code
stringlengths 13
6.09M
| order_type
stringclasses 2
values | original_example
dict | step_ids
listlengths 1
5
|
|---|---|---|---|
# pick three names
names = ["Mia", "Francis", "Eva"]
# propmpt user for his/her name
print("Please enter your name:")
user_name = input()
if user_name in names:
print("Hi there, {}!".format(user_name))
else:
print("Who goes there?")
|
normal
|
{
"blob_id": "59c33383365d10c108253f7b5a210d40718913a2",
"index": 9653,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Please enter your name:')\n<mask token>\nif user_name in names:\n print('Hi there, {}!'.format(user_name))\nelse:\n print('Who goes there?')\n",
"step-3": "names = ['Mia', 'Francis', 'Eva']\nprint('Please enter your name:')\nuser_name = input()\nif user_name in names:\n print('Hi there, {}!'.format(user_name))\nelse:\n print('Who goes there?')\n",
"step-4": "# pick three names\nnames = [\"Mia\", \"Francis\", \"Eva\"]\n\n# propmpt user for his/her name\nprint(\"Please enter your name:\")\nuser_name = input()\nif user_name in names:\n print(\"Hi there, {}!\".format(user_name))\nelse:\n print(\"Who goes there?\")\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
"""
В массиве случайных целых чисел поменять местами минимальный и максимальный элементы.
"""
import random
SIZE = 10
MIN_ITEM = -100
MAX_ITEM = 100
array = [random.randint(MIN_ITEM, MAX_ITEM) for _ in range(SIZE)]
print('Массив случайных чисел:\n', array)
min_el = array[0]
max_el = array[0]
max_el_inx = 0
min_el_inx = 0
for el in range(SIZE):
if array[el] > max_el:
max_el = array[el]
max_el_inx = el
if array[el] < min_el:
min_el = array[el]
min_el_inx = el
print('Минимальный и максимальный элементы массива:\n', min_el, 'и', max_el)
array.pop(min_el_inx)
array.insert(min_el_inx, max_el)
array.pop(max_el_inx)
array.insert(max_el_inx, min_el)
print('Массив, в котром поменяны местами минимальный и максимальный элементы:\n', array)
|
normal
|
{
"blob_id": "6027836b1b5d3cb8b842b1a1b77f5c9777269896",
"index": 7177,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Массив случайных чисел:\\n', array)\n<mask token>\nfor el in range(SIZE):\n if array[el] > max_el:\n max_el = array[el]\n max_el_inx = el\n if array[el] < min_el:\n min_el = array[el]\n min_el_inx = el\nprint('Минимальный и максимальный элементы массива:\\n', min_el, 'и', max_el)\narray.pop(min_el_inx)\narray.insert(min_el_inx, max_el)\narray.pop(max_el_inx)\narray.insert(max_el_inx, min_el)\nprint(\n 'Массив, в котром поменяны местами минимальный и максимальный элементы:\\n',\n array)\n",
"step-3": "<mask token>\nSIZE = 10\nMIN_ITEM = -100\nMAX_ITEM = 100\narray = [random.randint(MIN_ITEM, MAX_ITEM) for _ in range(SIZE)]\nprint('Массив случайных чисел:\\n', array)\nmin_el = array[0]\nmax_el = array[0]\nmax_el_inx = 0\nmin_el_inx = 0\nfor el in range(SIZE):\n if array[el] > max_el:\n max_el = array[el]\n max_el_inx = el\n if array[el] < min_el:\n min_el = array[el]\n min_el_inx = el\nprint('Минимальный и максимальный элементы массива:\\n', min_el, 'и', max_el)\narray.pop(min_el_inx)\narray.insert(min_el_inx, max_el)\narray.pop(max_el_inx)\narray.insert(max_el_inx, min_el)\nprint(\n 'Массив, в котром поменяны местами минимальный и максимальный элементы:\\n',\n array)\n",
"step-4": "<mask token>\nimport random\nSIZE = 10\nMIN_ITEM = -100\nMAX_ITEM = 100\narray = [random.randint(MIN_ITEM, MAX_ITEM) for _ in range(SIZE)]\nprint('Массив случайных чисел:\\n', array)\nmin_el = array[0]\nmax_el = array[0]\nmax_el_inx = 0\nmin_el_inx = 0\nfor el in range(SIZE):\n if array[el] > max_el:\n max_el = array[el]\n max_el_inx = el\n if array[el] < min_el:\n min_el = array[el]\n min_el_inx = el\nprint('Минимальный и максимальный элементы массива:\\n', min_el, 'и', max_el)\narray.pop(min_el_inx)\narray.insert(min_el_inx, max_el)\narray.pop(max_el_inx)\narray.insert(max_el_inx, min_el)\nprint(\n 'Массив, в котром поменяны местами минимальный и максимальный элементы:\\n',\n array)\n",
"step-5": "\"\"\"\n В массиве случайных целых чисел поменять местами минимальный и максимальный элементы.\n\"\"\"\n\n\nimport random\n\nSIZE = 10\nMIN_ITEM = -100\nMAX_ITEM = 100\narray = [random.randint(MIN_ITEM, MAX_ITEM) for _ in range(SIZE)]\nprint('Массив случайных чисел:\\n', array)\n\nmin_el = array[0]\nmax_el = array[0]\nmax_el_inx = 0\nmin_el_inx = 0\nfor el in range(SIZE):\n if array[el] > max_el:\n max_el = array[el]\n max_el_inx = el\n if array[el] < min_el:\n min_el = array[el]\n min_el_inx = el\nprint('Минимальный и максимальный элементы массива:\\n', min_el, 'и', max_el)\narray.pop(min_el_inx)\narray.insert(min_el_inx, max_el)\narray.pop(max_el_inx)\narray.insert(max_el_inx, min_el)\nprint('Массив, в котром поменяны местами минимальный и максимальный элементы:\\n', array)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('Hi Tom')
<|reserved_special_token_1|>
print("Hi Tom")
|
flexible
|
{
"blob_id": "e838a52fecbf69719acc6de38b5f045e792e1408",
"index": 9232,
"step-1": "<mask token>\n",
"step-2": "print('Hi Tom')\n",
"step-3": "print(\"Hi Tom\")",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_nukeboxQueue
----------------------------------
Tests for `nukebox2000` module.
"""
import sys
import unittest
from nukebox2000.MongoBox import NukeBoxDB
class TestNukeBoxDB(unittest.TestCase):
'''
'''
def setUp(self):
'''
B{Test} Data
- 2 User dict obj.
- contains basic data required by the MongoDB collection "Users"
- indexes exist on "mac_id" and "files" entries
- user entries contain a "set" of File elements which reference
the files (by obj id) that they have uploaded
- 2 File dict obj.
- contains basic data required for a File entry in the DB
- indexes exist on "track id"
'''
self.user_1 = {
'name': 'Terry',
'mac_id': '12341234'
}
self.user_2 = {
'name': 'Eric',
'mac_id': '43211234'
}
self.file_1 = {
'filetype': '.mp3',
'artist': 'Foals',
'path': 'temp_dir',
'track': 'Birch Tree',
'size': '10000',
'art': 'http://foals_art.jpeg'
}
self.file_2 = {
'filetype': '.mp3',
'artist': 'Foals',
'path': 'temp_dir',
'track': 'What Went Down',
'size': '10000',
'art': 'http://foals_art.jpeg'
}
def tearDown(self):
'''
B{Teardown} Test Data
- Deletes instance Data created in Setup
'''
for i in self.file_1, self.file_2, self.user_1, self.user_2:
del i
def test_Ensure_Indexes(self):
'''
B{Test 01}
Tests the DB method used to create the required indexes
- ensureInexes returns a list of booleans
- each item is True on success
'''
nbdb = NukeBoxDB(Debug=True)
_a, _b, _c = nbdb.ensureIndexes()
self.assertTrue(_a and _b and _c)
nbdb = None
def test_Create_User(self):
'''
B{Test 02}
Tests User entry creation in the DB
- createUser first checks if an matching entry already exists,
updating the existing entry if it does
- either way it returns the entries object id
'''
nbdb = NukeBoxDB()
user_1_result = nbdb.createUser(self.user_1)
self.assertTrue(user_1_result)
nbdb = None
def test_Create_User_Files(self):
'''
B{Test 03}
Tests File entry creation in th DB
- createFile first tries to retrieve an existing entry, updating it
if a match is found
- the method then uses the current "mac_id" instance variable to
retrieve the current user and updates their set of files
'''
nbdb = NukeBoxDB()
user_2_result = nbdb.createUser(self.user_2)
file_1_result = nbdb.createFile(self.file_1)
file_2_result = nbdb.createFile(self.file_2)
self.assertEquals(
file_1_result, nbdb.getTrack(self.file_1['track'])[1]
)
self.assertEquals(
file_2_result, nbdb.getTrack(self.file_2['track'])[1]
)
del user_2_result
nbdb = None
def test_Get_Valid_Track(self):
'''
'''
nbdb = NukeBoxDB()
track_value, track_id = nbdb.getTrack(self.file_1['track'])
self.assertTrue(track_value)
self.assertIsNotNone(track_id)
nbdb = None
def test_Get_Invalid_Track(self):
'''
'''
nbdb = NukeBoxDB()
track_value, track_id = nbdb.getTrack(
'Some Track That Should Not Exists in DB'
)
self.assertFalse(track_value)
self.assertIsNone(track_id)
nbdb = None
if __name__ == '__main__':
sys.exit(unittest.main())
|
normal
|
{
"blob_id": "bf63ceca2347f750cdf38dce620eaa3c73b556f1",
"index": 1733,
"step-1": "<mask token>\n\n\nclass TestNukeBoxDB(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n \"\"\"\n B{Test} Data\n\n - 2 User dict obj.\n - contains basic data required by the MongoDB collection \"Users\"\n - indexes exist on \"mac_id\" and \"files\" entries\n - user entries contain a \"set\" of File elements which reference\n the files (by obj id) that they have uploaded\n\n - 2 File dict obj.\n - contains basic data required for a File entry in the DB\n - indexes exist on \"track id\"\n \"\"\"\n self.user_1 = {'name': 'Terry', 'mac_id': '12341234'}\n self.user_2 = {'name': 'Eric', 'mac_id': '43211234'}\n self.file_1 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'Birch Tree', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n self.file_2 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'What Went Down', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n\n def tearDown(self):\n \"\"\"\n B{Teardown} Test Data\n\n - Deletes instance Data created in Setup\n \"\"\"\n for i in (self.file_1, self.file_2, self.user_1, self.user_2):\n del i\n\n def test_Ensure_Indexes(self):\n \"\"\"\n B{Test 01}\n\n Tests the DB method used to create the required indexes\n\n - ensureInexes returns a list of booleans\n - each item is True on success\n \"\"\"\n nbdb = NukeBoxDB(Debug=True)\n _a, _b, _c = nbdb.ensureIndexes()\n self.assertTrue(_a and _b and _c)\n nbdb = None\n <mask token>\n\n def test_Create_User_Files(self):\n \"\"\"\n B{Test 03}\n\n Tests File entry creation in th DB\n\n - createFile first tries to retrieve an existing entry, updating it\n if a match is found\n - the method then uses the current \"mac_id\" instance variable to\n retrieve the current user and updates their set of files\n \"\"\"\n nbdb = NukeBoxDB()\n user_2_result = nbdb.createUser(self.user_2)\n file_1_result = nbdb.createFile(self.file_1)\n file_2_result = nbdb.createFile(self.file_2)\n self.assertEquals(file_1_result, nbdb.getTrack(self.file_1['track'])[1]\n )\n self.assertEquals(file_2_result, nbdb.getTrack(self.file_2['track'])[1]\n )\n del user_2_result\n nbdb = None\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestNukeBoxDB(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n \"\"\"\n B{Test} Data\n\n - 2 User dict obj.\n - contains basic data required by the MongoDB collection \"Users\"\n - indexes exist on \"mac_id\" and \"files\" entries\n - user entries contain a \"set\" of File elements which reference\n the files (by obj id) that they have uploaded\n\n - 2 File dict obj.\n - contains basic data required for a File entry in the DB\n - indexes exist on \"track id\"\n \"\"\"\n self.user_1 = {'name': 'Terry', 'mac_id': '12341234'}\n self.user_2 = {'name': 'Eric', 'mac_id': '43211234'}\n self.file_1 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'Birch Tree', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n self.file_2 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'What Went Down', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n\n def tearDown(self):\n \"\"\"\n B{Teardown} Test Data\n\n - Deletes instance Data created in Setup\n \"\"\"\n for i in (self.file_1, self.file_2, self.user_1, self.user_2):\n del i\n\n def test_Ensure_Indexes(self):\n \"\"\"\n B{Test 01}\n\n Tests the DB method used to create the required indexes\n\n - ensureInexes returns a list of booleans\n - each item is True on success\n \"\"\"\n nbdb = NukeBoxDB(Debug=True)\n _a, _b, _c = nbdb.ensureIndexes()\n self.assertTrue(_a and _b and _c)\n nbdb = None\n <mask token>\n\n def test_Create_User_Files(self):\n \"\"\"\n B{Test 03}\n\n Tests File entry creation in th DB\n\n - createFile first tries to retrieve an existing entry, updating it\n if a match is found\n - the method then uses the current \"mac_id\" instance variable to\n retrieve the current user and updates their set of files\n \"\"\"\n nbdb = NukeBoxDB()\n user_2_result = nbdb.createUser(self.user_2)\n file_1_result = nbdb.createFile(self.file_1)\n file_2_result = nbdb.createFile(self.file_2)\n self.assertEquals(file_1_result, nbdb.getTrack(self.file_1['track'])[1]\n )\n self.assertEquals(file_2_result, nbdb.getTrack(self.file_2['track'])[1]\n )\n del user_2_result\n nbdb = None\n\n def test_Get_Valid_Track(self):\n \"\"\"\n \"\"\"\n nbdb = NukeBoxDB()\n track_value, track_id = nbdb.getTrack(self.file_1['track'])\n self.assertTrue(track_value)\n self.assertIsNotNone(track_id)\n nbdb = None\n\n def test_Get_Invalid_Track(self):\n \"\"\"\n \"\"\"\n nbdb = NukeBoxDB()\n track_value, track_id = nbdb.getTrack(\n 'Some Track That Should Not Exists in DB')\n self.assertFalse(track_value)\n self.assertIsNone(track_id)\n nbdb = None\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass TestNukeBoxDB(unittest.TestCase):\n \"\"\"\n \"\"\"\n\n def setUp(self):\n \"\"\"\n B{Test} Data\n\n - 2 User dict obj.\n - contains basic data required by the MongoDB collection \"Users\"\n - indexes exist on \"mac_id\" and \"files\" entries\n - user entries contain a \"set\" of File elements which reference\n the files (by obj id) that they have uploaded\n\n - 2 File dict obj.\n - contains basic data required for a File entry in the DB\n - indexes exist on \"track id\"\n \"\"\"\n self.user_1 = {'name': 'Terry', 'mac_id': '12341234'}\n self.user_2 = {'name': 'Eric', 'mac_id': '43211234'}\n self.file_1 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'Birch Tree', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n self.file_2 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'What Went Down', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n\n def tearDown(self):\n \"\"\"\n B{Teardown} Test Data\n\n - Deletes instance Data created in Setup\n \"\"\"\n for i in (self.file_1, self.file_2, self.user_1, self.user_2):\n del i\n\n def test_Ensure_Indexes(self):\n \"\"\"\n B{Test 01}\n\n Tests the DB method used to create the required indexes\n\n - ensureInexes returns a list of booleans\n - each item is True on success\n \"\"\"\n nbdb = NukeBoxDB(Debug=True)\n _a, _b, _c = nbdb.ensureIndexes()\n self.assertTrue(_a and _b and _c)\n nbdb = None\n\n def test_Create_User(self):\n \"\"\"\n B{Test 02}\n\n Tests User entry creation in the DB\n\n - createUser first checks if an matching entry already exists,\n updating the existing entry if it does\n - either way it returns the entries object id\n \"\"\"\n nbdb = NukeBoxDB()\n user_1_result = nbdb.createUser(self.user_1)\n self.assertTrue(user_1_result)\n nbdb = None\n\n def test_Create_User_Files(self):\n \"\"\"\n B{Test 03}\n\n Tests File entry creation in th DB\n\n - createFile first tries to retrieve an existing entry, updating it\n if a match is found\n - the method then uses the current \"mac_id\" instance variable to\n retrieve the current user and updates their set of files\n \"\"\"\n nbdb = NukeBoxDB()\n user_2_result = nbdb.createUser(self.user_2)\n file_1_result = nbdb.createFile(self.file_1)\n file_2_result = nbdb.createFile(self.file_2)\n self.assertEquals(file_1_result, nbdb.getTrack(self.file_1['track'])[1]\n )\n self.assertEquals(file_2_result, nbdb.getTrack(self.file_2['track'])[1]\n )\n del user_2_result\n nbdb = None\n\n def test_Get_Valid_Track(self):\n \"\"\"\n \"\"\"\n nbdb = NukeBoxDB()\n track_value, track_id = nbdb.getTrack(self.file_1['track'])\n self.assertTrue(track_value)\n self.assertIsNotNone(track_id)\n nbdb = None\n\n def test_Get_Invalid_Track(self):\n \"\"\"\n \"\"\"\n nbdb = NukeBoxDB()\n track_value, track_id = nbdb.getTrack(\n 'Some Track That Should Not Exists in DB')\n self.assertFalse(track_value)\n self.assertIsNone(track_id)\n nbdb = None\n\n\n<mask token>\n",
"step-4": "<mask token>\nimport sys\nimport unittest\nfrom nukebox2000.MongoBox import NukeBoxDB\n\n\nclass TestNukeBoxDB(unittest.TestCase):\n \"\"\"\n \"\"\"\n\n def setUp(self):\n \"\"\"\n B{Test} Data\n\n - 2 User dict obj.\n - contains basic data required by the MongoDB collection \"Users\"\n - indexes exist on \"mac_id\" and \"files\" entries\n - user entries contain a \"set\" of File elements which reference\n the files (by obj id) that they have uploaded\n\n - 2 File dict obj.\n - contains basic data required for a File entry in the DB\n - indexes exist on \"track id\"\n \"\"\"\n self.user_1 = {'name': 'Terry', 'mac_id': '12341234'}\n self.user_2 = {'name': 'Eric', 'mac_id': '43211234'}\n self.file_1 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'Birch Tree', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n self.file_2 = {'filetype': '.mp3', 'artist': 'Foals', 'path':\n 'temp_dir', 'track': 'What Went Down', 'size': '10000', 'art':\n 'http://foals_art.jpeg'}\n\n def tearDown(self):\n \"\"\"\n B{Teardown} Test Data\n\n - Deletes instance Data created in Setup\n \"\"\"\n for i in (self.file_1, self.file_2, self.user_1, self.user_2):\n del i\n\n def test_Ensure_Indexes(self):\n \"\"\"\n B{Test 01}\n\n Tests the DB method used to create the required indexes\n\n - ensureInexes returns a list of booleans\n - each item is True on success\n \"\"\"\n nbdb = NukeBoxDB(Debug=True)\n _a, _b, _c = nbdb.ensureIndexes()\n self.assertTrue(_a and _b and _c)\n nbdb = None\n\n def test_Create_User(self):\n \"\"\"\n B{Test 02}\n\n Tests User entry creation in the DB\n\n - createUser first checks if an matching entry already exists,\n updating the existing entry if it does\n - either way it returns the entries object id\n \"\"\"\n nbdb = NukeBoxDB()\n user_1_result = nbdb.createUser(self.user_1)\n self.assertTrue(user_1_result)\n nbdb = None\n\n def test_Create_User_Files(self):\n \"\"\"\n B{Test 03}\n\n Tests File entry creation in th DB\n\n - createFile first tries to retrieve an existing entry, updating it\n if a match is found\n - the method then uses the current \"mac_id\" instance variable to\n retrieve the current user and updates their set of files\n \"\"\"\n nbdb = NukeBoxDB()\n user_2_result = nbdb.createUser(self.user_2)\n file_1_result = nbdb.createFile(self.file_1)\n file_2_result = nbdb.createFile(self.file_2)\n self.assertEquals(file_1_result, nbdb.getTrack(self.file_1['track'])[1]\n )\n self.assertEquals(file_2_result, nbdb.getTrack(self.file_2['track'])[1]\n )\n del user_2_result\n nbdb = None\n\n def test_Get_Valid_Track(self):\n \"\"\"\n \"\"\"\n nbdb = NukeBoxDB()\n track_value, track_id = nbdb.getTrack(self.file_1['track'])\n self.assertTrue(track_value)\n self.assertIsNotNone(track_id)\n nbdb = None\n\n def test_Get_Invalid_Track(self):\n \"\"\"\n \"\"\"\n nbdb = NukeBoxDB()\n track_value, track_id = nbdb.getTrack(\n 'Some Track That Should Not Exists in DB')\n self.assertFalse(track_value)\n self.assertIsNone(track_id)\n nbdb = None\n\n\nif __name__ == '__main__':\n sys.exit(unittest.main())\n",
"step-5": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\ntest_nukeboxQueue\n----------------------------------\n\nTests for `nukebox2000` module.\n\"\"\"\n\n\nimport sys\nimport unittest\nfrom nukebox2000.MongoBox import NukeBoxDB\n\n\nclass TestNukeBoxDB(unittest.TestCase):\n\n '''\n '''\n\n def setUp(self):\n '''\n B{Test} Data\n\n - 2 User dict obj.\n - contains basic data required by the MongoDB collection \"Users\"\n - indexes exist on \"mac_id\" and \"files\" entries\n - user entries contain a \"set\" of File elements which reference\n the files (by obj id) that they have uploaded\n\n - 2 File dict obj.\n - contains basic data required for a File entry in the DB\n - indexes exist on \"track id\"\n '''\n\n self.user_1 = {\n 'name': 'Terry',\n 'mac_id': '12341234'\n }\n\n self.user_2 = {\n 'name': 'Eric',\n 'mac_id': '43211234'\n }\n\n self.file_1 = {\n 'filetype': '.mp3',\n 'artist': 'Foals',\n 'path': 'temp_dir',\n 'track': 'Birch Tree',\n 'size': '10000',\n 'art': 'http://foals_art.jpeg'\n }\n\n self.file_2 = {\n 'filetype': '.mp3',\n 'artist': 'Foals',\n 'path': 'temp_dir',\n 'track': 'What Went Down',\n 'size': '10000',\n 'art': 'http://foals_art.jpeg'\n }\n\n def tearDown(self):\n '''\n B{Teardown} Test Data\n\n - Deletes instance Data created in Setup\n '''\n\n for i in self.file_1, self.file_2, self.user_1, self.user_2:\n\n del i\n\n def test_Ensure_Indexes(self):\n '''\n B{Test 01}\n\n Tests the DB method used to create the required indexes\n\n - ensureInexes returns a list of booleans\n - each item is True on success\n '''\n\n nbdb = NukeBoxDB(Debug=True)\n\n _a, _b, _c = nbdb.ensureIndexes()\n\n self.assertTrue(_a and _b and _c)\n\n nbdb = None\n\n def test_Create_User(self):\n '''\n B{Test 02}\n\n Tests User entry creation in the DB\n\n - createUser first checks if an matching entry already exists,\n updating the existing entry if it does\n - either way it returns the entries object id\n '''\n\n nbdb = NukeBoxDB()\n\n user_1_result = nbdb.createUser(self.user_1)\n\n self.assertTrue(user_1_result)\n\n nbdb = None\n\n def test_Create_User_Files(self):\n '''\n B{Test 03}\n\n Tests File entry creation in th DB\n\n - createFile first tries to retrieve an existing entry, updating it\n if a match is found\n - the method then uses the current \"mac_id\" instance variable to\n retrieve the current user and updates their set of files\n '''\n\n nbdb = NukeBoxDB()\n\n user_2_result = nbdb.createUser(self.user_2)\n file_1_result = nbdb.createFile(self.file_1)\n file_2_result = nbdb.createFile(self.file_2)\n\n self.assertEquals(\n file_1_result, nbdb.getTrack(self.file_1['track'])[1]\n )\n\n self.assertEquals(\n file_2_result, nbdb.getTrack(self.file_2['track'])[1]\n )\n\n del user_2_result\n nbdb = None\n\n def test_Get_Valid_Track(self):\n '''\n '''\n\n nbdb = NukeBoxDB()\n\n track_value, track_id = nbdb.getTrack(self.file_1['track'])\n\n self.assertTrue(track_value)\n self.assertIsNotNone(track_id)\n\n nbdb = None\n\n def test_Get_Invalid_Track(self):\n '''\n '''\n\n nbdb = NukeBoxDB()\n\n track_value, track_id = nbdb.getTrack(\n 'Some Track That Should Not Exists in DB'\n )\n\n self.assertFalse(track_value)\n self.assertIsNone(track_id)\n\n nbdb = None\n\n\nif __name__ == '__main__':\n\n sys.exit(unittest.main())\n",
"step-ids": [
5,
7,
9,
11,
12
]
}
|
[
5,
7,
9,
11,
12
] |
<|reserved_special_token_0|>
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestMultipler(unittest.TestCase):
<|reserved_special_token_0|>
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestMultipler(unittest.TestCase):
def test__create__fixed_width(self):
self.assertIsInstance(Multiplier.create(23), FixedWidth)
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
def test__create__multiplier(self):
self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)
self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)
<|reserved_special_token_0|>
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestMultipler(unittest.TestCase):
def test__create__fixed_width(self):
self.assertIsInstance(Multiplier.create(23), FixedWidth)
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
def test__create__multiplier(self):
self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)
self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)
def test__create__bad_argument(self):
self.assertRaises(ValueError, Multiplier.create, '1234')
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
<|reserved_special_token_1|>
import unittest
from pattern.multiplier import Multiplier, FixedWidth, Range
from pattern.multiplier import WHATEVER, ONE_OR_MORE
class TestMultipler(unittest.TestCase):
def test__create__fixed_width(self):
self.assertIsInstance(Multiplier.create(23), FixedWidth)
def test__create__range(self):
self.assertIsInstance(Multiplier.create((23, 27)), Range)
def test__create__multiplier(self):
self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)
self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)
def test__create__bad_argument(self):
self.assertRaises(ValueError, Multiplier.create, '1234')
class TestWhatever(unittest.TestCase):
def test_compile(self):
self.assertEqual(WHATEVER.compile(), '*')
class TestOneOrMore(unittest.TestCase):
def test_compile(self):
self.assertEqual(ONE_OR_MORE.compile(), '+')
class TestFixedWidth(unittest.TestCase):
def test_compile(self):
self.assertEqual(FixedWidth(23).compile(), '{23}')
class TestRange(unittest.TestCase):
def test_compile(self):
self.assertEqual(Range((23, 27)).compile(), '{23,27}')
|
flexible
|
{
"blob_id": "5a7e535f2ae585f862cc792dab77f2fe0584fddc",
"index": 9986,
"step-1": "<mask token>\n\n\nclass TestWhatever(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(WHATEVER.compile(), '*')\n\n\nclass TestOneOrMore(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(ONE_OR_MORE.compile(), '+')\n\n\nclass TestFixedWidth(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(FixedWidth(23).compile(), '{23}')\n\n\nclass TestRange(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(Range((23, 27)).compile(), '{23,27}')\n",
"step-2": "<mask token>\n\n\nclass TestMultipler(unittest.TestCase):\n <mask token>\n\n def test__create__range(self):\n self.assertIsInstance(Multiplier.create((23, 27)), Range)\n <mask token>\n <mask token>\n\n\nclass TestWhatever(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(WHATEVER.compile(), '*')\n\n\nclass TestOneOrMore(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(ONE_OR_MORE.compile(), '+')\n\n\nclass TestFixedWidth(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(FixedWidth(23).compile(), '{23}')\n\n\nclass TestRange(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(Range((23, 27)).compile(), '{23,27}')\n",
"step-3": "<mask token>\n\n\nclass TestMultipler(unittest.TestCase):\n\n def test__create__fixed_width(self):\n self.assertIsInstance(Multiplier.create(23), FixedWidth)\n\n def test__create__range(self):\n self.assertIsInstance(Multiplier.create((23, 27)), Range)\n\n def test__create__multiplier(self):\n self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)\n self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)\n <mask token>\n\n\nclass TestWhatever(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(WHATEVER.compile(), '*')\n\n\nclass TestOneOrMore(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(ONE_OR_MORE.compile(), '+')\n\n\nclass TestFixedWidth(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(FixedWidth(23).compile(), '{23}')\n\n\nclass TestRange(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(Range((23, 27)).compile(), '{23,27}')\n",
"step-4": "<mask token>\n\n\nclass TestMultipler(unittest.TestCase):\n\n def test__create__fixed_width(self):\n self.assertIsInstance(Multiplier.create(23), FixedWidth)\n\n def test__create__range(self):\n self.assertIsInstance(Multiplier.create((23, 27)), Range)\n\n def test__create__multiplier(self):\n self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)\n self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)\n\n def test__create__bad_argument(self):\n self.assertRaises(ValueError, Multiplier.create, '1234')\n\n\nclass TestWhatever(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(WHATEVER.compile(), '*')\n\n\nclass TestOneOrMore(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(ONE_OR_MORE.compile(), '+')\n\n\nclass TestFixedWidth(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(FixedWidth(23).compile(), '{23}')\n\n\nclass TestRange(unittest.TestCase):\n\n def test_compile(self):\n self.assertEqual(Range((23, 27)).compile(), '{23,27}')\n",
"step-5": "import unittest\nfrom pattern.multiplier import Multiplier, FixedWidth, Range\nfrom pattern.multiplier import WHATEVER, ONE_OR_MORE\n\n\nclass TestMultipler(unittest.TestCase):\n def test__create__fixed_width(self):\n self.assertIsInstance(Multiplier.create(23), FixedWidth)\n\n def test__create__range(self):\n self.assertIsInstance(Multiplier.create((23, 27)), Range)\n\n def test__create__multiplier(self):\n self.assertEqual(Multiplier.create(WHATEVER), WHATEVER)\n self.assertEqual(Multiplier.create(ONE_OR_MORE), ONE_OR_MORE)\n\n def test__create__bad_argument(self):\n self.assertRaises(ValueError, Multiplier.create, '1234')\n\n\nclass TestWhatever(unittest.TestCase):\n def test_compile(self):\n self.assertEqual(WHATEVER.compile(), '*')\n\n\nclass TestOneOrMore(unittest.TestCase):\n def test_compile(self):\n self.assertEqual(ONE_OR_MORE.compile(), '+')\n\n\nclass TestFixedWidth(unittest.TestCase):\n def test_compile(self):\n self.assertEqual(FixedWidth(23).compile(), '{23}')\n\n\nclass TestRange(unittest.TestCase):\n def test_compile(self):\n self.assertEqual(Range((23, 27)).compile(), '{23,27}')\n\n",
"step-ids": [
8,
10,
12,
13,
15
]
}
|
[
8,
10,
12,
13,
15
] |
print ("Hello, Django girls!")
volume = 57
if volume < 20:
print("It's kinda quiet.")
elif 20 <= volume < 40:
print("It's nice for background music")
elif 40 <= volume < 60:
print("Perfect, I can hear all the details")
elif 60 <= volume < 80:
print("Nice for parties")
elif 80 <= volume < 100:
print("A bit loud!")
else:
print("My ears are hurting! :(")
def hi():
print('Hi there!')
print('How are you?')
hi()
|
normal
|
{
"blob_id": "00fd5efa4c66b7bd4617f4c886eddcdf38b951b7",
"index": 9507,
"step-1": "<mask token>\n",
"step-2": "print('Hello, Django girls!')\n<mask token>\nif volume < 20:\n print(\"It's kinda quiet.\")\nelif 20 <= volume < 40:\n print(\"It's nice for background music\")\nelif 40 <= volume < 60:\n print('Perfect, I can hear all the details')\nelif 60 <= volume < 80:\n print('Nice for parties')\nelif 80 <= volume < 100:\n print('A bit loud!')\nelse:\n print('My ears are hurting! :(')\n\n def hi():\n print('Hi there!')\n print('How are you?')\n hi()\n",
"step-3": "print('Hello, Django girls!')\nvolume = 57\nif volume < 20:\n print(\"It's kinda quiet.\")\nelif 20 <= volume < 40:\n print(\"It's nice for background music\")\nelif 40 <= volume < 60:\n print('Perfect, I can hear all the details')\nelif 60 <= volume < 80:\n print('Nice for parties')\nelif 80 <= volume < 100:\n print('A bit loud!')\nelse:\n print('My ears are hurting! :(')\n\n def hi():\n print('Hi there!')\n print('How are you?')\n hi()\n",
"step-4": "print (\"Hello, Django girls!\")\n\nvolume = 57\nif volume < 20:\n print(\"It's kinda quiet.\")\nelif 20 <= volume < 40:\n print(\"It's nice for background music\")\nelif 40 <= volume < 60:\n print(\"Perfect, I can hear all the details\")\nelif 60 <= volume < 80:\n print(\"Nice for parties\")\nelif 80 <= volume < 100:\n print(\"A bit loud!\")\nelse:\n print(\"My ears are hurting! :(\")\n\n def hi():\n print('Hi there!')\n print('How are you?')\n\n hi()",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
import os
import re
import sys
import traceback
import readline
from typing import NamedTuple, List
from PyInquirer import prompt
from pygments import highlight
from pygments.formatters.terminal import TerminalFormatter
from pygments.lexers.python import PythonLexer
import argparse
parser = argparse.ArgumentParser(description='Enter project endpoint')
parser.add_argument("proj", help="Run 'python3 cli.py <proj>', where <proj> is one of the following: hog cats ants scheme")
args = parser.parse_args()
proj = args.proj
from analyzer import get_problems, Comment
from finalizing import grade
from ok_interface import get_backup_ids, get_backup_code, submit_comment, submit_grade
from colorama import Fore, Style
from templates import template_completer, templates
import argparse
import config
parser = argparse.ArgumentParser(description='Enter project endpoint')
parser.add_argument("proj", help="Run 'python3 cli.py <proj>', where <proj> is one of the following: hog cats ants scheme")
args = parser.parse_args()
config.proj = args.proj
class Grade(NamedTuple):
score: int
message: str
comments: List[Comment]
def clear():
os.system("cls" if os.name == "nt" else "clear")
def display_code_with_accepted_and_potential_comments(
name, problem, accepted_comments, curr_comment=None
):
clear()
print(f"Problem: {name}")
highlighted_code = highlight(problem.code, PythonLexer(), TerminalFormatter())
for i, line in enumerate(highlighted_code.split("\n")):
line_num = problem.initial_line_number + i
if line_num in accepted_comments or (
curr_comment and line_num == curr_comment.line_num
):
print()
print(f"{Fore.GREEN}{line_num} {Style.RESET_ALL}{line}")
if line_num in accepted_comments or (
curr_comment and line_num == curr_comment.line_num
):
indent_level = len(line) - len(line.strip()) + 3
if line_num in accepted_comments:
for accepted_comment in accepted_comments[line_num]:
print(
Fore.MAGENTA
+ " " * indent_level
+ "# "
+ accepted_comment.comment
)
if curr_comment and line_num == curr_comment.line_num:
print(
Fore.RED
+ Style.BRIGHT
+ " " * indent_level
+ "# "
+ curr_comment.comment
)
print()
print()
def complete(comment):
if comment.fields:
print("Please provide supplementary information:")
field_vals = {}
for field in comment.fields:
q = {"type": "input", "name": "field", "message": field + ":"}
response = wrapped_prompt(q)
field_vals[field] = response["field"]
complete_text = comment.comment.format(**field_vals)
q = {
"type": "input",
"name": "final",
"message": "Final message",
"default": complete_text,
}
response = wrapped_prompt(q)
return Comment(comment.line_num, response["final"])
def add_comment(accepted_comments, new_comment):
if not new_comment:
return
if new_comment.line_num not in accepted_comments:
accepted_comments[new_comment.line_num] = []
accepted_comments[new_comment.line_num].append(new_comment)
class Interrupt(Exception):
def __init__(self, cmd):
super()
self.cmd = cmd
def wrapped_prompt(q):
ret = prompt([q])
if not ret:
receive_command()
return ret
def wrapped_input(q):
try:
ret = input(q)
except KeyboardInterrupt:
return receive_command()
return ret
def receive_command():
inp = input(
f"\n\n"
f"cancel = cancel this comment\n"
f"clear = clear all question comments\n"
f"reset = reset all student comments\n"
f"? {Style.BRIGHT}{Fore.RED}command: {Style.RESET_ALL}"
)
raise Interrupt(inp)
def main():
readline.parse_and_bind("tab: complete")
readline.set_completer_delims("")
print("cli.py main")
for id in get_backup_ids():
try:
code = get_backup_code(id)
problems = get_problems(code)
except Exception:
print(
f"{Fore.RED}An exception occurred while processing backup id #{id}",
file=sys.stderr,
)
traceback.print_exc(file=sys.stderr)
print(f"{Style.RESET_ALL}")
continue
grade = grade_backup(problems)
for comment in grade.comments:
print(comment)
assert not comment.fields, "fields not substituted!"
submit_comment(id, comment.line_num, comment.comment)
submit_grade(id, grade.score, grade.message)
def grade_backup(problems):
comments = []
try:
for name, problem in problems.items():
comments.extend(grade_problem(name, problem))
score, message = grade(comments)
print(message)
q = {
"type": "confirm",
"name": "ok",
"message": "Does this grade look reasonable?",
}
response = wrapped_prompt(q)
return Grade(score, message, comments)
except Interrupt as e:
if e.cmd == "reset":
return grade_backup(problems)
raise
def grade_problem(name, problem):
readline.set_completer(template_completer(name))
try:
accepted_comments = {}
for comment in problem.comments:
try:
display_code_with_accepted_and_potential_comments(
name, problem, accepted_comments, comment
)
print(f"{Fore.CYAN}Potential comment: {Style.RESET_ALL}")
print(
f"{Fore.GREEN}{comment.line_num}{Style.RESET_ALL} {comment.comment}"
)
q = {
"type": "confirm",
"name": "ok",
"message": "Add comment",
"default": True,
}
response = wrapped_prompt(q)
if response["ok"]:
add_comment(accepted_comments, complete(comment))
except Interrupt as e:
if e.cmd == "cancel":
continue
raise
while True:
try:
display_code_with_accepted_and_potential_comments(
name, problem, accepted_comments
)
response = wrapped_input(
f"? {Style.BRIGHT} Custom comment type: {Style.RESET_ALL}"
)
if not response:
q = {
"type": "confirm",
"name": "ok",
"message": "Go to next question?",
"default": True,
}
response = wrapped_prompt(q)
if response["ok"]:
break
continue
if response not in templates:
print(
f"{Fore.RED} Template {response} not found! {Style.RESET_ALL}"
)
continue
text = templates[response]
q = {"type": "input", "name": "line_num", "message": "Line number:"}
response = wrapped_prompt(q)
try:
line_num = int(response["line_num"])
except ValueError:
print(
f"{Fore.RED} Expected a number, received {response['line_num']} not found! {Style.RESET_ALL}"
)
continue
if text:
fields = list(set(re.findall(r"{(.*?)}", text)))
comment = Comment(line_num, text, fields)
add_comment(accepted_comments, complete(comment))
else:
q = {"type": "input", "name": "text", "message": "Comment:"}
response = wrapped_prompt(q)
comment = Comment(line_num, response["text"], [])
add_comment(accepted_comments, comment)
except Interrupt as e:
if e.cmd == "cancel":
continue
raise
print()
return list(sum(accepted_comments.values(), []))
except Interrupt as e:
if e.cmd == "clear":
return grade_problem(name, problem)
raise
if __name__ == "__main__":
try:
main()
except:
print(f"{Style.RESET_ALL}")
|
normal
|
{
"blob_id": "bec3d8546cd7d27f7da48f5658480cf17c36a255",
"index": 9462,
"step-1": "<mask token>\n\n\nclass Grade(NamedTuple):\n score: int\n message: str\n comments: List[Comment]\n\n\ndef clear():\n os.system('cls' if os.name == 'nt' else 'clear')\n\n\n<mask token>\n\n\ndef complete(comment):\n if comment.fields:\n print('Please provide supplementary information:')\n field_vals = {}\n for field in comment.fields:\n q = {'type': 'input', 'name': 'field', 'message': field + ':'}\n response = wrapped_prompt(q)\n field_vals[field] = response['field']\n complete_text = comment.comment.format(**field_vals)\n q = {'type': 'input', 'name': 'final', 'message': 'Final message',\n 'default': complete_text}\n response = wrapped_prompt(q)\n return Comment(comment.line_num, response['final'])\n\n\ndef add_comment(accepted_comments, new_comment):\n if not new_comment:\n return\n if new_comment.line_num not in accepted_comments:\n accepted_comments[new_comment.line_num] = []\n accepted_comments[new_comment.line_num].append(new_comment)\n\n\nclass Interrupt(Exception):\n\n def __init__(self, cmd):\n super()\n self.cmd = cmd\n\n\n<mask token>\n\n\ndef wrapped_input(q):\n try:\n ret = input(q)\n except KeyboardInterrupt:\n return receive_command()\n return ret\n\n\n<mask token>\n\n\ndef grade_backup(problems):\n comments = []\n try:\n for name, problem in problems.items():\n comments.extend(grade_problem(name, problem))\n score, message = grade(comments)\n print(message)\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Does this grade look reasonable?'}\n response = wrapped_prompt(q)\n return Grade(score, message, comments)\n except Interrupt as e:\n if e.cmd == 'reset':\n return grade_backup(problems)\n raise\n\n\ndef grade_problem(name, problem):\n readline.set_completer(template_completer(name))\n try:\n accepted_comments = {}\n for comment in problem.comments:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments, comment)\n print(f'{Fore.CYAN}Potential comment: {Style.RESET_ALL}')\n print(\n f'{Fore.GREEN}{comment.line_num}{Style.RESET_ALL} {comment.comment}'\n )\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Add comment', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n add_comment(accepted_comments, complete(comment))\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n while True:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments)\n response = wrapped_input(\n f'? {Style.BRIGHT} Custom comment type: {Style.RESET_ALL}')\n if not response:\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Go to next question?', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n break\n continue\n if response not in templates:\n print(\n f'{Fore.RED} Template {response} not found! {Style.RESET_ALL}'\n )\n continue\n text = templates[response]\n q = {'type': 'input', 'name': 'line_num', 'message':\n 'Line number:'}\n response = wrapped_prompt(q)\n try:\n line_num = int(response['line_num'])\n except ValueError:\n print(\n f\"{Fore.RED} Expected a number, received {response['line_num']} not found! {Style.RESET_ALL}\"\n )\n continue\n if text:\n fields = list(set(re.findall('{(.*?)}', text)))\n comment = Comment(line_num, text, fields)\n add_comment(accepted_comments, complete(comment))\n else:\n q = {'type': 'input', 'name': 'text', 'message': 'Comment:'\n }\n response = wrapped_prompt(q)\n comment = Comment(line_num, response['text'], [])\n add_comment(accepted_comments, comment)\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n print()\n return list(sum(accepted_comments.values(), []))\n except Interrupt as e:\n if e.cmd == 'clear':\n return grade_problem(name, problem)\n raise\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Grade(NamedTuple):\n score: int\n message: str\n comments: List[Comment]\n\n\ndef clear():\n os.system('cls' if os.name == 'nt' else 'clear')\n\n\ndef display_code_with_accepted_and_potential_comments(name, problem,\n accepted_comments, curr_comment=None):\n clear()\n print(f'Problem: {name}')\n highlighted_code = highlight(problem.code, PythonLexer(),\n TerminalFormatter())\n for i, line in enumerate(highlighted_code.split('\\n')):\n line_num = problem.initial_line_number + i\n if (line_num in accepted_comments or curr_comment and line_num ==\n curr_comment.line_num):\n print()\n print(f'{Fore.GREEN}{line_num} {Style.RESET_ALL}{line}')\n if (line_num in accepted_comments or curr_comment and line_num ==\n curr_comment.line_num):\n indent_level = len(line) - len(line.strip()) + 3\n if line_num in accepted_comments:\n for accepted_comment in accepted_comments[line_num]:\n print(Fore.MAGENTA + ' ' * indent_level + '# ' +\n accepted_comment.comment)\n if curr_comment and line_num == curr_comment.line_num:\n print(Fore.RED + Style.BRIGHT + ' ' * indent_level + '# ' +\n curr_comment.comment)\n print()\n print()\n\n\ndef complete(comment):\n if comment.fields:\n print('Please provide supplementary information:')\n field_vals = {}\n for field in comment.fields:\n q = {'type': 'input', 'name': 'field', 'message': field + ':'}\n response = wrapped_prompt(q)\n field_vals[field] = response['field']\n complete_text = comment.comment.format(**field_vals)\n q = {'type': 'input', 'name': 'final', 'message': 'Final message',\n 'default': complete_text}\n response = wrapped_prompt(q)\n return Comment(comment.line_num, response['final'])\n\n\ndef add_comment(accepted_comments, new_comment):\n if not new_comment:\n return\n if new_comment.line_num not in accepted_comments:\n accepted_comments[new_comment.line_num] = []\n accepted_comments[new_comment.line_num].append(new_comment)\n\n\nclass Interrupt(Exception):\n\n def __init__(self, cmd):\n super()\n self.cmd = cmd\n\n\ndef wrapped_prompt(q):\n ret = prompt([q])\n if not ret:\n receive_command()\n return ret\n\n\ndef wrapped_input(q):\n try:\n ret = input(q)\n except KeyboardInterrupt:\n return receive_command()\n return ret\n\n\ndef receive_command():\n inp = input(\n f\"\"\"\n\ncancel = cancel this comment\nclear = clear all question comments\nreset = reset all student comments\n? {Style.BRIGHT}{Fore.RED}command: {Style.RESET_ALL}\"\"\"\n )\n raise Interrupt(inp)\n\n\n<mask token>\n\n\ndef grade_backup(problems):\n comments = []\n try:\n for name, problem in problems.items():\n comments.extend(grade_problem(name, problem))\n score, message = grade(comments)\n print(message)\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Does this grade look reasonable?'}\n response = wrapped_prompt(q)\n return Grade(score, message, comments)\n except Interrupt as e:\n if e.cmd == 'reset':\n return grade_backup(problems)\n raise\n\n\ndef grade_problem(name, problem):\n readline.set_completer(template_completer(name))\n try:\n accepted_comments = {}\n for comment in problem.comments:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments, comment)\n print(f'{Fore.CYAN}Potential comment: {Style.RESET_ALL}')\n print(\n f'{Fore.GREEN}{comment.line_num}{Style.RESET_ALL} {comment.comment}'\n )\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Add comment', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n add_comment(accepted_comments, complete(comment))\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n while True:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments)\n response = wrapped_input(\n f'? {Style.BRIGHT} Custom comment type: {Style.RESET_ALL}')\n if not response:\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Go to next question?', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n break\n continue\n if response not in templates:\n print(\n f'{Fore.RED} Template {response} not found! {Style.RESET_ALL}'\n )\n continue\n text = templates[response]\n q = {'type': 'input', 'name': 'line_num', 'message':\n 'Line number:'}\n response = wrapped_prompt(q)\n try:\n line_num = int(response['line_num'])\n except ValueError:\n print(\n f\"{Fore.RED} Expected a number, received {response['line_num']} not found! {Style.RESET_ALL}\"\n )\n continue\n if text:\n fields = list(set(re.findall('{(.*?)}', text)))\n comment = Comment(line_num, text, fields)\n add_comment(accepted_comments, complete(comment))\n else:\n q = {'type': 'input', 'name': 'text', 'message': 'Comment:'\n }\n response = wrapped_prompt(q)\n comment = Comment(line_num, response['text'], [])\n add_comment(accepted_comments, comment)\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n print()\n return list(sum(accepted_comments.values(), []))\n except Interrupt as e:\n if e.cmd == 'clear':\n return grade_problem(name, problem)\n raise\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Grade(NamedTuple):\n score: int\n message: str\n comments: List[Comment]\n\n\ndef clear():\n os.system('cls' if os.name == 'nt' else 'clear')\n\n\ndef display_code_with_accepted_and_potential_comments(name, problem,\n accepted_comments, curr_comment=None):\n clear()\n print(f'Problem: {name}')\n highlighted_code = highlight(problem.code, PythonLexer(),\n TerminalFormatter())\n for i, line in enumerate(highlighted_code.split('\\n')):\n line_num = problem.initial_line_number + i\n if (line_num in accepted_comments or curr_comment and line_num ==\n curr_comment.line_num):\n print()\n print(f'{Fore.GREEN}{line_num} {Style.RESET_ALL}{line}')\n if (line_num in accepted_comments or curr_comment and line_num ==\n curr_comment.line_num):\n indent_level = len(line) - len(line.strip()) + 3\n if line_num in accepted_comments:\n for accepted_comment in accepted_comments[line_num]:\n print(Fore.MAGENTA + ' ' * indent_level + '# ' +\n accepted_comment.comment)\n if curr_comment and line_num == curr_comment.line_num:\n print(Fore.RED + Style.BRIGHT + ' ' * indent_level + '# ' +\n curr_comment.comment)\n print()\n print()\n\n\ndef complete(comment):\n if comment.fields:\n print('Please provide supplementary information:')\n field_vals = {}\n for field in comment.fields:\n q = {'type': 'input', 'name': 'field', 'message': field + ':'}\n response = wrapped_prompt(q)\n field_vals[field] = response['field']\n complete_text = comment.comment.format(**field_vals)\n q = {'type': 'input', 'name': 'final', 'message': 'Final message',\n 'default': complete_text}\n response = wrapped_prompt(q)\n return Comment(comment.line_num, response['final'])\n\n\ndef add_comment(accepted_comments, new_comment):\n if not new_comment:\n return\n if new_comment.line_num not in accepted_comments:\n accepted_comments[new_comment.line_num] = []\n accepted_comments[new_comment.line_num].append(new_comment)\n\n\nclass Interrupt(Exception):\n\n def __init__(self, cmd):\n super()\n self.cmd = cmd\n\n\ndef wrapped_prompt(q):\n ret = prompt([q])\n if not ret:\n receive_command()\n return ret\n\n\ndef wrapped_input(q):\n try:\n ret = input(q)\n except KeyboardInterrupt:\n return receive_command()\n return ret\n\n\ndef receive_command():\n inp = input(\n f\"\"\"\n\ncancel = cancel this comment\nclear = clear all question comments\nreset = reset all student comments\n? {Style.BRIGHT}{Fore.RED}command: {Style.RESET_ALL}\"\"\"\n )\n raise Interrupt(inp)\n\n\ndef main():\n readline.parse_and_bind('tab: complete')\n readline.set_completer_delims('')\n print('cli.py main')\n for id in get_backup_ids():\n try:\n code = get_backup_code(id)\n problems = get_problems(code)\n except Exception:\n print(\n f'{Fore.RED}An exception occurred while processing backup id #{id}'\n , file=sys.stderr)\n traceback.print_exc(file=sys.stderr)\n print(f'{Style.RESET_ALL}')\n continue\n grade = grade_backup(problems)\n for comment in grade.comments:\n print(comment)\n assert not comment.fields, 'fields not substituted!'\n submit_comment(id, comment.line_num, comment.comment)\n submit_grade(id, grade.score, grade.message)\n\n\ndef grade_backup(problems):\n comments = []\n try:\n for name, problem in problems.items():\n comments.extend(grade_problem(name, problem))\n score, message = grade(comments)\n print(message)\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Does this grade look reasonable?'}\n response = wrapped_prompt(q)\n return Grade(score, message, comments)\n except Interrupt as e:\n if e.cmd == 'reset':\n return grade_backup(problems)\n raise\n\n\ndef grade_problem(name, problem):\n readline.set_completer(template_completer(name))\n try:\n accepted_comments = {}\n for comment in problem.comments:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments, comment)\n print(f'{Fore.CYAN}Potential comment: {Style.RESET_ALL}')\n print(\n f'{Fore.GREEN}{comment.line_num}{Style.RESET_ALL} {comment.comment}'\n )\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Add comment', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n add_comment(accepted_comments, complete(comment))\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n while True:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments)\n response = wrapped_input(\n f'? {Style.BRIGHT} Custom comment type: {Style.RESET_ALL}')\n if not response:\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Go to next question?', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n break\n continue\n if response not in templates:\n print(\n f'{Fore.RED} Template {response} not found! {Style.RESET_ALL}'\n )\n continue\n text = templates[response]\n q = {'type': 'input', 'name': 'line_num', 'message':\n 'Line number:'}\n response = wrapped_prompt(q)\n try:\n line_num = int(response['line_num'])\n except ValueError:\n print(\n f\"{Fore.RED} Expected a number, received {response['line_num']} not found! {Style.RESET_ALL}\"\n )\n continue\n if text:\n fields = list(set(re.findall('{(.*?)}', text)))\n comment = Comment(line_num, text, fields)\n add_comment(accepted_comments, complete(comment))\n else:\n q = {'type': 'input', 'name': 'text', 'message': 'Comment:'\n }\n response = wrapped_prompt(q)\n comment = Comment(line_num, response['text'], [])\n add_comment(accepted_comments, comment)\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n print()\n return list(sum(accepted_comments.values(), []))\n except Interrupt as e:\n if e.cmd == 'clear':\n return grade_problem(name, problem)\n raise\n\n\n<mask token>\n",
"step-4": "<mask token>\nparser.add_argument('proj', help=\n \"Run 'python3 cli.py <proj>', where <proj> is one of the following: hog cats ants scheme\"\n )\n<mask token>\nparser.add_argument('proj', help=\n \"Run 'python3 cli.py <proj>', where <proj> is one of the following: hog cats ants scheme\"\n )\n<mask token>\n\n\nclass Grade(NamedTuple):\n score: int\n message: str\n comments: List[Comment]\n\n\ndef clear():\n os.system('cls' if os.name == 'nt' else 'clear')\n\n\ndef display_code_with_accepted_and_potential_comments(name, problem,\n accepted_comments, curr_comment=None):\n clear()\n print(f'Problem: {name}')\n highlighted_code = highlight(problem.code, PythonLexer(),\n TerminalFormatter())\n for i, line in enumerate(highlighted_code.split('\\n')):\n line_num = problem.initial_line_number + i\n if (line_num in accepted_comments or curr_comment and line_num ==\n curr_comment.line_num):\n print()\n print(f'{Fore.GREEN}{line_num} {Style.RESET_ALL}{line}')\n if (line_num in accepted_comments or curr_comment and line_num ==\n curr_comment.line_num):\n indent_level = len(line) - len(line.strip()) + 3\n if line_num in accepted_comments:\n for accepted_comment in accepted_comments[line_num]:\n print(Fore.MAGENTA + ' ' * indent_level + '# ' +\n accepted_comment.comment)\n if curr_comment and line_num == curr_comment.line_num:\n print(Fore.RED + Style.BRIGHT + ' ' * indent_level + '# ' +\n curr_comment.comment)\n print()\n print()\n\n\ndef complete(comment):\n if comment.fields:\n print('Please provide supplementary information:')\n field_vals = {}\n for field in comment.fields:\n q = {'type': 'input', 'name': 'field', 'message': field + ':'}\n response = wrapped_prompt(q)\n field_vals[field] = response['field']\n complete_text = comment.comment.format(**field_vals)\n q = {'type': 'input', 'name': 'final', 'message': 'Final message',\n 'default': complete_text}\n response = wrapped_prompt(q)\n return Comment(comment.line_num, response['final'])\n\n\ndef add_comment(accepted_comments, new_comment):\n if not new_comment:\n return\n if new_comment.line_num not in accepted_comments:\n accepted_comments[new_comment.line_num] = []\n accepted_comments[new_comment.line_num].append(new_comment)\n\n\nclass Interrupt(Exception):\n\n def __init__(self, cmd):\n super()\n self.cmd = cmd\n\n\ndef wrapped_prompt(q):\n ret = prompt([q])\n if not ret:\n receive_command()\n return ret\n\n\ndef wrapped_input(q):\n try:\n ret = input(q)\n except KeyboardInterrupt:\n return receive_command()\n return ret\n\n\ndef receive_command():\n inp = input(\n f\"\"\"\n\ncancel = cancel this comment\nclear = clear all question comments\nreset = reset all student comments\n? {Style.BRIGHT}{Fore.RED}command: {Style.RESET_ALL}\"\"\"\n )\n raise Interrupt(inp)\n\n\ndef main():\n readline.parse_and_bind('tab: complete')\n readline.set_completer_delims('')\n print('cli.py main')\n for id in get_backup_ids():\n try:\n code = get_backup_code(id)\n problems = get_problems(code)\n except Exception:\n print(\n f'{Fore.RED}An exception occurred while processing backup id #{id}'\n , file=sys.stderr)\n traceback.print_exc(file=sys.stderr)\n print(f'{Style.RESET_ALL}')\n continue\n grade = grade_backup(problems)\n for comment in grade.comments:\n print(comment)\n assert not comment.fields, 'fields not substituted!'\n submit_comment(id, comment.line_num, comment.comment)\n submit_grade(id, grade.score, grade.message)\n\n\ndef grade_backup(problems):\n comments = []\n try:\n for name, problem in problems.items():\n comments.extend(grade_problem(name, problem))\n score, message = grade(comments)\n print(message)\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Does this grade look reasonable?'}\n response = wrapped_prompt(q)\n return Grade(score, message, comments)\n except Interrupt as e:\n if e.cmd == 'reset':\n return grade_backup(problems)\n raise\n\n\ndef grade_problem(name, problem):\n readline.set_completer(template_completer(name))\n try:\n accepted_comments = {}\n for comment in problem.comments:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments, comment)\n print(f'{Fore.CYAN}Potential comment: {Style.RESET_ALL}')\n print(\n f'{Fore.GREEN}{comment.line_num}{Style.RESET_ALL} {comment.comment}'\n )\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Add comment', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n add_comment(accepted_comments, complete(comment))\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n while True:\n try:\n display_code_with_accepted_and_potential_comments(name,\n problem, accepted_comments)\n response = wrapped_input(\n f'? {Style.BRIGHT} Custom comment type: {Style.RESET_ALL}')\n if not response:\n q = {'type': 'confirm', 'name': 'ok', 'message':\n 'Go to next question?', 'default': True}\n response = wrapped_prompt(q)\n if response['ok']:\n break\n continue\n if response not in templates:\n print(\n f'{Fore.RED} Template {response} not found! {Style.RESET_ALL}'\n )\n continue\n text = templates[response]\n q = {'type': 'input', 'name': 'line_num', 'message':\n 'Line number:'}\n response = wrapped_prompt(q)\n try:\n line_num = int(response['line_num'])\n except ValueError:\n print(\n f\"{Fore.RED} Expected a number, received {response['line_num']} not found! {Style.RESET_ALL}\"\n )\n continue\n if text:\n fields = list(set(re.findall('{(.*?)}', text)))\n comment = Comment(line_num, text, fields)\n add_comment(accepted_comments, complete(comment))\n else:\n q = {'type': 'input', 'name': 'text', 'message': 'Comment:'\n }\n response = wrapped_prompt(q)\n comment = Comment(line_num, response['text'], [])\n add_comment(accepted_comments, comment)\n except Interrupt as e:\n if e.cmd == 'cancel':\n continue\n raise\n print()\n return list(sum(accepted_comments.values(), []))\n except Interrupt as e:\n if e.cmd == 'clear':\n return grade_problem(name, problem)\n raise\n\n\nif __name__ == '__main__':\n try:\n main()\n except:\n print(f'{Style.RESET_ALL}')\n",
"step-5": "import os\nimport re\nimport sys\nimport traceback\nimport readline\nfrom typing import NamedTuple, List\n\nfrom PyInquirer import prompt\nfrom pygments import highlight\nfrom pygments.formatters.terminal import TerminalFormatter\nfrom pygments.lexers.python import PythonLexer\n\nimport argparse\n\nparser = argparse.ArgumentParser(description='Enter project endpoint')\nparser.add_argument(\"proj\", help=\"Run 'python3 cli.py <proj>', where <proj> is one of the following: hog cats ants scheme\")\nargs = parser.parse_args()\nproj = args.proj\n\nfrom analyzer import get_problems, Comment\nfrom finalizing import grade\nfrom ok_interface import get_backup_ids, get_backup_code, submit_comment, submit_grade\nfrom colorama import Fore, Style\n\nfrom templates import template_completer, templates\n\nimport argparse\nimport config\n\nparser = argparse.ArgumentParser(description='Enter project endpoint')\nparser.add_argument(\"proj\", help=\"Run 'python3 cli.py <proj>', where <proj> is one of the following: hog cats ants scheme\")\nargs = parser.parse_args()\nconfig.proj = args.proj\n\nclass Grade(NamedTuple):\n score: int\n message: str\n comments: List[Comment]\n\n\ndef clear():\n os.system(\"cls\" if os.name == \"nt\" else \"clear\")\n\n\ndef display_code_with_accepted_and_potential_comments(\n name, problem, accepted_comments, curr_comment=None\n):\n clear()\n print(f\"Problem: {name}\")\n highlighted_code = highlight(problem.code, PythonLexer(), TerminalFormatter())\n for i, line in enumerate(highlighted_code.split(\"\\n\")):\n line_num = problem.initial_line_number + i\n if line_num in accepted_comments or (\n curr_comment and line_num == curr_comment.line_num\n ):\n print()\n print(f\"{Fore.GREEN}{line_num} {Style.RESET_ALL}{line}\")\n if line_num in accepted_comments or (\n curr_comment and line_num == curr_comment.line_num\n ):\n indent_level = len(line) - len(line.strip()) + 3\n if line_num in accepted_comments:\n for accepted_comment in accepted_comments[line_num]:\n print(\n Fore.MAGENTA\n + \" \" * indent_level\n + \"# \"\n + accepted_comment.comment\n )\n if curr_comment and line_num == curr_comment.line_num:\n print(\n Fore.RED\n + Style.BRIGHT\n + \" \" * indent_level\n + \"# \"\n + curr_comment.comment\n )\n print()\n print()\n\n\ndef complete(comment):\n if comment.fields:\n print(\"Please provide supplementary information:\")\n field_vals = {}\n for field in comment.fields:\n q = {\"type\": \"input\", \"name\": \"field\", \"message\": field + \":\"}\n response = wrapped_prompt(q)\n field_vals[field] = response[\"field\"]\n\n complete_text = comment.comment.format(**field_vals)\n q = {\n \"type\": \"input\",\n \"name\": \"final\",\n \"message\": \"Final message\",\n \"default\": complete_text,\n }\n response = wrapped_prompt(q)\n\n return Comment(comment.line_num, response[\"final\"])\n\n\ndef add_comment(accepted_comments, new_comment):\n if not new_comment:\n return\n if new_comment.line_num not in accepted_comments:\n accepted_comments[new_comment.line_num] = []\n accepted_comments[new_comment.line_num].append(new_comment)\n\n\nclass Interrupt(Exception):\n def __init__(self, cmd):\n super()\n self.cmd = cmd\n\n\ndef wrapped_prompt(q):\n ret = prompt([q])\n if not ret:\n receive_command()\n return ret\n\n\ndef wrapped_input(q):\n try:\n ret = input(q)\n except KeyboardInterrupt:\n return receive_command()\n return ret\n\n\ndef receive_command():\n inp = input(\n f\"\\n\\n\"\n f\"cancel = cancel this comment\\n\"\n f\"clear = clear all question comments\\n\"\n f\"reset = reset all student comments\\n\"\n f\"? {Style.BRIGHT}{Fore.RED}command: {Style.RESET_ALL}\"\n )\n raise Interrupt(inp)\n\n\ndef main():\n\n \n readline.parse_and_bind(\"tab: complete\")\n readline.set_completer_delims(\"\")\n print(\"cli.py main\")\n for id in get_backup_ids():\n try:\n code = get_backup_code(id)\n problems = get_problems(code)\n except Exception:\n print(\n f\"{Fore.RED}An exception occurred while processing backup id #{id}\",\n file=sys.stderr,\n )\n traceback.print_exc(file=sys.stderr)\n print(f\"{Style.RESET_ALL}\")\n continue\n\n grade = grade_backup(problems)\n for comment in grade.comments:\n print(comment)\n assert not comment.fields, \"fields not substituted!\"\n submit_comment(id, comment.line_num, comment.comment)\n submit_grade(id, grade.score, grade.message)\n\n\ndef grade_backup(problems):\n comments = []\n try:\n for name, problem in problems.items():\n comments.extend(grade_problem(name, problem))\n score, message = grade(comments)\n print(message)\n q = {\n \"type\": \"confirm\",\n \"name\": \"ok\",\n \"message\": \"Does this grade look reasonable?\",\n }\n response = wrapped_prompt(q)\n return Grade(score, message, comments)\n except Interrupt as e:\n if e.cmd == \"reset\":\n return grade_backup(problems)\n raise\n\n\ndef grade_problem(name, problem):\n readline.set_completer(template_completer(name))\n\n try:\n accepted_comments = {}\n for comment in problem.comments:\n try:\n display_code_with_accepted_and_potential_comments(\n name, problem, accepted_comments, comment\n )\n print(f\"{Fore.CYAN}Potential comment: {Style.RESET_ALL}\")\n print(\n f\"{Fore.GREEN}{comment.line_num}{Style.RESET_ALL} {comment.comment}\"\n )\n q = {\n \"type\": \"confirm\",\n \"name\": \"ok\",\n \"message\": \"Add comment\",\n \"default\": True,\n }\n response = wrapped_prompt(q)\n if response[\"ok\"]:\n add_comment(accepted_comments, complete(comment))\n except Interrupt as e:\n if e.cmd == \"cancel\":\n continue\n raise\n\n while True:\n try:\n display_code_with_accepted_and_potential_comments(\n name, problem, accepted_comments\n )\n response = wrapped_input(\n f\"? {Style.BRIGHT} Custom comment type: {Style.RESET_ALL}\"\n )\n if not response:\n q = {\n \"type\": \"confirm\",\n \"name\": \"ok\",\n \"message\": \"Go to next question?\",\n \"default\": True,\n }\n response = wrapped_prompt(q)\n if response[\"ok\"]:\n break\n continue\n if response not in templates:\n print(\n f\"{Fore.RED} Template {response} not found! {Style.RESET_ALL}\"\n )\n continue\n text = templates[response]\n q = {\"type\": \"input\", \"name\": \"line_num\", \"message\": \"Line number:\"}\n response = wrapped_prompt(q)\n try:\n line_num = int(response[\"line_num\"])\n except ValueError:\n print(\n f\"{Fore.RED} Expected a number, received {response['line_num']} not found! {Style.RESET_ALL}\"\n )\n continue\n\n if text:\n fields = list(set(re.findall(r\"{(.*?)}\", text)))\n comment = Comment(line_num, text, fields)\n add_comment(accepted_comments, complete(comment))\n else:\n q = {\"type\": \"input\", \"name\": \"text\", \"message\": \"Comment:\"}\n response = wrapped_prompt(q)\n comment = Comment(line_num, response[\"text\"], [])\n add_comment(accepted_comments, comment)\n except Interrupt as e:\n if e.cmd == \"cancel\":\n continue\n raise\n print()\n\n return list(sum(accepted_comments.values(), []))\n\n except Interrupt as e:\n if e.cmd == \"clear\":\n return grade_problem(name, problem)\n raise\n\n\nif __name__ == \"__main__\":\n try:\n\n main()\n except:\n print(f\"{Style.RESET_ALL}\")\n",
"step-ids": [
9,
12,
13,
14,
17
]
}
|
[
9,
12,
13,
14,
17
] |
#!/usr/bin/env python
"""
This is a Cog used to display processes/ programs running on the client to a discord text channel
Commented using reStructuredText (reST)
ToDo
create and use a database for multiple servers
"""
# Futures
# Built-in/Generic Imports
import os
import sys
import configparser
import shutil
import time
import codecs
# Libs
import discord
import psutil
from discord.ext import commands, tasks
# Own modules
__author__ = "Jack Draper"
__copyright__ = "Unofficial Copyright 2019, CyclopsBot"
__credits__ = ["Jack Draper"]
__license__ = "Developer"
__version__ = "0.0.4"
__maintainer__ = "Jack Draper"
__email__ = "thejaydwee@gmail.com"
__status__ = "Development"
# "Prototype", "Development", or "Production"
# Constants
CONFIG_PATH = "./configs/config.ini"
DEFAULT_EMBED = discord.Embed(
title=":desktop: Program Status",
colour=discord.Colour.blue()
)
# Checks for config file
if not os.path.exists("./configs/config.ini"):
print("No config file can be found in ./configs/.")
sys.exit("No config found.")
# Runs config file
config = configparser.ConfigParser()
try:
# config.read(os.path.abspath("./configs/config.ini"))
config.read_file(codecs.open(CONFIG_PATH, "r", "utf-8-sig"))
except FileNotFoundError:
try:
# shutil.copyfile("./configs/default_config.ini", "./configs/config.ini")
print("You need to set up the config file correctly.")
except shutil.Error:
print("Something is wrong with the default config file or the config folder.")
time.sleep(4)
sys.exit()
# Config Constants
ADMIN_ROLE = config["Credentials"]["admin_role"]
TEXT_CHANNEL = int(config["ProcessDisplay"]["text_channel_id"])
PROCESSES = eval(config["ProcessDisplay"]["processes"])
class ProcessDisplay(commands.Cog):
"""
The Cog for Process Display
"""
def __init__(self, client):
"""
:param client: the bot client parsed in from the main program
"""
self.started = False
self.client = client
self.inline = False
# Events
@commands.Cog.listener()
async def on_ready(self):
"""
Ran when bot is starting up and ready
Deletes messages from the bot in the TEXTCHANNEL
starts up find_processes method
:return:
"""
if not self.started:
channel = self.client.get_channel(TEXT_CHANNEL)
await self.delete_bot_msg(channel)
msg = await channel.send(embed=DEFAULT_EMBED)
self.find_processes.start(msg)
started = True
print("ProcessDisplay Running")
# Commands
@commands.command()
@commands.has_permissions(administrator=True)
async def toggle_inline(self,ctx):
"""
Toggles inline for process controls
:param ctx: The command Context
:return:
"""
self.inline = not self.inline
@commands.command()
@commands.has_permissions(administrator=True)
async def move_process(self, direction, process_name):
"""
need to make
:param direction:
:param process_name:
:return:
"""
for i in range(len(PROCESSES)):
if PROCESSES[i] == process_name:
if direction.lower() == "up":
pass
@commands.command()
@commands.has_permissions(administrator=True)
async def add_process(self, ctx, process, name):
"""
Adds a process to the process display.
Must be different from ones currently displayed.
:param ctx: Context of the command
:param process: The process (e.g. 'cmd.exe') to be added
:param name: The name to be displayed for the process (e.g. 'Command Prompt')
:return:
"""
name = self.fix_emoji_escapes(name)
if process in PROCESSES.keys():
await ctx.send(f"The process {process} is already being displayed")
elif name in PROCESSES.values():
await ctx.send(f"The process name {name} is already being displayed")
else:
PROCESSES[process] = name
self.update_processes_config()
await ctx.send(f"The process {name} has been added")
@commands.command()
@commands.has_permissions(administrator=True)
async def remove_process(self, ctx, *name):
"""
Removes a process from the process display
:param ctx: Context of the command
:param name: Name displayed for the process (e.g. Command Prompt)
:return:
"""
print(name)
name = self.fix_emoji_escapes(" ".join(name))
complete = False
for process in PROCESSES.keys():
if PROCESSES.get(process) == name:
PROCESSES.pop(process)
self.update_processes_config()
await ctx.send(f"The process {name} has been removed")
complete = True
break
if not complete:
await ctx.send(f"The process {name} doesn't exist")
@commands.command()
@commands.has_permissions(administrator=True)
async def edit_process(self, ctx, old_name, new_name):
"""
Edits the name of a process
:param ctx: The context of the command
:param old_name: The old name of the process (to be changed)
:param new_name: The new name of the process (changed to)
:return:
"""
old_name = self.fix_emoji_escapes(old_name)
new_name = self.fix_emoji_escapes(new_name)
if old_name in PROCESSES.values():
for process in PROCESSES:
if PROCESSES.get(process) == old_name:
PROCESSES.update({process: new_name})
self.update_processes_config()
else:
await ctx.send(f"Process name {old_name} doesn't exist")
@tasks.loop(seconds=1)
async def find_processes(self, msg):
"""
The processes with statuses are attached to the msg given
:param msg: The message to be edited with the processes
:return:
"""
running_processes = []
new_embed = DEFAULT_EMBED.copy()
for proc in psutil.process_iter():
if proc.name() in PROCESSES.keys():
running_processes.append(proc.name())
elif proc.name() in ["java.exe", "javaw.exe"] and proc.cwd() in PROCESSES.keys():
running_processes.append(proc.cwd())
for process in PROCESSES:
try:
if process in running_processes:
new_embed.add_field(name=PROCESSES.get(process),
value="Online <:GreenTick:592083498534174721>", inline=self.inline)
else:
new_embed.add_field(name=PROCESSES.get(process),
value="Offline <:RedCross:592082557961633877>", inline=self.inline)
except PermissionError:
new_embed.add_field(name=PROCESSES.get(process),
value="Admin Required <:OrangeUnknown:592082676891123722>", inline=self.inline)
await msg.edit(content="", embed=new_embed)
def is_me(self, m):
"""
Checks if a messages author is the bot
:param m: tbh idk, maybe message?
:return:
"""
return m.author == self.client.user
async def delete_bot_msg(self, channel):
"""
Deletes up to the last 100 messages sent by the bot in the given channel
:param channel: The channel that will have the messages deleted
:return: the message that says how many messages were deleted
"""
await channel.purge(limit=100, check=self.is_me)
@staticmethod
def update_processes_config():
"""
Updates the processes line in the config with the current PROCESSES
:return:
"""
config.set("ProcessDisplay", "processes", str(PROCESSES))
with open(CONFIG_PATH, 'w', encoding='utf-8') as configfile:
config.write(configfile)
@staticmethod
def fix_emoji_escapes(text):
"""
Fixes emoji escapes to add the < back on
:param text: The text that needs to be checked for an escape
:return: the fixed text
"""
new_text = text.split(":")
for i in range(2, len(new_text)):
if ">" in new_text[i]:
new_text[i-2] += "<"
return ":".join(new_text)
def setup(client):
"""
Ran on setup of the Cog
:param client: The bot client
:return:
"""
client.add_cog(ProcessDisplay(client))
|
normal
|
{
"blob_id": "8dfef0a4525328be8dfb4723f0a168dc22eb5eb2",
"index": 520,
"step-1": "<mask token>\n\n\nclass ProcessDisplay(commands.Cog):\n <mask token>\n <mask token>\n\n @commands.Cog.listener()\n async def on_ready(self):\n \"\"\"\n Ran when bot is starting up and ready\n Deletes messages from the bot in the TEXTCHANNEL\n starts up find_processes method\n :return:\n \"\"\"\n if not self.started:\n channel = self.client.get_channel(TEXT_CHANNEL)\n await self.delete_bot_msg(channel)\n msg = await channel.send(embed=DEFAULT_EMBED)\n self.find_processes.start(msg)\n started = True\n print('ProcessDisplay Running')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def toggle_inline(self, ctx):\n \"\"\"\n Toggles inline for process controls\n\n :param ctx: The command Context\n :return:\n \"\"\"\n self.inline = not self.inline\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def move_process(self, direction, process_name):\n \"\"\"\n need to make\n :param direction:\n :param process_name:\n :return:\n \"\"\"\n for i in range(len(PROCESSES)):\n if PROCESSES[i] == process_name:\n if direction.lower() == 'up':\n pass\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def add_process(self, ctx, process, name):\n \"\"\"\n Adds a process to the process display.\n Must be different from ones currently displayed.\n\n :param ctx: Context of the command\n :param process: The process (e.g. 'cmd.exe') to be added\n :param name: The name to be displayed for the process (e.g. 'Command Prompt')\n :return:\n \"\"\"\n name = self.fix_emoji_escapes(name)\n if process in PROCESSES.keys():\n await ctx.send(f'The process {process} is already being displayed')\n elif name in PROCESSES.values():\n await ctx.send(\n f'The process name {name} is already being displayed')\n else:\n PROCESSES[process] = name\n self.update_processes_config()\n await ctx.send(f'The process {name} has been added')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def remove_process(self, ctx, *name):\n \"\"\"\n Removes a process from the process display\n\n :param ctx: Context of the command\n :param name: Name displayed for the process (e.g. Command Prompt)\n :return:\n \"\"\"\n print(name)\n name = self.fix_emoji_escapes(' '.join(name))\n complete = False\n for process in PROCESSES.keys():\n if PROCESSES.get(process) == name:\n PROCESSES.pop(process)\n self.update_processes_config()\n await ctx.send(f'The process {name} has been removed')\n complete = True\n break\n if not complete:\n await ctx.send(f\"The process {name} doesn't exist\")\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def edit_process(self, ctx, old_name, new_name):\n \"\"\"\n Edits the name of a process\n :param ctx: The context of the command\n :param old_name: The old name of the process (to be changed)\n :param new_name: The new name of the process (changed to)\n :return:\n \"\"\"\n old_name = self.fix_emoji_escapes(old_name)\n new_name = self.fix_emoji_escapes(new_name)\n if old_name in PROCESSES.values():\n for process in PROCESSES:\n if PROCESSES.get(process) == old_name:\n PROCESSES.update({process: new_name})\n self.update_processes_config()\n else:\n await ctx.send(f\"Process name {old_name} doesn't exist\")\n\n @tasks.loop(seconds=1)\n async def find_processes(self, msg):\n \"\"\"\n The processes with statuses are attached to the msg given\n\n :param msg: The message to be edited with the processes\n :return:\n \"\"\"\n running_processes = []\n new_embed = DEFAULT_EMBED.copy()\n for proc in psutil.process_iter():\n if proc.name() in PROCESSES.keys():\n running_processes.append(proc.name())\n elif proc.name() in ['java.exe', 'javaw.exe'] and proc.cwd(\n ) in PROCESSES.keys():\n running_processes.append(proc.cwd())\n for process in PROCESSES:\n try:\n if process in running_processes:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Online <:GreenTick:592083498534174721>', inline=\n self.inline)\n else:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Offline <:RedCross:592082557961633877>', inline=\n self.inline)\n except PermissionError:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Admin Required <:OrangeUnknown:592082676891123722>',\n inline=self.inline)\n await msg.edit(content='', embed=new_embed)\n\n def is_me(self, m):\n \"\"\"\n Checks if a messages author is the bot\n :param m: tbh idk, maybe message?\n :return:\n \"\"\"\n return m.author == self.client.user\n\n async def delete_bot_msg(self, channel):\n \"\"\"\n Deletes up to the last 100 messages sent by the bot in the given channel\n :param channel: The channel that will have the messages deleted\n :return: the message that says how many messages were deleted\n \"\"\"\n await channel.purge(limit=100, check=self.is_me)\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass ProcessDisplay(commands.Cog):\n <mask token>\n\n def __init__(self, client):\n \"\"\"\n :param client: the bot client parsed in from the main program\n \"\"\"\n self.started = False\n self.client = client\n self.inline = False\n\n @commands.Cog.listener()\n async def on_ready(self):\n \"\"\"\n Ran when bot is starting up and ready\n Deletes messages from the bot in the TEXTCHANNEL\n starts up find_processes method\n :return:\n \"\"\"\n if not self.started:\n channel = self.client.get_channel(TEXT_CHANNEL)\n await self.delete_bot_msg(channel)\n msg = await channel.send(embed=DEFAULT_EMBED)\n self.find_processes.start(msg)\n started = True\n print('ProcessDisplay Running')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def toggle_inline(self, ctx):\n \"\"\"\n Toggles inline for process controls\n\n :param ctx: The command Context\n :return:\n \"\"\"\n self.inline = not self.inline\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def move_process(self, direction, process_name):\n \"\"\"\n need to make\n :param direction:\n :param process_name:\n :return:\n \"\"\"\n for i in range(len(PROCESSES)):\n if PROCESSES[i] == process_name:\n if direction.lower() == 'up':\n pass\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def add_process(self, ctx, process, name):\n \"\"\"\n Adds a process to the process display.\n Must be different from ones currently displayed.\n\n :param ctx: Context of the command\n :param process: The process (e.g. 'cmd.exe') to be added\n :param name: The name to be displayed for the process (e.g. 'Command Prompt')\n :return:\n \"\"\"\n name = self.fix_emoji_escapes(name)\n if process in PROCESSES.keys():\n await ctx.send(f'The process {process} is already being displayed')\n elif name in PROCESSES.values():\n await ctx.send(\n f'The process name {name} is already being displayed')\n else:\n PROCESSES[process] = name\n self.update_processes_config()\n await ctx.send(f'The process {name} has been added')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def remove_process(self, ctx, *name):\n \"\"\"\n Removes a process from the process display\n\n :param ctx: Context of the command\n :param name: Name displayed for the process (e.g. Command Prompt)\n :return:\n \"\"\"\n print(name)\n name = self.fix_emoji_escapes(' '.join(name))\n complete = False\n for process in PROCESSES.keys():\n if PROCESSES.get(process) == name:\n PROCESSES.pop(process)\n self.update_processes_config()\n await ctx.send(f'The process {name} has been removed')\n complete = True\n break\n if not complete:\n await ctx.send(f\"The process {name} doesn't exist\")\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def edit_process(self, ctx, old_name, new_name):\n \"\"\"\n Edits the name of a process\n :param ctx: The context of the command\n :param old_name: The old name of the process (to be changed)\n :param new_name: The new name of the process (changed to)\n :return:\n \"\"\"\n old_name = self.fix_emoji_escapes(old_name)\n new_name = self.fix_emoji_escapes(new_name)\n if old_name in PROCESSES.values():\n for process in PROCESSES:\n if PROCESSES.get(process) == old_name:\n PROCESSES.update({process: new_name})\n self.update_processes_config()\n else:\n await ctx.send(f\"Process name {old_name} doesn't exist\")\n\n @tasks.loop(seconds=1)\n async def find_processes(self, msg):\n \"\"\"\n The processes with statuses are attached to the msg given\n\n :param msg: The message to be edited with the processes\n :return:\n \"\"\"\n running_processes = []\n new_embed = DEFAULT_EMBED.copy()\n for proc in psutil.process_iter():\n if proc.name() in PROCESSES.keys():\n running_processes.append(proc.name())\n elif proc.name() in ['java.exe', 'javaw.exe'] and proc.cwd(\n ) in PROCESSES.keys():\n running_processes.append(proc.cwd())\n for process in PROCESSES:\n try:\n if process in running_processes:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Online <:GreenTick:592083498534174721>', inline=\n self.inline)\n else:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Offline <:RedCross:592082557961633877>', inline=\n self.inline)\n except PermissionError:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Admin Required <:OrangeUnknown:592082676891123722>',\n inline=self.inline)\n await msg.edit(content='', embed=new_embed)\n\n def is_me(self, m):\n \"\"\"\n Checks if a messages author is the bot\n :param m: tbh idk, maybe message?\n :return:\n \"\"\"\n return m.author == self.client.user\n\n async def delete_bot_msg(self, channel):\n \"\"\"\n Deletes up to the last 100 messages sent by the bot in the given channel\n :param channel: The channel that will have the messages deleted\n :return: the message that says how many messages were deleted\n \"\"\"\n await channel.purge(limit=100, check=self.is_me)\n\n @staticmethod\n def update_processes_config():\n \"\"\"\n Updates the processes line in the config with the current PROCESSES\n :return:\n \"\"\"\n config.set('ProcessDisplay', 'processes', str(PROCESSES))\n with open(CONFIG_PATH, 'w', encoding='utf-8') as configfile:\n config.write(configfile)\n <mask token>\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass ProcessDisplay(commands.Cog):\n \"\"\"\n The Cog for Process Display\n \"\"\"\n\n def __init__(self, client):\n \"\"\"\n :param client: the bot client parsed in from the main program\n \"\"\"\n self.started = False\n self.client = client\n self.inline = False\n\n @commands.Cog.listener()\n async def on_ready(self):\n \"\"\"\n Ran when bot is starting up and ready\n Deletes messages from the bot in the TEXTCHANNEL\n starts up find_processes method\n :return:\n \"\"\"\n if not self.started:\n channel = self.client.get_channel(TEXT_CHANNEL)\n await self.delete_bot_msg(channel)\n msg = await channel.send(embed=DEFAULT_EMBED)\n self.find_processes.start(msg)\n started = True\n print('ProcessDisplay Running')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def toggle_inline(self, ctx):\n \"\"\"\n Toggles inline for process controls\n\n :param ctx: The command Context\n :return:\n \"\"\"\n self.inline = not self.inline\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def move_process(self, direction, process_name):\n \"\"\"\n need to make\n :param direction:\n :param process_name:\n :return:\n \"\"\"\n for i in range(len(PROCESSES)):\n if PROCESSES[i] == process_name:\n if direction.lower() == 'up':\n pass\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def add_process(self, ctx, process, name):\n \"\"\"\n Adds a process to the process display.\n Must be different from ones currently displayed.\n\n :param ctx: Context of the command\n :param process: The process (e.g. 'cmd.exe') to be added\n :param name: The name to be displayed for the process (e.g. 'Command Prompt')\n :return:\n \"\"\"\n name = self.fix_emoji_escapes(name)\n if process in PROCESSES.keys():\n await ctx.send(f'The process {process} is already being displayed')\n elif name in PROCESSES.values():\n await ctx.send(\n f'The process name {name} is already being displayed')\n else:\n PROCESSES[process] = name\n self.update_processes_config()\n await ctx.send(f'The process {name} has been added')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def remove_process(self, ctx, *name):\n \"\"\"\n Removes a process from the process display\n\n :param ctx: Context of the command\n :param name: Name displayed for the process (e.g. Command Prompt)\n :return:\n \"\"\"\n print(name)\n name = self.fix_emoji_escapes(' '.join(name))\n complete = False\n for process in PROCESSES.keys():\n if PROCESSES.get(process) == name:\n PROCESSES.pop(process)\n self.update_processes_config()\n await ctx.send(f'The process {name} has been removed')\n complete = True\n break\n if not complete:\n await ctx.send(f\"The process {name} doesn't exist\")\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def edit_process(self, ctx, old_name, new_name):\n \"\"\"\n Edits the name of a process\n :param ctx: The context of the command\n :param old_name: The old name of the process (to be changed)\n :param new_name: The new name of the process (changed to)\n :return:\n \"\"\"\n old_name = self.fix_emoji_escapes(old_name)\n new_name = self.fix_emoji_escapes(new_name)\n if old_name in PROCESSES.values():\n for process in PROCESSES:\n if PROCESSES.get(process) == old_name:\n PROCESSES.update({process: new_name})\n self.update_processes_config()\n else:\n await ctx.send(f\"Process name {old_name} doesn't exist\")\n\n @tasks.loop(seconds=1)\n async def find_processes(self, msg):\n \"\"\"\n The processes with statuses are attached to the msg given\n\n :param msg: The message to be edited with the processes\n :return:\n \"\"\"\n running_processes = []\n new_embed = DEFAULT_EMBED.copy()\n for proc in psutil.process_iter():\n if proc.name() in PROCESSES.keys():\n running_processes.append(proc.name())\n elif proc.name() in ['java.exe', 'javaw.exe'] and proc.cwd(\n ) in PROCESSES.keys():\n running_processes.append(proc.cwd())\n for process in PROCESSES:\n try:\n if process in running_processes:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Online <:GreenTick:592083498534174721>', inline=\n self.inline)\n else:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Offline <:RedCross:592082557961633877>', inline=\n self.inline)\n except PermissionError:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Admin Required <:OrangeUnknown:592082676891123722>',\n inline=self.inline)\n await msg.edit(content='', embed=new_embed)\n\n def is_me(self, m):\n \"\"\"\n Checks if a messages author is the bot\n :param m: tbh idk, maybe message?\n :return:\n \"\"\"\n return m.author == self.client.user\n\n async def delete_bot_msg(self, channel):\n \"\"\"\n Deletes up to the last 100 messages sent by the bot in the given channel\n :param channel: The channel that will have the messages deleted\n :return: the message that says how many messages were deleted\n \"\"\"\n await channel.purge(limit=100, check=self.is_me)\n\n @staticmethod\n def update_processes_config():\n \"\"\"\n Updates the processes line in the config with the current PROCESSES\n :return:\n \"\"\"\n config.set('ProcessDisplay', 'processes', str(PROCESSES))\n with open(CONFIG_PATH, 'w', encoding='utf-8') as configfile:\n config.write(configfile)\n\n @staticmethod\n def fix_emoji_escapes(text):\n \"\"\"\n Fixes emoji escapes to add the < back on\n :param text: The text that needs to be checked for an escape\n :return: the fixed text\n \"\"\"\n new_text = text.split(':')\n for i in range(2, len(new_text)):\n if '>' in new_text[i]:\n new_text[i - 2] += '<'\n return ':'.join(new_text)\n\n\n<mask token>\n",
"step-4": "<mask token>\n__author__ = 'Jack Draper'\n__copyright__ = 'Unofficial Copyright 2019, CyclopsBot'\n__credits__ = ['Jack Draper']\n__license__ = 'Developer'\n__version__ = '0.0.4'\n__maintainer__ = 'Jack Draper'\n__email__ = 'thejaydwee@gmail.com'\n__status__ = 'Development'\nCONFIG_PATH = './configs/config.ini'\nDEFAULT_EMBED = discord.Embed(title=':desktop: Program Status', colour=\n discord.Colour.blue())\nif not os.path.exists('./configs/config.ini'):\n print('No config file can be found in ./configs/.')\n sys.exit('No config found.')\nconfig = configparser.ConfigParser()\ntry:\n config.read_file(codecs.open(CONFIG_PATH, 'r', 'utf-8-sig'))\nexcept FileNotFoundError:\n try:\n print('You need to set up the config file correctly.')\n except shutil.Error:\n print(\n 'Something is wrong with the default config file or the config folder.'\n )\n time.sleep(4)\n sys.exit()\nADMIN_ROLE = config['Credentials']['admin_role']\nTEXT_CHANNEL = int(config['ProcessDisplay']['text_channel_id'])\nPROCESSES = eval(config['ProcessDisplay']['processes'])\n\n\nclass ProcessDisplay(commands.Cog):\n \"\"\"\n The Cog for Process Display\n \"\"\"\n\n def __init__(self, client):\n \"\"\"\n :param client: the bot client parsed in from the main program\n \"\"\"\n self.started = False\n self.client = client\n self.inline = False\n\n @commands.Cog.listener()\n async def on_ready(self):\n \"\"\"\n Ran when bot is starting up and ready\n Deletes messages from the bot in the TEXTCHANNEL\n starts up find_processes method\n :return:\n \"\"\"\n if not self.started:\n channel = self.client.get_channel(TEXT_CHANNEL)\n await self.delete_bot_msg(channel)\n msg = await channel.send(embed=DEFAULT_EMBED)\n self.find_processes.start(msg)\n started = True\n print('ProcessDisplay Running')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def toggle_inline(self, ctx):\n \"\"\"\n Toggles inline for process controls\n\n :param ctx: The command Context\n :return:\n \"\"\"\n self.inline = not self.inline\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def move_process(self, direction, process_name):\n \"\"\"\n need to make\n :param direction:\n :param process_name:\n :return:\n \"\"\"\n for i in range(len(PROCESSES)):\n if PROCESSES[i] == process_name:\n if direction.lower() == 'up':\n pass\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def add_process(self, ctx, process, name):\n \"\"\"\n Adds a process to the process display.\n Must be different from ones currently displayed.\n\n :param ctx: Context of the command\n :param process: The process (e.g. 'cmd.exe') to be added\n :param name: The name to be displayed for the process (e.g. 'Command Prompt')\n :return:\n \"\"\"\n name = self.fix_emoji_escapes(name)\n if process in PROCESSES.keys():\n await ctx.send(f'The process {process} is already being displayed')\n elif name in PROCESSES.values():\n await ctx.send(\n f'The process name {name} is already being displayed')\n else:\n PROCESSES[process] = name\n self.update_processes_config()\n await ctx.send(f'The process {name} has been added')\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def remove_process(self, ctx, *name):\n \"\"\"\n Removes a process from the process display\n\n :param ctx: Context of the command\n :param name: Name displayed for the process (e.g. Command Prompt)\n :return:\n \"\"\"\n print(name)\n name = self.fix_emoji_escapes(' '.join(name))\n complete = False\n for process in PROCESSES.keys():\n if PROCESSES.get(process) == name:\n PROCESSES.pop(process)\n self.update_processes_config()\n await ctx.send(f'The process {name} has been removed')\n complete = True\n break\n if not complete:\n await ctx.send(f\"The process {name} doesn't exist\")\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def edit_process(self, ctx, old_name, new_name):\n \"\"\"\n Edits the name of a process\n :param ctx: The context of the command\n :param old_name: The old name of the process (to be changed)\n :param new_name: The new name of the process (changed to)\n :return:\n \"\"\"\n old_name = self.fix_emoji_escapes(old_name)\n new_name = self.fix_emoji_escapes(new_name)\n if old_name in PROCESSES.values():\n for process in PROCESSES:\n if PROCESSES.get(process) == old_name:\n PROCESSES.update({process: new_name})\n self.update_processes_config()\n else:\n await ctx.send(f\"Process name {old_name} doesn't exist\")\n\n @tasks.loop(seconds=1)\n async def find_processes(self, msg):\n \"\"\"\n The processes with statuses are attached to the msg given\n\n :param msg: The message to be edited with the processes\n :return:\n \"\"\"\n running_processes = []\n new_embed = DEFAULT_EMBED.copy()\n for proc in psutil.process_iter():\n if proc.name() in PROCESSES.keys():\n running_processes.append(proc.name())\n elif proc.name() in ['java.exe', 'javaw.exe'] and proc.cwd(\n ) in PROCESSES.keys():\n running_processes.append(proc.cwd())\n for process in PROCESSES:\n try:\n if process in running_processes:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Online <:GreenTick:592083498534174721>', inline=\n self.inline)\n else:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Offline <:RedCross:592082557961633877>', inline=\n self.inline)\n except PermissionError:\n new_embed.add_field(name=PROCESSES.get(process), value=\n 'Admin Required <:OrangeUnknown:592082676891123722>',\n inline=self.inline)\n await msg.edit(content='', embed=new_embed)\n\n def is_me(self, m):\n \"\"\"\n Checks if a messages author is the bot\n :param m: tbh idk, maybe message?\n :return:\n \"\"\"\n return m.author == self.client.user\n\n async def delete_bot_msg(self, channel):\n \"\"\"\n Deletes up to the last 100 messages sent by the bot in the given channel\n :param channel: The channel that will have the messages deleted\n :return: the message that says how many messages were deleted\n \"\"\"\n await channel.purge(limit=100, check=self.is_me)\n\n @staticmethod\n def update_processes_config():\n \"\"\"\n Updates the processes line in the config with the current PROCESSES\n :return:\n \"\"\"\n config.set('ProcessDisplay', 'processes', str(PROCESSES))\n with open(CONFIG_PATH, 'w', encoding='utf-8') as configfile:\n config.write(configfile)\n\n @staticmethod\n def fix_emoji_escapes(text):\n \"\"\"\n Fixes emoji escapes to add the < back on\n :param text: The text that needs to be checked for an escape\n :return: the fixed text\n \"\"\"\n new_text = text.split(':')\n for i in range(2, len(new_text)):\n if '>' in new_text[i]:\n new_text[i - 2] += '<'\n return ':'.join(new_text)\n\n\ndef setup(client):\n \"\"\"\n Ran on setup of the Cog\n :param client: The bot client\n :return:\n \"\"\"\n client.add_cog(ProcessDisplay(client))\n",
"step-5": "#!/usr/bin/env python\n\n\"\"\"\nThis is a Cog used to display processes/ programs running on the client to a discord text channel\n\nCommented using reStructuredText (reST)\n\nToDo\n create and use a database for multiple servers\n\"\"\"\n# Futures\n\n# Built-in/Generic Imports\nimport os\nimport sys\nimport configparser\nimport shutil\nimport time\nimport codecs\n\n# Libs\nimport discord\nimport psutil\nfrom discord.ext import commands, tasks\n\n# Own modules\n\n__author__ = \"Jack Draper\"\n__copyright__ = \"Unofficial Copyright 2019, CyclopsBot\"\n__credits__ = [\"Jack Draper\"]\n__license__ = \"Developer\"\n__version__ = \"0.0.4\"\n__maintainer__ = \"Jack Draper\"\n__email__ = \"thejaydwee@gmail.com\"\n__status__ = \"Development\"\n# \"Prototype\", \"Development\", or \"Production\"\n\n# Constants\nCONFIG_PATH = \"./configs/config.ini\"\nDEFAULT_EMBED = discord.Embed(\n title=\":desktop: Program Status\",\n colour=discord.Colour.blue()\n )\n\n\n# Checks for config file\nif not os.path.exists(\"./configs/config.ini\"):\n print(\"No config file can be found in ./configs/.\")\n sys.exit(\"No config found.\")\n# Runs config file\nconfig = configparser.ConfigParser()\ntry:\n # config.read(os.path.abspath(\"./configs/config.ini\"))\n config.read_file(codecs.open(CONFIG_PATH, \"r\", \"utf-8-sig\"))\nexcept FileNotFoundError:\n try:\n # shutil.copyfile(\"./configs/default_config.ini\", \"./configs/config.ini\")\n print(\"You need to set up the config file correctly.\")\n except shutil.Error:\n print(\"Something is wrong with the default config file or the config folder.\")\n time.sleep(4)\n sys.exit()\n\n# Config Constants\nADMIN_ROLE = config[\"Credentials\"][\"admin_role\"]\nTEXT_CHANNEL = int(config[\"ProcessDisplay\"][\"text_channel_id\"])\nPROCESSES = eval(config[\"ProcessDisplay\"][\"processes\"])\n\n\nclass ProcessDisplay(commands.Cog):\n \"\"\"\n The Cog for Process Display\n \"\"\"\n\n def __init__(self, client):\n \"\"\"\n :param client: the bot client parsed in from the main program\n \"\"\"\n self.started = False\n self.client = client\n self.inline = False\n\n # Events\n @commands.Cog.listener()\n async def on_ready(self):\n \"\"\"\n Ran when bot is starting up and ready\n Deletes messages from the bot in the TEXTCHANNEL\n starts up find_processes method\n :return:\n \"\"\"\n\n if not self.started:\n channel = self.client.get_channel(TEXT_CHANNEL)\n\n await self.delete_bot_msg(channel)\n msg = await channel.send(embed=DEFAULT_EMBED)\n self.find_processes.start(msg)\n started = True\n print(\"ProcessDisplay Running\")\n\n # Commands\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def toggle_inline(self,ctx):\n \"\"\"\n Toggles inline for process controls\n\n :param ctx: The command Context\n :return:\n \"\"\"\n self.inline = not self.inline\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def move_process(self, direction, process_name):\n \"\"\"\n need to make\n :param direction:\n :param process_name:\n :return:\n \"\"\"\n for i in range(len(PROCESSES)):\n if PROCESSES[i] == process_name:\n if direction.lower() == \"up\":\n pass\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def add_process(self, ctx, process, name):\n \"\"\"\n Adds a process to the process display.\n Must be different from ones currently displayed.\n\n :param ctx: Context of the command\n :param process: The process (e.g. 'cmd.exe') to be added\n :param name: The name to be displayed for the process (e.g. 'Command Prompt')\n :return:\n \"\"\"\n name = self.fix_emoji_escapes(name)\n if process in PROCESSES.keys():\n await ctx.send(f\"The process {process} is already being displayed\")\n elif name in PROCESSES.values():\n await ctx.send(f\"The process name {name} is already being displayed\")\n\n else:\n PROCESSES[process] = name\n self.update_processes_config()\n await ctx.send(f\"The process {name} has been added\")\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def remove_process(self, ctx, *name):\n \"\"\"\n Removes a process from the process display\n\n :param ctx: Context of the command\n :param name: Name displayed for the process (e.g. Command Prompt)\n :return:\n \"\"\"\n print(name)\n name = self.fix_emoji_escapes(\" \".join(name))\n complete = False\n for process in PROCESSES.keys():\n if PROCESSES.get(process) == name:\n PROCESSES.pop(process)\n self.update_processes_config()\n await ctx.send(f\"The process {name} has been removed\")\n complete = True\n break\n if not complete:\n await ctx.send(f\"The process {name} doesn't exist\")\n\n @commands.command()\n @commands.has_permissions(administrator=True)\n async def edit_process(self, ctx, old_name, new_name):\n \"\"\"\n Edits the name of a process\n :param ctx: The context of the command\n :param old_name: The old name of the process (to be changed)\n :param new_name: The new name of the process (changed to)\n :return:\n \"\"\"\n old_name = self.fix_emoji_escapes(old_name)\n new_name = self.fix_emoji_escapes(new_name)\n if old_name in PROCESSES.values():\n for process in PROCESSES:\n if PROCESSES.get(process) == old_name:\n PROCESSES.update({process: new_name})\n self.update_processes_config()\n\n else:\n await ctx.send(f\"Process name {old_name} doesn't exist\")\n\n @tasks.loop(seconds=1)\n async def find_processes(self, msg):\n \"\"\"\n The processes with statuses are attached to the msg given\n\n :param msg: The message to be edited with the processes\n :return:\n \"\"\"\n running_processes = []\n new_embed = DEFAULT_EMBED.copy()\n\n for proc in psutil.process_iter():\n if proc.name() in PROCESSES.keys():\n running_processes.append(proc.name())\n elif proc.name() in [\"java.exe\", \"javaw.exe\"] and proc.cwd() in PROCESSES.keys():\n running_processes.append(proc.cwd())\n\n for process in PROCESSES:\n try:\n if process in running_processes:\n new_embed.add_field(name=PROCESSES.get(process),\n value=\"Online <:GreenTick:592083498534174721>\", inline=self.inline)\n else:\n new_embed.add_field(name=PROCESSES.get(process),\n value=\"Offline <:RedCross:592082557961633877>\", inline=self.inline)\n except PermissionError:\n new_embed.add_field(name=PROCESSES.get(process),\n value=\"Admin Required <:OrangeUnknown:592082676891123722>\", inline=self.inline)\n await msg.edit(content=\"\", embed=new_embed)\n\n def is_me(self, m):\n \"\"\"\n Checks if a messages author is the bot\n :param m: tbh idk, maybe message?\n :return:\n \"\"\"\n return m.author == self.client.user\n\n async def delete_bot_msg(self, channel):\n \"\"\"\n Deletes up to the last 100 messages sent by the bot in the given channel\n :param channel: The channel that will have the messages deleted\n :return: the message that says how many messages were deleted\n \"\"\"\n await channel.purge(limit=100, check=self.is_me)\n\n @staticmethod\n def update_processes_config():\n \"\"\"\n Updates the processes line in the config with the current PROCESSES\n :return:\n \"\"\"\n\n config.set(\"ProcessDisplay\", \"processes\", str(PROCESSES))\n with open(CONFIG_PATH, 'w', encoding='utf-8') as configfile:\n config.write(configfile)\n\n @staticmethod\n def fix_emoji_escapes(text):\n \"\"\"\n Fixes emoji escapes to add the < back on\n :param text: The text that needs to be checked for an escape\n :return: the fixed text\n \"\"\"\n new_text = text.split(\":\")\n for i in range(2, len(new_text)):\n if \">\" in new_text[i]:\n new_text[i-2] += \"<\"\n return \":\".join(new_text)\n\n\ndef setup(client):\n \"\"\"\n Ran on setup of the Cog\n :param client: The bot client\n :return:\n \"\"\"\n client.add_cog(ProcessDisplay(client))\n",
"step-ids": [
2,
4,
6,
9,
11
]
}
|
[
2,
4,
6,
9,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
lst = []
user_input = int(input('Enter number of elements : '))
rotating_time = int(input('Enter how many times you want to rotate: '))
print('The numbers are:')
for i in range(0, user_input):
ele = int(input())
lst.append(ele)
numbers = rotation(lst, rotating_time)
print('Rotated by {0}: {1}'.format(rotating_time, numbers))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def rotation(list_of_number, ratating_time):
numbers = list_of_number[0]
numbers = [list_of_number[(i + ratating_time) % len(list_of_number)] for
i, x in enumerate(list_of_number)]
return numbers
def main():
lst = []
user_input = int(input('Enter number of elements : '))
rotating_time = int(input('Enter how many times you want to rotate: '))
print('The numbers are:')
for i in range(0, user_input):
ele = int(input())
lst.append(ele)
numbers = rotation(lst, rotating_time)
print('Rotated by {0}: {1}'.format(rotating_time, numbers))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def rotation(list_of_number, ratating_time):
numbers = list_of_number[0]
numbers = [list_of_number[(i + ratating_time) % len(list_of_number)] for
i, x in enumerate(list_of_number)]
return numbers
def main():
lst = []
user_input = int(input('Enter number of elements : '))
rotating_time = int(input('Enter how many times you want to rotate: '))
print('The numbers are:')
for i in range(0, user_input):
ele = int(input())
lst.append(ele)
numbers = rotation(lst, rotating_time)
print('Rotated by {0}: {1}'.format(rotating_time, numbers))
if __name__ == '__main__':
main()
<|reserved_special_token_1|>
#!/usr/bin/env python3
# Created by: Khang Le
# Created on: Dec 2019
# This program uses lists and rotation
def rotation(list_of_number, ratating_time):
numbers = list_of_number[0]
numbers = [list_of_number[(i + ratating_time) % len(list_of_number)]
for i, x in enumerate(list_of_number)]
return numbers
def main():
lst = []
# number of elemetns as input
user_input = int(input("Enter number of elements : "))
rotating_time = int(input("Enter how many times you want to rotate: "))
print("The numbers are:")
for i in range(0, user_input):
ele = int(input())
lst.append(ele) # adding the element
numbers = rotation(lst, rotating_time)
print("Rotated by {0}: {1}".format(rotating_time, numbers))
if __name__ == "__main__":
main()
|
flexible
|
{
"blob_id": "74de0da708c7eb792dea15afb23713d9d71af520",
"index": 5491,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n lst = []\n user_input = int(input('Enter number of elements : '))\n rotating_time = int(input('Enter how many times you want to rotate: '))\n print('The numbers are:')\n for i in range(0, user_input):\n ele = int(input())\n lst.append(ele)\n numbers = rotation(lst, rotating_time)\n print('Rotated by {0}: {1}'.format(rotating_time, numbers))\n\n\n<mask token>\n",
"step-3": "def rotation(list_of_number, ratating_time):\n numbers = list_of_number[0]\n numbers = [list_of_number[(i + ratating_time) % len(list_of_number)] for\n i, x in enumerate(list_of_number)]\n return numbers\n\n\ndef main():\n lst = []\n user_input = int(input('Enter number of elements : '))\n rotating_time = int(input('Enter how many times you want to rotate: '))\n print('The numbers are:')\n for i in range(0, user_input):\n ele = int(input())\n lst.append(ele)\n numbers = rotation(lst, rotating_time)\n print('Rotated by {0}: {1}'.format(rotating_time, numbers))\n\n\n<mask token>\n",
"step-4": "def rotation(list_of_number, ratating_time):\n numbers = list_of_number[0]\n numbers = [list_of_number[(i + ratating_time) % len(list_of_number)] for\n i, x in enumerate(list_of_number)]\n return numbers\n\n\ndef main():\n lst = []\n user_input = int(input('Enter number of elements : '))\n rotating_time = int(input('Enter how many times you want to rotate: '))\n print('The numbers are:')\n for i in range(0, user_input):\n ele = int(input())\n lst.append(ele)\n numbers = rotation(lst, rotating_time)\n print('Rotated by {0}: {1}'.format(rotating_time, numbers))\n\n\nif __name__ == '__main__':\n main()\n",
"step-5": "#!/usr/bin/env python3\n\n# Created by: Khang Le\n# Created on: Dec 2019\n# This program uses lists and rotation\n\n\ndef rotation(list_of_number, ratating_time):\n\n numbers = list_of_number[0]\n numbers = [list_of_number[(i + ratating_time) % len(list_of_number)]\n for i, x in enumerate(list_of_number)]\n return numbers\n\n\ndef main():\n\n lst = []\n # number of elemetns as input\n user_input = int(input(\"Enter number of elements : \"))\n rotating_time = int(input(\"Enter how many times you want to rotate: \"))\n print(\"The numbers are:\")\n for i in range(0, user_input):\n ele = int(input())\n lst.append(ele) # adding the element\n numbers = rotation(lst, rotating_time)\n print(\"Rotated by {0}: {1}\".format(rotating_time, numbers))\n\n\nif __name__ == \"__main__\":\n main()\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
class Solution:
# This would generate all permutations, but that's not what this question asks for
# def combo(self, cur, n, ret, arr):
# if cur == n:
# arr.append(ret)
# return
# self.combo(cur+1, n, ret + "1", arr)
# self.combo(cur+1, n, ret + "0", arr)
def combo(self, n):
if n == 0:
return ['0']
elif n == 1:
return ['0','1']
else:
prev = self.combo(n-1)
new = []
for combo in prev:
new.append('0' + combo)
prev.reverse()
for combo in prev:
new.append('1' + combo)
return new
def grayCode(self, n: int) -> List[int]:
combo = self.combo(n)
ret = []
for c in combo:
ret.append(int(c, 2))
return ret
|
normal
|
{
"blob_id": "e9a929dfef327737b54723579d3c57884fe61057",
"index": 7061,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n <mask token>\n",
"step-3": "class Solution:\n\n def combo(self, n):\n if n == 0:\n return ['0']\n elif n == 1:\n return ['0', '1']\n else:\n prev = self.combo(n - 1)\n new = []\n for combo in prev:\n new.append('0' + combo)\n prev.reverse()\n for combo in prev:\n new.append('1' + combo)\n return new\n <mask token>\n",
"step-4": "class Solution:\n\n def combo(self, n):\n if n == 0:\n return ['0']\n elif n == 1:\n return ['0', '1']\n else:\n prev = self.combo(n - 1)\n new = []\n for combo in prev:\n new.append('0' + combo)\n prev.reverse()\n for combo in prev:\n new.append('1' + combo)\n return new\n\n def grayCode(self, n: int) ->List[int]:\n combo = self.combo(n)\n ret = []\n for c in combo:\n ret.append(int(c, 2))\n return ret\n",
"step-5": "class Solution:\n\t# This would generate all permutations, but that's not what this question asks for\n # def combo(self, cur, n, ret, arr):\n # if cur == n:\n # arr.append(ret)\n # return\n # self.combo(cur+1, n, ret + \"1\", arr)\n # self.combo(cur+1, n, ret + \"0\", arr) \n def combo(self, n):\n if n == 0:\n return ['0']\n elif n == 1:\n return ['0','1']\n else:\n prev = self.combo(n-1)\n new = []\n for combo in prev:\n new.append('0' + combo)\n prev.reverse()\n for combo in prev:\n new.append('1' + combo)\n return new\n def grayCode(self, n: int) -> List[int]:\n combo = self.combo(n)\n ret = []\n for c in combo:\n ret.append(int(c, 2))\n return ret\n ",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@pytest.mark.parametrize('invalid_line', ['beginningGDG::',
'beginning::end', 'nothing'])
def test_parse_invalid_line(invalid_line):
assert gadget.parse_log_line(invalid_line) is None
<|reserved_special_token_1|>
import pytest
import gadget
@pytest.mark.parametrize('invalid_line', ['beginningGDG::',
'beginning::end', 'nothing'])
def test_parse_invalid_line(invalid_line):
assert gadget.parse_log_line(invalid_line) is None
<|reserved_special_token_1|>
import pytest
import gadget
@pytest.mark.parametrize('invalid_line', [
'beginningGDG::',
'beginning::end',
'nothing',
])
def test_parse_invalid_line(invalid_line):
assert gadget.parse_log_line(invalid_line) is None
|
flexible
|
{
"blob_id": "0dd361239d85ed485594ac0f5e7e2168f0684544",
"index": 915,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.parametrize('invalid_line', ['beginningGDG::',\n 'beginning::end', 'nothing'])\ndef test_parse_invalid_line(invalid_line):\n assert gadget.parse_log_line(invalid_line) is None\n",
"step-3": "import pytest\nimport gadget\n\n\n@pytest.mark.parametrize('invalid_line', ['beginningGDG::',\n 'beginning::end', 'nothing'])\ndef test_parse_invalid_line(invalid_line):\n assert gadget.parse_log_line(invalid_line) is None\n",
"step-4": "import pytest\nimport gadget\n\n@pytest.mark.parametrize('invalid_line', [\n 'beginningGDG::',\n 'beginning::end',\n 'nothing',\n])\ndef test_parse_invalid_line(invalid_line):\n assert gadget.parse_log_line(invalid_line) is None\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class person(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class person(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __unicode__(self):
return self.name
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class person(models.Model):
name = models.CharField(max_length=20, unique=True)
age = models.IntegerField()
email = models.CharField(max_length=20, unique=True)
phone = models.CharField(max_length=10, unique=True)
gender = models.CharField(max_length=10)
locations = [('ind', 'india'), ('aus', 'AUS')]
location = models.CharField(max_length=10, choices=locations)
marital_status = models.CharField(max_length=10)
def __unicode__(self):
return self.name
<|reserved_special_token_1|>
from django.db import models
class person(models.Model):
name = models.CharField(max_length=20, unique=True)
age = models.IntegerField()
email = models.CharField(max_length=20, unique=True)
phone = models.CharField(max_length=10, unique=True)
gender = models.CharField(max_length=10)
locations = [('ind', 'india'), ('aus', 'AUS')]
location = models.CharField(max_length=10, choices=locations)
marital_status = models.CharField(max_length=10)
def __unicode__(self):
return self.name
<|reserved_special_token_1|>
from django.db import models
# Create your models here.
class person(models.Model):
name=models.CharField(max_length=20,unique=True)
age=models.IntegerField()
email=models.CharField(max_length=20,unique=True)
phone=models.CharField(max_length=10, unique=True)
gender=models.CharField(max_length=10)
locations=[('ind',"india"),('aus',"AUS")]
location=models.CharField(max_length=10,choices=locations)
marital_status=models.CharField(max_length=10)
def __unicode__(self):
return self.name
|
flexible
|
{
"blob_id": "efe5df4005dbdb04cf4e7da1f350dab483c94c92",
"index": 4459,
"step-1": "<mask token>\n\n\nclass person(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass person(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __unicode__(self):\n return self.name\n",
"step-3": "<mask token>\n\n\nclass person(models.Model):\n name = models.CharField(max_length=20, unique=True)\n age = models.IntegerField()\n email = models.CharField(max_length=20, unique=True)\n phone = models.CharField(max_length=10, unique=True)\n gender = models.CharField(max_length=10)\n locations = [('ind', 'india'), ('aus', 'AUS')]\n location = models.CharField(max_length=10, choices=locations)\n marital_status = models.CharField(max_length=10)\n\n def __unicode__(self):\n return self.name\n",
"step-4": "from django.db import models\n\n\nclass person(models.Model):\n name = models.CharField(max_length=20, unique=True)\n age = models.IntegerField()\n email = models.CharField(max_length=20, unique=True)\n phone = models.CharField(max_length=10, unique=True)\n gender = models.CharField(max_length=10)\n locations = [('ind', 'india'), ('aus', 'AUS')]\n location = models.CharField(max_length=10, choices=locations)\n marital_status = models.CharField(max_length=10)\n\n def __unicode__(self):\n return self.name\n",
"step-5": "from django.db import models\n\n# Create your models here.\nclass person(models.Model):\n name=models.CharField(max_length=20,unique=True)\n age=models.IntegerField()\n email=models.CharField(max_length=20,unique=True)\n phone=models.CharField(max_length=10, unique=True)\n gender=models.CharField(max_length=10)\n locations=[('ind',\"india\"),('aus',\"AUS\")]\n location=models.CharField(max_length=10,choices=locations)\n marital_status=models.CharField(max_length=10)\n def __unicode__(self):\n return self.name\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
try:
var = int(input('Введите число: '))
c = 100 / var
print('Полет нормальный!')
except ZeroDivisionError:
c = 0
print('Попытка деления на ноль')
else:
print('Логика, которая выполняется только если нет исключений')
finally:
print('Критически важное действие')
print('Result', c)
<|reserved_special_token_1|>
a = 100
b = 0
try:
var = int(input('Введите число: '))
c = 100 / var
print('Полет нормальный!')
except ZeroDivisionError:
c = 0
print('Попытка деления на ноль')
else:
print('Логика, которая выполняется только если нет исключений')
finally:
print('Критически важное действие')
print('Result', c)
<|reserved_special_token_1|>
# *** Обработка исключений (исключительные события, искл. ситуации)***
# генерация исключения
a=100
b=0
# "деление на ноль" - пример ошибки (не рабочий)
# c=a/b
# решение - обработка исключений (отлов исключения)
# конструкция "try-except"
# try:
# c = a / b
# print("Все отлично")
# except:
# # тут должен быть код, который срабатывает при исключительных ситуациях
# # т.е. "запасной" код
# print("Что-то пошло не так")
# c=a/1
# # тут может быть код который выполняется после предыдущего блока
# print("Result: ", c)
# обработка множества исключений
# result=None
# try:
# var = int(input("Введите число, но не ноль: "))
# result = 50/var
# # обработка исключения конкретного типа (класса)
# except ZeroDivisionError: # в данном примере тип исключения - ZeroDivisionError
# print("Вы попытались поделить на ноль!")
# result=50/1
# except ValueError as val_error: # в данном примере тип исключения - ValueError,
# print(f"По-моему, Вы ввели не число. Инфо: {val_error}")
# result=0
# # обработка общего (базового) исключения - отлавливает все исключения
# except Exception as err:
# print(f"Что-то пошло не так: {err}")
# print("Result: ", result)
# конструкция "try-except-finally"
# try:
# var=int(input("Введите число: "))
# c = 100/var
# print("Полет нормальный!")
# except ZeroDivisionError:
# c=0
# print("Попытка деления на ноль")
# finally:
# # finally срабатывает в любом случае, даже если программа завершится аварийно
# # т.е. тут должна быть критически важная логика
# print("Критически важное действие")
# print("Result", c)
# конструкция "try-except-finally"
try:
var=int(input("Введите число: "))
c = 100/var
print("Полет нормальный!")
except ZeroDivisionError:
c=0
print("Попытка деления на ноль")
else:
#else срабатывает только тогда, когда нет исключений
print("Логика, которая выполняется только если нет исключений")
finally:
# finally срабатывает в любом случае, даже если программа завершится аварийно
# т.е. тут должна быть критически важная логика
print("Критически важное действие")
print("Result", c)
|
flexible
|
{
"blob_id": "bb02ba68eb6629dad364b5f015680e4126e655f3",
"index": 6173,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n var = int(input('Введите число: '))\n c = 100 / var\n print('Полет нормальный!')\nexcept ZeroDivisionError:\n c = 0\n print('Попытка деления на ноль')\nelse:\n print('Логика, которая выполняется только если нет исключений')\nfinally:\n print('Критически важное действие')\nprint('Result', c)\n",
"step-3": "a = 100\nb = 0\ntry:\n var = int(input('Введите число: '))\n c = 100 / var\n print('Полет нормальный!')\nexcept ZeroDivisionError:\n c = 0\n print('Попытка деления на ноль')\nelse:\n print('Логика, которая выполняется только если нет исключений')\nfinally:\n print('Критически важное действие')\nprint('Result', c)\n",
"step-4": "# *** Обработка исключений (исключительные события, искл. ситуации)***\n\n# генерация исключения\na=100\nb=0\n\n# \"деление на ноль\" - пример ошибки (не рабочий)\n# c=a/b\n\n# решение - обработка исключений (отлов исключения)\n# конструкция \"try-except\"\n\n# try:\n# c = a / b\n# print(\"Все отлично\")\n# except:\n# # тут должен быть код, который срабатывает при исключительных ситуациях\n# # т.е. \"запасной\" код\n# print(\"Что-то пошло не так\")\n# c=a/1\n\n# # тут может быть код который выполняется после предыдущего блока\n# print(\"Result: \", c)\n\n\n# обработка множества исключений\n\n# result=None\n\n# try:\n# var = int(input(\"Введите число, но не ноль: \"))\n# result = 50/var\n# # обработка исключения конкретного типа (класса)\n# except ZeroDivisionError: # в данном примере тип исключения - ZeroDivisionError\n# print(\"Вы попытались поделить на ноль!\")\n# result=50/1\n# except ValueError as val_error: # в данном примере тип исключения - ValueError, \n# print(f\"По-моему, Вы ввели не число. Инфо: {val_error}\")\n# result=0\n\n# # обработка общего (базового) исключения - отлавливает все исключения\n# except Exception as err:\n# print(f\"Что-то пошло не так: {err}\")\n\n# print(\"Result: \", result)\n\n\n# конструкция \"try-except-finally\"\n\n# try:\n# var=int(input(\"Введите число: \"))\n# c = 100/var\n# print(\"Полет нормальный!\")\n# except ZeroDivisionError:\n# c=0\n# print(\"Попытка деления на ноль\")\n# finally:\n# # finally срабатывает в любом случае, даже если программа завершится аварийно\n# # т.е. тут должна быть критически важная логика\n# print(\"Критически важное действие\")\n\n# print(\"Result\", c)\n\n# конструкция \"try-except-finally\"\n\ntry:\n var=int(input(\"Введите число: \"))\n c = 100/var\n print(\"Полет нормальный!\")\nexcept ZeroDivisionError:\n c=0\n print(\"Попытка деления на ноль\")\nelse: \n #else срабатывает только тогда, когда нет исключений\n print(\"Логика, которая выполняется только если нет исключений\")\nfinally:\n # finally срабатывает в любом случае, даже если программа завершится аварийно\n # т.е. тут должна быть критически важная логика\n print(\"Критически важное действие\")\n\nprint(\"Result\", c)",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
import boto3
from app.models import *
from app.config import *
from app.lib.log import save_races_to_db, save_laptimes_to_db
from app.utils.utils import get_sec
import pandas as pd
def import_csv_from_aws():
client = boto3.client(
's3',
aws_access_key_id=AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY
)
client.download_file('ergast-csv','filtered_laptimes.csv','filtered_laptimes.csv')
client.download_file('ergast-csv','filtered_races.csv','filtered_races.csv')
df_lapTimes = pd.read_csv('filtered_laptimes.csv')
df_races = pd.read_csv('filtered_races.csv')
df_races['round'] = df_races['round'].astype(int)
df_races['season'] = df_races['season'].astype(int)
df_lapTimes.rename(columns={"driverId":"driverRef"}, inplace=True)
df_lapTimes = pd.merge(df_lapTimes, df_races[['raceId', 'raceName', 'season', 'round']], on=['raceId'], how='left')
df_lapTimes.fillna("0:00:00", inplace=True)
df_lapTimes['time'] = df_lapTimes['time'].map(lambda x: get_sec(x))
df_lapTimes = df_lapTimes[["driverRef", "season", "raceId", "raceName", "round", "lap", "time", "position"]]
df_lapTimes.rename(columns={"round":"roundId"}, inplace=True)
save_races_to_db(df_races, db.session)
for i, group in df_lapTimes.groupby("raceId"):
g = group.drop(["raceId"], axis=1)
b.session.bulk_insert_mappings(LapTimes, g.to_dict("records"))
db.session.commit()
|
normal
|
{
"blob_id": "b573db8ea0845fb947636b8d82ed462904c6005d",
"index": 5519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef import_csv_from_aws():\n client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY_ID,\n aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\n client.download_file('ergast-csv', 'filtered_laptimes.csv',\n 'filtered_laptimes.csv')\n client.download_file('ergast-csv', 'filtered_races.csv',\n 'filtered_races.csv')\n df_lapTimes = pd.read_csv('filtered_laptimes.csv')\n df_races = pd.read_csv('filtered_races.csv')\n df_races['round'] = df_races['round'].astype(int)\n df_races['season'] = df_races['season'].astype(int)\n df_lapTimes.rename(columns={'driverId': 'driverRef'}, inplace=True)\n df_lapTimes = pd.merge(df_lapTimes, df_races[['raceId', 'raceName',\n 'season', 'round']], on=['raceId'], how='left')\n df_lapTimes.fillna('0:00:00', inplace=True)\n df_lapTimes['time'] = df_lapTimes['time'].map(lambda x: get_sec(x))\n df_lapTimes = df_lapTimes[['driverRef', 'season', 'raceId', 'raceName',\n 'round', 'lap', 'time', 'position']]\n df_lapTimes.rename(columns={'round': 'roundId'}, inplace=True)\n save_races_to_db(df_races, db.session)\n for i, group in df_lapTimes.groupby('raceId'):\n g = group.drop(['raceId'], axis=1)\n b.session.bulk_insert_mappings(LapTimes, g.to_dict('records'))\n db.session.commit()\n",
"step-3": "import boto3\nfrom app.models import *\nfrom app.config import *\nfrom app.lib.log import save_races_to_db, save_laptimes_to_db\nfrom app.utils.utils import get_sec\nimport pandas as pd\n\n\ndef import_csv_from_aws():\n client = boto3.client('s3', aws_access_key_id=AWS_ACCESS_KEY_ID,\n aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\n client.download_file('ergast-csv', 'filtered_laptimes.csv',\n 'filtered_laptimes.csv')\n client.download_file('ergast-csv', 'filtered_races.csv',\n 'filtered_races.csv')\n df_lapTimes = pd.read_csv('filtered_laptimes.csv')\n df_races = pd.read_csv('filtered_races.csv')\n df_races['round'] = df_races['round'].astype(int)\n df_races['season'] = df_races['season'].astype(int)\n df_lapTimes.rename(columns={'driverId': 'driverRef'}, inplace=True)\n df_lapTimes = pd.merge(df_lapTimes, df_races[['raceId', 'raceName',\n 'season', 'round']], on=['raceId'], how='left')\n df_lapTimes.fillna('0:00:00', inplace=True)\n df_lapTimes['time'] = df_lapTimes['time'].map(lambda x: get_sec(x))\n df_lapTimes = df_lapTimes[['driverRef', 'season', 'raceId', 'raceName',\n 'round', 'lap', 'time', 'position']]\n df_lapTimes.rename(columns={'round': 'roundId'}, inplace=True)\n save_races_to_db(df_races, db.session)\n for i, group in df_lapTimes.groupby('raceId'):\n g = group.drop(['raceId'], axis=1)\n b.session.bulk_insert_mappings(LapTimes, g.to_dict('records'))\n db.session.commit()\n",
"step-4": "import boto3\nfrom app.models import *\nfrom app.config import *\nfrom app.lib.log import save_races_to_db, save_laptimes_to_db\nfrom app.utils.utils import get_sec\nimport pandas as pd\n\ndef import_csv_from_aws():\n\n\tclient = boto3.client(\n\t\t's3',\n\t\taws_access_key_id=AWS_ACCESS_KEY_ID,\n\t\taws_secret_access_key=AWS_SECRET_ACCESS_KEY\n\t)\n\n\tclient.download_file('ergast-csv','filtered_laptimes.csv','filtered_laptimes.csv')\n\tclient.download_file('ergast-csv','filtered_races.csv','filtered_races.csv')\n\n\tdf_lapTimes = pd.read_csv('filtered_laptimes.csv')\n\tdf_races = pd.read_csv('filtered_races.csv')\n\n\tdf_races['round'] = df_races['round'].astype(int)\n\tdf_races['season'] = df_races['season'].astype(int)\n\n\tdf_lapTimes.rename(columns={\"driverId\":\"driverRef\"}, inplace=True)\n\tdf_lapTimes = pd.merge(df_lapTimes, df_races[['raceId', 'raceName', 'season', 'round']], on=['raceId'], how='left')\n\n\tdf_lapTimes.fillna(\"0:00:00\", inplace=True)\n\tdf_lapTimes['time'] = df_lapTimes['time'].map(lambda x: get_sec(x))\n\n\tdf_lapTimes = df_lapTimes[[\"driverRef\", \"season\", \"raceId\", \"raceName\", \"round\", \"lap\", \"time\", \"position\"]]\n\tdf_lapTimes.rename(columns={\"round\":\"roundId\"}, inplace=True)\n\n\tsave_races_to_db(df_races, db.session)\n\n\tfor i, group in df_lapTimes.groupby(\"raceId\"):\n\n\t\tg = group.drop([\"raceId\"], axis=1)\n\t\tb.session.bulk_insert_mappings(LapTimes, g.to_dict(\"records\"))\n\t\tdb.session.commit()\n\n\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
from airbot import resolvers
from airbot import utils
import unittest
from grapher import App
import pprint
OPENID_CONFIG = {
'ISSUER_URL': 'https://dev-545796.oktapreview.com',
'CLIENT_ID': '0oafvba1nlTwOqPN40h7',
'REDIRECT_URI': 'http://locahost/implicit/callback'
}
class TestEndToEnd(unittest.TestCase) :
@classmethod
def get_claim(cls):
claim = utils.OpenidHelper.get_claim(OPENID_CONFIG, "moshir.mikael@gmail.com","Azerty1!")
return claim
def test_entity_api(self):
event = {
"identity": {"claims" : TestEndToEnd.get_claim()},
"field" : "createBot",
"path" : "Mutation/createBot",
"arguments" : {
"accountid" : "testaccount",
"input" : {
"name" : "mytestbot",
"description" :"test"
}
}
}
self.assertTrue(True)
b= App.handler(event,{})
print b
event = {
"identity": {"claims": TestEndToEnd.get_claim()},
"field": "createEntity",
"path": "Mutation/createEntity",
"arguments": {
"botid": b["ID"],
"input": {
"name": "mytestbot",
"description": "test"
}
}
}
w= App.handler(event,{})
print w
event = {
"identity": {"claims": TestEndToEnd.get_claim()},
"field": "getEntity",
"path": "Query/getEntity",
"arguments": {
"entityid": w["ID"],
}
}
event = {
"identity": {"claims": TestEndToEnd.get_claim()},
"field": "updateEntity",
"path": "Mutation/updateEntity",
"arguments": {
"entityid": w["ID"],
"input": {
"tags" : "x,y,z"
}
}
}
u = App.handler(event, {})
print "U = ", u
event = {
"identity": {"claims": TestEndToEnd.get_claim()},
"field": "listEntities",
"path": "Query/listentities",
"arguments": {
"botid": b["ID"]
}
}
l = App.handler(event, {})
print "entities = ",l
event = {
"identity": {"claims": TestEndToEnd.get_claim()},
"field": "deleteEntity",
"path": "Mutation/deleteEntity",
"arguments": {
"entityid": w["ID"]
}
}
d = App.handler(event, {})
print "D = ", d
if __name__ == "__main__" :
unittest.main(verbosity=2)
|
normal
|
{
"blob_id": "9725c4bfea1215e2fb81c31cbb8948fd1656aca9",
"index": 3814,
"step-1": "from airbot import resolvers\nfrom airbot import utils\nimport unittest\nfrom grapher import App\nimport pprint\n\n\nOPENID_CONFIG = {\n 'ISSUER_URL': 'https://dev-545796.oktapreview.com',\n 'CLIENT_ID': '0oafvba1nlTwOqPN40h7',\n 'REDIRECT_URI': 'http://locahost/implicit/callback'\n}\n\n\nclass TestEndToEnd(unittest.TestCase) :\n\n @classmethod\n def get_claim(cls):\n claim = utils.OpenidHelper.get_claim(OPENID_CONFIG, \"moshir.mikael@gmail.com\",\"Azerty1!\")\n return claim\n\n def test_entity_api(self):\n event = {\n \"identity\": {\"claims\" : TestEndToEnd.get_claim()},\n \"field\" : \"createBot\",\n \"path\" : \"Mutation/createBot\",\n \"arguments\" : {\n \"accountid\" : \"testaccount\",\n \"input\" : {\n \"name\" : \"mytestbot\",\n \"description\" :\"test\"\n }\n }\n }\n self.assertTrue(True)\n b= App.handler(event,{})\n print b\n\n event = {\n \"identity\": {\"claims\": TestEndToEnd.get_claim()},\n \"field\": \"createEntity\",\n \"path\": \"Mutation/createEntity\",\n \"arguments\": {\n \"botid\": b[\"ID\"],\n \"input\": {\n \"name\": \"mytestbot\",\n \"description\": \"test\"\n }\n }\n }\n\n w= App.handler(event,{})\n print w\n\n event = {\n \"identity\": {\"claims\": TestEndToEnd.get_claim()},\n \"field\": \"getEntity\",\n \"path\": \"Query/getEntity\",\n \"arguments\": {\n \"entityid\": w[\"ID\"],\n }\n }\n\n event = {\n \"identity\": {\"claims\": TestEndToEnd.get_claim()},\n \"field\": \"updateEntity\",\n \"path\": \"Mutation/updateEntity\",\n \"arguments\": {\n \"entityid\": w[\"ID\"],\n \"input\": {\n \"tags\" : \"x,y,z\"\n }\n }\n }\n u = App.handler(event, {})\n print \"U = \", u\n\n event = {\n \"identity\": {\"claims\": TestEndToEnd.get_claim()},\n \"field\": \"listEntities\",\n \"path\": \"Query/listentities\",\n \"arguments\": {\n \"botid\": b[\"ID\"]\n }\n }\n l = App.handler(event, {})\n print \"entities = \",l\n\n event = {\n \"identity\": {\"claims\": TestEndToEnd.get_claim()},\n \"field\": \"deleteEntity\",\n \"path\": \"Mutation/deleteEntity\",\n \"arguments\": {\n \"entityid\": w[\"ID\"]\n }\n }\n d = App.handler(event, {})\n print \"D = \", d\n\n\n\nif __name__ == \"__main__\" :\n unittest.main(verbosity=2)\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
<|reserved_special_token_0|>
def get_ratio(username):
try:
print(username)
link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username
response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}
)
if response.status_code == 200:
collection = response.json()
if 'error' in collection:
return '-1'
else:
ratio = collection['stats']['curr_p9']['kd']['value']
return ratio
print('Invalid username')
return '-1'
else:
print('Error parsing data.')
return '-2'
except KeyError:
print('Error finding data. KeyError was returned.')
return '-3'
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_ratio(username):
try:
print(username)
link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username
response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}
)
if response.status_code == 200:
collection = response.json()
if 'error' in collection:
return '-1'
else:
ratio = collection['stats']['curr_p9']['kd']['value']
return ratio
print('Invalid username')
return '-1'
else:
print('Error parsing data.')
return '-2'
except KeyError:
print('Error finding data. KeyError was returned.')
return '-3'
@client.event
async def on_message(message):
if message.author == client.user:
return
if message.content.startswith('!patch'):
await message.channel.send(
'Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/'
)
if message.content.startswith('!help'):
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify Bot Help', icon_url='')
embed.add_field(name=
'Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify'
, value=
'You can change your nickname by typing "/nick *YourEpicIGN*". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\'t be able to verify you.'
, inline=False)
await message.channel.send(embed=embed)
if message.content.startswith('!verify'):
for list in LIST:
roles = discord.utils.get(message.guild.roles, name=list)
username = '{0.author.display_name}'.format(message)
ratio = float(get_ratio(username))
msgRatio = str(ratio)
msgVerified = str(VERIFIED)
print(ratio)
if ratio == -1.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name='Fortnite player **' + message.author.
display_name + '** not found.', value=
"""
Your Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again."""
, inline=False)
await message.channel.send(embed=embed)
elif ratio == -2.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name='Data not found.', value=
'Fortnite Tracker is down. Please try again shortly.',
inline=False)
await message.channel.send(embed=embed)
elif ratio == -3.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=
'No stats found for squad mode in the current season.',
value='Play some games and try again.', inline=False)
await message.channel.send(embed=embed)
elif ratio > 0 and ratio < VERIFIED:
print('🚫')
print('-')
embed = discord.Embed(colour=discord.Colour(4532110), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name +
' does not have over a ' + msgVerified + ' K/D.', value=
'Current season squads K/D: **' + msgRatio + '**', inline=False
)
await message.channel.send(embed=embed)
elif ratio >= VERIFIED:
print('✅')
print('-')
role = discord.utils.get(message.guild.roles, name=LIST[0])
embed = discord.Embed(colour=discord.Colour(4532110), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name +
' has over a ' + msgVerified + ' K/D. Verified!', value=
'Current season squads K/D: **' + msgRatio + '**', inline=False
)
user = message.author
await message.channel.send(embed=embed)
await user.add_roles(role)
@client.event
async def on_ready():
print('-')
print('Logged in as: ' + client.user.name)
print('With Client User ID: ' + str(client.user.id))
print('Verified set to: ' + str(VERIFIED))
print('-')
client.run(DISCORD_TOKEN)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
client = discord.Client()
DISCORD_TOKEN = GITHUB_DISCORD_TOKEN
FORTNITE_API_KEY = GITHUB_FORTNITE_API_KEY
LIST = ['Verified']
VERIFIED = 4
def get_ratio(username):
try:
print(username)
link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username
response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}
)
if response.status_code == 200:
collection = response.json()
if 'error' in collection:
return '-1'
else:
ratio = collection['stats']['curr_p9']['kd']['value']
return ratio
print('Invalid username')
return '-1'
else:
print('Error parsing data.')
return '-2'
except KeyError:
print('Error finding data. KeyError was returned.')
return '-3'
@client.event
async def on_message(message):
if message.author == client.user:
return
if message.content.startswith('!patch'):
await message.channel.send(
'Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/'
)
if message.content.startswith('!help'):
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify Bot Help', icon_url='')
embed.add_field(name=
'Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify'
, value=
'You can change your nickname by typing "/nick *YourEpicIGN*". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\'t be able to verify you.'
, inline=False)
await message.channel.send(embed=embed)
if message.content.startswith('!verify'):
for list in LIST:
roles = discord.utils.get(message.guild.roles, name=list)
username = '{0.author.display_name}'.format(message)
ratio = float(get_ratio(username))
msgRatio = str(ratio)
msgVerified = str(VERIFIED)
print(ratio)
if ratio == -1.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name='Fortnite player **' + message.author.
display_name + '** not found.', value=
"""
Your Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again."""
, inline=False)
await message.channel.send(embed=embed)
elif ratio == -2.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name='Data not found.', value=
'Fortnite Tracker is down. Please try again shortly.',
inline=False)
await message.channel.send(embed=embed)
elif ratio == -3.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=
'No stats found for squad mode in the current season.',
value='Play some games and try again.', inline=False)
await message.channel.send(embed=embed)
elif ratio > 0 and ratio < VERIFIED:
print('🚫')
print('-')
embed = discord.Embed(colour=discord.Colour(4532110), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name +
' does not have over a ' + msgVerified + ' K/D.', value=
'Current season squads K/D: **' + msgRatio + '**', inline=False
)
await message.channel.send(embed=embed)
elif ratio >= VERIFIED:
print('✅')
print('-')
role = discord.utils.get(message.guild.roles, name=LIST[0])
embed = discord.Embed(colour=discord.Colour(4532110), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name +
' has over a ' + msgVerified + ' K/D. Verified!', value=
'Current season squads K/D: **' + msgRatio + '**', inline=False
)
user = message.author
await message.channel.send(embed=embed)
await user.add_roles(role)
@client.event
async def on_ready():
print('-')
print('Logged in as: ' + client.user.name)
print('With Client User ID: ' + str(client.user.id))
print('Verified set to: ' + str(VERIFIED))
print('-')
client.run(DISCORD_TOKEN)
<|reserved_special_token_1|>
import discord
import requests
import math
from keys import GITHUB_DISCORD_TOKEN, GITHUB_FORTNITE_API_KEY
client = discord.Client()
DISCORD_TOKEN = GITHUB_DISCORD_TOKEN
FORTNITE_API_KEY = GITHUB_FORTNITE_API_KEY
LIST = ['Verified']
VERIFIED = 4
def get_ratio(username):
try:
print(username)
link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username
response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}
)
if response.status_code == 200:
collection = response.json()
if 'error' in collection:
return '-1'
else:
ratio = collection['stats']['curr_p9']['kd']['value']
return ratio
print('Invalid username')
return '-1'
else:
print('Error parsing data.')
return '-2'
except KeyError:
print('Error finding data. KeyError was returned.')
return '-3'
@client.event
async def on_message(message):
if message.author == client.user:
return
if message.content.startswith('!patch'):
await message.channel.send(
'Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/'
)
if message.content.startswith('!help'):
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify Bot Help', icon_url='')
embed.add_field(name=
'Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify'
, value=
'You can change your nickname by typing "/nick *YourEpicIGN*". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\'t be able to verify you.'
, inline=False)
await message.channel.send(embed=embed)
if message.content.startswith('!verify'):
for list in LIST:
roles = discord.utils.get(message.guild.roles, name=list)
username = '{0.author.display_name}'.format(message)
ratio = float(get_ratio(username))
msgRatio = str(ratio)
msgVerified = str(VERIFIED)
print(ratio)
if ratio == -1.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name='Fortnite player **' + message.author.
display_name + '** not found.', value=
"""
Your Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again."""
, inline=False)
await message.channel.send(embed=embed)
elif ratio == -2.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name='Data not found.', value=
'Fortnite Tracker is down. Please try again shortly.',
inline=False)
await message.channel.send(embed=embed)
elif ratio == -3.0:
embed = discord.Embed(colour=discord.Colour(9315878), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=
'No stats found for squad mode in the current season.',
value='Play some games and try again.', inline=False)
await message.channel.send(embed=embed)
elif ratio > 0 and ratio < VERIFIED:
print('🚫')
print('-')
embed = discord.Embed(colour=discord.Colour(4532110), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name +
' does not have over a ' + msgVerified + ' K/D.', value=
'Current season squads K/D: **' + msgRatio + '**', inline=False
)
await message.channel.send(embed=embed)
elif ratio >= VERIFIED:
print('✅')
print('-')
role = discord.utils.get(message.guild.roles, name=LIST[0])
embed = discord.Embed(colour=discord.Colour(4532110), url=
'https://github.com/af1/kdFortniteDiscordBot')
embed.set_author(name='Verify ' + message.author.display_name,
icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name +
' has over a ' + msgVerified + ' K/D. Verified!', value=
'Current season squads K/D: **' + msgRatio + '**', inline=False
)
user = message.author
await message.channel.send(embed=embed)
await user.add_roles(role)
@client.event
async def on_ready():
print('-')
print('Logged in as: ' + client.user.name)
print('With Client User ID: ' + str(client.user.id))
print('Verified set to: ' + str(VERIFIED))
print('-')
client.run(DISCORD_TOKEN)
<|reserved_special_token_1|>
import discord
import requests
import math
from keys import GITHUB_DISCORD_TOKEN, GITHUB_FORTNITE_API_KEY
client = discord.Client()
# Constant
DISCORD_TOKEN = GITHUB_DISCORD_TOKEN
FORTNITE_API_KEY = GITHUB_FORTNITE_API_KEY
LIST = ['Verified']
VERIFIED = 4
# Return the current season squad K/D of the fortnite player
def get_ratio(username):
try:
print(username)
link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username
response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY})
if response.status_code == 200:
collection = response.json()
if 'error' in collection:
return "-1"
else:
ratio = collection['stats']['curr_p9']['kd']['value']
return ratio
print("Invalid username")
return "-1"
else:
print("Error parsing data.")
return "-2"
except KeyError:
print("Error finding data. KeyError was returned.")
return "-3"
@client.event
async def on_message(message):
# we do not want the bot to reply to itself
if message.author == client.user:
return
# The command !patch return a link with the lastest patch note
if message.content.startswith('!patch'):
await message.channel.send('Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/')
# The command !help explains the one function
if message.content.startswith('!help'):
embed = discord.Embed(colour=discord.Colour(0x8e2626), url="https://github.com/af1/kdFortniteDiscordBot",)
embed.set_author(name="Verify Bot Help", icon_url="")
embed.add_field(name="Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify", value="You can change your nickname by typing \"/nick *YourEpicIGN*\". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\'t be able to verify you.", inline=False)
await message.channel.send(embed=embed)
# The command !verify return attribute a rank according to the K/D of the user
if message.content.startswith("!verify"):
for list in LIST:
roles = discord.utils.get(message.guild.roles, name=list)
username = '{0.author.display_name}'.format(message)
ratio = float(get_ratio(username))
msgRatio = str(ratio)
msgVerified = str(VERIFIED)
print(ratio)
if ratio == -1.0:
embed = discord.Embed(colour=discord.Colour(0x8e2626), url="https://github.com/af1/kdFortniteDiscordBot",)
embed.set_author(name="Verify " + message.author.display_name, icon_url=message.author.avatar_url)
embed.add_field(name="Fortnite player **" + message.author.display_name + "** not found.", value="\nYour Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again.", inline=False)
await message.channel.send(embed=embed)
elif ratio == -2.0:
embed = discord.Embed(colour=discord.Colour(0x8e2626), url="https://github.com/af1/kdFortniteDiscordBot",)
embed.set_author(name="Verify " + message.author.display_name, icon_url=message.author.avatar_url)
embed.add_field(name="Data not found.", value="Fortnite Tracker is down. Please try again shortly.", inline=False)
await message.channel.send(embed=embed)
elif ratio == -3.0:
embed = discord.Embed(colour=discord.Colour(0x8e2626), url="https://github.com/af1/kdFortniteDiscordBot",)
embed.set_author(name="Verify " + message.author.display_name, icon_url=message.author.avatar_url)
embed.add_field(name="No stats found for squad mode in the current season.", value="Play some games and try again.", inline=False)
await message.channel.send(embed=embed)
elif ratio > 0 and ratio < VERIFIED:
print("🚫")
print("-")
embed = discord.Embed(colour=discord.Colour(0x45278e), url="https://github.com/af1/kdFortniteDiscordBot",)
embed.set_author(name="Verify " + message.author.display_name, icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name + " does not have over a " + msgVerified + " K/D.", value="Current season squads K/D: **" + msgRatio + "**", inline=False)
await message.channel.send(embed=embed)
elif ratio >= VERIFIED:
print("✅")
print("-")
role = discord.utils.get(message.guild.roles, name=LIST[0])
embed = discord.Embed(colour=discord.Colour(0x45278e), url="https://github.com/af1/kdFortniteDiscordBot",)
embed.set_author(name="Verify " + message.author.display_name, icon_url=message.author.avatar_url)
embed.add_field(name=message.author.display_name + " has over a " + msgVerified + " K/D. Verified!", value="Current season squads K/D: **" + msgRatio + "**", inline=False)
user=message.author
await message.channel.send(embed=embed)
await user.add_roles(role)
@client.event
async def on_ready():
print("-")
print("Logged in as: " + client.user.name)
print("With Client User ID: " + str(client.user.id))
print("Verified set to: " + str(VERIFIED))
print("-")
client.run(DISCORD_TOKEN)
|
flexible
|
{
"blob_id": "6c6a49dfced680fe034cbbc2fa28d57d2aa1273e",
"index": 8973,
"step-1": "<mask token>\n\n\ndef get_ratio(username):\n try:\n print(username)\n link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username\n response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}\n )\n if response.status_code == 200:\n collection = response.json()\n if 'error' in collection:\n return '-1'\n else:\n ratio = collection['stats']['curr_p9']['kd']['value']\n return ratio\n print('Invalid username')\n return '-1'\n else:\n print('Error parsing data.')\n return '-2'\n except KeyError:\n print('Error finding data. KeyError was returned.')\n return '-3'\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_ratio(username):\n try:\n print(username)\n link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username\n response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}\n )\n if response.status_code == 200:\n collection = response.json()\n if 'error' in collection:\n return '-1'\n else:\n ratio = collection['stats']['curr_p9']['kd']['value']\n return ratio\n print('Invalid username')\n return '-1'\n else:\n print('Error parsing data.')\n return '-2'\n except KeyError:\n print('Error finding data. KeyError was returned.')\n return '-3'\n\n\n@client.event\nasync def on_message(message):\n if message.author == client.user:\n return\n if message.content.startswith('!patch'):\n await message.channel.send(\n 'Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/'\n )\n if message.content.startswith('!help'):\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify Bot Help', icon_url='')\n embed.add_field(name=\n 'Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify'\n , value=\n 'You can change your nickname by typing \"/nick *YourEpicIGN*\". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\\'t be able to verify you.'\n , inline=False)\n await message.channel.send(embed=embed)\n if message.content.startswith('!verify'):\n for list in LIST:\n roles = discord.utils.get(message.guild.roles, name=list)\n username = '{0.author.display_name}'.format(message)\n ratio = float(get_ratio(username))\n msgRatio = str(ratio)\n msgVerified = str(VERIFIED)\n print(ratio)\n if ratio == -1.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name='Fortnite player **' + message.author.\n display_name + '** not found.', value=\n \"\"\"\nYour Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again.\"\"\"\n , inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -2.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name='Data not found.', value=\n 'Fortnite Tracker is down. Please try again shortly.',\n inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -3.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=\n 'No stats found for squad mode in the current season.',\n value='Play some games and try again.', inline=False)\n await message.channel.send(embed=embed)\n elif ratio > 0 and ratio < VERIFIED:\n print('🚫')\n print('-')\n embed = discord.Embed(colour=discord.Colour(4532110), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name +\n ' does not have over a ' + msgVerified + ' K/D.', value=\n 'Current season squads K/D: **' + msgRatio + '**', inline=False\n )\n await message.channel.send(embed=embed)\n elif ratio >= VERIFIED:\n print('✅')\n print('-')\n role = discord.utils.get(message.guild.roles, name=LIST[0])\n embed = discord.Embed(colour=discord.Colour(4532110), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name +\n ' has over a ' + msgVerified + ' K/D. Verified!', value=\n 'Current season squads K/D: **' + msgRatio + '**', inline=False\n )\n user = message.author\n await message.channel.send(embed=embed)\n await user.add_roles(role)\n\n\n@client.event\nasync def on_ready():\n print('-')\n print('Logged in as: ' + client.user.name)\n print('With Client User ID: ' + str(client.user.id))\n print('Verified set to: ' + str(VERIFIED))\n print('-')\n\n\nclient.run(DISCORD_TOKEN)\n",
"step-3": "<mask token>\nclient = discord.Client()\nDISCORD_TOKEN = GITHUB_DISCORD_TOKEN\nFORTNITE_API_KEY = GITHUB_FORTNITE_API_KEY\nLIST = ['Verified']\nVERIFIED = 4\n\n\ndef get_ratio(username):\n try:\n print(username)\n link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username\n response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}\n )\n if response.status_code == 200:\n collection = response.json()\n if 'error' in collection:\n return '-1'\n else:\n ratio = collection['stats']['curr_p9']['kd']['value']\n return ratio\n print('Invalid username')\n return '-1'\n else:\n print('Error parsing data.')\n return '-2'\n except KeyError:\n print('Error finding data. KeyError was returned.')\n return '-3'\n\n\n@client.event\nasync def on_message(message):\n if message.author == client.user:\n return\n if message.content.startswith('!patch'):\n await message.channel.send(\n 'Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/'\n )\n if message.content.startswith('!help'):\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify Bot Help', icon_url='')\n embed.add_field(name=\n 'Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify'\n , value=\n 'You can change your nickname by typing \"/nick *YourEpicIGN*\". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\\'t be able to verify you.'\n , inline=False)\n await message.channel.send(embed=embed)\n if message.content.startswith('!verify'):\n for list in LIST:\n roles = discord.utils.get(message.guild.roles, name=list)\n username = '{0.author.display_name}'.format(message)\n ratio = float(get_ratio(username))\n msgRatio = str(ratio)\n msgVerified = str(VERIFIED)\n print(ratio)\n if ratio == -1.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name='Fortnite player **' + message.author.\n display_name + '** not found.', value=\n \"\"\"\nYour Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again.\"\"\"\n , inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -2.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name='Data not found.', value=\n 'Fortnite Tracker is down. Please try again shortly.',\n inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -3.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=\n 'No stats found for squad mode in the current season.',\n value='Play some games and try again.', inline=False)\n await message.channel.send(embed=embed)\n elif ratio > 0 and ratio < VERIFIED:\n print('🚫')\n print('-')\n embed = discord.Embed(colour=discord.Colour(4532110), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name +\n ' does not have over a ' + msgVerified + ' K/D.', value=\n 'Current season squads K/D: **' + msgRatio + '**', inline=False\n )\n await message.channel.send(embed=embed)\n elif ratio >= VERIFIED:\n print('✅')\n print('-')\n role = discord.utils.get(message.guild.roles, name=LIST[0])\n embed = discord.Embed(colour=discord.Colour(4532110), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name +\n ' has over a ' + msgVerified + ' K/D. Verified!', value=\n 'Current season squads K/D: **' + msgRatio + '**', inline=False\n )\n user = message.author\n await message.channel.send(embed=embed)\n await user.add_roles(role)\n\n\n@client.event\nasync def on_ready():\n print('-')\n print('Logged in as: ' + client.user.name)\n print('With Client User ID: ' + str(client.user.id))\n print('Verified set to: ' + str(VERIFIED))\n print('-')\n\n\nclient.run(DISCORD_TOKEN)\n",
"step-4": "import discord\nimport requests\nimport math\nfrom keys import GITHUB_DISCORD_TOKEN, GITHUB_FORTNITE_API_KEY\nclient = discord.Client()\nDISCORD_TOKEN = GITHUB_DISCORD_TOKEN\nFORTNITE_API_KEY = GITHUB_FORTNITE_API_KEY\nLIST = ['Verified']\nVERIFIED = 4\n\n\ndef get_ratio(username):\n try:\n print(username)\n link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username\n response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY}\n )\n if response.status_code == 200:\n collection = response.json()\n if 'error' in collection:\n return '-1'\n else:\n ratio = collection['stats']['curr_p9']['kd']['value']\n return ratio\n print('Invalid username')\n return '-1'\n else:\n print('Error parsing data.')\n return '-2'\n except KeyError:\n print('Error finding data. KeyError was returned.')\n return '-3'\n\n\n@client.event\nasync def on_message(message):\n if message.author == client.user:\n return\n if message.content.startswith('!patch'):\n await message.channel.send(\n 'Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/'\n )\n if message.content.startswith('!help'):\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify Bot Help', icon_url='')\n embed.add_field(name=\n 'Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify'\n , value=\n 'You can change your nickname by typing \"/nick *YourEpicIGN*\". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\\'t be able to verify you.'\n , inline=False)\n await message.channel.send(embed=embed)\n if message.content.startswith('!verify'):\n for list in LIST:\n roles = discord.utils.get(message.guild.roles, name=list)\n username = '{0.author.display_name}'.format(message)\n ratio = float(get_ratio(username))\n msgRatio = str(ratio)\n msgVerified = str(VERIFIED)\n print(ratio)\n if ratio == -1.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name='Fortnite player **' + message.author.\n display_name + '** not found.', value=\n \"\"\"\nYour Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again.\"\"\"\n , inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -2.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name='Data not found.', value=\n 'Fortnite Tracker is down. Please try again shortly.',\n inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -3.0:\n embed = discord.Embed(colour=discord.Colour(9315878), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=\n 'No stats found for squad mode in the current season.',\n value='Play some games and try again.', inline=False)\n await message.channel.send(embed=embed)\n elif ratio > 0 and ratio < VERIFIED:\n print('🚫')\n print('-')\n embed = discord.Embed(colour=discord.Colour(4532110), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name +\n ' does not have over a ' + msgVerified + ' K/D.', value=\n 'Current season squads K/D: **' + msgRatio + '**', inline=False\n )\n await message.channel.send(embed=embed)\n elif ratio >= VERIFIED:\n print('✅')\n print('-')\n role = discord.utils.get(message.guild.roles, name=LIST[0])\n embed = discord.Embed(colour=discord.Colour(4532110), url=\n 'https://github.com/af1/kdFortniteDiscordBot')\n embed.set_author(name='Verify ' + message.author.display_name,\n icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name +\n ' has over a ' + msgVerified + ' K/D. Verified!', value=\n 'Current season squads K/D: **' + msgRatio + '**', inline=False\n )\n user = message.author\n await message.channel.send(embed=embed)\n await user.add_roles(role)\n\n\n@client.event\nasync def on_ready():\n print('-')\n print('Logged in as: ' + client.user.name)\n print('With Client User ID: ' + str(client.user.id))\n print('Verified set to: ' + str(VERIFIED))\n print('-')\n\n\nclient.run(DISCORD_TOKEN)\n",
"step-5": "import discord\nimport requests\nimport math\nfrom keys import GITHUB_DISCORD_TOKEN, GITHUB_FORTNITE_API_KEY\n\nclient = discord.Client()\n\n# Constant\nDISCORD_TOKEN = GITHUB_DISCORD_TOKEN\nFORTNITE_API_KEY = GITHUB_FORTNITE_API_KEY\n\nLIST = ['Verified']\nVERIFIED = 4\n\n# Return the current season squad K/D of the fortnite player\ndef get_ratio(username):\n try:\n print(username)\n link = 'https://api.fortnitetracker.com/v1/profile/pc/' + username\n response = requests.get(link, headers={'TRN-Api-Key': FORTNITE_API_KEY})\n if response.status_code == 200:\n collection = response.json()\n if 'error' in collection:\n return \"-1\"\n else:\n ratio = collection['stats']['curr_p9']['kd']['value']\n return ratio\n print(\"Invalid username\")\n return \"-1\"\n else:\n print(\"Error parsing data.\")\n return \"-2\"\n except KeyError:\n print(\"Error finding data. KeyError was returned.\")\n return \"-3\"\n\n@client.event\nasync def on_message(message):\n # we do not want the bot to reply to itself\n if message.author == client.user:\n return\n # The command !patch return a link with the lastest patch note\n if message.content.startswith('!patch'):\n await message.channel.send('Latest patch notes: https://www.epicgames.com/fortnite/en-US/patch-notes/')\n # The command !help explains the one function\n if message.content.startswith('!help'):\n embed = discord.Embed(colour=discord.Colour(0x8e2626), url=\"https://github.com/af1/kdFortniteDiscordBot\",)\n embed.set_author(name=\"Verify Bot Help\", icon_url=\"\")\n embed.add_field(name=\"Set your Discord nickname to be exacly the same as your Epic Games player name. Then type: !verify\", value=\"You can change your nickname by typing \\\"/nick *YourEpicIGN*\\\". The bot looks at your squad K/D for the current season, so if you have no games played yet, the bot won\\'t be able to verify you.\", inline=False)\n await message.channel.send(embed=embed)\n # The command !verify return attribute a rank according to the K/D of the user\n if message.content.startswith(\"!verify\"):\n for list in LIST:\n roles = discord.utils.get(message.guild.roles, name=list)\n username = '{0.author.display_name}'.format(message)\n ratio = float(get_ratio(username))\n msgRatio = str(ratio)\n msgVerified = str(VERIFIED)\n print(ratio)\n if ratio == -1.0:\n embed = discord.Embed(colour=discord.Colour(0x8e2626), url=\"https://github.com/af1/kdFortniteDiscordBot\",)\n embed.set_author(name=\"Verify \" + message.author.display_name, icon_url=message.author.avatar_url)\n embed.add_field(name=\"Fortnite player **\" + message.author.display_name + \"** not found.\", value=\"\\nYour Discord nickname and IGN must be exactly the same. Change your Discord nickname to your IGN and try again.\", inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -2.0:\n embed = discord.Embed(colour=discord.Colour(0x8e2626), url=\"https://github.com/af1/kdFortniteDiscordBot\",)\n embed.set_author(name=\"Verify \" + message.author.display_name, icon_url=message.author.avatar_url)\n embed.add_field(name=\"Data not found.\", value=\"Fortnite Tracker is down. Please try again shortly.\", inline=False)\n await message.channel.send(embed=embed)\n elif ratio == -3.0:\n embed = discord.Embed(colour=discord.Colour(0x8e2626), url=\"https://github.com/af1/kdFortniteDiscordBot\",)\n embed.set_author(name=\"Verify \" + message.author.display_name, icon_url=message.author.avatar_url)\n embed.add_field(name=\"No stats found for squad mode in the current season.\", value=\"Play some games and try again.\", inline=False)\n await message.channel.send(embed=embed)\n elif ratio > 0 and ratio < VERIFIED:\n print(\"🚫\")\n print(\"-\")\n embed = discord.Embed(colour=discord.Colour(0x45278e), url=\"https://github.com/af1/kdFortniteDiscordBot\",)\n embed.set_author(name=\"Verify \" + message.author.display_name, icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name + \" does not have over a \" + msgVerified + \" K/D.\", value=\"Current season squads K/D: **\" + msgRatio + \"**\", inline=False)\n await message.channel.send(embed=embed)\n elif ratio >= VERIFIED:\n print(\"✅\")\n print(\"-\")\n role = discord.utils.get(message.guild.roles, name=LIST[0])\n embed = discord.Embed(colour=discord.Colour(0x45278e), url=\"https://github.com/af1/kdFortniteDiscordBot\",)\n embed.set_author(name=\"Verify \" + message.author.display_name, icon_url=message.author.avatar_url)\n embed.add_field(name=message.author.display_name + \" has over a \" + msgVerified + \" K/D. Verified!\", value=\"Current season squads K/D: **\" + msgRatio + \"**\", inline=False)\n user=message.author\n await message.channel.send(embed=embed)\n await user.add_roles(role) \n \n@client.event\nasync def on_ready():\n print(\"-\")\n print(\"Logged in as: \" + client.user.name)\n print(\"With Client User ID: \" + str(client.user.id))\n print(\"Verified set to: \" + str(VERIFIED))\n print(\"-\")\n\nclient.run(DISCORD_TOKEN)\n\n\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
# Generated by Django 2.0.4 on 2018-06-09 05:09
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('lists', '0004_auto_20180608_1835'),
]
operations = [
migrations.AlterModelOptions(
name='todo',
options={'ordering': ('-created_at',)},
),
migrations.AddField(
model_name='todo',
name='content',
field=models.TextField(default='', max_length=500),
),
]
|
normal
|
{
"blob_id": "b27913d2cd29f174d79652af6da2846e397373fc",
"index": 1549,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('lists', '0004_auto_20180608_1835')]\n operations = [migrations.AlterModelOptions(name='todo', options={\n 'ordering': ('-created_at',)}), migrations.AddField(model_name=\n 'todo', name='content', field=models.TextField(default='',\n max_length=500))]\n",
"step-4": "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('lists', '0004_auto_20180608_1835')]\n operations = [migrations.AlterModelOptions(name='todo', options={\n 'ordering': ('-created_at',)}), migrations.AddField(model_name=\n 'todo', name='content', field=models.TextField(default='',\n max_length=500))]\n",
"step-5": "# Generated by Django 2.0.4 on 2018-06-09 05:09\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('lists', '0004_auto_20180608_1835'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='todo',\n options={'ordering': ('-created_at',)},\n ),\n migrations.AddField(\n model_name='todo',\n name='content',\n field=models.TextField(default='', max_length=500),\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
#!/usr/bin/env python2
from Crypto.PublicKey import RSA
from Crypto.Util.number import *
from timeit import default_timer as timer
import os
import gmpy2
import itertools as it
def extract2(inp):
two = 0
while inp % 2 == 0:
inp //= 2
two += 1
return inp, two
def genkey():
while 1:
while 1:
p = getPrime(512)
q = getPrime(512)
if len(bin(abs(p - q))[2:]) < 450 or (p + q) & 1 != 0:
continue
phi = (p - 1) * (q - 1)
e = 257
print (gmpy2.gcd(phi, e))
try:
d = int(gmpy2.invert(e, phi))
except:
continue
assert (d * e) % phi == 1
ret = (d*e - 1) // phi
break
print ('d : ', d)
r = 256
mod = 2 ** r
d0 = d % mod
d0e = d0 * e
print (bin(d0e)[-10:])
if d0e & (1 << 2):
x = RSA.construct((p*q, e, d, p, q))
output = x.exportKey("PEM")
with open('pri.pem', 'w') as f:
f.write(output)
output = x.publickey().exportKey("PEM")
with open('pub.pem', 'w') as f:
f.write(output)
break
return ret
def solve():
x = RSA.importKey(open('pub.pem', 'rb').read())
# LSb you can get
# you should put the leaked private key here
d0 = THE_LEAKED_PRIVATE_KEY % mod
r = 304
mod = 2 ** r
e = x.e
N = x.n
d0e = d0 * e
cnt = 0
now = timer()
total_time = 0
for k in range(1, e, 2):
print ('k : ', k)
k_left, two_right = extract2(k)
k_left_1 = gmpy2.invert(k_left, mod)
left = N + 1 + k_left_1 - k_left_1 * d0e
left %= mod
_, two_left = extract2(left)
assert two_left - two_right > 0
poss_s = []
random_length = two_left - two_right - 1
poss_set = it.product('01', repeat=random_length)
poss_set = map(''.join, poss_set)
os.system('rm -rf ./ans')
for s in poss_set:
s += bin(left)[2:].rjust(r, '0')
assert len(s) == r
# Hensel
os.system('python3.6 ./tools_on_git/Hensel.py {} {}'.format(int(s, 2), N))
# solving univariate polynomial, similar to sage's small_roots
os.system('sage ./tools_on_git/coppersmith.sage {}'.format(N))
cnt += 1
total_time += timer() - now
now = timer()
print ('\tcnt : ', cnt)
print ('\tavg : ', total_time * 1.0 / cnt)
if os.path.isfile('ans'):
print ('answer found !')
exit()
#ret = genkey()
#print ('mutiplier : ', ret)
solve()
|
normal
|
{
"blob_id": "b29f85ccf396640c2a63bf634b549a3eaa0dbb1b",
"index": 1830,
"step-1": "<mask token>\n\n\ndef extract2(inp):\n two = 0\n while inp % 2 == 0:\n inp //= 2\n two += 1\n return inp, two\n\n\n<mask token>\n\n\ndef solve():\n x = RSA.importKey(open('pub.pem', 'rb').read())\n d0 = THE_LEAKED_PRIVATE_KEY % mod\n r = 304\n mod = 2 ** r\n e = x.e\n N = x.n\n d0e = d0 * e\n cnt = 0\n now = timer()\n total_time = 0\n for k in range(1, e, 2):\n print('k : ', k)\n k_left, two_right = extract2(k)\n k_left_1 = gmpy2.invert(k_left, mod)\n left = N + 1 + k_left_1 - k_left_1 * d0e\n left %= mod\n _, two_left = extract2(left)\n assert two_left - two_right > 0\n poss_s = []\n random_length = two_left - two_right - 1\n poss_set = it.product('01', repeat=random_length)\n poss_set = map(''.join, poss_set)\n os.system('rm -rf ./ans')\n for s in poss_set:\n s += bin(left)[2:].rjust(r, '0')\n assert len(s) == r\n os.system('python3.6 ./tools_on_git/Hensel.py {} {}'.format(int\n (s, 2), N))\n os.system('sage ./tools_on_git/coppersmith.sage {}'.format(N))\n cnt += 1\n total_time += timer() - now\n now = timer()\n print('\\tcnt : ', cnt)\n print('\\tavg : ', total_time * 1.0 / cnt)\n if os.path.isfile('ans'):\n print('answer found !')\n exit()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef extract2(inp):\n two = 0\n while inp % 2 == 0:\n inp //= 2\n two += 1\n return inp, two\n\n\ndef genkey():\n while 1:\n while 1:\n p = getPrime(512)\n q = getPrime(512)\n if len(bin(abs(p - q))[2:]) < 450 or p + q & 1 != 0:\n continue\n phi = (p - 1) * (q - 1)\n e = 257\n print(gmpy2.gcd(phi, e))\n try:\n d = int(gmpy2.invert(e, phi))\n except:\n continue\n assert d * e % phi == 1\n ret = (d * e - 1) // phi\n break\n print('d : ', d)\n r = 256\n mod = 2 ** r\n d0 = d % mod\n d0e = d0 * e\n print(bin(d0e)[-10:])\n if d0e & 1 << 2:\n x = RSA.construct((p * q, e, d, p, q))\n output = x.exportKey('PEM')\n with open('pri.pem', 'w') as f:\n f.write(output)\n output = x.publickey().exportKey('PEM')\n with open('pub.pem', 'w') as f:\n f.write(output)\n break\n return ret\n\n\ndef solve():\n x = RSA.importKey(open('pub.pem', 'rb').read())\n d0 = THE_LEAKED_PRIVATE_KEY % mod\n r = 304\n mod = 2 ** r\n e = x.e\n N = x.n\n d0e = d0 * e\n cnt = 0\n now = timer()\n total_time = 0\n for k in range(1, e, 2):\n print('k : ', k)\n k_left, two_right = extract2(k)\n k_left_1 = gmpy2.invert(k_left, mod)\n left = N + 1 + k_left_1 - k_left_1 * d0e\n left %= mod\n _, two_left = extract2(left)\n assert two_left - two_right > 0\n poss_s = []\n random_length = two_left - two_right - 1\n poss_set = it.product('01', repeat=random_length)\n poss_set = map(''.join, poss_set)\n os.system('rm -rf ./ans')\n for s in poss_set:\n s += bin(left)[2:].rjust(r, '0')\n assert len(s) == r\n os.system('python3.6 ./tools_on_git/Hensel.py {} {}'.format(int\n (s, 2), N))\n os.system('sage ./tools_on_git/coppersmith.sage {}'.format(N))\n cnt += 1\n total_time += timer() - now\n now = timer()\n print('\\tcnt : ', cnt)\n print('\\tavg : ', total_time * 1.0 / cnt)\n if os.path.isfile('ans'):\n print('answer found !')\n exit()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef extract2(inp):\n two = 0\n while inp % 2 == 0:\n inp //= 2\n two += 1\n return inp, two\n\n\ndef genkey():\n while 1:\n while 1:\n p = getPrime(512)\n q = getPrime(512)\n if len(bin(abs(p - q))[2:]) < 450 or p + q & 1 != 0:\n continue\n phi = (p - 1) * (q - 1)\n e = 257\n print(gmpy2.gcd(phi, e))\n try:\n d = int(gmpy2.invert(e, phi))\n except:\n continue\n assert d * e % phi == 1\n ret = (d * e - 1) // phi\n break\n print('d : ', d)\n r = 256\n mod = 2 ** r\n d0 = d % mod\n d0e = d0 * e\n print(bin(d0e)[-10:])\n if d0e & 1 << 2:\n x = RSA.construct((p * q, e, d, p, q))\n output = x.exportKey('PEM')\n with open('pri.pem', 'w') as f:\n f.write(output)\n output = x.publickey().exportKey('PEM')\n with open('pub.pem', 'w') as f:\n f.write(output)\n break\n return ret\n\n\ndef solve():\n x = RSA.importKey(open('pub.pem', 'rb').read())\n d0 = THE_LEAKED_PRIVATE_KEY % mod\n r = 304\n mod = 2 ** r\n e = x.e\n N = x.n\n d0e = d0 * e\n cnt = 0\n now = timer()\n total_time = 0\n for k in range(1, e, 2):\n print('k : ', k)\n k_left, two_right = extract2(k)\n k_left_1 = gmpy2.invert(k_left, mod)\n left = N + 1 + k_left_1 - k_left_1 * d0e\n left %= mod\n _, two_left = extract2(left)\n assert two_left - two_right > 0\n poss_s = []\n random_length = two_left - two_right - 1\n poss_set = it.product('01', repeat=random_length)\n poss_set = map(''.join, poss_set)\n os.system('rm -rf ./ans')\n for s in poss_set:\n s += bin(left)[2:].rjust(r, '0')\n assert len(s) == r\n os.system('python3.6 ./tools_on_git/Hensel.py {} {}'.format(int\n (s, 2), N))\n os.system('sage ./tools_on_git/coppersmith.sage {}'.format(N))\n cnt += 1\n total_time += timer() - now\n now = timer()\n print('\\tcnt : ', cnt)\n print('\\tavg : ', total_time * 1.0 / cnt)\n if os.path.isfile('ans'):\n print('answer found !')\n exit()\n\n\nsolve()\n",
"step-4": "from Crypto.PublicKey import RSA\nfrom Crypto.Util.number import *\nfrom timeit import default_timer as timer\nimport os\nimport gmpy2\nimport itertools as it\n\n\ndef extract2(inp):\n two = 0\n while inp % 2 == 0:\n inp //= 2\n two += 1\n return inp, two\n\n\ndef genkey():\n while 1:\n while 1:\n p = getPrime(512)\n q = getPrime(512)\n if len(bin(abs(p - q))[2:]) < 450 or p + q & 1 != 0:\n continue\n phi = (p - 1) * (q - 1)\n e = 257\n print(gmpy2.gcd(phi, e))\n try:\n d = int(gmpy2.invert(e, phi))\n except:\n continue\n assert d * e % phi == 1\n ret = (d * e - 1) // phi\n break\n print('d : ', d)\n r = 256\n mod = 2 ** r\n d0 = d % mod\n d0e = d0 * e\n print(bin(d0e)[-10:])\n if d0e & 1 << 2:\n x = RSA.construct((p * q, e, d, p, q))\n output = x.exportKey('PEM')\n with open('pri.pem', 'w') as f:\n f.write(output)\n output = x.publickey().exportKey('PEM')\n with open('pub.pem', 'w') as f:\n f.write(output)\n break\n return ret\n\n\ndef solve():\n x = RSA.importKey(open('pub.pem', 'rb').read())\n d0 = THE_LEAKED_PRIVATE_KEY % mod\n r = 304\n mod = 2 ** r\n e = x.e\n N = x.n\n d0e = d0 * e\n cnt = 0\n now = timer()\n total_time = 0\n for k in range(1, e, 2):\n print('k : ', k)\n k_left, two_right = extract2(k)\n k_left_1 = gmpy2.invert(k_left, mod)\n left = N + 1 + k_left_1 - k_left_1 * d0e\n left %= mod\n _, two_left = extract2(left)\n assert two_left - two_right > 0\n poss_s = []\n random_length = two_left - two_right - 1\n poss_set = it.product('01', repeat=random_length)\n poss_set = map(''.join, poss_set)\n os.system('rm -rf ./ans')\n for s in poss_set:\n s += bin(left)[2:].rjust(r, '0')\n assert len(s) == r\n os.system('python3.6 ./tools_on_git/Hensel.py {} {}'.format(int\n (s, 2), N))\n os.system('sage ./tools_on_git/coppersmith.sage {}'.format(N))\n cnt += 1\n total_time += timer() - now\n now = timer()\n print('\\tcnt : ', cnt)\n print('\\tavg : ', total_time * 1.0 / cnt)\n if os.path.isfile('ans'):\n print('answer found !')\n exit()\n\n\nsolve()\n",
"step-5": "#!/usr/bin/env python2\n\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Util.number import *\nfrom timeit import default_timer as timer\n\nimport os\nimport gmpy2\nimport itertools as it\n\n\ndef extract2(inp):\n two = 0\n while inp % 2 == 0:\n inp //= 2\n two += 1\n return inp, two\n\ndef genkey():\n while 1:\n while 1:\n p = getPrime(512)\n q = getPrime(512)\n if len(bin(abs(p - q))[2:]) < 450 or (p + q) & 1 != 0:\n continue\n\n phi = (p - 1) * (q - 1)\n e = 257\n print (gmpy2.gcd(phi, e))\n try:\n d = int(gmpy2.invert(e, phi))\n except:\n continue\n\n assert (d * e) % phi == 1\n ret = (d*e - 1) // phi\n break\n\n print ('d : ', d)\n r = 256\n mod = 2 ** r\n d0 = d % mod\n\n d0e = d0 * e\n print (bin(d0e)[-10:])\n\n if d0e & (1 << 2):\n x = RSA.construct((p*q, e, d, p, q))\n output = x.exportKey(\"PEM\")\n with open('pri.pem', 'w') as f:\n f.write(output)\n output = x.publickey().exportKey(\"PEM\")\n with open('pub.pem', 'w') as f:\n f.write(output)\n break\n return ret\n\n\ndef solve():\n x = RSA.importKey(open('pub.pem', 'rb').read())\n\n # LSb you can get\n # you should put the leaked private key here\n d0 = THE_LEAKED_PRIVATE_KEY % mod\n r = 304\n mod = 2 ** r\n e = x.e\n N = x.n\n d0e = d0 * e\n\n cnt = 0\n now = timer()\n total_time = 0\n for k in range(1, e, 2):\n print ('k : ', k)\n k_left, two_right = extract2(k)\n k_left_1 = gmpy2.invert(k_left, mod)\n\n left = N + 1 + k_left_1 - k_left_1 * d0e\n left %= mod\n _, two_left = extract2(left)\n assert two_left - two_right > 0\n\n poss_s = []\n random_length = two_left - two_right - 1\n poss_set = it.product('01', repeat=random_length)\n poss_set = map(''.join, poss_set)\n\n os.system('rm -rf ./ans')\n for s in poss_set:\n s += bin(left)[2:].rjust(r, '0')\n assert len(s) == r\n # Hensel\n os.system('python3.6 ./tools_on_git/Hensel.py {} {}'.format(int(s, 2), N))\n # solving univariate polynomial, similar to sage's small_roots\n os.system('sage ./tools_on_git/coppersmith.sage {}'.format(N))\n cnt += 1\n total_time += timer() - now\n now = timer()\n print ('\\tcnt : ', cnt)\n print ('\\tavg : ', total_time * 1.0 / cnt)\n if os.path.isfile('ans'):\n print ('answer found !')\n exit()\n\n\n#ret = genkey()\n#print ('mutiplier : ', ret)\nsolve()\n\n\n\n\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def process(json_file, outpur_dir, exclude_titles=None, include_titles=None):
"""
:param json_file: original data in json format
:param outpur_dir: the output directory of pre-processed data
:param exclude_titles: article titles to exclude
:param include_titles: article titles to include
"""
para_file = '{}/paras'.format(outpur_dir)
question_file = '{}/questions'.format(outpur_dir)
sent_file = '{}/sents'.format(outpur_dir)
answer_file = '{}/answers'.format(outpur_dir)
print('Generating {} raw data...'.format(json_file))
max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0
with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=
'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',
timeout=50000) as client:
source = json.load(fh)
for article in tqdm(source['data']):
title = article['title']
if include_titles and title not in include_titles:
continue
if exclude_titles and title in exclude_titles:
continue
for para in article['paragraphs']:
paragraphs, questions, answers, sents, ids = [], [], [], [], []
paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [
], [], []
paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [
], [], []
answers_index, sents_index = [], []
context = para['context']
if not context.strip():
continue
ann_para = client.annotate(context)
max_sent = max(max_sent, len(ann_para.sentence))
max_sent_len = max(max_sent_len, max(map(lambda x: len(x.
token), ann_para.sentence)))
(ann_para_tokens, paragraph_tokens, paragraph_pos,
paragraph_ner) = [], [], [], []
for sent in ann_para.sentence:
for token in sent.token:
ann_para_tokens.append(token)
paragraph_tokens.append(token.word)
paragraph_pos.append(token.pos)
paragraph_ner.append(token.ner)
for qa in para['qas']:
ques = qa['question']
id = qa['id']
if not ques.strip():
continue
ann_que = client.annotate(ques)
max_que_len = max(max_que_len, len(ann_que.sentence[0].
token))
question_tokens, question_pos, question_ner = [], [], []
for sent in ann_que.sentence:
for token in sent.token:
question_tokens.append(token.word)
question_pos.append(token.pos)
question_ner.append(token.ner)
(all_answer_tokens, all_answer_pos, all_answer_ner,
all_answer_index) = [], [], [], []
(all_sent_tokens, all_sent_pos, all_sent_ner,
all_sent_index) = [], [], [], []
for answer in qa['answers']:
answer_text = answer['text']
if not answer_text.strip():
continue
ann_ans = client.annotate(answer_text)
answer_tokens, answer_pos, answer_ner = [], [], []
for sent in ann_ans.sentence:
for token in sent.token:
answer_tokens.append(token.word)
answer_pos.append(token.pos)
answer_ner.append(token.ner)
all_answer_tokens.append(' '.join(answer_tokens))
all_answer_pos.append(' '.join(answer_pos))
all_answer_ner.append(' '.join(answer_ner))
answer_start = answer['answer_start']
answer_end = answer_start + len(answer_text)
sentence = []
for sent in ann_para.sentence:
if (sent.characterOffsetBegin <= answer_start <=
sent.characterOffsetEnd or sent.
characterOffsetBegin <= answer_end <= sent.
characterOffsetEnd):
sentence.append(sent)
sentence = [token for sent in sentence for token in
sent.token]
sentence_tokens = [token.word for token in sentence]
sentence_pos = [token.pos for token in sentence]
sentence_ner = [token.ner for token in sentence]
all_sent_tokens.append(' '.join(sentence_tokens))
all_sent_pos.append(' '.join(sentence_pos))
all_sent_ner.append(' '.join(sentence_ner))
y1_sent = sentence[0].tokenBeginIndex
y2_sent = sentence[-1].tokenBeginIndex
y1_ans = None
for i, token in enumerate(sentence):
if (token.beginChar - 1 <= answer_start <=
token.endChar):
y1_ans = sentence[0].tokenBeginIndex + i
try:
assert y1_ans != None
except:
continue
y2_ans = y1_ans + len(answer_tokens) - 1
all_answer_index.append('{},{}'.format(y1_ans, y2_ans))
all_sent_index.append('{},{}'.format(y1_sent, y2_sent))
paragraphs.append(' '.join(paragraph_tokens))
paragraphs_pos.append(' '.join(paragraph_pos))
paragraphs_ner.append(' '.join(paragraph_ner))
questions.append(' '.join(question_tokens))
questions_pos.append(' '.join(question_pos))
questions_ner.append(' '.join(question_ner))
answers.append('\t'.join(all_answer_tokens))
answers_pos.append('\t'.join(all_answer_pos))
answers_ner.append('\t'.join(all_answer_ner))
answers_index.append('\t'.join(all_answer_index))
sents.append('\t'.join(all_sent_tokens))
sents_pos.append('\t'.join(all_sent_pos))
sents_ner.append('\t'.join(all_sent_ner))
sents_index.append('\t'.join(all_sent_index))
ids.append(id)
with open('{}.tok'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs) + '\n')
with open('{}.pos'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_pos) + '\n')
with open('{}.ner'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_ner) + '\n')
with open('{}.id'.format(para_file), 'a') as f:
f.write('\n'.join(ids) + '\n')
with open('{}.tok'.format(question_file), 'a') as f:
f.write('\n'.join(questions) + '\n')
with open('{}.pos'.format(question_file), 'a') as f:
f.write('\n'.join(questions_pos) + '\n')
with open('{}.ner'.format(question_file), 'a') as f:
f.write('\n'.join(questions_ner) + '\n')
with open('{}.tok'.format(answer_file), 'a') as f:
f.write('\n'.join(answers) + '\n')
with open('{}.pos'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_pos) + '\n')
with open('{}.ner'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_ner) + '\n')
with open('{}.index'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_index) + '\n')
with open('{}.tok'.format(sent_file), 'a') as f:
f.write('\n'.join(sents) + '\n')
with open('{}.pos'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_pos) + '\n')
with open('{}.ner'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_ner) + '\n')
with open('{}.index'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_index) + '\n')
label(para_file, answer_file)
<|reserved_special_token_0|>
def get_data(train_json, dev_json, test_title_file, output_dir):
test_titles = open(test_title_file, 'r').readlines()
test_titles = set([line.strip() for line in test_titles])
process(train_json, '{}/train/'.format(output_dir), exclude_titles=
test_titles)
process(dev_json, '{}/dev/'.format(output_dir))
process(train_json, '{}/test/'.format(output_dir), include_titles=
test_titles)
def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,
vocab_file):
"""
get word embedding matrix from glove
"""
print('Generating word embedding...')
embedding_dict = {}
with open(emb_file, 'r', encoding='utf-8') as fh:
for line in tqdm(fh, total=emb_size):
array = line.split()
word = ''.join(array[0:-vec_size])
vector = list(map(float, array[-vec_size:]))
embedding_dict[word] = vector
TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',
'-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':
'(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}
SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']
words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:
x[1], reverse=True)))
words = SPECIAL_TOKENS + words
if vocab_size > 0:
words = words[:vocab_size]
with open(vocab_file, 'w') as f:
f.write('\n'.join(words[1:]))
embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))
word2idx_dict = {}
unknown_count = 0
for i, word in enumerate(words):
word2idx_dict[word] = i
if word in TRANSLATE:
word = TRANSLATE[word]
done = False
for w in (word, word.lower(), word.upper(), word.capitalize()):
if w in embedding_dict:
embedding[i] = embedding_dict[w]
done = True
break
if not done:
unknown_count += 1
return embedding, word2idx_dict, unknown_count
def get_tag_embedding(counter, data_type, vec_size):
"""
get pos/ner/label tags' embedding matrix
"""
print('Generating {} tag embedding...'.format(data_type))
SPECIAL_TOKENS = ['<NULL>', '<UNK>']
tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x
[1], reverse=True)))
tags = SPECIAL_TOKENS + tags
embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))
word2idx_dict = {w: i for i, w in enumerate(tags)}
return embedding, word2idx_dict
def get_vocab(config):
print('Get the vocabulary...')
word_counter, char_counter = Counter(), Counter()
pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()
files = [(config.train_para_file, config.train_question_file), (config.
dev_para_file, config.dev_question_file)]
for para_file, que_file in files:
with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.
format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'
) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(
'{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(
que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'
) as fpl:
while True:
para, question = fp.readline(), fq.readline()
pos, que_pos = fpp.readline(), fqp.readline()
ner, que_ner = fpn.readline(), fqn.readline()
label = fpl.readline()
if not question or not para:
break
if config.lower_word:
para = para.lower()
question = question.lower()
para_tokens = para.strip().split(' ')
que_tokens = question.strip().split(' ')
pos_tags = pos.strip().split(' ')
ner_tags = ner.strip().split(' ')
que_pos_tags = que_pos.strip().split(' ')
que_ner_tags = que_ner.strip().split(' ')
labels = label.strip().split(' ')
for token in (para_tokens + que_tokens):
word_counter[token] += 1
for char in list(token):
char_counter[char] += 1
for pos_tag in (pos_tags + que_pos_tags):
pos_counter[pos_tag] += 1
for ner_tag in (ner_tags + que_ner_tags):
ner_counter[ner_tag] += 1
for label in labels:
label_counter[label] += 1
word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,
emb_file=config.glove_word_file, emb_size=config.glove_word_size,
vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,
vocab_file=config.vocab_file)
char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',
vec_size=config.char_dim)
pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',
vec_size=config.pos_dim)
ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',
vec_size=config.ner_dim)
label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,
'label', vec_size=config.label_dim)
print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))
print('{} chars'.format(char_emb_mat.shape[0]))
print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(
pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],
char_emb_mat.shape[0]))
save(config.word_emb_file, word_emb_mat, message='word embedding')
save(config.char_emb_file, char_emb_mat, message='char embedding')
save(config.pos_emb_file, pos_emb_mat, message='pos embedding')
save(config.ner_emb_file, ner_emb_mat, message='ner embedding')
save(config.label_emb_file, label_emb_mat, message='label embedding')
save(config.word_dictionary, word2idx_dict, message='word dictionary')
save(config.char_dictionary, char2idx_dict, message='char dictionary')
save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')
save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')
save(config.label_dictionary, label2idx_dict, message='label dictionary')
print('Dump elmo word embedding...')
token_embedding_file = config.embedding_file
dump_token_embeddings(config.vocab_file, config.elmo_options_file,
config.elmo_weight_file, token_embedding_file)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def process(json_file, outpur_dir, exclude_titles=None, include_titles=None):
"""
:param json_file: original data in json format
:param outpur_dir: the output directory of pre-processed data
:param exclude_titles: article titles to exclude
:param include_titles: article titles to include
"""
para_file = '{}/paras'.format(outpur_dir)
question_file = '{}/questions'.format(outpur_dir)
sent_file = '{}/sents'.format(outpur_dir)
answer_file = '{}/answers'.format(outpur_dir)
print('Generating {} raw data...'.format(json_file))
max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0
with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=
'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',
timeout=50000) as client:
source = json.load(fh)
for article in tqdm(source['data']):
title = article['title']
if include_titles and title not in include_titles:
continue
if exclude_titles and title in exclude_titles:
continue
for para in article['paragraphs']:
paragraphs, questions, answers, sents, ids = [], [], [], [], []
paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [
], [], []
paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [
], [], []
answers_index, sents_index = [], []
context = para['context']
if not context.strip():
continue
ann_para = client.annotate(context)
max_sent = max(max_sent, len(ann_para.sentence))
max_sent_len = max(max_sent_len, max(map(lambda x: len(x.
token), ann_para.sentence)))
(ann_para_tokens, paragraph_tokens, paragraph_pos,
paragraph_ner) = [], [], [], []
for sent in ann_para.sentence:
for token in sent.token:
ann_para_tokens.append(token)
paragraph_tokens.append(token.word)
paragraph_pos.append(token.pos)
paragraph_ner.append(token.ner)
for qa in para['qas']:
ques = qa['question']
id = qa['id']
if not ques.strip():
continue
ann_que = client.annotate(ques)
max_que_len = max(max_que_len, len(ann_que.sentence[0].
token))
question_tokens, question_pos, question_ner = [], [], []
for sent in ann_que.sentence:
for token in sent.token:
question_tokens.append(token.word)
question_pos.append(token.pos)
question_ner.append(token.ner)
(all_answer_tokens, all_answer_pos, all_answer_ner,
all_answer_index) = [], [], [], []
(all_sent_tokens, all_sent_pos, all_sent_ner,
all_sent_index) = [], [], [], []
for answer in qa['answers']:
answer_text = answer['text']
if not answer_text.strip():
continue
ann_ans = client.annotate(answer_text)
answer_tokens, answer_pos, answer_ner = [], [], []
for sent in ann_ans.sentence:
for token in sent.token:
answer_tokens.append(token.word)
answer_pos.append(token.pos)
answer_ner.append(token.ner)
all_answer_tokens.append(' '.join(answer_tokens))
all_answer_pos.append(' '.join(answer_pos))
all_answer_ner.append(' '.join(answer_ner))
answer_start = answer['answer_start']
answer_end = answer_start + len(answer_text)
sentence = []
for sent in ann_para.sentence:
if (sent.characterOffsetBegin <= answer_start <=
sent.characterOffsetEnd or sent.
characterOffsetBegin <= answer_end <= sent.
characterOffsetEnd):
sentence.append(sent)
sentence = [token for sent in sentence for token in
sent.token]
sentence_tokens = [token.word for token in sentence]
sentence_pos = [token.pos for token in sentence]
sentence_ner = [token.ner for token in sentence]
all_sent_tokens.append(' '.join(sentence_tokens))
all_sent_pos.append(' '.join(sentence_pos))
all_sent_ner.append(' '.join(sentence_ner))
y1_sent = sentence[0].tokenBeginIndex
y2_sent = sentence[-1].tokenBeginIndex
y1_ans = None
for i, token in enumerate(sentence):
if (token.beginChar - 1 <= answer_start <=
token.endChar):
y1_ans = sentence[0].tokenBeginIndex + i
try:
assert y1_ans != None
except:
continue
y2_ans = y1_ans + len(answer_tokens) - 1
all_answer_index.append('{},{}'.format(y1_ans, y2_ans))
all_sent_index.append('{},{}'.format(y1_sent, y2_sent))
paragraphs.append(' '.join(paragraph_tokens))
paragraphs_pos.append(' '.join(paragraph_pos))
paragraphs_ner.append(' '.join(paragraph_ner))
questions.append(' '.join(question_tokens))
questions_pos.append(' '.join(question_pos))
questions_ner.append(' '.join(question_ner))
answers.append('\t'.join(all_answer_tokens))
answers_pos.append('\t'.join(all_answer_pos))
answers_ner.append('\t'.join(all_answer_ner))
answers_index.append('\t'.join(all_answer_index))
sents.append('\t'.join(all_sent_tokens))
sents_pos.append('\t'.join(all_sent_pos))
sents_ner.append('\t'.join(all_sent_ner))
sents_index.append('\t'.join(all_sent_index))
ids.append(id)
with open('{}.tok'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs) + '\n')
with open('{}.pos'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_pos) + '\n')
with open('{}.ner'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_ner) + '\n')
with open('{}.id'.format(para_file), 'a') as f:
f.write('\n'.join(ids) + '\n')
with open('{}.tok'.format(question_file), 'a') as f:
f.write('\n'.join(questions) + '\n')
with open('{}.pos'.format(question_file), 'a') as f:
f.write('\n'.join(questions_pos) + '\n')
with open('{}.ner'.format(question_file), 'a') as f:
f.write('\n'.join(questions_ner) + '\n')
with open('{}.tok'.format(answer_file), 'a') as f:
f.write('\n'.join(answers) + '\n')
with open('{}.pos'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_pos) + '\n')
with open('{}.ner'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_ner) + '\n')
with open('{}.index'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_index) + '\n')
with open('{}.tok'.format(sent_file), 'a') as f:
f.write('\n'.join(sents) + '\n')
with open('{}.pos'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_pos) + '\n')
with open('{}.ner'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_ner) + '\n')
with open('{}.index'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_index) + '\n')
label(para_file, answer_file)
def label(para_file, answer_file):
max_node = 0
with open('{}.tok'.format(para_file), 'r') as fp, open('{}.label'.
format(para_file), 'a') as fl, open('{}.index'.format(answer_file), 'r'
) as fa:
while True:
para = fp.readline()
if not para:
break
words = [p for p in para.strip().split(' ')]
max_node = max(len(words), max_node)
answer = fa.readline()
labels = []
try:
start, end = map(int, answer.split('\t')[0].split(','))
for i in range(len(words)):
if start <= i <= end:
if i == start:
labels.append('B')
else:
labels.append('I')
else:
labels.append('O')
except:
pass
fl.write(' '.join(labels) + '\n')
return max_node
def get_data(train_json, dev_json, test_title_file, output_dir):
test_titles = open(test_title_file, 'r').readlines()
test_titles = set([line.strip() for line in test_titles])
process(train_json, '{}/train/'.format(output_dir), exclude_titles=
test_titles)
process(dev_json, '{}/dev/'.format(output_dir))
process(train_json, '{}/test/'.format(output_dir), include_titles=
test_titles)
def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,
vocab_file):
"""
get word embedding matrix from glove
"""
print('Generating word embedding...')
embedding_dict = {}
with open(emb_file, 'r', encoding='utf-8') as fh:
for line in tqdm(fh, total=emb_size):
array = line.split()
word = ''.join(array[0:-vec_size])
vector = list(map(float, array[-vec_size:]))
embedding_dict[word] = vector
TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',
'-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':
'(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}
SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']
words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:
x[1], reverse=True)))
words = SPECIAL_TOKENS + words
if vocab_size > 0:
words = words[:vocab_size]
with open(vocab_file, 'w') as f:
f.write('\n'.join(words[1:]))
embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))
word2idx_dict = {}
unknown_count = 0
for i, word in enumerate(words):
word2idx_dict[word] = i
if word in TRANSLATE:
word = TRANSLATE[word]
done = False
for w in (word, word.lower(), word.upper(), word.capitalize()):
if w in embedding_dict:
embedding[i] = embedding_dict[w]
done = True
break
if not done:
unknown_count += 1
return embedding, word2idx_dict, unknown_count
def get_tag_embedding(counter, data_type, vec_size):
"""
get pos/ner/label tags' embedding matrix
"""
print('Generating {} tag embedding...'.format(data_type))
SPECIAL_TOKENS = ['<NULL>', '<UNK>']
tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x
[1], reverse=True)))
tags = SPECIAL_TOKENS + tags
embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))
word2idx_dict = {w: i for i, w in enumerate(tags)}
return embedding, word2idx_dict
def get_vocab(config):
print('Get the vocabulary...')
word_counter, char_counter = Counter(), Counter()
pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()
files = [(config.train_para_file, config.train_question_file), (config.
dev_para_file, config.dev_question_file)]
for para_file, que_file in files:
with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.
format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'
) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(
'{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(
que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'
) as fpl:
while True:
para, question = fp.readline(), fq.readline()
pos, que_pos = fpp.readline(), fqp.readline()
ner, que_ner = fpn.readline(), fqn.readline()
label = fpl.readline()
if not question or not para:
break
if config.lower_word:
para = para.lower()
question = question.lower()
para_tokens = para.strip().split(' ')
que_tokens = question.strip().split(' ')
pos_tags = pos.strip().split(' ')
ner_tags = ner.strip().split(' ')
que_pos_tags = que_pos.strip().split(' ')
que_ner_tags = que_ner.strip().split(' ')
labels = label.strip().split(' ')
for token in (para_tokens + que_tokens):
word_counter[token] += 1
for char in list(token):
char_counter[char] += 1
for pos_tag in (pos_tags + que_pos_tags):
pos_counter[pos_tag] += 1
for ner_tag in (ner_tags + que_ner_tags):
ner_counter[ner_tag] += 1
for label in labels:
label_counter[label] += 1
word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,
emb_file=config.glove_word_file, emb_size=config.glove_word_size,
vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,
vocab_file=config.vocab_file)
char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',
vec_size=config.char_dim)
pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',
vec_size=config.pos_dim)
ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',
vec_size=config.ner_dim)
label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,
'label', vec_size=config.label_dim)
print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))
print('{} chars'.format(char_emb_mat.shape[0]))
print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(
pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],
char_emb_mat.shape[0]))
save(config.word_emb_file, word_emb_mat, message='word embedding')
save(config.char_emb_file, char_emb_mat, message='char embedding')
save(config.pos_emb_file, pos_emb_mat, message='pos embedding')
save(config.ner_emb_file, ner_emb_mat, message='ner embedding')
save(config.label_emb_file, label_emb_mat, message='label embedding')
save(config.word_dictionary, word2idx_dict, message='word dictionary')
save(config.char_dictionary, char2idx_dict, message='char dictionary')
save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')
save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')
save(config.label_dictionary, label2idx_dict, message='label dictionary')
print('Dump elmo word embedding...')
token_embedding_file = config.embedding_file
dump_token_embeddings(config.vocab_file, config.elmo_options_file,
config.elmo_weight_file, token_embedding_file)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append('../..')
<|reserved_special_token_0|>
def process(json_file, outpur_dir, exclude_titles=None, include_titles=None):
"""
:param json_file: original data in json format
:param outpur_dir: the output directory of pre-processed data
:param exclude_titles: article titles to exclude
:param include_titles: article titles to include
"""
para_file = '{}/paras'.format(outpur_dir)
question_file = '{}/questions'.format(outpur_dir)
sent_file = '{}/sents'.format(outpur_dir)
answer_file = '{}/answers'.format(outpur_dir)
print('Generating {} raw data...'.format(json_file))
max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0
with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=
'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',
timeout=50000) as client:
source = json.load(fh)
for article in tqdm(source['data']):
title = article['title']
if include_titles and title not in include_titles:
continue
if exclude_titles and title in exclude_titles:
continue
for para in article['paragraphs']:
paragraphs, questions, answers, sents, ids = [], [], [], [], []
paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [
], [], []
paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [
], [], []
answers_index, sents_index = [], []
context = para['context']
if not context.strip():
continue
ann_para = client.annotate(context)
max_sent = max(max_sent, len(ann_para.sentence))
max_sent_len = max(max_sent_len, max(map(lambda x: len(x.
token), ann_para.sentence)))
(ann_para_tokens, paragraph_tokens, paragraph_pos,
paragraph_ner) = [], [], [], []
for sent in ann_para.sentence:
for token in sent.token:
ann_para_tokens.append(token)
paragraph_tokens.append(token.word)
paragraph_pos.append(token.pos)
paragraph_ner.append(token.ner)
for qa in para['qas']:
ques = qa['question']
id = qa['id']
if not ques.strip():
continue
ann_que = client.annotate(ques)
max_que_len = max(max_que_len, len(ann_que.sentence[0].
token))
question_tokens, question_pos, question_ner = [], [], []
for sent in ann_que.sentence:
for token in sent.token:
question_tokens.append(token.word)
question_pos.append(token.pos)
question_ner.append(token.ner)
(all_answer_tokens, all_answer_pos, all_answer_ner,
all_answer_index) = [], [], [], []
(all_sent_tokens, all_sent_pos, all_sent_ner,
all_sent_index) = [], [], [], []
for answer in qa['answers']:
answer_text = answer['text']
if not answer_text.strip():
continue
ann_ans = client.annotate(answer_text)
answer_tokens, answer_pos, answer_ner = [], [], []
for sent in ann_ans.sentence:
for token in sent.token:
answer_tokens.append(token.word)
answer_pos.append(token.pos)
answer_ner.append(token.ner)
all_answer_tokens.append(' '.join(answer_tokens))
all_answer_pos.append(' '.join(answer_pos))
all_answer_ner.append(' '.join(answer_ner))
answer_start = answer['answer_start']
answer_end = answer_start + len(answer_text)
sentence = []
for sent in ann_para.sentence:
if (sent.characterOffsetBegin <= answer_start <=
sent.characterOffsetEnd or sent.
characterOffsetBegin <= answer_end <= sent.
characterOffsetEnd):
sentence.append(sent)
sentence = [token for sent in sentence for token in
sent.token]
sentence_tokens = [token.word for token in sentence]
sentence_pos = [token.pos for token in sentence]
sentence_ner = [token.ner for token in sentence]
all_sent_tokens.append(' '.join(sentence_tokens))
all_sent_pos.append(' '.join(sentence_pos))
all_sent_ner.append(' '.join(sentence_ner))
y1_sent = sentence[0].tokenBeginIndex
y2_sent = sentence[-1].tokenBeginIndex
y1_ans = None
for i, token in enumerate(sentence):
if (token.beginChar - 1 <= answer_start <=
token.endChar):
y1_ans = sentence[0].tokenBeginIndex + i
try:
assert y1_ans != None
except:
continue
y2_ans = y1_ans + len(answer_tokens) - 1
all_answer_index.append('{},{}'.format(y1_ans, y2_ans))
all_sent_index.append('{},{}'.format(y1_sent, y2_sent))
paragraphs.append(' '.join(paragraph_tokens))
paragraphs_pos.append(' '.join(paragraph_pos))
paragraphs_ner.append(' '.join(paragraph_ner))
questions.append(' '.join(question_tokens))
questions_pos.append(' '.join(question_pos))
questions_ner.append(' '.join(question_ner))
answers.append('\t'.join(all_answer_tokens))
answers_pos.append('\t'.join(all_answer_pos))
answers_ner.append('\t'.join(all_answer_ner))
answers_index.append('\t'.join(all_answer_index))
sents.append('\t'.join(all_sent_tokens))
sents_pos.append('\t'.join(all_sent_pos))
sents_ner.append('\t'.join(all_sent_ner))
sents_index.append('\t'.join(all_sent_index))
ids.append(id)
with open('{}.tok'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs) + '\n')
with open('{}.pos'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_pos) + '\n')
with open('{}.ner'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_ner) + '\n')
with open('{}.id'.format(para_file), 'a') as f:
f.write('\n'.join(ids) + '\n')
with open('{}.tok'.format(question_file), 'a') as f:
f.write('\n'.join(questions) + '\n')
with open('{}.pos'.format(question_file), 'a') as f:
f.write('\n'.join(questions_pos) + '\n')
with open('{}.ner'.format(question_file), 'a') as f:
f.write('\n'.join(questions_ner) + '\n')
with open('{}.tok'.format(answer_file), 'a') as f:
f.write('\n'.join(answers) + '\n')
with open('{}.pos'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_pos) + '\n')
with open('{}.ner'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_ner) + '\n')
with open('{}.index'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_index) + '\n')
with open('{}.tok'.format(sent_file), 'a') as f:
f.write('\n'.join(sents) + '\n')
with open('{}.pos'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_pos) + '\n')
with open('{}.ner'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_ner) + '\n')
with open('{}.index'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_index) + '\n')
label(para_file, answer_file)
def label(para_file, answer_file):
max_node = 0
with open('{}.tok'.format(para_file), 'r') as fp, open('{}.label'.
format(para_file), 'a') as fl, open('{}.index'.format(answer_file), 'r'
) as fa:
while True:
para = fp.readline()
if not para:
break
words = [p for p in para.strip().split(' ')]
max_node = max(len(words), max_node)
answer = fa.readline()
labels = []
try:
start, end = map(int, answer.split('\t')[0].split(','))
for i in range(len(words)):
if start <= i <= end:
if i == start:
labels.append('B')
else:
labels.append('I')
else:
labels.append('O')
except:
pass
fl.write(' '.join(labels) + '\n')
return max_node
def get_data(train_json, dev_json, test_title_file, output_dir):
test_titles = open(test_title_file, 'r').readlines()
test_titles = set([line.strip() for line in test_titles])
process(train_json, '{}/train/'.format(output_dir), exclude_titles=
test_titles)
process(dev_json, '{}/dev/'.format(output_dir))
process(train_json, '{}/test/'.format(output_dir), include_titles=
test_titles)
def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,
vocab_file):
"""
get word embedding matrix from glove
"""
print('Generating word embedding...')
embedding_dict = {}
with open(emb_file, 'r', encoding='utf-8') as fh:
for line in tqdm(fh, total=emb_size):
array = line.split()
word = ''.join(array[0:-vec_size])
vector = list(map(float, array[-vec_size:]))
embedding_dict[word] = vector
TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',
'-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':
'(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}
SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']
words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:
x[1], reverse=True)))
words = SPECIAL_TOKENS + words
if vocab_size > 0:
words = words[:vocab_size]
with open(vocab_file, 'w') as f:
f.write('\n'.join(words[1:]))
embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))
word2idx_dict = {}
unknown_count = 0
for i, word in enumerate(words):
word2idx_dict[word] = i
if word in TRANSLATE:
word = TRANSLATE[word]
done = False
for w in (word, word.lower(), word.upper(), word.capitalize()):
if w in embedding_dict:
embedding[i] = embedding_dict[w]
done = True
break
if not done:
unknown_count += 1
return embedding, word2idx_dict, unknown_count
def get_tag_embedding(counter, data_type, vec_size):
"""
get pos/ner/label tags' embedding matrix
"""
print('Generating {} tag embedding...'.format(data_type))
SPECIAL_TOKENS = ['<NULL>', '<UNK>']
tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x
[1], reverse=True)))
tags = SPECIAL_TOKENS + tags
embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))
word2idx_dict = {w: i for i, w in enumerate(tags)}
return embedding, word2idx_dict
def get_vocab(config):
print('Get the vocabulary...')
word_counter, char_counter = Counter(), Counter()
pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()
files = [(config.train_para_file, config.train_question_file), (config.
dev_para_file, config.dev_question_file)]
for para_file, que_file in files:
with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.
format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'
) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(
'{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(
que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'
) as fpl:
while True:
para, question = fp.readline(), fq.readline()
pos, que_pos = fpp.readline(), fqp.readline()
ner, que_ner = fpn.readline(), fqn.readline()
label = fpl.readline()
if not question or not para:
break
if config.lower_word:
para = para.lower()
question = question.lower()
para_tokens = para.strip().split(' ')
que_tokens = question.strip().split(' ')
pos_tags = pos.strip().split(' ')
ner_tags = ner.strip().split(' ')
que_pos_tags = que_pos.strip().split(' ')
que_ner_tags = que_ner.strip().split(' ')
labels = label.strip().split(' ')
for token in (para_tokens + que_tokens):
word_counter[token] += 1
for char in list(token):
char_counter[char] += 1
for pos_tag in (pos_tags + que_pos_tags):
pos_counter[pos_tag] += 1
for ner_tag in (ner_tags + que_ner_tags):
ner_counter[ner_tag] += 1
for label in labels:
label_counter[label] += 1
word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,
emb_file=config.glove_word_file, emb_size=config.glove_word_size,
vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,
vocab_file=config.vocab_file)
char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',
vec_size=config.char_dim)
pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',
vec_size=config.pos_dim)
ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',
vec_size=config.ner_dim)
label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,
'label', vec_size=config.label_dim)
print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))
print('{} chars'.format(char_emb_mat.shape[0]))
print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(
pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],
char_emb_mat.shape[0]))
save(config.word_emb_file, word_emb_mat, message='word embedding')
save(config.char_emb_file, char_emb_mat, message='char embedding')
save(config.pos_emb_file, pos_emb_mat, message='pos embedding')
save(config.ner_emb_file, ner_emb_mat, message='ner embedding')
save(config.label_emb_file, label_emb_mat, message='label embedding')
save(config.word_dictionary, word2idx_dict, message='word dictionary')
save(config.char_dictionary, char2idx_dict, message='char dictionary')
save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')
save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')
save(config.label_dictionary, label2idx_dict, message='label dictionary')
print('Dump elmo word embedding...')
token_embedding_file = config.embedding_file
dump_token_embeddings(config.vocab_file, config.elmo_options_file,
config.elmo_weight_file, token_embedding_file)
if __name__ == '__main__':
os.system(
'mkdir data; mkdir data/processed; mkdir data/processed/train; mkdir data/processed/dev; mkdir data/processed/test'
)
get_data('../../LIB/squad/train-v1.1.json',
'../../LIB/squad/dev-v1.1.json', '../../LIB/squad/doclist-test.txt',
'data/processed')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
import os
import corenlp
import numpy as np
import ujson as json
from tqdm import tqdm
from collections import Counter
from bilm import dump_token_embeddings
import sys
sys.path.append('../..')
from LIB.utils import save
def process(json_file, outpur_dir, exclude_titles=None, include_titles=None):
"""
:param json_file: original data in json format
:param outpur_dir: the output directory of pre-processed data
:param exclude_titles: article titles to exclude
:param include_titles: article titles to include
"""
para_file = '{}/paras'.format(outpur_dir)
question_file = '{}/questions'.format(outpur_dir)
sent_file = '{}/sents'.format(outpur_dir)
answer_file = '{}/answers'.format(outpur_dir)
print('Generating {} raw data...'.format(json_file))
max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0
with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=
'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',
timeout=50000) as client:
source = json.load(fh)
for article in tqdm(source['data']):
title = article['title']
if include_titles and title not in include_titles:
continue
if exclude_titles and title in exclude_titles:
continue
for para in article['paragraphs']:
paragraphs, questions, answers, sents, ids = [], [], [], [], []
paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [
], [], []
paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [
], [], []
answers_index, sents_index = [], []
context = para['context']
if not context.strip():
continue
ann_para = client.annotate(context)
max_sent = max(max_sent, len(ann_para.sentence))
max_sent_len = max(max_sent_len, max(map(lambda x: len(x.
token), ann_para.sentence)))
(ann_para_tokens, paragraph_tokens, paragraph_pos,
paragraph_ner) = [], [], [], []
for sent in ann_para.sentence:
for token in sent.token:
ann_para_tokens.append(token)
paragraph_tokens.append(token.word)
paragraph_pos.append(token.pos)
paragraph_ner.append(token.ner)
for qa in para['qas']:
ques = qa['question']
id = qa['id']
if not ques.strip():
continue
ann_que = client.annotate(ques)
max_que_len = max(max_que_len, len(ann_que.sentence[0].
token))
question_tokens, question_pos, question_ner = [], [], []
for sent in ann_que.sentence:
for token in sent.token:
question_tokens.append(token.word)
question_pos.append(token.pos)
question_ner.append(token.ner)
(all_answer_tokens, all_answer_pos, all_answer_ner,
all_answer_index) = [], [], [], []
(all_sent_tokens, all_sent_pos, all_sent_ner,
all_sent_index) = [], [], [], []
for answer in qa['answers']:
answer_text = answer['text']
if not answer_text.strip():
continue
ann_ans = client.annotate(answer_text)
answer_tokens, answer_pos, answer_ner = [], [], []
for sent in ann_ans.sentence:
for token in sent.token:
answer_tokens.append(token.word)
answer_pos.append(token.pos)
answer_ner.append(token.ner)
all_answer_tokens.append(' '.join(answer_tokens))
all_answer_pos.append(' '.join(answer_pos))
all_answer_ner.append(' '.join(answer_ner))
answer_start = answer['answer_start']
answer_end = answer_start + len(answer_text)
sentence = []
for sent in ann_para.sentence:
if (sent.characterOffsetBegin <= answer_start <=
sent.characterOffsetEnd or sent.
characterOffsetBegin <= answer_end <= sent.
characterOffsetEnd):
sentence.append(sent)
sentence = [token for sent in sentence for token in
sent.token]
sentence_tokens = [token.word for token in sentence]
sentence_pos = [token.pos for token in sentence]
sentence_ner = [token.ner for token in sentence]
all_sent_tokens.append(' '.join(sentence_tokens))
all_sent_pos.append(' '.join(sentence_pos))
all_sent_ner.append(' '.join(sentence_ner))
y1_sent = sentence[0].tokenBeginIndex
y2_sent = sentence[-1].tokenBeginIndex
y1_ans = None
for i, token in enumerate(sentence):
if (token.beginChar - 1 <= answer_start <=
token.endChar):
y1_ans = sentence[0].tokenBeginIndex + i
try:
assert y1_ans != None
except:
continue
y2_ans = y1_ans + len(answer_tokens) - 1
all_answer_index.append('{},{}'.format(y1_ans, y2_ans))
all_sent_index.append('{},{}'.format(y1_sent, y2_sent))
paragraphs.append(' '.join(paragraph_tokens))
paragraphs_pos.append(' '.join(paragraph_pos))
paragraphs_ner.append(' '.join(paragraph_ner))
questions.append(' '.join(question_tokens))
questions_pos.append(' '.join(question_pos))
questions_ner.append(' '.join(question_ner))
answers.append('\t'.join(all_answer_tokens))
answers_pos.append('\t'.join(all_answer_pos))
answers_ner.append('\t'.join(all_answer_ner))
answers_index.append('\t'.join(all_answer_index))
sents.append('\t'.join(all_sent_tokens))
sents_pos.append('\t'.join(all_sent_pos))
sents_ner.append('\t'.join(all_sent_ner))
sents_index.append('\t'.join(all_sent_index))
ids.append(id)
with open('{}.tok'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs) + '\n')
with open('{}.pos'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_pos) + '\n')
with open('{}.ner'.format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_ner) + '\n')
with open('{}.id'.format(para_file), 'a') as f:
f.write('\n'.join(ids) + '\n')
with open('{}.tok'.format(question_file), 'a') as f:
f.write('\n'.join(questions) + '\n')
with open('{}.pos'.format(question_file), 'a') as f:
f.write('\n'.join(questions_pos) + '\n')
with open('{}.ner'.format(question_file), 'a') as f:
f.write('\n'.join(questions_ner) + '\n')
with open('{}.tok'.format(answer_file), 'a') as f:
f.write('\n'.join(answers) + '\n')
with open('{}.pos'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_pos) + '\n')
with open('{}.ner'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_ner) + '\n')
with open('{}.index'.format(answer_file), 'a') as f:
f.write('\n'.join(answers_index) + '\n')
with open('{}.tok'.format(sent_file), 'a') as f:
f.write('\n'.join(sents) + '\n')
with open('{}.pos'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_pos) + '\n')
with open('{}.ner'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_ner) + '\n')
with open('{}.index'.format(sent_file), 'a') as f:
f.write('\n'.join(sents_index) + '\n')
label(para_file, answer_file)
def label(para_file, answer_file):
max_node = 0
with open('{}.tok'.format(para_file), 'r') as fp, open('{}.label'.
format(para_file), 'a') as fl, open('{}.index'.format(answer_file), 'r'
) as fa:
while True:
para = fp.readline()
if not para:
break
words = [p for p in para.strip().split(' ')]
max_node = max(len(words), max_node)
answer = fa.readline()
labels = []
try:
start, end = map(int, answer.split('\t')[0].split(','))
for i in range(len(words)):
if start <= i <= end:
if i == start:
labels.append('B')
else:
labels.append('I')
else:
labels.append('O')
except:
pass
fl.write(' '.join(labels) + '\n')
return max_node
def get_data(train_json, dev_json, test_title_file, output_dir):
test_titles = open(test_title_file, 'r').readlines()
test_titles = set([line.strip() for line in test_titles])
process(train_json, '{}/train/'.format(output_dir), exclude_titles=
test_titles)
process(dev_json, '{}/dev/'.format(output_dir))
process(train_json, '{}/test/'.format(output_dir), include_titles=
test_titles)
def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,
vocab_file):
"""
get word embedding matrix from glove
"""
print('Generating word embedding...')
embedding_dict = {}
with open(emb_file, 'r', encoding='utf-8') as fh:
for line in tqdm(fh, total=emb_size):
array = line.split()
word = ''.join(array[0:-vec_size])
vector = list(map(float, array[-vec_size:]))
embedding_dict[word] = vector
TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',
'-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':
'(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}
SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']
words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:
x[1], reverse=True)))
words = SPECIAL_TOKENS + words
if vocab_size > 0:
words = words[:vocab_size]
with open(vocab_file, 'w') as f:
f.write('\n'.join(words[1:]))
embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))
word2idx_dict = {}
unknown_count = 0
for i, word in enumerate(words):
word2idx_dict[word] = i
if word in TRANSLATE:
word = TRANSLATE[word]
done = False
for w in (word, word.lower(), word.upper(), word.capitalize()):
if w in embedding_dict:
embedding[i] = embedding_dict[w]
done = True
break
if not done:
unknown_count += 1
return embedding, word2idx_dict, unknown_count
def get_tag_embedding(counter, data_type, vec_size):
"""
get pos/ner/label tags' embedding matrix
"""
print('Generating {} tag embedding...'.format(data_type))
SPECIAL_TOKENS = ['<NULL>', '<UNK>']
tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x
[1], reverse=True)))
tags = SPECIAL_TOKENS + tags
embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))
word2idx_dict = {w: i for i, w in enumerate(tags)}
return embedding, word2idx_dict
def get_vocab(config):
print('Get the vocabulary...')
word_counter, char_counter = Counter(), Counter()
pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()
files = [(config.train_para_file, config.train_question_file), (config.
dev_para_file, config.dev_question_file)]
for para_file, que_file in files:
with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.
format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'
) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(
'{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(
que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'
) as fpl:
while True:
para, question = fp.readline(), fq.readline()
pos, que_pos = fpp.readline(), fqp.readline()
ner, que_ner = fpn.readline(), fqn.readline()
label = fpl.readline()
if not question or not para:
break
if config.lower_word:
para = para.lower()
question = question.lower()
para_tokens = para.strip().split(' ')
que_tokens = question.strip().split(' ')
pos_tags = pos.strip().split(' ')
ner_tags = ner.strip().split(' ')
que_pos_tags = que_pos.strip().split(' ')
que_ner_tags = que_ner.strip().split(' ')
labels = label.strip().split(' ')
for token in (para_tokens + que_tokens):
word_counter[token] += 1
for char in list(token):
char_counter[char] += 1
for pos_tag in (pos_tags + que_pos_tags):
pos_counter[pos_tag] += 1
for ner_tag in (ner_tags + que_ner_tags):
ner_counter[ner_tag] += 1
for label in labels:
label_counter[label] += 1
word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,
emb_file=config.glove_word_file, emb_size=config.glove_word_size,
vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,
vocab_file=config.vocab_file)
char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',
vec_size=config.char_dim)
pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',
vec_size=config.pos_dim)
ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',
vec_size=config.ner_dim)
label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,
'label', vec_size=config.label_dim)
print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))
print('{} chars'.format(char_emb_mat.shape[0]))
print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(
pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],
char_emb_mat.shape[0]))
save(config.word_emb_file, word_emb_mat, message='word embedding')
save(config.char_emb_file, char_emb_mat, message='char embedding')
save(config.pos_emb_file, pos_emb_mat, message='pos embedding')
save(config.ner_emb_file, ner_emb_mat, message='ner embedding')
save(config.label_emb_file, label_emb_mat, message='label embedding')
save(config.word_dictionary, word2idx_dict, message='word dictionary')
save(config.char_dictionary, char2idx_dict, message='char dictionary')
save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')
save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')
save(config.label_dictionary, label2idx_dict, message='label dictionary')
print('Dump elmo word embedding...')
token_embedding_file = config.embedding_file
dump_token_embeddings(config.vocab_file, config.elmo_options_file,
config.elmo_weight_file, token_embedding_file)
if __name__ == '__main__':
os.system(
'mkdir data; mkdir data/processed; mkdir data/processed/train; mkdir data/processed/dev; mkdir data/processed/test'
)
get_data('../../LIB/squad/train-v1.1.json',
'../../LIB/squad/dev-v1.1.json', '../../LIB/squad/doclist-test.txt',
'data/processed')
<|reserved_special_token_1|>
"""
Data pre-processing
"""
import os
import corenlp
import numpy as np
import ujson as json
from tqdm import tqdm
from collections import Counter
from bilm import dump_token_embeddings
import sys
sys.path.append('../..')
from LIB.utils import save
def process(json_file, outpur_dir, exclude_titles=None, include_titles=None):
"""
:param json_file: original data in json format
:param outpur_dir: the output directory of pre-processed data
:param exclude_titles: article titles to exclude
:param include_titles: article titles to include
"""
para_file = "{}/paras".format(outpur_dir)
question_file = "{}/questions".format(outpur_dir)
sent_file = "{}/sents".format(outpur_dir)
answer_file = "{}/answers".format(outpur_dir)
print("Generating {} raw data...".format(json_file))
max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0
with open(json_file, "r") as fh, corenlp.CoreNLPClient(annotators="tokenize ssplit pos ner".split(),
endpoint="http://localhost:9099", timeout=50000) as client:
source = json.load(fh)
for article in tqdm(source["data"]):
title = article["title"]
if include_titles and title not in include_titles:
continue
if exclude_titles and title in exclude_titles:
continue
for para in article["paragraphs"]:
paragraphs, questions, answers, sents, ids = [], [], [], [], []
paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [], [], []
paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [], [], []
answers_index, sents_index = [], []
# paragraph
context = para["context"]
if not context.strip():
continue
ann_para = client.annotate(context)
max_sent = max(max_sent, len(ann_para.sentence))
max_sent_len = max(max_sent_len, max(map(lambda x: len(x.token), ann_para.sentence)))
ann_para_tokens, paragraph_tokens, paragraph_pos, paragraph_ner = [], [], [], []
for sent in ann_para.sentence:
for token in sent.token:
ann_para_tokens.append(token)
paragraph_tokens.append(token.word)
paragraph_pos.append(token.pos)
paragraph_ner.append(token.ner)
# questions
for qa in para["qas"]:
# question
ques = qa["question"]
id = qa["id"]
if not ques.strip():
continue
ann_que = client.annotate(ques)
max_que_len = max(max_que_len, len(ann_que.sentence[0].token))
question_tokens, question_pos, question_ner = [], [], []
for sent in ann_que.sentence:
for token in sent.token:
question_tokens.append(token.word)
question_pos.append(token.pos)
question_ner.append(token.ner)
# answer
all_answer_tokens, all_answer_pos, all_answer_ner, all_answer_index = [], [], [], []
all_sent_tokens, all_sent_pos, all_sent_ner, all_sent_index = [], [], [], []
for answer in qa["answers"]:
answer_text = answer["text"]
if not answer_text.strip():
continue
ann_ans = client.annotate(answer_text)
answer_tokens, answer_pos, answer_ner = [], [], []
for sent in ann_ans.sentence:
for token in sent.token:
answer_tokens.append(token.word)
answer_pos.append(token.pos)
answer_ner.append(token.ner)
all_answer_tokens.append(' '.join(answer_tokens))
all_answer_pos.append(' '.join(answer_pos))
all_answer_ner.append(' '.join(answer_ner))
answer_start = answer['answer_start']
answer_end = answer_start + len(answer_text)
# sentence
sentence = []
for sent in ann_para.sentence:
if sent.characterOffsetBegin <= answer_start <= sent.characterOffsetEnd or \
sent.characterOffsetBegin <= answer_end <= sent.characterOffsetEnd:
sentence.append(sent)
sentence = [token for sent in sentence for token in sent.token]
sentence_tokens = [token.word for token in sentence]
sentence_pos = [token.pos for token in sentence]
sentence_ner = [token.ner for token in sentence]
all_sent_tokens.append(' '.join(sentence_tokens))
all_sent_pos.append(' '.join(sentence_pos))
all_sent_ner.append(' '.join(sentence_ner))
# sentence index
y1_sent = sentence[0].tokenBeginIndex
y2_sent = sentence[-1].tokenBeginIndex
# answer index
y1_ans = None
for i, token in enumerate(sentence):
if token.beginChar - 1 <= answer_start <= token.endChar:
y1_ans = sentence[0].tokenBeginIndex + i
try:
assert y1_ans != None
except:
continue
y2_ans = y1_ans + len(answer_tokens) - 1
all_answer_index.append("{},{}".format(y1_ans, y2_ans))
all_sent_index.append("{},{}".format(y1_sent, y2_sent))
paragraphs.append(' '.join(paragraph_tokens))
paragraphs_pos.append(' '.join(paragraph_pos))
paragraphs_ner.append(' '.join(paragraph_ner))
questions.append(' '.join(question_tokens))
questions_pos.append(' '.join(question_pos))
questions_ner.append(' '.join(question_ner))
answers.append('\t'.join(all_answer_tokens))
answers_pos.append('\t'.join(all_answer_pos))
answers_ner.append('\t'.join(all_answer_ner))
answers_index.append('\t'.join(all_answer_index))
sents.append('\t'.join(all_sent_tokens))
sents_pos.append('\t'.join(all_sent_pos))
sents_ner.append('\t'.join(all_sent_ner))
sents_index.append('\t'.join(all_sent_index))
ids.append(id)
# save para
with open("{}.tok".format(para_file), 'a') as f:
f.write('\n'.join(paragraphs) + '\n')
with open("{}.pos".format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_pos) + '\n')
with open("{}.ner".format(para_file), 'a') as f:
f.write('\n'.join(paragraphs_ner) + '\n')
with open("{}.id".format(para_file), 'a') as f:
f.write('\n'.join(ids) + '\n')
# save question
with open("{}.tok".format(question_file), 'a') as f:
f.write('\n'.join(questions) + '\n')
with open("{}.pos".format(question_file), 'a') as f:
f.write('\n'.join(questions_pos) + '\n')
with open("{}.ner".format(question_file), 'a') as f:
f.write('\n'.join(questions_ner) + '\n')
# save answer
with open("{}.tok".format(answer_file), 'a') as f:
f.write('\n'.join(answers) + '\n')
with open("{}.pos".format(answer_file), 'a') as f:
f.write('\n'.join(answers_pos) + '\n')
with open("{}.ner".format(answer_file), 'a') as f:
f.write('\n'.join(answers_ner) + '\n')
with open("{}.index".format(answer_file), 'a') as f:
f.write("\n".join(answers_index) + '\n')
# save sent
with open("{}.tok".format(sent_file), 'a') as f:
f.write('\n'.join(sents) + '\n')
with open("{}.pos".format(sent_file), 'a') as f:
f.write('\n'.join(sents_pos) + '\n')
with open("{}.ner".format(sent_file), 'a') as f:
f.write('\n'.join(sents_ner) + '\n')
with open("{}.index".format(sent_file), 'a') as f:
f.write("\n".join(sents_index) + '\n')
# get BIO labels
label(para_file, answer_file)
def label(para_file, answer_file):
# get the answer BIO label for paragraph
max_node = 0
with open("{}.tok".format(para_file), 'r') as fp, open("{}.label".format(para_file), 'a') as fl, \
open("{}.index".format(answer_file), 'r') as fa:
while True:
para = fp.readline()
if not para:
break
words = [p for p in para.strip().split(' ')]
max_node = max(len(words), max_node)
answer = fa.readline()
labels = []
try:
start, end = map(int, answer.split('\t')[0].split(','))
for i in range(len(words)):
if start <= i <= end:
# answer words
if i == start:
labels.append('B')
else:
labels.append('I')
else:
# non answer words
labels.append('O')
except:
pass
fl.write(' '.join(labels) + '\n')
return max_node
def get_data(train_json, dev_json, test_title_file, output_dir):
test_titles = open(test_title_file, 'r').readlines()
test_titles = set([line.strip() for line in test_titles])
process(train_json, "{}/train/".format(output_dir), exclude_titles=test_titles)
process(dev_json, "{}/dev/".format(output_dir))
process(train_json, "{}/test/".format(output_dir), include_titles=test_titles)
def get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size, vocab_file):
"""
get word embedding matrix from glove
"""
print("Generating word embedding...")
# load word embeddings
embedding_dict = {}
with open(emb_file, "r", encoding="utf-8") as fh:
for line in tqdm(fh, total=emb_size):
array = line.split()
word = "".join(array[0:-vec_size])
vector = list(map(float, array[-vec_size:]))
embedding_dict[word] = vector
TRANSLATE = {
"-lsb-": "[", "-rsb-": "]", "-lrb-": "(", "-rrb-": ")", "-lcb-": "{",
"-rcb-": "}", "-LSB-": "[", "-RSB-": "]", "-LRB-": "(", "-RRB-": ")",
"-LCB-": "{", "-RCB-": "}"
}
SPECIAL_TOKENS = ["<NULL>", "<UNK>", "<S>", "</S>"]
words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))
words = SPECIAL_TOKENS + words
if vocab_size > 0:
words = words[:vocab_size]
with open(vocab_file, 'w') as f:
f.write('\n'.join(words[1:]))
embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))
word2idx_dict = {}
unknown_count = 0
for i, word in enumerate(words):
word2idx_dict[word] = i
if word in TRANSLATE:
word = TRANSLATE[word]
done = False
for w in (word, word.lower(), word.upper(), word.capitalize()):
if w in embedding_dict:
embedding[i] = embedding_dict[w]
done = True
break
if not done:
unknown_count += 1
return embedding, word2idx_dict, unknown_count
def get_tag_embedding(counter, data_type, vec_size):
"""
get pos/ner/label tags' embedding matrix
"""
print("Generating {} tag embedding...".format(data_type))
SPECIAL_TOKENS = ["<NULL>", "<UNK>"]
tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))
tags = SPECIAL_TOKENS + tags
embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))
word2idx_dict = {w: i for i, w in enumerate(tags)}
return embedding, word2idx_dict
def get_vocab(config):
print("Get the vocabulary...")
word_counter, char_counter = Counter(), Counter()
pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()
files = [(config.train_para_file, config.train_question_file), (config.dev_para_file, config.dev_question_file)]
for para_file, que_file in files:
with open("{}.tok".format(para_file), 'r') as fp, open("{}.tok".format(que_file), 'r') as fq, \
open("{}.pos".format(para_file), 'r') as fpp, open("{}.pos".format(que_file), 'r') as fqp, \
open("{}.ner".format(para_file), 'r') as fpn, open("{}.ner".format(que_file), 'r') as fqn, \
open("{}.label".format(para_file), 'r') as fpl:
while True:
para, question = fp.readline(), fq.readline()
pos, que_pos = fpp.readline(), fqp.readline()
ner, que_ner = fpn.readline(), fqn.readline()
label = fpl.readline()
if not question or not para:
break
if config.lower_word:
para = para.lower()
question = question.lower()
para_tokens = para.strip().split(' ')
que_tokens = question.strip().split(' ')
pos_tags = pos.strip().split(' ')
ner_tags = ner.strip().split(' ')
que_pos_tags = que_pos.strip().split(' ')
que_ner_tags = que_ner.strip().split(' ')
labels = label.strip().split(' ')
for token in para_tokens + que_tokens:
word_counter[token] += 1
for char in list(token):
char_counter[char] += 1
for pos_tag in pos_tags + que_pos_tags:
pos_counter[pos_tag] += 1
for ner_tag in ner_tags + que_ner_tags:
ner_counter[ner_tag] += 1
for label in labels:
label_counter[label] += 1
word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter, emb_file=config.glove_word_file,
emb_size=config.glove_word_size,
vocab_size=config.vocab_size_limit,
vec_size=config.glove_dim, vocab_file=config.vocab_file)
char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, "char", vec_size=config.char_dim)
pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, "pos", vec_size=config.pos_dim)
ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, "ner", vec_size=config.ner_dim)
label_emb_mat, label2idx_dict = get_tag_embedding(label_counter, "label", vec_size=config.label_dim)
print("{} out of {} are not in glove".format(unk_num, len(word2idx_dict)))
print("{} chars".format(char_emb_mat.shape[0]))
print("{} pos tags, {} ner tags, {} answer labels, {} chars".format(
pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0], char_emb_mat.shape[0]))
save(config.word_emb_file, word_emb_mat, message="word embedding")
save(config.char_emb_file, char_emb_mat, message="char embedding")
save(config.pos_emb_file, pos_emb_mat, message="pos embedding")
save(config.ner_emb_file, ner_emb_mat, message="ner embedding")
save(config.label_emb_file, label_emb_mat, message="label embedding")
save(config.word_dictionary, word2idx_dict, message="word dictionary")
save(config.char_dictionary, char2idx_dict, message="char dictionary")
save(config.pos_dictionary, pos2idx_dict, message="pos dictionary")
save(config.ner_dictionary, ner2idx_dict, message="ner dictionary")
save(config.label_dictionary, label2idx_dict, message="label dictionary")
print("Dump elmo word embedding...")
token_embedding_file = config.embedding_file
dump_token_embeddings(
config.vocab_file, config.elmo_options_file, config.elmo_weight_file, token_embedding_file
)
if __name__ == '__main__':
# process data
os.system("mkdir data; mkdir data/processed; mkdir data/processed/train; "
"mkdir data/processed/dev; mkdir data/processed/test")
get_data("../../LIB/squad/train-v1.1.json", "../../LIB/squad/dev-v1.1.json",
"../../LIB/squad/doclist-test.txt", "data/processed")
|
flexible
|
{
"blob_id": "0c37806f0a7c0976711edd685fd64d2616147cb6",
"index": 4623,
"step-1": "<mask token>\n\n\ndef process(json_file, outpur_dir, exclude_titles=None, include_titles=None):\n \"\"\"\n :param json_file: original data in json format\n :param outpur_dir: the output directory of pre-processed data\n :param exclude_titles: article titles to exclude\n :param include_titles: article titles to include\n \"\"\"\n para_file = '{}/paras'.format(outpur_dir)\n question_file = '{}/questions'.format(outpur_dir)\n sent_file = '{}/sents'.format(outpur_dir)\n answer_file = '{}/answers'.format(outpur_dir)\n print('Generating {} raw data...'.format(json_file))\n max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0\n with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=\n 'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',\n timeout=50000) as client:\n source = json.load(fh)\n for article in tqdm(source['data']):\n title = article['title']\n if include_titles and title not in include_titles:\n continue\n if exclude_titles and title in exclude_titles:\n continue\n for para in article['paragraphs']:\n paragraphs, questions, answers, sents, ids = [], [], [], [], []\n paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [\n ], [], []\n paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [\n ], [], []\n answers_index, sents_index = [], []\n context = para['context']\n if not context.strip():\n continue\n ann_para = client.annotate(context)\n max_sent = max(max_sent, len(ann_para.sentence))\n max_sent_len = max(max_sent_len, max(map(lambda x: len(x.\n token), ann_para.sentence)))\n (ann_para_tokens, paragraph_tokens, paragraph_pos,\n paragraph_ner) = [], [], [], []\n for sent in ann_para.sentence:\n for token in sent.token:\n ann_para_tokens.append(token)\n paragraph_tokens.append(token.word)\n paragraph_pos.append(token.pos)\n paragraph_ner.append(token.ner)\n for qa in para['qas']:\n ques = qa['question']\n id = qa['id']\n if not ques.strip():\n continue\n ann_que = client.annotate(ques)\n max_que_len = max(max_que_len, len(ann_que.sentence[0].\n token))\n question_tokens, question_pos, question_ner = [], [], []\n for sent in ann_que.sentence:\n for token in sent.token:\n question_tokens.append(token.word)\n question_pos.append(token.pos)\n question_ner.append(token.ner)\n (all_answer_tokens, all_answer_pos, all_answer_ner,\n all_answer_index) = [], [], [], []\n (all_sent_tokens, all_sent_pos, all_sent_ner,\n all_sent_index) = [], [], [], []\n for answer in qa['answers']:\n answer_text = answer['text']\n if not answer_text.strip():\n continue\n ann_ans = client.annotate(answer_text)\n answer_tokens, answer_pos, answer_ner = [], [], []\n for sent in ann_ans.sentence:\n for token in sent.token:\n answer_tokens.append(token.word)\n answer_pos.append(token.pos)\n answer_ner.append(token.ner)\n all_answer_tokens.append(' '.join(answer_tokens))\n all_answer_pos.append(' '.join(answer_pos))\n all_answer_ner.append(' '.join(answer_ner))\n answer_start = answer['answer_start']\n answer_end = answer_start + len(answer_text)\n sentence = []\n for sent in ann_para.sentence:\n if (sent.characterOffsetBegin <= answer_start <=\n sent.characterOffsetEnd or sent.\n characterOffsetBegin <= answer_end <= sent.\n characterOffsetEnd):\n sentence.append(sent)\n sentence = [token for sent in sentence for token in\n sent.token]\n sentence_tokens = [token.word for token in sentence]\n sentence_pos = [token.pos for token in sentence]\n sentence_ner = [token.ner for token in sentence]\n all_sent_tokens.append(' '.join(sentence_tokens))\n all_sent_pos.append(' '.join(sentence_pos))\n all_sent_ner.append(' '.join(sentence_ner))\n y1_sent = sentence[0].tokenBeginIndex\n y2_sent = sentence[-1].tokenBeginIndex\n y1_ans = None\n for i, token in enumerate(sentence):\n if (token.beginChar - 1 <= answer_start <=\n token.endChar):\n y1_ans = sentence[0].tokenBeginIndex + i\n try:\n assert y1_ans != None\n except:\n continue\n y2_ans = y1_ans + len(answer_tokens) - 1\n all_answer_index.append('{},{}'.format(y1_ans, y2_ans))\n all_sent_index.append('{},{}'.format(y1_sent, y2_sent))\n paragraphs.append(' '.join(paragraph_tokens))\n paragraphs_pos.append(' '.join(paragraph_pos))\n paragraphs_ner.append(' '.join(paragraph_ner))\n questions.append(' '.join(question_tokens))\n questions_pos.append(' '.join(question_pos))\n questions_ner.append(' '.join(question_ner))\n answers.append('\\t'.join(all_answer_tokens))\n answers_pos.append('\\t'.join(all_answer_pos))\n answers_ner.append('\\t'.join(all_answer_ner))\n answers_index.append('\\t'.join(all_answer_index))\n sents.append('\\t'.join(all_sent_tokens))\n sents_pos.append('\\t'.join(all_sent_pos))\n sents_ner.append('\\t'.join(all_sent_ner))\n sents_index.append('\\t'.join(all_sent_index))\n ids.append(id)\n with open('{}.tok'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs) + '\\n')\n with open('{}.pos'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_pos) + '\\n')\n with open('{}.ner'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_ner) + '\\n')\n with open('{}.id'.format(para_file), 'a') as f:\n f.write('\\n'.join(ids) + '\\n')\n with open('{}.tok'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions) + '\\n')\n with open('{}.pos'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_pos) + '\\n')\n with open('{}.ner'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_ner) + '\\n')\n with open('{}.tok'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers) + '\\n')\n with open('{}.pos'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_pos) + '\\n')\n with open('{}.ner'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_ner) + '\\n')\n with open('{}.index'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_index) + '\\n')\n with open('{}.tok'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents) + '\\n')\n with open('{}.pos'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_pos) + '\\n')\n with open('{}.ner'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_ner) + '\\n')\n with open('{}.index'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_index) + '\\n')\n label(para_file, answer_file)\n\n\n<mask token>\n\n\ndef get_data(train_json, dev_json, test_title_file, output_dir):\n test_titles = open(test_title_file, 'r').readlines()\n test_titles = set([line.strip() for line in test_titles])\n process(train_json, '{}/train/'.format(output_dir), exclude_titles=\n test_titles)\n process(dev_json, '{}/dev/'.format(output_dir))\n process(train_json, '{}/test/'.format(output_dir), include_titles=\n test_titles)\n\n\ndef get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,\n vocab_file):\n \"\"\"\n get word embedding matrix from glove\n \"\"\"\n print('Generating word embedding...')\n embedding_dict = {}\n with open(emb_file, 'r', encoding='utf-8') as fh:\n for line in tqdm(fh, total=emb_size):\n array = line.split()\n word = ''.join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n embedding_dict[word] = vector\n TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',\n '-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':\n '(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}\n SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:\n x[1], reverse=True)))\n words = SPECIAL_TOKENS + words\n if vocab_size > 0:\n words = words[:vocab_size]\n with open(vocab_file, 'w') as f:\n f.write('\\n'.join(words[1:]))\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\n word2idx_dict = {}\n unknown_count = 0\n for i, word in enumerate(words):\n word2idx_dict[word] = i\n if word in TRANSLATE:\n word = TRANSLATE[word]\n done = False\n for w in (word, word.lower(), word.upper(), word.capitalize()):\n if w in embedding_dict:\n embedding[i] = embedding_dict[w]\n done = True\n break\n if not done:\n unknown_count += 1\n return embedding, word2idx_dict, unknown_count\n\n\ndef get_tag_embedding(counter, data_type, vec_size):\n \"\"\"\n get pos/ner/label tags' embedding matrix\n \"\"\"\n print('Generating {} tag embedding...'.format(data_type))\n SPECIAL_TOKENS = ['<NULL>', '<UNK>']\n tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x\n [1], reverse=True)))\n tags = SPECIAL_TOKENS + tags\n embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))\n word2idx_dict = {w: i for i, w in enumerate(tags)}\n return embedding, word2idx_dict\n\n\ndef get_vocab(config):\n print('Get the vocabulary...')\n word_counter, char_counter = Counter(), Counter()\n pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()\n files = [(config.train_para_file, config.train_question_file), (config.\n dev_para_file, config.dev_question_file)]\n for para_file, que_file in files:\n with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.\n format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'\n ) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(\n '{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(\n que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'\n ) as fpl:\n while True:\n para, question = fp.readline(), fq.readline()\n pos, que_pos = fpp.readline(), fqp.readline()\n ner, que_ner = fpn.readline(), fqn.readline()\n label = fpl.readline()\n if not question or not para:\n break\n if config.lower_word:\n para = para.lower()\n question = question.lower()\n para_tokens = para.strip().split(' ')\n que_tokens = question.strip().split(' ')\n pos_tags = pos.strip().split(' ')\n ner_tags = ner.strip().split(' ')\n que_pos_tags = que_pos.strip().split(' ')\n que_ner_tags = que_ner.strip().split(' ')\n labels = label.strip().split(' ')\n for token in (para_tokens + que_tokens):\n word_counter[token] += 1\n for char in list(token):\n char_counter[char] += 1\n for pos_tag in (pos_tags + que_pos_tags):\n pos_counter[pos_tag] += 1\n for ner_tag in (ner_tags + que_ner_tags):\n ner_counter[ner_tag] += 1\n for label in labels:\n label_counter[label] += 1\n word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,\n emb_file=config.glove_word_file, emb_size=config.glove_word_size,\n vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,\n vocab_file=config.vocab_file)\n char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',\n vec_size=config.char_dim)\n pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',\n vec_size=config.pos_dim)\n ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',\n vec_size=config.ner_dim)\n label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,\n 'label', vec_size=config.label_dim)\n print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))\n print('{} chars'.format(char_emb_mat.shape[0]))\n print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(\n pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],\n char_emb_mat.shape[0]))\n save(config.word_emb_file, word_emb_mat, message='word embedding')\n save(config.char_emb_file, char_emb_mat, message='char embedding')\n save(config.pos_emb_file, pos_emb_mat, message='pos embedding')\n save(config.ner_emb_file, ner_emb_mat, message='ner embedding')\n save(config.label_emb_file, label_emb_mat, message='label embedding')\n save(config.word_dictionary, word2idx_dict, message='word dictionary')\n save(config.char_dictionary, char2idx_dict, message='char dictionary')\n save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')\n save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')\n save(config.label_dictionary, label2idx_dict, message='label dictionary')\n print('Dump elmo word embedding...')\n token_embedding_file = config.embedding_file\n dump_token_embeddings(config.vocab_file, config.elmo_options_file,\n config.elmo_weight_file, token_embedding_file)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef process(json_file, outpur_dir, exclude_titles=None, include_titles=None):\n \"\"\"\n :param json_file: original data in json format\n :param outpur_dir: the output directory of pre-processed data\n :param exclude_titles: article titles to exclude\n :param include_titles: article titles to include\n \"\"\"\n para_file = '{}/paras'.format(outpur_dir)\n question_file = '{}/questions'.format(outpur_dir)\n sent_file = '{}/sents'.format(outpur_dir)\n answer_file = '{}/answers'.format(outpur_dir)\n print('Generating {} raw data...'.format(json_file))\n max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0\n with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=\n 'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',\n timeout=50000) as client:\n source = json.load(fh)\n for article in tqdm(source['data']):\n title = article['title']\n if include_titles and title not in include_titles:\n continue\n if exclude_titles and title in exclude_titles:\n continue\n for para in article['paragraphs']:\n paragraphs, questions, answers, sents, ids = [], [], [], [], []\n paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [\n ], [], []\n paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [\n ], [], []\n answers_index, sents_index = [], []\n context = para['context']\n if not context.strip():\n continue\n ann_para = client.annotate(context)\n max_sent = max(max_sent, len(ann_para.sentence))\n max_sent_len = max(max_sent_len, max(map(lambda x: len(x.\n token), ann_para.sentence)))\n (ann_para_tokens, paragraph_tokens, paragraph_pos,\n paragraph_ner) = [], [], [], []\n for sent in ann_para.sentence:\n for token in sent.token:\n ann_para_tokens.append(token)\n paragraph_tokens.append(token.word)\n paragraph_pos.append(token.pos)\n paragraph_ner.append(token.ner)\n for qa in para['qas']:\n ques = qa['question']\n id = qa['id']\n if not ques.strip():\n continue\n ann_que = client.annotate(ques)\n max_que_len = max(max_que_len, len(ann_que.sentence[0].\n token))\n question_tokens, question_pos, question_ner = [], [], []\n for sent in ann_que.sentence:\n for token in sent.token:\n question_tokens.append(token.word)\n question_pos.append(token.pos)\n question_ner.append(token.ner)\n (all_answer_tokens, all_answer_pos, all_answer_ner,\n all_answer_index) = [], [], [], []\n (all_sent_tokens, all_sent_pos, all_sent_ner,\n all_sent_index) = [], [], [], []\n for answer in qa['answers']:\n answer_text = answer['text']\n if not answer_text.strip():\n continue\n ann_ans = client.annotate(answer_text)\n answer_tokens, answer_pos, answer_ner = [], [], []\n for sent in ann_ans.sentence:\n for token in sent.token:\n answer_tokens.append(token.word)\n answer_pos.append(token.pos)\n answer_ner.append(token.ner)\n all_answer_tokens.append(' '.join(answer_tokens))\n all_answer_pos.append(' '.join(answer_pos))\n all_answer_ner.append(' '.join(answer_ner))\n answer_start = answer['answer_start']\n answer_end = answer_start + len(answer_text)\n sentence = []\n for sent in ann_para.sentence:\n if (sent.characterOffsetBegin <= answer_start <=\n sent.characterOffsetEnd or sent.\n characterOffsetBegin <= answer_end <= sent.\n characterOffsetEnd):\n sentence.append(sent)\n sentence = [token for sent in sentence for token in\n sent.token]\n sentence_tokens = [token.word for token in sentence]\n sentence_pos = [token.pos for token in sentence]\n sentence_ner = [token.ner for token in sentence]\n all_sent_tokens.append(' '.join(sentence_tokens))\n all_sent_pos.append(' '.join(sentence_pos))\n all_sent_ner.append(' '.join(sentence_ner))\n y1_sent = sentence[0].tokenBeginIndex\n y2_sent = sentence[-1].tokenBeginIndex\n y1_ans = None\n for i, token in enumerate(sentence):\n if (token.beginChar - 1 <= answer_start <=\n token.endChar):\n y1_ans = sentence[0].tokenBeginIndex + i\n try:\n assert y1_ans != None\n except:\n continue\n y2_ans = y1_ans + len(answer_tokens) - 1\n all_answer_index.append('{},{}'.format(y1_ans, y2_ans))\n all_sent_index.append('{},{}'.format(y1_sent, y2_sent))\n paragraphs.append(' '.join(paragraph_tokens))\n paragraphs_pos.append(' '.join(paragraph_pos))\n paragraphs_ner.append(' '.join(paragraph_ner))\n questions.append(' '.join(question_tokens))\n questions_pos.append(' '.join(question_pos))\n questions_ner.append(' '.join(question_ner))\n answers.append('\\t'.join(all_answer_tokens))\n answers_pos.append('\\t'.join(all_answer_pos))\n answers_ner.append('\\t'.join(all_answer_ner))\n answers_index.append('\\t'.join(all_answer_index))\n sents.append('\\t'.join(all_sent_tokens))\n sents_pos.append('\\t'.join(all_sent_pos))\n sents_ner.append('\\t'.join(all_sent_ner))\n sents_index.append('\\t'.join(all_sent_index))\n ids.append(id)\n with open('{}.tok'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs) + '\\n')\n with open('{}.pos'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_pos) + '\\n')\n with open('{}.ner'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_ner) + '\\n')\n with open('{}.id'.format(para_file), 'a') as f:\n f.write('\\n'.join(ids) + '\\n')\n with open('{}.tok'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions) + '\\n')\n with open('{}.pos'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_pos) + '\\n')\n with open('{}.ner'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_ner) + '\\n')\n with open('{}.tok'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers) + '\\n')\n with open('{}.pos'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_pos) + '\\n')\n with open('{}.ner'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_ner) + '\\n')\n with open('{}.index'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_index) + '\\n')\n with open('{}.tok'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents) + '\\n')\n with open('{}.pos'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_pos) + '\\n')\n with open('{}.ner'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_ner) + '\\n')\n with open('{}.index'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_index) + '\\n')\n label(para_file, answer_file)\n\n\ndef label(para_file, answer_file):\n max_node = 0\n with open('{}.tok'.format(para_file), 'r') as fp, open('{}.label'.\n format(para_file), 'a') as fl, open('{}.index'.format(answer_file), 'r'\n ) as fa:\n while True:\n para = fp.readline()\n if not para:\n break\n words = [p for p in para.strip().split(' ')]\n max_node = max(len(words), max_node)\n answer = fa.readline()\n labels = []\n try:\n start, end = map(int, answer.split('\\t')[0].split(','))\n for i in range(len(words)):\n if start <= i <= end:\n if i == start:\n labels.append('B')\n else:\n labels.append('I')\n else:\n labels.append('O')\n except:\n pass\n fl.write(' '.join(labels) + '\\n')\n return max_node\n\n\ndef get_data(train_json, dev_json, test_title_file, output_dir):\n test_titles = open(test_title_file, 'r').readlines()\n test_titles = set([line.strip() for line in test_titles])\n process(train_json, '{}/train/'.format(output_dir), exclude_titles=\n test_titles)\n process(dev_json, '{}/dev/'.format(output_dir))\n process(train_json, '{}/test/'.format(output_dir), include_titles=\n test_titles)\n\n\ndef get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,\n vocab_file):\n \"\"\"\n get word embedding matrix from glove\n \"\"\"\n print('Generating word embedding...')\n embedding_dict = {}\n with open(emb_file, 'r', encoding='utf-8') as fh:\n for line in tqdm(fh, total=emb_size):\n array = line.split()\n word = ''.join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n embedding_dict[word] = vector\n TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',\n '-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':\n '(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}\n SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:\n x[1], reverse=True)))\n words = SPECIAL_TOKENS + words\n if vocab_size > 0:\n words = words[:vocab_size]\n with open(vocab_file, 'w') as f:\n f.write('\\n'.join(words[1:]))\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\n word2idx_dict = {}\n unknown_count = 0\n for i, word in enumerate(words):\n word2idx_dict[word] = i\n if word in TRANSLATE:\n word = TRANSLATE[word]\n done = False\n for w in (word, word.lower(), word.upper(), word.capitalize()):\n if w in embedding_dict:\n embedding[i] = embedding_dict[w]\n done = True\n break\n if not done:\n unknown_count += 1\n return embedding, word2idx_dict, unknown_count\n\n\ndef get_tag_embedding(counter, data_type, vec_size):\n \"\"\"\n get pos/ner/label tags' embedding matrix\n \"\"\"\n print('Generating {} tag embedding...'.format(data_type))\n SPECIAL_TOKENS = ['<NULL>', '<UNK>']\n tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x\n [1], reverse=True)))\n tags = SPECIAL_TOKENS + tags\n embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))\n word2idx_dict = {w: i for i, w in enumerate(tags)}\n return embedding, word2idx_dict\n\n\ndef get_vocab(config):\n print('Get the vocabulary...')\n word_counter, char_counter = Counter(), Counter()\n pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()\n files = [(config.train_para_file, config.train_question_file), (config.\n dev_para_file, config.dev_question_file)]\n for para_file, que_file in files:\n with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.\n format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'\n ) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(\n '{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(\n que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'\n ) as fpl:\n while True:\n para, question = fp.readline(), fq.readline()\n pos, que_pos = fpp.readline(), fqp.readline()\n ner, que_ner = fpn.readline(), fqn.readline()\n label = fpl.readline()\n if not question or not para:\n break\n if config.lower_word:\n para = para.lower()\n question = question.lower()\n para_tokens = para.strip().split(' ')\n que_tokens = question.strip().split(' ')\n pos_tags = pos.strip().split(' ')\n ner_tags = ner.strip().split(' ')\n que_pos_tags = que_pos.strip().split(' ')\n que_ner_tags = que_ner.strip().split(' ')\n labels = label.strip().split(' ')\n for token in (para_tokens + que_tokens):\n word_counter[token] += 1\n for char in list(token):\n char_counter[char] += 1\n for pos_tag in (pos_tags + que_pos_tags):\n pos_counter[pos_tag] += 1\n for ner_tag in (ner_tags + que_ner_tags):\n ner_counter[ner_tag] += 1\n for label in labels:\n label_counter[label] += 1\n word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,\n emb_file=config.glove_word_file, emb_size=config.glove_word_size,\n vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,\n vocab_file=config.vocab_file)\n char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',\n vec_size=config.char_dim)\n pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',\n vec_size=config.pos_dim)\n ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',\n vec_size=config.ner_dim)\n label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,\n 'label', vec_size=config.label_dim)\n print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))\n print('{} chars'.format(char_emb_mat.shape[0]))\n print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(\n pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],\n char_emb_mat.shape[0]))\n save(config.word_emb_file, word_emb_mat, message='word embedding')\n save(config.char_emb_file, char_emb_mat, message='char embedding')\n save(config.pos_emb_file, pos_emb_mat, message='pos embedding')\n save(config.ner_emb_file, ner_emb_mat, message='ner embedding')\n save(config.label_emb_file, label_emb_mat, message='label embedding')\n save(config.word_dictionary, word2idx_dict, message='word dictionary')\n save(config.char_dictionary, char2idx_dict, message='char dictionary')\n save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')\n save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')\n save(config.label_dictionary, label2idx_dict, message='label dictionary')\n print('Dump elmo word embedding...')\n token_embedding_file = config.embedding_file\n dump_token_embeddings(config.vocab_file, config.elmo_options_file,\n config.elmo_weight_file, token_embedding_file)\n\n\n<mask token>\n",
"step-3": "<mask token>\nsys.path.append('../..')\n<mask token>\n\n\ndef process(json_file, outpur_dir, exclude_titles=None, include_titles=None):\n \"\"\"\n :param json_file: original data in json format\n :param outpur_dir: the output directory of pre-processed data\n :param exclude_titles: article titles to exclude\n :param include_titles: article titles to include\n \"\"\"\n para_file = '{}/paras'.format(outpur_dir)\n question_file = '{}/questions'.format(outpur_dir)\n sent_file = '{}/sents'.format(outpur_dir)\n answer_file = '{}/answers'.format(outpur_dir)\n print('Generating {} raw data...'.format(json_file))\n max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0\n with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=\n 'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',\n timeout=50000) as client:\n source = json.load(fh)\n for article in tqdm(source['data']):\n title = article['title']\n if include_titles and title not in include_titles:\n continue\n if exclude_titles and title in exclude_titles:\n continue\n for para in article['paragraphs']:\n paragraphs, questions, answers, sents, ids = [], [], [], [], []\n paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [\n ], [], []\n paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [\n ], [], []\n answers_index, sents_index = [], []\n context = para['context']\n if not context.strip():\n continue\n ann_para = client.annotate(context)\n max_sent = max(max_sent, len(ann_para.sentence))\n max_sent_len = max(max_sent_len, max(map(lambda x: len(x.\n token), ann_para.sentence)))\n (ann_para_tokens, paragraph_tokens, paragraph_pos,\n paragraph_ner) = [], [], [], []\n for sent in ann_para.sentence:\n for token in sent.token:\n ann_para_tokens.append(token)\n paragraph_tokens.append(token.word)\n paragraph_pos.append(token.pos)\n paragraph_ner.append(token.ner)\n for qa in para['qas']:\n ques = qa['question']\n id = qa['id']\n if not ques.strip():\n continue\n ann_que = client.annotate(ques)\n max_que_len = max(max_que_len, len(ann_que.sentence[0].\n token))\n question_tokens, question_pos, question_ner = [], [], []\n for sent in ann_que.sentence:\n for token in sent.token:\n question_tokens.append(token.word)\n question_pos.append(token.pos)\n question_ner.append(token.ner)\n (all_answer_tokens, all_answer_pos, all_answer_ner,\n all_answer_index) = [], [], [], []\n (all_sent_tokens, all_sent_pos, all_sent_ner,\n all_sent_index) = [], [], [], []\n for answer in qa['answers']:\n answer_text = answer['text']\n if not answer_text.strip():\n continue\n ann_ans = client.annotate(answer_text)\n answer_tokens, answer_pos, answer_ner = [], [], []\n for sent in ann_ans.sentence:\n for token in sent.token:\n answer_tokens.append(token.word)\n answer_pos.append(token.pos)\n answer_ner.append(token.ner)\n all_answer_tokens.append(' '.join(answer_tokens))\n all_answer_pos.append(' '.join(answer_pos))\n all_answer_ner.append(' '.join(answer_ner))\n answer_start = answer['answer_start']\n answer_end = answer_start + len(answer_text)\n sentence = []\n for sent in ann_para.sentence:\n if (sent.characterOffsetBegin <= answer_start <=\n sent.characterOffsetEnd or sent.\n characterOffsetBegin <= answer_end <= sent.\n characterOffsetEnd):\n sentence.append(sent)\n sentence = [token for sent in sentence for token in\n sent.token]\n sentence_tokens = [token.word for token in sentence]\n sentence_pos = [token.pos for token in sentence]\n sentence_ner = [token.ner for token in sentence]\n all_sent_tokens.append(' '.join(sentence_tokens))\n all_sent_pos.append(' '.join(sentence_pos))\n all_sent_ner.append(' '.join(sentence_ner))\n y1_sent = sentence[0].tokenBeginIndex\n y2_sent = sentence[-1].tokenBeginIndex\n y1_ans = None\n for i, token in enumerate(sentence):\n if (token.beginChar - 1 <= answer_start <=\n token.endChar):\n y1_ans = sentence[0].tokenBeginIndex + i\n try:\n assert y1_ans != None\n except:\n continue\n y2_ans = y1_ans + len(answer_tokens) - 1\n all_answer_index.append('{},{}'.format(y1_ans, y2_ans))\n all_sent_index.append('{},{}'.format(y1_sent, y2_sent))\n paragraphs.append(' '.join(paragraph_tokens))\n paragraphs_pos.append(' '.join(paragraph_pos))\n paragraphs_ner.append(' '.join(paragraph_ner))\n questions.append(' '.join(question_tokens))\n questions_pos.append(' '.join(question_pos))\n questions_ner.append(' '.join(question_ner))\n answers.append('\\t'.join(all_answer_tokens))\n answers_pos.append('\\t'.join(all_answer_pos))\n answers_ner.append('\\t'.join(all_answer_ner))\n answers_index.append('\\t'.join(all_answer_index))\n sents.append('\\t'.join(all_sent_tokens))\n sents_pos.append('\\t'.join(all_sent_pos))\n sents_ner.append('\\t'.join(all_sent_ner))\n sents_index.append('\\t'.join(all_sent_index))\n ids.append(id)\n with open('{}.tok'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs) + '\\n')\n with open('{}.pos'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_pos) + '\\n')\n with open('{}.ner'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_ner) + '\\n')\n with open('{}.id'.format(para_file), 'a') as f:\n f.write('\\n'.join(ids) + '\\n')\n with open('{}.tok'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions) + '\\n')\n with open('{}.pos'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_pos) + '\\n')\n with open('{}.ner'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_ner) + '\\n')\n with open('{}.tok'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers) + '\\n')\n with open('{}.pos'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_pos) + '\\n')\n with open('{}.ner'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_ner) + '\\n')\n with open('{}.index'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_index) + '\\n')\n with open('{}.tok'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents) + '\\n')\n with open('{}.pos'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_pos) + '\\n')\n with open('{}.ner'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_ner) + '\\n')\n with open('{}.index'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_index) + '\\n')\n label(para_file, answer_file)\n\n\ndef label(para_file, answer_file):\n max_node = 0\n with open('{}.tok'.format(para_file), 'r') as fp, open('{}.label'.\n format(para_file), 'a') as fl, open('{}.index'.format(answer_file), 'r'\n ) as fa:\n while True:\n para = fp.readline()\n if not para:\n break\n words = [p for p in para.strip().split(' ')]\n max_node = max(len(words), max_node)\n answer = fa.readline()\n labels = []\n try:\n start, end = map(int, answer.split('\\t')[0].split(','))\n for i in range(len(words)):\n if start <= i <= end:\n if i == start:\n labels.append('B')\n else:\n labels.append('I')\n else:\n labels.append('O')\n except:\n pass\n fl.write(' '.join(labels) + '\\n')\n return max_node\n\n\ndef get_data(train_json, dev_json, test_title_file, output_dir):\n test_titles = open(test_title_file, 'r').readlines()\n test_titles = set([line.strip() for line in test_titles])\n process(train_json, '{}/train/'.format(output_dir), exclude_titles=\n test_titles)\n process(dev_json, '{}/dev/'.format(output_dir))\n process(train_json, '{}/test/'.format(output_dir), include_titles=\n test_titles)\n\n\ndef get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,\n vocab_file):\n \"\"\"\n get word embedding matrix from glove\n \"\"\"\n print('Generating word embedding...')\n embedding_dict = {}\n with open(emb_file, 'r', encoding='utf-8') as fh:\n for line in tqdm(fh, total=emb_size):\n array = line.split()\n word = ''.join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n embedding_dict[word] = vector\n TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',\n '-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':\n '(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}\n SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:\n x[1], reverse=True)))\n words = SPECIAL_TOKENS + words\n if vocab_size > 0:\n words = words[:vocab_size]\n with open(vocab_file, 'w') as f:\n f.write('\\n'.join(words[1:]))\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\n word2idx_dict = {}\n unknown_count = 0\n for i, word in enumerate(words):\n word2idx_dict[word] = i\n if word in TRANSLATE:\n word = TRANSLATE[word]\n done = False\n for w in (word, word.lower(), word.upper(), word.capitalize()):\n if w in embedding_dict:\n embedding[i] = embedding_dict[w]\n done = True\n break\n if not done:\n unknown_count += 1\n return embedding, word2idx_dict, unknown_count\n\n\ndef get_tag_embedding(counter, data_type, vec_size):\n \"\"\"\n get pos/ner/label tags' embedding matrix\n \"\"\"\n print('Generating {} tag embedding...'.format(data_type))\n SPECIAL_TOKENS = ['<NULL>', '<UNK>']\n tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x\n [1], reverse=True)))\n tags = SPECIAL_TOKENS + tags\n embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))\n word2idx_dict = {w: i for i, w in enumerate(tags)}\n return embedding, word2idx_dict\n\n\ndef get_vocab(config):\n print('Get the vocabulary...')\n word_counter, char_counter = Counter(), Counter()\n pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()\n files = [(config.train_para_file, config.train_question_file), (config.\n dev_para_file, config.dev_question_file)]\n for para_file, que_file in files:\n with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.\n format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'\n ) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(\n '{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(\n que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'\n ) as fpl:\n while True:\n para, question = fp.readline(), fq.readline()\n pos, que_pos = fpp.readline(), fqp.readline()\n ner, que_ner = fpn.readline(), fqn.readline()\n label = fpl.readline()\n if not question or not para:\n break\n if config.lower_word:\n para = para.lower()\n question = question.lower()\n para_tokens = para.strip().split(' ')\n que_tokens = question.strip().split(' ')\n pos_tags = pos.strip().split(' ')\n ner_tags = ner.strip().split(' ')\n que_pos_tags = que_pos.strip().split(' ')\n que_ner_tags = que_ner.strip().split(' ')\n labels = label.strip().split(' ')\n for token in (para_tokens + que_tokens):\n word_counter[token] += 1\n for char in list(token):\n char_counter[char] += 1\n for pos_tag in (pos_tags + que_pos_tags):\n pos_counter[pos_tag] += 1\n for ner_tag in (ner_tags + que_ner_tags):\n ner_counter[ner_tag] += 1\n for label in labels:\n label_counter[label] += 1\n word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,\n emb_file=config.glove_word_file, emb_size=config.glove_word_size,\n vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,\n vocab_file=config.vocab_file)\n char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',\n vec_size=config.char_dim)\n pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',\n vec_size=config.pos_dim)\n ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',\n vec_size=config.ner_dim)\n label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,\n 'label', vec_size=config.label_dim)\n print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))\n print('{} chars'.format(char_emb_mat.shape[0]))\n print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(\n pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],\n char_emb_mat.shape[0]))\n save(config.word_emb_file, word_emb_mat, message='word embedding')\n save(config.char_emb_file, char_emb_mat, message='char embedding')\n save(config.pos_emb_file, pos_emb_mat, message='pos embedding')\n save(config.ner_emb_file, ner_emb_mat, message='ner embedding')\n save(config.label_emb_file, label_emb_mat, message='label embedding')\n save(config.word_dictionary, word2idx_dict, message='word dictionary')\n save(config.char_dictionary, char2idx_dict, message='char dictionary')\n save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')\n save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')\n save(config.label_dictionary, label2idx_dict, message='label dictionary')\n print('Dump elmo word embedding...')\n token_embedding_file = config.embedding_file\n dump_token_embeddings(config.vocab_file, config.elmo_options_file,\n config.elmo_weight_file, token_embedding_file)\n\n\nif __name__ == '__main__':\n os.system(\n 'mkdir data; mkdir data/processed; mkdir data/processed/train; mkdir data/processed/dev; mkdir data/processed/test'\n )\n get_data('../../LIB/squad/train-v1.1.json',\n '../../LIB/squad/dev-v1.1.json', '../../LIB/squad/doclist-test.txt',\n 'data/processed')\n",
"step-4": "<mask token>\nimport os\nimport corenlp\nimport numpy as np\nimport ujson as json\nfrom tqdm import tqdm\nfrom collections import Counter\nfrom bilm import dump_token_embeddings\nimport sys\nsys.path.append('../..')\nfrom LIB.utils import save\n\n\ndef process(json_file, outpur_dir, exclude_titles=None, include_titles=None):\n \"\"\"\n :param json_file: original data in json format\n :param outpur_dir: the output directory of pre-processed data\n :param exclude_titles: article titles to exclude\n :param include_titles: article titles to include\n \"\"\"\n para_file = '{}/paras'.format(outpur_dir)\n question_file = '{}/questions'.format(outpur_dir)\n sent_file = '{}/sents'.format(outpur_dir)\n answer_file = '{}/answers'.format(outpur_dir)\n print('Generating {} raw data...'.format(json_file))\n max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0\n with open(json_file, 'r') as fh, corenlp.CoreNLPClient(annotators=\n 'tokenize ssplit pos ner'.split(), endpoint='http://localhost:9099',\n timeout=50000) as client:\n source = json.load(fh)\n for article in tqdm(source['data']):\n title = article['title']\n if include_titles and title not in include_titles:\n continue\n if exclude_titles and title in exclude_titles:\n continue\n for para in article['paragraphs']:\n paragraphs, questions, answers, sents, ids = [], [], [], [], []\n paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [\n ], [], []\n paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [\n ], [], []\n answers_index, sents_index = [], []\n context = para['context']\n if not context.strip():\n continue\n ann_para = client.annotate(context)\n max_sent = max(max_sent, len(ann_para.sentence))\n max_sent_len = max(max_sent_len, max(map(lambda x: len(x.\n token), ann_para.sentence)))\n (ann_para_tokens, paragraph_tokens, paragraph_pos,\n paragraph_ner) = [], [], [], []\n for sent in ann_para.sentence:\n for token in sent.token:\n ann_para_tokens.append(token)\n paragraph_tokens.append(token.word)\n paragraph_pos.append(token.pos)\n paragraph_ner.append(token.ner)\n for qa in para['qas']:\n ques = qa['question']\n id = qa['id']\n if not ques.strip():\n continue\n ann_que = client.annotate(ques)\n max_que_len = max(max_que_len, len(ann_que.sentence[0].\n token))\n question_tokens, question_pos, question_ner = [], [], []\n for sent in ann_que.sentence:\n for token in sent.token:\n question_tokens.append(token.word)\n question_pos.append(token.pos)\n question_ner.append(token.ner)\n (all_answer_tokens, all_answer_pos, all_answer_ner,\n all_answer_index) = [], [], [], []\n (all_sent_tokens, all_sent_pos, all_sent_ner,\n all_sent_index) = [], [], [], []\n for answer in qa['answers']:\n answer_text = answer['text']\n if not answer_text.strip():\n continue\n ann_ans = client.annotate(answer_text)\n answer_tokens, answer_pos, answer_ner = [], [], []\n for sent in ann_ans.sentence:\n for token in sent.token:\n answer_tokens.append(token.word)\n answer_pos.append(token.pos)\n answer_ner.append(token.ner)\n all_answer_tokens.append(' '.join(answer_tokens))\n all_answer_pos.append(' '.join(answer_pos))\n all_answer_ner.append(' '.join(answer_ner))\n answer_start = answer['answer_start']\n answer_end = answer_start + len(answer_text)\n sentence = []\n for sent in ann_para.sentence:\n if (sent.characterOffsetBegin <= answer_start <=\n sent.characterOffsetEnd or sent.\n characterOffsetBegin <= answer_end <= sent.\n characterOffsetEnd):\n sentence.append(sent)\n sentence = [token for sent in sentence for token in\n sent.token]\n sentence_tokens = [token.word for token in sentence]\n sentence_pos = [token.pos for token in sentence]\n sentence_ner = [token.ner for token in sentence]\n all_sent_tokens.append(' '.join(sentence_tokens))\n all_sent_pos.append(' '.join(sentence_pos))\n all_sent_ner.append(' '.join(sentence_ner))\n y1_sent = sentence[0].tokenBeginIndex\n y2_sent = sentence[-1].tokenBeginIndex\n y1_ans = None\n for i, token in enumerate(sentence):\n if (token.beginChar - 1 <= answer_start <=\n token.endChar):\n y1_ans = sentence[0].tokenBeginIndex + i\n try:\n assert y1_ans != None\n except:\n continue\n y2_ans = y1_ans + len(answer_tokens) - 1\n all_answer_index.append('{},{}'.format(y1_ans, y2_ans))\n all_sent_index.append('{},{}'.format(y1_sent, y2_sent))\n paragraphs.append(' '.join(paragraph_tokens))\n paragraphs_pos.append(' '.join(paragraph_pos))\n paragraphs_ner.append(' '.join(paragraph_ner))\n questions.append(' '.join(question_tokens))\n questions_pos.append(' '.join(question_pos))\n questions_ner.append(' '.join(question_ner))\n answers.append('\\t'.join(all_answer_tokens))\n answers_pos.append('\\t'.join(all_answer_pos))\n answers_ner.append('\\t'.join(all_answer_ner))\n answers_index.append('\\t'.join(all_answer_index))\n sents.append('\\t'.join(all_sent_tokens))\n sents_pos.append('\\t'.join(all_sent_pos))\n sents_ner.append('\\t'.join(all_sent_ner))\n sents_index.append('\\t'.join(all_sent_index))\n ids.append(id)\n with open('{}.tok'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs) + '\\n')\n with open('{}.pos'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_pos) + '\\n')\n with open('{}.ner'.format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_ner) + '\\n')\n with open('{}.id'.format(para_file), 'a') as f:\n f.write('\\n'.join(ids) + '\\n')\n with open('{}.tok'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions) + '\\n')\n with open('{}.pos'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_pos) + '\\n')\n with open('{}.ner'.format(question_file), 'a') as f:\n f.write('\\n'.join(questions_ner) + '\\n')\n with open('{}.tok'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers) + '\\n')\n with open('{}.pos'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_pos) + '\\n')\n with open('{}.ner'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_ner) + '\\n')\n with open('{}.index'.format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_index) + '\\n')\n with open('{}.tok'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents) + '\\n')\n with open('{}.pos'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_pos) + '\\n')\n with open('{}.ner'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_ner) + '\\n')\n with open('{}.index'.format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_index) + '\\n')\n label(para_file, answer_file)\n\n\ndef label(para_file, answer_file):\n max_node = 0\n with open('{}.tok'.format(para_file), 'r') as fp, open('{}.label'.\n format(para_file), 'a') as fl, open('{}.index'.format(answer_file), 'r'\n ) as fa:\n while True:\n para = fp.readline()\n if not para:\n break\n words = [p for p in para.strip().split(' ')]\n max_node = max(len(words), max_node)\n answer = fa.readline()\n labels = []\n try:\n start, end = map(int, answer.split('\\t')[0].split(','))\n for i in range(len(words)):\n if start <= i <= end:\n if i == start:\n labels.append('B')\n else:\n labels.append('I')\n else:\n labels.append('O')\n except:\n pass\n fl.write(' '.join(labels) + '\\n')\n return max_node\n\n\ndef get_data(train_json, dev_json, test_title_file, output_dir):\n test_titles = open(test_title_file, 'r').readlines()\n test_titles = set([line.strip() for line in test_titles])\n process(train_json, '{}/train/'.format(output_dir), exclude_titles=\n test_titles)\n process(dev_json, '{}/dev/'.format(output_dir))\n process(train_json, '{}/test/'.format(output_dir), include_titles=\n test_titles)\n\n\ndef get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size,\n vocab_file):\n \"\"\"\n get word embedding matrix from glove\n \"\"\"\n print('Generating word embedding...')\n embedding_dict = {}\n with open(emb_file, 'r', encoding='utf-8') as fh:\n for line in tqdm(fh, total=emb_size):\n array = line.split()\n word = ''.join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n embedding_dict[word] = vector\n TRANSLATE = {'-lsb-': '[', '-rsb-': ']', '-lrb-': '(', '-rrb-': ')',\n '-lcb-': '{', '-rcb-': '}', '-LSB-': '[', '-RSB-': ']', '-LRB-':\n '(', '-RRB-': ')', '-LCB-': '{', '-RCB-': '}'}\n SPECIAL_TOKENS = ['<NULL>', '<UNK>', '<S>', '</S>']\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x:\n x[1], reverse=True)))\n words = SPECIAL_TOKENS + words\n if vocab_size > 0:\n words = words[:vocab_size]\n with open(vocab_file, 'w') as f:\n f.write('\\n'.join(words[1:]))\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\n word2idx_dict = {}\n unknown_count = 0\n for i, word in enumerate(words):\n word2idx_dict[word] = i\n if word in TRANSLATE:\n word = TRANSLATE[word]\n done = False\n for w in (word, word.lower(), word.upper(), word.capitalize()):\n if w in embedding_dict:\n embedding[i] = embedding_dict[w]\n done = True\n break\n if not done:\n unknown_count += 1\n return embedding, word2idx_dict, unknown_count\n\n\ndef get_tag_embedding(counter, data_type, vec_size):\n \"\"\"\n get pos/ner/label tags' embedding matrix\n \"\"\"\n print('Generating {} tag embedding...'.format(data_type))\n SPECIAL_TOKENS = ['<NULL>', '<UNK>']\n tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x\n [1], reverse=True)))\n tags = SPECIAL_TOKENS + tags\n embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))\n word2idx_dict = {w: i for i, w in enumerate(tags)}\n return embedding, word2idx_dict\n\n\ndef get_vocab(config):\n print('Get the vocabulary...')\n word_counter, char_counter = Counter(), Counter()\n pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()\n files = [(config.train_para_file, config.train_question_file), (config.\n dev_para_file, config.dev_question_file)]\n for para_file, que_file in files:\n with open('{}.tok'.format(para_file), 'r') as fp, open('{}.tok'.\n format(que_file), 'r') as fq, open('{}.pos'.format(para_file), 'r'\n ) as fpp, open('{}.pos'.format(que_file), 'r') as fqp, open(\n '{}.ner'.format(para_file), 'r') as fpn, open('{}.ner'.format(\n que_file), 'r') as fqn, open('{}.label'.format(para_file), 'r'\n ) as fpl:\n while True:\n para, question = fp.readline(), fq.readline()\n pos, que_pos = fpp.readline(), fqp.readline()\n ner, que_ner = fpn.readline(), fqn.readline()\n label = fpl.readline()\n if not question or not para:\n break\n if config.lower_word:\n para = para.lower()\n question = question.lower()\n para_tokens = para.strip().split(' ')\n que_tokens = question.strip().split(' ')\n pos_tags = pos.strip().split(' ')\n ner_tags = ner.strip().split(' ')\n que_pos_tags = que_pos.strip().split(' ')\n que_ner_tags = que_ner.strip().split(' ')\n labels = label.strip().split(' ')\n for token in (para_tokens + que_tokens):\n word_counter[token] += 1\n for char in list(token):\n char_counter[char] += 1\n for pos_tag in (pos_tags + que_pos_tags):\n pos_counter[pos_tag] += 1\n for ner_tag in (ner_tags + que_ner_tags):\n ner_counter[ner_tag] += 1\n for label in labels:\n label_counter[label] += 1\n word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter,\n emb_file=config.glove_word_file, emb_size=config.glove_word_size,\n vocab_size=config.vocab_size_limit, vec_size=config.glove_dim,\n vocab_file=config.vocab_file)\n char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, 'char',\n vec_size=config.char_dim)\n pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, 'pos',\n vec_size=config.pos_dim)\n ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, 'ner',\n vec_size=config.ner_dim)\n label_emb_mat, label2idx_dict = get_tag_embedding(label_counter,\n 'label', vec_size=config.label_dim)\n print('{} out of {} are not in glove'.format(unk_num, len(word2idx_dict)))\n print('{} chars'.format(char_emb_mat.shape[0]))\n print('{} pos tags, {} ner tags, {} answer labels, {} chars'.format(\n pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0],\n char_emb_mat.shape[0]))\n save(config.word_emb_file, word_emb_mat, message='word embedding')\n save(config.char_emb_file, char_emb_mat, message='char embedding')\n save(config.pos_emb_file, pos_emb_mat, message='pos embedding')\n save(config.ner_emb_file, ner_emb_mat, message='ner embedding')\n save(config.label_emb_file, label_emb_mat, message='label embedding')\n save(config.word_dictionary, word2idx_dict, message='word dictionary')\n save(config.char_dictionary, char2idx_dict, message='char dictionary')\n save(config.pos_dictionary, pos2idx_dict, message='pos dictionary')\n save(config.ner_dictionary, ner2idx_dict, message='ner dictionary')\n save(config.label_dictionary, label2idx_dict, message='label dictionary')\n print('Dump elmo word embedding...')\n token_embedding_file = config.embedding_file\n dump_token_embeddings(config.vocab_file, config.elmo_options_file,\n config.elmo_weight_file, token_embedding_file)\n\n\nif __name__ == '__main__':\n os.system(\n 'mkdir data; mkdir data/processed; mkdir data/processed/train; mkdir data/processed/dev; mkdir data/processed/test'\n )\n get_data('../../LIB/squad/train-v1.1.json',\n '../../LIB/squad/dev-v1.1.json', '../../LIB/squad/doclist-test.txt',\n 'data/processed')\n",
"step-5": "\"\"\"\nData pre-processing\n\"\"\"\nimport os\nimport corenlp\nimport numpy as np\nimport ujson as json\nfrom tqdm import tqdm\nfrom collections import Counter\nfrom bilm import dump_token_embeddings\nimport sys\nsys.path.append('../..')\n\nfrom LIB.utils import save\n\n\ndef process(json_file, outpur_dir, exclude_titles=None, include_titles=None):\n \"\"\"\n :param json_file: original data in json format\n :param outpur_dir: the output directory of pre-processed data\n :param exclude_titles: article titles to exclude\n :param include_titles: article titles to include\n \"\"\"\n para_file = \"{}/paras\".format(outpur_dir)\n question_file = \"{}/questions\".format(outpur_dir)\n sent_file = \"{}/sents\".format(outpur_dir)\n answer_file = \"{}/answers\".format(outpur_dir)\n print(\"Generating {} raw data...\".format(json_file))\n max_sent, max_sent_len, max_que_len, max_ans_len = 0, 0, 0, 0\n with open(json_file, \"r\") as fh, corenlp.CoreNLPClient(annotators=\"tokenize ssplit pos ner\".split(),\n endpoint=\"http://localhost:9099\", timeout=50000) as client:\n source = json.load(fh)\n for article in tqdm(source[\"data\"]):\n title = article[\"title\"]\n if include_titles and title not in include_titles:\n continue\n if exclude_titles and title in exclude_titles:\n continue\n for para in article[\"paragraphs\"]:\n paragraphs, questions, answers, sents, ids = [], [], [], [], []\n paragraphs_pos, questions_pos, answers_pos, sents_pos = [], [], [], []\n paragraphs_ner, questions_ner, answers_ner, sents_ner = [], [], [], []\n answers_index, sents_index = [], []\n # paragraph\n context = para[\"context\"]\n if not context.strip():\n continue\n ann_para = client.annotate(context)\n max_sent = max(max_sent, len(ann_para.sentence))\n max_sent_len = max(max_sent_len, max(map(lambda x: len(x.token), ann_para.sentence)))\n ann_para_tokens, paragraph_tokens, paragraph_pos, paragraph_ner = [], [], [], []\n for sent in ann_para.sentence:\n for token in sent.token:\n ann_para_tokens.append(token)\n paragraph_tokens.append(token.word)\n paragraph_pos.append(token.pos)\n paragraph_ner.append(token.ner)\n\n # questions\n for qa in para[\"qas\"]:\n # question\n ques = qa[\"question\"]\n id = qa[\"id\"]\n if not ques.strip():\n continue\n ann_que = client.annotate(ques)\n max_que_len = max(max_que_len, len(ann_que.sentence[0].token))\n question_tokens, question_pos, question_ner = [], [], []\n for sent in ann_que.sentence:\n for token in sent.token:\n question_tokens.append(token.word)\n question_pos.append(token.pos)\n question_ner.append(token.ner)\n\n # answer\n all_answer_tokens, all_answer_pos, all_answer_ner, all_answer_index = [], [], [], []\n all_sent_tokens, all_sent_pos, all_sent_ner, all_sent_index = [], [], [], []\n for answer in qa[\"answers\"]:\n answer_text = answer[\"text\"]\n if not answer_text.strip():\n continue\n ann_ans = client.annotate(answer_text)\n answer_tokens, answer_pos, answer_ner = [], [], []\n for sent in ann_ans.sentence:\n for token in sent.token:\n answer_tokens.append(token.word)\n answer_pos.append(token.pos)\n answer_ner.append(token.ner)\n all_answer_tokens.append(' '.join(answer_tokens))\n all_answer_pos.append(' '.join(answer_pos))\n all_answer_ner.append(' '.join(answer_ner))\n\n answer_start = answer['answer_start']\n answer_end = answer_start + len(answer_text)\n # sentence\n sentence = []\n for sent in ann_para.sentence:\n if sent.characterOffsetBegin <= answer_start <= sent.characterOffsetEnd or \\\n sent.characterOffsetBegin <= answer_end <= sent.characterOffsetEnd:\n sentence.append(sent)\n sentence = [token for sent in sentence for token in sent.token]\n sentence_tokens = [token.word for token in sentence]\n sentence_pos = [token.pos for token in sentence]\n sentence_ner = [token.ner for token in sentence]\n all_sent_tokens.append(' '.join(sentence_tokens))\n all_sent_pos.append(' '.join(sentence_pos))\n all_sent_ner.append(' '.join(sentence_ner))\n\n # sentence index\n y1_sent = sentence[0].tokenBeginIndex\n y2_sent = sentence[-1].tokenBeginIndex\n # answer index\n y1_ans = None\n for i, token in enumerate(sentence):\n if token.beginChar - 1 <= answer_start <= token.endChar:\n y1_ans = sentence[0].tokenBeginIndex + i\n try:\n assert y1_ans != None\n except:\n continue\n y2_ans = y1_ans + len(answer_tokens) - 1\n all_answer_index.append(\"{},{}\".format(y1_ans, y2_ans))\n all_sent_index.append(\"{},{}\".format(y1_sent, y2_sent))\n\n paragraphs.append(' '.join(paragraph_tokens))\n paragraphs_pos.append(' '.join(paragraph_pos))\n paragraphs_ner.append(' '.join(paragraph_ner))\n questions.append(' '.join(question_tokens))\n questions_pos.append(' '.join(question_pos))\n questions_ner.append(' '.join(question_ner))\n answers.append('\\t'.join(all_answer_tokens))\n answers_pos.append('\\t'.join(all_answer_pos))\n answers_ner.append('\\t'.join(all_answer_ner))\n answers_index.append('\\t'.join(all_answer_index))\n sents.append('\\t'.join(all_sent_tokens))\n sents_pos.append('\\t'.join(all_sent_pos))\n sents_ner.append('\\t'.join(all_sent_ner))\n sents_index.append('\\t'.join(all_sent_index))\n ids.append(id)\n\n # save para\n with open(\"{}.tok\".format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs) + '\\n')\n with open(\"{}.pos\".format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_pos) + '\\n')\n with open(\"{}.ner\".format(para_file), 'a') as f:\n f.write('\\n'.join(paragraphs_ner) + '\\n')\n with open(\"{}.id\".format(para_file), 'a') as f:\n f.write('\\n'.join(ids) + '\\n')\n # save question\n with open(\"{}.tok\".format(question_file), 'a') as f:\n f.write('\\n'.join(questions) + '\\n')\n with open(\"{}.pos\".format(question_file), 'a') as f:\n f.write('\\n'.join(questions_pos) + '\\n')\n with open(\"{}.ner\".format(question_file), 'a') as f:\n f.write('\\n'.join(questions_ner) + '\\n')\n\n # save answer\n with open(\"{}.tok\".format(answer_file), 'a') as f:\n f.write('\\n'.join(answers) + '\\n')\n with open(\"{}.pos\".format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_pos) + '\\n')\n with open(\"{}.ner\".format(answer_file), 'a') as f:\n f.write('\\n'.join(answers_ner) + '\\n')\n with open(\"{}.index\".format(answer_file), 'a') as f:\n f.write(\"\\n\".join(answers_index) + '\\n')\n\n # save sent\n with open(\"{}.tok\".format(sent_file), 'a') as f:\n f.write('\\n'.join(sents) + '\\n')\n with open(\"{}.pos\".format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_pos) + '\\n')\n with open(\"{}.ner\".format(sent_file), 'a') as f:\n f.write('\\n'.join(sents_ner) + '\\n')\n with open(\"{}.index\".format(sent_file), 'a') as f:\n f.write(\"\\n\".join(sents_index) + '\\n')\n # get BIO labels\n label(para_file, answer_file)\n\n\ndef label(para_file, answer_file):\n # get the answer BIO label for paragraph\n max_node = 0\n with open(\"{}.tok\".format(para_file), 'r') as fp, open(\"{}.label\".format(para_file), 'a') as fl, \\\n open(\"{}.index\".format(answer_file), 'r') as fa:\n while True:\n para = fp.readline()\n if not para:\n break\n words = [p for p in para.strip().split(' ')]\n max_node = max(len(words), max_node)\n answer = fa.readline()\n labels = []\n try:\n start, end = map(int, answer.split('\\t')[0].split(','))\n for i in range(len(words)):\n if start <= i <= end:\n # answer words\n if i == start:\n labels.append('B')\n else:\n labels.append('I')\n else:\n # non answer words\n labels.append('O')\n except:\n pass\n fl.write(' '.join(labels) + '\\n')\n return max_node\n\n\ndef get_data(train_json, dev_json, test_title_file, output_dir):\n test_titles = open(test_title_file, 'r').readlines()\n test_titles = set([line.strip() for line in test_titles])\n\n process(train_json, \"{}/train/\".format(output_dir), exclude_titles=test_titles)\n process(dev_json, \"{}/dev/\".format(output_dir))\n process(train_json, \"{}/test/\".format(output_dir), include_titles=test_titles)\n\n\ndef get_word_embedding(counter, emb_file, emb_size, vocab_size, vec_size, vocab_file):\n \"\"\"\n get word embedding matrix from glove\n \"\"\"\n print(\"Generating word embedding...\")\n # load word embeddings\n embedding_dict = {}\n with open(emb_file, \"r\", encoding=\"utf-8\") as fh:\n for line in tqdm(fh, total=emb_size):\n array = line.split()\n word = \"\".join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n embedding_dict[word] = vector\n\n TRANSLATE = {\n \"-lsb-\": \"[\", \"-rsb-\": \"]\", \"-lrb-\": \"(\", \"-rrb-\": \")\", \"-lcb-\": \"{\",\n \"-rcb-\": \"}\", \"-LSB-\": \"[\", \"-RSB-\": \"]\", \"-LRB-\": \"(\", \"-RRB-\": \")\",\n \"-LCB-\": \"{\", \"-RCB-\": \"}\"\n }\n SPECIAL_TOKENS = [\"<NULL>\", \"<UNK>\", \"<S>\", \"</S>\"]\n words = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))\n words = SPECIAL_TOKENS + words\n if vocab_size > 0:\n words = words[:vocab_size]\n with open(vocab_file, 'w') as f:\n f.write('\\n'.join(words[1:]))\n embedding = np.random.normal(scale=0.1, size=(len(words), vec_size))\n word2idx_dict = {}\n unknown_count = 0\n for i, word in enumerate(words):\n word2idx_dict[word] = i\n if word in TRANSLATE:\n word = TRANSLATE[word]\n done = False\n for w in (word, word.lower(), word.upper(), word.capitalize()):\n if w in embedding_dict:\n embedding[i] = embedding_dict[w]\n done = True\n break\n if not done:\n unknown_count += 1\n return embedding, word2idx_dict, unknown_count\n\n\ndef get_tag_embedding(counter, data_type, vec_size):\n \"\"\"\n get pos/ner/label tags' embedding matrix\n \"\"\"\n print(\"Generating {} tag embedding...\".format(data_type))\n SPECIAL_TOKENS = [\"<NULL>\", \"<UNK>\"]\n tags = list(map(lambda x: x[0], sorted(counter.items(), key=lambda x: x[1], reverse=True)))\n tags = SPECIAL_TOKENS + tags\n embedding = np.random.normal(scale=0.1, size=(len(tags), vec_size))\n word2idx_dict = {w: i for i, w in enumerate(tags)}\n return embedding, word2idx_dict\n\n\ndef get_vocab(config):\n print(\"Get the vocabulary...\")\n word_counter, char_counter = Counter(), Counter()\n pos_counter, ner_counter, label_counter = Counter(), Counter(), Counter()\n files = [(config.train_para_file, config.train_question_file), (config.dev_para_file, config.dev_question_file)]\n for para_file, que_file in files:\n with open(\"{}.tok\".format(para_file), 'r') as fp, open(\"{}.tok\".format(que_file), 'r') as fq, \\\n open(\"{}.pos\".format(para_file), 'r') as fpp, open(\"{}.pos\".format(que_file), 'r') as fqp, \\\n open(\"{}.ner\".format(para_file), 'r') as fpn, open(\"{}.ner\".format(que_file), 'r') as fqn, \\\n open(\"{}.label\".format(para_file), 'r') as fpl:\n while True:\n para, question = fp.readline(), fq.readline()\n pos, que_pos = fpp.readline(), fqp.readline()\n ner, que_ner = fpn.readline(), fqn.readline()\n label = fpl.readline()\n if not question or not para:\n break\n if config.lower_word:\n para = para.lower()\n question = question.lower()\n para_tokens = para.strip().split(' ')\n que_tokens = question.strip().split(' ')\n pos_tags = pos.strip().split(' ')\n ner_tags = ner.strip().split(' ')\n que_pos_tags = que_pos.strip().split(' ')\n que_ner_tags = que_ner.strip().split(' ')\n labels = label.strip().split(' ')\n for token in para_tokens + que_tokens:\n word_counter[token] += 1\n for char in list(token):\n char_counter[char] += 1\n for pos_tag in pos_tags + que_pos_tags:\n pos_counter[pos_tag] += 1\n for ner_tag in ner_tags + que_ner_tags:\n ner_counter[ner_tag] += 1\n for label in labels:\n label_counter[label] += 1\n word_emb_mat, word2idx_dict, unk_num = get_word_embedding(word_counter, emb_file=config.glove_word_file,\n emb_size=config.glove_word_size,\n vocab_size=config.vocab_size_limit,\n vec_size=config.glove_dim, vocab_file=config.vocab_file)\n char_emb_mat, char2idx_dict = get_tag_embedding(char_counter, \"char\", vec_size=config.char_dim)\n pos_emb_mat, pos2idx_dict = get_tag_embedding(pos_counter, \"pos\", vec_size=config.pos_dim)\n ner_emb_mat, ner2idx_dict = get_tag_embedding(ner_counter, \"ner\", vec_size=config.ner_dim)\n label_emb_mat, label2idx_dict = get_tag_embedding(label_counter, \"label\", vec_size=config.label_dim)\n print(\"{} out of {} are not in glove\".format(unk_num, len(word2idx_dict)))\n print(\"{} chars\".format(char_emb_mat.shape[0]))\n print(\"{} pos tags, {} ner tags, {} answer labels, {} chars\".format(\n pos_emb_mat.shape[0], ner_emb_mat.shape[0], label_emb_mat.shape[0], char_emb_mat.shape[0]))\n save(config.word_emb_file, word_emb_mat, message=\"word embedding\")\n save(config.char_emb_file, char_emb_mat, message=\"char embedding\")\n save(config.pos_emb_file, pos_emb_mat, message=\"pos embedding\")\n save(config.ner_emb_file, ner_emb_mat, message=\"ner embedding\")\n save(config.label_emb_file, label_emb_mat, message=\"label embedding\")\n save(config.word_dictionary, word2idx_dict, message=\"word dictionary\")\n save(config.char_dictionary, char2idx_dict, message=\"char dictionary\")\n save(config.pos_dictionary, pos2idx_dict, message=\"pos dictionary\")\n save(config.ner_dictionary, ner2idx_dict, message=\"ner dictionary\")\n save(config.label_dictionary, label2idx_dict, message=\"label dictionary\")\n print(\"Dump elmo word embedding...\")\n token_embedding_file = config.embedding_file\n dump_token_embeddings(\n config.vocab_file, config.elmo_options_file, config.elmo_weight_file, token_embedding_file\n )\n\n\nif __name__ == '__main__':\n # process data\n os.system(\"mkdir data; mkdir data/processed; mkdir data/processed/train; \"\n \"mkdir data/processed/dev; mkdir data/processed/test\")\n get_data(\"../../LIB/squad/train-v1.1.json\", \"../../LIB/squad/dev-v1.1.json\",\n \"../../LIB/squad/doclist-test.txt\", \"data/processed\")",
"step-ids": [
5,
6,
7,
8,
9
]
}
|
[
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def check_controls(subpuc_names, subpuc_controls):
if len(subpuc_names) == len(subpuc_controls):
pass
else:
sys.exit(
'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(subpuc_controls)):
if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_controls.csv files.')
def check_1st_order_spec(subpuc_names, first_ord_spec):
if len(subpuc_names) == len(first_ord_spec):
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(first_ord_spec)):
if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec
[i, 0:3]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'
)
if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'
)
for j in range(len(first_ord_spec[0, :])):
if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'
)
def check_organic_spec(subpuc_names, organic_spec, chem_index):
if len(subpuc_names) == len(organic_spec[0, :]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(organic_spec[0, :])):
if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec
[1:, i]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'
)
if len(chem_index) == len(organic_spec[1:, 0]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'
)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def check_usetime(subpuc_names, subpuc_usetime):
if len(subpuc_names) == len(subpuc_usetime):
pass
else:
sys.exit(
'There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(subpuc_usetime)):
if subpuc_usetime[i, 1] >= 0.0 and subpuc_usetime[i, 1] <= 6.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_usetimescales.csv files.'
)
def check_controls(subpuc_names, subpuc_controls):
if len(subpuc_names) == len(subpuc_controls):
pass
else:
sys.exit(
'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(subpuc_controls)):
if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_controls.csv files.')
def check_1st_order_spec(subpuc_names, first_ord_spec):
if len(subpuc_names) == len(first_ord_spec):
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(first_ord_spec)):
if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec
[i, 0:3]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'
)
if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'
)
for j in range(len(first_ord_spec[0, :])):
if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'
)
def check_organic_spec(subpuc_names, organic_spec, chem_index):
if len(subpuc_names) == len(organic_spec[0, :]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(organic_spec[0, :])):
if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec
[1:, i]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'
)
if len(chem_index) == len(organic_spec[1:, 0]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'
)
def check_chem_assignments(chem_props_vars, chem_props_strs, chem_index):
if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(
chem_props_strs):
pass
else:
sys.exit(
'There is an issue with your chemical_assignments.csv file. Number of species is incorrect.'
)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def check_usage(subpuc_names, year, subpuc_usage):
if len(subpuc_names) == len(subpuc_usage[1:]):
pass
else:
sys.exit(
'There is an issue with your subpuc_usage.csv file. Number of sub-PUC(s) is incorrect.'
)
year_available = 0
for i in range(len(subpuc_usage[0, 1:])):
if subpuc_usage[0, 1 + i] == year:
year_available = 1
else:
pass
if year_available == 0:
sys.exit('There is an issue with your subpuc_usage.csv file. ' +
str(year) + ' is missing.')
else:
pass
def check_usetime(subpuc_names, subpuc_usetime):
if len(subpuc_names) == len(subpuc_usetime):
pass
else:
sys.exit(
'There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(subpuc_usetime)):
if subpuc_usetime[i, 1] >= 0.0 and subpuc_usetime[i, 1] <= 6.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_usetimescales.csv files.'
)
def check_controls(subpuc_names, subpuc_controls):
if len(subpuc_names) == len(subpuc_controls):
pass
else:
sys.exit(
'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(subpuc_controls)):
if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_controls.csv files.')
def check_1st_order_spec(subpuc_names, first_ord_spec):
if len(subpuc_names) == len(first_ord_spec):
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(first_ord_spec)):
if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec
[i, 0:3]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'
)
if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'
)
for j in range(len(first_ord_spec[0, :])):
if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'
)
def check_organic_spec(subpuc_names, organic_spec, chem_index):
if len(subpuc_names) == len(organic_spec[0, :]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(organic_spec[0, :])):
if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec
[1:, i]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'
)
if len(chem_index) == len(organic_spec[1:, 0]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'
)
def check_chem_assignments(chem_props_vars, chem_props_strs, chem_index):
if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(
chem_props_strs):
pass
else:
sys.exit(
'There is an issue with your chemical_assignments.csv file. Number of species is incorrect.'
)
<|reserved_special_token_1|>
import sys
import numpy as np
def check_usage(subpuc_names, year, subpuc_usage):
if len(subpuc_names) == len(subpuc_usage[1:]):
pass
else:
sys.exit(
'There is an issue with your subpuc_usage.csv file. Number of sub-PUC(s) is incorrect.'
)
year_available = 0
for i in range(len(subpuc_usage[0, 1:])):
if subpuc_usage[0, 1 + i] == year:
year_available = 1
else:
pass
if year_available == 0:
sys.exit('There is an issue with your subpuc_usage.csv file. ' +
str(year) + ' is missing.')
else:
pass
def check_usetime(subpuc_names, subpuc_usetime):
if len(subpuc_names) == len(subpuc_usetime):
pass
else:
sys.exit(
'There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(subpuc_usetime)):
if subpuc_usetime[i, 1] >= 0.0 and subpuc_usetime[i, 1] <= 6.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_usetimescales.csv files.'
)
def check_controls(subpuc_names, subpuc_controls):
if len(subpuc_names) == len(subpuc_controls):
pass
else:
sys.exit(
'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(subpuc_controls)):
if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_controls.csv files.')
def check_1st_order_spec(subpuc_names, first_ord_spec):
if len(subpuc_names) == len(first_ord_spec):
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(first_ord_spec)):
if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec
[i, 0:3]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'
)
if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:
pass
else:
sys.exit(
'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'
)
for j in range(len(first_ord_spec[0, :])):
if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:
pass
else:
sys.exit(
'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'
)
def check_organic_spec(subpuc_names, organic_spec, chem_index):
if len(subpuc_names) == len(organic_spec[0, :]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'
)
for i in range(len(organic_spec[0, :])):
if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec
[1:, i]) <= 1.01:
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'
)
if len(chem_index) == len(organic_spec[1:, 0]):
pass
else:
sys.exit(
'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'
)
def check_chem_assignments(chem_props_vars, chem_props_strs, chem_index):
if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(
chem_props_strs):
pass
else:
sys.exit(
'There is an issue with your chemical_assignments.csv file. Number of species is incorrect.'
)
<|reserved_special_token_1|>
import sys
import numpy as np
####################################################################################################
### These functions all perform QA checks on input files.
### These should catch many errors, but is not exhaustive.
####################################################################################################
####################################################################################################
def check_usage(subpuc_names,year,subpuc_usage):
if len(subpuc_names) == len(subpuc_usage[1:]):
pass
else: sys.exit('There is an issue with your subpuc_usage.csv file. Number of sub-PUC(s) is incorrect.')
year_available = 0
for i in range(len(subpuc_usage[0,1:])):
if subpuc_usage[0,1+i] == year:
year_available = 1
else: pass
if year_available == 0:
sys.exit('There is an issue with your subpuc_usage.csv file. '+str(year)+' is missing.')
else: pass
####################################################################################################
####################################################################################################
def check_usetime(subpuc_names,subpuc_usetime):
if len(subpuc_names) == len(subpuc_usetime):
pass
else: sys.exit('There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.')
for i in range(len(subpuc_usetime)):
if subpuc_usetime[i,1] >= 0.0 and subpuc_usetime[i,1] <= 6.0:
pass
else: sys.exit('There is a bounds issue in your subpuc_usetimescales.csv files.')
####################################################################################################
####################################################################################################
def check_controls(subpuc_names,subpuc_controls):
if len(subpuc_names) == len(subpuc_controls):
pass
else: sys.exit('There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.')
for i in range(len(subpuc_controls)):
if subpuc_controls[i,1] >= 0.0 and subpuc_controls[i,1] <= 1.0:
pass
else: sys.exit('There is a bounds issue in your subpuc_controls.csv files.')
####################################################################################################
####################################################################################################
def check_1st_order_spec(subpuc_names,first_ord_spec):
if len(subpuc_names) == len(first_ord_spec):
pass
else: sys.exit('There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.')
for i in range(len(first_ord_spec)):
if np.sum(first_ord_spec[i,0:3]) >= 0.99 and np.sum(first_ord_spec[i,0:3]) <= 1.01:
pass
else: sys.exit('There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.')
if first_ord_spec[i,2] >= first_ord_spec[i,3]:
pass
else: sys.exit('There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.')
for j in range(len(first_ord_spec[0,:])):
if first_ord_spec[i,j] >= 0.0 and first_ord_spec[i,j] <= 1.0:
pass
else: sys.exit('There is a bounds issue in your subpuc_1st_order_speciation.csv files.')
####################################################################################################
####################################################################################################
def check_organic_spec(subpuc_names,organic_spec,chem_index):
if len(subpuc_names) == len(organic_spec[0,:]):
pass
else: sys.exit('There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.')
for i in range(len(organic_spec[0,:])):
if np.nansum(organic_spec[1:,i]) >= 0.99 and np.nansum(organic_spec[1:,i]) <= 1.01:
pass
else: sys.exit('There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.')
if len(chem_index) == len(organic_spec[1:,0]):
pass
else: sys.exit('There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.')
####################################################################################################
####################################################################################################
def check_chem_assignments(chem_props_vars,chem_props_strs,chem_index):
if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(chem_props_strs):
pass
else: sys.exit('There is an issue with your chemical_assignments.csv file. Number of species is incorrect.')
####################################################################################################
|
flexible
|
{
"blob_id": "7413c06a990894c34ee5174d84f0e3bd20abf51f",
"index": 3294,
"step-1": "<mask token>\n\n\ndef check_controls(subpuc_names, subpuc_controls):\n if len(subpuc_names) == len(subpuc_controls):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(subpuc_controls)):\n if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_controls.csv files.')\n\n\ndef check_1st_order_spec(subpuc_names, first_ord_spec):\n if len(subpuc_names) == len(first_ord_spec):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(first_ord_spec)):\n if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec\n [i, 0:3]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'\n )\n if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'\n )\n for j in range(len(first_ord_spec[0, :])):\n if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'\n )\n\n\ndef check_organic_spec(subpuc_names, organic_spec, chem_index):\n if len(subpuc_names) == len(organic_spec[0, :]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(organic_spec[0, :])):\n if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec\n [1:, i]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'\n )\n if len(chem_index) == len(organic_spec[1:, 0]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'\n )\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef check_usetime(subpuc_names, subpuc_usetime):\n if len(subpuc_names) == len(subpuc_usetime):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(subpuc_usetime)):\n if subpuc_usetime[i, 1] >= 0.0 and subpuc_usetime[i, 1] <= 6.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_usetimescales.csv files.'\n )\n\n\ndef check_controls(subpuc_names, subpuc_controls):\n if len(subpuc_names) == len(subpuc_controls):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(subpuc_controls)):\n if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_controls.csv files.')\n\n\ndef check_1st_order_spec(subpuc_names, first_ord_spec):\n if len(subpuc_names) == len(first_ord_spec):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(first_ord_spec)):\n if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec\n [i, 0:3]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'\n )\n if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'\n )\n for j in range(len(first_ord_spec[0, :])):\n if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'\n )\n\n\ndef check_organic_spec(subpuc_names, organic_spec, chem_index):\n if len(subpuc_names) == len(organic_spec[0, :]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(organic_spec[0, :])):\n if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec\n [1:, i]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'\n )\n if len(chem_index) == len(organic_spec[1:, 0]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'\n )\n\n\ndef check_chem_assignments(chem_props_vars, chem_props_strs, chem_index):\n if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(\n chem_props_strs):\n pass\n else:\n sys.exit(\n 'There is an issue with your chemical_assignments.csv file. Number of species is incorrect.'\n )\n",
"step-3": "<mask token>\n\n\ndef check_usage(subpuc_names, year, subpuc_usage):\n if len(subpuc_names) == len(subpuc_usage[1:]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_usage.csv file. Number of sub-PUC(s) is incorrect.'\n )\n year_available = 0\n for i in range(len(subpuc_usage[0, 1:])):\n if subpuc_usage[0, 1 + i] == year:\n year_available = 1\n else:\n pass\n if year_available == 0:\n sys.exit('There is an issue with your subpuc_usage.csv file. ' +\n str(year) + ' is missing.')\n else:\n pass\n\n\ndef check_usetime(subpuc_names, subpuc_usetime):\n if len(subpuc_names) == len(subpuc_usetime):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(subpuc_usetime)):\n if subpuc_usetime[i, 1] >= 0.0 and subpuc_usetime[i, 1] <= 6.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_usetimescales.csv files.'\n )\n\n\ndef check_controls(subpuc_names, subpuc_controls):\n if len(subpuc_names) == len(subpuc_controls):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(subpuc_controls)):\n if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_controls.csv files.')\n\n\ndef check_1st_order_spec(subpuc_names, first_ord_spec):\n if len(subpuc_names) == len(first_ord_spec):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(first_ord_spec)):\n if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec\n [i, 0:3]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'\n )\n if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'\n )\n for j in range(len(first_ord_spec[0, :])):\n if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'\n )\n\n\ndef check_organic_spec(subpuc_names, organic_spec, chem_index):\n if len(subpuc_names) == len(organic_spec[0, :]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(organic_spec[0, :])):\n if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec\n [1:, i]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'\n )\n if len(chem_index) == len(organic_spec[1:, 0]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'\n )\n\n\ndef check_chem_assignments(chem_props_vars, chem_props_strs, chem_index):\n if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(\n chem_props_strs):\n pass\n else:\n sys.exit(\n 'There is an issue with your chemical_assignments.csv file. Number of species is incorrect.'\n )\n",
"step-4": "import sys\nimport numpy as np\n\n\ndef check_usage(subpuc_names, year, subpuc_usage):\n if len(subpuc_names) == len(subpuc_usage[1:]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_usage.csv file. Number of sub-PUC(s) is incorrect.'\n )\n year_available = 0\n for i in range(len(subpuc_usage[0, 1:])):\n if subpuc_usage[0, 1 + i] == year:\n year_available = 1\n else:\n pass\n if year_available == 0:\n sys.exit('There is an issue with your subpuc_usage.csv file. ' +\n str(year) + ' is missing.')\n else:\n pass\n\n\ndef check_usetime(subpuc_names, subpuc_usetime):\n if len(subpuc_names) == len(subpuc_usetime):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(subpuc_usetime)):\n if subpuc_usetime[i, 1] >= 0.0 and subpuc_usetime[i, 1] <= 6.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_usetimescales.csv files.'\n )\n\n\ndef check_controls(subpuc_names, subpuc_controls):\n if len(subpuc_names) == len(subpuc_controls):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(subpuc_controls)):\n if subpuc_controls[i, 1] >= 0.0 and subpuc_controls[i, 1] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_controls.csv files.')\n\n\ndef check_1st_order_spec(subpuc_names, first_ord_spec):\n if len(subpuc_names) == len(first_ord_spec):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(first_ord_spec)):\n if np.sum(first_ord_spec[i, 0:3]) >= 0.99 and np.sum(first_ord_spec\n [i, 0:3]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.'\n )\n if first_ord_spec[i, 2] >= first_ord_spec[i, 3]:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.'\n )\n for j in range(len(first_ord_spec[0, :])):\n if first_ord_spec[i, j] >= 0.0 and first_ord_spec[i, j] <= 1.0:\n pass\n else:\n sys.exit(\n 'There is a bounds issue in your subpuc_1st_order_speciation.csv files.'\n )\n\n\ndef check_organic_spec(subpuc_names, organic_spec, chem_index):\n if len(subpuc_names) == len(organic_spec[0, :]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.'\n )\n for i in range(len(organic_spec[0, :])):\n if np.nansum(organic_spec[1:, i]) >= 0.99 and np.nansum(organic_spec\n [1:, i]) <= 1.01:\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.'\n )\n if len(chem_index) == len(organic_spec[1:, 0]):\n pass\n else:\n sys.exit(\n 'There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.'\n )\n\n\ndef check_chem_assignments(chem_props_vars, chem_props_strs, chem_index):\n if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(\n chem_props_strs):\n pass\n else:\n sys.exit(\n 'There is an issue with your chemical_assignments.csv file. Number of species is incorrect.'\n )\n",
"step-5": "import sys\nimport numpy as np\n\n####################################################################################################\n### These functions all perform QA checks on input files. \n### These should catch many errors, but is not exhaustive. \n####################################################################################################\n\n####################################################################################################\ndef check_usage(subpuc_names,year,subpuc_usage):\n if len(subpuc_names) == len(subpuc_usage[1:]):\n pass\n else: sys.exit('There is an issue with your subpuc_usage.csv file. Number of sub-PUC(s) is incorrect.')\n year_available = 0\n for i in range(len(subpuc_usage[0,1:])):\n if subpuc_usage[0,1+i] == year:\n year_available = 1\n else: pass\n if year_available == 0:\n sys.exit('There is an issue with your subpuc_usage.csv file. '+str(year)+' is missing.')\n else: pass\n####################################################################################################\n\n####################################################################################################\ndef check_usetime(subpuc_names,subpuc_usetime):\n if len(subpuc_names) == len(subpuc_usetime):\n pass\n else: sys.exit('There is an issue with your subpuc_usetimescales.csv file. Number of sub-PUC(s) is incorrect.')\n for i in range(len(subpuc_usetime)):\n if subpuc_usetime[i,1] >= 0.0 and subpuc_usetime[i,1] <= 6.0:\n pass\n else: sys.exit('There is a bounds issue in your subpuc_usetimescales.csv files.')\n####################################################################################################\n\n####################################################################################################\ndef check_controls(subpuc_names,subpuc_controls):\n if len(subpuc_names) == len(subpuc_controls):\n pass\n else: sys.exit('There is an issue with your subpuc_controls.csv file. Number of sub-PUC(s) is incorrect.')\n for i in range(len(subpuc_controls)):\n if subpuc_controls[i,1] >= 0.0 and subpuc_controls[i,1] <= 1.0:\n pass\n else: sys.exit('There is a bounds issue in your subpuc_controls.csv files.')\n####################################################################################################\n\n####################################################################################################\ndef check_1st_order_spec(subpuc_names,first_ord_spec):\n if len(subpuc_names) == len(first_ord_spec):\n pass\n else: sys.exit('There is an issue with your subpuc_1st_order_speciation.csv file. Number of sub-PUC(s) is incorrect.')\n for i in range(len(first_ord_spec)):\n if np.sum(first_ord_spec[i,0:3]) >= 0.99 and np.sum(first_ord_spec[i,0:3]) <= 1.01:\n pass\n else: sys.exit('There is an issue with your subpuc_1st_order_speciation.csv file. Water + Inorganic + Organic out of bounds.')\n if first_ord_spec[i,2] >= first_ord_spec[i,3]:\n pass\n else: sys.exit('There is an issue with your subpuc_1st_order_speciation.csv file. TOG > Organic.')\n for j in range(len(first_ord_spec[0,:])):\n if first_ord_spec[i,j] >= 0.0 and first_ord_spec[i,j] <= 1.0:\n pass\n else: sys.exit('There is a bounds issue in your subpuc_1st_order_speciation.csv files.')\n####################################################################################################\n\n####################################################################################################\ndef check_organic_spec(subpuc_names,organic_spec,chem_index):\n if len(subpuc_names) == len(organic_spec[0,:]):\n pass\n else: sys.exit('There is an issue with your subpuc_organic_speciation.csv file. Number of sub-PUC(s) is incorrect.')\n for i in range(len(organic_spec[0,:])):\n if np.nansum(organic_spec[1:,i]) >= 0.99 and np.nansum(organic_spec[1:,i]) <= 1.01:\n pass\n else: sys.exit('There is an issue with your subpuc_organic_speciation.csv file. Total speciation out of bounds.')\n if len(chem_index) == len(organic_spec[1:,0]):\n pass\n else: sys.exit('There is an issue with your subpuc_organic_speciation.csv file. Number of species is incorrect.')\n####################################################################################################\n\n####################################################################################################\ndef check_chem_assignments(chem_props_vars,chem_props_strs,chem_index):\n if len(chem_index) == len(chem_props_vars) and len(chem_index) == len(chem_props_strs):\n pass\n else: sys.exit('There is an issue with your chemical_assignments.csv file. Number of species is incorrect.')\n####################################################################################################",
"step-ids": [
3,
5,
6,
7,
8
]
}
|
[
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(15), unique=True)
email = db.Column(db.String(50), unique=True)
password = db.Column(db.String(80))
def get_reset_token(self, expires_seconds=1800):
s = Serializer(app.config['SECRET_KEY'], expires_seconds)
return s.dumps({'user_id': self.id}).decode('utf-8')
@staticmethod
def verify_reset_token(token):
s = Serializer(app.config['SECRET_KEY'])
try:
user_id = s.loads(token)['user_id']
except:
return None
return user.query.get(user_id)
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class LoginForm(FlaskForm):
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
remember = BooleanField('Remember Me')
class RegisterForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class UpdateAccountForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
submit = SubmitField('Update')
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
if username.data != current_user.username:
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class RequestResetForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
submit = SubmitField('Request Password Reset')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user is None:
raise ValidationError(
'There is no accouunt with that email. You must register first.'
)
class ResetPasswordForm(FlaskForm):
password = PasswordField('Password', validators=[DataRequired()])
confirm_password = PasswordField('Confirm Password', validators=[
DataRequired(), EqualTo('password')])
submit = SubmitField('Reset Password')
@app.route('/', methods=['GET', 'POST'])
def home():
return render_template('index.html')
<|reserved_special_token_0|>
@app.route('/login/', methods=['GET', 'POST'])
def login():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = LoginForm()
if form.validate_on_submit():
user = User.query.filter_by(username=form.username.data).first()
if user:
if check_password_hash(user.password, form.password.data):
login_user(user, remember=form.remember.data)
flash('Account Created For {}!'.format(form.username.data))
return redirect(url_for('model_page'))
else:
return redirect(url_for('login_error'))
return render_template('login.html', form=form)
<|reserved_special_token_0|>
@app.route('/learn_more/', methods=['GET', 'POST'])
def learn_more():
return render_template('learn_more.html')
<|reserved_special_token_0|>
@app.route('/model_page/', methods=['GET', 'POST'])
@login_required
def model_page():
return render_template('model_page.html')
def send_reset_email(user):
token = user.get_reset_token()
msg = Message(subject='Password Reset Request', sender=
'noreply@syndicate.com', recipients=[user.email])
msg.body = f""" To reset your password, visit the following link :
{url_for('reset_token', token=token, _external=True)}
If you did not make this request then simply ignore this email and no changes will be made.
"""
mail.send(msg)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_config(fname):
"""
Creates connection to yaml file which holds the DB user and pass
"""
with open(fname) as f:
cfg = yaml.load(f, Loader=yaml.SafeLoader)
return cfg
<|reserved_special_token_0|>
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(15), unique=True)
email = db.Column(db.String(50), unique=True)
password = db.Column(db.String(80))
def get_reset_token(self, expires_seconds=1800):
s = Serializer(app.config['SECRET_KEY'], expires_seconds)
return s.dumps({'user_id': self.id}).decode('utf-8')
@staticmethod
def verify_reset_token(token):
s = Serializer(app.config['SECRET_KEY'])
try:
user_id = s.loads(token)['user_id']
except:
return None
return user.query.get(user_id)
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class LoginForm(FlaskForm):
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
remember = BooleanField('Remember Me')
class RegisterForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class UpdateAccountForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
submit = SubmitField('Update')
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
if username.data != current_user.username:
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class RequestResetForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
submit = SubmitField('Request Password Reset')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user is None:
raise ValidationError(
'There is no accouunt with that email. You must register first.'
)
class ResetPasswordForm(FlaskForm):
password = PasswordField('Password', validators=[DataRequired()])
confirm_password = PasswordField('Confirm Password', validators=[
DataRequired(), EqualTo('password')])
submit = SubmitField('Reset Password')
@app.route('/', methods=['GET', 'POST'])
def home():
return render_template('index.html')
<|reserved_special_token_0|>
@app.route('/login/', methods=['GET', 'POST'])
def login():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = LoginForm()
if form.validate_on_submit():
user = User.query.filter_by(username=form.username.data).first()
if user:
if check_password_hash(user.password, form.password.data):
login_user(user, remember=form.remember.data)
flash('Account Created For {}!'.format(form.username.data))
return redirect(url_for('model_page'))
else:
return redirect(url_for('login_error'))
return render_template('login.html', form=form)
<|reserved_special_token_0|>
@app.route('/learn_more/', methods=['GET', 'POST'])
def learn_more():
return render_template('learn_more.html')
<|reserved_special_token_0|>
@app.route('/model_page/', methods=['GET', 'POST'])
@login_required
def model_page():
return render_template('model_page.html')
def send_reset_email(user):
token = user.get_reset_token()
msg = Message(subject='Password Reset Request', sender=
'noreply@syndicate.com', recipients=[user.email])
msg.body = f""" To reset your password, visit the following link :
{url_for('reset_token', token=token, _external=True)}
If you did not make this request then simply ignore this email and no changes will be made.
"""
mail.send(msg)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_config(fname):
"""
Creates connection to yaml file which holds the DB user and pass
"""
with open(fname) as f:
cfg = yaml.load(f, Loader=yaml.SafeLoader)
return cfg
<|reserved_special_token_0|>
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(15), unique=True)
email = db.Column(db.String(50), unique=True)
password = db.Column(db.String(80))
def get_reset_token(self, expires_seconds=1800):
s = Serializer(app.config['SECRET_KEY'], expires_seconds)
return s.dumps({'user_id': self.id}).decode('utf-8')
@staticmethod
def verify_reset_token(token):
s = Serializer(app.config['SECRET_KEY'])
try:
user_id = s.loads(token)['user_id']
except:
return None
return user.query.get(user_id)
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class LoginForm(FlaskForm):
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
remember = BooleanField('Remember Me')
class RegisterForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class UpdateAccountForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
submit = SubmitField('Update')
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
if username.data != current_user.username:
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class RequestResetForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
submit = SubmitField('Request Password Reset')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user is None:
raise ValidationError(
'There is no accouunt with that email. You must register first.'
)
class ResetPasswordForm(FlaskForm):
password = PasswordField('Password', validators=[DataRequired()])
confirm_password = PasswordField('Confirm Password', validators=[
DataRequired(), EqualTo('password')])
submit = SubmitField('Reset Password')
@app.route('/', methods=['GET', 'POST'])
def home():
return render_template('index.html')
<|reserved_special_token_0|>
@app.route('/login/', methods=['GET', 'POST'])
def login():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = LoginForm()
if form.validate_on_submit():
user = User.query.filter_by(username=form.username.data).first()
if user:
if check_password_hash(user.password, form.password.data):
login_user(user, remember=form.remember.data)
flash('Account Created For {}!'.format(form.username.data))
return redirect(url_for('model_page'))
else:
return redirect(url_for('login_error'))
return render_template('login.html', form=form)
<|reserved_special_token_0|>
@app.route('/logout/')
@login_required
def logout():
logout_user()
return redirect(url_for('home'))
@app.route('/learn_more/', methods=['GET', 'POST'])
def learn_more():
return render_template('learn_more.html')
<|reserved_special_token_0|>
@app.route('/model_page/', methods=['GET', 'POST'])
@login_required
def model_page():
return render_template('model_page.html')
def send_reset_email(user):
token = user.get_reset_token()
msg = Message(subject='Password Reset Request', sender=
'noreply@syndicate.com', recipients=[user.email])
msg.body = f""" To reset your password, visit the following link :
{url_for('reset_token', token=token, _external=True)}
If you did not make this request then simply ignore this email and no changes will be made.
"""
mail.send(msg)
<|reserved_special_token_0|>
@app.route('/reset_password/<token>', methods=['GET', 'POST'])
def reset_token(token):
if current_user.is_authenticated:
return redirect(url_for('home'))
user = User.verify_reset_token(token)
if user is None:
flash('That is an invalid / expired token', 'warning')
return redirect(url_for('reset_request'))
form = ResetPasswordForm()
if form.validate_on_submit():
hashed_password = generate_password_hash(form.password.data, method
='sha256')
user.password = hashed_password
db.session.commit()
flash('Your password has been updated!', 'success')
return redirect(url_for('login'))
return render_template('reset_token.html', title='Rest Password', form=form
)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_config(fname):
"""
Creates connection to yaml file which holds the DB user and pass
"""
with open(fname) as f:
cfg = yaml.load(f, Loader=yaml.SafeLoader)
return cfg
if ENV == 'dev':
cfg = get_config('config.yml')
connection = cfg['connection'][ENV]
app.config['SECRET_KEY'] = connection['secret_key']
app.debug = True
app.config[connection['username']] = connection['password']
app.config['TESTING'] = False
app.config['MAIL_SERVER'] = 'smtp.gmail.com'
app.config['MAIL_PORT'] = 25
app.config['MAIL_USE_TLS'] = True
app.config['MAIL__USE_SSL'] = False
app.config['MAIL_USERNAME'] = connection['mail_user']
app.config['MAIL_PASSWORD'] = connection['mail_pass']
app.config['MAIL_DEFAULT_SENDER'] = 'mail@syndicate.com'
app.config['MAIL_MAX_EMAILS'] = None
app.config['MAIL_ASCII_ATTACHMENTS'] = False
else:
app.debug = False
app.config['SECRET_KEY'] = os.environ['SECRET_KEY']
app.config['MAIL_SERVER'] = os.environ['MAIL_SERVER']
app.config['MAIL_PORT'] = 25
app.config['MAIL_USE_TLS'] = False
app.config['MAIL__USE_SSL'] = False
app.config['MAIL_USERNAME'] = os.environ['MAIL_USERNAME']
app.config['MAIL_PASSWORD'] = os.environ['MAIL_PASSWORD']
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ['DATABASE_URL']
<|reserved_special_token_0|>
Bootstrap(app)
<|reserved_special_token_0|>
login_manager.init_app(app)
<|reserved_special_token_0|>
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(15), unique=True)
email = db.Column(db.String(50), unique=True)
password = db.Column(db.String(80))
def get_reset_token(self, expires_seconds=1800):
s = Serializer(app.config['SECRET_KEY'], expires_seconds)
return s.dumps({'user_id': self.id}).decode('utf-8')
@staticmethod
def verify_reset_token(token):
s = Serializer(app.config['SECRET_KEY'])
try:
user_id = s.loads(token)['user_id']
except:
return None
return user.query.get(user_id)
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class LoginForm(FlaskForm):
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
remember = BooleanField('Remember Me')
class RegisterForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
password = PasswordField('Password', validators=[InputRequired(),
Length(min=8, max=80)])
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class UpdateAccountForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
username = StringField('UserName', validators=[InputRequired(), Length(
min=4, max=15)])
submit = SubmitField('Update')
def validate_username(self, username):
"""
Raises a validation error if a user tries to register using an existing username
"""
if username.data != current_user.username:
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('Email Taken')
class RequestResetForm(FlaskForm):
email = StringField('email', validators=[InputRequired(), Email(message
='Invalid Email'), Length(max=50)])
submit = SubmitField('Request Password Reset')
def validate_email(self, email):
"""
Raises a validation error if a user tries to register using an existing email
"""
if email.data != current_user.email:
user = User.query.filter_by(email=email.data).first()
if user is None:
raise ValidationError(
'There is no accouunt with that email. You must register first.'
)
class ResetPasswordForm(FlaskForm):
password = PasswordField('Password', validators=[DataRequired()])
confirm_password = PasswordField('Confirm Password', validators=[
DataRequired(), EqualTo('password')])
submit = SubmitField('Reset Password')
@app.route('/', methods=['GET', 'POST'])
def home():
return render_template('index.html')
@app.route('/error/')
def error():
return render_template('error.html')
@app.route('/login_error/')
def login_error():
return render_template('login_error.html')
@app.route('/login/', methods=['GET', 'POST'])
def login():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = LoginForm()
if form.validate_on_submit():
user = User.query.filter_by(username=form.username.data).first()
if user:
if check_password_hash(user.password, form.password.data):
login_user(user, remember=form.remember.data)
flash('Account Created For {}!'.format(form.username.data))
return redirect(url_for('model_page'))
else:
return redirect(url_for('login_error'))
return render_template('login.html', form=form)
@app.route('/signup/', methods=['GET', 'POST'])
def signup():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = RegisterForm()
if form.validate_on_submit():
hashed_password = generate_password_hash(form.password.data, method
='sha256')
new_user = User(username=form.username.data, email=form.email.data,
password=hashed_password)
db.session.add(new_user)
db.session.commit()
return redirect(url_for('login'))
else:
return render_template('signup.html', form=form, message=
'Username / Email Already Exists')
return render_template('signup.html', form=form)
@app.route('/logout/')
@login_required
def logout():
logout_user()
return redirect(url_for('home'))
@app.route('/learn_more/', methods=['GET', 'POST'])
def learn_more():
return render_template('learn_more.html')
@app.route('/email_sent/', methods=['GET', 'POST'])
def email_sent():
return render_template('email_sent.html')
@app.route('/account/', methods=['GET', 'POST'])
@login_required
def account():
form = UpdateAccountForm()
if form.validate_on_submit():
current_user.username = form.username.data
current_user.email = form.email.data
db.session.commit()
flash('Your account has been updated', 'success')
return redirect(url_for('account'))
elif request.method == 'GET':
form.username.data = current_user.username
form.email.data = current_user.email
return render_template('account.html', title='Account', form=form)
@app.route('/model_page/', methods=['GET', 'POST'])
@login_required
def model_page():
return render_template('model_page.html')
def send_reset_email(user):
token = user.get_reset_token()
msg = Message(subject='Password Reset Request', sender=
'noreply@syndicate.com', recipients=[user.email])
msg.body = f""" To reset your password, visit the following link :
{url_for('reset_token', token=token, _external=True)}
If you did not make this request then simply ignore this email and no changes will be made.
"""
mail.send(msg)
@app.route('/reset_password/', methods=['GET', 'POST'])
def reset_request():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = RequestResetForm()
if form.validate_on_submit():
user = User.query.filter_by(email=form.email.data).first()
flask(
'An email has been sent with instructions to resset your password',
'info')
return redirect(url_for('login'))
return render_template('reset_request.html', title='Rest Password',
form=form)
@app.route('/reset_password/<token>', methods=['GET', 'POST'])
def reset_token(token):
if current_user.is_authenticated:
return redirect(url_for('home'))
user = User.verify_reset_token(token)
if user is None:
flash('That is an invalid / expired token', 'warning')
return redirect(url_for('reset_request'))
form = ResetPasswordForm()
if form.validate_on_submit():
hashed_password = generate_password_hash(form.password.data, method
='sha256')
user.password = hashed_password
db.session.commit()
flash('Your password has been updated!', 'success')
return redirect(url_for('login'))
return render_template('reset_token.html', title='Rest Password', form=form
)
@app.route('/predict_model', methods=['GET', 'POST'])
def predict_model():
int_features = [int(x) for x in request.form.values()]
final_features = [np.array(int_features)]
prediction = model.predict(final_features)
output = round(prediction[0], 2)
map_dict = {(1): 'DT Toronto', (3): 'North York', (4): 'Scarborough', (
6): 'Etobicoke'}
output = map_dict[output]
return render_template('model_page.html', prediction_text=
'The Crime Occurred in : {}'.format(output))
if __name__ == '__main__':
if ENV == 'prod':
app.run()
else:
app.run(debug=True)
<|reserved_special_token_1|>
import numpy as np
import yaml
import pickle
import os
from flask import Flask, request, jsonify, render_template, redirect, url_for, flash
from flask_mail import Mail, Message
from flask_wtf import FlaskForm
from flask_sqlalchemy import SQLAlchemy
from flask_bootstrap import Bootstrap
from wtforms import StringField, PasswordField, BooleanField, SubmitField
from wtforms.validators import ValidationError, DataRequired, EqualTo
from wtforms.validators import InputRequired, Email, Length
from werkzeug.security import generate_password_hash, check_password_hash
from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user
from itsdangerous import TimedJSONWebSignatureSerializer as Serializer
app = Flask(__name__)
model = pickle.load(open('model_GB.pkl', 'rb'))
ENV = 'prod'
def get_config(fname):
'''
Creates connection to yaml file which holds the DB user and pass
'''
with open(fname) as f:
cfg = yaml.load(f, Loader=yaml.SafeLoader)
return cfg
if ENV == 'dev':
cfg = get_config('config.yml')
connection = cfg['connection'][ENV]
app.config['SECRET_KEY'] = connection['secret_key']
app.debug = True
app.config[connection['username']] = connection['password']
app.config['TESTING'] = False
app.config['MAIL_SERVER'] = 'smtp.gmail.com'
app.config['MAIL_PORT'] = 25
app.config['MAIL_USE_TLS'] = True
app.config['MAIL__USE_SSL'] = False
app.config['MAIL_USERNAME'] = connection['mail_user']
app.config['MAIL_PASSWORD'] = connection['mail_pass']
app.config['MAIL_DEFAULT_SENDER'] = 'mail@syndicate.com'
app.config['MAIL_MAX_EMAILS'] = None
app.config['MAIL_ASCII_ATTACHMENTS'] = False
else:
app.debug = False
app.config['SECRET_KEY'] = os.environ['SECRET_KEY']
app.config['MAIL_SERVER'] = os.environ['MAIL_SERVER']
app.config['MAIL_PORT'] = 25
app.config['MAIL_USE_TLS'] = False
app.config['MAIL__USE_SSL'] = False
app.config['MAIL_USERNAME'] = os.environ['MAIL_USERNAME']
app.config['MAIL_PASSWORD'] = os.environ['MAIL_PASSWORD']
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ['DATABASE_URL']
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
mail = Mail(app)
Bootstrap(app)
db = SQLAlchemy(app)
login_manager = LoginManager()
login_manager.init_app(app)
login_manager.login_view = 'login'
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(15), unique=True)
email = db.Column(db.String(50), unique=True)
password = db.Column(db.String(80))
def get_reset_token(self, expires_seconds = 1800):
s = Serializer(app.config['SECRET_KEY'], expires_seconds)
return s.dumps({'user_id' : self.id}).decode('utf-8')
@staticmethod
def verify_reset_token(token):
s = Serializer(app.config['SECRET_KEY'])
try:
user_id = s.loads(token)['user_id']
except:
return None
return user.query.get(user_id)
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
class LoginForm(FlaskForm):
username = StringField('UserName', validators = [InputRequired(), Length(min = 4, max = 15)])
password = PasswordField('Password', validators = [InputRequired(), Length(min = 8, max = 80)])
remember = BooleanField('Remember Me')
class RegisterForm(FlaskForm):
email = StringField('email', validators = [InputRequired(), Email(message = 'Invalid Email'), Length(max = 50)])
username = StringField('UserName', validators = [InputRequired(), Length(min = 4, max = 15)])
password = PasswordField('Password', validators = [InputRequired(), Length(min = 8, max = 80)])
def validate_username(self, username):
'''
Raises a validation error if a user tries to register using an existing username
'''
user = User.query.filter_by(username = username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
'''
Raises a validation error if a user tries to register using an existing email
'''
user = User.query.filter_by(email = email.data).first()
if user:
raise ValidationError('Email Taken')
class UpdateAccountForm(FlaskForm):
email = StringField('email', validators = [InputRequired(), Email(message = 'Invalid Email'), Length(max = 50)])
username = StringField('UserName', validators = [InputRequired(), Length(min = 4, max = 15)])
submit = SubmitField('Update')
def validate_username(self, username):
'''
Raises a validation error if a user tries to register using an existing username
'''
if username.data != current_user.username:
user = User.query.filter_by(username = username.data).first()
if user:
raise ValidationError('Username Taken')
def validate_email(self, email):
'''
Raises a validation error if a user tries to register using an existing email
'''
if email.data != current_user.email:
user = User.query.filter_by(email = email.data).first()
if user:
raise ValidationError('Email Taken')
class RequestResetForm(FlaskForm):
email = StringField('email', validators = [InputRequired(), Email(message = 'Invalid Email'), Length(max = 50)])
submit = SubmitField('Request Password Reset')
def validate_email(self, email):
'''
Raises a validation error if a user tries to register using an existing email
'''
if email.data != current_user.email:
user = User.query.filter_by(email = email.data).first()
if user is None:
raise ValidationError('There is no accouunt with that email. You must register first.')
class ResetPasswordForm(FlaskForm):
password = PasswordField('Password', validators = [DataRequired()])
confirm_password = PasswordField('Confirm Password', validators = [DataRequired(), EqualTo('password')])
submit = SubmitField('Reset Password')
@app.route('/',methods=['GET', 'POST'])
def home():
return render_template('index.html')
@app.route('/error/')
def error():
return render_template('error.html')
@app.route('/login_error/')
def login_error():
return render_template('login_error.html')
@app.route('/login/',methods=['GET', 'POST'])
def login():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = LoginForm()
if form.validate_on_submit():
user = User.query.filter_by(username = form.username.data).first()
if user:
if check_password_hash(user.password, form.password.data):
login_user(user, remember = form.remember.data)
flash('Account Created For {}!'.format(form.username.data))
return redirect(url_for('model_page'))
else:
return redirect(url_for('login_error'))
return render_template('login.html', form=form)
@app.route('/signup/', methods = ['GET','POST'])
def signup():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = RegisterForm()
if form.validate_on_submit():
hashed_password = generate_password_hash(form.password.data, method = 'sha256') # sha256 will generate a hash which is 80 chars long
new_user = User(username = form.username.data, email = form.email.data, password = hashed_password)
db.session.add(new_user)
db.session.commit()
# send congrat email for registering
# msg = Message(subject = 'Welcome {}'.format(form.username.data), sender = app.config.get("MAIL_USERNAME"), recipients = [str(form.email.data)], body = 'Congratulations you have signed up and your account has been created!')
# mail.send(msg)
return redirect(url_for('login'))
else:
return render_template('signup.html', form = form, message= 'Username / Email Already Exists')
# return '<h1>' + form.email.data + ' ' + form.username.data + ' ' + form.password.data + '<h1>'
return render_template('signup.html', form = form)
@app.route('/logout/')
@login_required
def logout():
logout_user()
return redirect(url_for('home'))
@app.route('/learn_more/',methods=['GET', 'POST'])
def learn_more():
return render_template('learn_more.html')
@app.route('/email_sent/',methods=['GET', 'POST'])
def email_sent():
return render_template('email_sent.html')
@app.route('/account/',methods=['GET', 'POST'])
@login_required
def account():
form = UpdateAccountForm()
if form.validate_on_submit():
current_user.username = form.username.data
current_user.email = form.email.data
db.session.commit()
flash('Your account has been updated', 'success')
return redirect(url_for('account'))
elif request.method == 'GET':
form.username.data = current_user.username
form.email.data = current_user.email
return render_template('account.html', title = 'Account', form = form)
@app.route('/model_page/', methods = ['GET','POST'])
@login_required
def model_page():
return render_template('model_page.html')
def send_reset_email(user):
token = user.get_reset_token()
msg = Message(subject = 'Password Reset Request',
sender = 'noreply@syndicate.com',
recipients=[user.email])
msg.body = f''' To reset your password, visit the following link :
{url_for('reset_token', token = token, _external = True)}
If you did not make this request then simply ignore this email and no changes will be made.
'''
mail.send(msg)
@app.route('/reset_password/',methods=['GET', 'POST'])
def reset_request():
if current_user.is_authenticated:
return redirect(url_for('home'))
form = RequestResetForm()
if form.validate_on_submit():
user = User.query.filter_by(email = form.email.data).first()
flask('An email has been sent with instructions to resset your password', 'info')
return redirect(url_for('login'))
return render_template('reset_request.html', title = 'Rest Password', form = form)
@app.route('/reset_password/<token>',methods=['GET', 'POST'])
def reset_token(token):
if current_user.is_authenticated:
return redirect(url_for('home'))
user = User.verify_reset_token(token)
if user is None:
flash('That is an invalid / expired token', 'warning')
return redirect(url_for('reset_request'))
form = ResetPasswordForm()
if form.validate_on_submit():
hashed_password = generate_password_hash(form.password.data, method = 'sha256') # sha256 will generate a hash which is 80 chars long
user.password = hashed_password
db.session.commit()
flash('Your password has been updated!', 'success')
# send congrat email for registering
# msg = Message(subject = 'Welcome {}'.format(form.username.data), sender = app.config.get("MAIL_USERNAME"), recipients = [str(form.email.data)], body = 'Congratulations you have signed up and your account has been created!')
# mail.send(msg)
return redirect(url_for('login'))
return render_template('reset_token.html', title = 'Rest Password', form = form)
@app.route('/predict_model', methods=['GET', 'POST'])
def predict_model():
int_features = [int(x) for x in request.form.values()]
final_features = [np.array(int_features)]
prediction = model.predict(final_features)
output = round(prediction[0], 2)
map_dict = {1 : 'DT Toronto', 3 : 'North York', 4 : 'Scarborough', 6 : 'Etobicoke'}
output = map_dict[output]
return render_template('model_page.html', prediction_text = 'The Crime Occurred in : {}'.format(output))
if __name__ == "__main__":
if ENV == 'prod':
app.run()
else:
app.run(debug=True)
|
flexible
|
{
"blob_id": "f6a3693fe81e629d987067265bf4e410bf260bcf",
"index": 1663,
"step-1": "<mask token>\n\n\nclass User(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(15), unique=True)\n email = db.Column(db.String(50), unique=True)\n password = db.Column(db.String(80))\n\n def get_reset_token(self, expires_seconds=1800):\n s = Serializer(app.config['SECRET_KEY'], expires_seconds)\n return s.dumps({'user_id': self.id}).decode('utf-8')\n\n @staticmethod\n def verify_reset_token(token):\n s = Serializer(app.config['SECRET_KEY'])\n try:\n user_id = s.loads(token)['user_id']\n except:\n return None\n return user.query.get(user_id)\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\n\nclass LoginForm(FlaskForm):\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n remember = BooleanField('Remember Me')\n\n\nclass RegisterForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass UpdateAccountForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n submit = SubmitField('Update')\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n if username.data != current_user.username:\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass RequestResetForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n submit = SubmitField('Request Password Reset')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user is None:\n raise ValidationError(\n 'There is no accouunt with that email. You must register first.'\n )\n\n\nclass ResetPasswordForm(FlaskForm):\n password = PasswordField('Password', validators=[DataRequired()])\n confirm_password = PasswordField('Confirm Password', validators=[\n DataRequired(), EqualTo('password')])\n submit = SubmitField('Reset Password')\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n return render_template('index.html')\n\n\n<mask token>\n\n\n@app.route('/login/', methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(username=form.username.data).first()\n if user:\n if check_password_hash(user.password, form.password.data):\n login_user(user, remember=form.remember.data)\n flash('Account Created For {}!'.format(form.username.data))\n return redirect(url_for('model_page'))\n else:\n return redirect(url_for('login_error'))\n return render_template('login.html', form=form)\n\n\n<mask token>\n\n\n@app.route('/learn_more/', methods=['GET', 'POST'])\ndef learn_more():\n return render_template('learn_more.html')\n\n\n<mask token>\n\n\n@app.route('/model_page/', methods=['GET', 'POST'])\n@login_required\ndef model_page():\n return render_template('model_page.html')\n\n\ndef send_reset_email(user):\n token = user.get_reset_token()\n msg = Message(subject='Password Reset Request', sender=\n 'noreply@syndicate.com', recipients=[user.email])\n msg.body = f\"\"\" To reset your password, visit the following link :\n{url_for('reset_token', token=token, _external=True)}\n\nIf you did not make this request then simply ignore this email and no changes will be made.\n\"\"\"\n mail.send(msg)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_config(fname):\n \"\"\"\n Creates connection to yaml file which holds the DB user and pass\n \"\"\"\n with open(fname) as f:\n cfg = yaml.load(f, Loader=yaml.SafeLoader)\n return cfg\n\n\n<mask token>\n\n\nclass User(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(15), unique=True)\n email = db.Column(db.String(50), unique=True)\n password = db.Column(db.String(80))\n\n def get_reset_token(self, expires_seconds=1800):\n s = Serializer(app.config['SECRET_KEY'], expires_seconds)\n return s.dumps({'user_id': self.id}).decode('utf-8')\n\n @staticmethod\n def verify_reset_token(token):\n s = Serializer(app.config['SECRET_KEY'])\n try:\n user_id = s.loads(token)['user_id']\n except:\n return None\n return user.query.get(user_id)\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\n\nclass LoginForm(FlaskForm):\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n remember = BooleanField('Remember Me')\n\n\nclass RegisterForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass UpdateAccountForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n submit = SubmitField('Update')\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n if username.data != current_user.username:\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass RequestResetForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n submit = SubmitField('Request Password Reset')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user is None:\n raise ValidationError(\n 'There is no accouunt with that email. You must register first.'\n )\n\n\nclass ResetPasswordForm(FlaskForm):\n password = PasswordField('Password', validators=[DataRequired()])\n confirm_password = PasswordField('Confirm Password', validators=[\n DataRequired(), EqualTo('password')])\n submit = SubmitField('Reset Password')\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n return render_template('index.html')\n\n\n<mask token>\n\n\n@app.route('/login/', methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(username=form.username.data).first()\n if user:\n if check_password_hash(user.password, form.password.data):\n login_user(user, remember=form.remember.data)\n flash('Account Created For {}!'.format(form.username.data))\n return redirect(url_for('model_page'))\n else:\n return redirect(url_for('login_error'))\n return render_template('login.html', form=form)\n\n\n<mask token>\n\n\n@app.route('/learn_more/', methods=['GET', 'POST'])\ndef learn_more():\n return render_template('learn_more.html')\n\n\n<mask token>\n\n\n@app.route('/model_page/', methods=['GET', 'POST'])\n@login_required\ndef model_page():\n return render_template('model_page.html')\n\n\ndef send_reset_email(user):\n token = user.get_reset_token()\n msg = Message(subject='Password Reset Request', sender=\n 'noreply@syndicate.com', recipients=[user.email])\n msg.body = f\"\"\" To reset your password, visit the following link :\n{url_for('reset_token', token=token, _external=True)}\n\nIf you did not make this request then simply ignore this email and no changes will be made.\n\"\"\"\n mail.send(msg)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef get_config(fname):\n \"\"\"\n Creates connection to yaml file which holds the DB user and pass\n \"\"\"\n with open(fname) as f:\n cfg = yaml.load(f, Loader=yaml.SafeLoader)\n return cfg\n\n\n<mask token>\n\n\nclass User(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(15), unique=True)\n email = db.Column(db.String(50), unique=True)\n password = db.Column(db.String(80))\n\n def get_reset_token(self, expires_seconds=1800):\n s = Serializer(app.config['SECRET_KEY'], expires_seconds)\n return s.dumps({'user_id': self.id}).decode('utf-8')\n\n @staticmethod\n def verify_reset_token(token):\n s = Serializer(app.config['SECRET_KEY'])\n try:\n user_id = s.loads(token)['user_id']\n except:\n return None\n return user.query.get(user_id)\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\n\nclass LoginForm(FlaskForm):\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n remember = BooleanField('Remember Me')\n\n\nclass RegisterForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass UpdateAccountForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n submit = SubmitField('Update')\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n if username.data != current_user.username:\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass RequestResetForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n submit = SubmitField('Request Password Reset')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user is None:\n raise ValidationError(\n 'There is no accouunt with that email. You must register first.'\n )\n\n\nclass ResetPasswordForm(FlaskForm):\n password = PasswordField('Password', validators=[DataRequired()])\n confirm_password = PasswordField('Confirm Password', validators=[\n DataRequired(), EqualTo('password')])\n submit = SubmitField('Reset Password')\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n return render_template('index.html')\n\n\n<mask token>\n\n\n@app.route('/login/', methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(username=form.username.data).first()\n if user:\n if check_password_hash(user.password, form.password.data):\n login_user(user, remember=form.remember.data)\n flash('Account Created For {}!'.format(form.username.data))\n return redirect(url_for('model_page'))\n else:\n return redirect(url_for('login_error'))\n return render_template('login.html', form=form)\n\n\n<mask token>\n\n\n@app.route('/logout/')\n@login_required\ndef logout():\n logout_user()\n return redirect(url_for('home'))\n\n\n@app.route('/learn_more/', methods=['GET', 'POST'])\ndef learn_more():\n return render_template('learn_more.html')\n\n\n<mask token>\n\n\n@app.route('/model_page/', methods=['GET', 'POST'])\n@login_required\ndef model_page():\n return render_template('model_page.html')\n\n\ndef send_reset_email(user):\n token = user.get_reset_token()\n msg = Message(subject='Password Reset Request', sender=\n 'noreply@syndicate.com', recipients=[user.email])\n msg.body = f\"\"\" To reset your password, visit the following link :\n{url_for('reset_token', token=token, _external=True)}\n\nIf you did not make this request then simply ignore this email and no changes will be made.\n\"\"\"\n mail.send(msg)\n\n\n<mask token>\n\n\n@app.route('/reset_password/<token>', methods=['GET', 'POST'])\ndef reset_token(token):\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n user = User.verify_reset_token(token)\n if user is None:\n flash('That is an invalid / expired token', 'warning')\n return redirect(url_for('reset_request'))\n form = ResetPasswordForm()\n if form.validate_on_submit():\n hashed_password = generate_password_hash(form.password.data, method\n ='sha256')\n user.password = hashed_password\n db.session.commit()\n flash('Your password has been updated!', 'success')\n return redirect(url_for('login'))\n return render_template('reset_token.html', title='Rest Password', form=form\n )\n\n\n<mask token>\n",
"step-4": "<mask token>\n\n\ndef get_config(fname):\n \"\"\"\n Creates connection to yaml file which holds the DB user and pass\n \"\"\"\n with open(fname) as f:\n cfg = yaml.load(f, Loader=yaml.SafeLoader)\n return cfg\n\n\nif ENV == 'dev':\n cfg = get_config('config.yml')\n connection = cfg['connection'][ENV]\n app.config['SECRET_KEY'] = connection['secret_key']\n app.debug = True\n app.config[connection['username']] = connection['password']\n app.config['TESTING'] = False\n app.config['MAIL_SERVER'] = 'smtp.gmail.com'\n app.config['MAIL_PORT'] = 25\n app.config['MAIL_USE_TLS'] = True\n app.config['MAIL__USE_SSL'] = False\n app.config['MAIL_USERNAME'] = connection['mail_user']\n app.config['MAIL_PASSWORD'] = connection['mail_pass']\n app.config['MAIL_DEFAULT_SENDER'] = 'mail@syndicate.com'\n app.config['MAIL_MAX_EMAILS'] = None\n app.config['MAIL_ASCII_ATTACHMENTS'] = False\nelse:\n app.debug = False\n app.config['SECRET_KEY'] = os.environ['SECRET_KEY']\n app.config['MAIL_SERVER'] = os.environ['MAIL_SERVER']\n app.config['MAIL_PORT'] = 25\n app.config['MAIL_USE_TLS'] = False\n app.config['MAIL__USE_SSL'] = False\n app.config['MAIL_USERNAME'] = os.environ['MAIL_USERNAME']\n app.config['MAIL_PASSWORD'] = os.environ['MAIL_PASSWORD']\n app.config['SQLALCHEMY_DATABASE_URI'] = os.environ['DATABASE_URL']\n<mask token>\nBootstrap(app)\n<mask token>\nlogin_manager.init_app(app)\n<mask token>\n\n\nclass User(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(15), unique=True)\n email = db.Column(db.String(50), unique=True)\n password = db.Column(db.String(80))\n\n def get_reset_token(self, expires_seconds=1800):\n s = Serializer(app.config['SECRET_KEY'], expires_seconds)\n return s.dumps({'user_id': self.id}).decode('utf-8')\n\n @staticmethod\n def verify_reset_token(token):\n s = Serializer(app.config['SECRET_KEY'])\n try:\n user_id = s.loads(token)['user_id']\n except:\n return None\n return user.query.get(user_id)\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\n\nclass LoginForm(FlaskForm):\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n remember = BooleanField('Remember Me')\n\n\nclass RegisterForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n password = PasswordField('Password', validators=[InputRequired(),\n Length(min=8, max=80)])\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass UpdateAccountForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n username = StringField('UserName', validators=[InputRequired(), Length(\n min=4, max=15)])\n submit = SubmitField('Update')\n\n def validate_username(self, username):\n \"\"\"\n Raises a validation error if a user tries to register using an existing username\n \"\"\"\n if username.data != current_user.username:\n user = User.query.filter_by(username=username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\n\nclass RequestResetForm(FlaskForm):\n email = StringField('email', validators=[InputRequired(), Email(message\n ='Invalid Email'), Length(max=50)])\n submit = SubmitField('Request Password Reset')\n\n def validate_email(self, email):\n \"\"\"\n Raises a validation error if a user tries to register using an existing email\n \"\"\"\n if email.data != current_user.email:\n user = User.query.filter_by(email=email.data).first()\n if user is None:\n raise ValidationError(\n 'There is no accouunt with that email. You must register first.'\n )\n\n\nclass ResetPasswordForm(FlaskForm):\n password = PasswordField('Password', validators=[DataRequired()])\n confirm_password = PasswordField('Confirm Password', validators=[\n DataRequired(), EqualTo('password')])\n submit = SubmitField('Reset Password')\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n return render_template('index.html')\n\n\n@app.route('/error/')\ndef error():\n return render_template('error.html')\n\n\n@app.route('/login_error/')\ndef login_error():\n return render_template('login_error.html')\n\n\n@app.route('/login/', methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(username=form.username.data).first()\n if user:\n if check_password_hash(user.password, form.password.data):\n login_user(user, remember=form.remember.data)\n flash('Account Created For {}!'.format(form.username.data))\n return redirect(url_for('model_page'))\n else:\n return redirect(url_for('login_error'))\n return render_template('login.html', form=form)\n\n\n@app.route('/signup/', methods=['GET', 'POST'])\ndef signup():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = RegisterForm()\n if form.validate_on_submit():\n hashed_password = generate_password_hash(form.password.data, method\n ='sha256')\n new_user = User(username=form.username.data, email=form.email.data,\n password=hashed_password)\n db.session.add(new_user)\n db.session.commit()\n return redirect(url_for('login'))\n else:\n return render_template('signup.html', form=form, message=\n 'Username / Email Already Exists')\n return render_template('signup.html', form=form)\n\n\n@app.route('/logout/')\n@login_required\ndef logout():\n logout_user()\n return redirect(url_for('home'))\n\n\n@app.route('/learn_more/', methods=['GET', 'POST'])\ndef learn_more():\n return render_template('learn_more.html')\n\n\n@app.route('/email_sent/', methods=['GET', 'POST'])\ndef email_sent():\n return render_template('email_sent.html')\n\n\n@app.route('/account/', methods=['GET', 'POST'])\n@login_required\ndef account():\n form = UpdateAccountForm()\n if form.validate_on_submit():\n current_user.username = form.username.data\n current_user.email = form.email.data\n db.session.commit()\n flash('Your account has been updated', 'success')\n return redirect(url_for('account'))\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n return render_template('account.html', title='Account', form=form)\n\n\n@app.route('/model_page/', methods=['GET', 'POST'])\n@login_required\ndef model_page():\n return render_template('model_page.html')\n\n\ndef send_reset_email(user):\n token = user.get_reset_token()\n msg = Message(subject='Password Reset Request', sender=\n 'noreply@syndicate.com', recipients=[user.email])\n msg.body = f\"\"\" To reset your password, visit the following link :\n{url_for('reset_token', token=token, _external=True)}\n\nIf you did not make this request then simply ignore this email and no changes will be made.\n\"\"\"\n mail.send(msg)\n\n\n@app.route('/reset_password/', methods=['GET', 'POST'])\ndef reset_request():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n form = RequestResetForm()\n if form.validate_on_submit():\n user = User.query.filter_by(email=form.email.data).first()\n flask(\n 'An email has been sent with instructions to resset your password',\n 'info')\n return redirect(url_for('login'))\n return render_template('reset_request.html', title='Rest Password',\n form=form)\n\n\n@app.route('/reset_password/<token>', methods=['GET', 'POST'])\ndef reset_token(token):\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n user = User.verify_reset_token(token)\n if user is None:\n flash('That is an invalid / expired token', 'warning')\n return redirect(url_for('reset_request'))\n form = ResetPasswordForm()\n if form.validate_on_submit():\n hashed_password = generate_password_hash(form.password.data, method\n ='sha256')\n user.password = hashed_password\n db.session.commit()\n flash('Your password has been updated!', 'success')\n return redirect(url_for('login'))\n return render_template('reset_token.html', title='Rest Password', form=form\n )\n\n\n@app.route('/predict_model', methods=['GET', 'POST'])\ndef predict_model():\n int_features = [int(x) for x in request.form.values()]\n final_features = [np.array(int_features)]\n prediction = model.predict(final_features)\n output = round(prediction[0], 2)\n map_dict = {(1): 'DT Toronto', (3): 'North York', (4): 'Scarborough', (\n 6): 'Etobicoke'}\n output = map_dict[output]\n return render_template('model_page.html', prediction_text=\n 'The Crime Occurred in : {}'.format(output))\n\n\nif __name__ == '__main__':\n if ENV == 'prod':\n app.run()\n else:\n app.run(debug=True)\n",
"step-5": "import numpy as np\nimport yaml\nimport pickle\nimport os\n\nfrom flask import Flask, request, jsonify, render_template, redirect, url_for, flash\nfrom flask_mail import Mail, Message\nfrom flask_wtf import FlaskForm\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_bootstrap import Bootstrap\nfrom wtforms import StringField, PasswordField, BooleanField, SubmitField\nfrom wtforms.validators import ValidationError, DataRequired, EqualTo\nfrom wtforms.validators import InputRequired, Email, Length\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user\nfrom itsdangerous import TimedJSONWebSignatureSerializer as Serializer\n\n\napp = Flask(__name__)\nmodel = pickle.load(open('model_GB.pkl', 'rb'))\n\nENV = 'prod'\n\ndef get_config(fname):\n '''\n Creates connection to yaml file which holds the DB user and pass\n '''\n with open(fname) as f:\n cfg = yaml.load(f, Loader=yaml.SafeLoader)\n return cfg\n\nif ENV == 'dev':\n\n cfg = get_config('config.yml')\n connection = cfg['connection'][ENV]\n app.config['SECRET_KEY'] = connection['secret_key']\n app.debug = True\n app.config[connection['username']] = connection['password']\n\n app.config['TESTING'] = False\n app.config['MAIL_SERVER'] = 'smtp.gmail.com'\n app.config['MAIL_PORT'] = 25\n app.config['MAIL_USE_TLS'] = True\n app.config['MAIL__USE_SSL'] = False\n app.config['MAIL_USERNAME'] = connection['mail_user']\n app.config['MAIL_PASSWORD'] = connection['mail_pass']\n app.config['MAIL_DEFAULT_SENDER'] = 'mail@syndicate.com'\n app.config['MAIL_MAX_EMAILS'] = None\n app.config['MAIL_ASCII_ATTACHMENTS'] = False\n\nelse:\n app.debug = False\n app.config['SECRET_KEY'] = os.environ['SECRET_KEY']\n app.config['MAIL_SERVER'] = os.environ['MAIL_SERVER']\n app.config['MAIL_PORT'] = 25\n app.config['MAIL_USE_TLS'] = False\n app.config['MAIL__USE_SSL'] = False\n app.config['MAIL_USERNAME'] = os.environ['MAIL_USERNAME']\n app.config['MAIL_PASSWORD'] = os.environ['MAIL_PASSWORD']\n\n app.config['SQLALCHEMY_DATABASE_URI'] = os.environ['DATABASE_URL']\n\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\nmail = Mail(app)\nBootstrap(app)\ndb = SQLAlchemy(app)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\n\nclass User(UserMixin, db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(15), unique=True)\n email = db.Column(db.String(50), unique=True)\n password = db.Column(db.String(80))\n\n def get_reset_token(self, expires_seconds = 1800):\n s = Serializer(app.config['SECRET_KEY'], expires_seconds)\n return s.dumps({'user_id' : self.id}).decode('utf-8')\n\n @staticmethod\n def verify_reset_token(token):\n s = Serializer(app.config['SECRET_KEY'])\n try:\n user_id = s.loads(token)['user_id']\n except:\n return None\n return user.query.get(user_id)\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.query.get(int(user_id))\n\nclass LoginForm(FlaskForm):\n username = StringField('UserName', validators = [InputRequired(), Length(min = 4, max = 15)])\n password = PasswordField('Password', validators = [InputRequired(), Length(min = 8, max = 80)])\n remember = BooleanField('Remember Me')\n\nclass RegisterForm(FlaskForm):\n email = StringField('email', validators = [InputRequired(), Email(message = 'Invalid Email'), Length(max = 50)])\n username = StringField('UserName', validators = [InputRequired(), Length(min = 4, max = 15)])\n password = PasswordField('Password', validators = [InputRequired(), Length(min = 8, max = 80)])\n\n def validate_username(self, username):\n '''\n Raises a validation error if a user tries to register using an existing username\n '''\n user = User.query.filter_by(username = username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n '''\n Raises a validation error if a user tries to register using an existing email\n '''\n user = User.query.filter_by(email = email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\nclass UpdateAccountForm(FlaskForm):\n email = StringField('email', validators = [InputRequired(), Email(message = 'Invalid Email'), Length(max = 50)])\n username = StringField('UserName', validators = [InputRequired(), Length(min = 4, max = 15)])\n\n submit = SubmitField('Update')\n def validate_username(self, username):\n '''\n Raises a validation error if a user tries to register using an existing username\n '''\n if username.data != current_user.username:\n user = User.query.filter_by(username = username.data).first()\n if user:\n raise ValidationError('Username Taken')\n\n def validate_email(self, email):\n '''\n Raises a validation error if a user tries to register using an existing email\n '''\n if email.data != current_user.email:\n user = User.query.filter_by(email = email.data).first()\n if user:\n raise ValidationError('Email Taken')\n\nclass RequestResetForm(FlaskForm):\n email = StringField('email', validators = [InputRequired(), Email(message = 'Invalid Email'), Length(max = 50)])\n submit = SubmitField('Request Password Reset')\n\n def validate_email(self, email):\n '''\n Raises a validation error if a user tries to register using an existing email\n '''\n if email.data != current_user.email:\n user = User.query.filter_by(email = email.data).first()\n if user is None:\n raise ValidationError('There is no accouunt with that email. You must register first.')\n\nclass ResetPasswordForm(FlaskForm):\n password = PasswordField('Password', validators = [DataRequired()])\n confirm_password = PasswordField('Confirm Password', validators = [DataRequired(), EqualTo('password')])\n submit = SubmitField('Reset Password')\n\n\n\n\n@app.route('/',methods=['GET', 'POST'])\ndef home():\n return render_template('index.html')\n\n@app.route('/error/')\ndef error():\n return render_template('error.html')\n\n@app.route('/login_error/')\ndef login_error():\n return render_template('login_error.html')\n\n@app.route('/login/',methods=['GET', 'POST'])\ndef login():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n\n form = LoginForm()\n\n if form.validate_on_submit():\n user = User.query.filter_by(username = form.username.data).first()\n if user:\n if check_password_hash(user.password, form.password.data):\n login_user(user, remember = form.remember.data)\n flash('Account Created For {}!'.format(form.username.data))\n return redirect(url_for('model_page'))\n else:\n return redirect(url_for('login_error'))\n\n return render_template('login.html', form=form)\n\n@app.route('/signup/', methods = ['GET','POST'])\ndef signup():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n\n form = RegisterForm()\n\n if form.validate_on_submit():\n hashed_password = generate_password_hash(form.password.data, method = 'sha256') # sha256 will generate a hash which is 80 chars long\n new_user = User(username = form.username.data, email = form.email.data, password = hashed_password)\n db.session.add(new_user)\n db.session.commit()\n\n # send congrat email for registering\n # msg = Message(subject = 'Welcome {}'.format(form.username.data), sender = app.config.get(\"MAIL_USERNAME\"), recipients = [str(form.email.data)], body = 'Congratulations you have signed up and your account has been created!')\n # mail.send(msg)\n\n return redirect(url_for('login'))\n else:\n return render_template('signup.html', form = form, message= 'Username / Email Already Exists')\n # return '<h1>' + form.email.data + ' ' + form.username.data + ' ' + form.password.data + '<h1>'\n return render_template('signup.html', form = form)\n\n@app.route('/logout/')\n@login_required\ndef logout():\n logout_user()\n return redirect(url_for('home'))\n\n@app.route('/learn_more/',methods=['GET', 'POST'])\ndef learn_more():\n return render_template('learn_more.html')\n\n@app.route('/email_sent/',methods=['GET', 'POST'])\ndef email_sent():\n return render_template('email_sent.html')\n\n@app.route('/account/',methods=['GET', 'POST'])\n@login_required\ndef account():\n form = UpdateAccountForm()\n\n if form.validate_on_submit():\n current_user.username = form.username.data\n current_user.email = form.email.data\n db.session.commit()\n flash('Your account has been updated', 'success')\n return redirect(url_for('account'))\n\n elif request.method == 'GET':\n form.username.data = current_user.username\n form.email.data = current_user.email\n\n return render_template('account.html', title = 'Account', form = form)\n\n@app.route('/model_page/', methods = ['GET','POST'])\n@login_required\ndef model_page():\n return render_template('model_page.html')\n\ndef send_reset_email(user):\n token = user.get_reset_token()\n msg = Message(subject = 'Password Reset Request',\n sender = 'noreply@syndicate.com',\n recipients=[user.email])\n msg.body = f''' To reset your password, visit the following link :\n{url_for('reset_token', token = token, _external = True)}\n\nIf you did not make this request then simply ignore this email and no changes will be made.\n'''\n mail.send(msg)\n\n\n@app.route('/reset_password/',methods=['GET', 'POST'])\ndef reset_request():\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n\n form = RequestResetForm()\n if form.validate_on_submit():\n user = User.query.filter_by(email = form.email.data).first()\n flask('An email has been sent with instructions to resset your password', 'info')\n return redirect(url_for('login'))\n\n return render_template('reset_request.html', title = 'Rest Password', form = form)\n\n@app.route('/reset_password/<token>',methods=['GET', 'POST'])\ndef reset_token(token):\n if current_user.is_authenticated:\n return redirect(url_for('home'))\n\n user = User.verify_reset_token(token)\n if user is None:\n flash('That is an invalid / expired token', 'warning')\n return redirect(url_for('reset_request'))\n\n form = ResetPasswordForm()\n if form.validate_on_submit():\n hashed_password = generate_password_hash(form.password.data, method = 'sha256') # sha256 will generate a hash which is 80 chars long\n user.password = hashed_password\n db.session.commit()\n flash('Your password has been updated!', 'success')\n # send congrat email for registering\n # msg = Message(subject = 'Welcome {}'.format(form.username.data), sender = app.config.get(\"MAIL_USERNAME\"), recipients = [str(form.email.data)], body = 'Congratulations you have signed up and your account has been created!')\n # mail.send(msg)\n return redirect(url_for('login'))\n return render_template('reset_token.html', title = 'Rest Password', form = form)\n\n\n\n@app.route('/predict_model', methods=['GET', 'POST'])\ndef predict_model():\n int_features = [int(x) for x in request.form.values()]\n final_features = [np.array(int_features)]\n prediction = model.predict(final_features)\n\n output = round(prediction[0], 2)\n map_dict = {1 : 'DT Toronto', 3 : 'North York', 4 : 'Scarborough', 6 : 'Etobicoke'}\n output = map_dict[output]\n return render_template('model_page.html', prediction_text = 'The Crime Occurred in : {}'.format(output))\n\nif __name__ == \"__main__\":\n if ENV == 'prod':\n app.run()\n else:\n app.run(debug=True)\n",
"step-ids": [
25,
26,
28,
36,
39
]
}
|
[
25,
26,
28,
36,
39
] |
from django.urls import reverse
from django.utils.translation import get_language
from drf_dynamic_fields import DynamicFieldsMixin
from geotrek.api.v2.serializers import AttachmentSerializer
from mapentity.serializers import MapentityGeojsonModelSerializer
from rest_framework import serializers as rest_serializers
from rest_framework_gis import fields as rest_gis_fields
from rest_framework_gis.serializers import GeoFeatureModelSerializer
from geotrek.common.serializers import PictogramSerializerMixin, TranslatedModelSerializer
from . import models as sensitivity_models
class RuleSerializer(PictogramSerializerMixin, rest_serializers.ModelSerializer):
class Meta:
model = sensitivity_models.Rule
fields = ('id', 'code', 'name', 'pictogram', 'description', 'url')
class SportPracticeSerializer(TranslatedModelSerializer):
class Meta:
model = sensitivity_models.SportPractice
fields = ('id', 'name')
class SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):
practices = SportPracticeSerializer(many=True)
period = rest_serializers.SerializerMethodField()
def get_period(self, obj):
return [getattr(obj, 'period{:02}'.format(p)) for p in range(1, 13)]
class Meta:
model = sensitivity_models.Species
fields = ['id', 'name', 'practices', 'url', 'pictogram', 'period']
class SensitiveAreaSerializer(DynamicFieldsMixin, rest_serializers.ModelSerializer):
category = rest_serializers.CharField(source='category_display')
structure = rest_serializers.SlugRelatedField('name', read_only=True)
species = rest_serializers.CharField(source='species_display')
class Meta:
model = sensitivity_models.SensitiveArea
fields = "__all__"
class SensitiveAreaGeojsonSerializer(MapentityGeojsonModelSerializer):
radius = rest_serializers.IntegerField()
class Meta(MapentityGeojsonModelSerializer.Meta):
model = sensitivity_models.SensitiveArea
fields = ['id', 'species', 'radius', 'published']
class SensitiveAreaAPISerializer(TranslatedModelSerializer):
species = SpeciesSerializer()
kml_url = rest_serializers.SerializerMethodField()
attachments = AttachmentSerializer(many=True)
rules = RuleSerializer(many=True)
def get_kml_url(self, obj):
return reverse('sensitivity:sensitivearea_kml_detail', kwargs={'lang': get_language(), 'pk': obj.pk})
class Meta:
model = sensitivity_models.SensitiveArea
fields = ('id', 'species', 'description', 'contact', 'published', 'publication_date', 'kml_url', 'attachments', 'rules')
class SensitiveAreaAPIGeojsonSerializer(GeoFeatureModelSerializer, SensitiveAreaAPISerializer):
# Annotated geom field with API_SRID
geom2d_transformed = rest_gis_fields.GeometryField(read_only=True, precision=7)
class Meta(SensitiveAreaAPISerializer.Meta):
geo_field = 'geom2d_transformed'
fields = SensitiveAreaAPISerializer.Meta.fields + ('geom2d_transformed', )
|
normal
|
{
"blob_id": "dfd5915428dc8f15fb61c5d81f22dfecfe29af15",
"index": 6409,
"step-1": "<mask token>\n\n\nclass SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):\n <mask token>\n <mask token>\n <mask token>\n\n\n class Meta:\n model = sensitivity_models.Species\n fields = ['id', 'name', 'practices', 'url', 'pictogram', 'period']\n\n\nclass SensitiveAreaSerializer(DynamicFieldsMixin, rest_serializers.\n ModelSerializer):\n category = rest_serializers.CharField(source='category_display')\n structure = rest_serializers.SlugRelatedField('name', read_only=True)\n species = rest_serializers.CharField(source='species_display')\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = '__all__'\n\n\nclass SensitiveAreaGeojsonSerializer(MapentityGeojsonModelSerializer):\n radius = rest_serializers.IntegerField()\n\n\n class Meta(MapentityGeojsonModelSerializer.Meta):\n model = sensitivity_models.SensitiveArea\n fields = ['id', 'species', 'radius', 'published']\n\n\nclass SensitiveAreaAPISerializer(TranslatedModelSerializer):\n species = SpeciesSerializer()\n kml_url = rest_serializers.SerializerMethodField()\n attachments = AttachmentSerializer(many=True)\n rules = RuleSerializer(many=True)\n\n def get_kml_url(self, obj):\n return reverse('sensitivity:sensitivearea_kml_detail', kwargs={\n 'lang': get_language(), 'pk': obj.pk})\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = ('id', 'species', 'description', 'contact', 'published',\n 'publication_date', 'kml_url', 'attachments', 'rules')\n\n\nclass SensitiveAreaAPIGeojsonSerializer(GeoFeatureModelSerializer,\n SensitiveAreaAPISerializer):\n geom2d_transformed = rest_gis_fields.GeometryField(read_only=True,\n precision=7)\n\n\n class Meta(SensitiveAreaAPISerializer.Meta):\n geo_field = 'geom2d_transformed'\n fields = SensitiveAreaAPISerializer.Meta.fields + ('geom2d_transformed'\n ,)\n",
"step-2": "<mask token>\n\n\nclass SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):\n <mask token>\n <mask token>\n\n def get_period(self, obj):\n return [getattr(obj, 'period{:02}'.format(p)) for p in range(1, 13)]\n\n\n class Meta:\n model = sensitivity_models.Species\n fields = ['id', 'name', 'practices', 'url', 'pictogram', 'period']\n\n\nclass SensitiveAreaSerializer(DynamicFieldsMixin, rest_serializers.\n ModelSerializer):\n category = rest_serializers.CharField(source='category_display')\n structure = rest_serializers.SlugRelatedField('name', read_only=True)\n species = rest_serializers.CharField(source='species_display')\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = '__all__'\n\n\nclass SensitiveAreaGeojsonSerializer(MapentityGeojsonModelSerializer):\n radius = rest_serializers.IntegerField()\n\n\n class Meta(MapentityGeojsonModelSerializer.Meta):\n model = sensitivity_models.SensitiveArea\n fields = ['id', 'species', 'radius', 'published']\n\n\nclass SensitiveAreaAPISerializer(TranslatedModelSerializer):\n species = SpeciesSerializer()\n kml_url = rest_serializers.SerializerMethodField()\n attachments = AttachmentSerializer(many=True)\n rules = RuleSerializer(many=True)\n\n def get_kml_url(self, obj):\n return reverse('sensitivity:sensitivearea_kml_detail', kwargs={\n 'lang': get_language(), 'pk': obj.pk})\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = ('id', 'species', 'description', 'contact', 'published',\n 'publication_date', 'kml_url', 'attachments', 'rules')\n\n\nclass SensitiveAreaAPIGeojsonSerializer(GeoFeatureModelSerializer,\n SensitiveAreaAPISerializer):\n geom2d_transformed = rest_gis_fields.GeometryField(read_only=True,\n precision=7)\n\n\n class Meta(SensitiveAreaAPISerializer.Meta):\n geo_field = 'geom2d_transformed'\n fields = SensitiveAreaAPISerializer.Meta.fields + ('geom2d_transformed'\n ,)\n",
"step-3": "<mask token>\n\n\nclass SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):\n practices = SportPracticeSerializer(many=True)\n period = rest_serializers.SerializerMethodField()\n\n def get_period(self, obj):\n return [getattr(obj, 'period{:02}'.format(p)) for p in range(1, 13)]\n\n\n class Meta:\n model = sensitivity_models.Species\n fields = ['id', 'name', 'practices', 'url', 'pictogram', 'period']\n\n\nclass SensitiveAreaSerializer(DynamicFieldsMixin, rest_serializers.\n ModelSerializer):\n category = rest_serializers.CharField(source='category_display')\n structure = rest_serializers.SlugRelatedField('name', read_only=True)\n species = rest_serializers.CharField(source='species_display')\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = '__all__'\n\n\nclass SensitiveAreaGeojsonSerializer(MapentityGeojsonModelSerializer):\n radius = rest_serializers.IntegerField()\n\n\n class Meta(MapentityGeojsonModelSerializer.Meta):\n model = sensitivity_models.SensitiveArea\n fields = ['id', 'species', 'radius', 'published']\n\n\nclass SensitiveAreaAPISerializer(TranslatedModelSerializer):\n species = SpeciesSerializer()\n kml_url = rest_serializers.SerializerMethodField()\n attachments = AttachmentSerializer(many=True)\n rules = RuleSerializer(many=True)\n\n def get_kml_url(self, obj):\n return reverse('sensitivity:sensitivearea_kml_detail', kwargs={\n 'lang': get_language(), 'pk': obj.pk})\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = ('id', 'species', 'description', 'contact', 'published',\n 'publication_date', 'kml_url', 'attachments', 'rules')\n\n\nclass SensitiveAreaAPIGeojsonSerializer(GeoFeatureModelSerializer,\n SensitiveAreaAPISerializer):\n geom2d_transformed = rest_gis_fields.GeometryField(read_only=True,\n precision=7)\n\n\n class Meta(SensitiveAreaAPISerializer.Meta):\n geo_field = 'geom2d_transformed'\n fields = SensitiveAreaAPISerializer.Meta.fields + ('geom2d_transformed'\n ,)\n",
"step-4": "<mask token>\n\n\nclass SportPracticeSerializer(TranslatedModelSerializer):\n\n\n class Meta:\n model = sensitivity_models.SportPractice\n fields = 'id', 'name'\n\n\nclass SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):\n practices = SportPracticeSerializer(many=True)\n period = rest_serializers.SerializerMethodField()\n\n def get_period(self, obj):\n return [getattr(obj, 'period{:02}'.format(p)) for p in range(1, 13)]\n\n\n class Meta:\n model = sensitivity_models.Species\n fields = ['id', 'name', 'practices', 'url', 'pictogram', 'period']\n\n\nclass SensitiveAreaSerializer(DynamicFieldsMixin, rest_serializers.\n ModelSerializer):\n category = rest_serializers.CharField(source='category_display')\n structure = rest_serializers.SlugRelatedField('name', read_only=True)\n species = rest_serializers.CharField(source='species_display')\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = '__all__'\n\n\nclass SensitiveAreaGeojsonSerializer(MapentityGeojsonModelSerializer):\n radius = rest_serializers.IntegerField()\n\n\n class Meta(MapentityGeojsonModelSerializer.Meta):\n model = sensitivity_models.SensitiveArea\n fields = ['id', 'species', 'radius', 'published']\n\n\nclass SensitiveAreaAPISerializer(TranslatedModelSerializer):\n species = SpeciesSerializer()\n kml_url = rest_serializers.SerializerMethodField()\n attachments = AttachmentSerializer(many=True)\n rules = RuleSerializer(many=True)\n\n def get_kml_url(self, obj):\n return reverse('sensitivity:sensitivearea_kml_detail', kwargs={\n 'lang': get_language(), 'pk': obj.pk})\n\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = ('id', 'species', 'description', 'contact', 'published',\n 'publication_date', 'kml_url', 'attachments', 'rules')\n\n\nclass SensitiveAreaAPIGeojsonSerializer(GeoFeatureModelSerializer,\n SensitiveAreaAPISerializer):\n geom2d_transformed = rest_gis_fields.GeometryField(read_only=True,\n precision=7)\n\n\n class Meta(SensitiveAreaAPISerializer.Meta):\n geo_field = 'geom2d_transformed'\n fields = SensitiveAreaAPISerializer.Meta.fields + ('geom2d_transformed'\n ,)\n",
"step-5": "from django.urls import reverse\nfrom django.utils.translation import get_language\nfrom drf_dynamic_fields import DynamicFieldsMixin\nfrom geotrek.api.v2.serializers import AttachmentSerializer\nfrom mapentity.serializers import MapentityGeojsonModelSerializer\nfrom rest_framework import serializers as rest_serializers\nfrom rest_framework_gis import fields as rest_gis_fields\nfrom rest_framework_gis.serializers import GeoFeatureModelSerializer\n\nfrom geotrek.common.serializers import PictogramSerializerMixin, TranslatedModelSerializer\nfrom . import models as sensitivity_models\n\n\nclass RuleSerializer(PictogramSerializerMixin, rest_serializers.ModelSerializer):\n\n class Meta:\n model = sensitivity_models.Rule\n fields = ('id', 'code', 'name', 'pictogram', 'description', 'url')\n\n\nclass SportPracticeSerializer(TranslatedModelSerializer):\n class Meta:\n model = sensitivity_models.SportPractice\n fields = ('id', 'name')\n\n\nclass SpeciesSerializer(TranslatedModelSerializer, PictogramSerializerMixin):\n practices = SportPracticeSerializer(many=True)\n period = rest_serializers.SerializerMethodField()\n\n def get_period(self, obj):\n return [getattr(obj, 'period{:02}'.format(p)) for p in range(1, 13)]\n\n class Meta:\n model = sensitivity_models.Species\n fields = ['id', 'name', 'practices', 'url', 'pictogram', 'period']\n\n\nclass SensitiveAreaSerializer(DynamicFieldsMixin, rest_serializers.ModelSerializer):\n category = rest_serializers.CharField(source='category_display')\n structure = rest_serializers.SlugRelatedField('name', read_only=True)\n species = rest_serializers.CharField(source='species_display')\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = \"__all__\"\n\n\nclass SensitiveAreaGeojsonSerializer(MapentityGeojsonModelSerializer):\n radius = rest_serializers.IntegerField()\n\n class Meta(MapentityGeojsonModelSerializer.Meta):\n model = sensitivity_models.SensitiveArea\n fields = ['id', 'species', 'radius', 'published']\n\n\nclass SensitiveAreaAPISerializer(TranslatedModelSerializer):\n species = SpeciesSerializer()\n kml_url = rest_serializers.SerializerMethodField()\n attachments = AttachmentSerializer(many=True)\n rules = RuleSerializer(many=True)\n\n def get_kml_url(self, obj):\n return reverse('sensitivity:sensitivearea_kml_detail', kwargs={'lang': get_language(), 'pk': obj.pk})\n\n class Meta:\n model = sensitivity_models.SensitiveArea\n fields = ('id', 'species', 'description', 'contact', 'published', 'publication_date', 'kml_url', 'attachments', 'rules')\n\n\nclass SensitiveAreaAPIGeojsonSerializer(GeoFeatureModelSerializer, SensitiveAreaAPISerializer):\n # Annotated geom field with API_SRID\n geom2d_transformed = rest_gis_fields.GeometryField(read_only=True, precision=7)\n\n class Meta(SensitiveAreaAPISerializer.Meta):\n geo_field = 'geom2d_transformed'\n fields = SensitiveAreaAPISerializer.Meta.fields + ('geom2d_transformed', )\n",
"step-ids": [
10,
11,
12,
13,
16
]
}
|
[
10,
11,
12,
13,
16
] |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as tick
from statistics import mean
from tqdm import tqdm
import multiprocessing as mp
from . import model as dymod
class Filter:
"""誤ベクトル数の確認，誤ベクトル数によるフィルタリング処理"""
@classmethod
def get_incorrect_vector_example(cls, file_list, example_number):
"""含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数"""
incorrect_vector_list = []
try:
file_list = file_list[0:example_number]
except:
pass
for i, file in enumerate(tqdm(file_list)):
total_incorrect_vector = cls.get_total_incorrect_vector(file)
incorrect_vector_list.append(total_incorrect_vector)
return incorrect_vector_list
@classmethod
def get_incorrect_vector_all(cls, file_list):
"""含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する"""
incorrect_vector_list = []
for i, file in enumerate(tqdm(file_list)):
total_incorrect_vector = cls.get_total_incorrect_vector(file)
incorrect_vector_list.append(total_incorrect_vector)
return incorrect_vector_list
@classmethod
def show_incorrect_vector_example(cls, file_list, example_number):
"""含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数を表示する"""
incorrect_vector_list = []
try:
file_list = file_list[0:example_number]
except:
pass
for i, file in enumerate(tqdm(file_list)):
total_incorrect_vector = cls.get_total_incorrect_vector(file)
incorrect_vector_list.append(total_incorrect_vector)
incorrect_vector_mean = mean(incorrect_vector_list)
# plot
plt.title('incorrect vector NO. of first {} data'.format(example_number))
plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)
plt.axhline(incorrect_vector_mean, color='black')
plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(incorrect_vector_mean))
plt.gca().yaxis.set_minor_locator(tick.MultipleLocator(100))
plt.grid(which='minor')
plt.show()
@classmethod
def show_incorrect_vector_all(cls, file_list):
"""含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する"""
incorrect_vector_list = []
for i, file in enumerate(tqdm(file_list)):
total_incorrect_vector = cls.get_total_incorrect_vector(file)
incorrect_vector_list.append(total_incorrect_vector)
incorrect_vector_mean = mean(incorrect_vector_list)
# plot
plt.title('incorrect vector NO. of all data')
plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)
plt.axhline(incorrect_vector_mean, color='black')
plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(incorrect_vector_mean))
plt.grid()
plt.show()
@staticmethod
def filter_incorrect_vector(file_list, filter_value):
"""ファイル名のリストから，誤ベクトル数がfilter_value以上のファイルの名前を除外する"""
before = len(file_list)
print('Filtering...')
total_core = mp.cpu_count()
pool = mp.Pool(total_core)
args = [(file_list, total_core, i, filter_value) for i in range(total_core)]
callback = pool.map(parallel_task, args)
error_index_list = []
for each_error_index_list in callback:
for error_index in each_error_index_list:
error_index_list.append(error_index)
error_index_list.sort(reverse=True)
for error_index in error_index_list:
del file_list[error_index]
after = len(file_list)
print('Finish!\nFiltered data:', str(before - after) + '/' + str(before))
return file_list
@staticmethod
def get_total_incorrect_vector(file):
"""瞬時データに含まれる誤ベクトルの数を返す"""
data = dymod.InstantData(file)
status = data.get_data('Status')
return np.sum((status == 1) | (status == 17))
def parallel_task(args):
"""並列計算タスク"""
file_list, total_core, current_core, filter_value = args
file_count = len(file_list)
start = int(file_count * current_core / total_core)
end = int(file_count * (current_core + 1) / total_core) - 1
header = dymod.InstantData.get_header_row(file_list[0])
error_file_index_list = []
text = 'filtering task ' + str(current_core + 1) + '/' + str(total_core)
for i in tqdm(range(start, end), desc=text):
status = pd.read_csv(file_list[i], header=header)['Status']
if np.sum((status == 1) | (status == 17)) >= filter_value:
error_file_index_list.append(i)
return error_file_index_list
filtering = Filter()
|
normal
|
{
"blob_id": "5d4585dc96d4ebdbc15b7382038cfea959c9a6f3",
"index": 2495,
"step-1": "<mask token>\n\n\nclass Filter:\n <mask token>\n\n @classmethod\n def get_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n <mask token>\n\n @classmethod\n def show_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of first {} data'.format(\n example_number))\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.gca().yaxis.set_minor_locator(tick.MultipleLocator(100))\n plt.grid(which='minor')\n plt.show()\n\n @classmethod\n def show_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of all data')\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.grid()\n plt.show()\n <mask token>\n\n @staticmethod\n def get_total_incorrect_vector(file):\n \"\"\"瞬時データに含まれる誤ベクトルの数を返す\"\"\"\n data = dymod.InstantData(file)\n status = data.get_data('Status')\n return np.sum((status == 1) | (status == 17))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Filter:\n \"\"\"誤ベクトル数の確認，誤ベクトル数によるフィルタリング処理\"\"\"\n\n @classmethod\n def get_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def get_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def show_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of first {} data'.format(\n example_number))\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.gca().yaxis.set_minor_locator(tick.MultipleLocator(100))\n plt.grid(which='minor')\n plt.show()\n\n @classmethod\n def show_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of all data')\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.grid()\n plt.show()\n\n @staticmethod\n def filter_incorrect_vector(file_list, filter_value):\n \"\"\"ファイル名のリストから，誤ベクトル数がfilter_value以上のファイルの名前を除外する\"\"\"\n before = len(file_list)\n print('Filtering...')\n total_core = mp.cpu_count()\n pool = mp.Pool(total_core)\n args = [(file_list, total_core, i, filter_value) for i in range(\n total_core)]\n callback = pool.map(parallel_task, args)\n error_index_list = []\n for each_error_index_list in callback:\n for error_index in each_error_index_list:\n error_index_list.append(error_index)\n error_index_list.sort(reverse=True)\n for error_index in error_index_list:\n del file_list[error_index]\n after = len(file_list)\n print('Finish!\\nFiltered data:', str(before - after) + '/' + str(\n before))\n return file_list\n\n @staticmethod\n def get_total_incorrect_vector(file):\n \"\"\"瞬時データに含まれる誤ベクトルの数を返す\"\"\"\n data = dymod.InstantData(file)\n status = data.get_data('Status')\n return np.sum((status == 1) | (status == 17))\n\n\ndef parallel_task(args):\n \"\"\"並列計算タスク\"\"\"\n file_list, total_core, current_core, filter_value = args\n file_count = len(file_list)\n start = int(file_count * current_core / total_core)\n end = int(file_count * (current_core + 1) / total_core) - 1\n header = dymod.InstantData.get_header_row(file_list[0])\n error_file_index_list = []\n text = 'filtering task ' + str(current_core + 1) + '/' + str(total_core)\n for i in tqdm(range(start, end), desc=text):\n status = pd.read_csv(file_list[i], header=header)['Status']\n if np.sum((status == 1) | (status == 17)) >= filter_value:\n error_file_index_list.append(i)\n return error_file_index_list\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Filter:\n \"\"\"誤ベクトル数の確認，誤ベクトル数によるフィルタリング処理\"\"\"\n\n @classmethod\n def get_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def get_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def show_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of first {} data'.format(\n example_number))\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.gca().yaxis.set_minor_locator(tick.MultipleLocator(100))\n plt.grid(which='minor')\n plt.show()\n\n @classmethod\n def show_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of all data')\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.grid()\n plt.show()\n\n @staticmethod\n def filter_incorrect_vector(file_list, filter_value):\n \"\"\"ファイル名のリストから，誤ベクトル数がfilter_value以上のファイルの名前を除外する\"\"\"\n before = len(file_list)\n print('Filtering...')\n total_core = mp.cpu_count()\n pool = mp.Pool(total_core)\n args = [(file_list, total_core, i, filter_value) for i in range(\n total_core)]\n callback = pool.map(parallel_task, args)\n error_index_list = []\n for each_error_index_list in callback:\n for error_index in each_error_index_list:\n error_index_list.append(error_index)\n error_index_list.sort(reverse=True)\n for error_index in error_index_list:\n del file_list[error_index]\n after = len(file_list)\n print('Finish!\\nFiltered data:', str(before - after) + '/' + str(\n before))\n return file_list\n\n @staticmethod\n def get_total_incorrect_vector(file):\n \"\"\"瞬時データに含まれる誤ベクトルの数を返す\"\"\"\n data = dymod.InstantData(file)\n status = data.get_data('Status')\n return np.sum((status == 1) | (status == 17))\n\n\ndef parallel_task(args):\n \"\"\"並列計算タスク\"\"\"\n file_list, total_core, current_core, filter_value = args\n file_count = len(file_list)\n start = int(file_count * current_core / total_core)\n end = int(file_count * (current_core + 1) / total_core) - 1\n header = dymod.InstantData.get_header_row(file_list[0])\n error_file_index_list = []\n text = 'filtering task ' + str(current_core + 1) + '/' + str(total_core)\n for i in tqdm(range(start, end), desc=text):\n status = pd.read_csv(file_list[i], header=header)['Status']\n if np.sum((status == 1) | (status == 17)) >= filter_value:\n error_file_index_list.append(i)\n return error_file_index_list\n\n\nfiltering = Filter()\n",
"step-4": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as tick\nfrom statistics import mean\nfrom tqdm import tqdm\nimport multiprocessing as mp\nfrom . import model as dymod\n\n\nclass Filter:\n \"\"\"誤ベクトル数の確認，誤ベクトル数によるフィルタリング処理\"\"\"\n\n @classmethod\n def get_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def get_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def show_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of first {} data'.format(\n example_number))\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.gca().yaxis.set_minor_locator(tick.MultipleLocator(100))\n plt.grid(which='minor')\n plt.show()\n\n @classmethod\n def show_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n plt.title('incorrect vector NO. of all data')\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(\n incorrect_vector_mean))\n plt.grid()\n plt.show()\n\n @staticmethod\n def filter_incorrect_vector(file_list, filter_value):\n \"\"\"ファイル名のリストから，誤ベクトル数がfilter_value以上のファイルの名前を除外する\"\"\"\n before = len(file_list)\n print('Filtering...')\n total_core = mp.cpu_count()\n pool = mp.Pool(total_core)\n args = [(file_list, total_core, i, filter_value) for i in range(\n total_core)]\n callback = pool.map(parallel_task, args)\n error_index_list = []\n for each_error_index_list in callback:\n for error_index in each_error_index_list:\n error_index_list.append(error_index)\n error_index_list.sort(reverse=True)\n for error_index in error_index_list:\n del file_list[error_index]\n after = len(file_list)\n print('Finish!\\nFiltered data:', str(before - after) + '/' + str(\n before))\n return file_list\n\n @staticmethod\n def get_total_incorrect_vector(file):\n \"\"\"瞬時データに含まれる誤ベクトルの数を返す\"\"\"\n data = dymod.InstantData(file)\n status = data.get_data('Status')\n return np.sum((status == 1) | (status == 17))\n\n\ndef parallel_task(args):\n \"\"\"並列計算タスク\"\"\"\n file_list, total_core, current_core, filter_value = args\n file_count = len(file_list)\n start = int(file_count * current_core / total_core)\n end = int(file_count * (current_core + 1) / total_core) - 1\n header = dymod.InstantData.get_header_row(file_list[0])\n error_file_index_list = []\n text = 'filtering task ' + str(current_core + 1) + '/' + str(total_core)\n for i in tqdm(range(start, end), desc=text):\n status = pd.read_csv(file_list[i], header=header)['Status']\n if np.sum((status == 1) | (status == 17)) >= filter_value:\n error_file_index_list.append(i)\n return error_file_index_list\n\n\nfiltering = Filter()\n",
"step-5": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as tick\nfrom statistics import mean\nfrom tqdm import tqdm\nimport multiprocessing as mp\n\nfrom . import model as dymod\n\n\nclass Filter:\n \"\"\"誤ベクトル数の確認，誤ベクトル数によるフィルタリング処理\"\"\"\n\n @classmethod\n def get_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def get_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n return incorrect_vector_list\n\n @classmethod\n def show_incorrect_vector_example(cls, file_list, example_number):\n \"\"\"含まれる瞬時データの内指定した個数のデータがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n try:\n file_list = file_list[0:example_number]\n except:\n pass\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n\n # plot\n plt.title('incorrect vector NO. of first {} data'.format(example_number))\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(incorrect_vector_mean))\n plt.gca().yaxis.set_minor_locator(tick.MultipleLocator(100))\n plt.grid(which='minor')\n plt.show()\n\n @classmethod\n def show_incorrect_vector_all(cls, file_list):\n \"\"\"含まれる瞬時データ全てがそれぞれ持つ誤ベクトル数を表示する\"\"\"\n incorrect_vector_list = []\n for i, file in enumerate(tqdm(file_list)):\n total_incorrect_vector = cls.get_total_incorrect_vector(file)\n incorrect_vector_list.append(total_incorrect_vector)\n incorrect_vector_mean = mean(incorrect_vector_list)\n\n # plot\n plt.title('incorrect vector NO. of all data')\n plt.scatter(range(len(incorrect_vector_list)), incorrect_vector_list)\n plt.axhline(incorrect_vector_mean, color='black')\n plt.text(0, incorrect_vector_mean + 50, 'mean value = ' + str(incorrect_vector_mean))\n plt.grid()\n plt.show()\n\n @staticmethod\n def filter_incorrect_vector(file_list, filter_value):\n \"\"\"ファイル名のリストから，誤ベクトル数がfilter_value以上のファイルの名前を除外する\"\"\"\n before = len(file_list)\n print('Filtering...')\n total_core = mp.cpu_count()\n pool = mp.Pool(total_core)\n args = [(file_list, total_core, i, filter_value) for i in range(total_core)]\n callback = pool.map(parallel_task, args)\n error_index_list = []\n for each_error_index_list in callback:\n for error_index in each_error_index_list:\n error_index_list.append(error_index)\n error_index_list.sort(reverse=True)\n for error_index in error_index_list:\n del file_list[error_index]\n after = len(file_list)\n print('Finish!\\nFiltered data:', str(before - after) + '/' + str(before))\n return file_list\n\n @staticmethod\n def get_total_incorrect_vector(file):\n \"\"\"瞬時データに含まれる誤ベクトルの数を返す\"\"\"\n data = dymod.InstantData(file)\n status = data.get_data('Status')\n return np.sum((status == 1) | (status == 17))\n\n\ndef parallel_task(args):\n \"\"\"並列計算タスク\"\"\"\n file_list, total_core, current_core, filter_value = args\n file_count = len(file_list)\n start = int(file_count * current_core / total_core)\n end = int(file_count * (current_core + 1) / total_core) - 1\n header = dymod.InstantData.get_header_row(file_list[0])\n error_file_index_list = []\n text = 'filtering task ' + str(current_core + 1) + '/' + str(total_core)\n for i in tqdm(range(start, end), desc=text):\n status = pd.read_csv(file_list[i], header=header)['Status']\n if np.sum((status == 1) | (status == 17)) >= filter_value:\n error_file_index_list.append(i)\n return error_file_index_list\n\n\nfiltering = Filter()\n",
"step-ids": [
5,
9,
10,
11,
12
]
}
|
[
5,
9,
10,
11,
12
] |
<|reserved_special_token_0|>
def set_turn_time(time):
def next_turn(screen):
global stop
stop = False
tasks.append(Task(next_turn, time))
def add_attack(func):
attacks.append(func)
return func
<|reserved_special_token_0|>
def set_screen_angle(angle):
global screen_angle
screen_angle = angle
<|reserved_special_token_0|>
@add_attack
def yinchang_1():
global BOX_POS, BOX_SIZE
BOX_POS = [230, 230]
BOX_SIZE = [170, 160]
if DEBUG:
pass
sans.say('准备好了？')
<|reserved_special_token_0|>
@add_attack
def first_round2():
set_turn_time(50)
sans.hand_direction = LEFT
player.type = BLUE_SOUL
player.direction = LEFT
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -
BOX_POS[0]) // 10))
for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],
direction=LEFT, time1=8, time2=30, length=0, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],
direction=LEFT, time1=150, time2=38, length=40, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],
direction=LEFT, time1=8, time2=188, length=40, type_=2))
@add_attack
def first_round3():
set_turn_time(450)
player.type = RED_SOUL
for _ in range(0, 300, 2):
bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,
direction=UP, speed=[7, 0], time1=1000, time2=_))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) *
40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,
time2=_))
@add_attack
def first_round4():
sans.headtype = SANS_LOOK_LEFT
sans.say('只是第一个回合而已，何必用尽全力？')
<|reserved_special_token_0|>
@add_attack
def blue_bone():
set_turn_time(700)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 250]
BOX_SIZE = [350, 120]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE
[1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 -
8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=1))
bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=
1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))
<|reserved_special_token_0|>
@add_attack
def bone_gap():
set_turn_time(1000)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 230]
BOX_SIZE = [300, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color
=BLUE))
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=
DOWN, color=BLUE))
tasks.append(Task(shake, _ * 100 + 10))
tasks.append(Task(unshake, _ * 100 + 15))
tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))
y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,
color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=
RIGHT, color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -
BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,
speed=[(x - BOX_POS[0]) / 30, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],
length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *
100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],
type_=2))
bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=
DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /
30, 0], type_=1))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=
1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(
(BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))
<|reserved_special_token_0|>
@add_attack
def board_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
<|reserved_special_token_0|>
@add_attack
def board_2_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
<|reserved_special_token_0|>
@add_attack
def bone_lid3():
set_turn_time(1300)
player.type = RED_SOUL
for _ in range(20):
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1
=1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1
] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=
[0, -2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,
speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=
260, angle=-45, speed=[0, -2, 0, 0]))
<|reserved_special_token_0|>
@add_attack
def mercy2():
sans.say('这也是一个改过自新的机会，')
@add_attack
def mercy3():
sans.say('赶紧按下饶恕，')
<|reserved_special_token_0|>
@add_attack
def mercy5():
set_turn_time(0)
sans.headtype = SANS_NORMAL
<|reserved_special_token_0|>
def flash_round_4():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],
angle=45, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +
BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def set_turn_time(time):
def next_turn(screen):
global stop
stop = False
tasks.append(Task(next_turn, time))
def add_attack(func):
attacks.append(func)
return func
def shake(screen):
global screen_shaking
screen_shaking = True
<|reserved_special_token_0|>
def set_screen_angle(angle):
global screen_angle
screen_angle = angle
<|reserved_special_token_0|>
@add_attack
def yinchang_1():
global BOX_POS, BOX_SIZE
BOX_POS = [230, 230]
BOX_SIZE = [170, 160]
if DEBUG:
pass
sans.say('准备好了？')
@add_attack
def first_round1():
set_turn_time(50)
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7], speed=[0,
-5], direction=UP, time1=8, time2=40, length=1000, type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,
0], direction=UP, time1=200, time2=48, length=1000, type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,
5], direction=UP, time1=8, time2=248, length=1000, type_=1))
@add_attack
def first_round2():
set_turn_time(50)
sans.hand_direction = LEFT
player.type = BLUE_SOUL
player.direction = LEFT
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -
BOX_POS[0]) // 10))
for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],
direction=LEFT, time1=8, time2=30, length=0, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],
direction=LEFT, time1=150, time2=38, length=40, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],
direction=LEFT, time1=8, time2=188, length=40, type_=2))
@add_attack
def first_round3():
set_turn_time(450)
player.type = RED_SOUL
for _ in range(0, 300, 2):
bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,
direction=UP, speed=[7, 0], time1=1000, time2=_))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) *
40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,
time2=_))
@add_attack
def first_round4():
sans.headtype = SANS_LOOK_LEFT
sans.say('只是第一个回合而已，何必用尽全力？')
@add_attack
def first_round5():
set_turn_time(1)
sans.headtype = SANS_NORMAL
pygame.mixer.music.play(-1)
<|reserved_special_token_0|>
@add_attack
def blue_bone():
set_turn_time(700)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 250]
BOX_SIZE = [350, 120]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE
[1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 -
8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=1))
bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=
1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))
<|reserved_special_token_0|>
@add_attack
def bone_gap():
set_turn_time(1000)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 230]
BOX_SIZE = [300, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color
=BLUE))
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=
DOWN, color=BLUE))
tasks.append(Task(shake, _ * 100 + 10))
tasks.append(Task(unshake, _ * 100 + 15))
tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))
y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,
color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=
RIGHT, color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -
BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,
speed=[(x - BOX_POS[0]) / 30, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],
length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *
100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],
type_=2))
bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=
DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /
30, 0], type_=1))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=
1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(
(BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))
<|reserved_special_token_0|>
@add_attack
def board_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
@add_attack
def board_2():
set_turn_time(600)
tasks.append(Task(shake, 70))
tasks.append(Task(unshake, 75))
blasters.append(GasterBlaster(pos=[10, BOX_POS[1] + BOX_SIZE[1]], angle
=0, time1=10, time2=70, time3=10, width=70))
blasters.append(GasterBlaster(pos=[10, BOX_POS[1]], angle=0, time1=10,
time2=70, time3=10, width=30))
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30], length=
1000, direction=UP, time1=1000, time2=100, speed=[0, 0], type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] - 8], length=5, direction=DOWN,
time1=1000, time2=100, speed=[0, 0], type_=2))
boards.append(Board(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 40],
length=40, speed=[1, 0], time1=BOX_SIZE[0], time2=100, direction=UP))
for _ in range(0, 20, 4):
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] +
BOX_SIZE[1] - 40 - 25], length=1000, direction=UP, time1=
BOX_SIZE[0] // 4, time2=150 + _ * 30, speed=[-4, 0]))
def start_spinning(screen):
global spinning_left
spinning_left = True
def stop_spinning(screen):
global spinning_left
spinning_left = False
tasks.append(Task(start_spinning, 200))
tasks.append(Task(stop_spinning, 380))
tasks.append(Task(start_spinning, 500))
tasks.append(Task(stop_spinning, 680))
tasks.append(Task(lambda screen: set_screen_angle(0), 682))
<|reserved_special_token_0|>
@add_attack
def board_4():
set_turn_time(0)
bones.clear()
<|reserved_special_token_0|>
@add_attack
def board_2_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
<|reserved_special_token_0|>
@add_attack
def bone_lid3():
set_turn_time(1300)
player.type = RED_SOUL
for _ in range(20):
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1
=1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1
] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=
[0, -2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,
speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=
260, angle=-45, speed=[0, -2, 0, 0]))
<|reserved_special_token_0|>
@add_attack
def mercy1():
pygame.mixer.music.pause()
sans.say('好了，我也累了，不如我们休息一下？')
@add_attack
def mercy2():
sans.say('这也是一个改过自新的机会，')
@add_attack
def mercy3():
sans.say('赶紧按下饶恕，')
<|reserved_special_token_0|>
@add_attack
def mercy5():
set_turn_time(0)
sans.headtype = SANS_NORMAL
<|reserved_special_token_0|>
@add_attack
def before_flash():
sans.say('好吧，看来你已经做出了自己的选择。')
<|reserved_special_token_0|>
def flash_round_3():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [200, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],
angle=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],
angle=0, time1=10, time2=70, time3=0, width=60))
def flash_round_4():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],
angle=45, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +
BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))
def flash_round_5():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,
time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle
=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + 50], angle=0, time1
=10, time2=70, time3=0, width=100))
def flash_round_6():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,
time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle
=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],
angle=0, time1=10, time2=70, time3=0, width=100))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def set_turn_time(time):
def next_turn(screen):
global stop
stop = False
tasks.append(Task(next_turn, time))
def add_attack(func):
attacks.append(func)
return func
def shake(screen):
global screen_shaking
screen_shaking = True
<|reserved_special_token_0|>
def set_screen_angle(angle):
global screen_angle
screen_angle = angle
<|reserved_special_token_0|>
@add_attack
def yinchang_1():
global BOX_POS, BOX_SIZE
BOX_POS = [230, 230]
BOX_SIZE = [170, 160]
if DEBUG:
pass
sans.say('准备好了？')
@add_attack
def first_round1():
set_turn_time(50)
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7], speed=[0,
-5], direction=UP, time1=8, time2=40, length=1000, type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,
0], direction=UP, time1=200, time2=48, length=1000, type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,
5], direction=UP, time1=8, time2=248, length=1000, type_=1))
@add_attack
def first_round2():
set_turn_time(50)
sans.hand_direction = LEFT
player.type = BLUE_SOUL
player.direction = LEFT
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -
BOX_POS[0]) // 10))
for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],
direction=LEFT, time1=8, time2=30, length=0, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],
direction=LEFT, time1=150, time2=38, length=40, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],
direction=LEFT, time1=8, time2=188, length=40, type_=2))
@add_attack
def first_round3():
set_turn_time(450)
player.type = RED_SOUL
for _ in range(0, 300, 2):
bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,
direction=UP, speed=[7, 0], time1=1000, time2=_))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) *
40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,
time2=_))
@add_attack
def first_round4():
sans.headtype = SANS_LOOK_LEFT
sans.say('只是第一个回合而已，何必用尽全力？')
@add_attack
def first_round5():
set_turn_time(1)
sans.headtype = SANS_NORMAL
pygame.mixer.music.play(-1)
<|reserved_special_token_0|>
@add_attack
def blue_bone():
set_turn_time(700)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 250]
BOX_SIZE = [350, 120]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE
[1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 -
8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=1))
bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=
1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))
<|reserved_special_token_0|>
@add_attack
def bone_gap():
set_turn_time(1000)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 230]
BOX_SIZE = [300, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color
=BLUE))
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=
DOWN, color=BLUE))
tasks.append(Task(shake, _ * 100 + 10))
tasks.append(Task(unshake, _ * 100 + 15))
tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))
y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,
color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=
RIGHT, color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -
BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,
speed=[(x - BOX_POS[0]) / 30, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],
length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *
100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],
type_=2))
bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=
DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /
30, 0], type_=1))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=
1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(
(BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))
<|reserved_special_token_0|>
@add_attack
def board_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
@add_attack
def board_2():
set_turn_time(600)
tasks.append(Task(shake, 70))
tasks.append(Task(unshake, 75))
blasters.append(GasterBlaster(pos=[10, BOX_POS[1] + BOX_SIZE[1]], angle
=0, time1=10, time2=70, time3=10, width=70))
blasters.append(GasterBlaster(pos=[10, BOX_POS[1]], angle=0, time1=10,
time2=70, time3=10, width=30))
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30], length=
1000, direction=UP, time1=1000, time2=100, speed=[0, 0], type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] - 8], length=5, direction=DOWN,
time1=1000, time2=100, speed=[0, 0], type_=2))
boards.append(Board(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 40],
length=40, speed=[1, 0], time1=BOX_SIZE[0], time2=100, direction=UP))
for _ in range(0, 20, 4):
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] +
BOX_SIZE[1] - 40 - 25], length=1000, direction=UP, time1=
BOX_SIZE[0] // 4, time2=150 + _ * 30, speed=[-4, 0]))
def start_spinning(screen):
global spinning_left
spinning_left = True
def stop_spinning(screen):
global spinning_left
spinning_left = False
tasks.append(Task(start_spinning, 200))
tasks.append(Task(stop_spinning, 380))
tasks.append(Task(start_spinning, 500))
tasks.append(Task(stop_spinning, 680))
tasks.append(Task(lambda screen: set_screen_angle(0), 682))
<|reserved_special_token_0|>
@add_attack
def board_4():
set_turn_time(0)
bones.clear()
<|reserved_special_token_0|>
@add_attack
def board_2_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
<|reserved_special_token_0|>
@add_attack
def bone_lid2():
set_turn_time(60)
sans.hand_direction = UP
player.type = BLUE_SOUL
player.direction = UP
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (player.pos[1] - BOX_POS[1]) // 10))
tasks.append(Task(unshake, (player.pos[1] - BOX_POS[1]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
bones.append(RotatableBone(pos=[BOX_POS[0] - 20, BOX_POS[1]], time1=
1000, length=130, angle=-45, speed=[5, 0, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[
1]], time1=1000, length=130, angle=45, speed=[-5, 0, 0, 0]))
@add_attack
def bone_lid3():
set_turn_time(1300)
player.type = RED_SOUL
for _ in range(20):
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1
=1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1
] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=
[0, -2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,
speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=
260, angle=-45, speed=[0, -2, 0, 0]))
<|reserved_special_token_0|>
@add_attack
def mercy1():
pygame.mixer.music.pause()
sans.say('好了，我也累了，不如我们休息一下？')
@add_attack
def mercy2():
sans.say('这也是一个改过自新的机会，')
@add_attack
def mercy3():
sans.say('赶紧按下饶恕，')
<|reserved_special_token_0|>
@add_attack
def mercy5():
set_turn_time(0)
sans.headtype = SANS_NORMAL
<|reserved_special_token_0|>
@add_attack
def before_flash():
sans.say('好吧，看来你已经做出了自己的选择。')
<|reserved_special_token_0|>
def flash_round_2():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
def zjj(screen):
angle = random.randint(-140, -40)
d = random.randint(10, 200)
blasters.append(GasterBlaster(pos=[player.pos[0] + math.cos(math.
radians(angle)) * d, player.pos[1] + math.sin(math.radians(
angle)) * d], angle=angle - 180, time1=0, time2=20, width=50))
for _ in range(0, 50):
tasks.append(Task(zjj, _ / 2))
def flash_round_3():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [200, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],
angle=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],
angle=0, time1=10, time2=70, time3=0, width=60))
def flash_round_4():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],
angle=45, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +
BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))
def flash_round_5():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,
time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle
=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + 50], angle=0, time1
=10, time2=70, time3=0, width=100))
def flash_round_6():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,
time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle
=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],
angle=0, time1=10, time2=70, time3=0, width=100))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def set_turn_time(time):
def next_turn(screen):
global stop
stop = False
tasks.append(Task(next_turn, time))
def add_attack(func):
attacks.append(func)
return func
def shake(screen):
global screen_shaking
screen_shaking = True
<|reserved_special_token_0|>
def set_screen_angle(angle):
global screen_angle
screen_angle = angle
<|reserved_special_token_0|>
@add_attack
def yinchang_1():
global BOX_POS, BOX_SIZE
BOX_POS = [230, 230]
BOX_SIZE = [170, 160]
if DEBUG:
pass
sans.say('准备好了？')
@add_attack
def first_round1():
set_turn_time(50)
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7], speed=[0,
-5], direction=UP, time1=8, time2=40, length=1000, type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,
0], direction=UP, time1=200, time2=48, length=1000, type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,
5], direction=UP, time1=8, time2=248, length=1000, type_=1))
@add_attack
def first_round2():
set_turn_time(50)
sans.hand_direction = LEFT
player.type = BLUE_SOUL
player.direction = LEFT
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -
BOX_POS[0]) // 10))
for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],
direction=LEFT, time1=8, time2=30, length=0, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],
direction=LEFT, time1=150, time2=38, length=40, type_=2))
bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],
direction=LEFT, time1=8, time2=188, length=40, type_=2))
@add_attack
def first_round3():
set_turn_time(450)
player.type = RED_SOUL
for _ in range(0, 300, 2):
bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,
direction=UP, speed=[7, 0], time1=1000, time2=_))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) *
40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,
time2=_))
@add_attack
def first_round4():
sans.headtype = SANS_LOOK_LEFT
sans.say('只是第一个回合而已，何必用尽全力？')
@add_attack
def first_round5():
set_turn_time(1)
sans.headtype = SANS_NORMAL
pygame.mixer.music.play(-1)
<|reserved_special_token_0|>
@add_attack
def zjj_1():
set_turn_time(60)
global BOX_POS, BOX_SIZE
BOX_POS = [200, 230]
BOX_SIZE = [200, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
<|reserved_special_token_0|>
@add_attack
def blue_bone():
set_turn_time(700)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 250]
BOX_SIZE = [350, 120]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE
[1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 -
8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,
speed=[4, 0], type_=1))
bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=
1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))
@add_attack
def orange_bone():
def start_spinning(screen):
global spinning_left
spinning_left = True
def stop_spinning(screen):
global spinning_left
spinning_left = False
tasks.append(Task(start_spinning, 0))
tasks.append(Task(stop_spinning, 180))
tasks.append(Task(lambda screen: set_screen_angle(180), 181))
tasks.append(Task(start_spinning, 520))
tasks.append(Task(stop_spinning, 700))
tasks.append(Task(lambda screen: set_screen_angle(0), 701))
set_turn_time(700)
sans.hand_direction = UP
player.type = BLUE_SOUL
player.direction = UP
player.falling_speed = 10
tasks.append(Task(shake, (player.pos[1] - BOX_POS[1]) // 10))
tasks.append(Task(unshake, (player.pos[1] - BOX_POS[1]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=10,
direction=DOWN, time1=1000, time2=_ * 60 + 60, speed=[8, 0],
type_=2))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 30 + 16], length=
1000, direction=DOWN, time1=1000, time2=_ * 60 + 60, speed=[8,
0], type_=1))
bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=
1000, time2=_ * 60 + 60 + 8, speed=[8, 0], type_=1, color=ORANGE))
<|reserved_special_token_0|>
@add_attack
def bone_gap():
set_turn_time(1000)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 230]
BOX_SIZE = [300, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color
=BLUE))
bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=
DOWN, color=BLUE))
tasks.append(Task(shake, _ * 100 + 10))
tasks.append(Task(unshake, _ * 100 + 15))
tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))
y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,
speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,
color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=
RIGHT, color=ORANGE))
bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -
BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,
speed=[(x - BOX_POS[0]) / 30, 0], type_=2))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],
length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *
100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],
type_=2))
bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=
DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /
30, 0], type_=1))
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=
1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(
(BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))
<|reserved_special_token_0|>
@add_attack
def board_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
@add_attack
def board_2():
set_turn_time(600)
tasks.append(Task(shake, 70))
tasks.append(Task(unshake, 75))
blasters.append(GasterBlaster(pos=[10, BOX_POS[1] + BOX_SIZE[1]], angle
=0, time1=10, time2=70, time3=10, width=70))
blasters.append(GasterBlaster(pos=[10, BOX_POS[1]], angle=0, time1=10,
time2=70, time3=10, width=30))
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):
bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30], length=
1000, direction=UP, time1=1000, time2=100, speed=[0, 0], type_=1))
bones.append(Bone(pos=[x, BOX_POS[1] - 8], length=5, direction=DOWN,
time1=1000, time2=100, speed=[0, 0], type_=2))
boards.append(Board(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 40],
length=40, speed=[1, 0], time1=BOX_SIZE[0], time2=100, direction=UP))
for _ in range(0, 20, 4):
bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] +
BOX_SIZE[1] - 40 - 25], length=1000, direction=UP, time1=
BOX_SIZE[0] // 4, time2=150 + _ * 30, speed=[-4, 0]))
def start_spinning(screen):
global spinning_left
spinning_left = True
def stop_spinning(screen):
global spinning_left
spinning_left = False
tasks.append(Task(start_spinning, 200))
tasks.append(Task(stop_spinning, 380))
tasks.append(Task(start_spinning, 500))
tasks.append(Task(stop_spinning, 680))
tasks.append(Task(lambda screen: set_screen_angle(0), 682))
@add_attack
def board_3():
set_turn_time(100)
sans.hand_direction = LEFT
player.type = BLUE_SOUL
player.direction = LEFT
player.falling_speed = 10
tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -
BOX_POS[0]) // 10))
tasks.append(Task(shake, 60))
tasks.append(Task(unshake, 65))
blasters.append(GasterBlaster(pos=[BOX_POS[0], 10], angle=90, time1=10,
time2=50, time3=0, width=50))
@add_attack
def board_4():
set_turn_time(0)
bones.clear()
<|reserved_special_token_0|>
@add_attack
def board_2_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
<|reserved_special_token_0|>
@add_attack
def bone_lid1():
set_turn_time(70)
global BOX_SIZE, BOX_POS
BOX_POS = [200, 240]
BOX_SIZE = [200, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //
10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
bones.append(RotatableBone(pos=[BOX_POS[0] - 70, BOX_POS[1] + BOX_SIZE[
1]], time1=1000, length=130, angle=45, speed=[5, 0, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0] + 70, BOX_POS[
1] + BOX_SIZE[1]], time1=1000, length=130, angle=-45, speed=[-5, 0,
0, 0]))
@add_attack
def bone_lid2():
set_turn_time(60)
sans.hand_direction = UP
player.type = BLUE_SOUL
player.direction = UP
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake, (player.pos[1] - BOX_POS[1]) // 10))
tasks.append(Task(unshake, (player.pos[1] - BOX_POS[1]) // 10 + 5))
tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +
BOX_SIZE[1] - player.pos[1]) // 10))
bones.append(RotatableBone(pos=[BOX_POS[0] - 20, BOX_POS[1]], time1=
1000, length=130, angle=-45, speed=[5, 0, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[
1]], time1=1000, length=130, angle=45, speed=[-5, 0, 0, 0]))
@add_attack
def bone_lid3():
set_turn_time(1300)
player.type = RED_SOUL
for _ in range(20):
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1
=1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1
] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=
[0, -2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,
speed=[0, 2, 0, 0]))
bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1
] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=
260, angle=-45, speed=[0, -2, 0, 0]))
<|reserved_special_token_0|>
@add_attack
def mercy1():
pygame.mixer.music.pause()
sans.say('好了，我也累了，不如我们休息一下？')
@add_attack
def mercy2():
sans.say('这也是一个改过自新的机会，')
@add_attack
def mercy3():
sans.say('赶紧按下饶恕，')
<|reserved_special_token_0|>
@add_attack
def mercy5():
set_turn_time(0)
sans.headtype = SANS_NORMAL
<|reserved_special_token_0|>
@add_attack
def before_flash():
sans.say('好吧，看来你已经做出了自己的选择。')
<|reserved_special_token_0|>
def flash_round_2():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
def zjj(screen):
angle = random.randint(-140, -40)
d = random.randint(10, 200)
blasters.append(GasterBlaster(pos=[player.pos[0] + math.cos(math.
radians(angle)) * d, player.pos[1] + math.sin(math.radians(
angle)) * d], angle=angle - 180, time1=0, time2=20, width=50))
for _ in range(0, 50):
tasks.append(Task(zjj, _ / 2))
def flash_round_3():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [200, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],
angle=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],
angle=0, time1=10, time2=70, time3=0, width=60))
def flash_round_4():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],
angle=45, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +
BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))
def flash_round_5():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,
time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle
=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + 50], angle=0, time1
=10, time2=70, time3=0, width=100))
def flash_round_6():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,
time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle
=90, time1=10, time2=70, time3=0, width=60))
blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],
angle=0, time1=10, time2=70, time3=0, width=100))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
import pygame
import time as time_
import random
import os
from pygame.locals import *
from math import sin, cos, pi
from sys import exit
# ---------------------------
from unzip import *
unzip()
# ---------------------------
from others import *
from gaster_blaster import *
from board import *
from bone import *
from sans import *
from player import *
from functions import *
# ----------------------------------------------------------------
'''初始化'''
os.environ["SDL_VIDEO_WINDOW_POS"] = "100,100"
pygame.init()
if FULL_SCREEN:
display = pygame.display.set_mode((1920, 1080), FULLSCREEN)
else:
display = pygame.display.set_mode(SCREEN_SIZE)
screen = pygame.Surface(SCREEN_SIZE).convert_alpha()
mask_surface_blue = pygame.Surface(SCREEN_SIZE).convert_alpha() # 蓝色攻击的mask
mask_surface_orange = pygame.Surface(SCREEN_SIZE).convert_alpha() # 橙色攻击的mask
mask_surface_normal = pygame.Surface(SCREEN_SIZE).convert_alpha() # 普通攻击的mask
pygame.display.set_caption("UPPERTALE") #标题
pygame.display.set_icon(pygame.image.load("res/icon-32.png")) #图标
fps = pygame.time.Clock() # 帧数计时器
frames = 60
# -----------------------------------
'''因为需要修改全局变量
所以不得不写在主文件里的函数'''
def players_turn(text):
def tmp():
global is_players_turn, battle_text, shown_index
is_players_turn = True
battle_text = text
shown_index = 0
bones.clear()
blasters.clear()
boards.clear()
attacks.append(tmp)
def set_turn_time(time):
def next_turn(screen):
global stop
stop = False
tasks.append(Task(next_turn, time))
def add_attack(func):
attacks.append(func)
return func
def shake(screen):
global screen_shaking
screen_shaking = True
def unshake(screen):
global screen_shaking
screen_shaking = False
def set_screen_angle(angle):
global screen_angle
screen_angle = angle
def start_testing():
attacks.clear()
# -------------------------------------
'''回合'''
# 吟唱
@add_attack
def yinchang_1():
global BOX_POS, BOX_SIZE
BOX_POS = [230, 230]
BOX_SIZE = [170, 160]
if DEBUG:
# 测试区开始
pass
# 测试区结束
sans.say("准备好了？")
# 开头杀
@add_attack
def first_round1():
set_turn_time(50)
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake,
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake,
((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):
bones.append(
Bone(
pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7],
speed=[0, -5],
direction=UP,
time1=8,
time2=40,
length=1000,
type_=1
)
)
bones.append(
Bone(
pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47],
speed=[0, 0],
direction=UP,
time1=200,
time2=48,
length=1000,
type_=1
)
)
bones.append(
Bone(
pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47],
speed=[0, 5],
direction=UP,
time1=8,
time2=248,
length=1000,
type_=1
)
)
@add_attack
def first_round2():
set_turn_time(50)
sans.hand_direction = LEFT
player.type = BLUE_SOUL
player.direction = LEFT
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake,
(player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(unshake,
((player.pos[0] - BOX_POS[0]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(player.pos[0] - BOX_POS[0]) // 10))
for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):
bones.append(
Bone(
pos=[BOX_POS[0] - 7, y],
speed=[0, 0, 5],
direction=LEFT,
time1=8,
time2=30,
length=0,
type_=2
)
)
bones.append(
Bone(
pos=[BOX_POS[0] - 7, y],
speed=[0, 0, 0],
direction=LEFT,
time1=150,
time2=38,
length=40,
type_=2
)
)
bones.append(
Bone(
pos=[BOX_POS[0] - 7, y],
speed=[0, 0, -5],
direction=LEFT,
time1=8,
time2=188,
length=40,
type_=2
)
)
@add_attack
def first_round3():
set_turn_time(450)
player.type = RED_SOUL
for _ in range(0, 300, 2):
bones.append(
Bone(
pos=BOX_POS,
length=40 + sin(_ / 20) * 40,
direction=UP,
speed=[7, 0],
time1=1000,
time2=_,
)
)
bones.append(
Bone(
pos=[BOX_POS[0], BOX_POS[1] + 25 + (sin(_ / 20) * 40) + 60],
length=1000,
direction=UP,
speed=[7, 0],
time1=1000,
time2=_,
)
)
@add_attack
def first_round4():
sans.headtype = SANS_LOOK_LEFT
sans.say("只是第一个回合而已，何必用尽全力？")
@add_attack
def first_round5():
set_turn_time(1)
sans.headtype = SANS_NORMAL
pygame.mixer.music.play(-1)
players_turn("* ...")
@add_attack
def zjj_1():
set_turn_time(60)
global BOX_POS, BOX_SIZE
BOX_POS = [200, 230]
BOX_SIZE = [200, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake,
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake,
((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
@add_attack
def zjj_2():
set_turn_time(11 * 100)
def zjj(screen):
angle = random.randint(240, 300)
blasters.append(GasterBlaster(
pos=[
player.pos[0] + math.cos(math.radians(angle)) * 200,
player.pos[1] + math.sin(math.radians(angle)) * 200],
angle=angle - 180,
time1=10,
time2=30,
width=30,
color=BLUE
))
for _ in range(10):
tasks.append(Task(zjj, _ * 100))
bones.append(
Bone(
pos=[BOX_POS[0] - 20, BOX_POS[1] - 8],
length=BOX_SIZE[1] - 30 - 16,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[2, 0],
type_=2
))
bones.append(
Bone(
pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[1] - 8],
length=BOX_SIZE[1] - 30 - 16,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[-2, 0],
type_=2
))
bones.append(
Bone(
pos=[BOX_POS[0] - 20, BOX_POS[1] + BOX_SIZE[1] - 10 - 8],
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[2, 0],
type_=1
))
bones.append(
Bone(
pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[1] + BOX_SIZE[1] - 10 - 8],
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[-2, 0],
type_=1
))
players_turn("* ...")
@add_attack
def blue_bone():
set_turn_time(700)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 250]
BOX_SIZE = [350, 120]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake,
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake,
((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
bones.append(
Bone(
pos=[BOX_POS[0], BOX_POS[1] - 8],
length=BOX_SIZE[1] - 30 - 16,
direction=DOWN,
time1=1000,
time2=_ * 60 + 60,
speed=[4, 0],
type_=2
))
bones.append(
Bone(
pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 - 8],
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 60 + 60,
speed=[4, 0],
type_=1
))
bones.append(
Bone(
pos=BOX_POS,
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 60 + 60 + 16,
speed=[4, 0],
type_=1,
color=BLUE
))
@add_attack
def orange_bone():
def start_spinning(screen):
global spinning_left
spinning_left = True
def stop_spinning(screen):
global spinning_left
spinning_left = False
tasks.append(Task(start_spinning, 0))
tasks.append(Task(stop_spinning, 180))
tasks.append(Task(lambda screen:set_screen_angle(180), 181))
tasks.append(Task(start_spinning, 520))
tasks.append(Task(stop_spinning, 700))
tasks.append(Task(lambda screen:set_screen_angle(0), 701))
set_turn_time(700)
sans.hand_direction = UP
player.type = BLUE_SOUL
player.direction = UP
player.falling_speed = 10
tasks.append(Task(shake,
(player.pos[1] - BOX_POS[1]) // 10))
tasks.append(Task(unshake,
((player.pos[1] - BOX_POS[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
bones.append(
Bone(
pos=[BOX_POS[0], BOX_POS[1] - 8],
length=10,
direction=DOWN,
time1=1000,
time2=_ * 60 + 60,
speed=[8, 0],
type_=2
))
bones.append(
Bone(
pos=[BOX_POS[0], BOX_POS[1] + 30 + 16],
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 60 + 60,
speed=[8, 0],
type_=1
))
bones.append(
Bone(
pos=BOX_POS,
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 60 + 60 + 8,
speed=[8, 0],
type_=1,
color=ORANGE
))
players_turn("* ...")
@add_attack
def bone_gap():
set_turn_time(1000)
global BOX_POS, BOX_SIZE
BOX_POS = [150, 230]
BOX_SIZE = [300, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake,
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake,
((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
for _ in range(10):
x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)
bones.append(Bone(
pos=[x, BOX_POS[1]],
time1=10,
time2=_ * 100,
speed=[0, 0, BOX_SIZE[1] / 10],
length=0,
direction=DOWN,
color=BLUE
))
bones.append(Bone(
pos=[x, BOX_POS[1]],
time1=10,
time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[1] / 10],
length=BOX_SIZE[1],
direction=DOWN,
color=BLUE
))
tasks.append(Task(shake,_ * 100 + 10))
tasks.append(Task(unshake,_ * 100 + 15))
tasks.append(Task(lambda screen : slam_sound.play(),
_ * 100 + 15))
y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)
bones.append(Bone(
pos=[BOX_POS[0], y],
time1=10,
time2=_ * 100,
speed=[0, 0, BOX_SIZE[0] / 10],
length=0,
direction=RIGHT,
color=ORANGE
))
bones.append(Bone(
pos=[BOX_POS[0], y],
time1=10,
time2=_ * 100 + 10,
speed=[0, 0, -BOX_SIZE[0] / 10],
length=BOX_SIZE[0],
direction=RIGHT,
color=ORANGE
))
bones.append(
Bone(
pos=[BOX_POS[0], BOX_POS[1] - 8],
length=y - BOX_POS[1] - 16,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[(x - BOX_POS[0]) / 30, 0],
type_=2
))
bones.append(
Bone(
pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],
length=y - BOX_POS[1] - 16,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],
type_=2
))
bones.append(
Bone(
pos=[BOX_POS[0], y + 8],
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[(x - BOX_POS[0]) / 30, 0],
type_=1
))
bones.append(
Bone(
pos=[BOX_POS[0] + BOX_SIZE[0], y + 8],
length=1000,
direction=DOWN,
time1=1000,
time2=_ * 100 + 60,
speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],
type_=1
))
players_turn("* ...")
@add_attack
def board_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake,
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake,
((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
@add_attack
def board_2():
set_turn_time(600)
tasks.append(Task(shake, 70))
tasks.append(Task(unshake, 75))
blasters.append(
GasterBlaster(
pos=[10, BOX_POS[1] + BOX_SIZE[1]],
angle=0,
time1=10,
time2=70,
time3=10,
width=70
)
)
blasters.append(
GasterBlaster(
pos=[10, BOX_POS[1]],
angle=0,
time1=10,
time2=70,
time3=10,
width=30
)
)
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):
bones.append(
Bone(
pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30],
length=1000,
direction=UP,
time1=1000,
time2=100,
speed=[0, 0],
type_=1
)
)
bones.append(
Bone(
pos=[x, BOX_POS[1] - 8],
length=5,
direction=DOWN,
time1=1000,
time2=100,
speed=[0, 0],
type_=2
)
)
boards.append(
Board(
pos=[BOX_POS[0],BOX_POS[1] + BOX_SIZE[1] - 40],
length=40,
speed=[1, 0],
time1=BOX_SIZE[0],
time2=100,
direction=UP
)
)
for _ in range(0, 20, 4):
bones.append(
Bone(
pos=[BOX_POS[0] + BOX_SIZE[0],
BOX_POS[1] + BOX_SIZE[1] - 40 - 25],
length=1000,
direction=UP,
time1=BOX_SIZE[0] // 4,
time2=150 + (_ * 30),
speed=[-4, 0]
)
)
def start_spinning(screen):
global spinning_left
spinning_left = True
def stop_spinning(screen):
global spinning_left
spinning_left = False
tasks.append(Task(start_spinning, 200))
tasks.append(Task(stop_spinning, 380))
tasks.append(Task(start_spinning, 500))
tasks.append(Task(stop_spinning, 680))
tasks.append(Task(lambda screen:set_screen_angle(0), 682))
@add_attack
def board_3():
set_turn_time(100)
sans.hand_direction = LEFT
player.type = BLUE_SOUL
player.direction = LEFT
player.falling_speed = 10
tasks.append(Task(shake,
(player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(unshake,
((player.pos[0] - BOX_POS[0]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(player.pos[0] - BOX_POS[0]) // 10))
tasks.append(Task(shake, 60))
tasks.append(Task(unshake, 65))
blasters.append(
GasterBlaster(
pos=[BOX_POS[0], 10],
angle=90,
time1=10,
time2=50,
time3=0,
width=50
)
)
@add_attack
def board_4():
set_turn_time(0)
bones.clear()
players_turn("* ...")
@add_attack
def board_2_1():
set_turn_time(10)
global BOX_POS, BOX_SIZE
BOX_POS = [50, 240]
BOX_SIZE = [500, 140]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake,
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake,
((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
@add_attack
def board_2_2():
set_turn_time(600)
tasks.append(Task(shake, 70))
tasks.append(Task(unshake, 75))
blasters.append(
GasterBlaster(
pos=[10, BOX_POS[1] + BOX_SIZE[1]],
angle=0,
time1=10,
time2=70,
time3=10,
width=70
)
)
tasks.append(Task(shake, 250))
tasks.append(Task(unshake, 255))
blasters.append(
GasterBlaster(
pos=[10, BOX_POS[1] + BOX_SIZE[1] - 20],
angle=0,
time1=10,
time2=70,
time3=250,
width=70
)
)
boards.append(
Board(
pos=[BOX_POS[0] + BOX_SIZE[0],
BOX_POS[1] + BOX_SIZE[1] - 30 - 10],
time1=1000,
time2=0,
speed=[-2, 0],
length=40
)
)
boards.append(
Board(
pos=[BOX_POS[0] + BOX_SIZE[0],
BOX_POS[1] + BOX_SIZE[1] - 30 - 10],
time1=1000,
time2=100,
speed=[-1.5, 0],
length=40
)
)
boards.append(
Board(
pos=[BOX_POS[0] + BOX_SIZE[0],
BOX_POS[1] + BOX_SIZE[1] - 30 - 10],
time1=1000,
time2=200,
speed=[-1, 0],
length=40
)
)
boards.append(
Board(
pos=[BOX_POS[0] + BOX_SIZE[0],
BOX_POS[1] + BOX_SIZE[1] - 30 - 30],
time1=1000,
time2=300,
speed=[-3, 0],
length=80
)
)
for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):
bones.append(
Bone(
pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30],
length=1000,
direction=UP,
time1=400,
time2=100,
speed=[0, 0],
type_=1
)
)
bones.append(
Bone(
pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30],
length=1000,
direction=UP,
time1=1000,
time2=500,
speed=[0, 0],
type_=1
)
)
players_turn("* ...")
@add_attack
def bone_lid1():
set_turn_time(70)
global BOX_SIZE, BOX_POS
BOX_POS = [200, 240]
BOX_SIZE = [200, 150]
sans.hand_direction = DOWN
player.type = BLUE_SOUL
player.direction = DOWN
player.falling_speed = 10
tasks.append(Task(shake,
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
tasks.append(Task(unshake,
((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
bones.append(
RotatableBone(
pos=[BOX_POS[0] - 70, BOX_POS[1] + BOX_SIZE[1]],
time1=1000,
length=130,
angle=45,
speed=[5, 0, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0] + 70, BOX_POS[1] + BOX_SIZE[1]],
time1=1000,
length=130,
angle=-45,
speed=[-5, 0, 0, 0]
)
)
@add_attack
def bone_lid2():
set_turn_time(60)
sans.hand_direction = UP
player.type = BLUE_SOUL
player.direction = UP
player.falling_speed = 10
player.falling = True
tasks.append(Task(shake,
(player.pos[1] - BOX_POS[1]) // 10))
tasks.append(Task(unshake,
((player.pos[1] - BOX_POS[1]) // 10) + 5))
tasks.append(Task(lambda screen : slam_sound.play(),
(BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))
bones.append(
RotatableBone(
pos=[BOX_POS[0] - 20, BOX_POS[1]],
time1=1000,
length=130,
angle=-45,
speed=[5, 0, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[1]],
time1=1000,
length=130,
angle=45,
speed=[-5, 0, 0, 0]
)
)
@add_attack
def bone_lid3():
set_turn_time(1300)
player.type = RED_SOUL
for _ in range(20):
bones.append(
RotatableBone(
pos=[BOX_POS[0], BOX_POS[1] - 20],
time1=1000,
time2=_ * 60,
length=260,
angle=-45,
speed=[0, 2, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] + 20],
time1=1000,
time2=_ * 60,
length=260,
angle=45,
speed=[0, -2, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 20],
time1=1000,
time2=_ * 60 + 30,
length=260,
angle=45,
speed=[0, 2, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] + BOX_SIZE[1] + 20],
time1=1000,
time2=_ * 60 + 30,
length=260,
angle=-45,
speed=[0, -2, 0, 0]
)
)
players_turn("* ...")
@add_attack
def mercy1():
pygame.mixer.music.pause()
sans.say("好了，我也累了，不如我们休息一下？")
@add_attack
def mercy2():
sans.say("这也是一个改过自新的机会，")
@add_attack
def mercy3():
sans.say("赶紧按下饶恕，")
@add_attack
def mercy4():
sans.headtype = SANS_NO_EYES
sans.say("否则你绝对不想见到下一个回合")
@add_attack
def mercy5():
set_turn_time(0)
sans.headtype = SANS_NORMAL
players_turn("* ...")
@add_attack
def before_flash():
sans.say("好吧，看来你已经做出了自己的选择。")
@add_attack
def flash_round():
set_turn_time(10)
global blackout
flash_sound.play()
blackout = True
bones.clear()
blasters.clear()
boards.clear()
def flash(screen):
global blackout
blackout = False
flash_sound.play()
pygame.mixer.music.unpause()
tasks.append(Task(flash, 10))
def flash_round_1():
set_turn_time(150)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
player.type = BLUE_SOUL
player.direction = DOWN
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,
100000]
direction = random.randint(0, 1)
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] - 30, BOX_POS[1] + BOX_SIZE[1] - 30],
angle=0,
time1=0,
time2=30,
time3=10,
width=90
)
)
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] - 30, BOX_POS[1] - 30],
angle=0,
time1=0,
time2=30,
time3=60,
width=90
)
)
if direction:
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 30],
angle=90,
time1=0,
time2=30,
time3=10,
width=90
)
)
blasters.append(
GasterBlaster(
pos=[BOX_POS[0], BOX_POS[1] - 30],
angle=90,
time1=0,
time2=30,
time3=60,
width=90
)
)
else:
blasters.append(
GasterBlaster(
pos=[BOX_POS[0], BOX_POS[1] - 30],
angle=90,
time1=0,
time2=30,
time3=10,
width=90
)
)
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 30],
angle=90,
time1=0,
time2=30,
time3=60,
width=90
)
)
for angle in range(0, 360, 10):
bones.append(RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0] / 2 + cos(radians(angle)) * BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2 + 25 + sin(radians(angle)) * BOX_SIZE[1] / 2],
length=25,
angle=angle,
time1=150
)
)
if angle % 30 == 0:
bones.append(RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2 + 25],
length=40,
angle=angle,
speed=[0, 0, 0, 5],
time1=130,
time2=20
)
)
def flash_round_2():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2]
def zjj(screen):
angle = random.randint(-140, -40)
d = random.randint(10, 200)
blasters.append(GasterBlaster(
pos=[
player.pos[0] + math.cos(math.radians(angle)) * d,
player.pos[1] + math.sin(math.radians(angle)) * d],
angle=angle - 180,
time1=0,
time2=20,
width=50
))
for _ in range(0, 50):
tasks.append(Task(zjj, _ / 2))
def flash_round_3():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [200, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],
angle=90,
time1=10,
time2=70,
time3=0,
width=60
)
)
blasters.append(
GasterBlaster(
pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],
angle=0,
time1=10,
time2=70,
time3=0,
width=60
)
)
def flash_round_4():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],
angle=45,
time1=10,
time2=70,
time3=0,
width=60
)
)
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] - 10, BOX_POS[1] + BOX_SIZE[1] + 10],
angle=-45,
time1=10,
time2=70,
time3=0,
width=60
)
)
def flash_round_5():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(
GasterBlaster(
pos=[BOX_POS[0], 50],
angle=90,
time1=10,
time2=70,
time3=0,
width=60
)
)
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] + BOX_SIZE[0], 50],
angle=90,
time1=10,
time2=70,
time3=0,
width=60
)
)
blasters.append(
GasterBlaster(
pos=[50, BOX_POS[1] + 50],
angle=0,
time1=10,
time2=70,
time3=0,
width=100
)
)
def flash_round_6():
set_turn_time(100)
global _boxsize, _boxpos, BOX_POS, BOX_SIZE
BOX_SIZE = _boxsize = [150, 150]
BOX_POS = _boxpos = [230, 230]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2]
blasters.append(
GasterBlaster(
pos=[BOX_POS[0], 50],
angle=90,
time1=10,
time2=70,
time3=0,
width=60
)
)
blasters.append(
GasterBlaster(
pos=[BOX_POS[0] + BOX_SIZE[0], 50],
angle=90,
time1=10,
time2=70,
time3=0,
width=60
)
)
blasters.append(
GasterBlaster(
pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],
angle=0,
time1=10,
time2=70,
time3=0,
width=100
)
)
def flash_round_7():
set_turn_time(150)
global BOX_SIZE, BOX_POS, _boxpos, _boxsize
BOX_POS = _boxpos = [230, 230]
BOX_SIZE = _boxsize = [150, 150]
player.type = RED_SOUL
player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2]
for _ in range(3):
bones.append(
RotatableBone(
pos=[BOX_POS[0], BOX_POS[1] - 20],
time1=1000,
time2=_ * 50 + 20,
length=150,
angle=-20,
speed=[0, 4, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] + 20],
time1=1000,
time2=_ * 50 + 20,
length=150,
angle=20,
speed=[0, -4, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 20],
time1=1000,
time2=_ * 50 + 50,
length=150,
angle=20,
speed=[0, 4, 0, 0]
)
)
bones.append(
RotatableBone(
pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] + BOX_SIZE[1] + 20],
time1=1000,
time2=_ * 50 + 50,
length=150,
angle=-20,
speed=[0, -4, 0, 0]
)
)
random_attacks = [flash_round_1,
flash_round_2,
flash_round_3,
flash_round_4,
flash_round_5,
flash_round_6,
flash_round_7]
for _ in range(5):
attacks.append(random.choice(random_attacks))
attacks.append(flash_round)
players_turn("* ...")
@add_attack
def windmill():
set_turn_time(1200)
global BOX_POS, BOX_SIZE, before_strike, after_strike
def before_strike():
global sans_damage
sans_damage = 1
after_strike = lambda : ...
BOX_POS = [150, 240]
BOX_SIZE = [150, 150]
def movegb(screen):
for i in range(4):
blasters[i].angle += 1
blasters[i].end_angle += 1
blasters[i].radian += radians(-1)
blasters[i].back_speed = 0
for angle in range(360 * 5):
tasks.append(Task(movegb, angle * 0.4 + 100))
def enablerecoil(screen):
for b in blasters:
b.norecoil = False
tasks.append(Task(enablerecoil, 800))
for angle in range(0, 360, 90):
blasters.append(GasterBlaster(
pos=[150 + 150 / 2, 240 + 150 / 2],
angle=angle,
time1=10,
time2=1000,
width=30,
time3=0,
norecoil=True
))
players_turn("* ...")
@add_attack
def gameend():
...
# ------------------------------------
"""主程序"""
while True:
# ---------------------------------------------------------
'''实例化'''
from locals_ import *
time = 0
_boxpos = [0, 0]
_boxsize = SCREEN_SIZE[:]
rightdown = SCREEN_SIZE[:]
time1 = 0
time2 = 0
delta = 1
blasters = []
bones = []
tasks = []
warns = []
texts = []
boards = []
before_strike = None
after_strike = None
sans = Sans([280, 80])
player = Player([0, 0])
actions = {
"* check" : CHECK_SANS,
"* heal ({} time(s) left)" : HEAL_SANS
}
mc_actions = {
"* spare" : MERCY_SANS_SPARE,
"* flee" : MERCY_SANS_FLEE
}
pygame.mixer.music.stop()
if FULL_SCREEN:
display = pygame.display.set_mode((1920, 1080), FULLSCREEN)
else:
display = pygame.display.set_mode(SCREEN_SIZE)
while True:
time1 = time_.time()
# 屏幕震动
if screen_shaking:
screen_offset[0] = random.randint(-5, 5)
screen_offset[1] = random.randint(-5, 5)
else:
screen_offset = [0, 0]
# 屏幕旋转
if spinning_left:
screen_angle -= 1
# 屏幕旋转
if spinning_right:
screen_angle += 1
# 测试区
if DEBUG:...
# 战斗框位移
if _boxpos[0] != BOX_POS[0]:
if abs(BOX_POS[0] - _boxpos[0]) < 0.1:
_boxpos[0] = BOX_POS[0]
else:
_boxpos[0] += (BOX_POS[0] - _boxpos[0]) / 5
if _boxpos[1] != BOX_POS[1]:
if abs(BOX_POS[1] - _boxpos[1]) < 0.1:
_boxpos[1] = BOX_POS[1]
else:
_boxpos[1] += (BOX_POS[1] - _boxpos[1]) / 5
# 战斗框大小
if rightdown[0] != BOX_POS[0] + BOX_SIZE[0]:
if abs(BOX_POS[0] + BOX_SIZE[0] - rightdown[0]) < 0.1:
rightdown[0] = BOX_POS[0] + BOX_SIZE[0]
else:
rightdown[0] += (BOX_POS[0] + BOX_SIZE[0] - rightdown[0]) / 5
if rightdown[1] != BOX_POS[1] + BOX_SIZE[1]:
if abs(BOX_POS[1] + BOX_SIZE[1] - rightdown[1]) < 0.1:
rightdown[1] = BOX_POS[1] + BOX_SIZE[1]
else:
rightdown[1] += (BOX_POS[1] + BOX_SIZE[1] - rightdown[1]) / 5
_boxsize = [
rightdown[0] - _boxpos[0],
rightdown[1] - _boxpos[1]
]
if time >= len(attacks):
exit()
if not stop and not is_players_turn:
attacks[time]()
time += 1
stop = True
screen.fill((0, 0, 0, 255))
display.fill((0, 0, 0))
mask_surface_blue.fill((0, 0, 0, 0))
mask_surface_orange.fill((0, 0, 0, 0))
mask_surface_normal.fill((0, 0, 0, 0))
for event in pygame.event.get():
if event.type == QUIT:
pygame.quit()
exit()
if event.type == KEYDOWN:
if event.key == K_ESCAPE:
pygame.quit()
exit()
if event.key in (K_z, K_RETURN):
if sans.show_index >= len(sans.text) and sans.show_text == True:
sans.show_text = False
stop = False
elif page in (CHECK_SANS, HEAL_SANS, HEAL_SANS_CANT) and shown_index >= len(battle_text):
is_players_turn = False
stop = False
page = MAIN_PAGE
player.pos = [
BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2
]
player.select_sound.play()
else:
player.choose = is_players_turn
if is_players_turn and page != FIGHT_SANS:
player.select_sound.play()
if event.key in (K_x, K_RSHIFT):
sans.show_index = len(sans.text)
shown_index = len(battle_text)
player.back = True
player.choice = 0
if event.key == K_UP:
player.going_up = True
if event.key == K_DOWN:
player.going_down = True
if event.key == K_LEFT:
player.going_left = True
if event.key == K_RIGHT:
player.going_right = True
if event.key == K_F4:
if FULL_SCREEN:
display = pygame.display.set_mode(SCREEN_SIZE)
FULL_SCREEN = 0
else:
display = pygame.display.set_mode((1920, 1080), FULLSCREEN)
FULL_SCREEN = 1
if event.key == K_F2:
restarting = True
if DEBUG:
if event.key == K_n:
bones.clear()
boards.clear()
blasters.clear()
stop = False
if event.key == K_EQUALS:
frames += 1
if event.key == K_MINUS:
frames -= 1
if event.type == KEYUP:
if event.key == K_UP:
player.going_up = False
if event.key == K_DOWN:
player.going_down = False
if event.key == K_LEFT:
player.going_left = False
if event.key == K_RIGHT:
player.going_right = False
if event.key == K_ESCAPE:
pygame.quit()
exit()
if event.key in (K_z, K_RETURN):
player.choose = False
if event.key in (K_x, K_RSHIFT):
player.back = False
'''检测&更新'''
# 战斗框
pygame.draw.rect(screen, (255, 255, 255, 255), pygame.Rect((_boxpos[0] - 5, _boxpos[1] - 5),
(_boxsize[0] + 10, _boxsize[1] + 10)))
pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect(_boxpos, _boxsize)) # 内遮挡
# 骨头
for b in bones:
b.show(screen,
mask_surface_blue,
mask_surface_orange,
mask_surface_normal)
if b.stop:
bones.remove(b)
# 警告框
for w in warns:
w.show(screen)
if w.stop:
warns.remove(w)
# 板子
for b in boards:
b.show(screen)
if b.stop:
boards.remove(b)
if b.rect.colliderect(player.rect) and player.falling:
player.pos[0] += b.speed[0]
player.pos[1] += b.speed[1]
if player.direction == DOWN:
player.pos[1] = b.rect.top - 7
elif player.direction == UP:
player.pos[1] = b.rect.bottom - 1
elif player.direction == RIGHT:
player.pos[0] = b.rect.left - 7
elif player.direction == LEFT:
player.pos[0] = b.rect.right - 1
player.falling = False
"""外遮挡"""
pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((0, 0), (SCREEN_SIZE[0], _boxpos[1] - 5)))
pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((0, _boxpos[1] - 5), (_boxpos[0] - 5, _boxsize[1] + 10)))
pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((0, _boxpos[1] + _boxsize[1] + 5),
(SCREEN_SIZE[0], SCREEN_SIZE[1] - (_boxpos[1] + _boxsize[1]) - 5)))
pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((_boxpos[0] + _boxsize[0] + 5, _boxpos[1] - 5),
(SCREEN_SIZE[0] - (_boxpos[0] + _boxsize[0]) - 5, _boxsize[1] + 10)))
'''显示UI（外面）'''
pygame.draw.rect(screen, (191, 0, 0, 255), pygame.Rect((275, 400), (92, 20)))
if player.KR:
pygame.draw.rect(screen, (255, 0, 255, 255), pygame.Rect((275 + player.HP, 400), (round(player.KR), 20)))
pygame.draw.rect(screen, (255, 255, 0, 255), pygame.Rect((275, 400), (player.HP, 20)))
screen.blit(
font2.render(
"{:0>2.0f} / 92".format(player.HP + player.KR),
True,
(255, 255, 255) if not round(player.KR) else (255, 0, 255)
),
(
415,
400
)
)
screen.blit(hp_image, (240, 405))
screen.blit(kr_image, (375, 405))
screen.blit(
font2.render(
"Chara LV 19", True, (255, 255, 255)
), (30, 400)
)
# 显示文本
for text in texts:
screen.blit(
font.render(
text[1], True, (255, 255, 255)
), text[0]
)
if DEBUG:
screen.blit(
font2.render(
"DEBUG", True, (0, 0, 255)
), (200, 0)
)
# 显示帧数
screen.blit(
font2.render(
"FPS:{:0>3d}".format(round(1 / delta)), True, (0, 0, 255)
), (0, 0)
)
if fight:
screen.blit(fight_highlight_image, fight_pos)
else:
screen.blit(fight_default_image, fight_pos)
if act:
screen.blit(act_highlight_image, act_pos)
else:
screen.blit(act_default_image, act_pos)
if item:
screen.blit(item_highlight_image, item_pos)
else:
screen.blit(item_default_image, item_pos)
if mercy:
screen.blit(mercy_highlight_image, mercy_pos)
else:
screen.blit(mercy_default_image, mercy_pos)
# 鳝丝（要放在外面）
sans.show(screen)
if show_sans_damage:
if sans_damage == MISS:
screen.blit(miss_image, (250, 60))
# GB炮（要放在外面）
for t in blasters:
t.show(screen,
mask_surface_blue,
mask_surface_orange,
mask_surface_normal)
if t.stop:
blasters.remove(t)
# 其他东西，blahblahblah（外面）
for t in tasks:
t.show(screen)
if t.stop:
tasks.remove(t)
if is_players_turn: # 玩家回合
BOX_POS = [30, 250]
BOX_SIZE = [570, 130]
if page == MAIN_PAGE:
if shown_index < len(battle_text):
shown_index += 1
text_sound.play()
x = 40
y = 250
for char in battle_text[:shown_index]:
if char != '\n':
screen.blit(
battle_font.render(char, True, (255, 255, 255)),
(x, y)
)
x += 12
if x > BOX_POS[0] + BOX_SIZE[0] or char == "\n":
y += 16
x = 40
player.type = CURSOR_SOUL
player.options = (
(fight_pos[0] + 10, fight_pos[1] + 15),
( act_pos[0] + 10, act_pos[1] + 15),
( item_pos[0] + 10, item_pos[1] + 15),
(mercy_pos[0] + 10, mercy_pos[1] + 15)
)
if player.choice == 0:
fight = True
act = False
item = False
mercy = False
if player.choice == 1:
fight = False
act = True
item = False
mercy = False
if player.choice == 2:
fight = False
act = False
item = True
mercy = False
if player.choice == 3:
fight = False
act = False
item = False
mercy = True
if player.choose:
page = [FIGHT, ACT, 0, MERCY][player.choice]
player.choose = False
player.choice = 0
fight = False
act = False
item = False
mercy = False
if page == ACT:
player.options = [(40, 255)]
screen.blit(
battle_font.render("* sans", True, (255, 255, 255)),
(40, 250)
)
if player.choose:
page = [ACT_SANS][player.choice]
player.choose = False
player.choice = 0
if player.back:
page = MAIN_PAGE
if page == ACT_SANS:
player.options = []
y = 250
for _ in actions.keys():
if actions[_] == HEAL_SANS:
_ = _.format(heal_times_left)
screen.blit(
battle_font.render(_, True, (255, 255, 255)),
(40, y)
)
player.options.append((40, y + 5))
y += 20
if player.choose:
page = list(actions.values())[player.choice]
if page == HEAL_SANS:
if heal_times_left > 0:
heal(player, 92)
heal_times_left -= 1
else:
page = HEAL_SANS_CANT
player.choose = False
player.choice = 0
if player.back:
page = ACT
if page == CHECK_SANS:
player.type = RED_SOUL
player.pos = [
-100,
-100
]
battle_text = "* Sans\n The TRUE HERO.\n ATK:1\n DEF:1\n Nothing to say."
if shown_index < len(battle_text):
shown_index += 1
text_sound.play()
x = 40
y = 250
for char in battle_text[:shown_index]:
if char != '\n':
screen.blit(
battle_font.render(char, True, (255, 255, 255)),
(x, y)
)
x += 12
if x > BOX_POS[0] + BOX_SIZE[0] or char == "\n":
y += 20
x = 40
if page == HEAL_SANS:
player.type = RED_SOUL
player.pos = [
-100,
-100
]
battle_text = "* You are healthy again now.\n* {} time(s) left.".format(heal_times_left)
if shown_index < len(battle_text):
shown_index += 1
text_sound.play()
x = 40
y = 250
for char in battle_text[:shown_index]:
if char != '\n':
screen.blit(
battle_font.render(char, True, (255, 255, 255)),
(x, y)
)
x += 12
if x > BOX_POS[0] + BOX_SIZE[0] or char == "\n":
y += 20
x = 40
if page == HEAL_SANS_CANT:
player.type = RED_SOUL
player.pos = [
-100,
-100
]
battle_text = "* No more times for you to heal!"
if shown_index < len(battle_text):
shown_index += 1
text_sound.play()
x = 40
y = 250
for char in battle_text[:shown_index]:
if char != '\n':
screen.blit(
battle_font.render(char, True, (255, 255, 255)),
(x, y)
)
x += 12
if x > BOX_POS[0] + BOX_SIZE[0] or char == "\n":
y += 20
x = 40
if page == FIGHT:
player.options = [(40, 255)]
screen.blit(
battle_font.render("* sans", True, (255, 255, 255)),
(40, 250)
)
if player.choose:
page = [FIGHT_SANS][player.choice]
player.choose = False
player.choice = 0
choice_pos = [50, 250]
if player.back:
page = MAIN_PAGE
if page == FIGHT_SANS:
player.type = RED_SOUL
player.pos = [
-100,
-100
]
target_img.set_alpha(target_alpha)
if not choice_blink:
if target_alpha >= 255:
choice_going = True
else:
target_alpha += 10
screen.blit(target_img, [BOX_POS[0] + 10, BOX_POS[1] + 5])
screen.blit([choice_img, choice_blink_img][choice_ani_index // 5 % 2], choice_pos)
choice_ani_index += choice_blink
choice_pos[0] += choice_going * 8
if choice_going and (player.choose or choice_pos[0] > BOX_POS[0] + BOX_SIZE[0]):
choice_going = False
choice_blink = True
tasks.append(Strike(sans.pos[:]))
if not before_strike:
sans.target_pos = [100, 80]
else:
before_strike()
if choice_blink:
blink_time += 1
if blink_time > 60:
show_sans_damage = False
choice_going = False
choice_blink = False
choice_ani_index = 0
target_alpha = 0
blink_time = 0
is_players_turn = False
stop = False
page = MAIN_PAGE
if not after_strike:
sans.target_pos = [250, 80]
else:
after_strike()
player.pos = [
BOX_POS[0] + BOX_SIZE[0] / 2,
BOX_POS[1] + BOX_SIZE[1] / 2
]
elif blink_time > 30:
target_alpha -= 10
show_sans_damage = True
if page == MERCY:
player.options = [(40, 255)]
screen.blit(
battle_font.render("* sans", True, (255, 255, 255)),
(40, 250)
)
if player.choose:
page = [MERCY_SANS][player.choice]
player.choose = False
player.choice = 0
if player.back:
page = MAIN_PAGE
if page == MERCY_SANS:
player.options = []
y = 250
for _ in mc_actions.keys():
screen.blit(
battle_font.render(_, True, (255, 255, 255)),
(40, y)
)
player.options.append((40, y + 5))
y += 20
if player.choose:
page = list(mc_actions.values())[player.choice]
player.choose = False
player.choice = 0
if player.back:
page = MERCY
if page == MERCY_SANS_SPARE: # 你都饶恕了，想必也不想继续玩了（）
exit()
if page == MERCY_SANS_FLEE: # 你都逃跑了，想必也不想继续玩了（）
exit()
# 你死了
if player.HP + player.KR <= 0:
DEAD = True
if DEAD or restarting:
break
# 判定伤害
blue_mask = pygame.mask.from_surface(mask_surface_blue)
orange_mask = pygame.mask.from_surface(mask_surface_orange)
normal_mask = pygame.mask.from_surface(mask_surface_normal)
if mask_collide(blue_mask, player.mask, [0, 0], player.mask_pos):
if any([player.going_up, player.going_down, player.going_left, player.going_right, player.falling]):
damage(player)
if mask_collide(orange_mask, player.mask, [0, 0], player.mask_pos):
if not any([player.going_up, player.going_down, player.going_left, player.going_right, player.falling]):
damage(player)
if mask_collide(normal_mask, player.mask, [0, 0], player.mask_pos):
damage(player)
# 玩家
player.show(screen, _boxpos, _boxsize)
# 黑屏攻击
if blackout:
screen.fill(0x000000)
"""将screen的图像加工后放入display"""
if not FULL_SCREEN:
rotated_screen = pygame.transform.rotate(screen, screen_angle)
else:
screen_rect = screen.get_rect()
rotated_screen = pygame.transform.rotate(
pygame.transform.scale(
screen,
(
round(screen_rect.size[1] / screen_rect.size[0] * 1920),
1080
)
),
screen_angle
)
rotated_rect = rotated_screen.get_rect()
if not FULL_SCREEN:
rotated_rect.center = [SCREEN_SIZE[0] // 2, SCREEN_SIZE[1] // 2]
else:
rotated_rect.center = [960, 540]
display.blit(rotated_screen,
(rotated_rect.x + screen_offset[0],
rotated_rect.y + screen_offset[1]))
fps.tick(frames)
pygame.display.update()
time2 = time_.time()
delta = time2 - time1
if not restarting:
ticks = 0
heart_offset = [0, 0]
while True:
'''死后的'''
pygame.mixer.music.stop()
ticks += 1
screen.fill((0, 0, 0, 255))
if ticks >= 200:
break
if ticks >= 160:
screen.blit(alive_img, player.rect)
if ticks == 160:
split_sound.play()
elif ticks >= 100:
screen.blit(dead_img,
(player.rect.x + heart_offset[0],
player.rect.y + heart_offset[1]))
heart_offset = [random.randint(-2, 2), random.randint(-2, 2)]
elif ticks >= 60:
screen.blit(dead_img, player.rect)
if ticks == 60:
split_sound.play()
else:
screen.blit(alive_img, player.rect)
if not FULL_SCREEN:
rotated_screen = pygame.transform.rotate(screen, screen_angle)
else:
screen_rect = screen.get_rect()
rotated_screen = pygame.transform.rotate(
pygame.transform.scale(
screen,
(
round(screen_rect.size[1] / screen_rect.size[0] * 1920),
1080
)
),
screen_angle
)
rotated_rect = rotated_screen.get_rect()
if not FULL_SCREEN:
rotated_rect.center = [SCREEN_SIZE[0] // 2, SCREEN_SIZE[1] // 2]
else:
rotated_rect.center = [960, 540]
display.blit(rotated_screen,
(rotated_rect.x + screen_offset[0],
rotated_rect.y + screen_offset[1]))
fps.tick(frames)
pygame.display.update()
|
flexible
|
{
"blob_id": "46fd4b976526a1bc70cf902bdb191feea8b84ad9",
"index": 2633,
"step-1": "<mask token>\n\n\ndef set_turn_time(time):\n\n def next_turn(screen):\n global stop\n stop = False\n tasks.append(Task(next_turn, time))\n\n\ndef add_attack(func):\n attacks.append(func)\n return func\n\n\n<mask token>\n\n\ndef set_screen_angle(angle):\n global screen_angle\n screen_angle = angle\n\n\n<mask token>\n\n\n@add_attack\ndef yinchang_1():\n global BOX_POS, BOX_SIZE\n BOX_POS = [230, 230]\n BOX_SIZE = [170, 160]\n if DEBUG:\n pass\n sans.say('准备好了？')\n\n\n<mask token>\n\n\n@add_attack\ndef first_round2():\n set_turn_time(50)\n sans.hand_direction = LEFT\n player.type = BLUE_SOUL\n player.direction = LEFT\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))\n tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -\n BOX_POS[0]) // 10))\n for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],\n direction=LEFT, time1=8, time2=30, length=0, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],\n direction=LEFT, time1=150, time2=38, length=40, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],\n direction=LEFT, time1=8, time2=188, length=40, type_=2))\n\n\n@add_attack\ndef first_round3():\n set_turn_time(450)\n player.type = RED_SOUL\n for _ in range(0, 300, 2):\n bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,\n direction=UP, speed=[7, 0], time1=1000, time2=_))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) * \n 40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,\n time2=_))\n\n\n@add_attack\ndef first_round4():\n sans.headtype = SANS_LOOK_LEFT\n sans.say('只是第一个回合而已，何必用尽全力？')\n\n\n<mask token>\n\n\n@add_attack\ndef blue_bone():\n set_turn_time(700)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 250]\n BOX_SIZE = [350, 120]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE\n [1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 - \n 8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=1))\n bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=\n 1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_gap():\n set_turn_time(1000)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 230]\n BOX_SIZE = [300, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color\n =BLUE))\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=\n DOWN, color=BLUE))\n tasks.append(Task(shake, _ * 100 + 10))\n tasks.append(Task(unshake, _ * 100 + 15))\n tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))\n y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,\n color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=\n RIGHT, color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -\n BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,\n speed=[(x - BOX_POS[0]) / 30, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],\n length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *\n 100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],\n type_=2))\n bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=\n DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /\n 30, 0], type_=1))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=\n 1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(\n (BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))\n\n\n<mask token>\n\n\n@add_attack\ndef board_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n<mask token>\n\n\n@add_attack\ndef board_2_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_lid3():\n set_turn_time(1300)\n player.type = RED_SOUL\n for _ in range(20):\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1\n =1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1\n ] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=\n [0, -2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,\n speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=\n 260, angle=-45, speed=[0, -2, 0, 0]))\n\n\n<mask token>\n\n\n@add_attack\ndef mercy2():\n sans.say('这也是一个改过自新的机会，')\n\n\n@add_attack\ndef mercy3():\n sans.say('赶紧按下饶恕，')\n\n\n<mask token>\n\n\n@add_attack\ndef mercy5():\n set_turn_time(0)\n sans.headtype = SANS_NORMAL\n\n\n<mask token>\n\n\ndef flash_round_4():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],\n angle=45, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +\n BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef set_turn_time(time):\n\n def next_turn(screen):\n global stop\n stop = False\n tasks.append(Task(next_turn, time))\n\n\ndef add_attack(func):\n attacks.append(func)\n return func\n\n\ndef shake(screen):\n global screen_shaking\n screen_shaking = True\n\n\n<mask token>\n\n\ndef set_screen_angle(angle):\n global screen_angle\n screen_angle = angle\n\n\n<mask token>\n\n\n@add_attack\ndef yinchang_1():\n global BOX_POS, BOX_SIZE\n BOX_POS = [230, 230]\n BOX_SIZE = [170, 160]\n if DEBUG:\n pass\n sans.say('准备好了？')\n\n\n@add_attack\ndef first_round1():\n set_turn_time(50)\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7], speed=[0, \n -5], direction=UP, time1=8, time2=40, length=1000, type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,\n 0], direction=UP, time1=200, time2=48, length=1000, type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,\n 5], direction=UP, time1=8, time2=248, length=1000, type_=1))\n\n\n@add_attack\ndef first_round2():\n set_turn_time(50)\n sans.hand_direction = LEFT\n player.type = BLUE_SOUL\n player.direction = LEFT\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))\n tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -\n BOX_POS[0]) // 10))\n for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],\n direction=LEFT, time1=8, time2=30, length=0, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],\n direction=LEFT, time1=150, time2=38, length=40, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],\n direction=LEFT, time1=8, time2=188, length=40, type_=2))\n\n\n@add_attack\ndef first_round3():\n set_turn_time(450)\n player.type = RED_SOUL\n for _ in range(0, 300, 2):\n bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,\n direction=UP, speed=[7, 0], time1=1000, time2=_))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) * \n 40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,\n time2=_))\n\n\n@add_attack\ndef first_round4():\n sans.headtype = SANS_LOOK_LEFT\n sans.say('只是第一个回合而已，何必用尽全力？')\n\n\n@add_attack\ndef first_round5():\n set_turn_time(1)\n sans.headtype = SANS_NORMAL\n pygame.mixer.music.play(-1)\n\n\n<mask token>\n\n\n@add_attack\ndef blue_bone():\n set_turn_time(700)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 250]\n BOX_SIZE = [350, 120]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE\n [1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 - \n 8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=1))\n bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=\n 1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_gap():\n set_turn_time(1000)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 230]\n BOX_SIZE = [300, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color\n =BLUE))\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=\n DOWN, color=BLUE))\n tasks.append(Task(shake, _ * 100 + 10))\n tasks.append(Task(unshake, _ * 100 + 15))\n tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))\n y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,\n color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=\n RIGHT, color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -\n BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,\n speed=[(x - BOX_POS[0]) / 30, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],\n length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *\n 100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],\n type_=2))\n bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=\n DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /\n 30, 0], type_=1))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=\n 1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(\n (BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))\n\n\n<mask token>\n\n\n@add_attack\ndef board_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n@add_attack\ndef board_2():\n set_turn_time(600)\n tasks.append(Task(shake, 70))\n tasks.append(Task(unshake, 75))\n blasters.append(GasterBlaster(pos=[10, BOX_POS[1] + BOX_SIZE[1]], angle\n =0, time1=10, time2=70, time3=10, width=70))\n blasters.append(GasterBlaster(pos=[10, BOX_POS[1]], angle=0, time1=10,\n time2=70, time3=10, width=30))\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30], length=\n 1000, direction=UP, time1=1000, time2=100, speed=[0, 0], type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] - 8], length=5, direction=DOWN,\n time1=1000, time2=100, speed=[0, 0], type_=2))\n boards.append(Board(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 40],\n length=40, speed=[1, 0], time1=BOX_SIZE[0], time2=100, direction=UP))\n for _ in range(0, 20, 4):\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] +\n BOX_SIZE[1] - 40 - 25], length=1000, direction=UP, time1=\n BOX_SIZE[0] // 4, time2=150 + _ * 30, speed=[-4, 0]))\n\n def start_spinning(screen):\n global spinning_left\n spinning_left = True\n\n def stop_spinning(screen):\n global spinning_left\n spinning_left = False\n tasks.append(Task(start_spinning, 200))\n tasks.append(Task(stop_spinning, 380))\n tasks.append(Task(start_spinning, 500))\n tasks.append(Task(stop_spinning, 680))\n tasks.append(Task(lambda screen: set_screen_angle(0), 682))\n\n\n<mask token>\n\n\n@add_attack\ndef board_4():\n set_turn_time(0)\n bones.clear()\n\n\n<mask token>\n\n\n@add_attack\ndef board_2_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_lid3():\n set_turn_time(1300)\n player.type = RED_SOUL\n for _ in range(20):\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1\n =1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1\n ] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=\n [0, -2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,\n speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=\n 260, angle=-45, speed=[0, -2, 0, 0]))\n\n\n<mask token>\n\n\n@add_attack\ndef mercy1():\n pygame.mixer.music.pause()\n sans.say('好了，我也累了，不如我们休息一下？')\n\n\n@add_attack\ndef mercy2():\n sans.say('这也是一个改过自新的机会，')\n\n\n@add_attack\ndef mercy3():\n sans.say('赶紧按下饶恕，')\n\n\n<mask token>\n\n\n@add_attack\ndef mercy5():\n set_turn_time(0)\n sans.headtype = SANS_NORMAL\n\n\n<mask token>\n\n\n@add_attack\ndef before_flash():\n sans.say('好吧，看来你已经做出了自己的选择。')\n\n\n<mask token>\n\n\ndef flash_round_3():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [200, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],\n angle=90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],\n angle=0, time1=10, time2=70, time3=0, width=60))\n\n\ndef flash_round_4():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],\n angle=45, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +\n BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))\n\n\ndef flash_round_5():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,\n time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle\n =90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + 50], angle=0, time1\n =10, time2=70, time3=0, width=100))\n\n\ndef flash_round_6():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,\n time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle\n =90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],\n angle=0, time1=10, time2=70, time3=0, width=100))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef set_turn_time(time):\n\n def next_turn(screen):\n global stop\n stop = False\n tasks.append(Task(next_turn, time))\n\n\ndef add_attack(func):\n attacks.append(func)\n return func\n\n\ndef shake(screen):\n global screen_shaking\n screen_shaking = True\n\n\n<mask token>\n\n\ndef set_screen_angle(angle):\n global screen_angle\n screen_angle = angle\n\n\n<mask token>\n\n\n@add_attack\ndef yinchang_1():\n global BOX_POS, BOX_SIZE\n BOX_POS = [230, 230]\n BOX_SIZE = [170, 160]\n if DEBUG:\n pass\n sans.say('准备好了？')\n\n\n@add_attack\ndef first_round1():\n set_turn_time(50)\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7], speed=[0, \n -5], direction=UP, time1=8, time2=40, length=1000, type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,\n 0], direction=UP, time1=200, time2=48, length=1000, type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,\n 5], direction=UP, time1=8, time2=248, length=1000, type_=1))\n\n\n@add_attack\ndef first_round2():\n set_turn_time(50)\n sans.hand_direction = LEFT\n player.type = BLUE_SOUL\n player.direction = LEFT\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))\n tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -\n BOX_POS[0]) // 10))\n for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],\n direction=LEFT, time1=8, time2=30, length=0, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],\n direction=LEFT, time1=150, time2=38, length=40, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],\n direction=LEFT, time1=8, time2=188, length=40, type_=2))\n\n\n@add_attack\ndef first_round3():\n set_turn_time(450)\n player.type = RED_SOUL\n for _ in range(0, 300, 2):\n bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,\n direction=UP, speed=[7, 0], time1=1000, time2=_))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) * \n 40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,\n time2=_))\n\n\n@add_attack\ndef first_round4():\n sans.headtype = SANS_LOOK_LEFT\n sans.say('只是第一个回合而已，何必用尽全力？')\n\n\n@add_attack\ndef first_round5():\n set_turn_time(1)\n sans.headtype = SANS_NORMAL\n pygame.mixer.music.play(-1)\n\n\n<mask token>\n\n\n@add_attack\ndef blue_bone():\n set_turn_time(700)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 250]\n BOX_SIZE = [350, 120]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE\n [1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 - \n 8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=1))\n bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=\n 1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_gap():\n set_turn_time(1000)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 230]\n BOX_SIZE = [300, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color\n =BLUE))\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=\n DOWN, color=BLUE))\n tasks.append(Task(shake, _ * 100 + 10))\n tasks.append(Task(unshake, _ * 100 + 15))\n tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))\n y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,\n color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=\n RIGHT, color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -\n BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,\n speed=[(x - BOX_POS[0]) / 30, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],\n length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *\n 100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],\n type_=2))\n bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=\n DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /\n 30, 0], type_=1))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=\n 1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(\n (BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))\n\n\n<mask token>\n\n\n@add_attack\ndef board_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n@add_attack\ndef board_2():\n set_turn_time(600)\n tasks.append(Task(shake, 70))\n tasks.append(Task(unshake, 75))\n blasters.append(GasterBlaster(pos=[10, BOX_POS[1] + BOX_SIZE[1]], angle\n =0, time1=10, time2=70, time3=10, width=70))\n blasters.append(GasterBlaster(pos=[10, BOX_POS[1]], angle=0, time1=10,\n time2=70, time3=10, width=30))\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30], length=\n 1000, direction=UP, time1=1000, time2=100, speed=[0, 0], type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] - 8], length=5, direction=DOWN,\n time1=1000, time2=100, speed=[0, 0], type_=2))\n boards.append(Board(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 40],\n length=40, speed=[1, 0], time1=BOX_SIZE[0], time2=100, direction=UP))\n for _ in range(0, 20, 4):\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] +\n BOX_SIZE[1] - 40 - 25], length=1000, direction=UP, time1=\n BOX_SIZE[0] // 4, time2=150 + _ * 30, speed=[-4, 0]))\n\n def start_spinning(screen):\n global spinning_left\n spinning_left = True\n\n def stop_spinning(screen):\n global spinning_left\n spinning_left = False\n tasks.append(Task(start_spinning, 200))\n tasks.append(Task(stop_spinning, 380))\n tasks.append(Task(start_spinning, 500))\n tasks.append(Task(stop_spinning, 680))\n tasks.append(Task(lambda screen: set_screen_angle(0), 682))\n\n\n<mask token>\n\n\n@add_attack\ndef board_4():\n set_turn_time(0)\n bones.clear()\n\n\n<mask token>\n\n\n@add_attack\ndef board_2_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_lid2():\n set_turn_time(60)\n sans.hand_direction = UP\n player.type = BLUE_SOUL\n player.direction = UP\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (player.pos[1] - BOX_POS[1]) // 10))\n tasks.append(Task(unshake, (player.pos[1] - BOX_POS[1]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n bones.append(RotatableBone(pos=[BOX_POS[0] - 20, BOX_POS[1]], time1=\n 1000, length=130, angle=-45, speed=[5, 0, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[\n 1]], time1=1000, length=130, angle=45, speed=[-5, 0, 0, 0]))\n\n\n@add_attack\ndef bone_lid3():\n set_turn_time(1300)\n player.type = RED_SOUL\n for _ in range(20):\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1\n =1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1\n ] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=\n [0, -2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,\n speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=\n 260, angle=-45, speed=[0, -2, 0, 0]))\n\n\n<mask token>\n\n\n@add_attack\ndef mercy1():\n pygame.mixer.music.pause()\n sans.say('好了，我也累了，不如我们休息一下？')\n\n\n@add_attack\ndef mercy2():\n sans.say('这也是一个改过自新的机会，')\n\n\n@add_attack\ndef mercy3():\n sans.say('赶紧按下饶恕，')\n\n\n<mask token>\n\n\n@add_attack\ndef mercy5():\n set_turn_time(0)\n sans.headtype = SANS_NORMAL\n\n\n<mask token>\n\n\n@add_attack\ndef before_flash():\n sans.say('好吧，看来你已经做出了自己的选择。')\n\n\n<mask token>\n\n\ndef flash_round_2():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n\n def zjj(screen):\n angle = random.randint(-140, -40)\n d = random.randint(10, 200)\n blasters.append(GasterBlaster(pos=[player.pos[0] + math.cos(math.\n radians(angle)) * d, player.pos[1] + math.sin(math.radians(\n angle)) * d], angle=angle - 180, time1=0, time2=20, width=50))\n for _ in range(0, 50):\n tasks.append(Task(zjj, _ / 2))\n\n\ndef flash_round_3():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [200, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],\n angle=90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],\n angle=0, time1=10, time2=70, time3=0, width=60))\n\n\ndef flash_round_4():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],\n angle=45, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +\n BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))\n\n\ndef flash_round_5():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,\n time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle\n =90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + 50], angle=0, time1\n =10, time2=70, time3=0, width=100))\n\n\ndef flash_round_6():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,\n time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle\n =90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],\n angle=0, time1=10, time2=70, time3=0, width=100))\n\n\n<mask token>\n",
"step-4": "<mask token>\n\n\ndef set_turn_time(time):\n\n def next_turn(screen):\n global stop\n stop = False\n tasks.append(Task(next_turn, time))\n\n\ndef add_attack(func):\n attacks.append(func)\n return func\n\n\ndef shake(screen):\n global screen_shaking\n screen_shaking = True\n\n\n<mask token>\n\n\ndef set_screen_angle(angle):\n global screen_angle\n screen_angle = angle\n\n\n<mask token>\n\n\n@add_attack\ndef yinchang_1():\n global BOX_POS, BOX_SIZE\n BOX_POS = [230, 230]\n BOX_SIZE = [170, 160]\n if DEBUG:\n pass\n sans.say('准备好了？')\n\n\n@add_attack\ndef first_round1():\n set_turn_time(50)\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7], speed=[0, \n -5], direction=UP, time1=8, time2=40, length=1000, type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,\n 0], direction=UP, time1=200, time2=48, length=1000, type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47], speed=[0,\n 5], direction=UP, time1=8, time2=248, length=1000, type_=1))\n\n\n@add_attack\ndef first_round2():\n set_turn_time(50)\n sans.hand_direction = LEFT\n player.type = BLUE_SOUL\n player.direction = LEFT\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))\n tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -\n BOX_POS[0]) // 10))\n for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 5],\n direction=LEFT, time1=8, time2=30, length=0, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, 0],\n direction=LEFT, time1=150, time2=38, length=40, type_=2))\n bones.append(Bone(pos=[BOX_POS[0] - 7, y], speed=[0, 0, -5],\n direction=LEFT, time1=8, time2=188, length=40, type_=2))\n\n\n@add_attack\ndef first_round3():\n set_turn_time(450)\n player.type = RED_SOUL\n for _ in range(0, 300, 2):\n bones.append(Bone(pos=BOX_POS, length=40 + sin(_ / 20) * 40,\n direction=UP, speed=[7, 0], time1=1000, time2=_))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 25 + sin(_ / 20) * \n 40 + 60], length=1000, direction=UP, speed=[7, 0], time1=1000,\n time2=_))\n\n\n@add_attack\ndef first_round4():\n sans.headtype = SANS_LOOK_LEFT\n sans.say('只是第一个回合而已，何必用尽全力？')\n\n\n@add_attack\ndef first_round5():\n set_turn_time(1)\n sans.headtype = SANS_NORMAL\n pygame.mixer.music.play(-1)\n\n\n<mask token>\n\n\n@add_attack\ndef zjj_1():\n set_turn_time(60)\n global BOX_POS, BOX_SIZE\n BOX_POS = [200, 230]\n BOX_SIZE = [200, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n<mask token>\n\n\n@add_attack\ndef blue_bone():\n set_turn_time(700)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 250]\n BOX_SIZE = [350, 120]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=BOX_SIZE\n [1] - 30 - 16, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 - \n 8], length=1000, direction=DOWN, time1=1000, time2=_ * 60 + 60,\n speed=[4, 0], type_=1))\n bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=\n 1000, time2=_ * 60 + 60 + 16, speed=[4, 0], type_=1, color=BLUE))\n\n\n@add_attack\ndef orange_bone():\n\n def start_spinning(screen):\n global spinning_left\n spinning_left = True\n\n def stop_spinning(screen):\n global spinning_left\n spinning_left = False\n tasks.append(Task(start_spinning, 0))\n tasks.append(Task(stop_spinning, 180))\n tasks.append(Task(lambda screen: set_screen_angle(180), 181))\n tasks.append(Task(start_spinning, 520))\n tasks.append(Task(stop_spinning, 700))\n tasks.append(Task(lambda screen: set_screen_angle(0), 701))\n set_turn_time(700)\n sans.hand_direction = UP\n player.type = BLUE_SOUL\n player.direction = UP\n player.falling_speed = 10\n tasks.append(Task(shake, (player.pos[1] - BOX_POS[1]) // 10))\n tasks.append(Task(unshake, (player.pos[1] - BOX_POS[1]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=10,\n direction=DOWN, time1=1000, time2=_ * 60 + 60, speed=[8, 0],\n type_=2))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] + 30 + 16], length=\n 1000, direction=DOWN, time1=1000, time2=_ * 60 + 60, speed=[8, \n 0], type_=1))\n bones.append(Bone(pos=BOX_POS, length=1000, direction=DOWN, time1=\n 1000, time2=_ * 60 + 60 + 8, speed=[8, 0], type_=1, color=ORANGE))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_gap():\n set_turn_time(1000)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 230]\n BOX_SIZE = [300, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[1] / 10], length=0, direction=DOWN, color\n =BLUE))\n bones.append(Bone(pos=[x, BOX_POS[1]], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[1] / 10], length=BOX_SIZE[1], direction=\n DOWN, color=BLUE))\n tasks.append(Task(shake, _ * 100 + 10))\n tasks.append(Task(unshake, _ * 100 + 15))\n tasks.append(Task(lambda screen: slam_sound.play(), _ * 100 + 15))\n y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100,\n speed=[0, 0, BOX_SIZE[0] / 10], length=0, direction=RIGHT,\n color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], y], time1=10, time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[0] / 10], length=BOX_SIZE[0], direction=\n RIGHT, color=ORANGE))\n bones.append(Bone(pos=[BOX_POS[0], BOX_POS[1] - 8], length=y -\n BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ * 100 + 60,\n speed=[(x - BOX_POS[0]) / 30, 0], type_=2))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],\n length=y - BOX_POS[1] - 16, direction=DOWN, time1=1000, time2=_ *\n 100 + 60, speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],\n type_=2))\n bones.append(Bone(pos=[BOX_POS[0], y + 8], length=1000, direction=\n DOWN, time1=1000, time2=_ * 100 + 60, speed=[(x - BOX_POS[0]) /\n 30, 0], type_=1))\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], y + 8], length=\n 1000, direction=DOWN, time1=1000, time2=_ * 100 + 60, speed=[-(\n (BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0], type_=1))\n\n\n<mask token>\n\n\n@add_attack\ndef board_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n@add_attack\ndef board_2():\n set_turn_time(600)\n tasks.append(Task(shake, 70))\n tasks.append(Task(unshake, 75))\n blasters.append(GasterBlaster(pos=[10, BOX_POS[1] + BOX_SIZE[1]], angle\n =0, time1=10, time2=70, time3=10, width=70))\n blasters.append(GasterBlaster(pos=[10, BOX_POS[1]], angle=0, time1=10,\n time2=70, time3=10, width=30))\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):\n bones.append(Bone(pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30], length=\n 1000, direction=UP, time1=1000, time2=100, speed=[0, 0], type_=1))\n bones.append(Bone(pos=[x, BOX_POS[1] - 8], length=5, direction=DOWN,\n time1=1000, time2=100, speed=[0, 0], type_=2))\n boards.append(Board(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 40],\n length=40, speed=[1, 0], time1=BOX_SIZE[0], time2=100, direction=UP))\n for _ in range(0, 20, 4):\n bones.append(Bone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] +\n BOX_SIZE[1] - 40 - 25], length=1000, direction=UP, time1=\n BOX_SIZE[0] // 4, time2=150 + _ * 30, speed=[-4, 0]))\n\n def start_spinning(screen):\n global spinning_left\n spinning_left = True\n\n def stop_spinning(screen):\n global spinning_left\n spinning_left = False\n tasks.append(Task(start_spinning, 200))\n tasks.append(Task(stop_spinning, 380))\n tasks.append(Task(start_spinning, 500))\n tasks.append(Task(stop_spinning, 680))\n tasks.append(Task(lambda screen: set_screen_angle(0), 682))\n\n\n@add_attack\ndef board_3():\n set_turn_time(100)\n sans.hand_direction = LEFT\n player.type = BLUE_SOUL\n player.direction = LEFT\n player.falling_speed = 10\n tasks.append(Task(shake, (player.pos[0] - BOX_POS[0]) // 10))\n tasks.append(Task(unshake, (player.pos[0] - BOX_POS[0]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (player.pos[0] -\n BOX_POS[0]) // 10))\n tasks.append(Task(shake, 60))\n tasks.append(Task(unshake, 65))\n blasters.append(GasterBlaster(pos=[BOX_POS[0], 10], angle=90, time1=10,\n time2=50, time3=0, width=50))\n\n\n@add_attack\ndef board_4():\n set_turn_time(0)\n bones.clear()\n\n\n<mask token>\n\n\n@add_attack\ndef board_2_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n\n\n<mask token>\n\n\n@add_attack\ndef bone_lid1():\n set_turn_time(70)\n global BOX_SIZE, BOX_POS\n BOX_POS = [200, 240]\n BOX_SIZE = [200, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake, (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) //\n 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n bones.append(RotatableBone(pos=[BOX_POS[0] - 70, BOX_POS[1] + BOX_SIZE[\n 1]], time1=1000, length=130, angle=45, speed=[5, 0, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0] + 70, BOX_POS[\n 1] + BOX_SIZE[1]], time1=1000, length=130, angle=-45, speed=[-5, 0,\n 0, 0]))\n\n\n@add_attack\ndef bone_lid2():\n set_turn_time(60)\n sans.hand_direction = UP\n player.type = BLUE_SOUL\n player.direction = UP\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake, (player.pos[1] - BOX_POS[1]) // 10))\n tasks.append(Task(unshake, (player.pos[1] - BOX_POS[1]) // 10 + 5))\n tasks.append(Task(lambda screen: slam_sound.play(), (BOX_POS[1] +\n BOX_SIZE[1] - player.pos[1]) // 10))\n bones.append(RotatableBone(pos=[BOX_POS[0] - 20, BOX_POS[1]], time1=\n 1000, length=130, angle=-45, speed=[5, 0, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[\n 1]], time1=1000, length=130, angle=45, speed=[-5, 0, 0, 0]))\n\n\n@add_attack\ndef bone_lid3():\n set_turn_time(1300)\n player.type = RED_SOUL\n for _ in range(20):\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] - 20], time1\n =1000, time2=_ * 60, length=260, angle=-45, speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1\n ] + 20], time1=1000, time2=_ * 60, length=260, angle=45, speed=\n [0, -2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] - 20], time1=1000, time2=_ * 60 + 30, length=260, angle=45,\n speed=[0, 2, 0, 0]))\n bones.append(RotatableBone(pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1\n ] + BOX_SIZE[1] + 20], time1=1000, time2=_ * 60 + 30, length=\n 260, angle=-45, speed=[0, -2, 0, 0]))\n\n\n<mask token>\n\n\n@add_attack\ndef mercy1():\n pygame.mixer.music.pause()\n sans.say('好了，我也累了，不如我们休息一下？')\n\n\n@add_attack\ndef mercy2():\n sans.say('这也是一个改过自新的机会，')\n\n\n@add_attack\ndef mercy3():\n sans.say('赶紧按下饶恕，')\n\n\n<mask token>\n\n\n@add_attack\ndef mercy5():\n set_turn_time(0)\n sans.headtype = SANS_NORMAL\n\n\n<mask token>\n\n\n@add_attack\ndef before_flash():\n sans.say('好吧，看来你已经做出了自己的选择。')\n\n\n<mask token>\n\n\ndef flash_round_2():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n\n def zjj(screen):\n angle = random.randint(-140, -40)\n d = random.randint(10, 200)\n blasters.append(GasterBlaster(pos=[player.pos[0] + math.cos(math.\n radians(angle)) * d, player.pos[1] + math.sin(math.radians(\n angle)) * d], angle=angle - 180, time1=0, time2=20, width=50))\n for _ in range(0, 50):\n tasks.append(Task(zjj, _ / 2))\n\n\ndef flash_round_3():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [200, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],\n angle=90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],\n angle=0, time1=10, time2=70, time3=0, width=60))\n\n\ndef flash_round_4():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],\n angle=45, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] - 10, BOX_POS[1] +\n BOX_SIZE[1] + 10], angle=-45, time1=10, time2=70, time3=0, width=60))\n\n\ndef flash_round_5():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,\n time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle\n =90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + 50], angle=0, time1\n =10, time2=70, time3=0, width=100))\n\n\ndef flash_round_6():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2, BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(GasterBlaster(pos=[BOX_POS[0], 50], angle=90, time1=10,\n time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[BOX_POS[0] + BOX_SIZE[0], 50], angle\n =90, time1=10, time2=70, time3=0, width=60))\n blasters.append(GasterBlaster(pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],\n angle=0, time1=10, time2=70, time3=0, width=100))\n\n\n<mask token>\n",
"step-5": "import pygame\nimport time as time_\nimport random\nimport os\nfrom pygame.locals import *\nfrom math import sin, cos, pi\nfrom sys import exit\n# ---------------------------\nfrom unzip import *\nunzip()\n# ---------------------------\nfrom others import *\nfrom gaster_blaster import *\nfrom board import *\nfrom bone import *\nfrom sans import *\nfrom player import *\nfrom functions import *\n# ----------------------------------------------------------------\n'''初始化'''\nos.environ[\"SDL_VIDEO_WINDOW_POS\"] = \"100,100\"\npygame.init()\nif FULL_SCREEN:\n display = pygame.display.set_mode((1920, 1080), FULLSCREEN)\nelse:\n display = pygame.display.set_mode(SCREEN_SIZE)\nscreen = pygame.Surface(SCREEN_SIZE).convert_alpha()\nmask_surface_blue = pygame.Surface(SCREEN_SIZE).convert_alpha() # 蓝色攻击的mask\nmask_surface_orange = pygame.Surface(SCREEN_SIZE).convert_alpha() # 橙色攻击的mask\nmask_surface_normal = pygame.Surface(SCREEN_SIZE).convert_alpha() # 普通攻击的mask\npygame.display.set_caption(\"UPPERTALE\") #标题\npygame.display.set_icon(pygame.image.load(\"res/icon-32.png\")) #图标\n\nfps = pygame.time.Clock() # 帧数计时器\nframes = 60\n\n# -----------------------------------\n'''因为需要修改全局变量\n所以不得不写在主文件里的函数'''\ndef players_turn(text):\n def tmp():\n global is_players_turn, battle_text, shown_index\n is_players_turn = True\n battle_text = text\n shown_index = 0\n bones.clear()\n blasters.clear()\n boards.clear()\n attacks.append(tmp)\n\ndef set_turn_time(time):\n def next_turn(screen):\n global stop\n stop = False\n tasks.append(Task(next_turn, time))\n\ndef add_attack(func):\n attacks.append(func)\n return func\n\ndef shake(screen):\n global screen_shaking\n screen_shaking = True\n\ndef unshake(screen):\n global screen_shaking\n screen_shaking = False\n\ndef set_screen_angle(angle):\n global screen_angle\n screen_angle = angle\n\ndef start_testing():\n attacks.clear()\n\n# -------------------------------------\n'''回合'''\n# 吟唱\n@add_attack\ndef yinchang_1():\n global BOX_POS, BOX_SIZE\n BOX_POS = [230, 230]\n BOX_SIZE = [170, 160]\n if DEBUG:\n # 测试区开始\n pass\n # 测试区结束\n sans.say(\"准备好了？\")\n\n# 开头杀\n@add_attack\ndef first_round1():\n set_turn_time(50)\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake,\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake,\n ((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 10):\n bones.append(\n Bone(\n pos=[x, BOX_POS[1] + BOX_SIZE[1] - 7],\n speed=[0, -5],\n direction=UP,\n time1=8,\n time2=40,\n length=1000,\n type_=1\n )\n )\n\n bones.append(\n Bone(\n pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47],\n speed=[0, 0],\n direction=UP,\n time1=200,\n time2=48,\n length=1000,\n type_=1\n )\n )\n\n bones.append(\n Bone(\n pos=[x, BOX_POS[1] + BOX_SIZE[1] - 47],\n speed=[0, 5],\n direction=UP,\n time1=8,\n time2=248,\n length=1000,\n type_=1\n )\n )\n@add_attack\ndef first_round2():\n set_turn_time(50)\n sans.hand_direction = LEFT\n player.type = BLUE_SOUL\n player.direction = LEFT\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake,\n (player.pos[0] - BOX_POS[0]) // 10))\n tasks.append(Task(unshake,\n ((player.pos[0] - BOX_POS[0]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (player.pos[0] - BOX_POS[0]) // 10))\n for y in range(BOX_POS[1], BOX_POS[1] + BOX_SIZE[1], 10):\n bones.append(\n Bone(\n pos=[BOX_POS[0] - 7, y],\n speed=[0, 0, 5],\n direction=LEFT,\n time1=8,\n time2=30,\n length=0,\n type_=2\n )\n )\n bones.append(\n Bone(\n pos=[BOX_POS[0] - 7, y],\n speed=[0, 0, 0],\n direction=LEFT,\n time1=150,\n time2=38,\n length=40,\n type_=2\n )\n )\n bones.append(\n Bone(\n pos=[BOX_POS[0] - 7, y],\n speed=[0, 0, -5],\n direction=LEFT,\n time1=8,\n time2=188,\n length=40,\n type_=2\n )\n )\n\n@add_attack\ndef first_round3():\n set_turn_time(450)\n player.type = RED_SOUL\n for _ in range(0, 300, 2):\n bones.append(\n Bone(\n pos=BOX_POS,\n length=40 + sin(_ / 20) * 40,\n direction=UP,\n speed=[7, 0],\n time1=1000,\n time2=_,\n )\n )\n bones.append(\n Bone(\n pos=[BOX_POS[0], BOX_POS[1] + 25 + (sin(_ / 20) * 40) + 60],\n length=1000,\n direction=UP,\n speed=[7, 0],\n time1=1000,\n time2=_,\n )\n )\n\n@add_attack\ndef first_round4():\n sans.headtype = SANS_LOOK_LEFT\n sans.say(\"只是第一个回合而已，何必用尽全力？\")\n\n@add_attack\ndef first_round5():\n set_turn_time(1)\n sans.headtype = SANS_NORMAL\n pygame.mixer.music.play(-1)\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef zjj_1():\n set_turn_time(60)\n global BOX_POS, BOX_SIZE\n BOX_POS = [200, 230]\n BOX_SIZE = [200, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake,\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake,\n ((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n\n@add_attack\ndef zjj_2():\n set_turn_time(11 * 100)\n def zjj(screen):\n angle = random.randint(240, 300)\n blasters.append(GasterBlaster(\n pos=[\n player.pos[0] + math.cos(math.radians(angle)) * 200,\n player.pos[1] + math.sin(math.radians(angle)) * 200],\n angle=angle - 180,\n time1=10,\n time2=30,\n width=30,\n color=BLUE\n ))\n for _ in range(10):\n tasks.append(Task(zjj, _ * 100))\n bones.append(\n Bone(\n pos=[BOX_POS[0] - 20, BOX_POS[1] - 8],\n length=BOX_SIZE[1] - 30 - 16,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[2, 0],\n type_=2\n ))\n \n bones.append(\n Bone(\n pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[1] - 8],\n length=BOX_SIZE[1] - 30 - 16,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[-2, 0],\n type_=2\n ))\n\n \n bones.append(\n Bone(\n pos=[BOX_POS[0] - 20, BOX_POS[1] + BOX_SIZE[1] - 10 - 8],\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[2, 0],\n type_=1\n ))\n \n bones.append(\n Bone(\n pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[1] + BOX_SIZE[1] - 10 - 8],\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[-2, 0],\n type_=1\n ))\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef blue_bone():\n set_turn_time(700)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 250]\n BOX_SIZE = [350, 120]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake,\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake,\n ((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n bones.append(\n Bone(\n pos=[BOX_POS[0], BOX_POS[1] - 8],\n length=BOX_SIZE[1] - 30 - 16,\n direction=DOWN,\n time1=1000,\n time2=_ * 60 + 60,\n speed=[4, 0],\n type_=2\n ))\n \n bones.append(\n Bone(\n pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] - 10 - 8],\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 60 + 60,\n speed=[4, 0],\n type_=1\n ))\n \n bones.append(\n Bone(\n pos=BOX_POS,\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 60 + 60 + 16,\n speed=[4, 0],\n type_=1,\n color=BLUE\n ))\n \n@add_attack\ndef orange_bone():\n def start_spinning(screen):\n global spinning_left\n spinning_left = True\n def stop_spinning(screen):\n global spinning_left\n spinning_left = False\n tasks.append(Task(start_spinning, 0))\n tasks.append(Task(stop_spinning, 180))\n tasks.append(Task(lambda screen:set_screen_angle(180), 181))\n tasks.append(Task(start_spinning, 520))\n tasks.append(Task(stop_spinning, 700))\n tasks.append(Task(lambda screen:set_screen_angle(0), 701))\n set_turn_time(700)\n sans.hand_direction = UP\n player.type = BLUE_SOUL\n player.direction = UP\n player.falling_speed = 10\n tasks.append(Task(shake,\n (player.pos[1] - BOX_POS[1]) // 10))\n tasks.append(Task(unshake,\n ((player.pos[1] - BOX_POS[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n bones.append(\n Bone(\n pos=[BOX_POS[0], BOX_POS[1] - 8],\n length=10,\n direction=DOWN,\n time1=1000,\n time2=_ * 60 + 60,\n speed=[8, 0],\n type_=2\n ))\n \n bones.append(\n Bone(\n pos=[BOX_POS[0], BOX_POS[1] + 30 + 16],\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 60 + 60,\n speed=[8, 0],\n type_=1\n ))\n \n bones.append(\n Bone(\n pos=BOX_POS,\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 60 + 60 + 8,\n speed=[8, 0],\n type_=1,\n color=ORANGE\n ))\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef bone_gap():\n set_turn_time(1000)\n global BOX_POS, BOX_SIZE\n BOX_POS = [150, 230]\n BOX_SIZE = [300, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake,\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake,\n ((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n for _ in range(10):\n x = BOX_POS[0] + random.randint(100, BOX_SIZE[0] - 100)\n bones.append(Bone(\n pos=[x, BOX_POS[1]],\n time1=10,\n time2=_ * 100,\n speed=[0, 0, BOX_SIZE[1] / 10],\n length=0,\n direction=DOWN,\n color=BLUE\n ))\n bones.append(Bone(\n pos=[x, BOX_POS[1]],\n time1=10,\n time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[1] / 10],\n length=BOX_SIZE[1],\n direction=DOWN,\n color=BLUE\n ))\n tasks.append(Task(shake,_ * 100 + 10))\n tasks.append(Task(unshake,_ * 100 + 15))\n tasks.append(Task(lambda screen : slam_sound.play(),\n _ * 100 + 15))\n \n y = BOX_POS[1] + random.randint(70, BOX_SIZE[1] - 30)\n bones.append(Bone(\n pos=[BOX_POS[0], y],\n time1=10,\n time2=_ * 100,\n speed=[0, 0, BOX_SIZE[0] / 10],\n length=0,\n direction=RIGHT,\n color=ORANGE\n ))\n bones.append(Bone(\n pos=[BOX_POS[0], y],\n time1=10,\n time2=_ * 100 + 10,\n speed=[0, 0, -BOX_SIZE[0] / 10],\n length=BOX_SIZE[0],\n direction=RIGHT,\n color=ORANGE\n ))\n\n \n bones.append(\n Bone(\n pos=[BOX_POS[0], BOX_POS[1] - 8],\n length=y - BOX_POS[1] - 16,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[(x - BOX_POS[0]) / 30, 0],\n type_=2\n ))\n \n bones.append(\n Bone(\n pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 8],\n length=y - BOX_POS[1] - 16,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],\n type_=2\n ))\n\n \n bones.append(\n Bone(\n pos=[BOX_POS[0], y + 8],\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[(x - BOX_POS[0]) / 30, 0],\n type_=1\n ))\n \n bones.append(\n Bone(\n pos=[BOX_POS[0] + BOX_SIZE[0], y + 8],\n length=1000,\n direction=DOWN,\n time1=1000,\n time2=_ * 100 + 60,\n speed=[-((BOX_SIZE[0] + BOX_POS[0] - x) / 30), 0],\n type_=1\n ))\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef board_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake,\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake,\n ((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n \n@add_attack\ndef board_2():\n set_turn_time(600)\n tasks.append(Task(shake, 70))\n tasks.append(Task(unshake, 75))\n blasters.append(\n GasterBlaster(\n pos=[10, BOX_POS[1] + BOX_SIZE[1]],\n angle=0,\n time1=10,\n time2=70,\n time3=10,\n width=70\n )\n )\n\n blasters.append(\n GasterBlaster(\n pos=[10, BOX_POS[1]],\n angle=0,\n time1=10,\n time2=70,\n time3=10,\n width=30\n )\n )\n\n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):\n bones.append(\n Bone(\n pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30],\n length=1000,\n direction=UP,\n time1=1000,\n time2=100,\n speed=[0, 0],\n type_=1\n )\n )\n bones.append(\n Bone(\n pos=[x, BOX_POS[1] - 8],\n length=5,\n direction=DOWN,\n time1=1000,\n time2=100,\n speed=[0, 0],\n type_=2\n )\n )\n boards.append(\n Board(\n pos=[BOX_POS[0],BOX_POS[1] + BOX_SIZE[1] - 40],\n length=40,\n speed=[1, 0],\n time1=BOX_SIZE[0],\n time2=100,\n direction=UP\n )\n )\n\n for _ in range(0, 20, 4):\n bones.append(\n Bone(\n pos=[BOX_POS[0] + BOX_SIZE[0],\n BOX_POS[1] + BOX_SIZE[1] - 40 - 25],\n length=1000,\n direction=UP,\n time1=BOX_SIZE[0] // 4,\n time2=150 + (_ * 30),\n speed=[-4, 0]\n )\n )\n def start_spinning(screen):\n global spinning_left\n spinning_left = True\n def stop_spinning(screen):\n global spinning_left\n spinning_left = False\n tasks.append(Task(start_spinning, 200))\n tasks.append(Task(stop_spinning, 380))\n tasks.append(Task(start_spinning, 500))\n tasks.append(Task(stop_spinning, 680))\n tasks.append(Task(lambda screen:set_screen_angle(0), 682))\n\n@add_attack\ndef board_3():\n set_turn_time(100)\n sans.hand_direction = LEFT\n player.type = BLUE_SOUL\n player.direction = LEFT\n player.falling_speed = 10\n tasks.append(Task(shake,\n (player.pos[0] - BOX_POS[0]) // 10))\n tasks.append(Task(unshake,\n ((player.pos[0] - BOX_POS[0]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (player.pos[0] - BOX_POS[0]) // 10))\n \n tasks.append(Task(shake, 60))\n tasks.append(Task(unshake, 65))\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0], 10],\n angle=90,\n time1=10,\n time2=50,\n time3=0,\n width=50\n )\n )\n\n@add_attack\ndef board_4():\n set_turn_time(0)\n bones.clear()\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef board_2_1():\n set_turn_time(10)\n global BOX_POS, BOX_SIZE\n BOX_POS = [50, 240]\n BOX_SIZE = [500, 140]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake,\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake,\n ((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n\n@add_attack\ndef board_2_2():\n set_turn_time(600)\n tasks.append(Task(shake, 70))\n tasks.append(Task(unshake, 75))\n blasters.append(\n GasterBlaster(\n pos=[10, BOX_POS[1] + BOX_SIZE[1]],\n angle=0,\n time1=10,\n time2=70,\n time3=10,\n width=70\n )\n )\n \n tasks.append(Task(shake, 250))\n tasks.append(Task(unshake, 255))\n blasters.append(\n GasterBlaster(\n pos=[10, BOX_POS[1] + BOX_SIZE[1] - 20],\n angle=0,\n time1=10,\n time2=70,\n time3=250,\n width=70\n )\n )\n\n boards.append(\n Board(\n pos=[BOX_POS[0] + BOX_SIZE[0],\n BOX_POS[1] + BOX_SIZE[1] - 30 - 10],\n time1=1000,\n time2=0,\n speed=[-2, 0],\n length=40\n )\n )\n\n boards.append(\n Board(\n pos=[BOX_POS[0] + BOX_SIZE[0],\n BOX_POS[1] + BOX_SIZE[1] - 30 - 10],\n time1=1000,\n time2=100,\n speed=[-1.5, 0],\n length=40\n )\n )\n\n boards.append(\n Board(\n pos=[BOX_POS[0] + BOX_SIZE[0],\n BOX_POS[1] + BOX_SIZE[1] - 30 - 10],\n time1=1000,\n time2=200,\n speed=[-1, 0],\n length=40\n )\n )\n\n boards.append(\n Board(\n pos=[BOX_POS[0] + BOX_SIZE[0],\n BOX_POS[1] + BOX_SIZE[1] - 30 - 30],\n time1=1000,\n time2=300,\n speed=[-3, 0],\n length=80\n )\n )\n \n for x in range(BOX_POS[0], BOX_POS[0] + BOX_SIZE[0], 12):\n bones.append(\n Bone(\n pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30],\n length=1000,\n direction=UP,\n time1=400,\n time2=100,\n speed=[0, 0],\n type_=1\n )\n )\n\n bones.append(\n Bone(\n pos=[x, BOX_POS[1] + BOX_SIZE[1] - 30],\n length=1000,\n direction=UP,\n time1=1000,\n time2=500,\n speed=[0, 0],\n type_=1\n )\n )\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef bone_lid1():\n set_turn_time(70)\n global BOX_SIZE, BOX_POS\n BOX_POS = [200, 240]\n BOX_SIZE = [200, 150]\n sans.hand_direction = DOWN\n player.type = BLUE_SOUL\n player.direction = DOWN\n player.falling_speed = 10\n tasks.append(Task(shake,\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n tasks.append(Task(unshake,\n ((BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] - 70, BOX_POS[1] + BOX_SIZE[1]],\n time1=1000,\n length=130,\n angle=45,\n speed=[5, 0, 0, 0]\n )\n )\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0] + 70, BOX_POS[1] + BOX_SIZE[1]],\n time1=1000,\n length=130,\n angle=-45,\n speed=[-5, 0, 0, 0]\n )\n )\n\n@add_attack\ndef bone_lid2():\n set_turn_time(60)\n sans.hand_direction = UP\n player.type = BLUE_SOUL\n player.direction = UP\n player.falling_speed = 10\n player.falling = True\n tasks.append(Task(shake,\n (player.pos[1] - BOX_POS[1]) // 10))\n tasks.append(Task(unshake,\n ((player.pos[1] - BOX_POS[1]) // 10) + 5))\n tasks.append(Task(lambda screen : slam_sound.play(),\n (BOX_POS[1] + BOX_SIZE[1] - player.pos[1]) // 10))\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] - 20, BOX_POS[1]],\n time1=1000,\n length=130,\n angle=-45,\n speed=[5, 0, 0, 0]\n )\n )\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0] + 20, BOX_POS[1]],\n time1=1000,\n length=130,\n angle=45,\n speed=[-5, 0, 0, 0]\n )\n )\n\n@add_attack\ndef bone_lid3():\n set_turn_time(1300)\n player.type = RED_SOUL\n for _ in range(20):\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0], BOX_POS[1] - 20],\n time1=1000,\n time2=_ * 60,\n length=260,\n angle=-45,\n speed=[0, 2, 0, 0]\n )\n )\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] + 20],\n time1=1000,\n time2=_ * 60,\n length=260,\n angle=45,\n speed=[0, -2, 0, 0]\n )\n )\n \n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 20],\n time1=1000,\n time2=_ * 60 + 30,\n length=260,\n angle=45,\n speed=[0, 2, 0, 0]\n )\n )\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] + BOX_SIZE[1] + 20],\n time1=1000,\n time2=_ * 60 + 30,\n length=260,\n angle=-45,\n speed=[0, -2, 0, 0]\n )\n )\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef mercy1():\n pygame.mixer.music.pause()\n sans.say(\"好了，我也累了，不如我们休息一下？\")\n\n@add_attack\ndef mercy2():\n sans.say(\"这也是一个改过自新的机会，\")\n\n@add_attack\ndef mercy3():\n sans.say(\"赶紧按下饶恕，\")\n\n@add_attack\ndef mercy4():\n sans.headtype = SANS_NO_EYES\n sans.say(\"否则你绝对不想见到下一个回合\")\n\n@add_attack\ndef mercy5():\n set_turn_time(0)\n sans.headtype = SANS_NORMAL\n \nplayers_turn(\"* ...\")\n@add_attack\ndef before_flash():\n sans.say(\"好吧，看来你已经做出了自己的选择。\")\n \n@add_attack\ndef flash_round():\n set_turn_time(10)\n global blackout\n flash_sound.play()\n blackout = True\n bones.clear()\n blasters.clear()\n boards.clear()\n def flash(screen):\n global blackout\n blackout = False\n flash_sound.play()\n pygame.mixer.music.unpause()\n tasks.append(Task(flash, 10))\n \ndef flash_round_1():\n set_turn_time(150)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n player.type = BLUE_SOUL\n player.direction = DOWN\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,\n 100000]\n direction = random.randint(0, 1)\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] - 30, BOX_POS[1] + BOX_SIZE[1] - 30],\n angle=0,\n time1=0,\n time2=30,\n time3=10,\n width=90\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] - 30, BOX_POS[1] - 30],\n angle=0,\n time1=0,\n time2=30,\n time3=60,\n width=90\n )\n )\n if direction:\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 30],\n angle=90,\n time1=0,\n time2=30,\n time3=10,\n width=90\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0], BOX_POS[1] - 30],\n angle=90,\n time1=0,\n time2=30,\n time3=60,\n width=90\n )\n )\n else:\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0], BOX_POS[1] - 30],\n angle=90,\n time1=0,\n time2=30,\n time3=10,\n width=90\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 30],\n angle=90,\n time1=0,\n time2=30,\n time3=60,\n width=90\n )\n )\n for angle in range(0, 360, 10):\n bones.append(RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0] / 2 + cos(radians(angle)) * BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2 + 25 + sin(radians(angle)) * BOX_SIZE[1] / 2],\n length=25,\n angle=angle,\n time1=150\n )\n )\n if angle % 30 == 0:\n bones.append(RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2 + 25],\n length=40,\n angle=angle,\n speed=[0, 0, 0, 5],\n time1=130,\n time2=20\n )\n )\n\ndef flash_round_2():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2]\n def zjj(screen):\n angle = random.randint(-140, -40)\n d = random.randint(10, 200)\n blasters.append(GasterBlaster(\n pos=[\n player.pos[0] + math.cos(math.radians(angle)) * d,\n player.pos[1] + math.sin(math.radians(angle)) * d],\n angle=angle - 180,\n time1=0,\n time2=20,\n width=50\n ))\n for _ in range(0, 50):\n tasks.append(Task(zjj, _ / 2))\n\ndef flash_round_3():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [200, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] + BOX_SIZE[0] / 2, 50],\n angle=90,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[50, BOX_POS[1] + BOX_SIZE[1] / 2],\n angle=0,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n \ndef flash_round_4():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] - 10, BOX_POS[1] - 10],\n angle=45,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] - 10, BOX_POS[1] + BOX_SIZE[1] + 10],\n angle=-45,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n \ndef flash_round_5():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0], 50],\n angle=90,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] + BOX_SIZE[0], 50],\n angle=90,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[50, BOX_POS[1] + 50],\n angle=0,\n time1=10,\n time2=70,\n time3=0,\n width=100\n )\n )\n \ndef flash_round_6():\n set_turn_time(100)\n global _boxsize, _boxpos, BOX_POS, BOX_SIZE\n BOX_SIZE = _boxsize = [150, 150]\n BOX_POS = _boxpos = [230, 230]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2]\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0], 50],\n angle=90,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[BOX_POS[0] + BOX_SIZE[0], 50],\n angle=90,\n time1=10,\n time2=70,\n time3=0,\n width=60\n )\n )\n blasters.append(\n GasterBlaster(\n pos=[50, BOX_POS[1] + BOX_SIZE[1] - 50],\n angle=0,\n time1=10,\n time2=70,\n time3=0,\n width=100\n )\n )\n \ndef flash_round_7():\n set_turn_time(150)\n global BOX_SIZE, BOX_POS, _boxpos, _boxsize\n BOX_POS = _boxpos = [230, 230]\n BOX_SIZE = _boxsize = [150, 150]\n player.type = RED_SOUL\n player.pos = [BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2]\n for _ in range(3):\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0], BOX_POS[1] - 20],\n time1=1000,\n time2=_ * 50 + 20,\n length=150,\n angle=-20,\n speed=[0, 4, 0, 0]\n )\n )\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0], BOX_POS[1] + BOX_SIZE[1] + 20],\n time1=1000,\n time2=_ * 50 + 20,\n length=150,\n angle=20,\n speed=[0, -4, 0, 0]\n )\n )\n \n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] - 20],\n time1=1000,\n time2=_ * 50 + 50,\n length=150,\n angle=20,\n speed=[0, 4, 0, 0]\n )\n )\n bones.append(\n RotatableBone(\n pos=[BOX_POS[0] + BOX_SIZE[0], BOX_POS[1] + BOX_SIZE[1] + 20],\n time1=1000,\n time2=_ * 50 + 50,\n length=150,\n angle=-20,\n speed=[0, -4, 0, 0]\n )\n )\n \n\nrandom_attacks = [flash_round_1,\n flash_round_2,\n flash_round_3,\n flash_round_4,\n flash_round_5,\n flash_round_6,\n flash_round_7]\nfor _ in range(5):\n attacks.append(random.choice(random_attacks))\n attacks.append(flash_round)\n \nplayers_turn(\"* ...\")\n \n@add_attack\ndef windmill():\n set_turn_time(1200)\n global BOX_POS, BOX_SIZE, before_strike, after_strike\n def before_strike():\n global sans_damage\n sans_damage = 1\n after_strike = lambda : ...\n BOX_POS = [150, 240]\n BOX_SIZE = [150, 150]\n\n def movegb(screen):\n for i in range(4):\n blasters[i].angle += 1\n blasters[i].end_angle += 1\n blasters[i].radian += radians(-1)\n blasters[i].back_speed = 0\n\n for angle in range(360 * 5):\n tasks.append(Task(movegb, angle * 0.4 + 100))\n \n def enablerecoil(screen):\n for b in blasters:\n b.norecoil = False\n\n tasks.append(Task(enablerecoil, 800))\n\n for angle in range(0, 360, 90):\n blasters.append(GasterBlaster(\n pos=[150 + 150 / 2, 240 + 150 / 2],\n angle=angle,\n time1=10,\n time2=1000,\n width=30,\n time3=0,\n norecoil=True\n ))\n\nplayers_turn(\"* ...\")\n\n@add_attack\ndef gameend():\n ...\n\n# ------------------------------------\n\"\"\"主程序\"\"\"\n\nwhile True:\n # ---------------------------------------------------------\n '''实例化'''\n from locals_ import *\n time = 0\n _boxpos = [0, 0]\n _boxsize = SCREEN_SIZE[:]\n rightdown = SCREEN_SIZE[:]\n\n time1 = 0\n time2 = 0\n delta = 1\n blasters = []\n bones = []\n tasks = []\n warns = []\n texts = []\n boards = []\n before_strike = None\n after_strike = None\n sans = Sans([280, 80])\n player = Player([0, 0])\n actions = {\n \"* check\" : CHECK_SANS,\n \"* heal ({} time(s) left)\" : HEAL_SANS\n }\n mc_actions = {\n \"* spare\" : MERCY_SANS_SPARE,\n \"* flee\" : MERCY_SANS_FLEE\n }\n pygame.mixer.music.stop()\n if FULL_SCREEN:\n display = pygame.display.set_mode((1920, 1080), FULLSCREEN)\n else:\n display = pygame.display.set_mode(SCREEN_SIZE)\n while True:\n time1 = time_.time()\n # 屏幕震动\n if screen_shaking:\n screen_offset[0] = random.randint(-5, 5)\n screen_offset[1] = random.randint(-5, 5)\n else:\n screen_offset = [0, 0]\n # 屏幕旋转\n if spinning_left:\n screen_angle -= 1\n # 屏幕旋转\n if spinning_right:\n screen_angle += 1\n # 测试区\n if DEBUG:...\n # 战斗框位移\n if _boxpos[0] != BOX_POS[0]:\n if abs(BOX_POS[0] - _boxpos[0]) < 0.1:\n _boxpos[0] = BOX_POS[0]\n else:\n _boxpos[0] += (BOX_POS[0] - _boxpos[0]) / 5\n if _boxpos[1] != BOX_POS[1]:\n if abs(BOX_POS[1] - _boxpos[1]) < 0.1:\n _boxpos[1] = BOX_POS[1]\n else:\n _boxpos[1] += (BOX_POS[1] - _boxpos[1]) / 5\n\n # 战斗框大小\n if rightdown[0] != BOX_POS[0] + BOX_SIZE[0]:\n if abs(BOX_POS[0] + BOX_SIZE[0] - rightdown[0]) < 0.1:\n rightdown[0] = BOX_POS[0] + BOX_SIZE[0]\n else:\n rightdown[0] += (BOX_POS[0] + BOX_SIZE[0] - rightdown[0]) / 5\n if rightdown[1] != BOX_POS[1] + BOX_SIZE[1]:\n if abs(BOX_POS[1] + BOX_SIZE[1] - rightdown[1]) < 0.1:\n rightdown[1] = BOX_POS[1] + BOX_SIZE[1]\n else:\n rightdown[1] += (BOX_POS[1] + BOX_SIZE[1] - rightdown[1]) / 5\n _boxsize = [\n rightdown[0] - _boxpos[0],\n rightdown[1] - _boxpos[1]\n ]\n\n if time >= len(attacks):\n exit()\n if not stop and not is_players_turn:\n attacks[time]()\n time += 1\n stop = True\n\n screen.fill((0, 0, 0, 255))\n display.fill((0, 0, 0))\n mask_surface_blue.fill((0, 0, 0, 0))\n mask_surface_orange.fill((0, 0, 0, 0))\n mask_surface_normal.fill((0, 0, 0, 0))\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n exit()\n if event.type == KEYDOWN:\n if event.key == K_ESCAPE:\n pygame.quit()\n exit()\n if event.key in (K_z, K_RETURN):\n if sans.show_index >= len(sans.text) and sans.show_text == True:\n sans.show_text = False\n stop = False\n elif page in (CHECK_SANS, HEAL_SANS, HEAL_SANS_CANT) and shown_index >= len(battle_text):\n is_players_turn = False\n stop = False\n page = MAIN_PAGE\n player.pos = [\n BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2\n ]\n player.select_sound.play()\n else:\n player.choose = is_players_turn\n if is_players_turn and page != FIGHT_SANS:\n player.select_sound.play()\n if event.key in (K_x, K_RSHIFT):\n sans.show_index = len(sans.text)\n shown_index = len(battle_text)\n player.back = True\n player.choice = 0\n if event.key == K_UP:\n player.going_up = True\n if event.key == K_DOWN:\n player.going_down = True\n if event.key == K_LEFT:\n player.going_left = True\n if event.key == K_RIGHT:\n player.going_right = True\n if event.key == K_F4:\n if FULL_SCREEN:\n display = pygame.display.set_mode(SCREEN_SIZE)\n FULL_SCREEN = 0\n else:\n display = pygame.display.set_mode((1920, 1080), FULLSCREEN)\n FULL_SCREEN = 1\n if event.key == K_F2:\n restarting = True\n \n if DEBUG:\n if event.key == K_n:\n bones.clear()\n boards.clear()\n blasters.clear()\n stop = False\n if event.key == K_EQUALS:\n frames += 1\n if event.key == K_MINUS:\n frames -= 1\n if event.type == KEYUP:\n if event.key == K_UP:\n player.going_up = False\n if event.key == K_DOWN:\n player.going_down = False\n if event.key == K_LEFT:\n player.going_left = False\n if event.key == K_RIGHT:\n player.going_right = False\n if event.key == K_ESCAPE:\n pygame.quit()\n exit()\n if event.key in (K_z, K_RETURN):\n player.choose = False\n if event.key in (K_x, K_RSHIFT):\n player.back = False\n\n '''检测&更新'''\n \n # 战斗框\n pygame.draw.rect(screen, (255, 255, 255, 255), pygame.Rect((_boxpos[0] - 5, _boxpos[1] - 5),\n (_boxsize[0] + 10, _boxsize[1] + 10)))\n pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect(_boxpos, _boxsize)) # 内遮挡\n # 骨头\n for b in bones:\n b.show(screen,\n mask_surface_blue,\n mask_surface_orange,\n mask_surface_normal)\n if b.stop:\n bones.remove(b)\n # 警告框\n for w in warns:\n w.show(screen)\n if w.stop:\n warns.remove(w)\n # 板子\n for b in boards:\n b.show(screen)\n if b.stop:\n boards.remove(b)\n \n if b.rect.colliderect(player.rect) and player.falling:\n player.pos[0] += b.speed[0]\n player.pos[1] += b.speed[1]\n if player.direction == DOWN:\n player.pos[1] = b.rect.top - 7\n elif player.direction == UP:\n player.pos[1] = b.rect.bottom - 1\n elif player.direction == RIGHT:\n player.pos[0] = b.rect.left - 7\n elif player.direction == LEFT:\n player.pos[0] = b.rect.right - 1\n player.falling = False\n\n \"\"\"外遮挡\"\"\"\n pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((0, 0), (SCREEN_SIZE[0], _boxpos[1] - 5)))\n pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((0, _boxpos[1] - 5), (_boxpos[0] - 5, _boxsize[1] + 10)))\n pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((0, _boxpos[1] + _boxsize[1] + 5),\n (SCREEN_SIZE[0], SCREEN_SIZE[1] - (_boxpos[1] + _boxsize[1]) - 5)))\n pygame.draw.rect(screen, (0, 0, 0, 255), pygame.Rect((_boxpos[0] + _boxsize[0] + 5, _boxpos[1] - 5),\n (SCREEN_SIZE[0] - (_boxpos[0] + _boxsize[0]) - 5, _boxsize[1] + 10)))\n \n '''显示UI（外面）'''\n pygame.draw.rect(screen, (191, 0, 0, 255), pygame.Rect((275, 400), (92, 20)))\n if player.KR:\n pygame.draw.rect(screen, (255, 0, 255, 255), pygame.Rect((275 + player.HP, 400), (round(player.KR), 20)))\n pygame.draw.rect(screen, (255, 255, 0, 255), pygame.Rect((275, 400), (player.HP, 20)))\n screen.blit(\n font2.render(\n \"{:0>2.0f} / 92\".format(player.HP + player.KR),\n True,\n (255, 255, 255) if not round(player.KR) else (255, 0, 255)\n ),\n (\n 415,\n 400\n )\n )\n screen.blit(hp_image, (240, 405))\n screen.blit(kr_image, (375, 405))\n screen.blit(\n font2.render(\n \"Chara LV 19\", True, (255, 255, 255)\n ), (30, 400)\n )\n \n # 显示文本\n for text in texts:\n screen.blit(\n font.render(\n text[1], True, (255, 255, 255)\n ), text[0]\n )\n\n if DEBUG:\n screen.blit(\n font2.render(\n \"DEBUG\", True, (0, 0, 255)\n ), (200, 0)\n )\n # 显示帧数\n screen.blit(\n font2.render(\n \"FPS:{:0>3d}\".format(round(1 / delta)), True, (0, 0, 255)\n ), (0, 0)\n )\n if fight:\n screen.blit(fight_highlight_image, fight_pos)\n else:\n screen.blit(fight_default_image, fight_pos)\n if act:\n screen.blit(act_highlight_image, act_pos)\n else:\n screen.blit(act_default_image, act_pos)\n if item:\n screen.blit(item_highlight_image, item_pos)\n else:\n screen.blit(item_default_image, item_pos)\n if mercy:\n screen.blit(mercy_highlight_image, mercy_pos)\n else:\n screen.blit(mercy_default_image, mercy_pos)\n \n # 鳝丝（要放在外面）\n sans.show(screen)\n if show_sans_damage:\n if sans_damage == MISS:\n screen.blit(miss_image, (250, 60))\n \n # GB炮（要放在外面）\n for t in blasters:\n t.show(screen,\n mask_surface_blue,\n mask_surface_orange,\n mask_surface_normal)\n if t.stop:\n blasters.remove(t)\n\n # 其他东西，blahblahblah（外面）\n for t in tasks:\n t.show(screen)\n if t.stop:\n tasks.remove(t)\n\n if is_players_turn: # 玩家回合\n BOX_POS = [30, 250]\n BOX_SIZE = [570, 130]\n if page == MAIN_PAGE:\n if shown_index < len(battle_text):\n shown_index += 1\n text_sound.play()\n x = 40\n y = 250\n for char in battle_text[:shown_index]:\n if char != '\\n':\n screen.blit(\n battle_font.render(char, True, (255, 255, 255)),\n (x, y)\n )\n x += 12\n if x > BOX_POS[0] + BOX_SIZE[0] or char == \"\\n\":\n y += 16\n x = 40\n player.type = CURSOR_SOUL\n player.options = (\n (fight_pos[0] + 10, fight_pos[1] + 15),\n ( act_pos[0] + 10, act_pos[1] + 15),\n ( item_pos[0] + 10, item_pos[1] + 15),\n (mercy_pos[0] + 10, mercy_pos[1] + 15)\n )\n\n if player.choice == 0:\n fight = True\n act = False\n item = False\n mercy = False\n\n if player.choice == 1:\n fight = False\n act = True\n item = False\n mercy = False\n\n if player.choice == 2:\n fight = False\n act = False\n item = True\n mercy = False\n\n if player.choice == 3:\n fight = False\n act = False\n item = False\n mercy = True\n\n if player.choose:\n page = [FIGHT, ACT, 0, MERCY][player.choice]\n player.choose = False\n player.choice = 0\n fight = False\n act = False\n item = False\n mercy = False\n\n if page == ACT:\n player.options = [(40, 255)]\n screen.blit(\n battle_font.render(\"* sans\", True, (255, 255, 255)),\n (40, 250)\n )\n if player.choose:\n page = [ACT_SANS][player.choice]\n player.choose = False\n player.choice = 0\n if player.back:\n page = MAIN_PAGE\n\n if page == ACT_SANS:\n player.options = []\n y = 250\n for _ in actions.keys():\n if actions[_] == HEAL_SANS:\n _ = _.format(heal_times_left)\n screen.blit(\n battle_font.render(_, True, (255, 255, 255)),\n (40, y)\n )\n player.options.append((40, y + 5))\n y += 20\n \n if player.choose:\n page = list(actions.values())[player.choice]\n if page == HEAL_SANS:\n if heal_times_left > 0:\n heal(player, 92)\n heal_times_left -= 1\n else:\n page = HEAL_SANS_CANT\n player.choose = False\n player.choice = 0\n if player.back:\n page = ACT\n\n if page == CHECK_SANS:\n player.type = RED_SOUL\n player.pos = [\n -100,\n -100\n ]\n battle_text = \"* Sans\\n The TRUE HERO.\\n ATK:1\\n DEF:1\\n Nothing to say.\"\n if shown_index < len(battle_text):\n shown_index += 1\n text_sound.play()\n x = 40\n y = 250\n for char in battle_text[:shown_index]:\n if char != '\\n':\n screen.blit(\n battle_font.render(char, True, (255, 255, 255)),\n (x, y)\n )\n x += 12\n if x > BOX_POS[0] + BOX_SIZE[0] or char == \"\\n\":\n y += 20\n x = 40\n\n if page == HEAL_SANS:\n player.type = RED_SOUL\n player.pos = [\n -100,\n -100\n ]\n battle_text = \"* You are healthy again now.\\n* {} time(s) left.\".format(heal_times_left)\n if shown_index < len(battle_text):\n shown_index += 1\n text_sound.play()\n x = 40\n y = 250\n for char in battle_text[:shown_index]:\n if char != '\\n':\n screen.blit(\n battle_font.render(char, True, (255, 255, 255)),\n (x, y)\n )\n x += 12\n if x > BOX_POS[0] + BOX_SIZE[0] or char == \"\\n\":\n y += 20\n x = 40\n\n if page == HEAL_SANS_CANT:\n player.type = RED_SOUL\n player.pos = [\n -100,\n -100\n ]\n battle_text = \"* No more times for you to heal!\"\n if shown_index < len(battle_text):\n shown_index += 1\n text_sound.play()\n x = 40\n y = 250\n for char in battle_text[:shown_index]:\n if char != '\\n':\n screen.blit(\n battle_font.render(char, True, (255, 255, 255)),\n (x, y)\n )\n x += 12\n if x > BOX_POS[0] + BOX_SIZE[0] or char == \"\\n\":\n y += 20\n x = 40\n\n if page == FIGHT:\n player.options = [(40, 255)]\n screen.blit(\n battle_font.render(\"* sans\", True, (255, 255, 255)),\n (40, 250)\n )\n if player.choose:\n page = [FIGHT_SANS][player.choice]\n player.choose = False\n player.choice = 0\n choice_pos = [50, 250]\n if player.back:\n page = MAIN_PAGE\n\n if page == FIGHT_SANS:\n player.type = RED_SOUL\n player.pos = [\n -100,\n -100\n ]\n target_img.set_alpha(target_alpha)\n if not choice_blink:\n if target_alpha >= 255:\n choice_going = True\n else:\n target_alpha += 10\n screen.blit(target_img, [BOX_POS[0] + 10, BOX_POS[1] + 5])\n screen.blit([choice_img, choice_blink_img][choice_ani_index // 5 % 2], choice_pos)\n choice_ani_index += choice_blink\n choice_pos[0] += choice_going * 8\n if choice_going and (player.choose or choice_pos[0] > BOX_POS[0] + BOX_SIZE[0]):\n choice_going = False\n choice_blink = True\n tasks.append(Strike(sans.pos[:]))\n if not before_strike:\n sans.target_pos = [100, 80]\n else:\n before_strike()\n if choice_blink:\n blink_time += 1\n if blink_time > 60:\n show_sans_damage = False\n choice_going = False\n choice_blink = False\n choice_ani_index = 0\n target_alpha = 0\n blink_time = 0\n is_players_turn = False\n stop = False\n page = MAIN_PAGE\n if not after_strike:\n sans.target_pos = [250, 80]\n else:\n after_strike()\n player.pos = [\n BOX_POS[0] + BOX_SIZE[0] / 2,\n BOX_POS[1] + BOX_SIZE[1] / 2\n ]\n elif blink_time > 30:\n target_alpha -= 10\n show_sans_damage = True\n\n if page == MERCY:\n player.options = [(40, 255)]\n screen.blit(\n battle_font.render(\"* sans\", True, (255, 255, 255)),\n (40, 250)\n )\n if player.choose:\n page = [MERCY_SANS][player.choice]\n player.choose = False\n player.choice = 0\n if player.back:\n page = MAIN_PAGE\n\n if page == MERCY_SANS:\n player.options = []\n y = 250\n for _ in mc_actions.keys():\n screen.blit(\n battle_font.render(_, True, (255, 255, 255)),\n (40, y)\n )\n player.options.append((40, y + 5))\n y += 20\n \n if player.choose:\n page = list(mc_actions.values())[player.choice]\n player.choose = False\n player.choice = 0\n if player.back:\n page = MERCY\n\n if page == MERCY_SANS_SPARE: # 你都饶恕了，想必也不想继续玩了（）\n exit()\n\n if page == MERCY_SANS_FLEE: # 你都逃跑了，想必也不想继续玩了（）\n exit()\n\n # 你死了\n if player.HP + player.KR <= 0:\n DEAD = True\n if DEAD or restarting:\n break\n\n # 判定伤害\n blue_mask = pygame.mask.from_surface(mask_surface_blue)\n orange_mask = pygame.mask.from_surface(mask_surface_orange)\n normal_mask = pygame.mask.from_surface(mask_surface_normal)\n if mask_collide(blue_mask, player.mask, [0, 0], player.mask_pos):\n if any([player.going_up, player.going_down, player.going_left, player.going_right, player.falling]):\n damage(player)\n if mask_collide(orange_mask, player.mask, [0, 0], player.mask_pos):\n if not any([player.going_up, player.going_down, player.going_left, player.going_right, player.falling]):\n damage(player)\n if mask_collide(normal_mask, player.mask, [0, 0], player.mask_pos):\n damage(player)\n\n # 玩家\n player.show(screen, _boxpos, _boxsize)\n\n # 黑屏攻击\n if blackout:\n screen.fill(0x000000)\n\n \"\"\"将screen的图像加工后放入display\"\"\"\n if not FULL_SCREEN:\n rotated_screen = pygame.transform.rotate(screen, screen_angle)\n else:\n screen_rect = screen.get_rect()\n rotated_screen = pygame.transform.rotate(\n pygame.transform.scale(\n screen,\n (\n round(screen_rect.size[1] / screen_rect.size[0] * 1920),\n 1080\n )\n ),\n screen_angle\n )\n rotated_rect = rotated_screen.get_rect()\n if not FULL_SCREEN:\n rotated_rect.center = [SCREEN_SIZE[0] // 2, SCREEN_SIZE[1] // 2]\n else:\n rotated_rect.center = [960, 540]\n display.blit(rotated_screen,\n (rotated_rect.x + screen_offset[0],\n rotated_rect.y + screen_offset[1]))\n fps.tick(frames)\n pygame.display.update()\n time2 = time_.time()\n delta = time2 - time1\n\n if not restarting:\n ticks = 0\n heart_offset = [0, 0]\n while True:\n '''死后的'''\n pygame.mixer.music.stop()\n ticks += 1\n screen.fill((0, 0, 0, 255))\n if ticks >= 200:\n break\n \n if ticks >= 160:\n screen.blit(alive_img, player.rect)\n if ticks == 160:\n split_sound.play()\n \n elif ticks >= 100:\n screen.blit(dead_img,\n (player.rect.x + heart_offset[0],\n player.rect.y + heart_offset[1]))\n heart_offset = [random.randint(-2, 2), random.randint(-2, 2)]\n \n elif ticks >= 60:\n screen.blit(dead_img, player.rect)\n if ticks == 60:\n split_sound.play()\n \n else:\n screen.blit(alive_img, player.rect)\n \n if not FULL_SCREEN:\n rotated_screen = pygame.transform.rotate(screen, screen_angle)\n else:\n screen_rect = screen.get_rect()\n rotated_screen = pygame.transform.rotate(\n pygame.transform.scale(\n screen,\n (\n round(screen_rect.size[1] / screen_rect.size[0] * 1920),\n 1080\n )\n ),\n screen_angle\n )\n rotated_rect = rotated_screen.get_rect()\n if not FULL_SCREEN:\n rotated_rect.center = [SCREEN_SIZE[0] // 2, SCREEN_SIZE[1] // 2]\n else:\n rotated_rect.center = [960, 540]\n display.blit(rotated_screen,\n (rotated_rect.x + screen_offset[0],\n rotated_rect.y + screen_offset[1]))\n fps.tick(frames)\n pygame.display.update()\n",
"step-ids": [
16,
26,
28,
32,
47
]
}
|
[
16,
26,
28,
32,
47
] |
<|reserved_special_token_0|>
class SlicesDataset(utils.Dataset):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def load_image(self, image_id):
"""Returns an image with a given id."""
info = self.image_info[image_id]
patch_path = info['path']
width = info['width']
height = info['height']
impath = os.path.join(patch_path, 'images')
file_list = os.listdir(impath)
channels = info['channels']
image = []
for channel in channels:
if channel == 'none':
channel_image = skimage.img_as_ubyte(np.zeros((height, width)))
else:
channel_image_name = [x for x in file_list if channel in x][0]
channel_image_path = os.path.join(impath, channel_image_name)
channel_image = skimage.io.imread(channel_image_path)
channel_image = skimage.img_as_ubyte(channel_image)
image.append(channel_image)
image = np.stack(image, axis=2)
return image
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class SlicesDataset(utils.Dataset):
<|reserved_special_token_0|>
def load_slices(self, dataset_dir, n_images, n_patches, channels=['base']):
"""Load a subset of the Slices dataset.
dataset_dir: Root directory of the dataset.
n_images: number of images to load. Will load in os.listdir list order.
n_patches: number of patches to load per image.
channels: list of strings indicating channels to be stacked in the image.
currently "base", "mf", "edges" and "none" can be arbitrarily stacked.
"""
self.add_class('slices', 1, 'tissue')
self.add_class('slices', 2, 'mag')
image_list = os.listdir(dataset_dir)
image_counter = 0
patch_counter = 0
for i in range(n_images):
image_path = os.path.join(dataset_dir, image_list[i])
patch_list = os.listdir(image_path)
print(f'processing: image {i}')
for j in range(n_patches):
patch_path = os.path.join(image_path, patch_list[j])
patch_image_path = os.path.join(patch_path, 'images')
file_list = os.listdir(patch_image_path)
image_file_path = os.path.join(patch_image_path, file_list[0])
image = skimage.io.imread(image_file_path)
height, width = image.shape
self.add_image('slices', image_id=patch_counter, path=
patch_path, width=width, height=height, channels=channels)
patch_counter += 1
def load_image(self, image_id):
"""Returns an image with a given id."""
info = self.image_info[image_id]
patch_path = info['path']
width = info['width']
height = info['height']
impath = os.path.join(patch_path, 'images')
file_list = os.listdir(impath)
channels = info['channels']
image = []
for channel in channels:
if channel == 'none':
channel_image = skimage.img_as_ubyte(np.zeros((height, width)))
else:
channel_image_name = [x for x in file_list if channel in x][0]
channel_image_path = os.path.join(impath, channel_image_name)
channel_image = skimage.io.imread(channel_image_path)
channel_image = skimage.img_as_ubyte(channel_image)
image.append(channel_image)
image = np.stack(image, axis=2)
return image
def load_mask(self, image_id):
"""Loads masks from dataset.
"""
info = self.image_info[image_id]
patch_path = info['path']
height = info['height']
width = info['width']
mag_path = os.path.join(patch_path, 'mag')
tissue_path = os.path.join(patch_path, 'tissue')
mag_mask_list = os.listdir(mag_path)
tissue_mask_list = os.listdir(tissue_path)
classes = []
masks = []
if mag_mask_list:
for filename in mag_mask_list:
a = os.path.join(mag_path, filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(2)
if tissue_mask_list:
for filename in tissue_mask_list:
a = os.path.join(tissue_path, filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(1)
return np.stack(masks, axis=2), np.asarray(classes).astype(int)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class SlicesDataset(utils.Dataset):
""" Extension of maskrcnn dataset class to be used with our provided data. """
def load_slices(self, dataset_dir, n_images, n_patches, channels=['base']):
"""Load a subset of the Slices dataset.
dataset_dir: Root directory of the dataset.
n_images: number of images to load. Will load in os.listdir list order.
n_patches: number of patches to load per image.
channels: list of strings indicating channels to be stacked in the image.
currently "base", "mf", "edges" and "none" can be arbitrarily stacked.
"""
self.add_class('slices', 1, 'tissue')
self.add_class('slices', 2, 'mag')
image_list = os.listdir(dataset_dir)
image_counter = 0
patch_counter = 0
for i in range(n_images):
image_path = os.path.join(dataset_dir, image_list[i])
patch_list = os.listdir(image_path)
print(f'processing: image {i}')
for j in range(n_patches):
patch_path = os.path.join(image_path, patch_list[j])
patch_image_path = os.path.join(patch_path, 'images')
file_list = os.listdir(patch_image_path)
image_file_path = os.path.join(patch_image_path, file_list[0])
image = skimage.io.imread(image_file_path)
height, width = image.shape
self.add_image('slices', image_id=patch_counter, path=
patch_path, width=width, height=height, channels=channels)
patch_counter += 1
def load_image(self, image_id):
"""Returns an image with a given id."""
info = self.image_info[image_id]
patch_path = info['path']
width = info['width']
height = info['height']
impath = os.path.join(patch_path, 'images')
file_list = os.listdir(impath)
channels = info['channels']
image = []
for channel in channels:
if channel == 'none':
channel_image = skimage.img_as_ubyte(np.zeros((height, width)))
else:
channel_image_name = [x for x in file_list if channel in x][0]
channel_image_path = os.path.join(impath, channel_image_name)
channel_image = skimage.io.imread(channel_image_path)
channel_image = skimage.img_as_ubyte(channel_image)
image.append(channel_image)
image = np.stack(image, axis=2)
return image
def load_mask(self, image_id):
"""Loads masks from dataset.
"""
info = self.image_info[image_id]
patch_path = info['path']
height = info['height']
width = info['width']
mag_path = os.path.join(patch_path, 'mag')
tissue_path = os.path.join(patch_path, 'tissue')
mag_mask_list = os.listdir(mag_path)
tissue_mask_list = os.listdir(tissue_path)
classes = []
masks = []
if mag_mask_list:
for filename in mag_mask_list:
a = os.path.join(mag_path, filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(2)
if tissue_mask_list:
for filename in tissue_mask_list:
a = os.path.join(tissue_path, filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(1)
return np.stack(masks, axis=2), np.asarray(classes).astype(int)
<|reserved_special_token_1|>
from mrcnn import utils
import numpy as np
import os
import skimage
class SlicesDataset(utils.Dataset):
""" Extension of maskrcnn dataset class to be used with our provided data. """
def load_slices(self, dataset_dir, n_images, n_patches, channels=['base']):
"""Load a subset of the Slices dataset.
dataset_dir: Root directory of the dataset.
n_images: number of images to load. Will load in os.listdir list order.
n_patches: number of patches to load per image.
channels: list of strings indicating channels to be stacked in the image.
currently "base", "mf", "edges" and "none" can be arbitrarily stacked.
"""
self.add_class('slices', 1, 'tissue')
self.add_class('slices', 2, 'mag')
image_list = os.listdir(dataset_dir)
image_counter = 0
patch_counter = 0
for i in range(n_images):
image_path = os.path.join(dataset_dir, image_list[i])
patch_list = os.listdir(image_path)
print(f'processing: image {i}')
for j in range(n_patches):
patch_path = os.path.join(image_path, patch_list[j])
patch_image_path = os.path.join(patch_path, 'images')
file_list = os.listdir(patch_image_path)
image_file_path = os.path.join(patch_image_path, file_list[0])
image = skimage.io.imread(image_file_path)
height, width = image.shape
self.add_image('slices', image_id=patch_counter, path=
patch_path, width=width, height=height, channels=channels)
patch_counter += 1
def load_image(self, image_id):
"""Returns an image with a given id."""
info = self.image_info[image_id]
patch_path = info['path']
width = info['width']
height = info['height']
impath = os.path.join(patch_path, 'images')
file_list = os.listdir(impath)
channels = info['channels']
image = []
for channel in channels:
if channel == 'none':
channel_image = skimage.img_as_ubyte(np.zeros((height, width)))
else:
channel_image_name = [x for x in file_list if channel in x][0]
channel_image_path = os.path.join(impath, channel_image_name)
channel_image = skimage.io.imread(channel_image_path)
channel_image = skimage.img_as_ubyte(channel_image)
image.append(channel_image)
image = np.stack(image, axis=2)
return image
def load_mask(self, image_id):
"""Loads masks from dataset.
"""
info = self.image_info[image_id]
patch_path = info['path']
height = info['height']
width = info['width']
mag_path = os.path.join(patch_path, 'mag')
tissue_path = os.path.join(patch_path, 'tissue')
mag_mask_list = os.listdir(mag_path)
tissue_mask_list = os.listdir(tissue_path)
classes = []
masks = []
if mag_mask_list:
for filename in mag_mask_list:
a = os.path.join(mag_path, filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(2)
if tissue_mask_list:
for filename in tissue_mask_list:
a = os.path.join(tissue_path, filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(1)
return np.stack(masks, axis=2), np.asarray(classes).astype(int)
<|reserved_special_token_1|>
# coding: utf-8
from mrcnn import utils
import numpy as np
import os
import skimage
class SlicesDataset(utils.Dataset):
""" Extension of maskrcnn dataset class to be used with our provided data. """
def load_slices(self, dataset_dir, n_images, n_patches, channels = ["base"]):
"""Load a subset of the Slices dataset.
dataset_dir: Root directory of the dataset.
n_images: number of images to load. Will load in os.listdir list order.
n_patches: number of patches to load per image.
channels: list of strings indicating channels to be stacked in the image.
currently "base", "mf", "edges" and "none" can be arbitrarily stacked.
"""
# add classes to be trained on
self.add_class("slices", 1, "tissue")
self.add_class("slices", 2, "mag")
# collect image list and initialize counter
image_list = os.listdir(dataset_dir)
image_counter = 0
patch_counter = 0
# cycle over images and save patches to database.
for i in range(n_images):
image_path = os.path.join(dataset_dir,image_list[i])
patch_list = os.listdir(image_path)
print(f"processing: image {i}")
for j in range(n_patches):
patch_path = os.path.join(image_path, patch_list[j])
patch_image_path = os.path.join(patch_path,"images")
file_list = os.listdir(patch_image_path)
image_file_path = os.path.join(patch_image_path,file_list[0])
image = skimage.io.imread(image_file_path)
height, width = image.shape
self.add_image(
"slices",
image_id = patch_counter,
path = patch_path,
width = width, height = height,
channels = channels,
)
patch_counter += 1
def load_image(self, image_id):
"""Returns an image with a given id."""
# load image infos
info = self.image_info[image_id]
patch_path = info['path']
width = info['width']
height = info['height']
impath = os.path.join(patch_path,"images")
file_list = os.listdir(impath)
channels = info['channels']
image = []
# stack channels to be loaded.
for channel in channels:
if channel == "none":
channel_image = skimage.img_as_ubyte(np.zeros( (height,width) ) )
else:
channel_image_name = [x for x in file_list if channel in x][0]
channel_image_path = os.path.join(impath, channel_image_name)
channel_image = skimage.io.imread(channel_image_path)
channel_image = skimage.img_as_ubyte(channel_image)
image.append(channel_image)
image = np.stack(image, axis=2)
return image
def load_mask(self, image_id):
"""Loads masks from dataset.
"""
# load image infos
info = self.image_info[image_id]
patch_path = info['path']
height = info['height']
width = info['width']
mag_path = os.path.join(patch_path,"mag")
tissue_path = os.path.join(patch_path,"tissue")
# collect mask names
mag_mask_list = os.listdir(mag_path)
tissue_mask_list = os.listdir(tissue_path)
classes = []
masks = []
# append masks and ids in list
if mag_mask_list:
for filename in mag_mask_list:
a = os.path.join(mag_path,filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(2)
if tissue_mask_list:
for filename in tissue_mask_list:
a = os.path.join(tissue_path,filename)
masks.append(skimage.io.imread(a).astype(bool))
classes.append(1)
return np.stack(masks,axis=2), np.asarray(classes).astype(int)
|
flexible
|
{
"blob_id": "8675deb69eae04a722073432eaf69ce3d24a11ad",
"index": 9041,
"step-1": "<mask token>\n\n\nclass SlicesDataset(utils.Dataset):\n <mask token>\n <mask token>\n\n def load_image(self, image_id):\n \"\"\"Returns an image with a given id.\"\"\"\n info = self.image_info[image_id]\n patch_path = info['path']\n width = info['width']\n height = info['height']\n impath = os.path.join(patch_path, 'images')\n file_list = os.listdir(impath)\n channels = info['channels']\n image = []\n for channel in channels:\n if channel == 'none':\n channel_image = skimage.img_as_ubyte(np.zeros((height, width)))\n else:\n channel_image_name = [x for x in file_list if channel in x][0]\n channel_image_path = os.path.join(impath, channel_image_name)\n channel_image = skimage.io.imread(channel_image_path)\n channel_image = skimage.img_as_ubyte(channel_image)\n image.append(channel_image)\n image = np.stack(image, axis=2)\n return image\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass SlicesDataset(utils.Dataset):\n <mask token>\n\n def load_slices(self, dataset_dir, n_images, n_patches, channels=['base']):\n \"\"\"Load a subset of the Slices dataset.\n dataset_dir: Root directory of the dataset.\n n_images: number of images to load. Will load in os.listdir list order.\n n_patches: number of patches to load per image.\n channels: list of strings indicating channels to be stacked in the image.\n currently \"base\", \"mf\", \"edges\" and \"none\" can be arbitrarily stacked.\n \"\"\"\n self.add_class('slices', 1, 'tissue')\n self.add_class('slices', 2, 'mag')\n image_list = os.listdir(dataset_dir)\n image_counter = 0\n patch_counter = 0\n for i in range(n_images):\n image_path = os.path.join(dataset_dir, image_list[i])\n patch_list = os.listdir(image_path)\n print(f'processing: image {i}')\n for j in range(n_patches):\n patch_path = os.path.join(image_path, patch_list[j])\n patch_image_path = os.path.join(patch_path, 'images')\n file_list = os.listdir(patch_image_path)\n image_file_path = os.path.join(patch_image_path, file_list[0])\n image = skimage.io.imread(image_file_path)\n height, width = image.shape\n self.add_image('slices', image_id=patch_counter, path=\n patch_path, width=width, height=height, channels=channels)\n patch_counter += 1\n\n def load_image(self, image_id):\n \"\"\"Returns an image with a given id.\"\"\"\n info = self.image_info[image_id]\n patch_path = info['path']\n width = info['width']\n height = info['height']\n impath = os.path.join(patch_path, 'images')\n file_list = os.listdir(impath)\n channels = info['channels']\n image = []\n for channel in channels:\n if channel == 'none':\n channel_image = skimage.img_as_ubyte(np.zeros((height, width)))\n else:\n channel_image_name = [x for x in file_list if channel in x][0]\n channel_image_path = os.path.join(impath, channel_image_name)\n channel_image = skimage.io.imread(channel_image_path)\n channel_image = skimage.img_as_ubyte(channel_image)\n image.append(channel_image)\n image = np.stack(image, axis=2)\n return image\n\n def load_mask(self, image_id):\n \"\"\"Loads masks from dataset.\n \"\"\"\n info = self.image_info[image_id]\n patch_path = info['path']\n height = info['height']\n width = info['width']\n mag_path = os.path.join(patch_path, 'mag')\n tissue_path = os.path.join(patch_path, 'tissue')\n mag_mask_list = os.listdir(mag_path)\n tissue_mask_list = os.listdir(tissue_path)\n classes = []\n masks = []\n if mag_mask_list:\n for filename in mag_mask_list:\n a = os.path.join(mag_path, filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(2)\n if tissue_mask_list:\n for filename in tissue_mask_list:\n a = os.path.join(tissue_path, filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(1)\n return np.stack(masks, axis=2), np.asarray(classes).astype(int)\n",
"step-3": "<mask token>\n\n\nclass SlicesDataset(utils.Dataset):\n \"\"\" Extension of maskrcnn dataset class to be used with our provided data. \"\"\"\n\n def load_slices(self, dataset_dir, n_images, n_patches, channels=['base']):\n \"\"\"Load a subset of the Slices dataset.\n dataset_dir: Root directory of the dataset.\n n_images: number of images to load. Will load in os.listdir list order.\n n_patches: number of patches to load per image.\n channels: list of strings indicating channels to be stacked in the image.\n currently \"base\", \"mf\", \"edges\" and \"none\" can be arbitrarily stacked.\n \"\"\"\n self.add_class('slices', 1, 'tissue')\n self.add_class('slices', 2, 'mag')\n image_list = os.listdir(dataset_dir)\n image_counter = 0\n patch_counter = 0\n for i in range(n_images):\n image_path = os.path.join(dataset_dir, image_list[i])\n patch_list = os.listdir(image_path)\n print(f'processing: image {i}')\n for j in range(n_patches):\n patch_path = os.path.join(image_path, patch_list[j])\n patch_image_path = os.path.join(patch_path, 'images')\n file_list = os.listdir(patch_image_path)\n image_file_path = os.path.join(patch_image_path, file_list[0])\n image = skimage.io.imread(image_file_path)\n height, width = image.shape\n self.add_image('slices', image_id=patch_counter, path=\n patch_path, width=width, height=height, channels=channels)\n patch_counter += 1\n\n def load_image(self, image_id):\n \"\"\"Returns an image with a given id.\"\"\"\n info = self.image_info[image_id]\n patch_path = info['path']\n width = info['width']\n height = info['height']\n impath = os.path.join(patch_path, 'images')\n file_list = os.listdir(impath)\n channels = info['channels']\n image = []\n for channel in channels:\n if channel == 'none':\n channel_image = skimage.img_as_ubyte(np.zeros((height, width)))\n else:\n channel_image_name = [x for x in file_list if channel in x][0]\n channel_image_path = os.path.join(impath, channel_image_name)\n channel_image = skimage.io.imread(channel_image_path)\n channel_image = skimage.img_as_ubyte(channel_image)\n image.append(channel_image)\n image = np.stack(image, axis=2)\n return image\n\n def load_mask(self, image_id):\n \"\"\"Loads masks from dataset.\n \"\"\"\n info = self.image_info[image_id]\n patch_path = info['path']\n height = info['height']\n width = info['width']\n mag_path = os.path.join(patch_path, 'mag')\n tissue_path = os.path.join(patch_path, 'tissue')\n mag_mask_list = os.listdir(mag_path)\n tissue_mask_list = os.listdir(tissue_path)\n classes = []\n masks = []\n if mag_mask_list:\n for filename in mag_mask_list:\n a = os.path.join(mag_path, filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(2)\n if tissue_mask_list:\n for filename in tissue_mask_list:\n a = os.path.join(tissue_path, filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(1)\n return np.stack(masks, axis=2), np.asarray(classes).astype(int)\n",
"step-4": "from mrcnn import utils\nimport numpy as np\nimport os\nimport skimage\n\n\nclass SlicesDataset(utils.Dataset):\n \"\"\" Extension of maskrcnn dataset class to be used with our provided data. \"\"\"\n\n def load_slices(self, dataset_dir, n_images, n_patches, channels=['base']):\n \"\"\"Load a subset of the Slices dataset.\n dataset_dir: Root directory of the dataset.\n n_images: number of images to load. Will load in os.listdir list order.\n n_patches: number of patches to load per image.\n channels: list of strings indicating channels to be stacked in the image.\n currently \"base\", \"mf\", \"edges\" and \"none\" can be arbitrarily stacked.\n \"\"\"\n self.add_class('slices', 1, 'tissue')\n self.add_class('slices', 2, 'mag')\n image_list = os.listdir(dataset_dir)\n image_counter = 0\n patch_counter = 0\n for i in range(n_images):\n image_path = os.path.join(dataset_dir, image_list[i])\n patch_list = os.listdir(image_path)\n print(f'processing: image {i}')\n for j in range(n_patches):\n patch_path = os.path.join(image_path, patch_list[j])\n patch_image_path = os.path.join(patch_path, 'images')\n file_list = os.listdir(patch_image_path)\n image_file_path = os.path.join(patch_image_path, file_list[0])\n image = skimage.io.imread(image_file_path)\n height, width = image.shape\n self.add_image('slices', image_id=patch_counter, path=\n patch_path, width=width, height=height, channels=channels)\n patch_counter += 1\n\n def load_image(self, image_id):\n \"\"\"Returns an image with a given id.\"\"\"\n info = self.image_info[image_id]\n patch_path = info['path']\n width = info['width']\n height = info['height']\n impath = os.path.join(patch_path, 'images')\n file_list = os.listdir(impath)\n channels = info['channels']\n image = []\n for channel in channels:\n if channel == 'none':\n channel_image = skimage.img_as_ubyte(np.zeros((height, width)))\n else:\n channel_image_name = [x for x in file_list if channel in x][0]\n channel_image_path = os.path.join(impath, channel_image_name)\n channel_image = skimage.io.imread(channel_image_path)\n channel_image = skimage.img_as_ubyte(channel_image)\n image.append(channel_image)\n image = np.stack(image, axis=2)\n return image\n\n def load_mask(self, image_id):\n \"\"\"Loads masks from dataset.\n \"\"\"\n info = self.image_info[image_id]\n patch_path = info['path']\n height = info['height']\n width = info['width']\n mag_path = os.path.join(patch_path, 'mag')\n tissue_path = os.path.join(patch_path, 'tissue')\n mag_mask_list = os.listdir(mag_path)\n tissue_mask_list = os.listdir(tissue_path)\n classes = []\n masks = []\n if mag_mask_list:\n for filename in mag_mask_list:\n a = os.path.join(mag_path, filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(2)\n if tissue_mask_list:\n for filename in tissue_mask_list:\n a = os.path.join(tissue_path, filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(1)\n return np.stack(masks, axis=2), np.asarray(classes).astype(int)\n",
"step-5": "\n# coding: utf-8\n\n\n\n\nfrom mrcnn import utils\nimport numpy as np\nimport os\nimport skimage\n\n\nclass SlicesDataset(utils.Dataset):\n \"\"\" Extension of maskrcnn dataset class to be used with our provided data. \"\"\"\n \n \n def load_slices(self, dataset_dir, n_images, n_patches, channels = [\"base\"]):\n \"\"\"Load a subset of the Slices dataset.\n dataset_dir: Root directory of the dataset.\n n_images: number of images to load. Will load in os.listdir list order.\n n_patches: number of patches to load per image.\n channels: list of strings indicating channels to be stacked in the image.\n currently \"base\", \"mf\", \"edges\" and \"none\" can be arbitrarily stacked.\n \"\"\"\n \n # add classes to be trained on\n \n self.add_class(\"slices\", 1, \"tissue\")\n self.add_class(\"slices\", 2, \"mag\")\n \n # collect image list and initialize counter\n \n image_list = os.listdir(dataset_dir)\n image_counter = 0\n patch_counter = 0\n \n # cycle over images and save patches to database.\n \n for i in range(n_images):\n \n image_path = os.path.join(dataset_dir,image_list[i])\n patch_list = os.listdir(image_path)\n \n print(f\"processing: image {i}\") \n \n for j in range(n_patches):\n \n patch_path = os.path.join(image_path, patch_list[j])\n \n patch_image_path = os.path.join(patch_path,\"images\")\n \n file_list = os.listdir(patch_image_path)\n \n image_file_path = os.path.join(patch_image_path,file_list[0])\n \n image = skimage.io.imread(image_file_path)\n \n height, width = image.shape\n \n self.add_image(\n \"slices\",\n image_id = patch_counter,\n path = patch_path,\n width = width, height = height,\n channels = channels,\n )\n patch_counter += 1\n \n\n\n def load_image(self, image_id):\n \"\"\"Returns an image with a given id.\"\"\"\n \n # load image infos\n \n info = self.image_info[image_id]\n patch_path = info['path']\n width = info['width']\n height = info['height']\n impath = os.path.join(patch_path,\"images\")\n file_list = os.listdir(impath) \n channels = info['channels']\n \n image = []\n \n # stack channels to be loaded.\n \n for channel in channels:\n \n if channel == \"none\":\n channel_image = skimage.img_as_ubyte(np.zeros( (height,width) ) )\n \n else:\n channel_image_name = [x for x in file_list if channel in x][0] \n channel_image_path = os.path.join(impath, channel_image_name)\n channel_image = skimage.io.imread(channel_image_path)\n channel_image = skimage.img_as_ubyte(channel_image)\n image.append(channel_image)\n \n image = np.stack(image, axis=2)\n \n return image\n \n def load_mask(self, image_id):\n \"\"\"Loads masks from dataset.\n \"\"\"\n # load image infos\n \n info = self.image_info[image_id]\n patch_path = info['path']\n height = info['height']\n width = info['width']\n mag_path = os.path.join(patch_path,\"mag\")\n tissue_path = os.path.join(patch_path,\"tissue\")\n \n # collect mask names\n \n mag_mask_list = os.listdir(mag_path)\n tissue_mask_list = os.listdir(tissue_path)\n \n classes = []\n masks = []\n \n # append masks and ids in list\n \n if mag_mask_list:\n for filename in mag_mask_list:\n a = os.path.join(mag_path,filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(2)\n \n if tissue_mask_list:\n for filename in tissue_mask_list:\n a = os.path.join(tissue_path,filename)\n masks.append(skimage.io.imread(a).astype(bool))\n classes.append(1)\n \n return np.stack(masks,axis=2), np.asarray(classes).astype(int)\n\n",
"step-ids": [
2,
4,
5,
6,
7
]
}
|
[
2,
4,
5,
6,
7
] |
from models import Sensor
import mysql.connector as mariadb
## CREATE A DB WITH MARIADB ##
mariadb_connection = mariadb.connect(user='web', password='raspberry', database='PlantHubDB')
cursor = mariadb_connection.cursor()
def closeConnection():
cursor.close()
mariadb_connection.close()
return
def getTasks(amount):
mariadb_connection = mariadb.connect(user='web', password='raspberry', database='PlantHubDB')
cursor = mariadb_connection.cursor()
all_data = []
cursor.execute("SELECT * FROM Sensor")
all_entries = cursor.fetchall()
for row in all_entries:
entry = Sensor(row[0], row[1], row[2])
all_data.append(entry.data)
closeConnection()
return all_data
def getTask(task_id):
mariadb_connection = mariadb.connect(user='web', password='raspberry', database='PlantHubDB')
cursor = mariadb_connection.cursor()
cursor.execute("SELECT * FROM Sensor WHERE ID={}".format(task_id))
entry = cursor.fetchall()
data = Sensor(entry[0][0], entry[0][1], entry[0][2])
closeConnection()
return data.data
|
normal
|
{
"blob_id": "f471062573a5ec8cfeb194168edfba3d2700cac6",
"index": 9845,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef closeConnection():\n cursor.close()\n mariadb_connection.close()\n return\n\n\ndef getTasks(amount):\n mariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n all_data = []\n cursor.execute('SELECT * FROM Sensor')\n all_entries = cursor.fetchall()\n for row in all_entries:\n entry = Sensor(row[0], row[1], row[2])\n all_data.append(entry.data)\n closeConnection()\n return all_data\n\n\ndef getTask(task_id):\n mariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n cursor.execute('SELECT * FROM Sensor WHERE ID={}'.format(task_id))\n entry = cursor.fetchall()\n data = Sensor(entry[0][0], entry[0][1], entry[0][2])\n closeConnection()\n return data.data\n",
"step-3": "<mask token>\nmariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\ncursor = mariadb_connection.cursor()\n\n\ndef closeConnection():\n cursor.close()\n mariadb_connection.close()\n return\n\n\ndef getTasks(amount):\n mariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n all_data = []\n cursor.execute('SELECT * FROM Sensor')\n all_entries = cursor.fetchall()\n for row in all_entries:\n entry = Sensor(row[0], row[1], row[2])\n all_data.append(entry.data)\n closeConnection()\n return all_data\n\n\ndef getTask(task_id):\n mariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n cursor.execute('SELECT * FROM Sensor WHERE ID={}'.format(task_id))\n entry = cursor.fetchall()\n data = Sensor(entry[0][0], entry[0][1], entry[0][2])\n closeConnection()\n return data.data\n",
"step-4": "from models import Sensor\nimport mysql.connector as mariadb\nmariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\ncursor = mariadb_connection.cursor()\n\n\ndef closeConnection():\n cursor.close()\n mariadb_connection.close()\n return\n\n\ndef getTasks(amount):\n mariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n all_data = []\n cursor.execute('SELECT * FROM Sensor')\n all_entries = cursor.fetchall()\n for row in all_entries:\n entry = Sensor(row[0], row[1], row[2])\n all_data.append(entry.data)\n closeConnection()\n return all_data\n\n\ndef getTask(task_id):\n mariadb_connection = mariadb.connect(user='web', password='raspberry',\n database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n cursor.execute('SELECT * FROM Sensor WHERE ID={}'.format(task_id))\n entry = cursor.fetchall()\n data = Sensor(entry[0][0], entry[0][1], entry[0][2])\n closeConnection()\n return data.data\n",
"step-5": "from models import Sensor\nimport mysql.connector as mariadb\n\n## CREATE A DB WITH MARIADB ##\nmariadb_connection = mariadb.connect(user='web', password='raspberry', database='PlantHubDB')\ncursor = mariadb_connection.cursor()\n\ndef closeConnection():\n cursor.close()\n mariadb_connection.close()\n return\n\ndef getTasks(amount):\n mariadb_connection = mariadb.connect(user='web', password='raspberry', database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n all_data = []\n cursor.execute(\"SELECT * FROM Sensor\")\n all_entries = cursor.fetchall()\n\n for row in all_entries:\n entry = Sensor(row[0], row[1], row[2])\n all_data.append(entry.data)\n\n closeConnection()\n return all_data\n\ndef getTask(task_id):\n mariadb_connection = mariadb.connect(user='web', password='raspberry', database='PlantHubDB')\n cursor = mariadb_connection.cursor()\n cursor.execute(\"SELECT * FROM Sensor WHERE ID={}\".format(task_id))\n entry = cursor.fetchall()\n\n data = Sensor(entry[0][0], entry[0][1], entry[0][2])\n\n closeConnection()\n return data.data\n ",
"step-ids": [
0,
3,
4,
5,
6
]
}
|
[
0,
3,
4,
5,
6
] |
import sys
input = sys.stdin.readline
N = int(input())
A, B, C, D = [], [], [], []
for i in range(N):
a, b, c, d = map(int, input().split())
A.append(a)
B.append(b)
C.append(c)
D.append(d)
AB = []
CD = []
for i in range(N):
for j in range(N):
AB.append(A[i] + B[j])
CD.append(C[i] + D[j])
AB.sort()
CD.sort()
answer = 0
left, right = 0, len(CD) - 1
while left < len(AB) and right >= 0:
total = AB[left] + CD[right]
if total == 0:
left_count, right_count = 1, 1
left_tmp = left
left += 1
while left < len(AB) and AB[left] + CD[right] == 0:
left_count += 1
left += 1
right -= 1
while right >= 0 and AB[left_tmp] + CD[right] == 0:
right_count += 1
right -= 1
answer += (left_count * right_count)
elif total > 0:
right -= 1
else:
left += 1
print(answer)
|
normal
|
{
"blob_id": "2a9426653146603d9aa79a59ce181d97aa3c551c",
"index": 8525,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(N):\n a, b, c, d = map(int, input().split())\n A.append(a)\n B.append(b)\n C.append(c)\n D.append(d)\n<mask token>\nfor i in range(N):\n for j in range(N):\n AB.append(A[i] + B[j])\n CD.append(C[i] + D[j])\nAB.sort()\nCD.sort()\n<mask token>\nwhile left < len(AB) and right >= 0:\n total = AB[left] + CD[right]\n if total == 0:\n left_count, right_count = 1, 1\n left_tmp = left\n left += 1\n while left < len(AB) and AB[left] + CD[right] == 0:\n left_count += 1\n left += 1\n right -= 1\n while right >= 0 and AB[left_tmp] + CD[right] == 0:\n right_count += 1\n right -= 1\n answer += left_count * right_count\n elif total > 0:\n right -= 1\n else:\n left += 1\nprint(answer)\n",
"step-3": "<mask token>\ninput = sys.stdin.readline\nN = int(input())\nA, B, C, D = [], [], [], []\nfor i in range(N):\n a, b, c, d = map(int, input().split())\n A.append(a)\n B.append(b)\n C.append(c)\n D.append(d)\nAB = []\nCD = []\nfor i in range(N):\n for j in range(N):\n AB.append(A[i] + B[j])\n CD.append(C[i] + D[j])\nAB.sort()\nCD.sort()\nanswer = 0\nleft, right = 0, len(CD) - 1\nwhile left < len(AB) and right >= 0:\n total = AB[left] + CD[right]\n if total == 0:\n left_count, right_count = 1, 1\n left_tmp = left\n left += 1\n while left < len(AB) and AB[left] + CD[right] == 0:\n left_count += 1\n left += 1\n right -= 1\n while right >= 0 and AB[left_tmp] + CD[right] == 0:\n right_count += 1\n right -= 1\n answer += left_count * right_count\n elif total > 0:\n right -= 1\n else:\n left += 1\nprint(answer)\n",
"step-4": "import sys\ninput = sys.stdin.readline\nN = int(input())\nA, B, C, D = [], [], [], []\nfor i in range(N):\n a, b, c, d = map(int, input().split())\n A.append(a)\n B.append(b)\n C.append(c)\n D.append(d)\nAB = []\nCD = []\nfor i in range(N):\n for j in range(N):\n AB.append(A[i] + B[j])\n CD.append(C[i] + D[j])\nAB.sort()\nCD.sort()\nanswer = 0\nleft, right = 0, len(CD) - 1\nwhile left < len(AB) and right >= 0:\n total = AB[left] + CD[right]\n if total == 0:\n left_count, right_count = 1, 1\n left_tmp = left\n left += 1\n while left < len(AB) and AB[left] + CD[right] == 0:\n left_count += 1\n left += 1\n right -= 1\n while right >= 0 and AB[left_tmp] + CD[right] == 0:\n right_count += 1\n right -= 1\n answer += left_count * right_count\n elif total > 0:\n right -= 1\n else:\n left += 1\nprint(answer)\n",
"step-5": "import sys\ninput = sys.stdin.readline\n\nN = int(input())\nA, B, C, D = [], [], [], []\nfor i in range(N):\n a, b, c, d = map(int, input().split())\n A.append(a)\n B.append(b)\n C.append(c)\n D.append(d)\n\nAB = []\nCD = []\nfor i in range(N):\n for j in range(N):\n AB.append(A[i] + B[j])\n CD.append(C[i] + D[j])\n\nAB.sort()\nCD.sort()\n\nanswer = 0\nleft, right = 0, len(CD) - 1\nwhile left < len(AB) and right >= 0:\n total = AB[left] + CD[right]\n\n if total == 0: \n left_count, right_count = 1, 1\n left_tmp = left\n\n left += 1\n while left < len(AB) and AB[left] + CD[right] == 0:\n left_count += 1\n left += 1\n\n right -= 1\n while right >= 0 and AB[left_tmp] + CD[right] == 0:\n right_count += 1\n right -= 1\n \n answer += (left_count * right_count)\n\n elif total > 0:\n right -= 1\n else:\n left += 1\n\nprint(answer)",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [('products_app', '0003_auto_20200429_0739')]
operations = [migrations.CreateModel(name='User', fields=[('id', models
.AutoField(auto_created=True, primary_key=True, serialize=False,
verbose_name='ID')), ('name', models.CharField(max_length=100)), (
'email', models.EmailField(max_length=254))]), migrations.
RemoveField(model_name='item', name='stock'), migrations.
CreateModel(name='Order', fields=[('id', models.AutoField(
auto_created=True, primary_key=True, serialize=False, verbose_name=
'ID')), ('items', models.ManyToManyField(to='products_app.Item')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.
CASCADE, to='products_app.User'))])]
<|reserved_special_token_1|>
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [('products_app', '0003_auto_20200429_0739')]
operations = [migrations.CreateModel(name='User', fields=[('id', models
.AutoField(auto_created=True, primary_key=True, serialize=False,
verbose_name='ID')), ('name', models.CharField(max_length=100)), (
'email', models.EmailField(max_length=254))]), migrations.
RemoveField(model_name='item', name='stock'), migrations.
CreateModel(name='Order', fields=[('id', models.AutoField(
auto_created=True, primary_key=True, serialize=False, verbose_name=
'ID')), ('items', models.ManyToManyField(to='products_app.Item')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.
CASCADE, to='products_app.User'))])]
<|reserved_special_token_1|>
# Generated by Django 3.0.5 on 2020-04-30 06:26
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('products_app', '0003_auto_20200429_0739'),
]
operations = [
migrations.CreateModel(
name='User',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100)),
('email', models.EmailField(max_length=254)),
],
),
migrations.RemoveField(
model_name='item',
name='stock',
),
migrations.CreateModel(
name='Order',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('items', models.ManyToManyField(to='products_app.Item')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='products_app.User')),
],
),
]
|
flexible
|
{
"blob_id": "cdc8c8aba384b7b1b5e741ffe4309eaee30aaada",
"index": 5405,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('products_app', '0003_auto_20200429_0739')]\n operations = [migrations.CreateModel(name='User', fields=[('id', models\n .AutoField(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')), ('name', models.CharField(max_length=100)), (\n 'email', models.EmailField(max_length=254))]), migrations.\n RemoveField(model_name='item', name='stock'), migrations.\n CreateModel(name='Order', fields=[('id', models.AutoField(\n auto_created=True, primary_key=True, serialize=False, verbose_name=\n 'ID')), ('items', models.ManyToManyField(to='products_app.Item')),\n ('user', models.ForeignKey(on_delete=django.db.models.deletion.\n CASCADE, to='products_app.User'))])]\n",
"step-4": "from django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n dependencies = [('products_app', '0003_auto_20200429_0739')]\n operations = [migrations.CreateModel(name='User', fields=[('id', models\n .AutoField(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')), ('name', models.CharField(max_length=100)), (\n 'email', models.EmailField(max_length=254))]), migrations.\n RemoveField(model_name='item', name='stock'), migrations.\n CreateModel(name='Order', fields=[('id', models.AutoField(\n auto_created=True, primary_key=True, serialize=False, verbose_name=\n 'ID')), ('items', models.ManyToManyField(to='products_app.Item')),\n ('user', models.ForeignKey(on_delete=django.db.models.deletion.\n CASCADE, to='products_app.User'))])]\n",
"step-5": "# Generated by Django 3.0.5 on 2020-04-30 06:26\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('products_app', '0003_auto_20200429_0739'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='User',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=100)),\n ('email', models.EmailField(max_length=254)),\n ],\n ),\n migrations.RemoveField(\n model_name='item',\n name='stock',\n ),\n migrations.CreateModel(\n name='Order',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('items', models.ManyToManyField(to='products_app.Item')),\n ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='products_app.User')),\n ],\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
"""Create a new Node object and attach it a Linked List."""
class Node(object):
"""Build a node object."""
def __init__(self, data=None, next=None):
"""Constructor for the Node object."""
self.data = data
self.next = next
class LinkedList(object):
"""Build linked list."""
def __init__(self, iterable=()):
"""Constructor for the Linked List object."""
self.head = None
self._counter = 0
if isinstance(iterable, (str, tuple, list)):
for item in iterable:
self.push(item)
def push(self, val):
"""Add a new value to the head of the Linked List."""
new_head = Node(val, self.head)
self.head = new_head
self._counter += 1
def pop(self):
"""Remove and return the value if the head of the Linked List."""
if not self.head:
raise IndexError("Empty list, unable to pop")
output = self.head.data
self.head = self.head.next
self._counter -= 1
return output
def size(self):
"""Return size of our list."""
return self._counter
def search(self, val):
"""Search linked list for requested node."""
search_through = self.head
while search_through:
if val == search_through.data:
return search_through
else:
search_through = search_through.next
return search_through
def remove(self, node):
"""Remove selected node."""
current_node = self.head
previous_node = None
found = False
if current_node is None:
raise IndexError("Nothing in the list.")
try:
while current_node and found is False:
if node == current_node.data:
found = True
else:
previous_node = current_node
current_node = current_node.next
if previous_node is None:
self.pop()
elif current_node.next is None:
previous_node.next = None
else:
previous_node.next = current_node.next
except AttributeError:
raise ValueError("No such node.")
self._counter -= 1
def display(self):
"""Display nodes in linked list."""
node = self.head
display_this = []
while node:
display_this.append(node.data)
node = node.next
return str(display_this).replace("[", "(").replace("]", ")")
def __len__(self): # pragma: no cover
"""Return length of linked list."""
return self.size()
def __str__(self): # pragma: no cover
"""Display the linked list."""
return self.display()
|
normal
|
{
"blob_id": "192bd3c783f6f822f8e732ddf47d7fc3b22c032b",
"index": 1618,
"step-1": "<mask token>\n\n\nclass LinkedList(object):\n <mask token>\n\n def __init__(self, iterable=()):\n \"\"\"Constructor for the Linked List object.\"\"\"\n self.head = None\n self._counter = 0\n if isinstance(iterable, (str, tuple, list)):\n for item in iterable:\n self.push(item)\n\n def push(self, val):\n \"\"\"Add a new value to the head of the Linked List.\"\"\"\n new_head = Node(val, self.head)\n self.head = new_head\n self._counter += 1\n <mask token>\n <mask token>\n\n def search(self, val):\n \"\"\"Search linked list for requested node.\"\"\"\n search_through = self.head\n while search_through:\n if val == search_through.data:\n return search_through\n else:\n search_through = search_through.next\n return search_through\n\n def remove(self, node):\n \"\"\"Remove selected node.\"\"\"\n current_node = self.head\n previous_node = None\n found = False\n if current_node is None:\n raise IndexError('Nothing in the list.')\n try:\n while current_node and found is False:\n if node == current_node.data:\n found = True\n else:\n previous_node = current_node\n current_node = current_node.next\n if previous_node is None:\n self.pop()\n elif current_node.next is None:\n previous_node.next = None\n else:\n previous_node.next = current_node.next\n except AttributeError:\n raise ValueError('No such node.')\n self._counter -= 1\n <mask token>\n\n def __len__(self):\n \"\"\"Return length of linked list.\"\"\"\n return self.size()\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass LinkedList(object):\n <mask token>\n\n def __init__(self, iterable=()):\n \"\"\"Constructor for the Linked List object.\"\"\"\n self.head = None\n self._counter = 0\n if isinstance(iterable, (str, tuple, list)):\n for item in iterable:\n self.push(item)\n\n def push(self, val):\n \"\"\"Add a new value to the head of the Linked List.\"\"\"\n new_head = Node(val, self.head)\n self.head = new_head\n self._counter += 1\n\n def pop(self):\n \"\"\"Remove and return the value if the head of the Linked List.\"\"\"\n if not self.head:\n raise IndexError('Empty list, unable to pop')\n output = self.head.data\n self.head = self.head.next\n self._counter -= 1\n return output\n\n def size(self):\n \"\"\"Return size of our list.\"\"\"\n return self._counter\n\n def search(self, val):\n \"\"\"Search linked list for requested node.\"\"\"\n search_through = self.head\n while search_through:\n if val == search_through.data:\n return search_through\n else:\n search_through = search_through.next\n return search_through\n\n def remove(self, node):\n \"\"\"Remove selected node.\"\"\"\n current_node = self.head\n previous_node = None\n found = False\n if current_node is None:\n raise IndexError('Nothing in the list.')\n try:\n while current_node and found is False:\n if node == current_node.data:\n found = True\n else:\n previous_node = current_node\n current_node = current_node.next\n if previous_node is None:\n self.pop()\n elif current_node.next is None:\n previous_node.next = None\n else:\n previous_node.next = current_node.next\n except AttributeError:\n raise ValueError('No such node.')\n self._counter -= 1\n\n def display(self):\n \"\"\"Display nodes in linked list.\"\"\"\n node = self.head\n display_this = []\n while node:\n display_this.append(node.data)\n node = node.next\n return str(display_this).replace('[', '(').replace(']', ')')\n\n def __len__(self):\n \"\"\"Return length of linked list.\"\"\"\n return self.size()\n\n def __str__(self):\n \"\"\"Display the linked list.\"\"\"\n return self.display()\n",
"step-3": "<mask token>\n\n\nclass Node(object):\n <mask token>\n <mask token>\n\n\nclass LinkedList(object):\n \"\"\"Build linked list.\"\"\"\n\n def __init__(self, iterable=()):\n \"\"\"Constructor for the Linked List object.\"\"\"\n self.head = None\n self._counter = 0\n if isinstance(iterable, (str, tuple, list)):\n for item in iterable:\n self.push(item)\n\n def push(self, val):\n \"\"\"Add a new value to the head of the Linked List.\"\"\"\n new_head = Node(val, self.head)\n self.head = new_head\n self._counter += 1\n\n def pop(self):\n \"\"\"Remove and return the value if the head of the Linked List.\"\"\"\n if not self.head:\n raise IndexError('Empty list, unable to pop')\n output = self.head.data\n self.head = self.head.next\n self._counter -= 1\n return output\n\n def size(self):\n \"\"\"Return size of our list.\"\"\"\n return self._counter\n\n def search(self, val):\n \"\"\"Search linked list for requested node.\"\"\"\n search_through = self.head\n while search_through:\n if val == search_through.data:\n return search_through\n else:\n search_through = search_through.next\n return search_through\n\n def remove(self, node):\n \"\"\"Remove selected node.\"\"\"\n current_node = self.head\n previous_node = None\n found = False\n if current_node is None:\n raise IndexError('Nothing in the list.')\n try:\n while current_node and found is False:\n if node == current_node.data:\n found = True\n else:\n previous_node = current_node\n current_node = current_node.next\n if previous_node is None:\n self.pop()\n elif current_node.next is None:\n previous_node.next = None\n else:\n previous_node.next = current_node.next\n except AttributeError:\n raise ValueError('No such node.')\n self._counter -= 1\n\n def display(self):\n \"\"\"Display nodes in linked list.\"\"\"\n node = self.head\n display_this = []\n while node:\n display_this.append(node.data)\n node = node.next\n return str(display_this).replace('[', '(').replace(']', ')')\n\n def __len__(self):\n \"\"\"Return length of linked list.\"\"\"\n return self.size()\n\n def __str__(self):\n \"\"\"Display the linked list.\"\"\"\n return self.display()\n",
"step-4": "<mask token>\n\n\nclass Node(object):\n \"\"\"Build a node object.\"\"\"\n\n def __init__(self, data=None, next=None):\n \"\"\"Constructor for the Node object.\"\"\"\n self.data = data\n self.next = next\n\n\nclass LinkedList(object):\n \"\"\"Build linked list.\"\"\"\n\n def __init__(self, iterable=()):\n \"\"\"Constructor for the Linked List object.\"\"\"\n self.head = None\n self._counter = 0\n if isinstance(iterable, (str, tuple, list)):\n for item in iterable:\n self.push(item)\n\n def push(self, val):\n \"\"\"Add a new value to the head of the Linked List.\"\"\"\n new_head = Node(val, self.head)\n self.head = new_head\n self._counter += 1\n\n def pop(self):\n \"\"\"Remove and return the value if the head of the Linked List.\"\"\"\n if not self.head:\n raise IndexError('Empty list, unable to pop')\n output = self.head.data\n self.head = self.head.next\n self._counter -= 1\n return output\n\n def size(self):\n \"\"\"Return size of our list.\"\"\"\n return self._counter\n\n def search(self, val):\n \"\"\"Search linked list for requested node.\"\"\"\n search_through = self.head\n while search_through:\n if val == search_through.data:\n return search_through\n else:\n search_through = search_through.next\n return search_through\n\n def remove(self, node):\n \"\"\"Remove selected node.\"\"\"\n current_node = self.head\n previous_node = None\n found = False\n if current_node is None:\n raise IndexError('Nothing in the list.')\n try:\n while current_node and found is False:\n if node == current_node.data:\n found = True\n else:\n previous_node = current_node\n current_node = current_node.next\n if previous_node is None:\n self.pop()\n elif current_node.next is None:\n previous_node.next = None\n else:\n previous_node.next = current_node.next\n except AttributeError:\n raise ValueError('No such node.')\n self._counter -= 1\n\n def display(self):\n \"\"\"Display nodes in linked list.\"\"\"\n node = self.head\n display_this = []\n while node:\n display_this.append(node.data)\n node = node.next\n return str(display_this).replace('[', '(').replace(']', ')')\n\n def __len__(self):\n \"\"\"Return length of linked list.\"\"\"\n return self.size()\n\n def __str__(self):\n \"\"\"Display the linked list.\"\"\"\n return self.display()\n",
"step-5": "\"\"\"Create a new Node object and attach it a Linked List.\"\"\"\n\n\nclass Node(object):\n \"\"\"Build a node object.\"\"\"\n\n def __init__(self, data=None, next=None):\n \"\"\"Constructor for the Node object.\"\"\"\n self.data = data\n self.next = next\n\n\nclass LinkedList(object):\n \"\"\"Build linked list.\"\"\"\n\n def __init__(self, iterable=()):\n \"\"\"Constructor for the Linked List object.\"\"\"\n self.head = None\n self._counter = 0\n if isinstance(iterable, (str, tuple, list)):\n for item in iterable:\n self.push(item)\n\n def push(self, val):\n \"\"\"Add a new value to the head of the Linked List.\"\"\"\n new_head = Node(val, self.head)\n self.head = new_head\n self._counter += 1\n\n def pop(self):\n \"\"\"Remove and return the value if the head of the Linked List.\"\"\"\n if not self.head:\n raise IndexError(\"Empty list, unable to pop\")\n output = self.head.data\n self.head = self.head.next\n self._counter -= 1\n return output\n\n def size(self):\n \"\"\"Return size of our list.\"\"\"\n return self._counter\n\n def search(self, val):\n \"\"\"Search linked list for requested node.\"\"\"\n search_through = self.head\n while search_through:\n if val == search_through.data:\n return search_through\n else:\n search_through = search_through.next\n return search_through\n\n def remove(self, node):\n \"\"\"Remove selected node.\"\"\"\n current_node = self.head\n previous_node = None\n found = False\n if current_node is None:\n raise IndexError(\"Nothing in the list.\")\n try:\n while current_node and found is False:\n if node == current_node.data:\n found = True\n else:\n previous_node = current_node\n current_node = current_node.next\n if previous_node is None:\n self.pop()\n elif current_node.next is None:\n previous_node.next = None\n else:\n previous_node.next = current_node.next\n except AttributeError:\n raise ValueError(\"No such node.\")\n self._counter -= 1\n\n def display(self):\n \"\"\"Display nodes in linked list.\"\"\"\n node = self.head\n display_this = []\n while node:\n display_this.append(node.data)\n node = node.next\n return str(display_this).replace(\"[\", \"(\").replace(\"]\", \")\")\n\n def __len__(self): # pragma: no cover\n \"\"\"Return length of linked list.\"\"\"\n return self.size()\n\n def __str__(self): # pragma: no cover\n \"\"\"Display the linked list.\"\"\"\n return self.display()\n",
"step-ids": [
6,
10,
12,
14,
15
]
}
|
[
6,
10,
12,
14,
15
] |
REGION_LIST = [
'Центральный',
'Северо-Западный',
'Южный',
'Северо-Кавказский',
'Приволжский',
'Уральский',
'Сибирский',
'Дальневосточный',
]
CITY_LIST = {
'Абакан': 7,
'Альметьевск': 5,
'Ангарск': 7,
'Архангельск': 2,
'Астрахань': 3,
'Барнаул': 7,
'Батайск': 3,
'Белгород': 1,
'Бийск': 7,
'Благовещенск': 8,
'Братск': 7,
'Брянск': 1,
'Великий Новгород': 2,
'Владивосток': 8,
'Владикавказ': 4,
'Владимир': 1,
'Волгоград': 3,
'Волжский': 3,
'Вологда': 2,
'Воронеж': 1,
'Грозный': 4,
'Дзержинск': 5,
'Екатеринбург': 6,
'Иваново': 1,
'Ижевск': 5,
'Иркутск': 7,
'Йошкар-Ола': 5,
'Казань': 5,
'Калининград': 2,
'Калуга': 1,
'Кемерово': 7,
'Киров': 5,
'Комсомольск-на-Амуре': 8,
'Кострома': 1,
'Краснодар': 3,
'Красноярск': 7,
'Курган': 6,
'Курск': 1,
'Липецк': 1,
'Магнитогорск': 6,
'Махачкала': 4,
'Миасс': 6,
'Минеральные Воды': 4,
'Москва и Подмосковье': 1,
'Москва': 1,
'Мурманск': 2,
'Набережные Челны': 5,
'Нальчик': 4,
'Нефтекамск': 5,
'Нижневартовск': 6,
'Нижнекамск': 5,
'Нижний Новгород': 5,
'Нижний Тагил': 6,
'Новокузнецк': 7,
'Новомосковск': 1,
'Новороссийск': 3,
'Новосибирск': 7,
'Ноябрьск': 6,
'Обнинск': 1,
'Октябрьский': 5,
'Омск': 7,
'Орел': 1,
'Оренбург': 5,
'Орск': 5,
'Пенза': 5,
'Пермь': 5,
'Петрозаводск': 2,
'Петропавловск-Камчатский': 8,
'Прокопьевск': 7,
'Псков': 2,
'Пятигорск': 4,
'Ростов-на-Дону': 3,
'Рязань': 1,
'Самара': 5,
'Санкт-Петербург': 2,
'Саранск': 5,
'Саратов': 5,
'Севастополь': 3,
'Северодвинск': 2,
'Симферополь': 3,
'Смоленск': 1,
'Сочи': 3,
'Ставрополь': 4,
'Старый Оскол': 1,
'Стерлитамак': 5,
'Сургут': 6,
'Сыктывкар': 2,
'Таганрог': 3,
'Тамбов': 1,
'Тверь': 1,
'Тольятти': 5,
'Томск': 7,
'Тула': 1,
'Тюмень': 6,
'Улан-Удэ': 7,
'Ульяновск': 5,
'Уфа': 5,
'Хабаровск': 8,
'Чебоксары': 5,
'Челябинск': 6,
'Череповец': 2,
'Чита': 7,
'Шахты': 3,
'Энгельс': 5,
'Южно-Сахалинск': 8,
'Якутск': 8,
'Ярославль': 1,
}
|
normal
|
{
"blob_id": "2101299d6f6bfcd4726591fc256317968373ca1f",
"index": 3071,
"step-1": "<mask token>\n",
"step-2": "REGION_LIST = ['Центральный', 'Северо-Западный', 'Южный',\n 'Северо-Кавказский', 'Приволжский', 'Уральский', 'Сибирский',\n 'Дальневосточный']\nCITY_LIST = {'Абакан': 7, 'Альметьевск': 5, 'Ангарск': 7, 'Архангельск': 2,\n 'Астрахань': 3, 'Барнаул': 7, 'Батайск': 3, 'Белгород': 1, 'Бийск': 7,\n 'Благовещенск': 8, 'Братск': 7, 'Брянск': 1, 'Великий Новгород': 2,\n 'Владивосток': 8, 'Владикавказ': 4, 'Владимир': 1, 'Волгоград': 3,\n 'Волжский': 3, 'Вологда': 2, 'Воронеж': 1, 'Грозный': 4, 'Дзержинск': 5,\n 'Екатеринбург': 6, 'Иваново': 1, 'Ижевск': 5, 'Иркутск': 7,\n 'Йошкар-Ола': 5, 'Казань': 5, 'Калининград': 2, 'Калуга': 1, 'Кемерово':\n 7, 'Киров': 5, 'Комсомольск-на-Амуре': 8, 'Кострома': 1, 'Краснодар': 3,\n 'Красноярск': 7, 'Курган': 6, 'Курск': 1, 'Липецк': 1, 'Магнитогорск': \n 6, 'Махачкала': 4, 'Миасс': 6, 'Минеральные Воды': 4,\n 'Москва и Подмосковье': 1, 'Москва': 1, 'Мурманск': 2,\n 'Набережные Челны': 5, 'Нальчик': 4, 'Нефтекамск': 5, 'Нижневартовск': \n 6, 'Нижнекамск': 5, 'Нижний Новгород': 5, 'Нижний Тагил': 6,\n 'Новокузнецк': 7, 'Новомосковск': 1, 'Новороссийск': 3, 'Новосибирск': \n 7, 'Ноябрьск': 6, 'Обнинск': 1, 'Октябрьский': 5, 'Омск': 7, 'Орел': 1,\n 'Оренбург': 5, 'Орск': 5, 'Пенза': 5, 'Пермь': 5, 'Петрозаводск': 2,\n 'Петропавловск-Камчатский': 8, 'Прокопьевск': 7, 'Псков': 2,\n 'Пятигорск': 4, 'Ростов-на-Дону': 3, 'Рязань': 1, 'Самара': 5,\n 'Санкт-Петербург': 2, 'Саранск': 5, 'Саратов': 5, 'Севастополь': 3,\n 'Северодвинск': 2, 'Симферополь': 3, 'Смоленск': 1, 'Сочи': 3,\n 'Ставрополь': 4, 'Старый Оскол': 1, 'Стерлитамак': 5, 'Сургут': 6,\n 'Сыктывкар': 2, 'Таганрог': 3, 'Тамбов': 1, 'Тверь': 1, 'Тольятти': 5,\n 'Томск': 7, 'Тула': 1, 'Тюмень': 6, 'Улан-Удэ': 7, 'Ульяновск': 5,\n 'Уфа': 5, 'Хабаровск': 8, 'Чебоксары': 5, 'Челябинск': 6, 'Череповец': \n 2, 'Чита': 7, 'Шахты': 3, 'Энгельс': 5, 'Южно-Сахалинск': 8, 'Якутск': \n 8, 'Ярославль': 1}\n",
"step-3": "REGION_LIST = [\n 'Центральный',\n 'Северо-Западный',\n 'Южный',\n 'Северо-Кавказский',\n 'Приволжский',\n 'Уральский',\n 'Сибирский',\n 'Дальневосточный',\n]\n\nCITY_LIST = {\n 'Абакан': 7,\n 'Альметьевск': 5,\n 'Ангарск': 7,\n 'Архангельск': 2,\n 'Астрахань': 3,\n 'Барнаул': 7,\n 'Батайск': 3,\n 'Белгород': 1,\n 'Бийск': 7,\n 'Благовещенск': 8,\n 'Братск': 7,\n 'Брянск': 1,\n 'Великий Новгород': 2,\n 'Владивосток': 8,\n 'Владикавказ': 4,\n 'Владимир': 1,\n 'Волгоград': 3,\n 'Волжский': 3,\n 'Вологда': 2,\n 'Воронеж': 1,\n 'Грозный': 4,\n 'Дзержинск': 5,\n 'Екатеринбург': 6,\n 'Иваново': 1,\n 'Ижевск': 5,\n 'Иркутск': 7,\n 'Йошкар-Ола': 5,\n 'Казань': 5,\n 'Калининград': 2,\n 'Калуга': 1,\n 'Кемерово': 7,\n 'Киров': 5,\n 'Комсомольск-на-Амуре': 8,\n 'Кострома': 1,\n 'Краснодар': 3,\n 'Красноярск': 7,\n 'Курган': 6,\n 'Курск': 1,\n 'Липецк': 1,\n 'Магнитогорск': 6,\n 'Махачкала': 4,\n 'Миасс': 6,\n 'Минеральные Воды': 4,\n 'Москва и Подмосковье': 1,\n 'Москва': 1,\n 'Мурманск': 2,\n 'Набережные Челны': 5,\n 'Нальчик': 4,\n 'Нефтекамск': 5,\n 'Нижневартовск': 6,\n 'Нижнекамск': 5,\n 'Нижний Новгород': 5,\n 'Нижний Тагил': 6,\n 'Новокузнецк': 7,\n 'Новомосковск': 1,\n 'Новороссийск': 3,\n 'Новосибирск': 7,\n 'Ноябрьск': 6,\n 'Обнинск': 1,\n 'Октябрьский': 5,\n 'Омск': 7,\n 'Орел': 1,\n 'Оренбург': 5,\n 'Орск': 5,\n 'Пенза': 5,\n 'Пермь': 5,\n 'Петрозаводск': 2,\n 'Петропавловск-Камчатский': 8,\n 'Прокопьевск': 7,\n 'Псков': 2,\n 'Пятигорск': 4,\n 'Ростов-на-Дону': 3,\n 'Рязань': 1,\n 'Самара': 5,\n 'Санкт-Петербург': 2,\n 'Саранск': 5,\n 'Саратов': 5,\n 'Севастополь': 3,\n 'Северодвинск': 2,\n 'Симферополь': 3,\n 'Смоленск': 1,\n 'Сочи': 3,\n 'Ставрополь': 4,\n 'Старый Оскол': 1,\n 'Стерлитамак': 5,\n 'Сургут': 6,\n 'Сыктывкар': 2,\n 'Таганрог': 3,\n 'Тамбов': 1,\n 'Тверь': 1,\n 'Тольятти': 5,\n 'Томск': 7,\n 'Тула': 1,\n 'Тюмень': 6,\n 'Улан-Удэ': 7,\n 'Ульяновск': 5,\n 'Уфа': 5,\n 'Хабаровск': 8,\n 'Чебоксары': 5,\n 'Челябинск': 6,\n 'Череповец': 2,\n 'Чита': 7,\n 'Шахты': 3,\n 'Энгельс': 5,\n 'Южно-Сахалинск': 8,\n 'Якутск': 8,\n 'Ярославль': 1,\n}\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
embed()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
b = webdriver.Firefox()
embed()
<|reserved_special_token_1|>
from IPython import embed
from selenium import webdriver
b = webdriver.Firefox()
embed()
|
flexible
|
{
"blob_id": "9aa54f1259aceb052cfba74cedcfadfe68778ebd",
"index": 1020,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nembed()\n",
"step-3": "<mask token>\nb = webdriver.Firefox()\nembed()\n",
"step-4": "from IPython import embed\nfrom selenium import webdriver\nb = webdriver.Firefox()\nembed()\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
# import core modules and community packages
import sys, math, random
import pygame
# import configuration settings
from src.config import *
from src.board.levels import LEVEL_1
# import game elements
from src.pucman import Pucman
from src.ghast import Ghast
from src.board.board import Board
class Session():
def __init__(self, MODE="PLAYING"):
# init all game props
pygame.init()
# initialize game elements
board = Board(
size=BOARD_SIZE,
color=COLOR['BACKGROUND'],
level=LEVEL_1
)
pucman = Pucman(
start=board.findUniquePos(BOARD_ELEMENT_MAP['PUCMAN_START']),
size=board.tileSize,
color=COLOR['PUCMAN'],
MODE=MODE
)
ghasts = {
"blinky": Ghast(
name="Blinky",
start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']),
size=board.tileSize,
color=COLOR['BLINKY']
),
"pinky": Ghast(
name="Pinky",
start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']),
size=board.tileSize,
color=COLOR['PINKY']
),
"inky": Ghast(
name="Inky",
start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']),
size=board.tileSize,
color=COLOR['INKY']
),
"clyde": Ghast(
name="Clyde",
start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']),
size=board.tileSize,
color=COLOR['CLYDE']
)
}
self.board = board
self.pucman = pucman
self.ghasts = ghasts
self.clock = pygame.time.Clock()
self.MODE = MODE
def start(self):
# draw background & begin session
self.board.draw()
session = True
# while playing
while session:
# manage game time, 5 ticks per second
self.clock.tick(TICK_RATE[self.MODE])
# pygame.time.delay(50)
# update player state
self.pucman.move(self.board)
# Ghast-AI behavior
for ghast in self.ghasts:
sprite = self.ghasts[ghast]
sprite.move(self.pucman.pos, self.board)
if(sprite.atPucman(self.pucman.pos)):
session = False
print("You died to " + sprite.name)
# begin drawing back to front
self.board.draw()
self.pucman.draw(self.board)
for ghast in self.ghasts:
self.ghasts[ghast].draw(self.board._)
# update board
pygame.display.update()
|
normal
|
{
"blob_id": "f51a21ed71ede4e7462d9c77cb932a5f05b09e71",
"index": 9174,
"step-1": "<mask token>\n\n\nclass Session:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Session:\n <mask token>\n\n def start(self):\n self.board.draw()\n session = True\n while session:\n self.clock.tick(TICK_RATE[self.MODE])\n self.pucman.move(self.board)\n for ghast in self.ghasts:\n sprite = self.ghasts[ghast]\n sprite.move(self.pucman.pos, self.board)\n if sprite.atPucman(self.pucman.pos):\n session = False\n print('You died to ' + sprite.name)\n self.board.draw()\n self.pucman.draw(self.board)\n for ghast in self.ghasts:\n self.ghasts[ghast].draw(self.board._)\n pygame.display.update()\n",
"step-3": "<mask token>\n\n\nclass Session:\n\n def __init__(self, MODE='PLAYING'):\n pygame.init()\n board = Board(size=BOARD_SIZE, color=COLOR['BACKGROUND'], level=LEVEL_1\n )\n pucman = Pucman(start=board.findUniquePos(BOARD_ELEMENT_MAP[\n 'PUCMAN_START']), size=board.tileSize, color=COLOR['PUCMAN'],\n MODE=MODE)\n ghasts = {'blinky': Ghast(name='Blinky', start=board.findUniquePos(\n BOARD_ELEMENT_MAP['GHAST_SPAWN']), size=board.tileSize, color=\n COLOR['BLINKY']), 'pinky': Ghast(name='Pinky', start=board.\n findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']), size=board.\n tileSize, color=COLOR['PINKY']), 'inky': Ghast(name='Inky',\n start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']),\n size=board.tileSize, color=COLOR['INKY']), 'clyde': Ghast(name=\n 'Clyde', start=board.findUniquePos(BOARD_ELEMENT_MAP[\n 'GHAST_SPAWN']), size=board.tileSize, color=COLOR['CLYDE'])}\n self.board = board\n self.pucman = pucman\n self.ghasts = ghasts\n self.clock = pygame.time.Clock()\n self.MODE = MODE\n\n def start(self):\n self.board.draw()\n session = True\n while session:\n self.clock.tick(TICK_RATE[self.MODE])\n self.pucman.move(self.board)\n for ghast in self.ghasts:\n sprite = self.ghasts[ghast]\n sprite.move(self.pucman.pos, self.board)\n if sprite.atPucman(self.pucman.pos):\n session = False\n print('You died to ' + sprite.name)\n self.board.draw()\n self.pucman.draw(self.board)\n for ghast in self.ghasts:\n self.ghasts[ghast].draw(self.board._)\n pygame.display.update()\n",
"step-4": "import sys, math, random\nimport pygame\nfrom src.config import *\nfrom src.board.levels import LEVEL_1\nfrom src.pucman import Pucman\nfrom src.ghast import Ghast\nfrom src.board.board import Board\n\n\nclass Session:\n\n def __init__(self, MODE='PLAYING'):\n pygame.init()\n board = Board(size=BOARD_SIZE, color=COLOR['BACKGROUND'], level=LEVEL_1\n )\n pucman = Pucman(start=board.findUniquePos(BOARD_ELEMENT_MAP[\n 'PUCMAN_START']), size=board.tileSize, color=COLOR['PUCMAN'],\n MODE=MODE)\n ghasts = {'blinky': Ghast(name='Blinky', start=board.findUniquePos(\n BOARD_ELEMENT_MAP['GHAST_SPAWN']), size=board.tileSize, color=\n COLOR['BLINKY']), 'pinky': Ghast(name='Pinky', start=board.\n findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']), size=board.\n tileSize, color=COLOR['PINKY']), 'inky': Ghast(name='Inky',\n start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']),\n size=board.tileSize, color=COLOR['INKY']), 'clyde': Ghast(name=\n 'Clyde', start=board.findUniquePos(BOARD_ELEMENT_MAP[\n 'GHAST_SPAWN']), size=board.tileSize, color=COLOR['CLYDE'])}\n self.board = board\n self.pucman = pucman\n self.ghasts = ghasts\n self.clock = pygame.time.Clock()\n self.MODE = MODE\n\n def start(self):\n self.board.draw()\n session = True\n while session:\n self.clock.tick(TICK_RATE[self.MODE])\n self.pucman.move(self.board)\n for ghast in self.ghasts:\n sprite = self.ghasts[ghast]\n sprite.move(self.pucman.pos, self.board)\n if sprite.atPucman(self.pucman.pos):\n session = False\n print('You died to ' + sprite.name)\n self.board.draw()\n self.pucman.draw(self.board)\n for ghast in self.ghasts:\n self.ghasts[ghast].draw(self.board._)\n pygame.display.update()\n",
"step-5": "# import core modules and community packages\nimport sys, math, random\nimport pygame\n\n# import configuration settings\nfrom src.config import *\nfrom src.board.levels import LEVEL_1\n\n# import game elements\nfrom src.pucman import Pucman\nfrom src.ghast import Ghast\nfrom src.board.board import Board\n\nclass Session():\n def __init__(self, MODE=\"PLAYING\"):\n # init all game props\n pygame.init()\n\n # initialize game elements\n board = Board(\n size=BOARD_SIZE, \n color=COLOR['BACKGROUND'], \n level=LEVEL_1\n )\n pucman = Pucman(\n start=board.findUniquePos(BOARD_ELEMENT_MAP['PUCMAN_START']), \n size=board.tileSize, \n color=COLOR['PUCMAN'], \n MODE=MODE\n )\n ghasts = {\n \"blinky\": Ghast(\n name=\"Blinky\",\n start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']), \n size=board.tileSize, \n color=COLOR['BLINKY']\n ),\n \"pinky\": Ghast(\n name=\"Pinky\",\n start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']), \n size=board.tileSize, \n color=COLOR['PINKY']\n ),\n \"inky\": Ghast(\n name=\"Inky\",\n start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']), \n size=board.tileSize, \n color=COLOR['INKY']\n ),\n \"clyde\": Ghast(\n name=\"Clyde\",\n start=board.findUniquePos(BOARD_ELEMENT_MAP['GHAST_SPAWN']), \n size=board.tileSize, \n color=COLOR['CLYDE'] \n )\n }\n\n self.board = board\n self.pucman = pucman\n self.ghasts = ghasts\n self.clock = pygame.time.Clock()\n self.MODE = MODE\n\n def start(self):\n # draw background & begin session\n self.board.draw()\n session = True\n\n # while playing\n while session:\n # manage game time, 5 ticks per second\n self.clock.tick(TICK_RATE[self.MODE])\n # pygame.time.delay(50)\n\n # update player state\n self.pucman.move(self.board)\n\n # Ghast-AI behavior\n for ghast in self.ghasts:\n sprite = self.ghasts[ghast]\n\n sprite.move(self.pucman.pos, self.board)\n if(sprite.atPucman(self.pucman.pos)):\n session = False\n print(\"You died to \" + sprite.name)\n\n # begin drawing back to front\n self.board.draw()\n self.pucman.draw(self.board)\n for ghast in self.ghasts:\n self.ghasts[ghast].draw(self.board._)\n \n # update board\n pygame.display.update()\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
trainImageList.append(image)
<|reserved_special_token_0|>
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
testImageList.append(image)
<|reserved_special_token_0|>
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
valImageList.append(image)
<|reserved_special_token_0|>
pickle.dump(dataset, output_file)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
f = open('./digitdata/trainingimages', 'r')
reader = f.readlines()
labels = open('./digitdata/traininglabels', 'r')
lreader = labels.readlines()
trainImageList = []
j = 0
i = 0
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
trainImageList.append(image)
f = open('./digitdata/testimages', 'r')
reader = f.readlines()
labels = open('./digitdata/testlabels', 'r')
lreader = labels.readlines()
testImageList = []
j = 0
i = 0
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
testImageList.append(image)
f = open('./digitdata/validationimages', 'r')
reader = f.readlines()
labels = open('./digitdata/validationlabels', 'r')
lreader = labels.readlines()
valImageList = []
j = 0
i = 0
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
valImageList.append(image)
dataset = Dataset(trainImageList, testImageList, valImageList)
output_file = open('digits_dataset', 'wb')
pickle.dump(dataset, output_file)
<|reserved_special_token_1|>
from numpy import empty
import pickle
from dataset import Dataset
from image import Image
f = open('./digitdata/trainingimages', 'r')
reader = f.readlines()
labels = open('./digitdata/traininglabels', 'r')
lreader = labels.readlines()
trainImageList = []
j = 0
i = 0
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
trainImageList.append(image)
f = open('./digitdata/testimages', 'r')
reader = f.readlines()
labels = open('./digitdata/testlabels', 'r')
lreader = labels.readlines()
testImageList = []
j = 0
i = 0
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
testImageList.append(image)
f = open('./digitdata/validationimages', 'r')
reader = f.readlines()
labels = open('./digitdata/validationlabels', 'r')
lreader = labels.readlines()
valImageList = []
j = 0
i = 0
while j < len(reader):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
valImageList.append(image)
dataset = Dataset(trainImageList, testImageList, valImageList)
output_file = open('digits_dataset', 'wb')
pickle.dump(dataset, output_file)
<|reserved_special_token_1|>
from numpy import empty
import pickle
from dataset import Dataset
from image import Image
f = open("./digitdata/trainingimages", "r")
reader = f.readlines()
labels = open("./digitdata/traininglabels", "r")
lreader = labels.readlines()
trainImageList = []
j = 0
i = 0
while(j < len(reader)):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
trainImageList.append(image)
f = open("./digitdata/testimages", "r")
reader = f.readlines()
labels = open("./digitdata/testlabels", "r")
lreader = labels.readlines()
testImageList = []
j = 0
i = 0
while(j < len(reader)):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
testImageList.append(image)
f = open("./digitdata/validationimages", "r")
reader = f.readlines()
labels = open("./digitdata/validationlabels", "r")
lreader = labels.readlines()
valImageList = []
j = 0
i = 0
while(j < len(reader)):
image_array = empty([28, 28])
for r in range(0, 28):
row = reader[j]
j += 1
for c in range(0, 28):
if row[c] == '#' or row[c] == '+':
image_array[r][c] = 1
else:
image_array[r][c] = 0
label = lreader[i]
i += 1
image = Image(image_array, label)
valImageList.append(image)
dataset = Dataset(trainImageList, testImageList, valImageList)
output_file = open('digits_dataset', 'wb')
pickle.dump(dataset, output_file)
|
flexible
|
{
"blob_id": "aff439361716c35e5f492680a55e7470b4ee0c42",
"index": 5905,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n trainImageList.append(image)\n<mask token>\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n testImageList.append(image)\n<mask token>\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n valImageList.append(image)\n<mask token>\npickle.dump(dataset, output_file)\n",
"step-3": "<mask token>\nf = open('./digitdata/trainingimages', 'r')\nreader = f.readlines()\nlabels = open('./digitdata/traininglabels', 'r')\nlreader = labels.readlines()\ntrainImageList = []\nj = 0\ni = 0\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n trainImageList.append(image)\nf = open('./digitdata/testimages', 'r')\nreader = f.readlines()\nlabels = open('./digitdata/testlabels', 'r')\nlreader = labels.readlines()\ntestImageList = []\nj = 0\ni = 0\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n testImageList.append(image)\nf = open('./digitdata/validationimages', 'r')\nreader = f.readlines()\nlabels = open('./digitdata/validationlabels', 'r')\nlreader = labels.readlines()\nvalImageList = []\nj = 0\ni = 0\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n valImageList.append(image)\ndataset = Dataset(trainImageList, testImageList, valImageList)\noutput_file = open('digits_dataset', 'wb')\npickle.dump(dataset, output_file)\n",
"step-4": "from numpy import empty\nimport pickle\nfrom dataset import Dataset\nfrom image import Image\nf = open('./digitdata/trainingimages', 'r')\nreader = f.readlines()\nlabels = open('./digitdata/traininglabels', 'r')\nlreader = labels.readlines()\ntrainImageList = []\nj = 0\ni = 0\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n trainImageList.append(image)\nf = open('./digitdata/testimages', 'r')\nreader = f.readlines()\nlabels = open('./digitdata/testlabels', 'r')\nlreader = labels.readlines()\ntestImageList = []\nj = 0\ni = 0\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n testImageList.append(image)\nf = open('./digitdata/validationimages', 'r')\nreader = f.readlines()\nlabels = open('./digitdata/validationlabels', 'r')\nlreader = labels.readlines()\nvalImageList = []\nj = 0\ni = 0\nwhile j < len(reader):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n valImageList.append(image)\ndataset = Dataset(trainImageList, testImageList, valImageList)\noutput_file = open('digits_dataset', 'wb')\npickle.dump(dataset, output_file)\n",
"step-5": "from numpy import empty\nimport pickle\nfrom dataset import Dataset\nfrom image import Image\n\nf = open(\"./digitdata/trainingimages\", \"r\")\nreader = f.readlines()\n\nlabels = open(\"./digitdata/traininglabels\", \"r\")\nlreader = labels.readlines()\n\ntrainImageList = []\n\nj = 0\ni = 0\nwhile(j < len(reader)):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n trainImageList.append(image)\n\n\nf = open(\"./digitdata/testimages\", \"r\")\nreader = f.readlines()\n\nlabels = open(\"./digitdata/testlabels\", \"r\")\nlreader = labels.readlines()\n\ntestImageList = []\n\nj = 0\ni = 0\nwhile(j < len(reader)):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n testImageList.append(image)\n\nf = open(\"./digitdata/validationimages\", \"r\")\nreader = f.readlines()\n\nlabels = open(\"./digitdata/validationlabels\", \"r\")\nlreader = labels.readlines()\n\nvalImageList = []\n\nj = 0\ni = 0\nwhile(j < len(reader)):\n image_array = empty([28, 28])\n for r in range(0, 28):\n row = reader[j]\n j += 1\n for c in range(0, 28):\n if row[c] == '#' or row[c] == '+':\n image_array[r][c] = 1\n else:\n image_array[r][c] = 0\n label = lreader[i]\n i += 1\n image = Image(image_array, label)\n valImageList.append(image)\n\ndataset = Dataset(trainImageList, testImageList, valImageList)\noutput_file = open('digits_dataset', 'wb')\npickle.dump(dataset, output_file)\n\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
from django.contrib import admin
from ticket.models import Ticket, UserTicket, AuxiliaryTicket
@admin.register(Ticket)
class TicketAdmin(admin.ModelAdmin):
pass
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
|
normal
|
{
"blob_id": "c73a199d1c1c1867f3d53ceebf614bc9b65c0d5e",
"index": 280,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@admin.register(AuxiliaryTicket)\nclass AuxiliaryTicketAdmin(admin.ModelAdmin):\n pass\n\n\n@admin.register(UserTicket)\nclass UserTicketAdmin(admin.ModelAdmin):\n pass\n",
"step-3": "<mask token>\n\n\n@admin.register(Ticket)\nclass TicketAdmin(admin.ModelAdmin):\n pass\n\n\n@admin.register(AuxiliaryTicket)\nclass AuxiliaryTicketAdmin(admin.ModelAdmin):\n pass\n\n\n@admin.register(UserTicket)\nclass UserTicketAdmin(admin.ModelAdmin):\n pass\n",
"step-4": "from django.contrib import admin\nfrom ticket.models import Ticket, UserTicket, AuxiliaryTicket\n\n\n@admin.register(Ticket)\nclass TicketAdmin(admin.ModelAdmin):\n pass\n\n\n@admin.register(AuxiliaryTicket)\nclass AuxiliaryTicketAdmin(admin.ModelAdmin):\n pass\n\n\n@admin.register(UserTicket)\nclass UserTicketAdmin(admin.ModelAdmin):\n pass\n",
"step-5": null,
"step-ids": [
0,
2,
3,
4
]
}
|
[
0,
2,
3,
4
] |
# -*- coding: utf-8 -*-
""" This module provides a function for splitting datasets."""
from skmultilearn.model_selection import IterativeStratification
def iterative_train_test(X, y, test_size):
"""
Iteratively splits data with stratification.
This function is based on the iterative_train_test_split function from the
skmultilearn.model_selection package, but uses pandas dataframes as input and output.
Parameters
----------
X : pandas dataframe
Data samples.
y : array or sparse matrix
Indicator matrix.
test_size : float [0,1]
The proportion of the dataset to include in the test split, the rest will be put in the train set.
Returns
-------
X_train : pandas dataframe
Training samples.
y_train : array or sparse matrix
Indicator matrix of the training samples.
X_test : pandas dataframe
Test samples.
y_test : array or sparse matrix
Indicator matrix of the test samples.
"""
stratifier = IterativeStratification(n_splits=2, order=2, sample_distribution_per_fold=[test_size, 1.0-test_size])
train_indexes, test_indexes = next(stratifier.split(X, y))
X_train, y_train = X.iloc[train_indexes], y[train_indexes]
X_test, y_test = X.iloc[test_indexes], y[test_indexes]
return X_train, y_train, X_test, y_test
|
normal
|
{
"blob_id": "c4c068c7b50d1811f224701ad7e95d88f6734230",
"index": 2867,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef iterative_train_test(X, y, test_size):\n \"\"\"\n Iteratively splits data with stratification.\n\n This function is based on the iterative_train_test_split function from the\n skmultilearn.model_selection package, but uses pandas dataframes as input and output.\n\n Parameters\n ----------\n X : pandas dataframe\n Data samples.\n y : array or sparse matrix\n Indicator matrix.\n test_size : float [0,1]\n The proportion of the dataset to include in the test split, the rest will be put in the train set.\n\n Returns\n -------\n X_train : pandas dataframe\n Training samples.\n y_train : array or sparse matrix\n Indicator matrix of the training samples.\n X_test : pandas dataframe\n Test samples.\n y_test : array or sparse matrix\n Indicator matrix of the test samples.\n\n \"\"\"\n stratifier = IterativeStratification(n_splits=2, order=2,\n sample_distribution_per_fold=[test_size, 1.0 - test_size])\n train_indexes, test_indexes = next(stratifier.split(X, y))\n X_train, y_train = X.iloc[train_indexes], y[train_indexes]\n X_test, y_test = X.iloc[test_indexes], y[test_indexes]\n return X_train, y_train, X_test, y_test\n",
"step-3": "<mask token>\nfrom skmultilearn.model_selection import IterativeStratification\n\n\ndef iterative_train_test(X, y, test_size):\n \"\"\"\n Iteratively splits data with stratification.\n\n This function is based on the iterative_train_test_split function from the\n skmultilearn.model_selection package, but uses pandas dataframes as input and output.\n\n Parameters\n ----------\n X : pandas dataframe\n Data samples.\n y : array or sparse matrix\n Indicator matrix.\n test_size : float [0,1]\n The proportion of the dataset to include in the test split, the rest will be put in the train set.\n\n Returns\n -------\n X_train : pandas dataframe\n Training samples.\n y_train : array or sparse matrix\n Indicator matrix of the training samples.\n X_test : pandas dataframe\n Test samples.\n y_test : array or sparse matrix\n Indicator matrix of the test samples.\n\n \"\"\"\n stratifier = IterativeStratification(n_splits=2, order=2,\n sample_distribution_per_fold=[test_size, 1.0 - test_size])\n train_indexes, test_indexes = next(stratifier.split(X, y))\n X_train, y_train = X.iloc[train_indexes], y[train_indexes]\n X_test, y_test = X.iloc[test_indexes], y[test_indexes]\n return X_train, y_train, X_test, y_test\n",
"step-4": "# -*- coding: utf-8 -*-\n\"\"\" This module provides a function for splitting datasets.\"\"\"\n\nfrom skmultilearn.model_selection import IterativeStratification\n\ndef iterative_train_test(X, y, test_size):\n \"\"\"\n Iteratively splits data with stratification.\n\n This function is based on the iterative_train_test_split function from the\n skmultilearn.model_selection package, but uses pandas dataframes as input and output.\n\n Parameters\n ----------\n X : pandas dataframe\n Data samples.\n y : array or sparse matrix\n Indicator matrix.\n test_size : float [0,1]\n The proportion of the dataset to include in the test split, the rest will be put in the train set.\n\n Returns\n -------\n X_train : pandas dataframe\n Training samples.\n y_train : array or sparse matrix\n Indicator matrix of the training samples.\n X_test : pandas dataframe\n Test samples.\n y_test : array or sparse matrix\n Indicator matrix of the test samples.\n\n \"\"\"\n stratifier = IterativeStratification(n_splits=2, order=2, sample_distribution_per_fold=[test_size, 1.0-test_size])\n train_indexes, test_indexes = next(stratifier.split(X, y))\n\n X_train, y_train = X.iloc[train_indexes], y[train_indexes]\n X_test, y_test = X.iloc[test_indexes], y[test_indexes]\n\n return X_train, y_train, X_test, y_test\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class AssetID(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __repr__(self):
return '{:s}:{:s}'.format(self.asset_name, self.policy_id)
def __eq__(self, other):
if isinstance(other, AssetID):
return str(self) == str(other)
elif isinstance(other, str):
return str(self) == other
elif isinstance(other, bytes):
return str(self).encode() == other
return super(AssetID, self).__eq__(other)
def __hash__(self):
return hash(str(self))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AssetID(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self, asset_name, policy_id):
asset_name = asset_name if asset_name is not None else self.asset_name
policy_id = policy_id or self.policy_id
if isinstance(asset_name, bytes):
self.name_bytes = binascii.unhexlify(asset_name)
self.asset_name = asset_name.decode()
elif isinstance(asset_name, str):
self.name_bytes = binascii.unhexlify(asset_name.encode())
self.asset_name = asset_name
else:
raise ValueError('The asset_name is neither str or bytes but {}'
.format(type(self.asset_name)))
self.policy_id = policy_id
def __repr__(self):
return '{:s}:{:s}'.format(self.asset_name, self.policy_id)
def __eq__(self, other):
if isinstance(other, AssetID):
return str(self) == str(other)
elif isinstance(other, str):
return str(self) == other
elif isinstance(other, bytes):
return str(self).encode() == other
return super(AssetID, self).__eq__(other)
def __hash__(self):
return hash(str(self))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AssetID(object):
"""
Represents the ID of a native Cardano asset. It consists of asset name and policy ID.
It renders as string representation of ``asset_name:policy_id``.
The ``asset_name`` is always kept encoded as hexadecimal string and must be passed
to the constructor as such.
The ``.name_bytes`` property is a :class:`bytes` decoded representation of the hex.
Because Cardano allows full ASCII set to be used in asset names, some of them are not
safe to be displayed directly.
"""
asset_name = ''
policy_id = None
name_bytes = None
def __init__(self, asset_name, policy_id):
asset_name = asset_name if asset_name is not None else self.asset_name
policy_id = policy_id or self.policy_id
if isinstance(asset_name, bytes):
self.name_bytes = binascii.unhexlify(asset_name)
self.asset_name = asset_name.decode()
elif isinstance(asset_name, str):
self.name_bytes = binascii.unhexlify(asset_name.encode())
self.asset_name = asset_name
else:
raise ValueError('The asset_name is neither str or bytes but {}'
.format(type(self.asset_name)))
self.policy_id = policy_id
def __repr__(self):
return '{:s}:{:s}'.format(self.asset_name, self.policy_id)
def __eq__(self, other):
if isinstance(other, AssetID):
return str(self) == str(other)
elif isinstance(other, str):
return str(self) == other
elif isinstance(other, bytes):
return str(self).encode() == other
return super(AssetID, self).__eq__(other)
def __hash__(self):
return hash(str(self))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
Balance = collections.namedtuple('Balance', ['total', 'available', 'reward'])
Balance.__doc__ = (
'Represents a balance of asset, including total, principal and reward')
Balance.total.__doc__ = 'The total balance'
Balance.available.__doc__ = (
'The principal, i.e. the total minus staking rewards')
Balance.reward.__doc__ = 'The staking rewards (interest)'
BlockPosition = collections.namedtuple('BlockPosition', ['epoch', 'slot',
'absolute_slot', 'height'])
BlockPosition.__doc__ = "Represents block's position within the blockchain"
BlockPosition.epoch.__doc__ = 'Epoch number'
BlockPosition.slot.__doc__ = 'Slot number'
BlockPosition.absolute_slot.__doc__ = 'Absolute slot number'
BlockPosition.height.__doc__ = 'Block number (height of the chain) [optional]'
Epoch = collections.namedtuple('Epoch', ['number', 'starts'])
class AssetID(object):
"""
Represents the ID of a native Cardano asset. It consists of asset name and policy ID.
It renders as string representation of ``asset_name:policy_id``.
The ``asset_name`` is always kept encoded as hexadecimal string and must be passed
to the constructor as such.
The ``.name_bytes`` property is a :class:`bytes` decoded representation of the hex.
Because Cardano allows full ASCII set to be used in asset names, some of them are not
safe to be displayed directly.
"""
asset_name = ''
policy_id = None
name_bytes = None
def __init__(self, asset_name, policy_id):
asset_name = asset_name if asset_name is not None else self.asset_name
policy_id = policy_id or self.policy_id
if isinstance(asset_name, bytes):
self.name_bytes = binascii.unhexlify(asset_name)
self.asset_name = asset_name.decode()
elif isinstance(asset_name, str):
self.name_bytes = binascii.unhexlify(asset_name.encode())
self.asset_name = asset_name
else:
raise ValueError('The asset_name is neither str or bytes but {}'
.format(type(self.asset_name)))
self.policy_id = policy_id
def __repr__(self):
return '{:s}:{:s}'.format(self.asset_name, self.policy_id)
def __eq__(self, other):
if isinstance(other, AssetID):
return str(self) == str(other)
elif isinstance(other, str):
return str(self) == other
elif isinstance(other, bytes):
return str(self).encode() == other
return super(AssetID, self).__eq__(other)
def __hash__(self):
return hash(str(self))
StakePoolStatus = enum.Enum('StakePoolStatus', 'ACTIVE RETIRING DELISTED')
StakePoolStatus.__doc__ = 'Represents stake pool status'
StakeRewardMetrics = collections.namedtuple('StakeRewardMetrics', [
'expected', 'stake'])
StakeRewardMetrics.__doc__ = 'Represents stake pool reward metrics'
StakeRewardMetrics.expected.__doc__ = (
'Expected rewards at the end of an epoch, in ADA')
StakeRewardMetrics.stake.__doc__ = (
'Staked amount against which rewards were calculated, in ADA')
StakePoolInfo = collections.namedtuple('StakePoolInfo', ['id', 'status',
'ticker', 'name', 'description', 'homepage', 'rewards', 'cost',
'margin', 'pledge', 'relative_stake', 'saturation', 'produced_blocks',
'retirement'])
StakePoolInfo.__doc__ = 'Stores stake pool data'
StakePoolInfo.id.__doc__ = 'Unique ID'
StakePoolInfo.status.__doc__ = 'Status, one of :class:`StakePoolStatus` enum'
StakePoolInfo.ticker.__doc__ = '3-5 chars long ticker'
StakePoolInfo.name.__doc__ = 'Name'
StakePoolInfo.description.__doc__ = 'Description'
StakePoolInfo.homepage.__doc__ = 'Homepage URL'
StakePoolInfo.cost.__doc__ = 'Fixed pool running cost in ADA'
StakePoolInfo.margin.__doc__ = (
"Operator's margin on the total reward before splitting it among stakeholders (as :class:`Decimal` fraction)"
)
StakePoolInfo.pledge.__doc__ = (
'Minimal stake amount that the pool is willing to honor')
StakePoolInfo.relative_stake.__doc__ = (
'The live pool stake relative to the total stake')
StakePoolInfo.saturation.__doc__ = (
'Saturation-level of the pool based on the desired number of pools aimed by the network. A value above 1 indicates that the pool is saturated.'
)
StakePoolInfo.produced_blocks.__doc__ = (
'Number of blocks produced by a given stake pool in its lifetime.')
StakePoolInfo.retirement.__doc__ = (
'The :class:`Epoch` in which the pool retires')
StakingStatus = collections.namedtuple('StakingStatus', ['delegating',
'target_id', 'changes_at'])
StakingStatus.__doc__ = "Wallet's staking status"
StakingStatus.delegating.__doc__ = 'Whether the wallet is actively delegating'
StakingStatus.target_id.__doc__ = (
'The ID of the pool the wallet is delegating to')
StakingStatus.changes_at.__doc__ = (
':class:`Epoch` since which the change comes live')
<|reserved_special_token_1|>
import binascii
import collections
import enum
Balance = collections.namedtuple("Balance", ["total", "available", "reward"])
Balance.__doc__ = "Represents a balance of asset, including total, principal and reward"
Balance.total.__doc__ = "The total balance"
Balance.available.__doc__ = "The principal, i.e. the total minus staking rewards"
Balance.reward.__doc__ = "The staking rewards (interest)"
BlockPosition = collections.namedtuple(
"BlockPosition", ["epoch", "slot", "absolute_slot", "height"]
)
BlockPosition.__doc__ = "Represents block's position within the blockchain"
BlockPosition.epoch.__doc__ = "Epoch number"
BlockPosition.slot.__doc__ = "Slot number"
BlockPosition.absolute_slot.__doc__ = "Absolute slot number"
BlockPosition.height.__doc__ = "Block number (height of the chain) [optional]"
Epoch = collections.namedtuple("Epoch", ["number", "starts"])
class AssetID(object):
"""
Represents the ID of a native Cardano asset. It consists of asset name and policy ID.
It renders as string representation of ``asset_name:policy_id``.
The ``asset_name`` is always kept encoded as hexadecimal string and must be passed
to the constructor as such.
The ``.name_bytes`` property is a :class:`bytes` decoded representation of the hex.
Because Cardano allows full ASCII set to be used in asset names, some of them are not
safe to be displayed directly.
"""
asset_name = ""
policy_id = None
name_bytes = None
def __init__(self, asset_name, policy_id):
asset_name = asset_name if asset_name is not None else self.asset_name
policy_id = policy_id or self.policy_id
# binascii.hexlify() returns bytes() for some unknown reason. We may expect them to be
# passed here:
if isinstance(asset_name, bytes):
self.name_bytes = binascii.unhexlify(asset_name)
self.asset_name = asset_name.decode()
elif isinstance(asset_name, str):
self.name_bytes = binascii.unhexlify(asset_name.encode())
self.asset_name = asset_name
else:
raise ValueError(
"The asset_name is neither str or bytes but {}".format(
type(self.asset_name)
)
)
self.policy_id = policy_id
def __repr__(self):
return "{:s}:{:s}".format(self.asset_name, self.policy_id)
def __eq__(self, other):
if isinstance(other, AssetID):
return str(self) == str(other)
elif isinstance(other, str):
return str(self) == other
elif isinstance(other, bytes):
return str(self).encode() == other
return super(AssetID, self).__eq__(other)
def __hash__(self):
return hash(str(self))
StakePoolStatus = enum.Enum("StakePoolStatus", "ACTIVE RETIRING DELISTED")
StakePoolStatus.__doc__ = "Represents stake pool status"
StakeRewardMetrics = collections.namedtuple(
"StakeRewardMetrics",
[
"expected",
"stake",
],
)
StakeRewardMetrics.__doc__ = "Represents stake pool reward metrics"
StakeRewardMetrics.expected.__doc__ = "Expected rewards at the end of an epoch, in ADA"
StakeRewardMetrics.stake.__doc__ = (
"Staked amount against which rewards were calculated, in ADA"
)
StakePoolInfo = collections.namedtuple(
"StakePoolInfo",
[
"id",
"status",
"ticker",
"name",
"description",
"homepage",
"rewards",
"cost",
"margin",
"pledge",
"relative_stake",
"saturation",
"produced_blocks",
"retirement",
],
)
StakePoolInfo.__doc__ = "Stores stake pool data"
StakePoolInfo.id.__doc__ = "Unique ID"
StakePoolInfo.status.__doc__ = "Status, one of :class:`StakePoolStatus` enum"
StakePoolInfo.ticker.__doc__ = "3-5 chars long ticker"
StakePoolInfo.name.__doc__ = "Name"
StakePoolInfo.description.__doc__ = "Description"
StakePoolInfo.homepage.__doc__ = "Homepage URL"
StakePoolInfo.cost.__doc__ = "Fixed pool running cost in ADA"
StakePoolInfo.margin.__doc__ = "Operator's margin on the total reward before splitting it among stakeholders (as :class:`Decimal` fraction)"
StakePoolInfo.pledge.__doc__ = "Minimal stake amount that the pool is willing to honor"
StakePoolInfo.relative_stake.__doc__ = "The live pool stake relative to the total stake"
StakePoolInfo.saturation.__doc__ = (
"Saturation-level of the pool based on the desired number "
"of pools aimed by the network. A value above 1 indicates that the pool is saturated."
)
StakePoolInfo.produced_blocks.__doc__ = (
"Number of blocks produced by a given stake pool in its lifetime."
)
StakePoolInfo.retirement.__doc__ = "The :class:`Epoch` in which the pool retires"
StakingStatus = collections.namedtuple(
"StakingStatus",
[
"delegating",
"target_id",
"changes_at",
],
)
StakingStatus.__doc__ = "Wallet's staking status"
StakingStatus.delegating.__doc__ = "Whether the wallet is actively delegating"
StakingStatus.target_id.__doc__ = "The ID of the pool the wallet is delegating to"
StakingStatus.changes_at.__doc__ = ":class:`Epoch` since which the change comes live"
|
flexible
|
{
"blob_id": "5762271de166994b2f56e8e09c3f7ca5245b7ce0",
"index": 6249,
"step-1": "<mask token>\n\n\nclass AssetID(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __repr__(self):\n return '{:s}:{:s}'.format(self.asset_name, self.policy_id)\n\n def __eq__(self, other):\n if isinstance(other, AssetID):\n return str(self) == str(other)\n elif isinstance(other, str):\n return str(self) == other\n elif isinstance(other, bytes):\n return str(self).encode() == other\n return super(AssetID, self).__eq__(other)\n\n def __hash__(self):\n return hash(str(self))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass AssetID(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, asset_name, policy_id):\n asset_name = asset_name if asset_name is not None else self.asset_name\n policy_id = policy_id or self.policy_id\n if isinstance(asset_name, bytes):\n self.name_bytes = binascii.unhexlify(asset_name)\n self.asset_name = asset_name.decode()\n elif isinstance(asset_name, str):\n self.name_bytes = binascii.unhexlify(asset_name.encode())\n self.asset_name = asset_name\n else:\n raise ValueError('The asset_name is neither str or bytes but {}'\n .format(type(self.asset_name)))\n self.policy_id = policy_id\n\n def __repr__(self):\n return '{:s}:{:s}'.format(self.asset_name, self.policy_id)\n\n def __eq__(self, other):\n if isinstance(other, AssetID):\n return str(self) == str(other)\n elif isinstance(other, str):\n return str(self) == other\n elif isinstance(other, bytes):\n return str(self).encode() == other\n return super(AssetID, self).__eq__(other)\n\n def __hash__(self):\n return hash(str(self))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass AssetID(object):\n \"\"\"\n Represents the ID of a native Cardano asset. It consists of asset name and policy ID.\n It renders as string representation of ``asset_name:policy_id``.\n\n The ``asset_name`` is always kept encoded as hexadecimal string and must be passed\n to the constructor as such.\n\n The ``.name_bytes`` property is a :class:`bytes` decoded representation of the hex.\n Because Cardano allows full ASCII set to be used in asset names, some of them are not\n safe to be displayed directly.\n \"\"\"\n asset_name = ''\n policy_id = None\n name_bytes = None\n\n def __init__(self, asset_name, policy_id):\n asset_name = asset_name if asset_name is not None else self.asset_name\n policy_id = policy_id or self.policy_id\n if isinstance(asset_name, bytes):\n self.name_bytes = binascii.unhexlify(asset_name)\n self.asset_name = asset_name.decode()\n elif isinstance(asset_name, str):\n self.name_bytes = binascii.unhexlify(asset_name.encode())\n self.asset_name = asset_name\n else:\n raise ValueError('The asset_name is neither str or bytes but {}'\n .format(type(self.asset_name)))\n self.policy_id = policy_id\n\n def __repr__(self):\n return '{:s}:{:s}'.format(self.asset_name, self.policy_id)\n\n def __eq__(self, other):\n if isinstance(other, AssetID):\n return str(self) == str(other)\n elif isinstance(other, str):\n return str(self) == other\n elif isinstance(other, bytes):\n return str(self).encode() == other\n return super(AssetID, self).__eq__(other)\n\n def __hash__(self):\n return hash(str(self))\n\n\n<mask token>\n",
"step-4": "<mask token>\nBalance = collections.namedtuple('Balance', ['total', 'available', 'reward'])\nBalance.__doc__ = (\n 'Represents a balance of asset, including total, principal and reward')\nBalance.total.__doc__ = 'The total balance'\nBalance.available.__doc__ = (\n 'The principal, i.e. the total minus staking rewards')\nBalance.reward.__doc__ = 'The staking rewards (interest)'\nBlockPosition = collections.namedtuple('BlockPosition', ['epoch', 'slot',\n 'absolute_slot', 'height'])\nBlockPosition.__doc__ = \"Represents block's position within the blockchain\"\nBlockPosition.epoch.__doc__ = 'Epoch number'\nBlockPosition.slot.__doc__ = 'Slot number'\nBlockPosition.absolute_slot.__doc__ = 'Absolute slot number'\nBlockPosition.height.__doc__ = 'Block number (height of the chain) [optional]'\nEpoch = collections.namedtuple('Epoch', ['number', 'starts'])\n\n\nclass AssetID(object):\n \"\"\"\n Represents the ID of a native Cardano asset. It consists of asset name and policy ID.\n It renders as string representation of ``asset_name:policy_id``.\n\n The ``asset_name`` is always kept encoded as hexadecimal string and must be passed\n to the constructor as such.\n\n The ``.name_bytes`` property is a :class:`bytes` decoded representation of the hex.\n Because Cardano allows full ASCII set to be used in asset names, some of them are not\n safe to be displayed directly.\n \"\"\"\n asset_name = ''\n policy_id = None\n name_bytes = None\n\n def __init__(self, asset_name, policy_id):\n asset_name = asset_name if asset_name is not None else self.asset_name\n policy_id = policy_id or self.policy_id\n if isinstance(asset_name, bytes):\n self.name_bytes = binascii.unhexlify(asset_name)\n self.asset_name = asset_name.decode()\n elif isinstance(asset_name, str):\n self.name_bytes = binascii.unhexlify(asset_name.encode())\n self.asset_name = asset_name\n else:\n raise ValueError('The asset_name is neither str or bytes but {}'\n .format(type(self.asset_name)))\n self.policy_id = policy_id\n\n def __repr__(self):\n return '{:s}:{:s}'.format(self.asset_name, self.policy_id)\n\n def __eq__(self, other):\n if isinstance(other, AssetID):\n return str(self) == str(other)\n elif isinstance(other, str):\n return str(self) == other\n elif isinstance(other, bytes):\n return str(self).encode() == other\n return super(AssetID, self).__eq__(other)\n\n def __hash__(self):\n return hash(str(self))\n\n\nStakePoolStatus = enum.Enum('StakePoolStatus', 'ACTIVE RETIRING DELISTED')\nStakePoolStatus.__doc__ = 'Represents stake pool status'\nStakeRewardMetrics = collections.namedtuple('StakeRewardMetrics', [\n 'expected', 'stake'])\nStakeRewardMetrics.__doc__ = 'Represents stake pool reward metrics'\nStakeRewardMetrics.expected.__doc__ = (\n 'Expected rewards at the end of an epoch, in ADA')\nStakeRewardMetrics.stake.__doc__ = (\n 'Staked amount against which rewards were calculated, in ADA')\nStakePoolInfo = collections.namedtuple('StakePoolInfo', ['id', 'status',\n 'ticker', 'name', 'description', 'homepage', 'rewards', 'cost',\n 'margin', 'pledge', 'relative_stake', 'saturation', 'produced_blocks',\n 'retirement'])\nStakePoolInfo.__doc__ = 'Stores stake pool data'\nStakePoolInfo.id.__doc__ = 'Unique ID'\nStakePoolInfo.status.__doc__ = 'Status, one of :class:`StakePoolStatus` enum'\nStakePoolInfo.ticker.__doc__ = '3-5 chars long ticker'\nStakePoolInfo.name.__doc__ = 'Name'\nStakePoolInfo.description.__doc__ = 'Description'\nStakePoolInfo.homepage.__doc__ = 'Homepage URL'\nStakePoolInfo.cost.__doc__ = 'Fixed pool running cost in ADA'\nStakePoolInfo.margin.__doc__ = (\n \"Operator's margin on the total reward before splitting it among stakeholders (as :class:`Decimal` fraction)\"\n )\nStakePoolInfo.pledge.__doc__ = (\n 'Minimal stake amount that the pool is willing to honor')\nStakePoolInfo.relative_stake.__doc__ = (\n 'The live pool stake relative to the total stake')\nStakePoolInfo.saturation.__doc__ = (\n 'Saturation-level of the pool based on the desired number of pools aimed by the network. A value above 1 indicates that the pool is saturated.'\n )\nStakePoolInfo.produced_blocks.__doc__ = (\n 'Number of blocks produced by a given stake pool in its lifetime.')\nStakePoolInfo.retirement.__doc__ = (\n 'The :class:`Epoch` in which the pool retires')\nStakingStatus = collections.namedtuple('StakingStatus', ['delegating',\n 'target_id', 'changes_at'])\nStakingStatus.__doc__ = \"Wallet's staking status\"\nStakingStatus.delegating.__doc__ = 'Whether the wallet is actively delegating'\nStakingStatus.target_id.__doc__ = (\n 'The ID of the pool the wallet is delegating to')\nStakingStatus.changes_at.__doc__ = (\n ':class:`Epoch` since which the change comes live')\n",
"step-5": "import binascii\nimport collections\nimport enum\n\n\nBalance = collections.namedtuple(\"Balance\", [\"total\", \"available\", \"reward\"])\nBalance.__doc__ = \"Represents a balance of asset, including total, principal and reward\"\nBalance.total.__doc__ = \"The total balance\"\nBalance.available.__doc__ = \"The principal, i.e. the total minus staking rewards\"\nBalance.reward.__doc__ = \"The staking rewards (interest)\"\n\n\nBlockPosition = collections.namedtuple(\n \"BlockPosition\", [\"epoch\", \"slot\", \"absolute_slot\", \"height\"]\n)\nBlockPosition.__doc__ = \"Represents block's position within the blockchain\"\nBlockPosition.epoch.__doc__ = \"Epoch number\"\nBlockPosition.slot.__doc__ = \"Slot number\"\nBlockPosition.absolute_slot.__doc__ = \"Absolute slot number\"\nBlockPosition.height.__doc__ = \"Block number (height of the chain) [optional]\"\n\n\nEpoch = collections.namedtuple(\"Epoch\", [\"number\", \"starts\"])\n\n\nclass AssetID(object):\n \"\"\"\n Represents the ID of a native Cardano asset. It consists of asset name and policy ID.\n It renders as string representation of ``asset_name:policy_id``.\n\n The ``asset_name`` is always kept encoded as hexadecimal string and must be passed\n to the constructor as such.\n\n The ``.name_bytes`` property is a :class:`bytes` decoded representation of the hex.\n Because Cardano allows full ASCII set to be used in asset names, some of them are not\n safe to be displayed directly.\n \"\"\"\n\n asset_name = \"\"\n policy_id = None\n name_bytes = None\n\n def __init__(self, asset_name, policy_id):\n asset_name = asset_name if asset_name is not None else self.asset_name\n policy_id = policy_id or self.policy_id\n # binascii.hexlify() returns bytes() for some unknown reason. We may expect them to be\n # passed here:\n if isinstance(asset_name, bytes):\n self.name_bytes = binascii.unhexlify(asset_name)\n self.asset_name = asset_name.decode()\n elif isinstance(asset_name, str):\n self.name_bytes = binascii.unhexlify(asset_name.encode())\n self.asset_name = asset_name\n else:\n raise ValueError(\n \"The asset_name is neither str or bytes but {}\".format(\n type(self.asset_name)\n )\n )\n self.policy_id = policy_id\n\n def __repr__(self):\n return \"{:s}:{:s}\".format(self.asset_name, self.policy_id)\n\n def __eq__(self, other):\n if isinstance(other, AssetID):\n return str(self) == str(other)\n elif isinstance(other, str):\n return str(self) == other\n elif isinstance(other, bytes):\n return str(self).encode() == other\n return super(AssetID, self).__eq__(other)\n\n def __hash__(self):\n return hash(str(self))\n\n\nStakePoolStatus = enum.Enum(\"StakePoolStatus\", \"ACTIVE RETIRING DELISTED\")\nStakePoolStatus.__doc__ = \"Represents stake pool status\"\n\nStakeRewardMetrics = collections.namedtuple(\n \"StakeRewardMetrics\",\n [\n \"expected\",\n \"stake\",\n ],\n)\nStakeRewardMetrics.__doc__ = \"Represents stake pool reward metrics\"\nStakeRewardMetrics.expected.__doc__ = \"Expected rewards at the end of an epoch, in ADA\"\nStakeRewardMetrics.stake.__doc__ = (\n \"Staked amount against which rewards were calculated, in ADA\"\n)\n\nStakePoolInfo = collections.namedtuple(\n \"StakePoolInfo\",\n [\n \"id\",\n \"status\",\n \"ticker\",\n \"name\",\n \"description\",\n \"homepage\",\n \"rewards\",\n \"cost\",\n \"margin\",\n \"pledge\",\n \"relative_stake\",\n \"saturation\",\n \"produced_blocks\",\n \"retirement\",\n ],\n)\nStakePoolInfo.__doc__ = \"Stores stake pool data\"\nStakePoolInfo.id.__doc__ = \"Unique ID\"\nStakePoolInfo.status.__doc__ = \"Status, one of :class:`StakePoolStatus` enum\"\nStakePoolInfo.ticker.__doc__ = \"3-5 chars long ticker\"\nStakePoolInfo.name.__doc__ = \"Name\"\nStakePoolInfo.description.__doc__ = \"Description\"\nStakePoolInfo.homepage.__doc__ = \"Homepage URL\"\nStakePoolInfo.cost.__doc__ = \"Fixed pool running cost in ADA\"\nStakePoolInfo.margin.__doc__ = \"Operator's margin on the total reward before splitting it among stakeholders (as :class:`Decimal` fraction)\"\nStakePoolInfo.pledge.__doc__ = \"Minimal stake amount that the pool is willing to honor\"\nStakePoolInfo.relative_stake.__doc__ = \"The live pool stake relative to the total stake\"\nStakePoolInfo.saturation.__doc__ = (\n \"Saturation-level of the pool based on the desired number \"\n \"of pools aimed by the network. A value above 1 indicates that the pool is saturated.\"\n)\nStakePoolInfo.produced_blocks.__doc__ = (\n \"Number of blocks produced by a given stake pool in its lifetime.\"\n)\nStakePoolInfo.retirement.__doc__ = \"The :class:`Epoch` in which the pool retires\"\n\nStakingStatus = collections.namedtuple(\n \"StakingStatus\",\n [\n \"delegating\",\n \"target_id\",\n \"changes_at\",\n ],\n)\nStakingStatus.__doc__ = \"Wallet's staking status\"\nStakingStatus.delegating.__doc__ = \"Whether the wallet is actively delegating\"\nStakingStatus.target_id.__doc__ = \"The ID of the pool the wallet is delegating to\"\nStakingStatus.changes_at.__doc__ = \":class:`Epoch` since which the change comes live\"\n",
"step-ids": [
4,
5,
7,
8,
10
]
}
|
[
4,
5,
7,
8,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
InFileName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
for bfMatFile in InFileName:
if os.path.isfile(bfMatFile):
lsHTMLName = []
fo = open(bfMatFile)
lsHTMLName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
bfRow = ''
for rowHTML in lsHTMLName:
iPosic = rowHTML.find('<td><p>')
if iPosic > 0:
bfRowPart = rowHTML[iPosic + len('<td><p>'):]
bfRow += (bfRowPart[:bfRowPart.index('</p></td>')] + ','
).replace(' ', ',').strip()
if bfRow != '':
lsOutTmp.append(bfRow[:len(bfRow) - 1] + ';')
<|reserved_special_token_0|>
fo.write(bufferTmp)
fo.close()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
bfTmp = ''
lsOutTmp = []
InFileName = []
lsHTMLName = []
fileNameIn = sys.argv[1]
fileNameOu = sys.argv[2]
fo = open(fileNameIn)
InFileName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
for bfMatFile in InFileName:
if os.path.isfile(bfMatFile):
lsHTMLName = []
fo = open(bfMatFile)
lsHTMLName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
bfRow = ''
for rowHTML in lsHTMLName:
iPosic = rowHTML.find('<td><p>')
if iPosic > 0:
bfRowPart = rowHTML[iPosic + len('<td><p>'):]
bfRow += (bfRowPart[:bfRowPart.index('</p></td>')] + ','
).replace(' ', ',').strip()
if bfRow != '':
lsOutTmp.append(bfRow[:len(bfRow) - 1] + ';')
bufferTmp = '\n'
bufferTmp = bufferTmp.join(lsOutTmp)
fo = open(fileNameOu, 'w')
fo.write(bufferTmp)
fo.close()
<|reserved_special_token_1|>
import sys
import os
import os.path
bfTmp = ''
lsOutTmp = []
InFileName = []
lsHTMLName = []
fileNameIn = sys.argv[1]
fileNameOu = sys.argv[2]
fo = open(fileNameIn)
InFileName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
for bfMatFile in InFileName:
if os.path.isfile(bfMatFile):
lsHTMLName = []
fo = open(bfMatFile)
lsHTMLName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
bfRow = ''
for rowHTML in lsHTMLName:
iPosic = rowHTML.find('<td><p>')
if iPosic > 0:
bfRowPart = rowHTML[iPosic + len('<td><p>'):]
bfRow += (bfRowPart[:bfRowPart.index('</p></td>')] + ','
).replace(' ', ',').strip()
if bfRow != '':
lsOutTmp.append(bfRow[:len(bfRow) - 1] + ';')
bufferTmp = '\n'
bufferTmp = bufferTmp.join(lsOutTmp)
fo = open(fileNameOu, 'w')
fo.write(bufferTmp)
fo.close()
<|reserved_special_token_1|>
#C:\utils\Python\Python27\python.exe incompletosClean.py incompletos\inc.dat incompletos\out.dat
import sys
import os
import os.path
bfTmp = ''
lsOutTmp = []
InFileName = []
lsHTMLName = []
fileNameIn= sys.argv[1]
fileNameOu= sys.argv[2]
fo = open(fileNameIn)
InFileName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
for bfMatFile in InFileName:
if os.path.isfile(bfMatFile):
lsHTMLName = []
fo = open(bfMatFile)
lsHTMLName += [x.replace('\n', '') for x in fo.readlines()]
fo.close()
bfRow = ''
for rowHTML in lsHTMLName:
iPosic = rowHTML.find('<td><p>')
if iPosic > 0:
bfRowPart = rowHTML[iPosic + len('<td><p>'):]
bfRow += ((bfRowPart[:bfRowPart.index('</p></td>')] + ',').replace(' ', ',')).strip()
if bfRow != '':
lsOutTmp.append(bfRow[:len(bfRow)-1] + ';')
bufferTmp = '\n'
bufferTmp = bufferTmp.join(lsOutTmp)
fo= open(fileNameOu, 'w')
fo.write(bufferTmp)
fo.close()
|
flexible
|
{
"blob_id": "031727fa42b87260abb671518b2baeff1c9524f9",
"index": 8913,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nInFileName += [x.replace('\\n', '') for x in fo.readlines()]\nfo.close()\nfor bfMatFile in InFileName:\n if os.path.isfile(bfMatFile):\n lsHTMLName = []\n fo = open(bfMatFile)\n lsHTMLName += [x.replace('\\n', '') for x in fo.readlines()]\n fo.close()\n bfRow = ''\n for rowHTML in lsHTMLName:\n iPosic = rowHTML.find('<td><p>')\n if iPosic > 0:\n bfRowPart = rowHTML[iPosic + len('<td><p>'):]\n bfRow += (bfRowPart[:bfRowPart.index('</p></td>')] + ','\n ).replace(' ', ',').strip()\n if bfRow != '':\n lsOutTmp.append(bfRow[:len(bfRow) - 1] + ';')\n<mask token>\nfo.write(bufferTmp)\nfo.close()\n",
"step-3": "<mask token>\nbfTmp = ''\nlsOutTmp = []\nInFileName = []\nlsHTMLName = []\nfileNameIn = sys.argv[1]\nfileNameOu = sys.argv[2]\nfo = open(fileNameIn)\nInFileName += [x.replace('\\n', '') for x in fo.readlines()]\nfo.close()\nfor bfMatFile in InFileName:\n if os.path.isfile(bfMatFile):\n lsHTMLName = []\n fo = open(bfMatFile)\n lsHTMLName += [x.replace('\\n', '') for x in fo.readlines()]\n fo.close()\n bfRow = ''\n for rowHTML in lsHTMLName:\n iPosic = rowHTML.find('<td><p>')\n if iPosic > 0:\n bfRowPart = rowHTML[iPosic + len('<td><p>'):]\n bfRow += (bfRowPart[:bfRowPart.index('</p></td>')] + ','\n ).replace(' ', ',').strip()\n if bfRow != '':\n lsOutTmp.append(bfRow[:len(bfRow) - 1] + ';')\nbufferTmp = '\\n'\nbufferTmp = bufferTmp.join(lsOutTmp)\nfo = open(fileNameOu, 'w')\nfo.write(bufferTmp)\nfo.close()\n",
"step-4": "import sys\nimport os\nimport os.path\nbfTmp = ''\nlsOutTmp = []\nInFileName = []\nlsHTMLName = []\nfileNameIn = sys.argv[1]\nfileNameOu = sys.argv[2]\nfo = open(fileNameIn)\nInFileName += [x.replace('\\n', '') for x in fo.readlines()]\nfo.close()\nfor bfMatFile in InFileName:\n if os.path.isfile(bfMatFile):\n lsHTMLName = []\n fo = open(bfMatFile)\n lsHTMLName += [x.replace('\\n', '') for x in fo.readlines()]\n fo.close()\n bfRow = ''\n for rowHTML in lsHTMLName:\n iPosic = rowHTML.find('<td><p>')\n if iPosic > 0:\n bfRowPart = rowHTML[iPosic + len('<td><p>'):]\n bfRow += (bfRowPart[:bfRowPart.index('</p></td>')] + ','\n ).replace(' ', ',').strip()\n if bfRow != '':\n lsOutTmp.append(bfRow[:len(bfRow) - 1] + ';')\nbufferTmp = '\\n'\nbufferTmp = bufferTmp.join(lsOutTmp)\nfo = open(fileNameOu, 'w')\nfo.write(bufferTmp)\nfo.close()\n",
"step-5": "#C:\\utils\\Python\\Python27\\python.exe incompletosClean.py incompletos\\inc.dat incompletos\\out.dat\r\n\r\nimport sys\r\nimport os\r\nimport os.path\r\n\r\nbfTmp = ''\r\nlsOutTmp = []\r\nInFileName = []\r\nlsHTMLName = []\r\n\r\nfileNameIn= sys.argv[1]\r\nfileNameOu= sys.argv[2]\r\n\r\nfo = open(fileNameIn)\r\nInFileName += [x.replace('\\n', '') for x in fo.readlines()]\r\nfo.close()\r\n\r\nfor bfMatFile in InFileName:\r\n if os.path.isfile(bfMatFile):\r\n lsHTMLName = []\r\n fo = open(bfMatFile)\r\n lsHTMLName += [x.replace('\\n', '') for x in fo.readlines()]\r\n fo.close()\r\n\r\n bfRow = ''\r\n for rowHTML in lsHTMLName:\r\n iPosic = rowHTML.find('<td><p>')\r\n if iPosic > 0:\r\n bfRowPart = rowHTML[iPosic + len('<td><p>'):]\r\n bfRow += ((bfRowPart[:bfRowPart.index('</p></td>')] + ',').replace(' ', ',')).strip()\r\n\r\n if bfRow != '':\r\n lsOutTmp.append(bfRow[:len(bfRow)-1] + ';')\r\n\r\nbufferTmp = '\\n'\r\nbufferTmp = bufferTmp.join(lsOutTmp)\r\nfo= open(fileNameOu, 'w')\r\nfo.write(bufferTmp)\r\nfo.close()\r\n\r\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def make_word_dictionary(word_dict_pkl_path=params[
'default_word_dict_pkl_path'], training_data_path=params[
'default_training_data_path']):
word_dict = dict()
if os.path.isfile(word_dict_pkl_path):
with open(word_dict_pkl_path, 'rb') as f:
word_dict = pickle.load(f)
print('Existed word_dict loaded')
else:
print('No word_dict pkl file, start making word_dict...')
with codecs.open(training_data_path, 'r', encoding='utf-8') as f:
word_vocab = dict()
for line in f.read().split('\n')[1:]:
review = line.split('\t')[1]
tokens = tokenizer.morphs(review)
for token in tokens:
if token in word_vocab.keys():
word_vocab[token] += 1
else:
word_vocab[token] = 1
word_vocab = [word for word in word_vocab.keys() if word_vocab[
word] >= params['min_vocab_count']]
word_vocab = [params['PAD']] + word_vocab + [params['UNK']]
for idx, word in enumerate(word_vocab):
word_dict[word] = idx
print('Making word_dict ... Done and Saved')
with open(word_dict_pkl_path, 'wb') as f:
pickle.dump(word_dict, f)
return word_dict
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def make_word_dictionary(word_dict_pkl_path=params[
'default_word_dict_pkl_path'], training_data_path=params[
'default_training_data_path']):
word_dict = dict()
if os.path.isfile(word_dict_pkl_path):
with open(word_dict_pkl_path, 'rb') as f:
word_dict = pickle.load(f)
print('Existed word_dict loaded')
else:
print('No word_dict pkl file, start making word_dict...')
with codecs.open(training_data_path, 'r', encoding='utf-8') as f:
word_vocab = dict()
for line in f.read().split('\n')[1:]:
review = line.split('\t')[1]
tokens = tokenizer.morphs(review)
for token in tokens:
if token in word_vocab.keys():
word_vocab[token] += 1
else:
word_vocab[token] = 1
word_vocab = [word for word in word_vocab.keys() if word_vocab[
word] >= params['min_vocab_count']]
word_vocab = [params['PAD']] + word_vocab + [params['UNK']]
for idx, word in enumerate(word_vocab):
word_dict[word] = idx
print('Making word_dict ... Done and Saved')
with open(word_dict_pkl_path, 'wb') as f:
pickle.dump(word_dict, f)
return word_dict
<|reserved_special_token_0|>
def zero_padding(token_sentence, word_dict):
padded_sentence = token_sentence + [word_dict[params['PAD']]] * (params
['max_seq_length'] - len(token_sentence))
return padded_sentence
def dataset_iterator(filename, word_dict, batch_size):
with open(filename, 'r', encoding='utf8') as f_dataset:
context = []
sequence_length = []
label = []
text = f_dataset.read().split('\n')
for line in text[1:]:
class_label = [0, 0]
review = line.split('\t')[1]
polarity = int(line.split('\t')[2])
class_label[polarity] = 1
label.append(class_label)
tokens = tokenizer.morphs(review)
if len(tokens) > params['max_seq_length']:
tokens = tokens[:params['max_seq_length']]
sentence = [(word_dict[word] if word in word_dict else
word_dict[params['UNK']]) for word in tokens]
sequence_length.append(len(sentence))
sentence = zero_padding(sentence, word_dict)
context.append(sentence)
if len(context) == batch_size:
yield context, sequence_length, label
context = []
sequence_length = []
label = []
if len(context) > 0:
yield context, sequence_length, label
<|reserved_special_token_1|>
<|reserved_special_token_0|>
tokenizer = Okt()
def make_word_dictionary(word_dict_pkl_path=params[
'default_word_dict_pkl_path'], training_data_path=params[
'default_training_data_path']):
word_dict = dict()
if os.path.isfile(word_dict_pkl_path):
with open(word_dict_pkl_path, 'rb') as f:
word_dict = pickle.load(f)
print('Existed word_dict loaded')
else:
print('No word_dict pkl file, start making word_dict...')
with codecs.open(training_data_path, 'r', encoding='utf-8') as f:
word_vocab = dict()
for line in f.read().split('\n')[1:]:
review = line.split('\t')[1]
tokens = tokenizer.morphs(review)
for token in tokens:
if token in word_vocab.keys():
word_vocab[token] += 1
else:
word_vocab[token] = 1
word_vocab = [word for word in word_vocab.keys() if word_vocab[
word] >= params['min_vocab_count']]
word_vocab = [params['PAD']] + word_vocab + [params['UNK']]
for idx, word in enumerate(word_vocab):
word_dict[word] = idx
print('Making word_dict ... Done and Saved')
with open(word_dict_pkl_path, 'wb') as f:
pickle.dump(word_dict, f)
return word_dict
def make_word_embedding(word_dict, word_emb_pkl_path=params[
'default_word_emb_pkl_path'], fasttext_path=params['default_fasttext_path']
):
word_emb = np.zeros([len(word_dict), params['word_emb_dim']])
if os.path.isfile(word_emb_pkl_path):
with open(word_emb_pkl_path, 'rb') as f:
word_emb = pickle.load(f)
print('Existed trained word embedding loaded')
else:
fasttext_model = FastText.load_fasttext_format(fasttext_path,
encoding='utf8')
print('No word_emb pkl file, start making word_emb ...')
for word, idx in word_dict.items():
if idx == 0:
continue
else:
try:
word_emb[idx] = np.asarray(fasttext_model.wv[word])
except KeyError:
word_emb[idx] = np.random.uniform(-0.25, 0.25, params[
'word_emb_dim'])
with open(word_emb_pkl_path, 'wb') as f:
pickle.dump(word_emb, f)
print('Making word_emb ... Done and Saved')
return word_emb
def zero_padding(token_sentence, word_dict):
padded_sentence = token_sentence + [word_dict[params['PAD']]] * (params
['max_seq_length'] - len(token_sentence))
return padded_sentence
def dataset_iterator(filename, word_dict, batch_size):
with open(filename, 'r', encoding='utf8') as f_dataset:
context = []
sequence_length = []
label = []
text = f_dataset.read().split('\n')
for line in text[1:]:
class_label = [0, 0]
review = line.split('\t')[1]
polarity = int(line.split('\t')[2])
class_label[polarity] = 1
label.append(class_label)
tokens = tokenizer.morphs(review)
if len(tokens) > params['max_seq_length']:
tokens = tokens[:params['max_seq_length']]
sentence = [(word_dict[word] if word in word_dict else
word_dict[params['UNK']]) for word in tokens]
sequence_length.append(len(sentence))
sentence = zero_padding(sentence, word_dict)
context.append(sentence)
if len(context) == batch_size:
yield context, sequence_length, label
context = []
sequence_length = []
label = []
if len(context) > 0:
yield context, sequence_length, label
<|reserved_special_token_1|>
import os
import os.path
import numpy as np
import pickle
import codecs
from konlpy.tag import Okt
from hyperparams import params
from gensim.models import FastText
tokenizer = Okt()
def make_word_dictionary(word_dict_pkl_path=params[
'default_word_dict_pkl_path'], training_data_path=params[
'default_training_data_path']):
word_dict = dict()
if os.path.isfile(word_dict_pkl_path):
with open(word_dict_pkl_path, 'rb') as f:
word_dict = pickle.load(f)
print('Existed word_dict loaded')
else:
print('No word_dict pkl file, start making word_dict...')
with codecs.open(training_data_path, 'r', encoding='utf-8') as f:
word_vocab = dict()
for line in f.read().split('\n')[1:]:
review = line.split('\t')[1]
tokens = tokenizer.morphs(review)
for token in tokens:
if token in word_vocab.keys():
word_vocab[token] += 1
else:
word_vocab[token] = 1
word_vocab = [word for word in word_vocab.keys() if word_vocab[
word] >= params['min_vocab_count']]
word_vocab = [params['PAD']] + word_vocab + [params['UNK']]
for idx, word in enumerate(word_vocab):
word_dict[word] = idx
print('Making word_dict ... Done and Saved')
with open(word_dict_pkl_path, 'wb') as f:
pickle.dump(word_dict, f)
return word_dict
def make_word_embedding(word_dict, word_emb_pkl_path=params[
'default_word_emb_pkl_path'], fasttext_path=params['default_fasttext_path']
):
word_emb = np.zeros([len(word_dict), params['word_emb_dim']])
if os.path.isfile(word_emb_pkl_path):
with open(word_emb_pkl_path, 'rb') as f:
word_emb = pickle.load(f)
print('Existed trained word embedding loaded')
else:
fasttext_model = FastText.load_fasttext_format(fasttext_path,
encoding='utf8')
print('No word_emb pkl file, start making word_emb ...')
for word, idx in word_dict.items():
if idx == 0:
continue
else:
try:
word_emb[idx] = np.asarray(fasttext_model.wv[word])
except KeyError:
word_emb[idx] = np.random.uniform(-0.25, 0.25, params[
'word_emb_dim'])
with open(word_emb_pkl_path, 'wb') as f:
pickle.dump(word_emb, f)
print('Making word_emb ... Done and Saved')
return word_emb
def zero_padding(token_sentence, word_dict):
padded_sentence = token_sentence + [word_dict[params['PAD']]] * (params
['max_seq_length'] - len(token_sentence))
return padded_sentence
def dataset_iterator(filename, word_dict, batch_size):
with open(filename, 'r', encoding='utf8') as f_dataset:
context = []
sequence_length = []
label = []
text = f_dataset.read().split('\n')
for line in text[1:]:
class_label = [0, 0]
review = line.split('\t')[1]
polarity = int(line.split('\t')[2])
class_label[polarity] = 1
label.append(class_label)
tokens = tokenizer.morphs(review)
if len(tokens) > params['max_seq_length']:
tokens = tokens[:params['max_seq_length']]
sentence = [(word_dict[word] if word in word_dict else
word_dict[params['UNK']]) for word in tokens]
sequence_length.append(len(sentence))
sentence = zero_padding(sentence, word_dict)
context.append(sentence)
if len(context) == batch_size:
yield context, sequence_length, label
context = []
sequence_length = []
label = []
if len(context) > 0:
yield context, sequence_length, label
<|reserved_special_token_1|>
import os
import os.path
import numpy as np
import pickle
import codecs
from konlpy.tag import Okt
from hyperparams import params
from gensim.models import FastText
#tokenizer
tokenizer = Okt()
def make_word_dictionary(word_dict_pkl_path=params['default_word_dict_pkl_path'], training_data_path = params['default_training_data_path']):
#word_dict => 'Word':'index'
word_dict = dict()
if os.path.isfile(word_dict_pkl_path):
#if already existed, just load it
with open(word_dict_pkl_path, 'rb') as f:
word_dict = pickle.load(f)
print('Existed word_dict loaded')
else:
print('No word_dict pkl file, start making word_dict...')
with codecs.open(training_data_path, 'r', encoding='utf-8') as f:
word_vocab = dict()
# 'word':'frequency'
for line in f.read().split('\n')[1:]:
review = line.split('\t')[1]
#tokenizing
tokens = tokenizer.morphs(review)
for token in tokens:
if token in word_vocab.keys():
word_vocab[token] += 1
else:
word_vocab[token] = 1
word_vocab = [word for word in word_vocab.keys() if word_vocab[word] >= params['min_vocab_count']]
# add pad & unk token
word_vocab = [params['PAD']] + word_vocab + [params['UNK']]
for idx, word in enumerate(word_vocab):
word_dict[word] = idx
print('Making word_dict ... Done and Saved')
with open(word_dict_pkl_path, 'wb') as f:
pickle.dump(word_dict, f)
return word_dict
def make_word_embedding(word_dict, word_emb_pkl_path = params['default_word_emb_pkl_path'], fasttext_path = params['default_fasttext_path']):
word_emb = np.zeros([len(word_dict), params['word_emb_dim']])
if os.path.isfile(word_emb_pkl_path):
with open(word_emb_pkl_path, 'rb') as f:
word_emb = pickle.load(f)
print('Existed trained word embedding loaded')
else:
#load fasttext model
fasttext_model = FastText.load_fasttext_format(fasttext_path, encoding='utf8')
print('No word_emb pkl file, start making word_emb ...')
for word, idx in word_dict.items():
if idx==0:
# PAD = 0
continue
else:
try:
word_emb[idx] = np.asarray(fasttext_model.wv[word])
except KeyError:
# if there is no word vector for certain word, just assign random vector
word_emb[idx] = np.random.uniform(-0.25, 0.25, params['word_emb_dim'])
with open(word_emb_pkl_path, 'wb') as f:
pickle.dump(word_emb, f)
print('Making word_emb ... Done and Saved')
return word_emb
def zero_padding(token_sentence, word_dict):
#input : [1,4,3,2,1,15]
#output : [1,4,3,2,1,15,0,0,0,0]
padded_sentence = token_sentence + [word_dict[params['PAD']]]*(params['max_seq_length']-len(token_sentence))
return padded_sentence
def dataset_iterator(filename, word_dict, batch_size):
#yield batch for training
with open(filename, 'r', encoding='utf8') as f_dataset:
context = []
sequence_length = []
label = []
text = f_dataset.read().split('\n')
for line in text[1:]:
class_label = [0,0]
review = line.split('\t')[1]
polarity = int(line.split('\t')[2])
class_label[polarity] = 1 #mark polarity
label.append(class_label)
tokens = tokenizer.morphs(review)
#if the review is too long, cut it to adequate length
if len(tokens) > params['max_seq_length']:
tokens = tokens[:params['max_seq_length']]
sentence = [word_dict[word] if word in word_dict else word_dict[params['UNK']] for word in tokens]
sequence_length.append(len(sentence))
sentence = zero_padding(sentence, word_dict)
context.append(sentence)
if len(context) == batch_size:
yield (context, sequence_length, label)
context =[]
sequence_length = []
label = []
if len(context) > 0:
yield (context, sequence_length, label)
|
flexible
|
{
"blob_id": "430e971d2ae41bfd60e7416ecb2c26bb08e4df45",
"index": 6520,
"step-1": "<mask token>\n\n\ndef make_word_dictionary(word_dict_pkl_path=params[\n 'default_word_dict_pkl_path'], training_data_path=params[\n 'default_training_data_path']):\n word_dict = dict()\n if os.path.isfile(word_dict_pkl_path):\n with open(word_dict_pkl_path, 'rb') as f:\n word_dict = pickle.load(f)\n print('Existed word_dict loaded')\n else:\n print('No word_dict pkl file, start making word_dict...')\n with codecs.open(training_data_path, 'r', encoding='utf-8') as f:\n word_vocab = dict()\n for line in f.read().split('\\n')[1:]:\n review = line.split('\\t')[1]\n tokens = tokenizer.morphs(review)\n for token in tokens:\n if token in word_vocab.keys():\n word_vocab[token] += 1\n else:\n word_vocab[token] = 1\n word_vocab = [word for word in word_vocab.keys() if word_vocab[\n word] >= params['min_vocab_count']]\n word_vocab = [params['PAD']] + word_vocab + [params['UNK']]\n for idx, word in enumerate(word_vocab):\n word_dict[word] = idx\n print('Making word_dict ... Done and Saved')\n with open(word_dict_pkl_path, 'wb') as f:\n pickle.dump(word_dict, f)\n return word_dict\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef make_word_dictionary(word_dict_pkl_path=params[\n 'default_word_dict_pkl_path'], training_data_path=params[\n 'default_training_data_path']):\n word_dict = dict()\n if os.path.isfile(word_dict_pkl_path):\n with open(word_dict_pkl_path, 'rb') as f:\n word_dict = pickle.load(f)\n print('Existed word_dict loaded')\n else:\n print('No word_dict pkl file, start making word_dict...')\n with codecs.open(training_data_path, 'r', encoding='utf-8') as f:\n word_vocab = dict()\n for line in f.read().split('\\n')[1:]:\n review = line.split('\\t')[1]\n tokens = tokenizer.morphs(review)\n for token in tokens:\n if token in word_vocab.keys():\n word_vocab[token] += 1\n else:\n word_vocab[token] = 1\n word_vocab = [word for word in word_vocab.keys() if word_vocab[\n word] >= params['min_vocab_count']]\n word_vocab = [params['PAD']] + word_vocab + [params['UNK']]\n for idx, word in enumerate(word_vocab):\n word_dict[word] = idx\n print('Making word_dict ... Done and Saved')\n with open(word_dict_pkl_path, 'wb') as f:\n pickle.dump(word_dict, f)\n return word_dict\n\n\n<mask token>\n\n\ndef zero_padding(token_sentence, word_dict):\n padded_sentence = token_sentence + [word_dict[params['PAD']]] * (params\n ['max_seq_length'] - len(token_sentence))\n return padded_sentence\n\n\ndef dataset_iterator(filename, word_dict, batch_size):\n with open(filename, 'r', encoding='utf8') as f_dataset:\n context = []\n sequence_length = []\n label = []\n text = f_dataset.read().split('\\n')\n for line in text[1:]:\n class_label = [0, 0]\n review = line.split('\\t')[1]\n polarity = int(line.split('\\t')[2])\n class_label[polarity] = 1\n label.append(class_label)\n tokens = tokenizer.morphs(review)\n if len(tokens) > params['max_seq_length']:\n tokens = tokens[:params['max_seq_length']]\n sentence = [(word_dict[word] if word in word_dict else\n word_dict[params['UNK']]) for word in tokens]\n sequence_length.append(len(sentence))\n sentence = zero_padding(sentence, word_dict)\n context.append(sentence)\n if len(context) == batch_size:\n yield context, sequence_length, label\n context = []\n sequence_length = []\n label = []\n if len(context) > 0:\n yield context, sequence_length, label\n",
"step-3": "<mask token>\ntokenizer = Okt()\n\n\ndef make_word_dictionary(word_dict_pkl_path=params[\n 'default_word_dict_pkl_path'], training_data_path=params[\n 'default_training_data_path']):\n word_dict = dict()\n if os.path.isfile(word_dict_pkl_path):\n with open(word_dict_pkl_path, 'rb') as f:\n word_dict = pickle.load(f)\n print('Existed word_dict loaded')\n else:\n print('No word_dict pkl file, start making word_dict...')\n with codecs.open(training_data_path, 'r', encoding='utf-8') as f:\n word_vocab = dict()\n for line in f.read().split('\\n')[1:]:\n review = line.split('\\t')[1]\n tokens = tokenizer.morphs(review)\n for token in tokens:\n if token in word_vocab.keys():\n word_vocab[token] += 1\n else:\n word_vocab[token] = 1\n word_vocab = [word for word in word_vocab.keys() if word_vocab[\n word] >= params['min_vocab_count']]\n word_vocab = [params['PAD']] + word_vocab + [params['UNK']]\n for idx, word in enumerate(word_vocab):\n word_dict[word] = idx\n print('Making word_dict ... Done and Saved')\n with open(word_dict_pkl_path, 'wb') as f:\n pickle.dump(word_dict, f)\n return word_dict\n\n\ndef make_word_embedding(word_dict, word_emb_pkl_path=params[\n 'default_word_emb_pkl_path'], fasttext_path=params['default_fasttext_path']\n ):\n word_emb = np.zeros([len(word_dict), params['word_emb_dim']])\n if os.path.isfile(word_emb_pkl_path):\n with open(word_emb_pkl_path, 'rb') as f:\n word_emb = pickle.load(f)\n print('Existed trained word embedding loaded')\n else:\n fasttext_model = FastText.load_fasttext_format(fasttext_path,\n encoding='utf8')\n print('No word_emb pkl file, start making word_emb ...')\n for word, idx in word_dict.items():\n if idx == 0:\n continue\n else:\n try:\n word_emb[idx] = np.asarray(fasttext_model.wv[word])\n except KeyError:\n word_emb[idx] = np.random.uniform(-0.25, 0.25, params[\n 'word_emb_dim'])\n with open(word_emb_pkl_path, 'wb') as f:\n pickle.dump(word_emb, f)\n print('Making word_emb ... Done and Saved')\n return word_emb\n\n\ndef zero_padding(token_sentence, word_dict):\n padded_sentence = token_sentence + [word_dict[params['PAD']]] * (params\n ['max_seq_length'] - len(token_sentence))\n return padded_sentence\n\n\ndef dataset_iterator(filename, word_dict, batch_size):\n with open(filename, 'r', encoding='utf8') as f_dataset:\n context = []\n sequence_length = []\n label = []\n text = f_dataset.read().split('\\n')\n for line in text[1:]:\n class_label = [0, 0]\n review = line.split('\\t')[1]\n polarity = int(line.split('\\t')[2])\n class_label[polarity] = 1\n label.append(class_label)\n tokens = tokenizer.morphs(review)\n if len(tokens) > params['max_seq_length']:\n tokens = tokens[:params['max_seq_length']]\n sentence = [(word_dict[word] if word in word_dict else\n word_dict[params['UNK']]) for word in tokens]\n sequence_length.append(len(sentence))\n sentence = zero_padding(sentence, word_dict)\n context.append(sentence)\n if len(context) == batch_size:\n yield context, sequence_length, label\n context = []\n sequence_length = []\n label = []\n if len(context) > 0:\n yield context, sequence_length, label\n",
"step-4": "import os\nimport os.path\nimport numpy as np\nimport pickle\nimport codecs\nfrom konlpy.tag import Okt\nfrom hyperparams import params\nfrom gensim.models import FastText\ntokenizer = Okt()\n\n\ndef make_word_dictionary(word_dict_pkl_path=params[\n 'default_word_dict_pkl_path'], training_data_path=params[\n 'default_training_data_path']):\n word_dict = dict()\n if os.path.isfile(word_dict_pkl_path):\n with open(word_dict_pkl_path, 'rb') as f:\n word_dict = pickle.load(f)\n print('Existed word_dict loaded')\n else:\n print('No word_dict pkl file, start making word_dict...')\n with codecs.open(training_data_path, 'r', encoding='utf-8') as f:\n word_vocab = dict()\n for line in f.read().split('\\n')[1:]:\n review = line.split('\\t')[1]\n tokens = tokenizer.morphs(review)\n for token in tokens:\n if token in word_vocab.keys():\n word_vocab[token] += 1\n else:\n word_vocab[token] = 1\n word_vocab = [word for word in word_vocab.keys() if word_vocab[\n word] >= params['min_vocab_count']]\n word_vocab = [params['PAD']] + word_vocab + [params['UNK']]\n for idx, word in enumerate(word_vocab):\n word_dict[word] = idx\n print('Making word_dict ... Done and Saved')\n with open(word_dict_pkl_path, 'wb') as f:\n pickle.dump(word_dict, f)\n return word_dict\n\n\ndef make_word_embedding(word_dict, word_emb_pkl_path=params[\n 'default_word_emb_pkl_path'], fasttext_path=params['default_fasttext_path']\n ):\n word_emb = np.zeros([len(word_dict), params['word_emb_dim']])\n if os.path.isfile(word_emb_pkl_path):\n with open(word_emb_pkl_path, 'rb') as f:\n word_emb = pickle.load(f)\n print('Existed trained word embedding loaded')\n else:\n fasttext_model = FastText.load_fasttext_format(fasttext_path,\n encoding='utf8')\n print('No word_emb pkl file, start making word_emb ...')\n for word, idx in word_dict.items():\n if idx == 0:\n continue\n else:\n try:\n word_emb[idx] = np.asarray(fasttext_model.wv[word])\n except KeyError:\n word_emb[idx] = np.random.uniform(-0.25, 0.25, params[\n 'word_emb_dim'])\n with open(word_emb_pkl_path, 'wb') as f:\n pickle.dump(word_emb, f)\n print('Making word_emb ... Done and Saved')\n return word_emb\n\n\ndef zero_padding(token_sentence, word_dict):\n padded_sentence = token_sentence + [word_dict[params['PAD']]] * (params\n ['max_seq_length'] - len(token_sentence))\n return padded_sentence\n\n\ndef dataset_iterator(filename, word_dict, batch_size):\n with open(filename, 'r', encoding='utf8') as f_dataset:\n context = []\n sequence_length = []\n label = []\n text = f_dataset.read().split('\\n')\n for line in text[1:]:\n class_label = [0, 0]\n review = line.split('\\t')[1]\n polarity = int(line.split('\\t')[2])\n class_label[polarity] = 1\n label.append(class_label)\n tokens = tokenizer.morphs(review)\n if len(tokens) > params['max_seq_length']:\n tokens = tokens[:params['max_seq_length']]\n sentence = [(word_dict[word] if word in word_dict else\n word_dict[params['UNK']]) for word in tokens]\n sequence_length.append(len(sentence))\n sentence = zero_padding(sentence, word_dict)\n context.append(sentence)\n if len(context) == batch_size:\n yield context, sequence_length, label\n context = []\n sequence_length = []\n label = []\n if len(context) > 0:\n yield context, sequence_length, label\n",
"step-5": "import os\r\nimport os.path\r\nimport numpy as np\r\nimport pickle\r\nimport codecs\r\nfrom konlpy.tag import Okt\r\nfrom hyperparams import params\r\nfrom gensim.models import FastText\r\n\r\n#tokenizer\r\ntokenizer = Okt()\r\n\r\ndef make_word_dictionary(word_dict_pkl_path=params['default_word_dict_pkl_path'], training_data_path = params['default_training_data_path']):\r\n #word_dict => 'Word':'index'\r\n word_dict = dict()\r\n if os.path.isfile(word_dict_pkl_path):\r\n #if already existed, just load it\r\n with open(word_dict_pkl_path, 'rb') as f:\r\n word_dict = pickle.load(f)\r\n print('Existed word_dict loaded')\r\n else:\r\n print('No word_dict pkl file, start making word_dict...')\r\n with codecs.open(training_data_path, 'r', encoding='utf-8') as f:\r\n word_vocab = dict()\r\n # 'word':'frequency'\r\n for line in f.read().split('\\n')[1:]:\r\n review = line.split('\\t')[1]\r\n #tokenizing\r\n tokens = tokenizer.morphs(review)\r\n for token in tokens:\r\n if token in word_vocab.keys():\r\n word_vocab[token] += 1\r\n else:\r\n word_vocab[token] = 1\r\n word_vocab = [word for word in word_vocab.keys() if word_vocab[word] >= params['min_vocab_count']]\r\n # add pad & unk token\r\n word_vocab = [params['PAD']] + word_vocab + [params['UNK']]\r\n for idx, word in enumerate(word_vocab):\r\n word_dict[word] = idx\r\n print('Making word_dict ... Done and Saved')\r\n with open(word_dict_pkl_path, 'wb') as f:\r\n pickle.dump(word_dict, f)\r\n return word_dict\r\n\r\ndef make_word_embedding(word_dict, word_emb_pkl_path = params['default_word_emb_pkl_path'], fasttext_path = params['default_fasttext_path']):\r\n word_emb = np.zeros([len(word_dict), params['word_emb_dim']])\r\n if os.path.isfile(word_emb_pkl_path):\r\n with open(word_emb_pkl_path, 'rb') as f:\r\n word_emb = pickle.load(f)\r\n print('Existed trained word embedding loaded')\r\n else:\r\n #load fasttext model\r\n fasttext_model = FastText.load_fasttext_format(fasttext_path, encoding='utf8')\r\n print('No word_emb pkl file, start making word_emb ...')\r\n for word, idx in word_dict.items():\r\n if idx==0:\r\n # PAD = 0\r\n continue\r\n else:\r\n try:\r\n word_emb[idx] = np.asarray(fasttext_model.wv[word])\r\n except KeyError:\r\n # if there is no word vector for certain word, just assign random vector\r\n word_emb[idx] = np.random.uniform(-0.25, 0.25, params['word_emb_dim'])\r\n with open(word_emb_pkl_path, 'wb') as f:\r\n pickle.dump(word_emb, f)\r\n print('Making word_emb ... Done and Saved')\r\n return word_emb\r\n\r\ndef zero_padding(token_sentence, word_dict):\r\n #input : [1,4,3,2,1,15]\r\n #output : [1,4,3,2,1,15,0,0,0,0]\r\n padded_sentence = token_sentence + [word_dict[params['PAD']]]*(params['max_seq_length']-len(token_sentence))\r\n return padded_sentence\r\n\r\n\r\ndef dataset_iterator(filename, word_dict, batch_size):\r\n #yield batch for training\r\n with open(filename, 'r', encoding='utf8') as f_dataset:\r\n context = []\r\n sequence_length = []\r\n label = []\r\n text = f_dataset.read().split('\\n')\r\n for line in text[1:]:\r\n class_label = [0,0]\r\n review = line.split('\\t')[1]\r\n polarity = int(line.split('\\t')[2])\r\n class_label[polarity] = 1 #mark polarity\r\n label.append(class_label)\r\n tokens = tokenizer.morphs(review)\r\n #if the review is too long, cut it to adequate length\r\n if len(tokens) > params['max_seq_length']:\r\n tokens = tokens[:params['max_seq_length']]\r\n sentence = [word_dict[word] if word in word_dict else word_dict[params['UNK']] for word in tokens]\r\n sequence_length.append(len(sentence))\r\n sentence = zero_padding(sentence, word_dict)\r\n context.append(sentence)\r\n\r\n if len(context) == batch_size:\r\n yield (context, sequence_length, label)\r\n context =[]\r\n sequence_length = []\r\n label = []\r\n if len(context) > 0:\r\n yield (context, sequence_length, label)",
"step-ids": [
1,
3,
5,
6,
7
]
}
|
[
1,
3,
5,
6,
7
] |
__author__='rhyschris'
""" Defines the set of actions.
This functions exactly the same as
Actions.cs in the Unity game.
"""
from enum import Enum
class Actions(Enum):
doNothing = 0
crouch = 1
jump = 3
walkTowards = 0x1 << 2
runTowards = 0x2 << 2
moveAway = 0x3 << 2
blockUp = 0x1 << 4
blockDown = 0x2 << 4
attack1 = 0x3 << 4
attack2 = 0x4 << 4
attack3 = 0x5 << 4
attack4 = 0x6 << 4
if __name__ == '__main__':
print "Contents of actions:"
for act in Actions:
print repr(act)
|
normal
|
{
"blob_id": "bc0bfb0ff8eaf21b15b06eea2ea333381c70bc75",
"index": 6775,
"step-1": "__author__='rhyschris'\n\n\"\"\" Defines the set of actions.\n This functions exactly the same as \n Actions.cs in the Unity game.\n\"\"\"\nfrom enum import Enum\n\n\nclass Actions(Enum):\n doNothing = 0\n crouch = 1\n jump = 3\n walkTowards = 0x1 << 2\n runTowards = 0x2 << 2\n moveAway = 0x3 << 2\n blockUp = 0x1 << 4\n blockDown = 0x2 << 4\n attack1 = 0x3 << 4\n attack2 = 0x4 << 4\n attack3 = 0x5 << 4\n attack4 = 0x6 << 4\n\nif __name__ == '__main__':\n print \"Contents of actions:\"\n \n for act in Actions:\n print repr(act)\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
import time
from PyQt5.QtCore import (
QThread,
)
from common import attach_common
from database_downloader import DatabaseDownload
from ai_list_memorize import MemorizeList
from ai_list_morpheme import MorphemeList
from ai_list_ngram import NgramList
from ai_list_none import NoneList
from ai_bot_memorize import MemorizeBot
from ai_bot_morpheme import MorphemeBot
from ai_bot_ngram import NgramBot
from ai_bot_none import NoneBot
@attach_common
class TalkBotThread(QThread):
parent = None
bot = None
lister = None
text_target = ''
tokens_start_of_text = []
tokens_of_text = []
def run(self):
self.start_database()
self.update_bot_type()
self.start_main_loop()
def start_database(self):
if self.config.ENABLED_DOWNLOAD:
DatabaseDownload.remove_tempfiles()
DatabaseDownload.set_url('')
DatabaseDownload.do_download_html()
else:
DatabaseDownload.do_not_download_html()
self.text_target = DatabaseDownload().get_outcome()
def update_bot_type(self):
parent = self.parent
parent.is_bot_ready = False
parent.show_bot_not_ready_msg()
self.type_bot = parent.type_bot
self.select_token_list()
self.select_bot()
parent.bot = self.bot
parent.lister = self.lister
parent.is_bot_ready = True
parent.show_bot_ready_msg()
self.output_bot_type()
def output_bot_type(self):
parent = self.parent
msgs = (
'TalkBot:',
' id: {}'.format(parent.type_bot),
' bot: {}'.format(parent.bot.__class__.__name__),
' lister: {}'.format(parent.lister.__class__.__name__),
' tokens: {}'.format(str(parent.lister.get_token_list())[:60]),
)
for msg in msgs:
self.logger.w(msg)
def select_token_list(self):
if self.type_bot == 0:
self.lister = NoneList(
num_of_gram=self.config.DISABLE_NGRAM,
text_target=self.text_target,
)
self.tokens_start_of_text = self.lister.get_starting_token_list()
self.tokens_of_text = self.lister.get_token_list()
return
if self.type_bot == 1:
self.lister = NgramList(
num_of_gram=3,
text_target=self.text_target,
)
self.tokens_start_of_text = self.lister.get_starting_token_list()
self.tokens_of_text = self.lister.get_token_list()
return
if self.type_bot == 2:
self.lister = MorphemeList(
num_of_gram=self.config.DISABLE_NGRAM,
text_target=self.text_target,
)
self.tokens_start_of_text = self.lister.get_starting_token_list()
self.tokens_of_text = self.lister.get_token_list()
return
if self.type_bot == 3:
self.lister = MemorizeList(
num_of_gram=self.config.DISABLE_NGRAM,
text_target=self.text_target,
)
self.tokens_start_of_text = self.lister.get_starting_token_list()
self.tokens_of_text = self.lister.get_token_list()
return
err = self.type_bot
raise Exception(err)
def select_bot(self):
if self.type_bot == 0:
self.bot = NoneBot(
starting_token_list=self.tokens_start_of_text,
token_list=self.tokens_of_text,
)
return
if self.type_bot == 1:
self.bot = NgramBot(
starting_token_list=self.tokens_start_of_text,
token_list=self.tokens_of_text,
)
return
if self.type_bot == 2:
self.bot = MorphemeBot(
starting_token_list=self.tokens_start_of_text,
token_list=self.tokens_of_text,
)
return
if self.type_bot == 3:
self.bot = MemorizeBot(
starting_token_list=self.tokens_start_of_text,
token_list=self.tokens_of_text,
)
return
err = self.type_bot
raise Exception(err)
def start_main_loop(self):
parent = self.parent
while True:
time.sleep(0.2)
if parent.is_app_close:
break
if parent.type_bot != self.type_bot:
self.update_bot_type()
continue
parent.update_bot_msg_to_proper_latest_status()
msg = 'Stopped the database thread!'
self.logger.w(msg)
if __name__ == "__main__":
from gui_talkbot import MainWindow
TestClass = MainWindow
import sys
from PyQt5.QtWidgets import QApplication
qapp = QApplication(sys.argv)
window = TestClass()
window.show()
code = qapp.exec()
sys.exit(code)
|
normal
|
{
"blob_id": "77763f501c6776969d2594f987e5d7ab7d4377fb",
"index": 317,
"step-1": "<mask token>\n\n\n@attach_common\nclass TalkBotThread(QThread):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def run(self):\n self.start_database()\n self.update_bot_type()\n self.start_main_loop()\n\n def start_database(self):\n if self.config.ENABLED_DOWNLOAD:\n DatabaseDownload.remove_tempfiles()\n DatabaseDownload.set_url('')\n DatabaseDownload.do_download_html()\n else:\n DatabaseDownload.do_not_download_html()\n self.text_target = DatabaseDownload().get_outcome()\n <mask token>\n <mask token>\n\n def select_token_list(self):\n if self.type_bot == 0:\n self.lister = NoneList(num_of_gram=self.config.DISABLE_NGRAM,\n text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 1:\n self.lister = NgramList(num_of_gram=3, text_target=self.text_target\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 2:\n self.lister = MorphemeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 3:\n self.lister = MemorizeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n err = self.type_bot\n raise Exception(err)\n\n def select_bot(self):\n if self.type_bot == 0:\n self.bot = NoneBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 1:\n self.bot = NgramBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 2:\n self.bot = MorphemeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 3:\n self.bot = MemorizeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n err = self.type_bot\n raise Exception(err)\n\n def start_main_loop(self):\n parent = self.parent\n while True:\n time.sleep(0.2)\n if parent.is_app_close:\n break\n if parent.type_bot != self.type_bot:\n self.update_bot_type()\n continue\n parent.update_bot_msg_to_proper_latest_status()\n msg = 'Stopped the database thread!'\n self.logger.w(msg)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@attach_common\nclass TalkBotThread(QThread):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def run(self):\n self.start_database()\n self.update_bot_type()\n self.start_main_loop()\n\n def start_database(self):\n if self.config.ENABLED_DOWNLOAD:\n DatabaseDownload.remove_tempfiles()\n DatabaseDownload.set_url('')\n DatabaseDownload.do_download_html()\n else:\n DatabaseDownload.do_not_download_html()\n self.text_target = DatabaseDownload().get_outcome()\n <mask token>\n\n def output_bot_type(self):\n parent = self.parent\n msgs = 'TalkBot:', ' id: {}'.format(parent.type_bot\n ), ' bot: {}'.format(parent.bot.__class__.__name__\n ), ' lister: {}'.format(parent.lister.__class__.__name__\n ), ' tokens: {}'.format(str(parent.lister.get_token_list())[:60]\n )\n for msg in msgs:\n self.logger.w(msg)\n\n def select_token_list(self):\n if self.type_bot == 0:\n self.lister = NoneList(num_of_gram=self.config.DISABLE_NGRAM,\n text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 1:\n self.lister = NgramList(num_of_gram=3, text_target=self.text_target\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 2:\n self.lister = MorphemeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 3:\n self.lister = MemorizeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n err = self.type_bot\n raise Exception(err)\n\n def select_bot(self):\n if self.type_bot == 0:\n self.bot = NoneBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 1:\n self.bot = NgramBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 2:\n self.bot = MorphemeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 3:\n self.bot = MemorizeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n err = self.type_bot\n raise Exception(err)\n\n def start_main_loop(self):\n parent = self.parent\n while True:\n time.sleep(0.2)\n if parent.is_app_close:\n break\n if parent.type_bot != self.type_bot:\n self.update_bot_type()\n continue\n parent.update_bot_msg_to_proper_latest_status()\n msg = 'Stopped the database thread!'\n self.logger.w(msg)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\n@attach_common\nclass TalkBotThread(QThread):\n parent = None\n bot = None\n lister = None\n text_target = ''\n tokens_start_of_text = []\n tokens_of_text = []\n\n def run(self):\n self.start_database()\n self.update_bot_type()\n self.start_main_loop()\n\n def start_database(self):\n if self.config.ENABLED_DOWNLOAD:\n DatabaseDownload.remove_tempfiles()\n DatabaseDownload.set_url('')\n DatabaseDownload.do_download_html()\n else:\n DatabaseDownload.do_not_download_html()\n self.text_target = DatabaseDownload().get_outcome()\n\n def update_bot_type(self):\n parent = self.parent\n parent.is_bot_ready = False\n parent.show_bot_not_ready_msg()\n self.type_bot = parent.type_bot\n self.select_token_list()\n self.select_bot()\n parent.bot = self.bot\n parent.lister = self.lister\n parent.is_bot_ready = True\n parent.show_bot_ready_msg()\n self.output_bot_type()\n\n def output_bot_type(self):\n parent = self.parent\n msgs = 'TalkBot:', ' id: {}'.format(parent.type_bot\n ), ' bot: {}'.format(parent.bot.__class__.__name__\n ), ' lister: {}'.format(parent.lister.__class__.__name__\n ), ' tokens: {}'.format(str(parent.lister.get_token_list())[:60]\n )\n for msg in msgs:\n self.logger.w(msg)\n\n def select_token_list(self):\n if self.type_bot == 0:\n self.lister = NoneList(num_of_gram=self.config.DISABLE_NGRAM,\n text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 1:\n self.lister = NgramList(num_of_gram=3, text_target=self.text_target\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 2:\n self.lister = MorphemeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 3:\n self.lister = MemorizeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n err = self.type_bot\n raise Exception(err)\n\n def select_bot(self):\n if self.type_bot == 0:\n self.bot = NoneBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 1:\n self.bot = NgramBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 2:\n self.bot = MorphemeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 3:\n self.bot = MemorizeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n err = self.type_bot\n raise Exception(err)\n\n def start_main_loop(self):\n parent = self.parent\n while True:\n time.sleep(0.2)\n if parent.is_app_close:\n break\n if parent.type_bot != self.type_bot:\n self.update_bot_type()\n continue\n parent.update_bot_msg_to_proper_latest_status()\n msg = 'Stopped the database thread!'\n self.logger.w(msg)\n\n\n<mask token>\n",
"step-4": "import time\nfrom PyQt5.QtCore import QThread\nfrom common import attach_common\nfrom database_downloader import DatabaseDownload\nfrom ai_list_memorize import MemorizeList\nfrom ai_list_morpheme import MorphemeList\nfrom ai_list_ngram import NgramList\nfrom ai_list_none import NoneList\nfrom ai_bot_memorize import MemorizeBot\nfrom ai_bot_morpheme import MorphemeBot\nfrom ai_bot_ngram import NgramBot\nfrom ai_bot_none import NoneBot\n\n\n@attach_common\nclass TalkBotThread(QThread):\n parent = None\n bot = None\n lister = None\n text_target = ''\n tokens_start_of_text = []\n tokens_of_text = []\n\n def run(self):\n self.start_database()\n self.update_bot_type()\n self.start_main_loop()\n\n def start_database(self):\n if self.config.ENABLED_DOWNLOAD:\n DatabaseDownload.remove_tempfiles()\n DatabaseDownload.set_url('')\n DatabaseDownload.do_download_html()\n else:\n DatabaseDownload.do_not_download_html()\n self.text_target = DatabaseDownload().get_outcome()\n\n def update_bot_type(self):\n parent = self.parent\n parent.is_bot_ready = False\n parent.show_bot_not_ready_msg()\n self.type_bot = parent.type_bot\n self.select_token_list()\n self.select_bot()\n parent.bot = self.bot\n parent.lister = self.lister\n parent.is_bot_ready = True\n parent.show_bot_ready_msg()\n self.output_bot_type()\n\n def output_bot_type(self):\n parent = self.parent\n msgs = 'TalkBot:', ' id: {}'.format(parent.type_bot\n ), ' bot: {}'.format(parent.bot.__class__.__name__\n ), ' lister: {}'.format(parent.lister.__class__.__name__\n ), ' tokens: {}'.format(str(parent.lister.get_token_list())[:60]\n )\n for msg in msgs:\n self.logger.w(msg)\n\n def select_token_list(self):\n if self.type_bot == 0:\n self.lister = NoneList(num_of_gram=self.config.DISABLE_NGRAM,\n text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 1:\n self.lister = NgramList(num_of_gram=3, text_target=self.text_target\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 2:\n self.lister = MorphemeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n if self.type_bot == 3:\n self.lister = MemorizeList(num_of_gram=self.config.\n DISABLE_NGRAM, text_target=self.text_target)\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n err = self.type_bot\n raise Exception(err)\n\n def select_bot(self):\n if self.type_bot == 0:\n self.bot = NoneBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 1:\n self.bot = NgramBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 2:\n self.bot = MorphemeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n if self.type_bot == 3:\n self.bot = MemorizeBot(starting_token_list=self.\n tokens_start_of_text, token_list=self.tokens_of_text)\n return\n err = self.type_bot\n raise Exception(err)\n\n def start_main_loop(self):\n parent = self.parent\n while True:\n time.sleep(0.2)\n if parent.is_app_close:\n break\n if parent.type_bot != self.type_bot:\n self.update_bot_type()\n continue\n parent.update_bot_msg_to_proper_latest_status()\n msg = 'Stopped the database thread!'\n self.logger.w(msg)\n\n\nif __name__ == '__main__':\n from gui_talkbot import MainWindow\n TestClass = MainWindow\n import sys\n from PyQt5.QtWidgets import QApplication\n qapp = QApplication(sys.argv)\n window = TestClass()\n window.show()\n code = qapp.exec()\n sys.exit(code)\n",
"step-5": "import time\nfrom PyQt5.QtCore import (\n QThread,\n)\nfrom common import attach_common\nfrom database_downloader import DatabaseDownload\nfrom ai_list_memorize import MemorizeList\nfrom ai_list_morpheme import MorphemeList\nfrom ai_list_ngram import NgramList\nfrom ai_list_none import NoneList\nfrom ai_bot_memorize import MemorizeBot\nfrom ai_bot_morpheme import MorphemeBot\nfrom ai_bot_ngram import NgramBot\nfrom ai_bot_none import NoneBot\n\n\n@attach_common\nclass TalkBotThread(QThread):\n parent = None\n bot = None\n lister = None\n\n text_target = ''\n tokens_start_of_text = []\n tokens_of_text = []\n\n def run(self):\n self.start_database()\n self.update_bot_type()\n self.start_main_loop()\n\n def start_database(self):\n if self.config.ENABLED_DOWNLOAD:\n DatabaseDownload.remove_tempfiles()\n DatabaseDownload.set_url('')\n DatabaseDownload.do_download_html()\n else:\n DatabaseDownload.do_not_download_html()\n self.text_target = DatabaseDownload().get_outcome()\n\n def update_bot_type(self):\n parent = self.parent\n parent.is_bot_ready = False\n parent.show_bot_not_ready_msg()\n\n self.type_bot = parent.type_bot\n self.select_token_list()\n self.select_bot()\n\n parent.bot = self.bot\n parent.lister = self.lister\n parent.is_bot_ready = True\n parent.show_bot_ready_msg()\n self.output_bot_type()\n\n def output_bot_type(self):\n parent = self.parent\n msgs = (\n 'TalkBot:',\n ' id: {}'.format(parent.type_bot),\n ' bot: {}'.format(parent.bot.__class__.__name__),\n ' lister: {}'.format(parent.lister.__class__.__name__),\n ' tokens: {}'.format(str(parent.lister.get_token_list())[:60]),\n )\n for msg in msgs:\n self.logger.w(msg)\n\n def select_token_list(self):\n if self.type_bot == 0:\n self.lister = NoneList(\n num_of_gram=self.config.DISABLE_NGRAM,\n text_target=self.text_target,\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n\n if self.type_bot == 1:\n self.lister = NgramList(\n num_of_gram=3,\n text_target=self.text_target,\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n\n if self.type_bot == 2:\n self.lister = MorphemeList(\n num_of_gram=self.config.DISABLE_NGRAM,\n text_target=self.text_target,\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n\n if self.type_bot == 3:\n self.lister = MemorizeList(\n num_of_gram=self.config.DISABLE_NGRAM,\n text_target=self.text_target,\n )\n self.tokens_start_of_text = self.lister.get_starting_token_list()\n self.tokens_of_text = self.lister.get_token_list()\n return\n\n err = self.type_bot\n raise Exception(err)\n\n def select_bot(self):\n if self.type_bot == 0:\n self.bot = NoneBot(\n starting_token_list=self.tokens_start_of_text,\n token_list=self.tokens_of_text,\n )\n return\n\n if self.type_bot == 1:\n self.bot = NgramBot(\n starting_token_list=self.tokens_start_of_text,\n token_list=self.tokens_of_text,\n )\n return\n\n if self.type_bot == 2:\n self.bot = MorphemeBot(\n starting_token_list=self.tokens_start_of_text,\n token_list=self.tokens_of_text,\n )\n return\n\n if self.type_bot == 3:\n self.bot = MemorizeBot(\n starting_token_list=self.tokens_start_of_text,\n token_list=self.tokens_of_text,\n )\n return\n\n err = self.type_bot\n raise Exception(err)\n\n def start_main_loop(self):\n parent = self.parent\n while True:\n time.sleep(0.2)\n\n if parent.is_app_close:\n break\n\n if parent.type_bot != self.type_bot:\n self.update_bot_type()\n continue\n\n parent.update_bot_msg_to_proper_latest_status()\n\n msg = 'Stopped the database thread!'\n self.logger.w(msg)\n\n\nif __name__ == \"__main__\":\n from gui_talkbot import MainWindow\n TestClass = MainWindow\n\n import sys\n from PyQt5.QtWidgets import QApplication\n qapp = QApplication(sys.argv)\n window = TestClass()\n window.show()\n code = qapp.exec()\n sys.exit(code)\n",
"step-ids": [
6,
7,
9,
11,
12
]
}
|
[
6,
7,
9,
11,
12
] |
"""
OO 05-18-2020
Task
----------------------------------------------------------------------------------------------------------
Your company needs a function that meets the following requirements:
- For a given array of 'n' integers, the function returns the index of the element with the minimum value
in the array. If there is more than one element with the minimum value, the returned index should be
the smallest one.
- If an empty array is passed to the function, it should raise an Exception.
A colleague has written that function, and your task is to design 3 separated unit tests, testing if the
function behaves correctly. The implementation in Python is listed below (Implementations in other
languages can be found in the code template):
def minimum_index(seq):
if len(seq) == 0:
raise ValueError("Cannot get the minimum value index from an empty sequence")
min_idx = 0
for i in range(1, len(seq)):
if a[i] < a[min_idx]:
min_idx = i
return min_idx
Another co-worker has prepared functions that will perform the testing and validate returned results with
expectations. Your task is to implement 3 classes that will produce test data and the expected results for
the testing functions. More specifically: function 'get_array()' in 'TestDataEmptyArray' class and
functions 'get_array()' and 'get_expected_result()' in classes 'TestDataUniqueValues' and
'TestDataExactlyTwoDifferentMinimums' following the below specifications:
- get_array() method in class TestDataEmptyArray has to return an empty array.
- get_array() method in class TestDataUniqueValues has to return an array of size at least 2 with all
unique elements, while method get_expected_result() of this class has to return the expected minimum
value index for this array.
- get_array() method in class TestDataExactlyTwoDifferentMinimums has to return an array where there are
exactly two different minimum values, while method get_expected_result() of this class has to return
the expected minimum value index for this array.
"""
def minimum_index(seq):
if len(seq) == 0:
raise ValueError("Cannot get the minimum value index from an empty sequence")
min_idx = 0
for i in range(1, len(seq)):
if seq[i] < seq[min_idx]:
min_idx = i
return min_idx
class TestDataEmptyArray(object):
@staticmethod
def get_array():
return []
class TestDataUniqueValues(object):
@staticmethod
def get_array():
return [5, 3, 2]
@staticmethod
def get_expected_result():
return 2
class TestDataExactlyTwoDifferentMinimums(object):
@staticmethod
def get_array():
return [5, 3, 2, 2, 9]
@staticmethod
def get_expected_result():
return 2
def TestWithEmptyArray():
try:
seq = TestDataEmptyArray.get_array()
minimum_index(seq)
except ValueError:
pass
else:
assert False
def TestWithUniqueValues():
seq = TestDataUniqueValues.get_array()
assert len(seq) >= 2
assert len(list(set(seq))) == len(seq)
expected_result = TestDataUniqueValues.get_expected_result()
result = minimum_index(seq)
assert result == expected_result
def TestWithExactyTwoDifferentMinimums():
seq = TestDataExactlyTwoDifferentMinimums.get_array()
assert len(seq) >= 2
tmp = sorted(seq)
assert tmp[0] == tmp[1] and (len(tmp) == 2 or tmp[1] < tmp[2])
expected_result = TestDataExactlyTwoDifferentMinimums.get_expected_result()
result = minimum_index(seq)
assert result == expected_result
TestWithEmptyArray()
TestWithUniqueValues()
TestWithExactyTwoDifferentMinimums()
print("OK")
|
normal
|
{
"blob_id": "8fdc9a52b00686e10c97fa61e43ddbbccb64741b",
"index": 8946,
"step-1": "<mask token>\n\n\nclass TestDataEmptyArray(object):\n\n @staticmethod\n def get_array():\n return []\n\n\nclass TestDataUniqueValues(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\nclass TestDataExactlyTwoDifferentMinimums(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2, 2, 9]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\n<mask token>\n\n\ndef TestWithUniqueValues():\n seq = TestDataUniqueValues.get_array()\n assert len(seq) >= 2\n assert len(list(set(seq))) == len(seq)\n expected_result = TestDataUniqueValues.get_expected_result()\n result = minimum_index(seq)\n assert result == expected_result\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestDataEmptyArray(object):\n\n @staticmethod\n def get_array():\n return []\n\n\nclass TestDataUniqueValues(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\nclass TestDataExactlyTwoDifferentMinimums(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2, 2, 9]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\n<mask token>\n\n\ndef TestWithUniqueValues():\n seq = TestDataUniqueValues.get_array()\n assert len(seq) >= 2\n assert len(list(set(seq))) == len(seq)\n expected_result = TestDataUniqueValues.get_expected_result()\n result = minimum_index(seq)\n assert result == expected_result\n\n\ndef TestWithExactyTwoDifferentMinimums():\n seq = TestDataExactlyTwoDifferentMinimums.get_array()\n assert len(seq) >= 2\n tmp = sorted(seq)\n assert tmp[0] == tmp[1] and (len(tmp) == 2 or tmp[1] < tmp[2])\n expected_result = TestDataExactlyTwoDifferentMinimums.get_expected_result()\n result = minimum_index(seq)\n assert result == expected_result\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass TestDataEmptyArray(object):\n\n @staticmethod\n def get_array():\n return []\n\n\nclass TestDataUniqueValues(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\nclass TestDataExactlyTwoDifferentMinimums(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2, 2, 9]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\ndef TestWithEmptyArray():\n try:\n seq = TestDataEmptyArray.get_array()\n minimum_index(seq)\n except ValueError:\n pass\n else:\n assert False\n\n\ndef TestWithUniqueValues():\n seq = TestDataUniqueValues.get_array()\n assert len(seq) >= 2\n assert len(list(set(seq))) == len(seq)\n expected_result = TestDataUniqueValues.get_expected_result()\n result = minimum_index(seq)\n assert result == expected_result\n\n\ndef TestWithExactyTwoDifferentMinimums():\n seq = TestDataExactlyTwoDifferentMinimums.get_array()\n assert len(seq) >= 2\n tmp = sorted(seq)\n assert tmp[0] == tmp[1] and (len(tmp) == 2 or tmp[1] < tmp[2])\n expected_result = TestDataExactlyTwoDifferentMinimums.get_expected_result()\n result = minimum_index(seq)\n assert result == expected_result\n\n\n<mask token>\n",
"step-4": "<mask token>\n\n\ndef minimum_index(seq):\n if len(seq) == 0:\n raise ValueError(\n 'Cannot get the minimum value index from an empty sequence')\n min_idx = 0\n for i in range(1, len(seq)):\n if seq[i] < seq[min_idx]:\n min_idx = i\n return min_idx\n\n\nclass TestDataEmptyArray(object):\n\n @staticmethod\n def get_array():\n return []\n\n\nclass TestDataUniqueValues(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\nclass TestDataExactlyTwoDifferentMinimums(object):\n\n @staticmethod\n def get_array():\n return [5, 3, 2, 2, 9]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\ndef TestWithEmptyArray():\n try:\n seq = TestDataEmptyArray.get_array()\n minimum_index(seq)\n except ValueError:\n pass\n else:\n assert False\n\n\ndef TestWithUniqueValues():\n seq = TestDataUniqueValues.get_array()\n assert len(seq) >= 2\n assert len(list(set(seq))) == len(seq)\n expected_result = TestDataUniqueValues.get_expected_result()\n result = minimum_index(seq)\n assert result == expected_result\n\n\ndef TestWithExactyTwoDifferentMinimums():\n seq = TestDataExactlyTwoDifferentMinimums.get_array()\n assert len(seq) >= 2\n tmp = sorted(seq)\n assert tmp[0] == tmp[1] and (len(tmp) == 2 or tmp[1] < tmp[2])\n expected_result = TestDataExactlyTwoDifferentMinimums.get_expected_result()\n result = minimum_index(seq)\n assert result == expected_result\n\n\nTestWithEmptyArray()\nTestWithUniqueValues()\nTestWithExactyTwoDifferentMinimums()\nprint('OK')\n",
"step-5": "\"\"\"\n OO 05-18-2020\n\n Task\n ----------------------------------------------------------------------------------------------------------\n Your company needs a function that meets the following requirements:\n\n - For a given array of 'n' integers, the function returns the index of the element with the minimum value\n in the array. If there is more than one element with the minimum value, the returned index should be\n the smallest one.\n\n - If an empty array is passed to the function, it should raise an Exception.\n\n A colleague has written that function, and your task is to design 3 separated unit tests, testing if the\n function behaves correctly. The implementation in Python is listed below (Implementations in other\n languages can be found in the code template):\n \n def minimum_index(seq):\n if len(seq) == 0:\n raise ValueError(\"Cannot get the minimum value index from an empty sequence\")\n min_idx = 0\n for i in range(1, len(seq)):\n if a[i] < a[min_idx]:\n min_idx = i\n return min_idx\n\n Another co-worker has prepared functions that will perform the testing and validate returned results with\n expectations. Your task is to implement 3 classes that will produce test data and the expected results for\n the testing functions. More specifically: function 'get_array()' in 'TestDataEmptyArray' class and\n functions 'get_array()' and 'get_expected_result()' in classes 'TestDataUniqueValues' and\n 'TestDataExactlyTwoDifferentMinimums' following the below specifications:\n\n - get_array() method in class TestDataEmptyArray has to return an empty array.\n - get_array() method in class TestDataUniqueValues has to return an array of size at least 2 with all\n unique elements, while method get_expected_result() of this class has to return the expected minimum\n value index for this array.\n - get_array() method in class TestDataExactlyTwoDifferentMinimums has to return an array where there are\n exactly two different minimum values, while method get_expected_result() of this class has to return\n the expected minimum value index for this array.\n\n\"\"\"\n\n\ndef minimum_index(seq):\n if len(seq) == 0:\n raise ValueError(\"Cannot get the minimum value index from an empty sequence\")\n\n min_idx = 0\n for i in range(1, len(seq)):\n if seq[i] < seq[min_idx]:\n min_idx = i\n\n return min_idx\n\n\nclass TestDataEmptyArray(object):\n @staticmethod\n def get_array():\n return []\n\n\nclass TestDataUniqueValues(object):\n @staticmethod\n def get_array():\n return [5, 3, 2]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\nclass TestDataExactlyTwoDifferentMinimums(object):\n @staticmethod\n def get_array():\n return [5, 3, 2, 2, 9]\n\n @staticmethod\n def get_expected_result():\n return 2\n\n\ndef TestWithEmptyArray():\n try:\n seq = TestDataEmptyArray.get_array()\n minimum_index(seq)\n except ValueError:\n pass\n else:\n assert False\n\n\ndef TestWithUniqueValues():\n seq = TestDataUniqueValues.get_array()\n\n assert len(seq) >= 2\n assert len(list(set(seq))) == len(seq)\n\n expected_result = TestDataUniqueValues.get_expected_result()\n result = minimum_index(seq)\n\n assert result == expected_result\n\n\ndef TestWithExactyTwoDifferentMinimums():\n seq = TestDataExactlyTwoDifferentMinimums.get_array()\n\n assert len(seq) >= 2\n\n tmp = sorted(seq)\n\n assert tmp[0] == tmp[1] and (len(tmp) == 2 or tmp[1] < tmp[2])\n\n expected_result = TestDataExactlyTwoDifferentMinimums.get_expected_result()\n result = minimum_index(seq)\n\n assert result == expected_result\n\n\nTestWithEmptyArray()\nTestWithUniqueValues()\nTestWithExactyTwoDifferentMinimums()\nprint(\"OK\")\n",
"step-ids": [
9,
10,
11,
13,
14
]
}
|
[
9,
10,
11,
13,
14
] |
#!/bin/python
"""
len()
lower()
upper()
str()
"""
parrot = "Norwegian Blue"
print len(parrot)
|
normal
|
{
"blob_id": "cd8d95e2bf433020db2db06a21263f75e3f81331",
"index": 9740,
"step-1": "#!/bin/python\n\n\"\"\"\nlen()\nlower()\nupper()\nstr()\n\"\"\"\n\nparrot = \"Norwegian Blue\"\nprint len(parrot)\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
class ListNode:
def __init__(self, value=0, next=None):
self.value = value
self.next = next
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class ListNode:
def __init__(self, value=0, next=None):
self.value = value
self.next = next
def count(node: ListNode) ->int:
if node is None:
return 0
else:
return count(node.next) + 1
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class ListNode:
def __init__(self, value=0, next=None):
self.value = value
self.next = next
def count(node: ListNode) ->int:
if node is None:
return 0
else:
return count(node.next) + 1
<|reserved_special_token_0|>
print(count(None))
print(count(LL1))
print(count(ListNode()))
<|reserved_special_token_1|>
class ListNode:
def __init__(self, value=0, next=None):
self.value = value
self.next = next
def count(node: ListNode) ->int:
if node is None:
return 0
else:
return count(node.next) + 1
LL1 = ListNode(1, ListNode(4, ListNode(5)))
print(count(None))
print(count(LL1))
print(count(ListNode()))
<|reserved_special_token_1|>
class ListNode:
def __init__(self, value = 0, next = None):
self.value = value
self.next = next
def count(node: ListNode) -> int:
if node is None:
return 0
else:
return count(node.next) + 1
# Test Cases
LL1 = ListNode(1, ListNode(4, ListNode(5)))
print(count(None)) # 0
print(count(LL1)) # 3
print(count(ListNode())) # 1
|
flexible
|
{
"blob_id": "8c6169bd812a5f34693b12ce2c886969542f1ab8",
"index": 2352,
"step-1": "class ListNode:\n\n def __init__(self, value=0, next=None):\n self.value = value\n self.next = next\n\n\n<mask token>\n",
"step-2": "class ListNode:\n\n def __init__(self, value=0, next=None):\n self.value = value\n self.next = next\n\n\ndef count(node: ListNode) ->int:\n if node is None:\n return 0\n else:\n return count(node.next) + 1\n\n\n<mask token>\n",
"step-3": "class ListNode:\n\n def __init__(self, value=0, next=None):\n self.value = value\n self.next = next\n\n\ndef count(node: ListNode) ->int:\n if node is None:\n return 0\n else:\n return count(node.next) + 1\n\n\n<mask token>\nprint(count(None))\nprint(count(LL1))\nprint(count(ListNode()))\n",
"step-4": "class ListNode:\n\n def __init__(self, value=0, next=None):\n self.value = value\n self.next = next\n\n\ndef count(node: ListNode) ->int:\n if node is None:\n return 0\n else:\n return count(node.next) + 1\n\n\nLL1 = ListNode(1, ListNode(4, ListNode(5)))\nprint(count(None))\nprint(count(LL1))\nprint(count(ListNode()))\n",
"step-5": "class ListNode:\n def __init__(self, value = 0, next = None): \n self.value = value\n self.next = next\n \ndef count(node: ListNode) -> int:\n if node is None:\n return 0\n else:\n return count(node.next) + 1\n \n\n# Test Cases\nLL1 = ListNode(1, ListNode(4, ListNode(5)))\nprint(count(None)) # 0\nprint(count(LL1)) # 3\nprint(count(ListNode())) # 1\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
from calc1 import LispTranslator, RPNTranslator, Parser, Lexer
import unittest
class TestTranslators(unittest.TestCase):
def init_rpn(self, program):
return RPNTranslator(Parser(Lexer(program)))
def init_lisp(self, program):
return LispTranslator(Parser(Lexer(program)))
def test_simple_rpn(self):
self.assertEqual(self.init_rpn('2 + 3').interpret(), '2 3 +')
self.assertEqual(self.init_rpn('2 + 3 + 5').interpret(), '2 3 + 5 +')
self.assertEqual(self.init_rpn('2 + 3 * 5').interpret(), '2 3 5 * +')
self.assertEqual(self.init_rpn('(2 + 3) * 5').interpret(), '2 3 + 5 *')
def test_simple_lisp(self):
self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')
self.assertEqual(self.init_lisp('2 + 3 + 5').interpret(),
'(+ (+ 2 3) 5)')
self.assertEqual(self.init_lisp('2 + 3 * 5').interpret(),
'(+ 2 (* 3 5))')
self.assertEqual(self.init_lisp('(2 + 3) * 5').interpret(),
'(* (+ 2 3) 5)')
def test_examples_chapter_seven(self):
self.assertEqual(self.init_rpn('(5 + 3) * 12 DIV 3').interpret(),
'5 3 + 12 * 3 DIV')
self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')
self.assertEqual(self.init_lisp('(2 + 3 * 5)').interpret(),
'(+ 2 (* 3 5))')
if __name__ == '__main__':
unittest.main()
|
normal
|
{
"blob_id": "d0e957abfe5646fb84aed69902f2382d554dc825",
"index": 4401,
"step-1": "<mask token>\n\n\nclass TestTranslators(unittest.TestCase):\n <mask token>\n\n def init_lisp(self, program):\n return LispTranslator(Parser(Lexer(program)))\n <mask token>\n <mask token>\n\n def test_examples_chapter_seven(self):\n self.assertEqual(self.init_rpn('(5 + 3) * 12 DIV 3').interpret(),\n '5 3 + 12 * 3 DIV')\n self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')\n self.assertEqual(self.init_lisp('(2 + 3 * 5)').interpret(),\n '(+ 2 (* 3 5))')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestTranslators(unittest.TestCase):\n <mask token>\n\n def init_lisp(self, program):\n return LispTranslator(Parser(Lexer(program)))\n\n def test_simple_rpn(self):\n self.assertEqual(self.init_rpn('2 + 3').interpret(), '2 3 +')\n self.assertEqual(self.init_rpn('2 + 3 + 5').interpret(), '2 3 + 5 +')\n self.assertEqual(self.init_rpn('2 + 3 * 5').interpret(), '2 3 5 * +')\n self.assertEqual(self.init_rpn('(2 + 3) * 5').interpret(), '2 3 + 5 *')\n\n def test_simple_lisp(self):\n self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')\n self.assertEqual(self.init_lisp('2 + 3 + 5').interpret(),\n '(+ (+ 2 3) 5)')\n self.assertEqual(self.init_lisp('2 + 3 * 5').interpret(),\n '(+ 2 (* 3 5))')\n self.assertEqual(self.init_lisp('(2 + 3) * 5').interpret(),\n '(* (+ 2 3) 5)')\n\n def test_examples_chapter_seven(self):\n self.assertEqual(self.init_rpn('(5 + 3) * 12 DIV 3').interpret(),\n '5 3 + 12 * 3 DIV')\n self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')\n self.assertEqual(self.init_lisp('(2 + 3 * 5)').interpret(),\n '(+ 2 (* 3 5))')\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass TestTranslators(unittest.TestCase):\n\n def init_rpn(self, program):\n return RPNTranslator(Parser(Lexer(program)))\n\n def init_lisp(self, program):\n return LispTranslator(Parser(Lexer(program)))\n\n def test_simple_rpn(self):\n self.assertEqual(self.init_rpn('2 + 3').interpret(), '2 3 +')\n self.assertEqual(self.init_rpn('2 + 3 + 5').interpret(), '2 3 + 5 +')\n self.assertEqual(self.init_rpn('2 + 3 * 5').interpret(), '2 3 5 * +')\n self.assertEqual(self.init_rpn('(2 + 3) * 5').interpret(), '2 3 + 5 *')\n\n def test_simple_lisp(self):\n self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')\n self.assertEqual(self.init_lisp('2 + 3 + 5').interpret(),\n '(+ (+ 2 3) 5)')\n self.assertEqual(self.init_lisp('2 + 3 * 5').interpret(),\n '(+ 2 (* 3 5))')\n self.assertEqual(self.init_lisp('(2 + 3) * 5').interpret(),\n '(* (+ 2 3) 5)')\n\n def test_examples_chapter_seven(self):\n self.assertEqual(self.init_rpn('(5 + 3) * 12 DIV 3').interpret(),\n '5 3 + 12 * 3 DIV')\n self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')\n self.assertEqual(self.init_lisp('(2 + 3 * 5)').interpret(),\n '(+ 2 (* 3 5))')\n\n\n<mask token>\n",
"step-4": "from calc1 import LispTranslator, RPNTranslator, Parser, Lexer\nimport unittest\n\n\nclass TestTranslators(unittest.TestCase):\n\n def init_rpn(self, program):\n return RPNTranslator(Parser(Lexer(program)))\n\n def init_lisp(self, program):\n return LispTranslator(Parser(Lexer(program)))\n\n def test_simple_rpn(self):\n self.assertEqual(self.init_rpn('2 + 3').interpret(), '2 3 +')\n self.assertEqual(self.init_rpn('2 + 3 + 5').interpret(), '2 3 + 5 +')\n self.assertEqual(self.init_rpn('2 + 3 * 5').interpret(), '2 3 5 * +')\n self.assertEqual(self.init_rpn('(2 + 3) * 5').interpret(), '2 3 + 5 *')\n\n def test_simple_lisp(self):\n self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')\n self.assertEqual(self.init_lisp('2 + 3 + 5').interpret(),\n '(+ (+ 2 3) 5)')\n self.assertEqual(self.init_lisp('2 + 3 * 5').interpret(),\n '(+ 2 (* 3 5))')\n self.assertEqual(self.init_lisp('(2 + 3) * 5').interpret(),\n '(* (+ 2 3) 5)')\n\n def test_examples_chapter_seven(self):\n self.assertEqual(self.init_rpn('(5 + 3) * 12 DIV 3').interpret(),\n '5 3 + 12 * 3 DIV')\n self.assertEqual(self.init_lisp('2 + 3').interpret(), '(+ 2 3)')\n self.assertEqual(self.init_lisp('(2 + 3 * 5)').interpret(),\n '(+ 2 (* 3 5))')\n\n\nif __name__ == '__main__':\n unittest.main()\n",
"step-5": null,
"step-ids": [
3,
5,
6,
8
]
}
|
[
3,
5,
6,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while i >= 1:
print(i, end=' ')
i -= 1
<|reserved_special_token_1|>
num = int(input())
i = 10
while i >= 1:
print(i, end=' ')
i -= 1
<|reserved_special_token_1|>
num=int(input())
i=10
while i>=1:
print(i,end=" ")
i-=1
|
flexible
|
{
"blob_id": "ec0113dbd79e936e614bb7ee7e48d29aa616d511",
"index": 7389,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile i >= 1:\n print(i, end=' ')\n i -= 1\n",
"step-3": "num = int(input())\ni = 10\nwhile i >= 1:\n print(i, end=' ')\n i -= 1\n",
"step-4": "num=int(input())\r\ni=10\r\nwhile i>=1:\r\n print(i,end=\" \")\r\n i-=1\r\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
# Copyright (c) 2023 Intel Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import abstractmethod
from typing import Dict, List, Tuple, TypeVar
import pytest
from nncf.data import Dataset
from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
from nncf.quantization.advanced_parameters import OverflowFix
from nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend
from nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization
from tests.post_training.test_templates.helpers import ConvTestModel
from tests.post_training.test_templates.helpers import MultipleConvTestModel
from tests.post_training.test_templates.helpers import StaticDatasetMock
TModel = TypeVar("TModel")
TTensor = TypeVar("TTensor")
class TemplateTestBCAlgorithm:
@staticmethod
@abstractmethod
def list_to_backend_type(data: List) -> TTensor:
"""
Convert list to backend specific type
:param data: List of data.
:return: Converted data.
"""
@staticmethod
@abstractmethod
def get_backend() -> BiasCorrectionAlgoBackend:
"""
Get backend specific BiasCorrectionAlgoBackend
:return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend
"""
@staticmethod
def fn_to_type(tensor):
return tensor
@staticmethod
@abstractmethod
def get_transform_fn():
"""
Get transformation function for dataset.
"""
def get_dataset(self, input_size: Tuple):
"""
Return backend specific random dataset.
:param model: The model for which the dataset is being created.
"""
return StaticDatasetMock(input_size, self.fn_to_type)
@staticmethod
@abstractmethod
def backend_specific_model(model: TModel, tmp_dir: str):
"""
Return backend specific model.
"""
@staticmethod
@abstractmethod
def check_bias(model: TModel, ref_biases: Dict):
"""
Checks biases values.
"""
@staticmethod
def map_references(ref_biases: Dict) -> Dict[str, List]:
"""
Returns backend-specific reference.
"""
return ref_biases
@staticmethod
def get_quantization_algorithm():
return PostTrainingQuantization(
subset_size=1,
fast_bias_correction=False,
advanced_parameters=AdvancedQuantizationParameters(overflow_fix=OverflowFix.DISABLE),
)
@pytest.mark.parametrize(
"model_cls, ref_biases",
(
(
MultipleConvTestModel,
{
"/conv_1/Conv": [0.6658976, -0.70563036],
"/conv_2/Conv": [-0.307696, -0.42806846, 0.44965455],
"/conv_3/Conv": [-0.0033792169, 1.0661412],
"/conv_4/Conv": [-0.6941606, 0.9958957, 0.6081058],
# Disabled latest layer due to backends differences
# "/conv_5/Conv": [0.07476559, -0.75797373],
},
),
(ConvTestModel, {"/conv/Conv": [0.11085186, 1.0017344]}),
),
)
def test_update_bias(self, model_cls, ref_biases, tmpdir):
model = self.backend_specific_model(model_cls(), tmpdir)
dataset = Dataset(self.get_dataset(model_cls.INPUT_SIZE), self.get_transform_fn())
quantization_algorithm = self.get_quantization_algorithm()
quantized_model = quantization_algorithm.apply(model, dataset=dataset)
mapped_ref_biases = self.map_references(ref_biases)
self.check_bias(quantized_model, mapped_ref_biases)
|
normal
|
{
"blob_id": "de88e2d2cf165b35f247ea89300c91b3c8c07fea",
"index": 7844,
"step-1": "<mask token>\n\n\nclass TemplateTestBCAlgorithm:\n\n @staticmethod\n @abstractmethod\n def list_to_backend_type(data: List) ->TTensor:\n \"\"\"\n Convert list to backend specific type\n\n :param data: List of data.\n\n :return: Converted data.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def get_backend() ->BiasCorrectionAlgoBackend:\n \"\"\"\n Get backend specific BiasCorrectionAlgoBackend\n\n :return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend\n \"\"\"\n\n @staticmethod\n def fn_to_type(tensor):\n return tensor\n\n @staticmethod\n @abstractmethod\n def get_transform_fn():\n \"\"\"\n Get transformation function for dataset.\n \"\"\"\n\n def get_dataset(self, input_size: Tuple):\n \"\"\"\n Return backend specific random dataset.\n\n :param model: The model for which the dataset is being created.\n \"\"\"\n return StaticDatasetMock(input_size, self.fn_to_type)\n <mask token>\n\n @staticmethod\n @abstractmethod\n def check_bias(model: TModel, ref_biases: Dict):\n \"\"\"\n Checks biases values.\n \"\"\"\n\n @staticmethod\n def map_references(ref_biases: Dict) ->Dict[str, List]:\n \"\"\"\n Returns backend-specific reference.\n \"\"\"\n return ref_biases\n\n @staticmethod\n def get_quantization_algorithm():\n return PostTrainingQuantization(subset_size=1, fast_bias_correction\n =False, advanced_parameters=AdvancedQuantizationParameters(\n overflow_fix=OverflowFix.DISABLE))\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass TemplateTestBCAlgorithm:\n\n @staticmethod\n @abstractmethod\n def list_to_backend_type(data: List) ->TTensor:\n \"\"\"\n Convert list to backend specific type\n\n :param data: List of data.\n\n :return: Converted data.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def get_backend() ->BiasCorrectionAlgoBackend:\n \"\"\"\n Get backend specific BiasCorrectionAlgoBackend\n\n :return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend\n \"\"\"\n\n @staticmethod\n def fn_to_type(tensor):\n return tensor\n\n @staticmethod\n @abstractmethod\n def get_transform_fn():\n \"\"\"\n Get transformation function for dataset.\n \"\"\"\n\n def get_dataset(self, input_size: Tuple):\n \"\"\"\n Return backend specific random dataset.\n\n :param model: The model for which the dataset is being created.\n \"\"\"\n return StaticDatasetMock(input_size, self.fn_to_type)\n\n @staticmethod\n @abstractmethod\n def backend_specific_model(model: TModel, tmp_dir: str):\n \"\"\"\n Return backend specific model.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def check_bias(model: TModel, ref_biases: Dict):\n \"\"\"\n Checks biases values.\n \"\"\"\n\n @staticmethod\n def map_references(ref_biases: Dict) ->Dict[str, List]:\n \"\"\"\n Returns backend-specific reference.\n \"\"\"\n return ref_biases\n\n @staticmethod\n def get_quantization_algorithm():\n return PostTrainingQuantization(subset_size=1, fast_bias_correction\n =False, advanced_parameters=AdvancedQuantizationParameters(\n overflow_fix=OverflowFix.DISABLE))\n\n @pytest.mark.parametrize('model_cls, ref_biases', ((\n MultipleConvTestModel, {'/conv_1/Conv': [0.6658976, -0.70563036],\n '/conv_2/Conv': [-0.307696, -0.42806846, 0.44965455],\n '/conv_3/Conv': [-0.0033792169, 1.0661412], '/conv_4/Conv': [-\n 0.6941606, 0.9958957, 0.6081058]}), (ConvTestModel, {'/conv/Conv':\n [0.11085186, 1.0017344]})))\n def test_update_bias(self, model_cls, ref_biases, tmpdir):\n model = self.backend_specific_model(model_cls(), tmpdir)\n dataset = Dataset(self.get_dataset(model_cls.INPUT_SIZE), self.\n get_transform_fn())\n quantization_algorithm = self.get_quantization_algorithm()\n quantized_model = quantization_algorithm.apply(model, dataset=dataset)\n mapped_ref_biases = self.map_references(ref_biases)\n self.check_bias(quantized_model, mapped_ref_biases)\n",
"step-3": "<mask token>\nTModel = TypeVar('TModel')\nTTensor = TypeVar('TTensor')\n\n\nclass TemplateTestBCAlgorithm:\n\n @staticmethod\n @abstractmethod\n def list_to_backend_type(data: List) ->TTensor:\n \"\"\"\n Convert list to backend specific type\n\n :param data: List of data.\n\n :return: Converted data.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def get_backend() ->BiasCorrectionAlgoBackend:\n \"\"\"\n Get backend specific BiasCorrectionAlgoBackend\n\n :return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend\n \"\"\"\n\n @staticmethod\n def fn_to_type(tensor):\n return tensor\n\n @staticmethod\n @abstractmethod\n def get_transform_fn():\n \"\"\"\n Get transformation function for dataset.\n \"\"\"\n\n def get_dataset(self, input_size: Tuple):\n \"\"\"\n Return backend specific random dataset.\n\n :param model: The model for which the dataset is being created.\n \"\"\"\n return StaticDatasetMock(input_size, self.fn_to_type)\n\n @staticmethod\n @abstractmethod\n def backend_specific_model(model: TModel, tmp_dir: str):\n \"\"\"\n Return backend specific model.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def check_bias(model: TModel, ref_biases: Dict):\n \"\"\"\n Checks biases values.\n \"\"\"\n\n @staticmethod\n def map_references(ref_biases: Dict) ->Dict[str, List]:\n \"\"\"\n Returns backend-specific reference.\n \"\"\"\n return ref_biases\n\n @staticmethod\n def get_quantization_algorithm():\n return PostTrainingQuantization(subset_size=1, fast_bias_correction\n =False, advanced_parameters=AdvancedQuantizationParameters(\n overflow_fix=OverflowFix.DISABLE))\n\n @pytest.mark.parametrize('model_cls, ref_biases', ((\n MultipleConvTestModel, {'/conv_1/Conv': [0.6658976, -0.70563036],\n '/conv_2/Conv': [-0.307696, -0.42806846, 0.44965455],\n '/conv_3/Conv': [-0.0033792169, 1.0661412], '/conv_4/Conv': [-\n 0.6941606, 0.9958957, 0.6081058]}), (ConvTestModel, {'/conv/Conv':\n [0.11085186, 1.0017344]})))\n def test_update_bias(self, model_cls, ref_biases, tmpdir):\n model = self.backend_specific_model(model_cls(), tmpdir)\n dataset = Dataset(self.get_dataset(model_cls.INPUT_SIZE), self.\n get_transform_fn())\n quantization_algorithm = self.get_quantization_algorithm()\n quantized_model = quantization_algorithm.apply(model, dataset=dataset)\n mapped_ref_biases = self.map_references(ref_biases)\n self.check_bias(quantized_model, mapped_ref_biases)\n",
"step-4": "from abc import abstractmethod\nfrom typing import Dict, List, Tuple, TypeVar\nimport pytest\nfrom nncf.data import Dataset\nfrom nncf.quantization.advanced_parameters import AdvancedQuantizationParameters\nfrom nncf.quantization.advanced_parameters import OverflowFix\nfrom nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend\nfrom nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization\nfrom tests.post_training.test_templates.helpers import ConvTestModel\nfrom tests.post_training.test_templates.helpers import MultipleConvTestModel\nfrom tests.post_training.test_templates.helpers import StaticDatasetMock\nTModel = TypeVar('TModel')\nTTensor = TypeVar('TTensor')\n\n\nclass TemplateTestBCAlgorithm:\n\n @staticmethod\n @abstractmethod\n def list_to_backend_type(data: List) ->TTensor:\n \"\"\"\n Convert list to backend specific type\n\n :param data: List of data.\n\n :return: Converted data.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def get_backend() ->BiasCorrectionAlgoBackend:\n \"\"\"\n Get backend specific BiasCorrectionAlgoBackend\n\n :return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend\n \"\"\"\n\n @staticmethod\n def fn_to_type(tensor):\n return tensor\n\n @staticmethod\n @abstractmethod\n def get_transform_fn():\n \"\"\"\n Get transformation function for dataset.\n \"\"\"\n\n def get_dataset(self, input_size: Tuple):\n \"\"\"\n Return backend specific random dataset.\n\n :param model: The model for which the dataset is being created.\n \"\"\"\n return StaticDatasetMock(input_size, self.fn_to_type)\n\n @staticmethod\n @abstractmethod\n def backend_specific_model(model: TModel, tmp_dir: str):\n \"\"\"\n Return backend specific model.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def check_bias(model: TModel, ref_biases: Dict):\n \"\"\"\n Checks biases values.\n \"\"\"\n\n @staticmethod\n def map_references(ref_biases: Dict) ->Dict[str, List]:\n \"\"\"\n Returns backend-specific reference.\n \"\"\"\n return ref_biases\n\n @staticmethod\n def get_quantization_algorithm():\n return PostTrainingQuantization(subset_size=1, fast_bias_correction\n =False, advanced_parameters=AdvancedQuantizationParameters(\n overflow_fix=OverflowFix.DISABLE))\n\n @pytest.mark.parametrize('model_cls, ref_biases', ((\n MultipleConvTestModel, {'/conv_1/Conv': [0.6658976, -0.70563036],\n '/conv_2/Conv': [-0.307696, -0.42806846, 0.44965455],\n '/conv_3/Conv': [-0.0033792169, 1.0661412], '/conv_4/Conv': [-\n 0.6941606, 0.9958957, 0.6081058]}), (ConvTestModel, {'/conv/Conv':\n [0.11085186, 1.0017344]})))\n def test_update_bias(self, model_cls, ref_biases, tmpdir):\n model = self.backend_specific_model(model_cls(), tmpdir)\n dataset = Dataset(self.get_dataset(model_cls.INPUT_SIZE), self.\n get_transform_fn())\n quantization_algorithm = self.get_quantization_algorithm()\n quantized_model = quantization_algorithm.apply(model, dataset=dataset)\n mapped_ref_biases = self.map_references(ref_biases)\n self.check_bias(quantized_model, mapped_ref_biases)\n",
"step-5": "# Copyright (c) 2023 Intel Corporation\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom abc import abstractmethod\nfrom typing import Dict, List, Tuple, TypeVar\n\nimport pytest\n\nfrom nncf.data import Dataset\nfrom nncf.quantization.advanced_parameters import AdvancedQuantizationParameters\nfrom nncf.quantization.advanced_parameters import OverflowFix\nfrom nncf.quantization.algorithms.bias_correction.backend import BiasCorrectionAlgoBackend\nfrom nncf.quantization.algorithms.post_training.algorithm import PostTrainingQuantization\nfrom tests.post_training.test_templates.helpers import ConvTestModel\nfrom tests.post_training.test_templates.helpers import MultipleConvTestModel\nfrom tests.post_training.test_templates.helpers import StaticDatasetMock\n\nTModel = TypeVar(\"TModel\")\nTTensor = TypeVar(\"TTensor\")\n\n\nclass TemplateTestBCAlgorithm:\n @staticmethod\n @abstractmethod\n def list_to_backend_type(data: List) -> TTensor:\n \"\"\"\n Convert list to backend specific type\n\n :param data: List of data.\n\n :return: Converted data.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def get_backend() -> BiasCorrectionAlgoBackend:\n \"\"\"\n Get backend specific BiasCorrectionAlgoBackend\n\n :return BiasCorrectionAlgoBackend: Backend specific BiasCorrectionAlgoBackend\n \"\"\"\n\n @staticmethod\n def fn_to_type(tensor):\n return tensor\n\n @staticmethod\n @abstractmethod\n def get_transform_fn():\n \"\"\"\n Get transformation function for dataset.\n \"\"\"\n\n def get_dataset(self, input_size: Tuple):\n \"\"\"\n Return backend specific random dataset.\n\n :param model: The model for which the dataset is being created.\n \"\"\"\n return StaticDatasetMock(input_size, self.fn_to_type)\n\n @staticmethod\n @abstractmethod\n def backend_specific_model(model: TModel, tmp_dir: str):\n \"\"\"\n Return backend specific model.\n \"\"\"\n\n @staticmethod\n @abstractmethod\n def check_bias(model: TModel, ref_biases: Dict):\n \"\"\"\n Checks biases values.\n \"\"\"\n\n @staticmethod\n def map_references(ref_biases: Dict) -> Dict[str, List]:\n \"\"\"\n Returns backend-specific reference.\n \"\"\"\n return ref_biases\n\n @staticmethod\n def get_quantization_algorithm():\n return PostTrainingQuantization(\n subset_size=1,\n fast_bias_correction=False,\n advanced_parameters=AdvancedQuantizationParameters(overflow_fix=OverflowFix.DISABLE),\n )\n\n @pytest.mark.parametrize(\n \"model_cls, ref_biases\",\n (\n (\n MultipleConvTestModel,\n {\n \"/conv_1/Conv\": [0.6658976, -0.70563036],\n \"/conv_2/Conv\": [-0.307696, -0.42806846, 0.44965455],\n \"/conv_3/Conv\": [-0.0033792169, 1.0661412],\n \"/conv_4/Conv\": [-0.6941606, 0.9958957, 0.6081058],\n # Disabled latest layer due to backends differences\n # \"/conv_5/Conv\": [0.07476559, -0.75797373],\n },\n ),\n (ConvTestModel, {\"/conv/Conv\": [0.11085186, 1.0017344]}),\n ),\n )\n def test_update_bias(self, model_cls, ref_biases, tmpdir):\n model = self.backend_specific_model(model_cls(), tmpdir)\n dataset = Dataset(self.get_dataset(model_cls.INPUT_SIZE), self.get_transform_fn())\n\n quantization_algorithm = self.get_quantization_algorithm()\n quantized_model = quantization_algorithm.apply(model, dataset=dataset)\n\n mapped_ref_biases = self.map_references(ref_biases)\n self.check_bias(quantized_model, mapped_ref_biases)\n",
"step-ids": [
9,
11,
12,
13,
14
]
}
|
[
9,
11,
12,
13,
14
] |
from application.processing_data.twitter import TwitterAPIv2
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
from .twitter import TwitterAPIv2
categories={
'Noise Complaints': {
'loud',
'party',
'noisy',
'noise',
'hear',
'music',
},
'Animal Services' : {
'dog',
'cat',
'bird',
'rabbit',
'dead',
},
'Un-Sanitary conditions': {
'dirty',
'trash',
'mess',
'gross',
'litter',
},
'Water Infrastructure' : {
},
'Broken Roads' : {
}
}
def authenticate_client():
key = <key>
endpoint = 'https://textanalysishackathon.cognitiveservices.azure.com/'
ta_credential = AzureKeyCredential(key)
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint, credential=ta_credential)
return text_analytics_client
def filterNegativeTweets(tweets):
client = authenticate_client()
negative_tweets = []
documents = []
for tweet in tweets:
documents.append(tweet['text'])
response = client.analyze_sentiment(documents=documents)
twitterAPI = TwitterAPIv2()
result = [doc for doc in response if not doc.is_error]
#Iterate over the tweets and match them to the response values
for tweet in tweets:
for document in result:
#Tweet matches the document
if document.sentences[0].text in tweet['text']:
#if document is negative, save both the tweet, document, and get the keyphrases
if document.confidence_scores.negative >= 0.5:
negative_tweets.append({
'tweet': tweet,
'sentiment': document,
'key_phrases': client.extract_key_phrases(documents=[tweet['text']])[0],
'tweet_location_data' : twitterAPI.get_tweet_with_id_location(tweet['id'])
})
break
return negative_tweets
|
normal
|
{
"blob_id": "65aa761110877bd93c2d2cb3d097fa3e126f72b1",
"index": 1297,
"step-1": "from application.processing_data.twitter import TwitterAPIv2\nfrom azure.ai.textanalytics import TextAnalyticsClient\nfrom azure.core.credentials import AzureKeyCredential\nfrom .twitter import TwitterAPIv2\n\ncategories={\n 'Noise Complaints': {\n 'loud',\n 'party',\n 'noisy',\n 'noise',\n 'hear',\n 'music',\n },\n 'Animal Services' : {\n 'dog',\n 'cat',\n 'bird',\n 'rabbit',\n 'dead',\n },\n 'Un-Sanitary conditions': {\n 'dirty',\n 'trash',\n 'mess',\n 'gross',\n 'litter',\n },\n 'Water Infrastructure' : {\n\n },\n 'Broken Roads' : {\n\n }\n}\n\ndef authenticate_client():\n key = <key>\n endpoint = 'https://textanalysishackathon.cognitiveservices.azure.com/'\n ta_credential = AzureKeyCredential(key)\n text_analytics_client = TextAnalyticsClient(\n endpoint=endpoint, credential=ta_credential)\n return text_analytics_client\n\ndef filterNegativeTweets(tweets):\n client = authenticate_client()\n negative_tweets = []\n documents = []\n for tweet in tweets:\n documents.append(tweet['text'])\n response = client.analyze_sentiment(documents=documents)\n twitterAPI = TwitterAPIv2()\n result = [doc for doc in response if not doc.is_error]\n #Iterate over the tweets and match them to the response values\n for tweet in tweets:\n for document in result:\n #Tweet matches the document\n if document.sentences[0].text in tweet['text']:\n #if document is negative, save both the tweet, document, and get the keyphrases\n if document.confidence_scores.negative >= 0.5:\n negative_tweets.append({\n 'tweet': tweet,\n 'sentiment': document,\n 'key_phrases': client.extract_key_phrases(documents=[tweet['text']])[0],\n 'tweet_location_data' : twitterAPI.get_tweet_with_id_location(tweet['id'])\n })\n break\n\n return negative_tweets\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from django import template
register = template.Library()
@register.filter(name='phone_number')
def phone_number(number): # Convert a 10 character string into (xxx) xxx-xxxx.
first = number[0:3]
second = number[3:6]
third = number[6:10]
return '(' + first + ')' + ' ' + second + '-' + third
|
normal
|
{
"blob_id": "5e79a8a8fe79aac900fc0c2ff1caaa73ea08ada2",
"index": 5697,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@register.filter(name='phone_number')\ndef phone_number(number):\n first = number[0:3]\n second = number[3:6]\n third = number[6:10]\n return '(' + first + ')' + ' ' + second + '-' + third\n",
"step-3": "<mask token>\nregister = template.Library()\n\n\n@register.filter(name='phone_number')\ndef phone_number(number):\n first = number[0:3]\n second = number[3:6]\n third = number[6:10]\n return '(' + first + ')' + ' ' + second + '-' + third\n",
"step-4": "from django import template\nregister = template.Library()\n\n\n@register.filter(name='phone_number')\ndef phone_number(number):\n first = number[0:3]\n second = number[3:6]\n third = number[6:10]\n return '(' + first + ')' + ' ' + second + '-' + third\n",
"step-5": "from django import template\n\nregister = template.Library()\n\n\n@register.filter(name='phone_number')\ndef phone_number(number): # Convert a 10 character string into (xxx) xxx-xxxx.\n\tfirst = number[0:3]\n\tsecond = number[3:6]\n\tthird = number[6:10]\n\treturn '(' + first + ')' + ' ' + second + '-' + third\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.environ.setdefault('DJANGO_SETTINGS_MODULE',
'ecommerce.settings.development')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.environ.setdefault('DJANGO_SETTINGS_MODULE',
'ecommerce.settings.development')
application = get_asgi_application()
<|reserved_special_token_1|>
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE',
'ecommerce.settings.development')
application = get_asgi_application()
|
flexible
|
{
"blob_id": "1cb320cf57823511b0398adce097b770b2131eb6",
"index": 9307,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.environ.setdefault('DJANGO_SETTINGS_MODULE',\n 'ecommerce.settings.development')\n<mask token>\n",
"step-3": "<mask token>\nos.environ.setdefault('DJANGO_SETTINGS_MODULE',\n 'ecommerce.settings.development')\napplication = get_asgi_application()\n",
"step-4": "import os\nfrom django.core.asgi import get_asgi_application\nos.environ.setdefault('DJANGO_SETTINGS_MODULE',\n 'ecommerce.settings.development')\napplication = get_asgi_application()\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def get_url(url):
response = requests.get(url)
content = response.content.decode('utf8')
return content
def get_json_from_url(url):
content = get_url(url)
js = json.loads(content)
return js
def get_updates(TOKEN):
url = f'{base_url}{TOKEN}/getUpdates'
js = get_json_from_url(url)
if not js['ok']:
print('Error: Invalid telegram token!')
exit(0)
return js
<|reserved_special_token_0|>
def initialize_bot(TOKEN):
get_updates(TOKEN)
try:
f_cached = open('cached', 'rt')
except FileNotFoundError:
_, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))
with open('cached', 'wt') as f_cached:
json.dump({'chat_id': chat_id}, f_cached)
else:
chat_id = json.load(f_cached)['chat_id']
send_message(TOKEN, 'Bot initialized.', chat_id)
return chat_id
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_url(url):
response = requests.get(url)
content = response.content.decode('utf8')
return content
def get_json_from_url(url):
content = get_url(url)
js = json.loads(content)
return js
def get_updates(TOKEN):
url = f'{base_url}{TOKEN}/getUpdates'
js = get_json_from_url(url)
if not js['ok']:
print('Error: Invalid telegram token!')
exit(0)
return js
def get_last_chat_id_and_text(updates):
num_updates = len(updates['result'])
if num_updates == 0:
print('Error: Please send a message to the bot to initialize!')
exit(0)
last_update = num_updates - 1
text = updates['result'][last_update]['message']['text']
msg_timestamp = updates['result'][last_update]['message']['date']
chat_id = updates['result'][last_update]['message']['chat']['id']
return text, msg_timestamp, chat_id
def send_message(TOKEN, text, chat_id):
url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'
resp = json.loads(get_url(url))
if not resp['ok']:
print('Error: Invalid telegram chat_id! Please delete cached.')
exit(0)
def initialize_bot(TOKEN):
get_updates(TOKEN)
try:
f_cached = open('cached', 'rt')
except FileNotFoundError:
_, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))
with open('cached', 'wt') as f_cached:
json.dump({'chat_id': chat_id}, f_cached)
else:
chat_id = json.load(f_cached)['chat_id']
send_message(TOKEN, 'Bot initialized.', chat_id)
return chat_id
<|reserved_special_token_1|>
<|reserved_special_token_0|>
base_url = f'https://api.telegram.org/bot'
def get_url(url):
response = requests.get(url)
content = response.content.decode('utf8')
return content
def get_json_from_url(url):
content = get_url(url)
js = json.loads(content)
return js
def get_updates(TOKEN):
url = f'{base_url}{TOKEN}/getUpdates'
js = get_json_from_url(url)
if not js['ok']:
print('Error: Invalid telegram token!')
exit(0)
return js
def get_last_chat_id_and_text(updates):
num_updates = len(updates['result'])
if num_updates == 0:
print('Error: Please send a message to the bot to initialize!')
exit(0)
last_update = num_updates - 1
text = updates['result'][last_update]['message']['text']
msg_timestamp = updates['result'][last_update]['message']['date']
chat_id = updates['result'][last_update]['message']['chat']['id']
return text, msg_timestamp, chat_id
def send_message(TOKEN, text, chat_id):
url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'
resp = json.loads(get_url(url))
if not resp['ok']:
print('Error: Invalid telegram chat_id! Please delete cached.')
exit(0)
def initialize_bot(TOKEN):
get_updates(TOKEN)
try:
f_cached = open('cached', 'rt')
except FileNotFoundError:
_, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))
with open('cached', 'wt') as f_cached:
json.dump({'chat_id': chat_id}, f_cached)
else:
chat_id = json.load(f_cached)['chat_id']
send_message(TOKEN, 'Bot initialized.', chat_id)
return chat_id
<|reserved_special_token_1|>
import requests
import json
base_url = f'https://api.telegram.org/bot'
def get_url(url):
response = requests.get(url)
content = response.content.decode('utf8')
return content
def get_json_from_url(url):
content = get_url(url)
js = json.loads(content)
return js
def get_updates(TOKEN):
url = f'{base_url}{TOKEN}/getUpdates'
js = get_json_from_url(url)
if not js['ok']:
print('Error: Invalid telegram token!')
exit(0)
return js
def get_last_chat_id_and_text(updates):
num_updates = len(updates['result'])
if num_updates == 0:
print('Error: Please send a message to the bot to initialize!')
exit(0)
last_update = num_updates - 1
text = updates['result'][last_update]['message']['text']
msg_timestamp = updates['result'][last_update]['message']['date']
chat_id = updates['result'][last_update]['message']['chat']['id']
return text, msg_timestamp, chat_id
def send_message(TOKEN, text, chat_id):
url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'
resp = json.loads(get_url(url))
if not resp['ok']:
print('Error: Invalid telegram chat_id! Please delete cached.')
exit(0)
def initialize_bot(TOKEN):
get_updates(TOKEN)
try:
f_cached = open('cached', 'rt')
except FileNotFoundError:
_, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))
with open('cached', 'wt') as f_cached:
json.dump({'chat_id': chat_id}, f_cached)
else:
chat_id = json.load(f_cached)['chat_id']
send_message(TOKEN, 'Bot initialized.', chat_id)
return chat_id
<|reserved_special_token_1|>
import requests
import json
base_url = f"https://api.telegram.org/bot"
def get_url(url):
response = requests.get(url)
content = response.content.decode("utf8")
return content
def get_json_from_url(url):
content = get_url(url)
js = json.loads(content)
return js
def get_updates(TOKEN):
url = f'{base_url}{TOKEN}/getUpdates'
js = get_json_from_url(url)
if not js['ok']:
print('Error: Invalid telegram token!')
exit(0)
return js
def get_last_chat_id_and_text(updates):
num_updates = len(updates["result"])
if num_updates == 0:
print('Error: Please send a message to the bot to initialize!')
exit(0)
last_update = num_updates - 1
text = updates["result"][last_update]["message"]["text"]
msg_timestamp = updates["result"][last_update]["message"]["date"]
chat_id = updates["result"][last_update]["message"]["chat"]["id"]
return text, msg_timestamp, chat_id
def send_message(TOKEN, text, chat_id):
url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'
resp = json.loads(get_url(url))
if not resp['ok']:
print('Error: Invalid telegram chat_id! Please delete cached.')
exit(0)
def initialize_bot(TOKEN):
get_updates(TOKEN) # ensure token is valid
# in case the bot doesn't have a recent incoming message, cached will prevent failing
try:
f_cached = open('cached', 'rt')
except FileNotFoundError:
_, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))
with open('cached', 'wt') as f_cached:
json.dump({'chat_id': chat_id}, f_cached)
else:
chat_id = json.load(f_cached)['chat_id']
send_message(TOKEN, 'Bot initialized.', chat_id) # ensure chat_id is valid
return chat_id
|
flexible
|
{
"blob_id": "501614f9c7df3c862c9951ea343964b6ed47e74a",
"index": 3204,
"step-1": "<mask token>\n\n\ndef get_url(url):\n response = requests.get(url)\n content = response.content.decode('utf8')\n return content\n\n\ndef get_json_from_url(url):\n content = get_url(url)\n js = json.loads(content)\n return js\n\n\ndef get_updates(TOKEN):\n url = f'{base_url}{TOKEN}/getUpdates'\n js = get_json_from_url(url)\n if not js['ok']:\n print('Error: Invalid telegram token!')\n exit(0)\n return js\n\n\n<mask token>\n\n\ndef initialize_bot(TOKEN):\n get_updates(TOKEN)\n try:\n f_cached = open('cached', 'rt')\n except FileNotFoundError:\n _, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))\n with open('cached', 'wt') as f_cached:\n json.dump({'chat_id': chat_id}, f_cached)\n else:\n chat_id = json.load(f_cached)['chat_id']\n send_message(TOKEN, 'Bot initialized.', chat_id)\n return chat_id\n",
"step-2": "<mask token>\n\n\ndef get_url(url):\n response = requests.get(url)\n content = response.content.decode('utf8')\n return content\n\n\ndef get_json_from_url(url):\n content = get_url(url)\n js = json.loads(content)\n return js\n\n\ndef get_updates(TOKEN):\n url = f'{base_url}{TOKEN}/getUpdates'\n js = get_json_from_url(url)\n if not js['ok']:\n print('Error: Invalid telegram token!')\n exit(0)\n return js\n\n\ndef get_last_chat_id_and_text(updates):\n num_updates = len(updates['result'])\n if num_updates == 0:\n print('Error: Please send a message to the bot to initialize!')\n exit(0)\n last_update = num_updates - 1\n text = updates['result'][last_update]['message']['text']\n msg_timestamp = updates['result'][last_update]['message']['date']\n chat_id = updates['result'][last_update]['message']['chat']['id']\n return text, msg_timestamp, chat_id\n\n\ndef send_message(TOKEN, text, chat_id):\n url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'\n resp = json.loads(get_url(url))\n if not resp['ok']:\n print('Error: Invalid telegram chat_id! Please delete cached.')\n exit(0)\n\n\ndef initialize_bot(TOKEN):\n get_updates(TOKEN)\n try:\n f_cached = open('cached', 'rt')\n except FileNotFoundError:\n _, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))\n with open('cached', 'wt') as f_cached:\n json.dump({'chat_id': chat_id}, f_cached)\n else:\n chat_id = json.load(f_cached)['chat_id']\n send_message(TOKEN, 'Bot initialized.', chat_id)\n return chat_id\n",
"step-3": "<mask token>\nbase_url = f'https://api.telegram.org/bot'\n\n\ndef get_url(url):\n response = requests.get(url)\n content = response.content.decode('utf8')\n return content\n\n\ndef get_json_from_url(url):\n content = get_url(url)\n js = json.loads(content)\n return js\n\n\ndef get_updates(TOKEN):\n url = f'{base_url}{TOKEN}/getUpdates'\n js = get_json_from_url(url)\n if not js['ok']:\n print('Error: Invalid telegram token!')\n exit(0)\n return js\n\n\ndef get_last_chat_id_and_text(updates):\n num_updates = len(updates['result'])\n if num_updates == 0:\n print('Error: Please send a message to the bot to initialize!')\n exit(0)\n last_update = num_updates - 1\n text = updates['result'][last_update]['message']['text']\n msg_timestamp = updates['result'][last_update]['message']['date']\n chat_id = updates['result'][last_update]['message']['chat']['id']\n return text, msg_timestamp, chat_id\n\n\ndef send_message(TOKEN, text, chat_id):\n url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'\n resp = json.loads(get_url(url))\n if not resp['ok']:\n print('Error: Invalid telegram chat_id! Please delete cached.')\n exit(0)\n\n\ndef initialize_bot(TOKEN):\n get_updates(TOKEN)\n try:\n f_cached = open('cached', 'rt')\n except FileNotFoundError:\n _, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))\n with open('cached', 'wt') as f_cached:\n json.dump({'chat_id': chat_id}, f_cached)\n else:\n chat_id = json.load(f_cached)['chat_id']\n send_message(TOKEN, 'Bot initialized.', chat_id)\n return chat_id\n",
"step-4": "import requests\nimport json\nbase_url = f'https://api.telegram.org/bot'\n\n\ndef get_url(url):\n response = requests.get(url)\n content = response.content.decode('utf8')\n return content\n\n\ndef get_json_from_url(url):\n content = get_url(url)\n js = json.loads(content)\n return js\n\n\ndef get_updates(TOKEN):\n url = f'{base_url}{TOKEN}/getUpdates'\n js = get_json_from_url(url)\n if not js['ok']:\n print('Error: Invalid telegram token!')\n exit(0)\n return js\n\n\ndef get_last_chat_id_and_text(updates):\n num_updates = len(updates['result'])\n if num_updates == 0:\n print('Error: Please send a message to the bot to initialize!')\n exit(0)\n last_update = num_updates - 1\n text = updates['result'][last_update]['message']['text']\n msg_timestamp = updates['result'][last_update]['message']['date']\n chat_id = updates['result'][last_update]['message']['chat']['id']\n return text, msg_timestamp, chat_id\n\n\ndef send_message(TOKEN, text, chat_id):\n url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'\n resp = json.loads(get_url(url))\n if not resp['ok']:\n print('Error: Invalid telegram chat_id! Please delete cached.')\n exit(0)\n\n\ndef initialize_bot(TOKEN):\n get_updates(TOKEN)\n try:\n f_cached = open('cached', 'rt')\n except FileNotFoundError:\n _, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))\n with open('cached', 'wt') as f_cached:\n json.dump({'chat_id': chat_id}, f_cached)\n else:\n chat_id = json.load(f_cached)['chat_id']\n send_message(TOKEN, 'Bot initialized.', chat_id)\n return chat_id\n",
"step-5": "import requests\nimport json\n\nbase_url = f\"https://api.telegram.org/bot\"\n\n\ndef get_url(url):\n response = requests.get(url)\n content = response.content.decode(\"utf8\")\n return content\n\n\ndef get_json_from_url(url):\n content = get_url(url)\n js = json.loads(content)\n return js\n\n\ndef get_updates(TOKEN):\n url = f'{base_url}{TOKEN}/getUpdates'\n js = get_json_from_url(url)\n if not js['ok']:\n print('Error: Invalid telegram token!')\n exit(0)\n return js\n\n\ndef get_last_chat_id_and_text(updates):\n num_updates = len(updates[\"result\"])\n if num_updates == 0:\n print('Error: Please send a message to the bot to initialize!')\n exit(0)\n\n last_update = num_updates - 1\n text = updates[\"result\"][last_update][\"message\"][\"text\"]\n msg_timestamp = updates[\"result\"][last_update][\"message\"][\"date\"]\n chat_id = updates[\"result\"][last_update][\"message\"][\"chat\"][\"id\"]\n\n return text, msg_timestamp, chat_id\n\n\ndef send_message(TOKEN, text, chat_id):\n url = f'{base_url}{TOKEN}/sendMessage?text={text}&chat_id={chat_id}'\n resp = json.loads(get_url(url))\n if not resp['ok']:\n print('Error: Invalid telegram chat_id! Please delete cached.')\n exit(0)\n\n\ndef initialize_bot(TOKEN):\n\n get_updates(TOKEN) # ensure token is valid\n\n # in case the bot doesn't have a recent incoming message, cached will prevent failing\n try:\n f_cached = open('cached', 'rt')\n except FileNotFoundError:\n _, _, chat_id = get_last_chat_id_and_text(get_updates(TOKEN))\n with open('cached', 'wt') as f_cached:\n json.dump({'chat_id': chat_id}, f_cached)\n else:\n chat_id = json.load(f_cached)['chat_id']\n\n send_message(TOKEN, 'Bot initialized.', chat_id) # ensure chat_id is valid\n\n return chat_id\n",
"step-ids": [
4,
6,
7,
8,
9
]
}
|
[
4,
6,
7,
8,
9
] |
""" Codewars kata: Evaluate mathematical expression. https://www.codewars.com/kata/52a78825cdfc2cfc87000005/train/python """
#######################################################################################################################
#
# Import
#
#######################################################################################################################
import operator
import re
#######################################################################################################################
#
# Calculator
#
#######################################################################################################################
class Calculator(object):
re_num = r"(([-+])?(\d+)(\.\d+)?)"
def _float_to_string_(self, f, p=40):
# decimal.Decimal would let us avoid these shenanigans, but it's not available.
result = f"{f:+1.{p}f}"
if "." in result:
result = result.rstrip("0")
if result[-1] == ".": result += "0"
return result
def _muldiv_(self, m):
op = operator.mul if m.group("op") == "*" else operator.truediv
return self._float_to_string_(op(float(m.group('n1')), float(m.group('n2'))))
def _subber_(self, search, replace, target):
subs = -1
while subs != 0:
target, subs = re.subn(search, replace, target, count=1)
target = target.replace("--", "+")
target = target.replace("-+", "-")
return target
def _evaluate_(self, thing):
if type(thing) != str:
thing = thing[1]
thing = self._subber_(r"\(([^\(\)]*?)\)", self._evaluate_, thing)
thing = self._subber_(rf"(?P<n1>{self.re_num})(?P<op>\*|\/)(?P<n2>{self.re_num})", self._muldiv_, thing)
return self._float_to_string_(sum(float(val[0]) for val in re.findall(self.re_num, thing)))
def evaluate(self, thing):
return float(self._evaluate_(thing.replace(" ", "")))
def calc(expression):
return Calculator().evaluate(expression)
#######################################################################################################################
#
# __main__
#
#######################################################################################################################
if __name__ == "__main__":
print(f"result = {calc('-(-13) - (84 + 51 * (40)) * (5 / ((((83 * -32)))) / -93)')}") # 12.957005441119316
|
normal
|
{
"blob_id": "ac14e88810b848dbf4ff32ea99fd274cd0285e1c",
"index": 3539,
"step-1": "<mask token>\n\n\nclass Calculator(object):\n <mask token>\n\n def _float_to_string_(self, f, p=40):\n result = f'{f:+1.{p}f}'\n if '.' in result:\n result = result.rstrip('0')\n if result[-1] == '.':\n result += '0'\n return result\n\n def _muldiv_(self, m):\n op = operator.mul if m.group('op') == '*' else operator.truediv\n return self._float_to_string_(op(float(m.group('n1')), float(m.\n group('n2'))))\n\n def _subber_(self, search, replace, target):\n subs = -1\n while subs != 0:\n target, subs = re.subn(search, replace, target, count=1)\n target = target.replace('--', '+')\n target = target.replace('-+', '-')\n return target\n\n def _evaluate_(self, thing):\n if type(thing) != str:\n thing = thing[1]\n thing = self._subber_('\\\\(([^\\\\(\\\\)]*?)\\\\)', self._evaluate_, thing)\n thing = self._subber_(\n f'(?P<n1>{self.re_num})(?P<op>\\\\*|\\\\/)(?P<n2>{self.re_num})',\n self._muldiv_, thing)\n return self._float_to_string_(sum(float(val[0]) for val in re.\n findall(self.re_num, thing)))\n\n def evaluate(self, thing):\n return float(self._evaluate_(thing.replace(' ', '')))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Calculator(object):\n re_num = '(([-+])?(\\\\d+)(\\\\.\\\\d+)?)'\n\n def _float_to_string_(self, f, p=40):\n result = f'{f:+1.{p}f}'\n if '.' in result:\n result = result.rstrip('0')\n if result[-1] == '.':\n result += '0'\n return result\n\n def _muldiv_(self, m):\n op = operator.mul if m.group('op') == '*' else operator.truediv\n return self._float_to_string_(op(float(m.group('n1')), float(m.\n group('n2'))))\n\n def _subber_(self, search, replace, target):\n subs = -1\n while subs != 0:\n target, subs = re.subn(search, replace, target, count=1)\n target = target.replace('--', '+')\n target = target.replace('-+', '-')\n return target\n\n def _evaluate_(self, thing):\n if type(thing) != str:\n thing = thing[1]\n thing = self._subber_('\\\\(([^\\\\(\\\\)]*?)\\\\)', self._evaluate_, thing)\n thing = self._subber_(\n f'(?P<n1>{self.re_num})(?P<op>\\\\*|\\\\/)(?P<n2>{self.re_num})',\n self._muldiv_, thing)\n return self._float_to_string_(sum(float(val[0]) for val in re.\n findall(self.re_num, thing)))\n\n def evaluate(self, thing):\n return float(self._evaluate_(thing.replace(' ', '')))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Calculator(object):\n re_num = '(([-+])?(\\\\d+)(\\\\.\\\\d+)?)'\n\n def _float_to_string_(self, f, p=40):\n result = f'{f:+1.{p}f}'\n if '.' in result:\n result = result.rstrip('0')\n if result[-1] == '.':\n result += '0'\n return result\n\n def _muldiv_(self, m):\n op = operator.mul if m.group('op') == '*' else operator.truediv\n return self._float_to_string_(op(float(m.group('n1')), float(m.\n group('n2'))))\n\n def _subber_(self, search, replace, target):\n subs = -1\n while subs != 0:\n target, subs = re.subn(search, replace, target, count=1)\n target = target.replace('--', '+')\n target = target.replace('-+', '-')\n return target\n\n def _evaluate_(self, thing):\n if type(thing) != str:\n thing = thing[1]\n thing = self._subber_('\\\\(([^\\\\(\\\\)]*?)\\\\)', self._evaluate_, thing)\n thing = self._subber_(\n f'(?P<n1>{self.re_num})(?P<op>\\\\*|\\\\/)(?P<n2>{self.re_num})',\n self._muldiv_, thing)\n return self._float_to_string_(sum(float(val[0]) for val in re.\n findall(self.re_num, thing)))\n\n def evaluate(self, thing):\n return float(self._evaluate_(thing.replace(' ', '')))\n\n\ndef calc(expression):\n return Calculator().evaluate(expression)\n\n\nif __name__ == '__main__':\n print(\n f\"result = {calc('-(-13) - (84 + 51 * (40)) * (5 / ((((83 * -32)))) / -93)')}\"\n )\n",
"step-4": "<mask token>\nimport operator\nimport re\n\n\nclass Calculator(object):\n re_num = '(([-+])?(\\\\d+)(\\\\.\\\\d+)?)'\n\n def _float_to_string_(self, f, p=40):\n result = f'{f:+1.{p}f}'\n if '.' in result:\n result = result.rstrip('0')\n if result[-1] == '.':\n result += '0'\n return result\n\n def _muldiv_(self, m):\n op = operator.mul if m.group('op') == '*' else operator.truediv\n return self._float_to_string_(op(float(m.group('n1')), float(m.\n group('n2'))))\n\n def _subber_(self, search, replace, target):\n subs = -1\n while subs != 0:\n target, subs = re.subn(search, replace, target, count=1)\n target = target.replace('--', '+')\n target = target.replace('-+', '-')\n return target\n\n def _evaluate_(self, thing):\n if type(thing) != str:\n thing = thing[1]\n thing = self._subber_('\\\\(([^\\\\(\\\\)]*?)\\\\)', self._evaluate_, thing)\n thing = self._subber_(\n f'(?P<n1>{self.re_num})(?P<op>\\\\*|\\\\/)(?P<n2>{self.re_num})',\n self._muldiv_, thing)\n return self._float_to_string_(sum(float(val[0]) for val in re.\n findall(self.re_num, thing)))\n\n def evaluate(self, thing):\n return float(self._evaluate_(thing.replace(' ', '')))\n\n\ndef calc(expression):\n return Calculator().evaluate(expression)\n\n\nif __name__ == '__main__':\n print(\n f\"result = {calc('-(-13) - (84 + 51 * (40)) * (5 / ((((83 * -32)))) / -93)')}\"\n )\n",
"step-5": "\"\"\" Codewars kata: Evaluate mathematical expression. https://www.codewars.com/kata/52a78825cdfc2cfc87000005/train/python \"\"\"\n\n#######################################################################################################################\n#\n# Import\n#\n#######################################################################################################################\n\nimport operator\nimport re\n\n\n#######################################################################################################################\n#\n# Calculator\n#\n#######################################################################################################################\n\nclass Calculator(object):\n re_num = r\"(([-+])?(\\d+)(\\.\\d+)?)\"\n\n def _float_to_string_(self, f, p=40):\n # decimal.Decimal would let us avoid these shenanigans, but it's not available.\n result = f\"{f:+1.{p}f}\"\n if \".\" in result:\n result = result.rstrip(\"0\")\n if result[-1] == \".\": result += \"0\"\n return result\n\n def _muldiv_(self, m):\n op = operator.mul if m.group(\"op\") == \"*\" else operator.truediv\n return self._float_to_string_(op(float(m.group('n1')), float(m.group('n2'))))\n\n def _subber_(self, search, replace, target):\n subs = -1\n while subs != 0:\n target, subs = re.subn(search, replace, target, count=1)\n target = target.replace(\"--\", \"+\")\n target = target.replace(\"-+\", \"-\")\n return target\n\n def _evaluate_(self, thing):\n if type(thing) != str:\n thing = thing[1]\n thing = self._subber_(r\"\\(([^\\(\\)]*?)\\)\", self._evaluate_, thing)\n thing = self._subber_(rf\"(?P<n1>{self.re_num})(?P<op>\\*|\\/)(?P<n2>{self.re_num})\", self._muldiv_, thing)\n return self._float_to_string_(sum(float(val[0]) for val in re.findall(self.re_num, thing)))\n\n def evaluate(self, thing):\n return float(self._evaluate_(thing.replace(\" \", \"\")))\n\n\ndef calc(expression):\n return Calculator().evaluate(expression)\n\n\n#######################################################################################################################\n#\n# __main__\n#\n#######################################################################################################################\n\nif __name__ == \"__main__\":\n print(f\"result = {calc('-(-13) - (84 + 51 * (40)) * (5 / ((((83 * -32)))) / -93)')}\") # 12.957005441119316\n",
"step-ids": [
6,
7,
9,
10,
11
]
}
|
[
6,
7,
9,
10,
11
] |
#!/usr/bin/python3
__author__ = "yang.dd"
"""
example 090
"""
# list
# 新建list
testList = [10086, "中国移动", [1, 2, 3, 4]]
# 访问列表长度
print("list len: ", len(testList))
# 切片
print("切片（slice）:", testList[1:])
# 追加
print("追加一个元素")
testList.append("i'm new here!");
print("list len: ", len(testList))
print("last item :", testList[-1])
print("pop: ", testList.pop())
print("list len: ", len(testList))
print(testList)
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
print(matrix)
print(matrix[1])
col2 = [x[1] for x in matrix]
print(col2)
col2even = [x[1] for x in matrix if x[1] % 2 == 0]
print(col2even)
|
normal
|
{
"blob_id": "4f19eed272c12be137df92bfd3c72e978408c974",
"index": 3216,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('list len: ', len(testList))\nprint('切片（slice）:', testList[1:])\nprint('追加一个元素')\ntestList.append(\"i'm new here!\")\nprint('list len: ', len(testList))\nprint('last item :', testList[-1])\nprint('pop: ', testList.pop())\nprint('list len: ', len(testList))\nprint(testList)\n<mask token>\nprint(matrix)\nprint(matrix[1])\n<mask token>\nprint(col2)\n<mask token>\nprint(col2even)\n",
"step-3": "__author__ = 'yang.dd'\n<mask token>\ntestList = [10086, '中国移动', [1, 2, 3, 4]]\nprint('list len: ', len(testList))\nprint('切片（slice）:', testList[1:])\nprint('追加一个元素')\ntestList.append(\"i'm new here!\")\nprint('list len: ', len(testList))\nprint('last item :', testList[-1])\nprint('pop: ', testList.pop())\nprint('list len: ', len(testList))\nprint(testList)\nmatrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\nprint(matrix)\nprint(matrix[1])\ncol2 = [x[1] for x in matrix]\nprint(col2)\ncol2even = [x[1] for x in matrix if x[1] % 2 == 0]\nprint(col2even)\n",
"step-4": "#!/usr/bin/python3\r\n\r\n__author__ = \"yang.dd\"\r\n\r\n\"\"\"\r\n example 090\r\n\"\"\"\r\n# list\r\n# 新建list\r\ntestList = [10086, \"中国移动\", [1, 2, 3, 4]]\r\n\r\n# 访问列表长度\r\nprint(\"list len: \", len(testList))\r\n\r\n# 切片\r\nprint(\"切片（slice）:\", testList[1:])\r\n\r\n# 追加\r\n\r\nprint(\"追加一个元素\")\r\ntestList.append(\"i'm new here!\");\r\n\r\nprint(\"list len: \", len(testList))\r\nprint(\"last item :\", testList[-1])\r\n\r\nprint(\"pop: \", testList.pop())\r\nprint(\"list len: \", len(testList))\r\nprint(testList)\r\n\r\nmatrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\r\nprint(matrix)\r\n\r\nprint(matrix[1])\r\n\r\ncol2 = [x[1] for x in matrix]\r\nprint(col2)\r\n\r\ncol2even = [x[1] for x in matrix if x[1] % 2 == 0]\r\nprint(col2even)",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class ConfigError(ValueError):
pass
|
flexible
|
{
"blob_id": "76dd4d2b5f68683c77f9502a2298e65c97db7c8d",
"index": 1263,
"step-1": "<mask token>\n",
"step-2": "class ConfigError(ValueError):\n pass\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
<|reserved_special_token_0|>
class ProductForm(forms.Form):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class FindForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ProductForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
description = forms.CharField(max_length=2000, required=True, label=
'Описание', widget=forms.Textarea)
category = forms.ChoiceField(required=False, widget=forms.Select,
choices=PRODUCT_CATEGORY_CHOICES, label='Категория')
amount = forms.IntegerField(min_value=0, label='Остаток')
price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')
class FindForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
PRODUCT_OTHER_CHOICE = 'other'
PRODUCT_CATEGORY_CHOICES = (PRODUCT_OTHER_CHOICE, 'Разное'), ('food', 'Еда'), (
'drink', 'Вода'), ('cloth', 'Одежда'), ('electronics', 'Электроника')
class ProductForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
description = forms.CharField(max_length=2000, required=True, label=
'Описание', widget=forms.Textarea)
category = forms.ChoiceField(required=False, widget=forms.Select,
choices=PRODUCT_CATEGORY_CHOICES, label='Категория')
amount = forms.IntegerField(min_value=0, label='Остаток')
price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')
class FindForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
<|reserved_special_token_1|>
from django import forms
from django.forms import widgets
PRODUCT_OTHER_CHOICE = 'other'
PRODUCT_CATEGORY_CHOICES = (PRODUCT_OTHER_CHOICE, 'Разное'), ('food', 'Еда'), (
'drink', 'Вода'), ('cloth', 'Одежда'), ('electronics', 'Электроника')
class ProductForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
description = forms.CharField(max_length=2000, required=True, label=
'Описание', widget=forms.Textarea)
category = forms.ChoiceField(required=False, widget=forms.Select,
choices=PRODUCT_CATEGORY_CHOICES, label='Категория')
amount = forms.IntegerField(min_value=0, label='Остаток')
price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')
class FindForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
<|reserved_special_token_1|>
from django import forms
from django.forms import widgets
# from product.models import PRODUCT_OTHER_CHOICE, PRODUCT_CATEGORY_CHOICES
PRODUCT_OTHER_CHOICE = 'other'
PRODUCT_CATEGORY_CHOICES = (
(PRODUCT_OTHER_CHOICE, 'Разное'),
('food', 'Еда'),
('drink', 'Вода'),
('cloth', 'Одежда'),
('electronics', 'Электроника')
)
class ProductForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
description = forms.CharField(max_length=2000, required=True, label='Описание', widget=forms.Textarea)
category = forms.ChoiceField(required=False, widget=forms.Select, choices=PRODUCT_CATEGORY_CHOICES, label='Категория')
amount = forms.IntegerField(min_value=0, label='Остаток')
price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')
class FindForm(forms.Form):
name = forms.CharField(max_length=100, label='Наименование')
|
flexible
|
{
"blob_id": "e8a024796b6426e572571e46030678e90c537229",
"index": 7549,
"step-1": "<mask token>\n\n\nclass ProductForm(forms.Form):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass FindForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n",
"step-2": "<mask token>\n\n\nclass ProductForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n description = forms.CharField(max_length=2000, required=True, label=\n 'Описание', widget=forms.Textarea)\n category = forms.ChoiceField(required=False, widget=forms.Select,\n choices=PRODUCT_CATEGORY_CHOICES, label='Категория')\n amount = forms.IntegerField(min_value=0, label='Остаток')\n price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')\n\n\nclass FindForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n",
"step-3": "<mask token>\nPRODUCT_OTHER_CHOICE = 'other'\nPRODUCT_CATEGORY_CHOICES = (PRODUCT_OTHER_CHOICE, 'Разное'), ('food', 'Еда'), (\n 'drink', 'Вода'), ('cloth', 'Одежда'), ('electronics', 'Электроника')\n\n\nclass ProductForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n description = forms.CharField(max_length=2000, required=True, label=\n 'Описание', widget=forms.Textarea)\n category = forms.ChoiceField(required=False, widget=forms.Select,\n choices=PRODUCT_CATEGORY_CHOICES, label='Категория')\n amount = forms.IntegerField(min_value=0, label='Остаток')\n price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')\n\n\nclass FindForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n",
"step-4": "from django import forms\nfrom django.forms import widgets\nPRODUCT_OTHER_CHOICE = 'other'\nPRODUCT_CATEGORY_CHOICES = (PRODUCT_OTHER_CHOICE, 'Разное'), ('food', 'Еда'), (\n 'drink', 'Вода'), ('cloth', 'Одежда'), ('electronics', 'Электроника')\n\n\nclass ProductForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n description = forms.CharField(max_length=2000, required=True, label=\n 'Описание', widget=forms.Textarea)\n category = forms.ChoiceField(required=False, widget=forms.Select,\n choices=PRODUCT_CATEGORY_CHOICES, label='Категория')\n amount = forms.IntegerField(min_value=0, label='Остаток')\n price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')\n\n\nclass FindForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n",
"step-5": "from django import forms\nfrom django.forms import widgets\n# from product.models import PRODUCT_OTHER_CHOICE, PRODUCT_CATEGORY_CHOICES\n\nPRODUCT_OTHER_CHOICE = 'other'\nPRODUCT_CATEGORY_CHOICES = (\n (PRODUCT_OTHER_CHOICE, 'Разное'),\n ('food', 'Еда'),\n ('drink', 'Вода'),\n ('cloth', 'Одежда'),\n ('electronics', 'Электроника')\n)\n\nclass ProductForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n description = forms.CharField(max_length=2000, required=True, label='Описание', widget=forms.Textarea)\n category = forms.ChoiceField(required=False, widget=forms.Select, choices=PRODUCT_CATEGORY_CHOICES, label='Категория')\n amount = forms.IntegerField(min_value=0, label='Остаток')\n price = forms.DecimalField(max_digits=7, decimal_places=2, label='Цена')\n\nclass FindForm(forms.Form):\n name = forms.CharField(max_length=100, label='Наименование')\n\n\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
# =============================================================================
# ######################## Creator: Rhys Aeron Williams #######################
# ######################## Last update: 14th March 2019 #######################
# =============================================================================
# =============================================================================
# ####################### IMPORT THE LIBRARIES NECESSARY ######################
# =============================================================================
import cv2
from keras.preprocessing import image
import keras
import numpy as np
# =============================================================================
# ####################### LOAD THE MODEL TO RUN ###############################
# =============================================================================
model_file = "MODELS/5-2-19_v2.h5"
classifier = keras.models.load_model(model_file)
if classifier is not None:
print('model is loaded from ', model_file)
# =============================================================================
# ######################## USE THE WEBCAM ON THE LAPTOP #######################
# =============================================================================
cap = cv2.VideoCapture(0)
while(1):
# =============================================================================
# ######################## GET THE FRAME FROM WEBCAM ##########################
# =============================================================================
hasFrame, frame = cap.read()
if not hasFrame:
break
# =============================================================================
# ######################## RESIZE THE FRAME AND SAVE ##########################
# =============================================================================
h,w,bpp = np.shape(frame)
dim = (int(w/4), int(h/4))
frame_2 = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)
cv2.imwrite("frame.jpg", frame_2)
# =============================================================================
# ################## READ THE FRAME AND PUT INTO CLASSIFIER ###################
# =============================================================================
test_frame = image.load_img('frame.jpg', target_size = (60,40))
test_frame = image.img_to_array(test_frame)
test_frame = np.expand_dims(test_frame, axis = 0)
result = classifier.predict(test_frame)
# =============================================================================
# ####################### GET PREDICITION OF THE FRAME ########################
# =============================================================================
if result[0][0] == 1:
prediction = 'good'
text_colour = (0,255,0)
else:
prediction = 'bad'
text_colour = (0,0,255)
cv2.putText(frame, prediction, (5,80),cv2.FONT_HERSHEY_SIMPLEX,
fontScale = 3, color = text_colour,thickness = 3)
# =============================================================================
# ############################# SHOW FRAMES (VIDEO) ###########################
# =============================================================================
cv2.imshow('Video', frame)
k = cv2.waitKey(10)
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
|
normal
|
{
"blob_id": "e89ca4907373318bd55d0833730a30d981414992",
"index": 2677,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif classifier is not None:\n print('model is loaded from ', model_file)\n<mask token>\nwhile 1:\n hasFrame, frame = cap.read()\n if not hasFrame:\n break\n h, w, bpp = np.shape(frame)\n dim = int(w / 4), int(h / 4)\n frame_2 = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)\n cv2.imwrite('frame.jpg', frame_2)\n test_frame = image.load_img('frame.jpg', target_size=(60, 40))\n test_frame = image.img_to_array(test_frame)\n test_frame = np.expand_dims(test_frame, axis=0)\n result = classifier.predict(test_frame)\n if result[0][0] == 1:\n prediction = 'good'\n text_colour = 0, 255, 0\n else:\n prediction = 'bad'\n text_colour = 0, 0, 255\n cv2.putText(frame, prediction, (5, 80), cv2.FONT_HERSHEY_SIMPLEX,\n fontScale=3, color=text_colour, thickness=3)\n cv2.imshow('Video', frame)\n k = cv2.waitKey(10)\n if k == 27:\n break\ncap.release()\ncv2.destroyAllWindows()\n",
"step-3": "<mask token>\nmodel_file = 'MODELS/5-2-19_v2.h5'\nclassifier = keras.models.load_model(model_file)\nif classifier is not None:\n print('model is loaded from ', model_file)\ncap = cv2.VideoCapture(0)\nwhile 1:\n hasFrame, frame = cap.read()\n if not hasFrame:\n break\n h, w, bpp = np.shape(frame)\n dim = int(w / 4), int(h / 4)\n frame_2 = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)\n cv2.imwrite('frame.jpg', frame_2)\n test_frame = image.load_img('frame.jpg', target_size=(60, 40))\n test_frame = image.img_to_array(test_frame)\n test_frame = np.expand_dims(test_frame, axis=0)\n result = classifier.predict(test_frame)\n if result[0][0] == 1:\n prediction = 'good'\n text_colour = 0, 255, 0\n else:\n prediction = 'bad'\n text_colour = 0, 0, 255\n cv2.putText(frame, prediction, (5, 80), cv2.FONT_HERSHEY_SIMPLEX,\n fontScale=3, color=text_colour, thickness=3)\n cv2.imshow('Video', frame)\n k = cv2.waitKey(10)\n if k == 27:\n break\ncap.release()\ncv2.destroyAllWindows()\n",
"step-4": "import cv2\nfrom keras.preprocessing import image\nimport keras\nimport numpy as np\nmodel_file = 'MODELS/5-2-19_v2.h5'\nclassifier = keras.models.load_model(model_file)\nif classifier is not None:\n print('model is loaded from ', model_file)\ncap = cv2.VideoCapture(0)\nwhile 1:\n hasFrame, frame = cap.read()\n if not hasFrame:\n break\n h, w, bpp = np.shape(frame)\n dim = int(w / 4), int(h / 4)\n frame_2 = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)\n cv2.imwrite('frame.jpg', frame_2)\n test_frame = image.load_img('frame.jpg', target_size=(60, 40))\n test_frame = image.img_to_array(test_frame)\n test_frame = np.expand_dims(test_frame, axis=0)\n result = classifier.predict(test_frame)\n if result[0][0] == 1:\n prediction = 'good'\n text_colour = 0, 255, 0\n else:\n prediction = 'bad'\n text_colour = 0, 0, 255\n cv2.putText(frame, prediction, (5, 80), cv2.FONT_HERSHEY_SIMPLEX,\n fontScale=3, color=text_colour, thickness=3)\n cv2.imshow('Video', frame)\n k = cv2.waitKey(10)\n if k == 27:\n break\ncap.release()\ncv2.destroyAllWindows()\n",
"step-5": "# =============================================================================\n# ######################## Creator: Rhys Aeron Williams #######################\n# ######################## Last update: 14th March 2019 #######################\n# =============================================================================\n\n# =============================================================================\n# ####################### IMPORT THE LIBRARIES NECESSARY ######################\n# =============================================================================\n\nimport cv2\nfrom keras.preprocessing import image\nimport keras\nimport numpy as np\n\n\n# =============================================================================\n# ####################### LOAD THE MODEL TO RUN ###############################\n# =============================================================================\n\nmodel_file = \"MODELS/5-2-19_v2.h5\"\nclassifier = keras.models.load_model(model_file)\nif classifier is not None:\n print('model is loaded from ', model_file)\n \n\n# =============================================================================\n# ######################## USE THE WEBCAM ON THE LAPTOP #######################\n# =============================================================================\n \ncap = cv2.VideoCapture(0)\n\nwhile(1):\n\n# =============================================================================\n# ######################## GET THE FRAME FROM WEBCAM ##########################\n# =============================================================================\n\n hasFrame, frame = cap.read()\n if not hasFrame:\n break\n \n\n# =============================================================================\n# ######################## RESIZE THE FRAME AND SAVE ##########################\n# =============================================================================\n \n h,w,bpp = np.shape(frame)\n dim = (int(w/4), int(h/4))\n frame_2 = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n cv2.imwrite(\"frame.jpg\", frame_2)\n \n\n# =============================================================================\n# ################## READ THE FRAME AND PUT INTO CLASSIFIER ###################\n# =============================================================================\n \n test_frame = image.load_img('frame.jpg', target_size = (60,40))\n test_frame = image.img_to_array(test_frame)\n test_frame = np.expand_dims(test_frame, axis = 0)\n result = classifier.predict(test_frame)\n \n\n# =============================================================================\n# ####################### GET PREDICITION OF THE FRAME ########################\n# =============================================================================\n \n if result[0][0] == 1:\n prediction = 'good'\n text_colour = (0,255,0)\n else:\n prediction = 'bad'\n text_colour = (0,0,255)\n\n\n cv2.putText(frame, prediction, (5,80),cv2.FONT_HERSHEY_SIMPLEX, \n fontScale = 3, color = text_colour,thickness = 3)\n \n \n# =============================================================================\n# ############################# SHOW FRAMES (VIDEO) ###########################\n# =============================================================================\n \n cv2.imshow('Video', frame)\n k = cv2.waitKey(10)\n if k == 27:\n break\ncap.release()\ncv2.destroyAllWindows()\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def allocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_D_IJ:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_AD_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
def computeRHSDerivs():
for var in D:
cog.outl('\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,
var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,
var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,
var))
if var in DD:
cog.outl('\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +
var, var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +
var, var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +
var, PREFIX_D[1] + var))
cog.outl('\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +
var, var))
if var in AD:
cog.outl('\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
0] + var, var))
cog.outl('\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
1] + var, var))
cog.outl('\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
2] + var, var))
def deallocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_D_IJ:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_AD_I:
cog.outl('\t free(%s);' % deriv)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for f in D:
for p in PREFIX_D:
FUNC_D_I.append(p + f)
<|reserved_special_token_0|>
for f in DD:
for p in PREFIX_DD:
FUNC_D_IJ.append(p + f)
<|reserved_special_token_0|>
for f in AD:
for p in PREFIX_AD:
FUNC_AD_I.append(p + f)
<|reserved_special_token_0|>
for f in D:
for p in PREFIX_KOD:
FUNC_KOD_I.append(p + f)
<|reserved_special_token_0|>
for f in CONSTRAINT_D:
for p in PREFIX_D:
FUNC_CONS.append(p + f)
for f in CONSTRAINT_DD:
for p in PREFIX_DD:
FUNC_CONS.append(p + f)
def allocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_D_IJ:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_AD_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
def computeRHSDerivs():
for var in D:
cog.outl('\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,
var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,
var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,
var))
if var in DD:
cog.outl('\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +
var, var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +
var, var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +
var, PREFIX_D[1] + var))
cog.outl('\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +
var, var))
if var in AD:
cog.outl('\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
0] + var, var))
cog.outl('\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
1] + var, var))
cog.outl('\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
2] + var, var))
def deallocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_D_IJ:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_AD_I:
cog.outl('\t free(%s);' % deriv)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
D = ['alpha', 'beta0', 'beta1', 'beta2', 'B0', 'B1', 'B2', 'chi', 'Gt0',
'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0',
'At1', 'At2', 'At3', 'At4', 'At5']
custom_functions = {'grad': 'grad', 'grad2': 'grad2', 'agrad': 'agrad',
'kograd': 'kograd'}
DD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi', 'alpha', 'beta0',
'beta1', 'beta2']
AD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3',
'At4', 'At5', 'alpha', 'beta0', 'beta1', 'beta2', 'chi', 'Gt0', 'Gt1',
'Gt2', 'K', 'B0', 'B1', 'B2']
KO = AD
CONSTRAINT_D = ['chi', 'Gt0', 'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3',
'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3', 'At4', 'At5']
CONSTRAINT_DD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi']
PREFIX_D = ['grad_0_', 'grad_1_', 'grad_2_']
PREFIX_AD = ['agrad_0_', 'agrad_1_', 'agrad_2_']
PREFIX_KOD = ['kograd_0_', 'kograd_1_', 'kograd_2_']
PREFIX_DD = ['grad2_0_0_', 'grad2_0_1_', 'grad2_0_2_', 'grad2_1_1_',
'grad2_1_2_', 'grad2_2_2_']
FUNC_D_I = []
for f in D:
for p in PREFIX_D:
FUNC_D_I.append(p + f)
FUNC_D_IJ = []
for f in DD:
for p in PREFIX_DD:
FUNC_D_IJ.append(p + f)
FUNC_AD_I = []
for f in AD:
for p in PREFIX_AD:
FUNC_AD_I.append(p + f)
FUNC_KOD_I = []
for f in D:
for p in PREFIX_KOD:
FUNC_KOD_I.append(p + f)
FUNC_CONS = []
for f in CONSTRAINT_D:
for p in PREFIX_D:
FUNC_CONS.append(p + f)
for f in CONSTRAINT_DD:
for p in PREFIX_DD:
FUNC_CONS.append(p + f)
def allocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_D_IJ:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_AD_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
def computeRHSDerivs():
for var in D:
cog.outl('\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,
var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,
var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,
var))
if var in DD:
cog.outl('\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +
var, var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +
var, var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +
var, PREFIX_D[1] + var))
cog.outl('\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +
var, var))
if var in AD:
cog.outl('\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
0] + var, var))
cog.outl('\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
1] + var, var))
cog.outl('\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
2] + var, var))
def deallocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_D_IJ:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_AD_I:
cog.outl('\t free(%s);' % deriv)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
import cog
D = ['alpha', 'beta0', 'beta1', 'beta2', 'B0', 'B1', 'B2', 'chi', 'Gt0',
'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0',
'At1', 'At2', 'At3', 'At4', 'At5']
custom_functions = {'grad': 'grad', 'grad2': 'grad2', 'agrad': 'agrad',
'kograd': 'kograd'}
DD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi', 'alpha', 'beta0',
'beta1', 'beta2']
AD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3',
'At4', 'At5', 'alpha', 'beta0', 'beta1', 'beta2', 'chi', 'Gt0', 'Gt1',
'Gt2', 'K', 'B0', 'B1', 'B2']
KO = AD
CONSTRAINT_D = ['chi', 'Gt0', 'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3',
'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3', 'At4', 'At5']
CONSTRAINT_DD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi']
PREFIX_D = ['grad_0_', 'grad_1_', 'grad_2_']
PREFIX_AD = ['agrad_0_', 'agrad_1_', 'agrad_2_']
PREFIX_KOD = ['kograd_0_', 'kograd_1_', 'kograd_2_']
PREFIX_DD = ['grad2_0_0_', 'grad2_0_1_', 'grad2_0_2_', 'grad2_1_1_',
'grad2_1_2_', 'grad2_2_2_']
FUNC_D_I = []
for f in D:
for p in PREFIX_D:
FUNC_D_I.append(p + f)
FUNC_D_IJ = []
for f in DD:
for p in PREFIX_DD:
FUNC_D_IJ.append(p + f)
FUNC_AD_I = []
for f in AD:
for p in PREFIX_AD:
FUNC_AD_I.append(p + f)
FUNC_KOD_I = []
for f in D:
for p in PREFIX_KOD:
FUNC_KOD_I.append(p + f)
FUNC_CONS = []
for f in CONSTRAINT_D:
for p in PREFIX_D:
FUNC_CONS.append(p + f)
for f in CONSTRAINT_DD:
for p in PREFIX_DD:
FUNC_CONS.append(p + f)
def allocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_D_IJ:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
for deriv in FUNC_AD_I:
cog.outl('\t double* ' + deriv +
' = (double*)malloc(sizeof(double)*n);')
def computeRHSDerivs():
for var in D:
cog.outl('\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,
var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,
var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,
var))
if var in DD:
cog.outl('\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +
var, var))
cog.outl('\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +
var, PREFIX_D[0] + var))
cog.outl('\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +
var, var))
cog.outl('\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +
var, PREFIX_D[1] + var))
cog.outl('\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +
var, var))
if var in AD:
cog.outl('\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
0] + var, var))
cog.outl('\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
1] + var, var))
cog.outl('\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[
2] + var, var))
def deallocDerivMemory():
for deriv in FUNC_D_I:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_D_IJ:
cog.outl('\t free(%s);' % deriv)
for deriv in FUNC_AD_I:
cog.outl('\t free(%s);' % deriv)
<|reserved_special_token_1|>
"""
Contains derivative computation for BSSN formulation of ET equations.
"""
# first derivative
import cog
D = ["alpha", "beta0", "beta1", "beta2",
"B0", "B1", "B2",
"chi", "Gt0", "Gt1", "Gt2", "K",
"gt0", "gt1", "gt2", "gt3", "gt4", "gt5",
"At0", "At1", "At2", "At3", "At4", "At5" ]
# custom functions for code generation in cse.
custom_functions = {'grad': 'grad', 'grad2': 'grad2', 'agrad': 'agrad', 'kograd': 'kograd'}
# second derivs required for RHS
DD = ["gt0", "gt1", "gt2", "gt3", "gt4", "gt5", "chi",
"alpha", "beta0", "beta1", "beta2" ]
# advective derivatives
AD = ["gt0", "gt1", "gt2", "gt3", "gt4", "gt5",
"At0", "At1", "At2", "At3", "At4", "At5",
"alpha", "beta0", "beta1", "beta2", "chi", "Gt0", "Gt1", "Gt2", "K",
"B0", "B1", "B2"]
KO=AD
# first derivs required for constraints--no gauge variables
CONSTRAINT_D = [ "chi", "Gt0", "Gt1", "Gt2", "K",
"gt0", "gt1", "gt2", "gt3", "gt4", "gt5",
"At0", "At1", "At2", "At3", "At4", "At5" ]
# second derivs required for constraints--no gauge variables
CONSTRAINT_DD = ["gt0", "gt1", "gt2", "gt3", "gt4", "gt5", "chi"]
PREFIX_D = ["grad_0_", "grad_1_", "grad_2_"]
PREFIX_AD = ["agrad_0_", "agrad_1_", "agrad_2_"]
PREFIX_KOD = ["kograd_0_", "kograd_1_", "kograd_2_"]
PREFIX_DD = ["grad2_0_0_", "grad2_0_1_", "grad2_0_2_", "grad2_1_1_", "grad2_1_2_", "grad2_2_2_"]
# first derivative in i direction
FUNC_D_I=[]
for f in D:
for p in PREFIX_D:
FUNC_D_I.append(p+f)
# second derivative in ij direction
FUNC_D_IJ=[]
for f in DD:
for p in PREFIX_DD:
FUNC_D_IJ.append(p+f)
#advective derivative in i direction
FUNC_AD_I=[]
for f in AD:
for p in PREFIX_AD:
FUNC_AD_I.append(p+f)
#Kriess-Oliger derivative in i direction
FUNC_KOD_I=[]
for f in D:
for p in PREFIX_KOD:
FUNC_KOD_I.append(p+f)
FUNC_CONS=[]
for f in CONSTRAINT_D:
for p in PREFIX_D:
FUNC_CONS.append(p+f)
for f in CONSTRAINT_DD:
for p in PREFIX_DD:
FUNC_CONS.append(p+f)
def allocDerivMemory():
for deriv in FUNC_D_I:
cog.outl("\t double* "+deriv+" = (double*)malloc(sizeof(double)*n);")
for deriv in FUNC_D_IJ:
cog.outl("\t double* "+deriv+" = (double*)malloc(sizeof(double)*n);")
for deriv in FUNC_AD_I:
cog.outl("\t double* "+deriv+" = (double*)malloc(sizeof(double)*n);")
def computeRHSDerivs():
for var in D:
cog.outl("\t deriv_x(%s, %s, hx, sz, bflag);" %(PREFIX_D[0] + var ,var))
cog.outl("\t deriv_y(%s, %s, hx, sz, bflag);" %(PREFIX_D[1] + var ,var))
cog.outl("\t deriv_z(%s, %s, hx, sz, bflag);" %(PREFIX_D[2] + var ,var))
if var in DD:
cog.outl("\t deriv_xx(%s, %s, hx, sz, bflag);" %(PREFIX_DD[0] + var ,var))
cog.outl("\t deriv_y(%s, %s, hx, sz, bflag);" %(PREFIX_DD[1] + var , PREFIX_D[0] + var ))
cog.outl("\t deriv_z(%s, %s, hx, sz, bflag);" %(PREFIX_DD[2] + var , PREFIX_D[0] + var ))
cog.outl("\t deriv_yy(%s, %s, hx, sz, bflag);" %(PREFIX_DD[3] + var ,var))
cog.outl("\t deriv_z(%s, %s, hx, sz, bflag);" %(PREFIX_DD[4] + var , PREFIX_D[1] + var))
cog.outl("\t deriv_zz(%s, %s, hx, sz, bflag);" %(PREFIX_DD[5] + var ,var))
if var in AD:
cog.outl("\t adv_deriv_x(%s, %s, hx, sz, bflag);" %(PREFIX_AD[0] + var ,var))
cog.outl("\t adv_deriv_y(%s, %s, hx, sz, bflag);" %(PREFIX_AD[1] + var ,var))
cog.outl("\t adv_deriv_z(%s, %s, hx, sz, bflag);" %(PREFIX_AD[2] + var ,var))
def deallocDerivMemory():
for deriv in FUNC_D_I:
cog.outl("\t free(%s);" %(deriv))
for deriv in FUNC_D_IJ:
cog.outl("\t free(%s);" %(deriv))
for deriv in FUNC_AD_I:
cog.outl("\t free(%s);" %(deriv))
|
flexible
|
{
"blob_id": "20a238826640099e6c69aaa383c5fa7e9b02b13b",
"index": 5614,
"step-1": "<mask token>\n\n\ndef allocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_D_IJ:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_AD_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n\n\ndef computeRHSDerivs():\n for var in D:\n cog.outl('\\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,\n var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,\n var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,\n var))\n if var in DD:\n cog.outl('\\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +\n var, var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +\n var, var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +\n var, PREFIX_D[1] + var))\n cog.outl('\\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +\n var, var))\n if var in AD:\n cog.outl('\\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 0] + var, var))\n cog.outl('\\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 1] + var, var))\n cog.outl('\\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 2] + var, var))\n\n\ndef deallocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_D_IJ:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_AD_I:\n cog.outl('\\t free(%s);' % deriv)\n",
"step-2": "<mask token>\nfor f in D:\n for p in PREFIX_D:\n FUNC_D_I.append(p + f)\n<mask token>\nfor f in DD:\n for p in PREFIX_DD:\n FUNC_D_IJ.append(p + f)\n<mask token>\nfor f in AD:\n for p in PREFIX_AD:\n FUNC_AD_I.append(p + f)\n<mask token>\nfor f in D:\n for p in PREFIX_KOD:\n FUNC_KOD_I.append(p + f)\n<mask token>\nfor f in CONSTRAINT_D:\n for p in PREFIX_D:\n FUNC_CONS.append(p + f)\nfor f in CONSTRAINT_DD:\n for p in PREFIX_DD:\n FUNC_CONS.append(p + f)\n\n\ndef allocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_D_IJ:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_AD_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n\n\ndef computeRHSDerivs():\n for var in D:\n cog.outl('\\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,\n var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,\n var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,\n var))\n if var in DD:\n cog.outl('\\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +\n var, var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +\n var, var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +\n var, PREFIX_D[1] + var))\n cog.outl('\\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +\n var, var))\n if var in AD:\n cog.outl('\\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 0] + var, var))\n cog.outl('\\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 1] + var, var))\n cog.outl('\\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 2] + var, var))\n\n\ndef deallocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_D_IJ:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_AD_I:\n cog.outl('\\t free(%s);' % deriv)\n",
"step-3": "<mask token>\nD = ['alpha', 'beta0', 'beta1', 'beta2', 'B0', 'B1', 'B2', 'chi', 'Gt0',\n 'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0',\n 'At1', 'At2', 'At3', 'At4', 'At5']\ncustom_functions = {'grad': 'grad', 'grad2': 'grad2', 'agrad': 'agrad',\n 'kograd': 'kograd'}\nDD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi', 'alpha', 'beta0',\n 'beta1', 'beta2']\nAD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3',\n 'At4', 'At5', 'alpha', 'beta0', 'beta1', 'beta2', 'chi', 'Gt0', 'Gt1',\n 'Gt2', 'K', 'B0', 'B1', 'B2']\nKO = AD\nCONSTRAINT_D = ['chi', 'Gt0', 'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3',\n 'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3', 'At4', 'At5']\nCONSTRAINT_DD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi']\nPREFIX_D = ['grad_0_', 'grad_1_', 'grad_2_']\nPREFIX_AD = ['agrad_0_', 'agrad_1_', 'agrad_2_']\nPREFIX_KOD = ['kograd_0_', 'kograd_1_', 'kograd_2_']\nPREFIX_DD = ['grad2_0_0_', 'grad2_0_1_', 'grad2_0_2_', 'grad2_1_1_',\n 'grad2_1_2_', 'grad2_2_2_']\nFUNC_D_I = []\nfor f in D:\n for p in PREFIX_D:\n FUNC_D_I.append(p + f)\nFUNC_D_IJ = []\nfor f in DD:\n for p in PREFIX_DD:\n FUNC_D_IJ.append(p + f)\nFUNC_AD_I = []\nfor f in AD:\n for p in PREFIX_AD:\n FUNC_AD_I.append(p + f)\nFUNC_KOD_I = []\nfor f in D:\n for p in PREFIX_KOD:\n FUNC_KOD_I.append(p + f)\nFUNC_CONS = []\nfor f in CONSTRAINT_D:\n for p in PREFIX_D:\n FUNC_CONS.append(p + f)\nfor f in CONSTRAINT_DD:\n for p in PREFIX_DD:\n FUNC_CONS.append(p + f)\n\n\ndef allocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_D_IJ:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_AD_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n\n\ndef computeRHSDerivs():\n for var in D:\n cog.outl('\\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,\n var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,\n var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,\n var))\n if var in DD:\n cog.outl('\\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +\n var, var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +\n var, var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +\n var, PREFIX_D[1] + var))\n cog.outl('\\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +\n var, var))\n if var in AD:\n cog.outl('\\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 0] + var, var))\n cog.outl('\\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 1] + var, var))\n cog.outl('\\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 2] + var, var))\n\n\ndef deallocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_D_IJ:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_AD_I:\n cog.outl('\\t free(%s);' % deriv)\n",
"step-4": "<mask token>\nimport cog\nD = ['alpha', 'beta0', 'beta1', 'beta2', 'B0', 'B1', 'B2', 'chi', 'Gt0',\n 'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0',\n 'At1', 'At2', 'At3', 'At4', 'At5']\ncustom_functions = {'grad': 'grad', 'grad2': 'grad2', 'agrad': 'agrad',\n 'kograd': 'kograd'}\nDD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi', 'alpha', 'beta0',\n 'beta1', 'beta2']\nAD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3',\n 'At4', 'At5', 'alpha', 'beta0', 'beta1', 'beta2', 'chi', 'Gt0', 'Gt1',\n 'Gt2', 'K', 'B0', 'B1', 'B2']\nKO = AD\nCONSTRAINT_D = ['chi', 'Gt0', 'Gt1', 'Gt2', 'K', 'gt0', 'gt1', 'gt2', 'gt3',\n 'gt4', 'gt5', 'At0', 'At1', 'At2', 'At3', 'At4', 'At5']\nCONSTRAINT_DD = ['gt0', 'gt1', 'gt2', 'gt3', 'gt4', 'gt5', 'chi']\nPREFIX_D = ['grad_0_', 'grad_1_', 'grad_2_']\nPREFIX_AD = ['agrad_0_', 'agrad_1_', 'agrad_2_']\nPREFIX_KOD = ['kograd_0_', 'kograd_1_', 'kograd_2_']\nPREFIX_DD = ['grad2_0_0_', 'grad2_0_1_', 'grad2_0_2_', 'grad2_1_1_',\n 'grad2_1_2_', 'grad2_2_2_']\nFUNC_D_I = []\nfor f in D:\n for p in PREFIX_D:\n FUNC_D_I.append(p + f)\nFUNC_D_IJ = []\nfor f in DD:\n for p in PREFIX_DD:\n FUNC_D_IJ.append(p + f)\nFUNC_AD_I = []\nfor f in AD:\n for p in PREFIX_AD:\n FUNC_AD_I.append(p + f)\nFUNC_KOD_I = []\nfor f in D:\n for p in PREFIX_KOD:\n FUNC_KOD_I.append(p + f)\nFUNC_CONS = []\nfor f in CONSTRAINT_D:\n for p in PREFIX_D:\n FUNC_CONS.append(p + f)\nfor f in CONSTRAINT_DD:\n for p in PREFIX_DD:\n FUNC_CONS.append(p + f)\n\n\ndef allocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_D_IJ:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n for deriv in FUNC_AD_I:\n cog.outl('\\t double* ' + deriv +\n ' = (double*)malloc(sizeof(double)*n);')\n\n\ndef computeRHSDerivs():\n for var in D:\n cog.outl('\\t deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_D[0] + var,\n var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_D[1] + var,\n var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_D[2] + var,\n var))\n if var in DD:\n cog.outl('\\t deriv_xx(%s, %s, hx, sz, bflag);' % (PREFIX_DD[0] +\n var, var))\n cog.outl('\\t deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_DD[1] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[2] +\n var, PREFIX_D[0] + var))\n cog.outl('\\t deriv_yy(%s, %s, hx, sz, bflag);' % (PREFIX_DD[3] +\n var, var))\n cog.outl('\\t deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_DD[4] +\n var, PREFIX_D[1] + var))\n cog.outl('\\t deriv_zz(%s, %s, hx, sz, bflag);' % (PREFIX_DD[5] +\n var, var))\n if var in AD:\n cog.outl('\\t adv_deriv_x(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 0] + var, var))\n cog.outl('\\t adv_deriv_y(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 1] + var, var))\n cog.outl('\\t adv_deriv_z(%s, %s, hx, sz, bflag);' % (PREFIX_AD[\n 2] + var, var))\n\n\ndef deallocDerivMemory():\n for deriv in FUNC_D_I:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_D_IJ:\n cog.outl('\\t free(%s);' % deriv)\n for deriv in FUNC_AD_I:\n cog.outl('\\t free(%s);' % deriv)\n",
"step-5": "\"\"\"\nContains derivative computation for BSSN formulation of ET equations. \n\"\"\"\n\n# first derivative\nimport cog\n\nD = [\"alpha\", \"beta0\", \"beta1\", \"beta2\",\n \"B0\", \"B1\", \"B2\",\n \"chi\", \"Gt0\", \"Gt1\", \"Gt2\", \"K\",\n \"gt0\", \"gt1\", \"gt2\", \"gt3\", \"gt4\", \"gt5\",\n \"At0\", \"At1\", \"At2\", \"At3\", \"At4\", \"At5\" ]\n\n\n# custom functions for code generation in cse.\ncustom_functions = {'grad': 'grad', 'grad2': 'grad2', 'agrad': 'agrad', 'kograd': 'kograd'}\n\n# second derivs required for RHS\nDD = [\"gt0\", \"gt1\", \"gt2\", \"gt3\", \"gt4\", \"gt5\", \"chi\",\n \"alpha\", \"beta0\", \"beta1\", \"beta2\" ]\n\n# advective derivatives\nAD = [\"gt0\", \"gt1\", \"gt2\", \"gt3\", \"gt4\", \"gt5\",\n \"At0\", \"At1\", \"At2\", \"At3\", \"At4\", \"At5\",\n \"alpha\", \"beta0\", \"beta1\", \"beta2\", \"chi\", \"Gt0\", \"Gt1\", \"Gt2\", \"K\",\n \"B0\", \"B1\", \"B2\"] \n\nKO=AD\n\n# first derivs required for constraints--no gauge variables\nCONSTRAINT_D = [ \"chi\", \"Gt0\", \"Gt1\", \"Gt2\", \"K\",\n \"gt0\", \"gt1\", \"gt2\", \"gt3\", \"gt4\", \"gt5\",\n \"At0\", \"At1\", \"At2\", \"At3\", \"At4\", \"At5\" ]\n\n# second derivs required for constraints--no gauge variables\nCONSTRAINT_DD = [\"gt0\", \"gt1\", \"gt2\", \"gt3\", \"gt4\", \"gt5\", \"chi\"]\n\n\nPREFIX_D = [\"grad_0_\", \"grad_1_\", \"grad_2_\"]\nPREFIX_AD = [\"agrad_0_\", \"agrad_1_\", \"agrad_2_\"]\nPREFIX_KOD = [\"kograd_0_\", \"kograd_1_\", \"kograd_2_\"]\nPREFIX_DD = [\"grad2_0_0_\", \"grad2_0_1_\", \"grad2_0_2_\", \"grad2_1_1_\", \"grad2_1_2_\", \"grad2_2_2_\"]\n\n# first derivative in i direction\nFUNC_D_I=[]\nfor f in D:\n for p in PREFIX_D:\n FUNC_D_I.append(p+f)\n\n# second derivative in ij direction\nFUNC_D_IJ=[]\nfor f in DD:\n for p in PREFIX_DD:\n FUNC_D_IJ.append(p+f)\n\n#advective derivative in i direction\nFUNC_AD_I=[]\nfor f in AD:\n for p in PREFIX_AD:\n FUNC_AD_I.append(p+f)\n\n\n#Kriess-Oliger derivative in i direction\nFUNC_KOD_I=[]\nfor f in D:\n for p in PREFIX_KOD:\n FUNC_KOD_I.append(p+f)\n\nFUNC_CONS=[]\nfor f in CONSTRAINT_D:\n for p in PREFIX_D:\n FUNC_CONS.append(p+f)\n \nfor f in CONSTRAINT_DD:\n for p in PREFIX_DD:\n FUNC_CONS.append(p+f)\n\n\ndef allocDerivMemory():\n \n for deriv in FUNC_D_I:\n cog.outl(\"\\t double* \"+deriv+\" = (double*)malloc(sizeof(double)*n);\")\n\n for deriv in FUNC_D_IJ:\n cog.outl(\"\\t double* \"+deriv+\" = (double*)malloc(sizeof(double)*n);\")\n\n for deriv in FUNC_AD_I:\n cog.outl(\"\\t double* \"+deriv+\" = (double*)malloc(sizeof(double)*n);\")\n \n \ndef computeRHSDerivs():\n \n for var in D:\n cog.outl(\"\\t deriv_x(%s, %s, hx, sz, bflag);\" %(PREFIX_D[0] + var ,var))\n cog.outl(\"\\t deriv_y(%s, %s, hx, sz, bflag);\" %(PREFIX_D[1] + var ,var))\n cog.outl(\"\\t deriv_z(%s, %s, hx, sz, bflag);\" %(PREFIX_D[2] + var ,var))\n\n if var in DD:\n cog.outl(\"\\t deriv_xx(%s, %s, hx, sz, bflag);\" %(PREFIX_DD[0] + var ,var))\n cog.outl(\"\\t deriv_y(%s, %s, hx, sz, bflag);\" %(PREFIX_DD[1] + var , PREFIX_D[0] + var ))\n cog.outl(\"\\t deriv_z(%s, %s, hx, sz, bflag);\" %(PREFIX_DD[2] + var , PREFIX_D[0] + var ))\n\n cog.outl(\"\\t deriv_yy(%s, %s, hx, sz, bflag);\" %(PREFIX_DD[3] + var ,var))\n cog.outl(\"\\t deriv_z(%s, %s, hx, sz, bflag);\" %(PREFIX_DD[4] + var , PREFIX_D[1] + var))\n\n cog.outl(\"\\t deriv_zz(%s, %s, hx, sz, bflag);\" %(PREFIX_DD[5] + var ,var))\n\n if var in AD:\n cog.outl(\"\\t adv_deriv_x(%s, %s, hx, sz, bflag);\" %(PREFIX_AD[0] + var ,var))\n cog.outl(\"\\t adv_deriv_y(%s, %s, hx, sz, bflag);\" %(PREFIX_AD[1] + var ,var))\n cog.outl(\"\\t adv_deriv_z(%s, %s, hx, sz, bflag);\" %(PREFIX_AD[2] + var ,var))\n\n\n\ndef deallocDerivMemory():\n \n for deriv in FUNC_D_I:\n cog.outl(\"\\t free(%s);\" %(deriv))\n\n for deriv in FUNC_D_IJ:\n cog.outl(\"\\t free(%s);\" %(deriv))\n\n for deriv in FUNC_AD_I:\n cog.outl(\"\\t free(%s);\" %(deriv))\n\n\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from database_setup import Category, Base, CategoryItem, User
engine = create_engine('postgresql:///thegoodybasket')
# Bind the engine to the metadata of the Base class so that the
# declaratives can be accessed through a DBSession instance
Base.metadata.bind = engine
DBSession = sessionmaker(bind=engine)
# A DBSession() instance establishes all conversations with the database
# and represents a "staging zone" for all the objects loaded into the
# database session object. Any change made against the objects in the
# session won't be persisted into the database until you call
# session.commit(). If you're not happy about the changes, you can
# revert all of them back to the last commit by calling
# session.rollback()
session = DBSession()
# Create dummy user
User1 = User(name="Robo Barista", email="tinnyTim@udacity.com",
picture='profile1.jpg')
session.add(User1)
session.commit()
User2 = User(name="Lisa Rodriguez", email="lisarodriguez@gmail.com",
picture='profile2.jpg')
session.add(User2)
session.commit()
User3 = User(name="Hannah Martin", email="Hanzzymartin@hotmail.com",
picture='profile3.jpg')
session.add(User3)
session.commit()
User4 = User(name="Brad Phillips", email="Bradistheboss@gmail.com",
picture='profile4.jpg')
session.add(User4)
session.commit()
User5 = User(name="Marv Robins", email="Marvis1234rop@hotmail.com",
picture='profile5.jpg')
session.add(User5)
session.commit()
User6 = User(name="Jennifer Andrews", email="JennAndrews5426@gmail.com",
picture='profile6.jpg')
session.add(User6)
session.commit()
# items for Snowboarding
category1 = Category(user_id=1, name="Snowboarding")
session.add(category1)
session.commit()
categoryItem1 = CategoryItem(user_id=1, name="White Snowboard", description="Brand new white 145cm pro model. Also available in red, orange and grey.",
price="$250.00", picture="snowboard_white.jpg", category=category1)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=1, name="Snow Jacket", description="Warm and puffy red snow jacket. Perfect for keeping warm!",
price="$199.99", picture="jacket.jpg", category=category1)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=1, name="Snow Goggles", description="Brand new 2015 model anti-glare, removable lens and adjustable strap goggles.",
price="$49.99", picture="snow_goggles.jpg", category=category1)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=1, name="Snow Gloves", description="Thick and padded snow gloves to keep toasty hands. Available in red and black.",
price="$39.99", picture="ski_gloves.jpg", category=category1)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=1, name="Snow Hat", description="Keep your head warm with this knitted-by-hand snow hat.",
price="$17.99", picture="warm_hat.jpg", category=category1)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=1, name="Ray-Ban Aviators", description="Keep cool on the slopes with these huge aviators.",
price="$1.99", picture="ray_bans.jpg", category=category1)
session.add(categoryItem6)
session.commit()
# Items for Skiing
category2 = Category(user_id=2, name="Skiing")
session.add(category2)
session.commit()
categoryItem1 = CategoryItem(user_id=2, name="Ski Boots", description="Warm, lightweight and super rugged ski boots. Available in all sizes.",
price="$175.50", picture="ski_boots.jpg", category=category2)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=2, name="Ski Gloves", description="Padded and warm waterproof gloves, available in red and black.",
price="$52.99", picture="ski_gloves.jpg", category=category2)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=2, name="K2 Soloman Skis", description="Brand new 2015 Solomon K2 skis in size 175.",
price="$450.00", picture="k2_skis.jpg", category=category2)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=2, name="Walking Boots", description="Warm and weatherproof. Available in all sizes.",
price="$69.99", picture="walking_boots.jpg", category=category2)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=2, name="Enhanced walking boots", description="Made with grade A beef",
price="$7.99", picture="walking_boots.jpg", category=category2)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=2, name="Gold plated walking boots", description="16oz of refreshing goodness",
price="$1.99", picture="walking_boots.jpg", category=category2)
session.add(categoryItem6)
session.commit()
# Items for Laptops
category3 = Category(user_id=3, name="Laptops")
session.add(category3)
session.commit()
categoryItem1 = CategoryItem(user_id=3, name="Retina MacBook Pro 13 inch", description="MacBook Pro 13-inch dual-core i5 2.5GHz/4GB/500GB/HD Graphics 4000/SD",
price="$999.00", picture="macbook.jpg", category=category3)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=3, name="Microsoft Surface Pro 3", description="Microsoft Surface Pro 3 256GB Silver tablet with keyboard.",
price="$799.99", picture="surface_pro.jpg", category=category3)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=3, name="Sony Vaio", description="Sony Vaio VPCX13C7E Notebook Intel Atom (Z540).",
price="$5.50", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=3, name="Sony Vaio Mk 2", description="fresh baked and served with ice cream",
price="$3.99", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=3, name="Enhanced Sony Vaio", description="Made with grade A beef instead of silicon chips.",
price="$7.99", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=3, name="Root Beer", description="16oz of refreshing goodness",
price="$1.99", picture="sony_vaio.jpg", category=category3)
session.add(categoryItem6)
session.commit()
# Items for biking category.
category4 = Category(user_id=4, name="Biking")
session.add(category4)
session.commit()
categoryItem1 = CategoryItem(user_id=4, name="Racing Bike", description="Feel the speed with this super light and stiff carbon fibre racing bike.",
price="$1499.99", picture="racing_bike.jpg", category=category4)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=4, name="Bike Helmet", description="Protect your head from falls with a super strong helmet.",
price="$22.99", picture="bike_helmet.jpg", category=category4)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=4, name="Bike Chain", description="Spare chain with a full range of sizes and types available.",
price="$15.50", picture="bike_chain.jpg", category=category4)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=4, name="27 Inch Tyre", description="A wonderfully resistant tyre with Smart Guard protection.",
price="$33.99", picture="bike_tyres.jpg", category=category4)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=4, name="Puncture Repair Kit", description="Part of the essentials list when preparing for a bike ride.",
price="$15.99", picture="puncture_repair_kit.jpg", category=category4)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=4, name="White Stripe Crash Helmet", description="Colourful and stylish streamlined biking helmet.",
price="$29.99", picture="bike_helmet2.jpg", category=category4)
session.add(categoryItem6)
session.commit()
# Items for surfing category.
category5 = Category(user_id=5, name="Surfing")
session.add(category5)
session.commit()
categoryItem1 = CategoryItem(user_id=5, name="Surf Wax", description="Essential surfboard traction.",
price="$7.50", picture="surf_wax.jpg", category=category5)
session.add(categoryItem1)
session.commit()
categoryItem2 = CategoryItem(user_id=5, name="Surfboard", description="This versatile shape will glide you through the waves.",
price="$299.99", picture="surfboard.jpg", category=category5)
session.add(categoryItem2)
session.commit()
categoryItem3 = CategoryItem(user_id=5, name="Wetsuit", description="Keep warm and protected in the cold months.",
price="$150.00", picture="wetsuit.jpg", category=category5)
session.add(categoryItem3)
session.commit()
categoryItem4 = CategoryItem(user_id=5, name="Flip Flops", description="Blue, easy fit slip on.",
price="$3.99", picture="flip_flops.jpg", category=category5)
session.add(categoryItem4)
session.commit()
categoryItem5 = CategoryItem(user_id=5, name="Hat", description="Hat for chilling in the beach sun.",
price="$7.99", picture="flip_flops.jpg", category=category5)
session.add(categoryItem5)
session.commit()
categoryItem6 = CategoryItem(user_id=5, name="Root Beer soaked flip-flops", description="16oz of refreshing goodness poured over a fresh set of flip-flops",
price="$1.99", picture="flip_flops.jpg", category=category5)
session.add(categoryItem6)
session.commit()
print "Added Category items and users!"
|
normal
|
{
"blob_id": "964499c02548a7e790d96efcd780f471ab1fe1e3",
"index": 9923,
"step-1": "from sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom database_setup import Category, Base, CategoryItem, User\n\nengine = create_engine('postgresql:///thegoodybasket')\n# Bind the engine to the metadata of the Base class so that the\n# declaratives can be accessed through a DBSession instance\nBase.metadata.bind = engine\n\nDBSession = sessionmaker(bind=engine)\n# A DBSession() instance establishes all conversations with the database\n# and represents a \"staging zone\" for all the objects loaded into the\n# database session object. Any change made against the objects in the\n# session won't be persisted into the database until you call\n# session.commit(). If you're not happy about the changes, you can\n# revert all of them back to the last commit by calling\n# session.rollback()\nsession = DBSession()\n\n\n# Create dummy user\nUser1 = User(name=\"Robo Barista\", email=\"tinnyTim@udacity.com\",\n picture='profile1.jpg')\nsession.add(User1)\nsession.commit()\n\nUser2 = User(name=\"Lisa Rodriguez\", email=\"lisarodriguez@gmail.com\",\n picture='profile2.jpg')\nsession.add(User2)\nsession.commit()\n\nUser3 = User(name=\"Hannah Martin\", email=\"Hanzzymartin@hotmail.com\",\n picture='profile3.jpg')\nsession.add(User3)\nsession.commit()\n\nUser4 = User(name=\"Brad Phillips\", email=\"Bradistheboss@gmail.com\",\n picture='profile4.jpg')\nsession.add(User4)\nsession.commit()\n\nUser5 = User(name=\"Marv Robins\", email=\"Marvis1234rop@hotmail.com\",\n picture='profile5.jpg')\nsession.add(User5)\nsession.commit()\n\nUser6 = User(name=\"Jennifer Andrews\", email=\"JennAndrews5426@gmail.com\",\n picture='profile6.jpg')\nsession.add(User6)\nsession.commit()\n\n# items for Snowboarding\ncategory1 = Category(user_id=1, name=\"Snowboarding\")\n\nsession.add(category1)\nsession.commit()\n\ncategoryItem1 = CategoryItem(user_id=1, name=\"White Snowboard\", description=\"Brand new white 145cm pro model. Also available in red, orange and grey.\",\n price=\"$250.00\", picture=\"snowboard_white.jpg\", category=category1)\n\nsession.add(categoryItem1)\nsession.commit()\n\n\ncategoryItem2 = CategoryItem(user_id=1, name=\"Snow Jacket\", description=\"Warm and puffy red snow jacket. Perfect for keeping warm!\",\n price=\"$199.99\", picture=\"jacket.jpg\", category=category1)\n\nsession.add(categoryItem2)\nsession.commit()\n\ncategoryItem3 = CategoryItem(user_id=1, name=\"Snow Goggles\", description=\"Brand new 2015 model anti-glare, removable lens and adjustable strap goggles.\",\n price=\"$49.99\", picture=\"snow_goggles.jpg\", category=category1)\n\nsession.add(categoryItem3)\nsession.commit()\n\ncategoryItem4 = CategoryItem(user_id=1, name=\"Snow Gloves\", description=\"Thick and padded snow gloves to keep toasty hands. Available in red and black.\",\n price=\"$39.99\", picture=\"ski_gloves.jpg\", category=category1)\n\nsession.add(categoryItem4)\nsession.commit()\n\ncategoryItem5 = CategoryItem(user_id=1, name=\"Snow Hat\", description=\"Keep your head warm with this knitted-by-hand snow hat.\",\n price=\"$17.99\", picture=\"warm_hat.jpg\", category=category1)\n\nsession.add(categoryItem5)\nsession.commit()\n\ncategoryItem6 = CategoryItem(user_id=1, name=\"Ray-Ban Aviators\", description=\"Keep cool on the slopes with these huge aviators.\",\n price=\"$1.99\", picture=\"ray_bans.jpg\", category=category1)\n\nsession.add(categoryItem6)\nsession.commit()\n\n\n# Items for Skiing\ncategory2 = Category(user_id=2, name=\"Skiing\")\n\nsession.add(category2)\nsession.commit()\n\n\ncategoryItem1 = CategoryItem(user_id=2, name=\"Ski Boots\", description=\"Warm, lightweight and super rugged ski boots. Available in all sizes.\",\n price=\"$175.50\", picture=\"ski_boots.jpg\", category=category2)\n\nsession.add(categoryItem1)\nsession.commit()\n\n\ncategoryItem2 = CategoryItem(user_id=2, name=\"Ski Gloves\", description=\"Padded and warm waterproof gloves, available in red and black.\",\n price=\"$52.99\", picture=\"ski_gloves.jpg\", category=category2)\n\nsession.add(categoryItem2)\nsession.commit()\n\ncategoryItem3 = CategoryItem(user_id=2, name=\"K2 Soloman Skis\", description=\"Brand new 2015 Solomon K2 skis in size 175.\",\n price=\"$450.00\", picture=\"k2_skis.jpg\", category=category2)\n\nsession.add(categoryItem3)\nsession.commit()\n\ncategoryItem4 = CategoryItem(user_id=2, name=\"Walking Boots\", description=\"Warm and weatherproof. Available in all sizes.\",\n price=\"$69.99\", picture=\"walking_boots.jpg\", category=category2)\n\nsession.add(categoryItem4)\nsession.commit()\n\ncategoryItem5 = CategoryItem(user_id=2, name=\"Enhanced walking boots\", description=\"Made with grade A beef\",\n price=\"$7.99\", picture=\"walking_boots.jpg\", category=category2)\n\nsession.add(categoryItem5)\nsession.commit()\n\ncategoryItem6 = CategoryItem(user_id=2, name=\"Gold plated walking boots\", description=\"16oz of refreshing goodness\",\n price=\"$1.99\", picture=\"walking_boots.jpg\", category=category2)\n\nsession.add(categoryItem6)\nsession.commit()\n\n\n# Items for Laptops\ncategory3 = Category(user_id=3, name=\"Laptops\")\n\nsession.add(category3)\nsession.commit()\n\n\ncategoryItem1 = CategoryItem(user_id=3, name=\"Retina MacBook Pro 13 inch\", description=\"MacBook Pro 13-inch dual-core i5 2.5GHz/4GB/500GB/HD Graphics 4000/SD\",\n price=\"$999.00\", picture=\"macbook.jpg\", category=category3)\n\nsession.add(categoryItem1)\nsession.commit()\n\n\ncategoryItem2 = CategoryItem(user_id=3, name=\"Microsoft Surface Pro 3\", description=\"Microsoft Surface Pro 3 256GB Silver tablet with keyboard.\",\n price=\"$799.99\", picture=\"surface_pro.jpg\", category=category3)\n\nsession.add(categoryItem2)\nsession.commit()\n\ncategoryItem3 = CategoryItem(user_id=3, name=\"Sony Vaio\", description=\"Sony Vaio VPCX13C7E Notebook Intel Atom (Z540).\",\n price=\"$5.50\", picture=\"sony_vaio.jpg\", category=category3)\n\nsession.add(categoryItem3)\nsession.commit()\n\ncategoryItem4 = CategoryItem(user_id=3, name=\"Sony Vaio Mk 2\", description=\"fresh baked and served with ice cream\",\n price=\"$3.99\", picture=\"sony_vaio.jpg\", category=category3)\n\nsession.add(categoryItem4)\nsession.commit()\n\ncategoryItem5 = CategoryItem(user_id=3, name=\"Enhanced Sony Vaio\", description=\"Made with grade A beef instead of silicon chips.\",\n price=\"$7.99\", picture=\"sony_vaio.jpg\", category=category3)\n\nsession.add(categoryItem5)\nsession.commit()\n\ncategoryItem6 = CategoryItem(user_id=3, name=\"Root Beer\", description=\"16oz of refreshing goodness\",\n price=\"$1.99\", picture=\"sony_vaio.jpg\", category=category3)\n\nsession.add(categoryItem6)\nsession.commit()\n\n\n# Items for biking category.\ncategory4 = Category(user_id=4, name=\"Biking\")\n\nsession.add(category4)\nsession.commit()\n\n\ncategoryItem1 = CategoryItem(user_id=4, name=\"Racing Bike\", description=\"Feel the speed with this super light and stiff carbon fibre racing bike.\",\n price=\"$1499.99\", picture=\"racing_bike.jpg\", category=category4)\n\nsession.add(categoryItem1)\nsession.commit()\n\n\ncategoryItem2 = CategoryItem(user_id=4, name=\"Bike Helmet\", description=\"Protect your head from falls with a super strong helmet.\",\n price=\"$22.99\", picture=\"bike_helmet.jpg\", category=category4)\n\nsession.add(categoryItem2)\nsession.commit()\n\ncategoryItem3 = CategoryItem(user_id=4, name=\"Bike Chain\", description=\"Spare chain with a full range of sizes and types available.\",\n price=\"$15.50\", picture=\"bike_chain.jpg\", category=category4)\n\nsession.add(categoryItem3)\nsession.commit()\n\ncategoryItem4 = CategoryItem(user_id=4, name=\"27 Inch Tyre\", description=\"A wonderfully resistant tyre with Smart Guard protection.\",\n price=\"$33.99\", picture=\"bike_tyres.jpg\", category=category4)\n\nsession.add(categoryItem4)\nsession.commit()\n\ncategoryItem5 = CategoryItem(user_id=4, name=\"Puncture Repair Kit\", description=\"Part of the essentials list when preparing for a bike ride.\",\n price=\"$15.99\", picture=\"puncture_repair_kit.jpg\", category=category4)\n\nsession.add(categoryItem5)\nsession.commit()\n\ncategoryItem6 = CategoryItem(user_id=4, name=\"White Stripe Crash Helmet\", description=\"Colourful and stylish streamlined biking helmet.\",\n price=\"$29.99\", picture=\"bike_helmet2.jpg\", category=category4)\n\nsession.add(categoryItem6)\nsession.commit()\n\n\n# Items for surfing category.\ncategory5 = Category(user_id=5, name=\"Surfing\")\n\nsession.add(category5)\nsession.commit()\n\n\ncategoryItem1 = CategoryItem(user_id=5, name=\"Surf Wax\", description=\"Essential surfboard traction.\",\n price=\"$7.50\", picture=\"surf_wax.jpg\", category=category5)\n\nsession.add(categoryItem1)\nsession.commit()\n\n\ncategoryItem2 = CategoryItem(user_id=5, name=\"Surfboard\", description=\"This versatile shape will glide you through the waves.\",\n price=\"$299.99\", picture=\"surfboard.jpg\", category=category5)\n\nsession.add(categoryItem2)\nsession.commit()\n\ncategoryItem3 = CategoryItem(user_id=5, name=\"Wetsuit\", description=\"Keep warm and protected in the cold months.\",\n price=\"$150.00\", picture=\"wetsuit.jpg\", category=category5)\n\nsession.add(categoryItem3)\nsession.commit()\n\ncategoryItem4 = CategoryItem(user_id=5, name=\"Flip Flops\", description=\"Blue, easy fit slip on.\",\n price=\"$3.99\", picture=\"flip_flops.jpg\", category=category5)\n\nsession.add(categoryItem4)\nsession.commit()\n\ncategoryItem5 = CategoryItem(user_id=5, name=\"Hat\", description=\"Hat for chilling in the beach sun.\",\n price=\"$7.99\", picture=\"flip_flops.jpg\", category=category5)\n\nsession.add(categoryItem5)\nsession.commit()\n\ncategoryItem6 = CategoryItem(user_id=5, name=\"Root Beer soaked flip-flops\", description=\"16oz of refreshing goodness poured over a fresh set of flip-flops\",\n price=\"$1.99\", picture=\"flip_flops.jpg\", category=category5)\n\nsession.add(categoryItem6)\nsession.commit()\n\nprint \"Added Category items and users!\"",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
<|reserved_special_token_0|>
class CRITsAPI:
<|reserved_special_token_0|>
def get_object(self, obj_id, obj_type):
type_trans = self._type_translation(obj_type)
get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)
params = {'username': self.username, 'api_key': self.api_key}
r = requests.get(get_url, params=params, proxies=self.proxies,
verify=self.verify)
if r.status_code == 200:
return json.loads(r.text)
else:
print('Status code returned for query {}, was: {}'.format(
get_url, r.status_code))
return None
def add_indicator(self, source='', reference='', method='', campaign=
None, confidence=None, bucket_list=[], ticket='', add_domain=True,
add_relationship=True, indicator_confidence='unknown',
indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,
attack_type=iat.UNKNOWN, value=None, description=''):
data = {'api_key': self.api_key, 'username': self.username,
'source': source, 'reference': reference, 'method': '',
'campaign': campaign, 'confidence': confidence, 'bucket_list':
bucket_list, 'ticket': ticket, 'add_domain': True,
'add_relationship': True, 'indicator_confidence':
indicator_confidence, 'indicator_impact': indicator_impact,
'type': type, 'threat_type': threat_type, 'attack_type':
attack_type, 'value': value, 'description': description}
r = requests.post('{0}/indicators/'.format(self.url), data=data,
verify=self.verify, proxies=self.proxies)
if r.status_code == 200:
log.debug('Indicator uploaded successfully - {}'.format(value))
ind = json.loads(r.text)
return ind
return None
<|reserved_special_token_0|>
def forge_relationship(self, left_id, left_type, right_id, right_type,
rel_type, rel_date='', rel_confidence='high', rel_reason=''):
if not rel_date:
rel_date = datetime.datetime.now()
type_trans = self._type_translation(left_type)
submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)
headers = {'Content-Type': 'application/json'}
params = {'api_key': self.api_key, 'username': self.username}
data = {'action': 'forge_relationship', 'right_type': right_type,
'right_id': right_id, 'rel_type': rel_type, 'rel_date':
rel_date, 'rel_confidence': rel_confidence, 'rel_reason':
rel_reason}
r = requests.patch(submit_url, params=params, data=data, proxies=
self.proxies, verify=self.verify)
if r.status_code == 200:
log.debug('Relationship built successfully: {0} <-> {1}'.format
(left_id, right_id))
return True
else:
log.error(
'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'
.format(r.status_code, r.text, left_id, right_id))
return False
def add_campaign_to_object(self, id, type, campaign, confidence,
analyst, date, description):
obj = getattr(self.db, type)
result = obj.find({'_id': id, 'campaign.name': campaign})
if result:
import pdb
pdb.set_trace()
def _type_translation(self, str_type):
if str_type == 'Indicator':
return 'indicators'
if str_type == 'Domain':
return 'domains'
if str_type == 'IP':
return 'ips'
if str_type == 'Sample':
return 'samples'
if str_type == 'Event':
return 'events'
if str_type == 'Actor':
return 'actors'
if str_type == 'Email':
return 'emails'
if str_type == 'Backdoor':
return 'backdoors'
raise CRITsOperationalError('Invalid object type specified: {}'.
format(str_type))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class CRITsAPI:
<|reserved_special_token_0|>
def get_object(self, obj_id, obj_type):
type_trans = self._type_translation(obj_type)
get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)
params = {'username': self.username, 'api_key': self.api_key}
r = requests.get(get_url, params=params, proxies=self.proxies,
verify=self.verify)
if r.status_code == 200:
return json.loads(r.text)
else:
print('Status code returned for query {}, was: {}'.format(
get_url, r.status_code))
return None
def add_indicator(self, source='', reference='', method='', campaign=
None, confidence=None, bucket_list=[], ticket='', add_domain=True,
add_relationship=True, indicator_confidence='unknown',
indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,
attack_type=iat.UNKNOWN, value=None, description=''):
data = {'api_key': self.api_key, 'username': self.username,
'source': source, 'reference': reference, 'method': '',
'campaign': campaign, 'confidence': confidence, 'bucket_list':
bucket_list, 'ticket': ticket, 'add_domain': True,
'add_relationship': True, 'indicator_confidence':
indicator_confidence, 'indicator_impact': indicator_impact,
'type': type, 'threat_type': threat_type, 'attack_type':
attack_type, 'value': value, 'description': description}
r = requests.post('{0}/indicators/'.format(self.url), data=data,
verify=self.verify, proxies=self.proxies)
if r.status_code == 200:
log.debug('Indicator uploaded successfully - {}'.format(value))
ind = json.loads(r.text)
return ind
return None
def has_relationship(self, left_id, left_type, right_id, right_type,
rel_type='Related To'):
data = self.get_object(left_id, left_type)
if not data:
raise CRITsOperationalError(
'Crits Object not found with id {} and type {}'.format(
left_id, left_type))
if not 'relationships' in data:
return False
for relationship in data['relationships']:
if relationship['relationship'] != rel_type:
continue
if relationship['value'] != right_id:
continue
if relationship['type'] != right_type:
continue
return True
return False
def forge_relationship(self, left_id, left_type, right_id, right_type,
rel_type, rel_date='', rel_confidence='high', rel_reason=''):
if not rel_date:
rel_date = datetime.datetime.now()
type_trans = self._type_translation(left_type)
submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)
headers = {'Content-Type': 'application/json'}
params = {'api_key': self.api_key, 'username': self.username}
data = {'action': 'forge_relationship', 'right_type': right_type,
'right_id': right_id, 'rel_type': rel_type, 'rel_date':
rel_date, 'rel_confidence': rel_confidence, 'rel_reason':
rel_reason}
r = requests.patch(submit_url, params=params, data=data, proxies=
self.proxies, verify=self.verify)
if r.status_code == 200:
log.debug('Relationship built successfully: {0} <-> {1}'.format
(left_id, right_id))
return True
else:
log.error(
'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'
.format(r.status_code, r.text, left_id, right_id))
return False
def add_campaign_to_object(self, id, type, campaign, confidence,
analyst, date, description):
obj = getattr(self.db, type)
result = obj.find({'_id': id, 'campaign.name': campaign})
if result:
import pdb
pdb.set_trace()
def _type_translation(self, str_type):
if str_type == 'Indicator':
return 'indicators'
if str_type == 'Domain':
return 'domains'
if str_type == 'IP':
return 'ips'
if str_type == 'Sample':
return 'samples'
if str_type == 'Event':
return 'events'
if str_type == 'Actor':
return 'actors'
if str_type == 'Email':
return 'emails'
if str_type == 'Backdoor':
return 'backdoors'
raise CRITsOperationalError('Invalid object type specified: {}'.
format(str_type))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class CRITsAPI:
def __init__(self, api_url='', api_key='', username='', verify=True,
proxies={}):
self.url = api_url
if self.url[-1] == '/':
self.url = self.url[:-1]
self.api_key = api_key
self.username = username
self.verify = verify
self.proxies = proxies
def get_object(self, obj_id, obj_type):
type_trans = self._type_translation(obj_type)
get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)
params = {'username': self.username, 'api_key': self.api_key}
r = requests.get(get_url, params=params, proxies=self.proxies,
verify=self.verify)
if r.status_code == 200:
return json.loads(r.text)
else:
print('Status code returned for query {}, was: {}'.format(
get_url, r.status_code))
return None
def add_indicator(self, source='', reference='', method='', campaign=
None, confidence=None, bucket_list=[], ticket='', add_domain=True,
add_relationship=True, indicator_confidence='unknown',
indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,
attack_type=iat.UNKNOWN, value=None, description=''):
data = {'api_key': self.api_key, 'username': self.username,
'source': source, 'reference': reference, 'method': '',
'campaign': campaign, 'confidence': confidence, 'bucket_list':
bucket_list, 'ticket': ticket, 'add_domain': True,
'add_relationship': True, 'indicator_confidence':
indicator_confidence, 'indicator_impact': indicator_impact,
'type': type, 'threat_type': threat_type, 'attack_type':
attack_type, 'value': value, 'description': description}
r = requests.post('{0}/indicators/'.format(self.url), data=data,
verify=self.verify, proxies=self.proxies)
if r.status_code == 200:
log.debug('Indicator uploaded successfully - {}'.format(value))
ind = json.loads(r.text)
return ind
return None
def has_relationship(self, left_id, left_type, right_id, right_type,
rel_type='Related To'):
data = self.get_object(left_id, left_type)
if not data:
raise CRITsOperationalError(
'Crits Object not found with id {} and type {}'.format(
left_id, left_type))
if not 'relationships' in data:
return False
for relationship in data['relationships']:
if relationship['relationship'] != rel_type:
continue
if relationship['value'] != right_id:
continue
if relationship['type'] != right_type:
continue
return True
return False
def forge_relationship(self, left_id, left_type, right_id, right_type,
rel_type, rel_date='', rel_confidence='high', rel_reason=''):
if not rel_date:
rel_date = datetime.datetime.now()
type_trans = self._type_translation(left_type)
submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)
headers = {'Content-Type': 'application/json'}
params = {'api_key': self.api_key, 'username': self.username}
data = {'action': 'forge_relationship', 'right_type': right_type,
'right_id': right_id, 'rel_type': rel_type, 'rel_date':
rel_date, 'rel_confidence': rel_confidence, 'rel_reason':
rel_reason}
r = requests.patch(submit_url, params=params, data=data, proxies=
self.proxies, verify=self.verify)
if r.status_code == 200:
log.debug('Relationship built successfully: {0} <-> {1}'.format
(left_id, right_id))
return True
else:
log.error(
'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'
.format(r.status_code, r.text, left_id, right_id))
return False
def add_campaign_to_object(self, id, type, campaign, confidence,
analyst, date, description):
obj = getattr(self.db, type)
result = obj.find({'_id': id, 'campaign.name': campaign})
if result:
import pdb
pdb.set_trace()
def _type_translation(self, str_type):
if str_type == 'Indicator':
return 'indicators'
if str_type == 'Domain':
return 'domains'
if str_type == 'IP':
return 'ips'
if str_type == 'Sample':
return 'samples'
if str_type == 'Event':
return 'events'
if str_type == 'Actor':
return 'actors'
if str_type == 'Email':
return 'emails'
if str_type == 'Backdoor':
return 'backdoors'
raise CRITsOperationalError('Invalid object type specified: {}'.
format(str_type))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
log = logging.getLogger()
class CRITsAPI:
def __init__(self, api_url='', api_key='', username='', verify=True,
proxies={}):
self.url = api_url
if self.url[-1] == '/':
self.url = self.url[:-1]
self.api_key = api_key
self.username = username
self.verify = verify
self.proxies = proxies
def get_object(self, obj_id, obj_type):
type_trans = self._type_translation(obj_type)
get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)
params = {'username': self.username, 'api_key': self.api_key}
r = requests.get(get_url, params=params, proxies=self.proxies,
verify=self.verify)
if r.status_code == 200:
return json.loads(r.text)
else:
print('Status code returned for query {}, was: {}'.format(
get_url, r.status_code))
return None
def add_indicator(self, source='', reference='', method='', campaign=
None, confidence=None, bucket_list=[], ticket='', add_domain=True,
add_relationship=True, indicator_confidence='unknown',
indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,
attack_type=iat.UNKNOWN, value=None, description=''):
data = {'api_key': self.api_key, 'username': self.username,
'source': source, 'reference': reference, 'method': '',
'campaign': campaign, 'confidence': confidence, 'bucket_list':
bucket_list, 'ticket': ticket, 'add_domain': True,
'add_relationship': True, 'indicator_confidence':
indicator_confidence, 'indicator_impact': indicator_impact,
'type': type, 'threat_type': threat_type, 'attack_type':
attack_type, 'value': value, 'description': description}
r = requests.post('{0}/indicators/'.format(self.url), data=data,
verify=self.verify, proxies=self.proxies)
if r.status_code == 200:
log.debug('Indicator uploaded successfully - {}'.format(value))
ind = json.loads(r.text)
return ind
return None
def has_relationship(self, left_id, left_type, right_id, right_type,
rel_type='Related To'):
data = self.get_object(left_id, left_type)
if not data:
raise CRITsOperationalError(
'Crits Object not found with id {} and type {}'.format(
left_id, left_type))
if not 'relationships' in data:
return False
for relationship in data['relationships']:
if relationship['relationship'] != rel_type:
continue
if relationship['value'] != right_id:
continue
if relationship['type'] != right_type:
continue
return True
return False
def forge_relationship(self, left_id, left_type, right_id, right_type,
rel_type, rel_date='', rel_confidence='high', rel_reason=''):
if not rel_date:
rel_date = datetime.datetime.now()
type_trans = self._type_translation(left_type)
submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)
headers = {'Content-Type': 'application/json'}
params = {'api_key': self.api_key, 'username': self.username}
data = {'action': 'forge_relationship', 'right_type': right_type,
'right_id': right_id, 'rel_type': rel_type, 'rel_date':
rel_date, 'rel_confidence': rel_confidence, 'rel_reason':
rel_reason}
r = requests.patch(submit_url, params=params, data=data, proxies=
self.proxies, verify=self.verify)
if r.status_code == 200:
log.debug('Relationship built successfully: {0} <-> {1}'.format
(left_id, right_id))
return True
else:
log.error(
'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'
.format(r.status_code, r.text, left_id, right_id))
return False
def add_campaign_to_object(self, id, type, campaign, confidence,
analyst, date, description):
obj = getattr(self.db, type)
result = obj.find({'_id': id, 'campaign.name': campaign})
if result:
import pdb
pdb.set_trace()
def _type_translation(self, str_type):
if str_type == 'Indicator':
return 'indicators'
if str_type == 'Domain':
return 'domains'
if str_type == 'IP':
return 'ips'
if str_type == 'Sample':
return 'samples'
if str_type == 'Event':
return 'events'
if str_type == 'Actor':
return 'actors'
if str_type == 'Email':
return 'emails'
if str_type == 'Backdoor':
return 'backdoors'
raise CRITsOperationalError('Invalid object type specified: {}'.
format(str_type))
<|reserved_special_token_1|>
import datetime
import json
import logging
import requests
from lib.crits.exceptions import CRITsOperationalError
from lib.crits.vocabulary.indicators import IndicatorThreatTypes as itt
from lib.crits.vocabulary.indicators import IndicatorAttackTypes as iat
log = logging.getLogger()
class CRITsAPI():
def __init__(self, api_url='', api_key='', username='', verify=True,
proxies={}):
self.url = api_url
if self.url[-1] == '/':
self.url = self.url[:-1]
self.api_key = api_key
self.username = username
self.verify = verify
self.proxies = proxies
def get_object(self, obj_id, obj_type):
type_trans = self._type_translation(obj_type)
get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)
params = {
'username' : self.username,
'api_key' : self.api_key,
}
r = requests.get(get_url, params=params, proxies=self.proxies, verify=self.verify)
if r.status_code == 200:
return json.loads(r.text)
else:
print('Status code returned for query {}, '
'was: {}'.format(get_url, r.status_code))
return None
def add_indicator(self, source = '', reference = '', method = '',
campaign = None, confidence = None, bucket_list = [], ticket = '',
add_domain = True, add_relationship = True,
indicator_confidence = 'unknown', indicator_impact = 'unknown',
type = None, threat_type = itt.UNKNOWN, attack_type = iat.UNKNOWN,
value = None, description = ''):
# Time to upload these indicators
data = {
'api_key' : self.api_key,
'username' : self.username,
'source' : source,
'reference' : reference,
'method' : '',
'campaign' : campaign,
'confidence' : confidence,
'bucket_list' : bucket_list,
'ticket' : ticket,
'add_domain' : True,
'add_relationship' : True,
'indicator_confidence' : indicator_confidence,
'indicator_impact' : indicator_impact,
'type' : type,
'threat_type' : threat_type,
'attack_type' : attack_type,
'value' : value,
'description' : description,
}
r = requests.post("{0}/indicators/".format(self.url), data=data,
verify=self.verify, proxies=self.proxies)
if r.status_code == 200:
log.debug("Indicator uploaded successfully - {}".format(value))
ind = json.loads(r.text)
return ind
return None
def has_relationship(self, left_id, left_type, right_id, right_type,
rel_type='Related To'):
data = self.get_object(left_id, left_type)
if not data:
raise CRITsOperationalError('Crits Object not found with id {} and '
'type {}'.format(left_id, left_type))
if not 'relationships' in data:
return False
for relationship in data['relationships']:
if relationship['relationship'] != rel_type:
continue
if relationship['value'] != right_id:
continue
if relationship['type'] != right_type:
continue
return True
return False
def forge_relationship(self, left_id, left_type, right_id, right_type,
rel_type, rel_date='', rel_confidence='high',
rel_reason=''):
if not rel_date:
rel_date = datetime.datetime.now()
type_trans = self._type_translation(left_type)
submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)
headers = {
'Content-Type' : 'application/json',
}
params = {
'api_key' : self.api_key,
'username' : self.username,
}
data = {
'action' : 'forge_relationship',
'right_type' : right_type,
'right_id' : right_id,
'rel_type' : rel_type,
'rel_date' : rel_date,
'rel_confidence' : rel_confidence,
'rel_reason' : rel_reason
}
r = requests.patch(submit_url, params=params, data=data,
proxies=self.proxies, verify=self.verify)
if r.status_code == 200:
log.debug('Relationship built successfully: {0} <-> '
'{1}'.format(left_id, right_id))
return True
else:
log.error('Error with status code {0} and message {1} between '
'these indicators: {2} <-> '
'{3}'.format(r.status_code, r.text, left_id, right_id))
return False
def add_campaign_to_object(self, id, type, campaign, confidence, analyst,
date, description):
# TODO: Make sure the object does not already have the campaign
# Return if it does. Add it if it doesn't
obj = getattr(self.db, type)
result = obj.find( { '_id' : id, 'campaign.name' : campaign } )
if result:
import pdb
pdb.set_trace()
def _type_translation(self, str_type):
if str_type == 'Indicator':
return 'indicators'
if str_type == 'Domain':
return 'domains'
if str_type == 'IP':
return 'ips'
if str_type == 'Sample':
return 'samples'
if str_type == 'Event':
return 'events'
if str_type == 'Actor':
return 'actors'
if str_type == 'Email':
return 'emails'
if str_type == 'Backdoor':
return 'backdoors'
raise CRITsOperationalError('Invalid object type specified: '
'{}'.format(str_type))
|
flexible
|
{
"blob_id": "a505cc0e382554d65447a3fe3a56fac43c1964f2",
"index": 8133,
"step-1": "<mask token>\n\n\nclass CRITsAPI:\n <mask token>\n\n def get_object(self, obj_id, obj_type):\n type_trans = self._type_translation(obj_type)\n get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)\n params = {'username': self.username, 'api_key': self.api_key}\n r = requests.get(get_url, params=params, proxies=self.proxies,\n verify=self.verify)\n if r.status_code == 200:\n return json.loads(r.text)\n else:\n print('Status code returned for query {}, was: {}'.format(\n get_url, r.status_code))\n return None\n\n def add_indicator(self, source='', reference='', method='', campaign=\n None, confidence=None, bucket_list=[], ticket='', add_domain=True,\n add_relationship=True, indicator_confidence='unknown',\n indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,\n attack_type=iat.UNKNOWN, value=None, description=''):\n data = {'api_key': self.api_key, 'username': self.username,\n 'source': source, 'reference': reference, 'method': '',\n 'campaign': campaign, 'confidence': confidence, 'bucket_list':\n bucket_list, 'ticket': ticket, 'add_domain': True,\n 'add_relationship': True, 'indicator_confidence':\n indicator_confidence, 'indicator_impact': indicator_impact,\n 'type': type, 'threat_type': threat_type, 'attack_type':\n attack_type, 'value': value, 'description': description}\n r = requests.post('{0}/indicators/'.format(self.url), data=data,\n verify=self.verify, proxies=self.proxies)\n if r.status_code == 200:\n log.debug('Indicator uploaded successfully - {}'.format(value))\n ind = json.loads(r.text)\n return ind\n return None\n <mask token>\n\n def forge_relationship(self, left_id, left_type, right_id, right_type,\n rel_type, rel_date='', rel_confidence='high', rel_reason=''):\n if not rel_date:\n rel_date = datetime.datetime.now()\n type_trans = self._type_translation(left_type)\n submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)\n headers = {'Content-Type': 'application/json'}\n params = {'api_key': self.api_key, 'username': self.username}\n data = {'action': 'forge_relationship', 'right_type': right_type,\n 'right_id': right_id, 'rel_type': rel_type, 'rel_date':\n rel_date, 'rel_confidence': rel_confidence, 'rel_reason':\n rel_reason}\n r = requests.patch(submit_url, params=params, data=data, proxies=\n self.proxies, verify=self.verify)\n if r.status_code == 200:\n log.debug('Relationship built successfully: {0} <-> {1}'.format\n (left_id, right_id))\n return True\n else:\n log.error(\n 'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'\n .format(r.status_code, r.text, left_id, right_id))\n return False\n\n def add_campaign_to_object(self, id, type, campaign, confidence,\n analyst, date, description):\n obj = getattr(self.db, type)\n result = obj.find({'_id': id, 'campaign.name': campaign})\n if result:\n import pdb\n pdb.set_trace()\n\n def _type_translation(self, str_type):\n if str_type == 'Indicator':\n return 'indicators'\n if str_type == 'Domain':\n return 'domains'\n if str_type == 'IP':\n return 'ips'\n if str_type == 'Sample':\n return 'samples'\n if str_type == 'Event':\n return 'events'\n if str_type == 'Actor':\n return 'actors'\n if str_type == 'Email':\n return 'emails'\n if str_type == 'Backdoor':\n return 'backdoors'\n raise CRITsOperationalError('Invalid object type specified: {}'.\n format(str_type))\n",
"step-2": "<mask token>\n\n\nclass CRITsAPI:\n <mask token>\n\n def get_object(self, obj_id, obj_type):\n type_trans = self._type_translation(obj_type)\n get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)\n params = {'username': self.username, 'api_key': self.api_key}\n r = requests.get(get_url, params=params, proxies=self.proxies,\n verify=self.verify)\n if r.status_code == 200:\n return json.loads(r.text)\n else:\n print('Status code returned for query {}, was: {}'.format(\n get_url, r.status_code))\n return None\n\n def add_indicator(self, source='', reference='', method='', campaign=\n None, confidence=None, bucket_list=[], ticket='', add_domain=True,\n add_relationship=True, indicator_confidence='unknown',\n indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,\n attack_type=iat.UNKNOWN, value=None, description=''):\n data = {'api_key': self.api_key, 'username': self.username,\n 'source': source, 'reference': reference, 'method': '',\n 'campaign': campaign, 'confidence': confidence, 'bucket_list':\n bucket_list, 'ticket': ticket, 'add_domain': True,\n 'add_relationship': True, 'indicator_confidence':\n indicator_confidence, 'indicator_impact': indicator_impact,\n 'type': type, 'threat_type': threat_type, 'attack_type':\n attack_type, 'value': value, 'description': description}\n r = requests.post('{0}/indicators/'.format(self.url), data=data,\n verify=self.verify, proxies=self.proxies)\n if r.status_code == 200:\n log.debug('Indicator uploaded successfully - {}'.format(value))\n ind = json.loads(r.text)\n return ind\n return None\n\n def has_relationship(self, left_id, left_type, right_id, right_type,\n rel_type='Related To'):\n data = self.get_object(left_id, left_type)\n if not data:\n raise CRITsOperationalError(\n 'Crits Object not found with id {} and type {}'.format(\n left_id, left_type))\n if not 'relationships' in data:\n return False\n for relationship in data['relationships']:\n if relationship['relationship'] != rel_type:\n continue\n if relationship['value'] != right_id:\n continue\n if relationship['type'] != right_type:\n continue\n return True\n return False\n\n def forge_relationship(self, left_id, left_type, right_id, right_type,\n rel_type, rel_date='', rel_confidence='high', rel_reason=''):\n if not rel_date:\n rel_date = datetime.datetime.now()\n type_trans = self._type_translation(left_type)\n submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)\n headers = {'Content-Type': 'application/json'}\n params = {'api_key': self.api_key, 'username': self.username}\n data = {'action': 'forge_relationship', 'right_type': right_type,\n 'right_id': right_id, 'rel_type': rel_type, 'rel_date':\n rel_date, 'rel_confidence': rel_confidence, 'rel_reason':\n rel_reason}\n r = requests.patch(submit_url, params=params, data=data, proxies=\n self.proxies, verify=self.verify)\n if r.status_code == 200:\n log.debug('Relationship built successfully: {0} <-> {1}'.format\n (left_id, right_id))\n return True\n else:\n log.error(\n 'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'\n .format(r.status_code, r.text, left_id, right_id))\n return False\n\n def add_campaign_to_object(self, id, type, campaign, confidence,\n analyst, date, description):\n obj = getattr(self.db, type)\n result = obj.find({'_id': id, 'campaign.name': campaign})\n if result:\n import pdb\n pdb.set_trace()\n\n def _type_translation(self, str_type):\n if str_type == 'Indicator':\n return 'indicators'\n if str_type == 'Domain':\n return 'domains'\n if str_type == 'IP':\n return 'ips'\n if str_type == 'Sample':\n return 'samples'\n if str_type == 'Event':\n return 'events'\n if str_type == 'Actor':\n return 'actors'\n if str_type == 'Email':\n return 'emails'\n if str_type == 'Backdoor':\n return 'backdoors'\n raise CRITsOperationalError('Invalid object type specified: {}'.\n format(str_type))\n",
"step-3": "<mask token>\n\n\nclass CRITsAPI:\n\n def __init__(self, api_url='', api_key='', username='', verify=True,\n proxies={}):\n self.url = api_url\n if self.url[-1] == '/':\n self.url = self.url[:-1]\n self.api_key = api_key\n self.username = username\n self.verify = verify\n self.proxies = proxies\n\n def get_object(self, obj_id, obj_type):\n type_trans = self._type_translation(obj_type)\n get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)\n params = {'username': self.username, 'api_key': self.api_key}\n r = requests.get(get_url, params=params, proxies=self.proxies,\n verify=self.verify)\n if r.status_code == 200:\n return json.loads(r.text)\n else:\n print('Status code returned for query {}, was: {}'.format(\n get_url, r.status_code))\n return None\n\n def add_indicator(self, source='', reference='', method='', campaign=\n None, confidence=None, bucket_list=[], ticket='', add_domain=True,\n add_relationship=True, indicator_confidence='unknown',\n indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,\n attack_type=iat.UNKNOWN, value=None, description=''):\n data = {'api_key': self.api_key, 'username': self.username,\n 'source': source, 'reference': reference, 'method': '',\n 'campaign': campaign, 'confidence': confidence, 'bucket_list':\n bucket_list, 'ticket': ticket, 'add_domain': True,\n 'add_relationship': True, 'indicator_confidence':\n indicator_confidence, 'indicator_impact': indicator_impact,\n 'type': type, 'threat_type': threat_type, 'attack_type':\n attack_type, 'value': value, 'description': description}\n r = requests.post('{0}/indicators/'.format(self.url), data=data,\n verify=self.verify, proxies=self.proxies)\n if r.status_code == 200:\n log.debug('Indicator uploaded successfully - {}'.format(value))\n ind = json.loads(r.text)\n return ind\n return None\n\n def has_relationship(self, left_id, left_type, right_id, right_type,\n rel_type='Related To'):\n data = self.get_object(left_id, left_type)\n if not data:\n raise CRITsOperationalError(\n 'Crits Object not found with id {} and type {}'.format(\n left_id, left_type))\n if not 'relationships' in data:\n return False\n for relationship in data['relationships']:\n if relationship['relationship'] != rel_type:\n continue\n if relationship['value'] != right_id:\n continue\n if relationship['type'] != right_type:\n continue\n return True\n return False\n\n def forge_relationship(self, left_id, left_type, right_id, right_type,\n rel_type, rel_date='', rel_confidence='high', rel_reason=''):\n if not rel_date:\n rel_date = datetime.datetime.now()\n type_trans = self._type_translation(left_type)\n submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)\n headers = {'Content-Type': 'application/json'}\n params = {'api_key': self.api_key, 'username': self.username}\n data = {'action': 'forge_relationship', 'right_type': right_type,\n 'right_id': right_id, 'rel_type': rel_type, 'rel_date':\n rel_date, 'rel_confidence': rel_confidence, 'rel_reason':\n rel_reason}\n r = requests.patch(submit_url, params=params, data=data, proxies=\n self.proxies, verify=self.verify)\n if r.status_code == 200:\n log.debug('Relationship built successfully: {0} <-> {1}'.format\n (left_id, right_id))\n return True\n else:\n log.error(\n 'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'\n .format(r.status_code, r.text, left_id, right_id))\n return False\n\n def add_campaign_to_object(self, id, type, campaign, confidence,\n analyst, date, description):\n obj = getattr(self.db, type)\n result = obj.find({'_id': id, 'campaign.name': campaign})\n if result:\n import pdb\n pdb.set_trace()\n\n def _type_translation(self, str_type):\n if str_type == 'Indicator':\n return 'indicators'\n if str_type == 'Domain':\n return 'domains'\n if str_type == 'IP':\n return 'ips'\n if str_type == 'Sample':\n return 'samples'\n if str_type == 'Event':\n return 'events'\n if str_type == 'Actor':\n return 'actors'\n if str_type == 'Email':\n return 'emails'\n if str_type == 'Backdoor':\n return 'backdoors'\n raise CRITsOperationalError('Invalid object type specified: {}'.\n format(str_type))\n",
"step-4": "<mask token>\nlog = logging.getLogger()\n\n\nclass CRITsAPI:\n\n def __init__(self, api_url='', api_key='', username='', verify=True,\n proxies={}):\n self.url = api_url\n if self.url[-1] == '/':\n self.url = self.url[:-1]\n self.api_key = api_key\n self.username = username\n self.verify = verify\n self.proxies = proxies\n\n def get_object(self, obj_id, obj_type):\n type_trans = self._type_translation(obj_type)\n get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)\n params = {'username': self.username, 'api_key': self.api_key}\n r = requests.get(get_url, params=params, proxies=self.proxies,\n verify=self.verify)\n if r.status_code == 200:\n return json.loads(r.text)\n else:\n print('Status code returned for query {}, was: {}'.format(\n get_url, r.status_code))\n return None\n\n def add_indicator(self, source='', reference='', method='', campaign=\n None, confidence=None, bucket_list=[], ticket='', add_domain=True,\n add_relationship=True, indicator_confidence='unknown',\n indicator_impact='unknown', type=None, threat_type=itt.UNKNOWN,\n attack_type=iat.UNKNOWN, value=None, description=''):\n data = {'api_key': self.api_key, 'username': self.username,\n 'source': source, 'reference': reference, 'method': '',\n 'campaign': campaign, 'confidence': confidence, 'bucket_list':\n bucket_list, 'ticket': ticket, 'add_domain': True,\n 'add_relationship': True, 'indicator_confidence':\n indicator_confidence, 'indicator_impact': indicator_impact,\n 'type': type, 'threat_type': threat_type, 'attack_type':\n attack_type, 'value': value, 'description': description}\n r = requests.post('{0}/indicators/'.format(self.url), data=data,\n verify=self.verify, proxies=self.proxies)\n if r.status_code == 200:\n log.debug('Indicator uploaded successfully - {}'.format(value))\n ind = json.loads(r.text)\n return ind\n return None\n\n def has_relationship(self, left_id, left_type, right_id, right_type,\n rel_type='Related To'):\n data = self.get_object(left_id, left_type)\n if not data:\n raise CRITsOperationalError(\n 'Crits Object not found with id {} and type {}'.format(\n left_id, left_type))\n if not 'relationships' in data:\n return False\n for relationship in data['relationships']:\n if relationship['relationship'] != rel_type:\n continue\n if relationship['value'] != right_id:\n continue\n if relationship['type'] != right_type:\n continue\n return True\n return False\n\n def forge_relationship(self, left_id, left_type, right_id, right_type,\n rel_type, rel_date='', rel_confidence='high', rel_reason=''):\n if not rel_date:\n rel_date = datetime.datetime.now()\n type_trans = self._type_translation(left_type)\n submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)\n headers = {'Content-Type': 'application/json'}\n params = {'api_key': self.api_key, 'username': self.username}\n data = {'action': 'forge_relationship', 'right_type': right_type,\n 'right_id': right_id, 'rel_type': rel_type, 'rel_date':\n rel_date, 'rel_confidence': rel_confidence, 'rel_reason':\n rel_reason}\n r = requests.patch(submit_url, params=params, data=data, proxies=\n self.proxies, verify=self.verify)\n if r.status_code == 200:\n log.debug('Relationship built successfully: {0} <-> {1}'.format\n (left_id, right_id))\n return True\n else:\n log.error(\n 'Error with status code {0} and message {1} between these indicators: {2} <-> {3}'\n .format(r.status_code, r.text, left_id, right_id))\n return False\n\n def add_campaign_to_object(self, id, type, campaign, confidence,\n analyst, date, description):\n obj = getattr(self.db, type)\n result = obj.find({'_id': id, 'campaign.name': campaign})\n if result:\n import pdb\n pdb.set_trace()\n\n def _type_translation(self, str_type):\n if str_type == 'Indicator':\n return 'indicators'\n if str_type == 'Domain':\n return 'domains'\n if str_type == 'IP':\n return 'ips'\n if str_type == 'Sample':\n return 'samples'\n if str_type == 'Event':\n return 'events'\n if str_type == 'Actor':\n return 'actors'\n if str_type == 'Email':\n return 'emails'\n if str_type == 'Backdoor':\n return 'backdoors'\n raise CRITsOperationalError('Invalid object type specified: {}'.\n format(str_type))\n",
"step-5": "import datetime\nimport json\nimport logging\nimport requests\n\nfrom lib.crits.exceptions import CRITsOperationalError\nfrom lib.crits.vocabulary.indicators import IndicatorThreatTypes as itt\nfrom lib.crits.vocabulary.indicators import IndicatorAttackTypes as iat\n\nlog = logging.getLogger()\n\nclass CRITsAPI():\n\n def __init__(self, api_url='', api_key='', username='', verify=True,\n proxies={}):\n self.url = api_url\n if self.url[-1] == '/':\n self.url = self.url[:-1]\n self.api_key = api_key\n self.username = username\n self.verify = verify\n self.proxies = proxies\n\n def get_object(self, obj_id, obj_type):\n type_trans = self._type_translation(obj_type)\n get_url = '{}/{}/{}/'.format(self.url, type_trans, obj_id)\n params = {\n 'username' : self.username,\n 'api_key' : self.api_key,\n }\n r = requests.get(get_url, params=params, proxies=self.proxies, verify=self.verify)\n if r.status_code == 200:\n return json.loads(r.text)\n else:\n print('Status code returned for query {}, '\n 'was: {}'.format(get_url, r.status_code))\n return None\n\n def add_indicator(self, source = '', reference = '', method = '',\n campaign = None, confidence = None, bucket_list = [], ticket = '',\n add_domain = True, add_relationship = True,\n indicator_confidence = 'unknown', indicator_impact = 'unknown',\n type = None, threat_type = itt.UNKNOWN, attack_type = iat.UNKNOWN,\n value = None, description = ''):\n # Time to upload these indicators\n data = {\n 'api_key' : self.api_key,\n 'username' : self.username,\n 'source' : source,\n 'reference' : reference,\n 'method' : '',\n 'campaign' : campaign,\n 'confidence' : confidence,\n 'bucket_list' : bucket_list,\n 'ticket' : ticket,\n 'add_domain' : True,\n 'add_relationship' : True,\n 'indicator_confidence' : indicator_confidence,\n 'indicator_impact' : indicator_impact,\n 'type' : type,\n 'threat_type' : threat_type,\n 'attack_type' : attack_type,\n 'value' : value,\n 'description' : description,\n }\n\n r = requests.post(\"{0}/indicators/\".format(self.url), data=data,\n verify=self.verify, proxies=self.proxies)\n if r.status_code == 200:\n log.debug(\"Indicator uploaded successfully - {}\".format(value))\n ind = json.loads(r.text)\n return ind\n\n return None\n\n def has_relationship(self, left_id, left_type, right_id, right_type,\n rel_type='Related To'):\n data = self.get_object(left_id, left_type)\n if not data:\n raise CRITsOperationalError('Crits Object not found with id {} and '\n 'type {}'.format(left_id, left_type))\n if not 'relationships' in data:\n return False\n for relationship in data['relationships']:\n if relationship['relationship'] != rel_type:\n continue\n if relationship['value'] != right_id:\n continue\n if relationship['type'] != right_type:\n continue\n return True\n return False\n\n def forge_relationship(self, left_id, left_type, right_id, right_type,\n rel_type, rel_date='', rel_confidence='high',\n rel_reason=''):\n if not rel_date:\n rel_date = datetime.datetime.now()\n type_trans = self._type_translation(left_type)\n submit_url = '{}/{}/{}/'.format(self.url, type_trans, left_id)\n headers = {\n 'Content-Type' : 'application/json',\n }\n\n params = {\n 'api_key' : self.api_key,\n 'username' : self.username,\n }\n\n data = {\n 'action' : 'forge_relationship',\n 'right_type' : right_type,\n 'right_id' : right_id,\n 'rel_type' : rel_type,\n 'rel_date' : rel_date,\n 'rel_confidence' : rel_confidence,\n 'rel_reason' : rel_reason\n }\n\n r = requests.patch(submit_url, params=params, data=data,\n proxies=self.proxies, verify=self.verify)\n if r.status_code == 200:\n log.debug('Relationship built successfully: {0} <-> '\n '{1}'.format(left_id, right_id))\n return True\n else:\n log.error('Error with status code {0} and message {1} between '\n 'these indicators: {2} <-> '\n '{3}'.format(r.status_code, r.text, left_id, right_id))\n return False\n\n def add_campaign_to_object(self, id, type, campaign, confidence, analyst,\n date, description):\n # TODO: Make sure the object does not already have the campaign\n # Return if it does. Add it if it doesn't\n obj = getattr(self.db, type)\n result = obj.find( { '_id' : id, 'campaign.name' : campaign } )\n if result:\n import pdb\n pdb.set_trace()\n\n def _type_translation(self, str_type):\n if str_type == 'Indicator':\n return 'indicators'\n if str_type == 'Domain':\n return 'domains'\n if str_type == 'IP':\n return 'ips'\n if str_type == 'Sample':\n return 'samples'\n if str_type == 'Event':\n return 'events'\n if str_type == 'Actor':\n return 'actors'\n if str_type == 'Email':\n return 'emails'\n if str_type == 'Backdoor':\n return 'backdoors'\n\n raise CRITsOperationalError('Invalid object type specified: '\n '{}'.format(str_type))\n",
"step-ids": [
6,
7,
8,
9,
11
]
}
|
[
6,
7,
8,
9,
11
] |
#!/usr/bin/python
#==========================================================================
#
# Copyright Insight Software Consortium
#
# 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.txt
#
# 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.
#
#
#==========================================================================*/
# This script is used to automate the modularization process. The following
# steps are included:
# 1. Move the files in the monolithic ITK into modules of the modularized ITK.
# A manifest text file that lists all the files and their destinations is
# required to run the script.By default, the manifest file is named as
# "Manifest.txt" in the same directory of this script.
# 2. Create CMake Files and put them into modules.
# Modified by Guillaume Pasero <guillaume.pasero@c-s.fr>
# add dependencies in otb-module.cmake
# To run it, type ./modulizer.py OTB_PATH Manifest_PATH
# from the otb-modulizer root directory.
print "*************************************************************************"
print "WARNINGs! This modularization script is still in its experimental stage."
print "Current OTB users should not run this script."
print "*************************************************************************"
import shutil
import os.path as op
import re
import sys
import os
import stat
import glob
import documentationCheck
import analyseAppManifest
import dispatchTests
import dispatchExamples
from subprocess import call
def parseFullManifest(path):
sourceList = []
nbFields = 6
fd = open(path,'rb')
# skip first line and detect separator
firstLine = fd.readline()
sep = ','
if (len(firstLine.split(sep)) != nbFields):
sep = ';'
if (len(firstLine.split(sep)) != nbFields):
sep = '\t'
if (len(firstLine.split(sep)) != nbFields):
print "Unknown separator"
return sourceList
fd.seek(0)
# parse file
for line in fd:
if (line.strip()).startswith("#"):
continue
words = line.split(sep)
if (len(words) < (nbFields-1)):
print "Wrong number of fields, skipping this line"
continue
fullPath = words[0].strip(" ,;\t\n\r")
groupName = words[2].strip(" ,;\t\n\r")
moduleName = words[3].strip(" ,;\t\n\r")
subDir = words[4].strip(" ,;\t\n\r")
sourceName = op.basename(fullPath)
sourceList.append({"path":fullPath, "group":groupName, "module":moduleName, "subDir":subDir})
fd.close()
return sourceList
def parseDescriptions(path):
output = {}
sep = '|'
nbFields = 2
fd = open(path,'rb')
for line in fd:
if (line.strip()).startswith("#"):
continue
words = line.split(sep)
if len(words) != nbFields:
continue
moduleName = words[0].strip(" \"\t\n\r")
description = words[1].strip(" \"\t\n\r")
output[moduleName] = description
fd.close()
return output
if len(sys.argv) < 4:
print("USAGE: {0} monolithic_OTB_PATH OUTPUT_DIR Manifest_Path [module_dep [test_dep [mod_description]]]".format(sys.argv[0]))
print(" monolithic_OTB_PATH : checkout of OTB repository (will not be modified)")
print(" OUTPUT_DIR : output directory where OTB_Modular and OTB_remaining will be created ")
print(" Manifest_Path : path to manifest file, in CSV-like format. Fields are :")
print(" source_path/current_subDir/group/module/subDir/comment")
print(" module_dep : dependencies between modules")
print(" test_dep : additional dependencies for tests")
print(" mod_description : description for each module")
print(" migration_password : password to enable MIGRATION")
sys.exit(-1)
scriptDir = op.dirname(op.abspath(sys.argv[0]))
HeadOfOTBTree = sys.argv[1]
if (HeadOfOTBTree[-1] == '/'):
HeadOfOTBTree = HeadOfOTBTree[0:-1]
OutputDir = sys.argv[2]
HeadOfModularOTBTree = op.join(OutputDir,"OTB_Modular")
ManifestPath = sys.argv[3]
EdgePath = ""
if len(sys.argv) >= 5:
EdgePath = sys.argv[4]
testDependPath = ""
if len(sys.argv) >= 6:
testDependPath = sys.argv[5]
modDescriptionPath = ""
if len(sys.argv) >= 7:
modDescriptionPath = sys.argv[6]
enableMigration = False
if len(sys.argv) >= 8:
migrationPass = sys.argv[7]
if migrationPass == "redbutton":
enableMigration = True
# copy the whole OTB tree over to a temporary dir
HeadOfTempTree = op.join(OutputDir,"OTB_remaining")
if op.isdir(HeadOfTempTree):
shutil.rmtree(HeadOfTempTree)
if op.isdir(HeadOfModularOTBTree):
shutil.rmtree(HeadOfModularOTBTree)
print("Start to copy" + HeadOfOTBTree + " to ./OTB_remaining ...")
shutil.copytree(HeadOfOTBTree,HeadOfTempTree, ignore = shutil.ignore_patterns('.hg','.hg*'))
print("Done copying!")
# checkout OTB-Modular
cmd ='hg clone http://hg.orfeo-toolbox.org/OTB-Modular '+HeadOfModularOTBTree
os.system(cmd)
logDir = op.join(OutputDir,"logs")
if not op.isdir(logDir):
os.makedirs(logDir)
# read the manifest file
print ("moving files from ./OTB_remaining into modules in {0}".format(HeadOfModularOTBTree))
numOfMissingFiles = 0;
missingf = open(op.join(logDir,'missingFiles.log'),'w')
moduleList=[]
moduleDic={}
sourceList = parseFullManifest(ManifestPath)
for source in sourceList:
# build module list
moduleDic[source["module"]] = source["group"]
# create the path
inputfile = op.abspath(op.join(HeadOfTempTree,source["path"]))
outputPath = op.join(op.join(HeadOfModularOTBTree,"Modules"),op.join(source["group"],op.join(source["module"],source["subDir"])))
if not op.isdir(outputPath):
os.makedirs(outputPath)
# copying files to the destination
if op.isfile(inputfile):
if op.isfile(op.join(outputPath,op.basename(inputfile))):
os.remove(op.join(outputPath,op.basename(inputfile)))
shutil.move(inputfile, outputPath)
else:
missingf.write(inputfile+'\n')
numOfMissingFiles = numOfMissingFiles + 1
missingf.close()
print ("listed {0} missing files to logs/missingFiles.log").format(numOfMissingFiles)
moduleList = moduleDic.keys()
# after move, operate a documentation check
for source in sourceList:
outputPath = op.join(op.join(HeadOfModularOTBTree,"Modules"),op.join(source["group"],op.join(source["module"],source["subDir"])))
outputFile = op.join(outputPath,op.basename(source["path"]))
if op.isfile(outputFile):
if op.splitext(outputFile)[1] == ".h":
nextContent = documentationCheck.parserHeader(outputFile,source["module"])
fd = open(outputFile,'wb')
fd.writelines(nextContent)
fd.close()
# get dependencies (if file is present)
dependencies = {}
testDependencies = {}
exDependencies = {}
for mod in moduleList:
dependencies[mod] = []
testDependencies[mod] = []
exDependencies[mod] = []
if op.isfile(EdgePath):
fd = open(EdgePath,'rb')
for line in fd:
words = line.split(',')
if len(words) == 2:
depFrom = words[0].strip(" ,;\t\n\r")
depTo = words[1].strip(" ,;\t\n\r")
if dependencies.has_key(depFrom):
dependencies[depFrom].append(depTo)
else:
print("Bad dependency : "+depFrom+" -> "+depTo)
fd.close()
if op.isfile(testDependPath):
fd = open(testDependPath,'rb')
for line in fd:
words = line.split(',')
if len(words) == 2:
depFrom = words[0].strip(" ,;\t\n\r")
depTo = words[1].strip(" ,;\t\n\r")
if testDependencies.has_key(depFrom):
testDependencies[depFrom].append(depTo)
else:
print("Bad dependency : "+depFrom+" -> "+depTo)
fd.close()
"""
if op.isfile(exDependPath):
fd = open(exDependPath,'rb')
for line in fd:
words = line.split(',')
if len(words) == 2:
depFrom = words[0].strip(" ,;\t\n\r")
depTo = words[1].strip(" ,;\t\n\r")
if exDependencies.has_key(depFrom):
exDependencies[depFrom].append(depTo)
else:
print("Bad dependency : "+depFrom+" -> "+depTo)
fd.close()
"""
modDescriptions = {}
if op.isfile(modDescriptionPath):
modDescriptions = parseDescriptions(modDescriptionPath)
# list the new files
newf = open(op.join(logDir,'newFiles.log'),'w')
for (root, subDirs, files) in os.walk(HeadOfTempTree):
for afile in files:
newf.write(op.join(root, afile)+'\n')
newf.close()
print ("listed new files to logs/newFiles.log")
###########################################################################
print ('creating cmake files for each module (from the template module)')
#moduleList = os.listdir(HeadOfModularOTBTree)
for moduleName in moduleList:
moduleDir = op.join(op.join(HeadOfModularOTBTree,"Modules"),op.join(moduleDic[moduleName],moduleName))
cmakeModName = "OTB"+moduleName
if op.isdir(moduleDir):
# write CMakeLists.txt
filepath = moduleDir+'/CMakeLists.txt'
if not op.isfile(filepath):
o = open(filepath,'w')
if op.isdir(moduleDir+'/src'):
template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists.txt')
else:
template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists-nosrc.txt')
for line in open(template_cmakelist,'r'):
line = line.replace('otb-template-module',cmakeModName)
o.write(line);
o.close()
# write src/CMakeLists.txt
# list of CXX files
if op.isdir(moduleDir+'/src'):
cxxFiles = glob.glob(moduleDir+'/src/*.cxx')
cxxFileList='';
for cxxf in cxxFiles:
cxxFileList = cxxFileList+' '+cxxf.split('/')[-1]+'\n'
# build list of link dependencies
linkLibs = ""
for dep in dependencies[moduleName]:
#verify if dep is a header-onlymodule
depThirdParty = False
try:
moduleDic[dep]
except KeyError:
# this is a ThirdParty module
depThirdParty = True
if not depThirdParty:
depModuleDir = op.join(op.join(HeadOfModularOTBTree,"Modules"),op.join(moduleDic[dep],dep))
depcxx = glob.glob(depModuleDir+'/src/*.cxx')
if depcxx :
linkLibs = linkLibs + " ${OTB"+dep+"_LIBRARIES}" + "\n"
else:
linkLibs = linkLibs + " ${OTB"+dep+"_LIBRARIES}" + "\n"
if len(linkLibs) == 0:
linkLibs = " ${OTBITK_LIBRARIES}"
filepath = moduleDir+'/src/CMakeLists.txt'
if not op.isfile(filepath):
o = open(filepath,'w')
for line in open(op.join(scriptDir,'templateModule/otb-template-module/src/CMakeLists.txt'),'r'):
line = line.replace('otb-template-module',cmakeModName)
line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \n
line = line.replace('LINK_LIBRARIES_TO_BE_REPLACED',linkLibs)
o.write(line);
o.close()
# write app/CMakeLists.txt
if op.isdir(moduleDir+'/app'):
os.mkdir(moduleDir+'/test')
srcFiles = glob.glob(moduleDir+'/app/*.cxx')
srcFiles += glob.glob(moduleDir+'/app/*.h')
appList = {}
for srcf in srcFiles:
# get App name
appName = analyseAppManifest.findApplicationName(srcf)
if len(appName) == 0:
continue
appList[appName] = {"source":op.basename(srcf)}
# get original location
cmakeListPath = ""
for item in sourceList:
if op.basename(item["path"]) == op.basename(srcf) and \
moduleName == item["module"]:
appDir = op.basename(op.dirname(item["path"]))
cmakeListPath = op.join(HeadOfOTBTree,op.join("Testing/Applications"),op.join(appDir,"CMakeLists.txt"))
break
# get App tests
if not op.isfile(cmakeListPath):
continue
appList[appName]["test"] = analyseAppManifest.findTestFromApp(cmakeListPath,appName)
# build list of link dependencies
linkLibs = ""
for dep in dependencies[moduleName]:
linkLibs = linkLibs + " ${OTB"+dep+"_LIBRARIES}" + "\n"
filepath = moduleDir+'/app/CMakeLists.txt'
if not op.isfile(filepath):
o = open(filepath,'w')
# define link libraries
o.write("set("+cmakeModName+"_LINK_LIBS\n")
o.write(linkLibs)
o.write(")\n")
for appli in appList:
content = "\notb_create_application(\n"
content += " NAME " + appli + "\n"
content += " SOURCES " + appList[appli]["source"] + "\n"
content += " LINK_LIBRARIES ${${otb-module}_LIBRARIES})\n"
o.write(content)
o.close()
filepath = moduleDir+'/test/CMakeLists.txt'
if not op.isfile(filepath):
o = open(filepath,'w')
o.write("otb_module_test()")
for appli in appList:
if not appList[appli].has_key("test"):
continue
o.write("\n#----------- "+appli+" TESTS ----------------\n")
for test in appList[appli]["test"]:
if test.count("${"):
print "Warning : test name contains a variable : "+test
continue
testcode=appList[appli]["test"][test]
testcode=[s.replace('OTB_TEST_APPLICATION', 'otb_test_application') for s in testcode]
o.writelines(testcode)
o.write("\n")
o.close()
# write test/CMakeLists.txt : done by dispatchTests.py
"""
if op.isdir(moduleDir+'/test'):
cxxFiles = glob.glob(moduleDir+'/test/*.cxx')
cxxFileList='';
for cxxf in cxxFiles:
cxxFileList = cxxFileList+cxxf.split('/')[-1]+'\n'
filepath = moduleDir+'/test/CMakeLists.txt'
if not op.isfile(filepath):
o = open(filepath,'w')
for line in open('./templateModule/otb-template-module/test/CMakeLists.txt','r'):
# TODO : refactor for OTB
words= moduleName.split('-')
moduleNameMod='';
for word in words:
moduleNameMod=moduleNameMod + word.capitalize()
line = line.replace('itkTemplateModule',moduleNameMod)
line = line.replace('itk-template-module',moduleName)
line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \n
o.write(line);
o.close()
"""
# write otb-module.cmake, which contains dependency info
filepath = moduleDir+'/otb-module.cmake'
if not op.isfile(filepath):
o = open(filepath,'w')
for line in open(op.join(scriptDir,'templateModule/otb-template-module/otb-module.cmake'),'r'):
# replace documentation
if line.find("DESCRIPTION_TO_BE_REPLACED") >= 0:
docString = "\"TBD\""
if moduleName in modDescriptions:
descPos = line.find("DESCRIPTION_TO_BE_REPLACED")
limitChar = 80
docString = "\""+modDescriptions[moduleName]+"\""
curPos = 80 - descPos
while curPos < len(docString):
lastSpace = docString[0:curPos].rfind(' ')
if lastSpace > max(0,curPos-80):
docString = docString[0:lastSpace] + '\n' + docString[lastSpace+1:]
else:
docString = docString[0:curPos] + '\n' + docString[curPos:]
curPos += 81
line = line.replace('DESCRIPTION_TO_BE_REPLACED',docString)
# replace module name
line = line.replace('otb-template-module',cmakeModName)
# replace depend list
dependTagPos = line.find("DEPENDS_TO_BE_REPLACED")
if dependTagPos >= 0:
replacementStr = "DEPENDS"
indentStr = ""
for it in range(dependTagPos+2):
indentStr = indentStr + " "
if len(dependencies[moduleName]) > 0:
deplist = dependencies[moduleName]
else:
deplist = ["Common"]
for dep in deplist:
replacementStr = replacementStr + "\n" + indentStr +"OTB"+ dep
line = line.replace('DEPENDS_TO_BE_REPLACED',replacementStr)
# replace test_depend list
testDependTagPos = line.find("TESTDEP_TO_BE_REPLACED")
if testDependTagPos >= 0:
if moduleName.startswith("App"):
# for application : hardcode TestKernel and CommandLine
indentStr = ""
for it in range(testDependTagPos+2):
indentStr = indentStr + " "
replacementStr = "TEST_DEPENDS\n" + indentStr + "OTBTestKernel\n" + indentStr + "OTBCommandLine"
line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)
else:
# standard case
if len(testDependencies[moduleName]) > 0:
indentStr = ""
replacementStr = "TEST_DEPENDS"
for it in range(testDependTagPos+2):
indentStr = indentStr + " "
for dep in testDependencies[moduleName]:
replacementStr = replacementStr + "\n" + indentStr +"OTB"+ dep
line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)
else:
line = line.replace('TESTDEP_TO_BE_REPLACED','')
# replace example_depend list
exDependTagPos = line.find("EXDEP_TO_BE_REPLACED")
if exDependTagPos >= 0:
if len(exDependencies[moduleName]) > 0:
indentStr = ""
replacementStr = "EXAMPLE_DEPENDS"
for it in range(exDependTagPos+2):
indentStr = indentStr + " "
for dep in exDependencies[moduleName]:
replacementStr = replacementStr + "\n" + indentStr +"OTB"+ dep
line = line.replace('EXDEP_TO_BE_REPLACED',replacementStr)
else:
line = line.replace('EXDEP_TO_BE_REPLACED','')
o.write(line);
o.close()
# call dispatchTests to fill test/CMakeLists
if op.isfile(testDependPath):
dispatchTests.main(["dispatchTests.py",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,testDependPath])
"""
# call dispatchExamples to fill example/CMakeLists
if op.isfile(exDependPath):
dispatchExamples.main(["dispatchExamples.py",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,exDependPath])
"""
# examples
for i in sorted(os.listdir(HeadOfTempTree + "/Examples")):
if i == "CMakeLists.txt" or i == "README.txt" or i.startswith("DataRepresentation"):
continue
for j in sorted(os.listdir(HeadOfTempTree + "/Examples/" + i)):
if j == "CMakeLists.txt" or j.startswith("otb"):
continue
command = "mv %s/Examples/%s/%s %s/Examples/%s/%s" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j)
os.system(command)
for i in sorted(os.listdir(HeadOfTempTree + "/Examples/DataRepresentation")):
if i == "CMakeLists.txt" or i == "README.txt":
continue
for j in sorted(os.listdir(HeadOfTempTree + "/Examples/DataRepresentation/" + i)):
if j == "CMakeLists.txt" or j.startswith("otb"):
continue
command = "mv %s/Examples/DataRepresentation/%s/%s %s/Examples/DataRepresentation/%s/%s" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j)
os.system(command)
# save version without patches (so that we can regenerate patches later)
os.system( "cp -ar " + op.join(OutputDir,"OTB_Modular") + " " + op.join(OutputDir,"OTB_Modular-nopatch") )
# apply patches in OTB_Modular
curdir = op.abspath(op.dirname(__file__))
command = "cd " + op.join(OutputDir,"OTB_Modular") + " && patch -p1 < " + curdir + "/patches/otbmodular.patch"
print "Executing " + command
os.system( command )
# remove Copyright files we don't want to touch later
os.system( "rm -rf %s" % (op.join(HeadOfTempTree,"Copyright") ) )
os.system( "rm -rf %s" % (op.join(HeadOfTempTree,"RELEASE_NOTES.txt") ) )
os.system( "rm -rf %s" % (op.join(HeadOfTempTree,"README") ) )
# PREPARE MIGRATION COMMIT ON A CLONE OF ORIGINAL CHECKOUT
if enableMigration:
print("Executing migration on a clone of original checkout")
HeadOfTempTree = op.abspath(HeadOfTempTree)
OutputDir = op.abspath(OutputDir)
# clone original checkout
outputModular = op.join(OutputDir,"OTB_Modular")
outputMigration = op.join(OutputDir,"OTB_Migration")
if op.exists(outputMigration):
os.removedirs(outputMigration)
command = ["cp","-ar",HeadOfOTBTree,outputMigration]
call(command)
os.chdir(outputMigration)
# walk through OTB_Remaining and delete corresponding files in OTB checkout
print("DELETE STEP...")
for dirPath, dirNames, fileNames in os.walk(HeadOfTempTree):
currentSourceDir = dirPath.replace(HeadOfTempTree,'.')
for fileName in fileNames:
if op.exists(op.join(currentSourceDir,fileName)):
command = ["hg","remove",op.join(currentSourceDir,fileName)]
call(command)
else:
print("Unknown file : "+op.join(currentSourceDir,fileName))
command = ['hg','commit','-m','ENH: Remove files not necessary after modularization']
call(command)
# walk through manifest and rename files
print("MOVE STEP...")
for source in sourceList:
outputPath = op.join("./Modules",op.join(source["group"],op.join(source["module"],source["subDir"])))
command = ['hg','rename',source["path"],op.join(outputPath,op.basename(source["path"]))]
call(command)
command = ['hg','commit','-m','ENH: Move source and test files into their respective module']
call(command)
# add new files from OTB_Modular (files from OTB-Modular repo + generated files)
print("ADD STEP...")
for dirPath, dirNames, fileNames in os.walk(outputModular):
currentSourceDir = dirPath.replace(outputModular,'.')
if currentSourceDir.startswith("./.hg"):
print("skip .hg")
continue
for fileName in fileNames:
# skip hg files
if fileName.startswith(".hg"):
continue
targetFile = op.join(currentSourceDir,fileName)
if not op.exists(targetFile):
if not op.exists(currentSourceDir):
command = ["mkdir","-p",currentSourceDir]
call(command)
shutil.copy(op.join(dirPath,fileName),targetFile)
command = ['hg','add']
call(command)
command = ['hg','commit','-m','ENH: Add new files for modular build system']
call(command)
# apply patches on OTB Checkout
print("PATCH STEP...")
for dirPath, dirNames, fileNames in os.walk(outputModular):
currentSourceDir = dirPath.replace(outputModular,'.')
if currentSourceDir.startswith("./.hg"):
continue
for fileName in fileNames:
# skip hg files
if fileName.startswith(".hg"):
continue
targetFile = op.join(currentSourceDir,fileName)
if op.exists(targetFile):
command = ['cp',op.join(dirPath,fileName),targetFile]
call(command)
command = ['hg','commit','-m','ENH: Apply patches necessary after modularization']
call(command)
|
normal
|
{
"blob_id": "4f87c2602e3233889888e419296f67fe40a2db0f",
"index": 5886,
"step-1": "#!/usr/bin/python\n#==========================================================================\n#\n# Copyright Insight Software Consortium\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0.txt\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n#\n#==========================================================================*/\n# This script is used to automate the modularization process. The following\n# steps are included:\n# 1. Move the files in the monolithic ITK into modules of the modularized ITK.\n# A manifest text file that lists all the files and their destinations is\n# required to run the script.By default, the manifest file is named as\n# \"Manifest.txt\" in the same directory of this script.\n# 2. Create CMake Files and put them into modules.\n\n# Modified by Guillaume Pasero <guillaume.pasero@c-s.fr>\n# add dependencies in otb-module.cmake\n\n# To run it, type ./modulizer.py OTB_PATH Manifest_PATH\n# from the otb-modulizer root directory.\n\nprint \"*************************************************************************\"\nprint \"WARNINGs! This modularization script is still in its experimental stage.\"\nprint \"Current OTB users should not run this script.\"\nprint \"*************************************************************************\"\n\n\nimport shutil\nimport os.path as op\nimport re\nimport sys\nimport os\nimport stat\nimport glob\nimport documentationCheck\nimport analyseAppManifest\nimport dispatchTests\nimport dispatchExamples\nfrom subprocess import call\n\ndef parseFullManifest(path):\n sourceList = []\n nbFields = 6\n fd = open(path,'rb')\n # skip first line and detect separator\n firstLine = fd.readline()\n sep = ','\n if (len(firstLine.split(sep)) != nbFields):\n sep = ';'\n if (len(firstLine.split(sep)) != nbFields):\n sep = '\\t'\n if (len(firstLine.split(sep)) != nbFields):\n print \"Unknown separator\"\n return sourceList\n \n fd.seek(0)\n \n # parse file\n for line in fd:\n if (line.strip()).startswith(\"#\"):\n continue\n words = line.split(sep)\n if (len(words) < (nbFields-1)):\n print \"Wrong number of fields, skipping this line\"\n continue\n fullPath = words[0].strip(\" ,;\\t\\n\\r\")\n groupName = words[2].strip(\" ,;\\t\\n\\r\")\n moduleName = words[3].strip(\" ,;\\t\\n\\r\")\n subDir = words[4].strip(\" ,;\\t\\n\\r\")\n sourceName = op.basename(fullPath)\n \n sourceList.append({\"path\":fullPath, \"group\":groupName, \"module\":moduleName, \"subDir\":subDir})\n fd.close()\n \n return sourceList\n\n\ndef parseDescriptions(path):\n output = {}\n sep = '|'\n nbFields = 2\n fd = open(path,'rb')\n for line in fd:\n if (line.strip()).startswith(\"#\"):\n continue\n words = line.split(sep)\n if len(words) != nbFields:\n continue\n moduleName = words[0].strip(\" \\\"\\t\\n\\r\")\n description = words[1].strip(\" \\\"\\t\\n\\r\")\n output[moduleName] = description\n fd.close()\n \n return output\n\n\nif len(sys.argv) < 4:\n print(\"USAGE: {0} monolithic_OTB_PATH OUTPUT_DIR Manifest_Path [module_dep [test_dep [mod_description]]]\".format(sys.argv[0]))\n print(\" monolithic_OTB_PATH : checkout of OTB repository (will not be modified)\")\n print(\" OUTPUT_DIR : output directory where OTB_Modular and OTB_remaining will be created \")\n print(\" Manifest_Path : path to manifest file, in CSV-like format. Fields are :\")\n print(\" source_path/current_subDir/group/module/subDir/comment\")\n print(\" module_dep : dependencies between modules\")\n print(\" test_dep : additional dependencies for tests\")\n print(\" mod_description : description for each module\")\n print(\" migration_password : password to enable MIGRATION\")\n sys.exit(-1)\n\nscriptDir = op.dirname(op.abspath(sys.argv[0]))\n\nHeadOfOTBTree = sys.argv[1]\nif (HeadOfOTBTree[-1] == '/'):\n HeadOfOTBTree = HeadOfOTBTree[0:-1]\n\nOutputDir = sys.argv[2]\nHeadOfModularOTBTree = op.join(OutputDir,\"OTB_Modular\")\n\nManifestPath = sys.argv[3]\n\nEdgePath = \"\"\nif len(sys.argv) >= 5:\n EdgePath = sys.argv[4]\n \ntestDependPath = \"\"\nif len(sys.argv) >= 6:\n testDependPath = sys.argv[5]\n\nmodDescriptionPath = \"\"\nif len(sys.argv) >= 7:\n modDescriptionPath = sys.argv[6]\n\nenableMigration = False\nif len(sys.argv) >= 8:\n migrationPass = sys.argv[7]\n if migrationPass == \"redbutton\":\n enableMigration = True\n\n# copy the whole OTB tree over to a temporary dir\nHeadOfTempTree = op.join(OutputDir,\"OTB_remaining\")\n\nif op.isdir(HeadOfTempTree):\n shutil.rmtree(HeadOfTempTree)\n\nif op.isdir(HeadOfModularOTBTree):\n shutil.rmtree(HeadOfModularOTBTree)\n\nprint(\"Start to copy\" + HeadOfOTBTree + \" to ./OTB_remaining ...\")\nshutil.copytree(HeadOfOTBTree,HeadOfTempTree, ignore = shutil.ignore_patterns('.hg','.hg*'))\nprint(\"Done copying!\")\n\n# checkout OTB-Modular\ncmd ='hg clone http://hg.orfeo-toolbox.org/OTB-Modular '+HeadOfModularOTBTree\nos.system(cmd)\n\nlogDir = op.join(OutputDir,\"logs\")\nif not op.isdir(logDir):\n os.makedirs(logDir)\n\n# read the manifest file\nprint (\"moving files from ./OTB_remaining into modules in {0}\".format(HeadOfModularOTBTree))\nnumOfMissingFiles = 0;\nmissingf = open(op.join(logDir,'missingFiles.log'),'w')\nmoduleList=[]\nmoduleDic={}\nsourceList = parseFullManifest(ManifestPath)\n\nfor source in sourceList:\n # build module list\n moduleDic[source[\"module\"]] = source[\"group\"]\n \n # create the path\n inputfile = op.abspath(op.join(HeadOfTempTree,source[\"path\"]))\n outputPath = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n if not op.isdir(outputPath):\n os.makedirs(outputPath)\n \n # copying files to the destination\n if op.isfile(inputfile):\n if op.isfile(op.join(outputPath,op.basename(inputfile))):\n os.remove(op.join(outputPath,op.basename(inputfile)))\n shutil.move(inputfile, outputPath)\n else:\n missingf.write(inputfile+'\\n')\n numOfMissingFiles = numOfMissingFiles + 1\n\nmissingf.close()\nprint (\"listed {0} missing files to logs/missingFiles.log\").format(numOfMissingFiles)\n\nmoduleList = moduleDic.keys()\n\n# after move, operate a documentation check\nfor source in sourceList:\n outputPath = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n outputFile = op.join(outputPath,op.basename(source[\"path\"]))\n if op.isfile(outputFile):\n if op.splitext(outputFile)[1] == \".h\":\n nextContent = documentationCheck.parserHeader(outputFile,source[\"module\"])\n fd = open(outputFile,'wb')\n fd.writelines(nextContent)\n fd.close()\n\n\n# get dependencies (if file is present)\ndependencies = {}\ntestDependencies = {}\nexDependencies = {}\nfor mod in moduleList:\n dependencies[mod] = []\n testDependencies[mod] = []\n exDependencies[mod] = []\n\nif op.isfile(EdgePath):\n fd = open(EdgePath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if dependencies.has_key(depFrom):\n dependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\nif op.isfile(testDependPath):\n fd = open(testDependPath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if testDependencies.has_key(depFrom):\n testDependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\n\"\"\"\nif op.isfile(exDependPath):\n fd = open(exDependPath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if exDependencies.has_key(depFrom):\n exDependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\"\"\"\nmodDescriptions = {}\nif op.isfile(modDescriptionPath):\n modDescriptions = parseDescriptions(modDescriptionPath)\n\n\n\n# list the new files\nnewf = open(op.join(logDir,'newFiles.log'),'w')\nfor (root, subDirs, files) in os.walk(HeadOfTempTree):\n for afile in files:\n newf.write(op.join(root, afile)+'\\n')\nnewf.close()\nprint (\"listed new files to logs/newFiles.log\")\n\n###########################################################################\n\nprint ('creating cmake files for each module (from the template module)')\n#moduleList = os.listdir(HeadOfModularOTBTree)\nfor moduleName in moduleList:\n moduleDir = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(moduleDic[moduleName],moduleName))\n cmakeModName = \"OTB\"+moduleName\n \n if op.isdir(moduleDir):\n \n # write CMakeLists.txt\n filepath = moduleDir+'/CMakeLists.txt'\n \n if not op.isfile(filepath):\n o = open(filepath,'w')\n \n if op.isdir(moduleDir+'/src'):\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists.txt')\n else:\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists-nosrc.txt')\n \n for line in open(template_cmakelist,'r'):\n line = line.replace('otb-template-module',cmakeModName)\n o.write(line);\n o.close()\n\n # write src/CMakeLists.txt\n # list of CXX files\n if op.isdir(moduleDir+'/src'):\n cxxFiles = glob.glob(moduleDir+'/src/*.cxx')\n cxxFileList='';\n for cxxf in cxxFiles:\n cxxFileList = cxxFileList+' '+cxxf.split('/')[-1]+'\\n'\n # build list of link dependencies\n linkLibs = \"\"\n for dep in dependencies[moduleName]:\n #verify if dep is a header-onlymodule\n depThirdParty = False\n try:\n moduleDic[dep]\n except KeyError:\n # this is a ThirdParty module\n depThirdParty = True\n \n if not depThirdParty:\n depModuleDir = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(moduleDic[dep],dep))\n depcxx = glob.glob(depModuleDir+'/src/*.cxx')\n if depcxx :\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n else:\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n \n if len(linkLibs) == 0:\n linkLibs = \" ${OTBITK_LIBRARIES}\"\n filepath = moduleDir+'/src/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/src/CMakeLists.txt'),'r'):\n line = line.replace('otb-template-module',cmakeModName)\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\n\n line = line.replace('LINK_LIBRARIES_TO_BE_REPLACED',linkLibs)\n o.write(line);\n o.close()\n\n # write app/CMakeLists.txt\n if op.isdir(moduleDir+'/app'):\n os.mkdir(moduleDir+'/test')\n srcFiles = glob.glob(moduleDir+'/app/*.cxx')\n srcFiles += glob.glob(moduleDir+'/app/*.h')\n appList = {}\n \n for srcf in srcFiles:\n # get App name\n appName = analyseAppManifest.findApplicationName(srcf)\n if len(appName) == 0:\n continue\n \n appList[appName] = {\"source\":op.basename(srcf)}\n \n # get original location\n cmakeListPath = \"\"\n for item in sourceList:\n if op.basename(item[\"path\"]) == op.basename(srcf) and \\\n moduleName == item[\"module\"]:\n appDir = op.basename(op.dirname(item[\"path\"]))\n cmakeListPath = op.join(HeadOfOTBTree,op.join(\"Testing/Applications\"),op.join(appDir,\"CMakeLists.txt\"))\n break\n \n # get App tests\n if not op.isfile(cmakeListPath):\n continue\n \n appList[appName][\"test\"] = analyseAppManifest.findTestFromApp(cmakeListPath,appName)\n \n # build list of link dependencies\n linkLibs = \"\"\n for dep in dependencies[moduleName]:\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n \n filepath = moduleDir+'/app/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n # define link libraries \n o.write(\"set(\"+cmakeModName+\"_LINK_LIBS\\n\")\n o.write(linkLibs)\n o.write(\")\\n\")\n \n for appli in appList:\n content = \"\\notb_create_application(\\n\"\n content += \" NAME \" + appli + \"\\n\"\n content += \" SOURCES \" + appList[appli][\"source\"] + \"\\n\"\n content += \" LINK_LIBRARIES ${${otb-module}_LIBRARIES})\\n\"\n o.write(content)\n o.close()\n \n filepath = moduleDir+'/test/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n o.write(\"otb_module_test()\")\n for appli in appList:\n if not appList[appli].has_key(\"test\"):\n continue\n o.write(\"\\n#----------- \"+appli+\" TESTS ----------------\\n\")\n for test in appList[appli][\"test\"]:\n if test.count(\"${\"):\n print \"Warning : test name contains a variable : \"+test\n continue\n \n testcode=appList[appli][\"test\"][test]\n testcode=[s.replace('OTB_TEST_APPLICATION', 'otb_test_application') for s in testcode]\n o.writelines(testcode)\n o.write(\"\\n\")\n o.close()\n\n # write test/CMakeLists.txt : done by dispatchTests.py\n \"\"\"\n if op.isdir(moduleDir+'/test'):\n cxxFiles = glob.glob(moduleDir+'/test/*.cxx')\n cxxFileList='';\n for cxxf in cxxFiles:\n cxxFileList = cxxFileList+cxxf.split('/')[-1]+'\\n'\n filepath = moduleDir+'/test/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open('./templateModule/otb-template-module/test/CMakeLists.txt','r'):\n # TODO : refactor for OTB\n words= moduleName.split('-')\n moduleNameMod='';\n for word in words:\n moduleNameMod=moduleNameMod + word.capitalize()\n line = line.replace('itkTemplateModule',moduleNameMod)\n line = line.replace('itk-template-module',moduleName)\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\n\n o.write(line);\n o.close()\n \"\"\"\n \n # write otb-module.cmake, which contains dependency info\n filepath = moduleDir+'/otb-module.cmake'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/otb-module.cmake'),'r'):\n # replace documentation\n if line.find(\"DESCRIPTION_TO_BE_REPLACED\") >= 0:\n docString = \"\\\"TBD\\\"\"\n if moduleName in modDescriptions:\n descPos = line.find(\"DESCRIPTION_TO_BE_REPLACED\")\n limitChar = 80\n docString = \"\\\"\"+modDescriptions[moduleName]+\"\\\"\"\n curPos = 80 - descPos\n while curPos < len(docString):\n lastSpace = docString[0:curPos].rfind(' ')\n if lastSpace > max(0,curPos-80):\n docString = docString[0:lastSpace] + '\\n' + docString[lastSpace+1:]\n else:\n docString = docString[0:curPos] + '\\n' + docString[curPos:]\n curPos += 81\n line = line.replace('DESCRIPTION_TO_BE_REPLACED',docString)\n \n # replace module name\n line = line.replace('otb-template-module',cmakeModName)\n # replace depend list\n dependTagPos = line.find(\"DEPENDS_TO_BE_REPLACED\")\n if dependTagPos >= 0:\n replacementStr = \"DEPENDS\"\n indentStr = \"\"\n for it in range(dependTagPos+2):\n indentStr = indentStr + \" \"\n if len(dependencies[moduleName]) > 0:\n deplist = dependencies[moduleName]\n else:\n deplist = [\"Common\"]\n for dep in deplist:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep\n line = line.replace('DEPENDS_TO_BE_REPLACED',replacementStr)\n # replace test_depend list\n testDependTagPos = line.find(\"TESTDEP_TO_BE_REPLACED\")\n if testDependTagPos >= 0:\n if moduleName.startswith(\"App\"):\n # for application : hardcode TestKernel and CommandLine\n indentStr = \"\"\n for it in range(testDependTagPos+2):\n indentStr = indentStr + \" \"\n replacementStr = \"TEST_DEPENDS\\n\" + indentStr + \"OTBTestKernel\\n\" + indentStr + \"OTBCommandLine\"\n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\n else:\n # standard case\n\n if len(testDependencies[moduleName]) > 0:\n indentStr = \"\"\n replacementStr = \"TEST_DEPENDS\"\n for it in range(testDependTagPos+2):\n indentStr = indentStr + \" \"\n for dep in testDependencies[moduleName]:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep \n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\n else:\n line = line.replace('TESTDEP_TO_BE_REPLACED','')\n \n # replace example_depend list\n exDependTagPos = line.find(\"EXDEP_TO_BE_REPLACED\")\n if exDependTagPos >= 0:\n if len(exDependencies[moduleName]) > 0:\n indentStr = \"\"\n replacementStr = \"EXAMPLE_DEPENDS\"\n for it in range(exDependTagPos+2):\n indentStr = indentStr + \" \"\n for dep in exDependencies[moduleName]:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep \n line = line.replace('EXDEP_TO_BE_REPLACED',replacementStr)\n else:\n line = line.replace('EXDEP_TO_BE_REPLACED','')\n o.write(line);\n \n o.close()\n\n# call dispatchTests to fill test/CMakeLists\nif op.isfile(testDependPath):\n dispatchTests.main([\"dispatchTests.py\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,testDependPath])\n\n\"\"\"\n# call dispatchExamples to fill example/CMakeLists\nif op.isfile(exDependPath):\n dispatchExamples.main([\"dispatchExamples.py\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,exDependPath])\n\"\"\"\n\n# examples\nfor i in sorted(os.listdir(HeadOfTempTree + \"/Examples\")):\n if i == \"CMakeLists.txt\" or i == \"README.txt\" or i.startswith(\"DataRepresentation\"):\n continue\n\n for j in sorted(os.listdir(HeadOfTempTree + \"/Examples/\" + i)):\n if j == \"CMakeLists.txt\" or j.startswith(\"otb\"):\n continue\n \n command = \"mv %s/Examples/%s/%s %s/Examples/%s/%s\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j)\n os.system(command)\n\nfor i in sorted(os.listdir(HeadOfTempTree + \"/Examples/DataRepresentation\")):\n if i == \"CMakeLists.txt\" or i == \"README.txt\":\n continue\n\n for j in sorted(os.listdir(HeadOfTempTree + \"/Examples/DataRepresentation/\" + i)):\n if j == \"CMakeLists.txt\" or j.startswith(\"otb\"):\n continue\n \n command = \"mv %s/Examples/DataRepresentation/%s/%s %s/Examples/DataRepresentation/%s/%s\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j) \n os.system(command)\n\n\n# save version without patches (so that we can regenerate patches later)\nos.system( \"cp -ar \" + op.join(OutputDir,\"OTB_Modular\") + \" \" + op.join(OutputDir,\"OTB_Modular-nopatch\") )\n\n# apply patches in OTB_Modular\ncurdir = op.abspath(op.dirname(__file__))\ncommand = \"cd \" + op.join(OutputDir,\"OTB_Modular\") + \" && patch -p1 < \" + curdir + \"/patches/otbmodular.patch\"\nprint \"Executing \" + command\nos.system( command )\n\n# remove Copyright files we don't want to touch later\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"Copyright\") ) )\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"RELEASE_NOTES.txt\") ) )\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"README\") ) )\n\n# PREPARE MIGRATION COMMIT ON A CLONE OF ORIGINAL CHECKOUT\nif enableMigration:\n print(\"Executing migration on a clone of original checkout\")\n HeadOfTempTree = op.abspath(HeadOfTempTree)\n OutputDir = op.abspath(OutputDir)\n \n # clone original checkout\n outputModular = op.join(OutputDir,\"OTB_Modular\")\n outputMigration = op.join(OutputDir,\"OTB_Migration\")\n if op.exists(outputMigration):\n os.removedirs(outputMigration)\n command = [\"cp\",\"-ar\",HeadOfOTBTree,outputMigration]\n call(command)\n os.chdir(outputMigration)\n \n # walk through OTB_Remaining and delete corresponding files in OTB checkout\n print(\"DELETE STEP...\")\n for dirPath, dirNames, fileNames in os.walk(HeadOfTempTree):\n currentSourceDir = dirPath.replace(HeadOfTempTree,'.')\n for fileName in fileNames:\n if op.exists(op.join(currentSourceDir,fileName)):\n command = [\"hg\",\"remove\",op.join(currentSourceDir,fileName)]\n call(command)\n else:\n print(\"Unknown file : \"+op.join(currentSourceDir,fileName))\n command = ['hg','commit','-m','ENH: Remove files not necessary after modularization']\n call(command)\n \n # walk through manifest and rename files\n print(\"MOVE STEP...\")\n for source in sourceList:\n outputPath = op.join(\"./Modules\",op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n command = ['hg','rename',source[\"path\"],op.join(outputPath,op.basename(source[\"path\"]))]\n call(command)\n command = ['hg','commit','-m','ENH: Move source and test files into their respective module']\n call(command)\n \n # add new files from OTB_Modular (files from OTB-Modular repo + generated files)\n print(\"ADD STEP...\")\n for dirPath, dirNames, fileNames in os.walk(outputModular):\n currentSourceDir = dirPath.replace(outputModular,'.')\n if currentSourceDir.startswith(\"./.hg\"):\n print(\"skip .hg\")\n continue\n for fileName in fileNames:\n # skip hg files\n if fileName.startswith(\".hg\"):\n continue\n targetFile = op.join(currentSourceDir,fileName)\n if not op.exists(targetFile):\n if not op.exists(currentSourceDir):\n command = [\"mkdir\",\"-p\",currentSourceDir]\n call(command)\n shutil.copy(op.join(dirPath,fileName),targetFile)\n command = ['hg','add']\n call(command)\n command = ['hg','commit','-m','ENH: Add new files for modular build system']\n call(command)\n \n # apply patches on OTB Checkout\n print(\"PATCH STEP...\")\n for dirPath, dirNames, fileNames in os.walk(outputModular):\n currentSourceDir = dirPath.replace(outputModular,'.')\n if currentSourceDir.startswith(\"./.hg\"):\n continue\n for fileName in fileNames:\n # skip hg files\n if fileName.startswith(\".hg\"):\n continue\n targetFile = op.join(currentSourceDir,fileName)\n if op.exists(targetFile):\n command = ['cp',op.join(dirPath,fileName),targetFile]\n call(command)\n command = ['hg','commit','-m','ENH: Apply patches necessary after modularization']\n call(command)\n\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from marko.parser import Parser # type: ignore
from marko.block import Heading, Paragraph, CodeBlock, List # type: ignore
from marko.inline import CodeSpan # type: ignore
from langcreator.common import Generators, InputOutputGenerator, tag_regex, get_tags, builtin_generators
import collections
import re
def parse(content: str) -> Generators:
parser = Parser()
document = parser.parse(content)
tag_name = ""
last_output = None
generators: Generators = {}
for item in document.children:
if type(item) == Heading:
_check_previous_generator(generators, tag_name)
# TODO: test
last_output = None
tag_name = item.children[0].children
_check_tag_name(tag_name)
_check_defined_twice(generators, tag_name)
generators[tag_name] = {}
elif type(item) == Paragraph and type(item.children[0]) == CodeSpan:
current_generator = generators[tag_name]
if type(current_generator) == dict:
last_output = item.children[0].children.replace("<empty>", "")
current_generator[last_output] = []
else:
raise Exception(f"Mixing list and inputs/output in {tag_name}")
elif type(item) == CodeBlock:
current_generator = generators[tag_name]
if last_output is None:
# TODO: test
raise Exception(
f"Input example defined without output in {tag_name}")
elif type(current_generator) == dict:
inputs = item.children[0].children.strip().split("\n")
inputs = [x.replace("<empty>", "") for x in inputs]
current_generator[last_output] += inputs
_check_tags(current_generator, tag_name)
else:
raise Exception(f"Mixing list and inputs/output in {tag_name}")
elif type(item) == List:
generators[tag_name] = [
x.children[0].children[0].children for x in item.children
]
_check_previous_generator(generators, tag_name)
_check_all_used_tags(generators)
return generators
def _check_tags(generator: InputOutputGenerator, name: str):
for output, inputs in generator.items():
necessary_tags = dict(collections.Counter(get_tags(output)))
for index, input in enumerate(inputs):
input_tags = dict(collections.Counter(get_tags(input)))
for tag, count in necessary_tags.items():
tag = tag.replace("'", "")
if tag not in input_tags:
raise Exception(
f"missing {tag} in example {index + 1} of {name} `{output}`"
)
diff = count - input_tags[tag]
if diff > 0:
raise Exception(
f"missing {diff} {tag} in example {index + 1} of {name} `{output}`. "
+
f"Expected to find {count} {tag}, found {input_tags[tag]}."
)
def _check_tag_name(tag):
if not re.fullmatch(tag_regex, "#" + tag.strip()):
raise Exception("# %s is invalid, only letters and _ are allowed" %
(tag))
def _check_defined_twice(generators, tag):
if tag in generators:
raise Exception("# %s is being defined twice" % (tag))
def _check_previous_generator(generators, name):
if not name:
return
if type(generators[name]) == list:
return
if len(generators[name]) == 0:
raise Exception("output missing on # %s" % name)
for index, inputs in enumerate(generators[name].values()):
if len(inputs) == 0:
raise Exception(
f"input examples missing on # {name}, on example #{index}")
def _check_all_used_tags(generators):
available_tags = ["#" + x for x in builtin_generators
] + ["#" + x for x in generators.keys()]
for key, generator in generators.items():
if type(generator) == list:
for tag in generator:
if "#" + tag not in available_tags:
raise Exception(
"- %s is used in # %s but it's not defined anywhere. Defined tags are %s"
% (tag, key, ", ".join(available_tags)))
else:
for output in generator.keys():
tags = get_tags(output)
for tag in tags:
if tag not in available_tags:
raise Exception(
"%s is used in # %s but it's not defined anywhere. Defined tags are %s"
% (tag, key, ", ".join(available_tags)))
|
normal
|
{
"blob_id": "0bbc8aa77436193ab47c0fe8cf0d7c6dffcfe097",
"index": 8066,
"step-1": "<mask token>\n\n\ndef _check_tags(generator: InputOutputGenerator, name: str):\n for output, inputs in generator.items():\n necessary_tags = dict(collections.Counter(get_tags(output)))\n for index, input in enumerate(inputs):\n input_tags = dict(collections.Counter(get_tags(input)))\n for tag, count in necessary_tags.items():\n tag = tag.replace(\"'\", '')\n if tag not in input_tags:\n raise Exception(\n f'missing {tag} in example {index + 1} of {name} `{output}`'\n )\n diff = count - input_tags[tag]\n if diff > 0:\n raise Exception(\n f'missing {diff} {tag} in example {index + 1} of {name} `{output}`. '\n +\n f'Expected to find {count} {tag}, found {input_tags[tag]}.'\n )\n\n\ndef _check_tag_name(tag):\n if not re.fullmatch(tag_regex, '#' + tag.strip()):\n raise Exception('# %s is invalid, only letters and _ are allowed' % tag\n )\n\n\ndef _check_defined_twice(generators, tag):\n if tag in generators:\n raise Exception('# %s is being defined twice' % tag)\n\n\n<mask token>\n\n\ndef _check_all_used_tags(generators):\n available_tags = [('#' + x) for x in builtin_generators] + [('#' + x) for\n x in generators.keys()]\n for key, generator in generators.items():\n if type(generator) == list:\n for tag in generator:\n if '#' + tag not in available_tags:\n raise Exception(\n \"- %s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n else:\n for output in generator.keys():\n tags = get_tags(output)\n for tag in tags:\n if tag not in available_tags:\n raise Exception(\n \"%s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n",
"step-2": "<mask token>\n\n\ndef _check_tags(generator: InputOutputGenerator, name: str):\n for output, inputs in generator.items():\n necessary_tags = dict(collections.Counter(get_tags(output)))\n for index, input in enumerate(inputs):\n input_tags = dict(collections.Counter(get_tags(input)))\n for tag, count in necessary_tags.items():\n tag = tag.replace(\"'\", '')\n if tag not in input_tags:\n raise Exception(\n f'missing {tag} in example {index + 1} of {name} `{output}`'\n )\n diff = count - input_tags[tag]\n if diff > 0:\n raise Exception(\n f'missing {diff} {tag} in example {index + 1} of {name} `{output}`. '\n +\n f'Expected to find {count} {tag}, found {input_tags[tag]}.'\n )\n\n\ndef _check_tag_name(tag):\n if not re.fullmatch(tag_regex, '#' + tag.strip()):\n raise Exception('# %s is invalid, only letters and _ are allowed' % tag\n )\n\n\ndef _check_defined_twice(generators, tag):\n if tag in generators:\n raise Exception('# %s is being defined twice' % tag)\n\n\ndef _check_previous_generator(generators, name):\n if not name:\n return\n if type(generators[name]) == list:\n return\n if len(generators[name]) == 0:\n raise Exception('output missing on # %s' % name)\n for index, inputs in enumerate(generators[name].values()):\n if len(inputs) == 0:\n raise Exception(\n f'input examples missing on # {name}, on example #{index}')\n\n\ndef _check_all_used_tags(generators):\n available_tags = [('#' + x) for x in builtin_generators] + [('#' + x) for\n x in generators.keys()]\n for key, generator in generators.items():\n if type(generator) == list:\n for tag in generator:\n if '#' + tag not in available_tags:\n raise Exception(\n \"- %s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n else:\n for output in generator.keys():\n tags = get_tags(output)\n for tag in tags:\n if tag not in available_tags:\n raise Exception(\n \"%s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n",
"step-3": "<mask token>\n\n\ndef parse(content: str) ->Generators:\n parser = Parser()\n document = parser.parse(content)\n tag_name = ''\n last_output = None\n generators: Generators = {}\n for item in document.children:\n if type(item) == Heading:\n _check_previous_generator(generators, tag_name)\n last_output = None\n tag_name = item.children[0].children\n _check_tag_name(tag_name)\n _check_defined_twice(generators, tag_name)\n generators[tag_name] = {}\n elif type(item) == Paragraph and type(item.children[0]) == CodeSpan:\n current_generator = generators[tag_name]\n if type(current_generator) == dict:\n last_output = item.children[0].children.replace('<empty>', '')\n current_generator[last_output] = []\n else:\n raise Exception(f'Mixing list and inputs/output in {tag_name}')\n elif type(item) == CodeBlock:\n current_generator = generators[tag_name]\n if last_output is None:\n raise Exception(\n f'Input example defined without output in {tag_name}')\n elif type(current_generator) == dict:\n inputs = item.children[0].children.strip().split('\\n')\n inputs = [x.replace('<empty>', '') for x in inputs]\n current_generator[last_output] += inputs\n _check_tags(current_generator, tag_name)\n else:\n raise Exception(f'Mixing list and inputs/output in {tag_name}')\n elif type(item) == List:\n generators[tag_name] = [x.children[0].children[0].children for\n x in item.children]\n _check_previous_generator(generators, tag_name)\n _check_all_used_tags(generators)\n return generators\n\n\ndef _check_tags(generator: InputOutputGenerator, name: str):\n for output, inputs in generator.items():\n necessary_tags = dict(collections.Counter(get_tags(output)))\n for index, input in enumerate(inputs):\n input_tags = dict(collections.Counter(get_tags(input)))\n for tag, count in necessary_tags.items():\n tag = tag.replace(\"'\", '')\n if tag not in input_tags:\n raise Exception(\n f'missing {tag} in example {index + 1} of {name} `{output}`'\n )\n diff = count - input_tags[tag]\n if diff > 0:\n raise Exception(\n f'missing {diff} {tag} in example {index + 1} of {name} `{output}`. '\n +\n f'Expected to find {count} {tag}, found {input_tags[tag]}.'\n )\n\n\ndef _check_tag_name(tag):\n if not re.fullmatch(tag_regex, '#' + tag.strip()):\n raise Exception('# %s is invalid, only letters and _ are allowed' % tag\n )\n\n\ndef _check_defined_twice(generators, tag):\n if tag in generators:\n raise Exception('# %s is being defined twice' % tag)\n\n\ndef _check_previous_generator(generators, name):\n if not name:\n return\n if type(generators[name]) == list:\n return\n if len(generators[name]) == 0:\n raise Exception('output missing on # %s' % name)\n for index, inputs in enumerate(generators[name].values()):\n if len(inputs) == 0:\n raise Exception(\n f'input examples missing on # {name}, on example #{index}')\n\n\ndef _check_all_used_tags(generators):\n available_tags = [('#' + x) for x in builtin_generators] + [('#' + x) for\n x in generators.keys()]\n for key, generator in generators.items():\n if type(generator) == list:\n for tag in generator:\n if '#' + tag not in available_tags:\n raise Exception(\n \"- %s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n else:\n for output in generator.keys():\n tags = get_tags(output)\n for tag in tags:\n if tag not in available_tags:\n raise Exception(\n \"%s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n",
"step-4": "from marko.parser import Parser\nfrom marko.block import Heading, Paragraph, CodeBlock, List\nfrom marko.inline import CodeSpan\nfrom langcreator.common import Generators, InputOutputGenerator, tag_regex, get_tags, builtin_generators\nimport collections\nimport re\n\n\ndef parse(content: str) ->Generators:\n parser = Parser()\n document = parser.parse(content)\n tag_name = ''\n last_output = None\n generators: Generators = {}\n for item in document.children:\n if type(item) == Heading:\n _check_previous_generator(generators, tag_name)\n last_output = None\n tag_name = item.children[0].children\n _check_tag_name(tag_name)\n _check_defined_twice(generators, tag_name)\n generators[tag_name] = {}\n elif type(item) == Paragraph and type(item.children[0]) == CodeSpan:\n current_generator = generators[tag_name]\n if type(current_generator) == dict:\n last_output = item.children[0].children.replace('<empty>', '')\n current_generator[last_output] = []\n else:\n raise Exception(f'Mixing list and inputs/output in {tag_name}')\n elif type(item) == CodeBlock:\n current_generator = generators[tag_name]\n if last_output is None:\n raise Exception(\n f'Input example defined without output in {tag_name}')\n elif type(current_generator) == dict:\n inputs = item.children[0].children.strip().split('\\n')\n inputs = [x.replace('<empty>', '') for x in inputs]\n current_generator[last_output] += inputs\n _check_tags(current_generator, tag_name)\n else:\n raise Exception(f'Mixing list and inputs/output in {tag_name}')\n elif type(item) == List:\n generators[tag_name] = [x.children[0].children[0].children for\n x in item.children]\n _check_previous_generator(generators, tag_name)\n _check_all_used_tags(generators)\n return generators\n\n\ndef _check_tags(generator: InputOutputGenerator, name: str):\n for output, inputs in generator.items():\n necessary_tags = dict(collections.Counter(get_tags(output)))\n for index, input in enumerate(inputs):\n input_tags = dict(collections.Counter(get_tags(input)))\n for tag, count in necessary_tags.items():\n tag = tag.replace(\"'\", '')\n if tag not in input_tags:\n raise Exception(\n f'missing {tag} in example {index + 1} of {name} `{output}`'\n )\n diff = count - input_tags[tag]\n if diff > 0:\n raise Exception(\n f'missing {diff} {tag} in example {index + 1} of {name} `{output}`. '\n +\n f'Expected to find {count} {tag}, found {input_tags[tag]}.'\n )\n\n\ndef _check_tag_name(tag):\n if not re.fullmatch(tag_regex, '#' + tag.strip()):\n raise Exception('# %s is invalid, only letters and _ are allowed' % tag\n )\n\n\ndef _check_defined_twice(generators, tag):\n if tag in generators:\n raise Exception('# %s is being defined twice' % tag)\n\n\ndef _check_previous_generator(generators, name):\n if not name:\n return\n if type(generators[name]) == list:\n return\n if len(generators[name]) == 0:\n raise Exception('output missing on # %s' % name)\n for index, inputs in enumerate(generators[name].values()):\n if len(inputs) == 0:\n raise Exception(\n f'input examples missing on # {name}, on example #{index}')\n\n\ndef _check_all_used_tags(generators):\n available_tags = [('#' + x) for x in builtin_generators] + [('#' + x) for\n x in generators.keys()]\n for key, generator in generators.items():\n if type(generator) == list:\n for tag in generator:\n if '#' + tag not in available_tags:\n raise Exception(\n \"- %s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n else:\n for output in generator.keys():\n tags = get_tags(output)\n for tag in tags:\n if tag not in available_tags:\n raise Exception(\n \"%s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, ', '.join(available_tags)))\n",
"step-5": "from marko.parser import Parser # type: ignore\nfrom marko.block import Heading, Paragraph, CodeBlock, List # type: ignore\nfrom marko.inline import CodeSpan # type: ignore\nfrom langcreator.common import Generators, InputOutputGenerator, tag_regex, get_tags, builtin_generators\nimport collections\nimport re\n\n\ndef parse(content: str) -> Generators:\n parser = Parser()\n document = parser.parse(content)\n\n tag_name = \"\"\n last_output = None\n generators: Generators = {}\n for item in document.children:\n if type(item) == Heading:\n _check_previous_generator(generators, tag_name)\n # TODO: test\n last_output = None\n tag_name = item.children[0].children\n _check_tag_name(tag_name)\n _check_defined_twice(generators, tag_name)\n generators[tag_name] = {}\n elif type(item) == Paragraph and type(item.children[0]) == CodeSpan:\n current_generator = generators[tag_name]\n if type(current_generator) == dict:\n last_output = item.children[0].children.replace(\"<empty>\", \"\")\n current_generator[last_output] = []\n else:\n raise Exception(f\"Mixing list and inputs/output in {tag_name}\")\n elif type(item) == CodeBlock:\n current_generator = generators[tag_name]\n if last_output is None:\n # TODO: test\n raise Exception(\n f\"Input example defined without output in {tag_name}\")\n elif type(current_generator) == dict:\n inputs = item.children[0].children.strip().split(\"\\n\")\n inputs = [x.replace(\"<empty>\", \"\") for x in inputs]\n current_generator[last_output] += inputs\n _check_tags(current_generator, tag_name)\n else:\n raise Exception(f\"Mixing list and inputs/output in {tag_name}\")\n elif type(item) == List:\n generators[tag_name] = [\n x.children[0].children[0].children for x in item.children\n ]\n _check_previous_generator(generators, tag_name)\n\n _check_all_used_tags(generators)\n return generators\n\n\ndef _check_tags(generator: InputOutputGenerator, name: str):\n for output, inputs in generator.items():\n necessary_tags = dict(collections.Counter(get_tags(output)))\n\n for index, input in enumerate(inputs):\n input_tags = dict(collections.Counter(get_tags(input)))\n\n for tag, count in necessary_tags.items():\n tag = tag.replace(\"'\", \"\")\n if tag not in input_tags:\n raise Exception(\n f\"missing {tag} in example {index + 1} of {name} `{output}`\"\n )\n\n diff = count - input_tags[tag]\n if diff > 0:\n raise Exception(\n f\"missing {diff} {tag} in example {index + 1} of {name} `{output}`. \"\n +\n f\"Expected to find {count} {tag}, found {input_tags[tag]}.\"\n )\n\n\ndef _check_tag_name(tag):\n if not re.fullmatch(tag_regex, \"#\" + tag.strip()):\n raise Exception(\"# %s is invalid, only letters and _ are allowed\" %\n (tag))\n\n\ndef _check_defined_twice(generators, tag):\n if tag in generators:\n raise Exception(\"# %s is being defined twice\" % (tag))\n\n\ndef _check_previous_generator(generators, name):\n if not name:\n return\n if type(generators[name]) == list:\n return\n if len(generators[name]) == 0:\n raise Exception(\"output missing on # %s\" % name)\n for index, inputs in enumerate(generators[name].values()):\n if len(inputs) == 0:\n raise Exception(\n f\"input examples missing on # {name}, on example #{index}\")\n\n\ndef _check_all_used_tags(generators):\n available_tags = [\"#\" + x for x in builtin_generators\n ] + [\"#\" + x for x in generators.keys()]\n for key, generator in generators.items():\n if type(generator) == list:\n for tag in generator:\n if \"#\" + tag not in available_tags:\n raise Exception(\n \"- %s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, \", \".join(available_tags)))\n else:\n for output in generator.keys():\n tags = get_tags(output)\n for tag in tags:\n if tag not in available_tags:\n raise Exception(\n \"%s is used in # %s but it's not defined anywhere. Defined tags are %s\"\n % (tag, key, \", \".join(available_tags)))\n",
"step-ids": [
4,
5,
6,
7,
8
]
}
|
[
4,
5,
6,
7,
8
] |
ii = [('CoolWHM2.py', 73), ('MarrFDI3.py', 2), ('IrviWVD.py', 2), (
'CoolWHM3.py', 8), ('LewiMJW.py', 1), ('JacoWHI2.py', 4), ('EvarJSP.py', 1)
]
|
normal
|
{
"blob_id": "379c666f19537b513169c6b30e0c669dda6e372c",
"index": 9165,
"step-1": "<mask token>\n",
"step-2": "ii = [('CoolWHM2.py', 73), ('MarrFDI3.py', 2), ('IrviWVD.py', 2), (\n 'CoolWHM3.py', 8), ('LewiMJW.py', 1), ('JacoWHI2.py', 4), ('EvarJSP.py', 1)\n ]\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
<|reserved_special_token_0|>
def test_negativity():
assert make_it_negative(8) == -8
assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_negativity():
assert make_it_negative(8) == -8
assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'
<|reserved_special_token_0|>
def test_cleverness():
assert make_it_negative(-3) == 3
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_negativity():
assert make_it_negative(8) == -8
assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'
def test_easy():
assert 1 == 1
def test_cleverness():
assert make_it_negative(-3) == 3
<|reserved_special_token_1|>
import pytest
from debbiedowner import make_it_negative, complain_about
def test_negativity():
assert make_it_negative(8) == -8
assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'
def test_easy():
assert 1 == 1
def test_cleverness():
assert make_it_negative(-3) == 3
<|reserved_special_token_1|>
import pytest
from debbiedowner import make_it_negative, complain_about
def test_negativity():
assert make_it_negative(8) == -8
assert complain_about('enthusiasm') == "I hate enthusiasm. Totally boring."
def test_easy():
assert 1 == 1
def test_cleverness():
assert make_it_negative(-3) == 3
|
flexible
|
{
"blob_id": "e73e40a63b67ee1a6cca53a328af05e3eb3d8519",
"index": 703,
"step-1": "<mask token>\n\n\ndef test_negativity():\n assert make_it_negative(8) == -8\n assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_negativity():\n assert make_it_negative(8) == -8\n assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'\n\n\n<mask token>\n\n\ndef test_cleverness():\n assert make_it_negative(-3) == 3\n",
"step-3": "<mask token>\n\n\ndef test_negativity():\n assert make_it_negative(8) == -8\n assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'\n\n\ndef test_easy():\n assert 1 == 1\n\n\ndef test_cleverness():\n assert make_it_negative(-3) == 3\n",
"step-4": "import pytest\nfrom debbiedowner import make_it_negative, complain_about\n\n\ndef test_negativity():\n assert make_it_negative(8) == -8\n assert complain_about('enthusiasm') == 'I hate enthusiasm. Totally boring.'\n\n\ndef test_easy():\n assert 1 == 1\n\n\ndef test_cleverness():\n assert make_it_negative(-3) == 3\n",
"step-5": "import pytest\n\nfrom debbiedowner import make_it_negative, complain_about\n\ndef test_negativity():\n assert make_it_negative(8) == -8\n assert complain_about('enthusiasm') == \"I hate enthusiasm. Totally boring.\"\n\ndef test_easy():\n assert 1 == 1\n\ndef test_cleverness():\n assert make_it_negative(-3) == 3",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with os.scandir(os.path.abspath(beatmap_dir)) as it:
for entry in it:
if entry.is_dir():
try:
beatmap_id = int(str(entry.name).split(' ')[0])
except ValueError:
continue
beatmaps.append(entry.path)
<|reserved_special_token_0|>
for beatmap in beatmaps:
with os.scandir(os.path.abspath(beatmap)) as it:
bm = {'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),
'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(
beatmap)[1]).split(' ')[0]) + 1:], 'audio': None,
'audio_length': None, 'video': None}
print('{} {}'.format(bm['id'], bm['name']))
for entry in it:
if entry.is_file():
if entry.path.endswith('osu'):
with open(entry.path, 'r', encoding='utf-8') as f:
config_string = '[global]\n' + f.read()
a = ''
for x in config_string.split('\n')[:config_string.split
('\n').index('[Events]') - 1]:
a += x + '\n'
config = configparser.ConfigParser(allow_no_value=True)
config.read_string(a)
bm['audio'] = os.path.abspath(os.path.dirname(entry.
path) + '\\' + config.get('General', 'AudioFilename'))
elif entry.path.endswith('mp4') or entry.path.endswith('avi'
) or entry.path.endswith('mpg'):
bm['video'] = entry.path
bm_osu.append(bm)
<|reserved_special_token_0|>
for bm in bm_osu:
if bm['audio']:
text_playlist += '#EXTINF:0,{0}\n{1}\n'.format(bm['name'], bm['audio'])
<|reserved_special_token_0|>
try:
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
except:
open('osu.m3u', 'x')
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
<|reserved_special_token_0|>
for bm in bm_osu:
if bm['name']:
text_type += '{0}\n'.format(bm['name'])
<|reserved_special_token_0|>
try:
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
except:
open('osu.txt', 'x')
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
for bm in bm_osu:
if bm['audio']:
print('{} {}'.format(bm['id'], bm['name']))
if os.path.basename(bm['audio']).split('.')[-1] != '':
shutil.copy2(bm['audio'], '{}\\osu music\\{}.{}'.format(os.
getcwd(), bm['name'], os.path.basename(bm['audio']).split(
'.')[-1]))
if bm['video']:
shutil.copy2(bm['video'], '{}\\osu music\\{}.{}'.format(os.getcwd(),
bm['name'], os.path.basename(bm['video']).split('.')[-1]))
print('done, ty for use')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
beatmap_dir = os.path.abspath(os.environ['LOCALAPPDATA'] + '\\osu!\\Songs\\')
beatmaps = []
bm_osu = []
with os.scandir(os.path.abspath(beatmap_dir)) as it:
for entry in it:
if entry.is_dir():
try:
beatmap_id = int(str(entry.name).split(' ')[0])
except ValueError:
continue
beatmaps.append(entry.path)
beatmap_type = {'id': 0, 'name': 'Author - Title', 'audio':
'.\\somefile.mp3', 'video': '.\\something.mp4'}
for beatmap in beatmaps:
with os.scandir(os.path.abspath(beatmap)) as it:
bm = {'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),
'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(
beatmap)[1]).split(' ')[0]) + 1:], 'audio': None,
'audio_length': None, 'video': None}
print('{} {}'.format(bm['id'], bm['name']))
for entry in it:
if entry.is_file():
if entry.path.endswith('osu'):
with open(entry.path, 'r', encoding='utf-8') as f:
config_string = '[global]\n' + f.read()
a = ''
for x in config_string.split('\n')[:config_string.split
('\n').index('[Events]') - 1]:
a += x + '\n'
config = configparser.ConfigParser(allow_no_value=True)
config.read_string(a)
bm['audio'] = os.path.abspath(os.path.dirname(entry.
path) + '\\' + config.get('General', 'AudioFilename'))
elif entry.path.endswith('mp4') or entry.path.endswith('avi'
) or entry.path.endswith('mpg'):
bm['video'] = entry.path
bm_osu.append(bm)
text_playlist = ''
for bm in bm_osu:
if bm['audio']:
text_playlist += '#EXTINF:0,{0}\n{1}\n'.format(bm['name'], bm['audio'])
text_playlist = text_playlist[:-1]
try:
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
except:
open('osu.m3u', 'x')
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
text_type = ''
for bm in bm_osu:
if bm['name']:
text_type += '{0}\n'.format(bm['name'])
text_type = text_type[:-1]
try:
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
except:
open('osu.txt', 'x')
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
for bm in bm_osu:
if bm['audio']:
print('{} {}'.format(bm['id'], bm['name']))
if os.path.basename(bm['audio']).split('.')[-1] != '':
shutil.copy2(bm['audio'], '{}\\osu music\\{}.{}'.format(os.
getcwd(), bm['name'], os.path.basename(bm['audio']).split(
'.')[-1]))
if bm['video']:
shutil.copy2(bm['video'], '{}\\osu music\\{}.{}'.format(os.getcwd(),
bm['name'], os.path.basename(bm['video']).split('.')[-1]))
print('done, ty for use')
<|reserved_special_token_1|>
import os
import shutil
import configparser
beatmap_dir = os.path.abspath(os.environ['LOCALAPPDATA'] + '\\osu!\\Songs\\')
beatmaps = []
bm_osu = []
with os.scandir(os.path.abspath(beatmap_dir)) as it:
for entry in it:
if entry.is_dir():
try:
beatmap_id = int(str(entry.name).split(' ')[0])
except ValueError:
continue
beatmaps.append(entry.path)
beatmap_type = {'id': 0, 'name': 'Author - Title', 'audio':
'.\\somefile.mp3', 'video': '.\\something.mp4'}
for beatmap in beatmaps:
with os.scandir(os.path.abspath(beatmap)) as it:
bm = {'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),
'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(
beatmap)[1]).split(' ')[0]) + 1:], 'audio': None,
'audio_length': None, 'video': None}
print('{} {}'.format(bm['id'], bm['name']))
for entry in it:
if entry.is_file():
if entry.path.endswith('osu'):
with open(entry.path, 'r', encoding='utf-8') as f:
config_string = '[global]\n' + f.read()
a = ''
for x in config_string.split('\n')[:config_string.split
('\n').index('[Events]') - 1]:
a += x + '\n'
config = configparser.ConfigParser(allow_no_value=True)
config.read_string(a)
bm['audio'] = os.path.abspath(os.path.dirname(entry.
path) + '\\' + config.get('General', 'AudioFilename'))
elif entry.path.endswith('mp4') or entry.path.endswith('avi'
) or entry.path.endswith('mpg'):
bm['video'] = entry.path
bm_osu.append(bm)
text_playlist = ''
for bm in bm_osu:
if bm['audio']:
text_playlist += '#EXTINF:0,{0}\n{1}\n'.format(bm['name'], bm['audio'])
text_playlist = text_playlist[:-1]
try:
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
except:
open('osu.m3u', 'x')
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
text_type = ''
for bm in bm_osu:
if bm['name']:
text_type += '{0}\n'.format(bm['name'])
text_type = text_type[:-1]
try:
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
except:
open('osu.txt', 'x')
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
for bm in bm_osu:
if bm['audio']:
print('{} {}'.format(bm['id'], bm['name']))
if os.path.basename(bm['audio']).split('.')[-1] != '':
shutil.copy2(bm['audio'], '{}\\osu music\\{}.{}'.format(os.
getcwd(), bm['name'], os.path.basename(bm['audio']).split(
'.')[-1]))
if bm['video']:
shutil.copy2(bm['video'], '{}\\osu music\\{}.{}'.format(os.getcwd(),
bm['name'], os.path.basename(bm['video']).split('.')[-1]))
print('done, ty for use')
<|reserved_special_token_1|>
import os
import shutil
import configparser
beatmap_dir = os.path.abspath(os.environ['LOCALAPPDATA']+'\\osu!\\Songs\\')
beatmaps = []
bm_osu = []
with os.scandir(os.path.abspath(beatmap_dir)) as it:
for entry in it:
if entry.is_dir():
try:
beatmap_id = int(str(entry.name).split(' ')[0])
except ValueError:
# I'm not sure what to do about unranked maps right now, we will exclude them
continue
beatmaps.append(entry.path)
beatmap_type = {
"id": 0, # You may parse for "[Metadata]\n\nBeatmapSetID:{sid}" (WARN: Earlier maps will lack this parameter (osu file format v3 < osu file format v14)) or use the one provided with path
"name": 'Author - Title', # I should get it from osu files rather than directory, but that's how it happens
"audio": ".\\somefile.mp3", # Parse for "[General]\n\nAudioFilename: {filename}" | DONE
"video": ".\\something.mp4" # Parse for "[Events]\n\nVideo,{timestamp},{filename}" (found mp4,avi,mpg) | plz check, TODO
}
for beatmap in beatmaps:
with os.scandir(os.path.abspath(beatmap)) as it:
bm = {
'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),
'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(beatmap)[1]).split(' ')[0])+1:],
'audio': None,
'audio_length': None,
'video': None
}
print('{} {}'.format(bm['id'], bm['name']))
for entry in it:
if entry.is_file():
if entry.path.endswith('osu'):
# ConfigParser is actually overkill solution, although I set it up to work
# FixMe: This solution does not account for multiple (via diff) maps in one
# Although, ranked maps should never have this.
with open(entry.path, 'r', encoding="utf-8") as f:
config_string = '[global]\n' + f.read()
a = ''
for x in config_string.split('\n')[:config_string.split('\n').index('[Events]')-1]:
a += x+'\n'
config = configparser.ConfigParser(allow_no_value=True)
config.read_string(a)
# TODO: Rewrite to simple checks and add video checking.
bm['audio'] = os.path.abspath(os.path.dirname(entry.path)+'\\'+config.get('General', 'AudioFilename'))
elif entry.path.endswith('mp4') or entry.path.endswith('avi') or entry.path.endswith('mpg'):
bm['video'] = entry.path
bm_osu.append(bm)
text_playlist = ""
for bm in bm_osu:
if bm['audio']:
text_playlist += "#EXTINF:0,{0}\n{1}\n".format(bm['name'], bm['audio'])
text_playlist = text_playlist[:-1]
try:
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
except:
open('osu.m3u', 'x')
with open('osu.m3u', 'w', encoding='utf-8') as file:
file.write(text_playlist)
text_type = ""
for bm in bm_osu:
if bm['name']:
text_type += "{0}\n".format(bm['name'])
text_type = text_type[:-1]
try:
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
except:
open('osu.txt', 'x')
with open('osu.txt', 'w', encoding='utf-8') as file:
file.write(text_type)
for bm in bm_osu:
if bm['audio']:
print('{} {}'.format(bm['id'], bm['name']))
if os.path.basename(bm['audio']).split('.')[-1] != '':
shutil.copy2(bm['audio'], "{}\\osu music\\{}.{}".format(os.getcwd(), bm['name'], os.path.basename(bm['audio']).split('.')[-1]))
if bm['video']:
shutil.copy2(bm['video'], "{}\\osu music\\{}.{}".format(os.getcwd(), bm['name'], os.path.basename(bm['video']).split('.')[-1]))
print('done, ty for use')
|
flexible
|
{
"blob_id": "cd34f9ef100ae6d116f02258d22c114ec3f3e3e6",
"index": 1581,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith os.scandir(os.path.abspath(beatmap_dir)) as it:\n for entry in it:\n if entry.is_dir():\n try:\n beatmap_id = int(str(entry.name).split(' ')[0])\n except ValueError:\n continue\n beatmaps.append(entry.path)\n<mask token>\nfor beatmap in beatmaps:\n with os.scandir(os.path.abspath(beatmap)) as it:\n bm = {'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),\n 'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(\n beatmap)[1]).split(' ')[0]) + 1:], 'audio': None,\n 'audio_length': None, 'video': None}\n print('{} {}'.format(bm['id'], bm['name']))\n for entry in it:\n if entry.is_file():\n if entry.path.endswith('osu'):\n with open(entry.path, 'r', encoding='utf-8') as f:\n config_string = '[global]\\n' + f.read()\n a = ''\n for x in config_string.split('\\n')[:config_string.split\n ('\\n').index('[Events]') - 1]:\n a += x + '\\n'\n config = configparser.ConfigParser(allow_no_value=True)\n config.read_string(a)\n bm['audio'] = os.path.abspath(os.path.dirname(entry.\n path) + '\\\\' + config.get('General', 'AudioFilename'))\n elif entry.path.endswith('mp4') or entry.path.endswith('avi'\n ) or entry.path.endswith('mpg'):\n bm['video'] = entry.path\n bm_osu.append(bm)\n<mask token>\nfor bm in bm_osu:\n if bm['audio']:\n text_playlist += '#EXTINF:0,{0}\\n{1}\\n'.format(bm['name'], bm['audio'])\n<mask token>\ntry:\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\nexcept:\n open('osu.m3u', 'x')\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\n<mask token>\nfor bm in bm_osu:\n if bm['name']:\n text_type += '{0}\\n'.format(bm['name'])\n<mask token>\ntry:\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\nexcept:\n open('osu.txt', 'x')\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\nfor bm in bm_osu:\n if bm['audio']:\n print('{} {}'.format(bm['id'], bm['name']))\n if os.path.basename(bm['audio']).split('.')[-1] != '':\n shutil.copy2(bm['audio'], '{}\\\\osu music\\\\{}.{}'.format(os.\n getcwd(), bm['name'], os.path.basename(bm['audio']).split(\n '.')[-1]))\n if bm['video']:\n shutil.copy2(bm['video'], '{}\\\\osu music\\\\{}.{}'.format(os.getcwd(),\n bm['name'], os.path.basename(bm['video']).split('.')[-1]))\nprint('done, ty for use')\n",
"step-3": "<mask token>\nbeatmap_dir = os.path.abspath(os.environ['LOCALAPPDATA'] + '\\\\osu!\\\\Songs\\\\')\nbeatmaps = []\nbm_osu = []\nwith os.scandir(os.path.abspath(beatmap_dir)) as it:\n for entry in it:\n if entry.is_dir():\n try:\n beatmap_id = int(str(entry.name).split(' ')[0])\n except ValueError:\n continue\n beatmaps.append(entry.path)\nbeatmap_type = {'id': 0, 'name': 'Author - Title', 'audio':\n '.\\\\somefile.mp3', 'video': '.\\\\something.mp4'}\nfor beatmap in beatmaps:\n with os.scandir(os.path.abspath(beatmap)) as it:\n bm = {'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),\n 'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(\n beatmap)[1]).split(' ')[0]) + 1:], 'audio': None,\n 'audio_length': None, 'video': None}\n print('{} {}'.format(bm['id'], bm['name']))\n for entry in it:\n if entry.is_file():\n if entry.path.endswith('osu'):\n with open(entry.path, 'r', encoding='utf-8') as f:\n config_string = '[global]\\n' + f.read()\n a = ''\n for x in config_string.split('\\n')[:config_string.split\n ('\\n').index('[Events]') - 1]:\n a += x + '\\n'\n config = configparser.ConfigParser(allow_no_value=True)\n config.read_string(a)\n bm['audio'] = os.path.abspath(os.path.dirname(entry.\n path) + '\\\\' + config.get('General', 'AudioFilename'))\n elif entry.path.endswith('mp4') or entry.path.endswith('avi'\n ) or entry.path.endswith('mpg'):\n bm['video'] = entry.path\n bm_osu.append(bm)\ntext_playlist = ''\nfor bm in bm_osu:\n if bm['audio']:\n text_playlist += '#EXTINF:0,{0}\\n{1}\\n'.format(bm['name'], bm['audio'])\ntext_playlist = text_playlist[:-1]\ntry:\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\nexcept:\n open('osu.m3u', 'x')\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\ntext_type = ''\nfor bm in bm_osu:\n if bm['name']:\n text_type += '{0}\\n'.format(bm['name'])\ntext_type = text_type[:-1]\ntry:\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\nexcept:\n open('osu.txt', 'x')\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\nfor bm in bm_osu:\n if bm['audio']:\n print('{} {}'.format(bm['id'], bm['name']))\n if os.path.basename(bm['audio']).split('.')[-1] != '':\n shutil.copy2(bm['audio'], '{}\\\\osu music\\\\{}.{}'.format(os.\n getcwd(), bm['name'], os.path.basename(bm['audio']).split(\n '.')[-1]))\n if bm['video']:\n shutil.copy2(bm['video'], '{}\\\\osu music\\\\{}.{}'.format(os.getcwd(),\n bm['name'], os.path.basename(bm['video']).split('.')[-1]))\nprint('done, ty for use')\n",
"step-4": "import os\nimport shutil\nimport configparser\nbeatmap_dir = os.path.abspath(os.environ['LOCALAPPDATA'] + '\\\\osu!\\\\Songs\\\\')\nbeatmaps = []\nbm_osu = []\nwith os.scandir(os.path.abspath(beatmap_dir)) as it:\n for entry in it:\n if entry.is_dir():\n try:\n beatmap_id = int(str(entry.name).split(' ')[0])\n except ValueError:\n continue\n beatmaps.append(entry.path)\nbeatmap_type = {'id': 0, 'name': 'Author - Title', 'audio':\n '.\\\\somefile.mp3', 'video': '.\\\\something.mp4'}\nfor beatmap in beatmaps:\n with os.scandir(os.path.abspath(beatmap)) as it:\n bm = {'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),\n 'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(\n beatmap)[1]).split(' ')[0]) + 1:], 'audio': None,\n 'audio_length': None, 'video': None}\n print('{} {}'.format(bm['id'], bm['name']))\n for entry in it:\n if entry.is_file():\n if entry.path.endswith('osu'):\n with open(entry.path, 'r', encoding='utf-8') as f:\n config_string = '[global]\\n' + f.read()\n a = ''\n for x in config_string.split('\\n')[:config_string.split\n ('\\n').index('[Events]') - 1]:\n a += x + '\\n'\n config = configparser.ConfigParser(allow_no_value=True)\n config.read_string(a)\n bm['audio'] = os.path.abspath(os.path.dirname(entry.\n path) + '\\\\' + config.get('General', 'AudioFilename'))\n elif entry.path.endswith('mp4') or entry.path.endswith('avi'\n ) or entry.path.endswith('mpg'):\n bm['video'] = entry.path\n bm_osu.append(bm)\ntext_playlist = ''\nfor bm in bm_osu:\n if bm['audio']:\n text_playlist += '#EXTINF:0,{0}\\n{1}\\n'.format(bm['name'], bm['audio'])\ntext_playlist = text_playlist[:-1]\ntry:\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\nexcept:\n open('osu.m3u', 'x')\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\ntext_type = ''\nfor bm in bm_osu:\n if bm['name']:\n text_type += '{0}\\n'.format(bm['name'])\ntext_type = text_type[:-1]\ntry:\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\nexcept:\n open('osu.txt', 'x')\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\nfor bm in bm_osu:\n if bm['audio']:\n print('{} {}'.format(bm['id'], bm['name']))\n if os.path.basename(bm['audio']).split('.')[-1] != '':\n shutil.copy2(bm['audio'], '{}\\\\osu music\\\\{}.{}'.format(os.\n getcwd(), bm['name'], os.path.basename(bm['audio']).split(\n '.')[-1]))\n if bm['video']:\n shutil.copy2(bm['video'], '{}\\\\osu music\\\\{}.{}'.format(os.getcwd(),\n bm['name'], os.path.basename(bm['video']).split('.')[-1]))\nprint('done, ty for use')\n",
"step-5": "import os\nimport shutil\nimport configparser\n\nbeatmap_dir = os.path.abspath(os.environ['LOCALAPPDATA']+'\\\\osu!\\\\Songs\\\\')\nbeatmaps = []\nbm_osu = []\n\nwith os.scandir(os.path.abspath(beatmap_dir)) as it:\n for entry in it:\n if entry.is_dir():\n try:\n beatmap_id = int(str(entry.name).split(' ')[0])\n except ValueError:\n # I'm not sure what to do about unranked maps right now, we will exclude them\n continue\n beatmaps.append(entry.path)\n\nbeatmap_type = {\n \"id\": 0, # You may parse for \"[Metadata]\\n\\nBeatmapSetID:{sid}\" (WARN: Earlier maps will lack this parameter (osu file format v3 < osu file format v14)) or use the one provided with path\n \"name\": 'Author - Title', # I should get it from osu files rather than directory, but that's how it happens\n \"audio\": \".\\\\somefile.mp3\", # Parse for \"[General]\\n\\nAudioFilename: {filename}\" | DONE\n \"video\": \".\\\\something.mp4\" # Parse for \"[Events]\\n\\nVideo,{timestamp},{filename}\" (found mp4,avi,mpg) | plz check, TODO\n}\n\nfor beatmap in beatmaps:\n with os.scandir(os.path.abspath(beatmap)) as it:\n bm = {\n 'id': int(str(os.path.split(beatmap)[1]).split(' ')[0]),\n 'name': str(os.path.split(beatmap)[1])[len(str(os.path.split(beatmap)[1]).split(' ')[0])+1:],\n 'audio': None,\n 'audio_length': None,\n 'video': None\n }\n print('{} {}'.format(bm['id'], bm['name']))\n for entry in it:\n if entry.is_file():\n if entry.path.endswith('osu'):\n # ConfigParser is actually overkill solution, although I set it up to work\n # FixMe: This solution does not account for multiple (via diff) maps in one\n # Although, ranked maps should never have this.\n with open(entry.path, 'r', encoding=\"utf-8\") as f:\n config_string = '[global]\\n' + f.read()\n a = ''\n for x in config_string.split('\\n')[:config_string.split('\\n').index('[Events]')-1]:\n a += x+'\\n'\n config = configparser.ConfigParser(allow_no_value=True)\n config.read_string(a)\n # TODO: Rewrite to simple checks and add video checking.\n bm['audio'] = os.path.abspath(os.path.dirname(entry.path)+'\\\\'+config.get('General', 'AudioFilename'))\n elif entry.path.endswith('mp4') or entry.path.endswith('avi') or entry.path.endswith('mpg'):\n bm['video'] = entry.path\n bm_osu.append(bm)\n\n\ntext_playlist = \"\"\nfor bm in bm_osu:\n if bm['audio']:\n text_playlist += \"#EXTINF:0,{0}\\n{1}\\n\".format(bm['name'], bm['audio'])\n\ntext_playlist = text_playlist[:-1]\n\ntry:\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\nexcept:\n open('osu.m3u', 'x')\n with open('osu.m3u', 'w', encoding='utf-8') as file:\n file.write(text_playlist)\n\ntext_type = \"\"\nfor bm in bm_osu:\n if bm['name']:\n text_type += \"{0}\\n\".format(bm['name'])\ntext_type = text_type[:-1]\ntry:\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\nexcept:\n open('osu.txt', 'x')\n with open('osu.txt', 'w', encoding='utf-8') as file:\n file.write(text_type)\n\nfor bm in bm_osu:\n if bm['audio']:\n print('{} {}'.format(bm['id'], bm['name']))\n if os.path.basename(bm['audio']).split('.')[-1] != '':\n shutil.copy2(bm['audio'], \"{}\\\\osu music\\\\{}.{}\".format(os.getcwd(), bm['name'], os.path.basename(bm['audio']).split('.')[-1]))\n if bm['video']:\n shutil.copy2(bm['video'], \"{}\\\\osu music\\\\{}.{}\".format(os.getcwd(), bm['name'], os.path.basename(bm['video']).split('.')[-1]))\n\n\n\nprint('done, ty for use')",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_namespace(xml_path):
with open(xml_path) as f:
namespaces = re.findall('xmlns:(.*?)=\\"(.*?)\\"', f.read())
return dict(namespaces)
def get_comic_data(item, ns):
return {'title': item.find('title').text, 'post_date': item.find(
'pubDate').text, 'path': '', 'guid': item.find('guid').text,
'alt_text': '', 'tags': [child.text for child in item.findall(
'category')], 'text': item.find('content:encoded', ns).text}
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_namespace(xml_path):
with open(xml_path) as f:
namespaces = re.findall('xmlns:(.*?)=\\"(.*?)\\"', f.read())
return dict(namespaces)
def get_comic_data(item, ns):
return {'title': item.find('title').text, 'post_date': item.find(
'pubDate').text, 'path': '', 'guid': item.find('guid').text,
'alt_text': '', 'tags': [child.text for child in item.findall(
'category')], 'text': item.find('content:encoded', ns).text}
def get_comics(xml_path):
ns = get_namespace(xml_path)
tree = ElementTree.parse(xml_path)
root = tree.getroot()
assert root.tag == 'rss'
channel = root[0]
assert channel.tag == 'channel'
version = channel.find('wp:wxr_version', ns).text
assert version == '1.2'
return [get_comic_data(item, ns) for item in channel.findall('item')]
<|reserved_special_token_1|>
import re
from xml.etree import ElementTree
def get_namespace(xml_path):
with open(xml_path) as f:
namespaces = re.findall('xmlns:(.*?)=\\"(.*?)\\"', f.read())
return dict(namespaces)
def get_comic_data(item, ns):
return {'title': item.find('title').text, 'post_date': item.find(
'pubDate').text, 'path': '', 'guid': item.find('guid').text,
'alt_text': '', 'tags': [child.text for child in item.findall(
'category')], 'text': item.find('content:encoded', ns).text}
def get_comics(xml_path):
ns = get_namespace(xml_path)
tree = ElementTree.parse(xml_path)
root = tree.getroot()
assert root.tag == 'rss'
channel = root[0]
assert channel.tag == 'channel'
version = channel.find('wp:wxr_version', ns).text
assert version == '1.2'
return [get_comic_data(item, ns) for item in channel.findall('item')]
<|reserved_special_token_1|>
import re
from xml.etree import ElementTree
def get_namespace(xml_path):
with open(xml_path) as f:
namespaces = re.findall(r"xmlns:(.*?)=\"(.*?)\"", f.read())
return dict(namespaces)
def get_comic_data(item, ns):
return {
"title": item.find("title").text,
"post_date": item.find("pubDate").text,
"path": "",
"guid": item.find("guid").text,
"alt_text": "",
"tags": [child.text for child in item.findall("category")],
"text": item.find("content:encoded", ns).text,
}
def get_comics(xml_path):
ns = get_namespace(xml_path)
tree = ElementTree.parse(xml_path)
root = tree.getroot()
assert root.tag == "rss"
channel = root[0]
assert channel.tag == "channel"
version = channel.find("wp:wxr_version", ns).text
assert version == "1.2"
return [get_comic_data(item, ns) for item in channel.findall("item")]
|
flexible
|
{
"blob_id": "86a15bb2e4d59fb5c8763fa2de31164beb327685",
"index": 7928,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_namespace(xml_path):\n with open(xml_path) as f:\n namespaces = re.findall('xmlns:(.*?)=\\\\\"(.*?)\\\\\"', f.read())\n return dict(namespaces)\n\n\ndef get_comic_data(item, ns):\n return {'title': item.find('title').text, 'post_date': item.find(\n 'pubDate').text, 'path': '', 'guid': item.find('guid').text,\n 'alt_text': '', 'tags': [child.text for child in item.findall(\n 'category')], 'text': item.find('content:encoded', ns).text}\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef get_namespace(xml_path):\n with open(xml_path) as f:\n namespaces = re.findall('xmlns:(.*?)=\\\\\"(.*?)\\\\\"', f.read())\n return dict(namespaces)\n\n\ndef get_comic_data(item, ns):\n return {'title': item.find('title').text, 'post_date': item.find(\n 'pubDate').text, 'path': '', 'guid': item.find('guid').text,\n 'alt_text': '', 'tags': [child.text for child in item.findall(\n 'category')], 'text': item.find('content:encoded', ns).text}\n\n\ndef get_comics(xml_path):\n ns = get_namespace(xml_path)\n tree = ElementTree.parse(xml_path)\n root = tree.getroot()\n assert root.tag == 'rss'\n channel = root[0]\n assert channel.tag == 'channel'\n version = channel.find('wp:wxr_version', ns).text\n assert version == '1.2'\n return [get_comic_data(item, ns) for item in channel.findall('item')]\n",
"step-4": "import re\nfrom xml.etree import ElementTree\n\n\ndef get_namespace(xml_path):\n with open(xml_path) as f:\n namespaces = re.findall('xmlns:(.*?)=\\\\\"(.*?)\\\\\"', f.read())\n return dict(namespaces)\n\n\ndef get_comic_data(item, ns):\n return {'title': item.find('title').text, 'post_date': item.find(\n 'pubDate').text, 'path': '', 'guid': item.find('guid').text,\n 'alt_text': '', 'tags': [child.text for child in item.findall(\n 'category')], 'text': item.find('content:encoded', ns).text}\n\n\ndef get_comics(xml_path):\n ns = get_namespace(xml_path)\n tree = ElementTree.parse(xml_path)\n root = tree.getroot()\n assert root.tag == 'rss'\n channel = root[0]\n assert channel.tag == 'channel'\n version = channel.find('wp:wxr_version', ns).text\n assert version == '1.2'\n return [get_comic_data(item, ns) for item in channel.findall('item')]\n",
"step-5": "import re\nfrom xml.etree import ElementTree\n\n\ndef get_namespace(xml_path):\n with open(xml_path) as f:\n namespaces = re.findall(r\"xmlns:(.*?)=\\\"(.*?)\\\"\", f.read())\n return dict(namespaces)\n\n\ndef get_comic_data(item, ns):\n return {\n \"title\": item.find(\"title\").text,\n \"post_date\": item.find(\"pubDate\").text,\n \"path\": \"\",\n \"guid\": item.find(\"guid\").text,\n \"alt_text\": \"\",\n \"tags\": [child.text for child in item.findall(\"category\")],\n \"text\": item.find(\"content:encoded\", ns).text,\n }\n\n\ndef get_comics(xml_path):\n ns = get_namespace(xml_path)\n\n tree = ElementTree.parse(xml_path)\n root = tree.getroot()\n assert root.tag == \"rss\"\n channel = root[0]\n assert channel.tag == \"channel\"\n\n version = channel.find(\"wp:wxr_version\", ns).text\n assert version == \"1.2\"\n\n return [get_comic_data(item, ns) for item in channel.findall(\"item\")]\n",
"step-ids": [
0,
2,
3,
4,
5
]
}
|
[
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def write_command(serial, comm, verbose=False, dt=None):
""" Encodes a command and sends it over the serial port """
if verbose and comm != '':
if dt is None:
print('{} \t\t-> {}'.format(comm, serial.port))
else:
print('{} \t\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))
serial.write(comm.encode())
def read_buffer(serial):
""" Reads the serial port bufer and decodes it """
resp = serial.read_all()
return resp.decode()
def read_and_print(serial):
""" Obtains serial responser and prints it if it's not empty """
resp = read_buffer(serial)
if resp != '':
print(resp)
def runcommands(cs, ts, ps, serials, verbose=False, profiling=False):
""" Runs a series of commands at certain specified times """
if len(ts) == len(cs):
i = 0
t0 = time.time()
dt = time.time() - t0
while i < len(cs):
ser = serials[ps[i]]
comm = cs[i]
t = ts[i]
while dt - t < 0.0005:
dt = time.time() - t0
if verbose:
read_and_print(ser)
if profiling:
write_command(ser, comm, verbose, dt)
else:
write_command(ser, comm, verbose)
i += 1
else:
print('Error: Lists are not equally long. ')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def write_command(serial, comm, verbose=False, dt=None):
""" Encodes a command and sends it over the serial port """
if verbose and comm != '':
if dt is None:
print('{} \t\t-> {}'.format(comm, serial.port))
else:
print('{} \t\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))
serial.write(comm.encode())
def read_buffer(serial):
""" Reads the serial port bufer and decodes it """
resp = serial.read_all()
return resp.decode()
def read_and_print(serial):
""" Obtains serial responser and prints it if it's not empty """
resp = read_buffer(serial)
if resp != '':
print(resp)
def runcommands(cs, ts, ps, serials, verbose=False, profiling=False):
""" Runs a series of commands at certain specified times """
if len(ts) == len(cs):
i = 0
t0 = time.time()
dt = time.time() - t0
while i < len(cs):
ser = serials[ps[i]]
comm = cs[i]
t = ts[i]
while dt - t < 0.0005:
dt = time.time() - t0
if verbose:
read_and_print(ser)
if profiling:
write_command(ser, comm, verbose, dt)
else:
write_command(ser, comm, verbose)
i += 1
else:
print('Error: Lists are not equally long. ')
def load_csv(f):
delimiter = ','
ts = []
cs = []
ps = []
for l in f.readlines():
values = l.strip('\n').split(delimiter)
ts.append(float(values[0]))
cs.append(values[1])
if len(values) <= 3:
values.append('')
p = values[2].strip(' ')
if p == '':
ps.append(ps[-1])
else:
ps.append(p)
return ts, cs, ps
<|reserved_special_token_0|>
parser.add_argument('filename', type=str, help=
'CSV file with columns for time, commands and ports')
parser.add_argument('-r', '--reps', required=False, default=1, type=int,
help='Number of command sequence repetitions (default: %(default)s)')
parser.add_argument('-bd', '--baudrate', required=False, default=38400,
type=int, help='Baudrate (default: %(default)s)')
parser.add_argument('-v', '--verbose', required=False, action='store_true',
help='Print Commands as they are sent (default: %(default)s)')
parser.add_argument('-p', '--profiling', required=False, action=
'store_true', help=
'Show profiling information if verbose (default: %(default)s).')
<|reserved_special_token_0|>
try:
f = open(fname, 'r')
ts, cs, ps = load_csv(f)
ts_rep = []
for r in range(reps):
for t in ts:
ts_rep.append(t + ts[-1] * r)
cs_rep = cs * reps
ps_reps = ps * reps
try:
ports = list(set(ps))
serials = {}
for port in ports:
ser = serial.Serial(port=port, baudrate=baudrate, write_timeout
=0, bytesize=serial.EIGHTBITS, stopbits=serial.STOPBITS_ONE,
parity=serial.PARITY_NONE)
serials[port] = ser
runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)
finally:
time.sleep(0.5)
for ser in serials.values():
ser.close()
finally:
f.close()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def write_command(serial, comm, verbose=False, dt=None):
""" Encodes a command and sends it over the serial port """
if verbose and comm != '':
if dt is None:
print('{} \t\t-> {}'.format(comm, serial.port))
else:
print('{} \t\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))
serial.write(comm.encode())
def read_buffer(serial):
""" Reads the serial port bufer and decodes it """
resp = serial.read_all()
return resp.decode()
def read_and_print(serial):
""" Obtains serial responser and prints it if it's not empty """
resp = read_buffer(serial)
if resp != '':
print(resp)
def runcommands(cs, ts, ps, serials, verbose=False, profiling=False):
""" Runs a series of commands at certain specified times """
if len(ts) == len(cs):
i = 0
t0 = time.time()
dt = time.time() - t0
while i < len(cs):
ser = serials[ps[i]]
comm = cs[i]
t = ts[i]
while dt - t < 0.0005:
dt = time.time() - t0
if verbose:
read_and_print(ser)
if profiling:
write_command(ser, comm, verbose, dt)
else:
write_command(ser, comm, verbose)
i += 1
else:
print('Error: Lists are not equally long. ')
def load_csv(f):
delimiter = ','
ts = []
cs = []
ps = []
for l in f.readlines():
values = l.strip('\n').split(delimiter)
ts.append(float(values[0]))
cs.append(values[1])
if len(values) <= 3:
values.append('')
p = values[2].strip(' ')
if p == '':
ps.append(ps[-1])
else:
ps.append(p)
return ts, cs, ps
parser = argparse.ArgumentParser(description=
'sends a series of commands over the serial port')
parser.add_argument('filename', type=str, help=
'CSV file with columns for time, commands and ports')
parser.add_argument('-r', '--reps', required=False, default=1, type=int,
help='Number of command sequence repetitions (default: %(default)s)')
parser.add_argument('-bd', '--baudrate', required=False, default=38400,
type=int, help='Baudrate (default: %(default)s)')
parser.add_argument('-v', '--verbose', required=False, action='store_true',
help='Print Commands as they are sent (default: %(default)s)')
parser.add_argument('-p', '--profiling', required=False, action=
'store_true', help=
'Show profiling information if verbose (default: %(default)s).')
args = parser.parse_args()
fname = args.filename
reps = args.reps
baudrate = args.baudrate
verbose = args.verbose
profiling = args.profiling
try:
f = open(fname, 'r')
ts, cs, ps = load_csv(f)
ts_rep = []
for r in range(reps):
for t in ts:
ts_rep.append(t + ts[-1] * r)
cs_rep = cs * reps
ps_reps = ps * reps
try:
ports = list(set(ps))
serials = {}
for port in ports:
ser = serial.Serial(port=port, baudrate=baudrate, write_timeout
=0, bytesize=serial.EIGHTBITS, stopbits=serial.STOPBITS_ONE,
parity=serial.PARITY_NONE)
serials[port] = ser
runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)
finally:
time.sleep(0.5)
for ser in serials.values():
ser.close()
finally:
f.close()
<|reserved_special_token_1|>
import serial
import time
import argparse
def write_command(serial, comm, verbose=False, dt=None):
""" Encodes a command and sends it over the serial port """
if verbose and comm != '':
if dt is None:
print('{} \t\t-> {}'.format(comm, serial.port))
else:
print('{} \t\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))
serial.write(comm.encode())
def read_buffer(serial):
""" Reads the serial port bufer and decodes it """
resp = serial.read_all()
return resp.decode()
def read_and_print(serial):
""" Obtains serial responser and prints it if it's not empty """
resp = read_buffer(serial)
if resp != '':
print(resp)
def runcommands(cs, ts, ps, serials, verbose=False, profiling=False):
""" Runs a series of commands at certain specified times """
if len(ts) == len(cs):
i = 0
t0 = time.time()
dt = time.time() - t0
while i < len(cs):
ser = serials[ps[i]]
comm = cs[i]
t = ts[i]
while dt - t < 0.0005:
dt = time.time() - t0
if verbose:
read_and_print(ser)
if profiling:
write_command(ser, comm, verbose, dt)
else:
write_command(ser, comm, verbose)
i += 1
else:
print('Error: Lists are not equally long. ')
def load_csv(f):
delimiter = ','
ts = []
cs = []
ps = []
for l in f.readlines():
values = l.strip('\n').split(delimiter)
ts.append(float(values[0]))
cs.append(values[1])
if len(values) <= 3:
values.append('')
p = values[2].strip(' ')
if p == '':
ps.append(ps[-1])
else:
ps.append(p)
return ts, cs, ps
parser = argparse.ArgumentParser(description=
'sends a series of commands over the serial port')
parser.add_argument('filename', type=str, help=
'CSV file with columns for time, commands and ports')
parser.add_argument('-r', '--reps', required=False, default=1, type=int,
help='Number of command sequence repetitions (default: %(default)s)')
parser.add_argument('-bd', '--baudrate', required=False, default=38400,
type=int, help='Baudrate (default: %(default)s)')
parser.add_argument('-v', '--verbose', required=False, action='store_true',
help='Print Commands as they are sent (default: %(default)s)')
parser.add_argument('-p', '--profiling', required=False, action=
'store_true', help=
'Show profiling information if verbose (default: %(default)s).')
args = parser.parse_args()
fname = args.filename
reps = args.reps
baudrate = args.baudrate
verbose = args.verbose
profiling = args.profiling
try:
f = open(fname, 'r')
ts, cs, ps = load_csv(f)
ts_rep = []
for r in range(reps):
for t in ts:
ts_rep.append(t + ts[-1] * r)
cs_rep = cs * reps
ps_reps = ps * reps
try:
ports = list(set(ps))
serials = {}
for port in ports:
ser = serial.Serial(port=port, baudrate=baudrate, write_timeout
=0, bytesize=serial.EIGHTBITS, stopbits=serial.STOPBITS_ONE,
parity=serial.PARITY_NONE)
serials[port] = ser
runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)
finally:
time.sleep(0.5)
for ser in serials.values():
ser.close()
finally:
f.close()
<|reserved_special_token_1|>
# -*- coding: utf-8 -*-
import serial
import time
import argparse
def write_command(serial, comm, verbose = False, dt = None):
""" Encodes a command and sends it over the serial port """
if verbose and comm != "":
if dt is None:
print("{} \t\t-> {}".format(comm, serial.port))
else:
print("{} \t\t-> {} at {:2.3f} ms".format(comm, serial.port, dt))
serial.write(comm.encode())
def read_buffer(serial):
""" Reads the serial port bufer and decodes it """
resp = serial.read_all()
return resp.decode()
def read_and_print(serial):
""" Obtains serial responser and prints it if it's not empty """
resp = read_buffer(serial)
if resp != "":
print(resp)
def runcommands(cs, ts, ps, serials, verbose = False, profiling = False):
""" Runs a series of commands at certain specified times """
if len(ts) == len(cs):
i = 0
t0 = time.time()
dt = time.time() - t0 # elapsed time
while i < len(cs):
ser = serials[ps[i]]
comm = cs[i]
t = ts[i]
while (dt - t) < 0.0005:
dt = time.time() - t0
if verbose: read_and_print(ser)
if profiling:
write_command(ser, comm, verbose, dt)
else:
write_command(ser, comm, verbose)
i += 1
else:
print('Error: Lists are not equally long. ')
def load_csv(f):
delimiter = ','
ts = []
cs = []
ps = []
for l in f.readlines():
values = l.strip("\n").split(delimiter)
ts.append(float(values[0]))
cs.append(values[1])
if len(values) <= 3: # if there isn't a third field
values.append("") # add an empty one
p = values[2].strip(" ") # take all spaces out
if p == "":
ps.append(ps[-1]) # use previous one if it's empty
else:
ps.append(p)
return ts, cs, ps
# Create argument parser
parser = argparse.ArgumentParser(description='sends a series of commands over the serial port')
parser.add_argument('filename',
type=str, help='CSV file with columns for time, commands and ports')
parser.add_argument('-r', '--reps', required = False, default=1,
type=int, help='Number of command sequence repetitions (default: %(default)s)')
parser.add_argument('-bd', '--baudrate', required = False, default=38400,
type=int, help='Baudrate (default: %(default)s)')
parser.add_argument('-v', '--verbose', required = False,
action='store_true',
help='Print Commands as they are sent (default: %(default)s)')
parser.add_argument('-p', '--profiling', required = False,
action='store_true',
help='Show profiling information if verbose (default: %(default)s).')
# Get parameters
args = parser.parse_args()
#print(args.filename)
#print(args.reps)
#print(args.baudrate)
#print(args.verbose)
#print(args.profiling)
# Parameters
fname = args.filename
reps = args.reps
baudrate = args.baudrate
verbose = args.verbose
profiling = args.profiling
# test.csv -r 2 -b 38400 -v -p
#fname = 'test.csv'
#reps = 2
#baudrate = 38400
#verbose = True
#profiling = True
try:
f = open(fname, 'r')
ts, cs, ps = load_csv(f)
# Repeat all lists the specified number of times
ts_rep = [] # offset each rep's times
for r in range(reps):
for t in ts:
ts_rep.append(t + ts[-1]*r)
cs_rep = cs*reps
ps_reps = ps*reps
# Try to open the serial port connections and run the commands
try:
# Get list of unique portnames
ports = list(set(ps))
serials = {} # serial connections
for port in ports:
ser = serial.Serial(port = port,
baudrate=baudrate,
write_timeout=0,
bytesize=serial.EIGHTBITS,
stopbits=serial.STOPBITS_ONE,
parity=serial.PARITY_NONE)
serials[port] = ser
runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)
finally:
time.sleep(0.5)
for ser in serials.values():
ser.close()
finally:
f.close()
|
flexible
|
{
"blob_id": "3ffcab4b36c6ca05f1e667c628ebb873ebdc0d25",
"index": 7866,
"step-1": "<mask token>\n\n\ndef write_command(serial, comm, verbose=False, dt=None):\n \"\"\" Encodes a command and sends it over the serial port \"\"\"\n if verbose and comm != '':\n if dt is None:\n print('{} \\t\\t-> {}'.format(comm, serial.port))\n else:\n print('{} \\t\\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))\n serial.write(comm.encode())\n\n\ndef read_buffer(serial):\n \"\"\" Reads the serial port bufer and decodes it \"\"\"\n resp = serial.read_all()\n return resp.decode()\n\n\ndef read_and_print(serial):\n \"\"\" Obtains serial responser and prints it if it's not empty \"\"\"\n resp = read_buffer(serial)\n if resp != '':\n print(resp)\n\n\ndef runcommands(cs, ts, ps, serials, verbose=False, profiling=False):\n \"\"\" Runs a series of commands at certain specified times \"\"\"\n if len(ts) == len(cs):\n i = 0\n t0 = time.time()\n dt = time.time() - t0\n while i < len(cs):\n ser = serials[ps[i]]\n comm = cs[i]\n t = ts[i]\n while dt - t < 0.0005:\n dt = time.time() - t0\n if verbose:\n read_and_print(ser)\n if profiling:\n write_command(ser, comm, verbose, dt)\n else:\n write_command(ser, comm, verbose)\n i += 1\n else:\n print('Error: Lists are not equally long. ')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef write_command(serial, comm, verbose=False, dt=None):\n \"\"\" Encodes a command and sends it over the serial port \"\"\"\n if verbose and comm != '':\n if dt is None:\n print('{} \\t\\t-> {}'.format(comm, serial.port))\n else:\n print('{} \\t\\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))\n serial.write(comm.encode())\n\n\ndef read_buffer(serial):\n \"\"\" Reads the serial port bufer and decodes it \"\"\"\n resp = serial.read_all()\n return resp.decode()\n\n\ndef read_and_print(serial):\n \"\"\" Obtains serial responser and prints it if it's not empty \"\"\"\n resp = read_buffer(serial)\n if resp != '':\n print(resp)\n\n\ndef runcommands(cs, ts, ps, serials, verbose=False, profiling=False):\n \"\"\" Runs a series of commands at certain specified times \"\"\"\n if len(ts) == len(cs):\n i = 0\n t0 = time.time()\n dt = time.time() - t0\n while i < len(cs):\n ser = serials[ps[i]]\n comm = cs[i]\n t = ts[i]\n while dt - t < 0.0005:\n dt = time.time() - t0\n if verbose:\n read_and_print(ser)\n if profiling:\n write_command(ser, comm, verbose, dt)\n else:\n write_command(ser, comm, verbose)\n i += 1\n else:\n print('Error: Lists are not equally long. ')\n\n\ndef load_csv(f):\n delimiter = ','\n ts = []\n cs = []\n ps = []\n for l in f.readlines():\n values = l.strip('\\n').split(delimiter)\n ts.append(float(values[0]))\n cs.append(values[1])\n if len(values) <= 3:\n values.append('')\n p = values[2].strip(' ')\n if p == '':\n ps.append(ps[-1])\n else:\n ps.append(p)\n return ts, cs, ps\n\n\n<mask token>\nparser.add_argument('filename', type=str, help=\n 'CSV file with columns for time, commands and ports')\nparser.add_argument('-r', '--reps', required=False, default=1, type=int,\n help='Number of command sequence repetitions (default: %(default)s)')\nparser.add_argument('-bd', '--baudrate', required=False, default=38400,\n type=int, help='Baudrate (default: %(default)s)')\nparser.add_argument('-v', '--verbose', required=False, action='store_true',\n help='Print Commands as they are sent (default: %(default)s)')\nparser.add_argument('-p', '--profiling', required=False, action=\n 'store_true', help=\n 'Show profiling information if verbose (default: %(default)s).')\n<mask token>\ntry:\n f = open(fname, 'r')\n ts, cs, ps = load_csv(f)\n ts_rep = []\n for r in range(reps):\n for t in ts:\n ts_rep.append(t + ts[-1] * r)\n cs_rep = cs * reps\n ps_reps = ps * reps\n try:\n ports = list(set(ps))\n serials = {}\n for port in ports:\n ser = serial.Serial(port=port, baudrate=baudrate, write_timeout\n =0, bytesize=serial.EIGHTBITS, stopbits=serial.STOPBITS_ONE,\n parity=serial.PARITY_NONE)\n serials[port] = ser\n runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)\n finally:\n time.sleep(0.5)\n for ser in serials.values():\n ser.close()\nfinally:\n f.close()\n",
"step-3": "<mask token>\n\n\ndef write_command(serial, comm, verbose=False, dt=None):\n \"\"\" Encodes a command and sends it over the serial port \"\"\"\n if verbose and comm != '':\n if dt is None:\n print('{} \\t\\t-> {}'.format(comm, serial.port))\n else:\n print('{} \\t\\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))\n serial.write(comm.encode())\n\n\ndef read_buffer(serial):\n \"\"\" Reads the serial port bufer and decodes it \"\"\"\n resp = serial.read_all()\n return resp.decode()\n\n\ndef read_and_print(serial):\n \"\"\" Obtains serial responser and prints it if it's not empty \"\"\"\n resp = read_buffer(serial)\n if resp != '':\n print(resp)\n\n\ndef runcommands(cs, ts, ps, serials, verbose=False, profiling=False):\n \"\"\" Runs a series of commands at certain specified times \"\"\"\n if len(ts) == len(cs):\n i = 0\n t0 = time.time()\n dt = time.time() - t0\n while i < len(cs):\n ser = serials[ps[i]]\n comm = cs[i]\n t = ts[i]\n while dt - t < 0.0005:\n dt = time.time() - t0\n if verbose:\n read_and_print(ser)\n if profiling:\n write_command(ser, comm, verbose, dt)\n else:\n write_command(ser, comm, verbose)\n i += 1\n else:\n print('Error: Lists are not equally long. ')\n\n\ndef load_csv(f):\n delimiter = ','\n ts = []\n cs = []\n ps = []\n for l in f.readlines():\n values = l.strip('\\n').split(delimiter)\n ts.append(float(values[0]))\n cs.append(values[1])\n if len(values) <= 3:\n values.append('')\n p = values[2].strip(' ')\n if p == '':\n ps.append(ps[-1])\n else:\n ps.append(p)\n return ts, cs, ps\n\n\nparser = argparse.ArgumentParser(description=\n 'sends a series of commands over the serial port')\nparser.add_argument('filename', type=str, help=\n 'CSV file with columns for time, commands and ports')\nparser.add_argument('-r', '--reps', required=False, default=1, type=int,\n help='Number of command sequence repetitions (default: %(default)s)')\nparser.add_argument('-bd', '--baudrate', required=False, default=38400,\n type=int, help='Baudrate (default: %(default)s)')\nparser.add_argument('-v', '--verbose', required=False, action='store_true',\n help='Print Commands as they are sent (default: %(default)s)')\nparser.add_argument('-p', '--profiling', required=False, action=\n 'store_true', help=\n 'Show profiling information if verbose (default: %(default)s).')\nargs = parser.parse_args()\nfname = args.filename\nreps = args.reps\nbaudrate = args.baudrate\nverbose = args.verbose\nprofiling = args.profiling\ntry:\n f = open(fname, 'r')\n ts, cs, ps = load_csv(f)\n ts_rep = []\n for r in range(reps):\n for t in ts:\n ts_rep.append(t + ts[-1] * r)\n cs_rep = cs * reps\n ps_reps = ps * reps\n try:\n ports = list(set(ps))\n serials = {}\n for port in ports:\n ser = serial.Serial(port=port, baudrate=baudrate, write_timeout\n =0, bytesize=serial.EIGHTBITS, stopbits=serial.STOPBITS_ONE,\n parity=serial.PARITY_NONE)\n serials[port] = ser\n runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)\n finally:\n time.sleep(0.5)\n for ser in serials.values():\n ser.close()\nfinally:\n f.close()\n",
"step-4": "import serial\nimport time\nimport argparse\n\n\ndef write_command(serial, comm, verbose=False, dt=None):\n \"\"\" Encodes a command and sends it over the serial port \"\"\"\n if verbose and comm != '':\n if dt is None:\n print('{} \\t\\t-> {}'.format(comm, serial.port))\n else:\n print('{} \\t\\t-> {} at {:2.3f} ms'.format(comm, serial.port, dt))\n serial.write(comm.encode())\n\n\ndef read_buffer(serial):\n \"\"\" Reads the serial port bufer and decodes it \"\"\"\n resp = serial.read_all()\n return resp.decode()\n\n\ndef read_and_print(serial):\n \"\"\" Obtains serial responser and prints it if it's not empty \"\"\"\n resp = read_buffer(serial)\n if resp != '':\n print(resp)\n\n\ndef runcommands(cs, ts, ps, serials, verbose=False, profiling=False):\n \"\"\" Runs a series of commands at certain specified times \"\"\"\n if len(ts) == len(cs):\n i = 0\n t0 = time.time()\n dt = time.time() - t0\n while i < len(cs):\n ser = serials[ps[i]]\n comm = cs[i]\n t = ts[i]\n while dt - t < 0.0005:\n dt = time.time() - t0\n if verbose:\n read_and_print(ser)\n if profiling:\n write_command(ser, comm, verbose, dt)\n else:\n write_command(ser, comm, verbose)\n i += 1\n else:\n print('Error: Lists are not equally long. ')\n\n\ndef load_csv(f):\n delimiter = ','\n ts = []\n cs = []\n ps = []\n for l in f.readlines():\n values = l.strip('\\n').split(delimiter)\n ts.append(float(values[0]))\n cs.append(values[1])\n if len(values) <= 3:\n values.append('')\n p = values[2].strip(' ')\n if p == '':\n ps.append(ps[-1])\n else:\n ps.append(p)\n return ts, cs, ps\n\n\nparser = argparse.ArgumentParser(description=\n 'sends a series of commands over the serial port')\nparser.add_argument('filename', type=str, help=\n 'CSV file with columns for time, commands and ports')\nparser.add_argument('-r', '--reps', required=False, default=1, type=int,\n help='Number of command sequence repetitions (default: %(default)s)')\nparser.add_argument('-bd', '--baudrate', required=False, default=38400,\n type=int, help='Baudrate (default: %(default)s)')\nparser.add_argument('-v', '--verbose', required=False, action='store_true',\n help='Print Commands as they are sent (default: %(default)s)')\nparser.add_argument('-p', '--profiling', required=False, action=\n 'store_true', help=\n 'Show profiling information if verbose (default: %(default)s).')\nargs = parser.parse_args()\nfname = args.filename\nreps = args.reps\nbaudrate = args.baudrate\nverbose = args.verbose\nprofiling = args.profiling\ntry:\n f = open(fname, 'r')\n ts, cs, ps = load_csv(f)\n ts_rep = []\n for r in range(reps):\n for t in ts:\n ts_rep.append(t + ts[-1] * r)\n cs_rep = cs * reps\n ps_reps = ps * reps\n try:\n ports = list(set(ps))\n serials = {}\n for port in ports:\n ser = serial.Serial(port=port, baudrate=baudrate, write_timeout\n =0, bytesize=serial.EIGHTBITS, stopbits=serial.STOPBITS_ONE,\n parity=serial.PARITY_NONE)\n serials[port] = ser\n runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)\n finally:\n time.sleep(0.5)\n for ser in serials.values():\n ser.close()\nfinally:\n f.close()\n",
"step-5": "# -*- coding: utf-8 -*-\r\n\r\nimport serial\r\nimport time\r\nimport argparse\r\n\r\n \r\ndef write_command(serial, comm, verbose = False, dt = None):\r\n \"\"\" Encodes a command and sends it over the serial port \"\"\"\r\n if verbose and comm != \"\":\r\n if dt is None:\r\n print(\"{} \\t\\t-> {}\".format(comm, serial.port))\r\n else:\r\n print(\"{} \\t\\t-> {} at {:2.3f} ms\".format(comm, serial.port, dt))\r\n serial.write(comm.encode())\r\n \r\ndef read_buffer(serial):\r\n \"\"\" Reads the serial port bufer and decodes it \"\"\"\r\n resp = serial.read_all()\r\n return resp.decode()\r\n\r\ndef read_and_print(serial):\r\n \"\"\" Obtains serial responser and prints it if it's not empty \"\"\"\r\n resp = read_buffer(serial)\r\n if resp != \"\":\r\n print(resp)\r\n \r\n\r\ndef runcommands(cs, ts, ps, serials, verbose = False, profiling = False):\r\n \"\"\" Runs a series of commands at certain specified times \"\"\"\r\n if len(ts) == len(cs):\r\n i = 0\r\n t0 = time.time()\r\n dt = time.time() - t0 # elapsed time\r\n while i < len(cs):\r\n ser = serials[ps[i]]\r\n comm = cs[i]\r\n t = ts[i]\r\n while (dt - t) < 0.0005:\r\n dt = time.time() - t0\r\n if verbose: read_and_print(ser)\r\n if profiling:\r\n write_command(ser, comm, verbose, dt)\r\n else:\r\n write_command(ser, comm, verbose)\r\n i += 1\r\n else:\r\n print('Error: Lists are not equally long. ')\r\n\r\n\r\ndef load_csv(f):\r\n delimiter = ','\r\n ts = []\r\n cs = []\r\n ps = []\r\n for l in f.readlines():\r\n values = l.strip(\"\\n\").split(delimiter)\r\n ts.append(float(values[0]))\r\n cs.append(values[1])\r\n if len(values) <= 3: # if there isn't a third field\r\n values.append(\"\") # add an empty one\r\n p = values[2].strip(\" \") # take all spaces out\r\n if p == \"\": \r\n ps.append(ps[-1]) # use previous one if it's empty\r\n else:\r\n ps.append(p)\r\n return ts, cs, ps\r\n\r\n# Create argument parser\r\n \r\nparser = argparse.ArgumentParser(description='sends a series of commands over the serial port')\r\nparser.add_argument('filename',\r\n type=str, help='CSV file with columns for time, commands and ports')\r\nparser.add_argument('-r', '--reps', required = False, default=1,\r\n type=int, help='Number of command sequence repetitions (default: %(default)s)')\r\nparser.add_argument('-bd', '--baudrate', required = False, default=38400,\r\n type=int, help='Baudrate (default: %(default)s)')\r\nparser.add_argument('-v', '--verbose', required = False,\r\n action='store_true',\r\n help='Print Commands as they are sent (default: %(default)s)')\r\nparser.add_argument('-p', '--profiling', required = False,\r\n action='store_true',\r\n help='Show profiling information if verbose (default: %(default)s).')\r\n \r\n# Get parameters\r\nargs = parser.parse_args()\r\n#print(args.filename)\r\n#print(args.reps)\r\n#print(args.baudrate)\r\n#print(args.verbose)\r\n#print(args.profiling)\r\n\r\n# Parameters\r\nfname = args.filename\r\nreps = args.reps\r\nbaudrate = args.baudrate\r\nverbose = args.verbose\r\nprofiling = args.profiling\r\n\r\n# test.csv -r 2 -b 38400 -v -p\r\n#fname = 'test.csv'\r\n#reps = 2\r\n#baudrate = 38400\r\n#verbose = True\r\n#profiling = True\r\ntry: \r\n f = open(fname, 'r')\r\n ts, cs, ps = load_csv(f)\r\n\r\n # Repeat all lists the specified number of times\r\n ts_rep = [] # offset each rep's times\r\n for r in range(reps):\r\n for t in ts:\r\n ts_rep.append(t + ts[-1]*r)\r\n cs_rep = cs*reps\r\n ps_reps = ps*reps\r\n \r\n # Try to open the serial port connections and run the commands\r\n\r\n try:\r\n # Get list of unique portnames\r\n ports = list(set(ps))\r\n serials = {} # serial connections\r\n for port in ports:\r\n ser = serial.Serial(port = port, \r\n baudrate=baudrate,\r\n write_timeout=0,\r\n bytesize=serial.EIGHTBITS,\r\n stopbits=serial.STOPBITS_ONE,\r\n parity=serial.PARITY_NONE)\r\n serials[port] = ser\r\n runcommands(cs_rep, ts_rep, ps_reps, serials, verbose, profiling)\r\n finally:\r\n time.sleep(0.5)\r\n for ser in serials.values():\r\n ser.close()\r\nfinally:\r\n f.close()",
"step-ids": [
4,
6,
7,
8,
9
]
}
|
[
4,
6,
7,
8,
9
] |
def rank_and_file(l):
dict = {}
final_list = []
for each in l:
for num in each:
dict[num] = dict[num] + 1 if num in dict else 1
for key in dict:
if dict[key] % 2 != 0:
final_list.append(key)
final_list = sorted(final_list)
return " ".join(map(str, final_list))
f = open('B-large.in.txt', 'r')
f2 = open('outputLarge.txt', 'w')
final = ''
for i in range(1, int(f.readline().strip())+1):
l = []
for j in range(2*int(f.readline()) - 1):
l.append(map(int, f.readline().strip().split()))
final += 'Case #{}: {}\n'.format(i, rank_and_file(l))
f2.write(final)
|
normal
|
{
"blob_id": "359f4fa75379cc2dd80d372144ced08b8d15e0a4",
"index": 6758,
"step-1": "<mask token>\n",
"step-2": "def rank_and_file(l):\n dict = {}\n final_list = []\n for each in l:\n for num in each:\n dict[num] = dict[num] + 1 if num in dict else 1\n for key in dict:\n if dict[key] % 2 != 0:\n final_list.append(key)\n final_list = sorted(final_list)\n return ' '.join(map(str, final_list))\n\n\n<mask token>\n",
"step-3": "def rank_and_file(l):\n dict = {}\n final_list = []\n for each in l:\n for num in each:\n dict[num] = dict[num] + 1 if num in dict else 1\n for key in dict:\n if dict[key] % 2 != 0:\n final_list.append(key)\n final_list = sorted(final_list)\n return ' '.join(map(str, final_list))\n\n\n<mask token>\nfor i in range(1, int(f.readline().strip()) + 1):\n l = []\n for j in range(2 * int(f.readline()) - 1):\n l.append(map(int, f.readline().strip().split()))\n final += 'Case #{}: {}\\n'.format(i, rank_and_file(l))\nf2.write(final)\n",
"step-4": "def rank_and_file(l):\n dict = {}\n final_list = []\n for each in l:\n for num in each:\n dict[num] = dict[num] + 1 if num in dict else 1\n for key in dict:\n if dict[key] % 2 != 0:\n final_list.append(key)\n final_list = sorted(final_list)\n return ' '.join(map(str, final_list))\n\n\nf = open('B-large.in.txt', 'r')\nf2 = open('outputLarge.txt', 'w')\nfinal = ''\nfor i in range(1, int(f.readline().strip()) + 1):\n l = []\n for j in range(2 * int(f.readline()) - 1):\n l.append(map(int, f.readline().strip().split()))\n final += 'Case #{}: {}\\n'.format(i, rank_and_file(l))\nf2.write(final)\n",
"step-5": "def rank_and_file(l):\n\tdict = {}\n\tfinal_list = []\n\tfor each in l:\n\t\tfor num in each:\n\t\t\tdict[num] = dict[num] + 1 if num in dict else 1\n\tfor key in dict:\n\t\tif dict[key] % 2 != 0:\n\t\t\tfinal_list.append(key)\n\tfinal_list = sorted(final_list)\n\treturn \" \".join(map(str, final_list))\n\nf = open('B-large.in.txt', 'r')\nf2 = open('outputLarge.txt', 'w')\nfinal = ''\n\nfor i in range(1, int(f.readline().strip())+1):\n\tl = []\n\tfor j in range(2*int(f.readline()) - 1):\n\t\tl.append(map(int, f.readline().strip().split()))\n\tfinal += 'Case #{}: {}\\n'.format(i, rank_and_file(l))\n\nf2.write(final)",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
N = int(input("ingrese el numero de datos a ingresar "))
SP = 0
SO = 0
CP = 0
for i in range(1,N+1,1):
NUM = int(input("ingrese un numero entero "))
if NUM > 0:
SP += NUM
CP += 1
else:
SO += NUM
PG = (SP+SO)/N
PP = SP/CP
print(f"hay { CP } numeros positivos, el promedio general es de { PG } y el promedio de los numeros positivos es de { PP }")
|
normal
|
{
"blob_id": "efc0b8f1c4887810a9c85e34957d664b01c1e92e",
"index": 1453,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, N + 1, 1):\n NUM = int(input('ingrese un numero entero '))\n if NUM > 0:\n SP += NUM\n CP += 1\n else:\n SO += NUM\n<mask token>\nprint(\n f'hay {CP} numeros positivos, el promedio general es de {PG} y el promedio de los numeros positivos es de {PP}'\n )\n",
"step-3": "N = int(input('ingrese el numero de datos a ingresar '))\nSP = 0\nSO = 0\nCP = 0\nfor i in range(1, N + 1, 1):\n NUM = int(input('ingrese un numero entero '))\n if NUM > 0:\n SP += NUM\n CP += 1\n else:\n SO += NUM\nPG = (SP + SO) / N\nPP = SP / CP\nprint(\n f'hay {CP} numeros positivos, el promedio general es de {PG} y el promedio de los numeros positivos es de {PP}'\n )\n",
"step-4": "N = int(input(\"ingrese el numero de datos a ingresar \"))\nSP = 0\nSO = 0\nCP = 0\nfor i in range(1,N+1,1):\n NUM = int(input(\"ingrese un numero entero \"))\n if NUM > 0:\n SP += NUM\n CP += 1\n else:\n SO += NUM\nPG = (SP+SO)/N\nPP = SP/CP\nprint(f\"hay { CP } numeros positivos, el promedio general es de { PG } y el promedio de los numeros positivos es de { PP }\")\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
from . import cli
cli.run()
|
normal
|
{
"blob_id": "235623c3f557dbc28fbff855a618e4d26932ca65",
"index": 7630,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncli.run()\n",
"step-3": "from . import cli\ncli.run()\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
from flask import (Flask,
render_template,
request,
url_for,
redirect,
flash,
jsonify)
app = Flask(__name__)
@app.route('/', methods=['GET'])
def showHomepage():
return render_template('home.html')
if __name__ == '__main__':
print('app started')
app.secret_key = 'secretkey'
app.run(debug=True)
|
normal
|
{
"blob_id": "3001534be3364be1148cd51a4a943fd8c975d87e",
"index": 8384,
"step-1": "<mask token>\n\n\n@app.route('/', methods=['GET'])\ndef showHomepage():\n return render_template('home.html')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/', methods=['GET'])\ndef showHomepage():\n return render_template('home.html')\n\n\nif __name__ == '__main__':\n print('app started')\n app.secret_key = 'secretkey'\n app.run(debug=True)\n",
"step-3": "<mask token>\napp = Flask(__name__)\n\n\n@app.route('/', methods=['GET'])\ndef showHomepage():\n return render_template('home.html')\n\n\nif __name__ == '__main__':\n print('app started')\n app.secret_key = 'secretkey'\n app.run(debug=True)\n",
"step-4": "from flask import Flask, render_template, request, url_for, redirect, flash, jsonify\napp = Flask(__name__)\n\n\n@app.route('/', methods=['GET'])\ndef showHomepage():\n return render_template('home.html')\n\n\nif __name__ == '__main__':\n print('app started')\n app.secret_key = 'secretkey'\n app.run(debug=True)\n",
"step-5": "from flask import (Flask,\n\trender_template,\n\trequest,\n\turl_for,\n\tredirect,\n\tflash,\n\tjsonify)\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef showHomepage():\n\treturn render_template('home.html')\n\n\nif __name__ == '__main__':\n\tprint('app started')\n\tapp.secret_key = 'secretkey'\n\tapp.run(debug=True)\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def parse(node, array):
string = ''
string += '\t%s = ScrollView:create();\n' % node
if array.get('IsBounceEnabled') != None and array['IsBounceEnabled']:
string += '\t%s:setBounceEnabled(true);\n' % node
if array.get('InnerNodeSize') != None:
string += (
'\t%s:setInnerContainerSize({width = %.2f, height = %.2f});\n' %
(node, array['InnerNodeSize']['Width'], array['InnerNodeSize'][
'Height']))
if array['ScrollDirectionType'] == 'Horizontal':
string += '\t%s:setDirection(0);\n' % node
elif array['ScrollDirectionType'] == 'Vertical':
string += '\t%s:setDirection(1);\n' % node
return string
<|reserved_special_token_1|>
#!/usr/bin/python
# -*- coding: UTF-8 -*-
def parse(node, array):
string = ''
string += "\t%s = ScrollView:create();\n" % node
if (array.get('IsBounceEnabled') != None and array['IsBounceEnabled']):
string += "\t%s:setBounceEnabled(true);\n" % node
if (array.get('InnerNodeSize') != None):
string += "\t%s:setInnerContainerSize({width = %.2f, height = %.2f});\n" % (node, array['InnerNodeSize']['Width'], array['InnerNodeSize']['Height'])
if (array['ScrollDirectionType'] == 'Horizontal'):
string += "\t%s:setDirection(0);\n" % node
elif (array['ScrollDirectionType'] == 'Vertical'):
string += "\t%s:setDirection(1);\n" % node
return string
|
flexible
|
{
"blob_id": "97b94f3388a6e2473d43e3c4c4e281a86a031dbb",
"index": 9288,
"step-1": "<mask token>\n",
"step-2": "def parse(node, array):\n string = ''\n string += '\\t%s = ScrollView:create();\\n' % node\n if array.get('IsBounceEnabled') != None and array['IsBounceEnabled']:\n string += '\\t%s:setBounceEnabled(true);\\n' % node\n if array.get('InnerNodeSize') != None:\n string += (\n '\\t%s:setInnerContainerSize({width = %.2f, height = %.2f});\\n' %\n (node, array['InnerNodeSize']['Width'], array['InnerNodeSize'][\n 'Height']))\n if array['ScrollDirectionType'] == 'Horizontal':\n string += '\\t%s:setDirection(0);\\n' % node\n elif array['ScrollDirectionType'] == 'Vertical':\n string += '\\t%s:setDirection(1);\\n' % node\n return string\n",
"step-3": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\ndef parse(node, array):\n string = ''\n string += \"\\t%s = ScrollView:create();\\n\" % node\n\n if (array.get('IsBounceEnabled') != None and array['IsBounceEnabled']):\n string += \"\\t%s:setBounceEnabled(true);\\n\" % node\n \n if (array.get('InnerNodeSize') != None):\n string += \"\\t%s:setInnerContainerSize({width = %.2f, height = %.2f});\\n\" % (node, array['InnerNodeSize']['Width'], array['InnerNodeSize']['Height'])\n\n if (array['ScrollDirectionType'] == 'Horizontal'):\n string += \"\\t%s:setDirection(0);\\n\" % node\n elif (array['ScrollDirectionType'] == 'Vertical'):\n string += \"\\t%s:setDirection(1);\\n\" % node\n \n return string",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
<|reserved_special_token_0|>
class OneStepExplorerWithTrace(BaseExplorer):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def step(self, policy: BasePolicy):
if self.n_interaction > self.n_max_interaction:
raise InteractionExceeded()
a = policy.step(self.last_s)
s, r, done, info = self.env.step(a)
self.n_interaction += 1
self.trace['s'].append(self.last_s)
self.trace['a'].append(a)
self.trace['r'].append(r)
self.trace['done'].append(done)
self.trace['final_s'] = s
self.trace['final_a'] = policy.step(s)
self.last_s = s
if done:
self.last_s = self.env.reset()
self.n_ep += 1
self._update_stats(self.n_interaction, info['episode']['reward'])
return self.trace
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class OneStepExplorerWithTrace(BaseExplorer):
<|reserved_special_token_0|>
def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):
super().__init__(env)
self.n_step = n_step
self.n_max_interaction = n_max_interaction
self.last_s = self.env.reset()
self.trace = defaultdict(lambda : deque(maxlen=n_step))
self.n_interaction = 0
self.n_ep = 0
def step(self, policy: BasePolicy):
if self.n_interaction > self.n_max_interaction:
raise InteractionExceeded()
a = policy.step(self.last_s)
s, r, done, info = self.env.step(a)
self.n_interaction += 1
self.trace['s'].append(self.last_s)
self.trace['a'].append(a)
self.trace['r'].append(r)
self.trace['done'].append(done)
self.trace['final_s'] = s
self.trace['final_a'] = policy.step(s)
self.last_s = s
if done:
self.last_s = self.env.reset()
self.n_ep += 1
self._update_stats(self.n_interaction, info['episode']['reward'])
return self.trace
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class OneStepExplorerWithTrace(BaseExplorer):
"""one-step explorer but with n-step trace"""
def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):
super().__init__(env)
self.n_step = n_step
self.n_max_interaction = n_max_interaction
self.last_s = self.env.reset()
self.trace = defaultdict(lambda : deque(maxlen=n_step))
self.n_interaction = 0
self.n_ep = 0
def step(self, policy: BasePolicy):
if self.n_interaction > self.n_max_interaction:
raise InteractionExceeded()
a = policy.step(self.last_s)
s, r, done, info = self.env.step(a)
self.n_interaction += 1
self.trace['s'].append(self.last_s)
self.trace['a'].append(a)
self.trace['r'].append(r)
self.trace['done'].append(done)
self.trace['final_s'] = s
self.trace['final_a'] = policy.step(s)
self.last_s = s
if done:
self.last_s = self.env.reset()
self.n_ep += 1
self._update_stats(self.n_interaction, info['episode']['reward'])
return self.trace
<|reserved_special_token_1|>
from collections import defaultdict, deque
import numpy as np
import gym
from chula_rl.policy.base_policy import BasePolicy
from chula_rl.exception import *
from .base_explorer import BaseExplorer
class OneStepExplorerWithTrace(BaseExplorer):
"""one-step explorer but with n-step trace"""
def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):
super().__init__(env)
self.n_step = n_step
self.n_max_interaction = n_max_interaction
self.last_s = self.env.reset()
self.trace = defaultdict(lambda : deque(maxlen=n_step))
self.n_interaction = 0
self.n_ep = 0
def step(self, policy: BasePolicy):
if self.n_interaction > self.n_max_interaction:
raise InteractionExceeded()
a = policy.step(self.last_s)
s, r, done, info = self.env.step(a)
self.n_interaction += 1
self.trace['s'].append(self.last_s)
self.trace['a'].append(a)
self.trace['r'].append(r)
self.trace['done'].append(done)
self.trace['final_s'] = s
self.trace['final_a'] = policy.step(s)
self.last_s = s
if done:
self.last_s = self.env.reset()
self.n_ep += 1
self._update_stats(self.n_interaction, info['episode']['reward'])
return self.trace
<|reserved_special_token_1|>
from collections import defaultdict, deque
import numpy as np
import gym
from chula_rl.policy.base_policy import BasePolicy
from chula_rl.exception import *
from .base_explorer import BaseExplorer
class OneStepExplorerWithTrace(BaseExplorer):
"""one-step explorer but with n-step trace"""
def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):
super().__init__(env)
self.n_step = n_step
self.n_max_interaction = n_max_interaction
self.last_s = self.env.reset()
self.trace = defaultdict(lambda: deque(maxlen=n_step))
self.n_interaction = 0
self.n_ep = 0
def step(self, policy: BasePolicy):
if self.n_interaction > self.n_max_interaction:
raise InteractionExceeded()
# explore
a = policy.step(self.last_s)
s, r, done, info = self.env.step(a)
self.n_interaction += 1
# collect data
self.trace['s'].append(self.last_s)
self.trace['a'].append(a)
self.trace['r'].append(r)
self.trace['done'].append(done)
self.trace['final_s'] = s # for bootstrapping
self.trace['final_a'] = policy.step(s) # for SARSA
self.last_s = s
# if done reset
if done:
self.last_s = self.env.reset()
self.n_ep += 1
self._update_stats(self.n_interaction, info['episode']['reward'])
return self.trace
|
flexible
|
{
"blob_id": "958d7ec966179d63c6ba0a651e99fff70f0db31a",
"index": 5410,
"step-1": "<mask token>\n\n\nclass OneStepExplorerWithTrace(BaseExplorer):\n <mask token>\n <mask token>\n\n def step(self, policy: BasePolicy):\n if self.n_interaction > self.n_max_interaction:\n raise InteractionExceeded()\n a = policy.step(self.last_s)\n s, r, done, info = self.env.step(a)\n self.n_interaction += 1\n self.trace['s'].append(self.last_s)\n self.trace['a'].append(a)\n self.trace['r'].append(r)\n self.trace['done'].append(done)\n self.trace['final_s'] = s\n self.trace['final_a'] = policy.step(s)\n self.last_s = s\n if done:\n self.last_s = self.env.reset()\n self.n_ep += 1\n self._update_stats(self.n_interaction, info['episode']['reward'])\n return self.trace\n",
"step-2": "<mask token>\n\n\nclass OneStepExplorerWithTrace(BaseExplorer):\n <mask token>\n\n def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):\n super().__init__(env)\n self.n_step = n_step\n self.n_max_interaction = n_max_interaction\n self.last_s = self.env.reset()\n self.trace = defaultdict(lambda : deque(maxlen=n_step))\n self.n_interaction = 0\n self.n_ep = 0\n\n def step(self, policy: BasePolicy):\n if self.n_interaction > self.n_max_interaction:\n raise InteractionExceeded()\n a = policy.step(self.last_s)\n s, r, done, info = self.env.step(a)\n self.n_interaction += 1\n self.trace['s'].append(self.last_s)\n self.trace['a'].append(a)\n self.trace['r'].append(r)\n self.trace['done'].append(done)\n self.trace['final_s'] = s\n self.trace['final_a'] = policy.step(s)\n self.last_s = s\n if done:\n self.last_s = self.env.reset()\n self.n_ep += 1\n self._update_stats(self.n_interaction, info['episode']['reward'])\n return self.trace\n",
"step-3": "<mask token>\n\n\nclass OneStepExplorerWithTrace(BaseExplorer):\n \"\"\"one-step explorer but with n-step trace\"\"\"\n\n def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):\n super().__init__(env)\n self.n_step = n_step\n self.n_max_interaction = n_max_interaction\n self.last_s = self.env.reset()\n self.trace = defaultdict(lambda : deque(maxlen=n_step))\n self.n_interaction = 0\n self.n_ep = 0\n\n def step(self, policy: BasePolicy):\n if self.n_interaction > self.n_max_interaction:\n raise InteractionExceeded()\n a = policy.step(self.last_s)\n s, r, done, info = self.env.step(a)\n self.n_interaction += 1\n self.trace['s'].append(self.last_s)\n self.trace['a'].append(a)\n self.trace['r'].append(r)\n self.trace['done'].append(done)\n self.trace['final_s'] = s\n self.trace['final_a'] = policy.step(s)\n self.last_s = s\n if done:\n self.last_s = self.env.reset()\n self.n_ep += 1\n self._update_stats(self.n_interaction, info['episode']['reward'])\n return self.trace\n",
"step-4": "from collections import defaultdict, deque\nimport numpy as np\nimport gym\nfrom chula_rl.policy.base_policy import BasePolicy\nfrom chula_rl.exception import *\nfrom .base_explorer import BaseExplorer\n\n\nclass OneStepExplorerWithTrace(BaseExplorer):\n \"\"\"one-step explorer but with n-step trace\"\"\"\n\n def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):\n super().__init__(env)\n self.n_step = n_step\n self.n_max_interaction = n_max_interaction\n self.last_s = self.env.reset()\n self.trace = defaultdict(lambda : deque(maxlen=n_step))\n self.n_interaction = 0\n self.n_ep = 0\n\n def step(self, policy: BasePolicy):\n if self.n_interaction > self.n_max_interaction:\n raise InteractionExceeded()\n a = policy.step(self.last_s)\n s, r, done, info = self.env.step(a)\n self.n_interaction += 1\n self.trace['s'].append(self.last_s)\n self.trace['a'].append(a)\n self.trace['r'].append(r)\n self.trace['done'].append(done)\n self.trace['final_s'] = s\n self.trace['final_a'] = policy.step(s)\n self.last_s = s\n if done:\n self.last_s = self.env.reset()\n self.n_ep += 1\n self._update_stats(self.n_interaction, info['episode']['reward'])\n return self.trace\n",
"step-5": "from collections import defaultdict, deque\n\nimport numpy as np\n\nimport gym\nfrom chula_rl.policy.base_policy import BasePolicy\nfrom chula_rl.exception import *\n\nfrom .base_explorer import BaseExplorer\n\n\nclass OneStepExplorerWithTrace(BaseExplorer):\n \"\"\"one-step explorer but with n-step trace\"\"\"\n\n def __init__(self, n_step: int, n_max_interaction: int, env: gym.Env):\n super().__init__(env)\n self.n_step = n_step\n self.n_max_interaction = n_max_interaction\n\n self.last_s = self.env.reset()\n self.trace = defaultdict(lambda: deque(maxlen=n_step))\n\n self.n_interaction = 0\n self.n_ep = 0\n\n def step(self, policy: BasePolicy):\n if self.n_interaction > self.n_max_interaction:\n raise InteractionExceeded()\n\n # explore\n a = policy.step(self.last_s)\n s, r, done, info = self.env.step(a)\n self.n_interaction += 1\n\n # collect data\n self.trace['s'].append(self.last_s)\n self.trace['a'].append(a)\n self.trace['r'].append(r)\n self.trace['done'].append(done)\n self.trace['final_s'] = s # for bootstrapping\n self.trace['final_a'] = policy.step(s) # for SARSA\n\n self.last_s = s\n\n # if done reset\n if done:\n self.last_s = self.env.reset()\n self.n_ep += 1\n self._update_stats(self.n_interaction, info['episode']['reward'])\n\n return self.trace\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
# !/usr/bin/env python3
# -*- coding: UTF-8 -*-
# Copyright (c) 2021 Baidu, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Stochastic Gradient Descent
"""
from typing import Callable
import numpy as np
from tqdm import tqdm
from ..circuit import BasicCircuit
from .basic_optimizer import BasicOptimizer
class SGD(BasicOptimizer):
r"""SGD Optimizer class
"""
def __init__(self, iterations: int, circuit: BasicCircuit, learning_rate: float):
r"""The constructor of the SGD class
Args:
iterations (int): Number of iterations
circuit (BasicCircuit): Circuit whose parameters are to be optimized
learning_rate (float): Learning rate
"""
super().__init__(iterations, circuit)
self._learning_rate = learning_rate
def minimize(
self, shots: int,
loss_func: Callable[[np.ndarray, int], float],
grad_func: Callable[[np.ndarray, int], np.ndarray]
) -> None:
r"""Minimizes the given loss function
Args:
shots (int): Number of measurement shots
loss_func (Callable[[np.ndarray, int], float]): Loss function to be minimized
grad_func (Callable[[np.ndarray, int], np.ndarray]): Function for calculating gradients
"""
self._loss_history = []
for _ in tqdm(range(self._iterations)):
curr_param = self._circuit.parameters
gradient = grad_func(curr_param, shots)
new_param = curr_param - self._learning_rate * gradient
loss = loss_func(new_param, shots)
self._loss_history.append(loss)
|
normal
|
{
"blob_id": "129df937d7d295bae2009cfb65b2f85228206698",
"index": 8657,
"step-1": "<mask token>\n\n\nclass SGD(BasicOptimizer):\n <mask token>\n\n def __init__(self, iterations: int, circuit: BasicCircuit,\n learning_rate: float):\n \"\"\"The constructor of the SGD class\n\n Args:\n iterations (int): Number of iterations\n circuit (BasicCircuit): Circuit whose parameters are to be optimized\n learning_rate (float): Learning rate\n\n \"\"\"\n super().__init__(iterations, circuit)\n self._learning_rate = learning_rate\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass SGD(BasicOptimizer):\n <mask token>\n\n def __init__(self, iterations: int, circuit: BasicCircuit,\n learning_rate: float):\n \"\"\"The constructor of the SGD class\n\n Args:\n iterations (int): Number of iterations\n circuit (BasicCircuit): Circuit whose parameters are to be optimized\n learning_rate (float): Learning rate\n\n \"\"\"\n super().__init__(iterations, circuit)\n self._learning_rate = learning_rate\n\n def minimize(self, shots: int, loss_func: Callable[[np.ndarray, int],\n float], grad_func: Callable[[np.ndarray, int], np.ndarray]) ->None:\n \"\"\"Minimizes the given loss function\n\n Args:\n shots (int): Number of measurement shots\n loss_func (Callable[[np.ndarray, int], float]): Loss function to be minimized\n grad_func (Callable[[np.ndarray, int], np.ndarray]): Function for calculating gradients\n\n \"\"\"\n self._loss_history = []\n for _ in tqdm(range(self._iterations)):\n curr_param = self._circuit.parameters\n gradient = grad_func(curr_param, shots)\n new_param = curr_param - self._learning_rate * gradient\n loss = loss_func(new_param, shots)\n self._loss_history.append(loss)\n",
"step-3": "<mask token>\n\n\nclass SGD(BasicOptimizer):\n \"\"\"SGD Optimizer class\n \"\"\"\n\n def __init__(self, iterations: int, circuit: BasicCircuit,\n learning_rate: float):\n \"\"\"The constructor of the SGD class\n\n Args:\n iterations (int): Number of iterations\n circuit (BasicCircuit): Circuit whose parameters are to be optimized\n learning_rate (float): Learning rate\n\n \"\"\"\n super().__init__(iterations, circuit)\n self._learning_rate = learning_rate\n\n def minimize(self, shots: int, loss_func: Callable[[np.ndarray, int],\n float], grad_func: Callable[[np.ndarray, int], np.ndarray]) ->None:\n \"\"\"Minimizes the given loss function\n\n Args:\n shots (int): Number of measurement shots\n loss_func (Callable[[np.ndarray, int], float]): Loss function to be minimized\n grad_func (Callable[[np.ndarray, int], np.ndarray]): Function for calculating gradients\n\n \"\"\"\n self._loss_history = []\n for _ in tqdm(range(self._iterations)):\n curr_param = self._circuit.parameters\n gradient = grad_func(curr_param, shots)\n new_param = curr_param - self._learning_rate * gradient\n loss = loss_func(new_param, shots)\n self._loss_history.append(loss)\n",
"step-4": "<mask token>\nfrom typing import Callable\nimport numpy as np\nfrom tqdm import tqdm\nfrom ..circuit import BasicCircuit\nfrom .basic_optimizer import BasicOptimizer\n\n\nclass SGD(BasicOptimizer):\n \"\"\"SGD Optimizer class\n \"\"\"\n\n def __init__(self, iterations: int, circuit: BasicCircuit,\n learning_rate: float):\n \"\"\"The constructor of the SGD class\n\n Args:\n iterations (int): Number of iterations\n circuit (BasicCircuit): Circuit whose parameters are to be optimized\n learning_rate (float): Learning rate\n\n \"\"\"\n super().__init__(iterations, circuit)\n self._learning_rate = learning_rate\n\n def minimize(self, shots: int, loss_func: Callable[[np.ndarray, int],\n float], grad_func: Callable[[np.ndarray, int], np.ndarray]) ->None:\n \"\"\"Minimizes the given loss function\n\n Args:\n shots (int): Number of measurement shots\n loss_func (Callable[[np.ndarray, int], float]): Loss function to be minimized\n grad_func (Callable[[np.ndarray, int], np.ndarray]): Function for calculating gradients\n\n \"\"\"\n self._loss_history = []\n for _ in tqdm(range(self._iterations)):\n curr_param = self._circuit.parameters\n gradient = grad_func(curr_param, shots)\n new_param = curr_param - self._learning_rate * gradient\n loss = loss_func(new_param, shots)\n self._loss_history.append(loss)\n",
"step-5": "# !/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n# Copyright (c) 2021 Baidu, Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nStochastic Gradient Descent\n\"\"\"\n\nfrom typing import Callable\nimport numpy as np\nfrom tqdm import tqdm\nfrom ..circuit import BasicCircuit\nfrom .basic_optimizer import BasicOptimizer\n\n\nclass SGD(BasicOptimizer):\n r\"\"\"SGD Optimizer class\n \"\"\"\n def __init__(self, iterations: int, circuit: BasicCircuit, learning_rate: float):\n r\"\"\"The constructor of the SGD class\n\n Args:\n iterations (int): Number of iterations\n circuit (BasicCircuit): Circuit whose parameters are to be optimized\n learning_rate (float): Learning rate\n\n \"\"\"\n super().__init__(iterations, circuit)\n self._learning_rate = learning_rate\n\n def minimize(\n self, shots: int,\n loss_func: Callable[[np.ndarray, int], float],\n grad_func: Callable[[np.ndarray, int], np.ndarray]\n ) -> None:\n r\"\"\"Minimizes the given loss function\n\n Args:\n shots (int): Number of measurement shots\n loss_func (Callable[[np.ndarray, int], float]): Loss function to be minimized\n grad_func (Callable[[np.ndarray, int], np.ndarray]): Function for calculating gradients\n\n \"\"\"\n self._loss_history = []\n for _ in tqdm(range(self._iterations)):\n curr_param = self._circuit.parameters\n gradient = grad_func(curr_param, shots)\n new_param = curr_param - self._learning_rate * gradient\n loss = loss_func(new_param, shots)\n self._loss_history.append(loss)\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
BUILTINS_MODULE_NAME = 'builtins'
<|reserved_special_token_0|>
<|reserved_special_token_1|>
#!/usr/bin/env python3
# --------------------( LICENSE )--------------------
# Copyright (c) 2014-2023 Beartype authors.
# See "LICENSE" for further details.
'''
Project-wide **standard Python module globals** (i.e., global constants
describing modules and packages bundled with CPython's standard library).
This private submodule is *not* intended for importation by downstream callers.
'''
# ....................{ IMPORTS }....................
# ....................{ NAMES }....................
BUILTINS_MODULE_NAME = 'builtins'
'''
Fully-qualified name of the **builtins module** (i.e., objects defined by the
standard :mod:`builtins` module and thus globally available by default
*without* requiring explicit importation).
'''
|
flexible
|
{
"blob_id": "a42f36fca2f65d0c5c9b65055af1814d8b4b3d42",
"index": 89,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nBUILTINS_MODULE_NAME = 'builtins'\n<mask token>\n",
"step-3": "#!/usr/bin/env python3\n# --------------------( LICENSE )--------------------\n# Copyright (c) 2014-2023 Beartype authors.\n# See \"LICENSE\" for further details.\n\n'''\nProject-wide **standard Python module globals** (i.e., global constants\ndescribing modules and packages bundled with CPython's standard library).\n\nThis private submodule is *not* intended for importation by downstream callers.\n'''\n\n# ....................{ IMPORTS }....................\n\n# ....................{ NAMES }....................\nBUILTINS_MODULE_NAME = 'builtins'\n'''\nFully-qualified name of the **builtins module** (i.e., objects defined by the\nstandard :mod:`builtins` module and thus globally available by default\n*without* requiring explicit importation).\n'''\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
'''
A empresa Tchau de telefonia cobra:
-Abaixo de 200 minutos, R$ 0,20 por minuto
-Entre 200 e 400 minutos, R$ 0,18 por minuto
-Acima de 400 minutos, R$ 0,15 por minuto
- Bonus: - Acima de 800 minutos, R$ 0,08
Calcule a conta de telefone
'''
minutos = int(input('Minutos utilizados: '))
if minutos > 800:
total = minutos * 0.08
elif minutos > 400 and minutos <= 800:
total = minutos * 0.15
elif minutos < 200:
total = minutos * 0.2
else:
total = minutos * 0.18
print('Valor da conta: R$ %.2f' %total)
|
normal
|
{
"blob_id": "1b3e64be988495454535ca96c7a1b6c20aa27076",
"index": 2648,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif minutos > 800:\n total = minutos * 0.08\nelif minutos > 400 and minutos <= 800:\n total = minutos * 0.15\nelif minutos < 200:\n total = minutos * 0.2\nelse:\n total = minutos * 0.18\nprint('Valor da conta: R$ %.2f' % total)\n",
"step-3": "<mask token>\nminutos = int(input('Minutos utilizados: '))\nif minutos > 800:\n total = minutos * 0.08\nelif minutos > 400 and minutos <= 800:\n total = minutos * 0.15\nelif minutos < 200:\n total = minutos * 0.2\nelse:\n total = minutos * 0.18\nprint('Valor da conta: R$ %.2f' % total)\n",
"step-4": "'''\nA empresa Tchau de telefonia cobra:\n-Abaixo de 200 minutos, R$ 0,20 por minuto\n-Entre 200 e 400 minutos, R$ 0,18 por minuto\n-Acima de 400 minutos, R$ 0,15 por minuto\n\n\n- Bonus: - Acima de 800 minutos, R$ 0,08\nCalcule a conta de telefone\n'''\n\nminutos = int(input('Minutos utilizados: '))\n\nif minutos > 800:\n total = minutos * 0.08\nelif minutos > 400 and minutos <= 800:\n total = minutos * 0.15\nelif minutos < 200:\n total = minutos * 0.2\nelse:\n total = minutos * 0.18\n\nprint('Valor da conta: R$ %.2f' %total)\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
system.trajectories()
<|reserved_special_token_0|>
print('r is ' + str(r))
system.gillespieConcentrations(50000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
<|reserved_special_token_0|>
system.gillespieConcentrations(10000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
reactions = [Reaction(lambda X: 1, [1, 0]), Reaction(lambda X: 2 * X[0], [-
1, 1]), Reaction(lambda X: 0.02 * X[0] ** 2 * X[1], [1, -1]), Reaction(
lambda X: 0.04 * X[0], [-1, 0])]
system = ChemicalReactionsSystem(reactions, 2)
system.trajectories()
r = 1
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
print('r is ' + str(r))
system.gillespieConcentrations(50000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
r = 10
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
system.gillespieConcentrations(10000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
<|reserved_special_token_1|>
from simulateChemicals import *
reactions = [Reaction(lambda X: 1, [1, 0]), Reaction(lambda X: 2 * X[0], [-
1, 1]), Reaction(lambda X: 0.02 * X[0] ** 2 * X[1], [1, -1]), Reaction(
lambda X: 0.04 * X[0], [-1, 0])]
system = ChemicalReactionsSystem(reactions, 2)
system.trajectories()
r = 1
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
print('r is ' + str(r))
system.gillespieConcentrations(50000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
r = 10
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
system.gillespieConcentrations(10000 * r)
system.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)
<|reserved_special_token_1|>
#' % Computational Biology Lab 3
#' % Alois Klink
#' % 18 May 2017
#' # Converting Reaction Equations to a ODE
#' To convert many reaction equations to one ODE, one must first find the propensity
#' and the changes of each reaction.
#' The Reaction class takes a lambda function of the propensity and the change matrix
#' as inputs.
from simulateChemicals import *
#' Here are the reaction formulas:
#'
#'* $\emptyset \overset{1}{\to} X$
#'* $X \overset{2}{\to} Y$
#'* $2 X + Y \overset{0.02}{\to} 3 X$
#'* $X \overset{0.04}{\to} \emptyset$
reactions = [Reaction(lambda X: 1, [1,0]),
Reaction(lambda X: 2*X[0], [-1,1]),
Reaction(lambda X: 0.02* X[0]**2 *X[1], [1,-1]),
Reaction(lambda X: 0.04*X[0], [-1,0])]
#' # Displaying the ODE
#' The figure below shows how the system described by the above reactions
#' behaves when modelled with an ODE. Notice that as X is being created, it is
#' immediatly turned into Y. However, once Y passes a threshold point, and starts
#' combining with X, due to the X^2 factor, X dramatically jumps up, rapidly
#' converting all Y to X. Once Y runs out, X slowly begins to degrade to
#' an equilibrium position.
#+trajectories, caption='ODE Simulation Trajectories from 0 initialConditions'
system = ChemicalReactionsSystem(reactions, 2)
system.trajectories()
#' # Gillespie's Algorithm
#' The code below shows how the reaction changes when Gillespie's algorithm is
#' used to simulate the reactions. Gillespie's algorithm can be reduced by a
#' factor to increase the accuracy of the algorithm. This technically works by
#' increasing the number of molecules, and speeding up reactions. However, these
#' graphs have molecules split into pieces, which is not possible in the real world.
r = 1
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
#' This shows how the cocentration changes over time. Notice that due the high
#' randomness of the properties, the threshold point is reached much faster. As
#' r increases, however, and the Gillespie's algorithm is reduced, the variance
#' gets smaller and smaller, so that the threshold point is reached at the same
#' time as the ODE.
print("r is " + str(r))
system.gillespieConcentrations(50000*r)
#' This shows how the cocentration X changes in relation to the concentrations Y.
#' Notice that due the high randomness of the properties, the path is a lot tighter,
#' and the stable point seems to be a lot lower.
system.gillespieTrajectories([[0, 0], [4, 23]],
10000*r)
#' # Reduction
#' The following graphs have r = 10
r = 10
reactions = Reaction.rescaleReactions(reactions, r)
system = ChemicalReactionsSystem(reactions, 2)
#+caption='r = 10', width="15cm"
system.gillespieConcentrations(10000*r)
#+caption='r = 10', width="15cm"
system.gillespieTrajectories([[0, 0], [4, 23]],
10000*r)
|
flexible
|
{
"blob_id": "87a1624707e4a113a35d975518e432277c851e41",
"index": 9962,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsystem.trajectories()\n<mask token>\nprint('r is ' + str(r))\nsystem.gillespieConcentrations(50000 * r)\nsystem.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)\n<mask token>\nsystem.gillespieConcentrations(10000 * r)\nsystem.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)\n",
"step-3": "<mask token>\nreactions = [Reaction(lambda X: 1, [1, 0]), Reaction(lambda X: 2 * X[0], [-\n 1, 1]), Reaction(lambda X: 0.02 * X[0] ** 2 * X[1], [1, -1]), Reaction(\n lambda X: 0.04 * X[0], [-1, 0])]\nsystem = ChemicalReactionsSystem(reactions, 2)\nsystem.trajectories()\nr = 1\nreactions = Reaction.rescaleReactions(reactions, r)\nsystem = ChemicalReactionsSystem(reactions, 2)\nprint('r is ' + str(r))\nsystem.gillespieConcentrations(50000 * r)\nsystem.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)\nr = 10\nreactions = Reaction.rescaleReactions(reactions, r)\nsystem = ChemicalReactionsSystem(reactions, 2)\nsystem.gillespieConcentrations(10000 * r)\nsystem.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)\n",
"step-4": "from simulateChemicals import *\nreactions = [Reaction(lambda X: 1, [1, 0]), Reaction(lambda X: 2 * X[0], [-\n 1, 1]), Reaction(lambda X: 0.02 * X[0] ** 2 * X[1], [1, -1]), Reaction(\n lambda X: 0.04 * X[0], [-1, 0])]\nsystem = ChemicalReactionsSystem(reactions, 2)\nsystem.trajectories()\nr = 1\nreactions = Reaction.rescaleReactions(reactions, r)\nsystem = ChemicalReactionsSystem(reactions, 2)\nprint('r is ' + str(r))\nsystem.gillespieConcentrations(50000 * r)\nsystem.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)\nr = 10\nreactions = Reaction.rescaleReactions(reactions, r)\nsystem = ChemicalReactionsSystem(reactions, 2)\nsystem.gillespieConcentrations(10000 * r)\nsystem.gillespieTrajectories([[0, 0], [4, 23]], 10000 * r)\n",
"step-5": "#' % Computational Biology Lab 3\n#' % Alois Klink\n#' % 18 May 2017\n\n#' # Converting Reaction Equations to a ODE\n\n#' To convert many reaction equations to one ODE, one must first find the propensity\n#' and the changes of each reaction.\n\n#' The Reaction class takes a lambda function of the propensity and the change matrix\n#' as inputs.\n\nfrom simulateChemicals import *\n\n#' Here are the reaction formulas:\n#'\n#'* $\\emptyset \\overset{1}{\\to} X$\n#'* $X \\overset{2}{\\to} Y$\n#'* $2 X + Y \\overset{0.02}{\\to} 3 X$\n#'* $X \\overset{0.04}{\\to} \\emptyset$\n\nreactions = [Reaction(lambda X: 1, [1,0]),\n Reaction(lambda X: 2*X[0], [-1,1]),\n Reaction(lambda X: 0.02* X[0]**2 *X[1], [1,-1]),\n Reaction(lambda X: 0.04*X[0], [-1,0])]\n\n#' # Displaying the ODE\n\n#' The figure below shows how the system described by the above reactions\n#' behaves when modelled with an ODE. Notice that as X is being created, it is\n#' immediatly turned into Y. However, once Y passes a threshold point, and starts\n#' combining with X, due to the X^2 factor, X dramatically jumps up, rapidly\n#' converting all Y to X. Once Y runs out, X slowly begins to degrade to\n#' an equilibrium position.\n\n#+trajectories, caption='ODE Simulation Trajectories from 0 initialConditions'\nsystem = ChemicalReactionsSystem(reactions, 2)\nsystem.trajectories()\n\n#' # Gillespie's Algorithm\n\n#' The code below shows how the reaction changes when Gillespie's algorithm is\n#' used to simulate the reactions. Gillespie's algorithm can be reduced by a \n#' factor to increase the accuracy of the algorithm. This technically works by\n#' increasing the number of molecules, and speeding up reactions. However, these\n#' graphs have molecules split into pieces, which is not possible in the real world.\n\nr = 1\nreactions = Reaction.rescaleReactions(reactions, r)\nsystem = ChemicalReactionsSystem(reactions, 2)\n\t\n#' This shows how the cocentration changes over time. Notice that due the high\n#' randomness of the properties, the threshold point is reached much faster. As\n#' r increases, however, and the Gillespie's algorithm is reduced, the variance\n#' gets smaller and smaller, so that the threshold point is reached at the same\n#' time as the ODE.\n\nprint(\"r is \" + str(r))\nsystem.gillespieConcentrations(50000*r)\n\n#' This shows how the cocentration X changes in relation to the concentrations Y.\n#' Notice that due the high randomness of the properties, the path is a lot tighter,\n#' and the stable point seems to be a lot lower.\n\nsystem.gillespieTrajectories([[0, 0], [4, 23]],\n\t\t 10000*r)\n\t\t \n#' # Reduction\n\n#' The following graphs have r = 10\n\nr = 10\nreactions = Reaction.rescaleReactions(reactions, r)\nsystem = ChemicalReactionsSystem(reactions, 2)\n\n#+caption='r = 10', width=\"15cm\"\nsystem.gillespieConcentrations(10000*r)\n\n#+caption='r = 10', width=\"15cm\"\nsystem.gillespieTrajectories([[0, 0], [4, 23]],\n\t\t 10000*r)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table as dt
import plotly.express as px
import pandas as pd
import plotly.graph_objects as go
import numpy as np
from datetime import datetime as dat
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.feature_selection import f_regression
from sklearn.feature_selection import SelectKBest
# TODO:
# The model doesn't really take the opponent into consideration when calculating the win percentage. As you would expect, this is not ideal and is something that needs to be fixed
#
# The bar charts only graph 2019 data. Allowing the user to choose the year would be an easy addition. Future implemenatations could also include a previous X games option instead
#
# The bar chart only graphs stats correctly if they are selected in order. For example, if the set of possible stats are ['assists', 'rebounds', 'blocks'], they must all be selected
# in order to show all the values correctly. If the user selects only 'assists' and 'blocks' then 'assists' graphs correctly. 'blocks' is graphed but is given the value assigned
# to 'rebounds' because it assumes the second position in the array of stats to be graphed.
#
# The model doesn't run well (and generally fails) for small schools due to a lack of data for those teams. Error checking needs to be implemented to eliminate this problem.
def getStatsByYear(teamID, year, data):
''' Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 '''
teamStats = data[data["team_id"] == teamID]
for index, row in teamStats.iterrows():
if (row["season"] == year):
teamStatsForGivenYear = teamStats[data["season"] == row["season"]]
return teamStatsForGivenYear
def generate_bar_chart(team, opponent, stats, stat_names, data):
''' Generates a bar chart for a the user selected team, opponent and stats '''
teamStats = getStatsByYear(team, 2019, data)
opponentStats = getStatsByYear(opponent, 2019, data)
teamStatValues = teamStats[["assists", "assists_turnover_ratio", "blocked_att", "blocks", "defensive_rebounds", "fast_break_pts",
"field_goals_att", "field_goals_pct", "field_goals_made", "free_throws_att",
"free_throws_pct", "free_throws_made", "offensive_rebounds", "personal_fouls",
"points", "points_against", "points_in_paint", "points_off_turnovers",
"rebounds", "second_chance_pts", "steals", "team_rebounds", "three_points_att",
"three_points_pct", "three_points_made", "turnovers", "two_points_att",
"two_points_pct", "two_points_made"
]]
opponentStatValues = opponentStats[["assists", "assists_turnover_ratio", "blocked_att", "blocks", "defensive_rebounds", "fast_break_pts",
"field_goals_att", "field_goals_pct", "field_goals_made", "free_throws_att",
"free_throws_pct", "free_throws_made", "offensive_rebounds", "personal_fouls",
"points", "points_against", "points_in_paint", "points_off_turnovers",
"rebounds", "second_chance_pts", "steals", "team_rebounds", "three_points_att",
"three_points_pct", "three_points_made", "turnovers", "two_points_att",
"two_points_pct", "two_points_made"
]]
stats_to_be_graphed = []
for i in range(len(stat_names)):
if i in stats:
stats_to_be_graphed.append(stat_names[i])
# Graphs average stat values for the user's chosen team
teamVals = go.Bar(
x = stats_to_be_graphed,
y = teamStatValues.mean(),
name = data[(data.team_id == team)]['market'].iloc[0]
)
# Graphs average stat values for the opponent's team
opponentVals = go.Bar(
x = stats_to_be_graphed,
y = opponentStatValues.mean(),
name = data[(data.team_id == opponent)]['market'].iloc[0]
)
data = [teamVals, opponentVals]
layout = go.Layout(barmode = 'group')
fig = go.Figure(data = data, layout = layout)
return fig
def getAllTeamMatchRecords(teamID, df):
''' Returns all game records for a given teamID '''
return df[df["team_id"] == teamID]
def select_features(X_train, y_train, X_test):
''' Selects features '''
# configure to select all features
fs = SelectKBest(score_func=f_regression, k='all')
# learn relationship from training data
fs.fit(X_train, y_train)
# transform train input data
X_train_fs = fs.transform(X_train)
# transform test input data
X_test_fs = fs.transform(X_test)
return X_train_fs, X_test_fs, fs
def overallFeatures(df):
''' Return list of top four features '''
datasetForFS = df
datasetForFS.fillna(0)
X1 = datasetForFS[["assists","blocks","defensive_rebounds","opponent_drb","fast_break_pts","points_in_paint","points_off_turnovers","rebounds","steals","turnovers","efg","tov_pct","orb_pct","ftr"]]
y1 = datasetForFS['win']
X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0)
X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)
colList = X1.columns.values.tolist()
statScoreDF = pd.DataFrame(data={'Stat': pd.Series(colList), 'Score': pd.Series(fs.scores_.tolist())})
statScoreDF = statScoreDF.sort_values(by=['Score'], ascending=False)
return statScoreDF.head(n=4)['Stat'].tolist()
def avgDataRow(df):
''' Returns the average values of a dataFrame '''
df1 = dict()
for (columnName, columnData) in df.iteritems():
df1[columnName] = [df[columnName].mean()]
return pd.DataFrame(df1)
def updateWinPct(dfMain, dfWin, reg):
''' Return new win percentage '''
dfPred = reg.predict(dfMain)
return pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.around(dfPred), 'Predicted (float)': dfPred})
def filterRowsFS(df):
''' Return dataframe with selected features '''
return df[["assists","blocks","defensive_rebounds","opponent_drb","fast_break_pts","points_in_paint","points_off_turnovers","rebounds","steals","turnovers","efg","tov_pct","orb_pct","ftr"]]
def learn(dataset):
''' Trains the model '''
dataset = pd.read_csv("team_boxscores_v3.csv")
dataset = dataset.fillna(0)
# Shuffle
dataset = dataset.sample(frac = 1)
X1 = dataset[["assists","blocks","defensive_rebounds","opponent_drb","fast_break_pts","points_in_paint","points_off_turnovers","rebounds","steals","turnovers","efg","tov_pct","orb_pct","ftr"]]
y1 = dataset['win']
# No shuffle
# X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0)
# W/ shuffle
X_train = X1[int(len(X1)/5):]
X_test = X1[:int(len(X1)/5)]
y_train = y1[int(len(y1)/5):]
y_test = y1[:int(len(y1)/5)]
regressor = LinearRegression()
regressor.fit(X_train, y_train)
coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=['Coefficient'])
y_pred = regressor.predict(X_test)
y_pred_round = np.around(regressor.predict(X_test))
return regressor, pd.DataFrame({'Actual': y_test, 'Predicted (int)': y_pred_round, 'Predicted (float)': y_pred})
def calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data):
''' Calculates the win percentage for a team and the 4 selected stat values '''
temp = getAllTeamMatchRecords(team, data)
changed_stat1 = overallFeatures(temp)[0]
changed_stat2 = overallFeatures(temp)[1]
changed_stat3 = overallFeatures(temp)[2]
changed_stat4 = overallFeatures(temp)[3]
average_team_stats = avgDataRow(filterRowsFS(temp))
dfWin = temp["win"]
dfFinal = pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.around(regressor.predict(average_team_stats)), 'Predicted (float)': regressor.predict(average_team_stats)})
origWinPct = dfFinal.at[0, 'Predicted (float)']
average_team_stats.at[0, changed_stat1] = stat1
average_team_stats.at[0, changed_stat2] = stat2
average_team_stats.at[0, changed_stat3] = stat3
average_team_stats.at[0, changed_stat4] = stat4
win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[0,'Predicted (float)']
# Makes sure you can't have a win percentage of > 100% or < 0%
if win_percentage > 1:
win_percentage = 1
elif win_percentage < 0:
win_percentage = 0
win_percentage = win_percentage * 100
win_percentage = round(win_percentage, 2)
win_percentage_text = "Projected Win Percentage: " + str(win_percentage) + "%"
return win_percentage_text
def get_default_slider_values(team, data):
''' Gets the values the each of the 4 sliders should display. These values are what the model estimates the team will get in the matchup '''
numSliders = 4
stat_column_names = []
stat_column_values = []
estimated_stat_values = avgDataRow(filterRowsFS(getAllTeamMatchRecords(team, data)))
for i in range(numSliders):
stat_column_names.append(overallFeatures(getAllTeamMatchRecords(team, data))[i])
stat_column_values.append(estimated_stat_values.at[0, stat_column_names[i]])
return stat_column_names, stat_column_values
|
normal
|
{
"blob_id": "9b581df505765e895047584c5bb586faef95295f",
"index": 453,
"step-1": "<mask token>\n\n\ndef getStatsByYear(teamID, year, data):\n \"\"\" Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 \"\"\"\n teamStats = data[data['team_id'] == teamID]\n for index, row in teamStats.iterrows():\n if row['season'] == year:\n teamStatsForGivenYear = teamStats[data['season'] == row['season']]\n return teamStatsForGivenYear\n\n\ndef generate_bar_chart(team, opponent, stats, stat_names, data):\n \"\"\" Generates a bar chart for a the user selected team, opponent and stats \"\"\"\n teamStats = getStatsByYear(team, 2019, data)\n opponentStats = getStatsByYear(opponent, 2019, data)\n teamStatValues = teamStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n opponentStatValues = opponentStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n stats_to_be_graphed = []\n for i in range(len(stat_names)):\n if i in stats:\n stats_to_be_graphed.append(stat_names[i])\n teamVals = go.Bar(x=stats_to_be_graphed, y=teamStatValues.mean(), name=\n data[data.team_id == team]['market'].iloc[0])\n opponentVals = go.Bar(x=stats_to_be_graphed, y=opponentStatValues.mean(\n ), name=data[data.team_id == opponent]['market'].iloc[0])\n data = [teamVals, opponentVals]\n layout = go.Layout(barmode='group')\n fig = go.Figure(data=data, layout=layout)\n return fig\n\n\n<mask token>\n\n\ndef select_features(X_train, y_train, X_test):\n \"\"\" Selects features \"\"\"\n fs = SelectKBest(score_func=f_regression, k='all')\n fs.fit(X_train, y_train)\n X_train_fs = fs.transform(X_train)\n X_test_fs = fs.transform(X_test)\n return X_train_fs, X_test_fs, fs\n\n\ndef overallFeatures(df):\n \"\"\" Return list of top four features \"\"\"\n datasetForFS = df\n datasetForFS.fillna(0)\n X1 = datasetForFS[['assists', 'blocks', 'defensive_rebounds',\n 'opponent_drb', 'fast_break_pts', 'points_in_paint',\n 'points_off_turnovers', 'rebounds', 'steals', 'turnovers', 'efg',\n 'tov_pct', 'orb_pct', 'ftr']]\n y1 = datasetForFS['win']\n X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=\n 0.2, random_state=0)\n X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n colList = X1.columns.values.tolist()\n statScoreDF = pd.DataFrame(data={'Stat': pd.Series(colList), 'Score':\n pd.Series(fs.scores_.tolist())})\n statScoreDF = statScoreDF.sort_values(by=['Score'], ascending=False)\n return statScoreDF.head(n=4)['Stat'].tolist()\n\n\n<mask token>\n\n\ndef filterRowsFS(df):\n \"\"\" Return dataframe with selected features \"\"\"\n return df[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n\n\ndef learn(dataset):\n \"\"\" Trains the model \"\"\"\n dataset = pd.read_csv('team_boxscores_v3.csv')\n dataset = dataset.fillna(0)\n dataset = dataset.sample(frac=1)\n X1 = dataset[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n y1 = dataset['win']\n X_train = X1[int(len(X1) / 5):]\n X_test = X1[:int(len(X1) / 5)]\n y_train = y1[int(len(y1) / 5):]\n y_test = y1[:int(len(y1) / 5)]\n regressor = LinearRegression()\n regressor.fit(X_train, y_train)\n coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=[\n 'Coefficient'])\n y_pred = regressor.predict(X_test)\n y_pred_round = np.around(regressor.predict(X_test))\n return regressor, pd.DataFrame({'Actual': y_test, 'Predicted (int)':\n y_pred_round, 'Predicted (float)': y_pred})\n\n\ndef calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data\n ):\n \"\"\" Calculates the win percentage for a team and the 4 selected stat values \"\"\"\n temp = getAllTeamMatchRecords(team, data)\n changed_stat1 = overallFeatures(temp)[0]\n changed_stat2 = overallFeatures(temp)[1]\n changed_stat3 = overallFeatures(temp)[2]\n changed_stat4 = overallFeatures(temp)[3]\n average_team_stats = avgDataRow(filterRowsFS(temp))\n dfWin = temp['win']\n dfFinal = pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.\n around(regressor.predict(average_team_stats)), 'Predicted (float)':\n regressor.predict(average_team_stats)})\n origWinPct = dfFinal.at[0, 'Predicted (float)']\n average_team_stats.at[0, changed_stat1] = stat1\n average_team_stats.at[0, changed_stat2] = stat2\n average_team_stats.at[0, changed_stat3] = stat3\n average_team_stats.at[0, changed_stat4] = stat4\n win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[\n 0, 'Predicted (float)']\n if win_percentage > 1:\n win_percentage = 1\n elif win_percentage < 0:\n win_percentage = 0\n win_percentage = win_percentage * 100\n win_percentage = round(win_percentage, 2)\n win_percentage_text = 'Projected Win Percentage: ' + str(win_percentage\n ) + '%'\n return win_percentage_text\n\n\ndef get_default_slider_values(team, data):\n \"\"\" Gets the values the each of the 4 sliders should display. These values are what the model estimates the team will get in the matchup \"\"\"\n numSliders = 4\n stat_column_names = []\n stat_column_values = []\n estimated_stat_values = avgDataRow(filterRowsFS(getAllTeamMatchRecords(\n team, data)))\n for i in range(numSliders):\n stat_column_names.append(overallFeatures(getAllTeamMatchRecords(\n team, data))[i])\n stat_column_values.append(estimated_stat_values.at[0,\n stat_column_names[i]])\n return stat_column_names, stat_column_values\n",
"step-2": "<mask token>\n\n\ndef getStatsByYear(teamID, year, data):\n \"\"\" Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 \"\"\"\n teamStats = data[data['team_id'] == teamID]\n for index, row in teamStats.iterrows():\n if row['season'] == year:\n teamStatsForGivenYear = teamStats[data['season'] == row['season']]\n return teamStatsForGivenYear\n\n\ndef generate_bar_chart(team, opponent, stats, stat_names, data):\n \"\"\" Generates a bar chart for a the user selected team, opponent and stats \"\"\"\n teamStats = getStatsByYear(team, 2019, data)\n opponentStats = getStatsByYear(opponent, 2019, data)\n teamStatValues = teamStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n opponentStatValues = opponentStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n stats_to_be_graphed = []\n for i in range(len(stat_names)):\n if i in stats:\n stats_to_be_graphed.append(stat_names[i])\n teamVals = go.Bar(x=stats_to_be_graphed, y=teamStatValues.mean(), name=\n data[data.team_id == team]['market'].iloc[0])\n opponentVals = go.Bar(x=stats_to_be_graphed, y=opponentStatValues.mean(\n ), name=data[data.team_id == opponent]['market'].iloc[0])\n data = [teamVals, opponentVals]\n layout = go.Layout(barmode='group')\n fig = go.Figure(data=data, layout=layout)\n return fig\n\n\n<mask token>\n\n\ndef select_features(X_train, y_train, X_test):\n \"\"\" Selects features \"\"\"\n fs = SelectKBest(score_func=f_regression, k='all')\n fs.fit(X_train, y_train)\n X_train_fs = fs.transform(X_train)\n X_test_fs = fs.transform(X_test)\n return X_train_fs, X_test_fs, fs\n\n\ndef overallFeatures(df):\n \"\"\" Return list of top four features \"\"\"\n datasetForFS = df\n datasetForFS.fillna(0)\n X1 = datasetForFS[['assists', 'blocks', 'defensive_rebounds',\n 'opponent_drb', 'fast_break_pts', 'points_in_paint',\n 'points_off_turnovers', 'rebounds', 'steals', 'turnovers', 'efg',\n 'tov_pct', 'orb_pct', 'ftr']]\n y1 = datasetForFS['win']\n X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=\n 0.2, random_state=0)\n X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n colList = X1.columns.values.tolist()\n statScoreDF = pd.DataFrame(data={'Stat': pd.Series(colList), 'Score':\n pd.Series(fs.scores_.tolist())})\n statScoreDF = statScoreDF.sort_values(by=['Score'], ascending=False)\n return statScoreDF.head(n=4)['Stat'].tolist()\n\n\n<mask token>\n\n\ndef updateWinPct(dfMain, dfWin, reg):\n \"\"\" Return new win percentage \"\"\"\n dfPred = reg.predict(dfMain)\n return pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.\n around(dfPred), 'Predicted (float)': dfPred})\n\n\ndef filterRowsFS(df):\n \"\"\" Return dataframe with selected features \"\"\"\n return df[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n\n\ndef learn(dataset):\n \"\"\" Trains the model \"\"\"\n dataset = pd.read_csv('team_boxscores_v3.csv')\n dataset = dataset.fillna(0)\n dataset = dataset.sample(frac=1)\n X1 = dataset[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n y1 = dataset['win']\n X_train = X1[int(len(X1) / 5):]\n X_test = X1[:int(len(X1) / 5)]\n y_train = y1[int(len(y1) / 5):]\n y_test = y1[:int(len(y1) / 5)]\n regressor = LinearRegression()\n regressor.fit(X_train, y_train)\n coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=[\n 'Coefficient'])\n y_pred = regressor.predict(X_test)\n y_pred_round = np.around(regressor.predict(X_test))\n return regressor, pd.DataFrame({'Actual': y_test, 'Predicted (int)':\n y_pred_round, 'Predicted (float)': y_pred})\n\n\ndef calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data\n ):\n \"\"\" Calculates the win percentage for a team and the 4 selected stat values \"\"\"\n temp = getAllTeamMatchRecords(team, data)\n changed_stat1 = overallFeatures(temp)[0]\n changed_stat2 = overallFeatures(temp)[1]\n changed_stat3 = overallFeatures(temp)[2]\n changed_stat4 = overallFeatures(temp)[3]\n average_team_stats = avgDataRow(filterRowsFS(temp))\n dfWin = temp['win']\n dfFinal = pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.\n around(regressor.predict(average_team_stats)), 'Predicted (float)':\n regressor.predict(average_team_stats)})\n origWinPct = dfFinal.at[0, 'Predicted (float)']\n average_team_stats.at[0, changed_stat1] = stat1\n average_team_stats.at[0, changed_stat2] = stat2\n average_team_stats.at[0, changed_stat3] = stat3\n average_team_stats.at[0, changed_stat4] = stat4\n win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[\n 0, 'Predicted (float)']\n if win_percentage > 1:\n win_percentage = 1\n elif win_percentage < 0:\n win_percentage = 0\n win_percentage = win_percentage * 100\n win_percentage = round(win_percentage, 2)\n win_percentage_text = 'Projected Win Percentage: ' + str(win_percentage\n ) + '%'\n return win_percentage_text\n\n\ndef get_default_slider_values(team, data):\n \"\"\" Gets the values the each of the 4 sliders should display. These values are what the model estimates the team will get in the matchup \"\"\"\n numSliders = 4\n stat_column_names = []\n stat_column_values = []\n estimated_stat_values = avgDataRow(filterRowsFS(getAllTeamMatchRecords(\n team, data)))\n for i in range(numSliders):\n stat_column_names.append(overallFeatures(getAllTeamMatchRecords(\n team, data))[i])\n stat_column_values.append(estimated_stat_values.at[0,\n stat_column_names[i]])\n return stat_column_names, stat_column_values\n",
"step-3": "<mask token>\n\n\ndef getStatsByYear(teamID, year, data):\n \"\"\" Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 \"\"\"\n teamStats = data[data['team_id'] == teamID]\n for index, row in teamStats.iterrows():\n if row['season'] == year:\n teamStatsForGivenYear = teamStats[data['season'] == row['season']]\n return teamStatsForGivenYear\n\n\ndef generate_bar_chart(team, opponent, stats, stat_names, data):\n \"\"\" Generates a bar chart for a the user selected team, opponent and stats \"\"\"\n teamStats = getStatsByYear(team, 2019, data)\n opponentStats = getStatsByYear(opponent, 2019, data)\n teamStatValues = teamStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n opponentStatValues = opponentStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n stats_to_be_graphed = []\n for i in range(len(stat_names)):\n if i in stats:\n stats_to_be_graphed.append(stat_names[i])\n teamVals = go.Bar(x=stats_to_be_graphed, y=teamStatValues.mean(), name=\n data[data.team_id == team]['market'].iloc[0])\n opponentVals = go.Bar(x=stats_to_be_graphed, y=opponentStatValues.mean(\n ), name=data[data.team_id == opponent]['market'].iloc[0])\n data = [teamVals, opponentVals]\n layout = go.Layout(barmode='group')\n fig = go.Figure(data=data, layout=layout)\n return fig\n\n\ndef getAllTeamMatchRecords(teamID, df):\n \"\"\" Returns all game records for a given teamID \"\"\"\n return df[df['team_id'] == teamID]\n\n\ndef select_features(X_train, y_train, X_test):\n \"\"\" Selects features \"\"\"\n fs = SelectKBest(score_func=f_regression, k='all')\n fs.fit(X_train, y_train)\n X_train_fs = fs.transform(X_train)\n X_test_fs = fs.transform(X_test)\n return X_train_fs, X_test_fs, fs\n\n\ndef overallFeatures(df):\n \"\"\" Return list of top four features \"\"\"\n datasetForFS = df\n datasetForFS.fillna(0)\n X1 = datasetForFS[['assists', 'blocks', 'defensive_rebounds',\n 'opponent_drb', 'fast_break_pts', 'points_in_paint',\n 'points_off_turnovers', 'rebounds', 'steals', 'turnovers', 'efg',\n 'tov_pct', 'orb_pct', 'ftr']]\n y1 = datasetForFS['win']\n X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=\n 0.2, random_state=0)\n X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n colList = X1.columns.values.tolist()\n statScoreDF = pd.DataFrame(data={'Stat': pd.Series(colList), 'Score':\n pd.Series(fs.scores_.tolist())})\n statScoreDF = statScoreDF.sort_values(by=['Score'], ascending=False)\n return statScoreDF.head(n=4)['Stat'].tolist()\n\n\ndef avgDataRow(df):\n \"\"\" Returns the average values of a dataFrame \"\"\"\n df1 = dict()\n for columnName, columnData in df.iteritems():\n df1[columnName] = [df[columnName].mean()]\n return pd.DataFrame(df1)\n\n\ndef updateWinPct(dfMain, dfWin, reg):\n \"\"\" Return new win percentage \"\"\"\n dfPred = reg.predict(dfMain)\n return pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.\n around(dfPred), 'Predicted (float)': dfPred})\n\n\ndef filterRowsFS(df):\n \"\"\" Return dataframe with selected features \"\"\"\n return df[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n\n\ndef learn(dataset):\n \"\"\" Trains the model \"\"\"\n dataset = pd.read_csv('team_boxscores_v3.csv')\n dataset = dataset.fillna(0)\n dataset = dataset.sample(frac=1)\n X1 = dataset[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n y1 = dataset['win']\n X_train = X1[int(len(X1) / 5):]\n X_test = X1[:int(len(X1) / 5)]\n y_train = y1[int(len(y1) / 5):]\n y_test = y1[:int(len(y1) / 5)]\n regressor = LinearRegression()\n regressor.fit(X_train, y_train)\n coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=[\n 'Coefficient'])\n y_pred = regressor.predict(X_test)\n y_pred_round = np.around(regressor.predict(X_test))\n return regressor, pd.DataFrame({'Actual': y_test, 'Predicted (int)':\n y_pred_round, 'Predicted (float)': y_pred})\n\n\ndef calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data\n ):\n \"\"\" Calculates the win percentage for a team and the 4 selected stat values \"\"\"\n temp = getAllTeamMatchRecords(team, data)\n changed_stat1 = overallFeatures(temp)[0]\n changed_stat2 = overallFeatures(temp)[1]\n changed_stat3 = overallFeatures(temp)[2]\n changed_stat4 = overallFeatures(temp)[3]\n average_team_stats = avgDataRow(filterRowsFS(temp))\n dfWin = temp['win']\n dfFinal = pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.\n around(regressor.predict(average_team_stats)), 'Predicted (float)':\n regressor.predict(average_team_stats)})\n origWinPct = dfFinal.at[0, 'Predicted (float)']\n average_team_stats.at[0, changed_stat1] = stat1\n average_team_stats.at[0, changed_stat2] = stat2\n average_team_stats.at[0, changed_stat3] = stat3\n average_team_stats.at[0, changed_stat4] = stat4\n win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[\n 0, 'Predicted (float)']\n if win_percentage > 1:\n win_percentage = 1\n elif win_percentage < 0:\n win_percentage = 0\n win_percentage = win_percentage * 100\n win_percentage = round(win_percentage, 2)\n win_percentage_text = 'Projected Win Percentage: ' + str(win_percentage\n ) + '%'\n return win_percentage_text\n\n\ndef get_default_slider_values(team, data):\n \"\"\" Gets the values the each of the 4 sliders should display. These values are what the model estimates the team will get in the matchup \"\"\"\n numSliders = 4\n stat_column_names = []\n stat_column_values = []\n estimated_stat_values = avgDataRow(filterRowsFS(getAllTeamMatchRecords(\n team, data)))\n for i in range(numSliders):\n stat_column_names.append(overallFeatures(getAllTeamMatchRecords(\n team, data))[i])\n stat_column_values.append(estimated_stat_values.at[0,\n stat_column_names[i]])\n return stat_column_names, stat_column_values\n",
"step-4": "import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_table as dt\nimport plotly.express as px\nimport pandas as pd\nimport plotly.graph_objects as go\nimport numpy as np\nfrom datetime import datetime as dat\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import metrics\nfrom sklearn.feature_selection import f_regression\nfrom sklearn.feature_selection import SelectKBest\n\n\ndef getStatsByYear(teamID, year, data):\n \"\"\" Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 \"\"\"\n teamStats = data[data['team_id'] == teamID]\n for index, row in teamStats.iterrows():\n if row['season'] == year:\n teamStatsForGivenYear = teamStats[data['season'] == row['season']]\n return teamStatsForGivenYear\n\n\ndef generate_bar_chart(team, opponent, stats, stat_names, data):\n \"\"\" Generates a bar chart for a the user selected team, opponent and stats \"\"\"\n teamStats = getStatsByYear(team, 2019, data)\n opponentStats = getStatsByYear(opponent, 2019, data)\n teamStatValues = teamStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n opponentStatValues = opponentStats[['assists', 'assists_turnover_ratio',\n 'blocked_att', 'blocks', 'defensive_rebounds', 'fast_break_pts',\n 'field_goals_att', 'field_goals_pct', 'field_goals_made',\n 'free_throws_att', 'free_throws_pct', 'free_throws_made',\n 'offensive_rebounds', 'personal_fouls', 'points', 'points_against',\n 'points_in_paint', 'points_off_turnovers', 'rebounds',\n 'second_chance_pts', 'steals', 'team_rebounds', 'three_points_att',\n 'three_points_pct', 'three_points_made', 'turnovers',\n 'two_points_att', 'two_points_pct', 'two_points_made']]\n stats_to_be_graphed = []\n for i in range(len(stat_names)):\n if i in stats:\n stats_to_be_graphed.append(stat_names[i])\n teamVals = go.Bar(x=stats_to_be_graphed, y=teamStatValues.mean(), name=\n data[data.team_id == team]['market'].iloc[0])\n opponentVals = go.Bar(x=stats_to_be_graphed, y=opponentStatValues.mean(\n ), name=data[data.team_id == opponent]['market'].iloc[0])\n data = [teamVals, opponentVals]\n layout = go.Layout(barmode='group')\n fig = go.Figure(data=data, layout=layout)\n return fig\n\n\ndef getAllTeamMatchRecords(teamID, df):\n \"\"\" Returns all game records for a given teamID \"\"\"\n return df[df['team_id'] == teamID]\n\n\ndef select_features(X_train, y_train, X_test):\n \"\"\" Selects features \"\"\"\n fs = SelectKBest(score_func=f_regression, k='all')\n fs.fit(X_train, y_train)\n X_train_fs = fs.transform(X_train)\n X_test_fs = fs.transform(X_test)\n return X_train_fs, X_test_fs, fs\n\n\ndef overallFeatures(df):\n \"\"\" Return list of top four features \"\"\"\n datasetForFS = df\n datasetForFS.fillna(0)\n X1 = datasetForFS[['assists', 'blocks', 'defensive_rebounds',\n 'opponent_drb', 'fast_break_pts', 'points_in_paint',\n 'points_off_turnovers', 'rebounds', 'steals', 'turnovers', 'efg',\n 'tov_pct', 'orb_pct', 'ftr']]\n y1 = datasetForFS['win']\n X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=\n 0.2, random_state=0)\n X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\n colList = X1.columns.values.tolist()\n statScoreDF = pd.DataFrame(data={'Stat': pd.Series(colList), 'Score':\n pd.Series(fs.scores_.tolist())})\n statScoreDF = statScoreDF.sort_values(by=['Score'], ascending=False)\n return statScoreDF.head(n=4)['Stat'].tolist()\n\n\ndef avgDataRow(df):\n \"\"\" Returns the average values of a dataFrame \"\"\"\n df1 = dict()\n for columnName, columnData in df.iteritems():\n df1[columnName] = [df[columnName].mean()]\n return pd.DataFrame(df1)\n\n\ndef updateWinPct(dfMain, dfWin, reg):\n \"\"\" Return new win percentage \"\"\"\n dfPred = reg.predict(dfMain)\n return pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.\n around(dfPred), 'Predicted (float)': dfPred})\n\n\ndef filterRowsFS(df):\n \"\"\" Return dataframe with selected features \"\"\"\n return df[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n\n\ndef learn(dataset):\n \"\"\" Trains the model \"\"\"\n dataset = pd.read_csv('team_boxscores_v3.csv')\n dataset = dataset.fillna(0)\n dataset = dataset.sample(frac=1)\n X1 = dataset[['assists', 'blocks', 'defensive_rebounds', 'opponent_drb',\n 'fast_break_pts', 'points_in_paint', 'points_off_turnovers',\n 'rebounds', 'steals', 'turnovers', 'efg', 'tov_pct', 'orb_pct', 'ftr']]\n y1 = dataset['win']\n X_train = X1[int(len(X1) / 5):]\n X_test = X1[:int(len(X1) / 5)]\n y_train = y1[int(len(y1) / 5):]\n y_test = y1[:int(len(y1) / 5)]\n regressor = LinearRegression()\n regressor.fit(X_train, y_train)\n coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=[\n 'Coefficient'])\n y_pred = regressor.predict(X_test)\n y_pred_round = np.around(regressor.predict(X_test))\n return regressor, pd.DataFrame({'Actual': y_test, 'Predicted (int)':\n y_pred_round, 'Predicted (float)': y_pred})\n\n\ndef calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data\n ):\n \"\"\" Calculates the win percentage for a team and the 4 selected stat values \"\"\"\n temp = getAllTeamMatchRecords(team, data)\n changed_stat1 = overallFeatures(temp)[0]\n changed_stat2 = overallFeatures(temp)[1]\n changed_stat3 = overallFeatures(temp)[2]\n changed_stat4 = overallFeatures(temp)[3]\n average_team_stats = avgDataRow(filterRowsFS(temp))\n dfWin = temp['win']\n dfFinal = pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.\n around(regressor.predict(average_team_stats)), 'Predicted (float)':\n regressor.predict(average_team_stats)})\n origWinPct = dfFinal.at[0, 'Predicted (float)']\n average_team_stats.at[0, changed_stat1] = stat1\n average_team_stats.at[0, changed_stat2] = stat2\n average_team_stats.at[0, changed_stat3] = stat3\n average_team_stats.at[0, changed_stat4] = stat4\n win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[\n 0, 'Predicted (float)']\n if win_percentage > 1:\n win_percentage = 1\n elif win_percentage < 0:\n win_percentage = 0\n win_percentage = win_percentage * 100\n win_percentage = round(win_percentage, 2)\n win_percentage_text = 'Projected Win Percentage: ' + str(win_percentage\n ) + '%'\n return win_percentage_text\n\n\ndef get_default_slider_values(team, data):\n \"\"\" Gets the values the each of the 4 sliders should display. These values are what the model estimates the team will get in the matchup \"\"\"\n numSliders = 4\n stat_column_names = []\n stat_column_values = []\n estimated_stat_values = avgDataRow(filterRowsFS(getAllTeamMatchRecords(\n team, data)))\n for i in range(numSliders):\n stat_column_names.append(overallFeatures(getAllTeamMatchRecords(\n team, data))[i])\n stat_column_values.append(estimated_stat_values.at[0,\n stat_column_names[i]])\n return stat_column_names, stat_column_values\n",
"step-5": "import dash\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nimport dash_table as dt\r\nimport plotly.express as px\r\nimport pandas as pd\r\nimport plotly.graph_objects as go\r\nimport numpy as np\r\nfrom datetime import datetime as dat\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn import metrics\r\nfrom sklearn.feature_selection import f_regression\r\nfrom sklearn.feature_selection import SelectKBest\r\n\r\n# TODO: \r\n# The model doesn't really take the opponent into consideration when calculating the win percentage. As you would expect, this is not ideal and is something that needs to be fixed\r\n# \r\n# The bar charts only graph 2019 data. Allowing the user to choose the year would be an easy addition. Future implemenatations could also include a previous X games option instead\r\n# \r\n# The bar chart only graphs stats correctly if they are selected in order. For example, if the set of possible stats are ['assists', 'rebounds', 'blocks'], they must all be selected\r\n# in order to show all the values correctly. If the user selects only 'assists' and 'blocks' then 'assists' graphs correctly. 'blocks' is graphed but is given the value assigned\r\n# to 'rebounds' because it assumes the second position in the array of stats to be graphed. \r\n#\r\n# The model doesn't run well (and generally fails) for small schools due to a lack of data for those teams. Error checking needs to be implemented to eliminate this problem.\r\n\r\n\r\ndef getStatsByYear(teamID, year, data):\r\n ''' Returns the stats for a chosen team for a specific year. Choices are 2016 - 2019 '''\r\n teamStats = data[data[\"team_id\"] == teamID]\r\n \r\n for index, row in teamStats.iterrows():\r\n if (row[\"season\"] == year): \r\n teamStatsForGivenYear = teamStats[data[\"season\"] == row[\"season\"]]\r\n return teamStatsForGivenYear\r\n\r\ndef generate_bar_chart(team, opponent, stats, stat_names, data):\r\n ''' Generates a bar chart for a the user selected team, opponent and stats ''' \r\n\r\n teamStats = getStatsByYear(team, 2019, data)\r\n opponentStats = getStatsByYear(opponent, 2019, data)\r\n\r\n teamStatValues = teamStats[[\"assists\", \"assists_turnover_ratio\", \"blocked_att\", \"blocks\", \"defensive_rebounds\", \"fast_break_pts\",\r\n \"field_goals_att\", \"field_goals_pct\", \"field_goals_made\", \"free_throws_att\",\r\n \"free_throws_pct\", \"free_throws_made\", \"offensive_rebounds\", \"personal_fouls\", \r\n \"points\", \"points_against\", \"points_in_paint\", \"points_off_turnovers\",\r\n \"rebounds\", \"second_chance_pts\", \"steals\", \"team_rebounds\", \"three_points_att\",\r\n \"three_points_pct\", \"three_points_made\", \"turnovers\", \"two_points_att\",\r\n \"two_points_pct\", \"two_points_made\"\r\n ]]\r\n\r\n opponentStatValues = opponentStats[[\"assists\", \"assists_turnover_ratio\", \"blocked_att\", \"blocks\", \"defensive_rebounds\", \"fast_break_pts\",\r\n \"field_goals_att\", \"field_goals_pct\", \"field_goals_made\", \"free_throws_att\",\r\n \"free_throws_pct\", \"free_throws_made\", \"offensive_rebounds\", \"personal_fouls\", \r\n \"points\", \"points_against\", \"points_in_paint\", \"points_off_turnovers\",\r\n \"rebounds\", \"second_chance_pts\", \"steals\", \"team_rebounds\", \"three_points_att\",\r\n \"three_points_pct\", \"three_points_made\", \"turnovers\", \"two_points_att\",\r\n \"two_points_pct\", \"two_points_made\"\r\n ]]\r\n\r\n stats_to_be_graphed = []\r\n\r\n for i in range(len(stat_names)):\r\n if i in stats:\r\n stats_to_be_graphed.append(stat_names[i])\r\n\r\n # Graphs average stat values for the user's chosen team\r\n teamVals = go.Bar(\r\n x = stats_to_be_graphed,\r\n y = teamStatValues.mean(),\r\n name = data[(data.team_id == team)]['market'].iloc[0]\r\n )\r\n\r\n # Graphs average stat values for the opponent's team\r\n opponentVals = go.Bar(\r\n x = stats_to_be_graphed,\r\n y = opponentStatValues.mean(),\r\n name = data[(data.team_id == opponent)]['market'].iloc[0]\r\n )\r\n \r\n data = [teamVals, opponentVals]\r\n layout = go.Layout(barmode = 'group')\r\n fig = go.Figure(data = data, layout = layout)\r\n \r\n return fig\r\n\r\ndef getAllTeamMatchRecords(teamID, df):\r\n ''' Returns all game records for a given teamID '''\r\n return df[df[\"team_id\"] == teamID]\r\n\r\ndef select_features(X_train, y_train, X_test):\r\n ''' Selects features '''\r\n # configure to select all features\r\n fs = SelectKBest(score_func=f_regression, k='all')\r\n # learn relationship from training data\r\n fs.fit(X_train, y_train)\r\n # transform train input data\r\n X_train_fs = fs.transform(X_train)\r\n # transform test input data\r\n X_test_fs = fs.transform(X_test)\r\n return X_train_fs, X_test_fs, fs\r\n\r\ndef overallFeatures(df):\r\n ''' Return list of top four features '''\r\n datasetForFS = df\r\n datasetForFS.fillna(0)\r\n\r\n X1 = datasetForFS[[\"assists\",\"blocks\",\"defensive_rebounds\",\"opponent_drb\",\"fast_break_pts\",\"points_in_paint\",\"points_off_turnovers\",\"rebounds\",\"steals\",\"turnovers\",\"efg\",\"tov_pct\",\"orb_pct\",\"ftr\"]]\r\n y1 = datasetForFS['win']\r\n\r\n X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0)\r\n X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)\r\n\r\n colList = X1.columns.values.tolist()\r\n statScoreDF = pd.DataFrame(data={'Stat': pd.Series(colList), 'Score': pd.Series(fs.scores_.tolist())})\r\n statScoreDF = statScoreDF.sort_values(by=['Score'], ascending=False)\r\n \r\n return statScoreDF.head(n=4)['Stat'].tolist()\r\n\r\ndef avgDataRow(df):\r\n ''' Returns the average values of a dataFrame '''\r\n df1 = dict()\r\n for (columnName, columnData) in df.iteritems():\r\n df1[columnName] = [df[columnName].mean()]\r\n \r\n return pd.DataFrame(df1)\r\n\r\ndef updateWinPct(dfMain, dfWin, reg):\r\n ''' Return new win percentage '''\r\n dfPred = reg.predict(dfMain)\r\n return pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.around(dfPred), 'Predicted (float)': dfPred})\r\n\r\ndef filterRowsFS(df):\r\n ''' Return dataframe with selected features '''\r\n return df[[\"assists\",\"blocks\",\"defensive_rebounds\",\"opponent_drb\",\"fast_break_pts\",\"points_in_paint\",\"points_off_turnovers\",\"rebounds\",\"steals\",\"turnovers\",\"efg\",\"tov_pct\",\"orb_pct\",\"ftr\"]]\r\n\r\ndef learn(dataset):\r\n ''' Trains the model '''\r\n dataset = pd.read_csv(\"team_boxscores_v3.csv\")\r\n dataset = dataset.fillna(0)\r\n \r\n # Shuffle\r\n dataset = dataset.sample(frac = 1) \r\n \r\n X1 = dataset[[\"assists\",\"blocks\",\"defensive_rebounds\",\"opponent_drb\",\"fast_break_pts\",\"points_in_paint\",\"points_off_turnovers\",\"rebounds\",\"steals\",\"turnovers\",\"efg\",\"tov_pct\",\"orb_pct\",\"ftr\"]]\r\n y1 = dataset['win']\r\n \r\n # No shuffle\r\n # X_train, X_test, y_train, y_test = train_test_split(X1, y1, test_size=0.2, random_state=0)\r\n \r\n # W/ shuffle\r\n X_train = X1[int(len(X1)/5):]\r\n X_test = X1[:int(len(X1)/5)]\r\n \r\n y_train = y1[int(len(y1)/5):]\r\n y_test = y1[:int(len(y1)/5)]\r\n \r\n regressor = LinearRegression()\r\n regressor.fit(X_train, y_train)\r\n \r\n coeff_df = pd.DataFrame(regressor.coef_, X1.columns, columns=['Coefficient'])\r\n \r\n y_pred = regressor.predict(X_test)\r\n y_pred_round = np.around(regressor.predict(X_test))\r\n \r\n return regressor, pd.DataFrame({'Actual': y_test, 'Predicted (int)': y_pred_round, 'Predicted (float)': y_pred})\r\n\r\ndef calculate_win_percentage(team, stat1, stat2, stat3, stat4, regressor, data):\r\n ''' Calculates the win percentage for a team and the 4 selected stat values '''\r\n temp = getAllTeamMatchRecords(team, data)\r\n changed_stat1 = overallFeatures(temp)[0]\r\n changed_stat2 = overallFeatures(temp)[1]\r\n changed_stat3 = overallFeatures(temp)[2]\r\n changed_stat4 = overallFeatures(temp)[3]\r\n average_team_stats = avgDataRow(filterRowsFS(temp))\r\n dfWin = temp[\"win\"]\r\n\r\n dfFinal = pd.DataFrame({'Actual': dfWin.mean(), 'Predicted (int)': np.around(regressor.predict(average_team_stats)), 'Predicted (float)': regressor.predict(average_team_stats)})\r\n origWinPct = dfFinal.at[0, 'Predicted (float)']\r\n\r\n average_team_stats.at[0, changed_stat1] = stat1\r\n average_team_stats.at[0, changed_stat2] = stat2\r\n average_team_stats.at[0, changed_stat3] = stat3\r\n average_team_stats.at[0, changed_stat4] = stat4\r\n\r\n win_percentage = updateWinPct(average_team_stats, dfWin, regressor).at[0,'Predicted (float)']\r\n\r\n # Makes sure you can't have a win percentage of > 100% or < 0%\r\n if win_percentage > 1:\r\n win_percentage = 1\r\n elif win_percentage < 0:\r\n win_percentage = 0\r\n\r\n win_percentage = win_percentage * 100\r\n win_percentage = round(win_percentage, 2)\r\n\r\n win_percentage_text = \"Projected Win Percentage: \" + str(win_percentage) + \"%\"\r\n\r\n return win_percentage_text\r\n\r\ndef get_default_slider_values(team, data):\r\n ''' Gets the values the each of the 4 sliders should display. These values are what the model estimates the team will get in the matchup '''\r\n numSliders = 4\r\n stat_column_names = []\r\n stat_column_values = []\r\n\r\n estimated_stat_values = avgDataRow(filterRowsFS(getAllTeamMatchRecords(team, data)))\r\n\r\n for i in range(numSliders):\r\n stat_column_names.append(overallFeatures(getAllTeamMatchRecords(team, data))[i])\r\n stat_column_values.append(estimated_stat_values.at[0, stat_column_names[i]])\r\n\r\n return stat_column_names, stat_column_values",
"step-ids": [
8,
9,
11,
12,
13
]
}
|
[
8,
9,
11,
12,
13
] |
from lib.utility import start_time, end_time
from lib.prime import read_primes
from bisect import bisect_left
start_time()
primes = read_primes(100)
# limit = 10 ** 16
import random
# limit = random.randint(1000, 10 ** 5)
limit = 43268
# limit = 10 ** 16
print('limit=', limit)
v1 = set()
v2 = set()
def version_100_iq(limit):
nums = []
for x in range(2, limit):
facs = 0
n = x
for p in primes:
if n % p == 0:
facs += 1
while n % p == 0:
n //= p
if n == 1 or facs >= 4:
break
if facs >= 4:
nums.append(x)
return set(nums)
def version_1(limit):
def search(prod, i, num_distinct):
if prod >= limit or i >= len(primes):
return 0
if prod not in v1 and num_distinct >= 4:
v1.add(prod)
not_used = (prod % primes[i] != 0)
count = (num_distinct >= 4) and not not_used
count += search(prod * primes[i], i, num_distinct + not_used)
count += search(prod, i + 1, num_distinct)
return count
return search(1, 0, 0)
def version_2(limit):
def search(prod, i, num_distinct):
if prod >= limit:
return
if prod not in v1 and num_distinct >= 4:
v1.add(prod)
if i >= len(primes):
return
search(prod * primes[i], i + 1, num_distinct + 1)
search(prod, i + 1, num_distinct)
return search(1, 0, 0)
def find_prods_by_num_distinct_primes(limit, primes):
prods_by_num_distinct = [set() for _ in range(5)]
prods_by_num_distinct[0] |= {1}
def add_prods(prod, i, num_distinct):
if prod >= limit or i >= len(primes):
return
# not_used = (prod % primes[i] != 0)
# if not not_used:
prods_by_num_distinct[min(num_distinct, 4)].add(prod)
add_prods(prod * primes[i], i + 1, num_distinct + 1)
add_prods(prod, i + 1, num_distinct)
add_prods(1, 0, 0)
return [sorted(s) for s in prods_by_num_distinct]
version_2(limit)
pset = set(primes)
res = set()
count = 0
for n in sorted(v1):
for mult in range(1, 401):
if mult * n >= limit:
break
# if n % mult != 0 and mult in pset:
if mult in pset:
# assert(n * mult in v1), (n, mult)
continue
# print(n, mult)
if n * mult in res:
print(n, mult, n * mult)
res.add(n * mult)
count += 1
else:
print('not enough huh...', n)
count += (limit - 100*n) // n
print(len(res))
# n = 7 # a splitting point that seems to be the fastest
# lo = find_prods_by_num_distinct_primes(limit, primes[:n])
# hi = find_prods_by_num_distinct_primes(limit, primes[n:])
# max_sol = 0
# count = 0
# for lo_num_distinct_primes in range(0, 5):
# for prod in lo[lo_num_distinct_primes]:
# for hi_num_distinct_primes in range(4 - lo_num_distinct_primes, 5):
# # count += bisect_left(hi[hi_num_distinct_primes], limit / prod)
# for v in hi[hi_num_distinct_primes]:
# if v * prod < limit:
# count += 1
# # count += (limit - 1) // (v * prod)
print('Solution:', count)
iq_100 = version_100_iq(limit)
print('100 IQ version:', len(iq_100))
# if count != len(iq_100):
# print(iq_100 - v1)
# assert count == len(iq_100)
end_time()
|
normal
|
{
"blob_id": "ea8676a4c55bbe0ae2ff8abf924accfc0bd8f661",
"index": 1272,
"step-1": "<mask token>\n\n\ndef version_100_iq(limit):\n nums = []\n for x in range(2, limit):\n facs = 0\n n = x\n for p in primes:\n if n % p == 0:\n facs += 1\n while n % p == 0:\n n //= p\n if n == 1 or facs >= 4:\n break\n if facs >= 4:\n nums.append(x)\n return set(nums)\n\n\ndef version_1(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return 0\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n not_used = prod % primes[i] != 0\n count = num_distinct >= 4 and not not_used\n count += search(prod * primes[i], i, num_distinct + not_used)\n count += search(prod, i + 1, num_distinct)\n return count\n return search(1, 0, 0)\n\n\ndef version_2(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit:\n return\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n if i >= len(primes):\n return\n search(prod * primes[i], i + 1, num_distinct + 1)\n search(prod, i + 1, num_distinct)\n return search(1, 0, 0)\n\n\ndef find_prods_by_num_distinct_primes(limit, primes):\n prods_by_num_distinct = [set() for _ in range(5)]\n prods_by_num_distinct[0] |= {1}\n\n def add_prods(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return\n prods_by_num_distinct[min(num_distinct, 4)].add(prod)\n add_prods(prod * primes[i], i + 1, num_distinct + 1)\n add_prods(prod, i + 1, num_distinct)\n add_prods(1, 0, 0)\n return [sorted(s) for s in prods_by_num_distinct]\n\n\n<mask token>\n",
"step-2": "<mask token>\nstart_time()\n<mask token>\nprint('limit=', limit)\n<mask token>\n\n\ndef version_100_iq(limit):\n nums = []\n for x in range(2, limit):\n facs = 0\n n = x\n for p in primes:\n if n % p == 0:\n facs += 1\n while n % p == 0:\n n //= p\n if n == 1 or facs >= 4:\n break\n if facs >= 4:\n nums.append(x)\n return set(nums)\n\n\ndef version_1(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return 0\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n not_used = prod % primes[i] != 0\n count = num_distinct >= 4 and not not_used\n count += search(prod * primes[i], i, num_distinct + not_used)\n count += search(prod, i + 1, num_distinct)\n return count\n return search(1, 0, 0)\n\n\ndef version_2(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit:\n return\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n if i >= len(primes):\n return\n search(prod * primes[i], i + 1, num_distinct + 1)\n search(prod, i + 1, num_distinct)\n return search(1, 0, 0)\n\n\ndef find_prods_by_num_distinct_primes(limit, primes):\n prods_by_num_distinct = [set() for _ in range(5)]\n prods_by_num_distinct[0] |= {1}\n\n def add_prods(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return\n prods_by_num_distinct[min(num_distinct, 4)].add(prod)\n add_prods(prod * primes[i], i + 1, num_distinct + 1)\n add_prods(prod, i + 1, num_distinct)\n add_prods(1, 0, 0)\n return [sorted(s) for s in prods_by_num_distinct]\n\n\nversion_2(limit)\n<mask token>\nfor n in sorted(v1):\n for mult in range(1, 401):\n if mult * n >= limit:\n break\n if mult in pset:\n continue\n if n * mult in res:\n print(n, mult, n * mult)\n res.add(n * mult)\n count += 1\n else:\n print('not enough huh...', n)\n count += (limit - 100 * n) // n\nprint(len(res))\nprint('Solution:', count)\n<mask token>\nprint('100 IQ version:', len(iq_100))\nend_time()\n",
"step-3": "<mask token>\nstart_time()\nprimes = read_primes(100)\n<mask token>\nlimit = 43268\nprint('limit=', limit)\nv1 = set()\nv2 = set()\n\n\ndef version_100_iq(limit):\n nums = []\n for x in range(2, limit):\n facs = 0\n n = x\n for p in primes:\n if n % p == 0:\n facs += 1\n while n % p == 0:\n n //= p\n if n == 1 or facs >= 4:\n break\n if facs >= 4:\n nums.append(x)\n return set(nums)\n\n\ndef version_1(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return 0\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n not_used = prod % primes[i] != 0\n count = num_distinct >= 4 and not not_used\n count += search(prod * primes[i], i, num_distinct + not_used)\n count += search(prod, i + 1, num_distinct)\n return count\n return search(1, 0, 0)\n\n\ndef version_2(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit:\n return\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n if i >= len(primes):\n return\n search(prod * primes[i], i + 1, num_distinct + 1)\n search(prod, i + 1, num_distinct)\n return search(1, 0, 0)\n\n\ndef find_prods_by_num_distinct_primes(limit, primes):\n prods_by_num_distinct = [set() for _ in range(5)]\n prods_by_num_distinct[0] |= {1}\n\n def add_prods(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return\n prods_by_num_distinct[min(num_distinct, 4)].add(prod)\n add_prods(prod * primes[i], i + 1, num_distinct + 1)\n add_prods(prod, i + 1, num_distinct)\n add_prods(1, 0, 0)\n return [sorted(s) for s in prods_by_num_distinct]\n\n\nversion_2(limit)\npset = set(primes)\nres = set()\ncount = 0\nfor n in sorted(v1):\n for mult in range(1, 401):\n if mult * n >= limit:\n break\n if mult in pset:\n continue\n if n * mult in res:\n print(n, mult, n * mult)\n res.add(n * mult)\n count += 1\n else:\n print('not enough huh...', n)\n count += (limit - 100 * n) // n\nprint(len(res))\nprint('Solution:', count)\niq_100 = version_100_iq(limit)\nprint('100 IQ version:', len(iq_100))\nend_time()\n",
"step-4": "from lib.utility import start_time, end_time\nfrom lib.prime import read_primes\nfrom bisect import bisect_left\nstart_time()\nprimes = read_primes(100)\nimport random\nlimit = 43268\nprint('limit=', limit)\nv1 = set()\nv2 = set()\n\n\ndef version_100_iq(limit):\n nums = []\n for x in range(2, limit):\n facs = 0\n n = x\n for p in primes:\n if n % p == 0:\n facs += 1\n while n % p == 0:\n n //= p\n if n == 1 or facs >= 4:\n break\n if facs >= 4:\n nums.append(x)\n return set(nums)\n\n\ndef version_1(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return 0\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n not_used = prod % primes[i] != 0\n count = num_distinct >= 4 and not not_used\n count += search(prod * primes[i], i, num_distinct + not_used)\n count += search(prod, i + 1, num_distinct)\n return count\n return search(1, 0, 0)\n\n\ndef version_2(limit):\n\n def search(prod, i, num_distinct):\n if prod >= limit:\n return\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n if i >= len(primes):\n return\n search(prod * primes[i], i + 1, num_distinct + 1)\n search(prod, i + 1, num_distinct)\n return search(1, 0, 0)\n\n\ndef find_prods_by_num_distinct_primes(limit, primes):\n prods_by_num_distinct = [set() for _ in range(5)]\n prods_by_num_distinct[0] |= {1}\n\n def add_prods(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return\n prods_by_num_distinct[min(num_distinct, 4)].add(prod)\n add_prods(prod * primes[i], i + 1, num_distinct + 1)\n add_prods(prod, i + 1, num_distinct)\n add_prods(1, 0, 0)\n return [sorted(s) for s in prods_by_num_distinct]\n\n\nversion_2(limit)\npset = set(primes)\nres = set()\ncount = 0\nfor n in sorted(v1):\n for mult in range(1, 401):\n if mult * n >= limit:\n break\n if mult in pset:\n continue\n if n * mult in res:\n print(n, mult, n * mult)\n res.add(n * mult)\n count += 1\n else:\n print('not enough huh...', n)\n count += (limit - 100 * n) // n\nprint(len(res))\nprint('Solution:', count)\niq_100 = version_100_iq(limit)\nprint('100 IQ version:', len(iq_100))\nend_time()\n",
"step-5": "from lib.utility import start_time, end_time\nfrom lib.prime import read_primes\nfrom bisect import bisect_left\nstart_time()\n\nprimes = read_primes(100)\n# limit = 10 ** 16\nimport random\n# limit = random.randint(1000, 10 ** 5)\nlimit = 43268\n# limit = 10 ** 16\nprint('limit=', limit)\nv1 = set()\nv2 = set()\n\n\ndef version_100_iq(limit):\n nums = []\n for x in range(2, limit):\n facs = 0\n n = x\n for p in primes:\n if n % p == 0:\n facs += 1\n\n while n % p == 0:\n n //= p\n\n if n == 1 or facs >= 4:\n break\n\n if facs >= 4:\n nums.append(x)\n\n return set(nums)\n\n\ndef version_1(limit):\n def search(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return 0\n\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n\n not_used = (prod % primes[i] != 0)\n count = (num_distinct >= 4) and not not_used\n count += search(prod * primes[i], i, num_distinct + not_used)\n count += search(prod, i + 1, num_distinct)\n return count\n\n return search(1, 0, 0)\n\n\ndef version_2(limit):\n def search(prod, i, num_distinct):\n if prod >= limit:\n return\n\n if prod not in v1 and num_distinct >= 4:\n v1.add(prod)\n\n if i >= len(primes):\n return\n\n search(prod * primes[i], i + 1, num_distinct + 1)\n search(prod, i + 1, num_distinct)\n\n return search(1, 0, 0)\n\n\ndef find_prods_by_num_distinct_primes(limit, primes):\n prods_by_num_distinct = [set() for _ in range(5)]\n prods_by_num_distinct[0] |= {1}\n\n def add_prods(prod, i, num_distinct):\n if prod >= limit or i >= len(primes):\n return\n\n # not_used = (prod % primes[i] != 0)\n # if not not_used:\n prods_by_num_distinct[min(num_distinct, 4)].add(prod)\n\n add_prods(prod * primes[i], i + 1, num_distinct + 1)\n add_prods(prod, i + 1, num_distinct)\n\n add_prods(1, 0, 0)\n return [sorted(s) for s in prods_by_num_distinct]\n\n\nversion_2(limit)\n\npset = set(primes)\n\nres = set()\ncount = 0\nfor n in sorted(v1):\n for mult in range(1, 401):\n if mult * n >= limit:\n break\n # if n % mult != 0 and mult in pset:\n if mult in pset:\n # assert(n * mult in v1), (n, mult)\n continue\n\n\n # print(n, mult)\n if n * mult in res:\n print(n, mult, n * mult)\n res.add(n * mult)\n count += 1\n else:\n print('not enough huh...', n)\n count += (limit - 100*n) // n\n\n\nprint(len(res))\n# n = 7 # a splitting point that seems to be the fastest\n# lo = find_prods_by_num_distinct_primes(limit, primes[:n])\n# hi = find_prods_by_num_distinct_primes(limit, primes[n:])\n\n\n# max_sol = 0\n# count = 0\n# for lo_num_distinct_primes in range(0, 5):\n# for prod in lo[lo_num_distinct_primes]:\n# for hi_num_distinct_primes in range(4 - lo_num_distinct_primes, 5):\n# # count += bisect_left(hi[hi_num_distinct_primes], limit / prod)\n# for v in hi[hi_num_distinct_primes]:\n# if v * prod < limit:\n# count += 1\n# # count += (limit - 1) // (v * prod)\n\n\nprint('Solution:', count)\n\niq_100 = version_100_iq(limit)\nprint('100 IQ version:', len(iq_100))\n\n# if count != len(iq_100):\n # print(iq_100 - v1)\n# assert count == len(iq_100)\n\n\n\nend_time()\n",
"step-ids": [
4,
5,
6,
7,
8
]
}
|
[
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('gist test file4')
<|reserved_special_token_1|>
print("gist test file4")
|
flexible
|
{
"blob_id": "ec4725b5b60d10e86b29aab3723917ace5cf52f6",
"index": 8452,
"step-1": "<mask token>\n",
"step-2": "print('gist test file4')\n",
"step-3": "print(\"gist test file4\")",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class kdECSDemo(cdk.Stack):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class kdECSDemo(cdk.Stack):
def __init__(self, scope: cdk.Construct, construct_id: str, **kwargs
) ->None:
super().__init__(scope, construct_id, **kwargs)
vpc = ec2.Vpc(self, 'ECSVPC', cidr='10.0.0.0/16')
cluster = ecs.Cluster(self, 'ECSCluster', vpc=vpc)
task_definition = ecs.FargateTaskDefinition(self,
'ECSDemoTaskDefinition', task_role=iam.Role.from_role_arn(self,
'fargate_task_role',
'arn:aws-cn:iam::402202783068:role/ECS-Task-Role-Firelens'),
execution_role=iam.Role.from_role_arn(self,
'fargate_task_execution_role',
'arn:aws-cn:iam::402202783068:role/ecsTaskExecutionRole'))
task_definition.add_volume(name='data')
app_container = task_definition.add_container('AppContainer', image
=ecs.ContainerImage.from_ecr_repository(ecr.Repository.
from_repository_name(self, id='app-file-image', repository_name
='app-file')), logging=ecs.FireLensLogDriver())
app_container.add_mount_points(ecs.MountPoint(container_path=
'/data/logs', read_only=False, source_volume='data'))
fluentbit_container = ecs.FirelensLogRouter(self,
'fluentbit_container', firelens_config=ecs.FirelensConfig(type=
ecs.FirelensLogRouterType.FLUENTBIT, options=ecs.
FirelensOptions(config_file_value='/extra.conf')),
task_definition=task_definition, image=ecs.ContainerImage.
from_ecr_repository(ecr.Repository.from_repository_name(self,
id='log-router', repository_name='firelens-file')), logging=ecs
.AwsLogDriver(stream_prefix='/ecs/firelens-fluentbit-demo/'))
fluentbit_container.add_mount_points(ecs.MountPoint(container_path=
'/data/logs', read_only=False, source_volume='data'))
<|reserved_special_token_1|>
from aws_cdk import core as cdk
from aws_cdk import core, aws_ec2 as ec2, aws_ecs as ecs, aws_ecr as ecr, aws_iam as iam, aws_ecs_patterns as ecs_patterns
class kdECSDemo(cdk.Stack):
def __init__(self, scope: cdk.Construct, construct_id: str, **kwargs
) ->None:
super().__init__(scope, construct_id, **kwargs)
vpc = ec2.Vpc(self, 'ECSVPC', cidr='10.0.0.0/16')
cluster = ecs.Cluster(self, 'ECSCluster', vpc=vpc)
task_definition = ecs.FargateTaskDefinition(self,
'ECSDemoTaskDefinition', task_role=iam.Role.from_role_arn(self,
'fargate_task_role',
'arn:aws-cn:iam::402202783068:role/ECS-Task-Role-Firelens'),
execution_role=iam.Role.from_role_arn(self,
'fargate_task_execution_role',
'arn:aws-cn:iam::402202783068:role/ecsTaskExecutionRole'))
task_definition.add_volume(name='data')
app_container = task_definition.add_container('AppContainer', image
=ecs.ContainerImage.from_ecr_repository(ecr.Repository.
from_repository_name(self, id='app-file-image', repository_name
='app-file')), logging=ecs.FireLensLogDriver())
app_container.add_mount_points(ecs.MountPoint(container_path=
'/data/logs', read_only=False, source_volume='data'))
fluentbit_container = ecs.FirelensLogRouter(self,
'fluentbit_container', firelens_config=ecs.FirelensConfig(type=
ecs.FirelensLogRouterType.FLUENTBIT, options=ecs.
FirelensOptions(config_file_value='/extra.conf')),
task_definition=task_definition, image=ecs.ContainerImage.
from_ecr_repository(ecr.Repository.from_repository_name(self,
id='log-router', repository_name='firelens-file')), logging=ecs
.AwsLogDriver(stream_prefix='/ecs/firelens-fluentbit-demo/'))
fluentbit_container.add_mount_points(ecs.MountPoint(container_path=
'/data/logs', read_only=False, source_volume='data'))
<|reserved_special_token_1|>
from aws_cdk import core as cdk
# For consistency with other languages, `cdk` is the preferred import name for
# the CDK's core module. The following line also imports it as `core` for use
# with examples from the CDK Developer's Guide, which are in the process of
# being updated to use `cdk`. You may delete this import if you don't need it.
from aws_cdk import (core, aws_ec2 as ec2, aws_ecs as ecs, aws_ecr as ecr, aws_iam as iam,
aws_ecs_patterns as ecs_patterns)
class kdECSDemo(cdk.Stack):
def __init__(self, scope: cdk.Construct, construct_id: str, **kwargs) -> None:
super().__init__(scope, construct_id, **kwargs)
# 建VPC与ECS Cluster
# TODO: 即使指定 max_azs, 也只能部署2个AZ
vpc = ec2.Vpc(self, "ECSVPC", cidr='10.0.0.0/16')
cluster = ecs.Cluster(self, "ECSCluster", vpc=vpc)
#建Task Definition
task_definition = ecs.FargateTaskDefinition(self, "ECSDemoTaskDefinition",
task_role=iam.Role.from_role_arn(self, "fargate_task_role", "arn:aws-cn:iam::402202783068:role/ECS-Task-Role-Firelens"),
execution_role=iam.Role.from_role_arn(self, "fargate_task_execution_role", "arn:aws-cn:iam::402202783068:role/ecsTaskExecutionRole")
)
task_definition.add_volume(name="data")
# App Container
app_container = task_definition.add_container(
"AppContainer",
image=ecs.ContainerImage.from_ecr_repository(
ecr.Repository.from_repository_name(self, id="app-file-image", repository_name="app-file")
),
logging=ecs.FireLensLogDriver()
)
app_container.add_mount_points(ecs.MountPoint(
container_path="/data/logs",
read_only=False,
source_volume="data"
))
# app_container.add_port_mappings(ecs.PortMapping(container_port=80))
# Log Router
fluentbit_container = ecs.FirelensLogRouter(self, "fluentbit_container",
firelens_config=ecs.FirelensConfig(
type=ecs.FirelensLogRouterType.FLUENTBIT,
options=ecs.FirelensOptions(
config_file_value="/extra.conf"
)
),
task_definition=task_definition,
image=ecs.ContainerImage.from_ecr_repository(
ecr.Repository.from_repository_name(self, id="log-router", repository_name="firelens-file")
),
logging=ecs.AwsLogDriver(stream_prefix="/ecs/firelens-fluentbit-demo/")
)
fluentbit_container.add_mount_points(ecs.MountPoint(
container_path="/data/logs",
read_only=False,
source_volume="data"
))
# #建Service
# ecs_patterns.ApplicationLoadBalancedFargateService(self, "ServiceWithLogging",
# cluster=cluster,
# desired_count=1, # Default is 1
# task_definition=task_definition,
# public_load_balancer=True) # Default is False
|
flexible
|
{
"blob_id": "12cd3dbf211b202d25dc6f940156536c9fe3f76f",
"index": 3385,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass kdECSDemo(cdk.Stack):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass kdECSDemo(cdk.Stack):\n\n def __init__(self, scope: cdk.Construct, construct_id: str, **kwargs\n ) ->None:\n super().__init__(scope, construct_id, **kwargs)\n vpc = ec2.Vpc(self, 'ECSVPC', cidr='10.0.0.0/16')\n cluster = ecs.Cluster(self, 'ECSCluster', vpc=vpc)\n task_definition = ecs.FargateTaskDefinition(self,\n 'ECSDemoTaskDefinition', task_role=iam.Role.from_role_arn(self,\n 'fargate_task_role',\n 'arn:aws-cn:iam::402202783068:role/ECS-Task-Role-Firelens'),\n execution_role=iam.Role.from_role_arn(self,\n 'fargate_task_execution_role',\n 'arn:aws-cn:iam::402202783068:role/ecsTaskExecutionRole'))\n task_definition.add_volume(name='data')\n app_container = task_definition.add_container('AppContainer', image\n =ecs.ContainerImage.from_ecr_repository(ecr.Repository.\n from_repository_name(self, id='app-file-image', repository_name\n ='app-file')), logging=ecs.FireLensLogDriver())\n app_container.add_mount_points(ecs.MountPoint(container_path=\n '/data/logs', read_only=False, source_volume='data'))\n fluentbit_container = ecs.FirelensLogRouter(self,\n 'fluentbit_container', firelens_config=ecs.FirelensConfig(type=\n ecs.FirelensLogRouterType.FLUENTBIT, options=ecs.\n FirelensOptions(config_file_value='/extra.conf')),\n task_definition=task_definition, image=ecs.ContainerImage.\n from_ecr_repository(ecr.Repository.from_repository_name(self,\n id='log-router', repository_name='firelens-file')), logging=ecs\n .AwsLogDriver(stream_prefix='/ecs/firelens-fluentbit-demo/'))\n fluentbit_container.add_mount_points(ecs.MountPoint(container_path=\n '/data/logs', read_only=False, source_volume='data'))\n",
"step-4": "from aws_cdk import core as cdk\nfrom aws_cdk import core, aws_ec2 as ec2, aws_ecs as ecs, aws_ecr as ecr, aws_iam as iam, aws_ecs_patterns as ecs_patterns\n\n\nclass kdECSDemo(cdk.Stack):\n\n def __init__(self, scope: cdk.Construct, construct_id: str, **kwargs\n ) ->None:\n super().__init__(scope, construct_id, **kwargs)\n vpc = ec2.Vpc(self, 'ECSVPC', cidr='10.0.0.0/16')\n cluster = ecs.Cluster(self, 'ECSCluster', vpc=vpc)\n task_definition = ecs.FargateTaskDefinition(self,\n 'ECSDemoTaskDefinition', task_role=iam.Role.from_role_arn(self,\n 'fargate_task_role',\n 'arn:aws-cn:iam::402202783068:role/ECS-Task-Role-Firelens'),\n execution_role=iam.Role.from_role_arn(self,\n 'fargate_task_execution_role',\n 'arn:aws-cn:iam::402202783068:role/ecsTaskExecutionRole'))\n task_definition.add_volume(name='data')\n app_container = task_definition.add_container('AppContainer', image\n =ecs.ContainerImage.from_ecr_repository(ecr.Repository.\n from_repository_name(self, id='app-file-image', repository_name\n ='app-file')), logging=ecs.FireLensLogDriver())\n app_container.add_mount_points(ecs.MountPoint(container_path=\n '/data/logs', read_only=False, source_volume='data'))\n fluentbit_container = ecs.FirelensLogRouter(self,\n 'fluentbit_container', firelens_config=ecs.FirelensConfig(type=\n ecs.FirelensLogRouterType.FLUENTBIT, options=ecs.\n FirelensOptions(config_file_value='/extra.conf')),\n task_definition=task_definition, image=ecs.ContainerImage.\n from_ecr_repository(ecr.Repository.from_repository_name(self,\n id='log-router', repository_name='firelens-file')), logging=ecs\n .AwsLogDriver(stream_prefix='/ecs/firelens-fluentbit-demo/'))\n fluentbit_container.add_mount_points(ecs.MountPoint(container_path=\n '/data/logs', read_only=False, source_volume='data'))\n",
"step-5": "from aws_cdk import core as cdk\n\n# For consistency with other languages, `cdk` is the preferred import name for\n# the CDK's core module. The following line also imports it as `core` for use\n# with examples from the CDK Developer's Guide, which are in the process of\n# being updated to use `cdk`. You may delete this import if you don't need it.\nfrom aws_cdk import (core, aws_ec2 as ec2, aws_ecs as ecs, aws_ecr as ecr, aws_iam as iam,\n aws_ecs_patterns as ecs_patterns)\n\n\nclass kdECSDemo(cdk.Stack):\n\n def __init__(self, scope: cdk.Construct, construct_id: str, **kwargs) -> None:\n super().__init__(scope, construct_id, **kwargs)\n\n # 建VPC与ECS Cluster\n # TODO: 即使指定 max_azs, 也只能部署2个AZ\n vpc = ec2.Vpc(self, \"ECSVPC\", cidr='10.0.0.0/16') \n cluster = ecs.Cluster(self, \"ECSCluster\", vpc=vpc)\n\n #建Task Definition\n task_definition = ecs.FargateTaskDefinition(self, \"ECSDemoTaskDefinition\",\n task_role=iam.Role.from_role_arn(self, \"fargate_task_role\", \"arn:aws-cn:iam::402202783068:role/ECS-Task-Role-Firelens\"),\n execution_role=iam.Role.from_role_arn(self, \"fargate_task_execution_role\", \"arn:aws-cn:iam::402202783068:role/ecsTaskExecutionRole\")\n )\n\n task_definition.add_volume(name=\"data\")\n\n # App Container\n app_container = task_definition.add_container(\n \"AppContainer\",\n image=ecs.ContainerImage.from_ecr_repository(\n ecr.Repository.from_repository_name(self, id=\"app-file-image\", repository_name=\"app-file\")\n ), \n logging=ecs.FireLensLogDriver()\n )\n\n app_container.add_mount_points(ecs.MountPoint(\n container_path=\"/data/logs\",\n read_only=False,\n source_volume=\"data\"\n ))\n\n # app_container.add_port_mappings(ecs.PortMapping(container_port=80))\n \n # Log Router\n fluentbit_container = ecs.FirelensLogRouter(self, \"fluentbit_container\",\n firelens_config=ecs.FirelensConfig(\n type=ecs.FirelensLogRouterType.FLUENTBIT,\n options=ecs.FirelensOptions(\n config_file_value=\"/extra.conf\"\n )\n ),\n task_definition=task_definition,\n image=ecs.ContainerImage.from_ecr_repository(\n ecr.Repository.from_repository_name(self, id=\"log-router\", repository_name=\"firelens-file\")\n ),\n logging=ecs.AwsLogDriver(stream_prefix=\"/ecs/firelens-fluentbit-demo/\")\n )\n \n fluentbit_container.add_mount_points(ecs.MountPoint(\n container_path=\"/data/logs\",\n read_only=False,\n source_volume=\"data\"\n ))\n\n # #建Service\n # ecs_patterns.ApplicationLoadBalancedFargateService(self, \"ServiceWithLogging\",\n # cluster=cluster,\n # desired_count=1, # Default is 1\n # task_definition=task_definition,\n # public_load_balancer=True) # Default is False\n\n \n\n\n\n\n\n\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
################################################################################
# run_experiment.py #
# Ian Marci 2017 #
# Defines knn classifier and runs 4-fold cross validation on data in #
# Data/NetworkInput folder. #
# Prints accuracy for each fold as well as confusion matrix. #
################################################################################
# Imports
import tensorflow as tf
import numpy as np
from classifier_input_functions import choose_test_set, get_network_input
# Path and placeholder definitions
train_path = 'Data/NetworkTrain/'
test_path = 'Data/NetworkTest/'
x_train = tf.placeholder('float', [None, 200])
x_test = tf.placeholder('float', [200])
# Distance to decide nearest neighbor
distance = tf.reduce_sum(tf.abs(tf.add(x_train, tf.negative(x_test))),
reduction_indices=1)
# Prediction chooses lowest distance
pred = tf.argmin(distance, 0)
################################
# 4-fold cross validation loop #
################################
init = tf.global_variables_initializer()
with tf.Session() as sess:
predictions = []
labels = []
accuracies = []
for i in range(4):
sess.run(init)
choice = i + 1
choose_test_set(str(choice))
train_data, train_labels = get_network_input(train_path)
test_data, test_labels = get_network_input(test_path)
fold_accuracy = 0
for i in range(len(test_data)):
nn_index = sess.run(pred, feed_dict={x_train: train_data,
x_test: test_data[i, :]})
predictions.append(np.argmax(train_labels[nn_index]))
labels.append(np.argmax(test_labels[i]))
if predictions[-1] == labels[-1]:
fold_accuracy += 1./len(test_data)
accuracies.append(fold_accuracy)
overall_accuracy = np.mean(accuracies)
print('Average accuracy over 4 folds:', overall_accuracy)
confusion = tf.confusion_matrix(labels=labels, predictions=predictions)
print(confusion.eval())
|
normal
|
{
"blob_id": "dbc599a03d91f369d862f6cc90c31221747ead80",
"index": 2811,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith tf.Session() as sess:\n predictions = []\n labels = []\n accuracies = []\n for i in range(4):\n sess.run(init)\n choice = i + 1\n choose_test_set(str(choice))\n train_data, train_labels = get_network_input(train_path)\n test_data, test_labels = get_network_input(test_path)\n fold_accuracy = 0\n for i in range(len(test_data)):\n nn_index = sess.run(pred, feed_dict={x_train: train_data,\n x_test: test_data[i, :]})\n predictions.append(np.argmax(train_labels[nn_index]))\n labels.append(np.argmax(test_labels[i]))\n if predictions[-1] == labels[-1]:\n fold_accuracy += 1.0 / len(test_data)\n accuracies.append(fold_accuracy)\n overall_accuracy = np.mean(accuracies)\n print('Average accuracy over 4 folds:', overall_accuracy)\n confusion = tf.confusion_matrix(labels=labels, predictions=predictions)\n print(confusion.eval())\n",
"step-3": "<mask token>\ntrain_path = 'Data/NetworkTrain/'\ntest_path = 'Data/NetworkTest/'\nx_train = tf.placeholder('float', [None, 200])\nx_test = tf.placeholder('float', [200])\ndistance = tf.reduce_sum(tf.abs(tf.add(x_train, tf.negative(x_test))),\n reduction_indices=1)\npred = tf.argmin(distance, 0)\ninit = tf.global_variables_initializer()\nwith tf.Session() as sess:\n predictions = []\n labels = []\n accuracies = []\n for i in range(4):\n sess.run(init)\n choice = i + 1\n choose_test_set(str(choice))\n train_data, train_labels = get_network_input(train_path)\n test_data, test_labels = get_network_input(test_path)\n fold_accuracy = 0\n for i in range(len(test_data)):\n nn_index = sess.run(pred, feed_dict={x_train: train_data,\n x_test: test_data[i, :]})\n predictions.append(np.argmax(train_labels[nn_index]))\n labels.append(np.argmax(test_labels[i]))\n if predictions[-1] == labels[-1]:\n fold_accuracy += 1.0 / len(test_data)\n accuracies.append(fold_accuracy)\n overall_accuracy = np.mean(accuracies)\n print('Average accuracy over 4 folds:', overall_accuracy)\n confusion = tf.confusion_matrix(labels=labels, predictions=predictions)\n print(confusion.eval())\n",
"step-4": "import tensorflow as tf\nimport numpy as np\nfrom classifier_input_functions import choose_test_set, get_network_input\ntrain_path = 'Data/NetworkTrain/'\ntest_path = 'Data/NetworkTest/'\nx_train = tf.placeholder('float', [None, 200])\nx_test = tf.placeholder('float', [200])\ndistance = tf.reduce_sum(tf.abs(tf.add(x_train, tf.negative(x_test))),\n reduction_indices=1)\npred = tf.argmin(distance, 0)\ninit = tf.global_variables_initializer()\nwith tf.Session() as sess:\n predictions = []\n labels = []\n accuracies = []\n for i in range(4):\n sess.run(init)\n choice = i + 1\n choose_test_set(str(choice))\n train_data, train_labels = get_network_input(train_path)\n test_data, test_labels = get_network_input(test_path)\n fold_accuracy = 0\n for i in range(len(test_data)):\n nn_index = sess.run(pred, feed_dict={x_train: train_data,\n x_test: test_data[i, :]})\n predictions.append(np.argmax(train_labels[nn_index]))\n labels.append(np.argmax(test_labels[i]))\n if predictions[-1] == labels[-1]:\n fold_accuracy += 1.0 / len(test_data)\n accuracies.append(fold_accuracy)\n overall_accuracy = np.mean(accuracies)\n print('Average accuracy over 4 folds:', overall_accuracy)\n confusion = tf.confusion_matrix(labels=labels, predictions=predictions)\n print(confusion.eval())\n",
"step-5": "################################################################################\n# run_experiment.py #\n# Ian Marci 2017 #\n# Defines knn classifier and runs 4-fold cross validation on data in #\n# Data/NetworkInput folder. #\n# Prints accuracy for each fold as well as confusion matrix. #\n################################################################################\n\n# Imports\nimport tensorflow as tf\nimport numpy as np\nfrom classifier_input_functions import choose_test_set, get_network_input\n\n# Path and placeholder definitions\ntrain_path = 'Data/NetworkTrain/'\ntest_path = 'Data/NetworkTest/'\n\nx_train = tf.placeholder('float', [None, 200])\nx_test = tf.placeholder('float', [200])\n\n# Distance to decide nearest neighbor\ndistance = tf.reduce_sum(tf.abs(tf.add(x_train, tf.negative(x_test))),\n reduction_indices=1)\n# Prediction chooses lowest distance\npred = tf.argmin(distance, 0)\n\n################################\n# 4-fold cross validation loop #\n################################\n\ninit = tf.global_variables_initializer()\nwith tf.Session() as sess:\n predictions = []\n labels = []\n accuracies = []\n for i in range(4):\n sess.run(init)\n\n choice = i + 1\n choose_test_set(str(choice))\n\n train_data, train_labels = get_network_input(train_path)\n test_data, test_labels = get_network_input(test_path)\n\n fold_accuracy = 0\n\n for i in range(len(test_data)):\n nn_index = sess.run(pred, feed_dict={x_train: train_data,\n x_test: test_data[i, :]})\n predictions.append(np.argmax(train_labels[nn_index]))\n labels.append(np.argmax(test_labels[i]))\n\n if predictions[-1] == labels[-1]:\n fold_accuracy += 1./len(test_data)\n accuracies.append(fold_accuracy)\n\n overall_accuracy = np.mean(accuracies)\n print('Average accuracy over 4 folds:', overall_accuracy)\n confusion = tf.confusion_matrix(labels=labels, predictions=predictions)\n print(confusion.eval())\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class CommentSerializer(serializers.ModelSerializer):
class Meta:
model = Comment
fields = 'text', 'image', 'user', 'article', 'created_at'
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class FavoriteSerializer(serializers.ModelSerializer):
class Meta:
model = Favorite
fields = 'user', 'article', 'created_at'
class CommentSerializer(serializers.ModelSerializer):
class Meta:
model = Comment
fields = 'text', 'image', 'user', 'article', 'created_at'
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class GoodSerializer(serializers.ModelSerializer):
class Meta:
model = Good
fields = 'user', 'article', 'created_at'
class FavoriteSerializer(serializers.ModelSerializer):
class Meta:
model = Favorite
fields = 'user', 'article', 'created_at'
class CommentSerializer(serializers.ModelSerializer):
class Meta:
model = Comment
fields = 'text', 'image', 'user', 'article', 'created_at'
<|reserved_special_token_1|>
from rest_framework import serializers
from .models import Good, Favorite, Comment
class GoodSerializer(serializers.ModelSerializer):
class Meta:
model = Good
fields = 'user', 'article', 'created_at'
class FavoriteSerializer(serializers.ModelSerializer):
class Meta:
model = Favorite
fields = 'user', 'article', 'created_at'
class CommentSerializer(serializers.ModelSerializer):
class Meta:
model = Comment
fields = 'text', 'image', 'user', 'article', 'created_at'
<|reserved_special_token_1|>
from rest_framework import serializers
from .models import Good, Favorite, Comment
class GoodSerializer(serializers.ModelSerializer):
class Meta:
model = Good
fields = ('user', 'article', 'created_at')
class FavoriteSerializer(serializers.ModelSerializer):
class Meta:
model = Favorite
fields = ('user', 'article', 'created_at')
class CommentSerializer(serializers.ModelSerializer):
class Meta:
model = Comment
fields = ('text', 'image', 'user', 'article', 'created_at')
|
flexible
|
{
"blob_id": "fc8b9029955de6b11cbfe8e24107c687f49685c1",
"index": 9179,
"step-1": "<mask token>\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Comment\n fields = 'text', 'image', 'user', 'article', 'created_at'\n",
"step-2": "<mask token>\n\n\nclass FavoriteSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Favorite\n fields = 'user', 'article', 'created_at'\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Comment\n fields = 'text', 'image', 'user', 'article', 'created_at'\n",
"step-3": "<mask token>\n\n\nclass GoodSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Good\n fields = 'user', 'article', 'created_at'\n\n\nclass FavoriteSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Favorite\n fields = 'user', 'article', 'created_at'\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Comment\n fields = 'text', 'image', 'user', 'article', 'created_at'\n",
"step-4": "from rest_framework import serializers\nfrom .models import Good, Favorite, Comment\n\n\nclass GoodSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Good\n fields = 'user', 'article', 'created_at'\n\n\nclass FavoriteSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Favorite\n fields = 'user', 'article', 'created_at'\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Comment\n fields = 'text', 'image', 'user', 'article', 'created_at'\n",
"step-5": "from rest_framework import serializers\n\nfrom .models import Good, Favorite, Comment\n\n\nclass GoodSerializer(serializers.ModelSerializer):\n class Meta:\n model = Good\n fields = ('user', 'article', 'created_at')\n\n\nclass FavoriteSerializer(serializers.ModelSerializer):\n class Meta:\n model = Favorite\n fields = ('user', 'article', 'created_at')\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n class Meta:\n model = Comment\n fields = ('text', 'image', 'user', 'article', 'created_at')\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def solve(S, n):
Discriminante = S * S + 4 * n
r = int(Discriminante ** 0.5)
if r * r == Discriminante:
if r % 2 == S % 2:
return (r - S) // 2
else:
return -1
else:
return -1
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def digitSum(x):
if x < 10:
return x
return x % 10 + digitSum(x // 10)
def solve(S, n):
Discriminante = S * S + 4 * n
r = int(Discriminante ** 0.5)
if r * r == Discriminante:
if r % 2 == S % 2:
return (r - S) // 2
else:
return -1
else:
return -1
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def digitSum(x):
if x < 10:
return x
return x % 10 + digitSum(x // 10)
def solve(S, n):
Discriminante = S * S + 4 * n
r = int(Discriminante ** 0.5)
if r * r == Discriminante:
if r % 2 == S % 2:
return (r - S) // 2
else:
return -1
else:
return -1
<|reserved_special_token_0|>
for S in range(1, 163):
x = solve(S, n)
if x > 0 and digitSum(x) == S:
if ans == -1:
ans = x
else:
ans = min(ans, x)
print(ans)
<|reserved_special_token_1|>
def digitSum(x):
if x < 10:
return x
return x % 10 + digitSum(x // 10)
def solve(S, n):
Discriminante = S * S + 4 * n
r = int(Discriminante ** 0.5)
if r * r == Discriminante:
if r % 2 == S % 2:
return (r - S) // 2
else:
return -1
else:
return -1
n = int(input())
ans = -1
for S in range(1, 163):
x = solve(S, n)
if x > 0 and digitSum(x) == S:
if ans == -1:
ans = x
else:
ans = min(ans, x)
print(ans)
|
flexible
|
{
"blob_id": "f89800e0d8d4026c167381f275ca86c2cf7f011e",
"index": 4066,
"step-1": "<mask token>\n\n\ndef solve(S, n):\n Discriminante = S * S + 4 * n\n r = int(Discriminante ** 0.5)\n if r * r == Discriminante:\n if r % 2 == S % 2:\n return (r - S) // 2\n else:\n return -1\n else:\n return -1\n\n\n<mask token>\n",
"step-2": "def digitSum(x):\n if x < 10:\n return x\n return x % 10 + digitSum(x // 10)\n\n\ndef solve(S, n):\n Discriminante = S * S + 4 * n\n r = int(Discriminante ** 0.5)\n if r * r == Discriminante:\n if r % 2 == S % 2:\n return (r - S) // 2\n else:\n return -1\n else:\n return -1\n\n\n<mask token>\n",
"step-3": "def digitSum(x):\n if x < 10:\n return x\n return x % 10 + digitSum(x // 10)\n\n\ndef solve(S, n):\n Discriminante = S * S + 4 * n\n r = int(Discriminante ** 0.5)\n if r * r == Discriminante:\n if r % 2 == S % 2:\n return (r - S) // 2\n else:\n return -1\n else:\n return -1\n\n\n<mask token>\nfor S in range(1, 163):\n x = solve(S, n)\n if x > 0 and digitSum(x) == S:\n if ans == -1:\n ans = x\n else:\n ans = min(ans, x)\nprint(ans)\n",
"step-4": "def digitSum(x):\n if x < 10:\n return x\n return x % 10 + digitSum(x // 10)\n\n\ndef solve(S, n):\n Discriminante = S * S + 4 * n\n r = int(Discriminante ** 0.5)\n if r * r == Discriminante:\n if r % 2 == S % 2:\n return (r - S) // 2\n else:\n return -1\n else:\n return -1\n\n\nn = int(input())\nans = -1\nfor S in range(1, 163):\n x = solve(S, n)\n if x > 0 and digitSum(x) == S:\n if ans == -1:\n ans = x\n else:\n ans = min(ans, x)\nprint(ans)\n",
"step-5": null,
"step-ids": [
1,
2,
3,
4
]
}
|
[
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
vect = [0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,
5.723585101952381, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,
0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0,
0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,
0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,
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2.9444389791664403, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0, 0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 2.1972245773362196, 0, \n 0, 0.0, 0.0, 0.0, 1.0986122886681098, 0.0, 0.0, 3.044522437723423, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.6931471805599453,\n 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 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0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.1972245773362196, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 1.0986122886681098, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 3.9512437185814275, 0.0, 0, 0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 2.1972245773362196, 0, 0.0, 0, 0.0, 3.2188758248682006, 0.0, 0, 0,\n 0, 3.1354942159291497, 3.800574088041945, 0, 0.0, 0.0, 0.0, 0, \n 4.969640753475787, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 1.791759469228055, 0.0, 0.0, 0.0, 0.0, 0, 4.631631038266565, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 2.833213344056216, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.3978952727983707, 0, 0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 3.3322045101752034, 0.0, 0.0, 3.8918202981106265, 0, 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0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.791759469228055, 0.0, \n 0.0, 0.0, 1.6094379124341003, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 2.833213344056216,\n 0, 0.0, 0, 3.58351893845611, 0.0, 0, 0.0, 0, 0, 0, 0.0, 0.0, \n 2.302585092994046, 0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 4.553876891600541, 0, 0.0, 3.367295829986474, 0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 3.9120230054281455, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 2.550898738446989, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 2.302585092994046, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.6094379124341003, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.3978952727983707, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, \n 0.6931471805599453, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 1.9459101490553132, 3.295836866004329, 0, \n 3.9889840465642745, 0, 0.0, 0, 0.0, 0, 0.0, 1.791759469228055, 0, 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0.0, 0.0, 0.0, 0.0, 0.0, 5.662960480135945, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.6931471805599453, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0,\n 3.044522437723423, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 2.5649493574615367, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 2.6390573296152584, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 3.0841946160253877, 0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 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0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0, 0, 0.0, 1.0986122886681098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0, 0.0, 9.887510598012987, 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.6931471805599453, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 6.437751649736401, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 1.3862943611198906, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 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0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.8398715690385097, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 5.723585101952381, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 2.550898738446989, 5.101797476893978, 0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 3.8918202981106265, 0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 3.1354942159291497,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.550898738446989, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 2.833213344056216, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.4011973816621555, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 2.9957322735539913, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 2.302585092994046, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 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0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0,\n 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 1.3862943611198906, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 4.795790545596741, 0.0, 2.1972245773362196, 0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.8398715690385097, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.9459101490553132, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0910424533583156, 0.0, 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0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 4.581130849408909, 0.0, 0, 0.0, 0,\n 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 1.7412592803704001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 2.302585092994046, 0, 0.0, 0, 5.723585101952381, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 3.2958368660043296, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 2.1972245773362196, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 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0.0, 0, 0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 2.1972245773362196, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.550898738446989, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 4.394449154672439, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 1.6094379124341003, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 1.0986122886681098, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 5.723585101952381, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0,\n 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, \n 1.9459101490553132, 0.0, 0.0, 0.0, 0, 0.0, 2.1972245773362196, \n 1.9459101490553132, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0,\n 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, \n 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, \n 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0,\n 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, \n 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0.0, \n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, \n 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0, 0, 0.0, \n 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0, 0.0,\n 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, \n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0,\n 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0]\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def run_training(arguments_parser):
data = DatasetLoader(arguments_parser)
data.setup()
arguments_parser.num_training_steps = len(data.train_dataloader()
) * arguments_parser.max_epochs
dict_args = vars(arguments_parser)
model = Model(**dict_args)
arguments_parser.early_stop_callback = EarlyStopping('val_loss')
trainer = pl.Trainer.from_argparse_args(arguments_parser)
trainer.fit(model, data)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(os.getcwd())
<|reserved_special_token_0|>
def run_training(arguments_parser):
data = DatasetLoader(arguments_parser)
data.setup()
arguments_parser.num_training_steps = len(data.train_dataloader()
) * arguments_parser.max_epochs
dict_args = vars(arguments_parser)
model = Model(**dict_args)
arguments_parser.early_stop_callback = EarlyStopping('val_loss')
trainer = pl.Trainer.from_argparse_args(arguments_parser)
trainer.fit(model, data)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--pretrained', type=str, default='bert-base-uncased')
parser.add_argument('--nr_frozen_epochs', type=int, default=5)
parser.add_argument('--training_portion', type=float, default=0.9)
parser.add_argument('--batch_size', type=float, default=32)
parser.add_argument('--learning_rate', type=float, default=2e-05)
parser.add_argument('--frac', type=float, default=1)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
run_training(args)
<|reserved_special_token_1|>
import os
print(os.getcwd())
from TransformerModel.Model import Model
from dataset.DatasetLoader import DatasetLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
import argparse
from argparse import ArgumentParser, ArgumentTypeError
def run_training(arguments_parser):
data = DatasetLoader(arguments_parser)
data.setup()
arguments_parser.num_training_steps = len(data.train_dataloader()
) * arguments_parser.max_epochs
dict_args = vars(arguments_parser)
model = Model(**dict_args)
arguments_parser.early_stop_callback = EarlyStopping('val_loss')
trainer = pl.Trainer.from_argparse_args(arguments_parser)
trainer.fit(model, data)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('--pretrained', type=str, default='bert-base-uncased')
parser.add_argument('--nr_frozen_epochs', type=int, default=5)
parser.add_argument('--training_portion', type=float, default=0.9)
parser.add_argument('--batch_size', type=float, default=32)
parser.add_argument('--learning_rate', type=float, default=2e-05)
parser.add_argument('--frac', type=float, default=1)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
run_training(args)
<|reserved_special_token_1|>
# %%
import os
print(os.getcwd())
# %%
from TransformerModel.Model import Model
from dataset.DatasetLoader import DatasetLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
import argparse
from argparse import ArgumentParser, ArgumentTypeError
# %%
def run_training(arguments_parser):
data = DatasetLoader(arguments_parser)
data.setup()
arguments_parser.num_training_steps = (
len(data.train_dataloader()) * arguments_parser.max_epochs
)
dict_args = vars(arguments_parser)
model = Model(**dict_args)
arguments_parser.early_stop_callback = EarlyStopping("val_loss")
trainer = pl.Trainer.from_argparse_args(arguments_parser)
trainer.fit(model, data)
# %%
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--pretrained", type=str, default="bert-base-uncased")
parser.add_argument("--nr_frozen_epochs", type=int, default=5)
parser.add_argument("--training_portion", type=float, default=0.9)
parser.add_argument("--batch_size", type=float, default=32)
parser.add_argument("--learning_rate", type=float, default=2e-5)
parser.add_argument("--frac", type=float, default=1)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
run_training(args)
# %%
|
flexible
|
{
"blob_id": "328a03acab2a0550bea0795d22110a152db6c503",
"index": 806,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run_training(arguments_parser):\n data = DatasetLoader(arguments_parser)\n data.setup()\n arguments_parser.num_training_steps = len(data.train_dataloader()\n ) * arguments_parser.max_epochs\n dict_args = vars(arguments_parser)\n model = Model(**dict_args)\n arguments_parser.early_stop_callback = EarlyStopping('val_loss')\n trainer = pl.Trainer.from_argparse_args(arguments_parser)\n trainer.fit(model, data)\n\n\n<mask token>\n",
"step-3": "<mask token>\nprint(os.getcwd())\n<mask token>\n\n\ndef run_training(arguments_parser):\n data = DatasetLoader(arguments_parser)\n data.setup()\n arguments_parser.num_training_steps = len(data.train_dataloader()\n ) * arguments_parser.max_epochs\n dict_args = vars(arguments_parser)\n model = Model(**dict_args)\n arguments_parser.early_stop_callback = EarlyStopping('val_loss')\n trainer = pl.Trainer.from_argparse_args(arguments_parser)\n trainer.fit(model, data)\n\n\nif __name__ == '__main__':\n parser = ArgumentParser()\n parser.add_argument('--pretrained', type=str, default='bert-base-uncased')\n parser.add_argument('--nr_frozen_epochs', type=int, default=5)\n parser.add_argument('--training_portion', type=float, default=0.9)\n parser.add_argument('--batch_size', type=float, default=32)\n parser.add_argument('--learning_rate', type=float, default=2e-05)\n parser.add_argument('--frac', type=float, default=1)\n parser = pl.Trainer.add_argparse_args(parser)\n args = parser.parse_args()\n run_training(args)\n",
"step-4": "import os\nprint(os.getcwd())\nfrom TransformerModel.Model import Model\nfrom dataset.DatasetLoader import DatasetLoader\nimport pytorch_lightning as pl\nfrom pytorch_lightning.callbacks import EarlyStopping\nimport argparse\nfrom argparse import ArgumentParser, ArgumentTypeError\n\n\ndef run_training(arguments_parser):\n data = DatasetLoader(arguments_parser)\n data.setup()\n arguments_parser.num_training_steps = len(data.train_dataloader()\n ) * arguments_parser.max_epochs\n dict_args = vars(arguments_parser)\n model = Model(**dict_args)\n arguments_parser.early_stop_callback = EarlyStopping('val_loss')\n trainer = pl.Trainer.from_argparse_args(arguments_parser)\n trainer.fit(model, data)\n\n\nif __name__ == '__main__':\n parser = ArgumentParser()\n parser.add_argument('--pretrained', type=str, default='bert-base-uncased')\n parser.add_argument('--nr_frozen_epochs', type=int, default=5)\n parser.add_argument('--training_portion', type=float, default=0.9)\n parser.add_argument('--batch_size', type=float, default=32)\n parser.add_argument('--learning_rate', type=float, default=2e-05)\n parser.add_argument('--frac', type=float, default=1)\n parser = pl.Trainer.add_argparse_args(parser)\n args = parser.parse_args()\n run_training(args)\n",
"step-5": "# %%\nimport os\n\nprint(os.getcwd())\n# %%\nfrom TransformerModel.Model import Model\nfrom dataset.DatasetLoader import DatasetLoader\nimport pytorch_lightning as pl\nfrom pytorch_lightning.callbacks import EarlyStopping\nimport argparse\nfrom argparse import ArgumentParser, ArgumentTypeError\n\n# %%\n\n\ndef run_training(arguments_parser):\n data = DatasetLoader(arguments_parser)\n data.setup()\n\n arguments_parser.num_training_steps = (\n len(data.train_dataloader()) * arguments_parser.max_epochs\n )\n\n dict_args = vars(arguments_parser)\n\n model = Model(**dict_args)\n\n arguments_parser.early_stop_callback = EarlyStopping(\"val_loss\")\n\n trainer = pl.Trainer.from_argparse_args(arguments_parser)\n\n trainer.fit(model, data)\n\n\n# %%\nif __name__ == \"__main__\":\n\n parser = ArgumentParser()\n parser.add_argument(\"--pretrained\", type=str, default=\"bert-base-uncased\")\n parser.add_argument(\"--nr_frozen_epochs\", type=int, default=5)\n parser.add_argument(\"--training_portion\", type=float, default=0.9)\n parser.add_argument(\"--batch_size\", type=float, default=32)\n parser.add_argument(\"--learning_rate\", type=float, default=2e-5)\n parser.add_argument(\"--frac\", type=float, default=1)\n\n parser = pl.Trainer.add_argparse_args(parser)\n args = parser.parse_args()\n run_training(args)\n\n\n# %%\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
# The purpose of this bot is to cick the first black pixel.
# Testing a change here done by Git.
# changes through branches
import pyautogui
import keyboard
import win32api
import win32con
import time
# click function, with a 0.01 pause inorder to properly run the script
def click(x, y):
win32api.SetCursorPos((x, y))
win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0)
time.sleep(0.01)
win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0)
# pressing 's' to stop the function
while keyboard.is_pressed('s') == False:
# If the pixel is black (0), click on that pixel
if pyautogui.pixel(xPosition, yPosition)[0] == 0:
click(xPosition, yPosition)
|
normal
|
{
"blob_id": "9f831b8c90dd428879319b63712bd03fcc01b631",
"index": 8212,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef click(x, y):\n win32api.SetCursorPos((x, y))\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0)\n time.sleep(0.01)\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef click(x, y):\n win32api.SetCursorPos((x, y))\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0)\n time.sleep(0.01)\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0)\n\n\nwhile keyboard.is_pressed('s') == False:\n if pyautogui.pixel(xPosition, yPosition)[0] == 0:\n click(xPosition, yPosition)\n",
"step-4": "import pyautogui\nimport keyboard\nimport win32api\nimport win32con\nimport time\n\n\ndef click(x, y):\n win32api.SetCursorPos((x, y))\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0)\n time.sleep(0.01)\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0)\n\n\nwhile keyboard.is_pressed('s') == False:\n if pyautogui.pixel(xPosition, yPosition)[0] == 0:\n click(xPosition, yPosition)\n",
"step-5": "# The purpose of this bot is to cick the first black pixel.\r\n# Testing a change here done by Git. \r\n# changes through branches\r\n\r\nimport pyautogui\r\nimport keyboard\r\nimport win32api\r\nimport win32con\r\nimport time\r\n\r\n# click function, with a 0.01 pause inorder to properly run the script\r\n\r\n\r\ndef click(x, y):\r\n win32api.SetCursorPos((x, y))\r\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0)\r\n time.sleep(0.01)\r\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0)\r\n\r\n\r\n# pressing 's' to stop the function\r\n\r\nwhile keyboard.is_pressed('s') == False:\r\n\r\n # If the pixel is black (0), click on that pixel\r\n\r\n if pyautogui.pixel(xPosition, yPosition)[0] == 0:\r\n click(xPosition, yPosition)\r\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
num=int(input())
i=10
while i>=1:
print(i,end=" ")
i-=1
|
normal
|
{
"blob_id": "ec0113dbd79e936e614bb7ee7e48d29aa616d511",
"index": 7389,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile i >= 1:\n print(i, end=' ')\n i -= 1\n",
"step-3": "num = int(input())\ni = 10\nwhile i >= 1:\n print(i, end=' ')\n i -= 1\n",
"step-4": "num=int(input())\r\ni=10\r\nwhile i>=1:\r\n print(i,end=\" \")\r\n i-=1\r\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
'''
@mainpage Rat15S Compiler
@section intro_sec Introduction
This will become a Rat15S compiler. Currently working on Lexical Analyzer.
@author Reza Nikoopour
@author Eric Roe
'''
def main():
tokens = Lexer()
if __name__ == '__main__':
sys.path.append('Lib')
from lexicalanalyzer import Lexer
main(sys.argv[1], sys.argv[2])
|
normal
|
{
"blob_id": "d081abf3cd9bc323486772b4f6235fbbc9022099",
"index": 5498,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n tokens = Lexer()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef main():\n tokens = Lexer()\n\n\nif __name__ == '__main__':\n sys.path.append('Lib')\n from lexicalanalyzer import Lexer\n main(sys.argv[1], sys.argv[2])\n",
"step-4": "'''\n@mainpage Rat15S Compiler\n\n@section intro_sec Introduction\nThis will become a Rat15S compiler. Currently working on Lexical Analyzer.\n@author Reza Nikoopour\n@author Eric Roe\n'''\ndef main():\n tokens = Lexer()\n \nif __name__ == '__main__':\n sys.path.append('Lib')\n from lexicalanalyzer import Lexer\n main(sys.argv[1], sys.argv[2])\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def loadFiles(subdir, filetype):
"""
example:
dirs = ["dir1", "dir2"]
file_type = ".dat"
files, keys, data = loadFiles(dirs[0], file_type)
"""
dirname = os.path.dirname(__file__)
path = os.path.join(dirname, subdir + '/')
files_path = []
fileNamesFiltered = []
for root, dirs, files in os.walk(path):
for i, filename in enumerate(files):
if filename[len(filename) - len(filetype):] == filetype:
filename_path = path + filename
files_path.append(filename_path)
fileNamesFiltered.append(filename)
return fileNamesFiltered
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def loadFiles(subdir, filetype):
"""
example:
dirs = ["dir1", "dir2"]
file_type = ".dat"
files, keys, data = loadFiles(dirs[0], file_type)
"""
dirname = os.path.dirname(__file__)
path = os.path.join(dirname, subdir + '/')
files_path = []
fileNamesFiltered = []
for root, dirs, files in os.walk(path):
for i, filename in enumerate(files):
if filename[len(filename) - len(filetype):] == filetype:
filename_path = path + filename
files_path.append(filename_path)
fileNamesFiltered.append(filename)
return fileNamesFiltered
def sendMail(filename):
smtp_server = 'smtp.seznam.cz'
port = 25
sender_email = 'xxx@email.cz'
password = 'xxxx'
context = ssl.create_default_context()
receiver_email = sender_email
message = MIMEMultipart('alternative')
message['Subject'] = 'analysis status check: ' + str(filename)
message['From'] = sender_email
message['To'] = receiver_email
text = 'analysis status check'
part1 = MIMEText(text, 'plain')
message.attach(part1)
try:
with open(filename, 'rb') as attachment:
file = MIMEBase('application', 'octet-stream')
file.set_payload(attachment.read())
encoders.encode_base64(file)
file.add_header('Content-Disposition',
f'attachment; filename= {filename}')
message.attach(file)
except:
print('file not found')
try:
server = smtplib.SMTP(smtp_server, port)
server.ehlo()
server.starttls(context=context)
server.ehlo()
server.login(sender_email, password)
print('logged in')
server.sendmail(sender_email, receiver_email, message.as_string())
print('mail sent')
except Exception as e:
print(e)
finally:
server.quit()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def loadFiles(subdir, filetype):
"""
example:
dirs = ["dir1", "dir2"]
file_type = ".dat"
files, keys, data = loadFiles(dirs[0], file_type)
"""
dirname = os.path.dirname(__file__)
path = os.path.join(dirname, subdir + '/')
files_path = []
fileNamesFiltered = []
for root, dirs, files in os.walk(path):
for i, filename in enumerate(files):
if filename[len(filename) - len(filetype):] == filetype:
filename_path = path + filename
files_path.append(filename_path)
fileNamesFiltered.append(filename)
return fileNamesFiltered
def sendMail(filename):
smtp_server = 'smtp.seznam.cz'
port = 25
sender_email = 'xxx@email.cz'
password = 'xxxx'
context = ssl.create_default_context()
receiver_email = sender_email
message = MIMEMultipart('alternative')
message['Subject'] = 'analysis status check: ' + str(filename)
message['From'] = sender_email
message['To'] = receiver_email
text = 'analysis status check'
part1 = MIMEText(text, 'plain')
message.attach(part1)
try:
with open(filename, 'rb') as attachment:
file = MIMEBase('application', 'octet-stream')
file.set_payload(attachment.read())
encoders.encode_base64(file)
file.add_header('Content-Disposition',
f'attachment; filename= {filename}')
message.attach(file)
except:
print('file not found')
try:
server = smtplib.SMTP(smtp_server, port)
server.ehlo()
server.starttls(context=context)
server.ehlo()
server.login(sender_email, password)
print('logged in')
server.sendmail(sender_email, receiver_email, message.as_string())
print('mail sent')
except Exception as e:
print(e)
finally:
server.quit()
if __name__ == '__main__':
run = True
directory = '/folder/folder'
fileType = '.xxx'
name = 'xxxxxx_xxx__xxx.xxx'
while run == True:
names = loadFiles(directory, fileType)
print('running')
if name in names:
print('file found:', name)
f = open(name, 'r')
for line in f:
if 'THE ANALYSIS HAS' in line:
sendMail(name)
print('file sent')
run = False
print('done')
sys.exit()
time.sleep(300)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
import smtplib, ssl
from email import encoders
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
import os, sys
import time
def loadFiles(subdir, filetype):
"""
example:
dirs = ["dir1", "dir2"]
file_type = ".dat"
files, keys, data = loadFiles(dirs[0], file_type)
"""
dirname = os.path.dirname(__file__)
path = os.path.join(dirname, subdir + '/')
files_path = []
fileNamesFiltered = []
for root, dirs, files in os.walk(path):
for i, filename in enumerate(files):
if filename[len(filename) - len(filetype):] == filetype:
filename_path = path + filename
files_path.append(filename_path)
fileNamesFiltered.append(filename)
return fileNamesFiltered
def sendMail(filename):
smtp_server = 'smtp.seznam.cz'
port = 25
sender_email = 'xxx@email.cz'
password = 'xxxx'
context = ssl.create_default_context()
receiver_email = sender_email
message = MIMEMultipart('alternative')
message['Subject'] = 'analysis status check: ' + str(filename)
message['From'] = sender_email
message['To'] = receiver_email
text = 'analysis status check'
part1 = MIMEText(text, 'plain')
message.attach(part1)
try:
with open(filename, 'rb') as attachment:
file = MIMEBase('application', 'octet-stream')
file.set_payload(attachment.read())
encoders.encode_base64(file)
file.add_header('Content-Disposition',
f'attachment; filename= {filename}')
message.attach(file)
except:
print('file not found')
try:
server = smtplib.SMTP(smtp_server, port)
server.ehlo()
server.starttls(context=context)
server.ehlo()
server.login(sender_email, password)
print('logged in')
server.sendmail(sender_email, receiver_email, message.as_string())
print('mail sent')
except Exception as e:
print(e)
finally:
server.quit()
if __name__ == '__main__':
run = True
directory = '/folder/folder'
fileType = '.xxx'
name = 'xxxxxx_xxx__xxx.xxx'
while run == True:
names = loadFiles(directory, fileType)
print('running')
if name in names:
print('file found:', name)
f = open(name, 'r')
for line in f:
if 'THE ANALYSIS HAS' in line:
sendMail(name)
print('file sent')
run = False
print('done')
sys.exit()
time.sleep(300)
<|reserved_special_token_1|>
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 6 10:05:25 2019
@author: MCA
"""
import smtplib, ssl
from email import encoders
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
import os,sys
import time
def loadFiles(subdir, filetype):
"""
example:
dirs = ["dir1", "dir2"]
file_type = ".dat"
files, keys, data = loadFiles(dirs[0], file_type)
"""
dirname = os.path.dirname(__file__)
path = os.path.join(dirname, (subdir+"/"))
files_path = []
fileNamesFiltered = []
for root, dirs, files in os.walk(path):
for i, filename in enumerate(files):
if filename[(len(filename))-len(filetype):] == filetype:
# print(filename)
filename_path = path + filename
files_path.append(filename_path)
fileNamesFiltered.append(filename)
return fileNamesFiltered
def sendMail(filename):
smtp_server = "smtp.seznam.cz"
port = 25 # For starttls
sender_email = "xxx@email.cz"
#password = input("Type your password and press enter: ")
password = "xxxx"
# Create a secure SSL context
context = ssl.create_default_context()
receiver_email=sender_email
#compose an email
message = MIMEMultipart("alternative")
message["Subject"] = ("analysis status check: "+ str(filename))
message["From"] = sender_email
message["To"] = receiver_email
text = "analysis status check"
part1 = MIMEText(text, "plain")
message.attach(part1)
#send file
# filename = file
try:
with open(filename, "rb") as attachment:
# Add file as application/octet-stream
# Email client can usually download this automatically as attachment
file = MIMEBase("application", "octet-stream")
file.set_payload(attachment.read())
encoders.encode_base64(file)
file.add_header(
"Content-Disposition",
f"attachment; filename= {filename}",
)
message.attach(file)
except:
print("file not found")
# Try to log in to server and send email
try:
server = smtplib.SMTP(smtp_server,port)
server.ehlo() # Can be omitted
server.starttls(context=context) # Secure the connection
server.ehlo() # Can be omitted
server.login(sender_email, password)
print("logged in")
# TODO: Send email here
server.sendmail(sender_email, receiver_email, message.as_string())
print("mail sent")
except Exception as e:
# Print any error messages to stdout
print(e)
finally:
server.quit()
#--------------------------------------------------------------------------------------
if __name__ == "__main__":
run = True
directory = "/folder/folder"
fileType = ".xxx"
name = "xxxxxx_xxx__xxx.xxx"
while run == True:
names = loadFiles(directory, fileType)
print("running")
if name in names:
print("file found:", name)
f = open(name, "r")
for line in f:
if "THE ANALYSIS HAS" in line:
sendMail(name)
print("file sent")
run = False
print("done")
sys.exit()
time.sleep(300)
|
flexible
|
{
"blob_id": "b310c35b781e3221e2dacc7717ed77e20001bafa",
"index": 5109,
"step-1": "<mask token>\n\n\ndef loadFiles(subdir, filetype):\n \"\"\"\n example:\n dirs = [\"dir1\", \"dir2\"]\n file_type = \".dat\"\n files, keys, data = loadFiles(dirs[0], file_type)\n \n \"\"\"\n dirname = os.path.dirname(__file__)\n path = os.path.join(dirname, subdir + '/')\n files_path = []\n fileNamesFiltered = []\n for root, dirs, files in os.walk(path):\n for i, filename in enumerate(files):\n if filename[len(filename) - len(filetype):] == filetype:\n filename_path = path + filename\n files_path.append(filename_path)\n fileNamesFiltered.append(filename)\n return fileNamesFiltered\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef loadFiles(subdir, filetype):\n \"\"\"\n example:\n dirs = [\"dir1\", \"dir2\"]\n file_type = \".dat\"\n files, keys, data = loadFiles(dirs[0], file_type)\n \n \"\"\"\n dirname = os.path.dirname(__file__)\n path = os.path.join(dirname, subdir + '/')\n files_path = []\n fileNamesFiltered = []\n for root, dirs, files in os.walk(path):\n for i, filename in enumerate(files):\n if filename[len(filename) - len(filetype):] == filetype:\n filename_path = path + filename\n files_path.append(filename_path)\n fileNamesFiltered.append(filename)\n return fileNamesFiltered\n\n\ndef sendMail(filename):\n smtp_server = 'smtp.seznam.cz'\n port = 25\n sender_email = 'xxx@email.cz'\n password = 'xxxx'\n context = ssl.create_default_context()\n receiver_email = sender_email\n message = MIMEMultipart('alternative')\n message['Subject'] = 'analysis status check: ' + str(filename)\n message['From'] = sender_email\n message['To'] = receiver_email\n text = 'analysis status check'\n part1 = MIMEText(text, 'plain')\n message.attach(part1)\n try:\n with open(filename, 'rb') as attachment:\n file = MIMEBase('application', 'octet-stream')\n file.set_payload(attachment.read())\n encoders.encode_base64(file)\n file.add_header('Content-Disposition',\n f'attachment; filename= {filename}')\n message.attach(file)\n except:\n print('file not found')\n try:\n server = smtplib.SMTP(smtp_server, port)\n server.ehlo()\n server.starttls(context=context)\n server.ehlo()\n server.login(sender_email, password)\n print('logged in')\n server.sendmail(sender_email, receiver_email, message.as_string())\n print('mail sent')\n except Exception as e:\n print(e)\n finally:\n server.quit()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef loadFiles(subdir, filetype):\n \"\"\"\n example:\n dirs = [\"dir1\", \"dir2\"]\n file_type = \".dat\"\n files, keys, data = loadFiles(dirs[0], file_type)\n \n \"\"\"\n dirname = os.path.dirname(__file__)\n path = os.path.join(dirname, subdir + '/')\n files_path = []\n fileNamesFiltered = []\n for root, dirs, files in os.walk(path):\n for i, filename in enumerate(files):\n if filename[len(filename) - len(filetype):] == filetype:\n filename_path = path + filename\n files_path.append(filename_path)\n fileNamesFiltered.append(filename)\n return fileNamesFiltered\n\n\ndef sendMail(filename):\n smtp_server = 'smtp.seznam.cz'\n port = 25\n sender_email = 'xxx@email.cz'\n password = 'xxxx'\n context = ssl.create_default_context()\n receiver_email = sender_email\n message = MIMEMultipart('alternative')\n message['Subject'] = 'analysis status check: ' + str(filename)\n message['From'] = sender_email\n message['To'] = receiver_email\n text = 'analysis status check'\n part1 = MIMEText(text, 'plain')\n message.attach(part1)\n try:\n with open(filename, 'rb') as attachment:\n file = MIMEBase('application', 'octet-stream')\n file.set_payload(attachment.read())\n encoders.encode_base64(file)\n file.add_header('Content-Disposition',\n f'attachment; filename= {filename}')\n message.attach(file)\n except:\n print('file not found')\n try:\n server = smtplib.SMTP(smtp_server, port)\n server.ehlo()\n server.starttls(context=context)\n server.ehlo()\n server.login(sender_email, password)\n print('logged in')\n server.sendmail(sender_email, receiver_email, message.as_string())\n print('mail sent')\n except Exception as e:\n print(e)\n finally:\n server.quit()\n\n\nif __name__ == '__main__':\n run = True\n directory = '/folder/folder'\n fileType = '.xxx'\n name = 'xxxxxx_xxx__xxx.xxx'\n while run == True:\n names = loadFiles(directory, fileType)\n print('running')\n if name in names:\n print('file found:', name)\n f = open(name, 'r')\n for line in f:\n if 'THE ANALYSIS HAS' in line:\n sendMail(name)\n print('file sent')\n run = False\n print('done')\n sys.exit()\n time.sleep(300)\n",
"step-4": "<mask token>\nimport smtplib, ssl\nfrom email import encoders\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nimport os, sys\nimport time\n\n\ndef loadFiles(subdir, filetype):\n \"\"\"\n example:\n dirs = [\"dir1\", \"dir2\"]\n file_type = \".dat\"\n files, keys, data = loadFiles(dirs[0], file_type)\n \n \"\"\"\n dirname = os.path.dirname(__file__)\n path = os.path.join(dirname, subdir + '/')\n files_path = []\n fileNamesFiltered = []\n for root, dirs, files in os.walk(path):\n for i, filename in enumerate(files):\n if filename[len(filename) - len(filetype):] == filetype:\n filename_path = path + filename\n files_path.append(filename_path)\n fileNamesFiltered.append(filename)\n return fileNamesFiltered\n\n\ndef sendMail(filename):\n smtp_server = 'smtp.seznam.cz'\n port = 25\n sender_email = 'xxx@email.cz'\n password = 'xxxx'\n context = ssl.create_default_context()\n receiver_email = sender_email\n message = MIMEMultipart('alternative')\n message['Subject'] = 'analysis status check: ' + str(filename)\n message['From'] = sender_email\n message['To'] = receiver_email\n text = 'analysis status check'\n part1 = MIMEText(text, 'plain')\n message.attach(part1)\n try:\n with open(filename, 'rb') as attachment:\n file = MIMEBase('application', 'octet-stream')\n file.set_payload(attachment.read())\n encoders.encode_base64(file)\n file.add_header('Content-Disposition',\n f'attachment; filename= {filename}')\n message.attach(file)\n except:\n print('file not found')\n try:\n server = smtplib.SMTP(smtp_server, port)\n server.ehlo()\n server.starttls(context=context)\n server.ehlo()\n server.login(sender_email, password)\n print('logged in')\n server.sendmail(sender_email, receiver_email, message.as_string())\n print('mail sent')\n except Exception as e:\n print(e)\n finally:\n server.quit()\n\n\nif __name__ == '__main__':\n run = True\n directory = '/folder/folder'\n fileType = '.xxx'\n name = 'xxxxxx_xxx__xxx.xxx'\n while run == True:\n names = loadFiles(directory, fileType)\n print('running')\n if name in names:\n print('file found:', name)\n f = open(name, 'r')\n for line in f:\n if 'THE ANALYSIS HAS' in line:\n sendMail(name)\n print('file sent')\n run = False\n print('done')\n sys.exit()\n time.sleep(300)\n",
"step-5": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 6 10:05:25 2019\n\n@author: MCA\n\"\"\"\n\n\nimport smtplib, ssl\n\nfrom email import encoders\nfrom email.mime.text import MIMEText\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\n\nimport os,sys\nimport time\n\ndef loadFiles(subdir, filetype):\n \"\"\"\n example:\n dirs = [\"dir1\", \"dir2\"]\n file_type = \".dat\"\n files, keys, data = loadFiles(dirs[0], file_type)\n \n \"\"\" \n \n dirname = os.path.dirname(__file__)\n path = os.path.join(dirname, (subdir+\"/\"))\n files_path = []\n fileNamesFiltered = []\n for root, dirs, files in os.walk(path): \n for i, filename in enumerate(files):\n if filename[(len(filename))-len(filetype):] == filetype:\n# print(filename)\n filename_path = path + filename\n files_path.append(filename_path)\n fileNamesFiltered.append(filename)\n \n \n return fileNamesFiltered\n\n\n\ndef sendMail(filename):\n smtp_server = \"smtp.seznam.cz\"\n port = 25 # For starttls\n sender_email = \"xxx@email.cz\"\n #password = input(\"Type your password and press enter: \")\n password = \"xxxx\"\n \n # Create a secure SSL context\n context = ssl.create_default_context()\n receiver_email=sender_email\n \n \n #compose an email\n message = MIMEMultipart(\"alternative\")\n message[\"Subject\"] = (\"analysis status check: \"+ str(filename))\n message[\"From\"] = sender_email\n message[\"To\"] = receiver_email\n \n text = \"analysis status check\"\n part1 = MIMEText(text, \"plain\")\n message.attach(part1)\n \n \n #send file\n# filename = file\n try:\n with open(filename, \"rb\") as attachment:\n # Add file as application/octet-stream\n # Email client can usually download this automatically as attachment\n file = MIMEBase(\"application\", \"octet-stream\")\n file.set_payload(attachment.read())\n encoders.encode_base64(file)\n file.add_header(\n \"Content-Disposition\",\n f\"attachment; filename= {filename}\",\n )\n message.attach(file)\n except:\n print(\"file not found\")\n \n # Try to log in to server and send email\n try:\n server = smtplib.SMTP(smtp_server,port)\n server.ehlo() # Can be omitted\n server.starttls(context=context) # Secure the connection\n server.ehlo() # Can be omitted\n server.login(sender_email, password)\n print(\"logged in\")\n # TODO: Send email here\n server.sendmail(sender_email, receiver_email, message.as_string())\n print(\"mail sent\")\n except Exception as e:\n # Print any error messages to stdout\n print(e)\n finally:\n server.quit() \n\n\n#--------------------------------------------------------------------------------------\nif __name__ == \"__main__\":\n\n run = True\n directory = \"/folder/folder\"\n fileType = \".xxx\"\n name = \"xxxxxx_xxx__xxx.xxx\"\n \n while run == True:\n \n names = loadFiles(directory, fileType)\n print(\"running\")\n if name in names:\n print(\"file found:\", name)\n f = open(name, \"r\")\n for line in f:\n if \"THE ANALYSIS HAS\" in line: \n sendMail(name)\n print(\"file sent\")\n run = False\n print(\"done\")\n sys.exit()\n time.sleep(300)\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if w < 2 or w % 2 != 0 or w <= v:
print('INVALID INPUT')
else:
x = (4 * v - w) // 2
print('TW={0} FW={1}'.format(x, v - x))
<|reserved_special_token_1|>
v = int(input())
w = int(input())
if w < 2 or w % 2 != 0 or w <= v:
print('INVALID INPUT')
else:
x = (4 * v - w) // 2
print('TW={0} FW={1}'.format(x, v - x))
<|reserved_special_token_1|>
# Problem Statement – An automobile company manufactures both a two wheeler (TW) and a four wheeler (FW). A company manager wants to make the production of both types of vehicle according to the given data below:
# 1st data, Total number of vehicle (two-wheeler + four-wheeler)=v
# 2nd data, Total number of wheels = W
# The task is to find how many two-wheelers as well as four-wheelers need to manufacture as per the given data.
# Example :
# Input :
# 200 -> Value of V
# 540 -> Value of W
# Output :
# TW =130 FW=70
v=int(input())
w=int(input())
if (w<2 or w%2!=0 or w<=v):
print("INVALID INPUT")
else:
x=((4*v)-w)//2
print("TW={0} FW={1}".format(x,v-x))
|
flexible
|
{
"blob_id": "74939f81e999b8e239eb64fa10b56f48c47f7d94",
"index": 1622,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif w < 2 or w % 2 != 0 or w <= v:\n print('INVALID INPUT')\nelse:\n x = (4 * v - w) // 2\n print('TW={0} FW={1}'.format(x, v - x))\n",
"step-3": "v = int(input())\nw = int(input())\nif w < 2 or w % 2 != 0 or w <= v:\n print('INVALID INPUT')\nelse:\n x = (4 * v - w) // 2\n print('TW={0} FW={1}'.format(x, v - x))\n",
"step-4": "# Problem Statement – An automobile company manufactures both a two wheeler (TW) and a four wheeler (FW). A company manager wants to make the production of both types of vehicle according to the given data below:\n\n# 1st data, Total number of vehicle (two-wheeler + four-wheeler)=v\n# 2nd data, Total number of wheels = W\n \n\n# The task is to find how many two-wheelers as well as four-wheelers need to manufacture as per the given data.\n# Example :\n\n# Input :\n\n# 200 -> Value of V\n# 540 -> Value of W\n# Output :\n\n# TW =130 FW=70\nv=int(input())\nw=int(input())\nif (w<2 or w%2!=0 or w<=v):\n\tprint(\"INVALID INPUT\")\nelse:\n\tx=((4*v)-w)//2\n\tprint(\"TW={0} FW={1}\".format(x,v-x))\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
import pyreadstat
import matplotlib.pyplot as plt
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
# Set random seed for reproducible results
np.random.seed(1)
# Read sav file and create a pandas dataframe and extract metadata
df, meta = pyreadstat.read_sav("RESIDIV_Vimala.sav", usecols=["Sympt_blødning", "Sympt_smerter", "Sympt_ascites", "Sympt_fatigue", "Lengde_sympt_dager", "Lengde_sympt_uker", "Lengde_sympt_mnd", "kreftform"])
dataset = df.drop("kreftform", axis=1)
# dataset[0] is Y (kreftform), dataset[1, 2, 3 and 4] is X
X = dataset.values
Y = df["kreftform"].values
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one-hot encoded)
onehot_Y = np_utils.to_categorical(encoded_Y)
model = Sequential()
model.add(Dense(5, input_dim=(len(X[0]))))
model.add(Dense(32, activation="relu"))
model.add(Dense(len(onehot_Y[0]), activation="softmax"))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X, onehot_Y, validation_split=0.33, epochs=1000)
accuracy = "%.2f" % (model.evaluate(X, onehot_Y)[1]*100)
print("Accuracy:", accuracy, "%")
|
normal
|
{
"blob_id": "7282af4186a976296ac50840e9169b78a66e118b",
"index": 1683,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnp.random.seed(1)\n<mask token>\nencoder.fit(Y)\n<mask token>\nmodel.add(Dense(5, input_dim=len(X[0])))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(len(onehot_Y[0]), activation='softmax'))\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[\n 'accuracy'])\nmodel.fit(X, onehot_Y, validation_split=0.33, epochs=1000)\n<mask token>\nprint('Accuracy:', accuracy, '%')\n",
"step-3": "<mask token>\nnp.random.seed(1)\ndf, meta = pyreadstat.read_sav('RESIDIV_Vimala.sav', usecols=[\n 'Sympt_blødning', 'Sympt_smerter', 'Sympt_ascites', 'Sympt_fatigue',\n 'Lengde_sympt_dager', 'Lengde_sympt_uker', 'Lengde_sympt_mnd', 'kreftform']\n )\ndataset = df.drop('kreftform', axis=1)\nX = dataset.values\nY = df['kreftform'].values\nencoder = LabelEncoder()\nencoder.fit(Y)\nencoded_Y = encoder.transform(Y)\nonehot_Y = np_utils.to_categorical(encoded_Y)\nmodel = Sequential()\nmodel.add(Dense(5, input_dim=len(X[0])))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(len(onehot_Y[0]), activation='softmax'))\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[\n 'accuracy'])\nmodel.fit(X, onehot_Y, validation_split=0.33, epochs=1000)\naccuracy = '%.2f' % (model.evaluate(X, onehot_Y)[1] * 100)\nprint('Accuracy:', accuracy, '%')\n",
"step-4": "import pyreadstat\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.utils import np_utils\nfrom sklearn.preprocessing import LabelEncoder\nnp.random.seed(1)\ndf, meta = pyreadstat.read_sav('RESIDIV_Vimala.sav', usecols=[\n 'Sympt_blødning', 'Sympt_smerter', 'Sympt_ascites', 'Sympt_fatigue',\n 'Lengde_sympt_dager', 'Lengde_sympt_uker', 'Lengde_sympt_mnd', 'kreftform']\n )\ndataset = df.drop('kreftform', axis=1)\nX = dataset.values\nY = df['kreftform'].values\nencoder = LabelEncoder()\nencoder.fit(Y)\nencoded_Y = encoder.transform(Y)\nonehot_Y = np_utils.to_categorical(encoded_Y)\nmodel = Sequential()\nmodel.add(Dense(5, input_dim=len(X[0])))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(len(onehot_Y[0]), activation='softmax'))\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[\n 'accuracy'])\nmodel.fit(X, onehot_Y, validation_split=0.33, epochs=1000)\naccuracy = '%.2f' % (model.evaluate(X, onehot_Y)[1] * 100)\nprint('Accuracy:', accuracy, '%')\n",
"step-5": "import pyreadstat\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.utils import np_utils\nfrom sklearn.preprocessing import LabelEncoder\n\n# Set random seed for reproducible results\nnp.random.seed(1)\n\n# Read sav file and create a pandas dataframe and extract metadata\ndf, meta = pyreadstat.read_sav(\"RESIDIV_Vimala.sav\", usecols=[\"Sympt_blødning\", \"Sympt_smerter\", \"Sympt_ascites\", \"Sympt_fatigue\", \"Lengde_sympt_dager\", \"Lengde_sympt_uker\", \"Lengde_sympt_mnd\", \"kreftform\"])\n\ndataset = df.drop(\"kreftform\", axis=1)\n# dataset[0] is Y (kreftform), dataset[1, 2, 3 and 4] is X\nX = dataset.values\nY = df[\"kreftform\"].values\n\n# encode class values as integers\nencoder = LabelEncoder()\nencoder.fit(Y)\nencoded_Y = encoder.transform(Y)\n# convert integers to dummy variables (i.e. one-hot encoded)\nonehot_Y = np_utils.to_categorical(encoded_Y)\n\nmodel = Sequential()\nmodel.add(Dense(5, input_dim=(len(X[0]))))\nmodel.add(Dense(32, activation=\"relu\"))\nmodel.add(Dense(len(onehot_Y[0]), activation=\"softmax\"))\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\nmodel.fit(X, onehot_Y, validation_split=0.33, epochs=1000)\naccuracy = \"%.2f\" % (model.evaluate(X, onehot_Y)[1]*100)\n\nprint(\"Accuracy:\", accuracy, \"%\")\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
from scrapy import cmdline
cmdline.execute("scrapy crawl ariz".split())
|
normal
|
{
"blob_id": "abb2cfd2113e8de6c7bba42c357f0ec140b224a9",
"index": 3311,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncmdline.execute('scrapy crawl ariz'.split())\n",
"step-3": "from scrapy import cmdline\ncmdline.execute('scrapy crawl ariz'.split())\n",
"step-4": "from scrapy import cmdline\ncmdline.execute(\"scrapy crawl ariz\".split())",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
"""
Class: Dataset
This class is responsible of loading datasets
After initializing using load method the class results two parameter:
train: contains train set
test: contains test set
It's able of returning data structure in form of three lists:
- users
- items
- values (which are ratings)
"""
import pandas as pd
from Ratings import Ratings
class DatasetLoader(object):
# Default path where dataset files are located
base_path = './dataset/'
def __init__(self, ds_id, ds_name, ds_desc, ds_columns=None):
if ds_columns is None:
columns = ['user_id', 'item_id', 'values', 'timestamp']
else:
columns = ds_columns
self.id = ds_id
self.name = ds_name
self.desc = ds_desc
train_path = self.base_path + self.name + str(self.id) + '.base'
test_path = self.base_path + self.name + str(self.id) + '.test'
self.train = pd.read_csv(train_path, header=None, delim_whitespace=True)
self.train.columns = columns
self.test = pd.read_csv(test_path, header=None, delim_whitespace=True)
self.test.columns = columns
self.train_ratings = Ratings(self.to_lists(self.train))
self.test_ratings = Ratings(self.to_lists(self.test))
def to_lists(self, ds):
"""
:param ds_type: str [train || test]
:return: dataset in form of three list saved in a dict {users:u, items:i, values:v}
"""
#ds = getattr(self, ds_type)
lists = {
'users': ds['user_id'].values,
'items': ds['item_id'].values,
'values': ds['values'].values
}
return lists
def __str__(self):
return f'Dataset Id: {self.id}, File Name: {self.name}, Description: {self.desc}. \
train size: {len(self.train)}, test size: {len(self.test)}'
# Testing Area
# m_lens = Loader(2, 'u', 'MovieLens dataset, fold 1')
# print(len(m_lens.train))
# print(len(m_lens.test))
# print(m_lens)
|
normal
|
{
"blob_id": "b668945820abe893b92fdf26ccd8563ccff804ee",
"index": 1981,
"step-1": "<mask token>\n\n\nclass DatasetLoader(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass DatasetLoader(object):\n <mask token>\n\n def __init__(self, ds_id, ds_name, ds_desc, ds_columns=None):\n if ds_columns is None:\n columns = ['user_id', 'item_id', 'values', 'timestamp']\n else:\n columns = ds_columns\n self.id = ds_id\n self.name = ds_name\n self.desc = ds_desc\n train_path = self.base_path + self.name + str(self.id) + '.base'\n test_path = self.base_path + self.name + str(self.id) + '.test'\n self.train = pd.read_csv(train_path, header=None, delim_whitespace=True\n )\n self.train.columns = columns\n self.test = pd.read_csv(test_path, header=None, delim_whitespace=True)\n self.test.columns = columns\n self.train_ratings = Ratings(self.to_lists(self.train))\n self.test_ratings = Ratings(self.to_lists(self.test))\n\n def to_lists(self, ds):\n \"\"\"\n :param ds_type: str [train || test]\n :return: dataset in form of three list saved in a dict {users:u, items:i, values:v}\n \"\"\"\n lists = {'users': ds['user_id'].values, 'items': ds['item_id'].\n values, 'values': ds['values'].values}\n return lists\n\n def __str__(self):\n return (\n f'Dataset Id: {self.id}, File Name: {self.name}, Description: {self.desc}. train size: {len(self.train)}, test size: {len(self.test)}'\n )\n",
"step-3": "<mask token>\n\n\nclass DatasetLoader(object):\n base_path = './dataset/'\n\n def __init__(self, ds_id, ds_name, ds_desc, ds_columns=None):\n if ds_columns is None:\n columns = ['user_id', 'item_id', 'values', 'timestamp']\n else:\n columns = ds_columns\n self.id = ds_id\n self.name = ds_name\n self.desc = ds_desc\n train_path = self.base_path + self.name + str(self.id) + '.base'\n test_path = self.base_path + self.name + str(self.id) + '.test'\n self.train = pd.read_csv(train_path, header=None, delim_whitespace=True\n )\n self.train.columns = columns\n self.test = pd.read_csv(test_path, header=None, delim_whitespace=True)\n self.test.columns = columns\n self.train_ratings = Ratings(self.to_lists(self.train))\n self.test_ratings = Ratings(self.to_lists(self.test))\n\n def to_lists(self, ds):\n \"\"\"\n :param ds_type: str [train || test]\n :return: dataset in form of three list saved in a dict {users:u, items:i, values:v}\n \"\"\"\n lists = {'users': ds['user_id'].values, 'items': ds['item_id'].\n values, 'values': ds['values'].values}\n return lists\n\n def __str__(self):\n return (\n f'Dataset Id: {self.id}, File Name: {self.name}, Description: {self.desc}. train size: {len(self.train)}, test size: {len(self.test)}'\n )\n",
"step-4": "<mask token>\nimport pandas as pd\nfrom Ratings import Ratings\n\n\nclass DatasetLoader(object):\n base_path = './dataset/'\n\n def __init__(self, ds_id, ds_name, ds_desc, ds_columns=None):\n if ds_columns is None:\n columns = ['user_id', 'item_id', 'values', 'timestamp']\n else:\n columns = ds_columns\n self.id = ds_id\n self.name = ds_name\n self.desc = ds_desc\n train_path = self.base_path + self.name + str(self.id) + '.base'\n test_path = self.base_path + self.name + str(self.id) + '.test'\n self.train = pd.read_csv(train_path, header=None, delim_whitespace=True\n )\n self.train.columns = columns\n self.test = pd.read_csv(test_path, header=None, delim_whitespace=True)\n self.test.columns = columns\n self.train_ratings = Ratings(self.to_lists(self.train))\n self.test_ratings = Ratings(self.to_lists(self.test))\n\n def to_lists(self, ds):\n \"\"\"\n :param ds_type: str [train || test]\n :return: dataset in form of three list saved in a dict {users:u, items:i, values:v}\n \"\"\"\n lists = {'users': ds['user_id'].values, 'items': ds['item_id'].\n values, 'values': ds['values'].values}\n return lists\n\n def __str__(self):\n return (\n f'Dataset Id: {self.id}, File Name: {self.name}, Description: {self.desc}. train size: {len(self.train)}, test size: {len(self.test)}'\n )\n",
"step-5": "\"\"\"\nClass: Dataset\n\nThis class is responsible of loading datasets\n\nAfter initializing using load method the class results two parameter:\n train: contains train set\n test: contains test set\n\nIt's able of returning data structure in form of three lists:\n - users\n - items\n - values (which are ratings)\n\"\"\"\n\nimport pandas as pd\nfrom Ratings import Ratings\n\n\nclass DatasetLoader(object):\n\n # Default path where dataset files are located\n base_path = './dataset/'\n\n def __init__(self, ds_id, ds_name, ds_desc, ds_columns=None):\n\n if ds_columns is None:\n columns = ['user_id', 'item_id', 'values', 'timestamp']\n else:\n columns = ds_columns\n\n self.id = ds_id\n self.name = ds_name\n self.desc = ds_desc\n\n train_path = self.base_path + self.name + str(self.id) + '.base'\n test_path = self.base_path + self.name + str(self.id) + '.test'\n\n self.train = pd.read_csv(train_path, header=None, delim_whitespace=True)\n self.train.columns = columns\n\n self.test = pd.read_csv(test_path, header=None, delim_whitespace=True)\n self.test.columns = columns\n\n self.train_ratings = Ratings(self.to_lists(self.train))\n self.test_ratings = Ratings(self.to_lists(self.test))\n\n def to_lists(self, ds):\n \"\"\"\n :param ds_type: str [train || test]\n :return: dataset in form of three list saved in a dict {users:u, items:i, values:v}\n \"\"\"\n #ds = getattr(self, ds_type)\n\n lists = {\n 'users': ds['user_id'].values,\n 'items': ds['item_id'].values,\n 'values': ds['values'].values\n }\n\n return lists\n\n def __str__(self):\n return f'Dataset Id: {self.id}, File Name: {self.name}, Description: {self.desc}. \\\n train size: {len(self.train)}, test size: {len(self.test)}'\n\n\n# Testing Area\n# m_lens = Loader(2, 'u', 'MovieLens dataset, fold 1')\n# print(len(m_lens.train))\n# print(len(m_lens.test))\n# print(m_lens)\n",
"step-ids": [
1,
4,
5,
6,
7
]
}
|
[
1,
4,
5,
6,
7
] |
# Check given matrix is valid sudoku or Not.
|
normal
|
{
"blob_id": "502da0f0dafe42d3464fabb1d92ae1b0d7ef11f3",
"index": 431,
"step-1": "# Check given matrix is valid sudoku or Not.\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
1
]
}
|
[
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('status/', StatusAPIView.as_view(), name='status'),
path('log/', LogAPIView.as_view(), name='log'), path('state/',
StateAPIView.as_view(), name='state')]
<|reserved_special_token_1|>
from django.urls import path, include
from .views import StatusAPIView, StateAPIView, LogAPIView
urlpatterns = [path('status/', StatusAPIView.as_view(), name='status'),
path('log/', LogAPIView.as_view(), name='log'), path('state/',
StateAPIView.as_view(), name='state')]
<|reserved_special_token_1|>
from django.urls import path, include
from .views import StatusAPIView, StateAPIView, LogAPIView
urlpatterns = [
path('status/', StatusAPIView.as_view(), name='status'),
path('log/', LogAPIView.as_view(), name='log'),
path('state/', StateAPIView.as_view(), name='state'),
]
|
flexible
|
{
"blob_id": "1ae8d78c6581d35cd82194e2565e7a11edda1487",
"index": 7265,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('status/', StatusAPIView.as_view(), name='status'),\n path('log/', LogAPIView.as_view(), name='log'), path('state/',\n StateAPIView.as_view(), name='state')]\n",
"step-3": "from django.urls import path, include\nfrom .views import StatusAPIView, StateAPIView, LogAPIView\nurlpatterns = [path('status/', StatusAPIView.as_view(), name='status'),\n path('log/', LogAPIView.as_view(), name='log'), path('state/',\n StateAPIView.as_view(), name='state')]\n",
"step-4": "from django.urls import path, include\nfrom .views import StatusAPIView, StateAPIView, LogAPIView\n\n\nurlpatterns = [\n path('status/', StatusAPIView.as_view(), name='status'),\n path('log/', LogAPIView.as_view(), name='log'),\n path('state/', StateAPIView.as_view(), name='state'),\n]\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
from __future__ import absolute_import, print_function, division, unicode_literals
import tensorflow as tf
def get_encoder(conf):
if conf.encoder == 'linear':
model = tf.keras.Sequential([tf.keras.layers.Dense(conf.d_model * 2),
tf.keras.layers.ReLU(),
tf.keras.layers.Dense(conf.d_model)])
return model
if conf.encoder == 'rand_linear':
model = get_stochastic_linear(conf)
return model
if conf.encoder[:5] == 'cifar':
model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1, k=conf.k, linear=conf.linear)
return model
def get_stochastic_linear(conf):
model = tf.keras.Sequential([tf.keras.layers.GaussianNoise(.3),
tf.keras.layers.Dense(conf.d_model * 2),
tf.keras.layers.ReLU(),
tf.keras.layers.GaussianNoise(.3),
tf.keras.layers.Dense(conf.d_model)])
return model
# noinspection PyAbstractClass
class BasicBlock(tf.keras.layers.Layer):
EXPANSION = 1
def __init__(self, channels, filters, strides=1):
super().__init__()
self.conv_1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3, strides=strides, padding='same',
use_bias=False)
self.bn_1 = tf.keras.layers.BatchNormalization()
self.conv_2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3, strides=1, padding='same',
use_bias=False)
self.bn_2 = tf.keras.layers.BatchNormalization()
self.shortcut = tf.keras.Sequential()
if strides != 1 or channels != (filters * self.EXPANSION):
self.shortcut.add(tf.keras.layers.Conv2D(filters=self.EXPANSION * filters, kernel_size=1, strides=strides,
use_bias=False))
self.shortcut.add(tf.keras.layers.BatchNormalization())
def call(self, inputs, training=True, mask=None):
x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training), training=training))
x = self.bn_2(self.conv_2(x, training=training), training=training)
x += self.shortcut(inputs, training=training)
return tf.nn.relu(x)
# noinspection PyAbstractClass
class ResNet(tf.keras.Model):
def __init__(self, block, num_blocks, pool_len=4, low_dim=128, width=1, k=10, linear=True):
super().__init__()
self.channels = 64
self.pool_len = pool_len
self.k = k
self.linear = linear
self.conv_1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1, padding='same', use_bias=False)
self.bn_1 = tf.keras.layers.BatchNormalization()
self.base = int(64 * width)
self.residual = tf.keras.Sequential([
self._make_layer(block, self.base, num_blocks[0], stride=1),
self._make_layer(block, self.base * 2, num_blocks[1], stride=2),
self._make_layer(block, self.base * 4, num_blocks[2], stride=2),
self._make_layer(block, self.base * 8, num_blocks[3], stride=2)
])
if self.linear:
self.fc = tf.keras.layers.Dense(low_dim)
self.pool = tf.keras.layers.AveragePooling2D(pool_len, pool_len, data_format='channels_last')
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.channels, planes, stride))
self.channels = planes * block.EXPANSION
return tf.keras.Sequential(layers)
def call(self, inputs, training=True, mask=None):
x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training), training=training))
x = self.residual(x, training=training)
x = self.pool(x)
batch_size = tf.shape(x)[0]
x = tf.reshape(x, [batch_size, -1])
if self.linear:
x = self.fc(x, training=training)
return x
def test_resnet():
model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1)
a = tf.ones([7, 32, 32, 3])
b = model(a)
print(b)
if __name__ == '__main__':
test_resnet()
|
normal
|
{
"blob_id": "548eebb9628374df320021c714454e05d2c606c0",
"index": 5336,
"step-1": "<mask token>\n\n\ndef get_encoder(conf):\n if conf.encoder == 'linear':\n model = tf.keras.Sequential([tf.keras.layers.Dense(conf.d_model * 2\n ), tf.keras.layers.ReLU(), tf.keras.layers.Dense(conf.d_model)])\n return model\n if conf.encoder == 'rand_linear':\n model = get_stochastic_linear(conf)\n return model\n if conf.encoder[:5] == 'cifar':\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1, k\n =conf.k, linear=conf.linear)\n return model\n\n\n<mask token>\n\n\nclass BasicBlock(tf.keras.layers.Layer):\n EXPANSION = 1\n\n def __init__(self, channels, filters, strides=1):\n super().__init__()\n self.conv_1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=strides, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.conv_2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_2 = tf.keras.layers.BatchNormalization()\n self.shortcut = tf.keras.Sequential()\n if strides != 1 or channels != filters * self.EXPANSION:\n self.shortcut.add(tf.keras.layers.Conv2D(filters=self.EXPANSION *\n filters, kernel_size=1, strides=strides, use_bias=False))\n self.shortcut.add(tf.keras.layers.BatchNormalization())\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.bn_2(self.conv_2(x, training=training), training=training)\n x += self.shortcut(inputs, training=training)\n return tf.nn.relu(x)\n\n\nclass ResNet(tf.keras.Model):\n\n def __init__(self, block, num_blocks, pool_len=4, low_dim=128, width=1,\n k=10, linear=True):\n super().__init__()\n self.channels = 64\n self.pool_len = pool_len\n self.k = k\n self.linear = linear\n self.conv_1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.base = int(64 * width)\n self.residual = tf.keras.Sequential([self._make_layer(block, self.\n base, num_blocks[0], stride=1), self._make_layer(block, self.\n base * 2, num_blocks[1], stride=2), self._make_layer(block, \n self.base * 4, num_blocks[2], stride=2), self._make_layer(block,\n self.base * 8, num_blocks[3], stride=2)])\n if self.linear:\n self.fc = tf.keras.layers.Dense(low_dim)\n self.pool = tf.keras.layers.AveragePooling2D(pool_len, pool_len,\n data_format='channels_last')\n\n def _make_layer(self, block, planes, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.channels, planes, stride))\n self.channels = planes * block.EXPANSION\n return tf.keras.Sequential(layers)\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.residual(x, training=training)\n x = self.pool(x)\n batch_size = tf.shape(x)[0]\n x = tf.reshape(x, [batch_size, -1])\n if self.linear:\n x = self.fc(x, training=training)\n return x\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_encoder(conf):\n if conf.encoder == 'linear':\n model = tf.keras.Sequential([tf.keras.layers.Dense(conf.d_model * 2\n ), tf.keras.layers.ReLU(), tf.keras.layers.Dense(conf.d_model)])\n return model\n if conf.encoder == 'rand_linear':\n model = get_stochastic_linear(conf)\n return model\n if conf.encoder[:5] == 'cifar':\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1, k\n =conf.k, linear=conf.linear)\n return model\n\n\n<mask token>\n\n\nclass BasicBlock(tf.keras.layers.Layer):\n EXPANSION = 1\n\n def __init__(self, channels, filters, strides=1):\n super().__init__()\n self.conv_1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=strides, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.conv_2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_2 = tf.keras.layers.BatchNormalization()\n self.shortcut = tf.keras.Sequential()\n if strides != 1 or channels != filters * self.EXPANSION:\n self.shortcut.add(tf.keras.layers.Conv2D(filters=self.EXPANSION *\n filters, kernel_size=1, strides=strides, use_bias=False))\n self.shortcut.add(tf.keras.layers.BatchNormalization())\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.bn_2(self.conv_2(x, training=training), training=training)\n x += self.shortcut(inputs, training=training)\n return tf.nn.relu(x)\n\n\nclass ResNet(tf.keras.Model):\n\n def __init__(self, block, num_blocks, pool_len=4, low_dim=128, width=1,\n k=10, linear=True):\n super().__init__()\n self.channels = 64\n self.pool_len = pool_len\n self.k = k\n self.linear = linear\n self.conv_1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.base = int(64 * width)\n self.residual = tf.keras.Sequential([self._make_layer(block, self.\n base, num_blocks[0], stride=1), self._make_layer(block, self.\n base * 2, num_blocks[1], stride=2), self._make_layer(block, \n self.base * 4, num_blocks[2], stride=2), self._make_layer(block,\n self.base * 8, num_blocks[3], stride=2)])\n if self.linear:\n self.fc = tf.keras.layers.Dense(low_dim)\n self.pool = tf.keras.layers.AveragePooling2D(pool_len, pool_len,\n data_format='channels_last')\n\n def _make_layer(self, block, planes, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.channels, planes, stride))\n self.channels = planes * block.EXPANSION\n return tf.keras.Sequential(layers)\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.residual(x, training=training)\n x = self.pool(x)\n batch_size = tf.shape(x)[0]\n x = tf.reshape(x, [batch_size, -1])\n if self.linear:\n x = self.fc(x, training=training)\n return x\n\n\ndef test_resnet():\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1)\n a = tf.ones([7, 32, 32, 3])\n b = model(a)\n print(b)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef get_encoder(conf):\n if conf.encoder == 'linear':\n model = tf.keras.Sequential([tf.keras.layers.Dense(conf.d_model * 2\n ), tf.keras.layers.ReLU(), tf.keras.layers.Dense(conf.d_model)])\n return model\n if conf.encoder == 'rand_linear':\n model = get_stochastic_linear(conf)\n return model\n if conf.encoder[:5] == 'cifar':\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1, k\n =conf.k, linear=conf.linear)\n return model\n\n\ndef get_stochastic_linear(conf):\n model = tf.keras.Sequential([tf.keras.layers.GaussianNoise(0.3), tf.\n keras.layers.Dense(conf.d_model * 2), tf.keras.layers.ReLU(), tf.\n keras.layers.GaussianNoise(0.3), tf.keras.layers.Dense(conf.d_model)])\n return model\n\n\nclass BasicBlock(tf.keras.layers.Layer):\n EXPANSION = 1\n\n def __init__(self, channels, filters, strides=1):\n super().__init__()\n self.conv_1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=strides, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.conv_2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_2 = tf.keras.layers.BatchNormalization()\n self.shortcut = tf.keras.Sequential()\n if strides != 1 or channels != filters * self.EXPANSION:\n self.shortcut.add(tf.keras.layers.Conv2D(filters=self.EXPANSION *\n filters, kernel_size=1, strides=strides, use_bias=False))\n self.shortcut.add(tf.keras.layers.BatchNormalization())\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.bn_2(self.conv_2(x, training=training), training=training)\n x += self.shortcut(inputs, training=training)\n return tf.nn.relu(x)\n\n\nclass ResNet(tf.keras.Model):\n\n def __init__(self, block, num_blocks, pool_len=4, low_dim=128, width=1,\n k=10, linear=True):\n super().__init__()\n self.channels = 64\n self.pool_len = pool_len\n self.k = k\n self.linear = linear\n self.conv_1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.base = int(64 * width)\n self.residual = tf.keras.Sequential([self._make_layer(block, self.\n base, num_blocks[0], stride=1), self._make_layer(block, self.\n base * 2, num_blocks[1], stride=2), self._make_layer(block, \n self.base * 4, num_blocks[2], stride=2), self._make_layer(block,\n self.base * 8, num_blocks[3], stride=2)])\n if self.linear:\n self.fc = tf.keras.layers.Dense(low_dim)\n self.pool = tf.keras.layers.AveragePooling2D(pool_len, pool_len,\n data_format='channels_last')\n\n def _make_layer(self, block, planes, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.channels, planes, stride))\n self.channels = planes * block.EXPANSION\n return tf.keras.Sequential(layers)\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.residual(x, training=training)\n x = self.pool(x)\n batch_size = tf.shape(x)[0]\n x = tf.reshape(x, [batch_size, -1])\n if self.linear:\n x = self.fc(x, training=training)\n return x\n\n\ndef test_resnet():\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1)\n a = tf.ones([7, 32, 32, 3])\n b = model(a)\n print(b)\n\n\nif __name__ == '__main__':\n test_resnet()\n",
"step-4": "from __future__ import absolute_import, print_function, division, unicode_literals\nimport tensorflow as tf\n\n\ndef get_encoder(conf):\n if conf.encoder == 'linear':\n model = tf.keras.Sequential([tf.keras.layers.Dense(conf.d_model * 2\n ), tf.keras.layers.ReLU(), tf.keras.layers.Dense(conf.d_model)])\n return model\n if conf.encoder == 'rand_linear':\n model = get_stochastic_linear(conf)\n return model\n if conf.encoder[:5] == 'cifar':\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1, k\n =conf.k, linear=conf.linear)\n return model\n\n\ndef get_stochastic_linear(conf):\n model = tf.keras.Sequential([tf.keras.layers.GaussianNoise(0.3), tf.\n keras.layers.Dense(conf.d_model * 2), tf.keras.layers.ReLU(), tf.\n keras.layers.GaussianNoise(0.3), tf.keras.layers.Dense(conf.d_model)])\n return model\n\n\nclass BasicBlock(tf.keras.layers.Layer):\n EXPANSION = 1\n\n def __init__(self, channels, filters, strides=1):\n super().__init__()\n self.conv_1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=strides, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.conv_2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_2 = tf.keras.layers.BatchNormalization()\n self.shortcut = tf.keras.Sequential()\n if strides != 1 or channels != filters * self.EXPANSION:\n self.shortcut.add(tf.keras.layers.Conv2D(filters=self.EXPANSION *\n filters, kernel_size=1, strides=strides, use_bias=False))\n self.shortcut.add(tf.keras.layers.BatchNormalization())\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.bn_2(self.conv_2(x, training=training), training=training)\n x += self.shortcut(inputs, training=training)\n return tf.nn.relu(x)\n\n\nclass ResNet(tf.keras.Model):\n\n def __init__(self, block, num_blocks, pool_len=4, low_dim=128, width=1,\n k=10, linear=True):\n super().__init__()\n self.channels = 64\n self.pool_len = pool_len\n self.k = k\n self.linear = linear\n self.conv_1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3,\n strides=1, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.base = int(64 * width)\n self.residual = tf.keras.Sequential([self._make_layer(block, self.\n base, num_blocks[0], stride=1), self._make_layer(block, self.\n base * 2, num_blocks[1], stride=2), self._make_layer(block, \n self.base * 4, num_blocks[2], stride=2), self._make_layer(block,\n self.base * 8, num_blocks[3], stride=2)])\n if self.linear:\n self.fc = tf.keras.layers.Dense(low_dim)\n self.pool = tf.keras.layers.AveragePooling2D(pool_len, pool_len,\n data_format='channels_last')\n\n def _make_layer(self, block, planes, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.channels, planes, stride))\n self.channels = planes * block.EXPANSION\n return tf.keras.Sequential(layers)\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training),\n training=training))\n x = self.residual(x, training=training)\n x = self.pool(x)\n batch_size = tf.shape(x)[0]\n x = tf.reshape(x, [batch_size, -1])\n if self.linear:\n x = self.fc(x, training=training)\n return x\n\n\ndef test_resnet():\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1)\n a = tf.ones([7, 32, 32, 3])\n b = model(a)\n print(b)\n\n\nif __name__ == '__main__':\n test_resnet()\n",
"step-5": "from __future__ import absolute_import, print_function, division, unicode_literals\nimport tensorflow as tf\n\n\ndef get_encoder(conf):\n if conf.encoder == 'linear':\n model = tf.keras.Sequential([tf.keras.layers.Dense(conf.d_model * 2),\n tf.keras.layers.ReLU(),\n tf.keras.layers.Dense(conf.d_model)])\n return model\n\n if conf.encoder == 'rand_linear':\n model = get_stochastic_linear(conf)\n return model\n if conf.encoder[:5] == 'cifar':\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1, k=conf.k, linear=conf.linear)\n return model\n\n\ndef get_stochastic_linear(conf):\n model = tf.keras.Sequential([tf.keras.layers.GaussianNoise(.3),\n tf.keras.layers.Dense(conf.d_model * 2),\n tf.keras.layers.ReLU(),\n tf.keras.layers.GaussianNoise(.3),\n tf.keras.layers.Dense(conf.d_model)])\n return model\n\n\n# noinspection PyAbstractClass\nclass BasicBlock(tf.keras.layers.Layer):\n EXPANSION = 1\n\n def __init__(self, channels, filters, strides=1):\n super().__init__()\n self.conv_1 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3, strides=strides, padding='same',\n use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n self.conv_2 = tf.keras.layers.Conv2D(filters=filters, kernel_size=3, strides=1, padding='same',\n use_bias=False)\n self.bn_2 = tf.keras.layers.BatchNormalization()\n self.shortcut = tf.keras.Sequential()\n if strides != 1 or channels != (filters * self.EXPANSION):\n self.shortcut.add(tf.keras.layers.Conv2D(filters=self.EXPANSION * filters, kernel_size=1, strides=strides,\n use_bias=False))\n self.shortcut.add(tf.keras.layers.BatchNormalization())\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training), training=training))\n x = self.bn_2(self.conv_2(x, training=training), training=training)\n x += self.shortcut(inputs, training=training)\n return tf.nn.relu(x)\n\n\n# noinspection PyAbstractClass\nclass ResNet(tf.keras.Model):\n def __init__(self, block, num_blocks, pool_len=4, low_dim=128, width=1, k=10, linear=True):\n super().__init__()\n self.channels = 64\n self.pool_len = pool_len\n self.k = k\n self.linear = linear\n self.conv_1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1, padding='same', use_bias=False)\n self.bn_1 = tf.keras.layers.BatchNormalization()\n\n self.base = int(64 * width)\n self.residual = tf.keras.Sequential([\n self._make_layer(block, self.base, num_blocks[0], stride=1),\n self._make_layer(block, self.base * 2, num_blocks[1], stride=2),\n self._make_layer(block, self.base * 4, num_blocks[2], stride=2),\n self._make_layer(block, self.base * 8, num_blocks[3], stride=2)\n ])\n if self.linear:\n self.fc = tf.keras.layers.Dense(low_dim)\n self.pool = tf.keras.layers.AveragePooling2D(pool_len, pool_len, data_format='channels_last')\n\n def _make_layer(self, block, planes, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.channels, planes, stride))\n self.channels = planes * block.EXPANSION\n return tf.keras.Sequential(layers)\n\n def call(self, inputs, training=True, mask=None):\n x = tf.nn.relu(self.bn_1(self.conv_1(inputs, training=training), training=training))\n x = self.residual(x, training=training)\n x = self.pool(x)\n\n batch_size = tf.shape(x)[0]\n x = tf.reshape(x, [batch_size, -1])\n if self.linear:\n x = self.fc(x, training=training)\n return x\n\n\ndef test_resnet():\n model = ResNet(BasicBlock, [3, 4, 6, 3], 4, low_dim=128, width=1)\n a = tf.ones([7, 32, 32, 3])\n b = model(a)\n print(b)\n\n\nif __name__ == '__main__':\n test_resnet()\n",
"step-ids": [
9,
10,
12,
13,
14
]
}
|
[
9,
10,
12,
13,
14
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if a >= 70:
print: '통과입니다.'
print: '축하합니다.'
else:
print: '불합격입니다.'
print('안녕')
<|reserved_special_token_1|>
a = int(input('점수를 입력하세요'))
if a >= 70:
print: '통과입니다.'
print: '축하합니다.'
else:
print: '불합격입니다.'
print('안녕')
<|reserved_special_token_1|>
a = int(input('점수를 입력하세요'))
if a >= 70 :
print:('통과입니다.')
print:('축하합니다.')
else :
print:('불합격입니다.')
print("안녕")
|
flexible
|
{
"blob_id": "f8d0cc9cb0e5f8adf9077ffb39dd6abedfedaa12",
"index": 5427,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif a >= 70:\n print: '통과입니다.'\n print: '축하합니다.'\nelse:\n print: '불합격입니다.'\nprint('안녕')\n",
"step-3": "a = int(input('점수를 입력하세요'))\nif a >= 70:\n print: '통과입니다.'\n print: '축하합니다.'\nelse:\n print: '불합격입니다.'\nprint('안녕')\n",
"step-4": "a = int(input('점수를 입력하세요'))\r\nif a >= 70 :\r\n print:('통과입니다.')\r\n print:('축하합니다.')\r\nelse :\r\n print:('불합격입니다.')\r\nprint(\"안녕\")\r\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append('..')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append('..')
<|reserved_special_token_0|>
account = ACCOUNT.Account('577e4a03-540f9610-f686d434-qz5c4v5b6n',
'dd7b02f5-c286e9d4-f2cc78c3-bfab3')
bs = BS.Bitso(account)
currency_pair = CP.CurrencyPair('btc', 'xmn')
depth = bs.depth(currency_pair)
a = 1
<|reserved_special_token_1|>
import sys
sys.path.append('..')
from packages import bitso as BS
from packages import account as ACCOUNT
from packages import currency_pair as CP
account = ACCOUNT.Account('577e4a03-540f9610-f686d434-qz5c4v5b6n',
'dd7b02f5-c286e9d4-f2cc78c3-bfab3')
bs = BS.Bitso(account)
currency_pair = CP.CurrencyPair('btc', 'xmn')
depth = bs.depth(currency_pair)
a = 1
<|reserved_special_token_1|>
import sys
sys.path.append("..")
from packages import bitso as BS
from packages import account as ACCOUNT
from packages import currency_pair as CP
account=ACCOUNT.Account('577e4a03-540f9610-f686d434-qz5c4v5b6n','dd7b02f5-c286e9d4-f2cc78c3-bfab3')
bs=BS.Bitso(account)
currency_pair=CP.CurrencyPair('btc','xmn')
depth=bs.depth(currency_pair)
a=1
|
flexible
|
{
"blob_id": "03147de944c4f75417006a5087e75354dba644ec",
"index": 6339,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append('..')\n<mask token>\n",
"step-3": "<mask token>\nsys.path.append('..')\n<mask token>\naccount = ACCOUNT.Account('577e4a03-540f9610-f686d434-qz5c4v5b6n',\n 'dd7b02f5-c286e9d4-f2cc78c3-bfab3')\nbs = BS.Bitso(account)\ncurrency_pair = CP.CurrencyPair('btc', 'xmn')\ndepth = bs.depth(currency_pair)\na = 1\n",
"step-4": "import sys\nsys.path.append('..')\nfrom packages import bitso as BS\nfrom packages import account as ACCOUNT\nfrom packages import currency_pair as CP\naccount = ACCOUNT.Account('577e4a03-540f9610-f686d434-qz5c4v5b6n',\n 'dd7b02f5-c286e9d4-f2cc78c3-bfab3')\nbs = BS.Bitso(account)\ncurrency_pair = CP.CurrencyPair('btc', 'xmn')\ndepth = bs.depth(currency_pair)\na = 1\n",
"step-5": "import sys\nsys.path.append(\"..\")\nfrom packages import bitso as BS\nfrom packages import account as ACCOUNT\nfrom packages import currency_pair as CP\n\naccount=ACCOUNT.Account('577e4a03-540f9610-f686d434-qz5c4v5b6n','dd7b02f5-c286e9d4-f2cc78c3-bfab3')\nbs=BS.Bitso(account)\n\ncurrency_pair=CP.CurrencyPair('btc','xmn')\ndepth=bs.depth(currency_pair)\na=1\n\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
from mpi4py import MPI
from random import random
comm = MPI.COMM_WORLD
mydata = comm.rank
data = comm.gather(mydata)
if comm.rank == 0:
print("Data = ", data)
|
normal
|
{
"blob_id": "acf3d188bd6c99774ddf538dcc83f99ad56c7057",
"index": 7431,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif comm.rank == 0:\n print('Data = ', data)\n",
"step-3": "<mask token>\ncomm = MPI.COMM_WORLD\nmydata = comm.rank\ndata = comm.gather(mydata)\nif comm.rank == 0:\n print('Data = ', data)\n",
"step-4": "from mpi4py import MPI\nfrom random import random\ncomm = MPI.COMM_WORLD\nmydata = comm.rank\ndata = comm.gather(mydata)\nif comm.rank == 0:\n print('Data = ', data)\n",
"step-5": "from mpi4py import MPI\nfrom random import random\n\ncomm = MPI.COMM_WORLD\n\nmydata = comm.rank\n\ndata = comm.gather(mydata)\n\nif comm.rank == 0:\n print(\"Data = \", data)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
# ethermine.py, Copyright (c) 2019, Nicholas Saparoff <nick.saparoff@gmail.com>: Original implementation
from minermedic.pools.base_pool import BasePool
from phenome_core.util.rest_api import RestAPI
from minermedic.pools.helper import get_algo_index, get_coin_index, get_coin_cost
"""
EtherminePool
This is the main Pool API for Ethermine.
SEE: https://ethermine.org/api/worker#monitoring
"""
class EtherminePool(BasePool):
# PER WORKER
_MINER_URL_PER_WORKER = "https://api.ethermine.org/miner/:{MINER}/worker/:{WORKER}/currentStats"
# PER MINER
_MINER_URL_PER_MINER = "https://api.ethermine.org/miner/:{MINER}/currentStats"
# with Ethermine, the coin is Usually ETH, but could be ETC or ZCASH
_DEFAULT_COIN_ = "ETH"
def __init__(self, pool, pool_attrs):
super(EtherminePool, self).__init__(pool, pool_attrs)
def build_creation_parameters(self, pool, pool_attrs, pool_classname):
# get the default creation parameters
params = super(EtherminePool, self).build_creation_parameters(pool, pool_attrs, pool_classname)
server_location = "US"
if pool.startswith("eu1.etc") or pool.startswith("eu1.eth"):
server_location = "Europe"
elif pool.startswith("us1-etc"):
server_location = "US"
elif pool.startswith("us1.eth"):
server_location = "US East"
elif pool.startswith("us2.eth"):
server_location = "US West"
elif pool.startswith("asia1.eth"):
server_location = "Asia"
# Set the unique ID of the pool (give it a NAME, as the URL/IP may change)
# POOL - LOCATION (COIN)
params['unique_id'] = "ETHERMINE - " + server_location + " (" + self._DEFAULT_COIN_ + ")"
return params
def _clean_coin_address(self, miner):
coin_address = miner.coin_address.lower()
if coin_address.startswith('0x'):
coin_address = coin_address[2:]
elif coin_address.startswith('#0x'):
coin_address = coin_address[3:]
return coin_address
def get_worker_stats(self, miner, worker):
# build the miner URL
url = self._MINER_URL_PER_WORKER.replace("{MINER}",self._clean_coin_address(miner)).replace("{WORKER}",worker)
api = RestAPI(url=url, port=80)
return api.get_json()
def get_miner_stats(self, miner):
# build the miner URL
url = self._MINER_URL_PER_MINER.replace("{MINER}", self._clean_coin_address(miner))
api = RestAPI(url=url, port=80)
return api.get_json()
def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):
if algo == 'ethash':
algo_idx = get_algo_index('daggerhashimoto')
else:
algo_idx = get_algo_index(algo)
if algo_idx is -1:
return False
coin_idx = get_coin_index(self._DEFAULT_COIN_)
# get the cost of the coin
# TODO - get the currency from the config, do not assume USD
coin_cost = get_coin_cost(self._DEFAULT_COIN_,'USD')
success = False
json = self.get_worker_stats(miner, worker)
if json:
success = self.parse_json(json, results, miner, worker, pool_id, algo, algo_idx, coin_idx, coin_cost)
return success
def parse_json(self, json, results, miner, worker, pool, algo, algo_idx, coin_idx, coin_cost):
# get the record
record = json['data']
if record == 'NO DATA':
# check coin switch?
miner_coin_idx = None
if hasattr(miner, 'coin_idx'):
# we have been mining so far
miner_coin_idx = miner.coin
if miner_coin_idx is None or miner_coin_idx != coin_idx:
# reset the coin address, maybe switched coin
miner.coin_address = ''
# no data, just fail
return False
# API call results, speed is in units of Hashes
speed_suffix = 'H'
try:
# get accepted hashrate
speed_accepted = float(record['currentHashrate'])
except:
speed_accepted = 0.0
try:
# get "reported" hashrate
speed_reported = float(record['reportedHashrate'])
except:
speed_reported = None
# now get the miner stats for profitability
json_miner_stats = self.get_miner_stats(miner)
# get the record
record_miner_stats = json_miner_stats['data']
try:
coins_per_minute = float(record_miner_stats['coinsPerMin'])
except:
coins_per_minute = 0.0
try:
active_workers = float(record_miner_stats['activeWorkers'])
except:
active_workers = 1
# profitability is a measure of COIN / speed suffix / per DAY
# ETHERMINE only gives coin estimates per MINER per MINUTE, not per WORKER
# so we need to average it out by dividing by the # of active workers
profitability = ((coins_per_minute * (60 * 24))/speed_accepted)/active_workers
# finally set the API results into the main results object
results.populate_pool_results(miner, worker, pool, algo, algo_idx, coin_idx, coin_cost, profitability,
speed_accepted, speed_reported, speed_suffix)
# if we got here, we were successful
return True
|
normal
|
{
"blob_id": "921c7255fad46c767f2ec1030ef9498da05b9bb1",
"index": 9958,
"step-1": "<mask token>\n\n\nclass EtherminePool(BasePool):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def build_creation_parameters(self, pool, pool_attrs, pool_classname):\n params = super(EtherminePool, self).build_creation_parameters(pool,\n pool_attrs, pool_classname)\n server_location = 'US'\n if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):\n server_location = 'Europe'\n elif pool.startswith('us1-etc'):\n server_location = 'US'\n elif pool.startswith('us1.eth'):\n server_location = 'US East'\n elif pool.startswith('us2.eth'):\n server_location = 'US West'\n elif pool.startswith('asia1.eth'):\n server_location = 'Asia'\n params['unique_id'\n ] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'\n return params\n <mask token>\n\n def get_worker_stats(self, miner, worker):\n url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.\n _clean_coin_address(miner)).replace('{WORKER}', worker)\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_miner_stats(self, miner):\n url = self._MINER_URL_PER_MINER.replace('{MINER}', self.\n _clean_coin_address(miner))\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):\n if algo == 'ethash':\n algo_idx = get_algo_index('daggerhashimoto')\n else:\n algo_idx = get_algo_index(algo)\n if algo_idx is -1:\n return False\n coin_idx = get_coin_index(self._DEFAULT_COIN_)\n coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')\n success = False\n json = self.get_worker_stats(miner, worker)\n if json:\n success = self.parse_json(json, results, miner, worker, pool_id,\n algo, algo_idx, coin_idx, coin_cost)\n return success\n\n def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost):\n record = json['data']\n if record == 'NO DATA':\n miner_coin_idx = None\n if hasattr(miner, 'coin_idx'):\n miner_coin_idx = miner.coin\n if miner_coin_idx is None or miner_coin_idx != coin_idx:\n miner.coin_address = ''\n return False\n speed_suffix = 'H'\n try:\n speed_accepted = float(record['currentHashrate'])\n except:\n speed_accepted = 0.0\n try:\n speed_reported = float(record['reportedHashrate'])\n except:\n speed_reported = None\n json_miner_stats = self.get_miner_stats(miner)\n record_miner_stats = json_miner_stats['data']\n try:\n coins_per_minute = float(record_miner_stats['coinsPerMin'])\n except:\n coins_per_minute = 0.0\n try:\n active_workers = float(record_miner_stats['activeWorkers'])\n except:\n active_workers = 1\n profitability = coins_per_minute * (60 * 24\n ) / speed_accepted / active_workers\n results.populate_pool_results(miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost, profitability, speed_accepted,\n speed_reported, speed_suffix)\n return True\n",
"step-2": "<mask token>\n\n\nclass EtherminePool(BasePool):\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, pool, pool_attrs):\n super(EtherminePool, self).__init__(pool, pool_attrs)\n\n def build_creation_parameters(self, pool, pool_attrs, pool_classname):\n params = super(EtherminePool, self).build_creation_parameters(pool,\n pool_attrs, pool_classname)\n server_location = 'US'\n if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):\n server_location = 'Europe'\n elif pool.startswith('us1-etc'):\n server_location = 'US'\n elif pool.startswith('us1.eth'):\n server_location = 'US East'\n elif pool.startswith('us2.eth'):\n server_location = 'US West'\n elif pool.startswith('asia1.eth'):\n server_location = 'Asia'\n params['unique_id'\n ] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'\n return params\n\n def _clean_coin_address(self, miner):\n coin_address = miner.coin_address.lower()\n if coin_address.startswith('0x'):\n coin_address = coin_address[2:]\n elif coin_address.startswith('#0x'):\n coin_address = coin_address[3:]\n return coin_address\n\n def get_worker_stats(self, miner, worker):\n url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.\n _clean_coin_address(miner)).replace('{WORKER}', worker)\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_miner_stats(self, miner):\n url = self._MINER_URL_PER_MINER.replace('{MINER}', self.\n _clean_coin_address(miner))\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):\n if algo == 'ethash':\n algo_idx = get_algo_index('daggerhashimoto')\n else:\n algo_idx = get_algo_index(algo)\n if algo_idx is -1:\n return False\n coin_idx = get_coin_index(self._DEFAULT_COIN_)\n coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')\n success = False\n json = self.get_worker_stats(miner, worker)\n if json:\n success = self.parse_json(json, results, miner, worker, pool_id,\n algo, algo_idx, coin_idx, coin_cost)\n return success\n\n def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost):\n record = json['data']\n if record == 'NO DATA':\n miner_coin_idx = None\n if hasattr(miner, 'coin_idx'):\n miner_coin_idx = miner.coin\n if miner_coin_idx is None or miner_coin_idx != coin_idx:\n miner.coin_address = ''\n return False\n speed_suffix = 'H'\n try:\n speed_accepted = float(record['currentHashrate'])\n except:\n speed_accepted = 0.0\n try:\n speed_reported = float(record['reportedHashrate'])\n except:\n speed_reported = None\n json_miner_stats = self.get_miner_stats(miner)\n record_miner_stats = json_miner_stats['data']\n try:\n coins_per_minute = float(record_miner_stats['coinsPerMin'])\n except:\n coins_per_minute = 0.0\n try:\n active_workers = float(record_miner_stats['activeWorkers'])\n except:\n active_workers = 1\n profitability = coins_per_minute * (60 * 24\n ) / speed_accepted / active_workers\n results.populate_pool_results(miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost, profitability, speed_accepted,\n speed_reported, speed_suffix)\n return True\n",
"step-3": "<mask token>\n\n\nclass EtherminePool(BasePool):\n _MINER_URL_PER_WORKER = (\n 'https://api.ethermine.org/miner/:{MINER}/worker/:{WORKER}/currentStats'\n )\n _MINER_URL_PER_MINER = (\n 'https://api.ethermine.org/miner/:{MINER}/currentStats')\n _DEFAULT_COIN_ = 'ETH'\n\n def __init__(self, pool, pool_attrs):\n super(EtherminePool, self).__init__(pool, pool_attrs)\n\n def build_creation_parameters(self, pool, pool_attrs, pool_classname):\n params = super(EtherminePool, self).build_creation_parameters(pool,\n pool_attrs, pool_classname)\n server_location = 'US'\n if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):\n server_location = 'Europe'\n elif pool.startswith('us1-etc'):\n server_location = 'US'\n elif pool.startswith('us1.eth'):\n server_location = 'US East'\n elif pool.startswith('us2.eth'):\n server_location = 'US West'\n elif pool.startswith('asia1.eth'):\n server_location = 'Asia'\n params['unique_id'\n ] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'\n return params\n\n def _clean_coin_address(self, miner):\n coin_address = miner.coin_address.lower()\n if coin_address.startswith('0x'):\n coin_address = coin_address[2:]\n elif coin_address.startswith('#0x'):\n coin_address = coin_address[3:]\n return coin_address\n\n def get_worker_stats(self, miner, worker):\n url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.\n _clean_coin_address(miner)).replace('{WORKER}', worker)\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_miner_stats(self, miner):\n url = self._MINER_URL_PER_MINER.replace('{MINER}', self.\n _clean_coin_address(miner))\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):\n if algo == 'ethash':\n algo_idx = get_algo_index('daggerhashimoto')\n else:\n algo_idx = get_algo_index(algo)\n if algo_idx is -1:\n return False\n coin_idx = get_coin_index(self._DEFAULT_COIN_)\n coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')\n success = False\n json = self.get_worker_stats(miner, worker)\n if json:\n success = self.parse_json(json, results, miner, worker, pool_id,\n algo, algo_idx, coin_idx, coin_cost)\n return success\n\n def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost):\n record = json['data']\n if record == 'NO DATA':\n miner_coin_idx = None\n if hasattr(miner, 'coin_idx'):\n miner_coin_idx = miner.coin\n if miner_coin_idx is None or miner_coin_idx != coin_idx:\n miner.coin_address = ''\n return False\n speed_suffix = 'H'\n try:\n speed_accepted = float(record['currentHashrate'])\n except:\n speed_accepted = 0.0\n try:\n speed_reported = float(record['reportedHashrate'])\n except:\n speed_reported = None\n json_miner_stats = self.get_miner_stats(miner)\n record_miner_stats = json_miner_stats['data']\n try:\n coins_per_minute = float(record_miner_stats['coinsPerMin'])\n except:\n coins_per_minute = 0.0\n try:\n active_workers = float(record_miner_stats['activeWorkers'])\n except:\n active_workers = 1\n profitability = coins_per_minute * (60 * 24\n ) / speed_accepted / active_workers\n results.populate_pool_results(miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost, profitability, speed_accepted,\n speed_reported, speed_suffix)\n return True\n",
"step-4": "from minermedic.pools.base_pool import BasePool\nfrom phenome_core.util.rest_api import RestAPI\nfrom minermedic.pools.helper import get_algo_index, get_coin_index, get_coin_cost\n<mask token>\n\n\nclass EtherminePool(BasePool):\n _MINER_URL_PER_WORKER = (\n 'https://api.ethermine.org/miner/:{MINER}/worker/:{WORKER}/currentStats'\n )\n _MINER_URL_PER_MINER = (\n 'https://api.ethermine.org/miner/:{MINER}/currentStats')\n _DEFAULT_COIN_ = 'ETH'\n\n def __init__(self, pool, pool_attrs):\n super(EtherminePool, self).__init__(pool, pool_attrs)\n\n def build_creation_parameters(self, pool, pool_attrs, pool_classname):\n params = super(EtherminePool, self).build_creation_parameters(pool,\n pool_attrs, pool_classname)\n server_location = 'US'\n if pool.startswith('eu1.etc') or pool.startswith('eu1.eth'):\n server_location = 'Europe'\n elif pool.startswith('us1-etc'):\n server_location = 'US'\n elif pool.startswith('us1.eth'):\n server_location = 'US East'\n elif pool.startswith('us2.eth'):\n server_location = 'US West'\n elif pool.startswith('asia1.eth'):\n server_location = 'Asia'\n params['unique_id'\n ] = 'ETHERMINE - ' + server_location + ' (' + self._DEFAULT_COIN_ + ')'\n return params\n\n def _clean_coin_address(self, miner):\n coin_address = miner.coin_address.lower()\n if coin_address.startswith('0x'):\n coin_address = coin_address[2:]\n elif coin_address.startswith('#0x'):\n coin_address = coin_address[3:]\n return coin_address\n\n def get_worker_stats(self, miner, worker):\n url = self._MINER_URL_PER_WORKER.replace('{MINER}', self.\n _clean_coin_address(miner)).replace('{WORKER}', worker)\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_miner_stats(self, miner):\n url = self._MINER_URL_PER_MINER.replace('{MINER}', self.\n _clean_coin_address(miner))\n api = RestAPI(url=url, port=80)\n return api.get_json()\n\n def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):\n if algo == 'ethash':\n algo_idx = get_algo_index('daggerhashimoto')\n else:\n algo_idx = get_algo_index(algo)\n if algo_idx is -1:\n return False\n coin_idx = get_coin_index(self._DEFAULT_COIN_)\n coin_cost = get_coin_cost(self._DEFAULT_COIN_, 'USD')\n success = False\n json = self.get_worker_stats(miner, worker)\n if json:\n success = self.parse_json(json, results, miner, worker, pool_id,\n algo, algo_idx, coin_idx, coin_cost)\n return success\n\n def parse_json(self, json, results, miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost):\n record = json['data']\n if record == 'NO DATA':\n miner_coin_idx = None\n if hasattr(miner, 'coin_idx'):\n miner_coin_idx = miner.coin\n if miner_coin_idx is None or miner_coin_idx != coin_idx:\n miner.coin_address = ''\n return False\n speed_suffix = 'H'\n try:\n speed_accepted = float(record['currentHashrate'])\n except:\n speed_accepted = 0.0\n try:\n speed_reported = float(record['reportedHashrate'])\n except:\n speed_reported = None\n json_miner_stats = self.get_miner_stats(miner)\n record_miner_stats = json_miner_stats['data']\n try:\n coins_per_minute = float(record_miner_stats['coinsPerMin'])\n except:\n coins_per_minute = 0.0\n try:\n active_workers = float(record_miner_stats['activeWorkers'])\n except:\n active_workers = 1\n profitability = coins_per_minute * (60 * 24\n ) / speed_accepted / active_workers\n results.populate_pool_results(miner, worker, pool, algo, algo_idx,\n coin_idx, coin_cost, profitability, speed_accepted,\n speed_reported, speed_suffix)\n return True\n",
"step-5": "# ethermine.py, Copyright (c) 2019, Nicholas Saparoff <nick.saparoff@gmail.com>: Original implementation\n\nfrom minermedic.pools.base_pool import BasePool\nfrom phenome_core.util.rest_api import RestAPI\nfrom minermedic.pools.helper import get_algo_index, get_coin_index, get_coin_cost\n\n\"\"\"\n\nEtherminePool\n\n This is the main Pool API for Ethermine.\n SEE: https://ethermine.org/api/worker#monitoring\n \n\"\"\"\n\n\nclass EtherminePool(BasePool):\n\n # PER WORKER\n _MINER_URL_PER_WORKER = \"https://api.ethermine.org/miner/:{MINER}/worker/:{WORKER}/currentStats\"\n\n # PER MINER\n _MINER_URL_PER_MINER = \"https://api.ethermine.org/miner/:{MINER}/currentStats\"\n\n # with Ethermine, the coin is Usually ETH, but could be ETC or ZCASH\n _DEFAULT_COIN_ = \"ETH\"\n\n def __init__(self, pool, pool_attrs):\n super(EtherminePool, self).__init__(pool, pool_attrs)\n\n def build_creation_parameters(self, pool, pool_attrs, pool_classname):\n\n # get the default creation parameters\n params = super(EtherminePool, self).build_creation_parameters(pool, pool_attrs, pool_classname)\n\n server_location = \"US\"\n\n if pool.startswith(\"eu1.etc\") or pool.startswith(\"eu1.eth\"):\n server_location = \"Europe\"\n elif pool.startswith(\"us1-etc\"):\n server_location = \"US\"\n elif pool.startswith(\"us1.eth\"):\n server_location = \"US East\"\n elif pool.startswith(\"us2.eth\"):\n server_location = \"US West\"\n elif pool.startswith(\"asia1.eth\"):\n server_location = \"Asia\"\n\n # Set the unique ID of the pool (give it a NAME, as the URL/IP may change)\n # POOL - LOCATION (COIN)\n params['unique_id'] = \"ETHERMINE - \" + server_location + \" (\" + self._DEFAULT_COIN_ + \")\"\n\n return params\n\n def _clean_coin_address(self, miner):\n\n coin_address = miner.coin_address.lower()\n if coin_address.startswith('0x'):\n coin_address = coin_address[2:]\n elif coin_address.startswith('#0x'):\n coin_address = coin_address[3:]\n\n return coin_address\n\n def get_worker_stats(self, miner, worker):\n\n # build the miner URL\n url = self._MINER_URL_PER_WORKER.replace(\"{MINER}\",self._clean_coin_address(miner)).replace(\"{WORKER}\",worker)\n\n api = RestAPI(url=url, port=80)\n\n return api.get_json()\n\n def get_miner_stats(self, miner):\n\n # build the miner URL\n url = self._MINER_URL_PER_MINER.replace(\"{MINER}\", self._clean_coin_address(miner))\n\n api = RestAPI(url=url, port=80)\n\n return api.get_json()\n\n def get_pool_stats(self, results, miner, worker, algo, pool_id, pool_url):\n\n if algo == 'ethash':\n algo_idx = get_algo_index('daggerhashimoto')\n else:\n algo_idx = get_algo_index(algo)\n\n if algo_idx is -1:\n return False\n\n coin_idx = get_coin_index(self._DEFAULT_COIN_)\n\n # get the cost of the coin\n # TODO - get the currency from the config, do not assume USD\n coin_cost = get_coin_cost(self._DEFAULT_COIN_,'USD')\n\n success = False\n\n json = self.get_worker_stats(miner, worker)\n\n if json:\n success = self.parse_json(json, results, miner, worker, pool_id, algo, algo_idx, coin_idx, coin_cost)\n\n return success\n\n def parse_json(self, json, results, miner, worker, pool, algo, algo_idx, coin_idx, coin_cost):\n\n # get the record\n record = json['data']\n\n if record == 'NO DATA':\n\n # check coin switch?\n miner_coin_idx = None\n\n if hasattr(miner, 'coin_idx'):\n # we have been mining so far\n miner_coin_idx = miner.coin\n\n if miner_coin_idx is None or miner_coin_idx != coin_idx:\n # reset the coin address, maybe switched coin\n miner.coin_address = ''\n\n # no data, just fail\n return False\n\n # API call results, speed is in units of Hashes\n speed_suffix = 'H'\n\n try:\n # get accepted hashrate\n speed_accepted = float(record['currentHashrate'])\n except:\n speed_accepted = 0.0\n\n try:\n # get \"reported\" hashrate\n speed_reported = float(record['reportedHashrate'])\n except:\n speed_reported = None\n\n # now get the miner stats for profitability\n json_miner_stats = self.get_miner_stats(miner)\n\n # get the record\n record_miner_stats = json_miner_stats['data']\n\n try:\n coins_per_minute = float(record_miner_stats['coinsPerMin'])\n except:\n coins_per_minute = 0.0\n\n try:\n active_workers = float(record_miner_stats['activeWorkers'])\n except:\n active_workers = 1\n\n # profitability is a measure of COIN / speed suffix / per DAY\n # ETHERMINE only gives coin estimates per MINER per MINUTE, not per WORKER\n # so we need to average it out by dividing by the # of active workers\n profitability = ((coins_per_minute * (60 * 24))/speed_accepted)/active_workers\n\n # finally set the API results into the main results object\n results.populate_pool_results(miner, worker, pool, algo, algo_idx, coin_idx, coin_cost, profitability,\n speed_accepted, speed_reported, speed_suffix)\n\n # if we got here, we were successful\n return True\n\n",
"step-ids": [
6,
8,
9,
10,
11
]
}
|
[
6,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .simulator import SpatialSIRSimulator as Simulator
from .util import Prior
from .util import PriorExperiment
from .util import Truth
from .util import log_likelihood
|
flexible
|
{
"blob_id": "4f06eddfac38574a0ae3bdd0ea2ac81291380166",
"index": 9987,
"step-1": "<mask token>\n",
"step-2": "from .simulator import SpatialSIRSimulator as Simulator\nfrom .util import Prior\nfrom .util import PriorExperiment\nfrom .util import Truth\nfrom .util import log_likelihood\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
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