code
stringlengths 13
6.09M
| order_type
stringclasses 2
values | original_example
dict | step_ids
listlengths 1
5
|
|---|---|---|---|
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('Pepito')
print('Cumpleaños: 22 de enero')
<|reserved_special_token_0|>
print('Tengo', edad, 'años')
<|reserved_special_token_0|>
print('Me gusta la música de', cantante)
print('Me gusta cenar', comida)
print('Vivo en', ciudad)
<|reserved_special_token_1|>
print('Pepito')
print('Cumpleaños: 22 de enero')
edad = 42
print('Tengo', edad, 'años')
cantante = 'Suzanne Vega'
comida = 'rúcula'
ciudad = 'Barcelona'
print('Me gusta la música de', cantante)
print('Me gusta cenar', comida)
print('Vivo en', ciudad)
<|reserved_special_token_1|>
# Ejercicio 1
print('Pepito')
print('Cumpleaños: 22 de enero')
edad = 42
print('Tengo', edad, 'años')
cantante = 'Suzanne Vega'
comida = 'rúcula'
ciudad = 'Barcelona'
print('Me gusta la música de', cantante)
print('Me gusta cenar', comida)
print('Vivo en', ciudad)
|
flexible
|
{
"blob_id": "f26c624e8ae9711eb835e223407256e60dfc6d6e",
"index": 8945,
"step-1": "<mask token>\n",
"step-2": "print('Pepito')\nprint('Cumpleaños: 22 de enero')\n<mask token>\nprint('Tengo', edad, 'años')\n<mask token>\nprint('Me gusta la música de', cantante)\nprint('Me gusta cenar', comida)\nprint('Vivo en', ciudad)\n",
"step-3": "print('Pepito')\nprint('Cumpleaños: 22 de enero')\nedad = 42\nprint('Tengo', edad, 'años')\ncantante = 'Suzanne Vega'\ncomida = 'rúcula'\nciudad = 'Barcelona'\nprint('Me gusta la música de', cantante)\nprint('Me gusta cenar', comida)\nprint('Vivo en', ciudad)\n",
"step-4": "# Ejercicio 1\nprint('Pepito')\nprint('Cumpleaños: 22 de enero')\nedad = 42\nprint('Tengo', edad, 'años')\ncantante = 'Suzanne Vega'\ncomida = 'rúcula'\nciudad = 'Barcelona'\nprint('Me gusta la música de', cantante)\nprint('Me gusta cenar', comida)\nprint('Vivo en', ciudad)",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#/usr/bin/env python3
"""Demonstrates how to do deterministic task generation using l2l"""
import random
def fixed_random(func):
"""Create the data"""
def _func(self, i):
state = random.getstate()
if self.deterministic or self.seed is not None:
random.seed(self.seed + i)
results = func(self, i)
random.setstate(state)
else:
results = func(self, i)
return results
return _func
class RandomTest:
def __init__(self, seed=42, deterministic=False):
self.seed = seed
self.deterministic = deterministic
@fixed_random
def test_function(self, i):
return [random.randint(0, 10) for x in range(10)]
rt = RandomTest(0)
print(rt.test_function(0))
print(rt.test_function(0))
rt.seed = 1
print(rt.test_function(0))
print(rt.test_function(0))
|
normal
|
{
"blob_id": "7ee5779625d53ff1e18f73b20ba5849666f89b55",
"index": 2111,
"step-1": "<mask token>\n\n\nclass RandomTest:\n\n def __init__(self, seed=42, deterministic=False):\n self.seed = seed\n self.deterministic = deterministic\n\n @fixed_random\n def test_function(self, i):\n return [random.randint(0, 10) for x in range(10)]\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef fixed_random(func):\n \"\"\"Create the data\"\"\"\n\n def _func(self, i):\n state = random.getstate()\n if self.deterministic or self.seed is not None:\n random.seed(self.seed + i)\n results = func(self, i)\n random.setstate(state)\n else:\n results = func(self, i)\n return results\n return _func\n\n\nclass RandomTest:\n\n def __init__(self, seed=42, deterministic=False):\n self.seed = seed\n self.deterministic = deterministic\n\n @fixed_random\n def test_function(self, i):\n return [random.randint(0, 10) for x in range(10)]\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef fixed_random(func):\n \"\"\"Create the data\"\"\"\n\n def _func(self, i):\n state = random.getstate()\n if self.deterministic or self.seed is not None:\n random.seed(self.seed + i)\n results = func(self, i)\n random.setstate(state)\n else:\n results = func(self, i)\n return results\n return _func\n\n\nclass RandomTest:\n\n def __init__(self, seed=42, deterministic=False):\n self.seed = seed\n self.deterministic = deterministic\n\n @fixed_random\n def test_function(self, i):\n return [random.randint(0, 10) for x in range(10)]\n\n\nrt = RandomTest(0)\nprint(rt.test_function(0))\nprint(rt.test_function(0))\nrt.seed = 1\nprint(rt.test_function(0))\nprint(rt.test_function(0))\n",
"step-4": "<mask token>\nimport random\n\n\ndef fixed_random(func):\n \"\"\"Create the data\"\"\"\n\n def _func(self, i):\n state = random.getstate()\n if self.deterministic or self.seed is not None:\n random.seed(self.seed + i)\n results = func(self, i)\n random.setstate(state)\n else:\n results = func(self, i)\n return results\n return _func\n\n\nclass RandomTest:\n\n def __init__(self, seed=42, deterministic=False):\n self.seed = seed\n self.deterministic = deterministic\n\n @fixed_random\n def test_function(self, i):\n return [random.randint(0, 10) for x in range(10)]\n\n\nrt = RandomTest(0)\nprint(rt.test_function(0))\nprint(rt.test_function(0))\nrt.seed = 1\nprint(rt.test_function(0))\nprint(rt.test_function(0))\n",
"step-5": "#/usr/bin/env python3\n\n\"\"\"Demonstrates how to do deterministic task generation using l2l\"\"\"\nimport random\n\ndef fixed_random(func):\n \"\"\"Create the data\"\"\"\n def _func(self, i):\n state = random.getstate()\n if self.deterministic or self.seed is not None:\n random.seed(self.seed + i)\n results = func(self, i)\n random.setstate(state)\n else:\n results = func(self, i)\n return results\n\n return _func\n\n\nclass RandomTest:\n\n def __init__(self, seed=42, deterministic=False):\n self.seed = seed\n self.deterministic = deterministic\n\n @fixed_random\n def test_function(self, i):\n return [random.randint(0, 10) for x in range(10)]\n\n\nrt = RandomTest(0)\nprint(rt.test_function(0))\nprint(rt.test_function(0))\nrt.seed = 1\nprint(rt.test_function(0))\nprint(rt.test_function(0))\n",
"step-ids": [
3,
4,
6,
7,
8
]
}
|
[
3,
4,
6,
7,
8
] |
<|reserved_special_token_0|>
def _build_url(**kargs):
query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':
'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}
query.update(kargs)
query_str = '&'.join([f'{key}={val}' for key, val in query.items()])
return f'{url_base}?{query_str}'
<|reserved_special_token_0|>
def _get_symbol(symbol, **kargs):
kargs['symbol'] = symbol
kargs['datatype'] = 'csv'
req = _request(**kargs)
text = req.text
header, *text = text.split()
text = '\n'.join([l for l in text[::-1]])
csv_str = f'{header}\n{text}'
data = Data.load_csv(csv_str)
return Security(symbol, data)
def get(symbols, **kargs):
if not isinstance(symbols, list):
symbols = [symbols]
result = Securities()
for symbol in symbols:
kargs['symbol'] = symbol
result.add(id=symbol, security=_get_symbol(**kargs))
return result
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def _build_url(**kargs):
query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':
'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}
query.update(kargs)
query_str = '&'.join([f'{key}={val}' for key, val in query.items()])
return f'{url_base}?{query_str}'
def _request(**kargs):
url = _build_url(**kargs)
return r.get(url)
def _get_symbol(symbol, **kargs):
kargs['symbol'] = symbol
kargs['datatype'] = 'csv'
req = _request(**kargs)
text = req.text
header, *text = text.split()
text = '\n'.join([l for l in text[::-1]])
csv_str = f'{header}\n{text}'
data = Data.load_csv(csv_str)
return Security(symbol, data)
def get(symbols, **kargs):
if not isinstance(symbols, list):
symbols = [symbols]
result = Securities()
for symbol in symbols:
kargs['symbol'] = symbol
result.add(id=symbol, security=_get_symbol(**kargs))
return result
<|reserved_special_token_1|>
<|reserved_special_token_0|>
url_base = 'https://www.alphavantage.co/query'
def _build_url(**kargs):
query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':
'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}
query.update(kargs)
query_str = '&'.join([f'{key}={val}' for key, val in query.items()])
return f'{url_base}?{query_str}'
def _request(**kargs):
url = _build_url(**kargs)
return r.get(url)
def _get_symbol(symbol, **kargs):
kargs['symbol'] = symbol
kargs['datatype'] = 'csv'
req = _request(**kargs)
text = req.text
header, *text = text.split()
text = '\n'.join([l for l in text[::-1]])
csv_str = f'{header}\n{text}'
data = Data.load_csv(csv_str)
return Security(symbol, data)
def get(symbols, **kargs):
if not isinstance(symbols, list):
symbols = [symbols]
result = Securities()
for symbol in symbols:
kargs['symbol'] = symbol
result.add(id=symbol, security=_get_symbol(**kargs))
return result
<|reserved_special_token_1|>
import requests as r
from .security import Security, Securities
from .data import Data
url_base = 'https://www.alphavantage.co/query'
def _build_url(**kargs):
query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':
'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}
query.update(kargs)
query_str = '&'.join([f'{key}={val}' for key, val in query.items()])
return f'{url_base}?{query_str}'
def _request(**kargs):
url = _build_url(**kargs)
return r.get(url)
def _get_symbol(symbol, **kargs):
kargs['symbol'] = symbol
kargs['datatype'] = 'csv'
req = _request(**kargs)
text = req.text
header, *text = text.split()
text = '\n'.join([l for l in text[::-1]])
csv_str = f'{header}\n{text}'
data = Data.load_csv(csv_str)
return Security(symbol, data)
def get(symbols, **kargs):
if not isinstance(symbols, list):
symbols = [symbols]
result = Securities()
for symbol in symbols:
kargs['symbol'] = symbol
result.add(id=symbol, security=_get_symbol(**kargs))
return result
<|reserved_special_token_1|>
import requests as r
from .security import Security, Securities
from .data import Data
url_base = 'https://www.alphavantage.co/query'
def _build_url(**kargs):
query = {
'function': 'TIME_SERIES_DAILY',
'symbol': 'SPY',
'outputsize': 'full',
'datatype': 'json',
'apikey': 'JPIO2GNGBMFRLGMN'
}
query.update(kargs)
query_str = '&'.join([f'{key}={val}' for key, val in query.items()])
return f'{url_base}?{query_str}'
def _request(**kargs):
url = _build_url(**kargs)
return r.get(url)
def _get_symbol(symbol, **kargs):
kargs['symbol'] = symbol
kargs['datatype'] = 'csv'
req = _request(**kargs)
# Reverse dates to past to present
text = req.text
header, *text = text.split()
text = '\n'.join(
[l for l in text[::-1]]
)
csv_str = f'{header}\n{text}'
data = Data.load_csv(csv_str)
return Security(symbol, data)
def get(symbols, **kargs):
if not isinstance(symbols, list):
symbols = [symbols]
result = Securities()
for symbol in symbols:
kargs['symbol'] = symbol
result.add(
id=symbol,
security=_get_symbol(**kargs)
)
return result
|
flexible
|
{
"blob_id": "e99d3ae82d8eea38d29d6c4f09fdb3858e36ca50",
"index": 6518,
"step-1": "<mask token>\n\n\ndef _build_url(**kargs):\n query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':\n 'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}\n query.update(kargs)\n query_str = '&'.join([f'{key}={val}' for key, val in query.items()])\n return f'{url_base}?{query_str}'\n\n\n<mask token>\n\n\ndef _get_symbol(symbol, **kargs):\n kargs['symbol'] = symbol\n kargs['datatype'] = 'csv'\n req = _request(**kargs)\n text = req.text\n header, *text = text.split()\n text = '\\n'.join([l for l in text[::-1]])\n csv_str = f'{header}\\n{text}'\n data = Data.load_csv(csv_str)\n return Security(symbol, data)\n\n\ndef get(symbols, **kargs):\n if not isinstance(symbols, list):\n symbols = [symbols]\n result = Securities()\n for symbol in symbols:\n kargs['symbol'] = symbol\n result.add(id=symbol, security=_get_symbol(**kargs))\n return result\n",
"step-2": "<mask token>\n\n\ndef _build_url(**kargs):\n query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':\n 'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}\n query.update(kargs)\n query_str = '&'.join([f'{key}={val}' for key, val in query.items()])\n return f'{url_base}?{query_str}'\n\n\ndef _request(**kargs):\n url = _build_url(**kargs)\n return r.get(url)\n\n\ndef _get_symbol(symbol, **kargs):\n kargs['symbol'] = symbol\n kargs['datatype'] = 'csv'\n req = _request(**kargs)\n text = req.text\n header, *text = text.split()\n text = '\\n'.join([l for l in text[::-1]])\n csv_str = f'{header}\\n{text}'\n data = Data.load_csv(csv_str)\n return Security(symbol, data)\n\n\ndef get(symbols, **kargs):\n if not isinstance(symbols, list):\n symbols = [symbols]\n result = Securities()\n for symbol in symbols:\n kargs['symbol'] = symbol\n result.add(id=symbol, security=_get_symbol(**kargs))\n return result\n",
"step-3": "<mask token>\nurl_base = 'https://www.alphavantage.co/query'\n\n\ndef _build_url(**kargs):\n query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':\n 'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}\n query.update(kargs)\n query_str = '&'.join([f'{key}={val}' for key, val in query.items()])\n return f'{url_base}?{query_str}'\n\n\ndef _request(**kargs):\n url = _build_url(**kargs)\n return r.get(url)\n\n\ndef _get_symbol(symbol, **kargs):\n kargs['symbol'] = symbol\n kargs['datatype'] = 'csv'\n req = _request(**kargs)\n text = req.text\n header, *text = text.split()\n text = '\\n'.join([l for l in text[::-1]])\n csv_str = f'{header}\\n{text}'\n data = Data.load_csv(csv_str)\n return Security(symbol, data)\n\n\ndef get(symbols, **kargs):\n if not isinstance(symbols, list):\n symbols = [symbols]\n result = Securities()\n for symbol in symbols:\n kargs['symbol'] = symbol\n result.add(id=symbol, security=_get_symbol(**kargs))\n return result\n",
"step-4": "import requests as r\nfrom .security import Security, Securities\nfrom .data import Data\nurl_base = 'https://www.alphavantage.co/query'\n\n\ndef _build_url(**kargs):\n query = {'function': 'TIME_SERIES_DAILY', 'symbol': 'SPY', 'outputsize':\n 'full', 'datatype': 'json', 'apikey': 'JPIO2GNGBMFRLGMN'}\n query.update(kargs)\n query_str = '&'.join([f'{key}={val}' for key, val in query.items()])\n return f'{url_base}?{query_str}'\n\n\ndef _request(**kargs):\n url = _build_url(**kargs)\n return r.get(url)\n\n\ndef _get_symbol(symbol, **kargs):\n kargs['symbol'] = symbol\n kargs['datatype'] = 'csv'\n req = _request(**kargs)\n text = req.text\n header, *text = text.split()\n text = '\\n'.join([l for l in text[::-1]])\n csv_str = f'{header}\\n{text}'\n data = Data.load_csv(csv_str)\n return Security(symbol, data)\n\n\ndef get(symbols, **kargs):\n if not isinstance(symbols, list):\n symbols = [symbols]\n result = Securities()\n for symbol in symbols:\n kargs['symbol'] = symbol\n result.add(id=symbol, security=_get_symbol(**kargs))\n return result\n",
"step-5": "import requests as r\n\nfrom .security import Security, Securities\nfrom .data import Data\n\n\nurl_base = 'https://www.alphavantage.co/query'\n\ndef _build_url(**kargs):\n\tquery = {\n\t'function': 'TIME_SERIES_DAILY',\n\t'symbol': 'SPY',\n\t'outputsize': 'full',\n\t'datatype': 'json',\n\t'apikey': 'JPIO2GNGBMFRLGMN'\n\t}\n\tquery.update(kargs)\n\t\n\tquery_str = '&'.join([f'{key}={val}' for key, val in query.items()])\n\treturn f'{url_base}?{query_str}'\n\t\ndef _request(**kargs):\n\turl = _build_url(**kargs)\n\treturn r.get(url)\n\ndef _get_symbol(symbol, **kargs):\n\tkargs['symbol'] = symbol\n\tkargs['datatype'] = 'csv'\n\treq = _request(**kargs)\n\t# Reverse dates to past to present\n\ttext = req.text\n\theader, *text = text.split()\n\ttext = '\\n'.join(\n\t\t[l for l in text[::-1]]\n\t)\n\tcsv_str = f'{header}\\n{text}'\n\n\tdata = Data.load_csv(csv_str)\n\treturn Security(symbol, data) \n\t\ndef get(symbols, **kargs):\n\tif not isinstance(symbols, list):\n\t\tsymbols = [symbols]\n\t\t\n\tresult = Securities()\n\tfor symbol in symbols:\n\t\tkargs['symbol'] = symbol\n\t\tresult.add(\n\t\t\tid=symbol,\n\t\t\tsecurity=_get_symbol(**kargs)\n\t\t)\n\treturn result\n\t\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
# -*- coding: utf-8 -*-
from __future__ import print_function
"""phy main CLI tool.
Usage:
phy --help
"""
#------------------------------------------------------------------------------
# Imports
#------------------------------------------------------------------------------
import sys
import os.path as op
import argparse
from textwrap import dedent
import numpy as np
from six import exec_, string_types
#------------------------------------------------------------------------------
# Parser utilities
#------------------------------------------------------------------------------
class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,
argparse.RawDescriptionHelpFormatter):
pass
class Parser(argparse.ArgumentParser):
def error(self, message):
sys.stderr.write(message + '\n\n')
self.print_help()
sys.exit(2)
_examples = dedent("""
examples:
phy -v display the version of phy
phy download hybrid_120sec.dat -o data/
download a sample raw data file in `data/`
phy describe my_file.kwik
display information about a Kwik dataset
phy spikesort my_params.prm
run the whole suite (spike detection and clustering)
phy detect my_params.prm
run spike detection on a parameters file
phy cluster-auto my_file.kwik
run klustakwik on a dataset (after spike detection)
phy cluster-manual my_file.kwik
run the manual clustering GUI
""")
#------------------------------------------------------------------------------
# Parser creator
#------------------------------------------------------------------------------
class ParserCreator(object):
def __init__(self):
self.create_main()
self.create_download()
self.create_traces()
self.create_describe()
self.create_spikesort()
self.create_detect()
self.create_auto()
self.create_manual()
self.create_notebook()
@property
def parser(self):
return self._parser
def _add_sub_parser(self, name, desc):
p = self._subparsers.add_parser(name, help=desc, description=desc)
self._add_options(p)
return p
def _add_options(self, parser):
parser.add_argument('--debug', '-d',
action='store_true',
help='activate debug logging mode')
parser.add_argument('--hide-traceback',
action='store_true',
help='hide the traceback for cleaner error '
'messages')
parser.add_argument('--profiler', '-p',
action='store_true',
help='activate the profiler')
parser.add_argument('--line-profiler', '-lp',
dest='line_profiler',
action='store_true',
help='activate the line-profiler -- you '
'need to decorate the functions '
'to profile with `@profile` '
'in the code')
parser.add_argument('--ipython', '-i', action='store_true',
help='launch the script in an interactive '
'IPython console')
parser.add_argument('--pdb', action='store_true',
help='activate the Python debugger')
def create_main(self):
import phy
desc = sys.modules['phy'].__doc__
self._parser = Parser(description=desc,
epilog=_examples,
formatter_class=CustomFormatter,
)
self._parser.set_defaults(func=None)
self._parser.add_argument('--version', '-v',
action='version',
version=phy.__version_git__,
help='print the version of phy')
self._add_options(self._parser)
self._subparsers = self._parser.add_subparsers(dest='command',
title='subcommand',
)
def create_download(self):
desc = 'download a sample dataset'
p = self._add_sub_parser('download', desc)
p.add_argument('file', help='dataset filename')
p.add_argument('--output-dir', '-o', help='output directory')
p.add_argument('--base',
default='cortexlab',
choices=('cortexlab', 'github'),
help='data repository name: `cortexlab` or `github`',
)
p.set_defaults(func=download)
def create_describe(self):
desc = 'describe a `.kwik` file'
p = self._add_sub_parser('describe', desc)
p.add_argument('file', help='path to a `.kwik` file')
p.add_argument('--clustering', default='main',
help='name of the clustering to use')
p.set_defaults(func=describe)
def create_traces(self):
desc = 'show the traces of a raw data file'
p = self._add_sub_parser('traces', desc)
p.add_argument('file', help='path to a `.kwd` or `.dat` file')
p.add_argument('--interval',
help='detection interval in seconds (e.g. `0,10`)')
p.add_argument('--n-channels', '-n',
help='number of channels in the recording '
'(only required when using a flat binary file)')
p.add_argument('--dtype',
help='NumPy data type '
'(only required when using a flat binary file)',
default='int16',
)
p.add_argument('--sample-rate', '-s',
help='sample rate in Hz '
'(only required when using a flat binary file)')
p.set_defaults(func=traces)
def create_spikesort(self):
desc = 'launch the whole spike sorting pipeline on a `.prm` file'
p = self._add_sub_parser('spikesort', desc)
p.add_argument('file', help='path to a `.prm` file')
p.add_argument('--kwik-path', help='filename of the `.kwik` file '
'to create (by default, `"experiment_name".kwik`)')
p.add_argument('--overwrite', action='store_true', default=False,
help='overwrite the `.kwik` file ')
p.add_argument('--interval',
help='detection interval in seconds (e.g. `0,10`)')
p.set_defaults(func=spikesort)
def create_detect(self):
desc = 'launch the spike detection algorithm on a `.prm` file'
p = self._add_sub_parser('detect', desc)
p.add_argument('file', help='path to a `.prm` file')
p.add_argument('--kwik-path', help='filename of the `.kwik` file '
'to create (by default, `"experiment_name".kwik`)')
p.add_argument('--overwrite', action='store_true', default=False,
help='overwrite the `.kwik` file ')
p.add_argument('--interval',
help='detection interval in seconds (e.g. `0,10`)')
p.set_defaults(func=detect)
def create_auto(self):
desc = 'launch the automatic clustering algorithm on a `.kwik` file'
p = self._add_sub_parser('cluster-auto', desc)
p.add_argument('file', help='path to a `.kwik` file')
p.add_argument('--clustering', default='main',
help='name of the clustering to use')
p.set_defaults(func=cluster_auto)
def create_manual(self):
desc = 'launch the manual clustering GUI on a `.kwik` file'
p = self._add_sub_parser('cluster-manual', desc)
p.add_argument('file', help='path to a `.kwik` file')
p.add_argument('--clustering', default='main',
help='name of the clustering to use')
p.add_argument('--cluster-ids', '-c',
help='list of clusters to select initially')
p.add_argument('--no-store', action='store_true', default=False,
help='do not create the store (faster loading time, '
'slower GUI)')
p.set_defaults(func=cluster_manual)
def create_notebook(self):
# TODO
pass
def parse(self, args):
try:
return self._parser.parse_args(args)
except SystemExit as e:
if e.code != 0:
raise e
#------------------------------------------------------------------------------
# Subcommand functions
#------------------------------------------------------------------------------
def _get_kwik_path(args):
kwik_path = args.file
if not op.exists(kwik_path):
raise IOError("The file `{}` doesn't exist.".format(kwik_path))
return kwik_path
def _create_session(args, **kwargs):
from phy.session import Session
kwik_path = _get_kwik_path(args)
session = Session(kwik_path, **kwargs)
return session
def describe(args):
from phy.io.kwik import KwikModel
path = _get_kwik_path(args)
model = KwikModel(path, clustering=args.clustering)
return 'model.describe()', dict(model=model)
def download(args):
from phy import download_sample_data
download_sample_data(args.file,
output_dir=args.output_dir,
base=args.base,
)
def traces(args):
from vispy.app import run
from phy.plot.traces import TraceView
from phy.io.h5 import open_h5
from phy.io.traces import read_kwd, read_dat
path = args.file
if path.endswith('.kwd'):
f = open_h5(args.file)
traces = read_kwd(f)
elif path.endswith(('.dat', '.bin')):
if not args.n_channels:
raise ValueError("Please specify `--n-channels`.")
if not args.dtype:
raise ValueError("Please specify `--dtype`.")
if not args.sample_rate:
raise ValueError("Please specify `--sample-rate`.")
n_channels = int(args.n_channels)
dtype = np.dtype(args.dtype)
traces = read_dat(path, dtype=dtype, n_channels=n_channels)
start, end = map(int, args.interval.split(','))
sample_rate = float(args.sample_rate)
start = int(sample_rate * start)
end = int(sample_rate * end)
c = TraceView(keys='interactive')
c.visual.traces = .01 * traces[start:end, ...]
c.show()
run()
return None, None
def detect(args):
from phy.io import create_kwik
assert args.file.endswith('.prm')
kwik_path = args.kwik_path
kwik_path = create_kwik(args.file,
overwrite=args.overwrite,
kwik_path=kwik_path)
interval = args.interval
if interval is not None:
interval = list(map(float, interval.split(',')))
# Create the session with the newly-created .kwik file.
args.file = kwik_path
session = _create_session(args, use_store=False)
return ('session.detect(interval=interval)',
dict(session=session, interval=interval))
def cluster_auto(args):
from phy.utils._misc import _read_python
from phy.session import Session
assert args.file.endswith('.prm')
params = _read_python(args.file)
kwik_path = params['experiment_name'] + '.kwik'
session = Session(kwik_path)
ns = dict(session=session,
clustering=args.clustering,
)
cmd = ('session.cluster(clustering=clustering)')
return (cmd, ns)
def spikesort(args):
from phy.io import create_kwik
assert args.file.endswith('.prm')
kwik_path = args.kwik_path
kwik_path = create_kwik(args.file,
overwrite=args.overwrite,
kwik_path=kwik_path,
)
# Create the session with the newly-created .kwik file.
args.file = kwik_path
session = _create_session(args, use_store=False)
interval = args.interval
if interval is not None:
interval = list(map(float, interval.split(',')))
ns = dict(session=session,
interval=interval,
n_s_clusters=100, # TODO: better handling of KK parameters
)
cmd = ('session.detect(interval=interval); session.cluster();')
return (cmd, ns)
def cluster_manual(args):
session = _create_session(args,
clustering=args.clustering,
use_store=not(args.no_store),
)
cluster_ids = (list(map(int, args.cluster_ids.split(',')))
if args.cluster_ids else None)
session.model.describe()
from phy.gui import start_qt_app
start_qt_app()
gui = session.show_gui(cluster_ids=cluster_ids, show=False)
print("\nPress `ctrl+h` to see the list of keyboard shortcuts.\n")
return 'gui.show()', dict(session=session, gui=gui, requires_qt=True)
#------------------------------------------------------------------------------
# Main functions
#------------------------------------------------------------------------------
def main(args=None):
p = ParserCreator()
if args is None:
args = sys.argv[1:]
elif isinstance(args, string_types):
args = args.split(' ')
args = p.parse(args)
if args is None:
return
if args.profiler or args.line_profiler:
from phy.utils.testing import _enable_profiler, _profile
prof = _enable_profiler(args.line_profiler)
else:
prof = None
import phy
if args.debug:
phy.debug()
# Hide the traceback.
if args.hide_traceback:
def exception_handler(exception_type, exception, traceback):
print("{}: {}".format(exception_type.__name__, exception))
sys.excepthook = exception_handler
# Activate IPython debugger.
if args.pdb:
from IPython.core import ultratb
sys.excepthook = ultratb.FormattedTB(mode='Verbose',
color_scheme='Linux',
call_pdb=1,
)
func = args.func
if func is None:
p.parser.print_help()
return
out = func(args)
if not out:
return
cmd, ns = out
if not cmd:
return
requires_qt = ns.pop('requires_qt', False)
requires_vispy = ns.pop('requires_vispy', False)
# Default variables in namespace.
ns.update(phy=phy, path=args.file)
if 'session' in ns:
ns['model'] = ns['session'].model
# Interactive mode with IPython.
if args.ipython:
print("\nStarting IPython...")
from IPython import start_ipython
args_ipy = ["-i", "-c='{}'".format(cmd)]
if requires_qt or requires_vispy:
# Activate Qt event loop integration with Qt.
args_ipy += ["--gui=qt"]
start_ipython(args_ipy, user_ns=ns)
else:
if not prof:
exec_(cmd, {}, ns)
else:
_profile(prof, cmd, {}, ns)
if requires_qt:
# Launch the Qt app.
from phy.gui import run_qt_app
run_qt_app()
elif requires_vispy:
# Launch the VisPy Qt app.
from vispy.app import use_app, run
use_app('pyqt4')
run()
#------------------------------------------------------------------------------
# Entry point
#------------------------------------------------------------------------------
if __name__ == '__main__':
main()
|
normal
|
{
"blob_id": "539523f177e2c3c0e1fb0226d1fcd65463b68a0e",
"index": 6576,
"step-1": "<mask token>\n\n\nclass Parser(argparse.ArgumentParser):\n\n def error(self, message):\n sys.stderr.write(message + '\\n\\n')\n self.print_help()\n sys.exit(2)\n\n\n<mask token>\n\n\nclass ParserCreator(object):\n\n def __init__(self):\n self.create_main()\n self.create_download()\n self.create_traces()\n self.create_describe()\n self.create_spikesort()\n self.create_detect()\n self.create_auto()\n self.create_manual()\n self.create_notebook()\n\n @property\n def parser(self):\n return self._parser\n\n def _add_sub_parser(self, name, desc):\n p = self._subparsers.add_parser(name, help=desc, description=desc)\n self._add_options(p)\n return p\n\n def _add_options(self, parser):\n parser.add_argument('--debug', '-d', action='store_true', help=\n 'activate debug logging mode')\n parser.add_argument('--hide-traceback', action='store_true', help=\n 'hide the traceback for cleaner error messages')\n parser.add_argument('--profiler', '-p', action='store_true', help=\n 'activate the profiler')\n parser.add_argument('--line-profiler', '-lp', dest='line_profiler',\n action='store_true', help=\n 'activate the line-profiler -- you need to decorate the functions to profile with `@profile` in the code'\n )\n parser.add_argument('--ipython', '-i', action='store_true', help=\n 'launch the script in an interactive IPython console')\n parser.add_argument('--pdb', action='store_true', help=\n 'activate the Python debugger')\n\n def create_main(self):\n import phy\n desc = sys.modules['phy'].__doc__\n self._parser = Parser(description=desc, epilog=_examples,\n formatter_class=CustomFormatter)\n self._parser.set_defaults(func=None)\n self._parser.add_argument('--version', '-v', action='version',\n version=phy.__version_git__, help='print the version of phy')\n self._add_options(self._parser)\n self._subparsers = self._parser.add_subparsers(dest='command',\n title='subcommand')\n\n def create_download(self):\n desc = 'download a sample dataset'\n p = self._add_sub_parser('download', desc)\n p.add_argument('file', help='dataset filename')\n p.add_argument('--output-dir', '-o', help='output directory')\n p.add_argument('--base', default='cortexlab', choices=('cortexlab',\n 'github'), help='data repository name: `cortexlab` or `github`')\n p.set_defaults(func=download)\n\n def create_describe(self):\n desc = 'describe a `.kwik` file'\n p = self._add_sub_parser('describe', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=describe)\n\n def create_traces(self):\n desc = 'show the traces of a raw data file'\n p = self._add_sub_parser('traces', desc)\n p.add_argument('file', help='path to a `.kwd` or `.dat` file')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.add_argument('--n-channels', '-n', help=\n 'number of channels in the recording (only required when using a flat binary file)'\n )\n p.add_argument('--dtype', help=\n 'NumPy data type (only required when using a flat binary file)',\n default='int16')\n p.add_argument('--sample-rate', '-s', help=\n 'sample rate in Hz (only required when using a flat binary file)')\n p.set_defaults(func=traces)\n\n def create_spikesort(self):\n desc = 'launch the whole spike sorting pipeline on a `.prm` file'\n p = self._add_sub_parser('spikesort', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=spikesort)\n\n def create_detect(self):\n desc = 'launch the spike detection algorithm on a `.prm` file'\n p = self._add_sub_parser('detect', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=detect)\n\n def create_auto(self):\n desc = 'launch the automatic clustering algorithm on a `.kwik` file'\n p = self._add_sub_parser('cluster-auto', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=cluster_auto)\n\n def create_manual(self):\n desc = 'launch the manual clustering GUI on a `.kwik` file'\n p = self._add_sub_parser('cluster-manual', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.add_argument('--cluster-ids', '-c', help=\n 'list of clusters to select initially')\n p.add_argument('--no-store', action='store_true', default=False,\n help='do not create the store (faster loading time, slower GUI)')\n p.set_defaults(func=cluster_manual)\n\n def create_notebook(self):\n pass\n\n def parse(self, args):\n try:\n return self._parser.parse_args(args)\n except SystemExit as e:\n if e.code != 0:\n raise e\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass CustomFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.\n RawDescriptionHelpFormatter):\n pass\n\n\nclass Parser(argparse.ArgumentParser):\n\n def error(self, message):\n sys.stderr.write(message + '\\n\\n')\n self.print_help()\n sys.exit(2)\n\n\n<mask token>\n\n\nclass ParserCreator(object):\n\n def __init__(self):\n self.create_main()\n self.create_download()\n self.create_traces()\n self.create_describe()\n self.create_spikesort()\n self.create_detect()\n self.create_auto()\n self.create_manual()\n self.create_notebook()\n\n @property\n def parser(self):\n return self._parser\n\n def _add_sub_parser(self, name, desc):\n p = self._subparsers.add_parser(name, help=desc, description=desc)\n self._add_options(p)\n return p\n\n def _add_options(self, parser):\n parser.add_argument('--debug', '-d', action='store_true', help=\n 'activate debug logging mode')\n parser.add_argument('--hide-traceback', action='store_true', help=\n 'hide the traceback for cleaner error messages')\n parser.add_argument('--profiler', '-p', action='store_true', help=\n 'activate the profiler')\n parser.add_argument('--line-profiler', '-lp', dest='line_profiler',\n action='store_true', help=\n 'activate the line-profiler -- you need to decorate the functions to profile with `@profile` in the code'\n )\n parser.add_argument('--ipython', '-i', action='store_true', help=\n 'launch the script in an interactive IPython console')\n parser.add_argument('--pdb', action='store_true', help=\n 'activate the Python debugger')\n\n def create_main(self):\n import phy\n desc = sys.modules['phy'].__doc__\n self._parser = Parser(description=desc, epilog=_examples,\n formatter_class=CustomFormatter)\n self._parser.set_defaults(func=None)\n self._parser.add_argument('--version', '-v', action='version',\n version=phy.__version_git__, help='print the version of phy')\n self._add_options(self._parser)\n self._subparsers = self._parser.add_subparsers(dest='command',\n title='subcommand')\n\n def create_download(self):\n desc = 'download a sample dataset'\n p = self._add_sub_parser('download', desc)\n p.add_argument('file', help='dataset filename')\n p.add_argument('--output-dir', '-o', help='output directory')\n p.add_argument('--base', default='cortexlab', choices=('cortexlab',\n 'github'), help='data repository name: `cortexlab` or `github`')\n p.set_defaults(func=download)\n\n def create_describe(self):\n desc = 'describe a `.kwik` file'\n p = self._add_sub_parser('describe', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=describe)\n\n def create_traces(self):\n desc = 'show the traces of a raw data file'\n p = self._add_sub_parser('traces', desc)\n p.add_argument('file', help='path to a `.kwd` or `.dat` file')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.add_argument('--n-channels', '-n', help=\n 'number of channels in the recording (only required when using a flat binary file)'\n )\n p.add_argument('--dtype', help=\n 'NumPy data type (only required when using a flat binary file)',\n default='int16')\n p.add_argument('--sample-rate', '-s', help=\n 'sample rate in Hz (only required when using a flat binary file)')\n p.set_defaults(func=traces)\n\n def create_spikesort(self):\n desc = 'launch the whole spike sorting pipeline on a `.prm` file'\n p = self._add_sub_parser('spikesort', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=spikesort)\n\n def create_detect(self):\n desc = 'launch the spike detection algorithm on a `.prm` file'\n p = self._add_sub_parser('detect', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=detect)\n\n def create_auto(self):\n desc = 'launch the automatic clustering algorithm on a `.kwik` file'\n p = self._add_sub_parser('cluster-auto', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=cluster_auto)\n\n def create_manual(self):\n desc = 'launch the manual clustering GUI on a `.kwik` file'\n p = self._add_sub_parser('cluster-manual', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.add_argument('--cluster-ids', '-c', help=\n 'list of clusters to select initially')\n p.add_argument('--no-store', action='store_true', default=False,\n help='do not create the store (faster loading time, slower GUI)')\n p.set_defaults(func=cluster_manual)\n\n def create_notebook(self):\n pass\n\n def parse(self, args):\n try:\n return self._parser.parse_args(args)\n except SystemExit as e:\n if e.code != 0:\n raise e\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass CustomFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.\n RawDescriptionHelpFormatter):\n pass\n\n\nclass Parser(argparse.ArgumentParser):\n\n def error(self, message):\n sys.stderr.write(message + '\\n\\n')\n self.print_help()\n sys.exit(2)\n\n\n<mask token>\n\n\nclass ParserCreator(object):\n\n def __init__(self):\n self.create_main()\n self.create_download()\n self.create_traces()\n self.create_describe()\n self.create_spikesort()\n self.create_detect()\n self.create_auto()\n self.create_manual()\n self.create_notebook()\n\n @property\n def parser(self):\n return self._parser\n\n def _add_sub_parser(self, name, desc):\n p = self._subparsers.add_parser(name, help=desc, description=desc)\n self._add_options(p)\n return p\n\n def _add_options(self, parser):\n parser.add_argument('--debug', '-d', action='store_true', help=\n 'activate debug logging mode')\n parser.add_argument('--hide-traceback', action='store_true', help=\n 'hide the traceback for cleaner error messages')\n parser.add_argument('--profiler', '-p', action='store_true', help=\n 'activate the profiler')\n parser.add_argument('--line-profiler', '-lp', dest='line_profiler',\n action='store_true', help=\n 'activate the line-profiler -- you need to decorate the functions to profile with `@profile` in the code'\n )\n parser.add_argument('--ipython', '-i', action='store_true', help=\n 'launch the script in an interactive IPython console')\n parser.add_argument('--pdb', action='store_true', help=\n 'activate the Python debugger')\n\n def create_main(self):\n import phy\n desc = sys.modules['phy'].__doc__\n self._parser = Parser(description=desc, epilog=_examples,\n formatter_class=CustomFormatter)\n self._parser.set_defaults(func=None)\n self._parser.add_argument('--version', '-v', action='version',\n version=phy.__version_git__, help='print the version of phy')\n self._add_options(self._parser)\n self._subparsers = self._parser.add_subparsers(dest='command',\n title='subcommand')\n\n def create_download(self):\n desc = 'download a sample dataset'\n p = self._add_sub_parser('download', desc)\n p.add_argument('file', help='dataset filename')\n p.add_argument('--output-dir', '-o', help='output directory')\n p.add_argument('--base', default='cortexlab', choices=('cortexlab',\n 'github'), help='data repository name: `cortexlab` or `github`')\n p.set_defaults(func=download)\n\n def create_describe(self):\n desc = 'describe a `.kwik` file'\n p = self._add_sub_parser('describe', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=describe)\n\n def create_traces(self):\n desc = 'show the traces of a raw data file'\n p = self._add_sub_parser('traces', desc)\n p.add_argument('file', help='path to a `.kwd` or `.dat` file')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.add_argument('--n-channels', '-n', help=\n 'number of channels in the recording (only required when using a flat binary file)'\n )\n p.add_argument('--dtype', help=\n 'NumPy data type (only required when using a flat binary file)',\n default='int16')\n p.add_argument('--sample-rate', '-s', help=\n 'sample rate in Hz (only required when using a flat binary file)')\n p.set_defaults(func=traces)\n\n def create_spikesort(self):\n desc = 'launch the whole spike sorting pipeline on a `.prm` file'\n p = self._add_sub_parser('spikesort', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=spikesort)\n\n def create_detect(self):\n desc = 'launch the spike detection algorithm on a `.prm` file'\n p = self._add_sub_parser('detect', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=detect)\n\n def create_auto(self):\n desc = 'launch the automatic clustering algorithm on a `.kwik` file'\n p = self._add_sub_parser('cluster-auto', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=cluster_auto)\n\n def create_manual(self):\n desc = 'launch the manual clustering GUI on a `.kwik` file'\n p = self._add_sub_parser('cluster-manual', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.add_argument('--cluster-ids', '-c', help=\n 'list of clusters to select initially')\n p.add_argument('--no-store', action='store_true', default=False,\n help='do not create the store (faster loading time, slower GUI)')\n p.set_defaults(func=cluster_manual)\n\n def create_notebook(self):\n pass\n\n def parse(self, args):\n try:\n return self._parser.parse_args(args)\n except SystemExit as e:\n if e.code != 0:\n raise e\n\n\ndef _get_kwik_path(args):\n kwik_path = args.file\n if not op.exists(kwik_path):\n raise IOError(\"The file `{}` doesn't exist.\".format(kwik_path))\n return kwik_path\n\n\ndef _create_session(args, **kwargs):\n from phy.session import Session\n kwik_path = _get_kwik_path(args)\n session = Session(kwik_path, **kwargs)\n return session\n\n\ndef describe(args):\n from phy.io.kwik import KwikModel\n path = _get_kwik_path(args)\n model = KwikModel(path, clustering=args.clustering)\n return 'model.describe()', dict(model=model)\n\n\ndef download(args):\n from phy import download_sample_data\n download_sample_data(args.file, output_dir=args.output_dir, base=args.base)\n\n\ndef traces(args):\n from vispy.app import run\n from phy.plot.traces import TraceView\n from phy.io.h5 import open_h5\n from phy.io.traces import read_kwd, read_dat\n path = args.file\n if path.endswith('.kwd'):\n f = open_h5(args.file)\n traces = read_kwd(f)\n elif path.endswith(('.dat', '.bin')):\n if not args.n_channels:\n raise ValueError('Please specify `--n-channels`.')\n if not args.dtype:\n raise ValueError('Please specify `--dtype`.')\n if not args.sample_rate:\n raise ValueError('Please specify `--sample-rate`.')\n n_channels = int(args.n_channels)\n dtype = np.dtype(args.dtype)\n traces = read_dat(path, dtype=dtype, n_channels=n_channels)\n start, end = map(int, args.interval.split(','))\n sample_rate = float(args.sample_rate)\n start = int(sample_rate * start)\n end = int(sample_rate * end)\n c = TraceView(keys='interactive')\n c.visual.traces = 0.01 * traces[start:end, ...]\n c.show()\n run()\n return None, None\n\n\ndef detect(args):\n from phy.io import create_kwik\n assert args.file.endswith('.prm')\n kwik_path = args.kwik_path\n kwik_path = create_kwik(args.file, overwrite=args.overwrite, kwik_path=\n kwik_path)\n interval = args.interval\n if interval is not None:\n interval = list(map(float, interval.split(',')))\n args.file = kwik_path\n session = _create_session(args, use_store=False)\n return 'session.detect(interval=interval)', dict(session=session,\n interval=interval)\n\n\ndef cluster_auto(args):\n from phy.utils._misc import _read_python\n from phy.session import Session\n assert args.file.endswith('.prm')\n params = _read_python(args.file)\n kwik_path = params['experiment_name'] + '.kwik'\n session = Session(kwik_path)\n ns = dict(session=session, clustering=args.clustering)\n cmd = 'session.cluster(clustering=clustering)'\n return cmd, ns\n\n\ndef spikesort(args):\n from phy.io import create_kwik\n assert args.file.endswith('.prm')\n kwik_path = args.kwik_path\n kwik_path = create_kwik(args.file, overwrite=args.overwrite, kwik_path=\n kwik_path)\n args.file = kwik_path\n session = _create_session(args, use_store=False)\n interval = args.interval\n if interval is not None:\n interval = list(map(float, interval.split(',')))\n ns = dict(session=session, interval=interval, n_s_clusters=100)\n cmd = 'session.detect(interval=interval); session.cluster();'\n return cmd, ns\n\n\ndef cluster_manual(args):\n session = _create_session(args, clustering=args.clustering, use_store=\n not args.no_store)\n cluster_ids = list(map(int, args.cluster_ids.split(','))\n ) if args.cluster_ids else None\n session.model.describe()\n from phy.gui import start_qt_app\n start_qt_app()\n gui = session.show_gui(cluster_ids=cluster_ids, show=False)\n print('\\nPress `ctrl+h` to see the list of keyboard shortcuts.\\n')\n return 'gui.show()', dict(session=session, gui=gui, requires_qt=True)\n\n\ndef main(args=None):\n p = ParserCreator()\n if args is None:\n args = sys.argv[1:]\n elif isinstance(args, string_types):\n args = args.split(' ')\n args = p.parse(args)\n if args is None:\n return\n if args.profiler or args.line_profiler:\n from phy.utils.testing import _enable_profiler, _profile\n prof = _enable_profiler(args.line_profiler)\n else:\n prof = None\n import phy\n if args.debug:\n phy.debug()\n if args.hide_traceback:\n\n def exception_handler(exception_type, exception, traceback):\n print('{}: {}'.format(exception_type.__name__, exception))\n sys.excepthook = exception_handler\n if args.pdb:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose', color_scheme=\n 'Linux', call_pdb=1)\n func = args.func\n if func is None:\n p.parser.print_help()\n return\n out = func(args)\n if not out:\n return\n cmd, ns = out\n if not cmd:\n return\n requires_qt = ns.pop('requires_qt', False)\n requires_vispy = ns.pop('requires_vispy', False)\n ns.update(phy=phy, path=args.file)\n if 'session' in ns:\n ns['model'] = ns['session'].model\n if args.ipython:\n print('\\nStarting IPython...')\n from IPython import start_ipython\n args_ipy = ['-i', \"-c='{}'\".format(cmd)]\n if requires_qt or requires_vispy:\n args_ipy += ['--gui=qt']\n start_ipython(args_ipy, user_ns=ns)\n else:\n if not prof:\n exec_(cmd, {}, ns)\n else:\n _profile(prof, cmd, {}, ns)\n if requires_qt:\n from phy.gui import run_qt_app\n run_qt_app()\n elif requires_vispy:\n from vispy.app import use_app, run\n use_app('pyqt4')\n run()\n\n\nif __name__ == '__main__':\n main()\n",
"step-4": "<mask token>\n\n\nclass CustomFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.\n RawDescriptionHelpFormatter):\n pass\n\n\nclass Parser(argparse.ArgumentParser):\n\n def error(self, message):\n sys.stderr.write(message + '\\n\\n')\n self.print_help()\n sys.exit(2)\n\n\n_examples = dedent(\n \"\"\"\n\nexamples:\n phy -v display the version of phy\n phy download hybrid_120sec.dat -o data/\n download a sample raw data file in `data/`\n phy describe my_file.kwik\n display information about a Kwik dataset\n phy spikesort my_params.prm\n run the whole suite (spike detection and clustering)\n phy detect my_params.prm\n run spike detection on a parameters file\n phy cluster-auto my_file.kwik\n run klustakwik on a dataset (after spike detection)\n phy cluster-manual my_file.kwik\n run the manual clustering GUI\n\n\"\"\"\n )\n\n\nclass ParserCreator(object):\n\n def __init__(self):\n self.create_main()\n self.create_download()\n self.create_traces()\n self.create_describe()\n self.create_spikesort()\n self.create_detect()\n self.create_auto()\n self.create_manual()\n self.create_notebook()\n\n @property\n def parser(self):\n return self._parser\n\n def _add_sub_parser(self, name, desc):\n p = self._subparsers.add_parser(name, help=desc, description=desc)\n self._add_options(p)\n return p\n\n def _add_options(self, parser):\n parser.add_argument('--debug', '-d', action='store_true', help=\n 'activate debug logging mode')\n parser.add_argument('--hide-traceback', action='store_true', help=\n 'hide the traceback for cleaner error messages')\n parser.add_argument('--profiler', '-p', action='store_true', help=\n 'activate the profiler')\n parser.add_argument('--line-profiler', '-lp', dest='line_profiler',\n action='store_true', help=\n 'activate the line-profiler -- you need to decorate the functions to profile with `@profile` in the code'\n )\n parser.add_argument('--ipython', '-i', action='store_true', help=\n 'launch the script in an interactive IPython console')\n parser.add_argument('--pdb', action='store_true', help=\n 'activate the Python debugger')\n\n def create_main(self):\n import phy\n desc = sys.modules['phy'].__doc__\n self._parser = Parser(description=desc, epilog=_examples,\n formatter_class=CustomFormatter)\n self._parser.set_defaults(func=None)\n self._parser.add_argument('--version', '-v', action='version',\n version=phy.__version_git__, help='print the version of phy')\n self._add_options(self._parser)\n self._subparsers = self._parser.add_subparsers(dest='command',\n title='subcommand')\n\n def create_download(self):\n desc = 'download a sample dataset'\n p = self._add_sub_parser('download', desc)\n p.add_argument('file', help='dataset filename')\n p.add_argument('--output-dir', '-o', help='output directory')\n p.add_argument('--base', default='cortexlab', choices=('cortexlab',\n 'github'), help='data repository name: `cortexlab` or `github`')\n p.set_defaults(func=download)\n\n def create_describe(self):\n desc = 'describe a `.kwik` file'\n p = self._add_sub_parser('describe', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=describe)\n\n def create_traces(self):\n desc = 'show the traces of a raw data file'\n p = self._add_sub_parser('traces', desc)\n p.add_argument('file', help='path to a `.kwd` or `.dat` file')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.add_argument('--n-channels', '-n', help=\n 'number of channels in the recording (only required when using a flat binary file)'\n )\n p.add_argument('--dtype', help=\n 'NumPy data type (only required when using a flat binary file)',\n default='int16')\n p.add_argument('--sample-rate', '-s', help=\n 'sample rate in Hz (only required when using a flat binary file)')\n p.set_defaults(func=traces)\n\n def create_spikesort(self):\n desc = 'launch the whole spike sorting pipeline on a `.prm` file'\n p = self._add_sub_parser('spikesort', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=spikesort)\n\n def create_detect(self):\n desc = 'launch the spike detection algorithm on a `.prm` file'\n p = self._add_sub_parser('detect', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help=\n 'filename of the `.kwik` file to create (by default, `\"experiment_name\".kwik`)'\n )\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval', help=\n 'detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=detect)\n\n def create_auto(self):\n desc = 'launch the automatic clustering algorithm on a `.kwik` file'\n p = self._add_sub_parser('cluster-auto', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.set_defaults(func=cluster_auto)\n\n def create_manual(self):\n desc = 'launch the manual clustering GUI on a `.kwik` file'\n p = self._add_sub_parser('cluster-manual', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main', help=\n 'name of the clustering to use')\n p.add_argument('--cluster-ids', '-c', help=\n 'list of clusters to select initially')\n p.add_argument('--no-store', action='store_true', default=False,\n help='do not create the store (faster loading time, slower GUI)')\n p.set_defaults(func=cluster_manual)\n\n def create_notebook(self):\n pass\n\n def parse(self, args):\n try:\n return self._parser.parse_args(args)\n except SystemExit as e:\n if e.code != 0:\n raise e\n\n\ndef _get_kwik_path(args):\n kwik_path = args.file\n if not op.exists(kwik_path):\n raise IOError(\"The file `{}` doesn't exist.\".format(kwik_path))\n return kwik_path\n\n\ndef _create_session(args, **kwargs):\n from phy.session import Session\n kwik_path = _get_kwik_path(args)\n session = Session(kwik_path, **kwargs)\n return session\n\n\ndef describe(args):\n from phy.io.kwik import KwikModel\n path = _get_kwik_path(args)\n model = KwikModel(path, clustering=args.clustering)\n return 'model.describe()', dict(model=model)\n\n\ndef download(args):\n from phy import download_sample_data\n download_sample_data(args.file, output_dir=args.output_dir, base=args.base)\n\n\ndef traces(args):\n from vispy.app import run\n from phy.plot.traces import TraceView\n from phy.io.h5 import open_h5\n from phy.io.traces import read_kwd, read_dat\n path = args.file\n if path.endswith('.kwd'):\n f = open_h5(args.file)\n traces = read_kwd(f)\n elif path.endswith(('.dat', '.bin')):\n if not args.n_channels:\n raise ValueError('Please specify `--n-channels`.')\n if not args.dtype:\n raise ValueError('Please specify `--dtype`.')\n if not args.sample_rate:\n raise ValueError('Please specify `--sample-rate`.')\n n_channels = int(args.n_channels)\n dtype = np.dtype(args.dtype)\n traces = read_dat(path, dtype=dtype, n_channels=n_channels)\n start, end = map(int, args.interval.split(','))\n sample_rate = float(args.sample_rate)\n start = int(sample_rate * start)\n end = int(sample_rate * end)\n c = TraceView(keys='interactive')\n c.visual.traces = 0.01 * traces[start:end, ...]\n c.show()\n run()\n return None, None\n\n\ndef detect(args):\n from phy.io import create_kwik\n assert args.file.endswith('.prm')\n kwik_path = args.kwik_path\n kwik_path = create_kwik(args.file, overwrite=args.overwrite, kwik_path=\n kwik_path)\n interval = args.interval\n if interval is not None:\n interval = list(map(float, interval.split(',')))\n args.file = kwik_path\n session = _create_session(args, use_store=False)\n return 'session.detect(interval=interval)', dict(session=session,\n interval=interval)\n\n\ndef cluster_auto(args):\n from phy.utils._misc import _read_python\n from phy.session import Session\n assert args.file.endswith('.prm')\n params = _read_python(args.file)\n kwik_path = params['experiment_name'] + '.kwik'\n session = Session(kwik_path)\n ns = dict(session=session, clustering=args.clustering)\n cmd = 'session.cluster(clustering=clustering)'\n return cmd, ns\n\n\ndef spikesort(args):\n from phy.io import create_kwik\n assert args.file.endswith('.prm')\n kwik_path = args.kwik_path\n kwik_path = create_kwik(args.file, overwrite=args.overwrite, kwik_path=\n kwik_path)\n args.file = kwik_path\n session = _create_session(args, use_store=False)\n interval = args.interval\n if interval is not None:\n interval = list(map(float, interval.split(',')))\n ns = dict(session=session, interval=interval, n_s_clusters=100)\n cmd = 'session.detect(interval=interval); session.cluster();'\n return cmd, ns\n\n\ndef cluster_manual(args):\n session = _create_session(args, clustering=args.clustering, use_store=\n not args.no_store)\n cluster_ids = list(map(int, args.cluster_ids.split(','))\n ) if args.cluster_ids else None\n session.model.describe()\n from phy.gui import start_qt_app\n start_qt_app()\n gui = session.show_gui(cluster_ids=cluster_ids, show=False)\n print('\\nPress `ctrl+h` to see the list of keyboard shortcuts.\\n')\n return 'gui.show()', dict(session=session, gui=gui, requires_qt=True)\n\n\ndef main(args=None):\n p = ParserCreator()\n if args is None:\n args = sys.argv[1:]\n elif isinstance(args, string_types):\n args = args.split(' ')\n args = p.parse(args)\n if args is None:\n return\n if args.profiler or args.line_profiler:\n from phy.utils.testing import _enable_profiler, _profile\n prof = _enable_profiler(args.line_profiler)\n else:\n prof = None\n import phy\n if args.debug:\n phy.debug()\n if args.hide_traceback:\n\n def exception_handler(exception_type, exception, traceback):\n print('{}: {}'.format(exception_type.__name__, exception))\n sys.excepthook = exception_handler\n if args.pdb:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose', color_scheme=\n 'Linux', call_pdb=1)\n func = args.func\n if func is None:\n p.parser.print_help()\n return\n out = func(args)\n if not out:\n return\n cmd, ns = out\n if not cmd:\n return\n requires_qt = ns.pop('requires_qt', False)\n requires_vispy = ns.pop('requires_vispy', False)\n ns.update(phy=phy, path=args.file)\n if 'session' in ns:\n ns['model'] = ns['session'].model\n if args.ipython:\n print('\\nStarting IPython...')\n from IPython import start_ipython\n args_ipy = ['-i', \"-c='{}'\".format(cmd)]\n if requires_qt or requires_vispy:\n args_ipy += ['--gui=qt']\n start_ipython(args_ipy, user_ns=ns)\n else:\n if not prof:\n exec_(cmd, {}, ns)\n else:\n _profile(prof, cmd, {}, ns)\n if requires_qt:\n from phy.gui import run_qt_app\n run_qt_app()\n elif requires_vispy:\n from vispy.app import use_app, run\n use_app('pyqt4')\n run()\n\n\nif __name__ == '__main__':\n main()\n",
"step-5": "# -*- coding: utf-8 -*-\nfrom __future__ import print_function\n\n\"\"\"phy main CLI tool.\n\nUsage:\n\n phy --help\n\n\"\"\"\n\n#------------------------------------------------------------------------------\n# Imports\n#------------------------------------------------------------------------------\n\nimport sys\nimport os.path as op\nimport argparse\nfrom textwrap import dedent\n\nimport numpy as np\nfrom six import exec_, string_types\n\n\n#------------------------------------------------------------------------------\n# Parser utilities\n#------------------------------------------------------------------------------\n\nclass CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,\n argparse.RawDescriptionHelpFormatter):\n pass\n\n\nclass Parser(argparse.ArgumentParser):\n def error(self, message):\n sys.stderr.write(message + '\\n\\n')\n self.print_help()\n sys.exit(2)\n\n\n_examples = dedent(\"\"\"\n\nexamples:\n phy -v display the version of phy\n phy download hybrid_120sec.dat -o data/\n download a sample raw data file in `data/`\n phy describe my_file.kwik\n display information about a Kwik dataset\n phy spikesort my_params.prm\n run the whole suite (spike detection and clustering)\n phy detect my_params.prm\n run spike detection on a parameters file\n phy cluster-auto my_file.kwik\n run klustakwik on a dataset (after spike detection)\n phy cluster-manual my_file.kwik\n run the manual clustering GUI\n\n\"\"\")\n\n\n#------------------------------------------------------------------------------\n# Parser creator\n#------------------------------------------------------------------------------\n\nclass ParserCreator(object):\n def __init__(self):\n self.create_main()\n self.create_download()\n self.create_traces()\n self.create_describe()\n self.create_spikesort()\n self.create_detect()\n self.create_auto()\n self.create_manual()\n self.create_notebook()\n\n @property\n def parser(self):\n return self._parser\n\n def _add_sub_parser(self, name, desc):\n p = self._subparsers.add_parser(name, help=desc, description=desc)\n self._add_options(p)\n return p\n\n def _add_options(self, parser):\n parser.add_argument('--debug', '-d',\n action='store_true',\n help='activate debug logging mode')\n\n parser.add_argument('--hide-traceback',\n action='store_true',\n help='hide the traceback for cleaner error '\n 'messages')\n\n parser.add_argument('--profiler', '-p',\n action='store_true',\n help='activate the profiler')\n\n parser.add_argument('--line-profiler', '-lp',\n dest='line_profiler',\n action='store_true',\n help='activate the line-profiler -- you '\n 'need to decorate the functions '\n 'to profile with `@profile` '\n 'in the code')\n\n parser.add_argument('--ipython', '-i', action='store_true',\n help='launch the script in an interactive '\n 'IPython console')\n\n parser.add_argument('--pdb', action='store_true',\n help='activate the Python debugger')\n\n def create_main(self):\n import phy\n\n desc = sys.modules['phy'].__doc__\n self._parser = Parser(description=desc,\n epilog=_examples,\n formatter_class=CustomFormatter,\n )\n self._parser.set_defaults(func=None)\n self._parser.add_argument('--version', '-v',\n action='version',\n version=phy.__version_git__,\n help='print the version of phy')\n self._add_options(self._parser)\n self._subparsers = self._parser.add_subparsers(dest='command',\n title='subcommand',\n )\n\n def create_download(self):\n desc = 'download a sample dataset'\n p = self._add_sub_parser('download', desc)\n p.add_argument('file', help='dataset filename')\n p.add_argument('--output-dir', '-o', help='output directory')\n p.add_argument('--base',\n default='cortexlab',\n choices=('cortexlab', 'github'),\n help='data repository name: `cortexlab` or `github`',\n )\n p.set_defaults(func=download)\n\n def create_describe(self):\n desc = 'describe a `.kwik` file'\n p = self._add_sub_parser('describe', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main',\n help='name of the clustering to use')\n p.set_defaults(func=describe)\n\n def create_traces(self):\n desc = 'show the traces of a raw data file'\n p = self._add_sub_parser('traces', desc)\n p.add_argument('file', help='path to a `.kwd` or `.dat` file')\n p.add_argument('--interval',\n help='detection interval in seconds (e.g. `0,10`)')\n p.add_argument('--n-channels', '-n',\n help='number of channels in the recording '\n '(only required when using a flat binary file)')\n p.add_argument('--dtype',\n help='NumPy data type '\n '(only required when using a flat binary file)',\n default='int16',\n )\n p.add_argument('--sample-rate', '-s',\n help='sample rate in Hz '\n '(only required when using a flat binary file)')\n p.set_defaults(func=traces)\n\n def create_spikesort(self):\n desc = 'launch the whole spike sorting pipeline on a `.prm` file'\n p = self._add_sub_parser('spikesort', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help='filename of the `.kwik` file '\n 'to create (by default, `\"experiment_name\".kwik`)')\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval',\n help='detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=spikesort)\n\n def create_detect(self):\n desc = 'launch the spike detection algorithm on a `.prm` file'\n p = self._add_sub_parser('detect', desc)\n p.add_argument('file', help='path to a `.prm` file')\n p.add_argument('--kwik-path', help='filename of the `.kwik` file '\n 'to create (by default, `\"experiment_name\".kwik`)')\n p.add_argument('--overwrite', action='store_true', default=False,\n help='overwrite the `.kwik` file ')\n p.add_argument('--interval',\n help='detection interval in seconds (e.g. `0,10`)')\n p.set_defaults(func=detect)\n\n def create_auto(self):\n desc = 'launch the automatic clustering algorithm on a `.kwik` file'\n p = self._add_sub_parser('cluster-auto', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main',\n help='name of the clustering to use')\n p.set_defaults(func=cluster_auto)\n\n def create_manual(self):\n desc = 'launch the manual clustering GUI on a `.kwik` file'\n p = self._add_sub_parser('cluster-manual', desc)\n p.add_argument('file', help='path to a `.kwik` file')\n p.add_argument('--clustering', default='main',\n help='name of the clustering to use')\n p.add_argument('--cluster-ids', '-c',\n help='list of clusters to select initially')\n p.add_argument('--no-store', action='store_true', default=False,\n help='do not create the store (faster loading time, '\n 'slower GUI)')\n p.set_defaults(func=cluster_manual)\n\n def create_notebook(self):\n # TODO\n pass\n\n def parse(self, args):\n try:\n return self._parser.parse_args(args)\n except SystemExit as e:\n if e.code != 0:\n raise e\n\n\n#------------------------------------------------------------------------------\n# Subcommand functions\n#------------------------------------------------------------------------------\n\ndef _get_kwik_path(args):\n kwik_path = args.file\n\n if not op.exists(kwik_path):\n raise IOError(\"The file `{}` doesn't exist.\".format(kwik_path))\n\n return kwik_path\n\n\ndef _create_session(args, **kwargs):\n from phy.session import Session\n kwik_path = _get_kwik_path(args)\n session = Session(kwik_path, **kwargs)\n return session\n\n\ndef describe(args):\n from phy.io.kwik import KwikModel\n path = _get_kwik_path(args)\n model = KwikModel(path, clustering=args.clustering)\n return 'model.describe()', dict(model=model)\n\n\ndef download(args):\n from phy import download_sample_data\n download_sample_data(args.file,\n output_dir=args.output_dir,\n base=args.base,\n )\n\n\ndef traces(args):\n from vispy.app import run\n from phy.plot.traces import TraceView\n from phy.io.h5 import open_h5\n from phy.io.traces import read_kwd, read_dat\n\n path = args.file\n if path.endswith('.kwd'):\n f = open_h5(args.file)\n traces = read_kwd(f)\n elif path.endswith(('.dat', '.bin')):\n if not args.n_channels:\n raise ValueError(\"Please specify `--n-channels`.\")\n if not args.dtype:\n raise ValueError(\"Please specify `--dtype`.\")\n if not args.sample_rate:\n raise ValueError(\"Please specify `--sample-rate`.\")\n n_channels = int(args.n_channels)\n dtype = np.dtype(args.dtype)\n traces = read_dat(path, dtype=dtype, n_channels=n_channels)\n\n start, end = map(int, args.interval.split(','))\n sample_rate = float(args.sample_rate)\n start = int(sample_rate * start)\n end = int(sample_rate * end)\n\n c = TraceView(keys='interactive')\n c.visual.traces = .01 * traces[start:end, ...]\n c.show()\n run()\n\n return None, None\n\n\ndef detect(args):\n from phy.io import create_kwik\n\n assert args.file.endswith('.prm')\n kwik_path = args.kwik_path\n kwik_path = create_kwik(args.file,\n overwrite=args.overwrite,\n kwik_path=kwik_path)\n\n interval = args.interval\n if interval is not None:\n interval = list(map(float, interval.split(',')))\n\n # Create the session with the newly-created .kwik file.\n args.file = kwik_path\n session = _create_session(args, use_store=False)\n return ('session.detect(interval=interval)',\n dict(session=session, interval=interval))\n\n\ndef cluster_auto(args):\n from phy.utils._misc import _read_python\n from phy.session import Session\n\n assert args.file.endswith('.prm')\n\n params = _read_python(args.file)\n kwik_path = params['experiment_name'] + '.kwik'\n session = Session(kwik_path)\n\n ns = dict(session=session,\n clustering=args.clustering,\n )\n cmd = ('session.cluster(clustering=clustering)')\n return (cmd, ns)\n\n\ndef spikesort(args):\n from phy.io import create_kwik\n\n assert args.file.endswith('.prm')\n kwik_path = args.kwik_path\n kwik_path = create_kwik(args.file,\n overwrite=args.overwrite,\n kwik_path=kwik_path,\n )\n # Create the session with the newly-created .kwik file.\n args.file = kwik_path\n session = _create_session(args, use_store=False)\n\n interval = args.interval\n if interval is not None:\n interval = list(map(float, interval.split(',')))\n\n ns = dict(session=session,\n interval=interval,\n n_s_clusters=100, # TODO: better handling of KK parameters\n )\n cmd = ('session.detect(interval=interval); session.cluster();')\n return (cmd, ns)\n\n\ndef cluster_manual(args):\n session = _create_session(args,\n clustering=args.clustering,\n use_store=not(args.no_store),\n )\n cluster_ids = (list(map(int, args.cluster_ids.split(',')))\n if args.cluster_ids else None)\n\n session.model.describe()\n\n from phy.gui import start_qt_app\n start_qt_app()\n\n gui = session.show_gui(cluster_ids=cluster_ids, show=False)\n print(\"\\nPress `ctrl+h` to see the list of keyboard shortcuts.\\n\")\n return 'gui.show()', dict(session=session, gui=gui, requires_qt=True)\n\n\n#------------------------------------------------------------------------------\n# Main functions\n#------------------------------------------------------------------------------\n\ndef main(args=None):\n p = ParserCreator()\n if args is None:\n args = sys.argv[1:]\n elif isinstance(args, string_types):\n args = args.split(' ')\n args = p.parse(args)\n if args is None:\n return\n\n if args.profiler or args.line_profiler:\n from phy.utils.testing import _enable_profiler, _profile\n prof = _enable_profiler(args.line_profiler)\n else:\n prof = None\n\n import phy\n if args.debug:\n phy.debug()\n\n # Hide the traceback.\n if args.hide_traceback:\n def exception_handler(exception_type, exception, traceback):\n print(\"{}: {}\".format(exception_type.__name__, exception))\n\n sys.excepthook = exception_handler\n\n # Activate IPython debugger.\n if args.pdb:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n color_scheme='Linux',\n call_pdb=1,\n )\n\n func = args.func\n if func is None:\n p.parser.print_help()\n return\n\n out = func(args)\n if not out:\n return\n cmd, ns = out\n if not cmd:\n return\n requires_qt = ns.pop('requires_qt', False)\n requires_vispy = ns.pop('requires_vispy', False)\n\n # Default variables in namespace.\n ns.update(phy=phy, path=args.file)\n if 'session' in ns:\n ns['model'] = ns['session'].model\n\n # Interactive mode with IPython.\n if args.ipython:\n print(\"\\nStarting IPython...\")\n from IPython import start_ipython\n args_ipy = [\"-i\", \"-c='{}'\".format(cmd)]\n if requires_qt or requires_vispy:\n # Activate Qt event loop integration with Qt.\n args_ipy += [\"--gui=qt\"]\n start_ipython(args_ipy, user_ns=ns)\n else:\n if not prof:\n exec_(cmd, {}, ns)\n else:\n _profile(prof, cmd, {}, ns)\n\n if requires_qt:\n # Launch the Qt app.\n from phy.gui import run_qt_app\n run_qt_app()\n elif requires_vispy:\n # Launch the VisPy Qt app.\n from vispy.app import use_app, run\n use_app('pyqt4')\n run()\n\n\n#------------------------------------------------------------------------------\n# Entry point\n#------------------------------------------------------------------------------\n\nif __name__ == '__main__':\n main()\n",
"step-ids": [
17,
18,
29,
30,
32
]
}
|
[
17,
18,
29,
30,
32
] |
import random
import datetime
import os
import time
import json
#
l_target_path = "E:/code/PYTHON_TRAINING/Training/Apr2020/BillingSystem/bills/"
while True:
l_store_id = random.randint(1, 4)
now = datetime.datetime.now()
l_bill_id = now.strftime("%Y%m%d%H%M%S")
# Generate Random Date
start_date = datetime.date(2000, 1, 1)
end_date = datetime.date(2020, 1, 1)
time_between_dates = end_date - start_date
days_between_dates = time_between_dates.days
random_number_of_days = random.randrange(days_between_dates)
l_date = start_date + datetime.timedelta(days=random_number_of_days)
l_bill_details = {}
for i in range(random.randint(1, 25)):
l_prod_id = random.randint(1,25)
l_qty = random.randint(1,20)
l_bill_details[l_prod_id] = l_qty
l_data = { "bill_id":l_bill_id
,"store_id":l_store_id
,"bill_date":l_date
,"bill_details":l_bill_details}
print(l_data) #json.dumps(l_data)
new_file = open(l_target_path + l_bill_id + ".json", "w")
new_file.write(str(l_data))
new_file.close()
time.sleep(3)
|
normal
|
{
"blob_id": "fad2ad89e4d0f04fad61e27048397a5702870ca9",
"index": 6177,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n l_store_id = random.randint(1, 4)\n now = datetime.datetime.now()\n l_bill_id = now.strftime('%Y%m%d%H%M%S')\n start_date = datetime.date(2000, 1, 1)\n end_date = datetime.date(2020, 1, 1)\n time_between_dates = end_date - start_date\n days_between_dates = time_between_dates.days\n random_number_of_days = random.randrange(days_between_dates)\n l_date = start_date + datetime.timedelta(days=random_number_of_days)\n l_bill_details = {}\n for i in range(random.randint(1, 25)):\n l_prod_id = random.randint(1, 25)\n l_qty = random.randint(1, 20)\n l_bill_details[l_prod_id] = l_qty\n l_data = {'bill_id': l_bill_id, 'store_id': l_store_id, 'bill_date':\n l_date, 'bill_details': l_bill_details}\n print(l_data)\n new_file = open(l_target_path + l_bill_id + '.json', 'w')\n new_file.write(str(l_data))\n new_file.close()\n time.sleep(3)\n",
"step-3": "<mask token>\nl_target_path = 'E:/code/PYTHON_TRAINING/Training/Apr2020/BillingSystem/bills/'\nwhile True:\n l_store_id = random.randint(1, 4)\n now = datetime.datetime.now()\n l_bill_id = now.strftime('%Y%m%d%H%M%S')\n start_date = datetime.date(2000, 1, 1)\n end_date = datetime.date(2020, 1, 1)\n time_between_dates = end_date - start_date\n days_between_dates = time_between_dates.days\n random_number_of_days = random.randrange(days_between_dates)\n l_date = start_date + datetime.timedelta(days=random_number_of_days)\n l_bill_details = {}\n for i in range(random.randint(1, 25)):\n l_prod_id = random.randint(1, 25)\n l_qty = random.randint(1, 20)\n l_bill_details[l_prod_id] = l_qty\n l_data = {'bill_id': l_bill_id, 'store_id': l_store_id, 'bill_date':\n l_date, 'bill_details': l_bill_details}\n print(l_data)\n new_file = open(l_target_path + l_bill_id + '.json', 'w')\n new_file.write(str(l_data))\n new_file.close()\n time.sleep(3)\n",
"step-4": "import random\nimport datetime\nimport os\nimport time\nimport json\nl_target_path = 'E:/code/PYTHON_TRAINING/Training/Apr2020/BillingSystem/bills/'\nwhile True:\n l_store_id = random.randint(1, 4)\n now = datetime.datetime.now()\n l_bill_id = now.strftime('%Y%m%d%H%M%S')\n start_date = datetime.date(2000, 1, 1)\n end_date = datetime.date(2020, 1, 1)\n time_between_dates = end_date - start_date\n days_between_dates = time_between_dates.days\n random_number_of_days = random.randrange(days_between_dates)\n l_date = start_date + datetime.timedelta(days=random_number_of_days)\n l_bill_details = {}\n for i in range(random.randint(1, 25)):\n l_prod_id = random.randint(1, 25)\n l_qty = random.randint(1, 20)\n l_bill_details[l_prod_id] = l_qty\n l_data = {'bill_id': l_bill_id, 'store_id': l_store_id, 'bill_date':\n l_date, 'bill_details': l_bill_details}\n print(l_data)\n new_file = open(l_target_path + l_bill_id + '.json', 'w')\n new_file.write(str(l_data))\n new_file.close()\n time.sleep(3)\n",
"step-5": "import random\nimport datetime\nimport os\nimport time\nimport json\n\n#\nl_target_path = \"E:/code/PYTHON_TRAINING/Training/Apr2020/BillingSystem/bills/\"\n\n\nwhile True:\n\n l_store_id = random.randint(1, 4)\n now = datetime.datetime.now()\n l_bill_id = now.strftime(\"%Y%m%d%H%M%S\")\n\n\n # Generate Random Date\n start_date = datetime.date(2000, 1, 1)\n end_date = datetime.date(2020, 1, 1)\n time_between_dates = end_date - start_date\n days_between_dates = time_between_dates.days\n random_number_of_days = random.randrange(days_between_dates)\n\n l_date = start_date + datetime.timedelta(days=random_number_of_days)\n\n l_bill_details = {}\n\n for i in range(random.randint(1, 25)):\n\n l_prod_id = random.randint(1,25)\n l_qty = random.randint(1,20)\n l_bill_details[l_prod_id] = l_qty\n\n l_data = { \"bill_id\":l_bill_id\n ,\"store_id\":l_store_id\n ,\"bill_date\":l_date\n ,\"bill_details\":l_bill_details}\n \n print(l_data) #json.dumps(l_data)\n\n new_file = open(l_target_path + l_bill_id + \".json\", \"w\")\n new_file.write(str(l_data))\n new_file.close()\n\n\n time.sleep(3)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
from __future__ import annotations
from typing import TYPE_CHECKING
import abc
import tcod.event
if TYPE_CHECKING:
from tcodplus.canvas import Canvas
from tcodplus.event import CanvasDispatcher
class IDrawable(abc.ABC):
@property
@abc.abstractmethod
def force_redraw(self) -> bool:
pass
@property
@force_redraw.setter
def force_redraw(self, value: bool) -> None:
pass
@abc.abstractmethod
def draw(self, dest: Canvas) -> None:
pass
@abc.abstractmethod
def base_drawing(self, console: tcod.console.Console) -> None:
pass
class IFocusable(abc.ABC):
@property
@abc.abstractmethod
def focus_dispatcher(self) -> CanvasDispatcher:
pass
class IMouseFocusable(IFocusable):
@abc.abstractmethod
def mousefocus(self, event: tcod.event.MouseMotion) -> bool:
pass
class IKeyboardFocusable(IFocusable):
@property
@abc.abstractmethod
def kbdfocus(self) -> bool:
pass
@kbdfocus.setter
@abc.abstractmethod
def kbdfocus(self, val: bool) -> None:
pass
@property
@abc.abstractmethod
def kbdfocus_requested(self) -> bool:
pass
@kbdfocus_requested.setter
@abc.abstractmethod
def kbdfocus_requested(self, val: bool) -> None:
pass
class IUpdatable(abc.ABC):
@property
@abc.abstractmethod
def should_update(self) -> bool:
pass
@should_update.setter
@abc.abstractmethod
def should_update(self, value: bool) -> None:
pass
@abc.abstractmethod
def update(self) -> None:
pass
|
normal
|
{
"blob_id": "e37f958191c9481c6664e90c17f43419a0b5b606",
"index": 8131,
"step-1": "<mask token>\n\n\nclass IDrawable(abc.ABC):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass IFocusable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def focus_dispatcher(self) ->CanvasDispatcher:\n pass\n\n\nclass IMouseFocusable(IFocusable):\n\n @abc.abstractmethod\n def mousefocus(self, event: tcod.event.MouseMotion) ->bool:\n pass\n\n\nclass IKeyboardFocusable(IFocusable):\n\n @property\n @abc.abstractmethod\n def kbdfocus(self) ->bool:\n pass\n\n @kbdfocus.setter\n @abc.abstractmethod\n def kbdfocus(self, val: bool) ->None:\n pass\n\n @property\n @abc.abstractmethod\n def kbdfocus_requested(self) ->bool:\n pass\n\n @kbdfocus_requested.setter\n @abc.abstractmethod\n def kbdfocus_requested(self, val: bool) ->None:\n pass\n\n\nclass IUpdatable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def should_update(self) ->bool:\n pass\n\n @should_update.setter\n @abc.abstractmethod\n def should_update(self, value: bool) ->None:\n pass\n\n @abc.abstractmethod\n def update(self) ->None:\n pass\n",
"step-2": "<mask token>\n\n\nclass IDrawable(abc.ABC):\n <mask token>\n <mask token>\n <mask token>\n\n @abc.abstractmethod\n def base_drawing(self, console: tcod.console.Console) ->None:\n pass\n\n\nclass IFocusable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def focus_dispatcher(self) ->CanvasDispatcher:\n pass\n\n\nclass IMouseFocusable(IFocusable):\n\n @abc.abstractmethod\n def mousefocus(self, event: tcod.event.MouseMotion) ->bool:\n pass\n\n\nclass IKeyboardFocusable(IFocusable):\n\n @property\n @abc.abstractmethod\n def kbdfocus(self) ->bool:\n pass\n\n @kbdfocus.setter\n @abc.abstractmethod\n def kbdfocus(self, val: bool) ->None:\n pass\n\n @property\n @abc.abstractmethod\n def kbdfocus_requested(self) ->bool:\n pass\n\n @kbdfocus_requested.setter\n @abc.abstractmethod\n def kbdfocus_requested(self, val: bool) ->None:\n pass\n\n\nclass IUpdatable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def should_update(self) ->bool:\n pass\n\n @should_update.setter\n @abc.abstractmethod\n def should_update(self, value: bool) ->None:\n pass\n\n @abc.abstractmethod\n def update(self) ->None:\n pass\n",
"step-3": "<mask token>\n\n\nclass IDrawable(abc.ABC):\n <mask token>\n\n @property\n @force_redraw.setter\n def force_redraw(self, value: bool) ->None:\n pass\n\n @abc.abstractmethod\n def draw(self, dest: Canvas) ->None:\n pass\n\n @abc.abstractmethod\n def base_drawing(self, console: tcod.console.Console) ->None:\n pass\n\n\nclass IFocusable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def focus_dispatcher(self) ->CanvasDispatcher:\n pass\n\n\nclass IMouseFocusable(IFocusable):\n\n @abc.abstractmethod\n def mousefocus(self, event: tcod.event.MouseMotion) ->bool:\n pass\n\n\nclass IKeyboardFocusable(IFocusable):\n\n @property\n @abc.abstractmethod\n def kbdfocus(self) ->bool:\n pass\n\n @kbdfocus.setter\n @abc.abstractmethod\n def kbdfocus(self, val: bool) ->None:\n pass\n\n @property\n @abc.abstractmethod\n def kbdfocus_requested(self) ->bool:\n pass\n\n @kbdfocus_requested.setter\n @abc.abstractmethod\n def kbdfocus_requested(self, val: bool) ->None:\n pass\n\n\nclass IUpdatable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def should_update(self) ->bool:\n pass\n\n @should_update.setter\n @abc.abstractmethod\n def should_update(self, value: bool) ->None:\n pass\n\n @abc.abstractmethod\n def update(self) ->None:\n pass\n",
"step-4": "<mask token>\n\n\nclass IDrawable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def force_redraw(self) ->bool:\n pass\n\n @property\n @force_redraw.setter\n def force_redraw(self, value: bool) ->None:\n pass\n\n @abc.abstractmethod\n def draw(self, dest: Canvas) ->None:\n pass\n\n @abc.abstractmethod\n def base_drawing(self, console: tcod.console.Console) ->None:\n pass\n\n\nclass IFocusable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def focus_dispatcher(self) ->CanvasDispatcher:\n pass\n\n\nclass IMouseFocusable(IFocusable):\n\n @abc.abstractmethod\n def mousefocus(self, event: tcod.event.MouseMotion) ->bool:\n pass\n\n\nclass IKeyboardFocusable(IFocusable):\n\n @property\n @abc.abstractmethod\n def kbdfocus(self) ->bool:\n pass\n\n @kbdfocus.setter\n @abc.abstractmethod\n def kbdfocus(self, val: bool) ->None:\n pass\n\n @property\n @abc.abstractmethod\n def kbdfocus_requested(self) ->bool:\n pass\n\n @kbdfocus_requested.setter\n @abc.abstractmethod\n def kbdfocus_requested(self, val: bool) ->None:\n pass\n\n\nclass IUpdatable(abc.ABC):\n\n @property\n @abc.abstractmethod\n def should_update(self) ->bool:\n pass\n\n @should_update.setter\n @abc.abstractmethod\n def should_update(self, value: bool) ->None:\n pass\n\n @abc.abstractmethod\n def update(self) ->None:\n pass\n",
"step-5": "from __future__ import annotations\nfrom typing import TYPE_CHECKING\nimport abc\nimport tcod.event\n\nif TYPE_CHECKING:\n from tcodplus.canvas import Canvas\n from tcodplus.event import CanvasDispatcher\n\n\nclass IDrawable(abc.ABC):\n @property\n @abc.abstractmethod\n def force_redraw(self) -> bool:\n pass\n\n @property\n @force_redraw.setter\n def force_redraw(self, value: bool) -> None:\n pass\n\n @abc.abstractmethod\n def draw(self, dest: Canvas) -> None:\n pass\n\n @abc.abstractmethod\n def base_drawing(self, console: tcod.console.Console) -> None:\n pass\n\n\nclass IFocusable(abc.ABC):\n @property\n @abc.abstractmethod\n def focus_dispatcher(self) -> CanvasDispatcher:\n pass\n\n\nclass IMouseFocusable(IFocusable):\n @abc.abstractmethod\n def mousefocus(self, event: tcod.event.MouseMotion) -> bool:\n pass\n\n\nclass IKeyboardFocusable(IFocusable):\n @property\n @abc.abstractmethod\n def kbdfocus(self) -> bool:\n pass\n\n @kbdfocus.setter\n @abc.abstractmethod\n def kbdfocus(self, val: bool) -> None:\n pass\n\n @property\n @abc.abstractmethod\n def kbdfocus_requested(self) -> bool:\n pass\n\n @kbdfocus_requested.setter\n @abc.abstractmethod\n def kbdfocus_requested(self, val: bool) -> None:\n pass\n\n\nclass IUpdatable(abc.ABC):\n @property\n @abc.abstractmethod\n def should_update(self) -> bool:\n pass\n\n @should_update.setter\n @abc.abstractmethod\n def should_update(self, value: bool) -> None:\n pass\n\n @abc.abstractmethod\n def update(self) -> None:\n pass\n",
"step-ids": [
14,
15,
17,
18,
21
]
}
|
[
14,
15,
17,
18,
21
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
bw2_schema = Schema(name=TEXT(stored=True, sortable=True), comment=TEXT(
stored=True), product=TEXT(stored=True, sortable=True), categories=TEXT
(stored=True), location=TEXT(stored=True, sortable=True), database=TEXT
(stored=True), code=ID(unique=True, stored=True))
<|reserved_special_token_1|>
from __future__ import print_function, unicode_literals
from eight import *
from whoosh.fields import TEXT, ID, Schema
bw2_schema = Schema(name=TEXT(stored=True, sortable=True), comment=TEXT(
stored=True), product=TEXT(stored=True, sortable=True), categories=TEXT
(stored=True), location=TEXT(stored=True, sortable=True), database=TEXT
(stored=True), code=ID(unique=True, stored=True))
<|reserved_special_token_1|>
# -*- coding: utf-8 -*-
from __future__ import print_function, unicode_literals
from eight import *
from whoosh.fields import TEXT, ID, Schema
bw2_schema = Schema(
name=TEXT(stored=True, sortable=True),
comment=TEXT(stored=True),
product=TEXT(stored=True, sortable=True),
categories=TEXT(stored=True),
location=TEXT(stored=True, sortable=True),
database=TEXT(stored=True),
code=ID(unique=True, stored=True),
)
|
flexible
|
{
"blob_id": "07aafcb3db9c57ad09a29a827d72744ef0d22247",
"index": 3319,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nbw2_schema = Schema(name=TEXT(stored=True, sortable=True), comment=TEXT(\n stored=True), product=TEXT(stored=True, sortable=True), categories=TEXT\n (stored=True), location=TEXT(stored=True, sortable=True), database=TEXT\n (stored=True), code=ID(unique=True, stored=True))\n",
"step-3": "from __future__ import print_function, unicode_literals\nfrom eight import *\nfrom whoosh.fields import TEXT, ID, Schema\nbw2_schema = Schema(name=TEXT(stored=True, sortable=True), comment=TEXT(\n stored=True), product=TEXT(stored=True, sortable=True), categories=TEXT\n (stored=True), location=TEXT(stored=True, sortable=True), database=TEXT\n (stored=True), code=ID(unique=True, stored=True))\n",
"step-4": "# -*- coding: utf-8 -*-\nfrom __future__ import print_function, unicode_literals\nfrom eight import *\n\nfrom whoosh.fields import TEXT, ID, Schema\n\nbw2_schema = Schema(\n name=TEXT(stored=True, sortable=True),\n comment=TEXT(stored=True),\n product=TEXT(stored=True, sortable=True),\n categories=TEXT(stored=True),\n location=TEXT(stored=True, sortable=True),\n database=TEXT(stored=True),\n code=ID(unique=True, stored=True),\n)\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#! /usr/bin/env python
t = int(raw_input())
for i in xrange(1, t+1):
N = raw_input()
N1 = N
track = set()
if N == '0':
print "Case #%s: " % i + "INSOMNIA"
continue
count = 2
while len(track) !=10:
temp = set(x for x in N1)
track = temp | track
N1 = str(count*int(N))
count +=1
print "Case #%s: %d" % (i, int(N1) - int(N))
|
normal
|
{
"blob_id": "8c6b7032c85354740d59aa91108ad8b5279e1d45",
"index": 2570,
"step-1": "#! /usr/bin/env python\n\nt = int(raw_input())\nfor i in xrange(1, t+1):\n N = raw_input()\n N1 = N\n track = set()\n if N == '0':\n print \"Case #%s: \" % i + \"INSOMNIA\"\n continue\n count = 2\n while len(track) !=10:\n temp = set(x for x in N1)\n track = temp | track\n N1 = str(count*int(N))\n count +=1\n print \"Case #%s: %d\" % (i, int(N1) - int(N))\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
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)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
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)
<|reserved_special_token_1|>
'''
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)
|
flexible
|
{
"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|>
def find_and_display_patter_in_series(*, series, pattern):
"""I used that function when i don't remeber full name of a given column"""
res = series.loc[series.str.contains(pattern)]
return res
<|reserved_special_token_0|>
def find_patter_in_series(*, s, pat, tolist=True):
"""
I used that function when i don't remeber full name of a given column
"""
res = s.loc[s.str.contains(pat)]
if tolist == True:
return res.values.tolist()
else:
return res
def format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=
False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):
"""
formats columns in df into datetime dtype, and set all times to UTC
work with unix time units, ie. second number since 1970
columns in df, are find using full comlumn name or keywords in column name
"""
assert type(data
) == pd.DataFrame, 'please provide data in pandas dataframe format'
if isinstance(pattern_list, str):
pattern_list = [pattern_list]
else:
pass
for pat in pattern_list:
columns_with_potential_datetime_obj = list(
find_and_display_patter_in_series(series=pd.Series(data.columns
), pattern=pat))
for i in columns_with_potential_datetime_obj:
before_formatting = str(data.loc[0, i])
if unixtime == True:
s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'
).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)
else:
pass
else:
s = pd.to_datetime(data.loc[:, i], errors='coerce', format=
dt_format).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)
else:
pass
if verbose == True:
print(f'date time formatted in: {i}')
print(
f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'
)
print(
f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'
, end='\n')
else:
pass
return data
def replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):
"""
searches string with a given pattern and replace it with a new patter (fillna), eg: nan,
Parameters/Input
_________________ _______________________________________________________________________________
* df Pandas Dataframe
* searched_pattern "", str literal, used by pd.Series.str.contains()
* colnames default, "all", or list with selected colnames in df
* fillna default numpy.nan, or str literal
- what do you want to place instead of searched pattern in df
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
searched_pattern = pat
col_names = colnames
if col_names == 'all':
sel_col_names = list(df.columns)
else:
sel_col_names = col_names
if verbose == True:
print(
f'\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\n'
)
if verbose == False:
pass
for i, col_name in enumerate(sel_col_names):
if is_string_dtype(df[col_name]):
try:
positions_to_replace = df[col_name].str.contains(
searched_pattern, na=False).values
examples_to_display = [str(x) for x in list(df.loc[list(
positions_to_replace), col_name].str[0:20].values.
tolist()[0:3])]
df.loc[list(positions_to_replace), col_name] = [fillna
] * positions_to_replace.sum()
examples_of_positions_that_were_not_replaced = [str(x) for
x in list(df.loc[list(positions_to_replace == False),
col_name].str[0:20].values.tolist()[0:3])]
if verbose == True:
perc_of_replaced_pos_in_col = ''.join([str(
positions_to_replace.sum() / df.shape[0] * 100), '%'])
print(
f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'
)
print(
f" - three examples of replaced postions: {'; '.join(examples_to_display)}"
, end='\n')
print(
f" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}"
, end='\n\n')
else:
pass
except:
if verbose == True:
print(
f"""{i} - {col_name} - - probably only missing data datected, Values were not replaced!
"""
)
else:
pass
elif verbose == True:
print(
f'{i} - {col_name} - - is not of string type, Values were not replaced! \n'
)
else:
pass
return df.copy()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def find_and_display_patter_in_series(*, series, pattern):
"""I used that function when i don't remeber full name of a given column"""
res = series.loc[series.str.contains(pattern)]
return res
<|reserved_special_token_0|>
def find_patter_in_series(*, s, pat, tolist=True):
"""
I used that function when i don't remeber full name of a given column
"""
res = s.loc[s.str.contains(pat)]
if tolist == True:
return res.values.tolist()
else:
return res
def format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=
False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):
"""
formats columns in df into datetime dtype, and set all times to UTC
work with unix time units, ie. second number since 1970
columns in df, are find using full comlumn name or keywords in column name
"""
assert type(data
) == pd.DataFrame, 'please provide data in pandas dataframe format'
if isinstance(pattern_list, str):
pattern_list = [pattern_list]
else:
pass
for pat in pattern_list:
columns_with_potential_datetime_obj = list(
find_and_display_patter_in_series(series=pd.Series(data.columns
), pattern=pat))
for i in columns_with_potential_datetime_obj:
before_formatting = str(data.loc[0, i])
if unixtime == True:
s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'
).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)
else:
pass
else:
s = pd.to_datetime(data.loc[:, i], errors='coerce', format=
dt_format).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)
else:
pass
if verbose == True:
print(f'date time formatted in: {i}')
print(
f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'
)
print(
f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'
, end='\n')
else:
pass
return data
def replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):
"""
searches string with a given pattern and replace it with a new patter (fillna), eg: nan,
Parameters/Input
_________________ _______________________________________________________________________________
* df Pandas Dataframe
* searched_pattern "", str literal, used by pd.Series.str.contains()
* colnames default, "all", or list with selected colnames in df
* fillna default numpy.nan, or str literal
- what do you want to place instead of searched pattern in df
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
searched_pattern = pat
col_names = colnames
if col_names == 'all':
sel_col_names = list(df.columns)
else:
sel_col_names = col_names
if verbose == True:
print(
f'\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\n'
)
if verbose == False:
pass
for i, col_name in enumerate(sel_col_names):
if is_string_dtype(df[col_name]):
try:
positions_to_replace = df[col_name].str.contains(
searched_pattern, na=False).values
examples_to_display = [str(x) for x in list(df.loc[list(
positions_to_replace), col_name].str[0:20].values.
tolist()[0:3])]
df.loc[list(positions_to_replace), col_name] = [fillna
] * positions_to_replace.sum()
examples_of_positions_that_were_not_replaced = [str(x) for
x in list(df.loc[list(positions_to_replace == False),
col_name].str[0:20].values.tolist()[0:3])]
if verbose == True:
perc_of_replaced_pos_in_col = ''.join([str(
positions_to_replace.sum() / df.shape[0] * 100), '%'])
print(
f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'
)
print(
f" - three examples of replaced postions: {'; '.join(examples_to_display)}"
, end='\n')
print(
f" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}"
, end='\n\n')
else:
pass
except:
if verbose == True:
print(
f"""{i} - {col_name} - - probably only missing data datected, Values were not replaced!
"""
)
else:
pass
elif verbose == True:
print(
f'{i} - {col_name} - - is not of string type, Values were not replaced! \n'
)
else:
pass
return df.copy()
<|reserved_special_token_0|>
def drop_nan(df, method='any', row=True, verbose=True):
"""
function to dropna with thresholds from rows and columns
. method
. any : row/column wiht any missing data are removed
. all : row/column only wiht missing data are removed
. int, >0 : keeps row/clumns wiht this or larger number of non missing data
. float, >0 : as in the above, as fraction
"""
assert type(df) == pd.DataFrame, 'incorrect df dtype'
df = df.copy()
if verbose == True:
print(df.shape)
else:
pass
if row == True:
shapeidx, dfaxis = 1, 0
else:
shapeidx, dfaxis = 0, 1
if method == None:
pass
elif isinstance(method, str):
df = df.dropna(how=method, axis=dfaxis)
elif isinstance(method, int):
tr = method
if tr == 0:
pass
else:
if tr >= df.shape[shapeidx]:
tr = df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis)
elif isinstance(method, float):
tr = int(np.ceil(df.shape[shapeidx] * method))
if tr == 0:
pass
else:
if tr >= df.shape[shapeidx]:
tr = df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis)
else:
pass
if verbose == True:
print(df.shape)
else:
pass
return df
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def find_and_display_patter_in_series(*, series, pattern):
"""I used that function when i don't remeber full name of a given column"""
res = series.loc[series.str.contains(pattern)]
return res
<|reserved_special_token_0|>
def find_patter_in_series(*, s, pat, tolist=True):
"""
I used that function when i don't remeber full name of a given column
"""
res = s.loc[s.str.contains(pat)]
if tolist == True:
return res.values.tolist()
else:
return res
def format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=
False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):
"""
formats columns in df into datetime dtype, and set all times to UTC
work with unix time units, ie. second number since 1970
columns in df, are find using full comlumn name or keywords in column name
"""
assert type(data
) == pd.DataFrame, 'please provide data in pandas dataframe format'
if isinstance(pattern_list, str):
pattern_list = [pattern_list]
else:
pass
for pat in pattern_list:
columns_with_potential_datetime_obj = list(
find_and_display_patter_in_series(series=pd.Series(data.columns
), pattern=pat))
for i in columns_with_potential_datetime_obj:
before_formatting = str(data.loc[0, i])
if unixtime == True:
s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'
).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)
else:
pass
else:
s = pd.to_datetime(data.loc[:, i], errors='coerce', format=
dt_format).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)
else:
pass
if verbose == True:
print(f'date time formatted in: {i}')
print(
f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'
)
print(
f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'
, end='\n')
else:
pass
return data
def replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):
"""
searches string with a given pattern and replace it with a new patter (fillna), eg: nan,
Parameters/Input
_________________ _______________________________________________________________________________
* df Pandas Dataframe
* searched_pattern "", str literal, used by pd.Series.str.contains()
* colnames default, "all", or list with selected colnames in df
* fillna default numpy.nan, or str literal
- what do you want to place instead of searched pattern in df
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
searched_pattern = pat
col_names = colnames
if col_names == 'all':
sel_col_names = list(df.columns)
else:
sel_col_names = col_names
if verbose == True:
print(
f'\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\n'
)
if verbose == False:
pass
for i, col_name in enumerate(sel_col_names):
if is_string_dtype(df[col_name]):
try:
positions_to_replace = df[col_name].str.contains(
searched_pattern, na=False).values
examples_to_display = [str(x) for x in list(df.loc[list(
positions_to_replace), col_name].str[0:20].values.
tolist()[0:3])]
df.loc[list(positions_to_replace), col_name] = [fillna
] * positions_to_replace.sum()
examples_of_positions_that_were_not_replaced = [str(x) for
x in list(df.loc[list(positions_to_replace == False),
col_name].str[0:20].values.tolist()[0:3])]
if verbose == True:
perc_of_replaced_pos_in_col = ''.join([str(
positions_to_replace.sum() / df.shape[0] * 100), '%'])
print(
f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'
)
print(
f" - three examples of replaced postions: {'; '.join(examples_to_display)}"
, end='\n')
print(
f" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}"
, end='\n\n')
else:
pass
except:
if verbose == True:
print(
f"""{i} - {col_name} - - probably only missing data datected, Values were not replaced!
"""
)
else:
pass
elif verbose == True:
print(
f'{i} - {col_name} - - is not of string type, Values were not replaced! \n'
)
else:
pass
return df.copy()
def replace_numeric_values(*, df, colnames='all', lower_limit='none',
upper_limit='none', equal=False, replace_with=np.nan, verbose=True):
"""
Replace numerical values that are outside of range of a values
prediced with a theoretical limits of a given variable,
eg less then 0 in weight of a product,
Provide examples and numbers of replaced instances
Parameters/Input
_________________ _______________________________________________________________________________
* df : Pandas DataFrame
* cols_in_df : list, exact colnames of selected or all columns in df
* lower_limit : int,float,"none", if "none" no action is taken
* upper_limit : int,float,"none", if "none" no action is taken
* replace_with : str, np.nan, int, float
* equal : bool, if True, >= and <= values then limits will be replaced,
if False (default), > and < values then limits will be replaced,
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
cols_names = colnames
if cols_names == 'all':
cols = list(df.columns)
else:
cols = cols_names
if verbose == True:
print(
f"\n{''.join(['-'] * 80)} \n Replacing Numerical Values in {len(cols)} columns"
)
print(
f' lower filter={lower_limit}, upper filter ={upper_limit}')
if equal == True:
print(
f' Caution, equal=True, ie. values >= and <= then requested limits will be replaced'
)
print(f"{''.join(['-'] * 80)}\n")
if verbose == False:
pass
total_count = []
count = 0
for i, j in enumerate(cols):
info_lower_filter = 0
info_upper_filter = 0
if is_numeric_dtype(df[j]):
if lower_limit != 'none':
if equal == True:
lower_filter = df.loc[:, j] <= lower_limit
if equal == False:
lower_filter = df.loc[:, j] < lower_limit
info_lower_filter = lower_filter.sum()
df.loc[list(lower_filter), j] = replace_with
if upper_limit != 'none':
if equal == True:
upper_filter = df.loc[:, j] >= upper_limit
if equal == False:
upper_filter = df.loc[:, j] > upper_limit
info_upper_filter = upper_filter.sum()
df.loc[list(upper_filter), j] = replace_with
total_count.append(info_upper_filter + info_lower_filter)
if verbose == True:
if info_upper_filter + info_lower_filter > 0 and count < 4:
print(
f'eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}'
)
else:
pass
count += 1
elif verbose == True:
print(f'{i, j} is not of numeric type, values were not replaced !')
else:
pass
if verbose == True:
if len(total_count) > 3 and pd.Series(total_count).sum() > 0:
print(
f""". and {len(total_count) - 3} other columns had in total {pd.Series(total_count).sum()} replaced values
"""
)
if pd.Series(total_count).sum() == 0:
print('No values were replaced in requested columns....')
else:
pass
return df.copy()
def drop_nan(df, method='any', row=True, verbose=True):
"""
function to dropna with thresholds from rows and columns
. method
. any : row/column wiht any missing data are removed
. all : row/column only wiht missing data are removed
. int, >0 : keeps row/clumns wiht this or larger number of non missing data
. float, >0 : as in the above, as fraction
"""
assert type(df) == pd.DataFrame, 'incorrect df dtype'
df = df.copy()
if verbose == True:
print(df.shape)
else:
pass
if row == True:
shapeidx, dfaxis = 1, 0
else:
shapeidx, dfaxis = 0, 1
if method == None:
pass
elif isinstance(method, str):
df = df.dropna(how=method, axis=dfaxis)
elif isinstance(method, int):
tr = method
if tr == 0:
pass
else:
if tr >= df.shape[shapeidx]:
tr = df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis)
elif isinstance(method, float):
tr = int(np.ceil(df.shape[shapeidx] * method))
if tr == 0:
pass
else:
if tr >= df.shape[shapeidx]:
tr = df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis)
else:
pass
if verbose == True:
print(df.shape)
else:
pass
return df
def drop_columns(*, df, columns_to_drop, verbose=True):
"""
Small function to quickly remove columns from,
by column names stored in the list
- created to give info on removed columns and whether I am chnaging df in proper way,
- the function allows for column name duplicates,
"""
assert type(df
) == pd.DataFrame, 'please provide df in pandas dataframe format'
df = df.copy()
columns_to_drop = list(pd.Series(columns_to_drop).unique())
if verbose == True:
print(f'Removing {len(columns_to_drop)} columns from df')
else:
pass
for i, j in enumerate(columns_to_drop):
try:
df.drop(columns=[j], axis=1, inplace=True)
if verbose == True:
print(f'{i} removing: {j}, ==> new df.shape: {df.shape}')
else:
pass
except:
if verbose == True:
print(
f'{i} .... column: {j}, was not found in df, check if name is correct....'
)
else:
pass
return df
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def find_and_display_patter_in_series(*, series, pattern):
"""I used that function when i don't remeber full name of a given column"""
res = series.loc[series.str.contains(pattern)]
return res
def load_csv(*, path, filename, sep='\t', verbose=True):
"""
Loads csv into pandas df, based on pandas.read_scv(),
Returns error, if file or directoy not found
Parameters/Input
_________________ _______________________________________________________________________________
* path full path to directory
* csv_name. full csv file name
* separator " ", by default
* display_head bool, True, by default, display df.head(),
irrespectively when the futions was called.
Returns
_________________ _______________________________________________________________________________
* DataFrame by Pandas
"""
os.chdir(path)
if len(glob.glob(filename)) == 1:
df = pd.read_csv(filename, sep=sep, low_memory=False)
if verbose == True:
display(df.head(3))
print(df.shape)
else:
pass
return df
elif verbose == True:
print(f'ERROR :csv file {filename}, was not found in: \n {path}')
else:
pass
def find_patter_in_series(*, s, pat, tolist=True):
"""
I used that function when i don't remeber full name of a given column
"""
res = s.loc[s.str.contains(pat)]
if tolist == True:
return res.values.tolist()
else:
return res
def format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=
False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):
"""
formats columns in df into datetime dtype, and set all times to UTC
work with unix time units, ie. second number since 1970
columns in df, are find using full comlumn name or keywords in column name
"""
assert type(data
) == pd.DataFrame, 'please provide data in pandas dataframe format'
if isinstance(pattern_list, str):
pattern_list = [pattern_list]
else:
pass
for pat in pattern_list:
columns_with_potential_datetime_obj = list(
find_and_display_patter_in_series(series=pd.Series(data.columns
), pattern=pat))
for i in columns_with_potential_datetime_obj:
before_formatting = str(data.loc[0, i])
if unixtime == True:
s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'
).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)
else:
pass
else:
s = pd.to_datetime(data.loc[:, i], errors='coerce', format=
dt_format).copy()
data.loc[:, i] = s
if timezone != None:
data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)
else:
pass
if verbose == True:
print(f'date time formatted in: {i}')
print(
f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'
)
print(
f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'
, end='\n')
else:
pass
return data
def replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):
"""
searches string with a given pattern and replace it with a new patter (fillna), eg: nan,
Parameters/Input
_________________ _______________________________________________________________________________
* df Pandas Dataframe
* searched_pattern "", str literal, used by pd.Series.str.contains()
* colnames default, "all", or list with selected colnames in df
* fillna default numpy.nan, or str literal
- what do you want to place instead of searched pattern in df
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
searched_pattern = pat
col_names = colnames
if col_names == 'all':
sel_col_names = list(df.columns)
else:
sel_col_names = col_names
if verbose == True:
print(
f'\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\n'
)
if verbose == False:
pass
for i, col_name in enumerate(sel_col_names):
if is_string_dtype(df[col_name]):
try:
positions_to_replace = df[col_name].str.contains(
searched_pattern, na=False).values
examples_to_display = [str(x) for x in list(df.loc[list(
positions_to_replace), col_name].str[0:20].values.
tolist()[0:3])]
df.loc[list(positions_to_replace), col_name] = [fillna
] * positions_to_replace.sum()
examples_of_positions_that_were_not_replaced = [str(x) for
x in list(df.loc[list(positions_to_replace == False),
col_name].str[0:20].values.tolist()[0:3])]
if verbose == True:
perc_of_replaced_pos_in_col = ''.join([str(
positions_to_replace.sum() / df.shape[0] * 100), '%'])
print(
f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'
)
print(
f" - three examples of replaced postions: {'; '.join(examples_to_display)}"
, end='\n')
print(
f" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}"
, end='\n\n')
else:
pass
except:
if verbose == True:
print(
f"""{i} - {col_name} - - probably only missing data datected, Values were not replaced!
"""
)
else:
pass
elif verbose == True:
print(
f'{i} - {col_name} - - is not of string type, Values were not replaced! \n'
)
else:
pass
return df.copy()
def replace_numeric_values(*, df, colnames='all', lower_limit='none',
upper_limit='none', equal=False, replace_with=np.nan, verbose=True):
"""
Replace numerical values that are outside of range of a values
prediced with a theoretical limits of a given variable,
eg less then 0 in weight of a product,
Provide examples and numbers of replaced instances
Parameters/Input
_________________ _______________________________________________________________________________
* df : Pandas DataFrame
* cols_in_df : list, exact colnames of selected or all columns in df
* lower_limit : int,float,"none", if "none" no action is taken
* upper_limit : int,float,"none", if "none" no action is taken
* replace_with : str, np.nan, int, float
* equal : bool, if True, >= and <= values then limits will be replaced,
if False (default), > and < values then limits will be replaced,
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
cols_names = colnames
if cols_names == 'all':
cols = list(df.columns)
else:
cols = cols_names
if verbose == True:
print(
f"\n{''.join(['-'] * 80)} \n Replacing Numerical Values in {len(cols)} columns"
)
print(
f' lower filter={lower_limit}, upper filter ={upper_limit}')
if equal == True:
print(
f' Caution, equal=True, ie. values >= and <= then requested limits will be replaced'
)
print(f"{''.join(['-'] * 80)}\n")
if verbose == False:
pass
total_count = []
count = 0
for i, j in enumerate(cols):
info_lower_filter = 0
info_upper_filter = 0
if is_numeric_dtype(df[j]):
if lower_limit != 'none':
if equal == True:
lower_filter = df.loc[:, j] <= lower_limit
if equal == False:
lower_filter = df.loc[:, j] < lower_limit
info_lower_filter = lower_filter.sum()
df.loc[list(lower_filter), j] = replace_with
if upper_limit != 'none':
if equal == True:
upper_filter = df.loc[:, j] >= upper_limit
if equal == False:
upper_filter = df.loc[:, j] > upper_limit
info_upper_filter = upper_filter.sum()
df.loc[list(upper_filter), j] = replace_with
total_count.append(info_upper_filter + info_lower_filter)
if verbose == True:
if info_upper_filter + info_lower_filter > 0 and count < 4:
print(
f'eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}'
)
else:
pass
count += 1
elif verbose == True:
print(f'{i, j} is not of numeric type, values were not replaced !')
else:
pass
if verbose == True:
if len(total_count) > 3 and pd.Series(total_count).sum() > 0:
print(
f""". and {len(total_count) - 3} other columns had in total {pd.Series(total_count).sum()} replaced values
"""
)
if pd.Series(total_count).sum() == 0:
print('No values were replaced in requested columns....')
else:
pass
return df.copy()
def drop_nan(df, method='any', row=True, verbose=True):
"""
function to dropna with thresholds from rows and columns
. method
. any : row/column wiht any missing data are removed
. all : row/column only wiht missing data are removed
. int, >0 : keeps row/clumns wiht this or larger number of non missing data
. float, >0 : as in the above, as fraction
"""
assert type(df) == pd.DataFrame, 'incorrect df dtype'
df = df.copy()
if verbose == True:
print(df.shape)
else:
pass
if row == True:
shapeidx, dfaxis = 1, 0
else:
shapeidx, dfaxis = 0, 1
if method == None:
pass
elif isinstance(method, str):
df = df.dropna(how=method, axis=dfaxis)
elif isinstance(method, int):
tr = method
if tr == 0:
pass
else:
if tr >= df.shape[shapeidx]:
tr = df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis)
elif isinstance(method, float):
tr = int(np.ceil(df.shape[shapeidx] * method))
if tr == 0:
pass
else:
if tr >= df.shape[shapeidx]:
tr = df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis)
else:
pass
if verbose == True:
print(df.shape)
else:
pass
return df
def drop_columns(*, df, columns_to_drop, verbose=True):
"""
Small function to quickly remove columns from,
by column names stored in the list
- created to give info on removed columns and whether I am chnaging df in proper way,
- the function allows for column name duplicates,
"""
assert type(df
) == pd.DataFrame, 'please provide df in pandas dataframe format'
df = df.copy()
columns_to_drop = list(pd.Series(columns_to_drop).unique())
if verbose == True:
print(f'Removing {len(columns_to_drop)} columns from df')
else:
pass
for i, j in enumerate(columns_to_drop):
try:
df.drop(columns=[j], axis=1, inplace=True)
if verbose == True:
print(f'{i} removing: {j}, ==> new df.shape: {df.shape}')
else:
pass
except:
if verbose == True:
print(
f'{i} .... column: {j}, was not found in df, check if name is correct....'
)
else:
pass
return df
<|reserved_special_token_1|>
# ********************************************************************************** #
# #
# Project: Data Frame Explorer #
# Author: Pawel Rosikiewicz #
# Contact: prosikiewicz(a)gmail.com #
# #
# License: MIT License #
# Copyright (C) 2021.01.30 Pawel Rosikiewicz #
# #
# Permission is hereby granted, free of charge, to any person obtaining a copy #
# of this software and associated documentation files (the "Software"), to deal #
# in the Software without restriction, including without limitation the rights #
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #
# copies of the Software, and to permit persons to whom the Software is #
# furnished to do so, subject to the following conditions: #
# #
# The above copyright notice and this permission notice shall be included in all #
# copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #
# SOFTWARE. #
# #
# ********************************************************************************** #
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
import random
import glob
import re
import os
import seaborn as sns
from IPython.display import display
from pandas.api.types import is_numeric_dtype
from pandas.api.types import is_string_dtype
# Function, ............................................................................
def find_and_display_patter_in_series(*, series, pattern):
"I used that function when i don't remeber full name of a given column"
res = series.loc[series.str.contains(pattern)]
return res
# Function, ...........................................................................................
def load_csv(*, path, filename, sep="\t", verbose=True):
"""
Loads csv into pandas df, based on pandas.read_scv(),
Returns error, if file or directoy not found
Parameters/Input
_________________ _______________________________________________________________________________
* path full path to directory
* csv_name. full csv file name
* separator "\t", by default
* display_head bool, True, by default, display df.head(),
irrespectively when the futions was called.
Returns
_________________ _______________________________________________________________________________
* DataFrame by Pandas
"""
os.chdir(path)
if len(glob.glob(filename))==1:
df = pd.read_csv(filename, sep=sep, low_memory=False)
# display example,
if verbose==True:
display(df.head(3))
print(df.shape)
else:
pass
# return,
return df
else:
if verbose==True:
print(f"""ERROR :csv file {filename}, was not found in: \n {path}""")
else:
pass
# Function, ............................................................................
def find_patter_in_series(*, s, pat, tolist=True):
'''
I used that function when i don't remeber full name of a given column
'''
res = s.loc[s.str.contains(pat)]
if tolist==True:
return res.values.tolist()
else:
return res
# Function, ...........................................................................................
def format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):
'''
formats columns in df into datetime dtype, and set all times to UTC
work with unix time units, ie. second number since 1970
columns in df, are find using full comlumn name or keywords in column name
'''
assert type(data)==pd.DataFrame, "please provide data in pandas dataframe format"
if isinstance(pattern_list, str):
pattern_list = [pattern_list]
else:
pass
for pat in pattern_list:
# find column names using provided patterns or their full names,
columns_with_potential_datetime_obj = list(find_and_display_patter_in_series(series=pd.Series(data.columns), pattern=pat))
# replace
for i in columns_with_potential_datetime_obj:
# keep example of old cell
before_formatting = str(data.loc[0, i])
# convert to one format
if unixtime==True:
s = pd.to_datetime(data.loc[:, i], errors="coerce", unit='s').copy()#,format cannot be used with unit="s", but it will be the same
data.loc[:, i] = s
if timezone!=None:
data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)
else:
pass
else:
s = pd.to_datetime(data.loc[:, i], errors="coerce",format=dt_format).copy()
data.loc[:, i] = s
if timezone!=None:
data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)
else:
pass
# info
if verbose==True:
print(f"date time formatted in: {i}")
print(f" - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce")
print(f" - Example: {before_formatting} -->> {str(data.loc[0, i])}", end="\n")
else:
pass
return data
# Function, ...........................................................................................
def replace_text(*,df ,pat="", colnames="all", fillna=np.nan, verbose=True):
"""
searches string with a given pattern and replace it with a new patter (fillna), eg: nan,
Parameters/Input
_________________ _______________________________________________________________________________
* df Pandas Dataframe
* searched_pattern "", str literal, used by pd.Series.str.contains()
* colnames default, "all", or list with selected colnames in df
* fillna default numpy.nan, or str literal
- what do you want to place instead of searched pattern in df
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
# for older version,
searched_pattern = pat
col_names = colnames
# check col_names with values to replace,
if col_names=="all":
sel_col_names = list(df.columns)
else:
sel_col_names = col_names
# display message header,
if verbose==True:
print(f"""\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\n""")
if verbose==False:
pass
# exchnage searched pattern in each column separately,
for i, col_name in enumerate(sel_col_names):
# .. test if you really have string values in that column, otherwise it masy be float for all NaN in a column, and no action will be taken
if is_string_dtype(df[col_name]):
try:
# .... find postions with a given pattern and select three examples to display for the user,
positions_to_replace = df[col_name].str.contains(searched_pattern, na=False).values# arr
examples_to_display = [str(x) for x in list(df.loc[list(positions_to_replace), col_name].str[0:20].values.tolist()[0:3])]
# .... replace postions, and find examples of unchnaged postions,
df.loc[list(positions_to_replace), col_name] = [fillna]*positions_to_replace.sum()
examples_of_positions_that_were_not_replaced = [str(x) for x in list(df.loc[list(positions_to_replace==False), col_name].str[0:20].values.tolist()[0:3])]
# .... diplay info,
if verbose==True:
perc_of_replaced_pos_in_col = "".join([str(positions_to_replace.sum()/df.shape[0]*100),"%"])
print(f"{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}")
print(f" - three examples of replaced postions: {'; '.join(examples_to_display)}", end="\n")
print(f" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}", end="\n\n")
# the second print returns three first examples of exchanged values, just to see what i did,
else:
pass
except:
if verbose==True:
print(f"{i} - {col_name} - - probably only missing data datected, Values were not replaced! \n")
else:
pass
else:
if verbose==True:
print(f"{i} - {col_name} - - is not of string type, Values were not replaced! \n")
else:
pass
return df.copy()
# Function, ...........................................................................................
def replace_numeric_values(*, df, colnames="all", lower_limit="none", upper_limit="none", equal=False, replace_with=np.nan, verbose=True):
"""
Replace numerical values that are outside of range of a values
prediced with a theoretical limits of a given variable,
eg less then 0 in weight of a product,
Provide examples and numbers of replaced instances
Parameters/Input
_________________ _______________________________________________________________________________
* df : Pandas DataFrame
* cols_in_df : list, exact colnames of selected or all columns in df
* lower_limit : int,float,"none", if "none" no action is taken
* upper_limit : int,float,"none", if "none" no action is taken
* replace_with : str, np.nan, int, float
* equal : bool, if True, >= and <= values then limits will be replaced,
if False (default), > and < values then limits will be replaced,
Returns
_________________ _______________________________________________________________________________
* DataFrame DataFramne.copy() with new values,
* display messages. number of replaced straings in each column, and examples of replcaced values
"""
cols_names = colnames
# .. check provided col_names,
if cols_names=="all":
cols = list(df.columns)
else:
cols = cols_names
# .. info, header,
if verbose==True:
print(f"""\n{"".join(["-"]*80)} \n Replacing Numerical Values in {len(cols)} columns""")
print(f" lower filter={lower_limit}, upper filter ={upper_limit}")
if equal==True:
print(f" Caution, equal=True, ie. values >= and <= then requested limits will be replaced")
print(f'{"".join(["-"]*80)}\n')
if verbose==False:
pass
# .. intelligent info,
total_count=[]
# .. count, to limit the number of displayed messages,
count = 0
# .. replace values and collect examples,
for i, j in enumerate(cols):
# ..... assume no values were replaced, so the messages work later,
info_lower_filter = 0
info_upper_filter = 0
# ..... test if the column is of the numeric type:
# from pandas.api.types import is_numeric_dtype
if is_numeric_dtype(df[j]):
# * replace values < or <= lower limit,
# - ----------------------------------
if lower_limit!="none":
if equal == True:
lower_filter = df.loc[:,j]<=lower_limit
if equal == False:
lower_filter = df.loc[:,j]<lower_limit
# info,
info_lower_filter=lower_filter.sum()
df.loc[list(lower_filter),j]=replace_with
# * replace values > or >= upper limit,
# - ----------------------------------
if upper_limit!="none":
if equal == True:
upper_filter = df.loc[:,j]>=upper_limit
if equal == False:
upper_filter = df.loc[:,j]>upper_limit
# info,
info_upper_filter=upper_filter.sum()
df.loc[list(upper_filter),j]=replace_with
# * find how many values were replaced, and add that to the total_count list
total_count.append(info_upper_filter+info_lower_filter)
# * display examples for 3 first columns with replaced values,
if verbose==True:
if info_upper_filter+info_lower_filter>0 and count <4:
print(f"eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}")
else:
pass
# * add 1 to count, to limit the number of displayed examples,
count += 1
else:
if verbose==True:
print(f"{i, j} is not of numeric type, values were not replaced !")
else:
pass
# .. additional message, if more then 2 columns had replaced values,
if verbose==True:
if len(total_count)>3 and pd.Series(total_count).sum()>0:
print(f". and {len(total_count)-3} other columns had in total {pd.Series(total_count).sum()} replaced values \n")
# .. message in case no values vere replaced at all,
if pd.Series(total_count).sum()==0:
print("No values were replaced in requested columns....")
else:
pass
# .. return,
return df.copy()
# function, ...................................................
def drop_nan(df, method="any", row=True, verbose=True):
'''
function to dropna with thresholds from rows and columns
. method
. any : row/column wiht any missing data are removed
. all : row/column only wiht missing data are removed
. int, >0 : keeps row/clumns wiht this or larger number of non missing data
. float, >0 : as in the above, as fraction
'''
assert type(df)==pd.DataFrame, "incorrect df dtype"
df = df.copy()
if verbose==True:
print(df.shape)
else:
pass
# set funtion for rows or columns,
if row==True:
shapeidx, dfaxis = 1, 0
else:
shapeidx, dfaxis = 0, 1
# use threshold or "all", or None for do nothing,
if method==None:
pass
elif isinstance(method, str):
df = df.dropna(how=method, axis=dfaxis) # removes rows with NaN in all columns
elif isinstance(method, int):
tr = method
if tr==0:
pass
else:
if tr>=df.shape[shapeidx]:
tr=df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis) # eg Keep only the rows with at least 2 non-NA value
elif isinstance(method, float):
tr = int(np.ceil(df.shape[shapeidx]*(method)))
if tr==0:
pass
else:
if tr>=df.shape[shapeidx]:
tr=df.shape[shapeidx]
else:
pass
df = df.dropna(thresh=tr, axis=dfaxis) # eg Keep only the rows with at least 2 non-NA value
else:
pass
# info and return
if verbose==True:
print(df.shape)
else:
pass
return df
# Function, ...........................................................................................
def drop_columns(*, df, columns_to_drop, verbose=True):
"""
Small function to quickly remove columns from,
by column names stored in the list
- created to give info on removed columns and whether I am chnaging df in proper way,
- the function allows for column name duplicates,
"""
assert type(df)==pd.DataFrame, "please provide df in pandas dataframe format"
df = df.copy()
# find unique values in a list, just in case I made the mistake,
columns_to_drop = list(pd.Series(columns_to_drop).unique())
# .. info, header,
if verbose==True:
print(f"""Removing {len(columns_to_drop)} columns from df""")
else:
pass
# remove columns one by one,
for i,j in enumerate(columns_to_drop):
try:
df.drop(columns=[j], axis=1, inplace=True)
if verbose==True:
print(f"{i} removing: {j}, ==> new df.shape: {df.shape}")
else:
pass
except:
if verbose==True:
print(f"{i} .... column: {j}, was not found in df, check if name is correct....")
else:
pass
return df
|
flexible
|
{
"blob_id": "5f50b20bd044471ebb8e1350d1a75a250b255d8f",
"index": 8854,
"step-1": "<mask token>\n\n\ndef find_and_display_patter_in_series(*, series, pattern):\n \"\"\"I used that function when i don't remeber full name of a given column\"\"\"\n res = series.loc[series.str.contains(pattern)]\n return res\n\n\n<mask token>\n\n\ndef find_patter_in_series(*, s, pat, tolist=True):\n \"\"\"\n I used that function when i don't remeber full name of a given column\n \"\"\"\n res = s.loc[s.str.contains(pat)]\n if tolist == True:\n return res.values.tolist()\n else:\n return res\n\n\ndef format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=\n False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):\n \"\"\"\n formats columns in df into datetime dtype, and set all times to UTC\n work with unix time units, ie. second number since 1970\n columns in df, are find using full comlumn name or keywords in column name\n \"\"\"\n assert type(data\n ) == pd.DataFrame, 'please provide data in pandas dataframe format'\n if isinstance(pattern_list, str):\n pattern_list = [pattern_list]\n else:\n pass\n for pat in pattern_list:\n columns_with_potential_datetime_obj = list(\n find_and_display_patter_in_series(series=pd.Series(data.columns\n ), pattern=pat))\n for i in columns_with_potential_datetime_obj:\n before_formatting = str(data.loc[0, i])\n if unixtime == True:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'\n ).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)\n else:\n pass\n else:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', format=\n dt_format).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)\n else:\n pass\n if verbose == True:\n print(f'date time formatted in: {i}')\n print(\n f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'\n )\n print(\n f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'\n , end='\\n')\n else:\n pass\n return data\n\n\ndef replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):\n \"\"\" \n searches string with a given pattern and replace it with a new patter (fillna), eg: nan,\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df Pandas Dataframe\n * searched_pattern \"\", str literal, used by pd.Series.str.contains() \n * colnames default, \"all\", or list with selected colnames in df\n * fillna default numpy.nan, or str literal \n - what do you want to place instead of searched pattern in df\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\"\n searched_pattern = pat\n col_names = colnames\n if col_names == 'all':\n sel_col_names = list(df.columns)\n else:\n sel_col_names = col_names\n if verbose == True:\n print(\n f'\\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\\n'\n )\n if verbose == False:\n pass\n for i, col_name in enumerate(sel_col_names):\n if is_string_dtype(df[col_name]):\n try:\n positions_to_replace = df[col_name].str.contains(\n searched_pattern, na=False).values\n examples_to_display = [str(x) for x in list(df.loc[list(\n positions_to_replace), col_name].str[0:20].values.\n tolist()[0:3])]\n df.loc[list(positions_to_replace), col_name] = [fillna\n ] * positions_to_replace.sum()\n examples_of_positions_that_were_not_replaced = [str(x) for\n x in list(df.loc[list(positions_to_replace == False),\n col_name].str[0:20].values.tolist()[0:3])]\n if verbose == True:\n perc_of_replaced_pos_in_col = ''.join([str(\n positions_to_replace.sum() / df.shape[0] * 100), '%'])\n print(\n f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'\n )\n print(\n f\" - three examples of replaced postions: {'; '.join(examples_to_display)}\"\n , end='\\n')\n print(\n f\" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}\"\n , end='\\n\\n')\n else:\n pass\n except:\n if verbose == True:\n print(\n f\"\"\"{i} - {col_name} - - probably only missing data datected, Values were not replaced! \n\"\"\"\n )\n else:\n pass\n elif verbose == True:\n print(\n f'{i} - {col_name} - - is not of string type, Values were not replaced! \\n'\n )\n else:\n pass\n return df.copy()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef find_and_display_patter_in_series(*, series, pattern):\n \"\"\"I used that function when i don't remeber full name of a given column\"\"\"\n res = series.loc[series.str.contains(pattern)]\n return res\n\n\n<mask token>\n\n\ndef find_patter_in_series(*, s, pat, tolist=True):\n \"\"\"\n I used that function when i don't remeber full name of a given column\n \"\"\"\n res = s.loc[s.str.contains(pat)]\n if tolist == True:\n return res.values.tolist()\n else:\n return res\n\n\ndef format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=\n False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):\n \"\"\"\n formats columns in df into datetime dtype, and set all times to UTC\n work with unix time units, ie. second number since 1970\n columns in df, are find using full comlumn name or keywords in column name\n \"\"\"\n assert type(data\n ) == pd.DataFrame, 'please provide data in pandas dataframe format'\n if isinstance(pattern_list, str):\n pattern_list = [pattern_list]\n else:\n pass\n for pat in pattern_list:\n columns_with_potential_datetime_obj = list(\n find_and_display_patter_in_series(series=pd.Series(data.columns\n ), pattern=pat))\n for i in columns_with_potential_datetime_obj:\n before_formatting = str(data.loc[0, i])\n if unixtime == True:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'\n ).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)\n else:\n pass\n else:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', format=\n dt_format).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)\n else:\n pass\n if verbose == True:\n print(f'date time formatted in: {i}')\n print(\n f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'\n )\n print(\n f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'\n , end='\\n')\n else:\n pass\n return data\n\n\ndef replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):\n \"\"\" \n searches string with a given pattern and replace it with a new patter (fillna), eg: nan,\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df Pandas Dataframe\n * searched_pattern \"\", str literal, used by pd.Series.str.contains() \n * colnames default, \"all\", or list with selected colnames in df\n * fillna default numpy.nan, or str literal \n - what do you want to place instead of searched pattern in df\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\"\n searched_pattern = pat\n col_names = colnames\n if col_names == 'all':\n sel_col_names = list(df.columns)\n else:\n sel_col_names = col_names\n if verbose == True:\n print(\n f'\\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\\n'\n )\n if verbose == False:\n pass\n for i, col_name in enumerate(sel_col_names):\n if is_string_dtype(df[col_name]):\n try:\n positions_to_replace = df[col_name].str.contains(\n searched_pattern, na=False).values\n examples_to_display = [str(x) for x in list(df.loc[list(\n positions_to_replace), col_name].str[0:20].values.\n tolist()[0:3])]\n df.loc[list(positions_to_replace), col_name] = [fillna\n ] * positions_to_replace.sum()\n examples_of_positions_that_were_not_replaced = [str(x) for\n x in list(df.loc[list(positions_to_replace == False),\n col_name].str[0:20].values.tolist()[0:3])]\n if verbose == True:\n perc_of_replaced_pos_in_col = ''.join([str(\n positions_to_replace.sum() / df.shape[0] * 100), '%'])\n print(\n f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'\n )\n print(\n f\" - three examples of replaced postions: {'; '.join(examples_to_display)}\"\n , end='\\n')\n print(\n f\" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}\"\n , end='\\n\\n')\n else:\n pass\n except:\n if verbose == True:\n print(\n f\"\"\"{i} - {col_name} - - probably only missing data datected, Values were not replaced! \n\"\"\"\n )\n else:\n pass\n elif verbose == True:\n print(\n f'{i} - {col_name} - - is not of string type, Values were not replaced! \\n'\n )\n else:\n pass\n return df.copy()\n\n\n<mask token>\n\n\ndef drop_nan(df, method='any', row=True, verbose=True):\n \"\"\"\n function to dropna with thresholds from rows and columns\n . method\n . any : row/column wiht any missing data are removed\n . all : row/column only wiht missing data are removed\n . int, >0 : keeps row/clumns wiht this or larger number of non missing data\n . float, >0 : as in the above, as fraction\n \n \"\"\"\n assert type(df) == pd.DataFrame, 'incorrect df dtype'\n df = df.copy()\n if verbose == True:\n print(df.shape)\n else:\n pass\n if row == True:\n shapeidx, dfaxis = 1, 0\n else:\n shapeidx, dfaxis = 0, 1\n if method == None:\n pass\n elif isinstance(method, str):\n df = df.dropna(how=method, axis=dfaxis)\n elif isinstance(method, int):\n tr = method\n if tr == 0:\n pass\n else:\n if tr >= df.shape[shapeidx]:\n tr = df.shape[shapeidx]\n else:\n pass\n df = df.dropna(thresh=tr, axis=dfaxis)\n elif isinstance(method, float):\n tr = int(np.ceil(df.shape[shapeidx] * method))\n if tr == 0:\n pass\n else:\n if tr >= df.shape[shapeidx]:\n tr = df.shape[shapeidx]\n else:\n pass\n df = df.dropna(thresh=tr, axis=dfaxis)\n else:\n pass\n if verbose == True:\n print(df.shape)\n else:\n pass\n return df\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef find_and_display_patter_in_series(*, series, pattern):\n \"\"\"I used that function when i don't remeber full name of a given column\"\"\"\n res = series.loc[series.str.contains(pattern)]\n return res\n\n\n<mask token>\n\n\ndef find_patter_in_series(*, s, pat, tolist=True):\n \"\"\"\n I used that function when i don't remeber full name of a given column\n \"\"\"\n res = s.loc[s.str.contains(pat)]\n if tolist == True:\n return res.values.tolist()\n else:\n return res\n\n\ndef format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=\n False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):\n \"\"\"\n formats columns in df into datetime dtype, and set all times to UTC\n work with unix time units, ie. second number since 1970\n columns in df, are find using full comlumn name or keywords in column name\n \"\"\"\n assert type(data\n ) == pd.DataFrame, 'please provide data in pandas dataframe format'\n if isinstance(pattern_list, str):\n pattern_list = [pattern_list]\n else:\n pass\n for pat in pattern_list:\n columns_with_potential_datetime_obj = list(\n find_and_display_patter_in_series(series=pd.Series(data.columns\n ), pattern=pat))\n for i in columns_with_potential_datetime_obj:\n before_formatting = str(data.loc[0, i])\n if unixtime == True:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'\n ).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)\n else:\n pass\n else:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', format=\n dt_format).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)\n else:\n pass\n if verbose == True:\n print(f'date time formatted in: {i}')\n print(\n f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'\n )\n print(\n f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'\n , end='\\n')\n else:\n pass\n return data\n\n\ndef replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):\n \"\"\" \n searches string with a given pattern and replace it with a new patter (fillna), eg: nan,\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df Pandas Dataframe\n * searched_pattern \"\", str literal, used by pd.Series.str.contains() \n * colnames default, \"all\", or list with selected colnames in df\n * fillna default numpy.nan, or str literal \n - what do you want to place instead of searched pattern in df\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\"\n searched_pattern = pat\n col_names = colnames\n if col_names == 'all':\n sel_col_names = list(df.columns)\n else:\n sel_col_names = col_names\n if verbose == True:\n print(\n f'\\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\\n'\n )\n if verbose == False:\n pass\n for i, col_name in enumerate(sel_col_names):\n if is_string_dtype(df[col_name]):\n try:\n positions_to_replace = df[col_name].str.contains(\n searched_pattern, na=False).values\n examples_to_display = [str(x) for x in list(df.loc[list(\n positions_to_replace), col_name].str[0:20].values.\n tolist()[0:3])]\n df.loc[list(positions_to_replace), col_name] = [fillna\n ] * positions_to_replace.sum()\n examples_of_positions_that_were_not_replaced = [str(x) for\n x in list(df.loc[list(positions_to_replace == False),\n col_name].str[0:20].values.tolist()[0:3])]\n if verbose == True:\n perc_of_replaced_pos_in_col = ''.join([str(\n positions_to_replace.sum() / df.shape[0] * 100), '%'])\n print(\n f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'\n )\n print(\n f\" - three examples of replaced postions: {'; '.join(examples_to_display)}\"\n , end='\\n')\n print(\n f\" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}\"\n , end='\\n\\n')\n else:\n pass\n except:\n if verbose == True:\n print(\n f\"\"\"{i} - {col_name} - - probably only missing data datected, Values were not replaced! \n\"\"\"\n )\n else:\n pass\n elif verbose == True:\n print(\n f'{i} - {col_name} - - is not of string type, Values were not replaced! \\n'\n )\n else:\n pass\n return df.copy()\n\n\ndef replace_numeric_values(*, df, colnames='all', lower_limit='none',\n upper_limit='none', equal=False, replace_with=np.nan, verbose=True):\n \"\"\" \n\n Replace numerical values that are outside of range of a values \n prediced with a theoretical limits of a given variable, \n eg less then 0 in weight of a product, \n Provide examples and numbers of replaced instances\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df : Pandas DataFrame\n * cols_in_df : list, exact colnames of selected or all columns in df\n * lower_limit : int,float,\"none\", if \"none\" no action is taken\n * upper_limit : int,float,\"none\", if \"none\" no action is taken\n * replace_with : str, np.nan, int, float\n * equal : bool, if True, >= and <= values then limits will be replaced,\n if False (default), > and < values then limits will be replaced,\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\"\n cols_names = colnames\n if cols_names == 'all':\n cols = list(df.columns)\n else:\n cols = cols_names\n if verbose == True:\n print(\n f\"\\n{''.join(['-'] * 80)} \\n Replacing Numerical Values in {len(cols)} columns\"\n )\n print(\n f' lower filter={lower_limit}, upper filter ={upper_limit}')\n if equal == True:\n print(\n f' Caution, equal=True, ie. values >= and <= then requested limits will be replaced'\n )\n print(f\"{''.join(['-'] * 80)}\\n\")\n if verbose == False:\n pass\n total_count = []\n count = 0\n for i, j in enumerate(cols):\n info_lower_filter = 0\n info_upper_filter = 0\n if is_numeric_dtype(df[j]):\n if lower_limit != 'none':\n if equal == True:\n lower_filter = df.loc[:, j] <= lower_limit\n if equal == False:\n lower_filter = df.loc[:, j] < lower_limit\n info_lower_filter = lower_filter.sum()\n df.loc[list(lower_filter), j] = replace_with\n if upper_limit != 'none':\n if equal == True:\n upper_filter = df.loc[:, j] >= upper_limit\n if equal == False:\n upper_filter = df.loc[:, j] > upper_limit\n info_upper_filter = upper_filter.sum()\n df.loc[list(upper_filter), j] = replace_with\n total_count.append(info_upper_filter + info_lower_filter)\n if verbose == True:\n if info_upper_filter + info_lower_filter > 0 and count < 4:\n print(\n f'eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}'\n )\n else:\n pass\n count += 1\n elif verbose == True:\n print(f'{i, j} is not of numeric type, values were not replaced !')\n else:\n pass\n if verbose == True:\n if len(total_count) > 3 and pd.Series(total_count).sum() > 0:\n print(\n f\"\"\". and {len(total_count) - 3} other columns had in total {pd.Series(total_count).sum()} replaced values \n\"\"\"\n )\n if pd.Series(total_count).sum() == 0:\n print('No values were replaced in requested columns....')\n else:\n pass\n return df.copy()\n\n\ndef drop_nan(df, method='any', row=True, verbose=True):\n \"\"\"\n function to dropna with thresholds from rows and columns\n . method\n . any : row/column wiht any missing data are removed\n . all : row/column only wiht missing data are removed\n . int, >0 : keeps row/clumns wiht this or larger number of non missing data\n . float, >0 : as in the above, as fraction\n \n \"\"\"\n assert type(df) == pd.DataFrame, 'incorrect df dtype'\n df = df.copy()\n if verbose == True:\n print(df.shape)\n else:\n pass\n if row == True:\n shapeidx, dfaxis = 1, 0\n else:\n shapeidx, dfaxis = 0, 1\n if method == None:\n pass\n elif isinstance(method, str):\n df = df.dropna(how=method, axis=dfaxis)\n elif isinstance(method, int):\n tr = method\n if tr == 0:\n pass\n else:\n if tr >= df.shape[shapeidx]:\n tr = df.shape[shapeidx]\n else:\n pass\n df = df.dropna(thresh=tr, axis=dfaxis)\n elif isinstance(method, float):\n tr = int(np.ceil(df.shape[shapeidx] * method))\n if tr == 0:\n pass\n else:\n if tr >= df.shape[shapeidx]:\n tr = df.shape[shapeidx]\n else:\n pass\n df = df.dropna(thresh=tr, axis=dfaxis)\n else:\n pass\n if verbose == True:\n print(df.shape)\n else:\n pass\n return df\n\n\ndef drop_columns(*, df, columns_to_drop, verbose=True):\n \"\"\"\n Small function to quickly remove columns from, \n by column names stored in the list\n - created to give info on removed columns and whether I am chnaging df in proper way,\n - the function allows for column name duplicates, \n \"\"\"\n assert type(df\n ) == pd.DataFrame, 'please provide df in pandas dataframe format'\n df = df.copy()\n columns_to_drop = list(pd.Series(columns_to_drop).unique())\n if verbose == True:\n print(f'Removing {len(columns_to_drop)} columns from df')\n else:\n pass\n for i, j in enumerate(columns_to_drop):\n try:\n df.drop(columns=[j], axis=1, inplace=True)\n if verbose == True:\n print(f'{i} removing: {j}, ==> new df.shape: {df.shape}')\n else:\n pass\n except:\n if verbose == True:\n print(\n f'{i} .... column: {j}, was not found in df, check if name is correct....'\n )\n else:\n pass\n return df\n",
"step-4": "<mask token>\n\n\ndef find_and_display_patter_in_series(*, series, pattern):\n \"\"\"I used that function when i don't remeber full name of a given column\"\"\"\n res = series.loc[series.str.contains(pattern)]\n return res\n\n\ndef load_csv(*, path, filename, sep='\\t', verbose=True):\n \"\"\" \n Loads csv into pandas df, based on pandas.read_scv(), \n Returns error, if file or directoy not found\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * path full path to directory\n * csv_name. full csv file name\n * separator \"\t\", by default\n * display_head bool, True, by default, display df.head(), \n irrespectively when the futions was called. \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame by Pandas\n\n \"\"\"\n os.chdir(path)\n if len(glob.glob(filename)) == 1:\n df = pd.read_csv(filename, sep=sep, low_memory=False)\n if verbose == True:\n display(df.head(3))\n print(df.shape)\n else:\n pass\n return df\n elif verbose == True:\n print(f'ERROR :csv file {filename}, was not found in: \\n {path}')\n else:\n pass\n\n\ndef find_patter_in_series(*, s, pat, tolist=True):\n \"\"\"\n I used that function when i don't remeber full name of a given column\n \"\"\"\n res = s.loc[s.str.contains(pat)]\n if tolist == True:\n return res.values.tolist()\n else:\n return res\n\n\ndef format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=\n False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):\n \"\"\"\n formats columns in df into datetime dtype, and set all times to UTC\n work with unix time units, ie. second number since 1970\n columns in df, are find using full comlumn name or keywords in column name\n \"\"\"\n assert type(data\n ) == pd.DataFrame, 'please provide data in pandas dataframe format'\n if isinstance(pattern_list, str):\n pattern_list = [pattern_list]\n else:\n pass\n for pat in pattern_list:\n columns_with_potential_datetime_obj = list(\n find_and_display_patter_in_series(series=pd.Series(data.columns\n ), pattern=pat))\n for i in columns_with_potential_datetime_obj:\n before_formatting = str(data.loc[0, i])\n if unixtime == True:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', unit='s'\n ).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)\n else:\n pass\n else:\n s = pd.to_datetime(data.loc[:, i], errors='coerce', format=\n dt_format).copy()\n data.loc[:, i] = s\n if timezone != None:\n data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)\n else:\n pass\n if verbose == True:\n print(f'date time formatted in: {i}')\n print(\n f' - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce'\n )\n print(\n f' - Example: {before_formatting} -->> {str(data.loc[0, i])}'\n , end='\\n')\n else:\n pass\n return data\n\n\ndef replace_text(*, df, pat='', colnames='all', fillna=np.nan, verbose=True):\n \"\"\" \n searches string with a given pattern and replace it with a new patter (fillna), eg: nan,\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df Pandas Dataframe\n * searched_pattern \"\", str literal, used by pd.Series.str.contains() \n * colnames default, \"all\", or list with selected colnames in df\n * fillna default numpy.nan, or str literal \n - what do you want to place instead of searched pattern in df\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\"\n searched_pattern = pat\n col_names = colnames\n if col_names == 'all':\n sel_col_names = list(df.columns)\n else:\n sel_col_names = col_names\n if verbose == True:\n print(\n f'\\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\\n'\n )\n if verbose == False:\n pass\n for i, col_name in enumerate(sel_col_names):\n if is_string_dtype(df[col_name]):\n try:\n positions_to_replace = df[col_name].str.contains(\n searched_pattern, na=False).values\n examples_to_display = [str(x) for x in list(df.loc[list(\n positions_to_replace), col_name].str[0:20].values.\n tolist()[0:3])]\n df.loc[list(positions_to_replace), col_name] = [fillna\n ] * positions_to_replace.sum()\n examples_of_positions_that_were_not_replaced = [str(x) for\n x in list(df.loc[list(positions_to_replace == False),\n col_name].str[0:20].values.tolist()[0:3])]\n if verbose == True:\n perc_of_replaced_pos_in_col = ''.join([str(\n positions_to_replace.sum() / df.shape[0] * 100), '%'])\n print(\n f'{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}'\n )\n print(\n f\" - three examples of replaced postions: {'; '.join(examples_to_display)}\"\n , end='\\n')\n print(\n f\" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}\"\n , end='\\n\\n')\n else:\n pass\n except:\n if verbose == True:\n print(\n f\"\"\"{i} - {col_name} - - probably only missing data datected, Values were not replaced! \n\"\"\"\n )\n else:\n pass\n elif verbose == True:\n print(\n f'{i} - {col_name} - - is not of string type, Values were not replaced! \\n'\n )\n else:\n pass\n return df.copy()\n\n\ndef replace_numeric_values(*, df, colnames='all', lower_limit='none',\n upper_limit='none', equal=False, replace_with=np.nan, verbose=True):\n \"\"\" \n\n Replace numerical values that are outside of range of a values \n prediced with a theoretical limits of a given variable, \n eg less then 0 in weight of a product, \n Provide examples and numbers of replaced instances\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df : Pandas DataFrame\n * cols_in_df : list, exact colnames of selected or all columns in df\n * lower_limit : int,float,\"none\", if \"none\" no action is taken\n * upper_limit : int,float,\"none\", if \"none\" no action is taken\n * replace_with : str, np.nan, int, float\n * equal : bool, if True, >= and <= values then limits will be replaced,\n if False (default), > and < values then limits will be replaced,\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\"\n cols_names = colnames\n if cols_names == 'all':\n cols = list(df.columns)\n else:\n cols = cols_names\n if verbose == True:\n print(\n f\"\\n{''.join(['-'] * 80)} \\n Replacing Numerical Values in {len(cols)} columns\"\n )\n print(\n f' lower filter={lower_limit}, upper filter ={upper_limit}')\n if equal == True:\n print(\n f' Caution, equal=True, ie. values >= and <= then requested limits will be replaced'\n )\n print(f\"{''.join(['-'] * 80)}\\n\")\n if verbose == False:\n pass\n total_count = []\n count = 0\n for i, j in enumerate(cols):\n info_lower_filter = 0\n info_upper_filter = 0\n if is_numeric_dtype(df[j]):\n if lower_limit != 'none':\n if equal == True:\n lower_filter = df.loc[:, j] <= lower_limit\n if equal == False:\n lower_filter = df.loc[:, j] < lower_limit\n info_lower_filter = lower_filter.sum()\n df.loc[list(lower_filter), j] = replace_with\n if upper_limit != 'none':\n if equal == True:\n upper_filter = df.loc[:, j] >= upper_limit\n if equal == False:\n upper_filter = df.loc[:, j] > upper_limit\n info_upper_filter = upper_filter.sum()\n df.loc[list(upper_filter), j] = replace_with\n total_count.append(info_upper_filter + info_lower_filter)\n if verbose == True:\n if info_upper_filter + info_lower_filter > 0 and count < 4:\n print(\n f'eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}'\n )\n else:\n pass\n count += 1\n elif verbose == True:\n print(f'{i, j} is not of numeric type, values were not replaced !')\n else:\n pass\n if verbose == True:\n if len(total_count) > 3 and pd.Series(total_count).sum() > 0:\n print(\n f\"\"\". and {len(total_count) - 3} other columns had in total {pd.Series(total_count).sum()} replaced values \n\"\"\"\n )\n if pd.Series(total_count).sum() == 0:\n print('No values were replaced in requested columns....')\n else:\n pass\n return df.copy()\n\n\ndef drop_nan(df, method='any', row=True, verbose=True):\n \"\"\"\n function to dropna with thresholds from rows and columns\n . method\n . any : row/column wiht any missing data are removed\n . all : row/column only wiht missing data are removed\n . int, >0 : keeps row/clumns wiht this or larger number of non missing data\n . float, >0 : as in the above, as fraction\n \n \"\"\"\n assert type(df) == pd.DataFrame, 'incorrect df dtype'\n df = df.copy()\n if verbose == True:\n print(df.shape)\n else:\n pass\n if row == True:\n shapeidx, dfaxis = 1, 0\n else:\n shapeidx, dfaxis = 0, 1\n if method == None:\n pass\n elif isinstance(method, str):\n df = df.dropna(how=method, axis=dfaxis)\n elif isinstance(method, int):\n tr = method\n if tr == 0:\n pass\n else:\n if tr >= df.shape[shapeidx]:\n tr = df.shape[shapeidx]\n else:\n pass\n df = df.dropna(thresh=tr, axis=dfaxis)\n elif isinstance(method, float):\n tr = int(np.ceil(df.shape[shapeidx] * method))\n if tr == 0:\n pass\n else:\n if tr >= df.shape[shapeidx]:\n tr = df.shape[shapeidx]\n else:\n pass\n df = df.dropna(thresh=tr, axis=dfaxis)\n else:\n pass\n if verbose == True:\n print(df.shape)\n else:\n pass\n return df\n\n\ndef drop_columns(*, df, columns_to_drop, verbose=True):\n \"\"\"\n Small function to quickly remove columns from, \n by column names stored in the list\n - created to give info on removed columns and whether I am chnaging df in proper way,\n - the function allows for column name duplicates, \n \"\"\"\n assert type(df\n ) == pd.DataFrame, 'please provide df in pandas dataframe format'\n df = df.copy()\n columns_to_drop = list(pd.Series(columns_to_drop).unique())\n if verbose == True:\n print(f'Removing {len(columns_to_drop)} columns from df')\n else:\n pass\n for i, j in enumerate(columns_to_drop):\n try:\n df.drop(columns=[j], axis=1, inplace=True)\n if verbose == True:\n print(f'{i} removing: {j}, ==> new df.shape: {df.shape}')\n else:\n pass\n except:\n if verbose == True:\n print(\n f'{i} .... column: {j}, was not found in df, check if name is correct....'\n )\n else:\n pass\n return df\n",
"step-5": "# ********************************************************************************** #\n# #\n# Project: Data Frame Explorer # \n# Author: Pawel Rosikiewicz #\n# Contact: prosikiewicz(a)gmail.com #\n# #\n# License: MIT License #\n# Copyright (C) 2021.01.30 Pawel Rosikiewicz #\n# #\n# Permission is hereby granted, free of charge, to any person obtaining a copy #\n# of this software and associated documentation files (the \"Software\"), to deal #\n# in the Software without restriction, including without limitation the rights #\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #\n# copies of the Software, and to permit persons to whom the Software is #\n# furnished to do so, subject to the following conditions: #\n# # \n# The above copyright notice and this permission notice shall be included in all #\n# copies or substantial portions of the Software. #\n# #\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #\n# SOFTWARE. #\n# #\n# ********************************************************************************** #\n\n\n# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport numpy as np\nimport pandas as pd\nimport random\nimport glob\nimport re\nimport os\nimport seaborn as sns\n\nfrom IPython.display import display\nfrom pandas.api.types import is_numeric_dtype\nfrom pandas.api.types import is_string_dtype\n\n\n\n\n\n\n# Function, ............................................................................\ndef find_and_display_patter_in_series(*, series, pattern):\n \"I used that function when i don't remeber full name of a given column\"\n res = series.loc[series.str.contains(pattern)]\n return res\n\n\n\n# Function, ...........................................................................................\ndef load_csv(*, path, filename, sep=\"\\t\", verbose=True):\n \"\"\" \n Loads csv into pandas df, based on pandas.read_scv(), \n Returns error, if file or directoy not found\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * path full path to directory\n * csv_name. full csv file name\n * separator \"\\t\", by default\n * display_head bool, True, by default, display df.head(), \n irrespectively when the futions was called. \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame by Pandas\n\n \"\"\"\n \n os.chdir(path)\n if len(glob.glob(filename))==1: \n df = pd.read_csv(filename, sep=sep, low_memory=False)\n \n # display example,\n if verbose==True:\n display(df.head(3))\n print(df.shape)\n else:\n pass\n \n # return,\n return df\n \n else:\n if verbose==True:\n print(f\"\"\"ERROR :csv file {filename}, was not found in: \\n {path}\"\"\")\n else:\n pass\n\n\n \n \n \n \n# Function, ............................................................................\ndef find_patter_in_series(*, s, pat, tolist=True):\n '''\n I used that function when i don't remeber full name of a given column\n '''\n res = s.loc[s.str.contains(pat)]\n \n if tolist==True:\n return res.values.tolist()\n else:\n return res \n \n \n \n \n\n \n# Function, ........................................................................................... \ndef format_to_datetime(*, data, pattern_list, timezone='UTC', unixtime=False, dt_format='%Y-%m-%d %H:%M:%S', verbose=False):\n '''\n formats columns in df into datetime dtype, and set all times to UTC\n work with unix time units, ie. second number since 1970\n columns in df, are find using full comlumn name or keywords in column name\n '''\n assert type(data)==pd.DataFrame, \"please provide data in pandas dataframe format\"\n \n if isinstance(pattern_list, str):\n pattern_list = [pattern_list]\n else: \n pass\n \n for pat in pattern_list: \n # find column names using provided patterns or their full names, \n columns_with_potential_datetime_obj = list(find_and_display_patter_in_series(series=pd.Series(data.columns), pattern=pat))\n \n # replace \n for i in columns_with_potential_datetime_obj:\n # keep example of old cell \n before_formatting = str(data.loc[0, i])\n \n # convert to one format\n if unixtime==True:\n s = pd.to_datetime(data.loc[:, i], errors=\"coerce\", unit='s').copy()#,format cannot be used with unit=\"s\", but it will be the same\n data.loc[:, i] = s\n if timezone!=None:\n data.loc[:, i] = data.loc[:, i].dt.tz_localize(timezone)\n else:\n pass\n \n else: \n s = pd.to_datetime(data.loc[:, i], errors=\"coerce\",format=dt_format).copy()\n data.loc[:, i] = s\n if timezone!=None:\n data.loc[:, i] = data.loc[:, i].dt.tz_convert(timezone)\n else:\n pass\n \n # info\n if verbose==True:\n print(f\"date time formatted in: {i}\") \n print(f\" - {data.loc[:, i].isnull().sum()} NaN were instroduced by coerce\")\n print(f\" - Example: {before_formatting} -->> {str(data.loc[0, i])}\", end=\"\\n\")\n else:\n pass\n\n return data \n \n \n \n \n \n \n \n# Function, ...........................................................................................\ndef replace_text(*,df ,pat=\"\", colnames=\"all\", fillna=np.nan, verbose=True):\n \"\"\" \n searches string with a given pattern and replace it with a new patter (fillna), eg: nan,\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df Pandas Dataframe\n * searched_pattern \"\", str literal, used by pd.Series.str.contains() \n * colnames default, \"all\", or list with selected colnames in df\n * fillna default numpy.nan, or str literal \n - what do you want to place instead of searched pattern in df\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\"\n \n # for older version, \n searched_pattern = pat\n col_names = colnames\n \n # check col_names with values to replace, \n if col_names==\"all\": \n sel_col_names = list(df.columns)\n else: \n sel_col_names = col_names \n\n # display message header, \n if verbose==True:\n print(f\"\"\"\\nReplacing Text in {len(sel_col_names)} columns: {sel_col_names}\\n\"\"\") \n \n if verbose==False:\n pass\n\n # exchnage searched pattern in each column separately, \n for i, col_name in enumerate(sel_col_names):\n \n # .. test if you really have string values in that column, otherwise it masy be float for all NaN in a column, and no action will be taken \n if is_string_dtype(df[col_name]):\n \n try:\n # .... find postions with a given pattern and select three examples to display for the user, \n positions_to_replace = df[col_name].str.contains(searched_pattern, na=False).values# arr\n examples_to_display = [str(x) for x in list(df.loc[list(positions_to_replace), col_name].str[0:20].values.tolist()[0:3])]\n\n # .... replace postions, and find examples of unchnaged postions,\n df.loc[list(positions_to_replace), col_name] = [fillna]*positions_to_replace.sum() \n examples_of_positions_that_were_not_replaced = [str(x) for x in list(df.loc[list(positions_to_replace==False), col_name].str[0:20].values.tolist()[0:3])]\n\n # .... diplay info,\n if verbose==True:\n perc_of_replaced_pos_in_col = \"\".join([str(positions_to_replace.sum()/df.shape[0]*100),\"%\"])\n print(f\"{i} - {col_name} - - {positions_to_replace.sum()} positions out of {df.shape[0]}, were replaced with {fillna}, ie. {perc_of_replaced_pos_in_col}\")\n print(f\" - three examples of replaced postions: {'; '.join(examples_to_display)}\", end=\"\\n\")\n print(f\" - three examples of unchanged postions: {'; '.join(examples_of_positions_that_were_not_replaced)}\", end=\"\\n\\n\")\n # the second print returns three first examples of exchanged values, just to see what i did,\n else:\n pass\n \n except:\n if verbose==True:\n print(f\"{i} - {col_name} - - probably only missing data datected, Values were not replaced! \\n\") \n else:\n pass\n \n else:\n if verbose==True:\n print(f\"{i} - {col_name} - - is not of string type, Values were not replaced! \\n\") \n else:\n pass\n \n return df.copy()\n\n\n \n \n \n\n\n\n# Function, ...........................................................................................\ndef replace_numeric_values(*, df, colnames=\"all\", lower_limit=\"none\", upper_limit=\"none\", equal=False, replace_with=np.nan, verbose=True):\n \"\"\" \n\n Replace numerical values that are outside of range of a values \n prediced with a theoretical limits of a given variable, \n eg less then 0 in weight of a product, \n Provide examples and numbers of replaced instances\n \n Parameters/Input \n _________________ _______________________________________________________________________________ \n\n * df : Pandas DataFrame\n * cols_in_df : list, exact colnames of selected or all columns in df\n * lower_limit : int,float,\"none\", if \"none\" no action is taken\n * upper_limit : int,float,\"none\", if \"none\" no action is taken\n * replace_with : str, np.nan, int, float\n * equal : bool, if True, >= and <= values then limits will be replaced,\n if False (default), > and < values then limits will be replaced,\n \n Returns \n _________________ _______________________________________________________________________________ \n\n * DataFrame DataFramne.copy() with new values,\n * display messages. number of replaced straings in each column, and examples of replcaced values\n \"\"\" \n\n \n cols_names = colnames\n \n # .. check provided col_names,\n if cols_names==\"all\": \n cols = list(df.columns)\n else: \n cols = cols_names \n\n # .. info, header, \n if verbose==True:\n print(f\"\"\"\\n{\"\".join([\"-\"]*80)} \\n Replacing Numerical Values in {len(cols)} columns\"\"\") \n print(f\" lower filter={lower_limit}, upper filter ={upper_limit}\")\n if equal==True:\n print(f\" Caution, equal=True, ie. values >= and <= then requested limits will be replaced\")\n print(f'{\"\".join([\"-\"]*80)}\\n') \n \n if verbose==False:\n pass\n \n \n # .. intelligent info,\n total_count=[]\n\n # .. count, to limit the number of displayed messages,\n count = 0\n\n # .. replace values and collect examples, \n for i, j in enumerate(cols):\n\n # ..... assume no values were replaced, so the messages work later, \n info_lower_filter = 0\n info_upper_filter = 0 \n \n # ..... test if the column is of the numeric type:\n # from pandas.api.types import is_numeric_dtype\n if is_numeric_dtype(df[j]):\n \n \n # * replace values < or <= lower limit,\n # - ----------------------------------\n if lower_limit!=\"none\": \n if equal == True:\n lower_filter = df.loc[:,j]<=lower_limit\n if equal == False:\n lower_filter = df.loc[:,j]<lower_limit\n \n # info,\n info_lower_filter=lower_filter.sum()\n df.loc[list(lower_filter),j]=replace_with\n \n \n # * replace values > or >= upper limit,\n # - ----------------------------------\n if upper_limit!=\"none\": \n if equal == True:\n upper_filter = df.loc[:,j]>=upper_limit\n if equal == False:\n upper_filter = df.loc[:,j]>upper_limit\n \n # info,\n info_upper_filter=upper_filter.sum()\n df.loc[list(upper_filter),j]=replace_with \n \n # * find how many values were replaced, and add that to the total_count list \n total_count.append(info_upper_filter+info_lower_filter)\n \n # * display examples for 3 first columns with replaced values,\n if verbose==True:\n if info_upper_filter+info_lower_filter>0 and count <4:\n print(f\"eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}\")\n else:\n pass\n\n # * add 1 to count, to limit the number of displayed examples,\n count += 1 \n \n else:\n if verbose==True:\n print(f\"{i, j} is not of numeric type, values were not replaced !\")\n else:\n pass\n \n # .. additional message, if more then 2 columns had replaced values, \n if verbose==True:\n if len(total_count)>3 and pd.Series(total_count).sum()>0:\n print(f\". and {len(total_count)-3} other columns had in total {pd.Series(total_count).sum()} replaced values \\n\")\n\n # .. message in case no values vere replaced at all, \n if pd.Series(total_count).sum()==0:\n print(\"No values were replaced in requested columns....\")\n \n else:\n pass\n \n # .. return, \n return df.copy()\n \n \n \n \n\n \n \n# function, ...................................................\ndef drop_nan(df, method=\"any\", row=True, verbose=True): \n '''\n function to dropna with thresholds from rows and columns\n . method\n . any : row/column wiht any missing data are removed\n . all : row/column only wiht missing data are removed\n . int, >0 : keeps row/clumns wiht this or larger number of non missing data\n . float, >0 : as in the above, as fraction\n \n '''\n \n assert type(df)==pd.DataFrame, \"incorrect df dtype\"\n df = df.copy()\n \n if verbose==True:\n print(df.shape)\n else:\n pass\n \n # set funtion for rows or columns, \n if row==True:\n shapeidx, dfaxis = 1, 0\n else:\n shapeidx, dfaxis = 0, 1\n \n # use threshold or \"all\", or None for do nothing, \n if method==None:\n pass\n\n elif isinstance(method, str):\n df = df.dropna(how=method, axis=dfaxis) # removes rows with NaN in all columns \n\n elif isinstance(method, int):\n tr = method\n if tr==0:\n pass\n else:\n if tr>=df.shape[shapeidx]:\n tr=df.shape[shapeidx]\n else:\n pass \n df = df.dropna(thresh=tr, axis=dfaxis) # eg Keep only the rows with at least 2 non-NA value\n\n elif isinstance(method, float):\n tr = int(np.ceil(df.shape[shapeidx]*(method)))\n if tr==0:\n pass\n else:\n if tr>=df.shape[shapeidx]:\n tr=df.shape[shapeidx]\n else:\n pass \n df = df.dropna(thresh=tr, axis=dfaxis) # eg Keep only the rows with at least 2 non-NA value\n else:\n pass\n \n # info and return\n if verbose==True:\n print(df.shape)\n else:\n pass\n return df\n \n \n \n \n \n \n \n \n# Function, ...........................................................................................\ndef drop_columns(*, df, columns_to_drop, verbose=True):\n \"\"\"\n Small function to quickly remove columns from, \n by column names stored in the list\n - created to give info on removed columns and whether I am chnaging df in proper way,\n - the function allows for column name duplicates, \n \"\"\"\n \n assert type(df)==pd.DataFrame, \"please provide df in pandas dataframe format\"\n df = df.copy()\n \n # find unique values in a list, just in case I made the mistake, \n columns_to_drop = list(pd.Series(columns_to_drop).unique())\n\n # .. info, header, \n if verbose==True:\n print(f\"\"\"Removing {len(columns_to_drop)} columns from df\"\"\") \n else:\n pass\n\n \n # remove columns one by one, \n for i,j in enumerate(columns_to_drop):\n try:\n df.drop(columns=[j], axis=1, inplace=True)\n if verbose==True:\n print(f\"{i} removing: {j}, ==> new df.shape: {df.shape}\")\n else:\n pass\n \n except:\n if verbose==True:\n print(f\"{i} .... column: {j}, was not found in df, check if name is correct....\")\n else:\n pass\n \n return df\n\n",
"step-ids": [
4,
5,
7,
8,
10
]
}
|
[
4,
5,
7,
8,
10
] |
botName = "firstBot"
username = "mrthemafia"
password = "oblivion"
client_id = "Y3LQwponbEp07w"
client_secret = "R4oyCEj6hSTJWHfWMwb-DGUOBm8"
|
normal
|
{
"blob_id": "3031f695d57492cf3b29694fecd0a41c469a3e00",
"index": 7481,
"step-1": "<mask token>\n",
"step-2": "botName = 'firstBot'\nusername = 'mrthemafia'\npassword = 'oblivion'\nclient_id = 'Y3LQwponbEp07w'\nclient_secret = 'R4oyCEj6hSTJWHfWMwb-DGUOBm8'\n",
"step-3": "botName = \"firstBot\"\nusername = \"mrthemafia\"\npassword = \"oblivion\"\nclient_id = \"Y3LQwponbEp07w\"\nclient_secret = \"R4oyCEj6hSTJWHfWMwb-DGUOBm8\"\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
<|reserved_special_token_0|>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
<|reserved_special_token_0|>
net.load_state_dict(torch.load('net_data_multi.pkl'))
<|reserved_special_token_0|>
file_out.write('caseid,midprice\n')
<|reserved_special_token_0|>
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct * 4] = float(line[6])
x[ct * 4 + 1] = float(line[7]) / 10000
x[ct * 4 + 2] = float(line[8])
x[ct * 4 + 3] = float(line[9]) / 10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0 * average / 10
file_out.write(str(case) + ',' + str(average) + '\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
net = Net(n_feature=40, n_hidden=10, n_output=20)
net.load_state_dict(torch.load('net_data_multi.pkl'))
file_test = open('dataset/test_data.csv', 'r')
line = file_test.readline()
file_out = open('result_multi.csv', 'w')
file_out.write('caseid,midprice\n')
case = 1
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct * 4] = float(line[6])
x[ct * 4 + 1] = float(line[7]) / 10000
x[ct * 4 + 2] = float(line[8])
x[ct * 4 + 3] = float(line[9]) / 10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0 * average / 10
file_out.write(str(case) + ',' + str(average) + '\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
<|reserved_special_token_1|>
import torch
import torch.nn.functional as F
import csv
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
net = Net(n_feature=40, n_hidden=10, n_output=20)
net.load_state_dict(torch.load('net_data_multi.pkl'))
file_test = open('dataset/test_data.csv', 'r')
line = file_test.readline()
file_out = open('result_multi.csv', 'w')
file_out.write('caseid,midprice\n')
case = 1
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct * 4] = float(line[6])
x[ct * 4 + 1] = float(line[7]) / 10000
x[ct * 4 + 2] = float(line[8])
x[ct * 4 + 3] = float(line[9]) / 10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0 * average / 10
file_out.write(str(case) + ',' + str(average) + '\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
<|reserved_special_token_1|>
import torch
import torch.nn.functional as F
import csv
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
net = Net(n_feature=40, n_hidden=10, n_output=20)
net.load_state_dict(torch.load('net_data_multi.pkl'))
file_test = open('dataset/test_data.csv','r')
line = file_test.readline()
file_out = open('result_multi.csv','w')
file_out.write('caseid,midprice\n')
case = 1
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct*4] = float(line[6])
x[ct*4+1] = float(line[7])/10000
x[ct*4+2] = float(line[8])
x[ct*4+3] = float(line[9])/10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0*average/10
file_out.write(str(case)+','+str(average)+'\n')
#print(str(case)+','+str(average)+'\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
|
flexible
|
{
"blob_id": "e221553f866de8b3e175197a40982506bf8c1ef9",
"index": 205,
"step-1": "<mask token>\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self, n_feature, n_hidden, n_output):\n super(Net, self).__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden)\n self.predict = torch.nn.Linear(n_hidden, n_output)\n\n def forward(self, x):\n h1 = F.relu(self.hidden(x))\n y = self.predict(h1)\n return y\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self, n_feature, n_hidden, n_output):\n super(Net, self).__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden)\n self.predict = torch.nn.Linear(n_hidden, n_output)\n\n def forward(self, x):\n h1 = F.relu(self.hidden(x))\n y = self.predict(h1)\n return y\n\n\n<mask token>\nnet.load_state_dict(torch.load('net_data_multi.pkl'))\n<mask token>\nfile_out.write('caseid,midprice\\n')\n<mask token>\nwhile case < 143:\n line = file_test.readline().split(',')\n if len(line) < 9:\n case += 1\nwhile case <= 153:\n x = torch.FloatTensor(40).zero_()\n y = torch.FloatTensor(20).zero_()\n for ct in range(10):\n line = file_test.readline()\n if line == '':\n break\n line = line.split(',')\n x[ct * 4] = float(line[6])\n x[ct * 4 + 1] = float(line[7]) / 10000\n x[ct * 4 + 2] = float(line[8])\n x[ct * 4 + 3] = float(line[9]) / 10000\n prediction = net(x)\n average = 0\n for k in range(10):\n average += prediction.data.numpy()[k]\n average = 1.0 * average / 10\n file_out.write(str(case) + ',' + str(average) + '\\n')\n line = file_test.readline()\n case += 1\nfile_test.close()\nfile_out.close()\nprint('test complete')\n",
"step-3": "<mask token>\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self, n_feature, n_hidden, n_output):\n super(Net, self).__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden)\n self.predict = torch.nn.Linear(n_hidden, n_output)\n\n def forward(self, x):\n h1 = F.relu(self.hidden(x))\n y = self.predict(h1)\n return y\n\n\nnet = Net(n_feature=40, n_hidden=10, n_output=20)\nnet.load_state_dict(torch.load('net_data_multi.pkl'))\nfile_test = open('dataset/test_data.csv', 'r')\nline = file_test.readline()\nfile_out = open('result_multi.csv', 'w')\nfile_out.write('caseid,midprice\\n')\ncase = 1\nwhile case < 143:\n line = file_test.readline().split(',')\n if len(line) < 9:\n case += 1\nwhile case <= 153:\n x = torch.FloatTensor(40).zero_()\n y = torch.FloatTensor(20).zero_()\n for ct in range(10):\n line = file_test.readline()\n if line == '':\n break\n line = line.split(',')\n x[ct * 4] = float(line[6])\n x[ct * 4 + 1] = float(line[7]) / 10000\n x[ct * 4 + 2] = float(line[8])\n x[ct * 4 + 3] = float(line[9]) / 10000\n prediction = net(x)\n average = 0\n for k in range(10):\n average += prediction.data.numpy()[k]\n average = 1.0 * average / 10\n file_out.write(str(case) + ',' + str(average) + '\\n')\n line = file_test.readline()\n case += 1\nfile_test.close()\nfile_out.close()\nprint('test complete')\n",
"step-4": "import torch\nimport torch.nn.functional as F\nimport csv\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self, n_feature, n_hidden, n_output):\n super(Net, self).__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden)\n self.predict = torch.nn.Linear(n_hidden, n_output)\n\n def forward(self, x):\n h1 = F.relu(self.hidden(x))\n y = self.predict(h1)\n return y\n\n\nnet = Net(n_feature=40, n_hidden=10, n_output=20)\nnet.load_state_dict(torch.load('net_data_multi.pkl'))\nfile_test = open('dataset/test_data.csv', 'r')\nline = file_test.readline()\nfile_out = open('result_multi.csv', 'w')\nfile_out.write('caseid,midprice\\n')\ncase = 1\nwhile case < 143:\n line = file_test.readline().split(',')\n if len(line) < 9:\n case += 1\nwhile case <= 153:\n x = torch.FloatTensor(40).zero_()\n y = torch.FloatTensor(20).zero_()\n for ct in range(10):\n line = file_test.readline()\n if line == '':\n break\n line = line.split(',')\n x[ct * 4] = float(line[6])\n x[ct * 4 + 1] = float(line[7]) / 10000\n x[ct * 4 + 2] = float(line[8])\n x[ct * 4 + 3] = float(line[9]) / 10000\n prediction = net(x)\n average = 0\n for k in range(10):\n average += prediction.data.numpy()[k]\n average = 1.0 * average / 10\n file_out.write(str(case) + ',' + str(average) + '\\n')\n line = file_test.readline()\n case += 1\nfile_test.close()\nfile_out.close()\nprint('test complete')\n",
"step-5": "import torch\nimport torch.nn.functional as F\nimport csv\n\n\nclass Net(torch.nn.Module):\n\n def __init__(self, n_feature, n_hidden, n_output):\n super(Net, self).__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden)\n self.predict = torch.nn.Linear(n_hidden, n_output)\n\n def forward(self, x):\n h1 = F.relu(self.hidden(x))\n y = self.predict(h1) \n return y\n\n\nnet = Net(n_feature=40, n_hidden=10, n_output=20)\n\nnet.load_state_dict(torch.load('net_data_multi.pkl'))\n\nfile_test = open('dataset/test_data.csv','r')\nline = file_test.readline()\n\nfile_out = open('result_multi.csv','w')\nfile_out.write('caseid,midprice\\n')\n\ncase = 1\n\nwhile case < 143:\n line = file_test.readline().split(',')\n if len(line) < 9:\n case += 1\n \nwhile case <= 153:\n x = torch.FloatTensor(40).zero_()\n y = torch.FloatTensor(20).zero_()\n\n for ct in range(10):\n line = file_test.readline()\n if line == '':\n break\n line = line.split(',')\n\n x[ct*4] = float(line[6])\n x[ct*4+1] = float(line[7])/10000\n x[ct*4+2] = float(line[8])\n x[ct*4+3] = float(line[9])/10000\n\n prediction = net(x)\n\n average = 0\n for k in range(10):\n average += prediction.data.numpy()[k]\n average = 1.0*average/10\n\n file_out.write(str(case)+','+str(average)+'\\n')\n #print(str(case)+','+str(average)+'\\n')\n\n line = file_test.readline()\n case += 1\n\nfile_test.close()\nfile_out.close()\nprint('test complete')\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class GameManager:
def __init__(self):
self.screen = pygame.display.set_mode((1280, 720), flags=pygame.
FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)
self.running = True
self.delta_time = 1
self.active_scene = None
self.load_scene(MainGame.MainGame, (self,))
self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14
)
self.pygame_clock = pygame.time.Clock()
self.pygame_clock.tick()
pygame.joystick.init()
self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.
joystick.get_count())]
for joystick in self.joystick:
joystick.init()
random.seed(time.time())
self.player_joy = -1
<|reserved_special_token_0|>
def main_loop(self):
while self.running:
events = pygame.event.get()
for event in events:
if event.type == pygame.QUIT:
self.exit()
self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3
fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self
.delta_time)), False, (255, 255, 255))
self.active_scene.main_loop(events)
self.screen.blit(fps_text, (self.screen.get_width() - fps_text.
get_width(), 0))
pygame.display.flip()
def load_scene(self, scene_object, scene_parameters):
self.active_scene = scene_object(*scene_parameters)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class GameManager:
def __init__(self):
self.screen = pygame.display.set_mode((1280, 720), flags=pygame.
FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)
self.running = True
self.delta_time = 1
self.active_scene = None
self.load_scene(MainGame.MainGame, (self,))
self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14
)
self.pygame_clock = pygame.time.Clock()
self.pygame_clock.tick()
pygame.joystick.init()
self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.
joystick.get_count())]
for joystick in self.joystick:
joystick.init()
random.seed(time.time())
self.player_joy = -1
def __del__(self):
self.exit()
def main_loop(self):
while self.running:
events = pygame.event.get()
for event in events:
if event.type == pygame.QUIT:
self.exit()
self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3
fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self
.delta_time)), False, (255, 255, 255))
self.active_scene.main_loop(events)
self.screen.blit(fps_text, (self.screen.get_width() - fps_text.
get_width(), 0))
pygame.display.flip()
def load_scene(self, scene_object, scene_parameters):
self.active_scene = scene_object(*scene_parameters)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class GameManager:
def __init__(self):
self.screen = pygame.display.set_mode((1280, 720), flags=pygame.
FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)
self.running = True
self.delta_time = 1
self.active_scene = None
self.load_scene(MainGame.MainGame, (self,))
self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14
)
self.pygame_clock = pygame.time.Clock()
self.pygame_clock.tick()
pygame.joystick.init()
self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.
joystick.get_count())]
for joystick in self.joystick:
joystick.init()
random.seed(time.time())
self.player_joy = -1
def __del__(self):
self.exit()
def main_loop(self):
while self.running:
events = pygame.event.get()
for event in events:
if event.type == pygame.QUIT:
self.exit()
self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3
fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self
.delta_time)), False, (255, 255, 255))
self.active_scene.main_loop(events)
self.screen.blit(fps_text, (self.screen.get_width() - fps_text.
get_width(), 0))
pygame.display.flip()
def load_scene(self, scene_object, scene_parameters):
self.active_scene = scene_object(*scene_parameters)
def exit(self):
self.running = False
<|reserved_special_token_1|>
import pygame
import time
from menus import MainMenu
from scenes import TestWorldGen
from scenes import TestAnimation
from scenes import TestLevel2
from scenes import MainGame
import random
class GameManager:
def __init__(self):
self.screen = pygame.display.set_mode((1280, 720), flags=pygame.
FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)
self.running = True
self.delta_time = 1
self.active_scene = None
self.load_scene(MainGame.MainGame, (self,))
self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14
)
self.pygame_clock = pygame.time.Clock()
self.pygame_clock.tick()
pygame.joystick.init()
self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.
joystick.get_count())]
for joystick in self.joystick:
joystick.init()
random.seed(time.time())
self.player_joy = -1
def __del__(self):
self.exit()
def main_loop(self):
while self.running:
events = pygame.event.get()
for event in events:
if event.type == pygame.QUIT:
self.exit()
self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3
fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self
.delta_time)), False, (255, 255, 255))
self.active_scene.main_loop(events)
self.screen.blit(fps_text, (self.screen.get_width() - fps_text.
get_width(), 0))
pygame.display.flip()
def load_scene(self, scene_object, scene_parameters):
self.active_scene = scene_object(*scene_parameters)
def exit(self):
self.running = False
<|reserved_special_token_1|>
import pygame
import time
from menus import MainMenu
from scenes import TestWorldGen
from scenes import TestAnimation
from scenes import TestLevel2
from scenes import MainGame
import random
class GameManager:
def __init__(self):
self.screen = pygame.display.set_mode((1280, 720),
flags=pygame.FULLSCREEN |
pygame.HWSURFACE |
pygame.DOUBLEBUF) # type: pygame.Surface
self.running = True
self.delta_time = 1
self.active_scene = None
# self.load_scene(MainMenu.MainMenu, (self,))
# self.load_scene(TestWorldGen.TestWorldGen, (self,))
# self.load_scene(TestAnimation.TestAnimation, (self,))
# self.load_scene(TestLevel2.TestLevel, (self, ))
self.load_scene(MainGame.MainGame, (self,))
self.fps_font = pygame.font.Font("game_data/fonts/calling_code.ttf", 14)
self.pygame_clock = pygame.time.Clock() # type: pygame
self.pygame_clock.tick()
pygame.joystick.init()
self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.joystick.get_count())]
for joystick in self.joystick:
joystick.init()
random.seed(time.time())
self.player_joy = -1
def __del__(self):
self.exit()
def main_loop(self):
while self.running:
events = pygame.event.get()
for event in events:
if event.type == pygame.QUIT:
self.exit()
self.delta_time = float(self.pygame_clock.tick(60)) / (10 ** 3)
fps_text = self.fps_font.render("FPS: {}".format(round(1 / self.delta_time)), False, (255, 255, 255))
self.active_scene.main_loop(events)
self.screen.blit(fps_text, (self.screen.get_width() - fps_text.get_width(), 0))
pygame.display.flip()
def load_scene(self, scene_object, scene_parameters):
self.active_scene = scene_object(*scene_parameters)
def exit(self):
self.running = False
|
flexible
|
{
"blob_id": "91806afea92587476ac743346b88098b197a033c",
"index": 9706,
"step-1": "<mask token>\n\n\nclass GameManager:\n\n def __init__(self):\n self.screen = pygame.display.set_mode((1280, 720), flags=pygame.\n FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)\n self.running = True\n self.delta_time = 1\n self.active_scene = None\n self.load_scene(MainGame.MainGame, (self,))\n self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14\n )\n self.pygame_clock = pygame.time.Clock()\n self.pygame_clock.tick()\n pygame.joystick.init()\n self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.\n joystick.get_count())]\n for joystick in self.joystick:\n joystick.init()\n random.seed(time.time())\n self.player_joy = -1\n <mask token>\n\n def main_loop(self):\n while self.running:\n events = pygame.event.get()\n for event in events:\n if event.type == pygame.QUIT:\n self.exit()\n self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3\n fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self\n .delta_time)), False, (255, 255, 255))\n self.active_scene.main_loop(events)\n self.screen.blit(fps_text, (self.screen.get_width() - fps_text.\n get_width(), 0))\n pygame.display.flip()\n\n def load_scene(self, scene_object, scene_parameters):\n self.active_scene = scene_object(*scene_parameters)\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass GameManager:\n\n def __init__(self):\n self.screen = pygame.display.set_mode((1280, 720), flags=pygame.\n FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)\n self.running = True\n self.delta_time = 1\n self.active_scene = None\n self.load_scene(MainGame.MainGame, (self,))\n self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14\n )\n self.pygame_clock = pygame.time.Clock()\n self.pygame_clock.tick()\n pygame.joystick.init()\n self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.\n joystick.get_count())]\n for joystick in self.joystick:\n joystick.init()\n random.seed(time.time())\n self.player_joy = -1\n\n def __del__(self):\n self.exit()\n\n def main_loop(self):\n while self.running:\n events = pygame.event.get()\n for event in events:\n if event.type == pygame.QUIT:\n self.exit()\n self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3\n fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self\n .delta_time)), False, (255, 255, 255))\n self.active_scene.main_loop(events)\n self.screen.blit(fps_text, (self.screen.get_width() - fps_text.\n get_width(), 0))\n pygame.display.flip()\n\n def load_scene(self, scene_object, scene_parameters):\n self.active_scene = scene_object(*scene_parameters)\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass GameManager:\n\n def __init__(self):\n self.screen = pygame.display.set_mode((1280, 720), flags=pygame.\n FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)\n self.running = True\n self.delta_time = 1\n self.active_scene = None\n self.load_scene(MainGame.MainGame, (self,))\n self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14\n )\n self.pygame_clock = pygame.time.Clock()\n self.pygame_clock.tick()\n pygame.joystick.init()\n self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.\n joystick.get_count())]\n for joystick in self.joystick:\n joystick.init()\n random.seed(time.time())\n self.player_joy = -1\n\n def __del__(self):\n self.exit()\n\n def main_loop(self):\n while self.running:\n events = pygame.event.get()\n for event in events:\n if event.type == pygame.QUIT:\n self.exit()\n self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3\n fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self\n .delta_time)), False, (255, 255, 255))\n self.active_scene.main_loop(events)\n self.screen.blit(fps_text, (self.screen.get_width() - fps_text.\n get_width(), 0))\n pygame.display.flip()\n\n def load_scene(self, scene_object, scene_parameters):\n self.active_scene = scene_object(*scene_parameters)\n\n def exit(self):\n self.running = False\n",
"step-4": "import pygame\nimport time\nfrom menus import MainMenu\nfrom scenes import TestWorldGen\nfrom scenes import TestAnimation\nfrom scenes import TestLevel2\nfrom scenes import MainGame\nimport random\n\n\nclass GameManager:\n\n def __init__(self):\n self.screen = pygame.display.set_mode((1280, 720), flags=pygame.\n FULLSCREEN | pygame.HWSURFACE | pygame.DOUBLEBUF)\n self.running = True\n self.delta_time = 1\n self.active_scene = None\n self.load_scene(MainGame.MainGame, (self,))\n self.fps_font = pygame.font.Font('game_data/fonts/calling_code.ttf', 14\n )\n self.pygame_clock = pygame.time.Clock()\n self.pygame_clock.tick()\n pygame.joystick.init()\n self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.\n joystick.get_count())]\n for joystick in self.joystick:\n joystick.init()\n random.seed(time.time())\n self.player_joy = -1\n\n def __del__(self):\n self.exit()\n\n def main_loop(self):\n while self.running:\n events = pygame.event.get()\n for event in events:\n if event.type == pygame.QUIT:\n self.exit()\n self.delta_time = float(self.pygame_clock.tick(60)) / 10 ** 3\n fps_text = self.fps_font.render('FPS: {}'.format(round(1 / self\n .delta_time)), False, (255, 255, 255))\n self.active_scene.main_loop(events)\n self.screen.blit(fps_text, (self.screen.get_width() - fps_text.\n get_width(), 0))\n pygame.display.flip()\n\n def load_scene(self, scene_object, scene_parameters):\n self.active_scene = scene_object(*scene_parameters)\n\n def exit(self):\n self.running = False\n",
"step-5": "import pygame\nimport time\nfrom menus import MainMenu\nfrom scenes import TestWorldGen\nfrom scenes import TestAnimation\nfrom scenes import TestLevel2\nfrom scenes import MainGame\nimport random\n\n\nclass GameManager:\n def __init__(self):\n self.screen = pygame.display.set_mode((1280, 720),\n flags=pygame.FULLSCREEN |\n pygame.HWSURFACE |\n pygame.DOUBLEBUF) # type: pygame.Surface\n\n self.running = True\n\n self.delta_time = 1\n\n self.active_scene = None\n # self.load_scene(MainMenu.MainMenu, (self,))\n # self.load_scene(TestWorldGen.TestWorldGen, (self,))\n # self.load_scene(TestAnimation.TestAnimation, (self,))\n # self.load_scene(TestLevel2.TestLevel, (self, ))\n self.load_scene(MainGame.MainGame, (self,))\n\n self.fps_font = pygame.font.Font(\"game_data/fonts/calling_code.ttf\", 14)\n\n self.pygame_clock = pygame.time.Clock() # type: pygame\n self.pygame_clock.tick()\n pygame.joystick.init()\n self.joystick = [pygame.joystick.Joystick(i) for i in range(pygame.joystick.get_count())]\n for joystick in self.joystick:\n joystick.init()\n\n random.seed(time.time())\n\n self.player_joy = -1\n\n def __del__(self):\n self.exit()\n\n def main_loop(self):\n while self.running:\n events = pygame.event.get()\n for event in events:\n if event.type == pygame.QUIT:\n self.exit()\n\n self.delta_time = float(self.pygame_clock.tick(60)) / (10 ** 3)\n\n fps_text = self.fps_font.render(\"FPS: {}\".format(round(1 / self.delta_time)), False, (255, 255, 255))\n\n self.active_scene.main_loop(events)\n\n self.screen.blit(fps_text, (self.screen.get_width() - fps_text.get_width(), 0))\n\n pygame.display.flip()\n\n def load_scene(self, scene_object, scene_parameters):\n self.active_scene = scene_object(*scene_parameters)\n\n def exit(self):\n self.running = False\n",
"step-ids": [
4,
5,
6,
7,
8
]
}
|
[
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class Autorization:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class AutorizationClient(Autorization):
"""
Manejo de autorizaciones de clientes,
se listan los clientes, en orden de pendiente,
aprobado y rechazado, segun fecha
Para posterior aprovacion o rechazo
"""
@method_decorator(user_passes_test(role_admin_check()))
def list(self, request):
"""
Listado de clientes por autorizar,
se incluyen tambien clientes aprovados y rechazados
"""
obj_api = api()
token = request.session['token']
title_page = _('User - User Affiliation').title()
filters = {}
form_filters = AuthorizationClientFilter(request.GET)
if form_filters.is_valid():
filters = form_filters.cleaned_data
tools = Tools()
filters['from_date'] = tools.date_format_to_db(date=filters[
'from_date'])
filters['until_date'] = tools.date_format_to_db(date=filters[
'until_date'])
filters = form_filters.cleaned_data
if request.method == 'GET':
if 'approve' in request.GET and request.GET['approve']:
pk = request.GET['approve']
data = {'status': 1}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
if 'rejected' in request.GET and request.GET['rejected']:
pk = request.GET['rejected']
data = {'status': 2}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
data = obj_api.get(slug='authorizations/clients/', arg=filters,
token=token)
header_table = [('', 'code_seller'), ('', 'name'), ('',
'document_type_name'), ('', 'document'), ('', ''), ('', ''), (
'', 'document'), ('', 'approve'), ('', 'rejected'), ('',
'date_join')]
multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),
{'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':
'1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',
'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':
'2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(
'name or Social reason'), {}), (_('type document'), {}), (_(
'document number'), {}), (_('description'), {}), (_(
'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]
approve_column = {'type': 'submit', 'data': {'name': 'approve',
'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}
rejected_column = {'type': 'submit', 'data': {'name': 'rejected',
'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}
custom_column = {'date_join': {'type': 'date', 'data': ('date_join'
,)}, 'approve': {'type': 'if_eval', 'data': ('r["status"]=="0"'
,), 'next': approve_column}, 'rejected': {'type': 'if_eval',
'data': ('r["status"]=="0"',), 'next': rejected_column}}
table = convert(data, header=header_table, multi_header=
multi_header, custom_column=custom_column)
vars_page = self.generate_header(custom_title=title_page)
return render(request, 'admin/authorization/clients.html', {'table':
table, 'vars_page': vars_page, 'form_filters': form_filters})
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Autorization:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def generate_header(self, custom_title=None):
if custom_title:
title = '{} - '.format(_('authorizations')).title() + custom_title
else:
title = self.title_content_header
header = {'icon': self.logo_content_header, 'title': title}
return {**header, **self.vars_page}
class AutorizationClient(Autorization):
"""
Manejo de autorizaciones de clientes,
se listan los clientes, en orden de pendiente,
aprobado y rechazado, segun fecha
Para posterior aprovacion o rechazo
"""
@method_decorator(user_passes_test(role_admin_check()))
def list(self, request):
"""
Listado de clientes por autorizar,
se incluyen tambien clientes aprovados y rechazados
"""
obj_api = api()
token = request.session['token']
title_page = _('User - User Affiliation').title()
filters = {}
form_filters = AuthorizationClientFilter(request.GET)
if form_filters.is_valid():
filters = form_filters.cleaned_data
tools = Tools()
filters['from_date'] = tools.date_format_to_db(date=filters[
'from_date'])
filters['until_date'] = tools.date_format_to_db(date=filters[
'until_date'])
filters = form_filters.cleaned_data
if request.method == 'GET':
if 'approve' in request.GET and request.GET['approve']:
pk = request.GET['approve']
data = {'status': 1}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
if 'rejected' in request.GET and request.GET['rejected']:
pk = request.GET['rejected']
data = {'status': 2}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
data = obj_api.get(slug='authorizations/clients/', arg=filters,
token=token)
header_table = [('', 'code_seller'), ('', 'name'), ('',
'document_type_name'), ('', 'document'), ('', ''), ('', ''), (
'', 'document'), ('', 'approve'), ('', 'rejected'), ('',
'date_join')]
multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),
{'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':
'1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',
'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':
'2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(
'name or Social reason'), {}), (_('type document'), {}), (_(
'document number'), {}), (_('description'), {}), (_(
'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]
approve_column = {'type': 'submit', 'data': {'name': 'approve',
'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}
rejected_column = {'type': 'submit', 'data': {'name': 'rejected',
'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}
custom_column = {'date_join': {'type': 'date', 'data': ('date_join'
,)}, 'approve': {'type': 'if_eval', 'data': ('r["status"]=="0"'
,), 'next': approve_column}, 'rejected': {'type': 'if_eval',
'data': ('r["status"]=="0"',), 'next': rejected_column}}
table = convert(data, header=header_table, multi_header=
multi_header, custom_column=custom_column)
vars_page = self.generate_header(custom_title=title_page)
return render(request, 'admin/authorization/clients.html', {'table':
table, 'vars_page': vars_page, 'form_filters': form_filters})
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Autorization:
logo_content_header = 'fa fa-key'
vars_page = {}
def generate_header(self, custom_title=None):
if custom_title:
title = '{} - '.format(_('authorizations')).title() + custom_title
else:
title = self.title_content_header
header = {'icon': self.logo_content_header, 'title': title}
return {**header, **self.vars_page}
class AutorizationClient(Autorization):
"""
Manejo de autorizaciones de clientes,
se listan los clientes, en orden de pendiente,
aprobado y rechazado, segun fecha
Para posterior aprovacion o rechazo
"""
@method_decorator(user_passes_test(role_admin_check()))
def list(self, request):
"""
Listado de clientes por autorizar,
se incluyen tambien clientes aprovados y rechazados
"""
obj_api = api()
token = request.session['token']
title_page = _('User - User Affiliation').title()
filters = {}
form_filters = AuthorizationClientFilter(request.GET)
if form_filters.is_valid():
filters = form_filters.cleaned_data
tools = Tools()
filters['from_date'] = tools.date_format_to_db(date=filters[
'from_date'])
filters['until_date'] = tools.date_format_to_db(date=filters[
'until_date'])
filters = form_filters.cleaned_data
if request.method == 'GET':
if 'approve' in request.GET and request.GET['approve']:
pk = request.GET['approve']
data = {'status': 1}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
if 'rejected' in request.GET and request.GET['rejected']:
pk = request.GET['rejected']
data = {'status': 2}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
data = obj_api.get(slug='authorizations/clients/', arg=filters,
token=token)
header_table = [('', 'code_seller'), ('', 'name'), ('',
'document_type_name'), ('', 'document'), ('', ''), ('', ''), (
'', 'document'), ('', 'approve'), ('', 'rejected'), ('',
'date_join')]
multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),
{'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':
'1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',
'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':
'2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(
'name or Social reason'), {}), (_('type document'), {}), (_(
'document number'), {}), (_('description'), {}), (_(
'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]
approve_column = {'type': 'submit', 'data': {'name': 'approve',
'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}
rejected_column = {'type': 'submit', 'data': {'name': 'rejected',
'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}
custom_column = {'date_join': {'type': 'date', 'data': ('date_join'
,)}, 'approve': {'type': 'if_eval', 'data': ('r["status"]=="0"'
,), 'next': approve_column}, 'rejected': {'type': 'if_eval',
'data': ('r["status"]=="0"',), 'next': rejected_column}}
table = convert(data, header=header_table, multi_header=
multi_header, custom_column=custom_column)
vars_page = self.generate_header(custom_title=title_page)
return render(request, 'admin/authorization/clients.html', {'table':
table, 'vars_page': vars_page, 'form_filters': form_filters})
<|reserved_special_token_1|>
<|reserved_special_token_0|>
from django.shortcuts import render
from dashboard.json2table import convert
from django.utils.translation import ugettext_lazy as _
from api.connection import api
from login.utils.tools import role_admin_check
from django.utils.decorators import method_decorator
from django.contrib.auth.decorators import user_passes_test
from dashboard.tools import capitalize as cap, ToolsBackend as Tools
from dashboard.forms import AuthorizationClientFilter
class Autorization:
logo_content_header = 'fa fa-key'
vars_page = {}
def generate_header(self, custom_title=None):
if custom_title:
title = '{} - '.format(_('authorizations')).title() + custom_title
else:
title = self.title_content_header
header = {'icon': self.logo_content_header, 'title': title}
return {**header, **self.vars_page}
class AutorizationClient(Autorization):
"""
Manejo de autorizaciones de clientes,
se listan los clientes, en orden de pendiente,
aprobado y rechazado, segun fecha
Para posterior aprovacion o rechazo
"""
@method_decorator(user_passes_test(role_admin_check()))
def list(self, request):
"""
Listado de clientes por autorizar,
se incluyen tambien clientes aprovados y rechazados
"""
obj_api = api()
token = request.session['token']
title_page = _('User - User Affiliation').title()
filters = {}
form_filters = AuthorizationClientFilter(request.GET)
if form_filters.is_valid():
filters = form_filters.cleaned_data
tools = Tools()
filters['from_date'] = tools.date_format_to_db(date=filters[
'from_date'])
filters['until_date'] = tools.date_format_to_db(date=filters[
'until_date'])
filters = form_filters.cleaned_data
if request.method == 'GET':
if 'approve' in request.GET and request.GET['approve']:
pk = request.GET['approve']
data = {'status': 1}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
if 'rejected' in request.GET and request.GET['rejected']:
pk = request.GET['rejected']
data = {'status': 2}
obj_api.put(slug='authorizations/clients/' + pk, token=
token, arg=data)
data = obj_api.get(slug='authorizations/clients/', arg=filters,
token=token)
header_table = [('', 'code_seller'), ('', 'name'), ('',
'document_type_name'), ('', 'document'), ('', ''), ('', ''), (
'', 'document'), ('', 'approve'), ('', 'rejected'), ('',
'date_join')]
multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),
{'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':
'1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',
'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':
'2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(
'name or Social reason'), {}), (_('type document'), {}), (_(
'document number'), {}), (_('description'), {}), (_(
'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]
approve_column = {'type': 'submit', 'data': {'name': 'approve',
'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}
rejected_column = {'type': 'submit', 'data': {'name': 'rejected',
'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}
custom_column = {'date_join': {'type': 'date', 'data': ('date_join'
,)}, 'approve': {'type': 'if_eval', 'data': ('r["status"]=="0"'
,), 'next': approve_column}, 'rejected': {'type': 'if_eval',
'data': ('r["status"]=="0"',), 'next': rejected_column}}
table = convert(data, header=header_table, multi_header=
multi_header, custom_column=custom_column)
vars_page = self.generate_header(custom_title=title_page)
return render(request, 'admin/authorization/clients.html', {'table':
table, 'vars_page': vars_page, 'form_filters': form_filters})
<|reserved_special_token_1|>
"""Vista de Autorizaciones (Clientes/Especialistas/Vendedores)."""
from django.shortcuts import render
from dashboard.json2table import convert
from django.utils.translation import ugettext_lazy as _
from api.connection import api
from login.utils.tools import role_admin_check
from django.utils.decorators import method_decorator
from django.contrib.auth.decorators import user_passes_test
from dashboard.tools import capitalize as cap, ToolsBackend as Tools
from dashboard.forms import AuthorizationClientFilter
class Autorization:
logo_content_header = "fa fa-key"
vars_page = {}
def generate_header(self, custom_title=None):
if custom_title:
title = "{} - ".format(_("authorizations")).title() + custom_title
else:
title = self.title_content_header
header = {'icon': self.logo_content_header, 'title': title}
return {**header, **self.vars_page}
class AutorizationClient(Autorization):
"""
Manejo de autorizaciones de clientes,
se listan los clientes, en orden de pendiente,
aprobado y rechazado, segun fecha
Para posterior aprovacion o rechazo
"""
@method_decorator(user_passes_test(role_admin_check()))
def list(self, request):
"""
Listado de clientes por autorizar,
se incluyen tambien clientes aprovados y rechazados
"""
obj_api = api()
# actual_page = get_actual_page(request)
token = request.session['token']
title_page = _('User - User Affiliation').title()
filters = {}
form_filters = AuthorizationClientFilter(request.GET)
if form_filters.is_valid(): # Agregamos filtros de encontrarse alguno
filters = form_filters.cleaned_data
tools = Tools()
filters['from_date'] = tools.date_format_to_db(date=filters['from_date'])
filters['until_date'] = tools.date_format_to_db(date=filters['until_date'])
filters = form_filters.cleaned_data
if request.method == 'GET':
if 'approve' in request.GET and request.GET['approve']:
pk = request.GET['approve']
data = {"status":1}
obj_api.put(slug='authorizations/clients/' + pk, token=token, arg=data)
if 'rejected' in request.GET and request.GET['rejected']:
pk = request.GET['rejected']
data = {"status":2}
obj_api.put(slug='authorizations/clients/' + pk, token=token, arg=data)
# Traer data para el listado
data = obj_api.get(slug='authorizations/clients/', arg=filters, token=token)
header_table = [("", "code_seller"), ("", "name"),(
"", "document_type_name"), ( "", "document"),(
"", ""), ("", ""), (
"", "document"), (
"", "approve"), ("", "rejected"), (
"", "date_join")]
# Multiples header, una lista por cada nivel de la cabecera
multi_header = [
[
(_("seller code"), {'rowspan': '2'}),
(_('user'), {'rowspan': '1', 'colspan': '3'}),
(_('product'), {'rowspan': '1', 'colspan': '2'}),
(_('user code'), {'rowspan': '2', 'colspan': '1'}),
(_('validation'), {'rowspan': '1', 'colspan': '2'}),
(_('date'), {'rowspan': '2', 'colspan': '1'}),
],
[
(_('name or Social reason'), {}),
(_('type document'), {}),
(_('document number'), {}),
(_('description'), {}),
(_('Query Numbers'), {}),
(_('approve'), {}),
(_('deneis'), {}),
],
]
approve_column = {'type': 'submit', 'data': {'name':'approve','key':'id',
'cls':'btn btn-success','text':cap(_('approve'))}}
rejected_column = {'type': 'submit', 'data': {'name':'rejected','key':'id',
'cls':'btn btn-danger','text':cap(_('rejected'))}}
custom_column = {
"date_join": {'type': 'date', 'data': ('date_join',)},
"approve": {'type': 'if_eval', 'data': ('r["status"]=="0"',),
'next': approve_column},
"rejected": {
'type': 'if_eval',
'data': ('r["status"]=="0"',),
'next': rejected_column
},
}
table = convert(data, header=header_table, multi_header=multi_header, custom_column=custom_column)
# Titulo de la vista y variables de la Clase
vars_page = self.generate_header(custom_title=title_page)
return render(request, 'admin/authorization/clients.html',
{'table': table, 'vars_page': vars_page, 'form_filters':form_filters})
|
flexible
|
{
"blob_id": "b78ad3a55eb27fd91f89c22db07fadca297640ab",
"index": 2892,
"step-1": "<mask token>\n\n\nclass Autorization:\n <mask token>\n <mask token>\n <mask token>\n\n\nclass AutorizationClient(Autorization):\n \"\"\"\n Manejo de autorizaciones de clientes,\n se listan los clientes, en orden de pendiente,\n aprobado y rechazado, segun fecha\n Para posterior aprovacion o rechazo\n \"\"\"\n\n @method_decorator(user_passes_test(role_admin_check()))\n def list(self, request):\n \"\"\"\n Listado de clientes por autorizar,\n se incluyen tambien clientes aprovados y rechazados\n \"\"\"\n obj_api = api()\n token = request.session['token']\n title_page = _('User - User Affiliation').title()\n filters = {}\n form_filters = AuthorizationClientFilter(request.GET)\n if form_filters.is_valid():\n filters = form_filters.cleaned_data\n tools = Tools()\n filters['from_date'] = tools.date_format_to_db(date=filters[\n 'from_date'])\n filters['until_date'] = tools.date_format_to_db(date=filters[\n 'until_date'])\n filters = form_filters.cleaned_data\n if request.method == 'GET':\n if 'approve' in request.GET and request.GET['approve']:\n pk = request.GET['approve']\n data = {'status': 1}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n if 'rejected' in request.GET and request.GET['rejected']:\n pk = request.GET['rejected']\n data = {'status': 2}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n data = obj_api.get(slug='authorizations/clients/', arg=filters,\n token=token)\n header_table = [('', 'code_seller'), ('', 'name'), ('',\n 'document_type_name'), ('', 'document'), ('', ''), ('', ''), (\n '', 'document'), ('', 'approve'), ('', 'rejected'), ('',\n 'date_join')]\n multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),\n {'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':\n '1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',\n 'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':\n '2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(\n 'name or Social reason'), {}), (_('type document'), {}), (_(\n 'document number'), {}), (_('description'), {}), (_(\n 'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]\n approve_column = {'type': 'submit', 'data': {'name': 'approve',\n 'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}\n rejected_column = {'type': 'submit', 'data': {'name': 'rejected',\n 'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}\n custom_column = {'date_join': {'type': 'date', 'data': ('date_join'\n ,)}, 'approve': {'type': 'if_eval', 'data': ('r[\"status\"]==\"0\"'\n ,), 'next': approve_column}, 'rejected': {'type': 'if_eval',\n 'data': ('r[\"status\"]==\"0\"',), 'next': rejected_column}}\n table = convert(data, header=header_table, multi_header=\n multi_header, custom_column=custom_column)\n vars_page = self.generate_header(custom_title=title_page)\n return render(request, 'admin/authorization/clients.html', {'table':\n table, 'vars_page': vars_page, 'form_filters': form_filters})\n",
"step-2": "<mask token>\n\n\nclass Autorization:\n <mask token>\n <mask token>\n\n def generate_header(self, custom_title=None):\n if custom_title:\n title = '{} - '.format(_('authorizations')).title() + custom_title\n else:\n title = self.title_content_header\n header = {'icon': self.logo_content_header, 'title': title}\n return {**header, **self.vars_page}\n\n\nclass AutorizationClient(Autorization):\n \"\"\"\n Manejo de autorizaciones de clientes,\n se listan los clientes, en orden de pendiente,\n aprobado y rechazado, segun fecha\n Para posterior aprovacion o rechazo\n \"\"\"\n\n @method_decorator(user_passes_test(role_admin_check()))\n def list(self, request):\n \"\"\"\n Listado de clientes por autorizar,\n se incluyen tambien clientes aprovados y rechazados\n \"\"\"\n obj_api = api()\n token = request.session['token']\n title_page = _('User - User Affiliation').title()\n filters = {}\n form_filters = AuthorizationClientFilter(request.GET)\n if form_filters.is_valid():\n filters = form_filters.cleaned_data\n tools = Tools()\n filters['from_date'] = tools.date_format_to_db(date=filters[\n 'from_date'])\n filters['until_date'] = tools.date_format_to_db(date=filters[\n 'until_date'])\n filters = form_filters.cleaned_data\n if request.method == 'GET':\n if 'approve' in request.GET and request.GET['approve']:\n pk = request.GET['approve']\n data = {'status': 1}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n if 'rejected' in request.GET and request.GET['rejected']:\n pk = request.GET['rejected']\n data = {'status': 2}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n data = obj_api.get(slug='authorizations/clients/', arg=filters,\n token=token)\n header_table = [('', 'code_seller'), ('', 'name'), ('',\n 'document_type_name'), ('', 'document'), ('', ''), ('', ''), (\n '', 'document'), ('', 'approve'), ('', 'rejected'), ('',\n 'date_join')]\n multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),\n {'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':\n '1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',\n 'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':\n '2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(\n 'name or Social reason'), {}), (_('type document'), {}), (_(\n 'document number'), {}), (_('description'), {}), (_(\n 'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]\n approve_column = {'type': 'submit', 'data': {'name': 'approve',\n 'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}\n rejected_column = {'type': 'submit', 'data': {'name': 'rejected',\n 'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}\n custom_column = {'date_join': {'type': 'date', 'data': ('date_join'\n ,)}, 'approve': {'type': 'if_eval', 'data': ('r[\"status\"]==\"0\"'\n ,), 'next': approve_column}, 'rejected': {'type': 'if_eval',\n 'data': ('r[\"status\"]==\"0\"',), 'next': rejected_column}}\n table = convert(data, header=header_table, multi_header=\n multi_header, custom_column=custom_column)\n vars_page = self.generate_header(custom_title=title_page)\n return render(request, 'admin/authorization/clients.html', {'table':\n table, 'vars_page': vars_page, 'form_filters': form_filters})\n",
"step-3": "<mask token>\n\n\nclass Autorization:\n logo_content_header = 'fa fa-key'\n vars_page = {}\n\n def generate_header(self, custom_title=None):\n if custom_title:\n title = '{} - '.format(_('authorizations')).title() + custom_title\n else:\n title = self.title_content_header\n header = {'icon': self.logo_content_header, 'title': title}\n return {**header, **self.vars_page}\n\n\nclass AutorizationClient(Autorization):\n \"\"\"\n Manejo de autorizaciones de clientes,\n se listan los clientes, en orden de pendiente,\n aprobado y rechazado, segun fecha\n Para posterior aprovacion o rechazo\n \"\"\"\n\n @method_decorator(user_passes_test(role_admin_check()))\n def list(self, request):\n \"\"\"\n Listado de clientes por autorizar,\n se incluyen tambien clientes aprovados y rechazados\n \"\"\"\n obj_api = api()\n token = request.session['token']\n title_page = _('User - User Affiliation').title()\n filters = {}\n form_filters = AuthorizationClientFilter(request.GET)\n if form_filters.is_valid():\n filters = form_filters.cleaned_data\n tools = Tools()\n filters['from_date'] = tools.date_format_to_db(date=filters[\n 'from_date'])\n filters['until_date'] = tools.date_format_to_db(date=filters[\n 'until_date'])\n filters = form_filters.cleaned_data\n if request.method == 'GET':\n if 'approve' in request.GET and request.GET['approve']:\n pk = request.GET['approve']\n data = {'status': 1}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n if 'rejected' in request.GET and request.GET['rejected']:\n pk = request.GET['rejected']\n data = {'status': 2}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n data = obj_api.get(slug='authorizations/clients/', arg=filters,\n token=token)\n header_table = [('', 'code_seller'), ('', 'name'), ('',\n 'document_type_name'), ('', 'document'), ('', ''), ('', ''), (\n '', 'document'), ('', 'approve'), ('', 'rejected'), ('',\n 'date_join')]\n multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),\n {'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':\n '1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',\n 'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':\n '2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(\n 'name or Social reason'), {}), (_('type document'), {}), (_(\n 'document number'), {}), (_('description'), {}), (_(\n 'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]\n approve_column = {'type': 'submit', 'data': {'name': 'approve',\n 'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}\n rejected_column = {'type': 'submit', 'data': {'name': 'rejected',\n 'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}\n custom_column = {'date_join': {'type': 'date', 'data': ('date_join'\n ,)}, 'approve': {'type': 'if_eval', 'data': ('r[\"status\"]==\"0\"'\n ,), 'next': approve_column}, 'rejected': {'type': 'if_eval',\n 'data': ('r[\"status\"]==\"0\"',), 'next': rejected_column}}\n table = convert(data, header=header_table, multi_header=\n multi_header, custom_column=custom_column)\n vars_page = self.generate_header(custom_title=title_page)\n return render(request, 'admin/authorization/clients.html', {'table':\n table, 'vars_page': vars_page, 'form_filters': form_filters})\n",
"step-4": "<mask token>\nfrom django.shortcuts import render\nfrom dashboard.json2table import convert\nfrom django.utils.translation import ugettext_lazy as _\nfrom api.connection import api\nfrom login.utils.tools import role_admin_check\nfrom django.utils.decorators import method_decorator\nfrom django.contrib.auth.decorators import user_passes_test\nfrom dashboard.tools import capitalize as cap, ToolsBackend as Tools\nfrom dashboard.forms import AuthorizationClientFilter\n\n\nclass Autorization:\n logo_content_header = 'fa fa-key'\n vars_page = {}\n\n def generate_header(self, custom_title=None):\n if custom_title:\n title = '{} - '.format(_('authorizations')).title() + custom_title\n else:\n title = self.title_content_header\n header = {'icon': self.logo_content_header, 'title': title}\n return {**header, **self.vars_page}\n\n\nclass AutorizationClient(Autorization):\n \"\"\"\n Manejo de autorizaciones de clientes,\n se listan los clientes, en orden de pendiente,\n aprobado y rechazado, segun fecha\n Para posterior aprovacion o rechazo\n \"\"\"\n\n @method_decorator(user_passes_test(role_admin_check()))\n def list(self, request):\n \"\"\"\n Listado de clientes por autorizar,\n se incluyen tambien clientes aprovados y rechazados\n \"\"\"\n obj_api = api()\n token = request.session['token']\n title_page = _('User - User Affiliation').title()\n filters = {}\n form_filters = AuthorizationClientFilter(request.GET)\n if form_filters.is_valid():\n filters = form_filters.cleaned_data\n tools = Tools()\n filters['from_date'] = tools.date_format_to_db(date=filters[\n 'from_date'])\n filters['until_date'] = tools.date_format_to_db(date=filters[\n 'until_date'])\n filters = form_filters.cleaned_data\n if request.method == 'GET':\n if 'approve' in request.GET and request.GET['approve']:\n pk = request.GET['approve']\n data = {'status': 1}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n if 'rejected' in request.GET and request.GET['rejected']:\n pk = request.GET['rejected']\n data = {'status': 2}\n obj_api.put(slug='authorizations/clients/' + pk, token=\n token, arg=data)\n data = obj_api.get(slug='authorizations/clients/', arg=filters,\n token=token)\n header_table = [('', 'code_seller'), ('', 'name'), ('',\n 'document_type_name'), ('', 'document'), ('', ''), ('', ''), (\n '', 'document'), ('', 'approve'), ('', 'rejected'), ('',\n 'date_join')]\n multi_header = [[(_('seller code'), {'rowspan': '2'}), (_('user'),\n {'rowspan': '1', 'colspan': '3'}), (_('product'), {'rowspan':\n '1', 'colspan': '2'}), (_('user code'), {'rowspan': '2',\n 'colspan': '1'}), (_('validation'), {'rowspan': '1', 'colspan':\n '2'}), (_('date'), {'rowspan': '2', 'colspan': '1'})], [(_(\n 'name or Social reason'), {}), (_('type document'), {}), (_(\n 'document number'), {}), (_('description'), {}), (_(\n 'Query Numbers'), {}), (_('approve'), {}), (_('deneis'), {})]]\n approve_column = {'type': 'submit', 'data': {'name': 'approve',\n 'key': 'id', 'cls': 'btn btn-success', 'text': cap(_('approve'))}}\n rejected_column = {'type': 'submit', 'data': {'name': 'rejected',\n 'key': 'id', 'cls': 'btn btn-danger', 'text': cap(_('rejected'))}}\n custom_column = {'date_join': {'type': 'date', 'data': ('date_join'\n ,)}, 'approve': {'type': 'if_eval', 'data': ('r[\"status\"]==\"0\"'\n ,), 'next': approve_column}, 'rejected': {'type': 'if_eval',\n 'data': ('r[\"status\"]==\"0\"',), 'next': rejected_column}}\n table = convert(data, header=header_table, multi_header=\n multi_header, custom_column=custom_column)\n vars_page = self.generate_header(custom_title=title_page)\n return render(request, 'admin/authorization/clients.html', {'table':\n table, 'vars_page': vars_page, 'form_filters': form_filters})\n",
"step-5": "\"\"\"Vista de Autorizaciones (Clientes/Especialistas/Vendedores).\"\"\"\nfrom django.shortcuts import render\nfrom dashboard.json2table import convert\nfrom django.utils.translation import ugettext_lazy as _\nfrom api.connection import api\nfrom login.utils.tools import role_admin_check\nfrom django.utils.decorators import method_decorator\nfrom django.contrib.auth.decorators import user_passes_test\nfrom dashboard.tools import capitalize as cap, ToolsBackend as Tools\nfrom dashboard.forms import AuthorizationClientFilter\nclass Autorization:\n logo_content_header = \"fa fa-key\"\n vars_page = {}\n def generate_header(self, custom_title=None):\n if custom_title:\n title = \"{} - \".format(_(\"authorizations\")).title() + custom_title\n else:\n title = self.title_content_header\n\n header = {'icon': self.logo_content_header, 'title': title}\n return {**header, **self.vars_page}\n\n\nclass AutorizationClient(Autorization):\n \"\"\"\n Manejo de autorizaciones de clientes,\n se listan los clientes, en orden de pendiente,\n aprobado y rechazado, segun fecha\n Para posterior aprovacion o rechazo\n \"\"\"\n\n @method_decorator(user_passes_test(role_admin_check()))\n def list(self, request):\n \"\"\"\n Listado de clientes por autorizar,\n se incluyen tambien clientes aprovados y rechazados\n \"\"\"\n\n obj_api = api()\n # actual_page = get_actual_page(request)\n token = request.session['token']\n title_page = _('User - User Affiliation').title()\n filters = {}\n\n form_filters = AuthorizationClientFilter(request.GET)\n\n if form_filters.is_valid(): # Agregamos filtros de encontrarse alguno\n filters = form_filters.cleaned_data\n tools = Tools()\n filters['from_date'] = tools.date_format_to_db(date=filters['from_date'])\n filters['until_date'] = tools.date_format_to_db(date=filters['until_date'])\n filters = form_filters.cleaned_data\n \n if request.method == 'GET':\n if 'approve' in request.GET and request.GET['approve']:\n pk = request.GET['approve']\n data = {\"status\":1}\n obj_api.put(slug='authorizations/clients/' + pk, token=token, arg=data)\n\n if 'rejected' in request.GET and request.GET['rejected']:\n pk = request.GET['rejected']\n data = {\"status\":2}\n obj_api.put(slug='authorizations/clients/' + pk, token=token, arg=data)\n\n # Traer data para el listado\n data = obj_api.get(slug='authorizations/clients/', arg=filters, token=token)\n\n\n header_table = [(\"\", \"code_seller\"), (\"\", \"name\"),(\n \"\", \"document_type_name\"), ( \"\", \"document\"),(\n \"\", \"\"), (\"\", \"\"), (\n \"\", \"document\"), (\n \"\", \"approve\"), (\"\", \"rejected\"), (\n \"\", \"date_join\")]\n\n # Multiples header, una lista por cada nivel de la cabecera\n multi_header = [\n [\n (_(\"seller code\"), {'rowspan': '2'}),\n (_('user'), {'rowspan': '1', 'colspan': '3'}),\n (_('product'), {'rowspan': '1', 'colspan': '2'}),\n (_('user code'), {'rowspan': '2', 'colspan': '1'}),\n (_('validation'), {'rowspan': '1', 'colspan': '2'}),\n (_('date'), {'rowspan': '2', 'colspan': '1'}),\n ],\n [\n (_('name or Social reason'), {}),\n (_('type document'), {}),\n (_('document number'), {}),\n (_('description'), {}),\n (_('Query Numbers'), {}),\n (_('approve'), {}),\n (_('deneis'), {}),\n ],\n ]\n\n approve_column = {'type': 'submit', 'data': {'name':'approve','key':'id',\n 'cls':'btn btn-success','text':cap(_('approve'))}}\n rejected_column = {'type': 'submit', 'data': {'name':'rejected','key':'id',\n 'cls':'btn btn-danger','text':cap(_('rejected'))}}\n custom_column = {\n \"date_join\": {'type': 'date', 'data': ('date_join',)},\n \"approve\": {'type': 'if_eval', 'data': ('r[\"status\"]==\"0\"',),\n 'next': approve_column},\n \"rejected\": {\n 'type': 'if_eval',\n 'data': ('r[\"status\"]==\"0\"',),\n 'next': rejected_column\n },\n }\n\n table = convert(data, header=header_table, multi_header=multi_header, custom_column=custom_column)\n\n # Titulo de la vista y variables de la Clase\n vars_page = self.generate_header(custom_title=title_page)\n\n return render(request, 'admin/authorization/clients.html',\n {'table': table, 'vars_page': vars_page, 'form_filters':form_filters})",
"step-ids": [
4,
5,
6,
7,
8
]
}
|
[
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def is_even(a):
check_integer(a)
if a % 2 == 0:
print('true')
return True
else:
print('false')
return False
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def check_integer(a):
if type(a) != int:
print('please input an integer')
exit()
def is_even(a):
check_integer(a)
if a % 2 == 0:
print('true')
return True
else:
print('false')
return False
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def check_integer(a):
if type(a) != int:
print('please input an integer')
exit()
def is_even(a):
check_integer(a)
if a % 2 == 0:
print('true')
return True
else:
print('false')
return False
is_even(2)
is_even(3)
is_even('cat')
<|reserved_special_token_1|>
def check_integer(a):
if type(a) != int:
print("please input an integer")
exit()
def is_even(a):
check_integer(a)
if a % 2 == 0:
print("true")
return True
else:
print("false")
return False
is_even(2)
is_even(3)
is_even("cat")
|
flexible
|
{
"blob_id": "92391f17380b2e09cc9b3913f15ce35189d9893d",
"index": 8241,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef is_even(a):\n check_integer(a)\n if a % 2 == 0:\n print('true')\n return True\n else:\n print('false')\n return False\n\n\n<mask token>\n",
"step-3": "def check_integer(a):\n if type(a) != int:\n print('please input an integer')\n exit()\n\n\ndef is_even(a):\n check_integer(a)\n if a % 2 == 0:\n print('true')\n return True\n else:\n print('false')\n return False\n\n\n<mask token>\n",
"step-4": "def check_integer(a):\n if type(a) != int:\n print('please input an integer')\n exit()\n\n\ndef is_even(a):\n check_integer(a)\n if a % 2 == 0:\n print('true')\n return True\n else:\n print('false')\n return False\n\n\nis_even(2)\nis_even(3)\nis_even('cat')\n",
"step-5": "\n\ndef check_integer(a):\n if type(a) != int:\n print(\"please input an integer\")\n exit()\n\n\ndef is_even(a):\n check_integer(a)\n if a % 2 == 0:\n print(\"true\")\n return True\n else:\n print(\"false\")\n return False\n\n\nis_even(2)\nis_even(3)\nis_even(\"cat\")\n\n\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|>
for i in range(100):
numero = int(input('Digite um valor:'))
if numero % 2 == 0:
contador_pares += 1
else:
contador_impares += 1
print('A quantidade de números pares é igual a:', contador_pares)
print('A quantidade de números ímpares é igual a:', contador_impares)
<|reserved_special_token_1|>
contador_pares = 0
contador_impares = 0
for i in range(100):
numero = int(input('Digite um valor:'))
if numero % 2 == 0:
contador_pares += 1
else:
contador_impares += 1
print('A quantidade de números pares é igual a:', contador_pares)
print('A quantidade de números ímpares é igual a:', contador_impares)
|
flexible
|
{
"blob_id": "03aa33861def30a46de85c5b309878a1180a760f",
"index": 5211,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(100):\n numero = int(input('Digite um valor:'))\n if numero % 2 == 0:\n contador_pares += 1\n else:\n contador_impares += 1\nprint('A quantidade de números pares é igual a:', contador_pares)\nprint('A quantidade de números ímpares é igual a:', contador_impares)\n",
"step-3": "contador_pares = 0\ncontador_impares = 0\nfor i in range(100):\n numero = int(input('Digite um valor:'))\n if numero % 2 == 0:\n contador_pares += 1\n else:\n contador_impares += 1\nprint('A quantidade de números pares é igual a:', contador_pares)\nprint('A quantidade de números ímpares é igual a:', contador_impares)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
import torch
import numpy as np
# source: https://github.com/krasserm/bayesian-machine-learning/blob/master/gaussian_processes.ipynb
def kernel(X1, X2, l=1.0, sigma_f=1.0):
''' Isotropic squared exponential kernel. Computes a covariance matrix from points in X1 and X2. Args: X1: Array of m points (m x d). X2: Array of n points (n x d). Returns: Covariance matrix (m x n). '''
sqdist = np.sum(X1**2, 1).reshape(-1, 1) + np.sum(X2**2, 1) - 2 * np.dot(X1, X2.T)
return sigma_f**2 * np.exp(-0.5 / l**2 * sqdist)
# source: # https://github.com/krasserm/bayesian-machine-learning/blob/master/gaussian_processes.ipynb
def posterior_predictive(X_s, X_train, Y_train, l=1.0, sigma_f=1.0, sigma_y=1e-8):
''' Computes the sufficient statistics of the GP posterior predictive distribution from m training data X_train and Y_train and n new inputs X_s. Args: X_s: New input locations (n x d). X_train: Training locations (m x d). Y_train: Training targets (m x 1). l: Kernel length parameter. sigma_f: Kernel vertical variation parameter. sigma_y: Noise parameter. Returns: Posterior mean vector (n x d) and covariance matrix (n x n). '''
K = kernel(X_train, X_train, l, sigma_f) + sigma_y**2 * np.eye(len(X_train))
K_s = kernel(X_s, X_train, l, sigma_f)
K_ss = kernel(X_s, X_s, l, sigma_f) + sigma_y**2 * np.eye(len(X_s))
mu_s = np.matmul(K_s, np.linalg.solve(K, Y_train))
cov_s = K_ss - np.matmul(K_s, np.linalg.solve(K, K_s.T))
return mu_s, cov_s
class CNP(torch.nn.Module):
def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer, dec_layer):
super(CNP, self).__init__()
if en_layer == 1:
self.encoder = torch.nn.Linear(in_dim, hidden_dim)
else:
self.encoder = [
torch.nn.Linear(in_dim, hidden_dim),
torch.nn.ReLU()
]
for i in range(en_layer-2):
self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.encoder.append(torch.nn.ReLU())
self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.encoder = torch.nn.Sequential(*self.encoder)
if dec_layer == 1:
self.decoder = torch.nn.Linear(hidden_dim+query_dim, out_dim)
else:
self.decoder = [
torch.nn.Linear(hidden_dim+query_dim, hidden_dim),
torch.nn.ReLU()
]
for i in range(dec_layer-2):
self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.decoder.append(torch.nn.ReLU())
self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))
self.decoder = torch.nn.Sequential(*self.decoder)
def forward(self, context, query, key=None):
query = query.view(query.shape[0], -1)
# encode
h = self.encoder(context)
# aggregate
h = h.mean(dim=0)
h = torch.stack([h]*(query.shape[0]), dim=0)
r = torch.cat([h, query], dim=1)
# predict
out = self.decoder(r)
return out
class ANP(torch.nn.Module):
def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer, dec_layer, nhead):
super(ANP, self).__init__()
if en_layer == 1:
self.encoder = torch.nn.Linear(in_dim, hidden_dim)
else:
self.encoder = [
torch.nn.Linear(in_dim, hidden_dim),
torch.nn.ReLU()
]
for i in range(en_layer-2):
self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.encoder.append(torch.nn.ReLU())
self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.encoder = torch.nn.Sequential(*self.encoder)
if dec_layer == 1:
self.decoder = torch.nn.Linear(hidden_dim, out_dim)
else:
self.decoder = [
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU()
]
for i in range(dec_layer-2):
self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.decoder.append(torch.nn.ReLU())
self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))
self.decoder = torch.nn.Sequential(*self.decoder)
self.projector = torch.nn.Linear(query_dim, hidden_dim)
self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim, num_heads=nhead)
def forward(self, context, key, query):
query = query.view(query.shape[0], -1)
key = key.view(key.shape[0], -1)
# encode
h = self.encoder(context)
h.unsqueeze_(1)
# aggregate
q_t = self.projector(query)
k_t = self.projector(key)
q_t.unsqueeze_(1)
k_t.unsqueeze_(1)
h, _ = self.attention(query=q_t, key=k_t, value=h)
h.squeeze_(1)
# predict
pred = self.decoder(h)
return pred
class ANPv2(torch.nn.Module):
def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer, dec_layer, nhead):
super(ANPv2, self).__init__()
if en_layer == 1:
self.encoder = torch.nn.Linear(in_dim, hidden_dim)
else:
self.encoder = [
torch.nn.Linear(in_dim, hidden_dim),
torch.nn.ReLU()
]
for i in range(en_layer-2):
self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.encoder.append(torch.nn.ReLU())
self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.encoder = torch.nn.Sequential(*self.encoder)
if dec_layer == 1:
self.decoder = torch.nn.Linear(hidden_dim, out_dim)
else:
self.decoder = [
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU()
]
for i in range(dec_layer-2):
self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))
self.decoder.append(torch.nn.ReLU())
self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))
self.decoder = torch.nn.Sequential(*self.decoder)
self.key_mlp = torch.nn.Sequential(
torch.nn.Linear(query_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, hidden_dim)
)
self.query_mlp = torch.nn.Sequential(
torch.nn.Linear(query_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, hidden_dim)
)
self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim, num_heads=nhead)
def forward(self, context, key, query):
query = query.view(query.shape[0], -1)
key = key.view(key.shape[0], -1)
# encode
h = self.encoder(context)
h.unsqueeze_(1)
# aggregate
q_t = self.query_mlp(query)
k_t = self.key_mlp(key)
q_t.unsqueeze_(1)
k_t.unsqueeze_(1)
h, _ = self.attention(query=q_t, key=k_t, value=h)
h.squeeze_(1)
# predict
pred = self.decoder(h)
return pred
|
normal
|
{
"blob_id": "82c3bde5746d04c126a93851844f775e7ce65f4b",
"index": 9442,
"step-1": "<mask token>\n\n\nclass CNP(torch.nn.Module):\n <mask token>\n <mask token>\n\n\nclass ANP(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.projector = torch.nn.Linear(query_dim, hidden_dim)\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.projector(query)\n k_t = self.projector(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n\n\nclass ANPv2(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANPv2, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.key_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.query_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.query_mlp(query)\n k_t = self.key_mlp(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n",
"step-2": "<mask token>\n\n\nclass CNP(torch.nn.Module):\n <mask token>\n\n def forward(self, context, query, key=None):\n query = query.view(query.shape[0], -1)\n h = self.encoder(context)\n h = h.mean(dim=0)\n h = torch.stack([h] * query.shape[0], dim=0)\n r = torch.cat([h, query], dim=1)\n out = self.decoder(r)\n return out\n\n\nclass ANP(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.projector = torch.nn.Linear(query_dim, hidden_dim)\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.projector(query)\n k_t = self.projector(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n\n\nclass ANPv2(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANPv2, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.key_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.query_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.query_mlp(query)\n k_t = self.key_mlp(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n",
"step-3": "<mask token>\n\n\nclass CNP(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer):\n super(CNP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim + query_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim + query_dim,\n hidden_dim), torch.nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n\n def forward(self, context, query, key=None):\n query = query.view(query.shape[0], -1)\n h = self.encoder(context)\n h = h.mean(dim=0)\n h = torch.stack([h] * query.shape[0], dim=0)\n r = torch.cat([h, query], dim=1)\n out = self.decoder(r)\n return out\n\n\nclass ANP(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.projector = torch.nn.Linear(query_dim, hidden_dim)\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.projector(query)\n k_t = self.projector(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n\n\nclass ANPv2(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANPv2, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.key_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.query_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.query_mlp(query)\n k_t = self.key_mlp(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n",
"step-4": "<mask token>\n\n\ndef posterior_predictive(X_s, X_train, Y_train, l=1.0, sigma_f=1.0, sigma_y\n =1e-08):\n \"\"\" Computes the sufficient statistics of the GP posterior predictive distribution from m training data X_train and Y_train and n new inputs X_s. Args: X_s: New input locations (n x d). X_train: Training locations (m x d). Y_train: Training targets (m x 1). l: Kernel length parameter. sigma_f: Kernel vertical variation parameter. sigma_y: Noise parameter. Returns: Posterior mean vector (n x d) and covariance matrix (n x n). \"\"\"\n K = kernel(X_train, X_train, l, sigma_f) + sigma_y ** 2 * np.eye(len(\n X_train))\n K_s = kernel(X_s, X_train, l, sigma_f)\n K_ss = kernel(X_s, X_s, l, sigma_f) + sigma_y ** 2 * np.eye(len(X_s))\n mu_s = np.matmul(K_s, np.linalg.solve(K, Y_train))\n cov_s = K_ss - np.matmul(K_s, np.linalg.solve(K, K_s.T))\n return mu_s, cov_s\n\n\nclass CNP(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer):\n super(CNP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim + query_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim + query_dim,\n hidden_dim), torch.nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n\n def forward(self, context, query, key=None):\n query = query.view(query.shape[0], -1)\n h = self.encoder(context)\n h = h.mean(dim=0)\n h = torch.stack([h] * query.shape[0], dim=0)\n r = torch.cat([h, query], dim=1)\n out = self.decoder(r)\n return out\n\n\nclass ANP(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.projector = torch.nn.Linear(query_dim, hidden_dim)\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.projector(query)\n k_t = self.projector(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n\n\nclass ANPv2(torch.nn.Module):\n\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer,\n dec_layer, nhead):\n super(ANPv2, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [torch.nn.Linear(in_dim, hidden_dim), torch.nn.\n ReLU()]\n for i in range(en_layer - 2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [torch.nn.Linear(hidden_dim, hidden_dim), torch.\n nn.ReLU()]\n for i in range(dec_layer - 2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.key_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.query_mlp = torch.nn.Sequential(torch.nn.Linear(query_dim,\n hidden_dim), torch.nn.ReLU(), torch.nn.Linear(hidden_dim,\n hidden_dim))\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim,\n num_heads=nhead)\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n h = self.encoder(context)\n h.unsqueeze_(1)\n q_t = self.query_mlp(query)\n k_t = self.key_mlp(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n pred = self.decoder(h)\n return pred\n",
"step-5": "import torch\nimport numpy as np\n\n\n# source: https://github.com/krasserm/bayesian-machine-learning/blob/master/gaussian_processes.ipynb\ndef kernel(X1, X2, l=1.0, sigma_f=1.0):\n ''' Isotropic squared exponential kernel. Computes a covariance matrix from points in X1 and X2. Args: X1: Array of m points (m x d). X2: Array of n points (n x d). Returns: Covariance matrix (m x n). '''\n sqdist = np.sum(X1**2, 1).reshape(-1, 1) + np.sum(X2**2, 1) - 2 * np.dot(X1, X2.T)\n return sigma_f**2 * np.exp(-0.5 / l**2 * sqdist)\n \n# source: # https://github.com/krasserm/bayesian-machine-learning/blob/master/gaussian_processes.ipynb\ndef posterior_predictive(X_s, X_train, Y_train, l=1.0, sigma_f=1.0, sigma_y=1e-8):\n ''' Computes the sufficient statistics of the GP posterior predictive distribution from m training data X_train and Y_train and n new inputs X_s. Args: X_s: New input locations (n x d). X_train: Training locations (m x d). Y_train: Training targets (m x 1). l: Kernel length parameter. sigma_f: Kernel vertical variation parameter. sigma_y: Noise parameter. Returns: Posterior mean vector (n x d) and covariance matrix (n x n). '''\n K = kernel(X_train, X_train, l, sigma_f) + sigma_y**2 * np.eye(len(X_train))\n K_s = kernel(X_s, X_train, l, sigma_f)\n K_ss = kernel(X_s, X_s, l, sigma_f) + sigma_y**2 * np.eye(len(X_s))\n \n mu_s = np.matmul(K_s, np.linalg.solve(K, Y_train))\n cov_s = K_ss - np.matmul(K_s, np.linalg.solve(K, K_s.T))\n \n return mu_s, cov_s\n\nclass CNP(torch.nn.Module):\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer, dec_layer):\n super(CNP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [\n torch.nn.Linear(in_dim, hidden_dim),\n torch.nn.ReLU()\n ]\n for i in range(en_layer-2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n \n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim+query_dim, out_dim)\n else:\n self.decoder = [\n torch.nn.Linear(hidden_dim+query_dim, hidden_dim),\n torch.nn.ReLU()\n ]\n for i in range(dec_layer-2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n \n def forward(self, context, query, key=None):\n query = query.view(query.shape[0], -1)\n # encode\n h = self.encoder(context)\n # aggregate\n h = h.mean(dim=0)\n h = torch.stack([h]*(query.shape[0]), dim=0)\n r = torch.cat([h, query], dim=1)\n # predict\n out = self.decoder(r)\n return out\n\n\nclass ANP(torch.nn.Module):\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer, dec_layer, nhead):\n super(ANP, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [\n torch.nn.Linear(in_dim, hidden_dim),\n torch.nn.ReLU()\n ]\n for i in range(en_layer-2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n \n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [\n torch.nn.Linear(hidden_dim, hidden_dim),\n torch.nn.ReLU()\n ]\n for i in range(dec_layer-2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n self.projector = torch.nn.Linear(query_dim, hidden_dim)\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim, num_heads=nhead)\n\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n # encode\n h = self.encoder(context)\n h.unsqueeze_(1)\n # aggregate\n q_t = self.projector(query)\n k_t = self.projector(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n # predict\n pred = self.decoder(h)\n return pred\n\nclass ANPv2(torch.nn.Module):\n def __init__(self, in_dim, hidden_dim, query_dim, out_dim, en_layer, dec_layer, nhead):\n super(ANPv2, self).__init__()\n if en_layer == 1:\n self.encoder = torch.nn.Linear(in_dim, hidden_dim)\n else:\n self.encoder = [\n torch.nn.Linear(in_dim, hidden_dim),\n torch.nn.ReLU()\n ]\n for i in range(en_layer-2):\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder.append(torch.nn.ReLU())\n self.encoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.encoder = torch.nn.Sequential(*self.encoder)\n \n if dec_layer == 1:\n self.decoder = torch.nn.Linear(hidden_dim, out_dim)\n else:\n self.decoder = [\n torch.nn.Linear(hidden_dim, hidden_dim),\n torch.nn.ReLU()\n ]\n for i in range(dec_layer-2):\n self.decoder.append(torch.nn.Linear(hidden_dim, hidden_dim))\n self.decoder.append(torch.nn.ReLU())\n self.decoder.append(torch.nn.Linear(hidden_dim, out_dim))\n self.decoder = torch.nn.Sequential(*self.decoder)\n \n self.key_mlp = torch.nn.Sequential(\n torch.nn.Linear(query_dim, hidden_dim),\n torch.nn.ReLU(),\n torch.nn.Linear(hidden_dim, hidden_dim)\n )\n\n self.query_mlp = torch.nn.Sequential(\n torch.nn.Linear(query_dim, hidden_dim),\n torch.nn.ReLU(),\n torch.nn.Linear(hidden_dim, hidden_dim)\n )\n\n self.attention = torch.nn.MultiheadAttention(embed_dim=hidden_dim, num_heads=nhead)\n\n\n def forward(self, context, key, query):\n query = query.view(query.shape[0], -1)\n key = key.view(key.shape[0], -1)\n # encode\n h = self.encoder(context)\n h.unsqueeze_(1)\n # aggregate\n q_t = self.query_mlp(query)\n k_t = self.key_mlp(key)\n q_t.unsqueeze_(1)\n k_t.unsqueeze_(1)\n h, _ = self.attention(query=q_t, key=k_t, value=h)\n h.squeeze_(1)\n # predict\n pred = self.decoder(h)\n return pred\n",
"step-ids": [
7,
8,
9,
10,
13
]
}
|
[
7,
8,
9,
10,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class GetInTouchForm(forms.ModelForm):
class Meta:
model = GetInTouch
fields = '__all__'
<|reserved_special_token_1|>
from django import forms
from .models import GetInTouch
class GetInTouchForm(forms.ModelForm):
class Meta:
model = GetInTouch
fields = '__all__'
|
flexible
|
{
"blob_id": "c8dc143c09aa7f677167a4942ae1c4a0fbf75128",
"index": 3219,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass GetInTouchForm(forms.ModelForm):\n\n\n class Meta:\n model = GetInTouch\n fields = '__all__'\n",
"step-3": "from django import forms\nfrom .models import GetInTouch\n\n\nclass GetInTouchForm(forms.ModelForm):\n\n\n class Meta:\n model = GetInTouch\n fields = '__all__'\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
<|reserved_special_token_0|>
def md5_hexdigest(data):
return hashlib.md5(data.encode('utf-8')).hexdigest()
def sha1_hexdigest(data):
return hashlib.sha1(data.encode('utf-8')).hexdigest()
def sha224_hexdigest(data):
return hashlib.sha224(data.encode('utf-8')).hexdigest()
<|reserved_special_token_0|>
def sha384_hexdigest(data):
return hashlib.sha384(data.encode('utf-8')).hexdigest()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def md5_hexdigest(data):
return hashlib.md5(data.encode('utf-8')).hexdigest()
def sha1_hexdigest(data):
return hashlib.sha1(data.encode('utf-8')).hexdigest()
def sha224_hexdigest(data):
return hashlib.sha224(data.encode('utf-8')).hexdigest()
def sha256_hexdigest(data):
return hashlib.sha256(data.encode('utf-8')).hexdigest()
def sha384_hexdigest(data):
return hashlib.sha384(data.encode('utf-8')).hexdigest()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def md5_hexdigest(data):
return hashlib.md5(data.encode('utf-8')).hexdigest()
def sha1_hexdigest(data):
return hashlib.sha1(data.encode('utf-8')).hexdigest()
def sha224_hexdigest(data):
return hashlib.sha224(data.encode('utf-8')).hexdigest()
def sha256_hexdigest(data):
return hashlib.sha256(data.encode('utf-8')).hexdigest()
def sha384_hexdigest(data):
return hashlib.sha384(data.encode('utf-8')).hexdigest()
def sha512_hexdigest(data):
return hashlib.sha512(data.encode('utf-8')).hexdigest()
<|reserved_special_token_1|>
import hashlib
def md5_hexdigest(data):
return hashlib.md5(data.encode('utf-8')).hexdigest()
def sha1_hexdigest(data):
return hashlib.sha1(data.encode('utf-8')).hexdigest()
def sha224_hexdigest(data):
return hashlib.sha224(data.encode('utf-8')).hexdigest()
def sha256_hexdigest(data):
return hashlib.sha256(data.encode('utf-8')).hexdigest()
def sha384_hexdigest(data):
return hashlib.sha384(data.encode('utf-8')).hexdigest()
def sha512_hexdigest(data):
return hashlib.sha512(data.encode('utf-8')).hexdigest()
|
flexible
|
{
"blob_id": "35a95c49c2dc09b528329433a157cf313cf59667",
"index": 8955,
"step-1": "<mask token>\n\n\ndef md5_hexdigest(data):\n return hashlib.md5(data.encode('utf-8')).hexdigest()\n\n\ndef sha1_hexdigest(data):\n return hashlib.sha1(data.encode('utf-8')).hexdigest()\n\n\ndef sha224_hexdigest(data):\n return hashlib.sha224(data.encode('utf-8')).hexdigest()\n\n\n<mask token>\n\n\ndef sha384_hexdigest(data):\n return hashlib.sha384(data.encode('utf-8')).hexdigest()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef md5_hexdigest(data):\n return hashlib.md5(data.encode('utf-8')).hexdigest()\n\n\ndef sha1_hexdigest(data):\n return hashlib.sha1(data.encode('utf-8')).hexdigest()\n\n\ndef sha224_hexdigest(data):\n return hashlib.sha224(data.encode('utf-8')).hexdigest()\n\n\ndef sha256_hexdigest(data):\n return hashlib.sha256(data.encode('utf-8')).hexdigest()\n\n\ndef sha384_hexdigest(data):\n return hashlib.sha384(data.encode('utf-8')).hexdigest()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef md5_hexdigest(data):\n return hashlib.md5(data.encode('utf-8')).hexdigest()\n\n\ndef sha1_hexdigest(data):\n return hashlib.sha1(data.encode('utf-8')).hexdigest()\n\n\ndef sha224_hexdigest(data):\n return hashlib.sha224(data.encode('utf-8')).hexdigest()\n\n\ndef sha256_hexdigest(data):\n return hashlib.sha256(data.encode('utf-8')).hexdigest()\n\n\ndef sha384_hexdigest(data):\n return hashlib.sha384(data.encode('utf-8')).hexdigest()\n\n\ndef sha512_hexdigest(data):\n return hashlib.sha512(data.encode('utf-8')).hexdigest()\n",
"step-4": "import hashlib\n\n\ndef md5_hexdigest(data):\n return hashlib.md5(data.encode('utf-8')).hexdigest()\n\n\ndef sha1_hexdigest(data):\n return hashlib.sha1(data.encode('utf-8')).hexdigest()\n\n\ndef sha224_hexdigest(data):\n return hashlib.sha224(data.encode('utf-8')).hexdigest()\n\n\ndef sha256_hexdigest(data):\n return hashlib.sha256(data.encode('utf-8')).hexdigest()\n\n\ndef sha384_hexdigest(data):\n return hashlib.sha384(data.encode('utf-8')).hexdigest()\n\n\ndef sha512_hexdigest(data):\n return hashlib.sha512(data.encode('utf-8')).hexdigest()\n",
"step-5": null,
"step-ids": [
4,
5,
6,
7
]
}
|
[
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):
node = hou.pwd()
def getNaskCasting():
path = 'E:/WIP/Work/casting-nask.csv'
file = open(path, 'r')
fileText = file.readlines()
file.close()
fileText.pop(0)
assetDic = {}
for line in fileText:
assetType = line.split(',')
assetName = assetType[2]
assetType = assetType[1].split('/')[0]
assetDic[assetName] = assetType.lower()
return assetDic
assetList = getNaskCasting()
colorList = {'sets': (0, 0.4, 1), 'chars': (0.4, 1, 0.4), 'props': (0.6,
0.4, 1)}
assetTypeList = {'sets': [], 'props': [], 'chars': []}
nodeChildren = hou.node(nodePath).children()
for child in list(nodeChildren):
if str(child) in assetList.keys():
type = assetList[str(child)]
if colorNode == True:
child.setColor(hou.Color(colorList[type]))
assetTypeList[type].append(child)
if alignNode == True:
u = 0
v = 0
for type in sorted(assetTypeList.keys()):
v = 0
for asset in sorted(assetTypeList[type]):
pos = hou.Vector2(u, v)
asset.setPosition(pos)
v -= 1
u -= 3
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):
node = hou.pwd()
def getNaskCasting():
path = 'E:/WIP/Work/casting-nask.csv'
file = open(path, 'r')
fileText = file.readlines()
file.close()
fileText.pop(0)
assetDic = {}
for line in fileText:
assetType = line.split(',')
assetName = assetType[2]
assetType = assetType[1].split('/')[0]
assetDic[assetName] = assetType.lower()
return assetDic
assetList = getNaskCasting()
colorList = {'sets': (0, 0.4, 1), 'chars': (0.4, 1, 0.4), 'props': (0.6,
0.4, 1)}
assetTypeList = {'sets': [], 'props': [], 'chars': []}
nodeChildren = hou.node(nodePath).children()
for child in list(nodeChildren):
if str(child) in assetList.keys():
type = assetList[str(child)]
if colorNode == True:
child.setColor(hou.Color(colorList[type]))
assetTypeList[type].append(child)
if alignNode == True:
u = 0
v = 0
for type in sorted(assetTypeList.keys()):
v = 0
for asset in sorted(assetTypeList[type]):
pos = hou.Vector2(u, v)
asset.setPosition(pos)
v -= 1
u -= 3
reorderAssetsByTypes('/obj/geo1')
<|reserved_special_token_1|>
def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):
node = hou.pwd()
def getNaskCasting():
path = "E:/WIP/Work/casting-nask.csv"
file = open(path, "r")
fileText = file.readlines()
file.close()
fileText.pop(0)
assetDic = {}
for line in fileText:
assetType = line.split(",")
assetName = assetType[2]
assetType = assetType[1].split("/")[0]
assetDic[assetName] = assetType.lower()
return assetDic
assetList = getNaskCasting()
colorList = {"sets":(0, 0.4, 1), "chars":(0.4, 1, 0.4), "props":(0.6, 0.4, 1)}
assetTypeList = {"sets":[], "props":[], "chars":[]}
nodeChildren = hou.node(nodePath).children()
#colorize nodes by asset type
for child in list(nodeChildren):
if str(child) in assetList.keys():
type = assetList[str(child)]
if colorNode == True:
child.setColor(hou.Color(colorList[type]))
assetTypeList[type].append(child)
#reorder nodes layout by asset type
if alignNode == True:
u = 0
v = 0
for type in sorted(assetTypeList.keys()):
v = 0
for asset in sorted(assetTypeList[type]):
pos = hou.Vector2 (u,v)
asset.setPosition(pos)
v -= 1
u -= 3
reorderAssetsByTypes("/obj/geo1")
|
flexible
|
{
"blob_id": "3073850890eb7a61fb5200c5ab87c802cafe50bb",
"index": 7229,
"step-1": "<mask token>\n",
"step-2": "def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):\n node = hou.pwd()\n\n def getNaskCasting():\n path = 'E:/WIP/Work/casting-nask.csv'\n file = open(path, 'r')\n fileText = file.readlines()\n file.close()\n fileText.pop(0)\n assetDic = {}\n for line in fileText:\n assetType = line.split(',')\n assetName = assetType[2]\n assetType = assetType[1].split('/')[0]\n assetDic[assetName] = assetType.lower()\n return assetDic\n assetList = getNaskCasting()\n colorList = {'sets': (0, 0.4, 1), 'chars': (0.4, 1, 0.4), 'props': (0.6,\n 0.4, 1)}\n assetTypeList = {'sets': [], 'props': [], 'chars': []}\n nodeChildren = hou.node(nodePath).children()\n for child in list(nodeChildren):\n if str(child) in assetList.keys():\n type = assetList[str(child)]\n if colorNode == True:\n child.setColor(hou.Color(colorList[type]))\n assetTypeList[type].append(child)\n if alignNode == True:\n u = 0\n v = 0\n for type in sorted(assetTypeList.keys()):\n v = 0\n for asset in sorted(assetTypeList[type]):\n pos = hou.Vector2(u, v)\n asset.setPosition(pos)\n v -= 1\n u -= 3\n\n\n<mask token>\n",
"step-3": "def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):\n node = hou.pwd()\n\n def getNaskCasting():\n path = 'E:/WIP/Work/casting-nask.csv'\n file = open(path, 'r')\n fileText = file.readlines()\n file.close()\n fileText.pop(0)\n assetDic = {}\n for line in fileText:\n assetType = line.split(',')\n assetName = assetType[2]\n assetType = assetType[1].split('/')[0]\n assetDic[assetName] = assetType.lower()\n return assetDic\n assetList = getNaskCasting()\n colorList = {'sets': (0, 0.4, 1), 'chars': (0.4, 1, 0.4), 'props': (0.6,\n 0.4, 1)}\n assetTypeList = {'sets': [], 'props': [], 'chars': []}\n nodeChildren = hou.node(nodePath).children()\n for child in list(nodeChildren):\n if str(child) in assetList.keys():\n type = assetList[str(child)]\n if colorNode == True:\n child.setColor(hou.Color(colorList[type]))\n assetTypeList[type].append(child)\n if alignNode == True:\n u = 0\n v = 0\n for type in sorted(assetTypeList.keys()):\n v = 0\n for asset in sorted(assetTypeList[type]):\n pos = hou.Vector2(u, v)\n asset.setPosition(pos)\n v -= 1\n u -= 3\n\n\nreorderAssetsByTypes('/obj/geo1')\n",
"step-4": "def reorderAssetsByTypes(nodePath, colorNode=True, alignNode=True):\n node = hou.pwd()\n \n def getNaskCasting():\n path = \"E:/WIP/Work/casting-nask.csv\"\n\n file = open(path, \"r\")\n fileText = file.readlines()\n file.close()\n fileText.pop(0)\n\n assetDic = {}\n\n for line in fileText:\n assetType = line.split(\",\")\n assetName = assetType[2]\n assetType = assetType[1].split(\"/\")[0]\n assetDic[assetName] = assetType.lower()\n\n return assetDic\n \n assetList = getNaskCasting()\n colorList = {\"sets\":(0, 0.4, 1), \"chars\":(0.4, 1, 0.4), \"props\":(0.6, 0.4, 1)}\n assetTypeList = {\"sets\":[], \"props\":[], \"chars\":[]}\n \n nodeChildren = hou.node(nodePath).children()\n \n #colorize nodes by asset type\n for child in list(nodeChildren):\n if str(child) in assetList.keys():\n type = assetList[str(child)]\n if colorNode == True:\n child.setColor(hou.Color(colorList[type]))\n assetTypeList[type].append(child)\n \n #reorder nodes layout by asset type\n if alignNode == True:\n u = 0\n v = 0\n for type in sorted(assetTypeList.keys()):\n v = 0\n for asset in sorted(assetTypeList[type]):\n pos = hou.Vector2 (u,v)\n asset.setPosition(pos)\n v -= 1\n u -= 3\n\nreorderAssetsByTypes(\"/obj/geo1\")\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#! /usr/bin/python
from bs4 import BeautifulSoup
import requests
import sys
def exit(err):
print err
sys.exit(0)
def get_text(node, lower = True):
if lower:
return (''.join(node.findAll(text = True))).strip().lower()
return (''.join(node.findAll(text = True))).strip()
def get_method_signature(tag):
gold = 'Method signature:'.lower()
return tag.name == "td" and get_text(tag) == gold
def get_returns(tag):
gold = 'Returns:'.lower()
return tag.name == "pre" and gold in get_text(tag)
def main():
if len(sys.argv) != 3:
exit("Usage: %s <srm_number> <class_name>" % sys.argv[0])
srm = sys.argv[1].strip().lower()
class_name = sys.argv[2].strip().lower()
domain = "http://community.topcoder.com"
search_url = "%(domain)s/tc?module=ProblemArchive&class=%(class_name)s"
data = requests.get(search_url % locals()).text
# f = open('/tmp/data.html', 'w')
# f.write(data)
# f.close()
# data = open('/tmp/data.html', 'r')
soup = BeautifulSoup(data)
result_table = None
result_table_string = 'Challenge'
tables = soup.findAll('table')
tables.reverse()
for table in tables:
if result_table_string.lower() in get_text(table):
result_table = table
break
else:
exit("no problem found, please check class name")
result_row = None
actual_class_name = None
for row in result_table.findAll('tr'):
cells = row.findAll('td')
if len(cells) < 3:
continue
if get_text(cells[1]) == class_name and srm in get_text(cells[2]):
actual_class_name = get_text(cells[1], lower = False)
result_row = row
break
else:
exit("no problem found, please check class name and SRM number")
problem_url = "%s%s" % (domain, cells[1].find('a').get('href'))
data = requests.get(problem_url).text
# f = open('/tmp/problem.html', 'w')
# f.write(data)
# f.close()
#data = open('/tmp/problem.html', 'r')
soup = BeautifulSoup(data)
try:
method_signature_text = soup.findAll(get_method_signature)[-1]
method_signature = method_signature_text.nextSibling.string
returns_tr = method_signature_text.parent.previousSibling
return_type = returns_tr.findAll('td')[1].string.strip()
parameters_tr = returns_tr.previousSibling
parameters = parameters_tr.findAll('td')[1].string.split(",")
method_tr = parameters_tr.previousSibling
method_name = method_tr.findAll('td')[1].string.strip()
test_cases = soup.findAll(get_returns)
expected_return_values = []
inputs = []
for i in range(len(test_cases)):
inputs.append([])
for i, test_case in enumerate(test_cases):
expected_return_values.append(test_case.string.strip().split(": ")[1])
input_values = test_case.parent.parent.previousSibling.findAll('pre')
for input_value in input_values:
inputs[i].append(input_value.string.strip())
except:
raise
exit("error getting method signature, no luck")
# inject test cases into template
spaces = " "
input_test_case = "%(parameter)s var_%(index_1)d_%(index_2)d = %(value)s;\n"
invoke_method = "%(return_type)s expected_%(index_1)d = %(lower_actual_class_name)s.%(method_name)s(%(method_params)s);\n"
if return_type == "String":
compare_outputs = "System.out.println((expected_%(index_1)d.equals(%(expected_value)s) ? \"Passed\" : \"Failed\") + \" for case %(index_1)d\");"
else:
compare_outputs = "System.out.println(((expected_%(index_1)d == %(expected_value)s) ? \"Passed\" : \"Failed\") + \" for case %(index_1)d\");"
compare_outputs += "\n"
lower_actual_class_name = actual_class_name.lower()
test_case_str = ""
for index_1, input_case in enumerate(inputs):
# declare the inputs
method_params_list = []
for index_2, parameter in enumerate(parameters):
value = input_case[index_2]
test_case_str += spaces
test_case_str += input_test_case % locals()
method_params_list.append("var_%(index_1)d_%(index_2)d" % locals())
# invoke the function
method_params = ','.join(method_params_list)
test_case_str += spaces
test_case_str += invoke_method % locals()
# compare the output
expected_value = expected_return_values[index_1]
test_case_str += spaces
test_case_str += compare_outputs % locals()
# inject everything else into final template
template = open('template.java', 'r').read()
fp = open('%(actual_class_name)s.java' % locals(), 'w')
fp.write(template % locals())
fp.close()
print "done :) generated %(actual_class_name)s.java" % locals()
if __name__ == "__main__":
main()
|
normal
|
{
"blob_id": "e3119979028d3dd4e1061563db4ec20607e744d1",
"index": 3749,
"step-1": "#! /usr/bin/python\n\nfrom bs4 import BeautifulSoup\n\nimport requests\nimport sys\n\ndef exit(err):\n print err\n sys.exit(0)\n\ndef get_text(node, lower = True):\n if lower:\n return (''.join(node.findAll(text = True))).strip().lower()\n return (''.join(node.findAll(text = True))).strip()\n\ndef get_method_signature(tag):\n gold = 'Method signature:'.lower()\n return tag.name == \"td\" and get_text(tag) == gold\n\ndef get_returns(tag):\n gold = 'Returns:'.lower()\n return tag.name == \"pre\" and gold in get_text(tag)\n\ndef main():\n\n if len(sys.argv) != 3:\n exit(\"Usage: %s <srm_number> <class_name>\" % sys.argv[0])\n\n srm = sys.argv[1].strip().lower()\n class_name = sys.argv[2].strip().lower()\n\n domain = \"http://community.topcoder.com\"\n search_url = \"%(domain)s/tc?module=ProblemArchive&class=%(class_name)s\"\n\n data = requests.get(search_url % locals()).text\n # f = open('/tmp/data.html', 'w')\n # f.write(data)\n # f.close()\n # data = open('/tmp/data.html', 'r')\n\n soup = BeautifulSoup(data)\n result_table = None\n result_table_string = 'Challenge'\n tables = soup.findAll('table')\n tables.reverse()\n for table in tables:\n if result_table_string.lower() in get_text(table):\n result_table = table\n break\n else:\n exit(\"no problem found, please check class name\")\n\n result_row = None\n actual_class_name = None\n for row in result_table.findAll('tr'):\n cells = row.findAll('td')\n if len(cells) < 3:\n continue\n if get_text(cells[1]) == class_name and srm in get_text(cells[2]):\n actual_class_name = get_text(cells[1], lower = False)\n result_row = row\n break\n else:\n exit(\"no problem found, please check class name and SRM number\")\n\n problem_url = \"%s%s\" % (domain, cells[1].find('a').get('href'))\n\n data = requests.get(problem_url).text\n # f = open('/tmp/problem.html', 'w')\n # f.write(data)\n # f.close()\n #data = open('/tmp/problem.html', 'r')\n\n soup = BeautifulSoup(data)\n try:\n method_signature_text = soup.findAll(get_method_signature)[-1]\n method_signature = method_signature_text.nextSibling.string\n returns_tr = method_signature_text.parent.previousSibling\n return_type = returns_tr.findAll('td')[1].string.strip()\n parameters_tr = returns_tr.previousSibling\n parameters = parameters_tr.findAll('td')[1].string.split(\",\")\n method_tr = parameters_tr.previousSibling\n method_name = method_tr.findAll('td')[1].string.strip()\n test_cases = soup.findAll(get_returns)\n expected_return_values = []\n inputs = []\n for i in range(len(test_cases)):\n inputs.append([])\n for i, test_case in enumerate(test_cases):\n expected_return_values.append(test_case.string.strip().split(\": \")[1])\n input_values = test_case.parent.parent.previousSibling.findAll('pre')\n for input_value in input_values:\n inputs[i].append(input_value.string.strip())\n except:\n raise\n exit(\"error getting method signature, no luck\")\n\n # inject test cases into template\n spaces = \" \"\n input_test_case = \"%(parameter)s var_%(index_1)d_%(index_2)d = %(value)s;\\n\"\n invoke_method = \"%(return_type)s expected_%(index_1)d = %(lower_actual_class_name)s.%(method_name)s(%(method_params)s);\\n\"\n if return_type == \"String\":\n compare_outputs = \"System.out.println((expected_%(index_1)d.equals(%(expected_value)s) ? \\\"Passed\\\" : \\\"Failed\\\") + \\\" for case %(index_1)d\\\");\"\n else:\n compare_outputs = \"System.out.println(((expected_%(index_1)d == %(expected_value)s) ? \\\"Passed\\\" : \\\"Failed\\\") + \\\" for case %(index_1)d\\\");\"\n compare_outputs += \"\\n\"\n lower_actual_class_name = actual_class_name.lower()\n test_case_str = \"\"\n for index_1, input_case in enumerate(inputs):\n # declare the inputs\n method_params_list = []\n for index_2, parameter in enumerate(parameters):\n value = input_case[index_2]\n test_case_str += spaces\n test_case_str += input_test_case % locals()\n method_params_list.append(\"var_%(index_1)d_%(index_2)d\" % locals())\n # invoke the function\n method_params = ','.join(method_params_list)\n test_case_str += spaces\n test_case_str += invoke_method % locals()\n # compare the output\n expected_value = expected_return_values[index_1]\n test_case_str += spaces\n test_case_str += compare_outputs % locals()\n\n # inject everything else into final template\n template = open('template.java', 'r').read()\n fp = open('%(actual_class_name)s.java' % locals(), 'w')\n fp.write(template % locals())\n fp.close()\n print \"done :) generated %(actual_class_name)s.java\" % locals()\n\nif __name__ == \"__main__\":\n main()",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Config(object):
<|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_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_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_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_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Config(object):
from_train_file = 'data/dev.en'
to_train_file = 'data/dev.vi'
_PAD = b'_PAD'
_GO = b'_GO'
_EOS = b'_EOS'
_UNK = b'_UNK'
_START_VOCAB = [_PAD, _GO, _EOS, _UNK]
PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3
batch_size = 64
max_epochs = 1
early_stopping = 2
dropout = 0.9
lr = 0.5
l2 = 0.001
learning_rate_decay = 0.99
batch_size = 32
size = 1024
num_layers = 3
from_vocab_size = 10000
to_vocab_size = 10000
data_dir = 'data/'
dev_dir = 'data/'
max_train_data_size = 200
steps_per_checkpoint = 5
forward_only = False
buckets = [(10, 50)]
num_samples = 512
encode_layers = 3
encode_num_steps = 10
encode_hidden_size = 50
decode_layers = 3
encode_num_steps = 10
decode_hidden_size = 50
dtype = tf.float32
<|reserved_special_token_1|>
import tensorflow as tf
class Config(object):
from_train_file = 'data/dev.en'
to_train_file = 'data/dev.vi'
_PAD = b'_PAD'
_GO = b'_GO'
_EOS = b'_EOS'
_UNK = b'_UNK'
_START_VOCAB = [_PAD, _GO, _EOS, _UNK]
PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3
batch_size = 64
max_epochs = 1
early_stopping = 2
dropout = 0.9
lr = 0.5
l2 = 0.001
learning_rate_decay = 0.99
batch_size = 32
size = 1024
num_layers = 3
from_vocab_size = 10000
to_vocab_size = 10000
data_dir = 'data/'
dev_dir = 'data/'
max_train_data_size = 200
steps_per_checkpoint = 5
forward_only = False
buckets = [(10, 50)]
num_samples = 512
encode_layers = 3
encode_num_steps = 10
encode_hidden_size = 50
decode_layers = 3
encode_num_steps = 10
decode_hidden_size = 50
dtype = tf.float32
<|reserved_special_token_1|>
import tensorflow as tf
class Config(object):
# Source and Target files
from_train_file='data/dev.en'
to_train_file='data/dev.vi'
# Special characters and ID's
_PAD = b"_PAD"
_GO = b"_GO"
_EOS = b"_EOS"
_UNK = b"_UNK"
_START_VOCAB = [_PAD, _GO, _EOS, _UNK]
PAD_ID = 0
GO_ID = 1
EOS_ID = 2
UNK_ID = 3
# NMT hyperparameters
batch_size = 64
max_epochs = 1
early_stopping = 2
dropout = 0.9
lr = 0.5
l2 = 0.001
learning_rate_decay = 0.99
batch_size = 32
size = 1024
num_layers = 3
from_vocab_size = 10000
to_vocab_size = 10000
data_dir = "data/"
dev_dir = "data/"
max_train_data_size = 200
steps_per_checkpoint = 5
forward_only = False
# Buckets
buckets = [(10,50)]
# Other config variables
num_samples = 512
# Encoding parameters
encode_layers = 3
encode_num_steps = 10
encode_hidden_size = 50
# Encoding parameters
decode_layers = 3
encode_num_steps = 10
decode_hidden_size = 50
dtype = tf.float32
|
flexible
|
{
"blob_id": "c27c2df1830f066ca4f973c46967722869090d05",
"index": 1373,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Config(object):\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 <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 <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 <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 <mask token>\n",
"step-3": "<mask token>\n\n\nclass Config(object):\n from_train_file = 'data/dev.en'\n to_train_file = 'data/dev.vi'\n _PAD = b'_PAD'\n _GO = b'_GO'\n _EOS = b'_EOS'\n _UNK = b'_UNK'\n _START_VOCAB = [_PAD, _GO, _EOS, _UNK]\n PAD_ID = 0\n GO_ID = 1\n EOS_ID = 2\n UNK_ID = 3\n batch_size = 64\n max_epochs = 1\n early_stopping = 2\n dropout = 0.9\n lr = 0.5\n l2 = 0.001\n learning_rate_decay = 0.99\n batch_size = 32\n size = 1024\n num_layers = 3\n from_vocab_size = 10000\n to_vocab_size = 10000\n data_dir = 'data/'\n dev_dir = 'data/'\n max_train_data_size = 200\n steps_per_checkpoint = 5\n forward_only = False\n buckets = [(10, 50)]\n num_samples = 512\n encode_layers = 3\n encode_num_steps = 10\n encode_hidden_size = 50\n decode_layers = 3\n encode_num_steps = 10\n decode_hidden_size = 50\n dtype = tf.float32\n",
"step-4": "import tensorflow as tf\n\n\nclass Config(object):\n from_train_file = 'data/dev.en'\n to_train_file = 'data/dev.vi'\n _PAD = b'_PAD'\n _GO = b'_GO'\n _EOS = b'_EOS'\n _UNK = b'_UNK'\n _START_VOCAB = [_PAD, _GO, _EOS, _UNK]\n PAD_ID = 0\n GO_ID = 1\n EOS_ID = 2\n UNK_ID = 3\n batch_size = 64\n max_epochs = 1\n early_stopping = 2\n dropout = 0.9\n lr = 0.5\n l2 = 0.001\n learning_rate_decay = 0.99\n batch_size = 32\n size = 1024\n num_layers = 3\n from_vocab_size = 10000\n to_vocab_size = 10000\n data_dir = 'data/'\n dev_dir = 'data/'\n max_train_data_size = 200\n steps_per_checkpoint = 5\n forward_only = False\n buckets = [(10, 50)]\n num_samples = 512\n encode_layers = 3\n encode_num_steps = 10\n encode_hidden_size = 50\n decode_layers = 3\n encode_num_steps = 10\n decode_hidden_size = 50\n dtype = tf.float32\n",
"step-5": "import tensorflow as tf\n\nclass Config(object):\n\n # Source and Target files\n from_train_file='data/dev.en'\n to_train_file='data/dev.vi'\n\n # Special characters and ID's\n _PAD = b\"_PAD\"\n _GO = b\"_GO\"\n _EOS = b\"_EOS\"\n _UNK = b\"_UNK\"\n _START_VOCAB = [_PAD, _GO, _EOS, _UNK]\n\n PAD_ID = 0\n GO_ID = 1\n EOS_ID = 2\n UNK_ID = 3\n\n # NMT hyperparameters\n batch_size = 64\n max_epochs = 1\n early_stopping = 2\n dropout = 0.9\n lr = 0.5\n l2 = 0.001\n learning_rate_decay = 0.99\n batch_size = 32\n size = 1024\n num_layers = 3\n from_vocab_size = 10000\n to_vocab_size = 10000\n data_dir = \"data/\"\n dev_dir = \"data/\"\n max_train_data_size = 200\n steps_per_checkpoint = 5\n forward_only = False\n\n # Buckets\n buckets = [(10,50)]\n\n # Other config variables\n num_samples = 512\n\n # Encoding parameters\n encode_layers = 3\n encode_num_steps = 10\n encode_hidden_size = 50\n\n # Encoding parameters\n decode_layers = 3\n encode_num_steps = 10\n decode_hidden_size = 50\n\n dtype = tf.float32\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Solution:
def maxArea(self, h: int, w: int, horizontalCuts: List[int],
verticalCuts: List[int]) ->int:
horizontalCuts.sort()
verticalCuts.sort()
horizontalCuts.append(h)
verticalCuts.append(w)
hbreadth = 0
prev = 0
for h in horizontalCuts:
height = h - prev
hbreadth = max(height, hbreadth)
prev = h
prev = 0
vlength = 0
for v in verticalCuts:
height = v - prev
vlength = max(vlength, height)
prev = v
maxarea = hbreadth * vlength % (10 ** 9 + 7)
return maxarea
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
def maxArea(self, h: int, w: int, horizontalCuts: List[int],
verticalCuts: List[int]) ->int:
horizontalCuts.sort()
verticalCuts.sort()
horizontalCuts.append(h)
verticalCuts.append(w)
hbreadth = 0
prev = 0
for h in horizontalCuts:
height = h - prev
hbreadth = max(height, hbreadth)
prev = h
prev = 0
vlength = 0
for v in verticalCuts:
height = v - prev
vlength = max(vlength, height)
prev = v
maxarea = hbreadth * vlength % (10 ** 9 + 7)
return maxarea
<|reserved_special_token_0|>
print(obj.maxArea(h, w, horizontalCuts, verticalCuts))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
h = 5
w = 4
horizontalCuts = [3]
verticalCuts = [3]
class Solution:
def maxArea(self, h: int, w: int, horizontalCuts: List[int],
verticalCuts: List[int]) ->int:
horizontalCuts.sort()
verticalCuts.sort()
horizontalCuts.append(h)
verticalCuts.append(w)
hbreadth = 0
prev = 0
for h in horizontalCuts:
height = h - prev
hbreadth = max(height, hbreadth)
prev = h
prev = 0
vlength = 0
for v in verticalCuts:
height = v - prev
vlength = max(vlength, height)
prev = v
maxarea = hbreadth * vlength % (10 ** 9 + 7)
return maxarea
obj = Solution()
print(obj.maxArea(h, w, horizontalCuts, verticalCuts))
<|reserved_special_token_1|>
from typing import List
h = 5
w = 4
horizontalCuts = [3]
verticalCuts = [3]
class Solution:
def maxArea(self, h: int, w: int, horizontalCuts: List[int],
verticalCuts: List[int]) ->int:
horizontalCuts.sort()
verticalCuts.sort()
horizontalCuts.append(h)
verticalCuts.append(w)
hbreadth = 0
prev = 0
for h in horizontalCuts:
height = h - prev
hbreadth = max(height, hbreadth)
prev = h
prev = 0
vlength = 0
for v in verticalCuts:
height = v - prev
vlength = max(vlength, height)
prev = v
maxarea = hbreadth * vlength % (10 ** 9 + 7)
return maxarea
obj = Solution()
print(obj.maxArea(h, w, horizontalCuts, verticalCuts))
<|reserved_special_token_1|>
from typing import List
h = 5
w = 4
horizontalCuts = [3]
verticalCuts = [3]
class Solution:
def maxArea(self, h: int, w: int, horizontalCuts: List[int], verticalCuts: List[int]) -> int:
horizontalCuts.sort()
verticalCuts.sort()
horizontalCuts.append(h)
verticalCuts.append(w)
hbreadth= 0
prev=0
for h in horizontalCuts:
height= h-prev
hbreadth= max(height, hbreadth)
prev= h
prev=0
vlength=0
for v in verticalCuts:
height= v-prev
vlength= max(vlength, height)
prev=v
maxarea= (hbreadth * vlength) % ((10**9) + 7)
return maxarea
obj=Solution()
print(obj.maxArea(h, w, horizontalCuts, verticalCuts))
|
flexible
|
{
"blob_id": "8fb559810fbf79f0849ed98e51d3f2ad1ccc4b8b",
"index": 8296,
"step-1": "<mask token>\n\n\nclass Solution:\n\n def maxArea(self, h: int, w: int, horizontalCuts: List[int],\n verticalCuts: List[int]) ->int:\n horizontalCuts.sort()\n verticalCuts.sort()\n horizontalCuts.append(h)\n verticalCuts.append(w)\n hbreadth = 0\n prev = 0\n for h in horizontalCuts:\n height = h - prev\n hbreadth = max(height, hbreadth)\n prev = h\n prev = 0\n vlength = 0\n for v in verticalCuts:\n height = v - prev\n vlength = max(vlength, height)\n prev = v\n maxarea = hbreadth * vlength % (10 ** 9 + 7)\n return maxarea\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n\n def maxArea(self, h: int, w: int, horizontalCuts: List[int],\n verticalCuts: List[int]) ->int:\n horizontalCuts.sort()\n verticalCuts.sort()\n horizontalCuts.append(h)\n verticalCuts.append(w)\n hbreadth = 0\n prev = 0\n for h in horizontalCuts:\n height = h - prev\n hbreadth = max(height, hbreadth)\n prev = h\n prev = 0\n vlength = 0\n for v in verticalCuts:\n height = v - prev\n vlength = max(vlength, height)\n prev = v\n maxarea = hbreadth * vlength % (10 ** 9 + 7)\n return maxarea\n\n\n<mask token>\nprint(obj.maxArea(h, w, horizontalCuts, verticalCuts))\n",
"step-3": "<mask token>\nh = 5\nw = 4\nhorizontalCuts = [3]\nverticalCuts = [3]\n\n\nclass Solution:\n\n def maxArea(self, h: int, w: int, horizontalCuts: List[int],\n verticalCuts: List[int]) ->int:\n horizontalCuts.sort()\n verticalCuts.sort()\n horizontalCuts.append(h)\n verticalCuts.append(w)\n hbreadth = 0\n prev = 0\n for h in horizontalCuts:\n height = h - prev\n hbreadth = max(height, hbreadth)\n prev = h\n prev = 0\n vlength = 0\n for v in verticalCuts:\n height = v - prev\n vlength = max(vlength, height)\n prev = v\n maxarea = hbreadth * vlength % (10 ** 9 + 7)\n return maxarea\n\n\nobj = Solution()\nprint(obj.maxArea(h, w, horizontalCuts, verticalCuts))\n",
"step-4": "from typing import List\nh = 5\nw = 4\nhorizontalCuts = [3]\nverticalCuts = [3]\n\n\nclass Solution:\n\n def maxArea(self, h: int, w: int, horizontalCuts: List[int],\n verticalCuts: List[int]) ->int:\n horizontalCuts.sort()\n verticalCuts.sort()\n horizontalCuts.append(h)\n verticalCuts.append(w)\n hbreadth = 0\n prev = 0\n for h in horizontalCuts:\n height = h - prev\n hbreadth = max(height, hbreadth)\n prev = h\n prev = 0\n vlength = 0\n for v in verticalCuts:\n height = v - prev\n vlength = max(vlength, height)\n prev = v\n maxarea = hbreadth * vlength % (10 ** 9 + 7)\n return maxarea\n\n\nobj = Solution()\nprint(obj.maxArea(h, w, horizontalCuts, verticalCuts))\n",
"step-5": "from typing import List\nh = 5\nw = 4\nhorizontalCuts = [3]\nverticalCuts = [3]\nclass Solution:\n def maxArea(self, h: int, w: int, horizontalCuts: List[int], verticalCuts: List[int]) -> int:\n horizontalCuts.sort()\n verticalCuts.sort()\n horizontalCuts.append(h)\n verticalCuts.append(w)\n hbreadth= 0\n prev=0\n for h in horizontalCuts:\n height= h-prev\n hbreadth= max(height, hbreadth)\n prev= h\n\n prev=0\n vlength=0\n for v in verticalCuts:\n height= v-prev\n vlength= max(vlength, height)\n prev=v\n\n maxarea= (hbreadth * vlength) % ((10**9) + 7)\n return maxarea\n\nobj=Solution()\nprint(obj.maxArea(h, w, horizontalCuts, verticalCuts))\n",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
from web3 import Web3, HTTPProvider, IPCProvider
from tcmb.tcmb_parser import TCMB_Processor
from ecb.ecb_parser import ECB_Processor
from web3.contract import ConciseContract
from web3.middleware import geth_poa_middleware
import json
import time
tcmb_currencies = ["TRY", "USD", "AUD", "DKK", "EUR", "GBP", "CHF", "SEK", "CAD",
"KWD", "NOK", "SAR", "JPY", "BGN", "RON", "RUB", "IRR", "CNY", "PKR"]
ecb_currencies = ["EUR", "USD", "JPY", "BGN", "CZK", "DKK", "GBP", "HUF", "PLN",
"RON", "SEK", "CHF", "ISK", "NOK", "HRK", "RUB", "TRY", "AUD", "BRL",
"CAD", "CNY", "HKD", "IDR", "ILS", "INR", "KRW", "MXN", "MYR", "NZD",
"PHP", "SGD", "THB", "ZAR"]
def epoch_day(epoch_time):
epoch_time = int(epoch_time)
return(epoch_time - (epoch_time % 86400))
with open('config_ebloc.json') as json_data_file:
config_data = json.load(json_data_file)
owner_address = config_data["owner"]["address"]
owner_password = config_data["owner"]["password"]
contract_address = config_data["contract"]["address"]
contract_abi = config_data["contract"]["abi"]
gas = int(config_data["price"]["gas"])
gas_price = Web3.toWei( int(config_data["price"]["gas_price"]), 'gwei')
ecb_daily_log_path = config_data["log"]["ecb_daily"]
tcmb_daily_log_path = config_data["log"]["tcmb_daily"]
geth_ipc_path = config_data["geth"]["geth_ipc_path"]
contract_address = Web3.toChecksumAddress(contract_address)
web3 = Web3(IPCProvider(geth_ipc_path))
web3.middleware_stack.inject(geth_poa_middleware, layer=0)
web3.eth.defaultAccount = web3.eth.accounts[0]
web3.personal.unlockAccount(web3.eth.accounts[0], owner_password)
contract_instance = web3.eth.contract(abi=contract_abi, address=contract_address, ContractFactoryClass=ConciseContract)
unix_time = Web3.toInt(epoch_day(time.time()))
def add_ecb():
unix_time = Web3.toInt(epoch_day(time.time()))
ECB = ECB_Processor()
f = open(ecb_daily_log_path, "a")
if(time.strftime("%Y-%m-%d") == ECB.Currency_Dict["time"]):
for curr in ecb_currencies:
curr_code = bytes(curr, encoding='utf-8')
curr_value = web3.toInt(int(float(ECB.Currency_Dict[curr])*(10**9)))
tx_hash = contract_instance.add_ecb(unix_time, curr_code, curr_value, transact={'from': web3.eth.accounts[0]})
tx_hash = tx_hash.hex()
print(time.strftime("%Y-%m-%d %H:%M"), unix_time, tx_hash, curr_code, file=f)
else:
print(time.strftime("%Y-%m-%d %H:%M"), unix_time, "Weekend", file=f)
f.close()
def add_tcmb():
unix_time = Web3.toInt(epoch_day(time.time()))
TCMB = TCMB_Processor()
f = open(tcmb_daily_log_path, "a")
if(time.strftime("%m/%d/%Y") == TCMB.CURRENCY_DICT["Date"]):
for curr in tcmb_currencies:
curr_code = bytes(curr, encoding='utf-8')
curr_value_fb = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr]["ForexBuying"])*(10**9)))
curr_value_fs = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr]["ForexSelling"])*(10**9)))
# forex buying
tx_hash_fb = contract_instance.add_tcmb_forexbuying(unix_time, curr_code, curr_value_fb, transact={'from': web3.eth.accounts[0]})
tx_hash_fb = tx_hash_fb.hex()
print(time.strftime("%Y-%m-%d %H:%M"), unix_time, tx_hash_fb, curr_code, file=f)
# forex selling
tx_hash_fs = contract_instance.add_tcmb_forexselling(unix_time, curr_code, curr_value_fs, transact={'from': web3.eth.accounts[0]})
tx_hash_fs = tx_hash_fs.hex()
print(time.strftime("%Y-%m-%d %H:%M"), unix_time, tx_hash_fs, curr_code, file=f)
else:
print(time.strftime("%Y-%m-%d %H:%M"), unix_time, "Weekend", file=f)
f.close()
if __name__ == "__main__":
add_ecb()
add_tcmb()
print(time.strftime("%Y-%m-%d %H:%M"), " DONE EBLOC add_ecb & add_tcmb")
|
normal
|
{
"blob_id": "ecd5097d9d497b62b89217ee3c46506f21fc15d2",
"index": 5065,
"step-1": "<mask token>\n\n\ndef epoch_day(epoch_time):\n epoch_time = int(epoch_time)\n return epoch_time - epoch_time % 86400\n\n\n<mask token>\n\n\ndef add_ecb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n ECB = ECB_Processor()\n f = open(ecb_daily_log_path, 'a')\n if time.strftime('%Y-%m-%d') == ECB.Currency_Dict['time']:\n for curr in ecb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value = web3.toInt(int(float(ECB.Currency_Dict[curr]) * 10 **\n 9))\n tx_hash = contract_instance.add_ecb(unix_time, curr_code,\n curr_value, transact={'from': web3.eth.accounts[0]})\n tx_hash = tx_hash.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\ndef add_tcmb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n TCMB = TCMB_Processor()\n f = open(tcmb_daily_log_path, 'a')\n if time.strftime('%m/%d/%Y') == TCMB.CURRENCY_DICT['Date']:\n for curr in tcmb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value_fb = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexBuying']) * 10 ** 9))\n curr_value_fs = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexSelling']) * 10 ** 9))\n tx_hash_fb = contract_instance.add_tcmb_forexbuying(unix_time,\n curr_code, curr_value_fb, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fb = tx_hash_fb.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fb,\n curr_code, file=f)\n tx_hash_fs = contract_instance.add_tcmb_forexselling(unix_time,\n curr_code, curr_value_fs, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fs = tx_hash_fs.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fs,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef epoch_day(epoch_time):\n epoch_time = int(epoch_time)\n return epoch_time - epoch_time % 86400\n\n\nwith open('config_ebloc.json') as json_data_file:\n config_data = json.load(json_data_file)\n<mask token>\nweb3.middleware_stack.inject(geth_poa_middleware, layer=0)\n<mask token>\nweb3.personal.unlockAccount(web3.eth.accounts[0], owner_password)\n<mask token>\n\n\ndef add_ecb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n ECB = ECB_Processor()\n f = open(ecb_daily_log_path, 'a')\n if time.strftime('%Y-%m-%d') == ECB.Currency_Dict['time']:\n for curr in ecb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value = web3.toInt(int(float(ECB.Currency_Dict[curr]) * 10 **\n 9))\n tx_hash = contract_instance.add_ecb(unix_time, curr_code,\n curr_value, transact={'from': web3.eth.accounts[0]})\n tx_hash = tx_hash.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\ndef add_tcmb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n TCMB = TCMB_Processor()\n f = open(tcmb_daily_log_path, 'a')\n if time.strftime('%m/%d/%Y') == TCMB.CURRENCY_DICT['Date']:\n for curr in tcmb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value_fb = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexBuying']) * 10 ** 9))\n curr_value_fs = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexSelling']) * 10 ** 9))\n tx_hash_fb = contract_instance.add_tcmb_forexbuying(unix_time,\n curr_code, curr_value_fb, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fb = tx_hash_fb.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fb,\n curr_code, file=f)\n tx_hash_fs = contract_instance.add_tcmb_forexselling(unix_time,\n curr_code, curr_value_fs, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fs = tx_hash_fs.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fs,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\nif __name__ == '__main__':\n add_ecb()\n add_tcmb()\n print(time.strftime('%Y-%m-%d %H:%M'), ' DONE EBLOC add_ecb & add_tcmb')\n",
"step-3": "<mask token>\ntcmb_currencies = ['TRY', 'USD', 'AUD', 'DKK', 'EUR', 'GBP', 'CHF', 'SEK',\n 'CAD', 'KWD', 'NOK', 'SAR', 'JPY', 'BGN', 'RON', 'RUB', 'IRR', 'CNY', 'PKR'\n ]\necb_currencies = ['EUR', 'USD', 'JPY', 'BGN', 'CZK', 'DKK', 'GBP', 'HUF',\n 'PLN', 'RON', 'SEK', 'CHF', 'ISK', 'NOK', 'HRK', 'RUB', 'TRY', 'AUD',\n 'BRL', 'CAD', 'CNY', 'HKD', 'IDR', 'ILS', 'INR', 'KRW', 'MXN', 'MYR',\n 'NZD', 'PHP', 'SGD', 'THB', 'ZAR']\n\n\ndef epoch_day(epoch_time):\n epoch_time = int(epoch_time)\n return epoch_time - epoch_time % 86400\n\n\nwith open('config_ebloc.json') as json_data_file:\n config_data = json.load(json_data_file)\nowner_address = config_data['owner']['address']\nowner_password = config_data['owner']['password']\ncontract_address = config_data['contract']['address']\ncontract_abi = config_data['contract']['abi']\ngas = int(config_data['price']['gas'])\ngas_price = Web3.toWei(int(config_data['price']['gas_price']), 'gwei')\necb_daily_log_path = config_data['log']['ecb_daily']\ntcmb_daily_log_path = config_data['log']['tcmb_daily']\ngeth_ipc_path = config_data['geth']['geth_ipc_path']\ncontract_address = Web3.toChecksumAddress(contract_address)\nweb3 = Web3(IPCProvider(geth_ipc_path))\nweb3.middleware_stack.inject(geth_poa_middleware, layer=0)\nweb3.eth.defaultAccount = web3.eth.accounts[0]\nweb3.personal.unlockAccount(web3.eth.accounts[0], owner_password)\ncontract_instance = web3.eth.contract(abi=contract_abi, address=\n contract_address, ContractFactoryClass=ConciseContract)\nunix_time = Web3.toInt(epoch_day(time.time()))\n\n\ndef add_ecb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n ECB = ECB_Processor()\n f = open(ecb_daily_log_path, 'a')\n if time.strftime('%Y-%m-%d') == ECB.Currency_Dict['time']:\n for curr in ecb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value = web3.toInt(int(float(ECB.Currency_Dict[curr]) * 10 **\n 9))\n tx_hash = contract_instance.add_ecb(unix_time, curr_code,\n curr_value, transact={'from': web3.eth.accounts[0]})\n tx_hash = tx_hash.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\ndef add_tcmb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n TCMB = TCMB_Processor()\n f = open(tcmb_daily_log_path, 'a')\n if time.strftime('%m/%d/%Y') == TCMB.CURRENCY_DICT['Date']:\n for curr in tcmb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value_fb = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexBuying']) * 10 ** 9))\n curr_value_fs = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexSelling']) * 10 ** 9))\n tx_hash_fb = contract_instance.add_tcmb_forexbuying(unix_time,\n curr_code, curr_value_fb, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fb = tx_hash_fb.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fb,\n curr_code, file=f)\n tx_hash_fs = contract_instance.add_tcmb_forexselling(unix_time,\n curr_code, curr_value_fs, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fs = tx_hash_fs.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fs,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\nif __name__ == '__main__':\n add_ecb()\n add_tcmb()\n print(time.strftime('%Y-%m-%d %H:%M'), ' DONE EBLOC add_ecb & add_tcmb')\n",
"step-4": "from web3 import Web3, HTTPProvider, IPCProvider\nfrom tcmb.tcmb_parser import TCMB_Processor\nfrom ecb.ecb_parser import ECB_Processor\nfrom web3.contract import ConciseContract\nfrom web3.middleware import geth_poa_middleware\nimport json\nimport time\ntcmb_currencies = ['TRY', 'USD', 'AUD', 'DKK', 'EUR', 'GBP', 'CHF', 'SEK',\n 'CAD', 'KWD', 'NOK', 'SAR', 'JPY', 'BGN', 'RON', 'RUB', 'IRR', 'CNY', 'PKR'\n ]\necb_currencies = ['EUR', 'USD', 'JPY', 'BGN', 'CZK', 'DKK', 'GBP', 'HUF',\n 'PLN', 'RON', 'SEK', 'CHF', 'ISK', 'NOK', 'HRK', 'RUB', 'TRY', 'AUD',\n 'BRL', 'CAD', 'CNY', 'HKD', 'IDR', 'ILS', 'INR', 'KRW', 'MXN', 'MYR',\n 'NZD', 'PHP', 'SGD', 'THB', 'ZAR']\n\n\ndef epoch_day(epoch_time):\n epoch_time = int(epoch_time)\n return epoch_time - epoch_time % 86400\n\n\nwith open('config_ebloc.json') as json_data_file:\n config_data = json.load(json_data_file)\nowner_address = config_data['owner']['address']\nowner_password = config_data['owner']['password']\ncontract_address = config_data['contract']['address']\ncontract_abi = config_data['contract']['abi']\ngas = int(config_data['price']['gas'])\ngas_price = Web3.toWei(int(config_data['price']['gas_price']), 'gwei')\necb_daily_log_path = config_data['log']['ecb_daily']\ntcmb_daily_log_path = config_data['log']['tcmb_daily']\ngeth_ipc_path = config_data['geth']['geth_ipc_path']\ncontract_address = Web3.toChecksumAddress(contract_address)\nweb3 = Web3(IPCProvider(geth_ipc_path))\nweb3.middleware_stack.inject(geth_poa_middleware, layer=0)\nweb3.eth.defaultAccount = web3.eth.accounts[0]\nweb3.personal.unlockAccount(web3.eth.accounts[0], owner_password)\ncontract_instance = web3.eth.contract(abi=contract_abi, address=\n contract_address, ContractFactoryClass=ConciseContract)\nunix_time = Web3.toInt(epoch_day(time.time()))\n\n\ndef add_ecb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n ECB = ECB_Processor()\n f = open(ecb_daily_log_path, 'a')\n if time.strftime('%Y-%m-%d') == ECB.Currency_Dict['time']:\n for curr in ecb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value = web3.toInt(int(float(ECB.Currency_Dict[curr]) * 10 **\n 9))\n tx_hash = contract_instance.add_ecb(unix_time, curr_code,\n curr_value, transact={'from': web3.eth.accounts[0]})\n tx_hash = tx_hash.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\ndef add_tcmb():\n unix_time = Web3.toInt(epoch_day(time.time()))\n TCMB = TCMB_Processor()\n f = open(tcmb_daily_log_path, 'a')\n if time.strftime('%m/%d/%Y') == TCMB.CURRENCY_DICT['Date']:\n for curr in tcmb_currencies:\n curr_code = bytes(curr, encoding='utf-8')\n curr_value_fb = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexBuying']) * 10 ** 9))\n curr_value_fs = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\n 'ForexSelling']) * 10 ** 9))\n tx_hash_fb = contract_instance.add_tcmb_forexbuying(unix_time,\n curr_code, curr_value_fb, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fb = tx_hash_fb.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fb,\n curr_code, file=f)\n tx_hash_fs = contract_instance.add_tcmb_forexselling(unix_time,\n curr_code, curr_value_fs, transact={'from': web3.eth.\n accounts[0]})\n tx_hash_fs = tx_hash_fs.hex()\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, tx_hash_fs,\n curr_code, file=f)\n else:\n print(time.strftime('%Y-%m-%d %H:%M'), unix_time, 'Weekend', file=f)\n f.close()\n\n\nif __name__ == '__main__':\n add_ecb()\n add_tcmb()\n print(time.strftime('%Y-%m-%d %H:%M'), ' DONE EBLOC add_ecb & add_tcmb')\n",
"step-5": "from web3 import Web3, HTTPProvider, IPCProvider\nfrom tcmb.tcmb_parser import TCMB_Processor\nfrom ecb.ecb_parser import ECB_Processor\nfrom web3.contract import ConciseContract\nfrom web3.middleware import geth_poa_middleware\nimport json\nimport time\n\ntcmb_currencies = [\"TRY\", \"USD\", \"AUD\", \"DKK\", \"EUR\", \"GBP\", \"CHF\", \"SEK\", \"CAD\", \n\t\t\"KWD\", \"NOK\", \"SAR\", \"JPY\", \"BGN\", \"RON\", \"RUB\", \"IRR\", \"CNY\", \"PKR\"]\n\necb_currencies = [\"EUR\", \"USD\", \"JPY\", \"BGN\", \"CZK\", \"DKK\", \"GBP\", \"HUF\", \"PLN\", \n\t\t\"RON\", \"SEK\", \"CHF\", \"ISK\", \"NOK\", \"HRK\", \"RUB\", \"TRY\", \"AUD\", \"BRL\", \n\t\t\"CAD\", \"CNY\", \"HKD\", \"IDR\", \"ILS\", \"INR\", \"KRW\", \"MXN\", \"MYR\", \"NZD\", \n\t\t\"PHP\", \"SGD\", \"THB\", \"ZAR\"]\n\ndef epoch_day(epoch_time):\n\tepoch_time = int(epoch_time)\n\treturn(epoch_time - (epoch_time % 86400))\n\nwith open('config_ebloc.json') as json_data_file:\n\tconfig_data = json.load(json_data_file)\n\nowner_address = config_data[\"owner\"][\"address\"]\nowner_password = config_data[\"owner\"][\"password\"]\ncontract_address = config_data[\"contract\"][\"address\"]\ncontract_abi = config_data[\"contract\"][\"abi\"]\ngas = int(config_data[\"price\"][\"gas\"])\ngas_price = Web3.toWei( int(config_data[\"price\"][\"gas_price\"]), 'gwei')\necb_daily_log_path = config_data[\"log\"][\"ecb_daily\"]\ntcmb_daily_log_path = config_data[\"log\"][\"tcmb_daily\"]\ngeth_ipc_path = config_data[\"geth\"][\"geth_ipc_path\"]\n\ncontract_address = Web3.toChecksumAddress(contract_address)\n\nweb3 = Web3(IPCProvider(geth_ipc_path))\nweb3.middleware_stack.inject(geth_poa_middleware, layer=0)\n\nweb3.eth.defaultAccount = web3.eth.accounts[0]\nweb3.personal.unlockAccount(web3.eth.accounts[0], owner_password)\n\ncontract_instance = web3.eth.contract(abi=contract_abi, address=contract_address, ContractFactoryClass=ConciseContract)\n\nunix_time = Web3.toInt(epoch_day(time.time()))\n\ndef add_ecb():\n\tunix_time = Web3.toInt(epoch_day(time.time()))\n\tECB = ECB_Processor()\n\tf = open(ecb_daily_log_path, \"a\")\n\tif(time.strftime(\"%Y-%m-%d\") == ECB.Currency_Dict[\"time\"]):\n\t\tfor curr in ecb_currencies:\n\t\t\tcurr_code = bytes(curr, encoding='utf-8')\n\t\t\tcurr_value = web3.toInt(int(float(ECB.Currency_Dict[curr])*(10**9)))\n\t\t\ttx_hash = contract_instance.add_ecb(unix_time, curr_code, curr_value, transact={'from': web3.eth.accounts[0]})\n\t\t\ttx_hash = tx_hash.hex()\n\t\t\tprint(time.strftime(\"%Y-%m-%d %H:%M\"), unix_time, tx_hash, curr_code, file=f)\n\telse:\n\t\tprint(time.strftime(\"%Y-%m-%d %H:%M\"), unix_time, \"Weekend\", file=f)\n\tf.close()\n\ndef add_tcmb():\n\tunix_time = Web3.toInt(epoch_day(time.time()))\n\tTCMB = TCMB_Processor()\n\tf = open(tcmb_daily_log_path, \"a\")\n\tif(time.strftime(\"%m/%d/%Y\") == TCMB.CURRENCY_DICT[\"Date\"]):\n\t\tfor curr in tcmb_currencies:\n\t\t\tcurr_code = bytes(curr, encoding='utf-8')\n\t\t\tcurr_value_fb = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\"ForexBuying\"])*(10**9)))\n\t\t\tcurr_value_fs = web3.toInt(int(float(TCMB.CURRENCY_DICT[curr][\"ForexSelling\"])*(10**9)))\n\t\t\t# forex buying\n\t\t\ttx_hash_fb = contract_instance.add_tcmb_forexbuying(unix_time, curr_code, curr_value_fb, transact={'from': web3.eth.accounts[0]})\n\t\t\ttx_hash_fb = tx_hash_fb.hex()\n\t\t\tprint(time.strftime(\"%Y-%m-%d %H:%M\"), unix_time, tx_hash_fb, curr_code, file=f)\n\t\t\t# forex selling\n\t\t\ttx_hash_fs = contract_instance.add_tcmb_forexselling(unix_time, curr_code, curr_value_fs, transact={'from': web3.eth.accounts[0]})\n\t\t\ttx_hash_fs = tx_hash_fs.hex()\n\t\t\tprint(time.strftime(\"%Y-%m-%d %H:%M\"), unix_time, tx_hash_fs, curr_code, file=f)\n\telse:\n\t\tprint(time.strftime(\"%Y-%m-%d %H:%M\"), unix_time, \"Weekend\", file=f)\n\tf.close()\n\n\nif __name__ == \"__main__\":\n\tadd_ecb()\n\tadd_tcmb()\n\tprint(time.strftime(\"%Y-%m-%d %H:%M\"), \" DONE EBLOC add_ecb & add_tcmb\")",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def countVowels(string):
count = 0
vowels = ['a', 'e', 'i', 'o', 'u', 'y']
for vowel in vowels:
count += string.count(vowel)
return count
<|reserved_special_token_0|>
def isPalindrome(string):
return reverse(string) == string
def main():
count = 5
while count > 0:
string = input('Enter a string: ')
print('The middle character(s) is/are: ' + middle(string))
print('The string reversed is: ' + reverse(string))
print('The string contains ' + str(countVowels(string)) + ' vowels.')
if isPalindrome(string):
print('That is a palindrome.\n')
else:
print('That is not palindrome.\n')
count -= 1
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def middle(string):
characters = list(string)
length = len(characters)
middleNum = round((length + 0.5) / 2)
if length % 2 == 0:
return characters[middleNum - 1] + characters[middleNum]
else:
return characters[middleNum - 1]
def countVowels(string):
count = 0
vowels = ['a', 'e', 'i', 'o', 'u', 'y']
for vowel in vowels:
count += string.count(vowel)
return count
<|reserved_special_token_0|>
def isPalindrome(string):
return reverse(string) == string
def main():
count = 5
while count > 0:
string = input('Enter a string: ')
print('The middle character(s) is/are: ' + middle(string))
print('The string reversed is: ' + reverse(string))
print('The string contains ' + str(countVowels(string)) + ' vowels.')
if isPalindrome(string):
print('That is a palindrome.\n')
else:
print('That is not palindrome.\n')
count -= 1
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def middle(string):
characters = list(string)
length = len(characters)
middleNum = round((length + 0.5) / 2)
if length % 2 == 0:
return characters[middleNum - 1] + characters[middleNum]
else:
return characters[middleNum - 1]
def countVowels(string):
count = 0
vowels = ['a', 'e', 'i', 'o', 'u', 'y']
for vowel in vowels:
count += string.count(vowel)
return count
def reverse(string):
return string[::-1]
def isPalindrome(string):
return reverse(string) == string
def main():
count = 5
while count > 0:
string = input('Enter a string: ')
print('The middle character(s) is/are: ' + middle(string))
print('The string reversed is: ' + reverse(string))
print('The string contains ' + str(countVowels(string)) + ' vowels.')
if isPalindrome(string):
print('That is a palindrome.\n')
else:
print('That is not palindrome.\n')
count -= 1
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def middle(string):
characters = list(string)
length = len(characters)
middleNum = round((length + 0.5) / 2)
if length % 2 == 0:
return characters[middleNum - 1] + characters[middleNum]
else:
return characters[middleNum - 1]
def countVowels(string):
count = 0
vowels = ['a', 'e', 'i', 'o', 'u', 'y']
for vowel in vowels:
count += string.count(vowel)
return count
def reverse(string):
return string[::-1]
def isPalindrome(string):
return reverse(string) == string
def main():
count = 5
while count > 0:
string = input('Enter a string: ')
print('The middle character(s) is/are: ' + middle(string))
print('The string reversed is: ' + reverse(string))
print('The string contains ' + str(countVowels(string)) + ' vowels.')
if isPalindrome(string):
print('That is a palindrome.\n')
else:
print('That is not palindrome.\n')
count -= 1
main()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
# CIS 117 Python Programming - Lab 10
# Bryce DesBrisay
def middle(string):
characters = list(string)
length = len(characters)
middleNum = round((length + .5) / 2)
if length % 2 == 0:
return characters[middleNum - 1] + characters[middleNum]
else:
return characters[middleNum - 1]
def countVowels(string):
count = 0
vowels = ['a','e','i','o','u','y']
for vowel in vowels:
count += string.count(vowel)
return count
def reverse(string):
return string[::-1]
def isPalindrome(string):
return reverse(string) == string
def main():
count = 5
while count > 0:
string = input('Enter a string: ')
print('The middle character(s) is/are: ' + middle(string))
print('The string reversed is: ' + reverse(string))
print('The string contains ' + str(countVowels(string)) + ' vowels.')
if isPalindrome(string):
print('That is a palindrome.\n')
else:
print('That is not palindrome.\n')
count -= 1
main()
'''
Enter a string: racecar
The middle character(s) is/are: e
The string reversed is: racecar
The string contains 3 vowels.
That is a palindrome.
Enter a string: apple
The middle character(s) is/are: p
The string reversed is: elppa
The string contains 2 vowels.
That is not palindrome.
Enter a string: civic
The middle character(s) is/are: v
The string reversed is: civic
The string contains 2 vowels.
That is a palindrome.
Enter a string: bottle
The middle character(s) is/are: tt
The string reversed is: elttob
The string contains 2 vowels.
That is not palindrome.
Enter a string: noon
The middle character(s) is/are: oo
The string reversed is: noon
The string contains 2 vowels.
That is a palindrome.
'''
|
flexible
|
{
"blob_id": "d60690892eddda656c11470aacd1fdc9d07a721a",
"index": 3563,
"step-1": "<mask token>\n\n\ndef countVowels(string):\n count = 0\n vowels = ['a', 'e', 'i', 'o', 'u', 'y']\n for vowel in vowels:\n count += string.count(vowel)\n return count\n\n\n<mask token>\n\n\ndef isPalindrome(string):\n return reverse(string) == string\n\n\ndef main():\n count = 5\n while count > 0:\n string = input('Enter a string: ')\n print('The middle character(s) is/are: ' + middle(string))\n print('The string reversed is: ' + reverse(string))\n print('The string contains ' + str(countVowels(string)) + ' vowels.')\n if isPalindrome(string):\n print('That is a palindrome.\\n')\n else:\n print('That is not palindrome.\\n')\n count -= 1\n\n\n<mask token>\n",
"step-2": "def middle(string):\n characters = list(string)\n length = len(characters)\n middleNum = round((length + 0.5) / 2)\n if length % 2 == 0:\n return characters[middleNum - 1] + characters[middleNum]\n else:\n return characters[middleNum - 1]\n\n\ndef countVowels(string):\n count = 0\n vowels = ['a', 'e', 'i', 'o', 'u', 'y']\n for vowel in vowels:\n count += string.count(vowel)\n return count\n\n\n<mask token>\n\n\ndef isPalindrome(string):\n return reverse(string) == string\n\n\ndef main():\n count = 5\n while count > 0:\n string = input('Enter a string: ')\n print('The middle character(s) is/are: ' + middle(string))\n print('The string reversed is: ' + reverse(string))\n print('The string contains ' + str(countVowels(string)) + ' vowels.')\n if isPalindrome(string):\n print('That is a palindrome.\\n')\n else:\n print('That is not palindrome.\\n')\n count -= 1\n\n\n<mask token>\n",
"step-3": "def middle(string):\n characters = list(string)\n length = len(characters)\n middleNum = round((length + 0.5) / 2)\n if length % 2 == 0:\n return characters[middleNum - 1] + characters[middleNum]\n else:\n return characters[middleNum - 1]\n\n\ndef countVowels(string):\n count = 0\n vowels = ['a', 'e', 'i', 'o', 'u', 'y']\n for vowel in vowels:\n count += string.count(vowel)\n return count\n\n\ndef reverse(string):\n return string[::-1]\n\n\ndef isPalindrome(string):\n return reverse(string) == string\n\n\ndef main():\n count = 5\n while count > 0:\n string = input('Enter a string: ')\n print('The middle character(s) is/are: ' + middle(string))\n print('The string reversed is: ' + reverse(string))\n print('The string contains ' + str(countVowels(string)) + ' vowels.')\n if isPalindrome(string):\n print('That is a palindrome.\\n')\n else:\n print('That is not palindrome.\\n')\n count -= 1\n\n\n<mask token>\n",
"step-4": "def middle(string):\n characters = list(string)\n length = len(characters)\n middleNum = round((length + 0.5) / 2)\n if length % 2 == 0:\n return characters[middleNum - 1] + characters[middleNum]\n else:\n return characters[middleNum - 1]\n\n\ndef countVowels(string):\n count = 0\n vowels = ['a', 'e', 'i', 'o', 'u', 'y']\n for vowel in vowels:\n count += string.count(vowel)\n return count\n\n\ndef reverse(string):\n return string[::-1]\n\n\ndef isPalindrome(string):\n return reverse(string) == string\n\n\ndef main():\n count = 5\n while count > 0:\n string = input('Enter a string: ')\n print('The middle character(s) is/are: ' + middle(string))\n print('The string reversed is: ' + reverse(string))\n print('The string contains ' + str(countVowels(string)) + ' vowels.')\n if isPalindrome(string):\n print('That is a palindrome.\\n')\n else:\n print('That is not palindrome.\\n')\n count -= 1\n\n\nmain()\n<mask token>\n",
"step-5": "# CIS 117 Python Programming - Lab 10\n# Bryce DesBrisay\n\ndef middle(string):\n characters = list(string)\n length = len(characters)\n middleNum = round((length + .5) / 2)\n if length % 2 == 0:\n return characters[middleNum - 1] + characters[middleNum]\n else:\n return characters[middleNum - 1]\n\ndef countVowels(string):\n count = 0\n vowels = ['a','e','i','o','u','y']\n for vowel in vowels:\n count += string.count(vowel)\n return count\n\ndef reverse(string):\n return string[::-1]\n\ndef isPalindrome(string):\n return reverse(string) == string\n\ndef main():\n count = 5\n while count > 0:\n string = input('Enter a string: ')\n print('The middle character(s) is/are: ' + middle(string))\n print('The string reversed is: ' + reverse(string))\n print('The string contains ' + str(countVowels(string)) + ' vowels.')\n if isPalindrome(string):\n print('That is a palindrome.\\n')\n else:\n print('That is not palindrome.\\n')\n count -= 1\n\nmain()\n\n'''\nEnter a string: racecar\nThe middle character(s) is/are: e\nThe string reversed is: racecar\nThe string contains 3 vowels.\nThat is a palindrome.\n\nEnter a string: apple\nThe middle character(s) is/are: p\nThe string reversed is: elppa\nThe string contains 2 vowels.\nThat is not palindrome.\n\nEnter a string: civic\nThe middle character(s) is/are: v\nThe string reversed is: civic\nThe string contains 2 vowels.\nThat is a palindrome.\n\nEnter a string: bottle\nThe middle character(s) is/are: tt\nThe string reversed is: elttob\nThe string contains 2 vowels.\nThat is not palindrome.\n\nEnter a string: noon\nThe middle character(s) is/are: oo\nThe string reversed is: noon\nThe string contains 2 vowels.\nThat is a palindrome.\n'''\n\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../tools'))
import files
import genetics
def main(argv):
S = files.read_lines(argv[0])
S_rc = [genetics.dna_complement(s) for s in S]
S_u = set(S + S_rc)
B_k = []
for s in S_u:
B_k.append((s[:-1], s[1:]))
print '\n'.join('(%s, %s)' % b for b in sorted(B_k))
if __name__ == "__main__":
main(sys.argv[1:])
|
normal
|
{
"blob_id": "b616b907eb67fff97d57ee2b0d3ab8e01d154956",
"index": 2038,
"step-1": "import os\nimport sys\nsys.path.append(os.path.join(os.path.dirname(__file__), '../tools'))\n\nimport files\nimport genetics\n\n\ndef main(argv):\n S = files.read_lines(argv[0])\n S_rc = [genetics.dna_complement(s) for s in S]\n S_u = set(S + S_rc)\n\n B_k = []\n\n for s in S_u:\n B_k.append((s[:-1], s[1:]))\n\n print '\\n'.join('(%s, %s)' % b for b in sorted(B_k))\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
# -*- coding: utf-8 -*-
#
# File: PatrimonyCertificate.py
#
# Copyright (c) 2015 by CommunesPlone
# Generator: ArchGenXML Version 2.7
# http://plone.org/products/archgenxml
#
# GNU General Public License (GPL)
#
__author__ = """Gauthier BASTIEN <gbastien@commune.sambreville.be>, Stephan GEULETTE
<stephan.geulette@uvcw.be>, Jean-Michel Abe <jm.abe@la-bruyere.be>"""
__docformat__ = 'plaintext'
from AccessControl import ClassSecurityInfo
from Products.Archetypes.atapi import *
from zope.interface import implements
from Products.urban import interfaces
from Products.urban.content.licence.GenericLicence import GenericLicence
from Products.urban.content.Inquiry import Inquiry
from Products.CMFDynamicViewFTI.browserdefault import BrowserDefaultMixin
from Products.urban import UrbanMessage as _
from Products.urban.config import *
##code-section module-header #fill in your manual code here
from Products.urban.utils import setOptionalAttributes
from Products.urban.utils import setSchemataForInquiry
from Products.ATReferenceBrowserWidget.ATReferenceBrowserWidget import ReferenceBrowserWidget
optional_fields = ['architects']
##/code-section module-header
schema = Schema((
ReferenceField(
name='architects',
widget=ReferenceBrowserWidget(
allow_search=True,
only_for_review_states='enabled',
allow_browse=True,
force_close_on_insert=True,
startup_directory='urban/architects',
restrict_browsing_to_startup_directory=True,
wild_card_search=True,
show_index_selector=True,
label=_('urban_label_architects', default='Architect(s)'),
popup_name='contact_reference_popup',
),
required=False,
schemata='urban_description',
multiValued=True,
relationship="miscdemandarchitects",
allowed_types='Architect',
),
),
)
##code-section after-local-schema #fill in your manual code here
setOptionalAttributes(schema, optional_fields)
##/code-section after-local-schema
PatrimonyCertificate_schema = BaseFolderSchema.copy() + \
getattr(GenericLicence, 'schema', Schema(())).copy() + \
getattr(Inquiry, 'schema', Schema(())).copy() + \
schema.copy()
##code-section after-schema #fill in your manual code here
#put the the fields coming from Inquiry in a specific schemata
setSchemataForInquiry(PatrimonyCertificate_schema)
##/code-section after-schema
class PatrimonyCertificate(BaseFolder, GenericLicence, Inquiry, BrowserDefaultMixin):
"""
"""
security = ClassSecurityInfo()
implements(interfaces.IPatrimonyCertificate)
meta_type = 'PatrimonyCertificate'
_at_rename_after_creation = True
schema = PatrimonyCertificate_schema
##code-section class-header #fill in your manual code here
schemata_order = ['urban_description', 'urban_road', 'urban_location']
##/code-section class-header
# Methods
# Manually created methods
security.declarePublic('getRepresentatives')
def getRepresentatives(self):
"""
"""
return self.getArchitects()
def getLastDeposit(self):
return self.getLastEvent(interfaces.IDepositEvent)
def getLastCollegeReport(self):
return self.getLastEvent(interfaces.ICollegeReportEvent)
def getLastTheLicence(self):
return self.getLastEvent(interfaces.ITheLicenceEvent)
registerType(PatrimonyCertificate, PROJECTNAME)
# end of class PatrimonyCertificate
##code-section module-footer #fill in your manual code here
def finalizeSchema(schema, folderish=False, moveDiscussion=True):
"""
Finalizes the type schema to alter some fields
"""
schema.moveField('description', after='architects')
return schema
finalizeSchema(PatrimonyCertificate_schema)
##/code-section module-footer
|
normal
|
{
"blob_id": "6c0b2fa8166bb21a514dc188858e1de285ad9b0a",
"index": 166,
"step-1": "<mask token>\n\n\nclass PatrimonyCertificate(BaseFolder, GenericLicence, Inquiry,\n BrowserDefaultMixin):\n <mask token>\n security = ClassSecurityInfo()\n implements(interfaces.IPatrimonyCertificate)\n meta_type = 'PatrimonyCertificate'\n _at_rename_after_creation = True\n schema = PatrimonyCertificate_schema\n schemata_order = ['urban_description', 'urban_road', 'urban_location']\n security.declarePublic('getRepresentatives')\n\n def getRepresentatives(self):\n \"\"\"\n \"\"\"\n return self.getArchitects()\n\n def getLastDeposit(self):\n return self.getLastEvent(interfaces.IDepositEvent)\n\n def getLastCollegeReport(self):\n return self.getLastEvent(interfaces.ICollegeReportEvent)\n\n def getLastTheLicence(self):\n return self.getLastEvent(interfaces.ITheLicenceEvent)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass PatrimonyCertificate(BaseFolder, GenericLicence, Inquiry,\n BrowserDefaultMixin):\n \"\"\"\n \"\"\"\n security = ClassSecurityInfo()\n implements(interfaces.IPatrimonyCertificate)\n meta_type = 'PatrimonyCertificate'\n _at_rename_after_creation = True\n schema = PatrimonyCertificate_schema\n schemata_order = ['urban_description', 'urban_road', 'urban_location']\n security.declarePublic('getRepresentatives')\n\n def getRepresentatives(self):\n \"\"\"\n \"\"\"\n return self.getArchitects()\n\n def getLastDeposit(self):\n return self.getLastEvent(interfaces.IDepositEvent)\n\n def getLastCollegeReport(self):\n return self.getLastEvent(interfaces.ICollegeReportEvent)\n\n def getLastTheLicence(self):\n return self.getLastEvent(interfaces.ITheLicenceEvent)\n\n\n<mask token>\n\n\ndef finalizeSchema(schema, folderish=False, moveDiscussion=True):\n \"\"\"\n Finalizes the type schema to alter some fields\n \"\"\"\n schema.moveField('description', after='architects')\n return schema\n\n\n<mask token>\n",
"step-3": "<mask token>\nsetOptionalAttributes(schema, optional_fields)\n<mask token>\nsetSchemataForInquiry(PatrimonyCertificate_schema)\n\n\nclass PatrimonyCertificate(BaseFolder, GenericLicence, Inquiry,\n BrowserDefaultMixin):\n \"\"\"\n \"\"\"\n security = ClassSecurityInfo()\n implements(interfaces.IPatrimonyCertificate)\n meta_type = 'PatrimonyCertificate'\n _at_rename_after_creation = True\n schema = PatrimonyCertificate_schema\n schemata_order = ['urban_description', 'urban_road', 'urban_location']\n security.declarePublic('getRepresentatives')\n\n def getRepresentatives(self):\n \"\"\"\n \"\"\"\n return self.getArchitects()\n\n def getLastDeposit(self):\n return self.getLastEvent(interfaces.IDepositEvent)\n\n def getLastCollegeReport(self):\n return self.getLastEvent(interfaces.ICollegeReportEvent)\n\n def getLastTheLicence(self):\n return self.getLastEvent(interfaces.ITheLicenceEvent)\n\n\nregisterType(PatrimonyCertificate, PROJECTNAME)\n\n\ndef finalizeSchema(schema, folderish=False, moveDiscussion=True):\n \"\"\"\n Finalizes the type schema to alter some fields\n \"\"\"\n schema.moveField('description', after='architects')\n return schema\n\n\nfinalizeSchema(PatrimonyCertificate_schema)\n",
"step-4": "__author__ = \"\"\"Gauthier BASTIEN <gbastien@commune.sambreville.be>, Stephan GEULETTE\n<stephan.geulette@uvcw.be>, Jean-Michel Abe <jm.abe@la-bruyere.be>\"\"\"\n__docformat__ = 'plaintext'\n<mask token>\noptional_fields = ['architects']\nschema = Schema((ReferenceField(name='architects', widget=\n ReferenceBrowserWidget(allow_search=True, only_for_review_states=\n 'enabled', allow_browse=True, force_close_on_insert=True,\n startup_directory='urban/architects',\n restrict_browsing_to_startup_directory=True, wild_card_search=True,\n show_index_selector=True, label=_('urban_label_architects', default=\n 'Architect(s)'), popup_name='contact_reference_popup'), required=False,\n schemata='urban_description', multiValued=True, relationship=\n 'miscdemandarchitects', allowed_types='Architect'),))\nsetOptionalAttributes(schema, optional_fields)\nPatrimonyCertificate_schema = BaseFolderSchema.copy() + getattr(GenericLicence,\n 'schema', Schema(())).copy() + getattr(Inquiry, 'schema', Schema(())).copy(\n ) + schema.copy()\nsetSchemataForInquiry(PatrimonyCertificate_schema)\n\n\nclass PatrimonyCertificate(BaseFolder, GenericLicence, Inquiry,\n BrowserDefaultMixin):\n \"\"\"\n \"\"\"\n security = ClassSecurityInfo()\n implements(interfaces.IPatrimonyCertificate)\n meta_type = 'PatrimonyCertificate'\n _at_rename_after_creation = True\n schema = PatrimonyCertificate_schema\n schemata_order = ['urban_description', 'urban_road', 'urban_location']\n security.declarePublic('getRepresentatives')\n\n def getRepresentatives(self):\n \"\"\"\n \"\"\"\n return self.getArchitects()\n\n def getLastDeposit(self):\n return self.getLastEvent(interfaces.IDepositEvent)\n\n def getLastCollegeReport(self):\n return self.getLastEvent(interfaces.ICollegeReportEvent)\n\n def getLastTheLicence(self):\n return self.getLastEvent(interfaces.ITheLicenceEvent)\n\n\nregisterType(PatrimonyCertificate, PROJECTNAME)\n\n\ndef finalizeSchema(schema, folderish=False, moveDiscussion=True):\n \"\"\"\n Finalizes the type schema to alter some fields\n \"\"\"\n schema.moveField('description', after='architects')\n return schema\n\n\nfinalizeSchema(PatrimonyCertificate_schema)\n",
"step-5": "# -*- coding: utf-8 -*-\n#\n# File: PatrimonyCertificate.py\n#\n# Copyright (c) 2015 by CommunesPlone\n# Generator: ArchGenXML Version 2.7\n# http://plone.org/products/archgenxml\n#\n# GNU General Public License (GPL)\n#\n\n__author__ = \"\"\"Gauthier BASTIEN <gbastien@commune.sambreville.be>, Stephan GEULETTE\n<stephan.geulette@uvcw.be>, Jean-Michel Abe <jm.abe@la-bruyere.be>\"\"\"\n__docformat__ = 'plaintext'\n\nfrom AccessControl import ClassSecurityInfo\nfrom Products.Archetypes.atapi import *\nfrom zope.interface import implements\nfrom Products.urban import interfaces\nfrom Products.urban.content.licence.GenericLicence import GenericLicence\nfrom Products.urban.content.Inquiry import Inquiry\nfrom Products.CMFDynamicViewFTI.browserdefault import BrowserDefaultMixin\n\nfrom Products.urban import UrbanMessage as _\nfrom Products.urban.config import *\n\n##code-section module-header #fill in your manual code here\nfrom Products.urban.utils import setOptionalAttributes\nfrom Products.urban.utils import setSchemataForInquiry\nfrom Products.ATReferenceBrowserWidget.ATReferenceBrowserWidget import ReferenceBrowserWidget\noptional_fields = ['architects']\n##/code-section module-header\n\nschema = Schema((\n\n ReferenceField(\n name='architects',\n widget=ReferenceBrowserWidget(\n allow_search=True,\n only_for_review_states='enabled',\n allow_browse=True,\n force_close_on_insert=True,\n startup_directory='urban/architects',\n restrict_browsing_to_startup_directory=True,\n wild_card_search=True,\n show_index_selector=True,\n label=_('urban_label_architects', default='Architect(s)'),\n popup_name='contact_reference_popup',\n ),\n required=False,\n schemata='urban_description',\n multiValued=True,\n relationship=\"miscdemandarchitects\",\n allowed_types='Architect',\n ),\n\n),\n)\n\n##code-section after-local-schema #fill in your manual code here\nsetOptionalAttributes(schema, optional_fields)\n##/code-section after-local-schema\n\nPatrimonyCertificate_schema = BaseFolderSchema.copy() + \\\n getattr(GenericLicence, 'schema', Schema(())).copy() + \\\n getattr(Inquiry, 'schema', Schema(())).copy() + \\\n schema.copy()\n\n##code-section after-schema #fill in your manual code here\n#put the the fields coming from Inquiry in a specific schemata\nsetSchemataForInquiry(PatrimonyCertificate_schema)\n##/code-section after-schema\n\nclass PatrimonyCertificate(BaseFolder, GenericLicence, Inquiry, BrowserDefaultMixin):\n \"\"\"\n \"\"\"\n security = ClassSecurityInfo()\n implements(interfaces.IPatrimonyCertificate)\n\n meta_type = 'PatrimonyCertificate'\n _at_rename_after_creation = True\n\n schema = PatrimonyCertificate_schema\n\n ##code-section class-header #fill in your manual code here\n schemata_order = ['urban_description', 'urban_road', 'urban_location']\n ##/code-section class-header\n\n # Methods\n\n # Manually created methods\n\n security.declarePublic('getRepresentatives')\n def getRepresentatives(self):\n \"\"\"\n \"\"\"\n return self.getArchitects()\n\n def getLastDeposit(self):\n return self.getLastEvent(interfaces.IDepositEvent)\n\n def getLastCollegeReport(self):\n return self.getLastEvent(interfaces.ICollegeReportEvent)\n\n def getLastTheLicence(self):\n return self.getLastEvent(interfaces.ITheLicenceEvent)\n\n\n\nregisterType(PatrimonyCertificate, PROJECTNAME)\n# end of class PatrimonyCertificate\n\n##code-section module-footer #fill in your manual code here\ndef finalizeSchema(schema, folderish=False, moveDiscussion=True):\n \"\"\"\n Finalizes the type schema to alter some fields\n \"\"\"\n schema.moveField('description', after='architects')\n return schema\n\nfinalizeSchema(PatrimonyCertificate_schema)\n##/code-section module-footer\n\n",
"step-ids": [
6,
8,
9,
10,
12
]
}
|
[
6,
8,
9,
10,
12
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import base64
import json
import os
import re
import subprocess
import time
import traceback
import zipfile
from datetime import datetime
import requests
from flask import request, current_app
from library.oss import oss_upload_monkey_package_picture
from public_config import TCLOUD_FILE_TEMP_PATH
class ToolBusiness(object):
@classmethod
def get_tool_ip(cls):
ip = request.args.get('ip')
url = 'http://api.map.baidu.com/location/ip'
params = {"ip": ip, "ak": 'kqCYLKt8Uz9VnvHBXA7uOI51FIrei0OM'}
ret = requests.get(url=url, params=params)
ret = json.loads(ret.content)
if ret and 'status' in ret and ret['status'] == 0 and 'content' in ret and 'address' in ret:
return ret['status'], ret['content'], ret['address'], 'ok'
return 101, '', '', '获取失败'
@classmethod
def apk_analysis(cls, apk_download_url, type=1):
try:
# type 1 : not save , 2: save to db
target_path = "/tmp/packages/"
if not os.path.exists(target_path):
os.mkdir(target_path)
date_time_now = datetime.now().strftime('%Y%m%d-%H.%M.%S')
target_name = '{}.apk'.format(date_time_now)
download_apk_name = os.path.join(target_path, target_name)
current_app.logger.info('开始从 {} 下载到 {}'.format(apk_download_url, download_apk_name))
response = requests.get(url=apk_download_url, verify=False)
with open(download_apk_name, 'wb') as f:
f.write(response.content)
time.sleep(0.5)
# 下载失败
if not os.path.exists(download_apk_name):
current_app.logger.error('{} 下载失败!'.format(apk_download_url))
return 102, "下载失败"
current_app.logger.info('下载成功,保存地址 {}'.format(download_apk_name))
current_app.logger.info('开始分析')
package_info_re = re.compile(r"package: name='(.*)' versionCode='(.*)' versionName='(.*?)'.*", re.I)
label_icon_re = re.compile(r"application: label='(.+)'.*icon='(.+)'", re.I)
launchable_activity_re = re.compile(r"launchable-activity: name='(.+)'.*label.*", re.I)
apk_info = {}
cmd = '/usr/local/bin/aapt dump badging {}'.format(download_apk_name)
command_process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
infos = command_process.stdout.readlines()
for info in infos:
info = info.decode('utf-8')
if info.startswith('package:'):
temp = package_info_re.search(info)
apk_info['package_name'] = temp.group(1)
apk_info['version_code'] = temp.group(2) or 0
apk_info['version_name'] = temp.group(3)
elif info.startswith('application:'):
temp = label_icon_re.search(info)
apk_info['label'] = temp.group(1)
apk_info['icon'] = temp.group(2)
elif info.startswith('launchable-activity:'):
temp = launchable_activity_re.search(info)
apk_info['default_activity'] = temp.group(1)
try:
size = round(os.path.getsize(download_apk_name) / float(1024 * 1024), 2)
apk_info['size'] = str(size)
zip = zipfile.ZipFile(download_apk_name)
icon_binary = zip.read(apk_info['icon'])
time_now = datetime.now().strftime('%Y%m%d.%H%M%S')
picture = f'monkey-{time_now}.png'
dir_path = f'{TCLOUD_FILE_TEMP_PATH}/monkey'
if not os.path.exists(TCLOUD_FILE_TEMP_PATH):
os.mkdir(TCLOUD_FILE_TEMP_PATH)
if not os.path.exists(dir_path):
os.mkdir(dir_path)
with open(f'{dir_path}/{picture}', 'wb') as f:
f.write(icon_binary)
apk_info['icon'] = oss_upload_monkey_package_picture(dir_path, picture)
except Exception as e:
current_app.logger.warning(e)
current_app.logger.warning(traceback.format_exc())
current_app.logger.info(apk_info)
if type == 1:
pass
elif type == 2:
pass
return apk_info
except Exception as e:
current_app.logger.error(e)
current_app.logger.error(traceback.format_exc())
return {}
|
normal
|
{
"blob_id": "bf45349a9fdfcef7392c477e089c5e3916cb4c8e",
"index": 8502,
"step-1": "<mask token>\n\n\nclass ToolBusiness(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ToolBusiness(object):\n\n @classmethod\n def get_tool_ip(cls):\n ip = request.args.get('ip')\n url = 'http://api.map.baidu.com/location/ip'\n params = {'ip': ip, 'ak': 'kqCYLKt8Uz9VnvHBXA7uOI51FIrei0OM'}\n ret = requests.get(url=url, params=params)\n ret = json.loads(ret.content)\n if ret and 'status' in ret and ret['status'\n ] == 0 and 'content' in ret and 'address' in ret:\n return ret['status'], ret['content'], ret['address'], 'ok'\n return 101, '', '', '获取失败'\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ToolBusiness(object):\n\n @classmethod\n def get_tool_ip(cls):\n ip = request.args.get('ip')\n url = 'http://api.map.baidu.com/location/ip'\n params = {'ip': ip, 'ak': 'kqCYLKt8Uz9VnvHBXA7uOI51FIrei0OM'}\n ret = requests.get(url=url, params=params)\n ret = json.loads(ret.content)\n if ret and 'status' in ret and ret['status'\n ] == 0 and 'content' in ret and 'address' in ret:\n return ret['status'], ret['content'], ret['address'], 'ok'\n return 101, '', '', '获取失败'\n\n @classmethod\n def apk_analysis(cls, apk_download_url, type=1):\n try:\n target_path = '/tmp/packages/'\n if not os.path.exists(target_path):\n os.mkdir(target_path)\n date_time_now = datetime.now().strftime('%Y%m%d-%H.%M.%S')\n target_name = '{}.apk'.format(date_time_now)\n download_apk_name = os.path.join(target_path, target_name)\n current_app.logger.info('开始从 {} 下载到 {}'.format(apk_download_url,\n download_apk_name))\n response = requests.get(url=apk_download_url, verify=False)\n with open(download_apk_name, 'wb') as f:\n f.write(response.content)\n time.sleep(0.5)\n if not os.path.exists(download_apk_name):\n current_app.logger.error('{} 下载失败!'.format(apk_download_url))\n return 102, '下载失败'\n current_app.logger.info('下载成功,保存地址 {}'.format(download_apk_name))\n current_app.logger.info('开始分析')\n package_info_re = re.compile(\n \"package: name='(.*)' versionCode='(.*)' versionName='(.*?)'.*\"\n , re.I)\n label_icon_re = re.compile(\"application: label='(.+)'.*icon='(.+)'\"\n , re.I)\n launchable_activity_re = re.compile(\n \"launchable-activity: name='(.+)'.*label.*\", re.I)\n apk_info = {}\n cmd = '/usr/local/bin/aapt dump badging {}'.format(\n download_apk_name)\n command_process = subprocess.Popen(cmd, shell=True, stdout=\n subprocess.PIPE, stderr=subprocess.STDOUT)\n infos = command_process.stdout.readlines()\n for info in infos:\n info = info.decode('utf-8')\n if info.startswith('package:'):\n temp = package_info_re.search(info)\n apk_info['package_name'] = temp.group(1)\n apk_info['version_code'] = temp.group(2) or 0\n apk_info['version_name'] = temp.group(3)\n elif info.startswith('application:'):\n temp = label_icon_re.search(info)\n apk_info['label'] = temp.group(1)\n apk_info['icon'] = temp.group(2)\n elif info.startswith('launchable-activity:'):\n temp = launchable_activity_re.search(info)\n apk_info['default_activity'] = temp.group(1)\n try:\n size = round(os.path.getsize(download_apk_name) / float(\n 1024 * 1024), 2)\n apk_info['size'] = str(size)\n zip = zipfile.ZipFile(download_apk_name)\n icon_binary = zip.read(apk_info['icon'])\n time_now = datetime.now().strftime('%Y%m%d.%H%M%S')\n picture = f'monkey-{time_now}.png'\n dir_path = f'{TCLOUD_FILE_TEMP_PATH}/monkey'\n if not os.path.exists(TCLOUD_FILE_TEMP_PATH):\n os.mkdir(TCLOUD_FILE_TEMP_PATH)\n if not os.path.exists(dir_path):\n os.mkdir(dir_path)\n with open(f'{dir_path}/{picture}', 'wb') as f:\n f.write(icon_binary)\n apk_info['icon'] = oss_upload_monkey_package_picture(dir_path,\n picture)\n except Exception as e:\n current_app.logger.warning(e)\n current_app.logger.warning(traceback.format_exc())\n current_app.logger.info(apk_info)\n if type == 1:\n pass\n elif type == 2:\n pass\n return apk_info\n except Exception as e:\n current_app.logger.error(e)\n current_app.logger.error(traceback.format_exc())\n return {}\n",
"step-4": "import base64\nimport json\nimport os\nimport re\nimport subprocess\nimport time\nimport traceback\nimport zipfile\nfrom datetime import datetime\nimport requests\nfrom flask import request, current_app\nfrom library.oss import oss_upload_monkey_package_picture\nfrom public_config import TCLOUD_FILE_TEMP_PATH\n\n\nclass ToolBusiness(object):\n\n @classmethod\n def get_tool_ip(cls):\n ip = request.args.get('ip')\n url = 'http://api.map.baidu.com/location/ip'\n params = {'ip': ip, 'ak': 'kqCYLKt8Uz9VnvHBXA7uOI51FIrei0OM'}\n ret = requests.get(url=url, params=params)\n ret = json.loads(ret.content)\n if ret and 'status' in ret and ret['status'\n ] == 0 and 'content' in ret and 'address' in ret:\n return ret['status'], ret['content'], ret['address'], 'ok'\n return 101, '', '', '获取失败'\n\n @classmethod\n def apk_analysis(cls, apk_download_url, type=1):\n try:\n target_path = '/tmp/packages/'\n if not os.path.exists(target_path):\n os.mkdir(target_path)\n date_time_now = datetime.now().strftime('%Y%m%d-%H.%M.%S')\n target_name = '{}.apk'.format(date_time_now)\n download_apk_name = os.path.join(target_path, target_name)\n current_app.logger.info('开始从 {} 下载到 {}'.format(apk_download_url,\n download_apk_name))\n response = requests.get(url=apk_download_url, verify=False)\n with open(download_apk_name, 'wb') as f:\n f.write(response.content)\n time.sleep(0.5)\n if not os.path.exists(download_apk_name):\n current_app.logger.error('{} 下载失败!'.format(apk_download_url))\n return 102, '下载失败'\n current_app.logger.info('下载成功,保存地址 {}'.format(download_apk_name))\n current_app.logger.info('开始分析')\n package_info_re = re.compile(\n \"package: name='(.*)' versionCode='(.*)' versionName='(.*?)'.*\"\n , re.I)\n label_icon_re = re.compile(\"application: label='(.+)'.*icon='(.+)'\"\n , re.I)\n launchable_activity_re = re.compile(\n \"launchable-activity: name='(.+)'.*label.*\", re.I)\n apk_info = {}\n cmd = '/usr/local/bin/aapt dump badging {}'.format(\n download_apk_name)\n command_process = subprocess.Popen(cmd, shell=True, stdout=\n subprocess.PIPE, stderr=subprocess.STDOUT)\n infos = command_process.stdout.readlines()\n for info in infos:\n info = info.decode('utf-8')\n if info.startswith('package:'):\n temp = package_info_re.search(info)\n apk_info['package_name'] = temp.group(1)\n apk_info['version_code'] = temp.group(2) or 0\n apk_info['version_name'] = temp.group(3)\n elif info.startswith('application:'):\n temp = label_icon_re.search(info)\n apk_info['label'] = temp.group(1)\n apk_info['icon'] = temp.group(2)\n elif info.startswith('launchable-activity:'):\n temp = launchable_activity_re.search(info)\n apk_info['default_activity'] = temp.group(1)\n try:\n size = round(os.path.getsize(download_apk_name) / float(\n 1024 * 1024), 2)\n apk_info['size'] = str(size)\n zip = zipfile.ZipFile(download_apk_name)\n icon_binary = zip.read(apk_info['icon'])\n time_now = datetime.now().strftime('%Y%m%d.%H%M%S')\n picture = f'monkey-{time_now}.png'\n dir_path = f'{TCLOUD_FILE_TEMP_PATH}/monkey'\n if not os.path.exists(TCLOUD_FILE_TEMP_PATH):\n os.mkdir(TCLOUD_FILE_TEMP_PATH)\n if not os.path.exists(dir_path):\n os.mkdir(dir_path)\n with open(f'{dir_path}/{picture}', 'wb') as f:\n f.write(icon_binary)\n apk_info['icon'] = oss_upload_monkey_package_picture(dir_path,\n picture)\n except Exception as e:\n current_app.logger.warning(e)\n current_app.logger.warning(traceback.format_exc())\n current_app.logger.info(apk_info)\n if type == 1:\n pass\n elif type == 2:\n pass\n return apk_info\n except Exception as e:\n current_app.logger.error(e)\n current_app.logger.error(traceback.format_exc())\n return {}\n",
"step-5": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport base64\nimport json\nimport os\nimport re\nimport subprocess\nimport time\nimport traceback\nimport zipfile\nfrom datetime import datetime\n\nimport requests\nfrom flask import request, current_app\n\nfrom library.oss import oss_upload_monkey_package_picture\nfrom public_config import TCLOUD_FILE_TEMP_PATH\n\n\nclass ToolBusiness(object):\n\n @classmethod\n def get_tool_ip(cls):\n ip = request.args.get('ip')\n\n url = 'http://api.map.baidu.com/location/ip'\n params = {\"ip\": ip, \"ak\": 'kqCYLKt8Uz9VnvHBXA7uOI51FIrei0OM'}\n ret = requests.get(url=url, params=params)\n ret = json.loads(ret.content)\n\n if ret and 'status' in ret and ret['status'] == 0 and 'content' in ret and 'address' in ret:\n return ret['status'], ret['content'], ret['address'], 'ok'\n\n return 101, '', '', '获取失败'\n\n @classmethod\n def apk_analysis(cls, apk_download_url, type=1):\n try:\n # type 1 : not save , 2: save to db\n target_path = \"/tmp/packages/\"\n if not os.path.exists(target_path):\n os.mkdir(target_path)\n\n date_time_now = datetime.now().strftime('%Y%m%d-%H.%M.%S')\n target_name = '{}.apk'.format(date_time_now)\n\n download_apk_name = os.path.join(target_path, target_name)\n\n current_app.logger.info('开始从 {} 下载到 {}'.format(apk_download_url, download_apk_name))\n\n response = requests.get(url=apk_download_url, verify=False)\n\n with open(download_apk_name, 'wb') as f:\n f.write(response.content)\n\n time.sleep(0.5)\n # 下载失败\n if not os.path.exists(download_apk_name):\n current_app.logger.error('{} 下载失败!'.format(apk_download_url))\n return 102, \"下载失败\"\n\n current_app.logger.info('下载成功,保存地址 {}'.format(download_apk_name))\n current_app.logger.info('开始分析')\n\n package_info_re = re.compile(r\"package: name='(.*)' versionCode='(.*)' versionName='(.*?)'.*\", re.I)\n label_icon_re = re.compile(r\"application: label='(.+)'.*icon='(.+)'\", re.I)\n launchable_activity_re = re.compile(r\"launchable-activity: name='(.+)'.*label.*\", re.I)\n\n apk_info = {}\n\n cmd = '/usr/local/bin/aapt dump badging {}'.format(download_apk_name)\n\n command_process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n\n infos = command_process.stdout.readlines()\n\n for info in infos:\n info = info.decode('utf-8')\n if info.startswith('package:'):\n temp = package_info_re.search(info)\n apk_info['package_name'] = temp.group(1)\n apk_info['version_code'] = temp.group(2) or 0\n apk_info['version_name'] = temp.group(3)\n elif info.startswith('application:'):\n temp = label_icon_re.search(info)\n apk_info['label'] = temp.group(1)\n apk_info['icon'] = temp.group(2)\n elif info.startswith('launchable-activity:'):\n temp = launchable_activity_re.search(info)\n apk_info['default_activity'] = temp.group(1)\n\n try:\n size = round(os.path.getsize(download_apk_name) / float(1024 * 1024), 2)\n apk_info['size'] = str(size)\n zip = zipfile.ZipFile(download_apk_name)\n icon_binary = zip.read(apk_info['icon'])\n time_now = datetime.now().strftime('%Y%m%d.%H%M%S')\n picture = f'monkey-{time_now}.png'\n dir_path = f'{TCLOUD_FILE_TEMP_PATH}/monkey'\n\n if not os.path.exists(TCLOUD_FILE_TEMP_PATH):\n os.mkdir(TCLOUD_FILE_TEMP_PATH)\n\n if not os.path.exists(dir_path):\n os.mkdir(dir_path)\n with open(f'{dir_path}/{picture}', 'wb') as f:\n f.write(icon_binary)\n\n apk_info['icon'] = oss_upload_monkey_package_picture(dir_path, picture)\n except Exception as e:\n current_app.logger.warning(e)\n current_app.logger.warning(traceback.format_exc())\n\n current_app.logger.info(apk_info)\n\n if type == 1:\n pass\n elif type == 2:\n pass\n\n return apk_info\n except Exception as e:\n current_app.logger.error(e)\n current_app.logger.error(traceback.format_exc())\n return {}\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
import inspect
import re
import openquake.hazardlib.source as oqsrc
# List of valid attributes for an area source
AREAS_ATTRIBUTES = set(['source_id',
'name',
'tectonic_region_type',
'mfd',
'rupture_mesh_spacing',
'magnitude_scaling_relationship',
'rupture_aspect_ratio',
'temporal_occurrence_model',
'upper_seismogenic_depth',
'lower_seismogenic_depth',
'nodal_plane_distribution',
'hypocenter_distribution',
'polygon',
'area_discretization'])
AREAS_ATTRIBUTES |= set(['gr_aval',
'gr_bval',
'source_type'])
# List of valid attributes for a simple source
SIMPLE_FAULT_ATTRIBUTES = set(['source_id',
'name',
'tectonic_region_type',
'mfd',
'rupture_mesh_spacing',
'magnitude_scaling_relationship',
'rupture_aspect_ratio',
'temporal_occurrence_model',
'upper_seismogenic_depth',
'lower_seismogenic_depth',
'fault_trace',
'dip',
'rake',
'hypo_list',
'sliprate'])
SIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval',
'gr_bval',
'dip',
'rake',
'hypo_list',
'slip_list'])
SIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval',
'gr_bval',
'source_type'])
# This adds support for shapefiles created by the OpenQuake-engine
SIMPLE_FAULT_ATTRIBUTES |= set([''])
# Create the set of valid source types
SOURCE_TYPES = set()
for name, obj in inspect.getmembers(oqsrc):
if inspect.isclass(obj):
if not re.search('Rupture', name):
SOURCE_TYPES.add(name)
class OQtSource(object):
"""
A container for information necessary to build and/or characterise an
earthquake source
:parameter str source_id:
The ID of the source
:parameter str source_type:
Source type i.e. Object name amongst the ones admitted in the
OpenQuake Hazardlib.
"""
def __init__(self, *args, **kwargs):
# Checks
if len(args):
self.source_id = args[0]
if len(args) > 1:
self.source_type = args[1]
if len(kwargs):
self.__dict__.update(kwargs)
# Check mandatory attributes: ID
if 'source_id' not in self.__dict__:
raise ValueError('Source must have an ID')
elif not isinstance(self.source_id, str):
raise ValueError('ID must be a string')
# Check mandatory fields: SOURCE TYPE
if 'source_type' not in self.__dict__:
raise ValueError('Source must have a type')
if self.source_type not in SOURCE_TYPES:
raise ValueError('Unrecognized source type: %s' % self.source_type)
if 'source_type' in self.__dict__:
attribute_set = AREAS_ATTRIBUTES
elif 'source_type' in self.__dict__:
attribute_set = SIMPLE_FAULT_ATTRIBUTES
else:
raise ValueError('Unsupported source type')
# Check attributes
for key in self.__dict__:
if key not in attribute_set:
print ('Attribute set', attribute_set)
msg = 'Parameter %s not compatible with this source' % (key)
raise ValueError(msg)
def get_info(self):
for key in self.__dict__:
print ('%30s:' % (key), getattr(self, key))
|
normal
|
{
"blob_id": "8adf8cfc72d5af955bf7509d3573a9bcc7c0845e",
"index": 7537,
"step-1": "<mask token>\n\n\nclass OQtSource(object):\n <mask token>\n\n def __init__(self, *args, **kwargs):\n if len(args):\n self.source_id = args[0]\n if len(args) > 1:\n self.source_type = args[1]\n if len(kwargs):\n self.__dict__.update(kwargs)\n if 'source_id' not in self.__dict__:\n raise ValueError('Source must have an ID')\n elif not isinstance(self.source_id, str):\n raise ValueError('ID must be a string')\n if 'source_type' not in self.__dict__:\n raise ValueError('Source must have a type')\n if self.source_type not in SOURCE_TYPES:\n raise ValueError('Unrecognized source type: %s' % self.source_type)\n if 'source_type' in self.__dict__:\n attribute_set = AREAS_ATTRIBUTES\n elif 'source_type' in self.__dict__:\n attribute_set = SIMPLE_FAULT_ATTRIBUTES\n else:\n raise ValueError('Unsupported source type')\n for key in self.__dict__:\n if key not in attribute_set:\n print('Attribute set', attribute_set)\n msg = 'Parameter %s not compatible with this source' % key\n raise ValueError(msg)\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass OQtSource(object):\n \"\"\"\n A container for information necessary to build and/or characterise an\n earthquake source\n\n :parameter str source_id:\n The ID of the source\n :parameter str source_type:\n Source type i.e. Object name amongst the ones admitted in the\n OpenQuake Hazardlib.\n\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n if len(args):\n self.source_id = args[0]\n if len(args) > 1:\n self.source_type = args[1]\n if len(kwargs):\n self.__dict__.update(kwargs)\n if 'source_id' not in self.__dict__:\n raise ValueError('Source must have an ID')\n elif not isinstance(self.source_id, str):\n raise ValueError('ID must be a string')\n if 'source_type' not in self.__dict__:\n raise ValueError('Source must have a type')\n if self.source_type not in SOURCE_TYPES:\n raise ValueError('Unrecognized source type: %s' % self.source_type)\n if 'source_type' in self.__dict__:\n attribute_set = AREAS_ATTRIBUTES\n elif 'source_type' in self.__dict__:\n attribute_set = SIMPLE_FAULT_ATTRIBUTES\n else:\n raise ValueError('Unsupported source type')\n for key in self.__dict__:\n if key not in attribute_set:\n print('Attribute set', attribute_set)\n msg = 'Parameter %s not compatible with this source' % key\n raise ValueError(msg)\n\n def get_info(self):\n for key in self.__dict__:\n print('%30s:' % key, getattr(self, key))\n",
"step-3": "<mask token>\nAREAS_ATTRIBUTES |= set(['gr_aval', 'gr_bval', 'source_type'])\n<mask token>\nSIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval', 'gr_bval', 'dip', 'rake',\n 'hypo_list', 'slip_list'])\nSIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval', 'gr_bval', 'source_type'])\nSIMPLE_FAULT_ATTRIBUTES |= set([''])\n<mask token>\nfor name, obj in inspect.getmembers(oqsrc):\n if inspect.isclass(obj):\n if not re.search('Rupture', name):\n SOURCE_TYPES.add(name)\n\n\nclass OQtSource(object):\n \"\"\"\n A container for information necessary to build and/or characterise an\n earthquake source\n\n :parameter str source_id:\n The ID of the source\n :parameter str source_type:\n Source type i.e. Object name amongst the ones admitted in the\n OpenQuake Hazardlib.\n\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n if len(args):\n self.source_id = args[0]\n if len(args) > 1:\n self.source_type = args[1]\n if len(kwargs):\n self.__dict__.update(kwargs)\n if 'source_id' not in self.__dict__:\n raise ValueError('Source must have an ID')\n elif not isinstance(self.source_id, str):\n raise ValueError('ID must be a string')\n if 'source_type' not in self.__dict__:\n raise ValueError('Source must have a type')\n if self.source_type not in SOURCE_TYPES:\n raise ValueError('Unrecognized source type: %s' % self.source_type)\n if 'source_type' in self.__dict__:\n attribute_set = AREAS_ATTRIBUTES\n elif 'source_type' in self.__dict__:\n attribute_set = SIMPLE_FAULT_ATTRIBUTES\n else:\n raise ValueError('Unsupported source type')\n for key in self.__dict__:\n if key not in attribute_set:\n print('Attribute set', attribute_set)\n msg = 'Parameter %s not compatible with this source' % key\n raise ValueError(msg)\n\n def get_info(self):\n for key in self.__dict__:\n print('%30s:' % key, getattr(self, key))\n",
"step-4": "import inspect\nimport re\nimport openquake.hazardlib.source as oqsrc\nAREAS_ATTRIBUTES = set(['source_id', 'name', 'tectonic_region_type', 'mfd',\n 'rupture_mesh_spacing', 'magnitude_scaling_relationship',\n 'rupture_aspect_ratio', 'temporal_occurrence_model',\n 'upper_seismogenic_depth', 'lower_seismogenic_depth',\n 'nodal_plane_distribution', 'hypocenter_distribution', 'polygon',\n 'area_discretization'])\nAREAS_ATTRIBUTES |= set(['gr_aval', 'gr_bval', 'source_type'])\nSIMPLE_FAULT_ATTRIBUTES = set(['source_id', 'name', 'tectonic_region_type',\n 'mfd', 'rupture_mesh_spacing', 'magnitude_scaling_relationship',\n 'rupture_aspect_ratio', 'temporal_occurrence_model',\n 'upper_seismogenic_depth', 'lower_seismogenic_depth', 'fault_trace',\n 'dip', 'rake', 'hypo_list', 'sliprate'])\nSIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval', 'gr_bval', 'dip', 'rake',\n 'hypo_list', 'slip_list'])\nSIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval', 'gr_bval', 'source_type'])\nSIMPLE_FAULT_ATTRIBUTES |= set([''])\nSOURCE_TYPES = set()\nfor name, obj in inspect.getmembers(oqsrc):\n if inspect.isclass(obj):\n if not re.search('Rupture', name):\n SOURCE_TYPES.add(name)\n\n\nclass OQtSource(object):\n \"\"\"\n A container for information necessary to build and/or characterise an\n earthquake source\n\n :parameter str source_id:\n The ID of the source\n :parameter str source_type:\n Source type i.e. Object name amongst the ones admitted in the\n OpenQuake Hazardlib.\n\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n if len(args):\n self.source_id = args[0]\n if len(args) > 1:\n self.source_type = args[1]\n if len(kwargs):\n self.__dict__.update(kwargs)\n if 'source_id' not in self.__dict__:\n raise ValueError('Source must have an ID')\n elif not isinstance(self.source_id, str):\n raise ValueError('ID must be a string')\n if 'source_type' not in self.__dict__:\n raise ValueError('Source must have a type')\n if self.source_type not in SOURCE_TYPES:\n raise ValueError('Unrecognized source type: %s' % self.source_type)\n if 'source_type' in self.__dict__:\n attribute_set = AREAS_ATTRIBUTES\n elif 'source_type' in self.__dict__:\n attribute_set = SIMPLE_FAULT_ATTRIBUTES\n else:\n raise ValueError('Unsupported source type')\n for key in self.__dict__:\n if key not in attribute_set:\n print('Attribute set', attribute_set)\n msg = 'Parameter %s not compatible with this source' % key\n raise ValueError(msg)\n\n def get_info(self):\n for key in self.__dict__:\n print('%30s:' % key, getattr(self, key))\n",
"step-5": "import inspect\nimport re\nimport openquake.hazardlib.source as oqsrc\n\n# List of valid attributes for an area source\nAREAS_ATTRIBUTES = set(['source_id', \n\t\t\t'name', \n\t\t\t'tectonic_region_type', \n\t\t\t'mfd',\n 'rupture_mesh_spacing',\n 'magnitude_scaling_relationship',\n 'rupture_aspect_ratio',\n 'temporal_occurrence_model',\n 'upper_seismogenic_depth',\n 'lower_seismogenic_depth',\n 'nodal_plane_distribution',\n 'hypocenter_distribution',\n 'polygon',\n 'area_discretization'])\n \nAREAS_ATTRIBUTES |= set(['gr_aval', \n\t\t\t 'gr_bval', \n\t\t\t 'source_type'])\n\n# List of valid attributes for a simple source\nSIMPLE_FAULT_ATTRIBUTES = set(['source_id',\n 'name',\n 'tectonic_region_type',\n 'mfd',\n 'rupture_mesh_spacing',\n 'magnitude_scaling_relationship',\n 'rupture_aspect_ratio',\n 'temporal_occurrence_model',\n 'upper_seismogenic_depth',\n 'lower_seismogenic_depth',\n 'fault_trace',\n 'dip', \n 'rake', \n 'hypo_list', \n 'sliprate'])\n \nSIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval', \n 'gr_bval', \n 'dip',\n 'rake',\n 'hypo_list',\n 'slip_list'])\n\nSIMPLE_FAULT_ATTRIBUTES |= set(['gr_aval',\n 'gr_bval',\n 'source_type'])\n\n# This adds support for shapefiles created by the OpenQuake-engine\nSIMPLE_FAULT_ATTRIBUTES |= set([''])\n\n# Create the set of valid source types\nSOURCE_TYPES = set()\nfor name, obj in inspect.getmembers(oqsrc):\n if inspect.isclass(obj):\n if not re.search('Rupture', name):\n SOURCE_TYPES.add(name)\n\nclass OQtSource(object):\n \"\"\"\n A container for information necessary to build and/or characterise an\n earthquake source\n\n :parameter str source_id:\n The ID of the source\n :parameter str source_type:\n Source type i.e. Object name amongst the ones admitted in the\n OpenQuake Hazardlib.\n\n \"\"\"\n def __init__(self, *args, **kwargs):\n # Checks\n if len(args):\n self.source_id = args[0]\n if len(args) > 1:\n self.source_type = args[1]\n if len(kwargs):\n self.__dict__.update(kwargs)\n # Check mandatory attributes: ID\n if 'source_id' not in self.__dict__:\n raise ValueError('Source must have an ID')\n elif not isinstance(self.source_id, str):\n raise ValueError('ID must be a string')\n # Check mandatory fields: SOURCE TYPE\n if 'source_type' not in self.__dict__:\n raise ValueError('Source must have a type')\n if self.source_type not in SOURCE_TYPES:\n raise ValueError('Unrecognized source type: %s' % self.source_type)\n if 'source_type' in self.__dict__:\n attribute_set = AREAS_ATTRIBUTES\n elif 'source_type' in self.__dict__:\n attribute_set = SIMPLE_FAULT_ATTRIBUTES\n else:\n raise ValueError('Unsupported source type')\n # Check attributes\n for key in self.__dict__:\n if key not in attribute_set:\n print ('Attribute set', attribute_set)\n msg = 'Parameter %s not compatible with this source' % (key)\n raise ValueError(msg)\n\n def get_info(self):\n for key in self.__dict__:\n print ('%30s:' % (key), getattr(self, key))\n",
"step-ids": [
2,
4,
5,
7,
8
]
}
|
[
2,
4,
5,
7,
8
] |
from collections import Counter, defaultdict
from random import randrange
from copy import deepcopy
import sys
def election(votes, message=True, force_forward=False):
votes = deepcopy(votes)
N = len(votes)
for i in range(N):
obtained = Counter([v[-1] for v in votes if len(v)]).most_common()
M = len(obtained)
top = obtained[0]
if M == 1:
return top[0]
accum = [0]
for ob in obtained[::-1]:
accum.append(accum[-1] + ob[1])
accum = accum[:0:-1]
candidates = {top[0]}
for m in range(1,M):
if accum[m] < obtained[m-1][1]:
break
else:
candidates.add(obtained[m][0])
else:
m += 1
if message:
print('The {}-th vote: {}'.format(i+1, obtained))
if m == 1:
return top[0]
elif m >= M:
l = M-2
while l >= 0 and obtained[l][1] == obtained[-1][1]:
l -= 1
candidates = {obtained[i][0] for i in range(l+1)}
fighting = {obtained[i][0] for i in range(l+1,M)}
losers = set()
for f in fighting:
tmp_votes = deepcopy(votes)
tmp_candidates = candidates | {f}
for n in range(N):
while len(tmp_votes[n]) > 0 and not tmp_votes[n][-1] in tmp_candidates:
tmp_votes[n].pop()
tmp_result = election(tmp_votes, message=False)
if tmp_result != f and not (isinstance(tmp_result,list) and f in dict(tmp_result)):
losers.add(f)
candidates |= fighting
candidates -= losers
if losers:
if message:
print(' Candidates {} survived.'.format([ obtained[j][0] for j in range(m) if obtained[j][0] in candidates]))
else:
if message:
print(' All the candidates survived.')
if force_forward:
drop = obtained[randrange(l+1,M)][0]
candidates.discard(drop)
if message:
print(' Drop the candidate \'{}\'.'.format(drop))
elif message:
print(' Final winner was not determined.')
return obtained
elif message:
print(' Candidates {} survived.'.format([ obtained[j][0] for j in range(m)]))
for n in range(N):
while len(votes[n]) > 0 and not votes[n][-1] in candidates:
votes[n].pop()
if __name__ == '__main__':
args = sys.argv
if len(args) <= 1:
K = 0
else:
K = int(args[1])
votes = []
while True:
try:
votes.append(list(input().strip().upper()[::-1]))
except EOFError:
break
if K == 0:
winner = election(votes)
print('---')
if isinstance(winner, list):
print('The candidates \'{}\' are still surviving.'.format(winner))
else:
print('The candidate \'{}\' is the Final Winner !!!'.format(winner))
else:
win_times = defaultdict(int)
for _ in range(K):
win_times[election(votes, message=False, force_forward=True)] += 1
result = list(win_times.items())
if len(result) == 1:
winner = result[0][0]
print('The candidate \'{}\' is the Final Winner !!'.format(winner))
else:
print('Final winner was not determined.')
print('The winner distribution is: {}'.format(dict(win_times)))
|
normal
|
{
"blob_id": "05764d1cfd9573616fcd6b125280fddf2e5ce7ad",
"index": 3712,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef election(votes, message=True, force_forward=False):\n votes = deepcopy(votes)\n N = len(votes)\n for i in range(N):\n obtained = Counter([v[-1] for v in votes if len(v)]).most_common()\n M = len(obtained)\n top = obtained[0]\n if M == 1:\n return top[0]\n accum = [0]\n for ob in obtained[::-1]:\n accum.append(accum[-1] + ob[1])\n accum = accum[:0:-1]\n candidates = {top[0]}\n for m in range(1, M):\n if accum[m] < obtained[m - 1][1]:\n break\n else:\n candidates.add(obtained[m][0])\n else:\n m += 1\n if message:\n print('The {}-th vote: {}'.format(i + 1, obtained))\n if m == 1:\n return top[0]\n elif m >= M:\n l = M - 2\n while l >= 0 and obtained[l][1] == obtained[-1][1]:\n l -= 1\n candidates = {obtained[i][0] for i in range(l + 1)}\n fighting = {obtained[i][0] for i in range(l + 1, M)}\n losers = set()\n for f in fighting:\n tmp_votes = deepcopy(votes)\n tmp_candidates = candidates | {f}\n for n in range(N):\n while len(tmp_votes[n]) > 0 and not tmp_votes[n][-1\n ] in tmp_candidates:\n tmp_votes[n].pop()\n tmp_result = election(tmp_votes, message=False)\n if tmp_result != f and not (isinstance(tmp_result, list) and\n f in dict(tmp_result)):\n losers.add(f)\n candidates |= fighting\n candidates -= losers\n if losers:\n if message:\n print(' Candidates {} survived.'.format([obtained[j][0\n ] for j in range(m) if obtained[j][0] in candidates]))\n else:\n if message:\n print(' All the candidates survived.')\n if force_forward:\n drop = obtained[randrange(l + 1, M)][0]\n candidates.discard(drop)\n if message:\n print(\" Drop the candidate '{}'.\".format(drop))\n elif message:\n print(' Final winner was not determined.')\n return obtained\n elif message:\n print(' Candidates {} survived.'.format([obtained[j][0] for j in\n range(m)]))\n for n in range(N):\n while len(votes[n]) > 0 and not votes[n][-1] in candidates:\n votes[n].pop()\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef election(votes, message=True, force_forward=False):\n votes = deepcopy(votes)\n N = len(votes)\n for i in range(N):\n obtained = Counter([v[-1] for v in votes if len(v)]).most_common()\n M = len(obtained)\n top = obtained[0]\n if M == 1:\n return top[0]\n accum = [0]\n for ob in obtained[::-1]:\n accum.append(accum[-1] + ob[1])\n accum = accum[:0:-1]\n candidates = {top[0]}\n for m in range(1, M):\n if accum[m] < obtained[m - 1][1]:\n break\n else:\n candidates.add(obtained[m][0])\n else:\n m += 1\n if message:\n print('The {}-th vote: {}'.format(i + 1, obtained))\n if m == 1:\n return top[0]\n elif m >= M:\n l = M - 2\n while l >= 0 and obtained[l][1] == obtained[-1][1]:\n l -= 1\n candidates = {obtained[i][0] for i in range(l + 1)}\n fighting = {obtained[i][0] for i in range(l + 1, M)}\n losers = set()\n for f in fighting:\n tmp_votes = deepcopy(votes)\n tmp_candidates = candidates | {f}\n for n in range(N):\n while len(tmp_votes[n]) > 0 and not tmp_votes[n][-1\n ] in tmp_candidates:\n tmp_votes[n].pop()\n tmp_result = election(tmp_votes, message=False)\n if tmp_result != f and not (isinstance(tmp_result, list) and\n f in dict(tmp_result)):\n losers.add(f)\n candidates |= fighting\n candidates -= losers\n if losers:\n if message:\n print(' Candidates {} survived.'.format([obtained[j][0\n ] for j in range(m) if obtained[j][0] in candidates]))\n else:\n if message:\n print(' All the candidates survived.')\n if force_forward:\n drop = obtained[randrange(l + 1, M)][0]\n candidates.discard(drop)\n if message:\n print(\" Drop the candidate '{}'.\".format(drop))\n elif message:\n print(' Final winner was not determined.')\n return obtained\n elif message:\n print(' Candidates {} survived.'.format([obtained[j][0] for j in\n range(m)]))\n for n in range(N):\n while len(votes[n]) > 0 and not votes[n][-1] in candidates:\n votes[n].pop()\n\n\nif __name__ == '__main__':\n args = sys.argv\n if len(args) <= 1:\n K = 0\n else:\n K = int(args[1])\n votes = []\n while True:\n try:\n votes.append(list(input().strip().upper()[::-1]))\n except EOFError:\n break\n if K == 0:\n winner = election(votes)\n print('---')\n if isinstance(winner, list):\n print(\"The candidates '{}' are still surviving.\".format(winner))\n else:\n print(\"The candidate '{}' is the Final Winner !!!\".format(winner))\n else:\n win_times = defaultdict(int)\n for _ in range(K):\n win_times[election(votes, message=False, force_forward=True)] += 1\n result = list(win_times.items())\n if len(result) == 1:\n winner = result[0][0]\n print(\"The candidate '{}' is the Final Winner !!\".format(winner))\n else:\n print('Final winner was not determined.')\n print('The winner distribution is: {}'.format(dict(win_times)))\n",
"step-4": "from collections import Counter, defaultdict\nfrom random import randrange\nfrom copy import deepcopy\nimport sys\n\n\ndef election(votes, message=True, force_forward=False):\n votes = deepcopy(votes)\n N = len(votes)\n for i in range(N):\n obtained = Counter([v[-1] for v in votes if len(v)]).most_common()\n M = len(obtained)\n top = obtained[0]\n if M == 1:\n return top[0]\n accum = [0]\n for ob in obtained[::-1]:\n accum.append(accum[-1] + ob[1])\n accum = accum[:0:-1]\n candidates = {top[0]}\n for m in range(1, M):\n if accum[m] < obtained[m - 1][1]:\n break\n else:\n candidates.add(obtained[m][0])\n else:\n m += 1\n if message:\n print('The {}-th vote: {}'.format(i + 1, obtained))\n if m == 1:\n return top[0]\n elif m >= M:\n l = M - 2\n while l >= 0 and obtained[l][1] == obtained[-1][1]:\n l -= 1\n candidates = {obtained[i][0] for i in range(l + 1)}\n fighting = {obtained[i][0] for i in range(l + 1, M)}\n losers = set()\n for f in fighting:\n tmp_votes = deepcopy(votes)\n tmp_candidates = candidates | {f}\n for n in range(N):\n while len(tmp_votes[n]) > 0 and not tmp_votes[n][-1\n ] in tmp_candidates:\n tmp_votes[n].pop()\n tmp_result = election(tmp_votes, message=False)\n if tmp_result != f and not (isinstance(tmp_result, list) and\n f in dict(tmp_result)):\n losers.add(f)\n candidates |= fighting\n candidates -= losers\n if losers:\n if message:\n print(' Candidates {} survived.'.format([obtained[j][0\n ] for j in range(m) if obtained[j][0] in candidates]))\n else:\n if message:\n print(' All the candidates survived.')\n if force_forward:\n drop = obtained[randrange(l + 1, M)][0]\n candidates.discard(drop)\n if message:\n print(\" Drop the candidate '{}'.\".format(drop))\n elif message:\n print(' Final winner was not determined.')\n return obtained\n elif message:\n print(' Candidates {} survived.'.format([obtained[j][0] for j in\n range(m)]))\n for n in range(N):\n while len(votes[n]) > 0 and not votes[n][-1] in candidates:\n votes[n].pop()\n\n\nif __name__ == '__main__':\n args = sys.argv\n if len(args) <= 1:\n K = 0\n else:\n K = int(args[1])\n votes = []\n while True:\n try:\n votes.append(list(input().strip().upper()[::-1]))\n except EOFError:\n break\n if K == 0:\n winner = election(votes)\n print('---')\n if isinstance(winner, list):\n print(\"The candidates '{}' are still surviving.\".format(winner))\n else:\n print(\"The candidate '{}' is the Final Winner !!!\".format(winner))\n else:\n win_times = defaultdict(int)\n for _ in range(K):\n win_times[election(votes, message=False, force_forward=True)] += 1\n result = list(win_times.items())\n if len(result) == 1:\n winner = result[0][0]\n print(\"The candidate '{}' is the Final Winner !!\".format(winner))\n else:\n print('Final winner was not determined.')\n print('The winner distribution is: {}'.format(dict(win_times)))\n",
"step-5": "from collections import Counter, defaultdict\nfrom random import randrange\nfrom copy import deepcopy\nimport sys\n\ndef election(votes, message=True, force_forward=False):\n votes = deepcopy(votes)\n N = len(votes)\n for i in range(N):\n obtained = Counter([v[-1] for v in votes if len(v)]).most_common()\n M = len(obtained)\n top = obtained[0]\n if M == 1:\n return top[0]\n\n accum = [0]\n for ob in obtained[::-1]:\n accum.append(accum[-1] + ob[1])\n accum = accum[:0:-1]\n candidates = {top[0]}\n for m in range(1,M):\n if accum[m] < obtained[m-1][1]:\n break\n else:\n candidates.add(obtained[m][0])\n else:\n m += 1\n\n if message:\n print('The {}-th vote: {}'.format(i+1, obtained))\n if m == 1:\n return top[0]\n elif m >= M:\n l = M-2\n while l >= 0 and obtained[l][1] == obtained[-1][1]:\n l -= 1\n candidates = {obtained[i][0] for i in range(l+1)}\n fighting = {obtained[i][0] for i in range(l+1,M)}\n losers = set()\n for f in fighting:\n tmp_votes = deepcopy(votes)\n tmp_candidates = candidates | {f}\n for n in range(N):\n while len(tmp_votes[n]) > 0 and not tmp_votes[n][-1] in tmp_candidates:\n tmp_votes[n].pop()\n tmp_result = election(tmp_votes, message=False)\n if tmp_result != f and not (isinstance(tmp_result,list) and f in dict(tmp_result)):\n losers.add(f)\n candidates |= fighting\n candidates -= losers\n if losers:\n if message:\n print(' Candidates {} survived.'.format([ obtained[j][0] for j in range(m) if obtained[j][0] in candidates])) \n else:\n if message:\n print(' All the candidates survived.')\n if force_forward:\n drop = obtained[randrange(l+1,M)][0]\n candidates.discard(drop)\n if message:\n print(' Drop the candidate \\'{}\\'.'.format(drop))\n elif message:\n print(' Final winner was not determined.')\n return obtained\n elif message:\n print(' Candidates {} survived.'.format([ obtained[j][0] for j in range(m)]))\n \n for n in range(N):\n while len(votes[n]) > 0 and not votes[n][-1] in candidates:\n votes[n].pop()\n\nif __name__ == '__main__':\n args = sys.argv\n if len(args) <= 1:\n K = 0\n else:\n K = int(args[1])\n\n votes = []\n while True:\n try:\n votes.append(list(input().strip().upper()[::-1]))\n except EOFError:\n break\n\n if K == 0:\n winner = election(votes)\n print('---')\n if isinstance(winner, list):\n print('The candidates \\'{}\\' are still surviving.'.format(winner))\n else:\n print('The candidate \\'{}\\' is the Final Winner !!!'.format(winner))\n else:\n win_times = defaultdict(int)\n for _ in range(K):\n win_times[election(votes, message=False, force_forward=True)] += 1\n result = list(win_times.items())\n if len(result) == 1:\n winner = result[0][0]\n print('The candidate \\'{}\\' is the Final Winner !!'.format(winner)) \n else:\n print('Final winner was not determined.')\n print('The winner distribution is: {}'.format(dict(win_times)))\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import flask
import flask_sqlalchemy
app = flask.Flask(__name__)
app.config.from_pyfile('settings.py')
db = flask_sqlalchemy.SQLAlchemy(app)
|
normal
|
{
"blob_id": "2ed0ae48e8fec2c92effcbb3e495a1a9f4636c27",
"index": 6777,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.config.from_pyfile('settings.py')\n<mask token>\n",
"step-3": "<mask token>\napp = flask.Flask(__name__)\napp.config.from_pyfile('settings.py')\ndb = flask_sqlalchemy.SQLAlchemy(app)\n",
"step-4": "import flask\nimport flask_sqlalchemy\napp = flask.Flask(__name__)\napp.config.from_pyfile('settings.py')\ndb = flask_sqlalchemy.SQLAlchemy(app)\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
{
'variables': {
'node_shared_openssl%': 'true'
},
'targets': [
{
'target_name': 'keypair',
'sources': [
'secp256k1/keypair.cc'
],
'conditions': [
# For Windows, require either a 32-bit or 64-bit
# separately-compiled OpenSSL library.
# Currently set up to use with the following OpenSSL distro:
#
# http://slproweb.com/products/Win32OpenSSL.html
[
'OS=="win"',
{
'conditions':
[
[
'target_arch=="x64"',
{
'variables': {
'openssl_root%': 'C:/OpenSSL-Win64'
},
}, {
'variables': {
'openssl_root%': 'C:/OpenSSL-Win32'
}
}
]
],
'libraries': [
'-l<(openssl_root)/lib/libeay32.lib',
],
'include_dirs': [
'<(openssl_root)/include',
],
},
# Otherwise, if not Windows, link against the exposed OpenSSL
# in Node.
{
"conditions": [
['node_shared_openssl=="false"', {
# so when "node_shared_openssl" is "false", then OpenSSL has been
# bundled into the node executable. So we need to include the same
# header files that were used when building node.
'include_dirs': [
'<(node_root_dir)/deps/openssl/openssl/include'
],
"conditions" : [
["target_arch=='ia32'", {
"include_dirs": [ "<(node_root_dir)/deps/openssl/config/piii" ]
}],
["target_arch=='x64'", {
"include_dirs": [ "<(node_root_dir)/deps/openssl/config/k8" ]
}],
["target_arch=='arm'", {
"include_dirs": [ "<(node_root_dir)/deps/openssl/config/arm" ]
}]
]
}]
]}
]]
}
]
}
|
normal
|
{
"blob_id": "e7b30353fd25beb9d5cdeee688e4ffa6955d4221",
"index": 8437,
"step-1": "<mask token>\n",
"step-2": "{'variables': {'node_shared_openssl%': 'true'}, 'targets': [{'target_name':\n 'keypair', 'sources': ['secp256k1/keypair.cc'], 'conditions': [[\n 'OS==\"win\"', {'conditions': [['target_arch==\"x64\"', {'variables': {\n 'openssl_root%': 'C:/OpenSSL-Win64'}}, {'variables': {'openssl_root%':\n 'C:/OpenSSL-Win32'}}]], 'libraries': [\n '-l<(openssl_root)/lib/libeay32.lib'], 'include_dirs': [\n '<(openssl_root)/include']}, {'conditions': [[\n 'node_shared_openssl==\"false\"', {'include_dirs': [\n '<(node_root_dir)/deps/openssl/openssl/include'], 'conditions': [[\n \"target_arch=='ia32'\", {'include_dirs': [\n '<(node_root_dir)/deps/openssl/config/piii']}], [\"target_arch=='x64'\",\n {'include_dirs': ['<(node_root_dir)/deps/openssl/config/k8']}], [\n \"target_arch=='arm'\", {'include_dirs': [\n '<(node_root_dir)/deps/openssl/config/arm']}]]}]]}]]}]}\n",
"step-3": "{\n 'variables': {\n 'node_shared_openssl%': 'true'\n },\n 'targets': [\n {\n 'target_name': 'keypair',\n 'sources': [\n 'secp256k1/keypair.cc'\n ],\n 'conditions': [\n # For Windows, require either a 32-bit or 64-bit\n # separately-compiled OpenSSL library.\n\t# Currently set up to use with the following OpenSSL distro:\n\t#\n\t# http://slproweb.com/products/Win32OpenSSL.html\n [\n\t 'OS==\"win\"', \n\t {\n 'conditions':\n\t [\n [\n\t 'target_arch==\"x64\"',\n\t {\n\t 'variables': {\n 'openssl_root%': 'C:/OpenSSL-Win64'\n },\n }, {\n 'variables': {\n 'openssl_root%': 'C:/OpenSSL-Win32'\n }\n\t\t}\n\t ]\n ],\n 'libraries': [ \n '-l<(openssl_root)/lib/libeay32.lib',\n ],\n 'include_dirs': [\n '<(openssl_root)/include',\n ],\n },\n\n\n # Otherwise, if not Windows, link against the exposed OpenSSL\n\t # in Node.\n {\n \"conditions\": [\n ['node_shared_openssl==\"false\"', {\n # so when \"node_shared_openssl\" is \"false\", then OpenSSL has been\n # bundled into the node executable. So we need to include the same\n # header files that were used when building node.\n 'include_dirs': [\n '<(node_root_dir)/deps/openssl/openssl/include'\n ],\n \"conditions\" : [\n [\"target_arch=='ia32'\", {\n \"include_dirs\": [ \"<(node_root_dir)/deps/openssl/config/piii\" ]\n }],\n [\"target_arch=='x64'\", {\n \"include_dirs\": [ \"<(node_root_dir)/deps/openssl/config/k8\" ]\n }],\n [\"target_arch=='arm'\", {\n \"include_dirs\": [ \"<(node_root_dir)/deps/openssl/config/arm\" ]\n }]\n ]\n }]\n ]}\n ]]\n }\n ]\n}\n\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
#case1
print("My name is Jia-Chi. \nI have an older sister. \nI prefer Coke.\nMy favorite song is \"Amazing Grace\"")
#case2
print('''Liang, Jia-Chi
1
Coke
Amazing Grace''')
|
normal
|
{
"blob_id": "55986f6c2dafe650704660142cf85640e763b26d",
"index": 3291,
"step-1": "<mask token>\n",
"step-2": "print(\n \"\"\"My name is Jia-Chi. \nI have an older sister. \nI prefer Coke.\nMy favorite song is \"Amazing Grace\\\"\"\"\"\n )\nprint(\"\"\"Liang, Jia-Chi\n1\nCoke\nAmazing Grace\"\"\")\n",
"step-3": "#case1\nprint(\"My name is Jia-Chi. \\nI have an older sister. \\nI prefer Coke.\\nMy favorite song is \\\"Amazing Grace\\\"\")\n#case2\nprint('''Liang, Jia-Chi\n1\nCoke\nAmazing Grace''')\n\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
<|reserved_special_token_0|>
def write(output_filename, content):
with open(output_filename, 'w') as outfile:
outfile.write(content)
def main(argv):
"""
WebPerf Core Carbon Percentiles
Usage:
* run webperf-core test on all websites you want to use for your percentiles (with json as output file)
* run this file against your output file, for example like this: carbon-rating.py -i data\\carbon-references-2022.json -o tests\\energy_efficiency_carbon_percentiles.py
Options and arguments:
-h/--help : Help information on how to use script
-i/--input <file path> : input file path (.json)
-o/--output <file path> : output file path (.py)
"""
output_filename = ''
input_filename = ''
langCode = 'en'
language = False
language = gettext.translation('webperf-core', localedir='locales',
languages=[langCode])
language.install()
_ = language.gettext
try:
opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']
)
except getopt.GetoptError:
print(main.__doc__)
sys.exit(2)
if opts.__len__() == 0:
print(main.__doc__)
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
print(main.__doc__)
sys.exit(2)
elif opt in ('-i', '--input'):
input_filename = arg
file_ending = ''
file_long_ending = ''
if len(input_filename) > 4:
file_ending = input_filename[-4:].lower()
if len(input_filename) > 7:
file_long_ending = input_filename[-7:].lower()
if file_long_ending == '.sqlite':
from engines.sqlite import read_sites, add_site, delete_site
elif file_ending == '.csv':
from engines.csv import read_sites, add_site, delete_site
elif file_ending == '.xml':
from engines.sitemap import read_sites, add_site, delete_site
else:
from engines.json import read_tests, read_sites, add_site, delete_site
pass
elif opt in ('-o', '--output'):
output_filename = arg
pass
tests = read_tests(input_filename, 0, -1)
generated_date = False
co2s = list()
for test in tests:
if not generated_date:
generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]
).strftime('%Y-%m-%d')
str_data = test[FIELD_INDEX_DATA].replace("'", '"')
data = json.loads(str_data)
print(str_data)
co2s.append(data['co2'])
if not generated_date:
generated_date = datetime.today().strftime('%Y-%m-%d')
output_content = (
'# This array was last generated with carbon-rating.py on {0}\n'.
format(generated_date))
output_content += 'def get_generated_date():\n'
output_content += "\treturn '{0}'\n".format(generated_date)
output_content += '\n'
output_content += 'def get_percentiles():\n'
output_content += '\treturn [\n'
co2s_sorted = sorted(co2s)
intervals = list()
index = 1
while index <= 100:
percentile = getPercentile(co2s_sorted, index)
intervals.append(percentile)
position = index - 1
if index < 100:
if position % 10 == 0 and position != 0:
output_content += '\t\t# {0} percentile\n'.format(position)
output_content += '\t\t{0},\n'.format(percentile)
else:
output_content += '\t\t{0}\n'.format(percentile)
index += 1
output_content += '\t]'
print(output_content)
if len(output_filename) > 0:
write(output_filename, output_content)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def getPercentile(arr, percentile):
percentile = min(100, max(0, percentile))
index = percentile / 100 * (len(arr) - 1)
fractionPart = index - math.floor(index)
intPart = math.floor(index)
percentile = float(arr[intPart])
if fractionPart > 0:
percentile += fractionPart * (float(arr[intPart + 1]) - float(arr[
intPart]))
else:
percentile += 0
return percentile
def write(output_filename, content):
with open(output_filename, 'w') as outfile:
outfile.write(content)
def main(argv):
"""
WebPerf Core Carbon Percentiles
Usage:
* run webperf-core test on all websites you want to use for your percentiles (with json as output file)
* run this file against your output file, for example like this: carbon-rating.py -i data\\carbon-references-2022.json -o tests\\energy_efficiency_carbon_percentiles.py
Options and arguments:
-h/--help : Help information on how to use script
-i/--input <file path> : input file path (.json)
-o/--output <file path> : output file path (.py)
"""
output_filename = ''
input_filename = ''
langCode = 'en'
language = False
language = gettext.translation('webperf-core', localedir='locales',
languages=[langCode])
language.install()
_ = language.gettext
try:
opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']
)
except getopt.GetoptError:
print(main.__doc__)
sys.exit(2)
if opts.__len__() == 0:
print(main.__doc__)
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
print(main.__doc__)
sys.exit(2)
elif opt in ('-i', '--input'):
input_filename = arg
file_ending = ''
file_long_ending = ''
if len(input_filename) > 4:
file_ending = input_filename[-4:].lower()
if len(input_filename) > 7:
file_long_ending = input_filename[-7:].lower()
if file_long_ending == '.sqlite':
from engines.sqlite import read_sites, add_site, delete_site
elif file_ending == '.csv':
from engines.csv import read_sites, add_site, delete_site
elif file_ending == '.xml':
from engines.sitemap import read_sites, add_site, delete_site
else:
from engines.json import read_tests, read_sites, add_site, delete_site
pass
elif opt in ('-o', '--output'):
output_filename = arg
pass
tests = read_tests(input_filename, 0, -1)
generated_date = False
co2s = list()
for test in tests:
if not generated_date:
generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]
).strftime('%Y-%m-%d')
str_data = test[FIELD_INDEX_DATA].replace("'", '"')
data = json.loads(str_data)
print(str_data)
co2s.append(data['co2'])
if not generated_date:
generated_date = datetime.today().strftime('%Y-%m-%d')
output_content = (
'# This array was last generated with carbon-rating.py on {0}\n'.
format(generated_date))
output_content += 'def get_generated_date():\n'
output_content += "\treturn '{0}'\n".format(generated_date)
output_content += '\n'
output_content += 'def get_percentiles():\n'
output_content += '\treturn [\n'
co2s_sorted = sorted(co2s)
intervals = list()
index = 1
while index <= 100:
percentile = getPercentile(co2s_sorted, index)
intervals.append(percentile)
position = index - 1
if index < 100:
if position % 10 == 0 and position != 0:
output_content += '\t\t# {0} percentile\n'.format(position)
output_content += '\t\t{0},\n'.format(percentile)
else:
output_content += '\t\t{0}\n'.format(percentile)
index += 1
output_content += '\t]'
print(output_content)
if len(output_filename) > 0:
write(output_filename, output_content)
<|reserved_special_token_0|>
if __name__ == '__main__':
main(sys.argv[1:])
<|reserved_special_token_1|>
<|reserved_special_token_0|>
FIELD_INDEX_DATE = 0
FIELD_INDEX_DATA = 1
def getPercentile(arr, percentile):
percentile = min(100, max(0, percentile))
index = percentile / 100 * (len(arr) - 1)
fractionPart = index - math.floor(index)
intPart = math.floor(index)
percentile = float(arr[intPart])
if fractionPart > 0:
percentile += fractionPart * (float(arr[intPart + 1]) - float(arr[
intPart]))
else:
percentile += 0
return percentile
def write(output_filename, content):
with open(output_filename, 'w') as outfile:
outfile.write(content)
def main(argv):
"""
WebPerf Core Carbon Percentiles
Usage:
* run webperf-core test on all websites you want to use for your percentiles (with json as output file)
* run this file against your output file, for example like this: carbon-rating.py -i data\\carbon-references-2022.json -o tests\\energy_efficiency_carbon_percentiles.py
Options and arguments:
-h/--help : Help information on how to use script
-i/--input <file path> : input file path (.json)
-o/--output <file path> : output file path (.py)
"""
output_filename = ''
input_filename = ''
langCode = 'en'
language = False
language = gettext.translation('webperf-core', localedir='locales',
languages=[langCode])
language.install()
_ = language.gettext
try:
opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']
)
except getopt.GetoptError:
print(main.__doc__)
sys.exit(2)
if opts.__len__() == 0:
print(main.__doc__)
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
print(main.__doc__)
sys.exit(2)
elif opt in ('-i', '--input'):
input_filename = arg
file_ending = ''
file_long_ending = ''
if len(input_filename) > 4:
file_ending = input_filename[-4:].lower()
if len(input_filename) > 7:
file_long_ending = input_filename[-7:].lower()
if file_long_ending == '.sqlite':
from engines.sqlite import read_sites, add_site, delete_site
elif file_ending == '.csv':
from engines.csv import read_sites, add_site, delete_site
elif file_ending == '.xml':
from engines.sitemap import read_sites, add_site, delete_site
else:
from engines.json import read_tests, read_sites, add_site, delete_site
pass
elif opt in ('-o', '--output'):
output_filename = arg
pass
tests = read_tests(input_filename, 0, -1)
generated_date = False
co2s = list()
for test in tests:
if not generated_date:
generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]
).strftime('%Y-%m-%d')
str_data = test[FIELD_INDEX_DATA].replace("'", '"')
data = json.loads(str_data)
print(str_data)
co2s.append(data['co2'])
if not generated_date:
generated_date = datetime.today().strftime('%Y-%m-%d')
output_content = (
'# This array was last generated with carbon-rating.py on {0}\n'.
format(generated_date))
output_content += 'def get_generated_date():\n'
output_content += "\treturn '{0}'\n".format(generated_date)
output_content += '\n'
output_content += 'def get_percentiles():\n'
output_content += '\treturn [\n'
co2s_sorted = sorted(co2s)
intervals = list()
index = 1
while index <= 100:
percentile = getPercentile(co2s_sorted, index)
intervals.append(percentile)
position = index - 1
if index < 100:
if position % 10 == 0 and position != 0:
output_content += '\t\t# {0} percentile\n'.format(position)
output_content += '\t\t{0},\n'.format(percentile)
else:
output_content += '\t\t{0}\n'.format(percentile)
index += 1
output_content += '\t]'
print(output_content)
if len(output_filename) > 0:
write(output_filename, output_content)
<|reserved_special_token_0|>
if __name__ == '__main__':
main(sys.argv[1:])
<|reserved_special_token_1|>
import sys
import getopt
import datetime
import gettext
import math
import datetime
import json
import gettext
from datetime import datetime
FIELD_INDEX_DATE = 0
FIELD_INDEX_DATA = 1
def getPercentile(arr, percentile):
percentile = min(100, max(0, percentile))
index = percentile / 100 * (len(arr) - 1)
fractionPart = index - math.floor(index)
intPart = math.floor(index)
percentile = float(arr[intPart])
if fractionPart > 0:
percentile += fractionPart * (float(arr[intPart + 1]) - float(arr[
intPart]))
else:
percentile += 0
return percentile
def write(output_filename, content):
with open(output_filename, 'w') as outfile:
outfile.write(content)
def main(argv):
"""
WebPerf Core Carbon Percentiles
Usage:
* run webperf-core test on all websites you want to use for your percentiles (with json as output file)
* run this file against your output file, for example like this: carbon-rating.py -i data\\carbon-references-2022.json -o tests\\energy_efficiency_carbon_percentiles.py
Options and arguments:
-h/--help : Help information on how to use script
-i/--input <file path> : input file path (.json)
-o/--output <file path> : output file path (.py)
"""
output_filename = ''
input_filename = ''
langCode = 'en'
language = False
language = gettext.translation('webperf-core', localedir='locales',
languages=[langCode])
language.install()
_ = language.gettext
try:
opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']
)
except getopt.GetoptError:
print(main.__doc__)
sys.exit(2)
if opts.__len__() == 0:
print(main.__doc__)
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
print(main.__doc__)
sys.exit(2)
elif opt in ('-i', '--input'):
input_filename = arg
file_ending = ''
file_long_ending = ''
if len(input_filename) > 4:
file_ending = input_filename[-4:].lower()
if len(input_filename) > 7:
file_long_ending = input_filename[-7:].lower()
if file_long_ending == '.sqlite':
from engines.sqlite import read_sites, add_site, delete_site
elif file_ending == '.csv':
from engines.csv import read_sites, add_site, delete_site
elif file_ending == '.xml':
from engines.sitemap import read_sites, add_site, delete_site
else:
from engines.json import read_tests, read_sites, add_site, delete_site
pass
elif opt in ('-o', '--output'):
output_filename = arg
pass
tests = read_tests(input_filename, 0, -1)
generated_date = False
co2s = list()
for test in tests:
if not generated_date:
generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]
).strftime('%Y-%m-%d')
str_data = test[FIELD_INDEX_DATA].replace("'", '"')
data = json.loads(str_data)
print(str_data)
co2s.append(data['co2'])
if not generated_date:
generated_date = datetime.today().strftime('%Y-%m-%d')
output_content = (
'# This array was last generated with carbon-rating.py on {0}\n'.
format(generated_date))
output_content += 'def get_generated_date():\n'
output_content += "\treturn '{0}'\n".format(generated_date)
output_content += '\n'
output_content += 'def get_percentiles():\n'
output_content += '\treturn [\n'
co2s_sorted = sorted(co2s)
intervals = list()
index = 1
while index <= 100:
percentile = getPercentile(co2s_sorted, index)
intervals.append(percentile)
position = index - 1
if index < 100:
if position % 10 == 0 and position != 0:
output_content += '\t\t# {0} percentile\n'.format(position)
output_content += '\t\t{0},\n'.format(percentile)
else:
output_content += '\t\t{0}\n'.format(percentile)
index += 1
output_content += '\t]'
print(output_content)
if len(output_filename) > 0:
write(output_filename, output_content)
<|reserved_special_token_0|>
if __name__ == '__main__':
main(sys.argv[1:])
<|reserved_special_token_1|>
# -*- coding: utf-8 -*-
import sys
import getopt
import datetime
import gettext
import math
import datetime
import json
import gettext
from datetime import datetime
FIELD_INDEX_DATE = 0
FIELD_INDEX_DATA = 1
def getPercentile(arr, percentile):
percentile = min(100, max(0, percentile))
index = (percentile / 100) * (len(arr) - 1)
fractionPart = index - math.floor(index)
intPart = math.floor(index)
percentile = float(arr[intPart])
if fractionPart > 0:
percentile += fractionPart * \
(float(arr[intPart + 1]) - float(arr[intPart]))
else:
percentile += 0
return percentile
def write(output_filename, content):
with open(output_filename, 'w') as outfile:
outfile.write(content)
def main(argv):
"""
WebPerf Core Carbon Percentiles
Usage:
* run webperf-core test on all websites you want to use for your percentiles (with json as output file)
* run this file against your output file, for example like this: carbon-rating.py -i data\carbon-references-2022.json -o tests\energy_efficiency_carbon_percentiles.py
Options and arguments:
-h/--help\t\t\t: Help information on how to use script
-i/--input <file path>\t: input file path (.json)
-o/--output <file path>\t: output file path (.py)
"""
output_filename = ''
input_filename = ''
langCode = 'en'
language = False
# add support for default (en) language
language = gettext.translation(
'webperf-core', localedir='locales', languages=[langCode])
language.install()
_ = language.gettext
try:
opts, args = getopt.getopt(
argv, "hi:o:", ["help", "input=", "output="])
except getopt.GetoptError:
print(main.__doc__)
sys.exit(2)
if (opts.__len__() == 0):
print(main.__doc__)
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'): # help
print(main.__doc__)
sys.exit(2)
elif opt in ("-i", "--input"): # input file path
input_filename = arg
file_ending = ""
file_long_ending = ""
if (len(input_filename) > 4):
file_ending = input_filename[-4:].lower()
if (len(input_filename) > 7):
file_long_ending = input_filename[-7:].lower()
if file_long_ending == ".sqlite":
from engines.sqlite import read_sites, add_site, delete_site
elif (file_ending == ".csv"):
from engines.csv import read_sites, add_site, delete_site
elif (file_ending == ".xml"): # https://example.com/sitemap.xml
from engines.sitemap import read_sites, add_site, delete_site
else:
from engines.json import read_tests, read_sites, add_site, delete_site
pass
elif opt in ("-o", "--output"): # output file path
output_filename = arg
pass
tests = read_tests(input_filename, 0, -1)
generated_date = False
co2s = list()
for test in tests:
if not generated_date:
generated_date = datetime.fromisoformat(
test[FIELD_INDEX_DATE]).strftime('%Y-%m-%d')
str_data = test[FIELD_INDEX_DATA].replace('\'', '"')
data = json.loads(str_data)
print(str_data)
co2s.append(data['co2'])
if not generated_date:
generated_date = datetime.today().strftime('%Y-%m-%d')
output_content = "# This array was last generated with carbon-rating.py on {0}\n".format(
generated_date)
output_content += "def get_generated_date():\n"
output_content += "\treturn '{0}'\n".format(
generated_date)
output_content += "\n"
output_content += "def get_percentiles():\n"
output_content += "\treturn [\n"
co2s_sorted = sorted(co2s)
intervals = list()
index = 1
while (index <= 100):
percentile = getPercentile(co2s_sorted, index)
intervals.append(percentile)
position = index - 1
if index < 100:
if position % 10 == 0 and position != 0:
output_content += "\t\t# {0} percentile\n".format(position)
output_content += "\t\t{0},\n".format(percentile)
else:
output_content += "\t\t{0}\n".format(percentile)
index += 1
output_content += "\t]"
print(output_content)
if (len(output_filename) > 0):
write(output_filename, output_content)
"""
If file is executed on itself then call a definition, mostly for testing purposes
"""
if __name__ == '__main__':
main(sys.argv[1:])
|
flexible
|
{
"blob_id": "a801ca6ae90556d41fd278032af4e58a63709cec",
"index": 7977,
"step-1": "<mask token>\n\n\ndef write(output_filename, content):\n with open(output_filename, 'w') as outfile:\n outfile.write(content)\n\n\ndef main(argv):\n \"\"\"\n WebPerf Core Carbon Percentiles\n\n\n Usage:\n * run webperf-core test on all websites you want to use for your percentiles (with json as output file)\n * run this file against your output file, for example like this: carbon-rating.py -i data\\\\carbon-references-2022.json -o tests\\\\energy_efficiency_carbon_percentiles.py\n\n Options and arguments:\n -h/--help\t\t\t: Help information on how to use script\n -i/--input <file path>\t: input file path (.json)\n -o/--output <file path>\t: output file path (.py)\n \"\"\"\n output_filename = ''\n input_filename = ''\n langCode = 'en'\n language = False\n language = gettext.translation('webperf-core', localedir='locales',\n languages=[langCode])\n language.install()\n _ = language.gettext\n try:\n opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']\n )\n except getopt.GetoptError:\n print(main.__doc__)\n sys.exit(2)\n if opts.__len__() == 0:\n print(main.__doc__)\n sys.exit(2)\n for opt, arg in opts:\n if opt in ('-h', '--help'):\n print(main.__doc__)\n sys.exit(2)\n elif opt in ('-i', '--input'):\n input_filename = arg\n file_ending = ''\n file_long_ending = ''\n if len(input_filename) > 4:\n file_ending = input_filename[-4:].lower()\n if len(input_filename) > 7:\n file_long_ending = input_filename[-7:].lower()\n if file_long_ending == '.sqlite':\n from engines.sqlite import read_sites, add_site, delete_site\n elif file_ending == '.csv':\n from engines.csv import read_sites, add_site, delete_site\n elif file_ending == '.xml':\n from engines.sitemap import read_sites, add_site, delete_site\n else:\n from engines.json import read_tests, read_sites, add_site, delete_site\n pass\n elif opt in ('-o', '--output'):\n output_filename = arg\n pass\n tests = read_tests(input_filename, 0, -1)\n generated_date = False\n co2s = list()\n for test in tests:\n if not generated_date:\n generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]\n ).strftime('%Y-%m-%d')\n str_data = test[FIELD_INDEX_DATA].replace(\"'\", '\"')\n data = json.loads(str_data)\n print(str_data)\n co2s.append(data['co2'])\n if not generated_date:\n generated_date = datetime.today().strftime('%Y-%m-%d')\n output_content = (\n '# This array was last generated with carbon-rating.py on {0}\\n'.\n format(generated_date))\n output_content += 'def get_generated_date():\\n'\n output_content += \"\\treturn '{0}'\\n\".format(generated_date)\n output_content += '\\n'\n output_content += 'def get_percentiles():\\n'\n output_content += '\\treturn [\\n'\n co2s_sorted = sorted(co2s)\n intervals = list()\n index = 1\n while index <= 100:\n percentile = getPercentile(co2s_sorted, index)\n intervals.append(percentile)\n position = index - 1\n if index < 100:\n if position % 10 == 0 and position != 0:\n output_content += '\\t\\t# {0} percentile\\n'.format(position)\n output_content += '\\t\\t{0},\\n'.format(percentile)\n else:\n output_content += '\\t\\t{0}\\n'.format(percentile)\n index += 1\n output_content += '\\t]'\n print(output_content)\n if len(output_filename) > 0:\n write(output_filename, output_content)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef getPercentile(arr, percentile):\n percentile = min(100, max(0, percentile))\n index = percentile / 100 * (len(arr) - 1)\n fractionPart = index - math.floor(index)\n intPart = math.floor(index)\n percentile = float(arr[intPart])\n if fractionPart > 0:\n percentile += fractionPart * (float(arr[intPart + 1]) - float(arr[\n intPart]))\n else:\n percentile += 0\n return percentile\n\n\ndef write(output_filename, content):\n with open(output_filename, 'w') as outfile:\n outfile.write(content)\n\n\ndef main(argv):\n \"\"\"\n WebPerf Core Carbon Percentiles\n\n\n Usage:\n * run webperf-core test on all websites you want to use for your percentiles (with json as output file)\n * run this file against your output file, for example like this: carbon-rating.py -i data\\\\carbon-references-2022.json -o tests\\\\energy_efficiency_carbon_percentiles.py\n\n Options and arguments:\n -h/--help\t\t\t: Help information on how to use script\n -i/--input <file path>\t: input file path (.json)\n -o/--output <file path>\t: output file path (.py)\n \"\"\"\n output_filename = ''\n input_filename = ''\n langCode = 'en'\n language = False\n language = gettext.translation('webperf-core', localedir='locales',\n languages=[langCode])\n language.install()\n _ = language.gettext\n try:\n opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']\n )\n except getopt.GetoptError:\n print(main.__doc__)\n sys.exit(2)\n if opts.__len__() == 0:\n print(main.__doc__)\n sys.exit(2)\n for opt, arg in opts:\n if opt in ('-h', '--help'):\n print(main.__doc__)\n sys.exit(2)\n elif opt in ('-i', '--input'):\n input_filename = arg\n file_ending = ''\n file_long_ending = ''\n if len(input_filename) > 4:\n file_ending = input_filename[-4:].lower()\n if len(input_filename) > 7:\n file_long_ending = input_filename[-7:].lower()\n if file_long_ending == '.sqlite':\n from engines.sqlite import read_sites, add_site, delete_site\n elif file_ending == '.csv':\n from engines.csv import read_sites, add_site, delete_site\n elif file_ending == '.xml':\n from engines.sitemap import read_sites, add_site, delete_site\n else:\n from engines.json import read_tests, read_sites, add_site, delete_site\n pass\n elif opt in ('-o', '--output'):\n output_filename = arg\n pass\n tests = read_tests(input_filename, 0, -1)\n generated_date = False\n co2s = list()\n for test in tests:\n if not generated_date:\n generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]\n ).strftime('%Y-%m-%d')\n str_data = test[FIELD_INDEX_DATA].replace(\"'\", '\"')\n data = json.loads(str_data)\n print(str_data)\n co2s.append(data['co2'])\n if not generated_date:\n generated_date = datetime.today().strftime('%Y-%m-%d')\n output_content = (\n '# This array was last generated with carbon-rating.py on {0}\\n'.\n format(generated_date))\n output_content += 'def get_generated_date():\\n'\n output_content += \"\\treturn '{0}'\\n\".format(generated_date)\n output_content += '\\n'\n output_content += 'def get_percentiles():\\n'\n output_content += '\\treturn [\\n'\n co2s_sorted = sorted(co2s)\n intervals = list()\n index = 1\n while index <= 100:\n percentile = getPercentile(co2s_sorted, index)\n intervals.append(percentile)\n position = index - 1\n if index < 100:\n if position % 10 == 0 and position != 0:\n output_content += '\\t\\t# {0} percentile\\n'.format(position)\n output_content += '\\t\\t{0},\\n'.format(percentile)\n else:\n output_content += '\\t\\t{0}\\n'.format(percentile)\n index += 1\n output_content += '\\t]'\n print(output_content)\n if len(output_filename) > 0:\n write(output_filename, output_content)\n\n\n<mask token>\nif __name__ == '__main__':\n main(sys.argv[1:])\n",
"step-3": "<mask token>\nFIELD_INDEX_DATE = 0\nFIELD_INDEX_DATA = 1\n\n\ndef getPercentile(arr, percentile):\n percentile = min(100, max(0, percentile))\n index = percentile / 100 * (len(arr) - 1)\n fractionPart = index - math.floor(index)\n intPart = math.floor(index)\n percentile = float(arr[intPart])\n if fractionPart > 0:\n percentile += fractionPart * (float(arr[intPart + 1]) - float(arr[\n intPart]))\n else:\n percentile += 0\n return percentile\n\n\ndef write(output_filename, content):\n with open(output_filename, 'w') as outfile:\n outfile.write(content)\n\n\ndef main(argv):\n \"\"\"\n WebPerf Core Carbon Percentiles\n\n\n Usage:\n * run webperf-core test on all websites you want to use for your percentiles (with json as output file)\n * run this file against your output file, for example like this: carbon-rating.py -i data\\\\carbon-references-2022.json -o tests\\\\energy_efficiency_carbon_percentiles.py\n\n Options and arguments:\n -h/--help\t\t\t: Help information on how to use script\n -i/--input <file path>\t: input file path (.json)\n -o/--output <file path>\t: output file path (.py)\n \"\"\"\n output_filename = ''\n input_filename = ''\n langCode = 'en'\n language = False\n language = gettext.translation('webperf-core', localedir='locales',\n languages=[langCode])\n language.install()\n _ = language.gettext\n try:\n opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']\n )\n except getopt.GetoptError:\n print(main.__doc__)\n sys.exit(2)\n if opts.__len__() == 0:\n print(main.__doc__)\n sys.exit(2)\n for opt, arg in opts:\n if opt in ('-h', '--help'):\n print(main.__doc__)\n sys.exit(2)\n elif opt in ('-i', '--input'):\n input_filename = arg\n file_ending = ''\n file_long_ending = ''\n if len(input_filename) > 4:\n file_ending = input_filename[-4:].lower()\n if len(input_filename) > 7:\n file_long_ending = input_filename[-7:].lower()\n if file_long_ending == '.sqlite':\n from engines.sqlite import read_sites, add_site, delete_site\n elif file_ending == '.csv':\n from engines.csv import read_sites, add_site, delete_site\n elif file_ending == '.xml':\n from engines.sitemap import read_sites, add_site, delete_site\n else:\n from engines.json import read_tests, read_sites, add_site, delete_site\n pass\n elif opt in ('-o', '--output'):\n output_filename = arg\n pass\n tests = read_tests(input_filename, 0, -1)\n generated_date = False\n co2s = list()\n for test in tests:\n if not generated_date:\n generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]\n ).strftime('%Y-%m-%d')\n str_data = test[FIELD_INDEX_DATA].replace(\"'\", '\"')\n data = json.loads(str_data)\n print(str_data)\n co2s.append(data['co2'])\n if not generated_date:\n generated_date = datetime.today().strftime('%Y-%m-%d')\n output_content = (\n '# This array was last generated with carbon-rating.py on {0}\\n'.\n format(generated_date))\n output_content += 'def get_generated_date():\\n'\n output_content += \"\\treturn '{0}'\\n\".format(generated_date)\n output_content += '\\n'\n output_content += 'def get_percentiles():\\n'\n output_content += '\\treturn [\\n'\n co2s_sorted = sorted(co2s)\n intervals = list()\n index = 1\n while index <= 100:\n percentile = getPercentile(co2s_sorted, index)\n intervals.append(percentile)\n position = index - 1\n if index < 100:\n if position % 10 == 0 and position != 0:\n output_content += '\\t\\t# {0} percentile\\n'.format(position)\n output_content += '\\t\\t{0},\\n'.format(percentile)\n else:\n output_content += '\\t\\t{0}\\n'.format(percentile)\n index += 1\n output_content += '\\t]'\n print(output_content)\n if len(output_filename) > 0:\n write(output_filename, output_content)\n\n\n<mask token>\nif __name__ == '__main__':\n main(sys.argv[1:])\n",
"step-4": "import sys\nimport getopt\nimport datetime\nimport gettext\nimport math\nimport datetime\nimport json\nimport gettext\nfrom datetime import datetime\nFIELD_INDEX_DATE = 0\nFIELD_INDEX_DATA = 1\n\n\ndef getPercentile(arr, percentile):\n percentile = min(100, max(0, percentile))\n index = percentile / 100 * (len(arr) - 1)\n fractionPart = index - math.floor(index)\n intPart = math.floor(index)\n percentile = float(arr[intPart])\n if fractionPart > 0:\n percentile += fractionPart * (float(arr[intPart + 1]) - float(arr[\n intPart]))\n else:\n percentile += 0\n return percentile\n\n\ndef write(output_filename, content):\n with open(output_filename, 'w') as outfile:\n outfile.write(content)\n\n\ndef main(argv):\n \"\"\"\n WebPerf Core Carbon Percentiles\n\n\n Usage:\n * run webperf-core test on all websites you want to use for your percentiles (with json as output file)\n * run this file against your output file, for example like this: carbon-rating.py -i data\\\\carbon-references-2022.json -o tests\\\\energy_efficiency_carbon_percentiles.py\n\n Options and arguments:\n -h/--help\t\t\t: Help information on how to use script\n -i/--input <file path>\t: input file path (.json)\n -o/--output <file path>\t: output file path (.py)\n \"\"\"\n output_filename = ''\n input_filename = ''\n langCode = 'en'\n language = False\n language = gettext.translation('webperf-core', localedir='locales',\n languages=[langCode])\n language.install()\n _ = language.gettext\n try:\n opts, args = getopt.getopt(argv, 'hi:o:', ['help', 'input=', 'output=']\n )\n except getopt.GetoptError:\n print(main.__doc__)\n sys.exit(2)\n if opts.__len__() == 0:\n print(main.__doc__)\n sys.exit(2)\n for opt, arg in opts:\n if opt in ('-h', '--help'):\n print(main.__doc__)\n sys.exit(2)\n elif opt in ('-i', '--input'):\n input_filename = arg\n file_ending = ''\n file_long_ending = ''\n if len(input_filename) > 4:\n file_ending = input_filename[-4:].lower()\n if len(input_filename) > 7:\n file_long_ending = input_filename[-7:].lower()\n if file_long_ending == '.sqlite':\n from engines.sqlite import read_sites, add_site, delete_site\n elif file_ending == '.csv':\n from engines.csv import read_sites, add_site, delete_site\n elif file_ending == '.xml':\n from engines.sitemap import read_sites, add_site, delete_site\n else:\n from engines.json import read_tests, read_sites, add_site, delete_site\n pass\n elif opt in ('-o', '--output'):\n output_filename = arg\n pass\n tests = read_tests(input_filename, 0, -1)\n generated_date = False\n co2s = list()\n for test in tests:\n if not generated_date:\n generated_date = datetime.fromisoformat(test[FIELD_INDEX_DATE]\n ).strftime('%Y-%m-%d')\n str_data = test[FIELD_INDEX_DATA].replace(\"'\", '\"')\n data = json.loads(str_data)\n print(str_data)\n co2s.append(data['co2'])\n if not generated_date:\n generated_date = datetime.today().strftime('%Y-%m-%d')\n output_content = (\n '# This array was last generated with carbon-rating.py on {0}\\n'.\n format(generated_date))\n output_content += 'def get_generated_date():\\n'\n output_content += \"\\treturn '{0}'\\n\".format(generated_date)\n output_content += '\\n'\n output_content += 'def get_percentiles():\\n'\n output_content += '\\treturn [\\n'\n co2s_sorted = sorted(co2s)\n intervals = list()\n index = 1\n while index <= 100:\n percentile = getPercentile(co2s_sorted, index)\n intervals.append(percentile)\n position = index - 1\n if index < 100:\n if position % 10 == 0 and position != 0:\n output_content += '\\t\\t# {0} percentile\\n'.format(position)\n output_content += '\\t\\t{0},\\n'.format(percentile)\n else:\n output_content += '\\t\\t{0}\\n'.format(percentile)\n index += 1\n output_content += '\\t]'\n print(output_content)\n if len(output_filename) > 0:\n write(output_filename, output_content)\n\n\n<mask token>\nif __name__ == '__main__':\n main(sys.argv[1:])\n",
"step-5": "# -*- coding: utf-8 -*-\nimport sys\nimport getopt\nimport datetime\nimport gettext\nimport math\nimport datetime\nimport json\nimport gettext\nfrom datetime import datetime\n\nFIELD_INDEX_DATE = 0\nFIELD_INDEX_DATA = 1\n\n\ndef getPercentile(arr, percentile):\n percentile = min(100, max(0, percentile))\n index = (percentile / 100) * (len(arr) - 1)\n fractionPart = index - math.floor(index)\n intPart = math.floor(index)\n percentile = float(arr[intPart])\n\n if fractionPart > 0:\n percentile += fractionPart * \\\n (float(arr[intPart + 1]) - float(arr[intPart]))\n else:\n percentile += 0\n\n return percentile\n\n\ndef write(output_filename, content):\n with open(output_filename, 'w') as outfile:\n outfile.write(content)\n\n\ndef main(argv):\n \"\"\"\n WebPerf Core Carbon Percentiles\n\n\n Usage:\n * run webperf-core test on all websites you want to use for your percentiles (with json as output file)\n * run this file against your output file, for example like this: carbon-rating.py -i data\\carbon-references-2022.json -o tests\\energy_efficiency_carbon_percentiles.py\n\n Options and arguments:\n -h/--help\\t\\t\\t: Help information on how to use script\n -i/--input <file path>\\t: input file path (.json)\n -o/--output <file path>\\t: output file path (.py)\n \"\"\"\n\n output_filename = ''\n input_filename = ''\n langCode = 'en'\n language = False\n\n # add support for default (en) language\n language = gettext.translation(\n 'webperf-core', localedir='locales', languages=[langCode])\n language.install()\n _ = language.gettext\n\n try:\n opts, args = getopt.getopt(\n argv, \"hi:o:\", [\"help\", \"input=\", \"output=\"])\n except getopt.GetoptError:\n print(main.__doc__)\n sys.exit(2)\n\n if (opts.__len__() == 0):\n print(main.__doc__)\n sys.exit(2)\n\n for opt, arg in opts:\n if opt in ('-h', '--help'): # help\n print(main.__doc__)\n sys.exit(2)\n elif opt in (\"-i\", \"--input\"): # input file path\n input_filename = arg\n\n file_ending = \"\"\n file_long_ending = \"\"\n if (len(input_filename) > 4):\n file_ending = input_filename[-4:].lower()\n if (len(input_filename) > 7):\n file_long_ending = input_filename[-7:].lower()\n\n if file_long_ending == \".sqlite\":\n from engines.sqlite import read_sites, add_site, delete_site\n elif (file_ending == \".csv\"):\n from engines.csv import read_sites, add_site, delete_site\n elif (file_ending == \".xml\"): # https://example.com/sitemap.xml\n from engines.sitemap import read_sites, add_site, delete_site\n else:\n from engines.json import read_tests, read_sites, add_site, delete_site\n pass\n elif opt in (\"-o\", \"--output\"): # output file path\n output_filename = arg\n pass\n\n tests = read_tests(input_filename, 0, -1)\n generated_date = False\n co2s = list()\n\n for test in tests:\n if not generated_date:\n generated_date = datetime.fromisoformat(\n test[FIELD_INDEX_DATE]).strftime('%Y-%m-%d')\n\n str_data = test[FIELD_INDEX_DATA].replace('\\'', '\"')\n data = json.loads(str_data)\n print(str_data)\n co2s.append(data['co2'])\n\n if not generated_date:\n generated_date = datetime.today().strftime('%Y-%m-%d')\n\n output_content = \"# This array was last generated with carbon-rating.py on {0}\\n\".format(\n generated_date)\n output_content += \"def get_generated_date():\\n\"\n output_content += \"\\treturn '{0}'\\n\".format(\n generated_date)\n output_content += \"\\n\"\n output_content += \"def get_percentiles():\\n\"\n output_content += \"\\treturn [\\n\"\n\n co2s_sorted = sorted(co2s)\n\n intervals = list()\n\n index = 1\n while (index <= 100):\n percentile = getPercentile(co2s_sorted, index)\n intervals.append(percentile)\n position = index - 1\n if index < 100:\n if position % 10 == 0 and position != 0:\n output_content += \"\\t\\t# {0} percentile\\n\".format(position)\n\n output_content += \"\\t\\t{0},\\n\".format(percentile)\n else:\n output_content += \"\\t\\t{0}\\n\".format(percentile)\n index += 1\n\n output_content += \"\\t]\"\n\n print(output_content)\n if (len(output_filename) > 0):\n write(output_filename, output_content)\n\n\n\"\"\"\nIf file is executed on itself then call a definition, mostly for testing purposes\n\"\"\"\nif __name__ == '__main__':\n main(sys.argv[1:])\n",
"step-ids": [
2,
4,
5,
6,
7
]
}
|
[
2,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('Seu número é {} seu antecessor é {} e seu sucessor é {}'.format(n, m, o)
)
<|reserved_special_token_1|>
n = int(input('Digite um número'))
m = n - 1
o = n + 1
print('Seu número é {} seu antecessor é {} e seu sucessor é {}'.format(n, m, o)
)
<|reserved_special_token_1|>
n=int(input("Digite um número"))
m=n-1
o=n+1
print("Seu número é {} seu antecessor é {} e seu sucessor é {}".format(n,m,o))
|
flexible
|
{
"blob_id": "47d72379b894826dad335f098649702ade195f78",
"index": 7337,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Seu número é {} seu antecessor é {} e seu sucessor é {}'.format(n, m, o)\n )\n",
"step-3": "n = int(input('Digite um número'))\nm = n - 1\no = n + 1\nprint('Seu número é {} seu antecessor é {} e seu sucessor é {}'.format(n, m, o)\n )\n",
"step-4": "n=int(input(\"Digite um número\"))\nm=n-1\no=n+1\nprint(\"Seu número é {} seu antecessor é {} e seu sucessor é {}\".format(n,m,o))",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
# vim: expandtab
# -*- coding: utf-8 -*-
from poleno.utils.template import Library
from chcemvediet.apps.obligees.models import Obligee
register = Library()
@register.simple_tag
def gender(gender, masculine, feminine, neuter, plurale):
if gender == Obligee.GENDERS.MASCULINE:
return masculine
elif gender == Obligee.GENDERS.FEMININE:
return feminine
elif gender == Obligee.GENDERS.NEUTER:
return neuter
elif gender == Obligee.GENDERS.PLURALE:
return plurale
else:
return u''
|
normal
|
{
"blob_id": "c9d12f14fa0e46e4590746d45862fe255b415a1d",
"index": 396,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@register.simple_tag\ndef gender(gender, masculine, feminine, neuter, plurale):\n if gender == Obligee.GENDERS.MASCULINE:\n return masculine\n elif gender == Obligee.GENDERS.FEMININE:\n return feminine\n elif gender == Obligee.GENDERS.NEUTER:\n return neuter\n elif gender == Obligee.GENDERS.PLURALE:\n return plurale\n else:\n return u''\n",
"step-3": "<mask token>\nregister = Library()\n\n\n@register.simple_tag\ndef gender(gender, masculine, feminine, neuter, plurale):\n if gender == Obligee.GENDERS.MASCULINE:\n return masculine\n elif gender == Obligee.GENDERS.FEMININE:\n return feminine\n elif gender == Obligee.GENDERS.NEUTER:\n return neuter\n elif gender == Obligee.GENDERS.PLURALE:\n return plurale\n else:\n return u''\n",
"step-4": "from poleno.utils.template import Library\nfrom chcemvediet.apps.obligees.models import Obligee\nregister = Library()\n\n\n@register.simple_tag\ndef gender(gender, masculine, feminine, neuter, plurale):\n if gender == Obligee.GENDERS.MASCULINE:\n return masculine\n elif gender == Obligee.GENDERS.FEMININE:\n return feminine\n elif gender == Obligee.GENDERS.NEUTER:\n return neuter\n elif gender == Obligee.GENDERS.PLURALE:\n return plurale\n else:\n return u''\n",
"step-5": "# vim: expandtab\n# -*- coding: utf-8 -*-\nfrom poleno.utils.template import Library\nfrom chcemvediet.apps.obligees.models import Obligee\n\n\nregister = Library()\n\n@register.simple_tag\ndef gender(gender, masculine, feminine, neuter, plurale):\n if gender == Obligee.GENDERS.MASCULINE:\n return masculine\n elif gender == Obligee.GENDERS.FEMININE:\n return feminine\n elif gender == Obligee.GENDERS.NEUTER:\n return neuter\n elif gender == Obligee.GENDERS.PLURALE:\n return plurale\n else:\n return u''\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import asyncio
import logging
from datetime import datetime
from discord.ext import commands
from discord.ext.commands import Bot, Context
from humanize import precisedelta
from sqlalchemy.exc import SQLAlchemyError
from sqlalchemy_utils import ScalarListException
from config import CONFIG
from models import Reminder, db_session
from utils import (
DateTimeConverter,
get_database_user,
get_database_user_from_id,
get_name_string,
user_is_irc_bot,
)
LONG_HELP_TEXT = """
Add reminders for yourself or remove the last one you added.
"""
SHORT_HELP_TEXT = """Add or remove reminders."""
async def reminder_check(bot):
await bot.wait_until_ready()
while not bot.is_closed():
now = datetime.now()
reminders = (
db_session.query(Reminder)
.filter(Reminder.trigger_at <= now, Reminder.triggered == False) # noqa 712
.all()
)
for r in reminders:
if r.irc_name:
display_name = r.irc_name
else:
author_uid = r.user.user_uid
display_name = f"<@{author_uid}>"
channel = bot.get_channel(r.playback_channel_id)
message = f"Reminding {display_name}: " + r.reminder_content
await channel.send(message)
r.triggered = True
db_session.commit()
await asyncio.sleep(CONFIG.REMINDER_SEARCH_INTERVAL)
class Reminders(commands.Cog):
def __init__(self, bot: Bot):
self.bot = bot
self.bot.loop.create_task(reminder_check(self.bot))
@commands.group(help=LONG_HELP_TEXT, brief=SHORT_HELP_TEXT)
async def reminder(self, ctx: Context):
if not ctx.invoked_subcommand:
await ctx.send("Subcommand not found.")
@reminder.command(
help='Add a reminder, format "yyyy-mm-dd hh:mm" or "mm-dd hh:mm" or hh:mm:ss or hh:mm or xdxhxmxs or any ordered combination of the last format, then finally your reminder (rest of discord message).'
)
async def add(
self, ctx: Context, trigger_time: DateTimeConverter, *, reminder_content: str
):
now = datetime.now()
if not trigger_time:
await ctx.send("Incorrect time format, please see help text.")
elif trigger_time < now:
await ctx.send("That time is in the past.")
else:
# HURRAY the time is valid and not in the past, add the reminder
display_name = get_name_string(ctx.message)
# set the id to a random value if the author was the bridge bot, since we wont be using it anyways
# if ctx.message.clean_content.startswith("**<"): <---- FOR TESTING
if user_is_irc_bot(ctx):
author_id = 1
irc_n = display_name
else:
author_id = get_database_user(ctx.author).id
irc_n = None
trig_at = trigger_time
trig = False
playback_ch_id = ctx.message.channel.id
new_reminder = Reminder(
user_id=author_id,
reminder_content=reminder_content,
trigger_at=trig_at,
triggered=trig,
playback_channel_id=playback_ch_id,
irc_name=irc_n,
)
db_session.add(new_reminder)
try:
db_session.commit()
await ctx.send(
f"Thanks {display_name}, I have saved your reminder (but please note that my granularity is set at {precisedelta(CONFIG.REMINDER_SEARCH_INTERVAL, minimum_unit='seconds')})."
)
except (ScalarListException, SQLAlchemyError) as e:
db_session.rollback()
logging.exception(e)
await ctx.send(f"Something went wrong")
def setup(bot: Bot):
bot.add_cog(Reminders(bot))
|
normal
|
{
"blob_id": "0f54853901a26b66fe35106593ded6c92785b8db",
"index": 2682,
"step-1": "<mask token>\n\n\nclass Reminders(commands.Cog):\n\n def __init__(self, bot: Bot):\n self.bot = bot\n self.bot.loop.create_task(reminder_check(self.bot))\n\n @commands.group(help=LONG_HELP_TEXT, brief=SHORT_HELP_TEXT)\n async def reminder(self, ctx: Context):\n if not ctx.invoked_subcommand:\n await ctx.send('Subcommand not found.')\n\n @reminder.command(help=\n 'Add a reminder, format \"yyyy-mm-dd hh:mm\" or \"mm-dd hh:mm\" or hh:mm:ss or hh:mm or xdxhxmxs or any ordered combination of the last format, then finally your reminder (rest of discord message).'\n )\n async def add(self, ctx: Context, trigger_time: DateTimeConverter, *,\n reminder_content: str):\n now = datetime.now()\n if not trigger_time:\n await ctx.send('Incorrect time format, please see help text.')\n elif trigger_time < now:\n await ctx.send('That time is in the past.')\n else:\n display_name = get_name_string(ctx.message)\n if user_is_irc_bot(ctx):\n author_id = 1\n irc_n = display_name\n else:\n author_id = get_database_user(ctx.author).id\n irc_n = None\n trig_at = trigger_time\n trig = False\n playback_ch_id = ctx.message.channel.id\n new_reminder = Reminder(user_id=author_id, reminder_content=\n reminder_content, trigger_at=trig_at, triggered=trig,\n playback_channel_id=playback_ch_id, irc_name=irc_n)\n db_session.add(new_reminder)\n try:\n db_session.commit()\n await ctx.send(\n f\"Thanks {display_name}, I have saved your reminder (but please note that my granularity is set at {precisedelta(CONFIG.REMINDER_SEARCH_INTERVAL, minimum_unit='seconds')}).\"\n )\n except (ScalarListException, SQLAlchemyError) as e:\n db_session.rollback()\n logging.exception(e)\n await ctx.send(f'Something went wrong')\n\n\ndef setup(bot: Bot):\n bot.add_cog(Reminders(bot))\n",
"step-2": "<mask token>\n\n\nasync def reminder_check(bot):\n await bot.wait_until_ready()\n while not bot.is_closed():\n now = datetime.now()\n reminders = db_session.query(Reminder).filter(Reminder.trigger_at <=\n now, Reminder.triggered == False).all()\n for r in reminders:\n if r.irc_name:\n display_name = r.irc_name\n else:\n author_uid = r.user.user_uid\n display_name = f'<@{author_uid}>'\n channel = bot.get_channel(r.playback_channel_id)\n message = f'Reminding {display_name}: ' + r.reminder_content\n await channel.send(message)\n r.triggered = True\n db_session.commit()\n await asyncio.sleep(CONFIG.REMINDER_SEARCH_INTERVAL)\n\n\nclass Reminders(commands.Cog):\n\n def __init__(self, bot: Bot):\n self.bot = bot\n self.bot.loop.create_task(reminder_check(self.bot))\n\n @commands.group(help=LONG_HELP_TEXT, brief=SHORT_HELP_TEXT)\n async def reminder(self, ctx: Context):\n if not ctx.invoked_subcommand:\n await ctx.send('Subcommand not found.')\n\n @reminder.command(help=\n 'Add a reminder, format \"yyyy-mm-dd hh:mm\" or \"mm-dd hh:mm\" or hh:mm:ss or hh:mm or xdxhxmxs or any ordered combination of the last format, then finally your reminder (rest of discord message).'\n )\n async def add(self, ctx: Context, trigger_time: DateTimeConverter, *,\n reminder_content: str):\n now = datetime.now()\n if not trigger_time:\n await ctx.send('Incorrect time format, please see help text.')\n elif trigger_time < now:\n await ctx.send('That time is in the past.')\n else:\n display_name = get_name_string(ctx.message)\n if user_is_irc_bot(ctx):\n author_id = 1\n irc_n = display_name\n else:\n author_id = get_database_user(ctx.author).id\n irc_n = None\n trig_at = trigger_time\n trig = False\n playback_ch_id = ctx.message.channel.id\n new_reminder = Reminder(user_id=author_id, reminder_content=\n reminder_content, trigger_at=trig_at, triggered=trig,\n playback_channel_id=playback_ch_id, irc_name=irc_n)\n db_session.add(new_reminder)\n try:\n db_session.commit()\n await ctx.send(\n f\"Thanks {display_name}, I have saved your reminder (but please note that my granularity is set at {precisedelta(CONFIG.REMINDER_SEARCH_INTERVAL, minimum_unit='seconds')}).\"\n )\n except (ScalarListException, SQLAlchemyError) as e:\n db_session.rollback()\n logging.exception(e)\n await ctx.send(f'Something went wrong')\n\n\ndef setup(bot: Bot):\n bot.add_cog(Reminders(bot))\n",
"step-3": "<mask token>\nLONG_HELP_TEXT = \"\"\"\nAdd reminders for yourself or remove the last one you added.\n\"\"\"\nSHORT_HELP_TEXT = 'Add or remove reminders.'\n\n\nasync def reminder_check(bot):\n await bot.wait_until_ready()\n while not bot.is_closed():\n now = datetime.now()\n reminders = db_session.query(Reminder).filter(Reminder.trigger_at <=\n now, Reminder.triggered == False).all()\n for r in reminders:\n if r.irc_name:\n display_name = r.irc_name\n else:\n author_uid = r.user.user_uid\n display_name = f'<@{author_uid}>'\n channel = bot.get_channel(r.playback_channel_id)\n message = f'Reminding {display_name}: ' + r.reminder_content\n await channel.send(message)\n r.triggered = True\n db_session.commit()\n await asyncio.sleep(CONFIG.REMINDER_SEARCH_INTERVAL)\n\n\nclass Reminders(commands.Cog):\n\n def __init__(self, bot: Bot):\n self.bot = bot\n self.bot.loop.create_task(reminder_check(self.bot))\n\n @commands.group(help=LONG_HELP_TEXT, brief=SHORT_HELP_TEXT)\n async def reminder(self, ctx: Context):\n if not ctx.invoked_subcommand:\n await ctx.send('Subcommand not found.')\n\n @reminder.command(help=\n 'Add a reminder, format \"yyyy-mm-dd hh:mm\" or \"mm-dd hh:mm\" or hh:mm:ss or hh:mm or xdxhxmxs or any ordered combination of the last format, then finally your reminder (rest of discord message).'\n )\n async def add(self, ctx: Context, trigger_time: DateTimeConverter, *,\n reminder_content: str):\n now = datetime.now()\n if not trigger_time:\n await ctx.send('Incorrect time format, please see help text.')\n elif trigger_time < now:\n await ctx.send('That time is in the past.')\n else:\n display_name = get_name_string(ctx.message)\n if user_is_irc_bot(ctx):\n author_id = 1\n irc_n = display_name\n else:\n author_id = get_database_user(ctx.author).id\n irc_n = None\n trig_at = trigger_time\n trig = False\n playback_ch_id = ctx.message.channel.id\n new_reminder = Reminder(user_id=author_id, reminder_content=\n reminder_content, trigger_at=trig_at, triggered=trig,\n playback_channel_id=playback_ch_id, irc_name=irc_n)\n db_session.add(new_reminder)\n try:\n db_session.commit()\n await ctx.send(\n f\"Thanks {display_name}, I have saved your reminder (but please note that my granularity is set at {precisedelta(CONFIG.REMINDER_SEARCH_INTERVAL, minimum_unit='seconds')}).\"\n )\n except (ScalarListException, SQLAlchemyError) as e:\n db_session.rollback()\n logging.exception(e)\n await ctx.send(f'Something went wrong')\n\n\ndef setup(bot: Bot):\n bot.add_cog(Reminders(bot))\n",
"step-4": "import asyncio\nimport logging\nfrom datetime import datetime\nfrom discord.ext import commands\nfrom discord.ext.commands import Bot, Context\nfrom humanize import precisedelta\nfrom sqlalchemy.exc import SQLAlchemyError\nfrom sqlalchemy_utils import ScalarListException\nfrom config import CONFIG\nfrom models import Reminder, db_session\nfrom utils import DateTimeConverter, get_database_user, get_database_user_from_id, get_name_string, user_is_irc_bot\nLONG_HELP_TEXT = \"\"\"\nAdd reminders for yourself or remove the last one you added.\n\"\"\"\nSHORT_HELP_TEXT = 'Add or remove reminders.'\n\n\nasync def reminder_check(bot):\n await bot.wait_until_ready()\n while not bot.is_closed():\n now = datetime.now()\n reminders = db_session.query(Reminder).filter(Reminder.trigger_at <=\n now, Reminder.triggered == False).all()\n for r in reminders:\n if r.irc_name:\n display_name = r.irc_name\n else:\n author_uid = r.user.user_uid\n display_name = f'<@{author_uid}>'\n channel = bot.get_channel(r.playback_channel_id)\n message = f'Reminding {display_name}: ' + r.reminder_content\n await channel.send(message)\n r.triggered = True\n db_session.commit()\n await asyncio.sleep(CONFIG.REMINDER_SEARCH_INTERVAL)\n\n\nclass Reminders(commands.Cog):\n\n def __init__(self, bot: Bot):\n self.bot = bot\n self.bot.loop.create_task(reminder_check(self.bot))\n\n @commands.group(help=LONG_HELP_TEXT, brief=SHORT_HELP_TEXT)\n async def reminder(self, ctx: Context):\n if not ctx.invoked_subcommand:\n await ctx.send('Subcommand not found.')\n\n @reminder.command(help=\n 'Add a reminder, format \"yyyy-mm-dd hh:mm\" or \"mm-dd hh:mm\" or hh:mm:ss or hh:mm or xdxhxmxs or any ordered combination of the last format, then finally your reminder (rest of discord message).'\n )\n async def add(self, ctx: Context, trigger_time: DateTimeConverter, *,\n reminder_content: str):\n now = datetime.now()\n if not trigger_time:\n await ctx.send('Incorrect time format, please see help text.')\n elif trigger_time < now:\n await ctx.send('That time is in the past.')\n else:\n display_name = get_name_string(ctx.message)\n if user_is_irc_bot(ctx):\n author_id = 1\n irc_n = display_name\n else:\n author_id = get_database_user(ctx.author).id\n irc_n = None\n trig_at = trigger_time\n trig = False\n playback_ch_id = ctx.message.channel.id\n new_reminder = Reminder(user_id=author_id, reminder_content=\n reminder_content, trigger_at=trig_at, triggered=trig,\n playback_channel_id=playback_ch_id, irc_name=irc_n)\n db_session.add(new_reminder)\n try:\n db_session.commit()\n await ctx.send(\n f\"Thanks {display_name}, I have saved your reminder (but please note that my granularity is set at {precisedelta(CONFIG.REMINDER_SEARCH_INTERVAL, minimum_unit='seconds')}).\"\n )\n except (ScalarListException, SQLAlchemyError) as e:\n db_session.rollback()\n logging.exception(e)\n await ctx.send(f'Something went wrong')\n\n\ndef setup(bot: Bot):\n bot.add_cog(Reminders(bot))\n",
"step-5": "import asyncio\nimport logging\nfrom datetime import datetime\n\nfrom discord.ext import commands\nfrom discord.ext.commands import Bot, Context\nfrom humanize import precisedelta\nfrom sqlalchemy.exc import SQLAlchemyError\nfrom sqlalchemy_utils import ScalarListException\n\nfrom config import CONFIG\nfrom models import Reminder, db_session\nfrom utils import (\n DateTimeConverter,\n get_database_user,\n get_database_user_from_id,\n get_name_string,\n user_is_irc_bot,\n)\n\nLONG_HELP_TEXT = \"\"\"\nAdd reminders for yourself or remove the last one you added.\n\"\"\"\nSHORT_HELP_TEXT = \"\"\"Add or remove reminders.\"\"\"\n\n\nasync def reminder_check(bot):\n await bot.wait_until_ready()\n while not bot.is_closed():\n now = datetime.now()\n reminders = (\n db_session.query(Reminder)\n .filter(Reminder.trigger_at <= now, Reminder.triggered == False) # noqa 712\n .all()\n )\n for r in reminders:\n if r.irc_name:\n display_name = r.irc_name\n else:\n author_uid = r.user.user_uid\n display_name = f\"<@{author_uid}>\"\n channel = bot.get_channel(r.playback_channel_id)\n message = f\"Reminding {display_name}: \" + r.reminder_content\n await channel.send(message)\n r.triggered = True\n db_session.commit()\n\n await asyncio.sleep(CONFIG.REMINDER_SEARCH_INTERVAL)\n\n\nclass Reminders(commands.Cog):\n def __init__(self, bot: Bot):\n self.bot = bot\n self.bot.loop.create_task(reminder_check(self.bot))\n\n @commands.group(help=LONG_HELP_TEXT, brief=SHORT_HELP_TEXT)\n async def reminder(self, ctx: Context):\n if not ctx.invoked_subcommand:\n await ctx.send(\"Subcommand not found.\")\n\n @reminder.command(\n help='Add a reminder, format \"yyyy-mm-dd hh:mm\" or \"mm-dd hh:mm\" or hh:mm:ss or hh:mm or xdxhxmxs or any ordered combination of the last format, then finally your reminder (rest of discord message).'\n )\n async def add(\n self, ctx: Context, trigger_time: DateTimeConverter, *, reminder_content: str\n ):\n now = datetime.now()\n if not trigger_time:\n await ctx.send(\"Incorrect time format, please see help text.\")\n elif trigger_time < now:\n await ctx.send(\"That time is in the past.\")\n else:\n # HURRAY the time is valid and not in the past, add the reminder\n display_name = get_name_string(ctx.message)\n\n # set the id to a random value if the author was the bridge bot, since we wont be using it anyways\n # if ctx.message.clean_content.startswith(\"**<\"): <---- FOR TESTING\n if user_is_irc_bot(ctx):\n author_id = 1\n irc_n = display_name\n else:\n author_id = get_database_user(ctx.author).id\n irc_n = None\n\n trig_at = trigger_time\n trig = False\n playback_ch_id = ctx.message.channel.id\n new_reminder = Reminder(\n user_id=author_id,\n reminder_content=reminder_content,\n trigger_at=trig_at,\n triggered=trig,\n playback_channel_id=playback_ch_id,\n irc_name=irc_n,\n )\n db_session.add(new_reminder)\n try:\n db_session.commit()\n await ctx.send(\n f\"Thanks {display_name}, I have saved your reminder (but please note that my granularity is set at {precisedelta(CONFIG.REMINDER_SEARCH_INTERVAL, minimum_unit='seconds')}).\"\n )\n except (ScalarListException, SQLAlchemyError) as e:\n db_session.rollback()\n logging.exception(e)\n await ctx.send(f\"Something went wrong\")\n\n\ndef setup(bot: Bot):\n bot.add_cog(Reminders(bot))\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
"""
Main CLI endpoint for GeoCube
"""
import importlib.metadata
import click
from click import group
import geocube.cli.commands as cmd_modules
from geocube import show_versions
CONTEXT_SETTINGS = {
"help_option_names": ["-h", "--help"],
"token_normalize_func": lambda x: x.replace("-", "_"),
}
def check_version(ctx, _, value):
"""
Print current version, and check for latest version.
Called via 'geocube --version'
:param ctx: Application context object (click.Context)
:param value: Passed in by Click
:return None
"""
if not value or ctx.resilient_parsing:
return
click.echo(f"geocube v{importlib.metadata.version('geocube')}")
ctx.exit()
def cli_show_version(ctx, _, value):
"""
Print debugging version information.
:param ctx: Application context object (click.Context)
:param value: Passed in by Click
:return None
"""
if not value or ctx.resilient_parsing:
return
show_versions()
ctx.exit()
@group(context_settings=CONTEXT_SETTINGS)
@click.option(
"-v",
"--version",
is_flag=True,
is_eager=True,
expose_value=False,
callback=check_version,
help="Show the current version",
)
@click.option(
"--show-versions",
is_flag=True,
is_eager=True,
expose_value=False,
callback=cli_show_version,
help="Show debugging version information",
)
def geocube():
"""Top-level command and entry point into the GeoCube CLI"""
def _add_subcommands():
"""
Individual commands (and sub-commands) are encapsulated in separate files
under /commands. Collect these command groups, and add them underneath the
top-level command (geocube).
"""
geocube.add_command(cmd_modules.make_geocube.make_geocube)
_add_subcommands()
|
normal
|
{
"blob_id": "0964121d88fad2906311de7532eac52ff784fff6",
"index": 8306,
"step-1": "<mask token>\n\n\ndef check_version(ctx, _, value):\n \"\"\"\n Print current version, and check for latest version.\n\n Called via 'geocube --version'\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n click.echo(f\"geocube v{importlib.metadata.version('geocube')}\")\n ctx.exit()\n\n\ndef cli_show_version(ctx, _, value):\n \"\"\"\n Print debugging version information.\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n show_versions()\n ctx.exit()\n\n\n@group(context_settings=CONTEXT_SETTINGS)\n@click.option('-v', '--version', is_flag=True, is_eager=True, expose_value=\n False, callback=check_version, help='Show the current version')\n@click.option('--show-versions', is_flag=True, is_eager=True, expose_value=\n False, callback=cli_show_version, help='Show debugging version information'\n )\ndef geocube():\n \"\"\"Top-level command and entry point into the GeoCube CLI\"\"\"\n\n\ndef _add_subcommands():\n \"\"\"\n Individual commands (and sub-commands) are encapsulated in separate files\n under /commands. Collect these command groups, and add them underneath the\n top-level command (geocube).\n \"\"\"\n geocube.add_command(cmd_modules.make_geocube.make_geocube)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef check_version(ctx, _, value):\n \"\"\"\n Print current version, and check for latest version.\n\n Called via 'geocube --version'\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n click.echo(f\"geocube v{importlib.metadata.version('geocube')}\")\n ctx.exit()\n\n\ndef cli_show_version(ctx, _, value):\n \"\"\"\n Print debugging version information.\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n show_versions()\n ctx.exit()\n\n\n@group(context_settings=CONTEXT_SETTINGS)\n@click.option('-v', '--version', is_flag=True, is_eager=True, expose_value=\n False, callback=check_version, help='Show the current version')\n@click.option('--show-versions', is_flag=True, is_eager=True, expose_value=\n False, callback=cli_show_version, help='Show debugging version information'\n )\ndef geocube():\n \"\"\"Top-level command and entry point into the GeoCube CLI\"\"\"\n\n\ndef _add_subcommands():\n \"\"\"\n Individual commands (and sub-commands) are encapsulated in separate files\n under /commands. Collect these command groups, and add them underneath the\n top-level command (geocube).\n \"\"\"\n geocube.add_command(cmd_modules.make_geocube.make_geocube)\n\n\n_add_subcommands()\n",
"step-3": "<mask token>\nCONTEXT_SETTINGS = {'help_option_names': ['-h', '--help'],\n 'token_normalize_func': lambda x: x.replace('-', '_')}\n\n\ndef check_version(ctx, _, value):\n \"\"\"\n Print current version, and check for latest version.\n\n Called via 'geocube --version'\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n click.echo(f\"geocube v{importlib.metadata.version('geocube')}\")\n ctx.exit()\n\n\ndef cli_show_version(ctx, _, value):\n \"\"\"\n Print debugging version information.\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n show_versions()\n ctx.exit()\n\n\n@group(context_settings=CONTEXT_SETTINGS)\n@click.option('-v', '--version', is_flag=True, is_eager=True, expose_value=\n False, callback=check_version, help='Show the current version')\n@click.option('--show-versions', is_flag=True, is_eager=True, expose_value=\n False, callback=cli_show_version, help='Show debugging version information'\n )\ndef geocube():\n \"\"\"Top-level command and entry point into the GeoCube CLI\"\"\"\n\n\ndef _add_subcommands():\n \"\"\"\n Individual commands (and sub-commands) are encapsulated in separate files\n under /commands. Collect these command groups, and add them underneath the\n top-level command (geocube).\n \"\"\"\n geocube.add_command(cmd_modules.make_geocube.make_geocube)\n\n\n_add_subcommands()\n",
"step-4": "<mask token>\nimport importlib.metadata\nimport click\nfrom click import group\nimport geocube.cli.commands as cmd_modules\nfrom geocube import show_versions\nCONTEXT_SETTINGS = {'help_option_names': ['-h', '--help'],\n 'token_normalize_func': lambda x: x.replace('-', '_')}\n\n\ndef check_version(ctx, _, value):\n \"\"\"\n Print current version, and check for latest version.\n\n Called via 'geocube --version'\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n click.echo(f\"geocube v{importlib.metadata.version('geocube')}\")\n ctx.exit()\n\n\ndef cli_show_version(ctx, _, value):\n \"\"\"\n Print debugging version information.\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n show_versions()\n ctx.exit()\n\n\n@group(context_settings=CONTEXT_SETTINGS)\n@click.option('-v', '--version', is_flag=True, is_eager=True, expose_value=\n False, callback=check_version, help='Show the current version')\n@click.option('--show-versions', is_flag=True, is_eager=True, expose_value=\n False, callback=cli_show_version, help='Show debugging version information'\n )\ndef geocube():\n \"\"\"Top-level command and entry point into the GeoCube CLI\"\"\"\n\n\ndef _add_subcommands():\n \"\"\"\n Individual commands (and sub-commands) are encapsulated in separate files\n under /commands. Collect these command groups, and add them underneath the\n top-level command (geocube).\n \"\"\"\n geocube.add_command(cmd_modules.make_geocube.make_geocube)\n\n\n_add_subcommands()\n",
"step-5": "\"\"\"\nMain CLI endpoint for GeoCube\n\"\"\"\nimport importlib.metadata\n\nimport click\nfrom click import group\n\nimport geocube.cli.commands as cmd_modules\nfrom geocube import show_versions\n\nCONTEXT_SETTINGS = {\n \"help_option_names\": [\"-h\", \"--help\"],\n \"token_normalize_func\": lambda x: x.replace(\"-\", \"_\"),\n}\n\n\ndef check_version(ctx, _, value):\n \"\"\"\n Print current version, and check for latest version.\n\n Called via 'geocube --version'\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n\n click.echo(f\"geocube v{importlib.metadata.version('geocube')}\")\n\n ctx.exit()\n\n\ndef cli_show_version(ctx, _, value):\n \"\"\"\n Print debugging version information.\n\n :param ctx: Application context object (click.Context)\n :param value: Passed in by Click\n :return None\n \"\"\"\n if not value or ctx.resilient_parsing:\n return\n\n show_versions()\n\n ctx.exit()\n\n\n@group(context_settings=CONTEXT_SETTINGS)\n@click.option(\n \"-v\",\n \"--version\",\n is_flag=True,\n is_eager=True,\n expose_value=False,\n callback=check_version,\n help=\"Show the current version\",\n)\n@click.option(\n \"--show-versions\",\n is_flag=True,\n is_eager=True,\n expose_value=False,\n callback=cli_show_version,\n help=\"Show debugging version information\",\n)\ndef geocube():\n \"\"\"Top-level command and entry point into the GeoCube CLI\"\"\"\n\n\ndef _add_subcommands():\n \"\"\"\n Individual commands (and sub-commands) are encapsulated in separate files\n under /commands. Collect these command groups, and add them underneath the\n top-level command (geocube).\n \"\"\"\n geocube.add_command(cmd_modules.make_geocube.make_geocube)\n\n\n_add_subcommands()\n",
"step-ids": [
4,
5,
6,
7,
8
]
}
|
[
4,
5,
6,
7,
8
] |
# -*- coding: utf-8 -*-
"""Digital Forensics Virtual File System (dfVFS).
dfVFS, or Digital Forensics Virtual File System, is a Python module
that provides read-only access to file-system objects from various
storage media types and file formats.
"""
|
normal
|
{
"blob_id": "f7d3096d669946e13186a893ffc53067e0fd0a0a",
"index": 1065,
"step-1": "<mask token>\n",
"step-2": "# -*- coding: utf-8 -*-\n\"\"\"Digital Forensics Virtual File System (dfVFS).\n\ndfVFS, or Digital Forensics Virtual File System, is a Python module\nthat provides read-only access to file-system objects from various\nstorage media types and file formats.\n\"\"\"\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
import sys
minus = "-"
plus = "+"
divis = "/"
multi = "*"
power = "^"
unary = "-"
br_op = "("
br_cl = ")"
operations = [power, divis, multi, minus, plus]
digits = ['1','2','3','4','5','6','7','8','9','0','.']
def find_close_pos(the_string):
open_count = 0
close_count = 0
for i in range(len(the_string)):
if the_string[i] == br_op :
open_count = open_count + 1
if the_string[i] == br_cl :
close_count = close_count + 1
if close_count == open_count:
return i
def parse(the_string):
parsed_string = []
number = ""
for i in range(len(the_string)):
if the_string[i] == "-" and i == 0: ### string = "-2 + blablabla"
number += the_string[i]
elif the_string[i] in operations and the_string[i-1] not in operations: ### ^
parsed_string.append(float(number))
parsed_string.append(the_string[i])
number = ""
elif the_string[i] == "-" and the_string[i-1] in operations: ### ^-
number += the_string[i]
elif the_string[i] in digits: ### 2
number += the_string[i]
parsed_string.append(float(number))
return parsed_string
def single_operation(parsed_string):
if parsed_string[1] == "+":
return parsed_string[0] + parsed_string[2]
if parsed_string[1] == "-":
return parsed_string[0] - parsed_string[2]
if parsed_string[1] == "/":
return parsed_string[0] / parsed_string[2]
if parsed_string[1] == "*":
return parsed_string[0] * parsed_string[2]
if parsed_string[1] == "^":
return parsed_string[0] ** parsed_string[2]
def compute(the_string):
try:
the_string = the_string.replace(" ", "") ### delete space chars
while br_op in the_string :
open_pos = the_string.index(br_op)
close_pos = find_close_pos(the_string)
old = the_string[open_pos:close_pos+1]
new = compute(the_string[open_pos+1:close_pos])
the_string = the_string.replace(old, str(new))
parsed_string = parse(the_string)
for operation in operations:
while operation in parsed_string:
pos = len(parsed_string) - parsed_string[::-1].index(operation)
res = single_operation(parsed_string[pos-2:pos+1])
parsed_string[pos-2:pos+1] = [res]
return parsed_string[0]
except:
pass
def read_file(path):
lines = [ line for line in open (path,'r') if line.strip() != "" ]
return lines
def main(path):
try:
for line in read_file(path):
print str(round(float(compute(line)),5)).rstrip('0').rstrip('.')
except:
print line
print "Unexpected error:", sys.exc_info()[0]
if __name__ == '__main__':
path = sys.argv[1]
main(path)
|
normal
|
{
"blob_id": "c0c8f40e43f1c27f8efa47cfc366c6076b77b9c9",
"index": 9337,
"step-1": "import sys\n\nminus = \"-\"\nplus = \"+\"\ndivis = \"/\"\nmulti = \"*\"\npower = \"^\"\nunary = \"-\"\nbr_op = \"(\"\nbr_cl = \")\"\n\noperations = [power, divis, multi, minus, plus]\ndigits = ['1','2','3','4','5','6','7','8','9','0','.']\n\ndef find_close_pos(the_string):\n\topen_count = 0\n\tclose_count = 0\n\tfor i in range(len(the_string)):\n\t\tif the_string[i] == br_op :\n\t\t\topen_count = open_count + 1\n\t\tif the_string[i] == br_cl :\n\t\t\tclose_count = close_count + 1\n\t\t\tif close_count == open_count:\n\t\t\t\treturn i\n\ndef parse(the_string):\n\tparsed_string = []\n\tnumber = \"\"\n\tfor i in range(len(the_string)):\n\t\tif the_string[i] == \"-\" and i == 0: ### string = \"-2 + blablabla\"\n\t\t\tnumber += the_string[i]\n\t\telif the_string[i] in operations and the_string[i-1] not in operations: ### ^\n\t\t\tparsed_string.append(float(number))\n\t\t\tparsed_string.append(the_string[i])\n\t\t\tnumber = \"\"\n\t\telif the_string[i] == \"-\" and the_string[i-1] in operations: ### ^-\n\t\t\tnumber += the_string[i]\n\t\telif the_string[i] in digits: ### 2\n\t\t\tnumber += the_string[i]\n\tparsed_string.append(float(number))\n\treturn parsed_string\n\ndef single_operation(parsed_string):\n\tif parsed_string[1] == \"+\":\n\t\treturn parsed_string[0] + parsed_string[2]\n\tif parsed_string[1] == \"-\":\n\t\treturn parsed_string[0] - parsed_string[2]\n\tif parsed_string[1] == \"/\":\n\t\treturn parsed_string[0] / parsed_string[2]\n\tif parsed_string[1] == \"*\":\n\t\treturn parsed_string[0] * parsed_string[2]\n\tif parsed_string[1] == \"^\":\n\t\treturn parsed_string[0] ** parsed_string[2]\n\ndef compute(the_string):\n\ttry:\n\t\tthe_string = the_string.replace(\" \", \"\") ### delete space chars\n\t\twhile br_op in the_string : \n\t\t\topen_pos = the_string.index(br_op)\n\t\t\tclose_pos = find_close_pos(the_string)\n\t\t\told = the_string[open_pos:close_pos+1]\n\t\t\tnew = compute(the_string[open_pos+1:close_pos])\n\t\t\tthe_string = the_string.replace(old, str(new))\n\t\tparsed_string = parse(the_string)\n\t\tfor operation in operations:\n\t\t\twhile operation in parsed_string:\n\t\t\t\tpos = len(parsed_string) - parsed_string[::-1].index(operation)\n\t\t\t\tres = single_operation(parsed_string[pos-2:pos+1])\n\t\t\t\tparsed_string[pos-2:pos+1] = [res]\n\t\treturn parsed_string[0]\n\texcept:\n\t\tpass\n\ndef read_file(path):\n\tlines = [ line for line in open (path,'r') if line.strip() != \"\" ]\n\treturn lines\n\ndef main(path):\n\ttry:\n\t\tfor line in read_file(path):\n\t\t\tprint str(round(float(compute(line)),5)).rstrip('0').rstrip('.')\n\texcept:\n\t\tprint line\n\t\tprint \"Unexpected error:\", sys.exc_info()[0]\n\nif __name__ == '__main__':\n\tpath = sys.argv[1]\n\tmain(path)\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
class Node:
<|reserved_special_token_0|>
class Solution(object):
def postorder(self, root):
"""
:type root: Node
:rtype: List[int]
"""
if not root:
return []
if not root.children:
return [root.val]
result = []
for child in root.children:
result += self.postorder(child)
result += [root.val]
return result
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Node:
def __init__(self, val, children):
self.val = val
self.children = children
class Solution(object):
def postorder(self, root):
"""
:type root: Node
:rtype: List[int]
"""
if not root:
return []
if not root.children:
return [root.val]
result = []
for child in root.children:
result += self.postorder(child)
result += [root.val]
return result
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Node:
def __init__(self, val, children):
self.val = val
self.children = children
class Solution(object):
def postorder(self, root):
"""
:type root: Node
:rtype: List[int]
"""
if not root:
return []
if not root.children:
return [root.val]
result = []
for child in root.children:
result += self.postorder(child)
result += [root.val]
return result
<|reserved_special_token_0|>
print(result)
<|reserved_special_token_1|>
class Node:
def __init__(self, val, children):
self.val = val
self.children = children
class Solution(object):
def postorder(self, root):
"""
:type root: Node
:rtype: List[int]
"""
if not root:
return []
if not root.children:
return [root.val]
result = []
for child in root.children:
result += self.postorder(child)
result += [root.val]
return result
n5 = Node(5, None)
n6 = Node(6, None)
n3 = Node(2, None)
n4 = Node(4, None)
n2 = Node(3, [n5, n6])
n1 = Node(1, [n2, n3, n4])
s = Solution()
result = s.postorder(n1)
print(result)
<|reserved_special_token_1|>
# Definition for a Node.
class Node:
def __init__(self, val, children):
self.val = val
self.children = children
class Solution(object):
def postorder(self, root):
"""
:type root: Node
:rtype: List[int]
"""
if not root:
return([])
if not root.children:
return([root.val])
result = []
for child in root.children:
result += self.postorder(child)
result += [root.val]
return(result)
n5 = Node(5,None)
n6 = Node(6,None)
n3 = Node(2,None)
n4 = Node(4,None)
n2 = Node(3,[n5,n6])
n1 = Node(1,[n2,n3,n4])
s = Solution()
result = s.postorder(n1)
print(result)
|
flexible
|
{
"blob_id": "93ec15a37bd5f022e8f6e226e3bf0e91cc0457c6",
"index": 2178,
"step-1": "class Node:\n <mask token>\n\n\nclass Solution(object):\n\n def postorder(self, root):\n \"\"\"\n :type root: Node\n :rtype: List[int]\n \"\"\"\n if not root:\n return []\n if not root.children:\n return [root.val]\n result = []\n for child in root.children:\n result += self.postorder(child)\n result += [root.val]\n return result\n\n\n<mask token>\n",
"step-2": "class Node:\n\n def __init__(self, val, children):\n self.val = val\n self.children = children\n\n\nclass Solution(object):\n\n def postorder(self, root):\n \"\"\"\n :type root: Node\n :rtype: List[int]\n \"\"\"\n if not root:\n return []\n if not root.children:\n return [root.val]\n result = []\n for child in root.children:\n result += self.postorder(child)\n result += [root.val]\n return result\n\n\n<mask token>\n",
"step-3": "class Node:\n\n def __init__(self, val, children):\n self.val = val\n self.children = children\n\n\nclass Solution(object):\n\n def postorder(self, root):\n \"\"\"\n :type root: Node\n :rtype: List[int]\n \"\"\"\n if not root:\n return []\n if not root.children:\n return [root.val]\n result = []\n for child in root.children:\n result += self.postorder(child)\n result += [root.val]\n return result\n\n\n<mask token>\nprint(result)\n",
"step-4": "class Node:\n\n def __init__(self, val, children):\n self.val = val\n self.children = children\n\n\nclass Solution(object):\n\n def postorder(self, root):\n \"\"\"\n :type root: Node\n :rtype: List[int]\n \"\"\"\n if not root:\n return []\n if not root.children:\n return [root.val]\n result = []\n for child in root.children:\n result += self.postorder(child)\n result += [root.val]\n return result\n\n\nn5 = Node(5, None)\nn6 = Node(6, None)\nn3 = Node(2, None)\nn4 = Node(4, None)\nn2 = Node(3, [n5, n6])\nn1 = Node(1, [n2, n3, n4])\ns = Solution()\nresult = s.postorder(n1)\nprint(result)\n",
"step-5": "# Definition for a Node.\nclass Node:\n def __init__(self, val, children):\n self.val = val\n self.children = children\n\nclass Solution(object):\n def postorder(self, root):\n \"\"\"\n :type root: Node\n :rtype: List[int]\n \"\"\"\n if not root:\n return([])\n if not root.children:\n return([root.val])\n result = []\n for child in root.children:\n result += self.postorder(child)\n result += [root.val]\n return(result)\n\n \nn5 = Node(5,None)\nn6 = Node(6,None)\nn3 = Node(2,None)\nn4 = Node(4,None)\nn2 = Node(3,[n5,n6])\nn1 = Node(1,[n2,n3,n4])\n\ns = Solution()\nresult = s.postorder(n1)\nprint(result)\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def get_paths(debug, dataset):
if debug and dataset == 'OASIS':
project_wd = os.getcwd()
project_data = os.path.join(project_wd, 'data')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BANC':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BOSTON':
project_wd = os.getcwd()
project_data = None
project_sink = None
elif debug and dataset == 'BANC_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif debug and dataset == 'UKBIO_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif not debug and dataset == 'OASIS':
project_wd = '/code'
project_data = os.path.join(os.sep, 'NaN', 'data')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BANC':
project_wd = '/code'
project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BOSTON':
project_wd = '/code'
project_data = None
project_sink = None
elif not debug and (dataset == 'BANC_freesurf' or dataset ==
'UKBIO_freesurf' or dataset == 'freesurf_combined'):
project_wd = '/code'
project_data = os.path.join(os.sep, 'code', 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Code Path: %s' % project_wd)
print('Data Path: %s' % project_data)
print('Data Out: %s' % project_sink)
return project_wd, project_data, project_sink
def get_output_path(model, analysis, ngen, random_seed, population_size,
debug, mutation, crossover, predicted_attribute):
rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,
population_size, debug, mutation, crossover, predicted_attribute)
output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_all_random_seed_paths(model, analysis, ngen, population_size, debug,
mutation, crossover, predicted_attribute):
if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==
'feat_combi' or analysis == 'vanilla_combi' or analysis ==
'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or
analysis == 'uniform_dist'):
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen)
elif analysis == 'population':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
elif analysis == 'mutation':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (
mutation, crossover))
else:
raise IOError('Analysis path not defined. Passed analysis was %s' %
analysis)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):
"""
This function gets the original dataset and transforms it into a uniformly
distributed dataset.
"""
project_wd, project_data, project_sink = get_paths(debug, dataset)
demographics, imgs, dataframe = get_data(project_data, dataset, debug,
project_wd, resamplefactor, raw=raw, analysis=analysis)
demographics['age_int'] = demographics['age'].astype('int32', copy=False)
age_range = np.arange(demographics['age'].min(), demographics['age'].max())
max_n = 14
age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]
age_range = np.setdiff1d(age_range, age_to_remove)
ids_to_use = []
for age in age_range:
ids_to_use.append(demographics.index[demographics['age_int'] == age
].tolist()[:max_n])
ids_to_use = [item for sublist in ids_to_use for item in sublist]
demographics = demographics[demographics.index.isin(ids_to_use)]
dataframe = dataframe.loc[demographics['id']]
print('Shape of the new demographics:')
print(demographics.shape)
print('Oldest %d and youngest %d subject' % (demographics['age_int'].
max(), demographics['age_int'].min()))
print('Number of age bins %d' % len(demographics['age_int'].unique()))
return demographics, dataframe
def get_best_pipeline_paths(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute):
output_path = get_output_path(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute)
checkpoint_path = os.path.join(output_path, 'checkpoint_folder')
if os.path.exists(checkpoint_path):
shutil.rmtree(checkpoint_path)
print('Deleted pre-exiting checkpoint folder')
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
print('Creating checkpoint folder')
return checkpoint_path
def drop_missing_features(dataframe):
"""
This function takes a dataframe and removes the already defined missing
columns from the dataframe.
"""
missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',
'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',
'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',
'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',
'Left-non-WM-hypointensities', 'non-WM-hypointensities',
'Right-WM-hypointensities', 'Left-WM-hypointensities',
'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',
'Left-choroid-plexus', 'Left-Lateral-Ventricle',
'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']
cleaned_df = dataframe.drop(missing_features, axis=1)
return cleaned_df
def get_data_covariates(dataPath, rawsubjectsId, dataset):
if dataset == 'OASIS':
demographics = pd.read_csv(os.path.join(dataPath,
'oasis_cross-sectional.csv'))
demographics = demographics.sort_values('ID')
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = demographics.loc[(demographics['CDR'] == 0) |
demographics['CDR'].isnull(), 'ID']
demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]
elif dataset == 'BANC':
column_names = ['ID', 'original_dataset', 'sex', 'Age']
demographics = pd.read_csv(os.path.join(dataPath,
'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = rawsubjectsId
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
assert len(selectedSubId) == len(demographics)
return demographics, selectedSubId
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def _load_nibabel(filePath):
img = nib.load(os.path.join(filePath))
return img
def get_config_dictionary():
regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {
'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001,
0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {
'max_depth': range(1, 11), 'min_samples_split': range(2, 21),
'min_samples_leaf': range(1, 21)},
'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1,
101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},
'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},
'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',
'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [
1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1,
0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001,
0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},
'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,
0.001), 'score_func': {'sklearn.feature_selection.f_regression':
None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':
range(1, 100), 'score_func': {
'sklearn.feature_selection.f_regression': None}},
'sklearn.feature_selection.VarianceThreshold': {'threshold': [
0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}
return regressor_config_dic
def get_mean_age(df):
mean_age = df['Age'].mean()
std_age = df['Age'].std()
print('Mean Age %.2f +- %.2f' % (mean_age, std_age))
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def get_mae_for_all_generations(dataset, random_seed, generations,
config_dict, tpot_path):
"""
Get the MAE values for both the training and test dataset
:return:
"""
saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed,
'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,
generations))
logbook = joblib.load(saved_path)
gen = list(logbook['log'].keys())
print('There are %d optminal pipelines' % len(gen))
print('These are the best pipelines')
for generation in gen:
print(logbook['log'][generation]['pipeline_name'])
all_mae_test = []
all_mae_train = []
pipeline_complexity = []
curr_gen_idx = 0
for generation in range(generations):
if generation == gen[curr_gen_idx]:
all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_test_mae']))
all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_score']))
pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]
]['pipeline_sklearn_obj'].named_steps.keys()))
if len(gen) > 1 and len(gen) > curr_gen_idx + 1:
curr_gen_idx += 1
else:
all_mae_test.append(all_mae_test[-1])
all_mae_train.append(all_mae_train[-1])
pipeline_complexity.append(pipeline_complexity[-1])
pipeline_complexity = np.array(pipeline_complexity)
return all_mae_test, all_mae_train, pipeline_complexity
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def create_age_histogram(df, dataset):
"""
Get an age array and plot and save the age histogram for the analysed sample
"""
set_publication_style()
plt.figure()
path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset
min_age = df['age'].min()
max_age = df['age'].max()
plt.hist(df['age'], bins=65, range=(min_age, max_age))
plt.xlabel('Age')
plt.ylabel('# of Subjects')
plt.legend()
plt.savefig(path_to_save)
plt.close()
def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=
None, cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
cm = confusion_matrix(y_true, y_pred)
labels = [int(x) for x in unique_labels(y_true, y_pred)]
classes = classes[labels]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),
xticklabels=classes, yticklabels=classes, title=title, ylabel=
'True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',
color='white' if cm[i, j] > thresh else 'black')
fig.tight_layout()
return ax, cm
def plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
fig, ax = plt.subplots()
im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.
shape[0]), xticklabels=classes, yticklabels=classes, title=title,
ylabel='True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '{0:.2f} ± {1:.2f}'
thresh = cm_mean.max() / 2.0
for i in range(cm_mean.shape[0]):
for j in range(cm_mean.shape[1]):
ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=
'center', va='center', color='white' if cm_mean[i, j] >
thresh else 'black')
fig.tight_layout()
return ax
def plot_predicted_vs_true(true_y, predicted_y, save_path, metric):
fig = plt.figure()
plt.scatter(true_y, predicted_y, alpha=0.5)
plt.ylabel('Predicted %s' % metric)
plt.xlabel('True %s' % metric)
plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),
max(true_y)), alpha=0.3, linestyle='--', color='b')
if metric == 'Age':
plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.savefig(save_path)
plt.close()
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def ttest_ind_corrected(performance_a, performance_b, k=10, r=10):
"""Corrected repeated k-fold cv test.
The test assumes that the classifiers were evaluated using cross validation.
Ref:
Bouckaert, Remco R., and Eibe Frank. "Evaluating the replicability of significance tests for comparing learning
algorithms." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004
Args:
performance_a: performances from classifier A
performance_b: performances from classifier B
k: number of folds
r: number of repetitions
Returns:
t: t-statistic of the corrected test.
prob: p-value of the corrected test.
"""
df = k * r - 1
x = performance_a - performance_b
m = np.mean(x)
sigma_2 = np.var(x, ddof=1)
denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(m, denom)
prob = stats.t.sf(np.abs(t), df) * 2
return t, prob
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def get_paths(debug, dataset):
if debug and dataset == 'OASIS':
project_wd = os.getcwd()
project_data = os.path.join(project_wd, 'data')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BANC':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BOSTON':
project_wd = os.getcwd()
project_data = None
project_sink = None
elif debug and dataset == 'BANC_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif debug and dataset == 'UKBIO_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif not debug and dataset == 'OASIS':
project_wd = '/code'
project_data = os.path.join(os.sep, 'NaN', 'data')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BANC':
project_wd = '/code'
project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BOSTON':
project_wd = '/code'
project_data = None
project_sink = None
elif not debug and (dataset == 'BANC_freesurf' or dataset ==
'UKBIO_freesurf' or dataset == 'freesurf_combined'):
project_wd = '/code'
project_data = os.path.join(os.sep, 'code', 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Code Path: %s' % project_wd)
print('Data Path: %s' % project_data)
print('Data Out: %s' % project_sink)
return project_wd, project_data, project_sink
def get_output_path(model, analysis, ngen, random_seed, population_size,
debug, mutation, crossover, predicted_attribute):
rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,
population_size, debug, mutation, crossover, predicted_attribute)
output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_all_random_seed_paths(model, analysis, ngen, population_size, debug,
mutation, crossover, predicted_attribute):
if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==
'feat_combi' or analysis == 'vanilla_combi' or analysis ==
'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or
analysis == 'uniform_dist'):
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen)
elif analysis == 'population':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
elif analysis == 'mutation':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (
mutation, crossover))
else:
raise IOError('Analysis path not defined. Passed analysis was %s' %
analysis)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):
"""
This function gets the original dataset and transforms it into a uniformly
distributed dataset.
"""
project_wd, project_data, project_sink = get_paths(debug, dataset)
demographics, imgs, dataframe = get_data(project_data, dataset, debug,
project_wd, resamplefactor, raw=raw, analysis=analysis)
demographics['age_int'] = demographics['age'].astype('int32', copy=False)
age_range = np.arange(demographics['age'].min(), demographics['age'].max())
max_n = 14
age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]
age_range = np.setdiff1d(age_range, age_to_remove)
ids_to_use = []
for age in age_range:
ids_to_use.append(demographics.index[demographics['age_int'] == age
].tolist()[:max_n])
ids_to_use = [item for sublist in ids_to_use for item in sublist]
demographics = demographics[demographics.index.isin(ids_to_use)]
dataframe = dataframe.loc[demographics['id']]
print('Shape of the new demographics:')
print(demographics.shape)
print('Oldest %d and youngest %d subject' % (demographics['age_int'].
max(), demographics['age_int'].min()))
print('Number of age bins %d' % len(demographics['age_int'].unique()))
return demographics, dataframe
def get_best_pipeline_paths(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute):
output_path = get_output_path(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute)
checkpoint_path = os.path.join(output_path, 'checkpoint_folder')
if os.path.exists(checkpoint_path):
shutil.rmtree(checkpoint_path)
print('Deleted pre-exiting checkpoint folder')
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
print('Creating checkpoint folder')
return checkpoint_path
def drop_missing_features(dataframe):
"""
This function takes a dataframe and removes the already defined missing
columns from the dataframe.
"""
missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',
'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',
'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',
'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',
'Left-non-WM-hypointensities', 'non-WM-hypointensities',
'Right-WM-hypointensities', 'Left-WM-hypointensities',
'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',
'Left-choroid-plexus', 'Left-Lateral-Ventricle',
'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']
cleaned_df = dataframe.drop(missing_features, axis=1)
return cleaned_df
def get_data_covariates(dataPath, rawsubjectsId, dataset):
if dataset == 'OASIS':
demographics = pd.read_csv(os.path.join(dataPath,
'oasis_cross-sectional.csv'))
demographics = demographics.sort_values('ID')
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = demographics.loc[(demographics['CDR'] == 0) |
demographics['CDR'].isnull(), 'ID']
demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]
elif dataset == 'BANC':
column_names = ['ID', 'original_dataset', 'sex', 'Age']
demographics = pd.read_csv(os.path.join(dataPath,
'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = rawsubjectsId
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
assert len(selectedSubId) == len(demographics)
return demographics, selectedSubId
def _multiprocessing_resample(img, target_affine):
resampled_img = image.resample_img(img, target_affine=target_affine,
interpolation='nearest')
return resampled_img
def _load_nibabel(filePath):
img = nib.load(os.path.join(filePath))
return img
def get_config_dictionary():
regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {
'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001,
0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {
'max_depth': range(1, 11), 'min_samples_split': range(2, 21),
'min_samples_leaf': range(1, 21)},
'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1,
101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},
'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},
'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',
'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [
1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1,
0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001,
0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},
'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,
0.001), 'score_func': {'sklearn.feature_selection.f_regression':
None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':
range(1, 100), 'score_func': {
'sklearn.feature_selection.f_regression': None}},
'sklearn.feature_selection.VarianceThreshold': {'threshold': [
0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}
return regressor_config_dic
def get_mean_age(df):
mean_age = df['Age'].mean()
std_age = df['Age'].std()
print('Mean Age %.2f +- %.2f' % (mean_age, std_age))
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def get_mae_for_all_generations(dataset, random_seed, generations,
config_dict, tpot_path):
"""
Get the MAE values for both the training and test dataset
:return:
"""
saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed,
'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,
generations))
logbook = joblib.load(saved_path)
gen = list(logbook['log'].keys())
print('There are %d optminal pipelines' % len(gen))
print('These are the best pipelines')
for generation in gen:
print(logbook['log'][generation]['pipeline_name'])
all_mae_test = []
all_mae_train = []
pipeline_complexity = []
curr_gen_idx = 0
for generation in range(generations):
if generation == gen[curr_gen_idx]:
all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_test_mae']))
all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_score']))
pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]
]['pipeline_sklearn_obj'].named_steps.keys()))
if len(gen) > 1 and len(gen) > curr_gen_idx + 1:
curr_gen_idx += 1
else:
all_mae_test.append(all_mae_test[-1])
all_mae_train.append(all_mae_train[-1])
pipeline_complexity.append(pipeline_complexity[-1])
pipeline_complexity = np.array(pipeline_complexity)
return all_mae_test, all_mae_train, pipeline_complexity
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def create_age_histogram(df, dataset):
"""
Get an age array and plot and save the age histogram for the analysed sample
"""
set_publication_style()
plt.figure()
path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset
min_age = df['age'].min()
max_age = df['age'].max()
plt.hist(df['age'], bins=65, range=(min_age, max_age))
plt.xlabel('Age')
plt.ylabel('# of Subjects')
plt.legend()
plt.savefig(path_to_save)
plt.close()
def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=
None, cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
cm = confusion_matrix(y_true, y_pred)
labels = [int(x) for x in unique_labels(y_true, y_pred)]
classes = classes[labels]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),
xticklabels=classes, yticklabels=classes, title=title, ylabel=
'True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',
color='white' if cm[i, j] > thresh else 'black')
fig.tight_layout()
return ax, cm
def plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
fig, ax = plt.subplots()
im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.
shape[0]), xticklabels=classes, yticklabels=classes, title=title,
ylabel='True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '{0:.2f} ± {1:.2f}'
thresh = cm_mean.max() / 2.0
for i in range(cm_mean.shape[0]):
for j in range(cm_mean.shape[1]):
ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=
'center', va='center', color='white' if cm_mean[i, j] >
thresh else 'black')
fig.tight_layout()
return ax
def plot_predicted_vs_true(true_y, predicted_y, save_path, metric):
fig = plt.figure()
plt.scatter(true_y, predicted_y, alpha=0.5)
plt.ylabel('Predicted %s' % metric)
plt.xlabel('True %s' % metric)
plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),
max(true_y)), alpha=0.3, linestyle='--', color='b')
if metric == 'Age':
plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.savefig(save_path)
plt.close()
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def ttest_ind_corrected(performance_a, performance_b, k=10, r=10):
"""Corrected repeated k-fold cv test.
The test assumes that the classifiers were evaluated using cross validation.
Ref:
Bouckaert, Remco R., and Eibe Frank. "Evaluating the replicability of significance tests for comparing learning
algorithms." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004
Args:
performance_a: performances from classifier A
performance_b: performances from classifier B
k: number of folds
r: number of repetitions
Returns:
t: t-statistic of the corrected test.
prob: p-value of the corrected test.
"""
df = k * r - 1
x = performance_a - performance_b
m = np.mean(x)
sigma_2 = np.var(x, ddof=1)
denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(m, denom)
prob = stats.t.sf(np.abs(t), df) * 2
return t, prob
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def get_paths(debug, dataset):
if debug and dataset == 'OASIS':
project_wd = os.getcwd()
project_data = os.path.join(project_wd, 'data')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BANC':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BOSTON':
project_wd = os.getcwd()
project_data = None
project_sink = None
elif debug and dataset == 'BANC_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif debug and dataset == 'UKBIO_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif not debug and dataset == 'OASIS':
project_wd = '/code'
project_data = os.path.join(os.sep, 'NaN', 'data')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BANC':
project_wd = '/code'
project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BOSTON':
project_wd = '/code'
project_data = None
project_sink = None
elif not debug and (dataset == 'BANC_freesurf' or dataset ==
'UKBIO_freesurf' or dataset == 'freesurf_combined'):
project_wd = '/code'
project_data = os.path.join(os.sep, 'code', 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Code Path: %s' % project_wd)
print('Data Path: %s' % project_data)
print('Data Out: %s' % project_sink)
return project_wd, project_data, project_sink
def get_output_path(model, analysis, ngen, random_seed, population_size,
debug, mutation, crossover, predicted_attribute):
rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,
population_size, debug, mutation, crossover, predicted_attribute)
output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_all_random_seed_paths(model, analysis, ngen, population_size, debug,
mutation, crossover, predicted_attribute):
if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==
'feat_combi' or analysis == 'vanilla_combi' or analysis ==
'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or
analysis == 'uniform_dist'):
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen)
elif analysis == 'population':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
elif analysis == 'mutation':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (
mutation, crossover))
else:
raise IOError('Analysis path not defined. Passed analysis was %s' %
analysis)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):
"""
This function gets the original dataset and transforms it into a uniformly
distributed dataset.
"""
project_wd, project_data, project_sink = get_paths(debug, dataset)
demographics, imgs, dataframe = get_data(project_data, dataset, debug,
project_wd, resamplefactor, raw=raw, analysis=analysis)
demographics['age_int'] = demographics['age'].astype('int32', copy=False)
age_range = np.arange(demographics['age'].min(), demographics['age'].max())
max_n = 14
age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]
age_range = np.setdiff1d(age_range, age_to_remove)
ids_to_use = []
for age in age_range:
ids_to_use.append(demographics.index[demographics['age_int'] == age
].tolist()[:max_n])
ids_to_use = [item for sublist in ids_to_use for item in sublist]
demographics = demographics[demographics.index.isin(ids_to_use)]
dataframe = dataframe.loc[demographics['id']]
print('Shape of the new demographics:')
print(demographics.shape)
print('Oldest %d and youngest %d subject' % (demographics['age_int'].
max(), demographics['age_int'].min()))
print('Number of age bins %d' % len(demographics['age_int'].unique()))
return demographics, dataframe
def get_best_pipeline_paths(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute):
output_path = get_output_path(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute)
checkpoint_path = os.path.join(output_path, 'checkpoint_folder')
if os.path.exists(checkpoint_path):
shutil.rmtree(checkpoint_path)
print('Deleted pre-exiting checkpoint folder')
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
print('Creating checkpoint folder')
return checkpoint_path
def drop_missing_features(dataframe):
"""
This function takes a dataframe and removes the already defined missing
columns from the dataframe.
"""
missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',
'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',
'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',
'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',
'Left-non-WM-hypointensities', 'non-WM-hypointensities',
'Right-WM-hypointensities', 'Left-WM-hypointensities',
'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',
'Left-choroid-plexus', 'Left-Lateral-Ventricle',
'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']
cleaned_df = dataframe.drop(missing_features, axis=1)
return cleaned_df
def get_data_covariates(dataPath, rawsubjectsId, dataset):
if dataset == 'OASIS':
demographics = pd.read_csv(os.path.join(dataPath,
'oasis_cross-sectional.csv'))
demographics = demographics.sort_values('ID')
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = demographics.loc[(demographics['CDR'] == 0) |
demographics['CDR'].isnull(), 'ID']
demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]
elif dataset == 'BANC':
column_names = ['ID', 'original_dataset', 'sex', 'Age']
demographics = pd.read_csv(os.path.join(dataPath,
'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = rawsubjectsId
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
assert len(selectedSubId) == len(demographics)
return demographics, selectedSubId
def _multiprocessing_resample(img, target_affine):
resampled_img = image.resample_img(img, target_affine=target_affine,
interpolation='nearest')
return resampled_img
def _load_nibabel(filePath):
img = nib.load(os.path.join(filePath))
return img
def get_config_dictionary():
regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {
'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001,
0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {
'max_depth': range(1, 11), 'min_samples_split': range(2, 21),
'min_samples_leaf': range(1, 21)},
'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1,
101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},
'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},
'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',
'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [
1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1,
0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001,
0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},
'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,
0.001), 'score_func': {'sklearn.feature_selection.f_regression':
None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':
range(1, 100), 'score_func': {
'sklearn.feature_selection.f_regression': None}},
'sklearn.feature_selection.VarianceThreshold': {'threshold': [
0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}
return regressor_config_dic
def get_mean_age(df):
mean_age = df['Age'].mean()
std_age = df['Age'].std()
print('Mean Age %.2f +- %.2f' % (mean_age, std_age))
def get_data(project_data, dataset, debug, project_wd, resamplefactor, raw,
analysis):
""" Load the csv files and return
:param project_data:
:param dataset:
:param debug:
:param project_wd:
:param resamplefactor:
:raw: Which type of fressesfurfer should we analyse (the raw, where both
datasets have not been matched or the not raw where the number of columns
between dataset is the same)
:return: demographics:
:return: demographics:
:return: dataframe.values: Just the numeric values of the dataframe
"""
if dataset == 'freesurf_combined' and raw == True:
raise ValueError('The combined analysis cannot use the raw dataset')
print('Loading Brain image data')
elif dataset == 'OASIS':
fileList = os.listdir(project_data)
rawsubjectsId = [re.sub('^smwc1(.*?)\\_mpr-1_anon.nii$', '\\1',
file) for file in fileList if file.endswith('.nii')]
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, dataset)
get_mean_age(demographics)
imgs = [nib.load(os.path.join(project_data,
'smwc1%s_mpr-1_anon.nii' % subject)) for subject in tqdm(
selectedSubId)]
elif dataset == 'BANC':
project_data_path = os.path.join(project_data, 'wm_data')
fileList = os.listdir(project_data_path)
rawsubjectsId = [file[5:12] for file in fileList if file.endswith(
'.nii.gz')]
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, dataset)
get_mean_age(demographics)
subjectsFile = [os.path.join(project_data_path, file) for file in
fileList if file[5:12] in selectedSubId]
with Pool() as p:
imgs = list(tqdm(p.imap(_load_nibabel, subjectsFile), total=len
(selectedSubId)))
elif dataset == 'BANC_freesurf' and raw == True:
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'BANC',
'aparc_aseg_stats_BANC.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},
inplace=True)
return demographics, None, freesurf_df
elif dataset == 'UKBIO_freesurf' and raw == False and not analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_UKBIO.csv'), delimiter=',')
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',', index_col=False)
demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
freesurf_df = freesurf_df.set_index('id')
return demographics, None, freesurf_df
elif dataset == 'BANC_freesurf' and raw == False and not analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},
inplace=True)
return demographics, None, freesurf_df
elif dataset == 'UKBIO_freesurf' and raw == True and not analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
freesurf_df = freesurf_df.drop(columns='id.4844')
demographics = freesurf_df[['age', 'sex', 'id']].copy()
freesurf_df = freesurf_df.set_index('id')
return demographics, None, freesurf_df
elif dataset == 'UKBIO_freesurf' and raw == False and analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_UKBIO_summary.csv'), delimiter=',')
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
return demographics, None, freesurf_df
elif dataset == 'BANC_freesurf' and raw == False and analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_BANC_summary.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},
inplace=True)
return demographics, None, freesurf_df
elif dataset == 'freesurf_combined':
ukbio_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_UKBIO.csv'), delimiter=',', index_col=0)
banc_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
rawsubjectsId = banc_df.index
banc_demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
ukbio_demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
freesurfer_df = pd.concat([ukbio_df, banc_df])
tmp = banc_demographics.drop('original_dataset', axis=1)
tmp.rename(index=str, columns={'ID': 'id', 'Age': 'age'}, inplace=True)
tmp['sex'] = tmp['sex'].map({'F': 'female', 'M': 'male'})
tmp['dataset'] = 'banc'
ukbio_demographics['dataset'] = 'ukbio'
demographics = pd.concat([ukbio_demographics, tmp], sort=False)
bins = 17, 30, 40, 50, 60, 70, 80, 90
group_labels = range(1, len(bins))
demographics['age_band'] = pd.cut(demographics['age'], bins, labels
=group_labels)
sex_age_group = demographics.groupby(['sex', 'age_band'])
demographics['stratify'] = sex_age_group.grouper.group_info[0] + 1
return demographics, None, freesurfer_df
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Resample the dataset by a factor of %d' % resamplefactor)
print('Original image size: %s' % (imgs[0].shape,))
resampleby2affine = np.array([[resamplefactor, 1, 1, 1], [1,
resamplefactor, 1, 1], [1, 1, resamplefactor, 1], [1, 1, 1, 1]])
target_affine = np.multiply(imgs[0].affine, resampleby2affine)
print('Resampling Images')
with Pool() as p:
args = partial(_multiprocessing_resample, target_affine=target_affine)
resampledimgs = list(tqdm(p.imap(args, imgs), total=len(imgs)))
print('Resampled image size: %s' % (resampledimgs[0].shape,))
print('Compute brain mask')
MeanImgMask = masking.compute_multi_epi_mask(resampledimgs,
lower_cutoff=0.001, upper_cutoff=0.85, opening=False)
maskedData = [masking.apply_mask(img, MeanImgMask) for img in resampledimgs
]
if debug:
mask_path = os.path.join(project_wd, 'BayOptPy', 'tpot')
print('Saving brain mask: %s' % mask_path)
nib.save(MeanImgMask, os.path.join(mask_path, 'mask_%s.nii.gz' %
dataset))
print('Applied mask to the dataset')
maskedData = np.array(maskedData)
return demographics, imgs, maskedData
def get_mae_for_all_generations(dataset, random_seed, generations,
config_dict, tpot_path):
"""
Get the MAE values for both the training and test dataset
:return:
"""
saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed,
'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,
generations))
logbook = joblib.load(saved_path)
gen = list(logbook['log'].keys())
print('There are %d optminal pipelines' % len(gen))
print('These are the best pipelines')
for generation in gen:
print(logbook['log'][generation]['pipeline_name'])
all_mae_test = []
all_mae_train = []
pipeline_complexity = []
curr_gen_idx = 0
for generation in range(generations):
if generation == gen[curr_gen_idx]:
all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_test_mae']))
all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_score']))
pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]
]['pipeline_sklearn_obj'].named_steps.keys()))
if len(gen) > 1 and len(gen) > curr_gen_idx + 1:
curr_gen_idx += 1
else:
all_mae_test.append(all_mae_test[-1])
all_mae_train.append(all_mae_train[-1])
pipeline_complexity.append(pipeline_complexity[-1])
pipeline_complexity = np.array(pipeline_complexity)
return all_mae_test, all_mae_train, pipeline_complexity
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def create_age_histogram(df, dataset):
"""
Get an age array and plot and save the age histogram for the analysed sample
"""
set_publication_style()
plt.figure()
path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset
min_age = df['age'].min()
max_age = df['age'].max()
plt.hist(df['age'], bins=65, range=(min_age, max_age))
plt.xlabel('Age')
plt.ylabel('# of Subjects')
plt.legend()
plt.savefig(path_to_save)
plt.close()
def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=
None, cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
cm = confusion_matrix(y_true, y_pred)
labels = [int(x) for x in unique_labels(y_true, y_pred)]
classes = classes[labels]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),
xticklabels=classes, yticklabels=classes, title=title, ylabel=
'True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',
color='white' if cm[i, j] > thresh else 'black')
fig.tight_layout()
return ax, cm
def plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
fig, ax = plt.subplots()
im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.
shape[0]), xticklabels=classes, yticklabels=classes, title=title,
ylabel='True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '{0:.2f} ± {1:.2f}'
thresh = cm_mean.max() / 2.0
for i in range(cm_mean.shape[0]):
for j in range(cm_mean.shape[1]):
ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=
'center', va='center', color='white' if cm_mean[i, j] >
thresh else 'black')
fig.tight_layout()
return ax
def plot_predicted_vs_true(true_y, predicted_y, save_path, metric):
fig = plt.figure()
plt.scatter(true_y, predicted_y, alpha=0.5)
plt.ylabel('Predicted %s' % metric)
plt.xlabel('True %s' % metric)
plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),
max(true_y)), alpha=0.3, linestyle='--', color='b')
if metric == 'Age':
plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.savefig(save_path)
plt.close()
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def ttest_ind_corrected(performance_a, performance_b, k=10, r=10):
"""Corrected repeated k-fold cv test.
The test assumes that the classifiers were evaluated using cross validation.
Ref:
Bouckaert, Remco R., and Eibe Frank. "Evaluating the replicability of significance tests for comparing learning
algorithms." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004
Args:
performance_a: performances from classifier A
performance_b: performances from classifier B
k: number of folds
r: number of repetitions
Returns:
t: t-statistic of the corrected test.
prob: p-value of the corrected test.
"""
df = k * r - 1
x = performance_a - performance_b
m = np.mean(x)
sigma_2 = np.var(x, ddof=1)
denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(m, denom)
prob = stats.t.sf(np.abs(t), df) * 2
return t, prob
<|reserved_special_token_1|>
<|reserved_special_token_0|>
matplotlib.use('Agg')
<|reserved_special_token_0|>
sns.set()
def get_paths(debug, dataset):
if debug and dataset == 'OASIS':
project_wd = os.getcwd()
project_data = os.path.join(project_wd, 'data')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BANC':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BOSTON':
project_wd = os.getcwd()
project_data = None
project_sink = None
elif debug and dataset == 'BANC_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif debug and dataset == 'UKBIO_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif not debug and dataset == 'OASIS':
project_wd = '/code'
project_data = os.path.join(os.sep, 'NaN', 'data')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BANC':
project_wd = '/code'
project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BOSTON':
project_wd = '/code'
project_data = None
project_sink = None
elif not debug and (dataset == 'BANC_freesurf' or dataset ==
'UKBIO_freesurf' or dataset == 'freesurf_combined'):
project_wd = '/code'
project_data = os.path.join(os.sep, 'code', 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Code Path: %s' % project_wd)
print('Data Path: %s' % project_data)
print('Data Out: %s' % project_sink)
return project_wd, project_data, project_sink
def get_output_path(model, analysis, ngen, random_seed, population_size,
debug, mutation, crossover, predicted_attribute):
rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,
population_size, debug, mutation, crossover, predicted_attribute)
output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_all_random_seed_paths(model, analysis, ngen, population_size, debug,
mutation, crossover, predicted_attribute):
if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==
'feat_combi' or analysis == 'vanilla_combi' or analysis ==
'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or
analysis == 'uniform_dist'):
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen)
elif analysis == 'population':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%05d_population_size' % population_size,
'%03d_generations' % ngen)
elif analysis == 'mutation':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' % model,
'Output', analysis, predicted_attribute, '%03d_generations' %
ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' % model, 'Output', analysis, predicted_attribute,
'%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (
mutation, crossover))
else:
raise IOError('Analysis path not defined. Passed analysis was %s' %
analysis)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):
"""
This function gets the original dataset and transforms it into a uniformly
distributed dataset.
"""
project_wd, project_data, project_sink = get_paths(debug, dataset)
demographics, imgs, dataframe = get_data(project_data, dataset, debug,
project_wd, resamplefactor, raw=raw, analysis=analysis)
demographics['age_int'] = demographics['age'].astype('int32', copy=False)
age_range = np.arange(demographics['age'].min(), demographics['age'].max())
max_n = 14
age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]
age_range = np.setdiff1d(age_range, age_to_remove)
ids_to_use = []
for age in age_range:
ids_to_use.append(demographics.index[demographics['age_int'] == age
].tolist()[:max_n])
ids_to_use = [item for sublist in ids_to_use for item in sublist]
demographics = demographics[demographics.index.isin(ids_to_use)]
dataframe = dataframe.loc[demographics['id']]
print('Shape of the new demographics:')
print(demographics.shape)
print('Oldest %d and youngest %d subject' % (demographics['age_int'].
max(), demographics['age_int'].min()))
print('Number of age bins %d' % len(demographics['age_int'].unique()))
return demographics, dataframe
def get_best_pipeline_paths(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute):
output_path = get_output_path(model, analysis, ngen, random_seed,
population_size, debug, mutation, crossover, predicted_attribute)
checkpoint_path = os.path.join(output_path, 'checkpoint_folder')
if os.path.exists(checkpoint_path):
shutil.rmtree(checkpoint_path)
print('Deleted pre-exiting checkpoint folder')
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
print('Creating checkpoint folder')
return checkpoint_path
def drop_missing_features(dataframe):
"""
This function takes a dataframe and removes the already defined missing
columns from the dataframe.
"""
missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',
'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',
'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',
'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',
'Left-non-WM-hypointensities', 'non-WM-hypointensities',
'Right-WM-hypointensities', 'Left-WM-hypointensities',
'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',
'Left-choroid-plexus', 'Left-Lateral-Ventricle',
'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']
cleaned_df = dataframe.drop(missing_features, axis=1)
return cleaned_df
def get_data_covariates(dataPath, rawsubjectsId, dataset):
if dataset == 'OASIS':
demographics = pd.read_csv(os.path.join(dataPath,
'oasis_cross-sectional.csv'))
demographics = demographics.sort_values('ID')
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = demographics.loc[(demographics['CDR'] == 0) |
demographics['CDR'].isnull(), 'ID']
demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]
elif dataset == 'BANC':
column_names = ['ID', 'original_dataset', 'sex', 'Age']
demographics = pd.read_csv(os.path.join(dataPath,
'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
demographics = demographics.loc[~demographics['ID'].isin(
missingsubjectsId)]
selectedSubId = rawsubjectsId
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
assert len(selectedSubId) == len(demographics)
return demographics, selectedSubId
def _multiprocessing_resample(img, target_affine):
resampled_img = image.resample_img(img, target_affine=target_affine,
interpolation='nearest')
return resampled_img
def _load_nibabel(filePath):
img = nib.load(os.path.join(filePath))
return img
def get_config_dictionary():
regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {
'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001,
0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {
'max_depth': range(1, 11), 'min_samples_split': range(2, 21),
'min_samples_leaf': range(1, 21)},
'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1,
101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},
'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},
'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',
'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [
1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1,
0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001,
0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},
'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,
0.001), 'score_func': {'sklearn.feature_selection.f_regression':
None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':
range(1, 100), 'score_func': {
'sklearn.feature_selection.f_regression': None}},
'sklearn.feature_selection.VarianceThreshold': {'threshold': [
0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}
return regressor_config_dic
def get_mean_age(df):
mean_age = df['Age'].mean()
std_age = df['Age'].std()
print('Mean Age %.2f +- %.2f' % (mean_age, std_age))
def get_data(project_data, dataset, debug, project_wd, resamplefactor, raw,
analysis):
""" Load the csv files and return
:param project_data:
:param dataset:
:param debug:
:param project_wd:
:param resamplefactor:
:raw: Which type of fressesfurfer should we analyse (the raw, where both
datasets have not been matched or the not raw where the number of columns
between dataset is the same)
:return: demographics:
:return: demographics:
:return: dataframe.values: Just the numeric values of the dataframe
"""
if dataset == 'freesurf_combined' and raw == True:
raise ValueError('The combined analysis cannot use the raw dataset')
print('Loading Brain image data')
elif dataset == 'OASIS':
fileList = os.listdir(project_data)
rawsubjectsId = [re.sub('^smwc1(.*?)\\_mpr-1_anon.nii$', '\\1',
file) for file in fileList if file.endswith('.nii')]
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, dataset)
get_mean_age(demographics)
imgs = [nib.load(os.path.join(project_data,
'smwc1%s_mpr-1_anon.nii' % subject)) for subject in tqdm(
selectedSubId)]
elif dataset == 'BANC':
project_data_path = os.path.join(project_data, 'wm_data')
fileList = os.listdir(project_data_path)
rawsubjectsId = [file[5:12] for file in fileList if file.endswith(
'.nii.gz')]
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, dataset)
get_mean_age(demographics)
subjectsFile = [os.path.join(project_data_path, file) for file in
fileList if file[5:12] in selectedSubId]
with Pool() as p:
imgs = list(tqdm(p.imap(_load_nibabel, subjectsFile), total=len
(selectedSubId)))
elif dataset == 'BANC_freesurf' and raw == True:
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'BANC',
'aparc_aseg_stats_BANC.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},
inplace=True)
return demographics, None, freesurf_df
elif dataset == 'UKBIO_freesurf' and raw == False and not analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_UKBIO.csv'), delimiter=',')
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',', index_col=False)
demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
freesurf_df = freesurf_df.set_index('id')
return demographics, None, freesurf_df
elif dataset == 'BANC_freesurf' and raw == False and not analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},
inplace=True)
return demographics, None, freesurf_df
elif dataset == 'UKBIO_freesurf' and raw == True and not analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
freesurf_df = freesurf_df.drop(columns='id.4844')
demographics = freesurf_df[['age', 'sex', 'id']].copy()
freesurf_df = freesurf_df.set_index('id')
return demographics, None, freesurf_df
elif dataset == 'UKBIO_freesurf' and raw == False and analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_UKBIO_summary.csv'), delimiter=',')
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
return demographics, None, freesurf_df
elif dataset == 'BANC_freesurf' and raw == False and analysis == 'summary_data':
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_BANC_summary.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},
inplace=True)
return demographics, None, freesurf_df
elif dataset == 'freesurf_combined':
ukbio_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_UKBIO.csv'), delimiter=',', index_col=0)
banc_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'matched_dataset',
'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess', 'original_dataset', 'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
rawsubjectsId = banc_df.index
banc_demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId, 'BANC')
ukbio_demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
freesurfer_df = pd.concat([ukbio_df, banc_df])
tmp = banc_demographics.drop('original_dataset', axis=1)
tmp.rename(index=str, columns={'ID': 'id', 'Age': 'age'}, inplace=True)
tmp['sex'] = tmp['sex'].map({'F': 'female', 'M': 'male'})
tmp['dataset'] = 'banc'
ukbio_demographics['dataset'] = 'ukbio'
demographics = pd.concat([ukbio_demographics, tmp], sort=False)
bins = 17, 30, 40, 50, 60, 70, 80, 90
group_labels = range(1, len(bins))
demographics['age_band'] = pd.cut(demographics['age'], bins, labels
=group_labels)
sex_age_group = demographics.groupby(['sex', 'age_band'])
demographics['stratify'] = sex_age_group.grouper.group_info[0] + 1
return demographics, None, freesurfer_df
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Resample the dataset by a factor of %d' % resamplefactor)
print('Original image size: %s' % (imgs[0].shape,))
resampleby2affine = np.array([[resamplefactor, 1, 1, 1], [1,
resamplefactor, 1, 1], [1, 1, resamplefactor, 1], [1, 1, 1, 1]])
target_affine = np.multiply(imgs[0].affine, resampleby2affine)
print('Resampling Images')
with Pool() as p:
args = partial(_multiprocessing_resample, target_affine=target_affine)
resampledimgs = list(tqdm(p.imap(args, imgs), total=len(imgs)))
print('Resampled image size: %s' % (resampledimgs[0].shape,))
print('Compute brain mask')
MeanImgMask = masking.compute_multi_epi_mask(resampledimgs,
lower_cutoff=0.001, upper_cutoff=0.85, opening=False)
maskedData = [masking.apply_mask(img, MeanImgMask) for img in resampledimgs
]
if debug:
mask_path = os.path.join(project_wd, 'BayOptPy', 'tpot')
print('Saving brain mask: %s' % mask_path)
nib.save(MeanImgMask, os.path.join(mask_path, 'mask_%s.nii.gz' %
dataset))
print('Applied mask to the dataset')
maskedData = np.array(maskedData)
return demographics, imgs, maskedData
def get_mae_for_all_generations(dataset, random_seed, generations,
config_dict, tpot_path):
"""
Get the MAE values for both the training and test dataset
:return:
"""
saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed,
'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,
generations))
logbook = joblib.load(saved_path)
gen = list(logbook['log'].keys())
print('There are %d optminal pipelines' % len(gen))
print('These are the best pipelines')
for generation in gen:
print(logbook['log'][generation]['pipeline_name'])
all_mae_test = []
all_mae_train = []
pipeline_complexity = []
curr_gen_idx = 0
for generation in range(generations):
if generation == gen[curr_gen_idx]:
all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_test_mae']))
all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][
'pipeline_score']))
pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]
]['pipeline_sklearn_obj'].named_steps.keys()))
if len(gen) > 1 and len(gen) > curr_gen_idx + 1:
curr_gen_idx += 1
else:
all_mae_test.append(all_mae_test[-1])
all_mae_train.append(all_mae_train[-1])
pipeline_complexity.append(pipeline_complexity[-1])
pipeline_complexity = np.array(pipeline_complexity)
return all_mae_test, all_mae_train, pipeline_complexity
def set_publication_style():
plt.style.use(['seaborn-white', 'seaborn-talk'])
matplotlib.rc('font', family='Times New Roman')
sns.set_style('white', {'axes.spines.top': False, 'axes.spines.right':
False, 'axes.labelsize': 'large'})
def create_age_histogram(df, dataset):
"""
Get an age array and plot and save the age histogram for the analysed sample
"""
set_publication_style()
plt.figure()
path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset
min_age = df['age'].min()
max_age = df['age'].max()
plt.hist(df['age'], bins=65, range=(min_age, max_age))
plt.xlabel('Age')
plt.ylabel('# of Subjects')
plt.legend()
plt.savefig(path_to_save)
plt.close()
def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=
None, cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
cm = confusion_matrix(y_true, y_pred)
labels = [int(x) for x in unique_labels(y_true, y_pred)]
classes = classes[labels]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),
xticklabels=classes, yticklabels=classes, title=title, ylabel=
'True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.0
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',
color='white' if cm[i, j] > thresh else 'black')
fig.tight_layout()
return ax, cm
def plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
fig, ax = plt.subplots()
im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.
shape[0]), xticklabels=classes, yticklabels=classes, title=title,
ylabel='True label', xlabel='Predicted label')
plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=
'anchor')
fmt = '{0:.2f} ± {1:.2f}'
thresh = cm_mean.max() / 2.0
for i in range(cm_mean.shape[0]):
for j in range(cm_mean.shape[1]):
ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=
'center', va='center', color='white' if cm_mean[i, j] >
thresh else 'black')
fig.tight_layout()
return ax
def plot_predicted_vs_true(true_y, predicted_y, save_path, metric):
fig = plt.figure()
plt.scatter(true_y, predicted_y, alpha=0.5)
plt.ylabel('Predicted %s' % metric)
plt.xlabel('True %s' % metric)
plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),
max(true_y)), alpha=0.3, linestyle='--', color='b')
if metric == 'Age':
plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(
true_y), max(predicted_y)), step=10))
plt.savefig(save_path)
plt.close()
def load_cognitive_data(project_data):
cog_path = os.path.join(project_data, 'cog_ukbio')
cog_df = pd.read_csv(os.path.join(cog_path, 'UKB_10k_cog_bmi.csv'))
cog_df = cog_df.set_index('ID')
return cog_df
def ttest_ind_corrected(performance_a, performance_b, k=10, r=10):
"""Corrected repeated k-fold cv test.
The test assumes that the classifiers were evaluated using cross validation.
Ref:
Bouckaert, Remco R., and Eibe Frank. "Evaluating the replicability of significance tests for comparing learning
algorithms." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004
Args:
performance_a: performances from classifier A
performance_b: performances from classifier B
k: number of folds
r: number of repetitions
Returns:
t: t-statistic of the corrected test.
prob: p-value of the corrected test.
"""
df = k * r - 1
x = performance_a - performance_b
m = np.mean(x)
sigma_2 = np.var(x, ddof=1)
denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(m, denom)
prob = stats.t.sf(np.abs(t), df) * 2
return t, prob
<|reserved_special_token_1|>
import joblib
import os
import shutil
import re
from scipy import stats
from functools import partial
import pandas as pd
from multiprocessing import Process, Pool
from nilearn import masking, image
import nibabel as nib
import numpy as np
from tqdm import tqdm
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
import seaborn as sns
sns.set()
def get_paths(debug, dataset):
if debug and dataset == 'OASIS':
project_wd = os.getcwd()
project_data = os.path.join(project_wd, 'data')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BANC':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif debug and dataset == 'BOSTON':
project_wd = os.getcwd()
project_data = None
project_sink = None
elif debug and dataset == 'BANC_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif debug and dataset == 'UKBIO_freesurf':
project_wd = os.getcwd()
project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
elif not debug and dataset == 'OASIS':
project_wd = '/code'
project_data = os.path.join(os.sep, 'NaN', 'data')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BANC':
project_wd = '/code'
project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')
project_sink = os.path.join(project_data, 'output')
elif not debug and dataset == 'BOSTON':
project_wd = '/code'
project_data = None
project_sink = None
elif not debug and (dataset == 'BANC_freesurf' or
dataset == 'UKBIO_freesurf' or
dataset == 'freesurf_combined'
):
project_wd = '/code'
project_data = os.path.join(os.sep, 'code', 'BayOptPy',
'freesurfer_preprocess')
project_sink = None
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Code Path: %s' %project_wd)
print('Data Path: %s' %project_data)
print('Data Out: %s' %project_sink )
return project_wd, project_data, project_sink
def get_output_path(model, analysis, ngen, random_seed, population_size, debug,
mutation, crossover, predicted_attribute):
# Check if output path exists, otherwise create it
rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen, population_size,
debug, mutation, crossover,
predicted_attribute)
output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' %random_seed)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_all_random_seed_paths(model, analysis, ngen, population_size, debug, mutation,
crossover, predicted_attribute):
# As they should have been created by the get_output_path, do not create
# path but just find its location
if analysis == 'vanilla' or analysis == 'feat_selec' or \
analysis == 'feat_combi' or analysis == 'vanilla_combi' or \
analysis == 'random_seed' or analysis == 'ukbio' or \
analysis == 'summary_data' or analysis == 'uniform_dist':
if debug:
output_path = os.path.join('BayOptPy', 'tpot_%s' %model, 'Output',
analysis, predicted_attribute,
'%03d_generations' %ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' %model,
'Output', analysis,
predicted_attribute,
'%03d_generations' %ngen)
elif analysis == 'population':
if debug:
output_path = os.path.join('BayOptPy',
'tpot_%s' %model,
'Output', analysis, predicted_attribute,
'%05d_population_size' %population_size,
'%03d_generations' %ngen)
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' %model,
'Output', analysis,
predicted_attribute,
'%05d_population_size' %population_size,
'%03d_generations' %ngen)
elif analysis == 'mutation':
if debug:
output_path = os.path.join('BayOptPy',
'tpot_%s' %model,
'Output', analysis,
predicted_attribute,
'%03d_generations' %ngen,
'%.01f_mut_%.01f_cross' %(mutation, crossover))
else:
output_path = os.path.join(os.sep, 'code', 'BayOptPy',
'tpot_%s' %model,
'Output', analysis,
predicted_attribute,
'%03d_generations' %ngen,
'%.01f_mut_%.01f_cross' %(mutation, crossover))
else:
raise IOError('Analysis path not defined. Passed analysis was %s'
%analysis)
if not os.path.exists(output_path):
os.makedirs(output_path)
return output_path
def get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):
"""
This function gets the original dataset and transforms it into a uniformly
distributed dataset.
"""
project_wd, project_data, project_sink = get_paths(debug, dataset)
demographics, imgs, dataframe = get_data(project_data, dataset,
debug, project_wd,
resamplefactor,
raw=raw,
analysis=analysis)
# transform age into ints
demographics['age_int'] = demographics['age'].astype('int32', copy=False)
# Select 14 subjects for all ages that have 14 representatives.
age_range = np.arange(demographics['age'].min(), demographics['age'].max())
# remove entry where you don't have 14 subjects
max_n = 14
age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]
age_range = np.setdiff1d(age_range, age_to_remove)
# iterate over the dataframe and select 14 subjects for each age range
ids_to_use = []
for age in age_range:
ids_to_use.append(demographics.index[demographics['age_int'] ==
age].tolist()[:max_n])
# flatten ids_to_use
ids_to_use = [item for sublist in ids_to_use for item in sublist]
# Filter the demographics dataframe
demographics = demographics[demographics.index.isin(ids_to_use)]
# set subject's id as index
# filter dataset using index of the subjects
dataframe = dataframe.loc[demographics['id']]
# Print some diagnosis
print('Shape of the new demographics:')
print(demographics.shape)
print('Oldest %d and youngest %d subject' %(demographics['age_int'].max(),
demographics['age_int'].min()))
print('Number of age bins %d' %len(demographics['age_int'].unique()))
return demographics, dataframe
def get_best_pipeline_paths(model, analysis, ngen, random_seed, population_size, debug,
mutation, crossover, predicted_attribute):
# check if folder exists and in case yes, remove it as new runs will save
# new files without overwritting
output_path = get_output_path(model, analysis, ngen, random_seed, population_size,
debug, mutation, crossover,
predicted_attribute)
checkpoint_path = os.path.join(output_path, 'checkpoint_folder')
# Delete folder if it already exists and create a new one
if os.path.exists(checkpoint_path):
shutil.rmtree(checkpoint_path)
print('Deleted pre-exiting checkpoint folder')
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
print('Creating checkpoint folder')
return checkpoint_path
def drop_missing_features(dataframe):
'''
This function takes a dataframe and removes the already defined missing
columns from the dataframe.
'''
missing_features = [# This features are repeated or missing on the BIOBANK
# dataset
'BrainSegVolNotVent',
'BrainSegVolNotVent.1',
'BrainSegVolNotVent.2',
'eTIV',
'eTIV.1',
# Drop additional features that are 0 or have no
# biological meaning
'SurfaceHoles',
'rhSurfaceHoles',
'lhSurfaceHoles',
'BrainSegVolNotVentSurf',
'BrainSegVol',
'Optic-Chiasm',
'Right-non-WM-hypointensities',
'Left-non-WM-hypointensities',
'non-WM-hypointensities',
'Right-WM-hypointensities',
'Left-WM-hypointensities',
'WM-hypointensities',
'5th-Ventricle',
'Right-choroid-plexus',
'Left-choroid-plexus',
'Left-Lateral-Ventricle',
'Right-Lateral-Ventricle',
'Left-Inf-Lat-Vent',
'Right-Inf-Lat-Vent',
]
cleaned_df = dataframe.drop(missing_features, axis=1)
return cleaned_df
def get_data_covariates(dataPath, rawsubjectsId, dataset):
if dataset == 'OASIS':
# Load the demographic details from the dataset
demographics = pd.read_csv(os.path.join(dataPath, 'oasis_cross-sectional.csv'))
# sort demographics by ascending id
demographics = demographics.sort_values('ID')
# Check if there is any subject for which we have the fmri data but no demographics
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
# remove the demographic data from the missing subjects
demographics = demographics.loc[~demographics['ID'].isin(missingsubjectsId)]
# list of subjects that do not have dementia (CDR > 0)
selectedSubId = demographics.loc[(demographics['CDR'] == 0) | (demographics['CDR'].isnull()), 'ID']
# filter demographics to exclude those with CDR > 0
demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]
elif dataset == 'BANC':
# Load the demographic details from the dataset
column_names = ['ID', 'original_dataset', 'sex', 'Age']
demographics = pd.read_csv(os.path.join(dataPath,'original_dataset',
'BANC',
'BANC_2016.csv'), names=column_names)
# Check if there is any subject for which we have the fmri data but no demographics
missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))
# remove the demographic data from the missing subjects
demographics = demographics.loc[~demographics['ID'].isin(missingsubjectsId)]
selectedSubId = rawsubjectsId
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
# do some sanity checks
# Check if you have the same number of selectedsubjectsid as the demographic information
assert(len(selectedSubId) == len(demographics))
return demographics, selectedSubId
def _multiprocessing_resample(img, target_affine):
resampled_img = image.resample_img(img, target_affine=target_affine,
interpolation='nearest')
return resampled_img
def _load_nibabel(filePath):
img = nib.load(os.path.join(filePath))
return img
def get_config_dictionary():
# Define the same default pipeline as TPOT light but without the preprocessing operators
regressor_config_dic = {
'sklearn.linear_model.ElasticNetCV': {
'l1_ratio': np.arange(0.0, 1.01, 0.05),
'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
},
'sklearn.tree.DecisionTreeRegressor': {
'max_depth': range(1, 11),
'min_samples_split': range(2, 21),
'min_samples_leaf': range(1, 21)
},
'sklearn.neighbors.KNeighborsRegressor': {
'n_neighbors': range(1, 101),
'weights': ["uniform", "distance"],
'p': [1, 2]
},
'sklearn.linear_model.LassoLarsCV': {
'normalize': [True, False]
},
'sklearn.svm.LinearSVR': {
'loss': ["epsilon_insensitive", "squared_epsilon_insensitive"],
'dual': [True, False],
'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],
'C': [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1., 5., 10., 15., 20., 25.],
'epsilon': [1e-4, 1e-3, 1e-2, 1e-1, 1.]
},
'sklearn.linear_model.RidgeCV': {
},
# Selectors
'sklearn.feature_selection.SelectFwe': {
'alpha': np.arange(0, 0.05, 0.001),
'score_func': {
'sklearn.feature_selection.f_regression': None
}
},
'sklearn.feature_selection.SelectPercentile': {
'percentile': range(1, 100),
'score_func': {
'sklearn.feature_selection.f_regression': None
}
},
'sklearn.feature_selection.VarianceThreshold': {
'threshold': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]
}
}
return regressor_config_dic
def get_mean_age(df):
mean_age = df['Age'].mean()
std_age = df['Age'].std()
print('Mean Age %.2f +- %.2f' %(mean_age, std_age))
def get_data(project_data, dataset, debug, project_wd, resamplefactor, raw,
analysis):
''' Load the csv files and return
:param project_data:
:param dataset:
:param debug:
:param project_wd:
:param resamplefactor:
:raw: Which type of fressesfurfer should we analyse (the raw, where both
datasets have not been matched or the not raw where the number of columns
between dataset is the same)
:return: demographics:
:return: demographics:
:return: dataframe.values: Just the numeric values of the dataframe
'''
if dataset == 'freesurf_combined' and raw == True:
raise ValueError('The combined analysis cannot use the raw dataset')
print('Loading Brain image data')
elif dataset == 'OASIS':
# remove the file end and get list of all used subjects
fileList = os.listdir(project_data)
rawsubjectsId = [re.sub(r'^smwc1(.*?)\_mpr-1_anon.nii$', '\\1', file) for file in fileList if file.endswith('.nii')]
# TODO: Change this. For testing purpose select just the first 5 subjects
#rawsubjectsId = rawsubjectsId[:25]
# Load the demographics for each subject
demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, dataset)
# print subjects mean age
get_mean_age(demographics)
# Load image proxies
imgs = [nib.load(os.path.join(project_data, 'smwc1%s_mpr-1_anon.nii' %subject)) for subject in tqdm(selectedSubId)]
elif dataset == 'BANC':
# For now, performing analysis on White Matter.
project_data_path = os.path.join(project_data, 'wm_data')
# remove the file end and get list of all used subjects
fileList = os.listdir(project_data_path)
rawsubjectsId = [file[5:12] for file in fileList if file.endswith('.nii.gz')]
# TODO: select only a set of 5 subjects
# rawsubjectsId = rawsubjectsId[:5]
# Load the demographics for each subject
demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, dataset)
# print subjects mean age
get_mean_age(demographics)
# Get the file path of the selected subjects
subjectsFile = [os.path.join(project_data_path, file) for file in fileList if file[5:12] in selectedSubId]
# Load image proxies
with Pool() as p:
imgs = list(tqdm(p.imap(_load_nibabel, subjectsFile), total=len(selectedSubId)))
elif (dataset == 'BANC_freesurf' and raw==True):
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'original_dataset',
'BANC',
'aparc_aseg_stats_BANC.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
# Load the demographics for each subject
demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, 'BANC')
# return numpy array of the dataframe
# Rename columns to maintain consistency withe ukbio
demographics.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)
return demographics, None, freesurf_df
elif (dataset == 'UKBIO_freesurf' and raw==False and not
analysis=='summary_data'):
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'matched_dataset',
'aparc_aseg_UKBIO.csv'), delimiter=',')
# Read the full matrix to get the demographics information
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'original_dataset',
'UKBIO',
'UKB_10k_FS_4844_combined.csv'),
delimiter=',',
index_col=False)
demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
freesurf_df = freesurf_df.set_index('id')
return demographics, None, freesurf_df
elif (dataset == 'BANC_freesurf' and raw==False and not
analysis=='summary_data'):
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'matched_dataset',
'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
# Load the demographics for each subject
demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, 'BANC')
# return numpy array of the dataframe
# Rename columns to maintain consistency withe ukbio
demographics.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)
return demographics, None, freesurf_df
elif (dataset == 'UKBIO_freesurf' and raw==True and not
analysis=='summary_data'):
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'original_dataset',
'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
freesurf_df = freesurf_df.drop(columns='id.4844')
demographics = freesurf_df[['age', 'sex', 'id']].copy()
freesurf_df = freesurf_df.set_index('id')
return demographics, None, freesurf_df
elif (dataset == 'UKBIO_freesurf' and raw==False and
analysis=='summary_data'):
# This dataset contains only 21 feature that represent summary metrics
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'matched_dataset',
'aparc_aseg_UKBIO_summary.csv'), delimiter=',')
# Read the full matrix to get the demographics information
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'original_dataset',
'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
return demographics, None, freesurf_df
elif (dataset == 'BANC_freesurf' and raw==False and
analysis=='summary_data'):
# This dataset contains only 21 feature that represent summary metrics
freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'matched_dataset',
'aparc_aseg_BANC_summary.csv'),
delimiter=',', index_col=0)
rawsubjectsId = freesurf_df.index
# Load the demographics for each subject
demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, 'BANC')
# Rename columns to maintain consistency withe ukbio
demographics.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)
return demographics, None, freesurf_df
elif (dataset == 'freesurf_combined'):
ukbio_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'matched_dataset',
'aparc_aseg_UKBIO.csv'),
delimiter=',', index_col=0)
banc_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'matched_dataset',
'aparc_aseg_BANC.csv'),
delimiter=',', index_col=0)
ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',
'freesurfer_preprocess',
'original_dataset',
'UKBIO',
'UKB_10k_FS_4844_combined.csv'), delimiter=',')
rawsubjectsId = banc_df.index
# Load the demographics for each subject
banc_demographics, selectedSubId = get_data_covariates(project_data,
rawsubjectsId,
'BANC')
ukbio_demographics = ukbio_full_df[['age', 'sex', 'id']].copy()
# Concatenate both freesurfeer datasets
freesurfer_df = pd.concat([ukbio_df, banc_df])
# Concatenate demographics information (Age and Sex)
tmp = banc_demographics.drop('original_dataset', axis=1)
tmp.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)
# transform M/F into male/female
tmp['sex'] = tmp['sex'].map({'F': 'female', 'M': 'male'})
# Add column to specify dataset
tmp['dataset'] = 'banc'
ukbio_demographics['dataset'] = 'ukbio'
demographics = pd.concat([ukbio_demographics, tmp], sort=False)
# TODO: For now assume that the index in the BIOBANK correspond to th
# Stratify subjects. Divide them into classes <30, 30<40, 40<50, 50<60,
# 60<70, 70<80, 80<90, 90<100. Each age will be then further stratified
# into F/M.
bins = (17, 30, 40, 50, 60, 70, 80, 90)
group_labels = range(1,len(bins))
demographics['age_band'] = pd.cut(demographics['age'], bins,
labels=group_labels)
sex_age_group = demographics.groupby(['sex', 'age_band'])
# Note that the following groups are created:
# ('female', 1), ('female', 2), ('female', 3), ('female', 4), ('female', 5),
# ('female', 6), ('female', 7), ('male', 1), ('male', 2), ('male', 3),
# ('male', 4), ('male', 5), ('male', 6), ('male', 7)]
# This will label the groups cited above in a crescent order. In total
# you will have 1-14 groups, grouped according to their age and sex
demographics['stratify'] = sex_age_group.grouper.group_info[0] + 1
#same order between both fines
return demographics, None, freesurfer_df
else:
raise ValueError('Analysis for this dataset is not yet implemented!')
print('Resample the dataset by a factor of %d' %resamplefactor)
print('Original image size: %s' %(imgs[0].shape,))
# resample dataset to a lower quality. Increase the voxel size by two
resampleby2affine = np.array([[resamplefactor, 1, 1, 1],
[1, resamplefactor, 1, 1],
[1, 1, resamplefactor, 1],
[1, 1, 1, 1]])
target_affine = np.multiply(imgs[0].affine, resampleby2affine)
print('Resampling Images')
with Pool() as p:
args = partial(_multiprocessing_resample, target_affine=target_affine)
resampledimgs = list(tqdm(p.imap(args, imgs), total=len(imgs)))
print('Resampled image size: %s' %(resampledimgs[0].shape,))
# Use nilearn to mask only the brain voxels across subjects
print('Compute brain mask')
#The lower and the upper_cutoff represent the lower and the upper fraction of the histogram to be discarded
MeanImgMask = masking.compute_multi_epi_mask(resampledimgs, lower_cutoff=0.001, upper_cutoff=.85, opening=False)
# Apply the group mask on all subjects.
# Note: The apply_mask function returns the flattened data as a numpy array
maskedData = [masking.apply_mask(img, MeanImgMask) for img in resampledimgs]
# If debug option is set, save an nifti image of the image.
# Note: if you resampled the image you will not be able to overlay it on the original brain
if debug:
mask_path = os.path.join(project_wd, 'BayOptPy', 'tpot')
print('Saving brain mask: %s' %mask_path)
nib.save(MeanImgMask, os.path.join(mask_path, 'mask_%s.nii.gz' %dataset))
print('Applied mask to the dataset')
# Transform the imaging data into a np array (subjects x voxels)
maskedData = np.array(maskedData)
return demographics, imgs, maskedData
def get_mae_for_all_generations(dataset, random_seed, generations, config_dict,
tpot_path):
'''
Get the MAE values for both the training and test dataset
:return:
'''
# Load the scores for the best models
saved_path = os.path.join(tpot_path, 'random_seed_%03d' %random_seed,
'tpot_%s_%s_%03dgen_pipelines.dump'
%(dataset, config_dict, generations))
# Note that if a value is not present for a generation, that means that the
# score did not change from the previous generation
# sort the array in ascending order
logbook = joblib.load(saved_path)
gen = list(logbook['log'].keys())
print('There are %d optminal pipelines' %len(gen))
print('These are the best pipelines')
for generation in gen:
print(logbook['log'][generation]['pipeline_name'])
# Iterate over the the list of saved MAEs and repeat the values where one
# generation is missed
all_mae_test = []
all_mae_train = []
pipeline_complexity = []
curr_gen_idx = 0
# all generations
for generation in range(generations):
if generation == gen[curr_gen_idx]:
all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]]['pipeline_test_mae']))
all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]]['pipeline_score']))
pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]]['pipeline_sklearn_obj'].named_steps.keys()))
if len(gen) > 1 and (len(gen) > curr_gen_idx + 1):
curr_gen_idx += 1
else:
# repeat the same last value
all_mae_test.append(all_mae_test[-1])
all_mae_train.append(all_mae_train[-1])
pipeline_complexity.append(pipeline_complexity[-1])
# transform the pipeline_complexity into a numpy array, in order to perform
# fancy indexing
pipeline_complexity = np.array(pipeline_complexity)
return all_mae_test, all_mae_train, pipeline_complexity
def set_publication_style():
# Se font size to paper size
plt.style.use(['seaborn-white', 'seaborn-talk'])
matplotlib.rc("font", family="Times New Roman")
# Remove the spines
sns.set_style('white', {"axes.spines.top": False,
"axes.spines.right": False,
"axes.labelsize": 'large'})
def create_age_histogram(df, dataset):
'''
Get an age array and plot and save the age histogram for the analysed sample
'''
# Define plot styple
set_publication_style()
plt.figure()
path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' %dataset
min_age = df['age'].min()
max_age = df['age'].max()
plt.hist(df['age'], bins=65, range=(min_age,max_age))
plt.xlabel('Age')
plt.ylabel('# of Subjects')
plt.legend()
plt.savefig(path_to_save)
plt.close()
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
labels = [int(x) for x in unique_labels(y_true, y_pred)]
classes = classes[labels]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax, cm
def plot_confusion_matrix_boosting(cm_mean, cm_std,
classes,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
fig, ax = plt.subplots()
im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm_mean.shape[1]),
yticks=np.arange(cm_mean.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '{0:.2f} ± {1:.2f}'
thresh = cm_mean.max() / 2.
for i in range(cm_mean.shape[0]):
for j in range(cm_mean.shape[1]):
ax.text(j, i, fmt.format(cm_mean[i, j],cm_std[i, j]),
ha="center", va="center",
color="white" if cm_mean[i, j] > thresh else "black")
fig.tight_layout()
return ax
def plot_predicted_vs_true(true_y, predicted_y, save_path, metric):
fig = plt.figure()
plt.scatter(true_y, predicted_y, alpha=.5)
plt.ylabel('Predicted %s' %metric)
plt.xlabel('True %s'%metric)
plt.plot(np.arange(min(true_y),
max(true_y)),
np.arange(min(true_y),
max(true_y)), alpha=.3, linestyle='--',
color='b')
if metric == 'Age':
plt.xticks(np.arange(min(min(true_y), min(predicted_y)),
max(max(true_y), max(predicted_y)), step=10))
plt.yticks(np.arange(min(min(true_y), min(predicted_y)),
max(max(true_y), max(predicted_y)), step=10))
plt.savefig(save_path)
plt.close()
def load_cognitive_data(project_data):
cog_path = os.path.join(project_data, 'cog_ukbio')
cog_df = pd.read_csv(os.path.join(cog_path, 'UKB_10k_cog_bmi.csv'))
cog_df = cog_df.set_index('ID')
return cog_df
def ttest_ind_corrected(performance_a, performance_b, k=10, r=10):
"""Corrected repeated k-fold cv test.
The test assumes that the classifiers were evaluated using cross validation.
Ref:
Bouckaert, Remco R., and Eibe Frank. "Evaluating the replicability of significance tests for comparing learning
algorithms." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004
Args:
performance_a: performances from classifier A
performance_b: performances from classifier B
k: number of folds
r: number of repetitions
Returns:
t: t-statistic of the corrected test.
prob: p-value of the corrected test.
"""
df = k * r - 1
x = performance_a - performance_b
m = np.mean(x)
sigma_2 = np.var(x, ddof=1)
denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)
with np.errstate(divide='ignore', invalid='ignore'):
t = np.divide(m, denom)
prob = stats.t.sf(np.abs(t), df) * 2
return t, prob
|
flexible
|
{
"blob_id": "2e9d71b8055e1bab107cedae69ca3bc4219e7d38",
"index": 7460,
"step-1": "<mask token>\n\n\ndef get_paths(debug, dataset):\n if debug and dataset == 'OASIS':\n project_wd = os.getcwd()\n project_data = os.path.join(project_wd, 'data')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BANC':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BOSTON':\n project_wd = os.getcwd()\n project_data = None\n project_sink = None\n elif debug and dataset == 'BANC_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif debug and dataset == 'UKBIO_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif not debug and dataset == 'OASIS':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'NaN', 'data')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BANC':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BOSTON':\n project_wd = '/code'\n project_data = None\n project_sink = None\n elif not debug and (dataset == 'BANC_freesurf' or dataset ==\n 'UKBIO_freesurf' or dataset == 'freesurf_combined'):\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'code', 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n print('Code Path: %s' % project_wd)\n print('Data Path: %s' % project_data)\n print('Data Out: %s' % project_sink)\n return project_wd, project_data, project_sink\n\n\ndef get_output_path(model, analysis, ngen, random_seed, population_size,\n debug, mutation, crossover, predicted_attribute):\n rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,\n population_size, debug, mutation, crossover, predicted_attribute)\n output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_all_random_seed_paths(model, analysis, ngen, population_size, debug,\n mutation, crossover, predicted_attribute):\n if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==\n 'feat_combi' or analysis == 'vanilla_combi' or analysis ==\n 'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or\n analysis == 'uniform_dist'):\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen)\n elif analysis == 'population':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, \n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n elif analysis == 'mutation':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (\n mutation, crossover))\n else:\n raise IOError('Analysis path not defined. Passed analysis was %s' %\n analysis)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):\n \"\"\"\n This function gets the original dataset and transforms it into a uniformly\n distributed dataset.\n \"\"\"\n project_wd, project_data, project_sink = get_paths(debug, dataset)\n demographics, imgs, dataframe = get_data(project_data, dataset, debug,\n project_wd, resamplefactor, raw=raw, analysis=analysis)\n demographics['age_int'] = demographics['age'].astype('int32', copy=False)\n age_range = np.arange(demographics['age'].min(), demographics['age'].max())\n max_n = 14\n age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]\n age_range = np.setdiff1d(age_range, age_to_remove)\n ids_to_use = []\n for age in age_range:\n ids_to_use.append(demographics.index[demographics['age_int'] == age\n ].tolist()[:max_n])\n ids_to_use = [item for sublist in ids_to_use for item in sublist]\n demographics = demographics[demographics.index.isin(ids_to_use)]\n dataframe = dataframe.loc[demographics['id']]\n print('Shape of the new demographics:')\n print(demographics.shape)\n print('Oldest %d and youngest %d subject' % (demographics['age_int'].\n max(), demographics['age_int'].min()))\n print('Number of age bins %d' % len(demographics['age_int'].unique()))\n return demographics, dataframe\n\n\ndef get_best_pipeline_paths(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute):\n output_path = get_output_path(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute)\n checkpoint_path = os.path.join(output_path, 'checkpoint_folder')\n if os.path.exists(checkpoint_path):\n shutil.rmtree(checkpoint_path)\n print('Deleted pre-exiting checkpoint folder')\n if not os.path.exists(checkpoint_path):\n os.makedirs(checkpoint_path)\n print('Creating checkpoint folder')\n return checkpoint_path\n\n\ndef drop_missing_features(dataframe):\n \"\"\"\n This function takes a dataframe and removes the already defined missing\n columns from the dataframe.\n \"\"\"\n missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',\n 'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',\n 'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',\n 'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',\n 'Left-non-WM-hypointensities', 'non-WM-hypointensities',\n 'Right-WM-hypointensities', 'Left-WM-hypointensities',\n 'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',\n 'Left-choroid-plexus', 'Left-Lateral-Ventricle',\n 'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']\n cleaned_df = dataframe.drop(missing_features, axis=1)\n return cleaned_df\n\n\ndef get_data_covariates(dataPath, rawsubjectsId, dataset):\n if dataset == 'OASIS':\n demographics = pd.read_csv(os.path.join(dataPath,\n 'oasis_cross-sectional.csv'))\n demographics = demographics.sort_values('ID')\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = demographics.loc[(demographics['CDR'] == 0) |\n demographics['CDR'].isnull(), 'ID']\n demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]\n elif dataset == 'BANC':\n column_names = ['ID', 'original_dataset', 'sex', 'Age']\n demographics = pd.read_csv(os.path.join(dataPath,\n 'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = rawsubjectsId\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n assert len(selectedSubId) == len(demographics)\n return demographics, selectedSubId\n\n\n<mask token>\n\n\ndef _load_nibabel(filePath):\n img = nib.load(os.path.join(filePath))\n return img\n\n\ndef get_config_dictionary():\n regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {\n 'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001, \n 0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {\n 'max_depth': range(1, 11), 'min_samples_split': range(2, 21),\n 'min_samples_leaf': range(1, 21)},\n 'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1, \n 101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},\n 'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},\n 'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',\n 'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [\n 1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1, \n 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001, \n 0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},\n 'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,\n 0.001), 'score_func': {'sklearn.feature_selection.f_regression':\n None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':\n range(1, 100), 'score_func': {\n 'sklearn.feature_selection.f_regression': None}},\n 'sklearn.feature_selection.VarianceThreshold': {'threshold': [\n 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}\n return regressor_config_dic\n\n\ndef get_mean_age(df):\n mean_age = df['Age'].mean()\n std_age = df['Age'].std()\n print('Mean Age %.2f +- %.2f' % (mean_age, std_age))\n\n\n<mask token>\n\n\ndef get_mae_for_all_generations(dataset, random_seed, generations,\n config_dict, tpot_path):\n \"\"\"\n Get the MAE values for both the training and test dataset\n :return:\n \"\"\"\n saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed, \n 'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,\n generations))\n logbook = joblib.load(saved_path)\n gen = list(logbook['log'].keys())\n print('There are %d optminal pipelines' % len(gen))\n print('These are the best pipelines')\n for generation in gen:\n print(logbook['log'][generation]['pipeline_name'])\n all_mae_test = []\n all_mae_train = []\n pipeline_complexity = []\n curr_gen_idx = 0\n for generation in range(generations):\n if generation == gen[curr_gen_idx]:\n all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_test_mae']))\n all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_score']))\n pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]\n ]['pipeline_sklearn_obj'].named_steps.keys()))\n if len(gen) > 1 and len(gen) > curr_gen_idx + 1:\n curr_gen_idx += 1\n else:\n all_mae_test.append(all_mae_test[-1])\n all_mae_train.append(all_mae_train[-1])\n pipeline_complexity.append(pipeline_complexity[-1])\n pipeline_complexity = np.array(pipeline_complexity)\n return all_mae_test, all_mae_train, pipeline_complexity\n\n\n<mask token>\n\n\ndef create_age_histogram(df, dataset):\n \"\"\"\n Get an age array and plot and save the age histogram for the analysed sample\n \"\"\"\n set_publication_style()\n plt.figure()\n path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset\n min_age = df['age'].min()\n max_age = df['age'].max()\n plt.hist(df['age'], bins=65, range=(min_age, max_age))\n plt.xlabel('Age')\n plt.ylabel('# of Subjects')\n plt.legend()\n plt.savefig(path_to_save)\n plt.close()\n\n\ndef plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=\n None, cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n cm = confusion_matrix(y_true, y_pred)\n labels = [int(x) for x in unique_labels(y_true, y_pred)]\n classes = classes[labels]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print('Normalized confusion matrix')\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n fig, ax = plt.subplots()\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),\n xticklabels=classes, yticklabels=classes, title=title, ylabel=\n 'True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.0\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',\n color='white' if cm[i, j] > thresh else 'black')\n fig.tight_layout()\n return ax, cm\n\n\ndef plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n fig, ax = plt.subplots()\n im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.\n shape[0]), xticklabels=classes, yticklabels=classes, title=title,\n ylabel='True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '{0:.2f} ± {1:.2f}'\n thresh = cm_mean.max() / 2.0\n for i in range(cm_mean.shape[0]):\n for j in range(cm_mean.shape[1]):\n ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=\n 'center', va='center', color='white' if cm_mean[i, j] >\n thresh else 'black')\n fig.tight_layout()\n return ax\n\n\ndef plot_predicted_vs_true(true_y, predicted_y, save_path, metric):\n fig = plt.figure()\n plt.scatter(true_y, predicted_y, alpha=0.5)\n plt.ylabel('Predicted %s' % metric)\n plt.xlabel('True %s' % metric)\n plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),\n max(true_y)), alpha=0.3, linestyle='--', color='b')\n if metric == 'Age':\n plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.savefig(save_path)\n plt.close()\n\n\n<mask token>\n\n\ndef ttest_ind_corrected(performance_a, performance_b, k=10, r=10):\n \"\"\"Corrected repeated k-fold cv test.\n The test assumes that the classifiers were evaluated using cross validation.\n\n Ref:\n Bouckaert, Remco R., and Eibe Frank. \"Evaluating the replicability of significance tests for comparing learning\n algorithms.\" Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004\n\n Args:\n performance_a: performances from classifier A\n performance_b: performances from classifier B\n k: number of folds\n r: number of repetitions\n\n Returns:\n t: t-statistic of the corrected test.\n prob: p-value of the corrected test.\n \"\"\"\n df = k * r - 1\n x = performance_a - performance_b\n m = np.mean(x)\n sigma_2 = np.var(x, ddof=1)\n denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)\n with np.errstate(divide='ignore', invalid='ignore'):\n t = np.divide(m, denom)\n prob = stats.t.sf(np.abs(t), df) * 2\n return t, prob\n",
"step-2": "<mask token>\n\n\ndef get_paths(debug, dataset):\n if debug and dataset == 'OASIS':\n project_wd = os.getcwd()\n project_data = os.path.join(project_wd, 'data')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BANC':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BOSTON':\n project_wd = os.getcwd()\n project_data = None\n project_sink = None\n elif debug and dataset == 'BANC_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif debug and dataset == 'UKBIO_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif not debug and dataset == 'OASIS':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'NaN', 'data')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BANC':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BOSTON':\n project_wd = '/code'\n project_data = None\n project_sink = None\n elif not debug and (dataset == 'BANC_freesurf' or dataset ==\n 'UKBIO_freesurf' or dataset == 'freesurf_combined'):\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'code', 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n print('Code Path: %s' % project_wd)\n print('Data Path: %s' % project_data)\n print('Data Out: %s' % project_sink)\n return project_wd, project_data, project_sink\n\n\ndef get_output_path(model, analysis, ngen, random_seed, population_size,\n debug, mutation, crossover, predicted_attribute):\n rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,\n population_size, debug, mutation, crossover, predicted_attribute)\n output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_all_random_seed_paths(model, analysis, ngen, population_size, debug,\n mutation, crossover, predicted_attribute):\n if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==\n 'feat_combi' or analysis == 'vanilla_combi' or analysis ==\n 'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or\n analysis == 'uniform_dist'):\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen)\n elif analysis == 'population':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, \n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n elif analysis == 'mutation':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (\n mutation, crossover))\n else:\n raise IOError('Analysis path not defined. Passed analysis was %s' %\n analysis)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):\n \"\"\"\n This function gets the original dataset and transforms it into a uniformly\n distributed dataset.\n \"\"\"\n project_wd, project_data, project_sink = get_paths(debug, dataset)\n demographics, imgs, dataframe = get_data(project_data, dataset, debug,\n project_wd, resamplefactor, raw=raw, analysis=analysis)\n demographics['age_int'] = demographics['age'].astype('int32', copy=False)\n age_range = np.arange(demographics['age'].min(), demographics['age'].max())\n max_n = 14\n age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]\n age_range = np.setdiff1d(age_range, age_to_remove)\n ids_to_use = []\n for age in age_range:\n ids_to_use.append(demographics.index[demographics['age_int'] == age\n ].tolist()[:max_n])\n ids_to_use = [item for sublist in ids_to_use for item in sublist]\n demographics = demographics[demographics.index.isin(ids_to_use)]\n dataframe = dataframe.loc[demographics['id']]\n print('Shape of the new demographics:')\n print(demographics.shape)\n print('Oldest %d and youngest %d subject' % (demographics['age_int'].\n max(), demographics['age_int'].min()))\n print('Number of age bins %d' % len(demographics['age_int'].unique()))\n return demographics, dataframe\n\n\ndef get_best_pipeline_paths(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute):\n output_path = get_output_path(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute)\n checkpoint_path = os.path.join(output_path, 'checkpoint_folder')\n if os.path.exists(checkpoint_path):\n shutil.rmtree(checkpoint_path)\n print('Deleted pre-exiting checkpoint folder')\n if not os.path.exists(checkpoint_path):\n os.makedirs(checkpoint_path)\n print('Creating checkpoint folder')\n return checkpoint_path\n\n\ndef drop_missing_features(dataframe):\n \"\"\"\n This function takes a dataframe and removes the already defined missing\n columns from the dataframe.\n \"\"\"\n missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',\n 'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',\n 'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',\n 'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',\n 'Left-non-WM-hypointensities', 'non-WM-hypointensities',\n 'Right-WM-hypointensities', 'Left-WM-hypointensities',\n 'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',\n 'Left-choroid-plexus', 'Left-Lateral-Ventricle',\n 'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']\n cleaned_df = dataframe.drop(missing_features, axis=1)\n return cleaned_df\n\n\ndef get_data_covariates(dataPath, rawsubjectsId, dataset):\n if dataset == 'OASIS':\n demographics = pd.read_csv(os.path.join(dataPath,\n 'oasis_cross-sectional.csv'))\n demographics = demographics.sort_values('ID')\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = demographics.loc[(demographics['CDR'] == 0) |\n demographics['CDR'].isnull(), 'ID']\n demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]\n elif dataset == 'BANC':\n column_names = ['ID', 'original_dataset', 'sex', 'Age']\n demographics = pd.read_csv(os.path.join(dataPath,\n 'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = rawsubjectsId\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n assert len(selectedSubId) == len(demographics)\n return demographics, selectedSubId\n\n\ndef _multiprocessing_resample(img, target_affine):\n resampled_img = image.resample_img(img, target_affine=target_affine,\n interpolation='nearest')\n return resampled_img\n\n\ndef _load_nibabel(filePath):\n img = nib.load(os.path.join(filePath))\n return img\n\n\ndef get_config_dictionary():\n regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {\n 'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001, \n 0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {\n 'max_depth': range(1, 11), 'min_samples_split': range(2, 21),\n 'min_samples_leaf': range(1, 21)},\n 'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1, \n 101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},\n 'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},\n 'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',\n 'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [\n 1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1, \n 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001, \n 0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},\n 'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,\n 0.001), 'score_func': {'sklearn.feature_selection.f_regression':\n None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':\n range(1, 100), 'score_func': {\n 'sklearn.feature_selection.f_regression': None}},\n 'sklearn.feature_selection.VarianceThreshold': {'threshold': [\n 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}\n return regressor_config_dic\n\n\ndef get_mean_age(df):\n mean_age = df['Age'].mean()\n std_age = df['Age'].std()\n print('Mean Age %.2f +- %.2f' % (mean_age, std_age))\n\n\n<mask token>\n\n\ndef get_mae_for_all_generations(dataset, random_seed, generations,\n config_dict, tpot_path):\n \"\"\"\n Get the MAE values for both the training and test dataset\n :return:\n \"\"\"\n saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed, \n 'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,\n generations))\n logbook = joblib.load(saved_path)\n gen = list(logbook['log'].keys())\n print('There are %d optminal pipelines' % len(gen))\n print('These are the best pipelines')\n for generation in gen:\n print(logbook['log'][generation]['pipeline_name'])\n all_mae_test = []\n all_mae_train = []\n pipeline_complexity = []\n curr_gen_idx = 0\n for generation in range(generations):\n if generation == gen[curr_gen_idx]:\n all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_test_mae']))\n all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_score']))\n pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]\n ]['pipeline_sklearn_obj'].named_steps.keys()))\n if len(gen) > 1 and len(gen) > curr_gen_idx + 1:\n curr_gen_idx += 1\n else:\n all_mae_test.append(all_mae_test[-1])\n all_mae_train.append(all_mae_train[-1])\n pipeline_complexity.append(pipeline_complexity[-1])\n pipeline_complexity = np.array(pipeline_complexity)\n return all_mae_test, all_mae_train, pipeline_complexity\n\n\n<mask token>\n\n\ndef create_age_histogram(df, dataset):\n \"\"\"\n Get an age array and plot and save the age histogram for the analysed sample\n \"\"\"\n set_publication_style()\n plt.figure()\n path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset\n min_age = df['age'].min()\n max_age = df['age'].max()\n plt.hist(df['age'], bins=65, range=(min_age, max_age))\n plt.xlabel('Age')\n plt.ylabel('# of Subjects')\n plt.legend()\n plt.savefig(path_to_save)\n plt.close()\n\n\ndef plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=\n None, cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n cm = confusion_matrix(y_true, y_pred)\n labels = [int(x) for x in unique_labels(y_true, y_pred)]\n classes = classes[labels]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print('Normalized confusion matrix')\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n fig, ax = plt.subplots()\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),\n xticklabels=classes, yticklabels=classes, title=title, ylabel=\n 'True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.0\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',\n color='white' if cm[i, j] > thresh else 'black')\n fig.tight_layout()\n return ax, cm\n\n\ndef plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n fig, ax = plt.subplots()\n im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.\n shape[0]), xticklabels=classes, yticklabels=classes, title=title,\n ylabel='True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '{0:.2f} ± {1:.2f}'\n thresh = cm_mean.max() / 2.0\n for i in range(cm_mean.shape[0]):\n for j in range(cm_mean.shape[1]):\n ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=\n 'center', va='center', color='white' if cm_mean[i, j] >\n thresh else 'black')\n fig.tight_layout()\n return ax\n\n\ndef plot_predicted_vs_true(true_y, predicted_y, save_path, metric):\n fig = plt.figure()\n plt.scatter(true_y, predicted_y, alpha=0.5)\n plt.ylabel('Predicted %s' % metric)\n plt.xlabel('True %s' % metric)\n plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),\n max(true_y)), alpha=0.3, linestyle='--', color='b')\n if metric == 'Age':\n plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.savefig(save_path)\n plt.close()\n\n\n<mask token>\n\n\ndef ttest_ind_corrected(performance_a, performance_b, k=10, r=10):\n \"\"\"Corrected repeated k-fold cv test.\n The test assumes that the classifiers were evaluated using cross validation.\n\n Ref:\n Bouckaert, Remco R., and Eibe Frank. \"Evaluating the replicability of significance tests for comparing learning\n algorithms.\" Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004\n\n Args:\n performance_a: performances from classifier A\n performance_b: performances from classifier B\n k: number of folds\n r: number of repetitions\n\n Returns:\n t: t-statistic of the corrected test.\n prob: p-value of the corrected test.\n \"\"\"\n df = k * r - 1\n x = performance_a - performance_b\n m = np.mean(x)\n sigma_2 = np.var(x, ddof=1)\n denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)\n with np.errstate(divide='ignore', invalid='ignore'):\n t = np.divide(m, denom)\n prob = stats.t.sf(np.abs(t), df) * 2\n return t, prob\n",
"step-3": "<mask token>\n\n\ndef get_paths(debug, dataset):\n if debug and dataset == 'OASIS':\n project_wd = os.getcwd()\n project_data = os.path.join(project_wd, 'data')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BANC':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BOSTON':\n project_wd = os.getcwd()\n project_data = None\n project_sink = None\n elif debug and dataset == 'BANC_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif debug and dataset == 'UKBIO_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif not debug and dataset == 'OASIS':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'NaN', 'data')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BANC':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BOSTON':\n project_wd = '/code'\n project_data = None\n project_sink = None\n elif not debug and (dataset == 'BANC_freesurf' or dataset ==\n 'UKBIO_freesurf' or dataset == 'freesurf_combined'):\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'code', 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n print('Code Path: %s' % project_wd)\n print('Data Path: %s' % project_data)\n print('Data Out: %s' % project_sink)\n return project_wd, project_data, project_sink\n\n\ndef get_output_path(model, analysis, ngen, random_seed, population_size,\n debug, mutation, crossover, predicted_attribute):\n rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,\n population_size, debug, mutation, crossover, predicted_attribute)\n output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_all_random_seed_paths(model, analysis, ngen, population_size, debug,\n mutation, crossover, predicted_attribute):\n if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==\n 'feat_combi' or analysis == 'vanilla_combi' or analysis ==\n 'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or\n analysis == 'uniform_dist'):\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen)\n elif analysis == 'population':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, \n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n elif analysis == 'mutation':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (\n mutation, crossover))\n else:\n raise IOError('Analysis path not defined. Passed analysis was %s' %\n analysis)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):\n \"\"\"\n This function gets the original dataset and transforms it into a uniformly\n distributed dataset.\n \"\"\"\n project_wd, project_data, project_sink = get_paths(debug, dataset)\n demographics, imgs, dataframe = get_data(project_data, dataset, debug,\n project_wd, resamplefactor, raw=raw, analysis=analysis)\n demographics['age_int'] = demographics['age'].astype('int32', copy=False)\n age_range = np.arange(demographics['age'].min(), demographics['age'].max())\n max_n = 14\n age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]\n age_range = np.setdiff1d(age_range, age_to_remove)\n ids_to_use = []\n for age in age_range:\n ids_to_use.append(demographics.index[demographics['age_int'] == age\n ].tolist()[:max_n])\n ids_to_use = [item for sublist in ids_to_use for item in sublist]\n demographics = demographics[demographics.index.isin(ids_to_use)]\n dataframe = dataframe.loc[demographics['id']]\n print('Shape of the new demographics:')\n print(demographics.shape)\n print('Oldest %d and youngest %d subject' % (demographics['age_int'].\n max(), demographics['age_int'].min()))\n print('Number of age bins %d' % len(demographics['age_int'].unique()))\n return demographics, dataframe\n\n\ndef get_best_pipeline_paths(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute):\n output_path = get_output_path(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute)\n checkpoint_path = os.path.join(output_path, 'checkpoint_folder')\n if os.path.exists(checkpoint_path):\n shutil.rmtree(checkpoint_path)\n print('Deleted pre-exiting checkpoint folder')\n if not os.path.exists(checkpoint_path):\n os.makedirs(checkpoint_path)\n print('Creating checkpoint folder')\n return checkpoint_path\n\n\ndef drop_missing_features(dataframe):\n \"\"\"\n This function takes a dataframe and removes the already defined missing\n columns from the dataframe.\n \"\"\"\n missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',\n 'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',\n 'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',\n 'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',\n 'Left-non-WM-hypointensities', 'non-WM-hypointensities',\n 'Right-WM-hypointensities', 'Left-WM-hypointensities',\n 'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',\n 'Left-choroid-plexus', 'Left-Lateral-Ventricle',\n 'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']\n cleaned_df = dataframe.drop(missing_features, axis=1)\n return cleaned_df\n\n\ndef get_data_covariates(dataPath, rawsubjectsId, dataset):\n if dataset == 'OASIS':\n demographics = pd.read_csv(os.path.join(dataPath,\n 'oasis_cross-sectional.csv'))\n demographics = demographics.sort_values('ID')\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = demographics.loc[(demographics['CDR'] == 0) |\n demographics['CDR'].isnull(), 'ID']\n demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]\n elif dataset == 'BANC':\n column_names = ['ID', 'original_dataset', 'sex', 'Age']\n demographics = pd.read_csv(os.path.join(dataPath,\n 'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = rawsubjectsId\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n assert len(selectedSubId) == len(demographics)\n return demographics, selectedSubId\n\n\ndef _multiprocessing_resample(img, target_affine):\n resampled_img = image.resample_img(img, target_affine=target_affine,\n interpolation='nearest')\n return resampled_img\n\n\ndef _load_nibabel(filePath):\n img = nib.load(os.path.join(filePath))\n return img\n\n\ndef get_config_dictionary():\n regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {\n 'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001, \n 0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {\n 'max_depth': range(1, 11), 'min_samples_split': range(2, 21),\n 'min_samples_leaf': range(1, 21)},\n 'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1, \n 101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},\n 'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},\n 'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',\n 'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [\n 1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1, \n 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001, \n 0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},\n 'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,\n 0.001), 'score_func': {'sklearn.feature_selection.f_regression':\n None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':\n range(1, 100), 'score_func': {\n 'sklearn.feature_selection.f_regression': None}},\n 'sklearn.feature_selection.VarianceThreshold': {'threshold': [\n 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}\n return regressor_config_dic\n\n\ndef get_mean_age(df):\n mean_age = df['Age'].mean()\n std_age = df['Age'].std()\n print('Mean Age %.2f +- %.2f' % (mean_age, std_age))\n\n\ndef get_data(project_data, dataset, debug, project_wd, resamplefactor, raw,\n analysis):\n \"\"\" Load the csv files and return\n :param project_data:\n :param dataset:\n :param debug:\n :param project_wd:\n :param resamplefactor:\n :raw: Which type of fressesfurfer should we analyse (the raw, where both\n datasets have not been matched or the not raw where the number of columns\n between dataset is the same)\n :return: demographics:\n :return: demographics:\n :return: dataframe.values: Just the numeric values of the dataframe\n \"\"\"\n if dataset == 'freesurf_combined' and raw == True:\n raise ValueError('The combined analysis cannot use the raw dataset')\n print('Loading Brain image data')\n elif dataset == 'OASIS':\n fileList = os.listdir(project_data)\n rawsubjectsId = [re.sub('^smwc1(.*?)\\\\_mpr-1_anon.nii$', '\\\\1',\n file) for file in fileList if file.endswith('.nii')]\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, dataset)\n get_mean_age(demographics)\n imgs = [nib.load(os.path.join(project_data, \n 'smwc1%s_mpr-1_anon.nii' % subject)) for subject in tqdm(\n selectedSubId)]\n elif dataset == 'BANC':\n project_data_path = os.path.join(project_data, 'wm_data')\n fileList = os.listdir(project_data_path)\n rawsubjectsId = [file[5:12] for file in fileList if file.endswith(\n '.nii.gz')]\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, dataset)\n get_mean_age(demographics)\n subjectsFile = [os.path.join(project_data_path, file) for file in\n fileList if file[5:12] in selectedSubId]\n with Pool() as p:\n imgs = list(tqdm(p.imap(_load_nibabel, subjectsFile), total=len\n (selectedSubId)))\n elif dataset == 'BANC_freesurf' and raw == True:\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'BANC',\n 'aparc_aseg_stats_BANC.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},\n inplace=True)\n return demographics, None, freesurf_df\n elif dataset == 'UKBIO_freesurf' and raw == False and not analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_UKBIO.csv'), delimiter=',')\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',', index_col=False)\n demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n freesurf_df = freesurf_df.set_index('id')\n return demographics, None, freesurf_df\n elif dataset == 'BANC_freesurf' and raw == False and not analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},\n inplace=True)\n return demographics, None, freesurf_df\n elif dataset == 'UKBIO_freesurf' and raw == True and not analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n freesurf_df = freesurf_df.drop(columns='id.4844')\n demographics = freesurf_df[['age', 'sex', 'id']].copy()\n freesurf_df = freesurf_df.set_index('id')\n return demographics, None, freesurf_df\n elif dataset == 'UKBIO_freesurf' and raw == False and analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_UKBIO_summary.csv'), delimiter=',')\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n return demographics, None, freesurf_df\n elif dataset == 'BANC_freesurf' and raw == False and analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_BANC_summary.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},\n inplace=True)\n return demographics, None, freesurf_df\n elif dataset == 'freesurf_combined':\n ukbio_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_UKBIO.csv'), delimiter=',', index_col=0)\n banc_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n rawsubjectsId = banc_df.index\n banc_demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n ukbio_demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n freesurfer_df = pd.concat([ukbio_df, banc_df])\n tmp = banc_demographics.drop('original_dataset', axis=1)\n tmp.rename(index=str, columns={'ID': 'id', 'Age': 'age'}, inplace=True)\n tmp['sex'] = tmp['sex'].map({'F': 'female', 'M': 'male'})\n tmp['dataset'] = 'banc'\n ukbio_demographics['dataset'] = 'ukbio'\n demographics = pd.concat([ukbio_demographics, tmp], sort=False)\n bins = 17, 30, 40, 50, 60, 70, 80, 90\n group_labels = range(1, len(bins))\n demographics['age_band'] = pd.cut(demographics['age'], bins, labels\n =group_labels)\n sex_age_group = demographics.groupby(['sex', 'age_band'])\n demographics['stratify'] = sex_age_group.grouper.group_info[0] + 1\n return demographics, None, freesurfer_df\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n print('Resample the dataset by a factor of %d' % resamplefactor)\n print('Original image size: %s' % (imgs[0].shape,))\n resampleby2affine = np.array([[resamplefactor, 1, 1, 1], [1,\n resamplefactor, 1, 1], [1, 1, resamplefactor, 1], [1, 1, 1, 1]])\n target_affine = np.multiply(imgs[0].affine, resampleby2affine)\n print('Resampling Images')\n with Pool() as p:\n args = partial(_multiprocessing_resample, target_affine=target_affine)\n resampledimgs = list(tqdm(p.imap(args, imgs), total=len(imgs)))\n print('Resampled image size: %s' % (resampledimgs[0].shape,))\n print('Compute brain mask')\n MeanImgMask = masking.compute_multi_epi_mask(resampledimgs,\n lower_cutoff=0.001, upper_cutoff=0.85, opening=False)\n maskedData = [masking.apply_mask(img, MeanImgMask) for img in resampledimgs\n ]\n if debug:\n mask_path = os.path.join(project_wd, 'BayOptPy', 'tpot')\n print('Saving brain mask: %s' % mask_path)\n nib.save(MeanImgMask, os.path.join(mask_path, 'mask_%s.nii.gz' %\n dataset))\n print('Applied mask to the dataset')\n maskedData = np.array(maskedData)\n return demographics, imgs, maskedData\n\n\ndef get_mae_for_all_generations(dataset, random_seed, generations,\n config_dict, tpot_path):\n \"\"\"\n Get the MAE values for both the training and test dataset\n :return:\n \"\"\"\n saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed, \n 'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,\n generations))\n logbook = joblib.load(saved_path)\n gen = list(logbook['log'].keys())\n print('There are %d optminal pipelines' % len(gen))\n print('These are the best pipelines')\n for generation in gen:\n print(logbook['log'][generation]['pipeline_name'])\n all_mae_test = []\n all_mae_train = []\n pipeline_complexity = []\n curr_gen_idx = 0\n for generation in range(generations):\n if generation == gen[curr_gen_idx]:\n all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_test_mae']))\n all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_score']))\n pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]\n ]['pipeline_sklearn_obj'].named_steps.keys()))\n if len(gen) > 1 and len(gen) > curr_gen_idx + 1:\n curr_gen_idx += 1\n else:\n all_mae_test.append(all_mae_test[-1])\n all_mae_train.append(all_mae_train[-1])\n pipeline_complexity.append(pipeline_complexity[-1])\n pipeline_complexity = np.array(pipeline_complexity)\n return all_mae_test, all_mae_train, pipeline_complexity\n\n\n<mask token>\n\n\ndef create_age_histogram(df, dataset):\n \"\"\"\n Get an age array and plot and save the age histogram for the analysed sample\n \"\"\"\n set_publication_style()\n plt.figure()\n path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset\n min_age = df['age'].min()\n max_age = df['age'].max()\n plt.hist(df['age'], bins=65, range=(min_age, max_age))\n plt.xlabel('Age')\n plt.ylabel('# of Subjects')\n plt.legend()\n plt.savefig(path_to_save)\n plt.close()\n\n\ndef plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=\n None, cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n cm = confusion_matrix(y_true, y_pred)\n labels = [int(x) for x in unique_labels(y_true, y_pred)]\n classes = classes[labels]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print('Normalized confusion matrix')\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n fig, ax = plt.subplots()\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),\n xticklabels=classes, yticklabels=classes, title=title, ylabel=\n 'True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.0\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',\n color='white' if cm[i, j] > thresh else 'black')\n fig.tight_layout()\n return ax, cm\n\n\ndef plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n fig, ax = plt.subplots()\n im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.\n shape[0]), xticklabels=classes, yticklabels=classes, title=title,\n ylabel='True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '{0:.2f} ± {1:.2f}'\n thresh = cm_mean.max() / 2.0\n for i in range(cm_mean.shape[0]):\n for j in range(cm_mean.shape[1]):\n ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=\n 'center', va='center', color='white' if cm_mean[i, j] >\n thresh else 'black')\n fig.tight_layout()\n return ax\n\n\ndef plot_predicted_vs_true(true_y, predicted_y, save_path, metric):\n fig = plt.figure()\n plt.scatter(true_y, predicted_y, alpha=0.5)\n plt.ylabel('Predicted %s' % metric)\n plt.xlabel('True %s' % metric)\n plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),\n max(true_y)), alpha=0.3, linestyle='--', color='b')\n if metric == 'Age':\n plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.savefig(save_path)\n plt.close()\n\n\n<mask token>\n\n\ndef ttest_ind_corrected(performance_a, performance_b, k=10, r=10):\n \"\"\"Corrected repeated k-fold cv test.\n The test assumes that the classifiers were evaluated using cross validation.\n\n Ref:\n Bouckaert, Remco R., and Eibe Frank. \"Evaluating the replicability of significance tests for comparing learning\n algorithms.\" Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004\n\n Args:\n performance_a: performances from classifier A\n performance_b: performances from classifier B\n k: number of folds\n r: number of repetitions\n\n Returns:\n t: t-statistic of the corrected test.\n prob: p-value of the corrected test.\n \"\"\"\n df = k * r - 1\n x = performance_a - performance_b\n m = np.mean(x)\n sigma_2 = np.var(x, ddof=1)\n denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)\n with np.errstate(divide='ignore', invalid='ignore'):\n t = np.divide(m, denom)\n prob = stats.t.sf(np.abs(t), df) * 2\n return t, prob\n",
"step-4": "<mask token>\nmatplotlib.use('Agg')\n<mask token>\nsns.set()\n\n\ndef get_paths(debug, dataset):\n if debug and dataset == 'OASIS':\n project_wd = os.getcwd()\n project_data = os.path.join(project_wd, 'data')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BANC':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BOSTON':\n project_wd = os.getcwd()\n project_data = None\n project_sink = None\n elif debug and dataset == 'BANC_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif debug and dataset == 'UKBIO_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif not debug and dataset == 'OASIS':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'NaN', 'data')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BANC':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BOSTON':\n project_wd = '/code'\n project_data = None\n project_sink = None\n elif not debug and (dataset == 'BANC_freesurf' or dataset ==\n 'UKBIO_freesurf' or dataset == 'freesurf_combined'):\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'code', 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n print('Code Path: %s' % project_wd)\n print('Data Path: %s' % project_data)\n print('Data Out: %s' % project_sink)\n return project_wd, project_data, project_sink\n\n\ndef get_output_path(model, analysis, ngen, random_seed, population_size,\n debug, mutation, crossover, predicted_attribute):\n rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen,\n population_size, debug, mutation, crossover, predicted_attribute)\n output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' % random_seed)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_all_random_seed_paths(model, analysis, ngen, population_size, debug,\n mutation, crossover, predicted_attribute):\n if (analysis == 'vanilla' or analysis == 'feat_selec' or analysis ==\n 'feat_combi' or analysis == 'vanilla_combi' or analysis ==\n 'random_seed' or analysis == 'ukbio' or analysis == 'summary_data' or\n analysis == 'uniform_dist'):\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen)\n elif analysis == 'population':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, \n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%05d_population_size' % population_size, \n '%03d_generations' % ngen)\n elif analysis == 'mutation':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' % model,\n 'Output', analysis, predicted_attribute, '%03d_generations' %\n ngen, '%.01f_mut_%.01f_cross' % (mutation, crossover))\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy', \n 'tpot_%s' % model, 'Output', analysis, predicted_attribute,\n '%03d_generations' % ngen, '%.01f_mut_%.01f_cross' % (\n mutation, crossover))\n else:\n raise IOError('Analysis path not defined. Passed analysis was %s' %\n analysis)\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n return output_path\n\n\ndef get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):\n \"\"\"\n This function gets the original dataset and transforms it into a uniformly\n distributed dataset.\n \"\"\"\n project_wd, project_data, project_sink = get_paths(debug, dataset)\n demographics, imgs, dataframe = get_data(project_data, dataset, debug,\n project_wd, resamplefactor, raw=raw, analysis=analysis)\n demographics['age_int'] = demographics['age'].astype('int32', copy=False)\n age_range = np.arange(demographics['age'].min(), demographics['age'].max())\n max_n = 14\n age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]\n age_range = np.setdiff1d(age_range, age_to_remove)\n ids_to_use = []\n for age in age_range:\n ids_to_use.append(demographics.index[demographics['age_int'] == age\n ].tolist()[:max_n])\n ids_to_use = [item for sublist in ids_to_use for item in sublist]\n demographics = demographics[demographics.index.isin(ids_to_use)]\n dataframe = dataframe.loc[demographics['id']]\n print('Shape of the new demographics:')\n print(demographics.shape)\n print('Oldest %d and youngest %d subject' % (demographics['age_int'].\n max(), demographics['age_int'].min()))\n print('Number of age bins %d' % len(demographics['age_int'].unique()))\n return demographics, dataframe\n\n\ndef get_best_pipeline_paths(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute):\n output_path = get_output_path(model, analysis, ngen, random_seed,\n population_size, debug, mutation, crossover, predicted_attribute)\n checkpoint_path = os.path.join(output_path, 'checkpoint_folder')\n if os.path.exists(checkpoint_path):\n shutil.rmtree(checkpoint_path)\n print('Deleted pre-exiting checkpoint folder')\n if not os.path.exists(checkpoint_path):\n os.makedirs(checkpoint_path)\n print('Creating checkpoint folder')\n return checkpoint_path\n\n\ndef drop_missing_features(dataframe):\n \"\"\"\n This function takes a dataframe and removes the already defined missing\n columns from the dataframe.\n \"\"\"\n missing_features = ['BrainSegVolNotVent', 'BrainSegVolNotVent.1',\n 'BrainSegVolNotVent.2', 'eTIV', 'eTIV.1', 'SurfaceHoles',\n 'rhSurfaceHoles', 'lhSurfaceHoles', 'BrainSegVolNotVentSurf',\n 'BrainSegVol', 'Optic-Chiasm', 'Right-non-WM-hypointensities',\n 'Left-non-WM-hypointensities', 'non-WM-hypointensities',\n 'Right-WM-hypointensities', 'Left-WM-hypointensities',\n 'WM-hypointensities', '5th-Ventricle', 'Right-choroid-plexus',\n 'Left-choroid-plexus', 'Left-Lateral-Ventricle',\n 'Right-Lateral-Ventricle', 'Left-Inf-Lat-Vent', 'Right-Inf-Lat-Vent']\n cleaned_df = dataframe.drop(missing_features, axis=1)\n return cleaned_df\n\n\ndef get_data_covariates(dataPath, rawsubjectsId, dataset):\n if dataset == 'OASIS':\n demographics = pd.read_csv(os.path.join(dataPath,\n 'oasis_cross-sectional.csv'))\n demographics = demographics.sort_values('ID')\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = demographics.loc[(demographics['CDR'] == 0) |\n demographics['CDR'].isnull(), 'ID']\n demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]\n elif dataset == 'BANC':\n column_names = ['ID', 'original_dataset', 'sex', 'Age']\n demographics = pd.read_csv(os.path.join(dataPath,\n 'original_dataset', 'BANC', 'BANC_2016.csv'), names=column_names)\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n demographics = demographics.loc[~demographics['ID'].isin(\n missingsubjectsId)]\n selectedSubId = rawsubjectsId\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n assert len(selectedSubId) == len(demographics)\n return demographics, selectedSubId\n\n\ndef _multiprocessing_resample(img, target_affine):\n resampled_img = image.resample_img(img, target_affine=target_affine,\n interpolation='nearest')\n return resampled_img\n\n\ndef _load_nibabel(filePath):\n img = nib.load(os.path.join(filePath))\n return img\n\n\ndef get_config_dictionary():\n regressor_config_dic = {'sklearn.linear_model.ElasticNetCV': {\n 'l1_ratio': np.arange(0.0, 1.01, 0.05), 'tol': [1e-05, 0.0001, \n 0.001, 0.01, 0.1]}, 'sklearn.tree.DecisionTreeRegressor': {\n 'max_depth': range(1, 11), 'min_samples_split': range(2, 21),\n 'min_samples_leaf': range(1, 21)},\n 'sklearn.neighbors.KNeighborsRegressor': {'n_neighbors': range(1, \n 101), 'weights': ['uniform', 'distance'], 'p': [1, 2]},\n 'sklearn.linear_model.LassoLarsCV': {'normalize': [True, False]},\n 'sklearn.svm.LinearSVR': {'loss': ['epsilon_insensitive',\n 'squared_epsilon_insensitive'], 'dual': [True, False], 'tol': [\n 1e-05, 0.0001, 0.001, 0.01, 0.1], 'C': [0.0001, 0.001, 0.01, 0.1, \n 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0], 'epsilon': [0.0001, 0.001, \n 0.01, 0.1, 1.0]}, 'sklearn.linear_model.RidgeCV': {},\n 'sklearn.feature_selection.SelectFwe': {'alpha': np.arange(0, 0.05,\n 0.001), 'score_func': {'sklearn.feature_selection.f_regression':\n None}}, 'sklearn.feature_selection.SelectPercentile': {'percentile':\n range(1, 100), 'score_func': {\n 'sklearn.feature_selection.f_regression': None}},\n 'sklearn.feature_selection.VarianceThreshold': {'threshold': [\n 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}}\n return regressor_config_dic\n\n\ndef get_mean_age(df):\n mean_age = df['Age'].mean()\n std_age = df['Age'].std()\n print('Mean Age %.2f +- %.2f' % (mean_age, std_age))\n\n\ndef get_data(project_data, dataset, debug, project_wd, resamplefactor, raw,\n analysis):\n \"\"\" Load the csv files and return\n :param project_data:\n :param dataset:\n :param debug:\n :param project_wd:\n :param resamplefactor:\n :raw: Which type of fressesfurfer should we analyse (the raw, where both\n datasets have not been matched or the not raw where the number of columns\n between dataset is the same)\n :return: demographics:\n :return: demographics:\n :return: dataframe.values: Just the numeric values of the dataframe\n \"\"\"\n if dataset == 'freesurf_combined' and raw == True:\n raise ValueError('The combined analysis cannot use the raw dataset')\n print('Loading Brain image data')\n elif dataset == 'OASIS':\n fileList = os.listdir(project_data)\n rawsubjectsId = [re.sub('^smwc1(.*?)\\\\_mpr-1_anon.nii$', '\\\\1',\n file) for file in fileList if file.endswith('.nii')]\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, dataset)\n get_mean_age(demographics)\n imgs = [nib.load(os.path.join(project_data, \n 'smwc1%s_mpr-1_anon.nii' % subject)) for subject in tqdm(\n selectedSubId)]\n elif dataset == 'BANC':\n project_data_path = os.path.join(project_data, 'wm_data')\n fileList = os.listdir(project_data_path)\n rawsubjectsId = [file[5:12] for file in fileList if file.endswith(\n '.nii.gz')]\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, dataset)\n get_mean_age(demographics)\n subjectsFile = [os.path.join(project_data_path, file) for file in\n fileList if file[5:12] in selectedSubId]\n with Pool() as p:\n imgs = list(tqdm(p.imap(_load_nibabel, subjectsFile), total=len\n (selectedSubId)))\n elif dataset == 'BANC_freesurf' and raw == True:\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'BANC',\n 'aparc_aseg_stats_BANC.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},\n inplace=True)\n return demographics, None, freesurf_df\n elif dataset == 'UKBIO_freesurf' and raw == False and not analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_UKBIO.csv'), delimiter=',')\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',', index_col=False)\n demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n freesurf_df = freesurf_df.set_index('id')\n return demographics, None, freesurf_df\n elif dataset == 'BANC_freesurf' and raw == False and not analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},\n inplace=True)\n return demographics, None, freesurf_df\n elif dataset == 'UKBIO_freesurf' and raw == True and not analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n freesurf_df = freesurf_df.drop(columns='id.4844')\n demographics = freesurf_df[['age', 'sex', 'id']].copy()\n freesurf_df = freesurf_df.set_index('id')\n return demographics, None, freesurf_df\n elif dataset == 'UKBIO_freesurf' and raw == False and analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_UKBIO_summary.csv'), delimiter=',')\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n return demographics, None, freesurf_df\n elif dataset == 'BANC_freesurf' and raw == False and analysis == 'summary_data':\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_BANC_summary.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n demographics.rename(index=str, columns={'ID': 'id', 'Age': 'age'},\n inplace=True)\n return demographics, None, freesurf_df\n elif dataset == 'freesurf_combined':\n ukbio_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_UKBIO.csv'), delimiter=',', index_col=0)\n banc_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'matched_dataset',\n 'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess', 'original_dataset', 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n rawsubjectsId = banc_df.index\n banc_demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId, 'BANC')\n ukbio_demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n freesurfer_df = pd.concat([ukbio_df, banc_df])\n tmp = banc_demographics.drop('original_dataset', axis=1)\n tmp.rename(index=str, columns={'ID': 'id', 'Age': 'age'}, inplace=True)\n tmp['sex'] = tmp['sex'].map({'F': 'female', 'M': 'male'})\n tmp['dataset'] = 'banc'\n ukbio_demographics['dataset'] = 'ukbio'\n demographics = pd.concat([ukbio_demographics, tmp], sort=False)\n bins = 17, 30, 40, 50, 60, 70, 80, 90\n group_labels = range(1, len(bins))\n demographics['age_band'] = pd.cut(demographics['age'], bins, labels\n =group_labels)\n sex_age_group = demographics.groupby(['sex', 'age_band'])\n demographics['stratify'] = sex_age_group.grouper.group_info[0] + 1\n return demographics, None, freesurfer_df\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n print('Resample the dataset by a factor of %d' % resamplefactor)\n print('Original image size: %s' % (imgs[0].shape,))\n resampleby2affine = np.array([[resamplefactor, 1, 1, 1], [1,\n resamplefactor, 1, 1], [1, 1, resamplefactor, 1], [1, 1, 1, 1]])\n target_affine = np.multiply(imgs[0].affine, resampleby2affine)\n print('Resampling Images')\n with Pool() as p:\n args = partial(_multiprocessing_resample, target_affine=target_affine)\n resampledimgs = list(tqdm(p.imap(args, imgs), total=len(imgs)))\n print('Resampled image size: %s' % (resampledimgs[0].shape,))\n print('Compute brain mask')\n MeanImgMask = masking.compute_multi_epi_mask(resampledimgs,\n lower_cutoff=0.001, upper_cutoff=0.85, opening=False)\n maskedData = [masking.apply_mask(img, MeanImgMask) for img in resampledimgs\n ]\n if debug:\n mask_path = os.path.join(project_wd, 'BayOptPy', 'tpot')\n print('Saving brain mask: %s' % mask_path)\n nib.save(MeanImgMask, os.path.join(mask_path, 'mask_%s.nii.gz' %\n dataset))\n print('Applied mask to the dataset')\n maskedData = np.array(maskedData)\n return demographics, imgs, maskedData\n\n\ndef get_mae_for_all_generations(dataset, random_seed, generations,\n config_dict, tpot_path):\n \"\"\"\n Get the MAE values for both the training and test dataset\n :return:\n \"\"\"\n saved_path = os.path.join(tpot_path, 'random_seed_%03d' % random_seed, \n 'tpot_%s_%s_%03dgen_pipelines.dump' % (dataset, config_dict,\n generations))\n logbook = joblib.load(saved_path)\n gen = list(logbook['log'].keys())\n print('There are %d optminal pipelines' % len(gen))\n print('These are the best pipelines')\n for generation in gen:\n print(logbook['log'][generation]['pipeline_name'])\n all_mae_test = []\n all_mae_train = []\n pipeline_complexity = []\n curr_gen_idx = 0\n for generation in range(generations):\n if generation == gen[curr_gen_idx]:\n all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_test_mae']))\n all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]][\n 'pipeline_score']))\n pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]\n ]['pipeline_sklearn_obj'].named_steps.keys()))\n if len(gen) > 1 and len(gen) > curr_gen_idx + 1:\n curr_gen_idx += 1\n else:\n all_mae_test.append(all_mae_test[-1])\n all_mae_train.append(all_mae_train[-1])\n pipeline_complexity.append(pipeline_complexity[-1])\n pipeline_complexity = np.array(pipeline_complexity)\n return all_mae_test, all_mae_train, pipeline_complexity\n\n\ndef set_publication_style():\n plt.style.use(['seaborn-white', 'seaborn-talk'])\n matplotlib.rc('font', family='Times New Roman')\n sns.set_style('white', {'axes.spines.top': False, 'axes.spines.right': \n False, 'axes.labelsize': 'large'})\n\n\ndef create_age_histogram(df, dataset):\n \"\"\"\n Get an age array and plot and save the age histogram for the analysed sample\n \"\"\"\n set_publication_style()\n plt.figure()\n path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' % dataset\n min_age = df['age'].min()\n max_age = df['age'].max()\n plt.hist(df['age'], bins=65, range=(min_age, max_age))\n plt.xlabel('Age')\n plt.ylabel('# of Subjects')\n plt.legend()\n plt.savefig(path_to_save)\n plt.close()\n\n\ndef plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=\n None, cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n cm = confusion_matrix(y_true, y_pred)\n labels = [int(x) for x in unique_labels(y_true, y_pred)]\n classes = classes[labels]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print('Normalized confusion matrix')\n else:\n print('Confusion matrix, without normalization')\n print(cm)\n fig, ax = plt.subplots()\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm.shape[1]), yticks=np.arange(cm.shape[0]),\n xticklabels=classes, yticklabels=classes, title=title, ylabel=\n 'True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.0\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt), ha='center', va='center',\n color='white' if cm[i, j] > thresh else 'black')\n fig.tight_layout()\n return ax, cm\n\n\ndef plot_confusion_matrix_boosting(cm_mean, cm_std, classes, title=None,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n fig, ax = plt.subplots()\n im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(xticks=np.arange(cm_mean.shape[1]), yticks=np.arange(cm_mean.\n shape[0]), xticklabels=classes, yticklabels=classes, title=title,\n ylabel='True label', xlabel='Predicted label')\n plt.setp(ax.get_xticklabels(), rotation=45, ha='right', rotation_mode=\n 'anchor')\n fmt = '{0:.2f} ± {1:.2f}'\n thresh = cm_mean.max() / 2.0\n for i in range(cm_mean.shape[0]):\n for j in range(cm_mean.shape[1]):\n ax.text(j, i, fmt.format(cm_mean[i, j], cm_std[i, j]), ha=\n 'center', va='center', color='white' if cm_mean[i, j] >\n thresh else 'black')\n fig.tight_layout()\n return ax\n\n\ndef plot_predicted_vs_true(true_y, predicted_y, save_path, metric):\n fig = plt.figure()\n plt.scatter(true_y, predicted_y, alpha=0.5)\n plt.ylabel('Predicted %s' % metric)\n plt.xlabel('True %s' % metric)\n plt.plot(np.arange(min(true_y), max(true_y)), np.arange(min(true_y),\n max(true_y)), alpha=0.3, linestyle='--', color='b')\n if metric == 'Age':\n plt.xticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.yticks(np.arange(min(min(true_y), min(predicted_y)), max(max(\n true_y), max(predicted_y)), step=10))\n plt.savefig(save_path)\n plt.close()\n\n\ndef load_cognitive_data(project_data):\n cog_path = os.path.join(project_data, 'cog_ukbio')\n cog_df = pd.read_csv(os.path.join(cog_path, 'UKB_10k_cog_bmi.csv'))\n cog_df = cog_df.set_index('ID')\n return cog_df\n\n\ndef ttest_ind_corrected(performance_a, performance_b, k=10, r=10):\n \"\"\"Corrected repeated k-fold cv test.\n The test assumes that the classifiers were evaluated using cross validation.\n\n Ref:\n Bouckaert, Remco R., and Eibe Frank. \"Evaluating the replicability of significance tests for comparing learning\n algorithms.\" Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004\n\n Args:\n performance_a: performances from classifier A\n performance_b: performances from classifier B\n k: number of folds\n r: number of repetitions\n\n Returns:\n t: t-statistic of the corrected test.\n prob: p-value of the corrected test.\n \"\"\"\n df = k * r - 1\n x = performance_a - performance_b\n m = np.mean(x)\n sigma_2 = np.var(x, ddof=1)\n denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)\n with np.errstate(divide='ignore', invalid='ignore'):\n t = np.divide(m, denom)\n prob = stats.t.sf(np.abs(t), df) * 2\n return t, prob\n",
"step-5": "import joblib\nimport os\nimport shutil\nimport re\nfrom scipy import stats\nfrom functools import partial\n\nimport pandas as pd\nfrom multiprocessing import Process, Pool\nfrom nilearn import masking, image\nimport nibabel as nib\nimport numpy as np\nfrom tqdm import tqdm\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.utils.multiclass import unique_labels\nimport seaborn as sns\nsns.set()\n\ndef get_paths(debug, dataset):\n\n if debug and dataset == 'OASIS':\n project_wd = os.getcwd()\n project_data = os.path.join(project_wd, 'data')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BANC':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif debug and dataset == 'BOSTON':\n project_wd = os.getcwd()\n project_data = None\n project_sink = None\n elif debug and dataset == 'BANC_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif debug and dataset == 'UKBIO_freesurf':\n project_wd = os.getcwd()\n project_data = os.path.join(os.getenv('HOME'), 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n elif not debug and dataset == 'OASIS':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'NaN', 'data')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BANC':\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'data', 'NaN', 'BANC_2016')\n project_sink = os.path.join(project_data, 'output')\n elif not debug and dataset == 'BOSTON':\n project_wd = '/code'\n project_data = None\n project_sink = None\n elif not debug and (dataset == 'BANC_freesurf' or\n dataset == 'UKBIO_freesurf' or\n dataset == 'freesurf_combined'\n ):\n project_wd = '/code'\n project_data = os.path.join(os.sep, 'code', 'BayOptPy',\n 'freesurfer_preprocess')\n project_sink = None\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n\n print('Code Path: %s' %project_wd)\n print('Data Path: %s' %project_data)\n print('Data Out: %s' %project_sink )\n return project_wd, project_data, project_sink\n\ndef get_output_path(model, analysis, ngen, random_seed, population_size, debug,\n mutation, crossover, predicted_attribute):\n # Check if output path exists, otherwise create it\n rnd_seed_path = get_all_random_seed_paths(model, analysis, ngen, population_size,\n debug, mutation, crossover,\n predicted_attribute)\n output_path = os.path.join(rnd_seed_path, 'random_seed_%03d' %random_seed)\n\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n\n return output_path\n\ndef get_all_random_seed_paths(model, analysis, ngen, population_size, debug, mutation,\n crossover, predicted_attribute):\n # As they should have been created by the get_output_path, do not create\n # path but just find its location\n if analysis == 'vanilla' or analysis == 'feat_selec' or \\\n analysis == 'feat_combi' or analysis == 'vanilla_combi' or \\\n analysis == 'random_seed' or analysis == 'ukbio' or \\\n analysis == 'summary_data' or analysis == 'uniform_dist':\n if debug:\n output_path = os.path.join('BayOptPy', 'tpot_%s' %model, 'Output',\n analysis, predicted_attribute,\n '%03d_generations' %ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy',\n 'tpot_%s' %model,\n 'Output', analysis,\n predicted_attribute,\n '%03d_generations' %ngen)\n elif analysis == 'population':\n if debug:\n output_path = os.path.join('BayOptPy',\n 'tpot_%s' %model,\n 'Output', analysis, predicted_attribute,\n '%05d_population_size' %population_size,\n '%03d_generations' %ngen)\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy',\n 'tpot_%s' %model,\n 'Output', analysis,\n predicted_attribute,\n '%05d_population_size' %population_size,\n '%03d_generations' %ngen)\n elif analysis == 'mutation':\n if debug:\n output_path = os.path.join('BayOptPy',\n 'tpot_%s' %model,\n 'Output', analysis,\n predicted_attribute,\n '%03d_generations' %ngen,\n '%.01f_mut_%.01f_cross' %(mutation, crossover))\n else:\n output_path = os.path.join(os.sep, 'code', 'BayOptPy',\n 'tpot_%s' %model,\n 'Output', analysis,\n predicted_attribute,\n '%03d_generations' %ngen,\n '%.01f_mut_%.01f_cross' %(mutation, crossover))\n\n else:\n raise IOError('Analysis path not defined. Passed analysis was %s'\n %analysis)\n\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n\n return output_path\n\ndef get_uniform_dist_data(debug, dataset, resamplefactor, raw, analysis):\n \"\"\"\n This function gets the original dataset and transforms it into a uniformly\n distributed dataset.\n \"\"\"\n\n project_wd, project_data, project_sink = get_paths(debug, dataset)\n\n demographics, imgs, dataframe = get_data(project_data, dataset,\n debug, project_wd,\n resamplefactor,\n raw=raw,\n analysis=analysis)\n\n # transform age into ints\n demographics['age_int'] = demographics['age'].astype('int32', copy=False)\n\n # Select 14 subjects for all ages that have 14 representatives.\n age_range = np.arange(demographics['age'].min(), demographics['age'].max())\n # remove entry where you don't have 14 subjects\n max_n = 14\n age_to_remove = [35, 36, 39, 42, 78, 79, 80, 81, 82, 83, 85, 89]\n age_range = np.setdiff1d(age_range, age_to_remove)\n # iterate over the dataframe and select 14 subjects for each age range\n ids_to_use = []\n for age in age_range:\n ids_to_use.append(demographics.index[demographics['age_int'] ==\n age].tolist()[:max_n])\n\n # flatten ids_to_use\n ids_to_use = [item for sublist in ids_to_use for item in sublist]\n # Filter the demographics dataframe\n demographics = demographics[demographics.index.isin(ids_to_use)]\n # set subject's id as index\n # filter dataset using index of the subjects\n dataframe = dataframe.loc[demographics['id']]\n\n # Print some diagnosis\n print('Shape of the new demographics:')\n print(demographics.shape)\n print('Oldest %d and youngest %d subject' %(demographics['age_int'].max(),\n demographics['age_int'].min()))\n print('Number of age bins %d' %len(demographics['age_int'].unique()))\n return demographics, dataframe\n\n\ndef get_best_pipeline_paths(model, analysis, ngen, random_seed, population_size, debug,\n mutation, crossover, predicted_attribute):\n # check if folder exists and in case yes, remove it as new runs will save\n # new files without overwritting\n output_path = get_output_path(model, analysis, ngen, random_seed, population_size,\n debug, mutation, crossover,\n predicted_attribute)\n checkpoint_path = os.path.join(output_path, 'checkpoint_folder')\n\n # Delete folder if it already exists and create a new one\n if os.path.exists(checkpoint_path):\n shutil.rmtree(checkpoint_path)\n print('Deleted pre-exiting checkpoint folder')\n\n if not os.path.exists(checkpoint_path):\n os.makedirs(checkpoint_path)\n print('Creating checkpoint folder')\n\n return checkpoint_path\n\ndef drop_missing_features(dataframe):\n '''\n This function takes a dataframe and removes the already defined missing\n columns from the dataframe.\n '''\n missing_features = [# This features are repeated or missing on the BIOBANK\n # dataset\n 'BrainSegVolNotVent',\n 'BrainSegVolNotVent.1',\n 'BrainSegVolNotVent.2',\n 'eTIV',\n 'eTIV.1',\n # Drop additional features that are 0 or have no\n # biological meaning\n 'SurfaceHoles',\n 'rhSurfaceHoles',\n 'lhSurfaceHoles',\n 'BrainSegVolNotVentSurf',\n 'BrainSegVol',\n 'Optic-Chiasm',\n 'Right-non-WM-hypointensities',\n 'Left-non-WM-hypointensities',\n 'non-WM-hypointensities',\n 'Right-WM-hypointensities',\n 'Left-WM-hypointensities',\n 'WM-hypointensities',\n '5th-Ventricle',\n 'Right-choroid-plexus',\n 'Left-choroid-plexus',\n 'Left-Lateral-Ventricle',\n 'Right-Lateral-Ventricle',\n 'Left-Inf-Lat-Vent',\n 'Right-Inf-Lat-Vent',\n\n ]\n\n\n cleaned_df = dataframe.drop(missing_features, axis=1)\n return cleaned_df\n\ndef get_data_covariates(dataPath, rawsubjectsId, dataset):\n if dataset == 'OASIS':\n # Load the demographic details from the dataset\n demographics = pd.read_csv(os.path.join(dataPath, 'oasis_cross-sectional.csv'))\n # sort demographics by ascending id\n demographics = demographics.sort_values('ID')\n\n # Check if there is any subject for which we have the fmri data but no demographics\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n # remove the demographic data from the missing subjects\n demographics = demographics.loc[~demographics['ID'].isin(missingsubjectsId)]\n\n # list of subjects that do not have dementia (CDR > 0)\n selectedSubId = demographics.loc[(demographics['CDR'] == 0) | (demographics['CDR'].isnull()), 'ID']\n # filter demographics to exclude those with CDR > 0\n demographics = demographics.loc[demographics['ID'].isin(selectedSubId)]\n\n elif dataset == 'BANC':\n # Load the demographic details from the dataset\n column_names = ['ID', 'original_dataset', 'sex', 'Age']\n demographics = pd.read_csv(os.path.join(dataPath,'original_dataset',\n 'BANC',\n 'BANC_2016.csv'), names=column_names)\n # Check if there is any subject for which we have the fmri data but no demographics\n missingsubjectsId = list(set(demographics['ID']) ^ set(rawsubjectsId))\n # remove the demographic data from the missing subjects\n demographics = demographics.loc[~demographics['ID'].isin(missingsubjectsId)]\n selectedSubId = rawsubjectsId\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n\n # do some sanity checks\n # Check if you have the same number of selectedsubjectsid as the demographic information\n assert(len(selectedSubId) == len(demographics))\n\n return demographics, selectedSubId\n\n\ndef _multiprocessing_resample(img, target_affine):\n resampled_img = image.resample_img(img, target_affine=target_affine,\n interpolation='nearest')\n return resampled_img\n\n\ndef _load_nibabel(filePath):\n img = nib.load(os.path.join(filePath))\n return img\n\ndef get_config_dictionary():\n # Define the same default pipeline as TPOT light but without the preprocessing operators\n regressor_config_dic = {\n\n 'sklearn.linear_model.ElasticNetCV': {\n 'l1_ratio': np.arange(0.0, 1.01, 0.05),\n 'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]\n },\n\n 'sklearn.tree.DecisionTreeRegressor': {\n 'max_depth': range(1, 11),\n 'min_samples_split': range(2, 21),\n 'min_samples_leaf': range(1, 21)\n },\n\n 'sklearn.neighbors.KNeighborsRegressor': {\n 'n_neighbors': range(1, 101),\n 'weights': [\"uniform\", \"distance\"],\n 'p': [1, 2]\n },\n\n 'sklearn.linear_model.LassoLarsCV': {\n 'normalize': [True, False]\n },\n\n 'sklearn.svm.LinearSVR': {\n 'loss': [\"epsilon_insensitive\", \"squared_epsilon_insensitive\"],\n 'dual': [True, False],\n 'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],\n 'C': [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1., 5., 10., 15., 20., 25.],\n 'epsilon': [1e-4, 1e-3, 1e-2, 1e-1, 1.]\n },\n\n 'sklearn.linear_model.RidgeCV': {\n },\n\n # Selectors\n 'sklearn.feature_selection.SelectFwe': {\n 'alpha': np.arange(0, 0.05, 0.001),\n 'score_func': {\n 'sklearn.feature_selection.f_regression': None\n }\n },\n\n 'sklearn.feature_selection.SelectPercentile': {\n 'percentile': range(1, 100),\n 'score_func': {\n 'sklearn.feature_selection.f_regression': None\n }\n },\n\n 'sklearn.feature_selection.VarianceThreshold': {\n 'threshold': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.2]\n }\n\n }\n return regressor_config_dic\n\ndef get_mean_age(df):\n mean_age = df['Age'].mean()\n std_age = df['Age'].std()\n print('Mean Age %.2f +- %.2f' %(mean_age, std_age))\n\ndef get_data(project_data, dataset, debug, project_wd, resamplefactor, raw,\n analysis):\n ''' Load the csv files and return\n :param project_data:\n :param dataset:\n :param debug:\n :param project_wd:\n :param resamplefactor:\n :raw: Which type of fressesfurfer should we analyse (the raw, where both\n datasets have not been matched or the not raw where the number of columns\n between dataset is the same)\n :return: demographics:\n :return: demographics:\n :return: dataframe.values: Just the numeric values of the dataframe\n '''\n\n if dataset == 'freesurf_combined' and raw == True:\n raise ValueError('The combined analysis cannot use the raw dataset')\n print('Loading Brain image data')\n elif dataset == 'OASIS':\n # remove the file end and get list of all used subjects\n fileList = os.listdir(project_data)\n rawsubjectsId = [re.sub(r'^smwc1(.*?)\\_mpr-1_anon.nii$', '\\\\1', file) for file in fileList if file.endswith('.nii')]\n # TODO: Change this. For testing purpose select just the first 5 subjects\n #rawsubjectsId = rawsubjectsId[:25]\n\n # Load the demographics for each subject\n demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, dataset)\n # print subjects mean age\n get_mean_age(demographics)\n # Load image proxies\n imgs = [nib.load(os.path.join(project_data, 'smwc1%s_mpr-1_anon.nii' %subject)) for subject in tqdm(selectedSubId)]\n\n elif dataset == 'BANC':\n # For now, performing analysis on White Matter.\n project_data_path = os.path.join(project_data, 'wm_data')\n # remove the file end and get list of all used subjects\n fileList = os.listdir(project_data_path)\n rawsubjectsId = [file[5:12] for file in fileList if file.endswith('.nii.gz')]\n # TODO: select only a set of 5 subjects\n # rawsubjectsId = rawsubjectsId[:5]\n\n # Load the demographics for each subject\n demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, dataset)\n # print subjects mean age\n get_mean_age(demographics)\n # Get the file path of the selected subjects\n subjectsFile = [os.path.join(project_data_path, file) for file in fileList if file[5:12] in selectedSubId]\n\n # Load image proxies\n with Pool() as p:\n imgs = list(tqdm(p.imap(_load_nibabel, subjectsFile), total=len(selectedSubId)))\n\n elif (dataset == 'BANC_freesurf' and raw==True):\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'original_dataset',\n 'BANC',\n 'aparc_aseg_stats_BANC.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n\n # Load the demographics for each subject\n demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, 'BANC')\n # return numpy array of the dataframe\n # Rename columns to maintain consistency withe ukbio\n demographics.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)\n return demographics, None, freesurf_df\n\n elif (dataset == 'UKBIO_freesurf' and raw==False and not\n analysis=='summary_data'):\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'matched_dataset',\n 'aparc_aseg_UKBIO.csv'), delimiter=',')\n # Read the full matrix to get the demographics information\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'original_dataset',\n 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'),\n delimiter=',',\n index_col=False)\n demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n freesurf_df = freesurf_df.set_index('id')\n return demographics, None, freesurf_df\n elif (dataset == 'BANC_freesurf' and raw==False and not\n analysis=='summary_data'):\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'matched_dataset',\n 'aparc_aseg_BANC.csv'), delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n\n # Load the demographics for each subject\n demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, 'BANC')\n # return numpy array of the dataframe\n # Rename columns to maintain consistency withe ukbio\n demographics.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)\n return demographics, None, freesurf_df\n\n elif (dataset == 'UKBIO_freesurf' and raw==True and not\n analysis=='summary_data'):\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'original_dataset',\n 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n freesurf_df = freesurf_df.drop(columns='id.4844')\n demographics = freesurf_df[['age', 'sex', 'id']].copy()\n freesurf_df = freesurf_df.set_index('id')\n return demographics, None, freesurf_df\n elif (dataset == 'UKBIO_freesurf' and raw==False and\n analysis=='summary_data'):\n # This dataset contains only 21 feature that represent summary metrics\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'matched_dataset',\n 'aparc_aseg_UKBIO_summary.csv'), delimiter=',')\n # Read the full matrix to get the demographics information\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'original_dataset',\n 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n return demographics, None, freesurf_df\n elif (dataset == 'BANC_freesurf' and raw==False and\n analysis=='summary_data'):\n # This dataset contains only 21 feature that represent summary metrics\n freesurf_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'matched_dataset',\n 'aparc_aseg_BANC_summary.csv'),\n delimiter=',', index_col=0)\n rawsubjectsId = freesurf_df.index\n\n # Load the demographics for each subject\n demographics, selectedSubId = get_data_covariates(project_data, rawsubjectsId, 'BANC')\n # Rename columns to maintain consistency withe ukbio\n demographics.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)\n return demographics, None, freesurf_df\n\n elif (dataset == 'freesurf_combined'):\n ukbio_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'matched_dataset',\n 'aparc_aseg_UKBIO.csv'),\n delimiter=',', index_col=0)\n\n banc_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'matched_dataset',\n 'aparc_aseg_BANC.csv'),\n delimiter=',', index_col=0)\n ukbio_full_df = pd.read_csv(os.path.join(project_wd, 'BayOptPy',\n 'freesurfer_preprocess',\n 'original_dataset',\n 'UKBIO',\n 'UKB_10k_FS_4844_combined.csv'), delimiter=',')\n rawsubjectsId = banc_df.index\n # Load the demographics for each subject\n banc_demographics, selectedSubId = get_data_covariates(project_data,\n rawsubjectsId,\n 'BANC')\n ukbio_demographics = ukbio_full_df[['age', 'sex', 'id']].copy()\n # Concatenate both freesurfeer datasets\n freesurfer_df = pd.concat([ukbio_df, banc_df])\n\n # Concatenate demographics information (Age and Sex)\n tmp = banc_demographics.drop('original_dataset', axis=1)\n tmp.rename(index=str, columns={'ID':'id', 'Age': 'age'}, inplace=True)\n # transform M/F into male/female\n tmp['sex'] = tmp['sex'].map({'F': 'female', 'M': 'male'})\n # Add column to specify dataset\n tmp['dataset'] = 'banc'\n ukbio_demographics['dataset'] = 'ukbio'\n demographics = pd.concat([ukbio_demographics, tmp], sort=False)\n # TODO: For now assume that the index in the BIOBANK correspond to th\n # Stratify subjects. Divide them into classes <30, 30<40, 40<50, 50<60,\n # 60<70, 70<80, 80<90, 90<100. Each age will be then further stratified\n # into F/M.\n bins = (17, 30, 40, 50, 60, 70, 80, 90)\n group_labels = range(1,len(bins))\n demographics['age_band'] = pd.cut(demographics['age'], bins,\n labels=group_labels)\n sex_age_group = demographics.groupby(['sex', 'age_band'])\n # Note that the following groups are created:\n # ('female', 1), ('female', 2), ('female', 3), ('female', 4), ('female', 5),\n # ('female', 6), ('female', 7), ('male', 1), ('male', 2), ('male', 3),\n # ('male', 4), ('male', 5), ('male', 6), ('male', 7)]\n # This will label the groups cited above in a crescent order. In total\n # you will have 1-14 groups, grouped according to their age and sex\n demographics['stratify'] = sex_age_group.grouper.group_info[0] + 1\n #same order between both fines\n return demographics, None, freesurfer_df\n\n else:\n raise ValueError('Analysis for this dataset is not yet implemented!')\n\n print('Resample the dataset by a factor of %d' %resamplefactor)\n print('Original image size: %s' %(imgs[0].shape,))\n # resample dataset to a lower quality. Increase the voxel size by two\n resampleby2affine = np.array([[resamplefactor, 1, 1, 1],\n [1, resamplefactor, 1, 1],\n [1, 1, resamplefactor, 1],\n [1, 1, 1, 1]])\n target_affine = np.multiply(imgs[0].affine, resampleby2affine)\n print('Resampling Images')\n with Pool() as p:\n args = partial(_multiprocessing_resample, target_affine=target_affine)\n resampledimgs = list(tqdm(p.imap(args, imgs), total=len(imgs)))\n print('Resampled image size: %s' %(resampledimgs[0].shape,))\n\n # Use nilearn to mask only the brain voxels across subjects\n print('Compute brain mask')\n #The lower and the upper_cutoff represent the lower and the upper fraction of the histogram to be discarded\n MeanImgMask = masking.compute_multi_epi_mask(resampledimgs, lower_cutoff=0.001, upper_cutoff=.85, opening=False)\n # Apply the group mask on all subjects.\n # Note: The apply_mask function returns the flattened data as a numpy array\n maskedData = [masking.apply_mask(img, MeanImgMask) for img in resampledimgs]\n # If debug option is set, save an nifti image of the image.\n # Note: if you resampled the image you will not be able to overlay it on the original brain\n if debug:\n mask_path = os.path.join(project_wd, 'BayOptPy', 'tpot')\n print('Saving brain mask: %s' %mask_path)\n nib.save(MeanImgMask, os.path.join(mask_path, 'mask_%s.nii.gz' %dataset))\n print('Applied mask to the dataset')\n\n # Transform the imaging data into a np array (subjects x voxels)\n maskedData = np.array(maskedData)\n\n return demographics, imgs, maskedData\n\ndef get_mae_for_all_generations(dataset, random_seed, generations, config_dict,\n tpot_path):\n '''\n Get the MAE values for both the training and test dataset\n :return:\n '''\n # Load the scores for the best models\n saved_path = os.path.join(tpot_path, 'random_seed_%03d' %random_seed,\n 'tpot_%s_%s_%03dgen_pipelines.dump'\n %(dataset, config_dict, generations))\n # Note that if a value is not present for a generation, that means that the\n # score did not change from the previous generation\n # sort the array in ascending order\n logbook = joblib.load(saved_path)\n gen = list(logbook['log'].keys())\n\n print('There are %d optminal pipelines' %len(gen))\n print('These are the best pipelines')\n for generation in gen:\n print(logbook['log'][generation]['pipeline_name'])\n\n # Iterate over the the list of saved MAEs and repeat the values where one\n # generation is missed\n all_mae_test = []\n all_mae_train = []\n pipeline_complexity = []\n curr_gen_idx = 0\n # all generations\n for generation in range(generations):\n if generation == gen[curr_gen_idx]:\n all_mae_test.append(abs(logbook['log'][gen[curr_gen_idx]]['pipeline_test_mae']))\n all_mae_train.append(abs(logbook['log'][gen[curr_gen_idx]]['pipeline_score']))\n pipeline_complexity.append(len(logbook['log'][gen[curr_gen_idx]]['pipeline_sklearn_obj'].named_steps.keys()))\n if len(gen) > 1 and (len(gen) > curr_gen_idx + 1):\n curr_gen_idx += 1\n else:\n # repeat the same last value\n all_mae_test.append(all_mae_test[-1])\n all_mae_train.append(all_mae_train[-1])\n pipeline_complexity.append(pipeline_complexity[-1])\n\n # transform the pipeline_complexity into a numpy array, in order to perform\n # fancy indexing\n pipeline_complexity = np.array(pipeline_complexity)\n return all_mae_test, all_mae_train, pipeline_complexity\n\ndef set_publication_style():\n # Se font size to paper size\n plt.style.use(['seaborn-white', 'seaborn-talk'])\n matplotlib.rc(\"font\", family=\"Times New Roman\")\n # Remove the spines\n sns.set_style('white', {\"axes.spines.top\": False,\n \"axes.spines.right\": False,\n \"axes.labelsize\": 'large'})\n\ndef create_age_histogram(df, dataset):\n '''\n Get an age array and plot and save the age histogram for the analysed sample\n '''\n # Define plot styple\n set_publication_style()\n plt.figure()\n path_to_save = '/code/BayOptPy/tpot/age_histogram_%s.eps' %dataset\n min_age = df['age'].min()\n max_age = df['age'].max()\n plt.hist(df['age'], bins=65, range=(min_age,max_age))\n plt.xlabel('Age')\n plt.ylabel('# of Subjects')\n plt.legend()\n plt.savefig(path_to_save)\n plt.close()\n\n\n\ndef plot_confusion_matrix(y_true, y_pred, classes,\n normalize=False,\n title=None,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n\n # Compute confusion matrix\n cm = confusion_matrix(y_true, y_pred)\n # Only use the labels that appear in the data\n labels = [int(x) for x in unique_labels(y_true, y_pred)]\n classes = classes[labels]\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n fig, ax = plt.subplots()\n im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n # We want to show all ticks...\n ax.set(xticks=np.arange(cm.shape[1]),\n yticks=np.arange(cm.shape[0]),\n # ... and label them with the respective list entries\n xticklabels=classes, yticklabels=classes,\n title=title,\n ylabel='True label',\n xlabel='Predicted label')\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Loop over data dimensions and create text annotations.\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt),\n ha=\"center\", va=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n fig.tight_layout()\n return ax, cm\n\ndef plot_confusion_matrix_boosting(cm_mean, cm_std,\n classes,\n title=None,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n\n fig, ax = plt.subplots()\n im = ax.imshow(cm_mean, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n # We want to show all ticks...\n ax.set(xticks=np.arange(cm_mean.shape[1]),\n yticks=np.arange(cm_mean.shape[0]),\n # ... and label them with the respective list entries\n xticklabels=classes, yticklabels=classes,\n title=title,\n ylabel='True label',\n xlabel='Predicted label')\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Loop over data dimensions and create text annotations.\n fmt = '{0:.2f} ± {1:.2f}'\n thresh = cm_mean.max() / 2.\n for i in range(cm_mean.shape[0]):\n for j in range(cm_mean.shape[1]):\n ax.text(j, i, fmt.format(cm_mean[i, j],cm_std[i, j]),\n ha=\"center\", va=\"center\",\n color=\"white\" if cm_mean[i, j] > thresh else \"black\")\n fig.tight_layout()\n return ax\n\ndef plot_predicted_vs_true(true_y, predicted_y, save_path, metric):\n fig = plt.figure()\n plt.scatter(true_y, predicted_y, alpha=.5)\n plt.ylabel('Predicted %s' %metric)\n plt.xlabel('True %s'%metric)\n plt.plot(np.arange(min(true_y),\n max(true_y)),\n np.arange(min(true_y),\n max(true_y)), alpha=.3, linestyle='--',\n color='b')\n if metric == 'Age':\n plt.xticks(np.arange(min(min(true_y), min(predicted_y)),\n max(max(true_y), max(predicted_y)), step=10))\n plt.yticks(np.arange(min(min(true_y), min(predicted_y)),\n max(max(true_y), max(predicted_y)), step=10))\n plt.savefig(save_path)\n plt.close()\n\ndef load_cognitive_data(project_data):\n cog_path = os.path.join(project_data, 'cog_ukbio')\n cog_df = pd.read_csv(os.path.join(cog_path, 'UKB_10k_cog_bmi.csv'))\n cog_df = cog_df.set_index('ID')\n return cog_df\n\ndef ttest_ind_corrected(performance_a, performance_b, k=10, r=10):\n \"\"\"Corrected repeated k-fold cv test.\n The test assumes that the classifiers were evaluated using cross validation.\n\n Ref:\n Bouckaert, Remco R., and Eibe Frank. \"Evaluating the replicability of significance tests for comparing learning\n algorithms.\" Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2004\n\n Args:\n performance_a: performances from classifier A\n performance_b: performances from classifier B\n k: number of folds\n r: number of repetitions\n\n Returns:\n t: t-statistic of the corrected test.\n prob: p-value of the corrected test.\n \"\"\"\n df = k * r - 1\n\n x = performance_a - performance_b\n m = np.mean(x)\n\n sigma_2 = np.var(x, ddof=1)\n denom = np.sqrt((1 / k * r + 1 / (k - 1)) * sigma_2)\n\n with np.errstate(divide='ignore', invalid='ignore'):\n t = np.divide(m, denom)\n\n prob = stats.t.sf(np.abs(t), df) * 2\n\n return t, prob\n\n",
"step-ids": [
16,
17,
18,
21,
23
]
}
|
[
16,
17,
18,
21,
23
] |
<|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 = [('api', '0001_initial')]
operations = [migrations.RemoveField(model_name='replays', name='id'),
migrations.AddField(model_name='replays', name='oponent', field=
models.CharField(default='', max_length=200), preserve_default=
False), migrations.AddField(model_name='replays', name='player',
field=models.CharField(default='', max_length=200),
preserve_default=False), migrations.AddField(model_name='replays',
name='processed', field=models.BooleanField(default=False)),
migrations.AlterField(model_name='replays', name='title', field=
models.CharField(max_length=200, primary_key=True, serialize=False))]
<|reserved_special_token_1|>
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('api', '0001_initial')]
operations = [migrations.RemoveField(model_name='replays', name='id'),
migrations.AddField(model_name='replays', name='oponent', field=
models.CharField(default='', max_length=200), preserve_default=
False), migrations.AddField(model_name='replays', name='player',
field=models.CharField(default='', max_length=200),
preserve_default=False), migrations.AddField(model_name='replays',
name='processed', field=models.BooleanField(default=False)),
migrations.AlterField(model_name='replays', name='title', field=
models.CharField(max_length=200, primary_key=True, serialize=False))]
<|reserved_special_token_1|>
# Generated by Django 2.1 on 2018-12-09 21:53
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('api', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='replays',
name='id',
),
migrations.AddField(
model_name='replays',
name='oponent',
field=models.CharField(default='', max_length=200),
preserve_default=False,
),
migrations.AddField(
model_name='replays',
name='player',
field=models.CharField(default='', max_length=200),
preserve_default=False,
),
migrations.AddField(
model_name='replays',
name='processed',
field=models.BooleanField(default=False),
),
migrations.AlterField(
model_name='replays',
name='title',
field=models.CharField(max_length=200, primary_key=True, serialize=False),
),
]
|
flexible
|
{
"blob_id": "2c1ea45d3c7ee822ec58c2fadaf7fc182acc4422",
"index": 9264,
"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 = [('api', '0001_initial')]\n operations = [migrations.RemoveField(model_name='replays', name='id'),\n migrations.AddField(model_name='replays', name='oponent', field=\n models.CharField(default='', max_length=200), preserve_default=\n False), migrations.AddField(model_name='replays', name='player',\n field=models.CharField(default='', max_length=200),\n preserve_default=False), migrations.AddField(model_name='replays',\n name='processed', field=models.BooleanField(default=False)),\n migrations.AlterField(model_name='replays', name='title', field=\n models.CharField(max_length=200, primary_key=True, serialize=False))]\n",
"step-4": "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('api', '0001_initial')]\n operations = [migrations.RemoveField(model_name='replays', name='id'),\n migrations.AddField(model_name='replays', name='oponent', field=\n models.CharField(default='', max_length=200), preserve_default=\n False), migrations.AddField(model_name='replays', name='player',\n field=models.CharField(default='', max_length=200),\n preserve_default=False), migrations.AddField(model_name='replays',\n name='processed', field=models.BooleanField(default=False)),\n migrations.AlterField(model_name='replays', name='title', field=\n models.CharField(max_length=200, primary_key=True, serialize=False))]\n",
"step-5": "# Generated by Django 2.1 on 2018-12-09 21:53\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('api', '0001_initial'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='replays',\n name='id',\n ),\n migrations.AddField(\n model_name='replays',\n name='oponent',\n field=models.CharField(default='', max_length=200),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='replays',\n name='player',\n field=models.CharField(default='', max_length=200),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='replays',\n name='processed',\n field=models.BooleanField(default=False),\n ),\n migrations.AlterField(\n model_name='replays',\n name='title',\n field=models.CharField(max_length=200, primary_key=True, serialize=False),\n ),\n ]\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|>
ROUTE_LIST = [webapp2.Route('/api/history<name:/(?:[a-zA-Z0-9_-]+/?)*>',
handler=handlers.HistoryApi, name='historyApi'), webapp2.Route(
'/api<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.PageApi, name=
'pageApi'), webapp2.Route('/signup', handler=handlers.SignupPage, name=
'signup'), webapp2.Route('/login', handler=handlers.LoginPage, name=
'login'), webapp2.Route('/logout', handler=handlers.LogoutPage, name=
'logout'), webapp2.Route('/search', handler=handlers.SearchPage, name=
'search'), webapp2.Route('/_edit<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler
=handlers.EditPage, name='edit'), webapp2.Route(
'/_history<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.HistoryPage,
name='history'), webapp2.Route('<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler
=handlers.WikiPage, name='wiki')]
<|reserved_special_token_1|>
import webapp2
import handlers
ROUTE_LIST = [webapp2.Route('/api/history<name:/(?:[a-zA-Z0-9_-]+/?)*>',
handler=handlers.HistoryApi, name='historyApi'), webapp2.Route(
'/api<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.PageApi, name=
'pageApi'), webapp2.Route('/signup', handler=handlers.SignupPage, name=
'signup'), webapp2.Route('/login', handler=handlers.LoginPage, name=
'login'), webapp2.Route('/logout', handler=handlers.LogoutPage, name=
'logout'), webapp2.Route('/search', handler=handlers.SearchPage, name=
'search'), webapp2.Route('/_edit<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler
=handlers.EditPage, name='edit'), webapp2.Route(
'/_history<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.HistoryPage,
name='history'), webapp2.Route('<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler
=handlers.WikiPage, name='wiki')]
<|reserved_special_token_1|>
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2014 Vincent Celis
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import webapp2
import handlers
# A list containing webapp2.Route instances to define the routing tables
ROUTE_LIST = [
webapp2.Route(r'/api/history<name:/(?:[a-zA-Z0-9_-]+/?)*>',
handler=handlers.HistoryApi, name='historyApi'),
webapp2.Route(r'/api<name:/(?:[a-zA-Z0-9_-]+/?)*>',
handler=handlers.PageApi, name='pageApi'),
webapp2.Route(r'/signup', handler=handlers.SignupPage, name='signup'),
webapp2.Route(r'/login', handler=handlers.LoginPage, name='login'),
webapp2.Route(r'/logout', handler=handlers.LogoutPage, name='logout'),
webapp2.Route(r'/search', handler=handlers.SearchPage, name='search'),
webapp2.Route(r'/_edit<name:/(?:[a-zA-Z0-9_-]+/?)*>',
handler=handlers.EditPage, name='edit'),
webapp2.Route(r'/_history<name:/(?:[a-zA-Z0-9_-]+/?)*>',
handler=handlers.HistoryPage, name='history'),
webapp2.Route(r'<name:/(?:[a-zA-Z0-9_-]+/?)*>',
handler=handlers.WikiPage, name='wiki')
]
|
flexible
|
{
"blob_id": "a61bc654eecb4e44dce3e62df752f80559a2d055",
"index": 9184,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nROUTE_LIST = [webapp2.Route('/api/history<name:/(?:[a-zA-Z0-9_-]+/?)*>',\n handler=handlers.HistoryApi, name='historyApi'), webapp2.Route(\n '/api<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.PageApi, name=\n 'pageApi'), webapp2.Route('/signup', handler=handlers.SignupPage, name=\n 'signup'), webapp2.Route('/login', handler=handlers.LoginPage, name=\n 'login'), webapp2.Route('/logout', handler=handlers.LogoutPage, name=\n 'logout'), webapp2.Route('/search', handler=handlers.SearchPage, name=\n 'search'), webapp2.Route('/_edit<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler\n =handlers.EditPage, name='edit'), webapp2.Route(\n '/_history<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.HistoryPage,\n name='history'), webapp2.Route('<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler\n =handlers.WikiPage, name='wiki')]\n",
"step-3": "import webapp2\nimport handlers\nROUTE_LIST = [webapp2.Route('/api/history<name:/(?:[a-zA-Z0-9_-]+/?)*>',\n handler=handlers.HistoryApi, name='historyApi'), webapp2.Route(\n '/api<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.PageApi, name=\n 'pageApi'), webapp2.Route('/signup', handler=handlers.SignupPage, name=\n 'signup'), webapp2.Route('/login', handler=handlers.LoginPage, name=\n 'login'), webapp2.Route('/logout', handler=handlers.LogoutPage, name=\n 'logout'), webapp2.Route('/search', handler=handlers.SearchPage, name=\n 'search'), webapp2.Route('/_edit<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler\n =handlers.EditPage, name='edit'), webapp2.Route(\n '/_history<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler=handlers.HistoryPage,\n name='history'), webapp2.Route('<name:/(?:[a-zA-Z0-9_-]+/?)*>', handler\n =handlers.WikiPage, name='wiki')]\n",
"step-4": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Copyright (c) 2014 Vincent Celis\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\nimport webapp2\nimport handlers\n\n# A list containing webapp2.Route instances to define the routing tables\nROUTE_LIST = [\n webapp2.Route(r'/api/history<name:/(?:[a-zA-Z0-9_-]+/?)*>',\n handler=handlers.HistoryApi, name='historyApi'),\n webapp2.Route(r'/api<name:/(?:[a-zA-Z0-9_-]+/?)*>',\n handler=handlers.PageApi, name='pageApi'),\n webapp2.Route(r'/signup', handler=handlers.SignupPage, name='signup'),\n webapp2.Route(r'/login', handler=handlers.LoginPage, name='login'),\n webapp2.Route(r'/logout', handler=handlers.LogoutPage, name='logout'),\n webapp2.Route(r'/search', handler=handlers.SearchPage, name='search'),\n webapp2.Route(r'/_edit<name:/(?:[a-zA-Z0-9_-]+/?)*>',\n handler=handlers.EditPage, name='edit'),\n webapp2.Route(r'/_history<name:/(?:[a-zA-Z0-9_-]+/?)*>',\n handler=handlers.HistoryPage, name='history'),\n webapp2.Route(r'<name:/(?:[a-zA-Z0-9_-]+/?)*>',\n handler=handlers.WikiPage, name='wiki')\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|>
def cameras_get_info():
"""
cameras_get_info - reads the camera info from the XML file and
puts it into a python data structure and returns it.
"""
status = 0
xmldoc = minidom.parse(CAMERA_XML_FILE)
itemlist = xmldoc.getElementsByTagName('camera')
cameras_info = []
for i in xrange(len(itemlist)):
cameras_info.append({'id': itemlist[i].attributes['id'].value})
a = itemlist[i].getElementsByTagName('user')
cameras_info[i].update({'user': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('model')
cameras_info[i].update({'model': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('passwd')
cameras_info[i].update({'passwd': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('port')
cameras_info[i].update({'port': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('ip_address')
cameras_info[i].update({'ip_address': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('disk_location')
cameras_info[i].update({'disk_location': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('mfgr')
cameras_info[i].update({'mfgr': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('ftp_loc')
cameras_info[i].update({'ftp_loc': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('status')
cameras_info[i].update({'status': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('location')
cameras_info[i].update({'location': a[0].firstChild.data})
return status, cameras_info
<|reserved_special_token_1|>
<|reserved_special_token_0|>
CAMERA_XML_FILE = '/tmp/cameras.xml'
def cameras_get_info():
"""
cameras_get_info - reads the camera info from the XML file and
puts it into a python data structure and returns it.
"""
status = 0
xmldoc = minidom.parse(CAMERA_XML_FILE)
itemlist = xmldoc.getElementsByTagName('camera')
cameras_info = []
for i in xrange(len(itemlist)):
cameras_info.append({'id': itemlist[i].attributes['id'].value})
a = itemlist[i].getElementsByTagName('user')
cameras_info[i].update({'user': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('model')
cameras_info[i].update({'model': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('passwd')
cameras_info[i].update({'passwd': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('port')
cameras_info[i].update({'port': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('ip_address')
cameras_info[i].update({'ip_address': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('disk_location')
cameras_info[i].update({'disk_location': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('mfgr')
cameras_info[i].update({'mfgr': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('ftp_loc')
cameras_info[i].update({'ftp_loc': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('status')
cameras_info[i].update({'status': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('location')
cameras_info[i].update({'location': a[0].firstChild.data})
return status, cameras_info
<|reserved_special_token_1|>
<|reserved_special_token_0|>
from xml.dom import minidom, Node
CAMERA_XML_FILE = '/tmp/cameras.xml'
def cameras_get_info():
"""
cameras_get_info - reads the camera info from the XML file and
puts it into a python data structure and returns it.
"""
status = 0
xmldoc = minidom.parse(CAMERA_XML_FILE)
itemlist = xmldoc.getElementsByTagName('camera')
cameras_info = []
for i in xrange(len(itemlist)):
cameras_info.append({'id': itemlist[i].attributes['id'].value})
a = itemlist[i].getElementsByTagName('user')
cameras_info[i].update({'user': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('model')
cameras_info[i].update({'model': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('passwd')
cameras_info[i].update({'passwd': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('port')
cameras_info[i].update({'port': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('ip_address')
cameras_info[i].update({'ip_address': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('disk_location')
cameras_info[i].update({'disk_location': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('mfgr')
cameras_info[i].update({'mfgr': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('ftp_loc')
cameras_info[i].update({'ftp_loc': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('status')
cameras_info[i].update({'status': a[0].firstChild.data})
a = itemlist[i].getElementsByTagName('location')
cameras_info[i].update({'location': a[0].firstChild.data})
return status, cameras_info
<|reserved_special_token_1|>
#!/usr/bin/env python2.7
'''
lib script to encapsulate the camera info
'''
from xml.dom import minidom, Node
# what % of the file system remains before deleting files
# amount that we will cleanup relative to the filesystem total
CAMERA_XML_FILE = "/tmp/cameras.xml"
def cameras_get_info():
'''
cameras_get_info - reads the camera info from the XML file and
puts it into a python data structure and returns it.
'''
status = 0
xmldoc = minidom.parse(CAMERA_XML_FILE)
itemlist = xmldoc.getElementsByTagName('camera')
# camera info to return
cameras_info = []
for i in xrange(len(itemlist)):
cameras_info.append({'id':itemlist[i].attributes['id'].value})
a=itemlist[i].getElementsByTagName('user')
cameras_info[i].update({'user':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('model')
cameras_info[i].update({'model':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('passwd')
cameras_info[i].update({'passwd':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('port')
cameras_info[i].update({'port':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('ip_address')
cameras_info[i].update({'ip_address':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('disk_location')
cameras_info[i].update({'disk_location':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('mfgr')
cameras_info[i].update({'mfgr':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('ftp_loc')
cameras_info[i].update({'ftp_loc':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('status')
cameras_info[i].update({'status':a[0].firstChild.data})
a=itemlist[i].getElementsByTagName('location')
cameras_info[i].update({'location':a[0].firstChild.data})
return status, cameras_info
|
flexible
|
{
"blob_id": "510d411d79d5df8658703241f161b3e2a9ec5932",
"index": 4110,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef cameras_get_info():\n \"\"\"\n cameras_get_info - reads the camera info from the XML file and\n puts it into a python data structure and returns it.\n \"\"\"\n status = 0\n xmldoc = minidom.parse(CAMERA_XML_FILE)\n itemlist = xmldoc.getElementsByTagName('camera')\n cameras_info = []\n for i in xrange(len(itemlist)):\n cameras_info.append({'id': itemlist[i].attributes['id'].value})\n a = itemlist[i].getElementsByTagName('user')\n cameras_info[i].update({'user': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('model')\n cameras_info[i].update({'model': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('passwd')\n cameras_info[i].update({'passwd': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('port')\n cameras_info[i].update({'port': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('ip_address')\n cameras_info[i].update({'ip_address': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('disk_location')\n cameras_info[i].update({'disk_location': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('mfgr')\n cameras_info[i].update({'mfgr': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('ftp_loc')\n cameras_info[i].update({'ftp_loc': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('status')\n cameras_info[i].update({'status': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('location')\n cameras_info[i].update({'location': a[0].firstChild.data})\n return status, cameras_info\n",
"step-3": "<mask token>\nCAMERA_XML_FILE = '/tmp/cameras.xml'\n\n\ndef cameras_get_info():\n \"\"\"\n cameras_get_info - reads the camera info from the XML file and\n puts it into a python data structure and returns it.\n \"\"\"\n status = 0\n xmldoc = minidom.parse(CAMERA_XML_FILE)\n itemlist = xmldoc.getElementsByTagName('camera')\n cameras_info = []\n for i in xrange(len(itemlist)):\n cameras_info.append({'id': itemlist[i].attributes['id'].value})\n a = itemlist[i].getElementsByTagName('user')\n cameras_info[i].update({'user': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('model')\n cameras_info[i].update({'model': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('passwd')\n cameras_info[i].update({'passwd': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('port')\n cameras_info[i].update({'port': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('ip_address')\n cameras_info[i].update({'ip_address': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('disk_location')\n cameras_info[i].update({'disk_location': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('mfgr')\n cameras_info[i].update({'mfgr': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('ftp_loc')\n cameras_info[i].update({'ftp_loc': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('status')\n cameras_info[i].update({'status': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('location')\n cameras_info[i].update({'location': a[0].firstChild.data})\n return status, cameras_info\n",
"step-4": "<mask token>\nfrom xml.dom import minidom, Node\nCAMERA_XML_FILE = '/tmp/cameras.xml'\n\n\ndef cameras_get_info():\n \"\"\"\n cameras_get_info - reads the camera info from the XML file and\n puts it into a python data structure and returns it.\n \"\"\"\n status = 0\n xmldoc = minidom.parse(CAMERA_XML_FILE)\n itemlist = xmldoc.getElementsByTagName('camera')\n cameras_info = []\n for i in xrange(len(itemlist)):\n cameras_info.append({'id': itemlist[i].attributes['id'].value})\n a = itemlist[i].getElementsByTagName('user')\n cameras_info[i].update({'user': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('model')\n cameras_info[i].update({'model': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('passwd')\n cameras_info[i].update({'passwd': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('port')\n cameras_info[i].update({'port': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('ip_address')\n cameras_info[i].update({'ip_address': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('disk_location')\n cameras_info[i].update({'disk_location': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('mfgr')\n cameras_info[i].update({'mfgr': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('ftp_loc')\n cameras_info[i].update({'ftp_loc': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('status')\n cameras_info[i].update({'status': a[0].firstChild.data})\n a = itemlist[i].getElementsByTagName('location')\n cameras_info[i].update({'location': a[0].firstChild.data})\n return status, cameras_info\n",
"step-5": "#!/usr/bin/env python2.7\n'''\n lib script to encapsulate the camera info\n'''\nfrom xml.dom import minidom, Node\n\n# what % of the file system remains before deleting files\n# amount that we will cleanup relative to the filesystem total\nCAMERA_XML_FILE = \"/tmp/cameras.xml\"\n\n\ndef cameras_get_info():\n '''\n cameras_get_info - reads the camera info from the XML file and\n puts it into a python data structure and returns it.\n '''\n status = 0\n xmldoc = minidom.parse(CAMERA_XML_FILE)\n itemlist = xmldoc.getElementsByTagName('camera')\n # camera info to return\n cameras_info = []\n for i in xrange(len(itemlist)):\n cameras_info.append({'id':itemlist[i].attributes['id'].value})\n a=itemlist[i].getElementsByTagName('user')\n cameras_info[i].update({'user':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('model')\n cameras_info[i].update({'model':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('passwd')\n cameras_info[i].update({'passwd':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('port')\n cameras_info[i].update({'port':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('ip_address')\n cameras_info[i].update({'ip_address':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('disk_location')\n cameras_info[i].update({'disk_location':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('mfgr')\n cameras_info[i].update({'mfgr':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('ftp_loc')\n cameras_info[i].update({'ftp_loc':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('status')\n cameras_info[i].update({'status':a[0].firstChild.data})\n a=itemlist[i].getElementsByTagName('location')\n cameras_info[i].update({'location':a[0].firstChild.data})\n return status, cameras_info\n\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
# Given a string S, find the longest palindromic substring in S. You may assume that the maximum length of S is 1000, and there exists one unique longest palindromic substring.
class Solution(object):
def longestPalindrome(self, s):
"""
:type s: str
:rtype: str
"""
if len(s) == 0:
return ""
if len(s) == 1:
return s
start = -1
end = -2
for i in range(len(s)):
side = 1
while i - side >= 0 and i + side < len(s) and s[i - side] == s[i + side]:
side += 1
if (side - 1) * 2 + 1 > end - start + 1:
start = i - (side - 1)
end = i + side
out_string = s[start:end]
start = -1
end = -2
for i in range(len(s) - 1):
side = 0
while i - side >= 0 and i + 1 + side < len(s) and s[i - side] == s[i + 1 + side]:
side += 1
if side * 2 > end - start + 1:
start = i - side + 1
end = i + 1 + side
return out_string if len(out_string) > end - start else s[start:end]
|
normal
|
{
"blob_id": "7c39b3927bc0702818c54875785b4657c20c441e",
"index": 2272,
"step-1": "<mask token>\n",
"step-2": "class Solution(object):\n <mask token>\n",
"step-3": "class Solution(object):\n\n def longestPalindrome(self, s):\n \"\"\"\n :type s: str\n :rtype: str\n \"\"\"\n if len(s) == 0:\n return ''\n if len(s) == 1:\n return s\n start = -1\n end = -2\n for i in range(len(s)):\n side = 1\n while i - side >= 0 and i + side < len(s) and s[i - side] == s[\n i + side]:\n side += 1\n if (side - 1) * 2 + 1 > end - start + 1:\n start = i - (side - 1)\n end = i + side\n out_string = s[start:end]\n start = -1\n end = -2\n for i in range(len(s) - 1):\n side = 0\n while i - side >= 0 and i + 1 + side < len(s) and s[i - side] == s[\n i + 1 + side]:\n side += 1\n if side * 2 > end - start + 1:\n start = i - side + 1\n end = i + 1 + side\n return out_string if len(out_string) > end - start else s[start:end]\n",
"step-4": "# Given a string S, find the longest palindromic substring in S. You may assume that the maximum length of S is 1000, and there exists one unique longest palindromic substring.\n\nclass Solution(object):\n def longestPalindrome(self, s):\n \"\"\"\n :type s: str\n :rtype: str\n \"\"\"\n if len(s) == 0:\n return \"\"\n if len(s) == 1:\n return s\n \n start = -1\n end = -2\n for i in range(len(s)):\n side = 1\n while i - side >= 0 and i + side < len(s) and s[i - side] == s[i + side]:\n side += 1\n if (side - 1) * 2 + 1 > end - start + 1:\n start = i - (side - 1)\n end = i + side\n out_string = s[start:end]\n start = -1\n end = -2\n for i in range(len(s) - 1):\n side = 0\n while i - side >= 0 and i + 1 + side < len(s) and s[i - side] == s[i + 1 + side]:\n side += 1\n if side * 2 > end - start + 1:\n start = i - side + 1\n end = i + 1 + side\n return out_string if len(out_string) > end - start else s[start:end]\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def ConfigObject(config_path):
"""read a configuration file to retrieve access token"""
configDict = {}
with open(config_path, 'r') as config:
for line in config.readlines():
try:
configDict[line.split('=')[0]] = line.split('=')[1].rstrip()
except:
pass
return configDict
def uploadZippedToBox(zippedFolder, boxfolder=None):
if boxfolder is None:
boxfolder = accessUploadFolder()
try:
items = boxfolder.get_items()
for item in items:
if item.name == os.path.basename(zippedFolder):
try:
item.delete()
except Exception as e:
print(e)
return False
boxfolder.upload(zippedFolder)
uploaded = True
except Exception as e:
print(e)
uploaded = False
pass
finally:
return uploaded
<|reserved_special_token_0|>
def uploadAllZippedToBox(zipFolder):
"""uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed"""
zipFiles = listZipFiles(zipFolder)
tfolder = accessUploadFolder()
items = tfolder.get_items()
for item in items:
if item.name in zipFiles:
try:
item.delete()
except Exception as e:
print(e)
zipFiles.remove(item.name)
uploadedFiles = []
badUploads = []
for zipped in zipFiles:
try:
uploadZippedToBox(zipped, tfolder)
uploadedFiles.append((zipped, True))
except Exception as e:
print(e)
badUploads.append((zipped, False))
pass
return uploadedFiles, badUploads
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def ConfigObject(config_path):
"""read a configuration file to retrieve access token"""
configDict = {}
with open(config_path, 'r') as config:
for line in config.readlines():
try:
configDict[line.split('=')[0]] = line.split('=')[1].rstrip()
except:
pass
return configDict
def uploadZippedToBox(zippedFolder, boxfolder=None):
if boxfolder is None:
boxfolder = accessUploadFolder()
try:
items = boxfolder.get_items()
for item in items:
if item.name == os.path.basename(zippedFolder):
try:
item.delete()
except Exception as e:
print(e)
return False
boxfolder.upload(zippedFolder)
uploaded = True
except Exception as e:
print(e)
uploaded = False
pass
finally:
return uploaded
def accessUploadFolder(year=2020):
config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(
__file__)), 'instance', 'Boxapp.cfg']))
CLIENT_ID = config['client_id']
CLIENT_FOLDER = config['client_folder' + str(year)]
ACCESS_TOKEN = config['access_token']
auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=
ACCESS_TOKEN)
client = Client(auth)
try:
my = client.user(user_id='me').get()
print(my.name)
except:
sys.exit(
'ERROR: Invalid access token; try re-generating an access token from the app console on the web.'
)
tfolder = client.folder(CLIENT_FOLDER)
return tfolder
<|reserved_special_token_0|>
def uploadAllZippedToBox(zipFolder):
"""uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed"""
zipFiles = listZipFiles(zipFolder)
tfolder = accessUploadFolder()
items = tfolder.get_items()
for item in items:
if item.name in zipFiles:
try:
item.delete()
except Exception as e:
print(e)
zipFiles.remove(item.name)
uploadedFiles = []
badUploads = []
for zipped in zipFiles:
try:
uploadZippedToBox(zipped, tfolder)
uploadedFiles.append((zipped, True))
except Exception as e:
print(e)
badUploads.append((zipped, False))
pass
return uploadedFiles, badUploads
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def ConfigObject(config_path):
"""read a configuration file to retrieve access token"""
configDict = {}
with open(config_path, 'r') as config:
for line in config.readlines():
try:
configDict[line.split('=')[0]] = line.split('=')[1].rstrip()
except:
pass
return configDict
def uploadZippedToBox(zippedFolder, boxfolder=None):
if boxfolder is None:
boxfolder = accessUploadFolder()
try:
items = boxfolder.get_items()
for item in items:
if item.name == os.path.basename(zippedFolder):
try:
item.delete()
except Exception as e:
print(e)
return False
boxfolder.upload(zippedFolder)
uploaded = True
except Exception as e:
print(e)
uploaded = False
pass
finally:
return uploaded
def accessUploadFolder(year=2020):
config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(
__file__)), 'instance', 'Boxapp.cfg']))
CLIENT_ID = config['client_id']
CLIENT_FOLDER = config['client_folder' + str(year)]
ACCESS_TOKEN = config['access_token']
auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=
ACCESS_TOKEN)
client = Client(auth)
try:
my = client.user(user_id='me').get()
print(my.name)
except:
sys.exit(
'ERROR: Invalid access token; try re-generating an access token from the app console on the web.'
)
tfolder = client.folder(CLIENT_FOLDER)
return tfolder
def listZipFiles(directory_folder):
"""
Lists teh zip folders in teh directory folder, including subdirectortories
"""
zipFiles = []
for root, dirs, files in os.walk(directory_folder):
for name in files:
if name[-3:] == 'zip':
zipFiles.append(os.path.join(root, name))
return zipFiles
def uploadAllZippedToBox(zipFolder):
"""uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed"""
zipFiles = listZipFiles(zipFolder)
tfolder = accessUploadFolder()
items = tfolder.get_items()
for item in items:
if item.name in zipFiles:
try:
item.delete()
except Exception as e:
print(e)
zipFiles.remove(item.name)
uploadedFiles = []
badUploads = []
for zipped in zipFiles:
try:
uploadZippedToBox(zipped, tfolder)
uploadedFiles.append((zipped, True))
except Exception as e:
print(e)
badUploads.append((zipped, False))
pass
return uploadedFiles, badUploads
<|reserved_special_token_1|>
from boxsdk import Client, OAuth2
import os
import sys
def ConfigObject(config_path):
"""read a configuration file to retrieve access token"""
configDict = {}
with open(config_path, 'r') as config:
for line in config.readlines():
try:
configDict[line.split('=')[0]] = line.split('=')[1].rstrip()
except:
pass
return configDict
def uploadZippedToBox(zippedFolder, boxfolder=None):
if boxfolder is None:
boxfolder = accessUploadFolder()
try:
items = boxfolder.get_items()
for item in items:
if item.name == os.path.basename(zippedFolder):
try:
item.delete()
except Exception as e:
print(e)
return False
boxfolder.upload(zippedFolder)
uploaded = True
except Exception as e:
print(e)
uploaded = False
pass
finally:
return uploaded
def accessUploadFolder(year=2020):
config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(
__file__)), 'instance', 'Boxapp.cfg']))
CLIENT_ID = config['client_id']
CLIENT_FOLDER = config['client_folder' + str(year)]
ACCESS_TOKEN = config['access_token']
auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=
ACCESS_TOKEN)
client = Client(auth)
try:
my = client.user(user_id='me').get()
print(my.name)
except:
sys.exit(
'ERROR: Invalid access token; try re-generating an access token from the app console on the web.'
)
tfolder = client.folder(CLIENT_FOLDER)
return tfolder
def listZipFiles(directory_folder):
"""
Lists teh zip folders in teh directory folder, including subdirectortories
"""
zipFiles = []
for root, dirs, files in os.walk(directory_folder):
for name in files:
if name[-3:] == 'zip':
zipFiles.append(os.path.join(root, name))
return zipFiles
def uploadAllZippedToBox(zipFolder):
"""uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed"""
zipFiles = listZipFiles(zipFolder)
tfolder = accessUploadFolder()
items = tfolder.get_items()
for item in items:
if item.name in zipFiles:
try:
item.delete()
except Exception as e:
print(e)
zipFiles.remove(item.name)
uploadedFiles = []
badUploads = []
for zipped in zipFiles:
try:
uploadZippedToBox(zipped, tfolder)
uploadedFiles.append((zipped, True))
except Exception as e:
print(e)
badUploads.append((zipped, False))
pass
return uploadedFiles, badUploads
<|reserved_special_token_1|>
from boxsdk import Client, OAuth2
import os
import sys
def ConfigObject(config_path):
"read a configuration file to retrieve access token"
configDict = {}
with open(config_path,'r') as config:
for line in config.readlines():
try:
configDict[line.split("=")[0]] = line.split("=")[1].rstrip()
except:
pass
return configDict
def uploadZippedToBox(zippedFolder, boxfolder = None):
if boxfolder is None:
boxfolder = accessUploadFolder()
try:
items = boxfolder.get_items()
for item in items:
if item.name == os.path.basename(zippedFolder):
try:
item.delete()
except Exception as e:
print(e)
return False
boxfolder.upload(zippedFolder)
uploaded = True
except Exception as e:
print(e)
uploaded = False
pass
finally:
return uploaded
def accessUploadFolder(year=2020):
# Define client ID, client secret, and developer token.path = os.path.join(*[os.path.dirname(os.path.abspath(__file__)),"instance"])
# Read app info from text file
config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(__file__)),"instance", 'Boxapp.cfg']))
CLIENT_ID = config['client_id']
CLIENT_FOLDER = config['client_folder' + str(year)]
ACCESS_TOKEN = config['access_token']
# Create OAuth2 object.
auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=ACCESS_TOKEN)
# Create the authenticated client
client = Client(auth)
# make sure we connected
try:
my = client.user(user_id='me').get()
print(my.name) # developer name tied to the token
except:
sys.exit("ERROR: Invalid access token; try re-generating an "
"access token from the app console on the web.")
tfolder = client.folder(CLIENT_FOLDER) # 2020 scada data folder
return tfolder
def listZipFiles(directory_folder):
'''
Lists teh zip folders in teh directory folder, including subdirectortories
'''
zipFiles = []
for root, dirs, files in os.walk(directory_folder):
for name in files:
if name[-3:] == 'zip':
zipFiles.append(os.path.join(root, name))
return zipFiles
def uploadAllZippedToBox(zipFolder):
'''uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed'''
#files to upload
zipFiles = listZipFiles(zipFolder)
tfolder = accessUploadFolder()
items = tfolder.get_items()
for item in items:
if item.name in zipFiles:
try:
item.delete()
#tfolder.file(file_id=item.id).delete()
except Exception as e:
print(e)
#If we coudn't delete the existing zip file don't try to upload a new one.
zipFiles.remove(item.name)
uploadedFiles = []
badUploads = []
for zipped in zipFiles:
try:
uploadZippedToBox(zipped, tfolder)
uploadedFiles.append((zipped,True))
except Exception as e:
print(e)
badUploads.append((zipped,False))
pass
return uploadedFiles, badUploads
|
flexible
|
{
"blob_id": "e76ebbe8dab2e5169ef40b559f783c49ba4de825",
"index": 1750,
"step-1": "<mask token>\n\n\ndef ConfigObject(config_path):\n \"\"\"read a configuration file to retrieve access token\"\"\"\n configDict = {}\n with open(config_path, 'r') as config:\n for line in config.readlines():\n try:\n configDict[line.split('=')[0]] = line.split('=')[1].rstrip()\n except:\n pass\n return configDict\n\n\ndef uploadZippedToBox(zippedFolder, boxfolder=None):\n if boxfolder is None:\n boxfolder = accessUploadFolder()\n try:\n items = boxfolder.get_items()\n for item in items:\n if item.name == os.path.basename(zippedFolder):\n try:\n item.delete()\n except Exception as e:\n print(e)\n return False\n boxfolder.upload(zippedFolder)\n uploaded = True\n except Exception as e:\n print(e)\n uploaded = False\n pass\n finally:\n return uploaded\n\n\n<mask token>\n\n\ndef uploadAllZippedToBox(zipFolder):\n \"\"\"uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed\"\"\"\n zipFiles = listZipFiles(zipFolder)\n tfolder = accessUploadFolder()\n items = tfolder.get_items()\n for item in items:\n if item.name in zipFiles:\n try:\n item.delete()\n except Exception as e:\n print(e)\n zipFiles.remove(item.name)\n uploadedFiles = []\n badUploads = []\n for zipped in zipFiles:\n try:\n uploadZippedToBox(zipped, tfolder)\n uploadedFiles.append((zipped, True))\n except Exception as e:\n print(e)\n badUploads.append((zipped, False))\n pass\n return uploadedFiles, badUploads\n",
"step-2": "<mask token>\n\n\ndef ConfigObject(config_path):\n \"\"\"read a configuration file to retrieve access token\"\"\"\n configDict = {}\n with open(config_path, 'r') as config:\n for line in config.readlines():\n try:\n configDict[line.split('=')[0]] = line.split('=')[1].rstrip()\n except:\n pass\n return configDict\n\n\ndef uploadZippedToBox(zippedFolder, boxfolder=None):\n if boxfolder is None:\n boxfolder = accessUploadFolder()\n try:\n items = boxfolder.get_items()\n for item in items:\n if item.name == os.path.basename(zippedFolder):\n try:\n item.delete()\n except Exception as e:\n print(e)\n return False\n boxfolder.upload(zippedFolder)\n uploaded = True\n except Exception as e:\n print(e)\n uploaded = False\n pass\n finally:\n return uploaded\n\n\ndef accessUploadFolder(year=2020):\n config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(\n __file__)), 'instance', 'Boxapp.cfg']))\n CLIENT_ID = config['client_id']\n CLIENT_FOLDER = config['client_folder' + str(year)]\n ACCESS_TOKEN = config['access_token']\n auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=\n ACCESS_TOKEN)\n client = Client(auth)\n try:\n my = client.user(user_id='me').get()\n print(my.name)\n except:\n sys.exit(\n 'ERROR: Invalid access token; try re-generating an access token from the app console on the web.'\n )\n tfolder = client.folder(CLIENT_FOLDER)\n return tfolder\n\n\n<mask token>\n\n\ndef uploadAllZippedToBox(zipFolder):\n \"\"\"uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed\"\"\"\n zipFiles = listZipFiles(zipFolder)\n tfolder = accessUploadFolder()\n items = tfolder.get_items()\n for item in items:\n if item.name in zipFiles:\n try:\n item.delete()\n except Exception as e:\n print(e)\n zipFiles.remove(item.name)\n uploadedFiles = []\n badUploads = []\n for zipped in zipFiles:\n try:\n uploadZippedToBox(zipped, tfolder)\n uploadedFiles.append((zipped, True))\n except Exception as e:\n print(e)\n badUploads.append((zipped, False))\n pass\n return uploadedFiles, badUploads\n",
"step-3": "<mask token>\n\n\ndef ConfigObject(config_path):\n \"\"\"read a configuration file to retrieve access token\"\"\"\n configDict = {}\n with open(config_path, 'r') as config:\n for line in config.readlines():\n try:\n configDict[line.split('=')[0]] = line.split('=')[1].rstrip()\n except:\n pass\n return configDict\n\n\ndef uploadZippedToBox(zippedFolder, boxfolder=None):\n if boxfolder is None:\n boxfolder = accessUploadFolder()\n try:\n items = boxfolder.get_items()\n for item in items:\n if item.name == os.path.basename(zippedFolder):\n try:\n item.delete()\n except Exception as e:\n print(e)\n return False\n boxfolder.upload(zippedFolder)\n uploaded = True\n except Exception as e:\n print(e)\n uploaded = False\n pass\n finally:\n return uploaded\n\n\ndef accessUploadFolder(year=2020):\n config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(\n __file__)), 'instance', 'Boxapp.cfg']))\n CLIENT_ID = config['client_id']\n CLIENT_FOLDER = config['client_folder' + str(year)]\n ACCESS_TOKEN = config['access_token']\n auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=\n ACCESS_TOKEN)\n client = Client(auth)\n try:\n my = client.user(user_id='me').get()\n print(my.name)\n except:\n sys.exit(\n 'ERROR: Invalid access token; try re-generating an access token from the app console on the web.'\n )\n tfolder = client.folder(CLIENT_FOLDER)\n return tfolder\n\n\ndef listZipFiles(directory_folder):\n \"\"\"\n Lists teh zip folders in teh directory folder, including subdirectortories\n \"\"\"\n zipFiles = []\n for root, dirs, files in os.walk(directory_folder):\n for name in files:\n if name[-3:] == 'zip':\n zipFiles.append(os.path.join(root, name))\n return zipFiles\n\n\ndef uploadAllZippedToBox(zipFolder):\n \"\"\"uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed\"\"\"\n zipFiles = listZipFiles(zipFolder)\n tfolder = accessUploadFolder()\n items = tfolder.get_items()\n for item in items:\n if item.name in zipFiles:\n try:\n item.delete()\n except Exception as e:\n print(e)\n zipFiles.remove(item.name)\n uploadedFiles = []\n badUploads = []\n for zipped in zipFiles:\n try:\n uploadZippedToBox(zipped, tfolder)\n uploadedFiles.append((zipped, True))\n except Exception as e:\n print(e)\n badUploads.append((zipped, False))\n pass\n return uploadedFiles, badUploads\n",
"step-4": "from boxsdk import Client, OAuth2\nimport os\nimport sys\n\n\ndef ConfigObject(config_path):\n \"\"\"read a configuration file to retrieve access token\"\"\"\n configDict = {}\n with open(config_path, 'r') as config:\n for line in config.readlines():\n try:\n configDict[line.split('=')[0]] = line.split('=')[1].rstrip()\n except:\n pass\n return configDict\n\n\ndef uploadZippedToBox(zippedFolder, boxfolder=None):\n if boxfolder is None:\n boxfolder = accessUploadFolder()\n try:\n items = boxfolder.get_items()\n for item in items:\n if item.name == os.path.basename(zippedFolder):\n try:\n item.delete()\n except Exception as e:\n print(e)\n return False\n boxfolder.upload(zippedFolder)\n uploaded = True\n except Exception as e:\n print(e)\n uploaded = False\n pass\n finally:\n return uploaded\n\n\ndef accessUploadFolder(year=2020):\n config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(\n __file__)), 'instance', 'Boxapp.cfg']))\n CLIENT_ID = config['client_id']\n CLIENT_FOLDER = config['client_folder' + str(year)]\n ACCESS_TOKEN = config['access_token']\n auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=\n ACCESS_TOKEN)\n client = Client(auth)\n try:\n my = client.user(user_id='me').get()\n print(my.name)\n except:\n sys.exit(\n 'ERROR: Invalid access token; try re-generating an access token from the app console on the web.'\n )\n tfolder = client.folder(CLIENT_FOLDER)\n return tfolder\n\n\ndef listZipFiles(directory_folder):\n \"\"\"\n Lists teh zip folders in teh directory folder, including subdirectortories\n \"\"\"\n zipFiles = []\n for root, dirs, files in os.walk(directory_folder):\n for name in files:\n if name[-3:] == 'zip':\n zipFiles.append(os.path.join(root, name))\n return zipFiles\n\n\ndef uploadAllZippedToBox(zipFolder):\n \"\"\"uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed\"\"\"\n zipFiles = listZipFiles(zipFolder)\n tfolder = accessUploadFolder()\n items = tfolder.get_items()\n for item in items:\n if item.name in zipFiles:\n try:\n item.delete()\n except Exception as e:\n print(e)\n zipFiles.remove(item.name)\n uploadedFiles = []\n badUploads = []\n for zipped in zipFiles:\n try:\n uploadZippedToBox(zipped, tfolder)\n uploadedFiles.append((zipped, True))\n except Exception as e:\n print(e)\n badUploads.append((zipped, False))\n pass\n return uploadedFiles, badUploads\n",
"step-5": "from boxsdk import Client, OAuth2\n\nimport os\nimport sys\n\ndef ConfigObject(config_path):\n \"read a configuration file to retrieve access token\"\n configDict = {}\n with open(config_path,'r') as config:\n for line in config.readlines():\n try:\n configDict[line.split(\"=\")[0]] = line.split(\"=\")[1].rstrip()\n except:\n pass\n return configDict\ndef uploadZippedToBox(zippedFolder, boxfolder = None):\n if boxfolder is None:\n boxfolder = accessUploadFolder()\n try:\n items = boxfolder.get_items()\n for item in items:\n if item.name == os.path.basename(zippedFolder):\n try:\n item.delete()\n except Exception as e:\n print(e)\n return False\n boxfolder.upload(zippedFolder)\n uploaded = True\n except Exception as e:\n print(e)\n uploaded = False\n pass\n finally:\n return uploaded\n\ndef accessUploadFolder(year=2020):\n # Define client ID, client secret, and developer token.path = os.path.join(*[os.path.dirname(os.path.abspath(__file__)),\"instance\"])\n\n # Read app info from text file\n config = ConfigObject(os.path.join(*[os.path.dirname(os.path.abspath(__file__)),\"instance\", 'Boxapp.cfg']))\n CLIENT_ID = config['client_id']\n CLIENT_FOLDER = config['client_folder' + str(year)]\n ACCESS_TOKEN = config['access_token']\n\n # Create OAuth2 object.\n auth = OAuth2(client_id=CLIENT_ID, client_secret='', access_token=ACCESS_TOKEN)\n # Create the authenticated client\n client = Client(auth)\n\n # make sure we connected\n try:\n my = client.user(user_id='me').get()\n print(my.name) # developer name tied to the token\n except:\n sys.exit(\"ERROR: Invalid access token; try re-generating an \"\n \"access token from the app console on the web.\")\n\n tfolder = client.folder(CLIENT_FOLDER) # 2020 scada data folder\n return tfolder\ndef listZipFiles(directory_folder):\n '''\n Lists teh zip folders in teh directory folder, including subdirectortories\n '''\n zipFiles = []\n for root, dirs, files in os.walk(directory_folder):\n for name in files:\n if name[-3:] == 'zip':\n zipFiles.append(os.path.join(root, name))\n return zipFiles\ndef uploadAllZippedToBox(zipFolder):\n '''uploads new zip folders to box. Will not upload a zip folder if it already exists on Box even if the contents have changed'''\n #files to upload\n zipFiles = listZipFiles(zipFolder)\n tfolder = accessUploadFolder()\n items = tfolder.get_items()\n for item in items:\n if item.name in zipFiles:\n try:\n item.delete()\n #tfolder.file(file_id=item.id).delete()\n except Exception as e:\n print(e)\n #If we coudn't delete the existing zip file don't try to upload a new one.\n zipFiles.remove(item.name)\n uploadedFiles = []\n badUploads = []\n\n for zipped in zipFiles:\n try:\n uploadZippedToBox(zipped, tfolder)\n uploadedFiles.append((zipped,True))\n except Exception as e:\n print(e)\n badUploads.append((zipped,False))\n pass\n\n return uploadedFiles, badUploads\n\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
from django import template
from ..models import Article
# 得到django 负责管理标签和过滤器的类
register = template.Library()
@register.simple_tag
def getlatestarticle():
latearticle = Article.objects.all().order_by("-atime")
return latearticle
|
normal
|
{
"blob_id": "804c75b3ab0b115e5187d44e4d139cfb553269a9",
"index": 6791,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@register.simple_tag\ndef getlatestarticle():\n latearticle = Article.objects.all().order_by('-atime')\n return latearticle\n",
"step-3": "<mask token>\nregister = template.Library()\n\n\n@register.simple_tag\ndef getlatestarticle():\n latearticle = Article.objects.all().order_by('-atime')\n return latearticle\n",
"step-4": "from django import template\nfrom ..models import Article\nregister = template.Library()\n\n\n@register.simple_tag\ndef getlatestarticle():\n latearticle = Article.objects.all().order_by('-atime')\n return latearticle\n",
"step-5": "from django import template\nfrom ..models import Article\n# 得到django 负责管理标签和过滤器的类\nregister = template.Library()\n\n@register.simple_tag\ndef getlatestarticle():\n latearticle = Article.objects.all().order_by(\"-atime\")\n return latearticle",
"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 prep_folder(args):
""" Append to slash to filepath if needed, and generate folder if it doesn't exist"""
if args.save_folder[-1] != '/':
args.save_folder += '/'
if not os.path.isdir(args.save_folder):
os.mkdir(args.save_folder)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def prep_folder(args):
""" Append to slash to filepath if needed, and generate folder if it doesn't exist"""
if args.save_folder[-1] != '/':
args.save_folder += '/'
if not os.path.isdir(args.save_folder):
os.mkdir(args.save_folder)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=3, dest='epochs',
help='Number of epochs to run')
parser.add_argument('--batch-size', type=int, default=50, dest=
'batch_size', help='Batch size')
parser.add_argument('--max-out-length', type=int, default=128, dest=
'max_out_length', help=
'Maximum output length (outputs truncated if longer)')
parser.add_argument('--adversarial-model', type=str, default=None, dest
='adv_model', help=
'Type of adversarial model to use. Will use traditional teacher forcing if None.'
)
parser.add_argument('--train-disc-only-steps', type=int, default=0,
dest='train_disc_only_steps', help=
'Number of steps for which to train discriminator only (without updating generator)'
)
parser.add_argument('--gen_weight_decay', type=float, default=0, dest=
'gen_weight_decay', help=
"Weight decay for the generator's training scheduler")
parser.add_argument('--gen_lr', type=float, default=2e-05, dest=
'gen_lr', help='Learning rate for generator')
parser.add_argument('--gen_epsilon', type=float, default=1e-08, dest=
'gen_epsilon', help='Epsilon parameter for generator optimizer')
parser.add_argument('--gen_warmup_steps', type=int, default=0, dest=
'gen_warmup_steps', help=
'Number of warmup steps for training generator')
parser.add_argument('--disc_weight_decay', type=float, default=0, dest=
'disc_weight_decay', help=
"Weight decay for the discriminator's training scheduler")
parser.add_argument('--disc_lr', type=float, default=2e-05, dest=
'disc_lr', help='Learning rate for discriminator')
parser.add_argument('--disc_epsilon', type=float, default=1e-08, dest=
'disc_epsilon', help='Epsilon parameter for discriminator optimizer')
parser.add_argument('--disc_warmup_steps', type=int, default=0, dest=
'disc_warmup_steps', help=
'Number of warmup steps for training discriminator')
parser.add_argument('--train-data-path', type=str, dest=
'train_data_path', help='Filepath to preprocessed data')
parser.add_argument('--save-folder', type=str, dest='save_folder', help
='Filepath to folder where checkpoints should be saved')
parser.add_argument('--pretrained-gen', type=str, default=None, dest=
'pretrained_gen', help=
'Filepath to trained generator. If None, will instantiate a default pretrained generator.'
)
parser.add_argument('--pretrained-disc', type=str, default=None, dest=
'pretrained_disc', help=
'Filepath to trained discriminator. If None, will instantiate a default pretrained discriminator of type specified by --adversarial-model option.'
)
args = parser.parse_args()
assert args.train_data_path is not None
assert args.save_folder is not None
prep_folder(args)
eos_token_id = GPT2Tokenizer.from_pretrained('gpt2').eos_token_id
train_dataset = DialogDataset(args.train_data_path, eos_token_id)
train_loader = train_dataset.get_loader(args.batch_size, shuffle=True)
gen_opt_params = {'weight_decay': args.gen_weight_decay, 'lr': args.
gen_lr, 'warmup_steps': args.gen_warmup_steps, 'epsilon': args.
gen_epsilon, 'total_steps': int(len(train_dataset) / args.
batch_size) * args.epochs}
generator = DialogGenerator(args.pretrained_gen, args.save_folder,
gen_opt_params)
if args.adv_model is not None:
disc_opt_params = {'weight_decay': args.disc_weight_decay, 'lr':
args.disc_lr, 'warmup_steps': args.disc_warmup_steps, 'epsilon':
args.disc_epsilon, 'total_steps': int(len(train_dataset) / args
.batch_size) * args.epochs}
discriminator = DialogDiscriminator(args.adv_model, args.
pretrained_disc, args.save_folder, disc_opt_params)
generator.train_adversarial(train_loader, args.epochs, args.
max_out_length, discriminator, args.train_disc_only_steps)
else:
generator.train_traditional(train_loader, args.epochs, args.
max_out_length)
<|reserved_special_token_1|>
import torch
import argparse
from DialogGenerator import DialogGenerator
from DialogDataset import DialogDataset
from DialogDiscriminator import DialogDiscriminator
from transformers import GPT2Tokenizer
import os
def prep_folder(args):
""" Append to slash to filepath if needed, and generate folder if it doesn't exist"""
if args.save_folder[-1] != '/':
args.save_folder += '/'
if not os.path.isdir(args.save_folder):
os.mkdir(args.save_folder)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=3, dest='epochs',
help='Number of epochs to run')
parser.add_argument('--batch-size', type=int, default=50, dest=
'batch_size', help='Batch size')
parser.add_argument('--max-out-length', type=int, default=128, dest=
'max_out_length', help=
'Maximum output length (outputs truncated if longer)')
parser.add_argument('--adversarial-model', type=str, default=None, dest
='adv_model', help=
'Type of adversarial model to use. Will use traditional teacher forcing if None.'
)
parser.add_argument('--train-disc-only-steps', type=int, default=0,
dest='train_disc_only_steps', help=
'Number of steps for which to train discriminator only (without updating generator)'
)
parser.add_argument('--gen_weight_decay', type=float, default=0, dest=
'gen_weight_decay', help=
"Weight decay for the generator's training scheduler")
parser.add_argument('--gen_lr', type=float, default=2e-05, dest=
'gen_lr', help='Learning rate for generator')
parser.add_argument('--gen_epsilon', type=float, default=1e-08, dest=
'gen_epsilon', help='Epsilon parameter for generator optimizer')
parser.add_argument('--gen_warmup_steps', type=int, default=0, dest=
'gen_warmup_steps', help=
'Number of warmup steps for training generator')
parser.add_argument('--disc_weight_decay', type=float, default=0, dest=
'disc_weight_decay', help=
"Weight decay for the discriminator's training scheduler")
parser.add_argument('--disc_lr', type=float, default=2e-05, dest=
'disc_lr', help='Learning rate for discriminator')
parser.add_argument('--disc_epsilon', type=float, default=1e-08, dest=
'disc_epsilon', help='Epsilon parameter for discriminator optimizer')
parser.add_argument('--disc_warmup_steps', type=int, default=0, dest=
'disc_warmup_steps', help=
'Number of warmup steps for training discriminator')
parser.add_argument('--train-data-path', type=str, dest=
'train_data_path', help='Filepath to preprocessed data')
parser.add_argument('--save-folder', type=str, dest='save_folder', help
='Filepath to folder where checkpoints should be saved')
parser.add_argument('--pretrained-gen', type=str, default=None, dest=
'pretrained_gen', help=
'Filepath to trained generator. If None, will instantiate a default pretrained generator.'
)
parser.add_argument('--pretrained-disc', type=str, default=None, dest=
'pretrained_disc', help=
'Filepath to trained discriminator. If None, will instantiate a default pretrained discriminator of type specified by --adversarial-model option.'
)
args = parser.parse_args()
assert args.train_data_path is not None
assert args.save_folder is not None
prep_folder(args)
eos_token_id = GPT2Tokenizer.from_pretrained('gpt2').eos_token_id
train_dataset = DialogDataset(args.train_data_path, eos_token_id)
train_loader = train_dataset.get_loader(args.batch_size, shuffle=True)
gen_opt_params = {'weight_decay': args.gen_weight_decay, 'lr': args.
gen_lr, 'warmup_steps': args.gen_warmup_steps, 'epsilon': args.
gen_epsilon, 'total_steps': int(len(train_dataset) / args.
batch_size) * args.epochs}
generator = DialogGenerator(args.pretrained_gen, args.save_folder,
gen_opt_params)
if args.adv_model is not None:
disc_opt_params = {'weight_decay': args.disc_weight_decay, 'lr':
args.disc_lr, 'warmup_steps': args.disc_warmup_steps, 'epsilon':
args.disc_epsilon, 'total_steps': int(len(train_dataset) / args
.batch_size) * args.epochs}
discriminator = DialogDiscriminator(args.adv_model, args.
pretrained_disc, args.save_folder, disc_opt_params)
generator.train_adversarial(train_loader, args.epochs, args.
max_out_length, discriminator, args.train_disc_only_steps)
else:
generator.train_traditional(train_loader, args.epochs, args.
max_out_length)
<|reserved_special_token_1|>
import torch
import argparse
from DialogGenerator import DialogGenerator
from DialogDataset import DialogDataset
from DialogDiscriminator import DialogDiscriminator
from transformers import GPT2Tokenizer
import os
def prep_folder(args):
""" Append to slash to filepath if needed, and generate folder if it doesn't exist"""
if(args.save_folder[-1]!='/'):
args.save_folder += '/'
if(not os.path.isdir(args.save_folder)):
os.mkdir(args.save_folder)
if(__name__=="__main__"):
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=3, dest="epochs", help='Number of epochs to run')
parser.add_argument('--batch-size', type=int, default=50, dest="batch_size", help='Batch size')
parser.add_argument('--max-out-length', type=int, default=128, dest="max_out_length", help='Maximum output length (outputs truncated if longer)')
parser.add_argument('--adversarial-model', type=str, default=None, dest="adv_model", help='Type of adversarial model to use. Will use traditional teacher forcing if None.')
parser.add_argument('--train-disc-only-steps', type=int, default=0, dest="train_disc_only_steps", help='Number of steps for which to train discriminator only (without updating generator)')
parser.add_argument('--gen_weight_decay', type=float, default=0, dest="gen_weight_decay", help='Weight decay for the generator\'s training scheduler')
parser.add_argument('--gen_lr', type=float, default=2e-5, dest="gen_lr", help='Learning rate for generator')
parser.add_argument('--gen_epsilon', type=float, default=1e-8, dest="gen_epsilon", help='Epsilon parameter for generator optimizer')
parser.add_argument('--gen_warmup_steps', type=int, default=0, dest="gen_warmup_steps", help='Number of warmup steps for training generator')
parser.add_argument('--disc_weight_decay', type=float, default=0, dest="disc_weight_decay", help='Weight decay for the discriminator\'s training scheduler')
parser.add_argument('--disc_lr', type=float, default=2e-5, dest="disc_lr", help='Learning rate for discriminator')
parser.add_argument('--disc_epsilon', type=float, default=1e-8, dest="disc_epsilon", help='Epsilon parameter for discriminator optimizer')
parser.add_argument('--disc_warmup_steps', type=int, default=0, dest="disc_warmup_steps", help='Number of warmup steps for training discriminator')
parser.add_argument('--train-data-path', type=str, dest="train_data_path", help="Filepath to preprocessed data")
parser.add_argument('--save-folder', type=str, dest="save_folder", help="Filepath to folder where checkpoints should be saved")
parser.add_argument('--pretrained-gen', type=str, default=None, dest="pretrained_gen", help="Filepath to trained generator. If None, will instantiate a default pretrained generator.")
parser.add_argument('--pretrained-disc', type=str, default=None, dest="pretrained_disc", help="Filepath to trained discriminator. If None, will instantiate a default pretrained discriminator of type specified by --adversarial-model option.")
args = parser.parse_args()
assert args.train_data_path is not None
assert args.save_folder is not None
prep_folder(args)
eos_token_id = GPT2Tokenizer.from_pretrained("gpt2").eos_token_id
train_dataset = DialogDataset(args.train_data_path, eos_token_id)
train_loader = train_dataset.get_loader(args.batch_size, shuffle=True)
gen_opt_params = {"weight_decay": args.gen_weight_decay,
"lr": args.gen_lr,
"warmup_steps": args.gen_warmup_steps,
"epsilon": args.gen_epsilon,
"total_steps": int(len(train_dataset) / args.batch_size) * args.epochs }
generator = DialogGenerator(args.pretrained_gen, args.save_folder, gen_opt_params)
if(args.adv_model is not None):
disc_opt_params = {"weight_decay": args.disc_weight_decay,
"lr": args.disc_lr,
"warmup_steps": args.disc_warmup_steps,
"epsilon": args.disc_epsilon,
"total_steps": int(len(train_dataset) / args.batch_size) * args.epochs }
discriminator = DialogDiscriminator(args.adv_model, args.pretrained_disc, args.save_folder, disc_opt_params)
generator.train_adversarial(train_loader, args.epochs, args.max_out_length, discriminator, args.train_disc_only_steps)
else:
generator.train_traditional(train_loader, args.epochs, args.max_out_length)
|
flexible
|
{
"blob_id": "18be97061c65185fcebf10c628e0e51bb08522cf",
"index": 3609,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef prep_folder(args):\n \"\"\" Append to slash to filepath if needed, and generate folder if it doesn't exist\"\"\"\n if args.save_folder[-1] != '/':\n args.save_folder += '/'\n if not os.path.isdir(args.save_folder):\n os.mkdir(args.save_folder)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef prep_folder(args):\n \"\"\" Append to slash to filepath if needed, and generate folder if it doesn't exist\"\"\"\n if args.save_folder[-1] != '/':\n args.save_folder += '/'\n if not os.path.isdir(args.save_folder):\n os.mkdir(args.save_folder)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--epochs', type=int, default=3, dest='epochs',\n help='Number of epochs to run')\n parser.add_argument('--batch-size', type=int, default=50, dest=\n 'batch_size', help='Batch size')\n parser.add_argument('--max-out-length', type=int, default=128, dest=\n 'max_out_length', help=\n 'Maximum output length (outputs truncated if longer)')\n parser.add_argument('--adversarial-model', type=str, default=None, dest\n ='adv_model', help=\n 'Type of adversarial model to use. Will use traditional teacher forcing if None.'\n )\n parser.add_argument('--train-disc-only-steps', type=int, default=0,\n dest='train_disc_only_steps', help=\n 'Number of steps for which to train discriminator only (without updating generator)'\n )\n parser.add_argument('--gen_weight_decay', type=float, default=0, dest=\n 'gen_weight_decay', help=\n \"Weight decay for the generator's training scheduler\")\n parser.add_argument('--gen_lr', type=float, default=2e-05, dest=\n 'gen_lr', help='Learning rate for generator')\n parser.add_argument('--gen_epsilon', type=float, default=1e-08, dest=\n 'gen_epsilon', help='Epsilon parameter for generator optimizer')\n parser.add_argument('--gen_warmup_steps', type=int, default=0, dest=\n 'gen_warmup_steps', help=\n 'Number of warmup steps for training generator')\n parser.add_argument('--disc_weight_decay', type=float, default=0, dest=\n 'disc_weight_decay', help=\n \"Weight decay for the discriminator's training scheduler\")\n parser.add_argument('--disc_lr', type=float, default=2e-05, dest=\n 'disc_lr', help='Learning rate for discriminator')\n parser.add_argument('--disc_epsilon', type=float, default=1e-08, dest=\n 'disc_epsilon', help='Epsilon parameter for discriminator optimizer')\n parser.add_argument('--disc_warmup_steps', type=int, default=0, dest=\n 'disc_warmup_steps', help=\n 'Number of warmup steps for training discriminator')\n parser.add_argument('--train-data-path', type=str, dest=\n 'train_data_path', help='Filepath to preprocessed data')\n parser.add_argument('--save-folder', type=str, dest='save_folder', help\n ='Filepath to folder where checkpoints should be saved')\n parser.add_argument('--pretrained-gen', type=str, default=None, dest=\n 'pretrained_gen', help=\n 'Filepath to trained generator. If None, will instantiate a default pretrained generator.'\n )\n parser.add_argument('--pretrained-disc', type=str, default=None, dest=\n 'pretrained_disc', help=\n 'Filepath to trained discriminator. If None, will instantiate a default pretrained discriminator of type specified by --adversarial-model option.'\n )\n args = parser.parse_args()\n assert args.train_data_path is not None\n assert args.save_folder is not None\n prep_folder(args)\n eos_token_id = GPT2Tokenizer.from_pretrained('gpt2').eos_token_id\n train_dataset = DialogDataset(args.train_data_path, eos_token_id)\n train_loader = train_dataset.get_loader(args.batch_size, shuffle=True)\n gen_opt_params = {'weight_decay': args.gen_weight_decay, 'lr': args.\n gen_lr, 'warmup_steps': args.gen_warmup_steps, 'epsilon': args.\n gen_epsilon, 'total_steps': int(len(train_dataset) / args.\n batch_size) * args.epochs}\n generator = DialogGenerator(args.pretrained_gen, args.save_folder,\n gen_opt_params)\n if args.adv_model is not None:\n disc_opt_params = {'weight_decay': args.disc_weight_decay, 'lr':\n args.disc_lr, 'warmup_steps': args.disc_warmup_steps, 'epsilon':\n args.disc_epsilon, 'total_steps': int(len(train_dataset) / args\n .batch_size) * args.epochs}\n discriminator = DialogDiscriminator(args.adv_model, args.\n pretrained_disc, args.save_folder, disc_opt_params)\n generator.train_adversarial(train_loader, args.epochs, args.\n max_out_length, discriminator, args.train_disc_only_steps)\n else:\n generator.train_traditional(train_loader, args.epochs, args.\n max_out_length)\n",
"step-4": "import torch\nimport argparse\nfrom DialogGenerator import DialogGenerator\nfrom DialogDataset import DialogDataset\nfrom DialogDiscriminator import DialogDiscriminator\nfrom transformers import GPT2Tokenizer\nimport os\n\n\ndef prep_folder(args):\n \"\"\" Append to slash to filepath if needed, and generate folder if it doesn't exist\"\"\"\n if args.save_folder[-1] != '/':\n args.save_folder += '/'\n if not os.path.isdir(args.save_folder):\n os.mkdir(args.save_folder)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--epochs', type=int, default=3, dest='epochs',\n help='Number of epochs to run')\n parser.add_argument('--batch-size', type=int, default=50, dest=\n 'batch_size', help='Batch size')\n parser.add_argument('--max-out-length', type=int, default=128, dest=\n 'max_out_length', help=\n 'Maximum output length (outputs truncated if longer)')\n parser.add_argument('--adversarial-model', type=str, default=None, dest\n ='adv_model', help=\n 'Type of adversarial model to use. Will use traditional teacher forcing if None.'\n )\n parser.add_argument('--train-disc-only-steps', type=int, default=0,\n dest='train_disc_only_steps', help=\n 'Number of steps for which to train discriminator only (without updating generator)'\n )\n parser.add_argument('--gen_weight_decay', type=float, default=0, dest=\n 'gen_weight_decay', help=\n \"Weight decay for the generator's training scheduler\")\n parser.add_argument('--gen_lr', type=float, default=2e-05, dest=\n 'gen_lr', help='Learning rate for generator')\n parser.add_argument('--gen_epsilon', type=float, default=1e-08, dest=\n 'gen_epsilon', help='Epsilon parameter for generator optimizer')\n parser.add_argument('--gen_warmup_steps', type=int, default=0, dest=\n 'gen_warmup_steps', help=\n 'Number of warmup steps for training generator')\n parser.add_argument('--disc_weight_decay', type=float, default=0, dest=\n 'disc_weight_decay', help=\n \"Weight decay for the discriminator's training scheduler\")\n parser.add_argument('--disc_lr', type=float, default=2e-05, dest=\n 'disc_lr', help='Learning rate for discriminator')\n parser.add_argument('--disc_epsilon', type=float, default=1e-08, dest=\n 'disc_epsilon', help='Epsilon parameter for discriminator optimizer')\n parser.add_argument('--disc_warmup_steps', type=int, default=0, dest=\n 'disc_warmup_steps', help=\n 'Number of warmup steps for training discriminator')\n parser.add_argument('--train-data-path', type=str, dest=\n 'train_data_path', help='Filepath to preprocessed data')\n parser.add_argument('--save-folder', type=str, dest='save_folder', help\n ='Filepath to folder where checkpoints should be saved')\n parser.add_argument('--pretrained-gen', type=str, default=None, dest=\n 'pretrained_gen', help=\n 'Filepath to trained generator. If None, will instantiate a default pretrained generator.'\n )\n parser.add_argument('--pretrained-disc', type=str, default=None, dest=\n 'pretrained_disc', help=\n 'Filepath to trained discriminator. If None, will instantiate a default pretrained discriminator of type specified by --adversarial-model option.'\n )\n args = parser.parse_args()\n assert args.train_data_path is not None\n assert args.save_folder is not None\n prep_folder(args)\n eos_token_id = GPT2Tokenizer.from_pretrained('gpt2').eos_token_id\n train_dataset = DialogDataset(args.train_data_path, eos_token_id)\n train_loader = train_dataset.get_loader(args.batch_size, shuffle=True)\n gen_opt_params = {'weight_decay': args.gen_weight_decay, 'lr': args.\n gen_lr, 'warmup_steps': args.gen_warmup_steps, 'epsilon': args.\n gen_epsilon, 'total_steps': int(len(train_dataset) / args.\n batch_size) * args.epochs}\n generator = DialogGenerator(args.pretrained_gen, args.save_folder,\n gen_opt_params)\n if args.adv_model is not None:\n disc_opt_params = {'weight_decay': args.disc_weight_decay, 'lr':\n args.disc_lr, 'warmup_steps': args.disc_warmup_steps, 'epsilon':\n args.disc_epsilon, 'total_steps': int(len(train_dataset) / args\n .batch_size) * args.epochs}\n discriminator = DialogDiscriminator(args.adv_model, args.\n pretrained_disc, args.save_folder, disc_opt_params)\n generator.train_adversarial(train_loader, args.epochs, args.\n max_out_length, discriminator, args.train_disc_only_steps)\n else:\n generator.train_traditional(train_loader, args.epochs, args.\n max_out_length)\n",
"step-5": "import torch\nimport argparse\nfrom DialogGenerator import DialogGenerator\nfrom DialogDataset import DialogDataset\nfrom DialogDiscriminator import DialogDiscriminator\nfrom transformers import GPT2Tokenizer\nimport os\n\ndef prep_folder(args):\n \"\"\" Append to slash to filepath if needed, and generate folder if it doesn't exist\"\"\"\n if(args.save_folder[-1]!='/'):\n args.save_folder += '/'\n if(not os.path.isdir(args.save_folder)):\n os.mkdir(args.save_folder)\n\nif(__name__==\"__main__\"):\n parser = argparse.ArgumentParser()\n parser.add_argument('--epochs', type=int, default=3, dest=\"epochs\", help='Number of epochs to run')\n parser.add_argument('--batch-size', type=int, default=50, dest=\"batch_size\", help='Batch size')\n parser.add_argument('--max-out-length', type=int, default=128, dest=\"max_out_length\", help='Maximum output length (outputs truncated if longer)')\n parser.add_argument('--adversarial-model', type=str, default=None, dest=\"adv_model\", help='Type of adversarial model to use. Will use traditional teacher forcing if None.')\n parser.add_argument('--train-disc-only-steps', type=int, default=0, dest=\"train_disc_only_steps\", help='Number of steps for which to train discriminator only (without updating generator)')\n\n parser.add_argument('--gen_weight_decay', type=float, default=0, dest=\"gen_weight_decay\", help='Weight decay for the generator\\'s training scheduler')\n parser.add_argument('--gen_lr', type=float, default=2e-5, dest=\"gen_lr\", help='Learning rate for generator')\n parser.add_argument('--gen_epsilon', type=float, default=1e-8, dest=\"gen_epsilon\", help='Epsilon parameter for generator optimizer')\n parser.add_argument('--gen_warmup_steps', type=int, default=0, dest=\"gen_warmup_steps\", help='Number of warmup steps for training generator')\n\n parser.add_argument('--disc_weight_decay', type=float, default=0, dest=\"disc_weight_decay\", help='Weight decay for the discriminator\\'s training scheduler')\n parser.add_argument('--disc_lr', type=float, default=2e-5, dest=\"disc_lr\", help='Learning rate for discriminator')\n parser.add_argument('--disc_epsilon', type=float, default=1e-8, dest=\"disc_epsilon\", help='Epsilon parameter for discriminator optimizer')\n parser.add_argument('--disc_warmup_steps', type=int, default=0, dest=\"disc_warmup_steps\", help='Number of warmup steps for training discriminator')\n\n parser.add_argument('--train-data-path', type=str, dest=\"train_data_path\", help=\"Filepath to preprocessed data\")\n parser.add_argument('--save-folder', type=str, dest=\"save_folder\", help=\"Filepath to folder where checkpoints should be saved\")\n parser.add_argument('--pretrained-gen', type=str, default=None, dest=\"pretrained_gen\", help=\"Filepath to trained generator. If None, will instantiate a default pretrained generator.\")\n parser.add_argument('--pretrained-disc', type=str, default=None, dest=\"pretrained_disc\", help=\"Filepath to trained discriminator. If None, will instantiate a default pretrained discriminator of type specified by --adversarial-model option.\")\n\n args = parser.parse_args()\n\n assert args.train_data_path is not None\n assert args.save_folder is not None\n\n prep_folder(args)\n \n eos_token_id = GPT2Tokenizer.from_pretrained(\"gpt2\").eos_token_id\n train_dataset = DialogDataset(args.train_data_path, eos_token_id)\n train_loader = train_dataset.get_loader(args.batch_size, shuffle=True)\n\n gen_opt_params = {\"weight_decay\": args.gen_weight_decay, \n \"lr\": args.gen_lr, \n \"warmup_steps\": args.gen_warmup_steps,\n \"epsilon\": args.gen_epsilon,\n \"total_steps\": int(len(train_dataset) / args.batch_size) * args.epochs }\n\n generator = DialogGenerator(args.pretrained_gen, args.save_folder, gen_opt_params)\n\n if(args.adv_model is not None):\n disc_opt_params = {\"weight_decay\": args.disc_weight_decay, \n \"lr\": args.disc_lr, \n \"warmup_steps\": args.disc_warmup_steps,\n \"epsilon\": args.disc_epsilon,\n \"total_steps\": int(len(train_dataset) / args.batch_size) * args.epochs }\n discriminator = DialogDiscriminator(args.adv_model, args.pretrained_disc, args.save_folder, disc_opt_params)\n \n generator.train_adversarial(train_loader, args.epochs, args.max_out_length, discriminator, args.train_disc_only_steps)\n else:\n generator.train_traditional(train_loader, args.epochs, args.max_out_length)\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|>
while True:
o = sys.stdin.read(byte)
if qlty > qlty * n % 1:
oo = o
sys.stdout.write(o)
else:
sys.stdout.write(oo)
if not o:
break
n = n + 1
<|reserved_special_token_1|>
<|reserved_special_token_0|>
byte = int(sys.argv[1])
qlty = float(sys.argv[2])
n = 0
while True:
o = sys.stdin.read(byte)
if qlty > qlty * n % 1:
oo = o
sys.stdout.write(o)
else:
sys.stdout.write(oo)
if not o:
break
n = n + 1
<|reserved_special_token_1|>
import sys
byte = int(sys.argv[1])
qlty = float(sys.argv[2])
n = 0
while True:
o = sys.stdin.read(byte)
if qlty > qlty * n % 1:
oo = o
sys.stdout.write(o)
else:
sys.stdout.write(oo)
if not o:
break
n = n + 1
<|reserved_special_token_1|>
import sys
byte = int(sys.argv[1])
qlty = float(sys.argv[2])
n = 0
while True:
o = sys.stdin.read(byte)
if qlty>(qlty*n)%1:
oo = o
sys.stdout.write(o)
else:
sys.stdout.write(oo)
if not o:
break
n=n+1
|
flexible
|
{
"blob_id": "70845ab4aab80d988a5c01d0b4fb76e63b800527",
"index": 6484,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n o = sys.stdin.read(byte)\n if qlty > qlty * n % 1:\n oo = o\n sys.stdout.write(o)\n else:\n sys.stdout.write(oo)\n if not o:\n break\n n = n + 1\n",
"step-3": "<mask token>\nbyte = int(sys.argv[1])\nqlty = float(sys.argv[2])\nn = 0\nwhile True:\n o = sys.stdin.read(byte)\n if qlty > qlty * n % 1:\n oo = o\n sys.stdout.write(o)\n else:\n sys.stdout.write(oo)\n if not o:\n break\n n = n + 1\n",
"step-4": "import sys\nbyte = int(sys.argv[1])\nqlty = float(sys.argv[2])\nn = 0\nwhile True:\n o = sys.stdin.read(byte)\n if qlty > qlty * n % 1:\n oo = o\n sys.stdout.write(o)\n else:\n sys.stdout.write(oo)\n if not o:\n break\n n = n + 1\n",
"step-5": "import sys\r\nbyte = int(sys.argv[1])\r\nqlty = float(sys.argv[2])\r\nn = 0\r\nwhile True:\r\n o = sys.stdin.read(byte)\r\n if qlty>(qlty*n)%1:\r\n oo = o\r\n sys.stdout.write(o)\r\n else:\r\n sys.stdout.write(oo)\r\n if not o:\r\n break\r\n n=n+1",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def process_frame(img):
global vid_data
img = cv2.resize(img, (w, h))
cv2.imshow('Frame', img)
cv2.waitKey(1)
vid_data = np.append(vid_data, img, axis=0)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def process_frame(img):
global vid_data
img = cv2.resize(img, (w, h))
cv2.imshow('Frame', img)
cv2.waitKey(1)
vid_data = np.append(vid_data, img, axis=0)
<|reserved_special_token_0|>
while vid.isOpened():
ret, frame = vid.read()
if ret:
process_frame(frame)
n = n + 1
"""
cv2.imshow('Frame', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
"""
else:
break
vid.release()
cv2.destroyAllWindows()
print(vid_data.shape)
<|reserved_special_token_0|>
print(vid_data.shape)
np.savetxt('trackmania_vid_data2D_360x640.csv', vid_data, delimiter=',')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
vid = cv2.VideoCapture('trackmania_test_vid.mp4')
w = 1280 // 2
h = 720 // 2
vid_data = np.empty((360, 640, 3))
def process_frame(img):
global vid_data
img = cv2.resize(img, (w, h))
cv2.imshow('Frame', img)
cv2.waitKey(1)
vid_data = np.append(vid_data, img, axis=0)
n = 0
while vid.isOpened():
ret, frame = vid.read()
if ret:
process_frame(frame)
n = n + 1
"""
cv2.imshow('Frame', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
"""
else:
break
vid.release()
cv2.destroyAllWindows()
print(vid_data.shape)
vid_data = vid_data.reshape((vid_data.shape[0], -1))
print(vid_data.shape)
np.savetxt('trackmania_vid_data2D_360x640.csv', vid_data, delimiter=',')
<|reserved_special_token_1|>
import numpy as np
import cv2
vid = cv2.VideoCapture('trackmania_test_vid.mp4')
w = 1280 // 2
h = 720 // 2
vid_data = np.empty((360, 640, 3))
def process_frame(img):
global vid_data
img = cv2.resize(img, (w, h))
cv2.imshow('Frame', img)
cv2.waitKey(1)
vid_data = np.append(vid_data, img, axis=0)
n = 0
while vid.isOpened():
ret, frame = vid.read()
if ret:
process_frame(frame)
n = n + 1
"""
cv2.imshow('Frame', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
"""
else:
break
vid.release()
cv2.destroyAllWindows()
print(vid_data.shape)
vid_data = vid_data.reshape((vid_data.shape[0], -1))
print(vid_data.shape)
np.savetxt('trackmania_vid_data2D_360x640.csv', vid_data, delimiter=',')
<|reserved_special_token_1|>
#train a neural network from input video feed
import numpy as np
import cv2
vid = cv2.VideoCapture('trackmania_test_vid.mp4')
w = 1280//2
h = 720//2
vid_data = np.empty((360, 640, 3))
#print(vid_data.shape)
def process_frame(img):
global vid_data
img = cv2.resize(img, (w, h))
cv2.imshow('Frame', img)
cv2.waitKey(1)
vid_data = np.append(vid_data, img, axis=0)
#print(img.shape)
# Read until video is completed
n = 0
while vid.isOpened():
# Capture frame-by-frame
ret, frame = vid.read()
if ret:
#print("frame = {}".format(frame.shape))
process_frame(frame)
n = n + 1
'''
cv2.imshow('Frame', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
'''
else:
break
# When everything done, release the video capture object
vid.release()
# Closes all the frames
cv2.destroyAllWindows()
print(vid_data.shape)
vid_data = vid_data.reshape((vid_data.shape[0], -1))
print(vid_data.shape)
# n = 1340
#print('No. of frames = {}'.format(n))
np.savetxt("trackmania_vid_data2D_360x640.csv", vid_data, delimiter=",")
#50580,320,3 ---> 281,180,320,3
#101160,640,3 ---> 281,360,640,3
|
flexible
|
{
"blob_id": "eb81b0e41743e1785b82e88f6a618dc91eba73e5",
"index": 1389,
"step-1": "<mask token>\n\n\ndef process_frame(img):\n global vid_data\n img = cv2.resize(img, (w, h))\n cv2.imshow('Frame', img)\n cv2.waitKey(1)\n vid_data = np.append(vid_data, img, axis=0)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef process_frame(img):\n global vid_data\n img = cv2.resize(img, (w, h))\n cv2.imshow('Frame', img)\n cv2.waitKey(1)\n vid_data = np.append(vid_data, img, axis=0)\n\n\n<mask token>\nwhile vid.isOpened():\n ret, frame = vid.read()\n if ret:\n process_frame(frame)\n n = n + 1\n \"\"\"\n cv2.imshow('Frame', frame)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n break\n \"\"\"\n else:\n break\nvid.release()\ncv2.destroyAllWindows()\nprint(vid_data.shape)\n<mask token>\nprint(vid_data.shape)\nnp.savetxt('trackmania_vid_data2D_360x640.csv', vid_data, delimiter=',')\n",
"step-3": "<mask token>\nvid = cv2.VideoCapture('trackmania_test_vid.mp4')\nw = 1280 // 2\nh = 720 // 2\nvid_data = np.empty((360, 640, 3))\n\n\ndef process_frame(img):\n global vid_data\n img = cv2.resize(img, (w, h))\n cv2.imshow('Frame', img)\n cv2.waitKey(1)\n vid_data = np.append(vid_data, img, axis=0)\n\n\nn = 0\nwhile vid.isOpened():\n ret, frame = vid.read()\n if ret:\n process_frame(frame)\n n = n + 1\n \"\"\"\n cv2.imshow('Frame', frame)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n break\n \"\"\"\n else:\n break\nvid.release()\ncv2.destroyAllWindows()\nprint(vid_data.shape)\nvid_data = vid_data.reshape((vid_data.shape[0], -1))\nprint(vid_data.shape)\nnp.savetxt('trackmania_vid_data2D_360x640.csv', vid_data, delimiter=',')\n",
"step-4": "import numpy as np\nimport cv2\nvid = cv2.VideoCapture('trackmania_test_vid.mp4')\nw = 1280 // 2\nh = 720 // 2\nvid_data = np.empty((360, 640, 3))\n\n\ndef process_frame(img):\n global vid_data\n img = cv2.resize(img, (w, h))\n cv2.imshow('Frame', img)\n cv2.waitKey(1)\n vid_data = np.append(vid_data, img, axis=0)\n\n\nn = 0\nwhile vid.isOpened():\n ret, frame = vid.read()\n if ret:\n process_frame(frame)\n n = n + 1\n \"\"\"\n cv2.imshow('Frame', frame)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n break\n \"\"\"\n else:\n break\nvid.release()\ncv2.destroyAllWindows()\nprint(vid_data.shape)\nvid_data = vid_data.reshape((vid_data.shape[0], -1))\nprint(vid_data.shape)\nnp.savetxt('trackmania_vid_data2D_360x640.csv', vid_data, delimiter=',')\n",
"step-5": "#train a neural network from input video feed\nimport numpy as np\nimport cv2\nvid = cv2.VideoCapture('trackmania_test_vid.mp4')\nw = 1280//2\nh = 720//2\n\nvid_data = np.empty((360, 640, 3))\n#print(vid_data.shape)\n\n\ndef process_frame(img):\n global vid_data\n img = cv2.resize(img, (w, h))\n cv2.imshow('Frame', img)\n cv2.waitKey(1)\n vid_data = np.append(vid_data, img, axis=0)\n #print(img.shape)\n\n\n# Read until video is completed\nn = 0\nwhile vid.isOpened():\n # Capture frame-by-frame\n ret, frame = vid.read()\n if ret:\n #print(\"frame = {}\".format(frame.shape))\n process_frame(frame)\n n = n + 1\n '''\n cv2.imshow('Frame', frame)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n break\n '''\n else:\n break\n\n# When everything done, release the video capture object\nvid.release()\n\n# Closes all the frames\ncv2.destroyAllWindows()\nprint(vid_data.shape)\nvid_data = vid_data.reshape((vid_data.shape[0], -1))\nprint(vid_data.shape)\n# n = 1340\n#print('No. of frames = {}'.format(n))\n\nnp.savetxt(\"trackmania_vid_data2D_360x640.csv\", vid_data, delimiter=\",\")\n\n#50580,320,3 ---> 281,180,320,3\n#101160,640,3 ---> 281,360,640,3\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class AuthorizationError(ValueError):
pass
class BearerTokenValidator:
def __init__(self, access_token, app_context: AppContext):
self.access_token = access_token
user_service = app_context.user_service
self.blacklist_token_repo = app_context.blacklist_token_repo
self.payload = user_service.decode_auth_token(access_token, get_jwk())
def check_is_blacklisted(self):
is_blacklisted_token = BlacklistToken.check_blacklist(self.
access_token, self.blacklist_token_repo)
if is_blacklisted_token:
LOGGER.debug('Token blacklisted.')
raise AuthenticationError('Invalid token.')
return self
def check_username_claim(self):
if not self.payload.get('sub'):
LOGGER.debug('Token missing sub.')
raise AuthorizationError('Forbidden.')
return self
def check_user_exists(self, user):
if not user:
LOGGER.debug('Token user not found.')
raise AuthorizationError('Forbidden.')
return self
def check_has_permissions(self, user: User, permissions: list):
has_permissions = True
for permission in permissions:
if not user.role.has_permission(Permission.from_enum(permission)):
LOGGER.debug(f'Missing permission {permission}.')
has_permissions = False
LOGGER.debug(f'Required permissions: {permissions}')
if not has_permissions:
raise AuthorizationError('Forbidden.')
return self
@staticmethod
def from_authorization_header(authorization_header: str, app_context:
AppContext):
if not authorization_header:
LOGGER.debug('Authorization header not found.')
raise AuthenticationError('Invalid token.')
if 'Bearer ' not in authorization_header:
LOGGER.debug('Bearer token not found.')
raise AuthenticationError('Invalid token.')
access_token = authorization_header.split('Bearer')[1].strip()
LOGGER.debug(f'Bearer token is:\n"{access_token}"')
return BearerTokenValidator(access_token, app_context)
<|reserved_special_token_0|>
class ExceptionHandlers:
def __init__(self, app):
@app.errorhandler(AuthorizationError)
def handle_authorization_exception(e):
"""Return403 forbidden."""
return jsonify(str(e)), 403
@app.errorhandler(AuthenticationError)
def handle_authentication_exception(e):
"""Return401 authentication error."""
return jsonify(str(e)), 401
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AuthenticationError(ValueError):
pass
class AuthorizationError(ValueError):
pass
class BearerTokenValidator:
def __init__(self, access_token, app_context: AppContext):
self.access_token = access_token
user_service = app_context.user_service
self.blacklist_token_repo = app_context.blacklist_token_repo
self.payload = user_service.decode_auth_token(access_token, get_jwk())
def check_is_blacklisted(self):
is_blacklisted_token = BlacklistToken.check_blacklist(self.
access_token, self.blacklist_token_repo)
if is_blacklisted_token:
LOGGER.debug('Token blacklisted.')
raise AuthenticationError('Invalid token.')
return self
def check_username_claim(self):
if not self.payload.get('sub'):
LOGGER.debug('Token missing sub.')
raise AuthorizationError('Forbidden.')
return self
def check_user_exists(self, user):
if not user:
LOGGER.debug('Token user not found.')
raise AuthorizationError('Forbidden.')
return self
def check_has_permissions(self, user: User, permissions: list):
has_permissions = True
for permission in permissions:
if not user.role.has_permission(Permission.from_enum(permission)):
LOGGER.debug(f'Missing permission {permission}.')
has_permissions = False
LOGGER.debug(f'Required permissions: {permissions}')
if not has_permissions:
raise AuthorizationError('Forbidden.')
return self
@staticmethod
def from_authorization_header(authorization_header: str, app_context:
AppContext):
if not authorization_header:
LOGGER.debug('Authorization header not found.')
raise AuthenticationError('Invalid token.')
if 'Bearer ' not in authorization_header:
LOGGER.debug('Bearer token not found.')
raise AuthenticationError('Invalid token.')
access_token = authorization_header.split('Bearer')[1].strip()
LOGGER.debug(f'Bearer token is:\n"{access_token}"')
return BearerTokenValidator(access_token, app_context)
<|reserved_special_token_0|>
class ExceptionHandlers:
def __init__(self, app):
@app.errorhandler(AuthorizationError)
def handle_authorization_exception(e):
"""Return403 forbidden."""
return jsonify(str(e)), 403
@app.errorhandler(AuthenticationError)
def handle_authentication_exception(e):
"""Return401 authentication error."""
return jsonify(str(e)), 401
<|reserved_special_token_0|>
def issue_token_for_user(user: User):
access_token = new_token({'iss': 'lorem.ipsum.dev', 'aud':
'lorem.ipsum.auth', 'sub': user.username, 'email': user.email,
'roles': [user.role.name], 'exp': datetime.datetime.now(tz=datetime
.timezone.utc) + datetime.timedelta(hours=4), 'iat': datetime.
datetime.now(tz=datetime.timezone.utc)})
return access_token
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def app_context():
if 'app_context' not in g:
g.app_context = lorem_ipsum.create_app_context()
return g.app_context
@lru_cache()
def get_jwk():
LOGGER.debug('Loading jwk from public key...')
key_data = None
with open(app_context().config['jwk_public_key_path'], 'rb') as _key_file:
key_data = _key_file.read()
LOGGER.debug(key_data)
key = JsonWebKey.import_key(key_data, {'kty': 'RSA'})
_jwks = {'keys': [{**key.as_dict(), 'kid': 'demo_key'}]}
LOGGER.debug(_jwks)
return _jwks
class AuthenticationError(ValueError):
pass
class AuthorizationError(ValueError):
pass
class BearerTokenValidator:
def __init__(self, access_token, app_context: AppContext):
self.access_token = access_token
user_service = app_context.user_service
self.blacklist_token_repo = app_context.blacklist_token_repo
self.payload = user_service.decode_auth_token(access_token, get_jwk())
def check_is_blacklisted(self):
is_blacklisted_token = BlacklistToken.check_blacklist(self.
access_token, self.blacklist_token_repo)
if is_blacklisted_token:
LOGGER.debug('Token blacklisted.')
raise AuthenticationError('Invalid token.')
return self
def check_username_claim(self):
if not self.payload.get('sub'):
LOGGER.debug('Token missing sub.')
raise AuthorizationError('Forbidden.')
return self
def check_user_exists(self, user):
if not user:
LOGGER.debug('Token user not found.')
raise AuthorizationError('Forbidden.')
return self
def check_has_permissions(self, user: User, permissions: list):
has_permissions = True
for permission in permissions:
if not user.role.has_permission(Permission.from_enum(permission)):
LOGGER.debug(f'Missing permission {permission}.')
has_permissions = False
LOGGER.debug(f'Required permissions: {permissions}')
if not has_permissions:
raise AuthorizationError('Forbidden.')
return self
@staticmethod
def from_authorization_header(authorization_header: str, app_context:
AppContext):
if not authorization_header:
LOGGER.debug('Authorization header not found.')
raise AuthenticationError('Invalid token.')
if 'Bearer ' not in authorization_header:
LOGGER.debug('Bearer token not found.')
raise AuthenticationError('Invalid token.')
access_token = authorization_header.split('Bearer')[1].strip()
LOGGER.debug(f'Bearer token is:\n"{access_token}"')
return BearerTokenValidator(access_token, app_context)
def should_skip_auth(flask_request):
"""
Return true if should skip auth, e.g. when method is OPTIONS like when performing a React request.
:param flask_request: Flask request.
:return:
"""
return flask_request.method in ['HEAD', 'OPTIONS']
<|reserved_special_token_0|>
class ExceptionHandlers:
def __init__(self, app):
@app.errorhandler(AuthorizationError)
def handle_authorization_exception(e):
"""Return403 forbidden."""
return jsonify(str(e)), 403
@app.errorhandler(AuthenticationError)
def handle_authentication_exception(e):
"""Return401 authentication error."""
return jsonify(str(e)), 401
@lru_cache()
def jwk_key():
jwk_path = os.environ.get('jwk_private_key_path') or app_context().config[
'jwk_private_key_path']
with open(jwk_path, 'rb') as f:
key = JsonWebKey.import_key(f.read())
return key
def new_token(payload: dict):
key = jwk_key()
header = {'alg': 'RS256', 'kid': 'demo_key'}
token = jwt.encode(header, payload, key)
LOGGER.debug(token)
return token.decode('utf-8')
def issue_token_for_user(user: User):
access_token = new_token({'iss': 'lorem.ipsum.dev', 'aud':
'lorem.ipsum.auth', 'sub': user.username, 'email': user.email,
'roles': [user.role.name], 'exp': datetime.datetime.now(tz=datetime
.timezone.utc) + datetime.timedelta(hours=4), 'iat': datetime.
datetime.now(tz=datetime.timezone.utc)})
return access_token
<|reserved_special_token_1|>
<|reserved_special_token_0|>
LOGGER = logging.getLogger('lorem-ipsum')
def app_context():
if 'app_context' not in g:
g.app_context = lorem_ipsum.create_app_context()
return g.app_context
@lru_cache()
def get_jwk():
LOGGER.debug('Loading jwk from public key...')
key_data = None
with open(app_context().config['jwk_public_key_path'], 'rb') as _key_file:
key_data = _key_file.read()
LOGGER.debug(key_data)
key = JsonWebKey.import_key(key_data, {'kty': 'RSA'})
_jwks = {'keys': [{**key.as_dict(), 'kid': 'demo_key'}]}
LOGGER.debug(_jwks)
return _jwks
class AuthenticationError(ValueError):
pass
class AuthorizationError(ValueError):
pass
class BearerTokenValidator:
def __init__(self, access_token, app_context: AppContext):
self.access_token = access_token
user_service = app_context.user_service
self.blacklist_token_repo = app_context.blacklist_token_repo
self.payload = user_service.decode_auth_token(access_token, get_jwk())
def check_is_blacklisted(self):
is_blacklisted_token = BlacklistToken.check_blacklist(self.
access_token, self.blacklist_token_repo)
if is_blacklisted_token:
LOGGER.debug('Token blacklisted.')
raise AuthenticationError('Invalid token.')
return self
def check_username_claim(self):
if not self.payload.get('sub'):
LOGGER.debug('Token missing sub.')
raise AuthorizationError('Forbidden.')
return self
def check_user_exists(self, user):
if not user:
LOGGER.debug('Token user not found.')
raise AuthorizationError('Forbidden.')
return self
def check_has_permissions(self, user: User, permissions: list):
has_permissions = True
for permission in permissions:
if not user.role.has_permission(Permission.from_enum(permission)):
LOGGER.debug(f'Missing permission {permission}.')
has_permissions = False
LOGGER.debug(f'Required permissions: {permissions}')
if not has_permissions:
raise AuthorizationError('Forbidden.')
return self
@staticmethod
def from_authorization_header(authorization_header: str, app_context:
AppContext):
if not authorization_header:
LOGGER.debug('Authorization header not found.')
raise AuthenticationError('Invalid token.')
if 'Bearer ' not in authorization_header:
LOGGER.debug('Bearer token not found.')
raise AuthenticationError('Invalid token.')
access_token = authorization_header.split('Bearer')[1].strip()
LOGGER.debug(f'Bearer token is:\n"{access_token}"')
return BearerTokenValidator(access_token, app_context)
def should_skip_auth(flask_request):
"""
Return true if should skip auth, e.g. when method is OPTIONS like when performing a React request.
:param flask_request: Flask request.
:return:
"""
return flask_request.method in ['HEAD', 'OPTIONS']
def requires_permission(permissions: list):
def requires_permission_decorator(function):
def wrapper(*args, **kwargs):
LOGGER.info(f'Authorization...\n{request.headers}')
if should_skip_auth(request):
return jsonify('ok')
authorization_header = request.headers.get('Authorization')
context = app_context()
with context.transaction_manager.transaction:
bearer_token_validator = (BearerTokenValidator.
from_authorization_header(authorization_header, context
).check_is_blacklisted().check_username_claim())
user = context.user_repo.get(username=
bearer_token_validator.payload['sub'])
bearer_token_validator.check_user_exists(user
).check_has_permissions(user, permissions)
g.access_token = bearer_token_validator.access_token
g.user = user
_result = function(*args, **kwargs)
return _result
wrapper.__name__ = function.__name__
return wrapper
return requires_permission_decorator
class ExceptionHandlers:
def __init__(self, app):
@app.errorhandler(AuthorizationError)
def handle_authorization_exception(e):
"""Return403 forbidden."""
return jsonify(str(e)), 403
@app.errorhandler(AuthenticationError)
def handle_authentication_exception(e):
"""Return401 authentication error."""
return jsonify(str(e)), 401
@lru_cache()
def jwk_key():
jwk_path = os.environ.get('jwk_private_key_path') or app_context().config[
'jwk_private_key_path']
with open(jwk_path, 'rb') as f:
key = JsonWebKey.import_key(f.read())
return key
def new_token(payload: dict):
key = jwk_key()
header = {'alg': 'RS256', 'kid': 'demo_key'}
token = jwt.encode(header, payload, key)
LOGGER.debug(token)
return token.decode('utf-8')
def issue_token_for_user(user: User):
access_token = new_token({'iss': 'lorem.ipsum.dev', 'aud':
'lorem.ipsum.auth', 'sub': user.username, 'email': user.email,
'roles': [user.role.name], 'exp': datetime.datetime.now(tz=datetime
.timezone.utc) + datetime.timedelta(hours=4), 'iat': datetime.
datetime.now(tz=datetime.timezone.utc)})
return access_token
<|reserved_special_token_1|>
import datetime
import logging
import os
from functools import lru_cache
from authlib.jose import JsonWebKey, jwt
from flask import g, request, jsonify
from lorem_ipsum.model import User, AppContext
import lorem_ipsum
from lorem_ipsum.model import Permission, BlacklistToken
LOGGER = logging.getLogger('lorem-ipsum')
def app_context():
if 'app_context' not in g:
g.app_context = lorem_ipsum.create_app_context()
return g.app_context
@lru_cache()
def get_jwk():
LOGGER.debug('Loading jwk from public key...')
key_data = None
with open(app_context().config['jwk_public_key_path'], 'rb') as _key_file:
key_data = _key_file.read()
LOGGER.debug(key_data)
key = JsonWebKey.import_key(key_data, {'kty': 'RSA'})
_jwks = {'keys': [{**key.as_dict(), 'kid': 'demo_key'}]}
LOGGER.debug(_jwks)
return _jwks
class AuthenticationError(ValueError):
pass
class AuthorizationError(ValueError):
pass
class BearerTokenValidator:
def __init__(self, access_token, app_context: AppContext):
self.access_token = access_token
user_service = app_context.user_service
self.blacklist_token_repo = app_context.blacklist_token_repo
self.payload = user_service.decode_auth_token(access_token, get_jwk())
def check_is_blacklisted(self):
is_blacklisted_token = BlacklistToken.check_blacklist(self.access_token, self.blacklist_token_repo)
if is_blacklisted_token:
LOGGER.debug('Token blacklisted.')
raise AuthenticationError('Invalid token.')
return self
def check_username_claim(self):
if not self.payload.get('sub'):
LOGGER.debug('Token missing sub.')
raise AuthorizationError('Forbidden.')
return self
def check_user_exists(self, user):
if not user:
LOGGER.debug('Token user not found.')
raise AuthorizationError('Forbidden.')
return self
def check_has_permissions(self, user: User, permissions: list):
has_permissions = True
for permission in permissions:
if not user.role.has_permission(Permission.from_enum(permission)):
LOGGER.debug(f'Missing permission {permission}.')
has_permissions = False
LOGGER.debug(f'Required permissions: {permissions}')
if not has_permissions:
raise AuthorizationError('Forbidden.')
return self
@staticmethod
def from_authorization_header(authorization_header: str, app_context: AppContext):
if not authorization_header:
LOGGER.debug('Authorization header not found.')
raise AuthenticationError('Invalid token.')
if 'Bearer ' not in authorization_header:
LOGGER.debug('Bearer token not found.')
raise AuthenticationError('Invalid token.')
access_token = authorization_header.split('Bearer')[1].strip()
LOGGER.debug(f'Bearer token is:\n"{access_token}"')
return BearerTokenValidator(access_token, app_context)
def should_skip_auth(flask_request):
"""
Return true if should skip auth, e.g. when method is OPTIONS like when performing a React request.
:param flask_request: Flask request.
:return:
"""
return flask_request.method in ['HEAD', 'OPTIONS']
def requires_permission(permissions: list):
def requires_permission_decorator(function):
def wrapper(*args, **kwargs):
LOGGER.info(f'Authorization...\n{request.headers}')
if should_skip_auth(request):
return jsonify('ok')
authorization_header = request.headers.get('Authorization')
context = app_context()
with context.transaction_manager.transaction:
bearer_token_validator = BearerTokenValidator.from_authorization_header(authorization_header, context) \
.check_is_blacklisted() \
.check_username_claim()
user = context.user_repo.get(username=bearer_token_validator.payload['sub'])
bearer_token_validator.check_user_exists(user) \
.check_has_permissions(user, permissions)
g.access_token = bearer_token_validator.access_token
g.user = user
_result = function(*args, **kwargs)
return _result
wrapper.__name__ = function.__name__
return wrapper
return requires_permission_decorator
class ExceptionHandlers:
def __init__(self, app):
@app.errorhandler(AuthorizationError)
def handle_authorization_exception(e):
"""Return403 forbidden."""
return jsonify(str(e)), 403
@app.errorhandler(AuthenticationError)
def handle_authentication_exception(e):
"""Return401 authentication error."""
return jsonify(str(e)), 401
@lru_cache()
def jwk_key():
jwk_path = os.environ.get('jwk_private_key_path') or app_context().config['jwk_private_key_path']
with open(jwk_path, 'rb') as f:
key = JsonWebKey.import_key(f.read())
return key
def new_token(payload: dict):
key = jwk_key()
header = {'alg': 'RS256', 'kid': 'demo_key'}
token = jwt.encode(header, payload, key)
LOGGER.debug(token)
return token.decode('utf-8')
def issue_token_for_user(user: User):
access_token = new_token({
"iss": "lorem.ipsum.dev",
"aud": "lorem.ipsum.auth",
"sub": user.username,
"email": user.email,
"roles": [
user.role.name
],
"exp": datetime.datetime.now(tz=datetime.timezone.utc) + datetime.timedelta(hours=4),
"iat": datetime.datetime.now(tz=datetime.timezone.utc)
})
return access_token
|
flexible
|
{
"blob_id": "97d4387c7bfd141b5a7019b221adb550105d4351",
"index": 604,
"step-1": "<mask token>\n\n\nclass AuthorizationError(ValueError):\n pass\n\n\nclass BearerTokenValidator:\n\n def __init__(self, access_token, app_context: AppContext):\n self.access_token = access_token\n user_service = app_context.user_service\n self.blacklist_token_repo = app_context.blacklist_token_repo\n self.payload = user_service.decode_auth_token(access_token, get_jwk())\n\n def check_is_blacklisted(self):\n is_blacklisted_token = BlacklistToken.check_blacklist(self.\n access_token, self.blacklist_token_repo)\n if is_blacklisted_token:\n LOGGER.debug('Token blacklisted.')\n raise AuthenticationError('Invalid token.')\n return self\n\n def check_username_claim(self):\n if not self.payload.get('sub'):\n LOGGER.debug('Token missing sub.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_user_exists(self, user):\n if not user:\n LOGGER.debug('Token user not found.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_has_permissions(self, user: User, permissions: list):\n has_permissions = True\n for permission in permissions:\n if not user.role.has_permission(Permission.from_enum(permission)):\n LOGGER.debug(f'Missing permission {permission}.')\n has_permissions = False\n LOGGER.debug(f'Required permissions: {permissions}')\n if not has_permissions:\n raise AuthorizationError('Forbidden.')\n return self\n\n @staticmethod\n def from_authorization_header(authorization_header: str, app_context:\n AppContext):\n if not authorization_header:\n LOGGER.debug('Authorization header not found.')\n raise AuthenticationError('Invalid token.')\n if 'Bearer ' not in authorization_header:\n LOGGER.debug('Bearer token not found.')\n raise AuthenticationError('Invalid token.')\n access_token = authorization_header.split('Bearer')[1].strip()\n LOGGER.debug(f'Bearer token is:\\n\"{access_token}\"')\n return BearerTokenValidator(access_token, app_context)\n\n\n<mask token>\n\n\nclass ExceptionHandlers:\n\n def __init__(self, app):\n\n @app.errorhandler(AuthorizationError)\n def handle_authorization_exception(e):\n \"\"\"Return403 forbidden.\"\"\"\n return jsonify(str(e)), 403\n\n @app.errorhandler(AuthenticationError)\n def handle_authentication_exception(e):\n \"\"\"Return401 authentication error.\"\"\"\n return jsonify(str(e)), 401\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass AuthenticationError(ValueError):\n pass\n\n\nclass AuthorizationError(ValueError):\n pass\n\n\nclass BearerTokenValidator:\n\n def __init__(self, access_token, app_context: AppContext):\n self.access_token = access_token\n user_service = app_context.user_service\n self.blacklist_token_repo = app_context.blacklist_token_repo\n self.payload = user_service.decode_auth_token(access_token, get_jwk())\n\n def check_is_blacklisted(self):\n is_blacklisted_token = BlacklistToken.check_blacklist(self.\n access_token, self.blacklist_token_repo)\n if is_blacklisted_token:\n LOGGER.debug('Token blacklisted.')\n raise AuthenticationError('Invalid token.')\n return self\n\n def check_username_claim(self):\n if not self.payload.get('sub'):\n LOGGER.debug('Token missing sub.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_user_exists(self, user):\n if not user:\n LOGGER.debug('Token user not found.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_has_permissions(self, user: User, permissions: list):\n has_permissions = True\n for permission in permissions:\n if not user.role.has_permission(Permission.from_enum(permission)):\n LOGGER.debug(f'Missing permission {permission}.')\n has_permissions = False\n LOGGER.debug(f'Required permissions: {permissions}')\n if not has_permissions:\n raise AuthorizationError('Forbidden.')\n return self\n\n @staticmethod\n def from_authorization_header(authorization_header: str, app_context:\n AppContext):\n if not authorization_header:\n LOGGER.debug('Authorization header not found.')\n raise AuthenticationError('Invalid token.')\n if 'Bearer ' not in authorization_header:\n LOGGER.debug('Bearer token not found.')\n raise AuthenticationError('Invalid token.')\n access_token = authorization_header.split('Bearer')[1].strip()\n LOGGER.debug(f'Bearer token is:\\n\"{access_token}\"')\n return BearerTokenValidator(access_token, app_context)\n\n\n<mask token>\n\n\nclass ExceptionHandlers:\n\n def __init__(self, app):\n\n @app.errorhandler(AuthorizationError)\n def handle_authorization_exception(e):\n \"\"\"Return403 forbidden.\"\"\"\n return jsonify(str(e)), 403\n\n @app.errorhandler(AuthenticationError)\n def handle_authentication_exception(e):\n \"\"\"Return401 authentication error.\"\"\"\n return jsonify(str(e)), 401\n\n\n<mask token>\n\n\ndef issue_token_for_user(user: User):\n access_token = new_token({'iss': 'lorem.ipsum.dev', 'aud':\n 'lorem.ipsum.auth', 'sub': user.username, 'email': user.email,\n 'roles': [user.role.name], 'exp': datetime.datetime.now(tz=datetime\n .timezone.utc) + datetime.timedelta(hours=4), 'iat': datetime.\n datetime.now(tz=datetime.timezone.utc)})\n return access_token\n",
"step-3": "<mask token>\n\n\ndef app_context():\n if 'app_context' not in g:\n g.app_context = lorem_ipsum.create_app_context()\n return g.app_context\n\n\n@lru_cache()\ndef get_jwk():\n LOGGER.debug('Loading jwk from public key...')\n key_data = None\n with open(app_context().config['jwk_public_key_path'], 'rb') as _key_file:\n key_data = _key_file.read()\n LOGGER.debug(key_data)\n key = JsonWebKey.import_key(key_data, {'kty': 'RSA'})\n _jwks = {'keys': [{**key.as_dict(), 'kid': 'demo_key'}]}\n LOGGER.debug(_jwks)\n return _jwks\n\n\nclass AuthenticationError(ValueError):\n pass\n\n\nclass AuthorizationError(ValueError):\n pass\n\n\nclass BearerTokenValidator:\n\n def __init__(self, access_token, app_context: AppContext):\n self.access_token = access_token\n user_service = app_context.user_service\n self.blacklist_token_repo = app_context.blacklist_token_repo\n self.payload = user_service.decode_auth_token(access_token, get_jwk())\n\n def check_is_blacklisted(self):\n is_blacklisted_token = BlacklistToken.check_blacklist(self.\n access_token, self.blacklist_token_repo)\n if is_blacklisted_token:\n LOGGER.debug('Token blacklisted.')\n raise AuthenticationError('Invalid token.')\n return self\n\n def check_username_claim(self):\n if not self.payload.get('sub'):\n LOGGER.debug('Token missing sub.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_user_exists(self, user):\n if not user:\n LOGGER.debug('Token user not found.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_has_permissions(self, user: User, permissions: list):\n has_permissions = True\n for permission in permissions:\n if not user.role.has_permission(Permission.from_enum(permission)):\n LOGGER.debug(f'Missing permission {permission}.')\n has_permissions = False\n LOGGER.debug(f'Required permissions: {permissions}')\n if not has_permissions:\n raise AuthorizationError('Forbidden.')\n return self\n\n @staticmethod\n def from_authorization_header(authorization_header: str, app_context:\n AppContext):\n if not authorization_header:\n LOGGER.debug('Authorization header not found.')\n raise AuthenticationError('Invalid token.')\n if 'Bearer ' not in authorization_header:\n LOGGER.debug('Bearer token not found.')\n raise AuthenticationError('Invalid token.')\n access_token = authorization_header.split('Bearer')[1].strip()\n LOGGER.debug(f'Bearer token is:\\n\"{access_token}\"')\n return BearerTokenValidator(access_token, app_context)\n\n\ndef should_skip_auth(flask_request):\n \"\"\"\n Return true if should skip auth, e.g. when method is OPTIONS like when performing a React request.\n :param flask_request: Flask request.\n :return:\n \"\"\"\n return flask_request.method in ['HEAD', 'OPTIONS']\n\n\n<mask token>\n\n\nclass ExceptionHandlers:\n\n def __init__(self, app):\n\n @app.errorhandler(AuthorizationError)\n def handle_authorization_exception(e):\n \"\"\"Return403 forbidden.\"\"\"\n return jsonify(str(e)), 403\n\n @app.errorhandler(AuthenticationError)\n def handle_authentication_exception(e):\n \"\"\"Return401 authentication error.\"\"\"\n return jsonify(str(e)), 401\n\n\n@lru_cache()\ndef jwk_key():\n jwk_path = os.environ.get('jwk_private_key_path') or app_context().config[\n 'jwk_private_key_path']\n with open(jwk_path, 'rb') as f:\n key = JsonWebKey.import_key(f.read())\n return key\n\n\ndef new_token(payload: dict):\n key = jwk_key()\n header = {'alg': 'RS256', 'kid': 'demo_key'}\n token = jwt.encode(header, payload, key)\n LOGGER.debug(token)\n return token.decode('utf-8')\n\n\ndef issue_token_for_user(user: User):\n access_token = new_token({'iss': 'lorem.ipsum.dev', 'aud':\n 'lorem.ipsum.auth', 'sub': user.username, 'email': user.email,\n 'roles': [user.role.name], 'exp': datetime.datetime.now(tz=datetime\n .timezone.utc) + datetime.timedelta(hours=4), 'iat': datetime.\n datetime.now(tz=datetime.timezone.utc)})\n return access_token\n",
"step-4": "<mask token>\nLOGGER = logging.getLogger('lorem-ipsum')\n\n\ndef app_context():\n if 'app_context' not in g:\n g.app_context = lorem_ipsum.create_app_context()\n return g.app_context\n\n\n@lru_cache()\ndef get_jwk():\n LOGGER.debug('Loading jwk from public key...')\n key_data = None\n with open(app_context().config['jwk_public_key_path'], 'rb') as _key_file:\n key_data = _key_file.read()\n LOGGER.debug(key_data)\n key = JsonWebKey.import_key(key_data, {'kty': 'RSA'})\n _jwks = {'keys': [{**key.as_dict(), 'kid': 'demo_key'}]}\n LOGGER.debug(_jwks)\n return _jwks\n\n\nclass AuthenticationError(ValueError):\n pass\n\n\nclass AuthorizationError(ValueError):\n pass\n\n\nclass BearerTokenValidator:\n\n def __init__(self, access_token, app_context: AppContext):\n self.access_token = access_token\n user_service = app_context.user_service\n self.blacklist_token_repo = app_context.blacklist_token_repo\n self.payload = user_service.decode_auth_token(access_token, get_jwk())\n\n def check_is_blacklisted(self):\n is_blacklisted_token = BlacklistToken.check_blacklist(self.\n access_token, self.blacklist_token_repo)\n if is_blacklisted_token:\n LOGGER.debug('Token blacklisted.')\n raise AuthenticationError('Invalid token.')\n return self\n\n def check_username_claim(self):\n if not self.payload.get('sub'):\n LOGGER.debug('Token missing sub.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_user_exists(self, user):\n if not user:\n LOGGER.debug('Token user not found.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_has_permissions(self, user: User, permissions: list):\n has_permissions = True\n for permission in permissions:\n if not user.role.has_permission(Permission.from_enum(permission)):\n LOGGER.debug(f'Missing permission {permission}.')\n has_permissions = False\n LOGGER.debug(f'Required permissions: {permissions}')\n if not has_permissions:\n raise AuthorizationError('Forbidden.')\n return self\n\n @staticmethod\n def from_authorization_header(authorization_header: str, app_context:\n AppContext):\n if not authorization_header:\n LOGGER.debug('Authorization header not found.')\n raise AuthenticationError('Invalid token.')\n if 'Bearer ' not in authorization_header:\n LOGGER.debug('Bearer token not found.')\n raise AuthenticationError('Invalid token.')\n access_token = authorization_header.split('Bearer')[1].strip()\n LOGGER.debug(f'Bearer token is:\\n\"{access_token}\"')\n return BearerTokenValidator(access_token, app_context)\n\n\ndef should_skip_auth(flask_request):\n \"\"\"\n Return true if should skip auth, e.g. when method is OPTIONS like when performing a React request.\n :param flask_request: Flask request.\n :return:\n \"\"\"\n return flask_request.method in ['HEAD', 'OPTIONS']\n\n\ndef requires_permission(permissions: list):\n\n def requires_permission_decorator(function):\n\n def wrapper(*args, **kwargs):\n LOGGER.info(f'Authorization...\\n{request.headers}')\n if should_skip_auth(request):\n return jsonify('ok')\n authorization_header = request.headers.get('Authorization')\n context = app_context()\n with context.transaction_manager.transaction:\n bearer_token_validator = (BearerTokenValidator.\n from_authorization_header(authorization_header, context\n ).check_is_blacklisted().check_username_claim())\n user = context.user_repo.get(username=\n bearer_token_validator.payload['sub'])\n bearer_token_validator.check_user_exists(user\n ).check_has_permissions(user, permissions)\n g.access_token = bearer_token_validator.access_token\n g.user = user\n _result = function(*args, **kwargs)\n return _result\n wrapper.__name__ = function.__name__\n return wrapper\n return requires_permission_decorator\n\n\nclass ExceptionHandlers:\n\n def __init__(self, app):\n\n @app.errorhandler(AuthorizationError)\n def handle_authorization_exception(e):\n \"\"\"Return403 forbidden.\"\"\"\n return jsonify(str(e)), 403\n\n @app.errorhandler(AuthenticationError)\n def handle_authentication_exception(e):\n \"\"\"Return401 authentication error.\"\"\"\n return jsonify(str(e)), 401\n\n\n@lru_cache()\ndef jwk_key():\n jwk_path = os.environ.get('jwk_private_key_path') or app_context().config[\n 'jwk_private_key_path']\n with open(jwk_path, 'rb') as f:\n key = JsonWebKey.import_key(f.read())\n return key\n\n\ndef new_token(payload: dict):\n key = jwk_key()\n header = {'alg': 'RS256', 'kid': 'demo_key'}\n token = jwt.encode(header, payload, key)\n LOGGER.debug(token)\n return token.decode('utf-8')\n\n\ndef issue_token_for_user(user: User):\n access_token = new_token({'iss': 'lorem.ipsum.dev', 'aud':\n 'lorem.ipsum.auth', 'sub': user.username, 'email': user.email,\n 'roles': [user.role.name], 'exp': datetime.datetime.now(tz=datetime\n .timezone.utc) + datetime.timedelta(hours=4), 'iat': datetime.\n datetime.now(tz=datetime.timezone.utc)})\n return access_token\n",
"step-5": "import datetime\nimport logging\nimport os\nfrom functools import lru_cache\nfrom authlib.jose import JsonWebKey, jwt\n\nfrom flask import g, request, jsonify\nfrom lorem_ipsum.model import User, AppContext\nimport lorem_ipsum\nfrom lorem_ipsum.model import Permission, BlacklistToken\n\nLOGGER = logging.getLogger('lorem-ipsum')\n\n\ndef app_context():\n if 'app_context' not in g:\n g.app_context = lorem_ipsum.create_app_context()\n return g.app_context\n\n\n@lru_cache()\ndef get_jwk():\n LOGGER.debug('Loading jwk from public key...')\n key_data = None\n with open(app_context().config['jwk_public_key_path'], 'rb') as _key_file:\n key_data = _key_file.read()\n LOGGER.debug(key_data)\n key = JsonWebKey.import_key(key_data, {'kty': 'RSA'})\n _jwks = {'keys': [{**key.as_dict(), 'kid': 'demo_key'}]}\n LOGGER.debug(_jwks)\n return _jwks\n\n\nclass AuthenticationError(ValueError):\n pass\n\n\nclass AuthorizationError(ValueError):\n pass\n\n\nclass BearerTokenValidator:\n def __init__(self, access_token, app_context: AppContext):\n self.access_token = access_token\n user_service = app_context.user_service\n self.blacklist_token_repo = app_context.blacklist_token_repo\n self.payload = user_service.decode_auth_token(access_token, get_jwk())\n\n def check_is_blacklisted(self):\n is_blacklisted_token = BlacklistToken.check_blacklist(self.access_token, self.blacklist_token_repo)\n if is_blacklisted_token:\n LOGGER.debug('Token blacklisted.')\n raise AuthenticationError('Invalid token.')\n return self\n\n def check_username_claim(self):\n if not self.payload.get('sub'):\n LOGGER.debug('Token missing sub.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_user_exists(self, user):\n if not user:\n LOGGER.debug('Token user not found.')\n raise AuthorizationError('Forbidden.')\n return self\n\n def check_has_permissions(self, user: User, permissions: list):\n has_permissions = True\n for permission in permissions:\n if not user.role.has_permission(Permission.from_enum(permission)):\n LOGGER.debug(f'Missing permission {permission}.')\n has_permissions = False\n LOGGER.debug(f'Required permissions: {permissions}')\n if not has_permissions:\n raise AuthorizationError('Forbidden.')\n return self\n\n @staticmethod\n def from_authorization_header(authorization_header: str, app_context: AppContext):\n if not authorization_header:\n LOGGER.debug('Authorization header not found.')\n raise AuthenticationError('Invalid token.')\n if 'Bearer ' not in authorization_header:\n LOGGER.debug('Bearer token not found.')\n raise AuthenticationError('Invalid token.')\n access_token = authorization_header.split('Bearer')[1].strip()\n LOGGER.debug(f'Bearer token is:\\n\"{access_token}\"')\n return BearerTokenValidator(access_token, app_context)\n\n\ndef should_skip_auth(flask_request):\n \"\"\"\n Return true if should skip auth, e.g. when method is OPTIONS like when performing a React request.\n :param flask_request: Flask request.\n :return:\n \"\"\"\n return flask_request.method in ['HEAD', 'OPTIONS']\n\n\ndef requires_permission(permissions: list):\n def requires_permission_decorator(function):\n def wrapper(*args, **kwargs):\n LOGGER.info(f'Authorization...\\n{request.headers}')\n if should_skip_auth(request):\n return jsonify('ok')\n authorization_header = request.headers.get('Authorization')\n context = app_context()\n with context.transaction_manager.transaction:\n bearer_token_validator = BearerTokenValidator.from_authorization_header(authorization_header, context) \\\n .check_is_blacklisted() \\\n .check_username_claim()\n user = context.user_repo.get(username=bearer_token_validator.payload['sub'])\n\n bearer_token_validator.check_user_exists(user) \\\n .check_has_permissions(user, permissions)\n g.access_token = bearer_token_validator.access_token\n g.user = user\n\n _result = function(*args, **kwargs)\n return _result\n\n wrapper.__name__ = function.__name__\n return wrapper\n\n return requires_permission_decorator\n\n\nclass ExceptionHandlers:\n def __init__(self, app):\n @app.errorhandler(AuthorizationError)\n def handle_authorization_exception(e):\n \"\"\"Return403 forbidden.\"\"\"\n return jsonify(str(e)), 403\n\n @app.errorhandler(AuthenticationError)\n def handle_authentication_exception(e):\n \"\"\"Return401 authentication error.\"\"\"\n return jsonify(str(e)), 401\n\n\n@lru_cache()\ndef jwk_key():\n jwk_path = os.environ.get('jwk_private_key_path') or app_context().config['jwk_private_key_path']\n with open(jwk_path, 'rb') as f:\n key = JsonWebKey.import_key(f.read())\n return key\n\n\ndef new_token(payload: dict):\n key = jwk_key()\n header = {'alg': 'RS256', 'kid': 'demo_key'}\n token = jwt.encode(header, payload, key)\n LOGGER.debug(token)\n return token.decode('utf-8')\n\n\ndef issue_token_for_user(user: User):\n access_token = new_token({\n \"iss\": \"lorem.ipsum.dev\",\n \"aud\": \"lorem.ipsum.auth\",\n \"sub\": user.username,\n \"email\": user.email,\n \"roles\": [\n user.role.name\n ],\n \"exp\": datetime.datetime.now(tz=datetime.timezone.utc) + datetime.timedelta(hours=4),\n \"iat\": datetime.datetime.now(tz=datetime.timezone.utc)\n })\n return access_token\n",
"step-ids": [
10,
12,
17,
19,
21
]
}
|
[
10,
12,
17,
19,
21
] |
import csv
with open('./csvs/users.csv', encoding='utf-8', newline='') as users_csv:
reader = csv.reader(users_csv)
d = {}
for row in reader:
userId, profileName = row
if profileName == 'A Customer':
continue
value = d.get(profileName)
if not value:
d.setdefault(profileName, userId)
else:
if value != userId:
print(f'{userId}, {value}, {profileName}')
|
normal
|
{
"blob_id": "3b77f7ea5137174e6723368502659390ea064c5a",
"index": 8968,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('./csvs/users.csv', encoding='utf-8', newline='') as users_csv:\n reader = csv.reader(users_csv)\n d = {}\n for row in reader:\n userId, profileName = row\n if profileName == 'A Customer':\n continue\n value = d.get(profileName)\n if not value:\n d.setdefault(profileName, userId)\n elif value != userId:\n print(f'{userId}, {value}, {profileName}')\n",
"step-3": "import csv\nwith open('./csvs/users.csv', encoding='utf-8', newline='') as users_csv:\n reader = csv.reader(users_csv)\n d = {}\n for row in reader:\n userId, profileName = row\n if profileName == 'A Customer':\n continue\n value = d.get(profileName)\n if not value:\n d.setdefault(profileName, userId)\n elif value != userId:\n print(f'{userId}, {value}, {profileName}')\n",
"step-4": "import csv\n\nwith open('./csvs/users.csv', encoding='utf-8', newline='') as users_csv:\n reader = csv.reader(users_csv)\n d = {}\n for row in reader:\n userId, profileName = row\n if profileName == 'A Customer':\n continue\n value = d.get(profileName)\n if not value:\n d.setdefault(profileName, userId)\n else:\n if value != userId:\n print(f'{userId}, {value}, {profileName}')",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
# coding: utf-8
import logging
def __gen_logger():
result = logging.getLogger('superslick')
return result
logger = __gen_logger()
|
normal
|
{
"blob_id": "cee9deeeabfec46ee5c132704e8fd653e55987f3",
"index": 3430,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef __gen_logger():\n result = logging.getLogger('superslick')\n return result\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef __gen_logger():\n result = logging.getLogger('superslick')\n return result\n\n\nlogger = __gen_logger()\n",
"step-4": "import logging\n\n\ndef __gen_logger():\n result = logging.getLogger('superslick')\n return result\n\n\nlogger = __gen_logger()\n",
"step-5": "# coding: utf-8\n\nimport logging\n\ndef __gen_logger():\n result = logging.getLogger('superslick')\n return result\n\nlogger = __gen_logger()",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
"""
stanCode Breakout Project
Adapted from Eric Roberts's Breakout by
Sonja Johnson-Yu, Kylie Jue, Nick Bowman,
and Jerry Liao
YOUR DESCRIPTION HERE
"""
from campy.gui.events.timer import pause
from breakoutgraphics import BreakoutGraphics
FRAME_RATE = 1000 / 120 # 120 frames per second.
NUM_LIVES = 3
def main():
graphics = BreakoutGraphics()
lives = NUM_LIVES # 生命
graphics.window.add(graphics.scoreboard, 0, graphics.window_height) # 計分板
# Add animation loop here!
while True:
pause(FRAME_RATE)
if graphics.ball_fall_down():
lives -= 1
if lives > 0:
graphics.reset_ball()
else:
graphics.game_over()
break
if graphics.you_win():
break
vx = graphics.getx()
vy = graphics.gety()
graphics.ball.move(vx, vy)
graphics.boundary()
graphics.collision()
if __name__ == '__main__':
main()
|
normal
|
{
"blob_id": "b218f5e401510f844006cb6079737b54aa86827b",
"index": 2194,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n graphics = BreakoutGraphics()\n lives = NUM_LIVES\n graphics.window.add(graphics.scoreboard, 0, graphics.window_height)\n while True:\n pause(FRAME_RATE)\n if graphics.ball_fall_down():\n lives -= 1\n if lives > 0:\n graphics.reset_ball()\n else:\n graphics.game_over()\n break\n if graphics.you_win():\n break\n vx = graphics.getx()\n vy = graphics.gety()\n graphics.ball.move(vx, vy)\n graphics.boundary()\n graphics.collision()\n\n\nif __name__ == '__main__':\n main()\n",
"step-3": "<mask token>\nFRAME_RATE = 1000 / 120\nNUM_LIVES = 3\n\n\ndef main():\n graphics = BreakoutGraphics()\n lives = NUM_LIVES\n graphics.window.add(graphics.scoreboard, 0, graphics.window_height)\n while True:\n pause(FRAME_RATE)\n if graphics.ball_fall_down():\n lives -= 1\n if lives > 0:\n graphics.reset_ball()\n else:\n graphics.game_over()\n break\n if graphics.you_win():\n break\n vx = graphics.getx()\n vy = graphics.gety()\n graphics.ball.move(vx, vy)\n graphics.boundary()\n graphics.collision()\n\n\nif __name__ == '__main__':\n main()\n",
"step-4": "<mask token>\nfrom campy.gui.events.timer import pause\nfrom breakoutgraphics import BreakoutGraphics\nFRAME_RATE = 1000 / 120\nNUM_LIVES = 3\n\n\ndef main():\n graphics = BreakoutGraphics()\n lives = NUM_LIVES\n graphics.window.add(graphics.scoreboard, 0, graphics.window_height)\n while True:\n pause(FRAME_RATE)\n if graphics.ball_fall_down():\n lives -= 1\n if lives > 0:\n graphics.reset_ball()\n else:\n graphics.game_over()\n break\n if graphics.you_win():\n break\n vx = graphics.getx()\n vy = graphics.gety()\n graphics.ball.move(vx, vy)\n graphics.boundary()\n graphics.collision()\n\n\nif __name__ == '__main__':\n main()\n",
"step-5": "\"\"\"\nstanCode Breakout Project\nAdapted from Eric Roberts's Breakout by\nSonja Johnson-Yu, Kylie Jue, Nick Bowman,\nand Jerry Liao\n\nYOUR DESCRIPTION HERE\n\"\"\"\n\nfrom campy.gui.events.timer import pause\nfrom breakoutgraphics import BreakoutGraphics\n\nFRAME_RATE = 1000 / 120 # 120 frames per second.\nNUM_LIVES = 3\n\n\ndef main():\n graphics = BreakoutGraphics()\n lives = NUM_LIVES # 生命\n\n graphics.window.add(graphics.scoreboard, 0, graphics.window_height) # 計分板\n\n # Add animation loop here!\n while True:\n pause(FRAME_RATE)\n if graphics.ball_fall_down():\n lives -= 1\n if lives > 0:\n graphics.reset_ball()\n else:\n graphics.game_over()\n break\n if graphics.you_win():\n break\n vx = graphics.getx()\n vy = graphics.gety()\n graphics.ball.move(vx, vy)\n graphics.boundary()\n graphics.collision()\n\n\nif __name__ == '__main__':\n main()\n",
"step-ids": [
0,
2,
3,
4,
5
]
}
|
[
0,
2,
3,
4,
5
] |
from ..core.helpers import itemize
from ..core.files import backendRep, expandDir, prefixSlash, normpath
from .helpers import splitModRef
from .repo import checkoutRepo
from .links import provenanceLink
# GET DATA FOR MAIN SOURCE AND ALL MODULES
class AppData:
def __init__(
self, app, backend, moduleRefs, locations, modules, version, checkout, silent
):
"""Collects TF data according to specifications.
The specifications are passed as arguments when the object is initialized.
Parameters
----------
backend: string
`github` or `gitlab` or a GitLab instance such as `gitlab.huc.knaw.nl`.
app: obj
The high-level API object
moduleRefs: tuple
Each member consists of a module ref, which is a tuple of information
that defines a module.
locations: string|tuple
One or more directory paths. They will be combined with the `modules`
argument and used as locations to search for TF data files.
modules: string|tuple
One or more directory path segments. They will be appended to the
paths given by the `locations` argument to form search locations
for TF data files.
version: string
The version of TF data that should be retrievend. Version is a directory
level just below the search locations.
checkout: string
A specifier to use a specific release or commit of a data repository.
silent: string, optional tf.core.timestamp.SILENT_D
See `tf.core.timestamp.Timestamp`
"""
self.backend = backend
self.app = app
self.moduleRefs = (
[]
if moduleRefs is None
else moduleRefs.split(",")
if type(moduleRefs) is str
else list(moduleRefs)
)
self.locationsArg = locations
self.modulesArg = modules
self.version = version
self.checkout = checkout
self.silent = silent
def getMain(self):
"""Get the main data of the corpus.
This is specified by the `org`, `repo` and `relative` settings under
`provenanceSpec` in `config.yaml`.
See Also
--------
tf.advanced.settings: options allowed in `config.yaml`
"""
app = self.app
checkout = self.checkout
aContext = app.context
org = aContext.org
repo = aContext.repo
relative = prefixSlash(aContext.relative)
appPath = aContext.appPath
appName = aContext.appName
if appName.startswith("app:"):
appParent = appPath.rsplit("/", 1)[0]
relative = f"{appParent}{relative}"
elif org is None or repo is None:
appPathRep = f"{appPath}/" if appPath else ""
relative = f"{appPathRep}{appName}"
self.checkout = "local"
if not self.getModule(org, repo, prefixSlash(relative), checkout, isBase=True):
self.good = False
def getStandard(self):
"""Get the data of the standard modules specified by the settings of the corpus.
These are specified in the `moduleSpecs` setting under
`provenanceSpecs` in `config.yaml`.
They will be loaded *after* the extra modules specified in the **mod**
parameter, and only in as far they have not been specifief in the
**mod** parameter. In this way you can pass overriding
checkout specifiers to the standard modules.
See Also
--------
tf.advanced.settings: options allowed in `config.yaml`
"""
app = self.app
loadData = app.loadData
if not loadData or loadData == "core":
return
aContext = app.context
moduleSpecs = aContext.moduleSpecs
seen = self.seen
checkout = self.checkout
backend = self.backend
for m in moduleSpecs or []:
org = m["org"]
repo = m["repo"]
relative = m["relative"]
theCheckout = m.get("checkout", checkout)
theBackend = m.get("backend", backend)
bRep = backendRep(theBackend, "spec", default=backend)
ref = f"{bRep}{org}/{repo}{relative}"
if ref in seen:
continue
if not self.getModule(
org,
repo,
relative,
theCheckout,
backend=theBackend,
specs=m,
):
self.good = False
def getRefs(self):
"""Get data from additional modules.
These are specified in the `moduleRefs` parameter of `AppData`.
We store the set of special modules in order to skip them
later when we are loading the standard modules.
"""
backend = self.backend
refs = self.moduleRefs
for ref in refs:
refPure = ref.rsplit(":", 1)[0]
if refPure in self.seen:
continue
parts = splitModRef(ref)
if not parts:
self.good = False
continue
parts[2] = prefixSlash(normpath(parts[2])) # the relative bit
theBackend = (
None if parts[-1] is None or parts[-1] == backend else parts[-1]
)
if not self.getModule(*parts[0:-1], backend=theBackend):
self.good = False
def getModules(self):
"""Get data from additional local directories.
These are specified in the `locations` and `modules` parameters of `AppData`.
"""
self.provenance = []
provenance = self.provenance
self.mLocations = []
mLocations = self.mLocations
self.locations = None
self.modules = None
self.good = True
self.seen = set()
self.getMain()
self.getRefs()
self.getStandard()
version = self.version
good = self.good
app = self.app
if good:
app.mLocations = mLocations
app.provenance = provenance
else:
return
mModules = []
if mLocations:
mModules.append(version or "")
locations = self.locationsArg
modules = self.modulesArg
givenLocations = (
[]
if locations is None
else [expandDir(app, x.strip()) for x in itemize(locations, "\n")]
if type(locations) is str
else [str(x) for x in locations]
)
givenModules = (
[]
if modules is None
else [normpath(x.strip()) for x in itemize(modules, "\n")]
if type(modules) is str
else [normpath(str(x)) for x in modules]
)
self.locations = mLocations + givenLocations
self.modules = mModules + givenModules
def getModule(
self, org, repo, relative, checkout, backend=None, isBase=False, specs=None
):
"""Prepare to load a single module.
Eventually, all TF data will be downloaded from local directories, bases
on a list of location paths and module paths.
This function computes the contribution of a single module to both the
location paths and the module paths.
Parameters
----------
org: string
GitHub organization or GitLab group of the module
repo: string:
GitHub repository or GitLab project of the module
relative: string
Path within the repository of the module
checkout: string
A specifier to use a specific release or commit of a data repository.
backend: string
The backend if different from the backend of the main module
isBase: boolean, optional False
Whether this module is the main data of the corpus.
specs: dict, optional False
Additional informational attributes of the module, e.g. a DOI
"""
backend = self.backend if backend is None else backendRep(backend, "norm")
bRep = backendRep(backend, "spec", default=self.backend)
version = self.version
silent = self.silent
mLocations = self.mLocations
provenance = self.provenance
seen = self.seen
app = self.app
_browse = app._browse
aContext = app.context
branch = aContext.provenanceSpec["branch"]
relative = prefixSlash(normpath(relative))
moduleRef = f"{bRep}{org}/{repo}{relative}"
if moduleRef in self.seen:
return True
if org is None or repo is None:
relativeBare = relative.removeprefix("/")
repoLocation = relativeBare
mLocations.append(relativeBare)
(commit, local, release) = (None, None, None)
else:
(commit, release, local, localBase, localDir) = checkoutRepo(
backend,
_browse=_browse,
org=org,
repo=repo,
folder=relative,
version=version,
checkout=checkout,
withPaths=False,
keep=False,
silent=silent,
)
if not localBase:
return False
repoLocation = f"{localBase}/{org}/{repo}"
mLocations.append(f"{localBase}/{localDir}")
seen.add(moduleRef)
if isBase:
app.repoLocation = repoLocation
info = {}
for item in (
("doi", None),
("corpus", f"{org}/{repo}{relative}"),
):
(key, default) = item
info[key] = (
getattr(aContext, key)
if isBase
else specs[key]
if specs and key in specs
else default
)
provenance.append(
(
("corpus", info["corpus"]),
("version", version),
("commit", commit or "??"),
("release", release or "none"),
(
"live",
provenanceLink(
backend, org, repo, version, branch, commit, local, release, relative
),
),
("doi", info["doi"]),
)
)
return True
def getModulesData(*args):
"""Retrieve all data for a corpus.
Parameters
----------
args: list
All parameters needed to retrieve all associated data.
They are the same as are needed to construct an `AppData` object.
"""
mData = AppData(*args)
mData.getModules()
if not mData.good or mData.locations is None:
return None
return (mData.locations, mData.modules)
|
normal
|
{
"blob_id": "7be54b2bd99680beed3e8e9cb14225756a71a4ea",
"index": 1135,
"step-1": "<mask token>\n\n\nclass AppData:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass AppData:\n\n def __init__(self, app, backend, moduleRefs, locations, modules,\n version, checkout, silent):\n \"\"\"Collects TF data according to specifications.\n\n The specifications are passed as arguments when the object is initialized.\n\n Parameters\n ----------\n backend: string\n `github` or `gitlab` or a GitLab instance such as `gitlab.huc.knaw.nl`.\n app: obj\n The high-level API object\n moduleRefs: tuple\n Each member consists of a module ref, which is a tuple of information\n that defines a module.\n locations: string|tuple\n One or more directory paths. They will be combined with the `modules`\n argument and used as locations to search for TF data files.\n modules: string|tuple\n One or more directory path segments. They will be appended to the\n paths given by the `locations` argument to form search locations\n for TF data files.\n version: string\n The version of TF data that should be retrievend. Version is a directory\n level just below the search locations.\n checkout: string\n A specifier to use a specific release or commit of a data repository.\n silent: string, optional tf.core.timestamp.SILENT_D\n See `tf.core.timestamp.Timestamp`\n\n \"\"\"\n self.backend = backend\n self.app = app\n self.moduleRefs = [] if moduleRefs is None else moduleRefs.split(','\n ) if type(moduleRefs) is str else list(moduleRefs)\n self.locationsArg = locations\n self.modulesArg = modules\n self.version = version\n self.checkout = checkout\n self.silent = silent\n\n def getMain(self):\n \"\"\"Get the main data of the corpus.\n\n This is specified by the `org`, `repo` and `relative` settings under\n `provenanceSpec` in `config.yaml`.\n\n See Also\n --------\n tf.advanced.settings: options allowed in `config.yaml`\n \"\"\"\n app = self.app\n checkout = self.checkout\n aContext = app.context\n org = aContext.org\n repo = aContext.repo\n relative = prefixSlash(aContext.relative)\n appPath = aContext.appPath\n appName = aContext.appName\n if appName.startswith('app:'):\n appParent = appPath.rsplit('/', 1)[0]\n relative = f'{appParent}{relative}'\n elif org is None or repo is None:\n appPathRep = f'{appPath}/' if appPath else ''\n relative = f'{appPathRep}{appName}'\n self.checkout = 'local'\n if not self.getModule(org, repo, prefixSlash(relative), checkout,\n isBase=True):\n self.good = False\n <mask token>\n\n def getRefs(self):\n \"\"\"Get data from additional modules.\n\n These are specified in the `moduleRefs` parameter of `AppData`.\n We store the set of special modules in order to skip them\n later when we are loading the standard modules.\n \"\"\"\n backend = self.backend\n refs = self.moduleRefs\n for ref in refs:\n refPure = ref.rsplit(':', 1)[0]\n if refPure in self.seen:\n continue\n parts = splitModRef(ref)\n if not parts:\n self.good = False\n continue\n parts[2] = prefixSlash(normpath(parts[2]))\n theBackend = None if parts[-1] is None or parts[-1\n ] == backend else parts[-1]\n if not self.getModule(*parts[0:-1], backend=theBackend):\n self.good = False\n\n def getModules(self):\n \"\"\"Get data from additional local directories.\n\n These are specified in the `locations` and `modules` parameters of `AppData`.\n \"\"\"\n self.provenance = []\n provenance = self.provenance\n self.mLocations = []\n mLocations = self.mLocations\n self.locations = None\n self.modules = None\n self.good = True\n self.seen = set()\n self.getMain()\n self.getRefs()\n self.getStandard()\n version = self.version\n good = self.good\n app = self.app\n if good:\n app.mLocations = mLocations\n app.provenance = provenance\n else:\n return\n mModules = []\n if mLocations:\n mModules.append(version or '')\n locations = self.locationsArg\n modules = self.modulesArg\n givenLocations = [] if locations is None else [expandDir(app, x.\n strip()) for x in itemize(locations, '\\n')] if type(locations\n ) is str else [str(x) for x in locations]\n givenModules = [] if modules is None else [normpath(x.strip()) for\n x in itemize(modules, '\\n')] if type(modules) is str else [normpath\n (str(x)) for x in modules]\n self.locations = mLocations + givenLocations\n self.modules = mModules + givenModules\n <mask token>\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass AppData:\n\n def __init__(self, app, backend, moduleRefs, locations, modules,\n version, checkout, silent):\n \"\"\"Collects TF data according to specifications.\n\n The specifications are passed as arguments when the object is initialized.\n\n Parameters\n ----------\n backend: string\n `github` or `gitlab` or a GitLab instance such as `gitlab.huc.knaw.nl`.\n app: obj\n The high-level API object\n moduleRefs: tuple\n Each member consists of a module ref, which is a tuple of information\n that defines a module.\n locations: string|tuple\n One or more directory paths. They will be combined with the `modules`\n argument and used as locations to search for TF data files.\n modules: string|tuple\n One or more directory path segments. They will be appended to the\n paths given by the `locations` argument to form search locations\n for TF data files.\n version: string\n The version of TF data that should be retrievend. Version is a directory\n level just below the search locations.\n checkout: string\n A specifier to use a specific release or commit of a data repository.\n silent: string, optional tf.core.timestamp.SILENT_D\n See `tf.core.timestamp.Timestamp`\n\n \"\"\"\n self.backend = backend\n self.app = app\n self.moduleRefs = [] if moduleRefs is None else moduleRefs.split(','\n ) if type(moduleRefs) is str else list(moduleRefs)\n self.locationsArg = locations\n self.modulesArg = modules\n self.version = version\n self.checkout = checkout\n self.silent = silent\n\n def getMain(self):\n \"\"\"Get the main data of the corpus.\n\n This is specified by the `org`, `repo` and `relative` settings under\n `provenanceSpec` in `config.yaml`.\n\n See Also\n --------\n tf.advanced.settings: options allowed in `config.yaml`\n \"\"\"\n app = self.app\n checkout = self.checkout\n aContext = app.context\n org = aContext.org\n repo = aContext.repo\n relative = prefixSlash(aContext.relative)\n appPath = aContext.appPath\n appName = aContext.appName\n if appName.startswith('app:'):\n appParent = appPath.rsplit('/', 1)[0]\n relative = f'{appParent}{relative}'\n elif org is None or repo is None:\n appPathRep = f'{appPath}/' if appPath else ''\n relative = f'{appPathRep}{appName}'\n self.checkout = 'local'\n if not self.getModule(org, repo, prefixSlash(relative), checkout,\n isBase=True):\n self.good = False\n\n def getStandard(self):\n \"\"\"Get the data of the standard modules specified by the settings of the corpus.\n\n These are specified in the `moduleSpecs` setting under\n `provenanceSpecs` in `config.yaml`.\n\n They will be loaded *after* the extra modules specified in the **mod**\n parameter, and only in as far they have not been specifief in the\n **mod** parameter. In this way you can pass overriding\n checkout specifiers to the standard modules.\n\n See Also\n --------\n tf.advanced.settings: options allowed in `config.yaml`\n \"\"\"\n app = self.app\n loadData = app.loadData\n if not loadData or loadData == 'core':\n return\n aContext = app.context\n moduleSpecs = aContext.moduleSpecs\n seen = self.seen\n checkout = self.checkout\n backend = self.backend\n for m in (moduleSpecs or []):\n org = m['org']\n repo = m['repo']\n relative = m['relative']\n theCheckout = m.get('checkout', checkout)\n theBackend = m.get('backend', backend)\n bRep = backendRep(theBackend, 'spec', default=backend)\n ref = f'{bRep}{org}/{repo}{relative}'\n if ref in seen:\n continue\n if not self.getModule(org, repo, relative, theCheckout, backend\n =theBackend, specs=m):\n self.good = False\n\n def getRefs(self):\n \"\"\"Get data from additional modules.\n\n These are specified in the `moduleRefs` parameter of `AppData`.\n We store the set of special modules in order to skip them\n later when we are loading the standard modules.\n \"\"\"\n backend = self.backend\n refs = self.moduleRefs\n for ref in refs:\n refPure = ref.rsplit(':', 1)[0]\n if refPure in self.seen:\n continue\n parts = splitModRef(ref)\n if not parts:\n self.good = False\n continue\n parts[2] = prefixSlash(normpath(parts[2]))\n theBackend = None if parts[-1] is None or parts[-1\n ] == backend else parts[-1]\n if not self.getModule(*parts[0:-1], backend=theBackend):\n self.good = False\n\n def getModules(self):\n \"\"\"Get data from additional local directories.\n\n These are specified in the `locations` and `modules` parameters of `AppData`.\n \"\"\"\n self.provenance = []\n provenance = self.provenance\n self.mLocations = []\n mLocations = self.mLocations\n self.locations = None\n self.modules = None\n self.good = True\n self.seen = set()\n self.getMain()\n self.getRefs()\n self.getStandard()\n version = self.version\n good = self.good\n app = self.app\n if good:\n app.mLocations = mLocations\n app.provenance = provenance\n else:\n return\n mModules = []\n if mLocations:\n mModules.append(version or '')\n locations = self.locationsArg\n modules = self.modulesArg\n givenLocations = [] if locations is None else [expandDir(app, x.\n strip()) for x in itemize(locations, '\\n')] if type(locations\n ) is str else [str(x) for x in locations]\n givenModules = [] if modules is None else [normpath(x.strip()) for\n x in itemize(modules, '\\n')] if type(modules) is str else [normpath\n (str(x)) for x in modules]\n self.locations = mLocations + givenLocations\n self.modules = mModules + givenModules\n\n def getModule(self, org, repo, relative, checkout, backend=None, isBase\n =False, specs=None):\n \"\"\"Prepare to load a single module.\n\n Eventually, all TF data will be downloaded from local directories, bases\n on a list of location paths and module paths.\n\n This function computes the contribution of a single module to both the\n location paths and the module paths.\n\n Parameters\n ----------\n org: string\n GitHub organization or GitLab group of the module\n repo: string:\n GitHub repository or GitLab project of the module\n relative: string\n Path within the repository of the module\n checkout: string\n A specifier to use a specific release or commit of a data repository.\n backend: string\n The backend if different from the backend of the main module\n isBase: boolean, optional False\n Whether this module is the main data of the corpus.\n specs: dict, optional False\n Additional informational attributes of the module, e.g. a DOI\n \"\"\"\n backend = self.backend if backend is None else backendRep(backend,\n 'norm')\n bRep = backendRep(backend, 'spec', default=self.backend)\n version = self.version\n silent = self.silent\n mLocations = self.mLocations\n provenance = self.provenance\n seen = self.seen\n app = self.app\n _browse = app._browse\n aContext = app.context\n branch = aContext.provenanceSpec['branch']\n relative = prefixSlash(normpath(relative))\n moduleRef = f'{bRep}{org}/{repo}{relative}'\n if moduleRef in self.seen:\n return True\n if org is None or repo is None:\n relativeBare = relative.removeprefix('/')\n repoLocation = relativeBare\n mLocations.append(relativeBare)\n commit, local, release = None, None, None\n else:\n commit, release, local, localBase, localDir = checkoutRepo(backend,\n _browse=_browse, org=org, repo=repo, folder=relative,\n version=version, checkout=checkout, withPaths=False, keep=\n False, silent=silent)\n if not localBase:\n return False\n repoLocation = f'{localBase}/{org}/{repo}'\n mLocations.append(f'{localBase}/{localDir}')\n seen.add(moduleRef)\n if isBase:\n app.repoLocation = repoLocation\n info = {}\n for item in (('doi', None), ('corpus', f'{org}/{repo}{relative}')):\n key, default = item\n info[key] = getattr(aContext, key) if isBase else specs[key\n ] if specs and key in specs else default\n provenance.append((('corpus', info['corpus']), ('version', version),\n ('commit', commit or '??'), ('release', release or 'none'), (\n 'live', provenanceLink(backend, org, repo, version, branch,\n commit, local, release, relative)), ('doi', info['doi'])))\n return True\n\n\ndef getModulesData(*args):\n \"\"\"Retrieve all data for a corpus.\n\n Parameters\n ----------\n args: list\n All parameters needed to retrieve all associated data.\n They are the same as are needed to construct an `AppData` object.\n \"\"\"\n mData = AppData(*args)\n mData.getModules()\n if not mData.good or mData.locations is None:\n return None\n return mData.locations, mData.modules\n",
"step-4": "from ..core.helpers import itemize\nfrom ..core.files import backendRep, expandDir, prefixSlash, normpath\nfrom .helpers import splitModRef\nfrom .repo import checkoutRepo\nfrom .links import provenanceLink\n\n\nclass AppData:\n\n def __init__(self, app, backend, moduleRefs, locations, modules,\n version, checkout, silent):\n \"\"\"Collects TF data according to specifications.\n\n The specifications are passed as arguments when the object is initialized.\n\n Parameters\n ----------\n backend: string\n `github` or `gitlab` or a GitLab instance such as `gitlab.huc.knaw.nl`.\n app: obj\n The high-level API object\n moduleRefs: tuple\n Each member consists of a module ref, which is a tuple of information\n that defines a module.\n locations: string|tuple\n One or more directory paths. They will be combined with the `modules`\n argument and used as locations to search for TF data files.\n modules: string|tuple\n One or more directory path segments. They will be appended to the\n paths given by the `locations` argument to form search locations\n for TF data files.\n version: string\n The version of TF data that should be retrievend. Version is a directory\n level just below the search locations.\n checkout: string\n A specifier to use a specific release or commit of a data repository.\n silent: string, optional tf.core.timestamp.SILENT_D\n See `tf.core.timestamp.Timestamp`\n\n \"\"\"\n self.backend = backend\n self.app = app\n self.moduleRefs = [] if moduleRefs is None else moduleRefs.split(','\n ) if type(moduleRefs) is str else list(moduleRefs)\n self.locationsArg = locations\n self.modulesArg = modules\n self.version = version\n self.checkout = checkout\n self.silent = silent\n\n def getMain(self):\n \"\"\"Get the main data of the corpus.\n\n This is specified by the `org`, `repo` and `relative` settings under\n `provenanceSpec` in `config.yaml`.\n\n See Also\n --------\n tf.advanced.settings: options allowed in `config.yaml`\n \"\"\"\n app = self.app\n checkout = self.checkout\n aContext = app.context\n org = aContext.org\n repo = aContext.repo\n relative = prefixSlash(aContext.relative)\n appPath = aContext.appPath\n appName = aContext.appName\n if appName.startswith('app:'):\n appParent = appPath.rsplit('/', 1)[0]\n relative = f'{appParent}{relative}'\n elif org is None or repo is None:\n appPathRep = f'{appPath}/' if appPath else ''\n relative = f'{appPathRep}{appName}'\n self.checkout = 'local'\n if not self.getModule(org, repo, prefixSlash(relative), checkout,\n isBase=True):\n self.good = False\n\n def getStandard(self):\n \"\"\"Get the data of the standard modules specified by the settings of the corpus.\n\n These are specified in the `moduleSpecs` setting under\n `provenanceSpecs` in `config.yaml`.\n\n They will be loaded *after* the extra modules specified in the **mod**\n parameter, and only in as far they have not been specifief in the\n **mod** parameter. In this way you can pass overriding\n checkout specifiers to the standard modules.\n\n See Also\n --------\n tf.advanced.settings: options allowed in `config.yaml`\n \"\"\"\n app = self.app\n loadData = app.loadData\n if not loadData or loadData == 'core':\n return\n aContext = app.context\n moduleSpecs = aContext.moduleSpecs\n seen = self.seen\n checkout = self.checkout\n backend = self.backend\n for m in (moduleSpecs or []):\n org = m['org']\n repo = m['repo']\n relative = m['relative']\n theCheckout = m.get('checkout', checkout)\n theBackend = m.get('backend', backend)\n bRep = backendRep(theBackend, 'spec', default=backend)\n ref = f'{bRep}{org}/{repo}{relative}'\n if ref in seen:\n continue\n if not self.getModule(org, repo, relative, theCheckout, backend\n =theBackend, specs=m):\n self.good = False\n\n def getRefs(self):\n \"\"\"Get data from additional modules.\n\n These are specified in the `moduleRefs` parameter of `AppData`.\n We store the set of special modules in order to skip them\n later when we are loading the standard modules.\n \"\"\"\n backend = self.backend\n refs = self.moduleRefs\n for ref in refs:\n refPure = ref.rsplit(':', 1)[0]\n if refPure in self.seen:\n continue\n parts = splitModRef(ref)\n if not parts:\n self.good = False\n continue\n parts[2] = prefixSlash(normpath(parts[2]))\n theBackend = None if parts[-1] is None or parts[-1\n ] == backend else parts[-1]\n if not self.getModule(*parts[0:-1], backend=theBackend):\n self.good = False\n\n def getModules(self):\n \"\"\"Get data from additional local directories.\n\n These are specified in the `locations` and `modules` parameters of `AppData`.\n \"\"\"\n self.provenance = []\n provenance = self.provenance\n self.mLocations = []\n mLocations = self.mLocations\n self.locations = None\n self.modules = None\n self.good = True\n self.seen = set()\n self.getMain()\n self.getRefs()\n self.getStandard()\n version = self.version\n good = self.good\n app = self.app\n if good:\n app.mLocations = mLocations\n app.provenance = provenance\n else:\n return\n mModules = []\n if mLocations:\n mModules.append(version or '')\n locations = self.locationsArg\n modules = self.modulesArg\n givenLocations = [] if locations is None else [expandDir(app, x.\n strip()) for x in itemize(locations, '\\n')] if type(locations\n ) is str else [str(x) for x in locations]\n givenModules = [] if modules is None else [normpath(x.strip()) for\n x in itemize(modules, '\\n')] if type(modules) is str else [normpath\n (str(x)) for x in modules]\n self.locations = mLocations + givenLocations\n self.modules = mModules + givenModules\n\n def getModule(self, org, repo, relative, checkout, backend=None, isBase\n =False, specs=None):\n \"\"\"Prepare to load a single module.\n\n Eventually, all TF data will be downloaded from local directories, bases\n on a list of location paths and module paths.\n\n This function computes the contribution of a single module to both the\n location paths and the module paths.\n\n Parameters\n ----------\n org: string\n GitHub organization or GitLab group of the module\n repo: string:\n GitHub repository or GitLab project of the module\n relative: string\n Path within the repository of the module\n checkout: string\n A specifier to use a specific release or commit of a data repository.\n backend: string\n The backend if different from the backend of the main module\n isBase: boolean, optional False\n Whether this module is the main data of the corpus.\n specs: dict, optional False\n Additional informational attributes of the module, e.g. a DOI\n \"\"\"\n backend = self.backend if backend is None else backendRep(backend,\n 'norm')\n bRep = backendRep(backend, 'spec', default=self.backend)\n version = self.version\n silent = self.silent\n mLocations = self.mLocations\n provenance = self.provenance\n seen = self.seen\n app = self.app\n _browse = app._browse\n aContext = app.context\n branch = aContext.provenanceSpec['branch']\n relative = prefixSlash(normpath(relative))\n moduleRef = f'{bRep}{org}/{repo}{relative}'\n if moduleRef in self.seen:\n return True\n if org is None or repo is None:\n relativeBare = relative.removeprefix('/')\n repoLocation = relativeBare\n mLocations.append(relativeBare)\n commit, local, release = None, None, None\n else:\n commit, release, local, localBase, localDir = checkoutRepo(backend,\n _browse=_browse, org=org, repo=repo, folder=relative,\n version=version, checkout=checkout, withPaths=False, keep=\n False, silent=silent)\n if not localBase:\n return False\n repoLocation = f'{localBase}/{org}/{repo}'\n mLocations.append(f'{localBase}/{localDir}')\n seen.add(moduleRef)\n if isBase:\n app.repoLocation = repoLocation\n info = {}\n for item in (('doi', None), ('corpus', f'{org}/{repo}{relative}')):\n key, default = item\n info[key] = getattr(aContext, key) if isBase else specs[key\n ] if specs and key in specs else default\n provenance.append((('corpus', info['corpus']), ('version', version),\n ('commit', commit or '??'), ('release', release or 'none'), (\n 'live', provenanceLink(backend, org, repo, version, branch,\n commit, local, release, relative)), ('doi', info['doi'])))\n return True\n\n\ndef getModulesData(*args):\n \"\"\"Retrieve all data for a corpus.\n\n Parameters\n ----------\n args: list\n All parameters needed to retrieve all associated data.\n They are the same as are needed to construct an `AppData` object.\n \"\"\"\n mData = AppData(*args)\n mData.getModules()\n if not mData.good or mData.locations is None:\n return None\n return mData.locations, mData.modules\n",
"step-5": "from ..core.helpers import itemize\nfrom ..core.files import backendRep, expandDir, prefixSlash, normpath\nfrom .helpers import splitModRef\nfrom .repo import checkoutRepo\nfrom .links import provenanceLink\n\n\n# GET DATA FOR MAIN SOURCE AND ALL MODULES\n\n\nclass AppData:\n def __init__(\n self, app, backend, moduleRefs, locations, modules, version, checkout, silent\n ):\n \"\"\"Collects TF data according to specifications.\n\n The specifications are passed as arguments when the object is initialized.\n\n Parameters\n ----------\n backend: string\n `github` or `gitlab` or a GitLab instance such as `gitlab.huc.knaw.nl`.\n app: obj\n The high-level API object\n moduleRefs: tuple\n Each member consists of a module ref, which is a tuple of information\n that defines a module.\n locations: string|tuple\n One or more directory paths. They will be combined with the `modules`\n argument and used as locations to search for TF data files.\n modules: string|tuple\n One or more directory path segments. They will be appended to the\n paths given by the `locations` argument to form search locations\n for TF data files.\n version: string\n The version of TF data that should be retrievend. Version is a directory\n level just below the search locations.\n checkout: string\n A specifier to use a specific release or commit of a data repository.\n silent: string, optional tf.core.timestamp.SILENT_D\n See `tf.core.timestamp.Timestamp`\n\n \"\"\"\n self.backend = backend\n self.app = app\n self.moduleRefs = (\n []\n if moduleRefs is None\n else moduleRefs.split(\",\")\n if type(moduleRefs) is str\n else list(moduleRefs)\n )\n self.locationsArg = locations\n self.modulesArg = modules\n self.version = version\n self.checkout = checkout\n self.silent = silent\n\n def getMain(self):\n \"\"\"Get the main data of the corpus.\n\n This is specified by the `org`, `repo` and `relative` settings under\n `provenanceSpec` in `config.yaml`.\n\n See Also\n --------\n tf.advanced.settings: options allowed in `config.yaml`\n \"\"\"\n\n app = self.app\n checkout = self.checkout\n aContext = app.context\n org = aContext.org\n repo = aContext.repo\n relative = prefixSlash(aContext.relative)\n appPath = aContext.appPath\n appName = aContext.appName\n\n if appName.startswith(\"app:\"):\n appParent = appPath.rsplit(\"/\", 1)[0]\n relative = f\"{appParent}{relative}\"\n elif org is None or repo is None:\n appPathRep = f\"{appPath}/\" if appPath else \"\"\n relative = f\"{appPathRep}{appName}\"\n self.checkout = \"local\"\n\n if not self.getModule(org, repo, prefixSlash(relative), checkout, isBase=True):\n self.good = False\n\n def getStandard(self):\n \"\"\"Get the data of the standard modules specified by the settings of the corpus.\n\n These are specified in the `moduleSpecs` setting under\n `provenanceSpecs` in `config.yaml`.\n\n They will be loaded *after* the extra modules specified in the **mod**\n parameter, and only in as far they have not been specifief in the\n **mod** parameter. In this way you can pass overriding\n checkout specifiers to the standard modules.\n\n See Also\n --------\n tf.advanced.settings: options allowed in `config.yaml`\n \"\"\"\n\n app = self.app\n loadData = app.loadData\n\n if not loadData or loadData == \"core\":\n return\n\n aContext = app.context\n moduleSpecs = aContext.moduleSpecs\n seen = self.seen\n checkout = self.checkout\n backend = self.backend\n\n for m in moduleSpecs or []:\n org = m[\"org\"]\n repo = m[\"repo\"]\n relative = m[\"relative\"]\n theCheckout = m.get(\"checkout\", checkout)\n theBackend = m.get(\"backend\", backend)\n bRep = backendRep(theBackend, \"spec\", default=backend)\n\n ref = f\"{bRep}{org}/{repo}{relative}\"\n if ref in seen:\n continue\n\n if not self.getModule(\n org,\n repo,\n relative,\n theCheckout,\n backend=theBackend,\n specs=m,\n ):\n self.good = False\n\n def getRefs(self):\n \"\"\"Get data from additional modules.\n\n These are specified in the `moduleRefs` parameter of `AppData`.\n We store the set of special modules in order to skip them\n later when we are loading the standard modules.\n \"\"\"\n\n backend = self.backend\n refs = self.moduleRefs\n for ref in refs:\n refPure = ref.rsplit(\":\", 1)[0]\n if refPure in self.seen:\n continue\n\n parts = splitModRef(ref)\n if not parts:\n self.good = False\n continue\n\n parts[2] = prefixSlash(normpath(parts[2])) # the relative bit\n theBackend = (\n None if parts[-1] is None or parts[-1] == backend else parts[-1]\n )\n\n if not self.getModule(*parts[0:-1], backend=theBackend):\n self.good = False\n\n def getModules(self):\n \"\"\"Get data from additional local directories.\n\n These are specified in the `locations` and `modules` parameters of `AppData`.\n \"\"\"\n\n self.provenance = []\n provenance = self.provenance\n self.mLocations = []\n mLocations = self.mLocations\n\n self.locations = None\n self.modules = None\n\n self.good = True\n self.seen = set()\n\n self.getMain()\n self.getRefs()\n self.getStandard()\n\n version = self.version\n good = self.good\n app = self.app\n\n if good:\n app.mLocations = mLocations\n app.provenance = provenance\n else:\n return\n\n mModules = []\n if mLocations:\n mModules.append(version or \"\")\n\n locations = self.locationsArg\n modules = self.modulesArg\n\n givenLocations = (\n []\n if locations is None\n else [expandDir(app, x.strip()) for x in itemize(locations, \"\\n\")]\n if type(locations) is str\n else [str(x) for x in locations]\n )\n givenModules = (\n []\n if modules is None\n else [normpath(x.strip()) for x in itemize(modules, \"\\n\")]\n if type(modules) is str\n else [normpath(str(x)) for x in modules]\n )\n\n self.locations = mLocations + givenLocations\n self.modules = mModules + givenModules\n\n def getModule(\n self, org, repo, relative, checkout, backend=None, isBase=False, specs=None\n ):\n \"\"\"Prepare to load a single module.\n\n Eventually, all TF data will be downloaded from local directories, bases\n on a list of location paths and module paths.\n\n This function computes the contribution of a single module to both the\n location paths and the module paths.\n\n Parameters\n ----------\n org: string\n GitHub organization or GitLab group of the module\n repo: string:\n GitHub repository or GitLab project of the module\n relative: string\n Path within the repository of the module\n checkout: string\n A specifier to use a specific release or commit of a data repository.\n backend: string\n The backend if different from the backend of the main module\n isBase: boolean, optional False\n Whether this module is the main data of the corpus.\n specs: dict, optional False\n Additional informational attributes of the module, e.g. a DOI\n \"\"\"\n\n backend = self.backend if backend is None else backendRep(backend, \"norm\")\n bRep = backendRep(backend, \"spec\", default=self.backend)\n version = self.version\n silent = self.silent\n mLocations = self.mLocations\n provenance = self.provenance\n seen = self.seen\n app = self.app\n _browse = app._browse\n aContext = app.context\n branch = aContext.provenanceSpec[\"branch\"]\n\n relative = prefixSlash(normpath(relative))\n\n moduleRef = f\"{bRep}{org}/{repo}{relative}\"\n if moduleRef in self.seen:\n return True\n\n if org is None or repo is None:\n relativeBare = relative.removeprefix(\"/\")\n repoLocation = relativeBare\n mLocations.append(relativeBare)\n (commit, local, release) = (None, None, None)\n else:\n (commit, release, local, localBase, localDir) = checkoutRepo(\n backend,\n _browse=_browse,\n org=org,\n repo=repo,\n folder=relative,\n version=version,\n checkout=checkout,\n withPaths=False,\n keep=False,\n silent=silent,\n )\n if not localBase:\n return False\n\n repoLocation = f\"{localBase}/{org}/{repo}\"\n mLocations.append(f\"{localBase}/{localDir}\")\n\n seen.add(moduleRef)\n if isBase:\n app.repoLocation = repoLocation\n\n info = {}\n for item in (\n (\"doi\", None),\n (\"corpus\", f\"{org}/{repo}{relative}\"),\n ):\n (key, default) = item\n info[key] = (\n getattr(aContext, key)\n if isBase\n else specs[key]\n if specs and key in specs\n else default\n )\n provenance.append(\n (\n (\"corpus\", info[\"corpus\"]),\n (\"version\", version),\n (\"commit\", commit or \"??\"),\n (\"release\", release or \"none\"),\n (\n \"live\",\n provenanceLink(\n backend, org, repo, version, branch, commit, local, release, relative\n ),\n ),\n (\"doi\", info[\"doi\"]),\n )\n )\n return True\n\n\ndef getModulesData(*args):\n \"\"\"Retrieve all data for a corpus.\n\n Parameters\n ----------\n args: list\n All parameters needed to retrieve all associated data.\n They are the same as are needed to construct an `AppData` object.\n \"\"\"\n\n mData = AppData(*args)\n mData.getModules()\n\n if not mData.good or mData.locations is None:\n return None\n\n return (mData.locations, mData.modules)\n",
"step-ids": [
1,
5,
8,
9,
10
]
}
|
[
1,
5,
8,
9,
10
] |
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as scio
import estimateGaussian as eg
import multivariateGaussian as mvg
import visualizeFit as vf
import selectThreshold as st
plt.ion()
# np.set_printoptions(formatter={'float': '{: 0.6f}'.format})
'''第1部分 加载示例数据集'''
#先通过一个小数据集进行异常检测 便于可视化
# 数据集包含两个特征
# 一些机器的等待时间和吞吐量 实验目的找出其中可能有异常的机器
print('Visualizing example dataset for outlier detection.')
data = scio.loadmat('ex8data1.mat')
X = data['X']#训练集样本特征矩阵
Xval = data['Xval'] #验证集样本特征矩阵
yval = data['yval'].flatten() #验证集样本标签 异常/正常
# 可视化样例训练集
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c='b', marker='x', s=15, linewidth=1)
plt.axis([0, 30, 0, 30])
plt.xlabel('Latency (ms)') #x1等待时间
plt.ylabel('Throughput (mb/s') #x2吞吐量
input('Program paused. Press ENTER to continue')
'''第2部分 估计训练集的分布'''
# 假设数据集的各个特征服从高斯分布
print('Visualizing Gaussian fit.')
# 参数估计
mu, sigma2 = eg.estimate_gaussian(X)
# 计算训练集的概率分布
p = mvg.multivariate_gaussian(X, mu, sigma2)
#可视化训练集的概率分布 画出等高线图
vf.visualize_fit(X, mu, sigma2)
plt.xlabel('Latency (ms)')
plt.ylabel('Throughput (mb/s')
input('Program paused. Press ENTER to continue')
'''第3部分 基于验证集 得到一个最好的概率分布阈值'''
pval = mvg.multivariate_gaussian(Xval, mu, sigma2) #根据训练集的概率分布 得到验证集样本的概率
epsilon, f1 = st.select_threshold(yval, pval) #选择合适的概率阈值
print('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))
print('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))
print('(you should see a value epsilon of about 8.99e-05 and F1 of about 0.875)')
# 标出训练集中的异常值
outliers = np.where(p < epsilon)
plt.scatter(X[outliers, 0], X[outliers, 1], marker='o', facecolors='none', edgecolors='r')
input('Program paused. Press ENTER to continue')
'''第4部分 基于大数据集 进行异常检测（特征数很多）'''
data = scio.loadmat('ex8data2.mat')
X = data['X'] #训练集样本特征矩阵
Xval = data['Xval'] #验证集样本特征矩阵
yval = data['yval'].flatten() #验证集样本标签 1异常 0正常
#参数估计
mu, sigma2 = eg.estimate_gaussian(X)
# 计算训练集的概率分布
p = mvg.multivariate_gaussian(X, mu, sigma2)
# 得到验证集每个样本的概率
pval = mvg.multivariate_gaussian(Xval, mu, sigma2)
# 选择一个最好的阈值
epsilon, f1 = st.select_threshold(yval, pval)
#验证程序正确性
print('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))
print('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))
print('# Outliers found: {}'.format(np.sum(np.less(p, epsilon)))) #训练集上的异常样本数量
print('(you should see a value epsilon of about 1.38e-18, F1 of about 0.615, and 117 outliers)')
input('ex8 Finished. Press ENTER to exit')
|
normal
|
{
"blob_id": "de6b9961e0572338c87802314e7ae3cded5168b4",
"index": 487,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.ion()\n<mask token>\nprint('Visualizing example dataset for outlier detection.')\n<mask token>\nplt.figure()\nplt.scatter(X[:, 0], X[:, 1], c='b', marker='x', s=15, linewidth=1)\nplt.axis([0, 30, 0, 30])\nplt.xlabel('Latency (ms)')\nplt.ylabel('Throughput (mb/s')\ninput('Program paused. Press ENTER to continue')\n<mask token>\nprint('Visualizing Gaussian fit.')\n<mask token>\nvf.visualize_fit(X, mu, sigma2)\nplt.xlabel('Latency (ms)')\nplt.ylabel('Throughput (mb/s')\ninput('Program paused. Press ENTER to continue')\n<mask token>\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint(\n '(you should see a value epsilon of about 8.99e-05 and F1 of about 0.875)')\n<mask token>\nplt.scatter(X[outliers, 0], X[outliers, 1], marker='o', facecolors='none',\n edgecolors='r')\ninput('Program paused. Press ENTER to continue')\n<mask token>\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint('# Outliers found: {}'.format(np.sum(np.less(p, epsilon))))\nprint(\n '(you should see a value epsilon of about 1.38e-18, F1 of about 0.615, and 117 outliers)'\n )\ninput('ex8 Finished. Press ENTER to exit')\n",
"step-3": "<mask token>\nplt.ion()\n<mask token>\nprint('Visualizing example dataset for outlier detection.')\ndata = scio.loadmat('ex8data1.mat')\nX = data['X']\nXval = data['Xval']\nyval = data['yval'].flatten()\nplt.figure()\nplt.scatter(X[:, 0], X[:, 1], c='b', marker='x', s=15, linewidth=1)\nplt.axis([0, 30, 0, 30])\nplt.xlabel('Latency (ms)')\nplt.ylabel('Throughput (mb/s')\ninput('Program paused. Press ENTER to continue')\n<mask token>\nprint('Visualizing Gaussian fit.')\nmu, sigma2 = eg.estimate_gaussian(X)\np = mvg.multivariate_gaussian(X, mu, sigma2)\nvf.visualize_fit(X, mu, sigma2)\nplt.xlabel('Latency (ms)')\nplt.ylabel('Throughput (mb/s')\ninput('Program paused. Press ENTER to continue')\n<mask token>\npval = mvg.multivariate_gaussian(Xval, mu, sigma2)\nepsilon, f1 = st.select_threshold(yval, pval)\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint(\n '(you should see a value epsilon of about 8.99e-05 and F1 of about 0.875)')\noutliers = np.where(p < epsilon)\nplt.scatter(X[outliers, 0], X[outliers, 1], marker='o', facecolors='none',\n edgecolors='r')\ninput('Program paused. Press ENTER to continue')\n<mask token>\ndata = scio.loadmat('ex8data2.mat')\nX = data['X']\nXval = data['Xval']\nyval = data['yval'].flatten()\nmu, sigma2 = eg.estimate_gaussian(X)\np = mvg.multivariate_gaussian(X, mu, sigma2)\npval = mvg.multivariate_gaussian(Xval, mu, sigma2)\nepsilon, f1 = st.select_threshold(yval, pval)\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint('# Outliers found: {}'.format(np.sum(np.less(p, epsilon))))\nprint(\n '(you should see a value epsilon of about 1.38e-18, F1 of about 0.615, and 117 outliers)'\n )\ninput('ex8 Finished. Press ENTER to exit')\n",
"step-4": "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.io as scio\nimport estimateGaussian as eg\nimport multivariateGaussian as mvg\nimport visualizeFit as vf\nimport selectThreshold as st\nplt.ion()\n<mask token>\nprint('Visualizing example dataset for outlier detection.')\ndata = scio.loadmat('ex8data1.mat')\nX = data['X']\nXval = data['Xval']\nyval = data['yval'].flatten()\nplt.figure()\nplt.scatter(X[:, 0], X[:, 1], c='b', marker='x', s=15, linewidth=1)\nplt.axis([0, 30, 0, 30])\nplt.xlabel('Latency (ms)')\nplt.ylabel('Throughput (mb/s')\ninput('Program paused. Press ENTER to continue')\n<mask token>\nprint('Visualizing Gaussian fit.')\nmu, sigma2 = eg.estimate_gaussian(X)\np = mvg.multivariate_gaussian(X, mu, sigma2)\nvf.visualize_fit(X, mu, sigma2)\nplt.xlabel('Latency (ms)')\nplt.ylabel('Throughput (mb/s')\ninput('Program paused. Press ENTER to continue')\n<mask token>\npval = mvg.multivariate_gaussian(Xval, mu, sigma2)\nepsilon, f1 = st.select_threshold(yval, pval)\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint(\n '(you should see a value epsilon of about 8.99e-05 and F1 of about 0.875)')\noutliers = np.where(p < epsilon)\nplt.scatter(X[outliers, 0], X[outliers, 1], marker='o', facecolors='none',\n edgecolors='r')\ninput('Program paused. Press ENTER to continue')\n<mask token>\ndata = scio.loadmat('ex8data2.mat')\nX = data['X']\nXval = data['Xval']\nyval = data['yval'].flatten()\nmu, sigma2 = eg.estimate_gaussian(X)\np = mvg.multivariate_gaussian(X, mu, sigma2)\npval = mvg.multivariate_gaussian(Xval, mu, sigma2)\nepsilon, f1 = st.select_threshold(yval, pval)\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint('# Outliers found: {}'.format(np.sum(np.less(p, epsilon))))\nprint(\n '(you should see a value epsilon of about 1.38e-18, F1 of about 0.615, and 117 outliers)'\n )\ninput('ex8 Finished. Press ENTER to exit')\n",
"step-5": "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.io as scio\n\nimport estimateGaussian as eg\nimport multivariateGaussian as mvg\nimport visualizeFit as vf\nimport selectThreshold as st\n\nplt.ion()\n# np.set_printoptions(formatter={'float': '{: 0.6f}'.format})\n\n'''第1部分 加载示例数据集'''\n\n#先通过一个小数据集进行异常检测 便于可视化\n\n# 数据集包含两个特征 \n# 一些机器的等待时间和吞吐量 实验目的找出其中可能有异常的机器\n\n\nprint('Visualizing example dataset for outlier detection.')\n\n\ndata = scio.loadmat('ex8data1.mat')\nX = data['X']#训练集样本特征矩阵\nXval = data['Xval'] #验证集样本特征矩阵\nyval = data['yval'].flatten() #验证集样本标签 异常/正常 \n\n# 可视化样例训练集\nplt.figure()\nplt.scatter(X[:, 0], X[:, 1], c='b', marker='x', s=15, linewidth=1)\nplt.axis([0, 30, 0, 30])\nplt.xlabel('Latency (ms)') #x1等待时间\nplt.ylabel('Throughput (mb/s') #x2吞吐量\n\n\ninput('Program paused. Press ENTER to continue')\n\n'''第2部分 估计训练集的分布'''\n# 假设数据集的各个特征服从高斯分布\n\nprint('Visualizing Gaussian fit.')\n\n# 参数估计 \nmu, sigma2 = eg.estimate_gaussian(X)\n\n# 计算训练集的概率分布\np = mvg.multivariate_gaussian(X, mu, sigma2)\n#可视化训练集的概率分布 画出等高线图\nvf.visualize_fit(X, mu, sigma2)\nplt.xlabel('Latency (ms)')\nplt.ylabel('Throughput (mb/s')\n\ninput('Program paused. Press ENTER to continue')\n\n'''第3部分 基于验证集 得到一个最好的概率分布阈值'''\npval = mvg.multivariate_gaussian(Xval, mu, sigma2) #根据训练集的概率分布 得到验证集样本的概率\n\nepsilon, f1 = st.select_threshold(yval, pval) #选择合适的概率阈值\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint('(you should see a value epsilon of about 8.99e-05 and F1 of about 0.875)')\n\n# 标出训练集中的异常值\noutliers = np.where(p < epsilon)\nplt.scatter(X[outliers, 0], X[outliers, 1], marker='o', facecolors='none', edgecolors='r')\n\ninput('Program paused. Press ENTER to continue')\n\n\n'''第4部分 基于大数据集 进行异常检测（特征数很多）'''\ndata = scio.loadmat('ex8data2.mat')\nX = data['X'] #训练集样本特征矩阵\nXval = data['Xval'] #验证集样本特征矩阵\nyval = data['yval'].flatten() #验证集样本标签 1异常 0正常\n\n#参数估计\nmu, sigma2 = eg.estimate_gaussian(X)\n\n# 计算训练集的概率分布\np = mvg.multivariate_gaussian(X, mu, sigma2)\n\n# 得到验证集每个样本的概率\npval = mvg.multivariate_gaussian(Xval, mu, sigma2)\n\n# 选择一个最好的阈值\nepsilon, f1 = st.select_threshold(yval, pval)\n\n#验证程序正确性\nprint('Best epsilon found using cross-validation: {:0.4e}'.format(epsilon))\nprint('Best F1 on Cross Validation Set: {:0.6f}'.format(f1))\nprint('# Outliers found: {}'.format(np.sum(np.less(p, epsilon)))) #训练集上的异常样本数量\nprint('(you should see a value epsilon of about 1.38e-18, F1 of about 0.615, and 117 outliers)')\n\ninput('ex8 Finished. Press ENTER to exit')\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|>
app.config.from_pyfile('config.py', silent=True)
<|reserved_special_token_0|>
app.register_blueprint(static_blueprint)
app.register_blueprint(admin_blueprint)
app.register_blueprint(cart_blueprint)
app.register_blueprint(product_blueprint)
app.register_blueprint(account_blueprint)
app.register_blueprint(category_blueprint)
app.register_blueprint(order_blueprint)
<|reserved_special_token_0|>
try:
AccountUser.query.first()
except Exception as e:
db.create_all()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app = Flask(__name__, instance_relative_config=True)
app.config.from_pyfile('config.py', silent=True)
db = SQLAlchemy(app)
<|reserved_special_token_0|>
app.register_blueprint(static_blueprint)
app.register_blueprint(admin_blueprint)
app.register_blueprint(cart_blueprint)
app.register_blueprint(product_blueprint)
app.register_blueprint(account_blueprint)
app.register_blueprint(category_blueprint)
app.register_blueprint(order_blueprint)
<|reserved_special_token_0|>
try:
AccountUser.query.first()
except Exception as e:
db.create_all()
user_datastore = SQLAlchemySessionUserDatastore(db.session, AccountUser,
AccountRole)
security = Security(app, user_datastore, register_form=RegistrationForm,
login_form=LoginForm)
<|reserved_special_token_1|>
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_security import SQLAlchemySessionUserDatastore, Security
app = Flask(__name__, instance_relative_config=True)
app.config.from_pyfile('config.py', silent=True)
db = SQLAlchemy(app)
from .blueprints.cart.views import cart_blueprint
from .blueprints.admin.views import admin_blueprint
from .blueprints.products.views import product_blueprint
from .blueprints.orders.views import order_blueprint
from .blueprints.account.views import account_blueprint
from .blueprints.categories.views import category_blueprint
from .blueprints.static_pages.views import static_blueprint
app.register_blueprint(static_blueprint)
app.register_blueprint(admin_blueprint)
app.register_blueprint(cart_blueprint)
app.register_blueprint(product_blueprint)
app.register_blueprint(account_blueprint)
app.register_blueprint(category_blueprint)
app.register_blueprint(order_blueprint)
from .blueprints.account.models import AccountUser, AccountRole
from .blueprints.account.forms import RegistrationForm, LoginForm
try:
AccountUser.query.first()
except Exception as e:
db.create_all()
user_datastore = SQLAlchemySessionUserDatastore(db.session, AccountUser,
AccountRole)
security = Security(app, user_datastore, register_form=RegistrationForm,
login_form=LoginForm)
<|reserved_special_token_1|>
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_security import SQLAlchemySessionUserDatastore, Security
app = Flask(__name__, instance_relative_config=True)
app.config.from_pyfile("config.py", silent=True)
db = SQLAlchemy(app)
from .blueprints.cart.views import cart_blueprint
from .blueprints.admin.views import admin_blueprint
from .blueprints.products.views import product_blueprint
from .blueprints.orders.views import order_blueprint
from .blueprints.account.views import account_blueprint
from .blueprints.categories.views import category_blueprint
from .blueprints.static_pages.views import static_blueprint
app.register_blueprint(static_blueprint)
app.register_blueprint(admin_blueprint)
app.register_blueprint(cart_blueprint)
app.register_blueprint(product_blueprint)
app.register_blueprint(account_blueprint)
app.register_blueprint(category_blueprint)
app.register_blueprint(order_blueprint)
from .blueprints.account.models import AccountUser, AccountRole
from .blueprints.account.forms import RegistrationForm, LoginForm
try:
AccountUser.query.first()
except Exception as e:
db.create_all()
user_datastore = SQLAlchemySessionUserDatastore(db.session, AccountUser, AccountRole)
security = Security(
app, user_datastore, register_form=RegistrationForm, login_form=LoginForm
)
|
flexible
|
{
"blob_id": "5d97a2afed26ec4826c8bce30c84863d21f86001",
"index": 9370,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.config.from_pyfile('config.py', silent=True)\n<mask token>\napp.register_blueprint(static_blueprint)\napp.register_blueprint(admin_blueprint)\napp.register_blueprint(cart_blueprint)\napp.register_blueprint(product_blueprint)\napp.register_blueprint(account_blueprint)\napp.register_blueprint(category_blueprint)\napp.register_blueprint(order_blueprint)\n<mask token>\ntry:\n AccountUser.query.first()\nexcept Exception as e:\n db.create_all()\n<mask token>\n",
"step-3": "<mask token>\napp = Flask(__name__, instance_relative_config=True)\napp.config.from_pyfile('config.py', silent=True)\ndb = SQLAlchemy(app)\n<mask token>\napp.register_blueprint(static_blueprint)\napp.register_blueprint(admin_blueprint)\napp.register_blueprint(cart_blueprint)\napp.register_blueprint(product_blueprint)\napp.register_blueprint(account_blueprint)\napp.register_blueprint(category_blueprint)\napp.register_blueprint(order_blueprint)\n<mask token>\ntry:\n AccountUser.query.first()\nexcept Exception as e:\n db.create_all()\nuser_datastore = SQLAlchemySessionUserDatastore(db.session, AccountUser,\n AccountRole)\nsecurity = Security(app, user_datastore, register_form=RegistrationForm,\n login_form=LoginForm)\n",
"step-4": "from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_security import SQLAlchemySessionUserDatastore, Security\napp = Flask(__name__, instance_relative_config=True)\napp.config.from_pyfile('config.py', silent=True)\ndb = SQLAlchemy(app)\nfrom .blueprints.cart.views import cart_blueprint\nfrom .blueprints.admin.views import admin_blueprint\nfrom .blueprints.products.views import product_blueprint\nfrom .blueprints.orders.views import order_blueprint\nfrom .blueprints.account.views import account_blueprint\nfrom .blueprints.categories.views import category_blueprint\nfrom .blueprints.static_pages.views import static_blueprint\napp.register_blueprint(static_blueprint)\napp.register_blueprint(admin_blueprint)\napp.register_blueprint(cart_blueprint)\napp.register_blueprint(product_blueprint)\napp.register_blueprint(account_blueprint)\napp.register_blueprint(category_blueprint)\napp.register_blueprint(order_blueprint)\nfrom .blueprints.account.models import AccountUser, AccountRole\nfrom .blueprints.account.forms import RegistrationForm, LoginForm\ntry:\n AccountUser.query.first()\nexcept Exception as e:\n db.create_all()\nuser_datastore = SQLAlchemySessionUserDatastore(db.session, AccountUser,\n AccountRole)\nsecurity = Security(app, user_datastore, register_form=RegistrationForm,\n login_form=LoginForm)\n",
"step-5": "from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_security import SQLAlchemySessionUserDatastore, Security\n\napp = Flask(__name__, instance_relative_config=True)\napp.config.from_pyfile(\"config.py\", silent=True)\n\ndb = SQLAlchemy(app)\n\nfrom .blueprints.cart.views import cart_blueprint\nfrom .blueprints.admin.views import admin_blueprint\nfrom .blueprints.products.views import product_blueprint\nfrom .blueprints.orders.views import order_blueprint\nfrom .blueprints.account.views import account_blueprint\nfrom .blueprints.categories.views import category_blueprint\nfrom .blueprints.static_pages.views import static_blueprint\n\n\napp.register_blueprint(static_blueprint)\napp.register_blueprint(admin_blueprint)\napp.register_blueprint(cart_blueprint)\napp.register_blueprint(product_blueprint)\napp.register_blueprint(account_blueprint)\napp.register_blueprint(category_blueprint)\napp.register_blueprint(order_blueprint)\n\nfrom .blueprints.account.models import AccountUser, AccountRole\nfrom .blueprints.account.forms import RegistrationForm, LoginForm\n\ntry:\n AccountUser.query.first()\nexcept Exception as e:\n db.create_all()\n\nuser_datastore = SQLAlchemySessionUserDatastore(db.session, AccountUser, AccountRole)\nsecurity = Security(\n app, user_datastore, register_form=RegistrationForm, login_form=LoginForm\n)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
try:
i = float(input('Enter the score : '))
if i > 1 or i < 0:
print("Entered score isn't valid.")
elif i < 0.6:
print('Grade: F')
elif i < 0.7:
print('Grade: D')
elif i < 0.8:
print('Grade: C')
elif i < 0.9:
print('Grade: B')
elif i <= 1.0:
print('Grade: A')
except Exception as e:
print(str(e))
<|reserved_special_token_1|>
# Exercise 3: Write a program to prompt for a score between 0.0 and 1.0. If the score is out of range, print an error message.
# If the score is between 0.0 and 1.0, print a grade using the following table:
# Score Grade
# >= 0.9 A
# >= 0.8 B
# >= 0.7 C
# >= 0.6 D
# < 0.6 F
# Vinayak Nayak
# 27th December 2018
# 12:30 pm
try:
i = float(input("Enter the score : "))
if(i > 1 or i < 0):
print("Entered score isn't valid.")
else:
if (i < 0.6):
print("Grade: F")
elif (i < 0.7):
print("Grade: D")
elif (i < 0.8):
print("Grade: C")
elif (i < 0.9):
print("Grade: B")
elif (i <= 1.0):
print("Grade: A")
except Exception as e:
print(str(e))
|
flexible
|
{
"blob_id": "6f253da5dc1caa504a3a8aadae7bce6537b5c8c6",
"index": 6237,
"step-1": "<mask token>\n",
"step-2": "try:\n i = float(input('Enter the score : '))\n if i > 1 or i < 0:\n print(\"Entered score isn't valid.\")\n elif i < 0.6:\n print('Grade: F')\n elif i < 0.7:\n print('Grade: D')\n elif i < 0.8:\n print('Grade: C')\n elif i < 0.9:\n print('Grade: B')\n elif i <= 1.0:\n print('Grade: A')\nexcept Exception as e:\n print(str(e))\n",
"step-3": "# Exercise 3: Write a program to prompt for a score between 0.0 and 1.0. If the score is out of range, print an error message.\n# If the score is between 0.0 and 1.0, print a grade using the following table:\n# Score Grade\n# >= 0.9 A\n# >= 0.8 B\n# >= 0.7 C\n# >= 0.6 D\n# < 0.6 F\n\n# Vinayak Nayak\n# 27th December 2018\n# 12:30 pm\n\ntry:\n i = float(input(\"Enter the score : \"))\n\n if(i > 1 or i < 0):\n print(\"Entered score isn't valid.\")\n else:\n if (i < 0.6):\n print(\"Grade: F\")\n\n elif (i < 0.7):\n print(\"Grade: D\")\n\n elif (i < 0.8):\n print(\"Grade: C\")\n\n elif (i < 0.9):\n print(\"Grade: B\")\n\n elif (i <= 1.0):\n print(\"Grade: A\")\n\nexcept Exception as e:\n print(str(e))\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
import tty
import sys
import termios
def init():
orig_settings = termios.tcgetattr(sys.stdin)
tty.setcbreak(sys.stdin)
return orig_settings
def get_input():
return sys.stdin.read(1)
def exit(orig_settings):
termios.tcsetattr(sys.stdin, termios.TCSADRAIN, orig_settings)
if __name__ == "__main__":
settings = init()
key = 0
while key != chr(27): # esc
key = get_input()
print("'" + str(key) + "'")
exit(settings)
|
normal
|
{
"blob_id": "c64e41609a19a20f59446399a2e864ff8834c3f0",
"index": 4322,
"step-1": "<mask token>\n\n\ndef get_input():\n return sys.stdin.read(1)\n\n\ndef exit(orig_settings):\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, orig_settings)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef init():\n orig_settings = termios.tcgetattr(sys.stdin)\n tty.setcbreak(sys.stdin)\n return orig_settings\n\n\ndef get_input():\n return sys.stdin.read(1)\n\n\ndef exit(orig_settings):\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, orig_settings)\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef init():\n orig_settings = termios.tcgetattr(sys.stdin)\n tty.setcbreak(sys.stdin)\n return orig_settings\n\n\ndef get_input():\n return sys.stdin.read(1)\n\n\ndef exit(orig_settings):\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, orig_settings)\n\n\nif __name__ == '__main__':\n settings = init()\n key = 0\n while key != chr(27):\n key = get_input()\n print(\"'\" + str(key) + \"'\")\n exit(settings)\n",
"step-4": "import tty\nimport sys\nimport termios\n\n\ndef init():\n orig_settings = termios.tcgetattr(sys.stdin)\n tty.setcbreak(sys.stdin)\n return orig_settings\n\n\ndef get_input():\n return sys.stdin.read(1)\n\n\ndef exit(orig_settings):\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, orig_settings)\n\n\nif __name__ == '__main__':\n settings = init()\n key = 0\n while key != chr(27):\n key = get_input()\n print(\"'\" + str(key) + \"'\")\n exit(settings)\n",
"step-5": "import tty\nimport sys\nimport termios\n\n\ndef init():\n orig_settings = termios.tcgetattr(sys.stdin)\n tty.setcbreak(sys.stdin)\n return orig_settings\n\ndef get_input():\n return sys.stdin.read(1)\n\ndef exit(orig_settings):\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, orig_settings) \n\n\n\nif __name__ == \"__main__\":\n settings = init()\n key = 0\n while key != chr(27): # esc\n key = get_input()\n print(\"'\" + str(key) + \"'\")\n exit(settings)",
"step-ids": [
2,
3,
4,
5,
6
]
}
|
[
2,
3,
4,
5,
6
] |
"""
purpose :Take an string as input and construct an algorithm
to input a string of characters and check whether
it is a palindrome.
@Author : Reshma Y. Kale
"""
from com.bridgelabz.utility.Data_structure_utility import *
if __name__=="__main__":
dq = Deque()
dq.palindrom()
|
normal
|
{
"blob_id": "d4d47f7abc5c8224188430546a65bfb8f358802f",
"index": 1472,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n dq = Deque()\n dq.palindrom()\n",
"step-3": "<mask token>\nfrom com.bridgelabz.utility.Data_structure_utility import *\nif __name__ == '__main__':\n dq = Deque()\n dq.palindrom()\n",
"step-4": "\"\"\"\npurpose :Take an string as input and construct an algorithm\n to input a string of characters and check whether\n it is a palindrome.\n\n@Author : Reshma Y. Kale\n\n\"\"\"\nfrom com.bridgelabz.utility.Data_structure_utility import *\nif __name__==\"__main__\":\n\n dq = Deque()\n dq.palindrom()",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from prettytable import PrettyTable
from time import sleep
from customization import *
import urllib.request,json
chrome_options=webdriver.ChromeOptions()
chrome_options.add_argument("--headless")
chrome_options.add_argument("--incognito")
chrome_options.add_experimental_option('excludeSwitches', ['enable-logging'])
chromeBrowser = webdriver.Chrome(chromePath, options=chrome_options)
def bio_shortener(bio):
lines=[]
x=len(bio)/30
y=0
Status=True
while Status:
y=y+1
lines.append(bio[0:30])
lines.append("\n")
bio=bio[30:]
if y==int(x)+1:
Status=False
A=''.join(lines)
return A
def nb_checker(nb):
if nb!='None':
return nb.text
else:
nb
def quick_search(username):
print("Collecting username information...")
insta_url="https://instagram.com/"+username+"/"
chromeBrowser.get(insta_url)
WebDriverWait(chromeBrowser,5).until(lambda d: d.find_element_by_xpath('//*[@id="loginForm"]/div/div[1]/div/label/input'))
chromeBrowser.find_element_by_xpath('//*[@id="loginForm"]/div/div[1]/div/label/input').send_keys(i_email)
chromeBrowser.find_element_by_xpath('//*[@id="loginForm"]/div/div[2]/div/label/input').send_keys(i_password)
chromeBrowser.find_element_by_xpath('//*[@id="loginForm"]/div[1]/div[3]/button').click()
WebDriverWait(chromeBrowser,10).until(lambda d: d.find_element_by_xpath('//*[@id="react-root"]/section/main/div/div/div/div/button'))
chromeBrowser.find_element_by_xpath('//*[@id="react-root"]/section/main/div/div/div/div/button').click()
try:
instaName=chromeBrowser.find_element_by_class_name('rhpdm').text
except:
instaName="None"
try:
instaBio=chromeBrowser.find_element_by_xpath('/html/body/div[1]/section/main/div/header/section/div[2]/span').text
except:
instaBio="None"
try:
instaPersonalSite=chromeBrowser.find_element_by_xpath('//*[@id="react-root"]/section/main/div/header/section/div[2]/a[1]').text
except NameError:
instaPersonalSite=chromeBrowser.find_element_by_xpath('//*[@id="react-root"]/section/main/div/header/section/div[2]/a').text
except:
instaPersonalSite='None'
sleep(1)
chromeBrowser.get('https://stackoverflow.com/users/')
WebDriverWait(chromeBrowser, 10).until(lambda d: d.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[1]/div[1]/input'))
chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[1]/div[1]/input').send_keys(username)
sleep(1)
try:
Name=chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a')
if str(Name.text.lower())==username.lower():
placeholder=True
except:
placeholder=False
try:
sofLocation=chromeBrowser.find_element_by_class_name('user-location').text
except:
sofLocation='None'
try:
sofUser_tag = chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[3]').text
except:
sofUser_tag='None'
try:
chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a').click()
WebDriverWait(chromeBrowser, 10).until(lambda d: d.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[2]/div[1]/div/div[2]/div/div[1]/div/div[2]'))
except:
placeholder=True
try:
sofBio=chromeBrowser.find_element_by_xpath('//*[@id="user-card"]/div/div[2]/div/div[1]/div/div[2]').text
except:
sofBio='None'
githubUrl = "https://api.github.com/users/" + username
try:
with urllib.request.urlopen(githubUrl) as url:
githubData = json.loads(url.read().decode())
gitName=str(githubData['name'])
gitCompany=str(githubData['company'])
gitBlog=str(githubData['blog'])
gitEmail=str(githubData['email'])
gitBio=str(githubData['bio'])
gitTwitter=str(githubData['twitter_username'])
gitLocation=str(githubData['location'])
except:
placeholder=True
pt = PrettyTable(
[' ', ' Instagram ', ' StackOverflow ', ' GitHub '])
pt.add_row(["Name", instaName,"X", gitName])
pt.add_row(["Email", "X","X",gitEmail])
pt.add_row(["Company","X","X", gitCompany])
pt.add_row(["Personal Site", instaPersonalSite,"X", gitBlog])
pt.add_row(["Location", "X", sofLocation, gitLocation])
pt.add_row(["Twitter", "X", "X", gitTwitter])
pt.add_row(["Tags", "X", sofUser_tag, "X"])
pt.add_row(["Biography", bio_shortener(instaBio), bio_shortener(sofBio), bio_shortener(gitBio)])
print(pt)
input()
|
normal
|
{
"blob_id": "e1c902ef340a0a5538b41a03cc93686e0dd31672",
"index": 8788,
"step-1": "<mask token>\n\n\ndef bio_shortener(bio):\n lines = []\n x = len(bio) / 30\n y = 0\n Status = True\n while Status:\n y = y + 1\n lines.append(bio[0:30])\n lines.append('\\n')\n bio = bio[30:]\n if y == int(x) + 1:\n Status = False\n A = ''.join(lines)\n return A\n\n\ndef nb_checker(nb):\n if nb != 'None':\n return nb.text\n else:\n nb\n\n\ndef quick_search(username):\n print('Collecting username information...')\n insta_url = 'https://instagram.com/' + username + '/'\n chromeBrowser.get(insta_url)\n WebDriverWait(chromeBrowser, 5).until(lambda d: d.find_element_by_xpath\n ('//*[@id=\"loginForm\"]/div/div[1]/div/label/input'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[1]/div/label/input').send_keys(i_email)\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[2]/div/label/input').send_keys(i_password\n )\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div[1]/div[3]/button').click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button').click()\n try:\n instaName = chromeBrowser.find_element_by_class_name('rhpdm').text\n except:\n instaName = 'None'\n try:\n instaBio = chromeBrowser.find_element_by_xpath(\n '/html/body/div[1]/section/main/div/header/section/div[2]/span'\n ).text\n except:\n instaBio = 'None'\n try:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a[1]'\n ).text\n except NameError:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a'\n ).text\n except:\n instaPersonalSite = 'None'\n sleep(1)\n chromeBrowser.get('https://stackoverflow.com/users/')\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input'))\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input').send_keys(username)\n sleep(1)\n try:\n Name = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a')\n if str(Name.text.lower()) == username.lower():\n placeholder = True\n except:\n placeholder = False\n try:\n sofLocation = chromeBrowser.find_element_by_class_name('user-location'\n ).text\n except:\n sofLocation = 'None'\n try:\n sofUser_tag = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[3]').text\n except:\n sofUser_tag = 'None'\n try:\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a'\n ).click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[2]/div[1]/div/div[2]/div/div[1]/div/div[2]'\n ))\n except:\n placeholder = True\n try:\n sofBio = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"user-card\"]/div/div[2]/div/div[1]/div/div[2]').text\n except:\n sofBio = 'None'\n githubUrl = 'https://api.github.com/users/' + username\n try:\n with urllib.request.urlopen(githubUrl) as url:\n githubData = json.loads(url.read().decode())\n gitName = str(githubData['name'])\n gitCompany = str(githubData['company'])\n gitBlog = str(githubData['blog'])\n gitEmail = str(githubData['email'])\n gitBio = str(githubData['bio'])\n gitTwitter = str(githubData['twitter_username'])\n gitLocation = str(githubData['location'])\n except:\n placeholder = True\n pt = PrettyTable([' ', ' Instagram ',\n ' StackOverflow ', ' GitHub '])\n pt.add_row(['Name', instaName, 'X', gitName])\n pt.add_row(['Email', 'X', 'X', gitEmail])\n pt.add_row(['Company', 'X', 'X', gitCompany])\n pt.add_row(['Personal Site', instaPersonalSite, 'X', gitBlog])\n pt.add_row(['Location', 'X', sofLocation, gitLocation])\n pt.add_row(['Twitter', 'X', 'X', gitTwitter])\n pt.add_row(['Tags', 'X', sofUser_tag, 'X'])\n pt.add_row(['Biography', bio_shortener(instaBio), bio_shortener(sofBio),\n bio_shortener(gitBio)])\n print(pt)\n input()\n",
"step-2": "<mask token>\nchrome_options.add_argument('--headless')\nchrome_options.add_argument('--incognito')\nchrome_options.add_experimental_option('excludeSwitches', ['enable-logging'])\n<mask token>\n\n\ndef bio_shortener(bio):\n lines = []\n x = len(bio) / 30\n y = 0\n Status = True\n while Status:\n y = y + 1\n lines.append(bio[0:30])\n lines.append('\\n')\n bio = bio[30:]\n if y == int(x) + 1:\n Status = False\n A = ''.join(lines)\n return A\n\n\ndef nb_checker(nb):\n if nb != 'None':\n return nb.text\n else:\n nb\n\n\ndef quick_search(username):\n print('Collecting username information...')\n insta_url = 'https://instagram.com/' + username + '/'\n chromeBrowser.get(insta_url)\n WebDriverWait(chromeBrowser, 5).until(lambda d: d.find_element_by_xpath\n ('//*[@id=\"loginForm\"]/div/div[1]/div/label/input'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[1]/div/label/input').send_keys(i_email)\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[2]/div/label/input').send_keys(i_password\n )\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div[1]/div[3]/button').click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button').click()\n try:\n instaName = chromeBrowser.find_element_by_class_name('rhpdm').text\n except:\n instaName = 'None'\n try:\n instaBio = chromeBrowser.find_element_by_xpath(\n '/html/body/div[1]/section/main/div/header/section/div[2]/span'\n ).text\n except:\n instaBio = 'None'\n try:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a[1]'\n ).text\n except NameError:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a'\n ).text\n except:\n instaPersonalSite = 'None'\n sleep(1)\n chromeBrowser.get('https://stackoverflow.com/users/')\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input'))\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input').send_keys(username)\n sleep(1)\n try:\n Name = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a')\n if str(Name.text.lower()) == username.lower():\n placeholder = True\n except:\n placeholder = False\n try:\n sofLocation = chromeBrowser.find_element_by_class_name('user-location'\n ).text\n except:\n sofLocation = 'None'\n try:\n sofUser_tag = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[3]').text\n except:\n sofUser_tag = 'None'\n try:\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a'\n ).click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[2]/div[1]/div/div[2]/div/div[1]/div/div[2]'\n ))\n except:\n placeholder = True\n try:\n sofBio = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"user-card\"]/div/div[2]/div/div[1]/div/div[2]').text\n except:\n sofBio = 'None'\n githubUrl = 'https://api.github.com/users/' + username\n try:\n with urllib.request.urlopen(githubUrl) as url:\n githubData = json.loads(url.read().decode())\n gitName = str(githubData['name'])\n gitCompany = str(githubData['company'])\n gitBlog = str(githubData['blog'])\n gitEmail = str(githubData['email'])\n gitBio = str(githubData['bio'])\n gitTwitter = str(githubData['twitter_username'])\n gitLocation = str(githubData['location'])\n except:\n placeholder = True\n pt = PrettyTable([' ', ' Instagram ',\n ' StackOverflow ', ' GitHub '])\n pt.add_row(['Name', instaName, 'X', gitName])\n pt.add_row(['Email', 'X', 'X', gitEmail])\n pt.add_row(['Company', 'X', 'X', gitCompany])\n pt.add_row(['Personal Site', instaPersonalSite, 'X', gitBlog])\n pt.add_row(['Location', 'X', sofLocation, gitLocation])\n pt.add_row(['Twitter', 'X', 'X', gitTwitter])\n pt.add_row(['Tags', 'X', sofUser_tag, 'X'])\n pt.add_row(['Biography', bio_shortener(instaBio), bio_shortener(sofBio),\n bio_shortener(gitBio)])\n print(pt)\n input()\n",
"step-3": "<mask token>\nchrome_options = webdriver.ChromeOptions()\nchrome_options.add_argument('--headless')\nchrome_options.add_argument('--incognito')\nchrome_options.add_experimental_option('excludeSwitches', ['enable-logging'])\nchromeBrowser = webdriver.Chrome(chromePath, options=chrome_options)\n\n\ndef bio_shortener(bio):\n lines = []\n x = len(bio) / 30\n y = 0\n Status = True\n while Status:\n y = y + 1\n lines.append(bio[0:30])\n lines.append('\\n')\n bio = bio[30:]\n if y == int(x) + 1:\n Status = False\n A = ''.join(lines)\n return A\n\n\ndef nb_checker(nb):\n if nb != 'None':\n return nb.text\n else:\n nb\n\n\ndef quick_search(username):\n print('Collecting username information...')\n insta_url = 'https://instagram.com/' + username + '/'\n chromeBrowser.get(insta_url)\n WebDriverWait(chromeBrowser, 5).until(lambda d: d.find_element_by_xpath\n ('//*[@id=\"loginForm\"]/div/div[1]/div/label/input'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[1]/div/label/input').send_keys(i_email)\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[2]/div/label/input').send_keys(i_password\n )\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div[1]/div[3]/button').click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button').click()\n try:\n instaName = chromeBrowser.find_element_by_class_name('rhpdm').text\n except:\n instaName = 'None'\n try:\n instaBio = chromeBrowser.find_element_by_xpath(\n '/html/body/div[1]/section/main/div/header/section/div[2]/span'\n ).text\n except:\n instaBio = 'None'\n try:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a[1]'\n ).text\n except NameError:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a'\n ).text\n except:\n instaPersonalSite = 'None'\n sleep(1)\n chromeBrowser.get('https://stackoverflow.com/users/')\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input'))\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input').send_keys(username)\n sleep(1)\n try:\n Name = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a')\n if str(Name.text.lower()) == username.lower():\n placeholder = True\n except:\n placeholder = False\n try:\n sofLocation = chromeBrowser.find_element_by_class_name('user-location'\n ).text\n except:\n sofLocation = 'None'\n try:\n sofUser_tag = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[3]').text\n except:\n sofUser_tag = 'None'\n try:\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a'\n ).click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[2]/div[1]/div/div[2]/div/div[1]/div/div[2]'\n ))\n except:\n placeholder = True\n try:\n sofBio = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"user-card\"]/div/div[2]/div/div[1]/div/div[2]').text\n except:\n sofBio = 'None'\n githubUrl = 'https://api.github.com/users/' + username\n try:\n with urllib.request.urlopen(githubUrl) as url:\n githubData = json.loads(url.read().decode())\n gitName = str(githubData['name'])\n gitCompany = str(githubData['company'])\n gitBlog = str(githubData['blog'])\n gitEmail = str(githubData['email'])\n gitBio = str(githubData['bio'])\n gitTwitter = str(githubData['twitter_username'])\n gitLocation = str(githubData['location'])\n except:\n placeholder = True\n pt = PrettyTable([' ', ' Instagram ',\n ' StackOverflow ', ' GitHub '])\n pt.add_row(['Name', instaName, 'X', gitName])\n pt.add_row(['Email', 'X', 'X', gitEmail])\n pt.add_row(['Company', 'X', 'X', gitCompany])\n pt.add_row(['Personal Site', instaPersonalSite, 'X', gitBlog])\n pt.add_row(['Location', 'X', sofLocation, gitLocation])\n pt.add_row(['Twitter', 'X', 'X', gitTwitter])\n pt.add_row(['Tags', 'X', sofUser_tag, 'X'])\n pt.add_row(['Biography', bio_shortener(instaBio), bio_shortener(sofBio),\n bio_shortener(gitBio)])\n print(pt)\n input()\n",
"step-4": "from selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom prettytable import PrettyTable\nfrom time import sleep\nfrom customization import *\nimport urllib.request, json\nchrome_options = webdriver.ChromeOptions()\nchrome_options.add_argument('--headless')\nchrome_options.add_argument('--incognito')\nchrome_options.add_experimental_option('excludeSwitches', ['enable-logging'])\nchromeBrowser = webdriver.Chrome(chromePath, options=chrome_options)\n\n\ndef bio_shortener(bio):\n lines = []\n x = len(bio) / 30\n y = 0\n Status = True\n while Status:\n y = y + 1\n lines.append(bio[0:30])\n lines.append('\\n')\n bio = bio[30:]\n if y == int(x) + 1:\n Status = False\n A = ''.join(lines)\n return A\n\n\ndef nb_checker(nb):\n if nb != 'None':\n return nb.text\n else:\n nb\n\n\ndef quick_search(username):\n print('Collecting username information...')\n insta_url = 'https://instagram.com/' + username + '/'\n chromeBrowser.get(insta_url)\n WebDriverWait(chromeBrowser, 5).until(lambda d: d.find_element_by_xpath\n ('//*[@id=\"loginForm\"]/div/div[1]/div/label/input'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[1]/div/label/input').send_keys(i_email)\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div/div[2]/div/label/input').send_keys(i_password\n )\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"loginForm\"]/div[1]/div[3]/button').click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button'))\n chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/div/div/div/button').click()\n try:\n instaName = chromeBrowser.find_element_by_class_name('rhpdm').text\n except:\n instaName = 'None'\n try:\n instaBio = chromeBrowser.find_element_by_xpath(\n '/html/body/div[1]/section/main/div/header/section/div[2]/span'\n ).text\n except:\n instaBio = 'None'\n try:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a[1]'\n ).text\n except NameError:\n instaPersonalSite = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a'\n ).text\n except:\n instaPersonalSite = 'None'\n sleep(1)\n chromeBrowser.get('https://stackoverflow.com/users/')\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input'))\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[1]/div[1]/input').send_keys(username)\n sleep(1)\n try:\n Name = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a')\n if str(Name.text.lower()) == username.lower():\n placeholder = True\n except:\n placeholder = False\n try:\n sofLocation = chromeBrowser.find_element_by_class_name('user-location'\n ).text\n except:\n sofLocation = 'None'\n try:\n sofUser_tag = chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[3]').text\n except:\n sofUser_tag = 'None'\n try:\n chromeBrowser.find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a'\n ).click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.\n find_element_by_xpath(\n '/html/body/div[4]/div[2]/div/div[2]/div[1]/div/div[2]/div/div[1]/div/div[2]'\n ))\n except:\n placeholder = True\n try:\n sofBio = chromeBrowser.find_element_by_xpath(\n '//*[@id=\"user-card\"]/div/div[2]/div/div[1]/div/div[2]').text\n except:\n sofBio = 'None'\n githubUrl = 'https://api.github.com/users/' + username\n try:\n with urllib.request.urlopen(githubUrl) as url:\n githubData = json.loads(url.read().decode())\n gitName = str(githubData['name'])\n gitCompany = str(githubData['company'])\n gitBlog = str(githubData['blog'])\n gitEmail = str(githubData['email'])\n gitBio = str(githubData['bio'])\n gitTwitter = str(githubData['twitter_username'])\n gitLocation = str(githubData['location'])\n except:\n placeholder = True\n pt = PrettyTable([' ', ' Instagram ',\n ' StackOverflow ', ' GitHub '])\n pt.add_row(['Name', instaName, 'X', gitName])\n pt.add_row(['Email', 'X', 'X', gitEmail])\n pt.add_row(['Company', 'X', 'X', gitCompany])\n pt.add_row(['Personal Site', instaPersonalSite, 'X', gitBlog])\n pt.add_row(['Location', 'X', sofLocation, gitLocation])\n pt.add_row(['Twitter', 'X', 'X', gitTwitter])\n pt.add_row(['Tags', 'X', sofUser_tag, 'X'])\n pt.add_row(['Biography', bio_shortener(instaBio), bio_shortener(sofBio),\n bio_shortener(gitBio)])\n print(pt)\n input()\n",
"step-5": "from selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom prettytable import PrettyTable\nfrom time import sleep\nfrom customization import *\n\nimport urllib.request,json\nchrome_options=webdriver.ChromeOptions()\nchrome_options.add_argument(\"--headless\")\nchrome_options.add_argument(\"--incognito\")\nchrome_options.add_experimental_option('excludeSwitches', ['enable-logging'])\nchromeBrowser = webdriver.Chrome(chromePath, options=chrome_options)\ndef bio_shortener(bio):\n lines=[]\n x=len(bio)/30\n y=0\n Status=True\n while Status:\n y=y+1\n lines.append(bio[0:30])\n lines.append(\"\\n\")\n bio=bio[30:]\n if y==int(x)+1:\n Status=False\n\n A=''.join(lines)\n return A\n\ndef nb_checker(nb):\n if nb!='None':\n return nb.text\n else:\n nb\n\n\ndef quick_search(username):\n print(\"Collecting username information...\")\n insta_url=\"https://instagram.com/\"+username+\"/\"\n chromeBrowser.get(insta_url)\n WebDriverWait(chromeBrowser,5).until(lambda d: d.find_element_by_xpath('//*[@id=\"loginForm\"]/div/div[1]/div/label/input'))\n chromeBrowser.find_element_by_xpath('//*[@id=\"loginForm\"]/div/div[1]/div/label/input').send_keys(i_email)\n chromeBrowser.find_element_by_xpath('//*[@id=\"loginForm\"]/div/div[2]/div/label/input').send_keys(i_password)\n chromeBrowser.find_element_by_xpath('//*[@id=\"loginForm\"]/div[1]/div[3]/button').click()\n WebDriverWait(chromeBrowser,10).until(lambda d: d.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/div/div/div/button'))\n chromeBrowser.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/div/div/div/button').click()\n try:\n instaName=chromeBrowser.find_element_by_class_name('rhpdm').text\n except:\n instaName=\"None\"\n try:\n instaBio=chromeBrowser.find_element_by_xpath('/html/body/div[1]/section/main/div/header/section/div[2]/span').text\n except:\n instaBio=\"None\"\n try:\n instaPersonalSite=chromeBrowser.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a[1]').text\n except NameError:\n instaPersonalSite=chromeBrowser.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/header/section/div[2]/a').text\n except:\n instaPersonalSite='None'\n\n sleep(1)\n chromeBrowser.get('https://stackoverflow.com/users/')\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[1]/div[1]/input'))\n chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[1]/div[1]/input').send_keys(username)\n sleep(1)\n try:\n Name=chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a')\n if str(Name.text.lower())==username.lower():\n placeholder=True\n except:\n placeholder=False\n try:\n sofLocation=chromeBrowser.find_element_by_class_name('user-location').text\n except:\n sofLocation='None'\n try:\n sofUser_tag = chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[3]').text\n except:\n sofUser_tag='None'\n try:\n chromeBrowser.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[3]/div[1]/div[1]/div[2]/a').click()\n WebDriverWait(chromeBrowser, 10).until(lambda d: d.find_element_by_xpath('/html/body/div[4]/div[2]/div/div[2]/div[1]/div/div[2]/div/div[1]/div/div[2]'))\n except:\n placeholder=True\n try:\n sofBio=chromeBrowser.find_element_by_xpath('//*[@id=\"user-card\"]/div/div[2]/div/div[1]/div/div[2]').text\n except:\n sofBio='None'\n\n githubUrl = \"https://api.github.com/users/\" + username\n\n try:\n with urllib.request.urlopen(githubUrl) as url:\n githubData = json.loads(url.read().decode())\n gitName=str(githubData['name'])\n gitCompany=str(githubData['company'])\n gitBlog=str(githubData['blog'])\n gitEmail=str(githubData['email'])\n gitBio=str(githubData['bio'])\n gitTwitter=str(githubData['twitter_username'])\n gitLocation=str(githubData['location'])\n except:\n placeholder=True\n\n pt = PrettyTable(\n [' ', ' Instagram ', ' StackOverflow ', ' GitHub '])\n pt.add_row([\"Name\", instaName,\"X\", gitName])\n pt.add_row([\"Email\", \"X\",\"X\",gitEmail])\n pt.add_row([\"Company\",\"X\",\"X\", gitCompany])\n pt.add_row([\"Personal Site\", instaPersonalSite,\"X\", gitBlog])\n pt.add_row([\"Location\", \"X\", sofLocation, gitLocation])\n pt.add_row([\"Twitter\", \"X\", \"X\", gitTwitter])\n pt.add_row([\"Tags\", \"X\", sofUser_tag, \"X\"])\n pt.add_row([\"Biography\", bio_shortener(instaBio), bio_shortener(sofBio), bio_shortener(gitBio)])\n print(pt)\n input()\n",
"step-ids": [
3,
4,
5,
6,
7
]
}
|
[
3,
4,
5,
6,
7
] |
from flask import request
from flask_restful import abort
from sqlalchemy.exc import SQLAlchemyError
from gm.main.models.model import db, Metric, QuantModelMetricSchema, \
MlModelMetricSchema, Frequency, QuantModelMetric, MlModelMetric, \
ThresholdType
from gm.main.resources import success, get_metric_by_id, BaseResource
class MetricsResource(BaseResource):
"""
This resource handles the HTTP requests coming to the endpoint "/metrics".
Note: no trailing slash ("/") should be used.
Accepted HTTP methods: GET, POST
"""
def get(self):
"""
Implements the GET method for endpoint "/metrics". By default the results are
order by 'metric_id' ascending.
Implemented Query Parameters:
- is_active: to filter results that are either active or inactive. Boolean and
case insensitive.
- frequency: filter results based on a metric frequency. Values of this enum must
be respected. Case insensitive.
- threshold_type: filter results based on a metric threshold type. Values of this
enum must be respected. Case insensitive.
- sort: allows one to order the resulting collecting by 'metric_id' in descending
order. This should be done by specifying the query parameter as "sort=-metric_id".
Case insensitive.
Note: if unknown query parameters are given these will be ignored.
:return: a collection of metrics
"""
query = self.build_query()
metrics = query.all()
result = self.schema_collection.dump(metrics)
return success(result)
def build_query(self):
"""
Builds the query (without executing it) to the be used in the GET method.
:return: query with all the query conditions specified for obtaining the metrics
that are in the database and respect the desired filters (query parameters).
"""
# this filter is required
query = Metric.query.filter(Metric.metric_type == self.metric_type)
# get query parameters (parameters which are not here are ignored)
is_active = request.args.get('is_active')
frequency = request.args.get('frequency')
threshold_type = request.args.get('threshold_type')
sort = request.args.get('sort')
# process each parameter, and if valid add it as a query condition
if is_active is not None:
is_active = is_active.lower() == 'true'
query = Metric.query.filter_by(is_active=is_active)
if frequency is not None:
try:
frequency = Frequency.from_name(frequency)
except ValueError as e:
msg = f"Invalid 'frequency': {frequency}. Use one of {Frequency.values()}"
abort(400, message=msg)
query = query.filter_by(frequency=frequency)
if threshold_type is not None:
try:
threshold_type = ThresholdType.from_name(threshold_type)
except ValueError as e:
msg = f"Invalid 'threshold_type': {threshold_type}. Use one of " \
f"{ThresholdType.values()}"
abort(400, message=msg)
query = query.filter_by(threshold_type=threshold_type)
if sort is not None and sort.lstrip("-") == 'metric_id':
query = query.order_by(Metric.metric_id.desc())
else:
query = query.order_by(Metric.metric_id)
return query
def post(self):
"""
Implements the POST method for endpoint "/metrics". It should be used to create a
new metric.
:return: the metric as a json created in the database (in case of success)
"""
json_data = request.get_json(force=True)
if not json_data:
abort(400, message='No input data provided')
# make sure the metric_id (temporary) and metric_type (model) are filled
json_data["metric_id"] = "TBD"
json_data["metric_type"] = "model"
# validate and deserialize input
new_metric = self.load(json_data, session=db.session)
# get the next metric id and update metric object
try:
db.session.add(new_metric)
db.session.commit()
except SQLAlchemyError as e:
abort(400, message=f'Database error. Reason: {e}')
# dump to json and return result
result = self.schema.dump(new_metric)
return success(result, code=201)
class QuantModelMetricsResource(MetricsResource):
"""
This resource handles the HTTP requests coming to the endpoint
"/quant_model/metrics/{metric_id}".
This subclass uses almost everything from the base class, it only needs to specify the
appropriate schemas in the constructor, and to override the build_query method so that
the appropriate metric_type is filtered and the remaining query parameters (specific
to this endpoint) are processed.
Implemented Query Parameters:
- asset_class: to filter results by a given asset class.
- model_name: to filter results by a given model name.
- pricing_library: to filter results for a given pricing library.
Note: no trailing slash ("/") should be used.
Accepted HTTP methods: GET, POST
"""
def __init__(self, **kwargs):
"""
Initialize schemas with appropriate classes.
:param kwargs: pass through to base constructor (service and metric_type)
"""
schema = QuantModelMetricSchema()
schema_collection = QuantModelMetricSchema(many=True)
super().__init__(schema, schema_collection, **kwargs)
def build_query(self):
"""
Override method to include specific query parameters to this model endpoint.
"""
# build query from base class add required field for joining with parent
query = super().build_query()
query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)
# get the remaining query parameters
asset_class = request.args.get('asset_class')
model_name = request.args.get('model_name')
pricing_library = request.args.get('pricing_library')
# process each parameter and, if valid, add as a query condition
if asset_class is not None:
query = query.filter(QuantModelMetric.asset_class == asset_class)
if model_name is not None:
query = query.filter(QuantModelMetric.model_name == model_name)
if pricing_library is not None:
query = query.filter(QuantModelMetric.pricing_library == pricing_library)
return query
class MlModelMetricsResource(MetricsResource):
"""
This resource handles the HTTP requests coming to the endpoint
"/ml_model/metrics/{metric_id}".
This subclass uses almost everything from the base class, it only needs to specify the
appropriate schemas in the constructor, and to override the build_query method so that
the appropriate metric_type is filtered and the remaining query parameters (specific
to this endpoint) are processed.
Implemented Query Parameters:
- algorithm: to filter results by a given algorithm.
Note: no trailing slash ("/") should be used.
Accepted HTTP methods: GET, POST
"""
def __init__(self, **kwargs):
"""
Initialize schemas with appropriate classes.
:param kwargs: pass through to base constructor (service and metric_type)
"""
schema = MlModelMetricSchema()
schema_collection = MlModelMetricSchema(many=True)
super().__init__(schema, schema_collection, **kwargs)
def build_query(self):
"""
Override method to include specific query parameters to this ml_model
endpoint.
"""
query = super().build_query()
query = query.filter(Metric.metric_id == MlModelMetric.metric_id)
algorithm = request.args.get('algorithm')
if algorithm is not None:
query = query.filter(MlModelMetric.algorithm == algorithm)
return query
class MetricResource(BaseResource):
"""
This resource handles the HTTP requests coming to the endpoint "/metrics/{metric_id}".
Note: no trailing slash ("/") should be used.
Accepted HTTP methods: GET, PUT, DELETE
"""
def get(self, metric_id):
"""
Implements the GET method for endpoint "/metrics/{metric_id}". It should be used
to get a single metric from the database.
:param metric_id: the metric_id associated with this endpoint
:return: the json object of metric found in the database (if it exists)
"""
metric = get_metric_by_id(metric_id)
return self.schema.jsonify(metric)
def put(self, metric_id):
"""
Implements the PUT method for endpoint "/metrics/{metric_id}". It should be used
to update a metric.
:param metric_id: the metric_id associated with this endpoint
:return: the metric as a json after the update (in case of success)
"""
json_data = request.get_json(force=True)
if not json_data:
abort(400, message='No input data provided')
# Validate and deserialize input
metric = get_metric_by_id(metric_id)
self.load(json_data, metric, db.session, partial=True)
# if it was found and deserialized successfully try to commit
try:
db.session.commit()
except SQLAlchemyError as e:
abort(400, message=f'Database error. Reason: {e}')
return success(json_data)
def delete(self, metric_id):
"""
Implements the DELETE method for endpoint "/metrics/{metric_id}". It should be
used to delete a metric result matching the provided metric_id and cob_date.
:param metric_id: the metric_id associated with this endpoint
:return: the metric as a json after the delete (in case of success)
"""
metric = get_metric_by_id(metric_id)
# dump as json to send in the end if del is successful
result = self.schema.dump(metric)
# if result was found, delete it from database
try:
db.session.delete(metric)
db.session.commit()
except SQLAlchemyError as e:
abort(400, message=f'Database error. Reason: {e}')
return success(result)
class QuantModelMetricResource(MetricResource):
"""
This resource handles the HTTP requests coming to the endpoint
"/quant_model/metrics/{metric_id}".
This subclass uses everything from the base class and only needs to specify the
appropriate schemas in the constructor.
Note: no trailing slash ("/") should be used.
Accepted HTTP methods: GET, PUT, DELETE
"""
def __init__(self, **kwargs):
"""
Initialize schemas with appropriate classes.
:param kwargs: pass through to base constructor (service and metric_type)
"""
schema = QuantModelMetricSchema()
schema_collection = QuantModelMetricSchema(many=True)
super().__init__(schema, schema_collection, **kwargs)
class MlModelMetricResource(MetricResource):
"""
This resource handles the HTTP requests coming to the endpoint
"/ml_model/metrics/{metric_id}".
This subclass uses everything from the base class and only needs to specify the
appropriate schemas in the constructor.
Note: no trailing slash ("/") should be used.
Accepted HTTP methods: GET, PUT, DELETE
"""
def __init__(self, **kwargs):
"""
Initialize schemas with appropriate classes.
:param kwargs: pass through to base constructor (service and metric_type)
"""
schema = MlModelMetricSchema()
schema_collection = MlModelMetricSchema(many=True)
super().__init__(schema, schema_collection, **kwargs)
|
normal
|
{
"blob_id": "1431a0049c05a99e0b68052f56bf8e2e3c48e1aa",
"index": 622,
"step-1": "<mask token>\n\n\nclass QuantModelMetricsResource(MetricsResource):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass MlModelMetricsResource(MetricsResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses almost everything from the base class, it only needs to specify the\n appropriate schemas in the constructor, and to override the build_query method so that\n the appropriate metric_type is filtered and the remaining query parameters (specific\n to this endpoint) are processed.\n\n Implemented Query Parameters:\n - algorithm: to filter results by a given algorithm.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n def build_query(self):\n \"\"\"\n Override method to include specific query parameters to this ml_model\n endpoint.\n \"\"\"\n query = super().build_query()\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\n algorithm = request.args.get('algorithm')\n if algorithm is not None:\n query = query.filter(MlModelMetric.algorithm == algorithm)\n return query\n\n\nclass MetricResource(BaseResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint \"/metrics/{metric_id}\".\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def get(self, metric_id):\n \"\"\"\n Implements the GET method for endpoint \"/metrics/{metric_id}\". It should be used\n to get a single metric from the database.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the json object of metric found in the database (if it exists)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n return self.schema.jsonify(metric)\n\n def put(self, metric_id):\n \"\"\"\n Implements the PUT method for endpoint \"/metrics/{metric_id}\". It should be used\n to update a metric.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the update (in case of success)\n \"\"\"\n json_data = request.get_json(force=True)\n if not json_data:\n abort(400, message='No input data provided')\n metric = get_metric_by_id(metric_id)\n self.load(json_data, metric, db.session, partial=True)\n try:\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(json_data)\n\n def delete(self, metric_id):\n \"\"\"\n Implements the DELETE method for endpoint \"/metrics/{metric_id}\". It should be\n used to delete a metric result matching the provided metric_id and cob_date.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the delete (in case of success)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n result = self.schema.dump(metric)\n try:\n db.session.delete(metric)\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(result)\n\n\nclass QuantModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/quant_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = QuantModelMetricSchema()\n schema_collection = QuantModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n\nclass MlModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n",
"step-2": "<mask token>\n\n\nclass QuantModelMetricsResource(MetricsResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/quant_model/metrics/{metric_id}\".\n\n This subclass uses almost everything from the base class, it only needs to specify the\n appropriate schemas in the constructor, and to override the build_query method so that\n the appropriate metric_type is filtered and the remaining query parameters (specific\n to this endpoint) are processed.\n\n Implemented Query Parameters:\n - asset_class: to filter results by a given asset class.\n - model_name: to filter results by a given model name.\n - pricing_library: to filter results for a given pricing library.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = QuantModelMetricSchema()\n schema_collection = QuantModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n def build_query(self):\n \"\"\"\n Override method to include specific query parameters to this model endpoint.\n \"\"\"\n query = super().build_query()\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\n asset_class = request.args.get('asset_class')\n model_name = request.args.get('model_name')\n pricing_library = request.args.get('pricing_library')\n if asset_class is not None:\n query = query.filter(QuantModelMetric.asset_class == asset_class)\n if model_name is not None:\n query = query.filter(QuantModelMetric.model_name == model_name)\n if pricing_library is not None:\n query = query.filter(QuantModelMetric.pricing_library ==\n pricing_library)\n return query\n\n\nclass MlModelMetricsResource(MetricsResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses almost everything from the base class, it only needs to specify the\n appropriate schemas in the constructor, and to override the build_query method so that\n the appropriate metric_type is filtered and the remaining query parameters (specific\n to this endpoint) are processed.\n\n Implemented Query Parameters:\n - algorithm: to filter results by a given algorithm.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n def build_query(self):\n \"\"\"\n Override method to include specific query parameters to this ml_model\n endpoint.\n \"\"\"\n query = super().build_query()\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\n algorithm = request.args.get('algorithm')\n if algorithm is not None:\n query = query.filter(MlModelMetric.algorithm == algorithm)\n return query\n\n\nclass MetricResource(BaseResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint \"/metrics/{metric_id}\".\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def get(self, metric_id):\n \"\"\"\n Implements the GET method for endpoint \"/metrics/{metric_id}\". It should be used\n to get a single metric from the database.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the json object of metric found in the database (if it exists)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n return self.schema.jsonify(metric)\n\n def put(self, metric_id):\n \"\"\"\n Implements the PUT method for endpoint \"/metrics/{metric_id}\". It should be used\n to update a metric.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the update (in case of success)\n \"\"\"\n json_data = request.get_json(force=True)\n if not json_data:\n abort(400, message='No input data provided')\n metric = get_metric_by_id(metric_id)\n self.load(json_data, metric, db.session, partial=True)\n try:\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(json_data)\n\n def delete(self, metric_id):\n \"\"\"\n Implements the DELETE method for endpoint \"/metrics/{metric_id}\". It should be\n used to delete a metric result matching the provided metric_id and cob_date.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the delete (in case of success)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n result = self.schema.dump(metric)\n try:\n db.session.delete(metric)\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(result)\n\n\nclass QuantModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/quant_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = QuantModelMetricSchema()\n schema_collection = QuantModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n\nclass MlModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n",
"step-3": "<mask token>\n\n\nclass MetricsResource(BaseResource):\n <mask token>\n\n def get(self):\n \"\"\"\n Implements the GET method for endpoint \"/metrics\". By default the results are\n order by 'metric_id' ascending.\n\n Implemented Query Parameters:\n - is_active: to filter results that are either active or inactive. Boolean and\n case insensitive.\n - frequency: filter results based on a metric frequency. Values of this enum must\n be respected. Case insensitive.\n - threshold_type: filter results based on a metric threshold type. Values of this\n enum must be respected. Case insensitive.\n - sort: allows one to order the resulting collecting by 'metric_id' in descending\n order. This should be done by specifying the query parameter as \"sort=-metric_id\".\n Case insensitive.\n\n Note: if unknown query parameters are given these will be ignored.\n\n :return: a collection of metrics\n \"\"\"\n query = self.build_query()\n metrics = query.all()\n result = self.schema_collection.dump(metrics)\n return success(result)\n\n def build_query(self):\n \"\"\"\n Builds the query (without executing it) to the be used in the GET method.\n :return: query with all the query conditions specified for obtaining the metrics\n that are in the database and respect the desired filters (query parameters).\n \"\"\"\n query = Metric.query.filter(Metric.metric_type == self.metric_type)\n is_active = request.args.get('is_active')\n frequency = request.args.get('frequency')\n threshold_type = request.args.get('threshold_type')\n sort = request.args.get('sort')\n if is_active is not None:\n is_active = is_active.lower() == 'true'\n query = Metric.query.filter_by(is_active=is_active)\n if frequency is not None:\n try:\n frequency = Frequency.from_name(frequency)\n except ValueError as e:\n msg = (\n f\"Invalid 'frequency': {frequency}. Use one of {Frequency.values()}\"\n )\n abort(400, message=msg)\n query = query.filter_by(frequency=frequency)\n if threshold_type is not None:\n try:\n threshold_type = ThresholdType.from_name(threshold_type)\n except ValueError as e:\n msg = (\n f\"Invalid 'threshold_type': {threshold_type}. Use one of {ThresholdType.values()}\"\n )\n abort(400, message=msg)\n query = query.filter_by(threshold_type=threshold_type)\n if sort is not None and sort.lstrip('-') == 'metric_id':\n query = query.order_by(Metric.metric_id.desc())\n else:\n query = query.order_by(Metric.metric_id)\n return query\n\n def post(self):\n \"\"\"\n Implements the POST method for endpoint \"/metrics\". It should be used to create a\n new metric.\n\n :return: the metric as a json created in the database (in case of success)\n \"\"\"\n json_data = request.get_json(force=True)\n if not json_data:\n abort(400, message='No input data provided')\n json_data['metric_id'] = 'TBD'\n json_data['metric_type'] = 'model'\n new_metric = self.load(json_data, session=db.session)\n try:\n db.session.add(new_metric)\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n result = self.schema.dump(new_metric)\n return success(result, code=201)\n\n\nclass QuantModelMetricsResource(MetricsResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/quant_model/metrics/{metric_id}\".\n\n This subclass uses almost everything from the base class, it only needs to specify the\n appropriate schemas in the constructor, and to override the build_query method so that\n the appropriate metric_type is filtered and the remaining query parameters (specific\n to this endpoint) are processed.\n\n Implemented Query Parameters:\n - asset_class: to filter results by a given asset class.\n - model_name: to filter results by a given model name.\n - pricing_library: to filter results for a given pricing library.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = QuantModelMetricSchema()\n schema_collection = QuantModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n def build_query(self):\n \"\"\"\n Override method to include specific query parameters to this model endpoint.\n \"\"\"\n query = super().build_query()\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\n asset_class = request.args.get('asset_class')\n model_name = request.args.get('model_name')\n pricing_library = request.args.get('pricing_library')\n if asset_class is not None:\n query = query.filter(QuantModelMetric.asset_class == asset_class)\n if model_name is not None:\n query = query.filter(QuantModelMetric.model_name == model_name)\n if pricing_library is not None:\n query = query.filter(QuantModelMetric.pricing_library ==\n pricing_library)\n return query\n\n\nclass MlModelMetricsResource(MetricsResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses almost everything from the base class, it only needs to specify the\n appropriate schemas in the constructor, and to override the build_query method so that\n the appropriate metric_type is filtered and the remaining query parameters (specific\n to this endpoint) are processed.\n\n Implemented Query Parameters:\n - algorithm: to filter results by a given algorithm.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n def build_query(self):\n \"\"\"\n Override method to include specific query parameters to this ml_model\n endpoint.\n \"\"\"\n query = super().build_query()\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\n algorithm = request.args.get('algorithm')\n if algorithm is not None:\n query = query.filter(MlModelMetric.algorithm == algorithm)\n return query\n\n\nclass MetricResource(BaseResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint \"/metrics/{metric_id}\".\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def get(self, metric_id):\n \"\"\"\n Implements the GET method for endpoint \"/metrics/{metric_id}\". It should be used\n to get a single metric from the database.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the json object of metric found in the database (if it exists)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n return self.schema.jsonify(metric)\n\n def put(self, metric_id):\n \"\"\"\n Implements the PUT method for endpoint \"/metrics/{metric_id}\". It should be used\n to update a metric.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the update (in case of success)\n \"\"\"\n json_data = request.get_json(force=True)\n if not json_data:\n abort(400, message='No input data provided')\n metric = get_metric_by_id(metric_id)\n self.load(json_data, metric, db.session, partial=True)\n try:\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(json_data)\n\n def delete(self, metric_id):\n \"\"\"\n Implements the DELETE method for endpoint \"/metrics/{metric_id}\". It should be\n used to delete a metric result matching the provided metric_id and cob_date.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the delete (in case of success)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n result = self.schema.dump(metric)\n try:\n db.session.delete(metric)\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(result)\n\n\nclass QuantModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/quant_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = QuantModelMetricSchema()\n schema_collection = QuantModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n\nclass MlModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n",
"step-4": "from flask import request\nfrom flask_restful import abort\nfrom sqlalchemy.exc import SQLAlchemyError\nfrom gm.main.models.model import db, Metric, QuantModelMetricSchema, MlModelMetricSchema, Frequency, QuantModelMetric, MlModelMetric, ThresholdType\nfrom gm.main.resources import success, get_metric_by_id, BaseResource\n\n\nclass MetricsResource(BaseResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint \"/metrics\".\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def get(self):\n \"\"\"\n Implements the GET method for endpoint \"/metrics\". By default the results are\n order by 'metric_id' ascending.\n\n Implemented Query Parameters:\n - is_active: to filter results that are either active or inactive. Boolean and\n case insensitive.\n - frequency: filter results based on a metric frequency. Values of this enum must\n be respected. Case insensitive.\n - threshold_type: filter results based on a metric threshold type. Values of this\n enum must be respected. Case insensitive.\n - sort: allows one to order the resulting collecting by 'metric_id' in descending\n order. This should be done by specifying the query parameter as \"sort=-metric_id\".\n Case insensitive.\n\n Note: if unknown query parameters are given these will be ignored.\n\n :return: a collection of metrics\n \"\"\"\n query = self.build_query()\n metrics = query.all()\n result = self.schema_collection.dump(metrics)\n return success(result)\n\n def build_query(self):\n \"\"\"\n Builds the query (without executing it) to the be used in the GET method.\n :return: query with all the query conditions specified for obtaining the metrics\n that are in the database and respect the desired filters (query parameters).\n \"\"\"\n query = Metric.query.filter(Metric.metric_type == self.metric_type)\n is_active = request.args.get('is_active')\n frequency = request.args.get('frequency')\n threshold_type = request.args.get('threshold_type')\n sort = request.args.get('sort')\n if is_active is not None:\n is_active = is_active.lower() == 'true'\n query = Metric.query.filter_by(is_active=is_active)\n if frequency is not None:\n try:\n frequency = Frequency.from_name(frequency)\n except ValueError as e:\n msg = (\n f\"Invalid 'frequency': {frequency}. Use one of {Frequency.values()}\"\n )\n abort(400, message=msg)\n query = query.filter_by(frequency=frequency)\n if threshold_type is not None:\n try:\n threshold_type = ThresholdType.from_name(threshold_type)\n except ValueError as e:\n msg = (\n f\"Invalid 'threshold_type': {threshold_type}. Use one of {ThresholdType.values()}\"\n )\n abort(400, message=msg)\n query = query.filter_by(threshold_type=threshold_type)\n if sort is not None and sort.lstrip('-') == 'metric_id':\n query = query.order_by(Metric.metric_id.desc())\n else:\n query = query.order_by(Metric.metric_id)\n return query\n\n def post(self):\n \"\"\"\n Implements the POST method for endpoint \"/metrics\". It should be used to create a\n new metric.\n\n :return: the metric as a json created in the database (in case of success)\n \"\"\"\n json_data = request.get_json(force=True)\n if not json_data:\n abort(400, message='No input data provided')\n json_data['metric_id'] = 'TBD'\n json_data['metric_type'] = 'model'\n new_metric = self.load(json_data, session=db.session)\n try:\n db.session.add(new_metric)\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n result = self.schema.dump(new_metric)\n return success(result, code=201)\n\n\nclass QuantModelMetricsResource(MetricsResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/quant_model/metrics/{metric_id}\".\n\n This subclass uses almost everything from the base class, it only needs to specify the\n appropriate schemas in the constructor, and to override the build_query method so that\n the appropriate metric_type is filtered and the remaining query parameters (specific\n to this endpoint) are processed.\n\n Implemented Query Parameters:\n - asset_class: to filter results by a given asset class.\n - model_name: to filter results by a given model name.\n - pricing_library: to filter results for a given pricing library.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = QuantModelMetricSchema()\n schema_collection = QuantModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n def build_query(self):\n \"\"\"\n Override method to include specific query parameters to this model endpoint.\n \"\"\"\n query = super().build_query()\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\n asset_class = request.args.get('asset_class')\n model_name = request.args.get('model_name')\n pricing_library = request.args.get('pricing_library')\n if asset_class is not None:\n query = query.filter(QuantModelMetric.asset_class == asset_class)\n if model_name is not None:\n query = query.filter(QuantModelMetric.model_name == model_name)\n if pricing_library is not None:\n query = query.filter(QuantModelMetric.pricing_library ==\n pricing_library)\n return query\n\n\nclass MlModelMetricsResource(MetricsResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses almost everything from the base class, it only needs to specify the\n appropriate schemas in the constructor, and to override the build_query method so that\n the appropriate metric_type is filtered and the remaining query parameters (specific\n to this endpoint) are processed.\n\n Implemented Query Parameters:\n - algorithm: to filter results by a given algorithm.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, POST\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n def build_query(self):\n \"\"\"\n Override method to include specific query parameters to this ml_model\n endpoint.\n \"\"\"\n query = super().build_query()\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\n algorithm = request.args.get('algorithm')\n if algorithm is not None:\n query = query.filter(MlModelMetric.algorithm == algorithm)\n return query\n\n\nclass MetricResource(BaseResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint \"/metrics/{metric_id}\".\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def get(self, metric_id):\n \"\"\"\n Implements the GET method for endpoint \"/metrics/{metric_id}\". It should be used\n to get a single metric from the database.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the json object of metric found in the database (if it exists)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n return self.schema.jsonify(metric)\n\n def put(self, metric_id):\n \"\"\"\n Implements the PUT method for endpoint \"/metrics/{metric_id}\". It should be used\n to update a metric.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the update (in case of success)\n \"\"\"\n json_data = request.get_json(force=True)\n if not json_data:\n abort(400, message='No input data provided')\n metric = get_metric_by_id(metric_id)\n self.load(json_data, metric, db.session, partial=True)\n try:\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(json_data)\n\n def delete(self, metric_id):\n \"\"\"\n Implements the DELETE method for endpoint \"/metrics/{metric_id}\". It should be\n used to delete a metric result matching the provided metric_id and cob_date.\n\n :param metric_id: the metric_id associated with this endpoint\n :return: the metric as a json after the delete (in case of success)\n \"\"\"\n metric = get_metric_by_id(metric_id)\n result = self.schema.dump(metric)\n try:\n db.session.delete(metric)\n db.session.commit()\n except SQLAlchemyError as e:\n abort(400, message=f'Database error. Reason: {e}')\n return success(result)\n\n\nclass QuantModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/quant_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = QuantModelMetricSchema()\n schema_collection = QuantModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n\n\nclass MlModelMetricResource(MetricResource):\n \"\"\"\n This resource handles the HTTP requests coming to the endpoint\n \"/ml_model/metrics/{metric_id}\".\n\n This subclass uses everything from the base class and only needs to specify the\n appropriate schemas in the constructor.\n\n Note: no trailing slash (\"/\") should be used.\n\n Accepted HTTP methods: GET, PUT, DELETE\n \"\"\"\n\n def __init__(self, **kwargs):\n \"\"\"\n Initialize schemas with appropriate classes.\n\n :param kwargs: pass through to base constructor (service and metric_type)\n \"\"\"\n schema = MlModelMetricSchema()\n schema_collection = MlModelMetricSchema(many=True)\n super().__init__(schema, schema_collection, **kwargs)\n",
"step-5": "from flask import request\r\nfrom flask_restful import abort\r\nfrom sqlalchemy.exc import SQLAlchemyError\r\n\r\nfrom gm.main.models.model import db, Metric, QuantModelMetricSchema, \\\r\n MlModelMetricSchema, Frequency, QuantModelMetric, MlModelMetric, \\\r\n ThresholdType\r\nfrom gm.main.resources import success, get_metric_by_id, BaseResource\r\n\r\n\r\nclass MetricsResource(BaseResource):\r\n \"\"\"\r\n This resource handles the HTTP requests coming to the endpoint \"/metrics\".\r\n\r\n Note: no trailing slash (\"/\") should be used.\r\n\r\n Accepted HTTP methods: GET, POST\r\n \"\"\"\r\n\r\n def get(self):\r\n \"\"\"\r\n Implements the GET method for endpoint \"/metrics\". By default the results are\r\n order by 'metric_id' ascending.\r\n\r\n Implemented Query Parameters:\r\n - is_active: to filter results that are either active or inactive. Boolean and\r\n case insensitive.\r\n - frequency: filter results based on a metric frequency. Values of this enum must\r\n be respected. Case insensitive.\r\n - threshold_type: filter results based on a metric threshold type. Values of this\r\n enum must be respected. Case insensitive.\r\n - sort: allows one to order the resulting collecting by 'metric_id' in descending\r\n order. This should be done by specifying the query parameter as \"sort=-metric_id\".\r\n Case insensitive.\r\n\r\n Note: if unknown query parameters are given these will be ignored.\r\n\r\n :return: a collection of metrics\r\n \"\"\"\r\n query = self.build_query()\r\n metrics = query.all()\r\n result = self.schema_collection.dump(metrics)\r\n return success(result)\r\n\r\n def build_query(self):\r\n \"\"\"\r\n Builds the query (without executing it) to the be used in the GET method.\r\n :return: query with all the query conditions specified for obtaining the metrics\r\n that are in the database and respect the desired filters (query parameters).\r\n \"\"\"\r\n\r\n # this filter is required\r\n query = Metric.query.filter(Metric.metric_type == self.metric_type)\r\n\r\n # get query parameters (parameters which are not here are ignored)\r\n is_active = request.args.get('is_active')\r\n frequency = request.args.get('frequency')\r\n threshold_type = request.args.get('threshold_type')\r\n sort = request.args.get('sort')\r\n\r\n # process each parameter, and if valid add it as a query condition\r\n if is_active is not None:\r\n is_active = is_active.lower() == 'true'\r\n query = Metric.query.filter_by(is_active=is_active)\r\n if frequency is not None:\r\n try:\r\n frequency = Frequency.from_name(frequency)\r\n except ValueError as e:\r\n msg = f\"Invalid 'frequency': {frequency}. Use one of {Frequency.values()}\"\r\n abort(400, message=msg)\r\n query = query.filter_by(frequency=frequency)\r\n if threshold_type is not None:\r\n try:\r\n threshold_type = ThresholdType.from_name(threshold_type)\r\n except ValueError as e:\r\n msg = f\"Invalid 'threshold_type': {threshold_type}. Use one of \" \\\r\n f\"{ThresholdType.values()}\"\r\n abort(400, message=msg)\r\n query = query.filter_by(threshold_type=threshold_type)\r\n if sort is not None and sort.lstrip(\"-\") == 'metric_id':\r\n query = query.order_by(Metric.metric_id.desc())\r\n else:\r\n query = query.order_by(Metric.metric_id)\r\n\r\n return query\r\n\r\n\r\n def post(self):\r\n \"\"\"\r\n Implements the POST method for endpoint \"/metrics\". It should be used to create a\r\n new metric.\r\n\r\n :return: the metric as a json created in the database (in case of success)\r\n \"\"\"\r\n json_data = request.get_json(force=True)\r\n if not json_data:\r\n abort(400, message='No input data provided')\r\n # make sure the metric_id (temporary) and metric_type (model) are filled\r\n json_data[\"metric_id\"] = \"TBD\"\r\n json_data[\"metric_type\"] = \"model\"\r\n\r\n # validate and deserialize input\r\n new_metric = self.load(json_data, session=db.session)\r\n\r\n # get the next metric id and update metric object\r\n try:\r\n db.session.add(new_metric)\r\n db.session.commit()\r\n except SQLAlchemyError as e:\r\n abort(400, message=f'Database error. Reason: {e}')\r\n\r\n # dump to json and return result\r\n result = self.schema.dump(new_metric)\r\n return success(result, code=201)\r\n\r\n\r\nclass QuantModelMetricsResource(MetricsResource):\r\n \"\"\"\r\n This resource handles the HTTP requests coming to the endpoint\r\n \"/quant_model/metrics/{metric_id}\".\r\n\r\n This subclass uses almost everything from the base class, it only needs to specify the\r\n appropriate schemas in the constructor, and to override the build_query method so that\r\n the appropriate metric_type is filtered and the remaining query parameters (specific\r\n to this endpoint) are processed.\r\n\r\n Implemented Query Parameters:\r\n - asset_class: to filter results by a given asset class.\r\n - model_name: to filter results by a given model name.\r\n - pricing_library: to filter results for a given pricing library.\r\n\r\n Note: no trailing slash (\"/\") should be used.\r\n\r\n Accepted HTTP methods: GET, POST\r\n \"\"\"\r\n\r\n def __init__(self, **kwargs):\r\n \"\"\"\r\n Initialize schemas with appropriate classes.\r\n\r\n :param kwargs: pass through to base constructor (service and metric_type)\r\n \"\"\"\r\n schema = QuantModelMetricSchema()\r\n schema_collection = QuantModelMetricSchema(many=True)\r\n super().__init__(schema, schema_collection, **kwargs)\r\n\r\n def build_query(self):\r\n \"\"\"\r\n Override method to include specific query parameters to this model endpoint.\r\n \"\"\"\r\n # build query from base class add required field for joining with parent\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\r\n\r\n # get the remaining query parameters\r\n asset_class = request.args.get('asset_class')\r\n model_name = request.args.get('model_name')\r\n pricing_library = request.args.get('pricing_library')\r\n\r\n # process each parameter and, if valid, add as a query condition\r\n if asset_class is not None:\r\n query = query.filter(QuantModelMetric.asset_class == asset_class)\r\n if model_name is not None:\r\n query = query.filter(QuantModelMetric.model_name == model_name)\r\n if pricing_library is not None:\r\n query = query.filter(QuantModelMetric.pricing_library == pricing_library)\r\n return query\r\n\r\n\r\nclass MlModelMetricsResource(MetricsResource):\r\n \"\"\"\r\n This resource handles the HTTP requests coming to the endpoint\r\n \"/ml_model/metrics/{metric_id}\".\r\n\r\n This subclass uses almost everything from the base class, it only needs to specify the\r\n appropriate schemas in the constructor, and to override the build_query method so that\r\n the appropriate metric_type is filtered and the remaining query parameters (specific\r\n to this endpoint) are processed.\r\n\r\n Implemented Query Parameters:\r\n - algorithm: to filter results by a given algorithm.\r\n\r\n Note: no trailing slash (\"/\") should be used.\r\n\r\n Accepted HTTP methods: GET, POST\r\n \"\"\"\r\n\r\n def __init__(self, **kwargs):\r\n \"\"\"\r\n Initialize schemas with appropriate classes.\r\n\r\n :param kwargs: pass through to base constructor (service and metric_type)\r\n \"\"\"\r\n schema = MlModelMetricSchema()\r\n schema_collection = MlModelMetricSchema(many=True)\r\n super().__init__(schema, schema_collection, **kwargs)\r\n\r\n def build_query(self):\r\n \"\"\"\r\n Override method to include specific query parameters to this ml_model\r\n endpoint.\r\n \"\"\"\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\r\n algorithm = request.args.get('algorithm')\r\n if algorithm is not None:\r\n query = query.filter(MlModelMetric.algorithm == algorithm)\r\n return query\r\n\r\n\r\nclass MetricResource(BaseResource):\r\n \"\"\"\r\n This resource handles the HTTP requests coming to the endpoint \"/metrics/{metric_id}\".\r\n\r\n Note: no trailing slash (\"/\") should be used.\r\n\r\n Accepted HTTP methods: GET, PUT, DELETE\r\n \"\"\"\r\n\r\n def get(self, metric_id):\r\n \"\"\"\r\n Implements the GET method for endpoint \"/metrics/{metric_id}\". It should be used\r\n to get a single metric from the database.\r\n\r\n :param metric_id: the metric_id associated with this endpoint\r\n :return: the json object of metric found in the database (if it exists)\r\n \"\"\"\r\n metric = get_metric_by_id(metric_id)\r\n return self.schema.jsonify(metric)\r\n\r\n def put(self, metric_id):\r\n \"\"\"\r\n Implements the PUT method for endpoint \"/metrics/{metric_id}\". It should be used\r\n to update a metric.\r\n\r\n :param metric_id: the metric_id associated with this endpoint\r\n :return: the metric as a json after the update (in case of success)\r\n \"\"\"\r\n json_data = request.get_json(force=True)\r\n if not json_data:\r\n abort(400, message='No input data provided')\r\n\r\n # Validate and deserialize input\r\n metric = get_metric_by_id(metric_id)\r\n self.load(json_data, metric, db.session, partial=True)\r\n\r\n # if it was found and deserialized successfully try to commit\r\n try:\r\n db.session.commit()\r\n except SQLAlchemyError as e:\r\n abort(400, message=f'Database error. Reason: {e}')\r\n\r\n return success(json_data)\r\n\r\n def delete(self, metric_id):\r\n \"\"\"\r\n Implements the DELETE method for endpoint \"/metrics/{metric_id}\". It should be\r\n used to delete a metric result matching the provided metric_id and cob_date.\r\n\r\n :param metric_id: the metric_id associated with this endpoint\r\n :return: the metric as a json after the delete (in case of success)\r\n \"\"\"\r\n metric = get_metric_by_id(metric_id)\r\n # dump as json to send in the end if del is successful\r\n result = self.schema.dump(metric)\r\n\r\n # if result was found, delete it from database\r\n try:\r\n db.session.delete(metric)\r\n db.session.commit()\r\n except SQLAlchemyError as e:\r\n abort(400, message=f'Database error. Reason: {e}')\r\n return success(result)\r\n\r\n\r\nclass QuantModelMetricResource(MetricResource):\r\n \"\"\"\r\n This resource handles the HTTP requests coming to the endpoint\r\n \"/quant_model/metrics/{metric_id}\".\r\n\r\n This subclass uses everything from the base class and only needs to specify the\r\n appropriate schemas in the constructor.\r\n\r\n Note: no trailing slash (\"/\") should be used.\r\n\r\n Accepted HTTP methods: GET, PUT, DELETE\r\n \"\"\"\r\n\r\n def __init__(self, **kwargs):\r\n \"\"\"\r\n Initialize schemas with appropriate classes.\r\n\r\n :param kwargs: pass through to base constructor (service and metric_type)\r\n \"\"\"\r\n schema = QuantModelMetricSchema()\r\n schema_collection = QuantModelMetricSchema(many=True)\r\n super().__init__(schema, schema_collection, **kwargs)\r\n\r\n\r\nclass MlModelMetricResource(MetricResource):\r\n \"\"\"\r\n This resource handles the HTTP requests coming to the endpoint\r\n \"/ml_model/metrics/{metric_id}\".\r\n\r\n This subclass uses everything from the base class and only needs to specify the\r\n appropriate schemas in the constructor.\r\n\r\n Note: no trailing slash (\"/\") should be used.\r\n\r\n Accepted HTTP methods: GET, PUT, DELETE\r\n \"\"\"\r\n\r\n def __init__(self, **kwargs):\r\n \"\"\"\r\n Initialize schemas with appropriate classes.\r\n\r\n :param kwargs: pass through to base constructor (service and metric_type)\r\n \"\"\"\r\n schema = MlModelMetricSchema()\r\n schema_collection = MlModelMetricSchema(many=True)\r\n super().__init__(schema, schema_collection, **kwargs)",
"step-ids": [
16,
19,
23,
25,
26
]
}
|
[
16,
19,
23,
25,
26
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
PULPNNInstallPath = cwd = os.getcwd() + '/../'
PULPNNSrcDirs = {'script': PULPNNInstallPath + 'scripts/'}
PULPNNInstallPath32bit = cwd = os.getcwd() + '/../32bit/'
PULPNNInstallPath64bit = cwd = os.getcwd() + '/../64bit/'
PULPNNTestFolder32bit = PULPNNInstallPath32bit + 'test/'
PULPNNTestFolder64bit = PULPNNInstallPath64bit + 'test/'
PULPNNSrcDirs32bit = {'pulp_nn_inc': PULPNNInstallPath32bit + 'include/',
'pulp_nn_pointwise_convolution': PULPNNInstallPath32bit +
'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath32bit +
'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution':
PULPNNInstallPath32bit + 'src/DepthwiseConvolutions/',
'pulp_nn_linear_convolution_nq': PULPNNInstallPath32bit +
'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q':
PULPNNInstallPath32bit + 'src/LinearConvolutionsQuant/',
'pulp_nn_support_function': PULPNNInstallPath32bit +
'src/SupportFunctions/', 'include': PULPNNTestFolder32bit + 'include/',
'src': PULPNNTestFolder32bit + 'src/', 'pointwise_convolution':
PULPNNTestFolder32bit + 'src/StandardConvolutions/', 'matmul':
PULPNNTestFolder32bit + 'src/MatrixMultiplications/',
'depthwise_convolution': PULPNNTestFolder32bit +
'src/DepthwiseConvolutions/', 'linear_convolution_nq':
PULPNNTestFolder32bit + 'src/LinearConvolutionsNoQuant/',
'linear_convolution_q': PULPNNTestFolder32bit +
'src/LinearConvolutionsQuant/', 'support_function':
PULPNNTestFolder32bit + 'src/SupportFunctions/', 'data_allocation_pw':
PULPNNTestFolder32bit + 'include/DataAllocationStandardConvolutions/',
'data_allocation_dw': PULPNNTestFolder32bit +
'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':
PULPNNTestFolder32bit +
'include/DataAllocationLinearConvolutionsNoQuant/',
'data_allocation_ln_q': PULPNNTestFolder32bit +
'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw':
PULPNNTestFolder32bit + 'include/GoldenModelStandardConvolutions/',
'golden_model_dw': PULPNNTestFolder32bit +
'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq':
PULPNNTestFolder32bit + 'include/GoldenModelLinearConvolutionsNoQuant/',
'golden_model_ln_q': PULPNNTestFolder32bit +
'include/GoldenModelLinearConvolutionsQuant/', 'test':
PULPNNTestFolder32bit}
PULPNNSrcDirs64bit = {'pulp_nn_inc': PULPNNInstallPath64bit + 'include/',
'pulp_nn_pointwise_convolution': PULPNNInstallPath64bit +
'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath64bit +
'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution':
PULPNNInstallPath64bit + 'src/DepthwiseConvolutions/',
'pulp_nn_linear_convolution_nq': PULPNNInstallPath64bit +
'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q':
PULPNNInstallPath64bit + 'src/LinearConvolutionsQuant/',
'pulp_nn_support_function': PULPNNInstallPath64bit +
'src/SupportFunctions/', 'include': PULPNNTestFolder64bit + 'include/',
'src': PULPNNTestFolder64bit + 'src/', 'pointwise_convolution':
PULPNNTestFolder64bit + 'src/StandardConvolutions/', 'matmul':
PULPNNTestFolder64bit + 'src/MatrixMultiplications/',
'depthwise_convolution': PULPNNTestFolder64bit +
'src/DepthwiseConvolutions/', 'linear_convolution_nq':
PULPNNTestFolder64bit + 'src/LinearConvolutionsNoQuant/',
'linear_convolution_q': PULPNNTestFolder64bit +
'src/LinearConvolutionsQuant/', 'support_function':
PULPNNTestFolder64bit + 'src/SupportFunctions/', 'data_allocation_pw':
PULPNNTestFolder64bit + 'include/DataAllocationStandardConvolutions/',
'data_allocation_dw': PULPNNTestFolder64bit +
'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':
PULPNNTestFolder64bit +
'include/DataAllocationLinearConvolutionsNoQuant/',
'data_allocation_ln_q': PULPNNTestFolder64bit +
'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw':
PULPNNTestFolder64bit + 'include/GoldenModelStandardConvolutions/',
'golden_model_dw': PULPNNTestFolder64bit +
'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq':
PULPNNTestFolder64bit + 'include/GoldenModelLinearConvolutionsNoQuant/',
'golden_model_ln_q': PULPNNTestFolder64bit +
'include/GoldenModelLinearConvolutionsQuant/', 'test':
PULPNNTestFolder64bit}
<|reserved_special_token_1|>
import os
PULPNNInstallPath = cwd = os.getcwd() + '/../'
PULPNNSrcDirs = {'script': PULPNNInstallPath + 'scripts/'}
PULPNNInstallPath32bit = cwd = os.getcwd() + '/../32bit/'
PULPNNInstallPath64bit = cwd = os.getcwd() + '/../64bit/'
PULPNNTestFolder32bit = PULPNNInstallPath32bit + 'test/'
PULPNNTestFolder64bit = PULPNNInstallPath64bit + 'test/'
PULPNNSrcDirs32bit = {'pulp_nn_inc': PULPNNInstallPath32bit + 'include/',
'pulp_nn_pointwise_convolution': PULPNNInstallPath32bit +
'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath32bit +
'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution':
PULPNNInstallPath32bit + 'src/DepthwiseConvolutions/',
'pulp_nn_linear_convolution_nq': PULPNNInstallPath32bit +
'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q':
PULPNNInstallPath32bit + 'src/LinearConvolutionsQuant/',
'pulp_nn_support_function': PULPNNInstallPath32bit +
'src/SupportFunctions/', 'include': PULPNNTestFolder32bit + 'include/',
'src': PULPNNTestFolder32bit + 'src/', 'pointwise_convolution':
PULPNNTestFolder32bit + 'src/StandardConvolutions/', 'matmul':
PULPNNTestFolder32bit + 'src/MatrixMultiplications/',
'depthwise_convolution': PULPNNTestFolder32bit +
'src/DepthwiseConvolutions/', 'linear_convolution_nq':
PULPNNTestFolder32bit + 'src/LinearConvolutionsNoQuant/',
'linear_convolution_q': PULPNNTestFolder32bit +
'src/LinearConvolutionsQuant/', 'support_function':
PULPNNTestFolder32bit + 'src/SupportFunctions/', 'data_allocation_pw':
PULPNNTestFolder32bit + 'include/DataAllocationStandardConvolutions/',
'data_allocation_dw': PULPNNTestFolder32bit +
'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':
PULPNNTestFolder32bit +
'include/DataAllocationLinearConvolutionsNoQuant/',
'data_allocation_ln_q': PULPNNTestFolder32bit +
'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw':
PULPNNTestFolder32bit + 'include/GoldenModelStandardConvolutions/',
'golden_model_dw': PULPNNTestFolder32bit +
'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq':
PULPNNTestFolder32bit + 'include/GoldenModelLinearConvolutionsNoQuant/',
'golden_model_ln_q': PULPNNTestFolder32bit +
'include/GoldenModelLinearConvolutionsQuant/', 'test':
PULPNNTestFolder32bit}
PULPNNSrcDirs64bit = {'pulp_nn_inc': PULPNNInstallPath64bit + 'include/',
'pulp_nn_pointwise_convolution': PULPNNInstallPath64bit +
'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath64bit +
'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution':
PULPNNInstallPath64bit + 'src/DepthwiseConvolutions/',
'pulp_nn_linear_convolution_nq': PULPNNInstallPath64bit +
'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q':
PULPNNInstallPath64bit + 'src/LinearConvolutionsQuant/',
'pulp_nn_support_function': PULPNNInstallPath64bit +
'src/SupportFunctions/', 'include': PULPNNTestFolder64bit + 'include/',
'src': PULPNNTestFolder64bit + 'src/', 'pointwise_convolution':
PULPNNTestFolder64bit + 'src/StandardConvolutions/', 'matmul':
PULPNNTestFolder64bit + 'src/MatrixMultiplications/',
'depthwise_convolution': PULPNNTestFolder64bit +
'src/DepthwiseConvolutions/', 'linear_convolution_nq':
PULPNNTestFolder64bit + 'src/LinearConvolutionsNoQuant/',
'linear_convolution_q': PULPNNTestFolder64bit +
'src/LinearConvolutionsQuant/', 'support_function':
PULPNNTestFolder64bit + 'src/SupportFunctions/', 'data_allocation_pw':
PULPNNTestFolder64bit + 'include/DataAllocationStandardConvolutions/',
'data_allocation_dw': PULPNNTestFolder64bit +
'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':
PULPNNTestFolder64bit +
'include/DataAllocationLinearConvolutionsNoQuant/',
'data_allocation_ln_q': PULPNNTestFolder64bit +
'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw':
PULPNNTestFolder64bit + 'include/GoldenModelStandardConvolutions/',
'golden_model_dw': PULPNNTestFolder64bit +
'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq':
PULPNNTestFolder64bit + 'include/GoldenModelLinearConvolutionsNoQuant/',
'golden_model_ln_q': PULPNNTestFolder64bit +
'include/GoldenModelLinearConvolutionsQuant/', 'test':
PULPNNTestFolder64bit}
<|reserved_special_token_1|>
#
# struct_test.py
# Nazareno Bruschi <nazareno.bruschi@unibo.it>
#
# Copyright (C) 2019-2020 University of Bologna
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
PULPNNInstallPath = cwd = os.getcwd() + "/../"
PULPNNSrcDirs = {'script': PULPNNInstallPath + "scripts/"}
PULPNNInstallPath32bit = cwd = os.getcwd() + "/../32bit/"
PULPNNInstallPath64bit = cwd = os.getcwd() + "/../64bit/"
PULPNNTestFolder32bit = PULPNNInstallPath32bit + "test/"
PULPNNTestFolder64bit = PULPNNInstallPath64bit + "test/"
PULPNNSrcDirs32bit = {'pulp_nn_inc': PULPNNInstallPath32bit + "include/",
'pulp_nn_pointwise_convolution': PULPNNInstallPath32bit + "src/StandardConvolutions/",
'pulp_nn_matmul': PULPNNInstallPath32bit + "src/MatrixMultiplications/",
'pulp_nn_depthwise_convolution': PULPNNInstallPath32bit + "src/DepthwiseConvolutions/",
'pulp_nn_linear_convolution_nq': PULPNNInstallPath32bit + "src/LinearConvolutionsNoQuant/",
'pulp_nn_linear_convolution_q': PULPNNInstallPath32bit + "src/LinearConvolutionsQuant/",
'pulp_nn_support_function': PULPNNInstallPath32bit + "src/SupportFunctions/",
'include': PULPNNTestFolder32bit + "include/",
'src': PULPNNTestFolder32bit + "src/",
'pointwise_convolution': PULPNNTestFolder32bit + "src/StandardConvolutions/",
'matmul': PULPNNTestFolder32bit + "src/MatrixMultiplications/",
'depthwise_convolution': PULPNNTestFolder32bit + "src/DepthwiseConvolutions/",
'linear_convolution_nq': PULPNNTestFolder32bit + "src/LinearConvolutionsNoQuant/",
'linear_convolution_q': PULPNNTestFolder32bit + "src/LinearConvolutionsQuant/",
'support_function': PULPNNTestFolder32bit + "src/SupportFunctions/",
'data_allocation_pw': PULPNNTestFolder32bit + "include/DataAllocationStandardConvolutions/",
'data_allocation_dw': PULPNNTestFolder32bit + "include/DataAllocationDepthwiseConvolutions/",
'data_allocation_ln_nq': PULPNNTestFolder32bit + "include/DataAllocationLinearConvolutionsNoQuant/",
'data_allocation_ln_q': PULPNNTestFolder32bit + "include/DataAllocationLinearConvolutionsQuant/",
'golden_model_pw': PULPNNTestFolder32bit + "include/GoldenModelStandardConvolutions/",
'golden_model_dw': PULPNNTestFolder32bit + "include/GoldenModelDepthwiseConvolutions/",
'golden_model_ln_nq': PULPNNTestFolder32bit + "include/GoldenModelLinearConvolutionsNoQuant/",
'golden_model_ln_q': PULPNNTestFolder32bit + "include/GoldenModelLinearConvolutionsQuant/",
'test': PULPNNTestFolder32bit}
PULPNNSrcDirs64bit = {'pulp_nn_inc': PULPNNInstallPath64bit + "include/",
'pulp_nn_pointwise_convolution': PULPNNInstallPath64bit + "src/StandardConvolutions/",
'pulp_nn_matmul': PULPNNInstallPath64bit + "src/MatrixMultiplications/",
'pulp_nn_depthwise_convolution': PULPNNInstallPath64bit + "src/DepthwiseConvolutions/",
'pulp_nn_linear_convolution_nq': PULPNNInstallPath64bit + "src/LinearConvolutionsNoQuant/",
'pulp_nn_linear_convolution_q': PULPNNInstallPath64bit + "src/LinearConvolutionsQuant/",
'pulp_nn_support_function': PULPNNInstallPath64bit + "src/SupportFunctions/",
'include': PULPNNTestFolder64bit + "include/",
'src': PULPNNTestFolder64bit + "src/",
'pointwise_convolution': PULPNNTestFolder64bit + "src/StandardConvolutions/",
'matmul': PULPNNTestFolder64bit + "src/MatrixMultiplications/",
'depthwise_convolution': PULPNNTestFolder64bit + "src/DepthwiseConvolutions/",
'linear_convolution_nq': PULPNNTestFolder64bit + "src/LinearConvolutionsNoQuant/",
'linear_convolution_q': PULPNNTestFolder64bit + "src/LinearConvolutionsQuant/",
'support_function': PULPNNTestFolder64bit + "src/SupportFunctions/",
'data_allocation_pw': PULPNNTestFolder64bit + "include/DataAllocationStandardConvolutions/",
'data_allocation_dw': PULPNNTestFolder64bit + "include/DataAllocationDepthwiseConvolutions/",
'data_allocation_ln_nq': PULPNNTestFolder64bit + "include/DataAllocationLinearConvolutionsNoQuant/",
'data_allocation_ln_q': PULPNNTestFolder64bit + "include/DataAllocationLinearConvolutionsQuant/",
'golden_model_pw': PULPNNTestFolder64bit + "include/GoldenModelStandardConvolutions/",
'golden_model_dw': PULPNNTestFolder64bit + "include/GoldenModelDepthwiseConvolutions/",
'golden_model_ln_nq': PULPNNTestFolder64bit + "include/GoldenModelLinearConvolutionsNoQuant/",
'golden_model_ln_q': PULPNNTestFolder64bit + "include/GoldenModelLinearConvolutionsQuant/",
'test': PULPNNTestFolder64bit}
|
flexible
|
{
"blob_id": "d8d0c181fcfc9e0692369cc7a65259c43a68e931",
"index": 5688,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nPULPNNInstallPath = cwd = os.getcwd() + '/../'\nPULPNNSrcDirs = {'script': PULPNNInstallPath + 'scripts/'}\nPULPNNInstallPath32bit = cwd = os.getcwd() + '/../32bit/'\nPULPNNInstallPath64bit = cwd = os.getcwd() + '/../64bit/'\nPULPNNTestFolder32bit = PULPNNInstallPath32bit + 'test/'\nPULPNNTestFolder64bit = PULPNNInstallPath64bit + 'test/'\nPULPNNSrcDirs32bit = {'pulp_nn_inc': PULPNNInstallPath32bit + 'include/',\n 'pulp_nn_pointwise_convolution': PULPNNInstallPath32bit +\n 'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath32bit +\n 'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution': \n PULPNNInstallPath32bit + 'src/DepthwiseConvolutions/',\n 'pulp_nn_linear_convolution_nq': PULPNNInstallPath32bit +\n 'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q': \n PULPNNInstallPath32bit + 'src/LinearConvolutionsQuant/',\n 'pulp_nn_support_function': PULPNNInstallPath32bit +\n 'src/SupportFunctions/', 'include': PULPNNTestFolder32bit + 'include/',\n 'src': PULPNNTestFolder32bit + 'src/', 'pointwise_convolution': \n PULPNNTestFolder32bit + 'src/StandardConvolutions/', 'matmul': \n PULPNNTestFolder32bit + 'src/MatrixMultiplications/',\n 'depthwise_convolution': PULPNNTestFolder32bit +\n 'src/DepthwiseConvolutions/', 'linear_convolution_nq': \n PULPNNTestFolder32bit + 'src/LinearConvolutionsNoQuant/',\n 'linear_convolution_q': PULPNNTestFolder32bit +\n 'src/LinearConvolutionsQuant/', 'support_function': \n PULPNNTestFolder32bit + 'src/SupportFunctions/', 'data_allocation_pw': \n PULPNNTestFolder32bit + 'include/DataAllocationStandardConvolutions/',\n 'data_allocation_dw': PULPNNTestFolder32bit +\n 'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':\n PULPNNTestFolder32bit +\n 'include/DataAllocationLinearConvolutionsNoQuant/',\n 'data_allocation_ln_q': PULPNNTestFolder32bit +\n 'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw': \n PULPNNTestFolder32bit + 'include/GoldenModelStandardConvolutions/',\n 'golden_model_dw': PULPNNTestFolder32bit +\n 'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq': \n PULPNNTestFolder32bit + 'include/GoldenModelLinearConvolutionsNoQuant/',\n 'golden_model_ln_q': PULPNNTestFolder32bit +\n 'include/GoldenModelLinearConvolutionsQuant/', 'test':\n PULPNNTestFolder32bit}\nPULPNNSrcDirs64bit = {'pulp_nn_inc': PULPNNInstallPath64bit + 'include/',\n 'pulp_nn_pointwise_convolution': PULPNNInstallPath64bit +\n 'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath64bit +\n 'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution': \n PULPNNInstallPath64bit + 'src/DepthwiseConvolutions/',\n 'pulp_nn_linear_convolution_nq': PULPNNInstallPath64bit +\n 'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q': \n PULPNNInstallPath64bit + 'src/LinearConvolutionsQuant/',\n 'pulp_nn_support_function': PULPNNInstallPath64bit +\n 'src/SupportFunctions/', 'include': PULPNNTestFolder64bit + 'include/',\n 'src': PULPNNTestFolder64bit + 'src/', 'pointwise_convolution': \n PULPNNTestFolder64bit + 'src/StandardConvolutions/', 'matmul': \n PULPNNTestFolder64bit + 'src/MatrixMultiplications/',\n 'depthwise_convolution': PULPNNTestFolder64bit +\n 'src/DepthwiseConvolutions/', 'linear_convolution_nq': \n PULPNNTestFolder64bit + 'src/LinearConvolutionsNoQuant/',\n 'linear_convolution_q': PULPNNTestFolder64bit +\n 'src/LinearConvolutionsQuant/', 'support_function': \n PULPNNTestFolder64bit + 'src/SupportFunctions/', 'data_allocation_pw': \n PULPNNTestFolder64bit + 'include/DataAllocationStandardConvolutions/',\n 'data_allocation_dw': PULPNNTestFolder64bit +\n 'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':\n PULPNNTestFolder64bit +\n 'include/DataAllocationLinearConvolutionsNoQuant/',\n 'data_allocation_ln_q': PULPNNTestFolder64bit +\n 'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw': \n PULPNNTestFolder64bit + 'include/GoldenModelStandardConvolutions/',\n 'golden_model_dw': PULPNNTestFolder64bit +\n 'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq': \n PULPNNTestFolder64bit + 'include/GoldenModelLinearConvolutionsNoQuant/',\n 'golden_model_ln_q': PULPNNTestFolder64bit +\n 'include/GoldenModelLinearConvolutionsQuant/', 'test':\n PULPNNTestFolder64bit}\n",
"step-3": "import os\nPULPNNInstallPath = cwd = os.getcwd() + '/../'\nPULPNNSrcDirs = {'script': PULPNNInstallPath + 'scripts/'}\nPULPNNInstallPath32bit = cwd = os.getcwd() + '/../32bit/'\nPULPNNInstallPath64bit = cwd = os.getcwd() + '/../64bit/'\nPULPNNTestFolder32bit = PULPNNInstallPath32bit + 'test/'\nPULPNNTestFolder64bit = PULPNNInstallPath64bit + 'test/'\nPULPNNSrcDirs32bit = {'pulp_nn_inc': PULPNNInstallPath32bit + 'include/',\n 'pulp_nn_pointwise_convolution': PULPNNInstallPath32bit +\n 'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath32bit +\n 'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution': \n PULPNNInstallPath32bit + 'src/DepthwiseConvolutions/',\n 'pulp_nn_linear_convolution_nq': PULPNNInstallPath32bit +\n 'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q': \n PULPNNInstallPath32bit + 'src/LinearConvolutionsQuant/',\n 'pulp_nn_support_function': PULPNNInstallPath32bit +\n 'src/SupportFunctions/', 'include': PULPNNTestFolder32bit + 'include/',\n 'src': PULPNNTestFolder32bit + 'src/', 'pointwise_convolution': \n PULPNNTestFolder32bit + 'src/StandardConvolutions/', 'matmul': \n PULPNNTestFolder32bit + 'src/MatrixMultiplications/',\n 'depthwise_convolution': PULPNNTestFolder32bit +\n 'src/DepthwiseConvolutions/', 'linear_convolution_nq': \n PULPNNTestFolder32bit + 'src/LinearConvolutionsNoQuant/',\n 'linear_convolution_q': PULPNNTestFolder32bit +\n 'src/LinearConvolutionsQuant/', 'support_function': \n PULPNNTestFolder32bit + 'src/SupportFunctions/', 'data_allocation_pw': \n PULPNNTestFolder32bit + 'include/DataAllocationStandardConvolutions/',\n 'data_allocation_dw': PULPNNTestFolder32bit +\n 'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':\n PULPNNTestFolder32bit +\n 'include/DataAllocationLinearConvolutionsNoQuant/',\n 'data_allocation_ln_q': PULPNNTestFolder32bit +\n 'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw': \n PULPNNTestFolder32bit + 'include/GoldenModelStandardConvolutions/',\n 'golden_model_dw': PULPNNTestFolder32bit +\n 'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq': \n PULPNNTestFolder32bit + 'include/GoldenModelLinearConvolutionsNoQuant/',\n 'golden_model_ln_q': PULPNNTestFolder32bit +\n 'include/GoldenModelLinearConvolutionsQuant/', 'test':\n PULPNNTestFolder32bit}\nPULPNNSrcDirs64bit = {'pulp_nn_inc': PULPNNInstallPath64bit + 'include/',\n 'pulp_nn_pointwise_convolution': PULPNNInstallPath64bit +\n 'src/StandardConvolutions/', 'pulp_nn_matmul': PULPNNInstallPath64bit +\n 'src/MatrixMultiplications/', 'pulp_nn_depthwise_convolution': \n PULPNNInstallPath64bit + 'src/DepthwiseConvolutions/',\n 'pulp_nn_linear_convolution_nq': PULPNNInstallPath64bit +\n 'src/LinearConvolutionsNoQuant/', 'pulp_nn_linear_convolution_q': \n PULPNNInstallPath64bit + 'src/LinearConvolutionsQuant/',\n 'pulp_nn_support_function': PULPNNInstallPath64bit +\n 'src/SupportFunctions/', 'include': PULPNNTestFolder64bit + 'include/',\n 'src': PULPNNTestFolder64bit + 'src/', 'pointwise_convolution': \n PULPNNTestFolder64bit + 'src/StandardConvolutions/', 'matmul': \n PULPNNTestFolder64bit + 'src/MatrixMultiplications/',\n 'depthwise_convolution': PULPNNTestFolder64bit +\n 'src/DepthwiseConvolutions/', 'linear_convolution_nq': \n PULPNNTestFolder64bit + 'src/LinearConvolutionsNoQuant/',\n 'linear_convolution_q': PULPNNTestFolder64bit +\n 'src/LinearConvolutionsQuant/', 'support_function': \n PULPNNTestFolder64bit + 'src/SupportFunctions/', 'data_allocation_pw': \n PULPNNTestFolder64bit + 'include/DataAllocationStandardConvolutions/',\n 'data_allocation_dw': PULPNNTestFolder64bit +\n 'include/DataAllocationDepthwiseConvolutions/', 'data_allocation_ln_nq':\n PULPNNTestFolder64bit +\n 'include/DataAllocationLinearConvolutionsNoQuant/',\n 'data_allocation_ln_q': PULPNNTestFolder64bit +\n 'include/DataAllocationLinearConvolutionsQuant/', 'golden_model_pw': \n PULPNNTestFolder64bit + 'include/GoldenModelStandardConvolutions/',\n 'golden_model_dw': PULPNNTestFolder64bit +\n 'include/GoldenModelDepthwiseConvolutions/', 'golden_model_ln_nq': \n PULPNNTestFolder64bit + 'include/GoldenModelLinearConvolutionsNoQuant/',\n 'golden_model_ln_q': PULPNNTestFolder64bit +\n 'include/GoldenModelLinearConvolutionsQuant/', 'test':\n PULPNNTestFolder64bit}\n",
"step-4": "#\n# struct_test.py\n# Nazareno Bruschi <nazareno.bruschi@unibo.it>\n#\n# Copyright (C) 2019-2020 University of Bologna\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\nimport os\n\nPULPNNInstallPath = cwd = os.getcwd() + \"/../\"\nPULPNNSrcDirs = {'script': PULPNNInstallPath + \"scripts/\"}\nPULPNNInstallPath32bit = cwd = os.getcwd() + \"/../32bit/\"\nPULPNNInstallPath64bit = cwd = os.getcwd() + \"/../64bit/\"\nPULPNNTestFolder32bit = PULPNNInstallPath32bit + \"test/\"\nPULPNNTestFolder64bit = PULPNNInstallPath64bit + \"test/\"\nPULPNNSrcDirs32bit = {'pulp_nn_inc': PULPNNInstallPath32bit + \"include/\",\n 'pulp_nn_pointwise_convolution': PULPNNInstallPath32bit + \"src/StandardConvolutions/\",\n 'pulp_nn_matmul': PULPNNInstallPath32bit + \"src/MatrixMultiplications/\",\n 'pulp_nn_depthwise_convolution': PULPNNInstallPath32bit + \"src/DepthwiseConvolutions/\",\n 'pulp_nn_linear_convolution_nq': PULPNNInstallPath32bit + \"src/LinearConvolutionsNoQuant/\",\n 'pulp_nn_linear_convolution_q': PULPNNInstallPath32bit + \"src/LinearConvolutionsQuant/\",\n 'pulp_nn_support_function': PULPNNInstallPath32bit + \"src/SupportFunctions/\",\n 'include': PULPNNTestFolder32bit + \"include/\",\n 'src': PULPNNTestFolder32bit + \"src/\",\n 'pointwise_convolution': PULPNNTestFolder32bit + \"src/StandardConvolutions/\",\n 'matmul': PULPNNTestFolder32bit + \"src/MatrixMultiplications/\",\n 'depthwise_convolution': PULPNNTestFolder32bit + \"src/DepthwiseConvolutions/\",\n 'linear_convolution_nq': PULPNNTestFolder32bit + \"src/LinearConvolutionsNoQuant/\",\n 'linear_convolution_q': PULPNNTestFolder32bit + \"src/LinearConvolutionsQuant/\",\n 'support_function': PULPNNTestFolder32bit + \"src/SupportFunctions/\",\n 'data_allocation_pw': PULPNNTestFolder32bit + \"include/DataAllocationStandardConvolutions/\",\n 'data_allocation_dw': PULPNNTestFolder32bit + \"include/DataAllocationDepthwiseConvolutions/\",\n 'data_allocation_ln_nq': PULPNNTestFolder32bit + \"include/DataAllocationLinearConvolutionsNoQuant/\",\n 'data_allocation_ln_q': PULPNNTestFolder32bit + \"include/DataAllocationLinearConvolutionsQuant/\",\n 'golden_model_pw': PULPNNTestFolder32bit + \"include/GoldenModelStandardConvolutions/\",\n 'golden_model_dw': PULPNNTestFolder32bit + \"include/GoldenModelDepthwiseConvolutions/\",\n 'golden_model_ln_nq': PULPNNTestFolder32bit + \"include/GoldenModelLinearConvolutionsNoQuant/\",\n 'golden_model_ln_q': PULPNNTestFolder32bit + \"include/GoldenModelLinearConvolutionsQuant/\",\n 'test': PULPNNTestFolder32bit}\nPULPNNSrcDirs64bit = {'pulp_nn_inc': PULPNNInstallPath64bit + \"include/\",\n 'pulp_nn_pointwise_convolution': PULPNNInstallPath64bit + \"src/StandardConvolutions/\",\n 'pulp_nn_matmul': PULPNNInstallPath64bit + \"src/MatrixMultiplications/\",\n 'pulp_nn_depthwise_convolution': PULPNNInstallPath64bit + \"src/DepthwiseConvolutions/\",\n 'pulp_nn_linear_convolution_nq': PULPNNInstallPath64bit + \"src/LinearConvolutionsNoQuant/\",\n 'pulp_nn_linear_convolution_q': PULPNNInstallPath64bit + \"src/LinearConvolutionsQuant/\",\n 'pulp_nn_support_function': PULPNNInstallPath64bit + \"src/SupportFunctions/\",\n 'include': PULPNNTestFolder64bit + \"include/\",\n 'src': PULPNNTestFolder64bit + \"src/\",\n 'pointwise_convolution': PULPNNTestFolder64bit + \"src/StandardConvolutions/\",\n 'matmul': PULPNNTestFolder64bit + \"src/MatrixMultiplications/\",\n 'depthwise_convolution': PULPNNTestFolder64bit + \"src/DepthwiseConvolutions/\",\n 'linear_convolution_nq': PULPNNTestFolder64bit + \"src/LinearConvolutionsNoQuant/\",\n 'linear_convolution_q': PULPNNTestFolder64bit + \"src/LinearConvolutionsQuant/\",\n 'support_function': PULPNNTestFolder64bit + \"src/SupportFunctions/\",\n 'data_allocation_pw': PULPNNTestFolder64bit + \"include/DataAllocationStandardConvolutions/\",\n 'data_allocation_dw': PULPNNTestFolder64bit + \"include/DataAllocationDepthwiseConvolutions/\",\n 'data_allocation_ln_nq': PULPNNTestFolder64bit + \"include/DataAllocationLinearConvolutionsNoQuant/\",\n 'data_allocation_ln_q': PULPNNTestFolder64bit + \"include/DataAllocationLinearConvolutionsQuant/\",\n 'golden_model_pw': PULPNNTestFolder64bit + \"include/GoldenModelStandardConvolutions/\",\n 'golden_model_dw': PULPNNTestFolder64bit + \"include/GoldenModelDepthwiseConvolutions/\",\n 'golden_model_ln_nq': PULPNNTestFolder64bit + \"include/GoldenModelLinearConvolutionsNoQuant/\",\n 'golden_model_ln_q': PULPNNTestFolder64bit + \"include/GoldenModelLinearConvolutionsQuant/\",\n 'test': PULPNNTestFolder64bit}",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class OfferApi(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,
**kwargs):
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method find_eligible_items"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'
)
collection_formats = {}
path_params = {}
query_params = []
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'offset' in params:
query_params.append(('offset', params['offset']))
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
auth_settings = ['api_auth']
return self.api_client.call_api('/find_eligible_items', 'GET',
path_params, query_params, header_params, body=body_params,
post_params=form_params, files=local_var_files, response_type=
'PagedEligibleItemCollection', auth_settings=auth_settings,
async_req=params.get('async_req'), _return_http_data_only=
params.get('_return_http_data_only'), _preload_content=params.
get('_preload_content', True), _request_timeout=params.get(
'_request_timeout'), collection_formats=collection_formats)
def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs
):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
else:
data = self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
return data
def send_offer_to_interested_buyers_with_http_info(self,
x_ebay_c_marketplace_id, **kwargs):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'body']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'
)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
header_params['Content-Type'
] = self.api_client.select_header_content_type(['application/json']
)
auth_settings = ['api_auth']
return self.api_client.call_api('/send_offer_to_interested_buyers',
'POST', path_params, query_params, header_params, body=
body_params, post_params=form_params, files=local_var_files,
response_type='SendOfferToInterestedBuyersCollectionResponse',
auth_settings=auth_settings, async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class OfferApi(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs):
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.find_eligible_items_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
else:
data = self.find_eligible_items_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
return data
def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,
**kwargs):
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method find_eligible_items"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'
)
collection_formats = {}
path_params = {}
query_params = []
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'offset' in params:
query_params.append(('offset', params['offset']))
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
auth_settings = ['api_auth']
return self.api_client.call_api('/find_eligible_items', 'GET',
path_params, query_params, header_params, body=body_params,
post_params=form_params, files=local_var_files, response_type=
'PagedEligibleItemCollection', auth_settings=auth_settings,
async_req=params.get('async_req'), _return_http_data_only=
params.get('_return_http_data_only'), _preload_content=params.
get('_preload_content', True), _request_timeout=params.get(
'_request_timeout'), collection_formats=collection_formats)
def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs
):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
else:
data = self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
return data
def send_offer_to_interested_buyers_with_http_info(self,
x_ebay_c_marketplace_id, **kwargs):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'body']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'
)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
header_params['Content-Type'
] = self.api_client.select_header_content_type(['application/json']
)
auth_settings = ['api_auth']
return self.api_client.call_api('/send_offer_to_interested_buyers',
'POST', path_params, query_params, header_params, body=
body_params, post_params=form_params, files=local_var_files,
response_type='SendOfferToInterestedBuyersCollectionResponse',
auth_settings=auth_settings, async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class OfferApi(object):
<|reserved_special_token_0|>
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs):
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.find_eligible_items_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
else:
data = self.find_eligible_items_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
return data
def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,
**kwargs):
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method find_eligible_items"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'
)
collection_formats = {}
path_params = {}
query_params = []
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'offset' in params:
query_params.append(('offset', params['offset']))
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
auth_settings = ['api_auth']
return self.api_client.call_api('/find_eligible_items', 'GET',
path_params, query_params, header_params, body=body_params,
post_params=form_params, files=local_var_files, response_type=
'PagedEligibleItemCollection', auth_settings=auth_settings,
async_req=params.get('async_req'), _return_http_data_only=
params.get('_return_http_data_only'), _preload_content=params.
get('_preload_content', True), _request_timeout=params.get(
'_request_timeout'), collection_formats=collection_formats)
def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs
):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
else:
data = self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
return data
def send_offer_to_interested_buyers_with_http_info(self,
x_ebay_c_marketplace_id, **kwargs):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'body']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'
)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
header_params['Content-Type'
] = self.api_client.select_header_content_type(['application/json']
)
auth_settings = ['api_auth']
return self.api_client.call_api('/send_offer_to_interested_buyers',
'POST', path_params, query_params, header_params, body=
body_params, post_params=form_params, files=local_var_files,
response_type='SendOfferToInterestedBuyersCollectionResponse',
auth_settings=auth_settings, async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
from __future__ import absolute_import
import re
import six
from ...sell_negotiation.api_client import ApiClient
class OfferApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs):
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.find_eligible_items_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
else:
data = self.find_eligible_items_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
return data
def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,
**kwargs):
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method find_eligible_items"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'
)
collection_formats = {}
path_params = {}
query_params = []
if 'limit' in params:
query_params.append(('limit', params['limit']))
if 'offset' in params:
query_params.append(('offset', params['offset']))
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
auth_settings = ['api_auth']
return self.api_client.call_api('/find_eligible_items', 'GET',
path_params, query_params, header_params, body=body_params,
post_params=form_params, files=local_var_files, response_type=
'PagedEligibleItemCollection', auth_settings=auth_settings,
async_req=params.get('async_req'), _return_http_data_only=
params.get('_return_http_data_only'), _preload_content=params.
get('_preload_content', True), _request_timeout=params.get(
'_request_timeout'), collection_formats=collection_formats)
def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs
):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
else:
data = self.send_offer_to_interested_buyers_with_http_info(
x_ebay_c_marketplace_id, **kwargs)
return data
def send_offer_to_interested_buyers_with_http_info(self,
x_ebay_c_marketplace_id, **kwargs):
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'body']
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers"
% key)
params[key] = val
del params['kwargs']
if 'x_ebay_c_marketplace_id' not in params or params[
'x_ebay_c_marketplace_id'] is None:
raise ValueError(
'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'
)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params[
'x_ebay_c_marketplace_id']
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
header_params['Accept'] = self.api_client.select_header_accept([
'application/json'])
header_params['Content-Type'
] = self.api_client.select_header_content_type(['application/json']
)
auth_settings = ['api_auth']
return self.api_client.call_api('/send_offer_to_interested_buyers',
'POST', path_params, query_params, header_params, body=
body_params, post_params=form_params, files=local_var_files,
response_type='SendOfferToInterestedBuyersCollectionResponse',
auth_settings=auth_settings, async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
<|reserved_special_token_1|>
# coding: utf-8
"""
Negotiation API
The <b>Negotiations API</b> gives sellers the ability to proactively send discount offers to buyers who have shown an \"interest\" in their listings. <br><br>By sending buyers discount offers on listings where they have shown an interest, sellers can increase the velocity of their sales. <br><br>There are various ways for a buyer to show <i>interest </i> in a listing. For example, if a buyer adds the listing to their <b>Watch</b> list, or if they add the listing to their shopping cart and later abandon the cart, they are deemed to have shown an interest in the listing. <br><br>In the offers that sellers send, they can discount their listings by either a percentage off the listing price, or they can set a new discounted price that is lower than the original listing price. <br><br>For details about how seller offers work, see <a href=\"/api-docs/sell/static/marketing/offers-to-buyers.html\" title=\"Selling Integration Guide\">Sending offers to buyers</a>. # noqa: E501
OpenAPI spec version: v1.1.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from ...sell_negotiation.api_client import ApiClient
class OfferApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501
else:
(data) = self.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501
return data
def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501
"""find_eligible_items # noqa: E501
This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10
:param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0
:return: PagedEligibleItemCollection
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method find_eligible_items" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'x_ebay_c_marketplace_id' is set
if ('x_ebay_c_marketplace_id' not in params or
params['x_ebay_c_marketplace_id'] is None):
raise ValueError("Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'offset' in params:
query_params.append(('offset', params['offset'])) # noqa: E501
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params['x_ebay_c_marketplace_id'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_auth'] # noqa: E501
return self.api_client.call_api(
'/find_eligible_items', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PagedEligibleItemCollection', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501
else:
(data) = self.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501
return data
def send_offer_to_interested_buyers_with_http_info(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501
"""send_offer_to_interested_buyers # noqa: E501
This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)
:param CreateOffersRequest body: Send offer to eligible items request.
:return: SendOfferToInterestedBuyersCollectionResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['x_ebay_c_marketplace_id', 'body'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method send_offer_to_interested_buyers" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'x_ebay_c_marketplace_id' is set
if ('x_ebay_c_marketplace_id' not in params or
params['x_ebay_c_marketplace_id'] is None):
raise ValueError("Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
if 'x_ebay_c_marketplace_id' in params:
header_params['X-EBAY-C-MARKETPLACE-ID'] = params['x_ebay_c_marketplace_id'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_auth'] # noqa: E501
return self.api_client.call_api(
'/send_offer_to_interested_buyers', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SendOfferToInterestedBuyersCollectionResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
|
flexible
|
{
"blob_id": "a93818440410bde004f0203f18112fa1b666959c",
"index": 9615,
"step-1": "<mask token>\n\n\nclass OfferApi(object):\n <mask token>\n <mask token>\n <mask token>\n\n def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,\n **kwargs):\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method find_eligible_items\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n if 'limit' in params:\n query_params.append(('limit', params['limit']))\n if 'offset' in params:\n query_params.append(('offset', params['offset']))\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n auth_settings = ['api_auth']\n return self.api_client.call_api('/find_eligible_items', 'GET',\n path_params, query_params, header_params, body=body_params,\n post_params=form_params, files=local_var_files, response_type=\n 'PagedEligibleItemCollection', auth_settings=auth_settings,\n async_req=params.get('async_req'), _return_http_data_only=\n params.get('_return_http_data_only'), _preload_content=params.\n get('_preload_content', True), _request_timeout=params.get(\n '_request_timeout'), collection_formats=collection_formats)\n\n def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs\n ):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n else:\n data = self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n return data\n\n def send_offer_to_interested_buyers_with_http_info(self,\n x_ebay_c_marketplace_id, **kwargs):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'body']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n if 'body' in params:\n body_params = params['body']\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n header_params['Content-Type'\n ] = self.api_client.select_header_content_type(['application/json']\n )\n auth_settings = ['api_auth']\n return self.api_client.call_api('/send_offer_to_interested_buyers',\n 'POST', path_params, query_params, header_params, body=\n body_params, post_params=form_params, files=local_var_files,\n response_type='SendOfferToInterestedBuyersCollectionResponse',\n auth_settings=auth_settings, async_req=params.get('async_req'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)\n",
"step-2": "<mask token>\n\n\nclass OfferApi(object):\n <mask token>\n <mask token>\n\n def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs):\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.find_eligible_items_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n else:\n data = self.find_eligible_items_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n return data\n\n def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,\n **kwargs):\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method find_eligible_items\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n if 'limit' in params:\n query_params.append(('limit', params['limit']))\n if 'offset' in params:\n query_params.append(('offset', params['offset']))\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n auth_settings = ['api_auth']\n return self.api_client.call_api('/find_eligible_items', 'GET',\n path_params, query_params, header_params, body=body_params,\n post_params=form_params, files=local_var_files, response_type=\n 'PagedEligibleItemCollection', auth_settings=auth_settings,\n async_req=params.get('async_req'), _return_http_data_only=\n params.get('_return_http_data_only'), _preload_content=params.\n get('_preload_content', True), _request_timeout=params.get(\n '_request_timeout'), collection_formats=collection_formats)\n\n def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs\n ):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n else:\n data = self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n return data\n\n def send_offer_to_interested_buyers_with_http_info(self,\n x_ebay_c_marketplace_id, **kwargs):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'body']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n if 'body' in params:\n body_params = params['body']\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n header_params['Content-Type'\n ] = self.api_client.select_header_content_type(['application/json']\n )\n auth_settings = ['api_auth']\n return self.api_client.call_api('/send_offer_to_interested_buyers',\n 'POST', path_params, query_params, header_params, body=\n body_params, post_params=form_params, files=local_var_files,\n response_type='SendOfferToInterestedBuyersCollectionResponse',\n auth_settings=auth_settings, async_req=params.get('async_req'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)\n",
"step-3": "<mask token>\n\n\nclass OfferApi(object):\n <mask token>\n\n def __init__(self, api_client=None):\n if api_client is None:\n api_client = ApiClient()\n self.api_client = api_client\n\n def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs):\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.find_eligible_items_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n else:\n data = self.find_eligible_items_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n return data\n\n def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,\n **kwargs):\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method find_eligible_items\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n if 'limit' in params:\n query_params.append(('limit', params['limit']))\n if 'offset' in params:\n query_params.append(('offset', params['offset']))\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n auth_settings = ['api_auth']\n return self.api_client.call_api('/find_eligible_items', 'GET',\n path_params, query_params, header_params, body=body_params,\n post_params=form_params, files=local_var_files, response_type=\n 'PagedEligibleItemCollection', auth_settings=auth_settings,\n async_req=params.get('async_req'), _return_http_data_only=\n params.get('_return_http_data_only'), _preload_content=params.\n get('_preload_content', True), _request_timeout=params.get(\n '_request_timeout'), collection_formats=collection_formats)\n\n def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs\n ):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n else:\n data = self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n return data\n\n def send_offer_to_interested_buyers_with_http_info(self,\n x_ebay_c_marketplace_id, **kwargs):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'body']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n if 'body' in params:\n body_params = params['body']\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n header_params['Content-Type'\n ] = self.api_client.select_header_content_type(['application/json']\n )\n auth_settings = ['api_auth']\n return self.api_client.call_api('/send_offer_to_interested_buyers',\n 'POST', path_params, query_params, header_params, body=\n body_params, post_params=form_params, files=local_var_files,\n response_type='SendOfferToInterestedBuyersCollectionResponse',\n auth_settings=auth_settings, async_req=params.get('async_req'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)\n",
"step-4": "<mask token>\nfrom __future__ import absolute_import\nimport re\nimport six\nfrom ...sell_negotiation.api_client import ApiClient\n\n\nclass OfferApi(object):\n \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n Do not edit the class manually.\n Ref: https://github.com/swagger-api/swagger-codegen\n \"\"\"\n\n def __init__(self, api_client=None):\n if api_client is None:\n api_client = ApiClient()\n self.api_client = api_client\n\n def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs):\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.find_eligible_items_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n else:\n data = self.find_eligible_items_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n return data\n\n def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id,\n **kwargs):\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method find_eligible_items\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n if 'limit' in params:\n query_params.append(('limit', params['limit']))\n if 'offset' in params:\n query_params.append(('offset', params['offset']))\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n auth_settings = ['api_auth']\n return self.api_client.call_api('/find_eligible_items', 'GET',\n path_params, query_params, header_params, body=body_params,\n post_params=form_params, files=local_var_files, response_type=\n 'PagedEligibleItemCollection', auth_settings=auth_settings,\n async_req=params.get('async_req'), _return_http_data_only=\n params.get('_return_http_data_only'), _preload_content=params.\n get('_preload_content', True), _request_timeout=params.get(\n '_request_timeout'), collection_formats=collection_formats)\n\n def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs\n ):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n else:\n data = self.send_offer_to_interested_buyers_with_http_info(\n x_ebay_c_marketplace_id, **kwargs)\n return data\n\n def send_offer_to_interested_buyers_with_http_info(self,\n x_ebay_c_marketplace_id, **kwargs):\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n all_params = ['x_ebay_c_marketplace_id', 'body']\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s' to method send_offer_to_interested_buyers\"\n % key)\n params[key] = val\n del params['kwargs']\n if 'x_ebay_c_marketplace_id' not in params or params[\n 'x_ebay_c_marketplace_id'] is None:\n raise ValueError(\n 'Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`'\n )\n collection_formats = {}\n path_params = {}\n query_params = []\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params[\n 'x_ebay_c_marketplace_id']\n form_params = []\n local_var_files = {}\n body_params = None\n if 'body' in params:\n body_params = params['body']\n header_params['Accept'] = self.api_client.select_header_accept([\n 'application/json'])\n header_params['Content-Type'\n ] = self.api_client.select_header_content_type(['application/json']\n )\n auth_settings = ['api_auth']\n return self.api_client.call_api('/send_offer_to_interested_buyers',\n 'POST', path_params, query_params, header_params, body=\n body_params, post_params=form_params, files=local_var_files,\n response_type='SendOfferToInterestedBuyersCollectionResponse',\n auth_settings=auth_settings, async_req=params.get('async_req'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)\n",
"step-5": "# coding: utf-8\n\n\"\"\"\n Negotiation API\n\n The <b>Negotiations API</b> gives sellers the ability to proactively send discount offers to buyers who have shown an \\\"interest\\\" in their listings. <br><br>By sending buyers discount offers on listings where they have shown an interest, sellers can increase the velocity of their sales. <br><br>There are various ways for a buyer to show <i>interest </i> in a listing. For example, if a buyer adds the listing to their <b>Watch</b> list, or if they add the listing to their shopping cart and later abandon the cart, they are deemed to have shown an interest in the listing. <br><br>In the offers that sellers send, they can discount their listings by either a percentage off the listing price, or they can set a new discounted price that is lower than the original listing price. <br><br>For details about how seller offers work, see <a href=\\\"/api-docs/sell/static/marketing/offers-to-buyers.html\\\" title=\\\"Selling Integration Guide\\\">Sending offers to buyers</a>. # noqa: E501\n\n OpenAPI spec version: v1.1.0\n \n Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\nfrom __future__ import absolute_import\n\nimport re # noqa: F401\n\n# python 2 and python 3 compatibility library\nimport six\n\nfrom ...sell_negotiation.api_client import ApiClient\n\n\nclass OfferApi(object):\n \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n Do not edit the class manually.\n Ref: https://github.com/swagger-api/swagger-codegen\n \"\"\"\n\n def __init__(self, api_client=None):\n if api_client is None:\n api_client = ApiClient()\n self.api_client = api_client\n\n def find_eligible_items(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501\n else:\n (data) = self.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501\n return data\n\n def find_eligible_items_with_http_info(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501\n \"\"\"find_eligible_items # noqa: E501\n\n This method evaluates a seller's current listings and returns the set of IDs that are eligible for a seller-initiated discount offer to a buyer. A listing ID is returned only when one or more buyers have shown an "interest" in the listing. If any buyers have shown interest in a listing, the seller can initiate a "negotiation" with them by calling sendOfferToInterestedBuyers, which sends all interested buyers a message that offers the listing at a discount. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.find_eligible_items_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which you want to search for eligible listings. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param str limit: This query parameter specifies the maximum number of items to return from the result set on a page in the paginated response. Minimum: 1 Maximum: 200 Default: 10\n :param str offset: This query parameter specifies the number of results to skip in the result set before returning the first result in the paginated response. Combine offset with the limit query parameter to control the items returned in the response. For example, if you supply an offset of 0 and a limit of 10, the first page of the response contains the first 10 results from the complete list of items retrieved by the call. If offset is 10 and limit is 20, the first page of the response contains items 11-30 from the complete result set. Default: 0\n :return: PagedEligibleItemCollection\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n\n all_params = ['x_ebay_c_marketplace_id', 'limit', 'offset'] # noqa: E501\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method find_eligible_items\" % key\n )\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'x_ebay_c_marketplace_id' is set\n if ('x_ebay_c_marketplace_id' not in params or\n params['x_ebay_c_marketplace_id'] is None):\n raise ValueError(\"Missing the required parameter `x_ebay_c_marketplace_id` when calling `find_eligible_items`\") # noqa: E501\n\n collection_formats = {}\n\n path_params = {}\n\n query_params = []\n if 'limit' in params:\n query_params.append(('limit', params['limit'])) # noqa: E501\n if 'offset' in params:\n query_params.append(('offset', params['offset'])) # noqa: E501\n\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params['x_ebay_c_marketplace_id'] # noqa: E501\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.select_header_accept(\n ['application/json']) # noqa: E501\n\n # Authentication setting\n auth_settings = ['api_auth'] # noqa: E501\n\n return self.api_client.call_api(\n '/find_eligible_items', 'GET',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='PagedEligibleItemCollection', # noqa: E501\n auth_settings=auth_settings,\n async_req=params.get('async_req'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)\n\n def send_offer_to_interested_buyers(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n kwargs['_return_http_data_only'] = True\n if kwargs.get('async_req'):\n return self.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501\n else:\n (data) = self.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, **kwargs) # noqa: E501\n return data\n\n def send_offer_to_interested_buyers_with_http_info(self, x_ebay_c_marketplace_id, **kwargs): # noqa: E501\n \"\"\"send_offer_to_interested_buyers # noqa: E501\n\n This method sends eligible buyers offers to purchase items in a listing at a discount. When a buyer has shown interest in a listing, they become "eligible" to receive a seller-initiated offer to purchase the item(s). Sellers use findEligibleItems to get the set of listings that have interested buyers. If a listing has interested buyers, sellers can use this method (sendOfferToInterestedBuyers) to send an offer to the buyers who are interested in the listing. The offer gives buyers the ability to purchase the associated listings at a discounted price. For details about how to create seller offers to buyers, see Sending offers to buyers. # noqa: E501\n This method makes a synchronous HTTP request by default. To make an\n asynchronous HTTP request, please pass async_req=True\n >>> thread = api.send_offer_to_interested_buyers_with_http_info(x_ebay_c_marketplace_id, async_req=True)\n >>> result = thread.get()\n\n :param async_req bool\n :param str x_ebay_c_marketplace_id: The eBay marketplace on which your listings with "eligible" buyers appear. For a complete list of supported marketplaces, see Negotiation API requirements and restrictions. (required)\n :param CreateOffersRequest body: Send offer to eligible items request.\n :return: SendOfferToInterestedBuyersCollectionResponse\n If the method is called asynchronously,\n returns the request thread.\n \"\"\"\n\n all_params = ['x_ebay_c_marketplace_id', 'body'] # noqa: E501\n all_params.append('async_req')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method send_offer_to_interested_buyers\" % key\n )\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'x_ebay_c_marketplace_id' is set\n if ('x_ebay_c_marketplace_id' not in params or\n params['x_ebay_c_marketplace_id'] is None):\n raise ValueError(\"Missing the required parameter `x_ebay_c_marketplace_id` when calling `send_offer_to_interested_buyers`\") # noqa: E501\n\n collection_formats = {}\n\n path_params = {}\n\n query_params = []\n\n header_params = {}\n if 'x_ebay_c_marketplace_id' in params:\n header_params['X-EBAY-C-MARKETPLACE-ID'] = params['x_ebay_c_marketplace_id'] # noqa: E501\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n if 'body' in params:\n body_params = params['body']\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.select_header_accept(\n ['application/json']) # noqa: E501\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501\n ['application/json']) # noqa: E501\n\n # Authentication setting\n auth_settings = ['api_auth'] # noqa: E501\n\n return self.api_client.call_api(\n '/send_offer_to_interested_buyers', 'POST',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='SendOfferToInterestedBuyersCollectionResponse', # noqa: E501\n auth_settings=auth_settings,\n async_req=params.get('async_req'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)\n",
"step-ids": [
4,
5,
6,
8,
9
]
}
|
[
4,
5,
6,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('test_9feats.csv', 'w') as f:
df = pd.DataFrame(file, columns=['dst_host_srv_serror_rate',
'dst_host_serror_rate', 'serror_rate', 'srv_serror_rate', 'count',
'flag', 'same_srv_rate', 'dst_host_srv_count',
'dst_host_diff_srv_rate', 'Malicious'])
df.to_csv(f, index=False, header=True, line_terminator='\n')
print(df)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
file = pd.read_csv('KDDTest+.csv')
with open('test_9feats.csv', 'w') as f:
df = pd.DataFrame(file, columns=['dst_host_srv_serror_rate',
'dst_host_serror_rate', 'serror_rate', 'srv_serror_rate', 'count',
'flag', 'same_srv_rate', 'dst_host_srv_count',
'dst_host_diff_srv_rate', 'Malicious'])
df.to_csv(f, index=False, header=True, line_terminator='\n')
print(df)
<|reserved_special_token_1|>
import pandas as pd
file = pd.read_csv('KDDTest+.csv')
with open('test_9feats.csv', 'w') as f:
df = pd.DataFrame(file, columns=['dst_host_srv_serror_rate',
'dst_host_serror_rate', 'serror_rate', 'srv_serror_rate', 'count',
'flag', 'same_srv_rate', 'dst_host_srv_count',
'dst_host_diff_srv_rate', 'Malicious'])
df.to_csv(f, index=False, header=True, line_terminator='\n')
print(df)
<|reserved_special_token_1|>
import pandas as pd
file = pd.read_csv("KDDTest+.csv")
with open("test_9feats.csv", "w") as f:
df = pd.DataFrame(file,
columns=[
"dst_host_srv_serror_rate", "dst_host_serror_rate",
"serror_rate", "srv_serror_rate", "count", "flag",
"same_srv_rate", "dst_host_srv_count",
"dst_host_diff_srv_rate", "Malicious"
])
df.to_csv(f, index=False, header=True, line_terminator='\n')
print(df)
|
flexible
|
{
"blob_id": "ce28330db66dcdfad63bdac698ce9d285964d288",
"index": 5124,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('test_9feats.csv', 'w') as f:\n df = pd.DataFrame(file, columns=['dst_host_srv_serror_rate',\n 'dst_host_serror_rate', 'serror_rate', 'srv_serror_rate', 'count',\n 'flag', 'same_srv_rate', 'dst_host_srv_count',\n 'dst_host_diff_srv_rate', 'Malicious'])\n df.to_csv(f, index=False, header=True, line_terminator='\\n')\n print(df)\n",
"step-3": "<mask token>\nfile = pd.read_csv('KDDTest+.csv')\nwith open('test_9feats.csv', 'w') as f:\n df = pd.DataFrame(file, columns=['dst_host_srv_serror_rate',\n 'dst_host_serror_rate', 'serror_rate', 'srv_serror_rate', 'count',\n 'flag', 'same_srv_rate', 'dst_host_srv_count',\n 'dst_host_diff_srv_rate', 'Malicious'])\n df.to_csv(f, index=False, header=True, line_terminator='\\n')\n print(df)\n",
"step-4": "import pandas as pd\nfile = pd.read_csv('KDDTest+.csv')\nwith open('test_9feats.csv', 'w') as f:\n df = pd.DataFrame(file, columns=['dst_host_srv_serror_rate',\n 'dst_host_serror_rate', 'serror_rate', 'srv_serror_rate', 'count',\n 'flag', 'same_srv_rate', 'dst_host_srv_count',\n 'dst_host_diff_srv_rate', 'Malicious'])\n df.to_csv(f, index=False, header=True, line_terminator='\\n')\n print(df)\n",
"step-5": "import pandas as pd\n\nfile = pd.read_csv(\"KDDTest+.csv\")\nwith open(\"test_9feats.csv\", \"w\") as f:\n df = pd.DataFrame(file,\n columns=[\n \"dst_host_srv_serror_rate\", \"dst_host_serror_rate\",\n \"serror_rate\", \"srv_serror_rate\", \"count\", \"flag\",\n \"same_srv_rate\", \"dst_host_srv_count\",\n \"dst_host_diff_srv_rate\", \"Malicious\"\n ])\n df.to_csv(f, index=False, header=True, line_terminator='\\n')\n print(df)",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# from sklearn import tree
# import joblib
music_data = pd.read_csv(r"C:\Users\junha\PythonProjects\predict_music_preferences\music.csv")
# print(music_data)
X = music_data.drop(columns=['genre'])
y = music_data['genre']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(predictions)
score = accuracy_score(y_test, predictions)
print(score)
# joblib.dump(model, 'music-recommender.joblib')
# tree.export_graphviz(model, out_file='music-recommender.dot',
# feature_names=['age', 'gender'],
# class_names=sorted(y.unique()),
# label='all', rounded= True,
# filled=True)
|
normal
|
{
"blob_id": "8dbcd7bba09f8acff860890d8201e016b587796d",
"index": 6149,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmodel.fit(X_train, y_train)\n<mask token>\nprint(predictions)\n<mask token>\nprint(score)\n",
"step-3": "<mask token>\nmusic_data = pd.read_csv(\n 'C:\\\\Users\\\\junha\\\\PythonProjects\\\\predict_music_preferences\\\\music.csv')\nX = music_data.drop(columns=['genre'])\ny = music_data['genre']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\nmodel = DecisionTreeClassifier()\nmodel.fit(X_train, y_train)\npredictions = model.predict(X_test)\nprint(predictions)\nscore = accuracy_score(y_test, predictions)\nprint(score)\n",
"step-4": "import pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nmusic_data = pd.read_csv(\n 'C:\\\\Users\\\\junha\\\\PythonProjects\\\\predict_music_preferences\\\\music.csv')\nX = music_data.drop(columns=['genre'])\ny = music_data['genre']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\nmodel = DecisionTreeClassifier()\nmodel.fit(X_train, y_train)\npredictions = model.predict(X_test)\nprint(predictions)\nscore = accuracy_score(y_test, predictions)\nprint(score)\n",
"step-5": "\nimport pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n# from sklearn import tree\n# import joblib\nmusic_data = pd.read_csv(r\"C:\\Users\\junha\\PythonProjects\\predict_music_preferences\\music.csv\")\n# print(music_data)\n\n\nX = music_data.drop(columns=['genre'])\ny = music_data['genre']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nmodel = DecisionTreeClassifier()\nmodel.fit(X_train, y_train)\npredictions = model.predict(X_test)\nprint(predictions)\n\nscore = accuracy_score(y_test, predictions)\nprint(score)\n\n# joblib.dump(model, 'music-recommender.joblib')\n\n# tree.export_graphviz(model, out_file='music-recommender.dot',\n# feature_names=['age', 'gender'],\n# class_names=sorted(y.unique()), \n# label='all', rounded= True,\n# filled=True)",
"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 = [('trades', '0001_initial')]
operations = [migrations.AddField(model_name='orderinfo', name=
'nonce_str', field=models.CharField(blank=True, max_length=50, null
=True, unique=True, verbose_name='随机加密串'))]
<|reserved_special_token_1|>
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('trades', '0001_initial')]
operations = [migrations.AddField(model_name='orderinfo', name=
'nonce_str', field=models.CharField(blank=True, max_length=50, null
=True, unique=True, verbose_name='随机加密串'))]
<|reserved_special_token_1|>
# Generated by Django 2.2.16 on 2020-10-27 14:55
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('trades', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='orderinfo',
name='nonce_str',
field=models.CharField(blank=True, max_length=50, null=True, unique=True, verbose_name='随机加密串'),
),
]
|
flexible
|
{
"blob_id": "4e04e748a97c59a26a394b049c15d96476b98517",
"index": 9382,
"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 = [('trades', '0001_initial')]\n operations = [migrations.AddField(model_name='orderinfo', name=\n 'nonce_str', field=models.CharField(blank=True, max_length=50, null\n =True, unique=True, verbose_name='随机加密串'))]\n",
"step-4": "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [('trades', '0001_initial')]\n operations = [migrations.AddField(model_name='orderinfo', name=\n 'nonce_str', field=models.CharField(blank=True, max_length=50, null\n =True, unique=True, verbose_name='随机加密串'))]\n",
"step-5": "# Generated by Django 2.2.16 on 2020-10-27 14:55\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('trades', '0001_initial'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='orderinfo',\n name='nonce_str',\n field=models.CharField(blank=True, max_length=50, null=True, unique=True, verbose_name='随机加密串'),\n ),\n ]\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Ui_Window(QDialog):
def __init__(self):
super(Ui_Window, self).__init__()
self.ui = Ui_Dialog()
self.ui.setupUi(self)
regex = QRegExp('\\w+')
validator = QRegExpValidator(regex)
self.ui.usernameLineEdit.setValidator(validator)
self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)
self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)
self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)
self.figure = plt.figure()
self.canvas = FigureCanvas(self.figure)
self.toolbar = NavigationToolbar(self.canvas, self)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.ui.plotDisplayGroupBox.setLayout(layout)
self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)
self.ui.exportPushButton.clicked.connect(self.exportValues)
settings = configparser.ConfigParser()
settings.read(SETTINGS_FILE)
helper.print_with_stars('Initializing APIs')
twitterApi, googleClient, errors = phase2Functions.init_apis(settings
['KEYS']['api_key'], settings['KEYS']['api_secret_key'])
if len(errors) > 0:
self.printMessages(errors)
sys.exit(1)
else:
self.twitterApi = twitterApi
self.googleClient = googleClient
self.show()
<|reserved_special_token_0|>
def plotSentiment(self):
QApplication.setOverrideCursor(Qt.WaitCursor)
startDate = self.get_start_date()
endDate = self.get_end_date()
if startDate is None or endDate is None:
return
dateList, scoreList, magnitudeList, tweetList, errors = (
phase2Functions.generate_data_lists(self.twitterApi, self.
googleClient, self.get_username(), startDate, endDate))
QApplication.restoreOverrideCursor()
if len(errors) > 0:
self.printMessages(errors)
else:
self.plotData = dateList, scoreList, magnitudeList
self.tweetList = tweetList
self.figure.clear()
ax = self.figure.add_subplot(111)
self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,
right=0.9, hspace=0.2, wspace=0.2)
ax.set_title("Sentiment Analysis of @{}'s tweets".format(self.
get_username()))
ax.set_xlabel('Date')
ax.set_ylabel('Sentiment Value')
ax.xaxis.set_major_locator(plt.MaxNLocator(10))
for tick in ax.get_xticklabels():
tick.set_rotation(45)
ax.plot(self.plotData[0], self.plotData[1], '-bo', label=
'Sentiment Score')
ax.plot(self.plotData[0], self.plotData[2], '-ro', label=
'Sentiment Magnitude')
ax.legend(loc='lower right')
self.canvas.draw()
self.enableExport()
<|reserved_special_token_0|>
def get_username(self):
return self.ui.usernameLineEdit.text()
<|reserved_special_token_0|>
def get_start_date(self):
start_month = self.ui.startMonthSpinBox.value()
start_day = self.ui.startDaySpinBox.value()
start_year = self.ui.startYearSpinBox.value()
try:
startDate = datetime.datetime(start_year, start_month, start_day)
except:
self.printMessages([
'Start date is improperly set. Check to see that the date is correct/exists.'
])
return None
return startDate
<|reserved_special_token_0|>
def get_end_date(self):
end_month = self.ui.endMonthSpinBox.value()
end_day = self.ui.endDaySpinBox.value()
end_year = self.ui.endYearSpinBox.value()
try:
endDate = datetime.datetime(end_year, end_month, end_day)
except:
self.printMessages([
'End date is improperly set. Check to see that the date is correct/exists.'
])
return None
return endDate
<|reserved_special_token_0|>
def enableExport(self):
self.ui.exportPushButton.setEnabled(True)
<|reserved_special_token_0|>
def exportValues(self):
currentTimeDate = datetime.datetime.now()
currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate
.month) + '-' + str(currentTimeDate.day) + '-' + str(
currentTimeDate.hour) + '-' + str(currentTimeDate.minute
) + '-' + str(currentTimeDate.second)
with open(currentTimeDate + '_' + self.get_username() +
'_score.csv', mode='w') as score_file:
writer = csv.writer(score_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[1]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
with open(currentTimeDate + '_' + self.get_username() +
'_magnitude.csv', mode='w') as magnitude_file:
writer = csv.writer(magnitude_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[2]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
msgBox = QMessageBox()
msgBox.setText('CSV files exported!')
msgBox.exec()
<|reserved_special_token_0|>
def printMessages(self, messageList):
msgBox = QMessageBox()
msgBox.setIcon(QMessageBox.Critical)
msgBox.setWindowTitle('Errors occured!')
tempString = ''
for message in messageList:
tempString += message + '\n'
msgBox.setText(tempString)
msgBox.exec()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.insert(1, '..\\SharedFiles\\')
<|reserved_special_token_0|>
class Ui_Window(QDialog):
def __init__(self):
super(Ui_Window, self).__init__()
self.ui = Ui_Dialog()
self.ui.setupUi(self)
regex = QRegExp('\\w+')
validator = QRegExpValidator(regex)
self.ui.usernameLineEdit.setValidator(validator)
self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)
self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)
self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)
self.figure = plt.figure()
self.canvas = FigureCanvas(self.figure)
self.toolbar = NavigationToolbar(self.canvas, self)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.ui.plotDisplayGroupBox.setLayout(layout)
self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)
self.ui.exportPushButton.clicked.connect(self.exportValues)
settings = configparser.ConfigParser()
settings.read(SETTINGS_FILE)
helper.print_with_stars('Initializing APIs')
twitterApi, googleClient, errors = phase2Functions.init_apis(settings
['KEYS']['api_key'], settings['KEYS']['api_secret_key'])
if len(errors) > 0:
self.printMessages(errors)
sys.exit(1)
else:
self.twitterApi = twitterApi
self.googleClient = googleClient
self.show()
"""
Plot the sentiment score
Input - self:Ui_Window
Output - None
"""
def plotSentiment(self):
QApplication.setOverrideCursor(Qt.WaitCursor)
startDate = self.get_start_date()
endDate = self.get_end_date()
if startDate is None or endDate is None:
return
dateList, scoreList, magnitudeList, tweetList, errors = (
phase2Functions.generate_data_lists(self.twitterApi, self.
googleClient, self.get_username(), startDate, endDate))
QApplication.restoreOverrideCursor()
if len(errors) > 0:
self.printMessages(errors)
else:
self.plotData = dateList, scoreList, magnitudeList
self.tweetList = tweetList
self.figure.clear()
ax = self.figure.add_subplot(111)
self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,
right=0.9, hspace=0.2, wspace=0.2)
ax.set_title("Sentiment Analysis of @{}'s tweets".format(self.
get_username()))
ax.set_xlabel('Date')
ax.set_ylabel('Sentiment Value')
ax.xaxis.set_major_locator(plt.MaxNLocator(10))
for tick in ax.get_xticklabels():
tick.set_rotation(45)
ax.plot(self.plotData[0], self.plotData[1], '-bo', label=
'Sentiment Score')
ax.plot(self.plotData[0], self.plotData[2], '-ro', label=
'Sentiment Magnitude')
ax.legend(loc='lower right')
self.canvas.draw()
self.enableExport()
"""
Gets username from text field
Input - self:Ui_Window
Output - string
"""
def get_username(self):
return self.ui.usernameLineEdit.text()
"""
Gets start date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
"""
def get_start_date(self):
start_month = self.ui.startMonthSpinBox.value()
start_day = self.ui.startDaySpinBox.value()
start_year = self.ui.startYearSpinBox.value()
try:
startDate = datetime.datetime(start_year, start_month, start_day)
except:
self.printMessages([
'Start date is improperly set. Check to see that the date is correct/exists.'
])
return None
return startDate
"""
Gets end date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
"""
def get_end_date(self):
end_month = self.ui.endMonthSpinBox.value()
end_day = self.ui.endDaySpinBox.value()
end_year = self.ui.endYearSpinBox.value()
try:
endDate = datetime.datetime(end_year, end_month, end_day)
except:
self.printMessages([
'End date is improperly set. Check to see that the date is correct/exists.'
])
return None
return endDate
"""
Toggles the export button.
Input - self:Ui_Window
Output - None
"""
def enableExport(self):
self.ui.exportPushButton.setEnabled(True)
"""
Exports date, score/magntitude, and tweet text to csv and pops up a window when done
Input - self:Ui_Window
Output - None
"""
def exportValues(self):
currentTimeDate = datetime.datetime.now()
currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate
.month) + '-' + str(currentTimeDate.day) + '-' + str(
currentTimeDate.hour) + '-' + str(currentTimeDate.minute
) + '-' + str(currentTimeDate.second)
with open(currentTimeDate + '_' + self.get_username() +
'_score.csv', mode='w') as score_file:
writer = csv.writer(score_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[1]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
with open(currentTimeDate + '_' + self.get_username() +
'_magnitude.csv', mode='w') as magnitude_file:
writer = csv.writer(magnitude_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[2]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
msgBox = QMessageBox()
msgBox.setText('CSV files exported!')
msgBox.exec()
"""
Prints out messages in a pop up window
Input - self:Ui_Window
Output - None
"""
def printMessages(self, messageList):
msgBox = QMessageBox()
msgBox.setIcon(QMessageBox.Critical)
msgBox.setWindowTitle('Errors occured!')
tempString = ''
for message in messageList:
tempString += message + '\n'
msgBox.setText(tempString)
msgBox.exec()
if __name__ == '__main__':
app = QApplication(sys.argv)
window = Ui_Window()
window.show()
sys.exit(app.exec_())
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.insert(1, '..\\SharedFiles\\')
<|reserved_special_token_0|>
SETTINGS_FILE = '..\\SharedFiles\\settings.ini'
class Ui_Window(QDialog):
def __init__(self):
super(Ui_Window, self).__init__()
self.ui = Ui_Dialog()
self.ui.setupUi(self)
regex = QRegExp('\\w+')
validator = QRegExpValidator(regex)
self.ui.usernameLineEdit.setValidator(validator)
self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)
self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)
self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)
self.figure = plt.figure()
self.canvas = FigureCanvas(self.figure)
self.toolbar = NavigationToolbar(self.canvas, self)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.ui.plotDisplayGroupBox.setLayout(layout)
self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)
self.ui.exportPushButton.clicked.connect(self.exportValues)
settings = configparser.ConfigParser()
settings.read(SETTINGS_FILE)
helper.print_with_stars('Initializing APIs')
twitterApi, googleClient, errors = phase2Functions.init_apis(settings
['KEYS']['api_key'], settings['KEYS']['api_secret_key'])
if len(errors) > 0:
self.printMessages(errors)
sys.exit(1)
else:
self.twitterApi = twitterApi
self.googleClient = googleClient
self.show()
"""
Plot the sentiment score
Input - self:Ui_Window
Output - None
"""
def plotSentiment(self):
QApplication.setOverrideCursor(Qt.WaitCursor)
startDate = self.get_start_date()
endDate = self.get_end_date()
if startDate is None or endDate is None:
return
dateList, scoreList, magnitudeList, tweetList, errors = (
phase2Functions.generate_data_lists(self.twitterApi, self.
googleClient, self.get_username(), startDate, endDate))
QApplication.restoreOverrideCursor()
if len(errors) > 0:
self.printMessages(errors)
else:
self.plotData = dateList, scoreList, magnitudeList
self.tweetList = tweetList
self.figure.clear()
ax = self.figure.add_subplot(111)
self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,
right=0.9, hspace=0.2, wspace=0.2)
ax.set_title("Sentiment Analysis of @{}'s tweets".format(self.
get_username()))
ax.set_xlabel('Date')
ax.set_ylabel('Sentiment Value')
ax.xaxis.set_major_locator(plt.MaxNLocator(10))
for tick in ax.get_xticklabels():
tick.set_rotation(45)
ax.plot(self.plotData[0], self.plotData[1], '-bo', label=
'Sentiment Score')
ax.plot(self.plotData[0], self.plotData[2], '-ro', label=
'Sentiment Magnitude')
ax.legend(loc='lower right')
self.canvas.draw()
self.enableExport()
"""
Gets username from text field
Input - self:Ui_Window
Output - string
"""
def get_username(self):
return self.ui.usernameLineEdit.text()
"""
Gets start date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
"""
def get_start_date(self):
start_month = self.ui.startMonthSpinBox.value()
start_day = self.ui.startDaySpinBox.value()
start_year = self.ui.startYearSpinBox.value()
try:
startDate = datetime.datetime(start_year, start_month, start_day)
except:
self.printMessages([
'Start date is improperly set. Check to see that the date is correct/exists.'
])
return None
return startDate
"""
Gets end date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
"""
def get_end_date(self):
end_month = self.ui.endMonthSpinBox.value()
end_day = self.ui.endDaySpinBox.value()
end_year = self.ui.endYearSpinBox.value()
try:
endDate = datetime.datetime(end_year, end_month, end_day)
except:
self.printMessages([
'End date is improperly set. Check to see that the date is correct/exists.'
])
return None
return endDate
"""
Toggles the export button.
Input - self:Ui_Window
Output - None
"""
def enableExport(self):
self.ui.exportPushButton.setEnabled(True)
"""
Exports date, score/magntitude, and tweet text to csv and pops up a window when done
Input - self:Ui_Window
Output - None
"""
def exportValues(self):
currentTimeDate = datetime.datetime.now()
currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate
.month) + '-' + str(currentTimeDate.day) + '-' + str(
currentTimeDate.hour) + '-' + str(currentTimeDate.minute
) + '-' + str(currentTimeDate.second)
with open(currentTimeDate + '_' + self.get_username() +
'_score.csv', mode='w') as score_file:
writer = csv.writer(score_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[1]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
with open(currentTimeDate + '_' + self.get_username() +
'_magnitude.csv', mode='w') as magnitude_file:
writer = csv.writer(magnitude_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[2]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
msgBox = QMessageBox()
msgBox.setText('CSV files exported!')
msgBox.exec()
"""
Prints out messages in a pop up window
Input - self:Ui_Window
Output - None
"""
def printMessages(self, messageList):
msgBox = QMessageBox()
msgBox.setIcon(QMessageBox.Critical)
msgBox.setWindowTitle('Errors occured!')
tempString = ''
for message in messageList:
tempString += message + '\n'
msgBox.setText(tempString)
msgBox.exec()
if __name__ == '__main__':
app = QApplication(sys.argv)
window = Ui_Window()
window.show()
sys.exit(app.exec_())
<|reserved_special_token_1|>
from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout, QMessageBox
from PySide2.QtCore import Qt, QFile, QRegExp
from PySide2.QtGui import QRegExpValidator
from phase2GUI import Ui_Dialog
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
import configparser, csv, datetime, sys
sys.path.insert(1, '..\\SharedFiles\\')
import matplotlib.pyplot as plt
import helper, phase2Functions
SETTINGS_FILE = '..\\SharedFiles\\settings.ini'
class Ui_Window(QDialog):
def __init__(self):
super(Ui_Window, self).__init__()
self.ui = Ui_Dialog()
self.ui.setupUi(self)
regex = QRegExp('\\w+')
validator = QRegExpValidator(regex)
self.ui.usernameLineEdit.setValidator(validator)
self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)
self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)
self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)
self.figure = plt.figure()
self.canvas = FigureCanvas(self.figure)
self.toolbar = NavigationToolbar(self.canvas, self)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.ui.plotDisplayGroupBox.setLayout(layout)
self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)
self.ui.exportPushButton.clicked.connect(self.exportValues)
settings = configparser.ConfigParser()
settings.read(SETTINGS_FILE)
helper.print_with_stars('Initializing APIs')
twitterApi, googleClient, errors = phase2Functions.init_apis(settings
['KEYS']['api_key'], settings['KEYS']['api_secret_key'])
if len(errors) > 0:
self.printMessages(errors)
sys.exit(1)
else:
self.twitterApi = twitterApi
self.googleClient = googleClient
self.show()
"""
Plot the sentiment score
Input - self:Ui_Window
Output - None
"""
def plotSentiment(self):
QApplication.setOverrideCursor(Qt.WaitCursor)
startDate = self.get_start_date()
endDate = self.get_end_date()
if startDate is None or endDate is None:
return
dateList, scoreList, magnitudeList, tweetList, errors = (
phase2Functions.generate_data_lists(self.twitterApi, self.
googleClient, self.get_username(), startDate, endDate))
QApplication.restoreOverrideCursor()
if len(errors) > 0:
self.printMessages(errors)
else:
self.plotData = dateList, scoreList, magnitudeList
self.tweetList = tweetList
self.figure.clear()
ax = self.figure.add_subplot(111)
self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,
right=0.9, hspace=0.2, wspace=0.2)
ax.set_title("Sentiment Analysis of @{}'s tweets".format(self.
get_username()))
ax.set_xlabel('Date')
ax.set_ylabel('Sentiment Value')
ax.xaxis.set_major_locator(plt.MaxNLocator(10))
for tick in ax.get_xticklabels():
tick.set_rotation(45)
ax.plot(self.plotData[0], self.plotData[1], '-bo', label=
'Sentiment Score')
ax.plot(self.plotData[0], self.plotData[2], '-ro', label=
'Sentiment Magnitude')
ax.legend(loc='lower right')
self.canvas.draw()
self.enableExport()
"""
Gets username from text field
Input - self:Ui_Window
Output - string
"""
def get_username(self):
return self.ui.usernameLineEdit.text()
"""
Gets start date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
"""
def get_start_date(self):
start_month = self.ui.startMonthSpinBox.value()
start_day = self.ui.startDaySpinBox.value()
start_year = self.ui.startYearSpinBox.value()
try:
startDate = datetime.datetime(start_year, start_month, start_day)
except:
self.printMessages([
'Start date is improperly set. Check to see that the date is correct/exists.'
])
return None
return startDate
"""
Gets end date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
"""
def get_end_date(self):
end_month = self.ui.endMonthSpinBox.value()
end_day = self.ui.endDaySpinBox.value()
end_year = self.ui.endYearSpinBox.value()
try:
endDate = datetime.datetime(end_year, end_month, end_day)
except:
self.printMessages([
'End date is improperly set. Check to see that the date is correct/exists.'
])
return None
return endDate
"""
Toggles the export button.
Input - self:Ui_Window
Output - None
"""
def enableExport(self):
self.ui.exportPushButton.setEnabled(True)
"""
Exports date, score/magntitude, and tweet text to csv and pops up a window when done
Input - self:Ui_Window
Output - None
"""
def exportValues(self):
currentTimeDate = datetime.datetime.now()
currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate
.month) + '-' + str(currentTimeDate.day) + '-' + str(
currentTimeDate.hour) + '-' + str(currentTimeDate.minute
) + '-' + str(currentTimeDate.second)
with open(currentTimeDate + '_' + self.get_username() +
'_score.csv', mode='w') as score_file:
writer = csv.writer(score_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[1]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
with open(currentTimeDate + '_' + self.get_username() +
'_magnitude.csv', mode='w') as magnitude_file:
writer = csv.writer(magnitude_file)
for i in range(len(self.plotData[0])):
writer.writerow([str(self.plotData[0][i]), self.plotData[2]
[i], self.tweetList[i].full_text.encode(encoding=
'UTF-8', errors='replace')])
msgBox = QMessageBox()
msgBox.setText('CSV files exported!')
msgBox.exec()
"""
Prints out messages in a pop up window
Input - self:Ui_Window
Output - None
"""
def printMessages(self, messageList):
msgBox = QMessageBox()
msgBox.setIcon(QMessageBox.Critical)
msgBox.setWindowTitle('Errors occured!')
tempString = ''
for message in messageList:
tempString += message + '\n'
msgBox.setText(tempString)
msgBox.exec()
if __name__ == '__main__':
app = QApplication(sys.argv)
window = Ui_Window()
window.show()
sys.exit(app.exec_())
<|reserved_special_token_1|>
#---------------------------------------------
# File name: phase2app.py
# Description: Launches GUI for Twitter User Timeline Sentiment Analysis program
# Author: Gilbert Yap (gilberty@bu.edu)
# Date: October 03, 2020
#---------------------------------------------
from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout, QMessageBox
from PySide2.QtCore import Qt, QFile, QRegExp
from PySide2.QtGui import QRegExpValidator
from phase2GUI import Ui_Dialog
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
import configparser, csv, datetime, sys
sys.path.insert(1, '..\\SharedFiles\\')
import matplotlib.pyplot as plt
import helper, phase2Functions
SETTINGS_FILE = '..\\SharedFiles\\settings.ini'
class Ui_Window(QDialog):
def __init__(self):
super(Ui_Window, self).__init__()
self.ui = Ui_Dialog()
self.ui.setupUi(self)
# Set regex validator for the username
regex = QRegExp("\w+")
validator = QRegExpValidator(regex)
self.ui.usernameLineEdit.setValidator(validator)
# Set the end date to today by default
self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)
self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)
self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)
# Place a plot inside of plotDisplayGroupBox
self.figure = plt.figure()
self.canvas = FigureCanvas(self.figure)
self.toolbar = NavigationToolbar(self.canvas, self)
layout = QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.ui.plotDisplayGroupBox.setLayout(layout)
# Set up signals
self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)
self.ui.exportPushButton.clicked.connect(self.exportValues)
# Init APIs
settings = configparser.ConfigParser()
settings.read(SETTINGS_FILE)
helper.print_with_stars('Initializing APIs')
(twitterApi, googleClient, errors) = phase2Functions.init_apis(settings['KEYS']['api_key'], settings['KEYS']['api_secret_key'])
if(len(errors) > 0):
self.printMessages(errors)
sys.exit(1)
else:
self.twitterApi = twitterApi
self.googleClient = googleClient
self.show()
'''
Plot the sentiment score
Input - self:Ui_Window
Output - None
'''
def plotSentiment(self):
QApplication.setOverrideCursor(Qt.WaitCursor)
# Get the sentiment data
startDate = self.get_start_date()
endDate = self.get_end_date()
if (startDate is None) or (endDate is None):
return
(dateList, scoreList, magnitudeList, tweetList, errors) = phase2Functions.generate_data_lists(self.twitterApi, self.googleClient, self.get_username(), startDate, endDate)
QApplication.restoreOverrideCursor()
# If there were any errors, print them out
if(len(errors) > 0):
self.printMessages(errors)
else:
# If there are no errors, format and plot out the data
self.plotData = (dateList, scoreList, magnitudeList)
self.tweetList = tweetList
self.figure.clear()
ax = self.figure.add_subplot(111)
self.figure.subplots_adjust(top=0.88,
bottom=0.255,
left=0.17,
right=0.9,
hspace=0.2,
wspace=0.2)
ax.set_title("Sentiment Analysis of @{}'s tweets".format(self.get_username(),))
ax.set_xlabel("Date")
ax.set_ylabel("Sentiment Value")
ax.xaxis.set_major_locator(plt.MaxNLocator(10))
for tick in ax.get_xticklabels():
tick.set_rotation(45)
ax.plot(self.plotData[0],self.plotData[1],"-bo",label='Sentiment Score')
ax.plot(self.plotData[0],self.plotData[2], "-ro",label='Sentiment Magnitude')
ax.legend(loc="lower right")
self.canvas.draw()
self.enableExport()
'''
Gets username from text field
Input - self:Ui_Window
Output - string
'''
def get_username(self):
return (self.ui.usernameLineEdit.text())
'''
Gets start date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
'''
def get_start_date(self):
start_month = self.ui.startMonthSpinBox.value()
start_day = self.ui.startDaySpinBox.value()
start_year = self.ui.startYearSpinBox.value()
try:
startDate = datetime.datetime(start_year, start_month,start_day)
except:
self.printMessages(['Start date is improperly set. Check to see that the date is correct/exists.'])
return None
return startDate
'''
Gets end date from spin boxes
Input - self:Ui_Window
Output - datetime.datetime
'''
def get_end_date(self):
end_month = self.ui.endMonthSpinBox.value()
end_day = self.ui.endDaySpinBox.value()
end_year = self.ui.endYearSpinBox.value()
try:
endDate = datetime.datetime(end_year, end_month,end_day)
except:
self.printMessages(['End date is improperly set. Check to see that the date is correct/exists.'])
return None
return endDate
'''
Toggles the export button.
Input - self:Ui_Window
Output - None
'''
def enableExport(self):
self.ui.exportPushButton.setEnabled(True)
'''
Exports date, score/magntitude, and tweet text to csv and pops up a window when done
Input - self:Ui_Window
Output - None
'''
def exportValues(self):
currentTimeDate = datetime.datetime.now()
currentTimeDate = str(currentTimeDate.year)+'-'+str(currentTimeDate.month)+'-'+str(currentTimeDate.day)+'-'+str(currentTimeDate.hour)+'-'+str(currentTimeDate.minute)+'-'+str(currentTimeDate.second)
with open(currentTimeDate+'_'+self.get_username()+'_score.csv', mode='w') as score_file:
writer = csv.writer(score_file)
for i in range(len(self.plotData[0])):
writer.writerow( [ str(self.plotData[0][i]), self.plotData[1][i],
self.tweetList[i].full_text.encode(encoding='UTF-8', errors='replace') ] )
with open(currentTimeDate+'_'+self.get_username()+'_magnitude.csv', mode='w') as magnitude_file:
writer = csv.writer(magnitude_file)
for i in range(len(self.plotData[0])):
writer.writerow( [ str(self.plotData[0][i]), self.plotData[2][i],
self.tweetList[i].full_text.encode(encoding='UTF-8', errors='replace') ] )
msgBox = QMessageBox()
msgBox.setText('CSV files exported!')
msgBox.exec()
'''
Prints out messages in a pop up window
Input - self:Ui_Window
Output - None
'''
def printMessages(self, messageList):
msgBox = QMessageBox()
msgBox.setIcon(QMessageBox.Critical)
msgBox.setWindowTitle('Errors occured!')
tempString = ''
for message in messageList:
tempString += (message + '\n')
msgBox.setText(tempString)
msgBox.exec()
if __name__ == "__main__":
app = QApplication(sys.argv)
window = Ui_Window()
window.show()
sys.exit(app.exec_())
|
flexible
|
{
"blob_id": "8cabacb64f3b193b957c61d6e1ca21f2046e52d1",
"index": 8199,
"step-1": "<mask token>\n\n\nclass Ui_Window(QDialog):\n\n def __init__(self):\n super(Ui_Window, self).__init__()\n self.ui = Ui_Dialog()\n self.ui.setupUi(self)\n regex = QRegExp('\\\\w+')\n validator = QRegExpValidator(regex)\n self.ui.usernameLineEdit.setValidator(validator)\n self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)\n self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)\n self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)\n self.figure = plt.figure()\n self.canvas = FigureCanvas(self.figure)\n self.toolbar = NavigationToolbar(self.canvas, self)\n layout = QVBoxLayout()\n layout.addWidget(self.toolbar)\n layout.addWidget(self.canvas)\n self.ui.plotDisplayGroupBox.setLayout(layout)\n self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)\n self.ui.exportPushButton.clicked.connect(self.exportValues)\n settings = configparser.ConfigParser()\n settings.read(SETTINGS_FILE)\n helper.print_with_stars('Initializing APIs')\n twitterApi, googleClient, errors = phase2Functions.init_apis(settings\n ['KEYS']['api_key'], settings['KEYS']['api_secret_key'])\n if len(errors) > 0:\n self.printMessages(errors)\n sys.exit(1)\n else:\n self.twitterApi = twitterApi\n self.googleClient = googleClient\n self.show()\n <mask token>\n\n def plotSentiment(self):\n QApplication.setOverrideCursor(Qt.WaitCursor)\n startDate = self.get_start_date()\n endDate = self.get_end_date()\n if startDate is None or endDate is None:\n return\n dateList, scoreList, magnitudeList, tweetList, errors = (\n phase2Functions.generate_data_lists(self.twitterApi, self.\n googleClient, self.get_username(), startDate, endDate))\n QApplication.restoreOverrideCursor()\n if len(errors) > 0:\n self.printMessages(errors)\n else:\n self.plotData = dateList, scoreList, magnitudeList\n self.tweetList = tweetList\n self.figure.clear()\n ax = self.figure.add_subplot(111)\n self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,\n right=0.9, hspace=0.2, wspace=0.2)\n ax.set_title(\"Sentiment Analysis of @{}'s tweets\".format(self.\n get_username()))\n ax.set_xlabel('Date')\n ax.set_ylabel('Sentiment Value')\n ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n for tick in ax.get_xticklabels():\n tick.set_rotation(45)\n ax.plot(self.plotData[0], self.plotData[1], '-bo', label=\n 'Sentiment Score')\n ax.plot(self.plotData[0], self.plotData[2], '-ro', label=\n 'Sentiment Magnitude')\n ax.legend(loc='lower right')\n self.canvas.draw()\n self.enableExport()\n <mask token>\n\n def get_username(self):\n return self.ui.usernameLineEdit.text()\n <mask token>\n\n def get_start_date(self):\n start_month = self.ui.startMonthSpinBox.value()\n start_day = self.ui.startDaySpinBox.value()\n start_year = self.ui.startYearSpinBox.value()\n try:\n startDate = datetime.datetime(start_year, start_month, start_day)\n except:\n self.printMessages([\n 'Start date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return startDate\n <mask token>\n\n def get_end_date(self):\n end_month = self.ui.endMonthSpinBox.value()\n end_day = self.ui.endDaySpinBox.value()\n end_year = self.ui.endYearSpinBox.value()\n try:\n endDate = datetime.datetime(end_year, end_month, end_day)\n except:\n self.printMessages([\n 'End date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return endDate\n <mask token>\n\n def enableExport(self):\n self.ui.exportPushButton.setEnabled(True)\n <mask token>\n\n def exportValues(self):\n currentTimeDate = datetime.datetime.now()\n currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate\n .month) + '-' + str(currentTimeDate.day) + '-' + str(\n currentTimeDate.hour) + '-' + str(currentTimeDate.minute\n ) + '-' + str(currentTimeDate.second)\n with open(currentTimeDate + '_' + self.get_username() +\n '_score.csv', mode='w') as score_file:\n writer = csv.writer(score_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[1]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n with open(currentTimeDate + '_' + self.get_username() +\n '_magnitude.csv', mode='w') as magnitude_file:\n writer = csv.writer(magnitude_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[2]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n msgBox = QMessageBox()\n msgBox.setText('CSV files exported!')\n msgBox.exec()\n <mask token>\n\n def printMessages(self, messageList):\n msgBox = QMessageBox()\n msgBox.setIcon(QMessageBox.Critical)\n msgBox.setWindowTitle('Errors occured!')\n tempString = ''\n for message in messageList:\n tempString += message + '\\n'\n msgBox.setText(tempString)\n msgBox.exec()\n\n\n<mask token>\n",
"step-2": "<mask token>\nsys.path.insert(1, '..\\\\SharedFiles\\\\')\n<mask token>\n\n\nclass Ui_Window(QDialog):\n\n def __init__(self):\n super(Ui_Window, self).__init__()\n self.ui = Ui_Dialog()\n self.ui.setupUi(self)\n regex = QRegExp('\\\\w+')\n validator = QRegExpValidator(regex)\n self.ui.usernameLineEdit.setValidator(validator)\n self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)\n self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)\n self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)\n self.figure = plt.figure()\n self.canvas = FigureCanvas(self.figure)\n self.toolbar = NavigationToolbar(self.canvas, self)\n layout = QVBoxLayout()\n layout.addWidget(self.toolbar)\n layout.addWidget(self.canvas)\n self.ui.plotDisplayGroupBox.setLayout(layout)\n self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)\n self.ui.exportPushButton.clicked.connect(self.exportValues)\n settings = configparser.ConfigParser()\n settings.read(SETTINGS_FILE)\n helper.print_with_stars('Initializing APIs')\n twitterApi, googleClient, errors = phase2Functions.init_apis(settings\n ['KEYS']['api_key'], settings['KEYS']['api_secret_key'])\n if len(errors) > 0:\n self.printMessages(errors)\n sys.exit(1)\n else:\n self.twitterApi = twitterApi\n self.googleClient = googleClient\n self.show()\n \"\"\"\n Plot the sentiment score\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def plotSentiment(self):\n QApplication.setOverrideCursor(Qt.WaitCursor)\n startDate = self.get_start_date()\n endDate = self.get_end_date()\n if startDate is None or endDate is None:\n return\n dateList, scoreList, magnitudeList, tweetList, errors = (\n phase2Functions.generate_data_lists(self.twitterApi, self.\n googleClient, self.get_username(), startDate, endDate))\n QApplication.restoreOverrideCursor()\n if len(errors) > 0:\n self.printMessages(errors)\n else:\n self.plotData = dateList, scoreList, magnitudeList\n self.tweetList = tweetList\n self.figure.clear()\n ax = self.figure.add_subplot(111)\n self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,\n right=0.9, hspace=0.2, wspace=0.2)\n ax.set_title(\"Sentiment Analysis of @{}'s tweets\".format(self.\n get_username()))\n ax.set_xlabel('Date')\n ax.set_ylabel('Sentiment Value')\n ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n for tick in ax.get_xticklabels():\n tick.set_rotation(45)\n ax.plot(self.plotData[0], self.plotData[1], '-bo', label=\n 'Sentiment Score')\n ax.plot(self.plotData[0], self.plotData[2], '-ro', label=\n 'Sentiment Magnitude')\n ax.legend(loc='lower right')\n self.canvas.draw()\n self.enableExport()\n \"\"\"\n Gets username from text field\n Input - self:Ui_Window\n Output - string\n \"\"\"\n\n def get_username(self):\n return self.ui.usernameLineEdit.text()\n \"\"\"\n Gets start date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n \"\"\"\n\n def get_start_date(self):\n start_month = self.ui.startMonthSpinBox.value()\n start_day = self.ui.startDaySpinBox.value()\n start_year = self.ui.startYearSpinBox.value()\n try:\n startDate = datetime.datetime(start_year, start_month, start_day)\n except:\n self.printMessages([\n 'Start date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return startDate\n \"\"\"\n Gets end date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n \"\"\"\n\n def get_end_date(self):\n end_month = self.ui.endMonthSpinBox.value()\n end_day = self.ui.endDaySpinBox.value()\n end_year = self.ui.endYearSpinBox.value()\n try:\n endDate = datetime.datetime(end_year, end_month, end_day)\n except:\n self.printMessages([\n 'End date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return endDate\n \"\"\"\n Toggles the export button.\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def enableExport(self):\n self.ui.exportPushButton.setEnabled(True)\n \"\"\"\n Exports date, score/magntitude, and tweet text to csv and pops up a window when done\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def exportValues(self):\n currentTimeDate = datetime.datetime.now()\n currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate\n .month) + '-' + str(currentTimeDate.day) + '-' + str(\n currentTimeDate.hour) + '-' + str(currentTimeDate.minute\n ) + '-' + str(currentTimeDate.second)\n with open(currentTimeDate + '_' + self.get_username() +\n '_score.csv', mode='w') as score_file:\n writer = csv.writer(score_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[1]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n with open(currentTimeDate + '_' + self.get_username() +\n '_magnitude.csv', mode='w') as magnitude_file:\n writer = csv.writer(magnitude_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[2]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n msgBox = QMessageBox()\n msgBox.setText('CSV files exported!')\n msgBox.exec()\n \"\"\"\n Prints out messages in a pop up window\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def printMessages(self, messageList):\n msgBox = QMessageBox()\n msgBox.setIcon(QMessageBox.Critical)\n msgBox.setWindowTitle('Errors occured!')\n tempString = ''\n for message in messageList:\n tempString += message + '\\n'\n msgBox.setText(tempString)\n msgBox.exec()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n window = Ui_Window()\n window.show()\n sys.exit(app.exec_())\n",
"step-3": "<mask token>\nsys.path.insert(1, '..\\\\SharedFiles\\\\')\n<mask token>\nSETTINGS_FILE = '..\\\\SharedFiles\\\\settings.ini'\n\n\nclass Ui_Window(QDialog):\n\n def __init__(self):\n super(Ui_Window, self).__init__()\n self.ui = Ui_Dialog()\n self.ui.setupUi(self)\n regex = QRegExp('\\\\w+')\n validator = QRegExpValidator(regex)\n self.ui.usernameLineEdit.setValidator(validator)\n self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)\n self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)\n self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)\n self.figure = plt.figure()\n self.canvas = FigureCanvas(self.figure)\n self.toolbar = NavigationToolbar(self.canvas, self)\n layout = QVBoxLayout()\n layout.addWidget(self.toolbar)\n layout.addWidget(self.canvas)\n self.ui.plotDisplayGroupBox.setLayout(layout)\n self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)\n self.ui.exportPushButton.clicked.connect(self.exportValues)\n settings = configparser.ConfigParser()\n settings.read(SETTINGS_FILE)\n helper.print_with_stars('Initializing APIs')\n twitterApi, googleClient, errors = phase2Functions.init_apis(settings\n ['KEYS']['api_key'], settings['KEYS']['api_secret_key'])\n if len(errors) > 0:\n self.printMessages(errors)\n sys.exit(1)\n else:\n self.twitterApi = twitterApi\n self.googleClient = googleClient\n self.show()\n \"\"\"\n Plot the sentiment score\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def plotSentiment(self):\n QApplication.setOverrideCursor(Qt.WaitCursor)\n startDate = self.get_start_date()\n endDate = self.get_end_date()\n if startDate is None or endDate is None:\n return\n dateList, scoreList, magnitudeList, tweetList, errors = (\n phase2Functions.generate_data_lists(self.twitterApi, self.\n googleClient, self.get_username(), startDate, endDate))\n QApplication.restoreOverrideCursor()\n if len(errors) > 0:\n self.printMessages(errors)\n else:\n self.plotData = dateList, scoreList, magnitudeList\n self.tweetList = tweetList\n self.figure.clear()\n ax = self.figure.add_subplot(111)\n self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,\n right=0.9, hspace=0.2, wspace=0.2)\n ax.set_title(\"Sentiment Analysis of @{}'s tweets\".format(self.\n get_username()))\n ax.set_xlabel('Date')\n ax.set_ylabel('Sentiment Value')\n ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n for tick in ax.get_xticklabels():\n tick.set_rotation(45)\n ax.plot(self.plotData[0], self.plotData[1], '-bo', label=\n 'Sentiment Score')\n ax.plot(self.plotData[0], self.plotData[2], '-ro', label=\n 'Sentiment Magnitude')\n ax.legend(loc='lower right')\n self.canvas.draw()\n self.enableExport()\n \"\"\"\n Gets username from text field\n Input - self:Ui_Window\n Output - string\n \"\"\"\n\n def get_username(self):\n return self.ui.usernameLineEdit.text()\n \"\"\"\n Gets start date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n \"\"\"\n\n def get_start_date(self):\n start_month = self.ui.startMonthSpinBox.value()\n start_day = self.ui.startDaySpinBox.value()\n start_year = self.ui.startYearSpinBox.value()\n try:\n startDate = datetime.datetime(start_year, start_month, start_day)\n except:\n self.printMessages([\n 'Start date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return startDate\n \"\"\"\n Gets end date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n \"\"\"\n\n def get_end_date(self):\n end_month = self.ui.endMonthSpinBox.value()\n end_day = self.ui.endDaySpinBox.value()\n end_year = self.ui.endYearSpinBox.value()\n try:\n endDate = datetime.datetime(end_year, end_month, end_day)\n except:\n self.printMessages([\n 'End date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return endDate\n \"\"\"\n Toggles the export button.\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def enableExport(self):\n self.ui.exportPushButton.setEnabled(True)\n \"\"\"\n Exports date, score/magntitude, and tweet text to csv and pops up a window when done\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def exportValues(self):\n currentTimeDate = datetime.datetime.now()\n currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate\n .month) + '-' + str(currentTimeDate.day) + '-' + str(\n currentTimeDate.hour) + '-' + str(currentTimeDate.minute\n ) + '-' + str(currentTimeDate.second)\n with open(currentTimeDate + '_' + self.get_username() +\n '_score.csv', mode='w') as score_file:\n writer = csv.writer(score_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[1]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n with open(currentTimeDate + '_' + self.get_username() +\n '_magnitude.csv', mode='w') as magnitude_file:\n writer = csv.writer(magnitude_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[2]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n msgBox = QMessageBox()\n msgBox.setText('CSV files exported!')\n msgBox.exec()\n \"\"\"\n Prints out messages in a pop up window\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def printMessages(self, messageList):\n msgBox = QMessageBox()\n msgBox.setIcon(QMessageBox.Critical)\n msgBox.setWindowTitle('Errors occured!')\n tempString = ''\n for message in messageList:\n tempString += message + '\\n'\n msgBox.setText(tempString)\n msgBox.exec()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n window = Ui_Window()\n window.show()\n sys.exit(app.exec_())\n",
"step-4": "from PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout, QMessageBox\nfrom PySide2.QtCore import Qt, QFile, QRegExp\nfrom PySide2.QtGui import QRegExpValidator\nfrom phase2GUI import Ui_Dialog\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar\nimport configparser, csv, datetime, sys\nsys.path.insert(1, '..\\\\SharedFiles\\\\')\nimport matplotlib.pyplot as plt\nimport helper, phase2Functions\nSETTINGS_FILE = '..\\\\SharedFiles\\\\settings.ini'\n\n\nclass Ui_Window(QDialog):\n\n def __init__(self):\n super(Ui_Window, self).__init__()\n self.ui = Ui_Dialog()\n self.ui.setupUi(self)\n regex = QRegExp('\\\\w+')\n validator = QRegExpValidator(regex)\n self.ui.usernameLineEdit.setValidator(validator)\n self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)\n self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)\n self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)\n self.figure = plt.figure()\n self.canvas = FigureCanvas(self.figure)\n self.toolbar = NavigationToolbar(self.canvas, self)\n layout = QVBoxLayout()\n layout.addWidget(self.toolbar)\n layout.addWidget(self.canvas)\n self.ui.plotDisplayGroupBox.setLayout(layout)\n self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)\n self.ui.exportPushButton.clicked.connect(self.exportValues)\n settings = configparser.ConfigParser()\n settings.read(SETTINGS_FILE)\n helper.print_with_stars('Initializing APIs')\n twitterApi, googleClient, errors = phase2Functions.init_apis(settings\n ['KEYS']['api_key'], settings['KEYS']['api_secret_key'])\n if len(errors) > 0:\n self.printMessages(errors)\n sys.exit(1)\n else:\n self.twitterApi = twitterApi\n self.googleClient = googleClient\n self.show()\n \"\"\"\n Plot the sentiment score\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def plotSentiment(self):\n QApplication.setOverrideCursor(Qt.WaitCursor)\n startDate = self.get_start_date()\n endDate = self.get_end_date()\n if startDate is None or endDate is None:\n return\n dateList, scoreList, magnitudeList, tweetList, errors = (\n phase2Functions.generate_data_lists(self.twitterApi, self.\n googleClient, self.get_username(), startDate, endDate))\n QApplication.restoreOverrideCursor()\n if len(errors) > 0:\n self.printMessages(errors)\n else:\n self.plotData = dateList, scoreList, magnitudeList\n self.tweetList = tweetList\n self.figure.clear()\n ax = self.figure.add_subplot(111)\n self.figure.subplots_adjust(top=0.88, bottom=0.255, left=0.17,\n right=0.9, hspace=0.2, wspace=0.2)\n ax.set_title(\"Sentiment Analysis of @{}'s tweets\".format(self.\n get_username()))\n ax.set_xlabel('Date')\n ax.set_ylabel('Sentiment Value')\n ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n for tick in ax.get_xticklabels():\n tick.set_rotation(45)\n ax.plot(self.plotData[0], self.plotData[1], '-bo', label=\n 'Sentiment Score')\n ax.plot(self.plotData[0], self.plotData[2], '-ro', label=\n 'Sentiment Magnitude')\n ax.legend(loc='lower right')\n self.canvas.draw()\n self.enableExport()\n \"\"\"\n Gets username from text field\n Input - self:Ui_Window\n Output - string\n \"\"\"\n\n def get_username(self):\n return self.ui.usernameLineEdit.text()\n \"\"\"\n Gets start date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n \"\"\"\n\n def get_start_date(self):\n start_month = self.ui.startMonthSpinBox.value()\n start_day = self.ui.startDaySpinBox.value()\n start_year = self.ui.startYearSpinBox.value()\n try:\n startDate = datetime.datetime(start_year, start_month, start_day)\n except:\n self.printMessages([\n 'Start date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return startDate\n \"\"\"\n Gets end date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n \"\"\"\n\n def get_end_date(self):\n end_month = self.ui.endMonthSpinBox.value()\n end_day = self.ui.endDaySpinBox.value()\n end_year = self.ui.endYearSpinBox.value()\n try:\n endDate = datetime.datetime(end_year, end_month, end_day)\n except:\n self.printMessages([\n 'End date is improperly set. Check to see that the date is correct/exists.'\n ])\n return None\n return endDate\n \"\"\"\n Toggles the export button.\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def enableExport(self):\n self.ui.exportPushButton.setEnabled(True)\n \"\"\"\n Exports date, score/magntitude, and tweet text to csv and pops up a window when done\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def exportValues(self):\n currentTimeDate = datetime.datetime.now()\n currentTimeDate = str(currentTimeDate.year) + '-' + str(currentTimeDate\n .month) + '-' + str(currentTimeDate.day) + '-' + str(\n currentTimeDate.hour) + '-' + str(currentTimeDate.minute\n ) + '-' + str(currentTimeDate.second)\n with open(currentTimeDate + '_' + self.get_username() +\n '_score.csv', mode='w') as score_file:\n writer = csv.writer(score_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[1]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n with open(currentTimeDate + '_' + self.get_username() +\n '_magnitude.csv', mode='w') as magnitude_file:\n writer = csv.writer(magnitude_file)\n for i in range(len(self.plotData[0])):\n writer.writerow([str(self.plotData[0][i]), self.plotData[2]\n [i], self.tweetList[i].full_text.encode(encoding=\n 'UTF-8', errors='replace')])\n msgBox = QMessageBox()\n msgBox.setText('CSV files exported!')\n msgBox.exec()\n \"\"\"\n Prints out messages in a pop up window\n Input - self:Ui_Window\n Output - None\n \"\"\"\n\n def printMessages(self, messageList):\n msgBox = QMessageBox()\n msgBox.setIcon(QMessageBox.Critical)\n msgBox.setWindowTitle('Errors occured!')\n tempString = ''\n for message in messageList:\n tempString += message + '\\n'\n msgBox.setText(tempString)\n msgBox.exec()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n window = Ui_Window()\n window.show()\n sys.exit(app.exec_())\n",
"step-5": "#---------------------------------------------\n# File name: phase2app.py\n# Description: Launches GUI for Twitter User Timeline Sentiment Analysis program\n# Author: Gilbert Yap (gilberty@bu.edu)\n# Date: October 03, 2020\n#---------------------------------------------\n\nfrom PySide2.QtWidgets import QApplication, QDialog, QVBoxLayout, QMessageBox\nfrom PySide2.QtCore import Qt, QFile, QRegExp\nfrom PySide2.QtGui import QRegExpValidator\nfrom phase2GUI import Ui_Dialog\n\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar\n\nimport configparser, csv, datetime, sys\nsys.path.insert(1, '..\\\\SharedFiles\\\\')\nimport matplotlib.pyplot as plt\nimport helper, phase2Functions\n\nSETTINGS_FILE = '..\\\\SharedFiles\\\\settings.ini'\n\nclass Ui_Window(QDialog):\n def __init__(self):\n super(Ui_Window, self).__init__()\n self.ui = Ui_Dialog()\n self.ui.setupUi(self)\n\n # Set regex validator for the username\n regex = QRegExp(\"\\w+\")\n validator = QRegExpValidator(regex)\n self.ui.usernameLineEdit.setValidator(validator)\n\n # Set the end date to today by default\n self.ui.endMonthSpinBox.setValue(datetime.datetime.now().month)\n self.ui.endDaySpinBox.setValue(datetime.datetime.now().day)\n self.ui.endYearSpinBox.setValue(datetime.datetime.now().year)\n \n # Place a plot inside of plotDisplayGroupBox\n self.figure = plt.figure()\n self.canvas = FigureCanvas(self.figure)\n self.toolbar = NavigationToolbar(self.canvas, self)\n layout = QVBoxLayout()\n layout.addWidget(self.toolbar)\n layout.addWidget(self.canvas)\n self.ui.plotDisplayGroupBox.setLayout(layout)\n\n # Set up signals\n self.ui.processDatesPushButton.clicked.connect(self.plotSentiment)\n self.ui.exportPushButton.clicked.connect(self.exportValues)\n\n # Init APIs\n settings = configparser.ConfigParser()\n settings.read(SETTINGS_FILE)\n\n helper.print_with_stars('Initializing APIs')\n (twitterApi, googleClient, errors) = phase2Functions.init_apis(settings['KEYS']['api_key'], settings['KEYS']['api_secret_key'])\n\n if(len(errors) > 0):\n self.printMessages(errors)\n sys.exit(1)\n else:\n self.twitterApi = twitterApi\n self.googleClient = googleClient\n self.show()\n\n '''\n Plot the sentiment score\n Input - self:Ui_Window\n Output - None\n '''\n def plotSentiment(self):\n QApplication.setOverrideCursor(Qt.WaitCursor)\n # Get the sentiment data\n startDate = self.get_start_date()\n endDate = self.get_end_date()\n \n if (startDate is None) or (endDate is None):\n return\n \n (dateList, scoreList, magnitudeList, tweetList, errors) = phase2Functions.generate_data_lists(self.twitterApi, self.googleClient, self.get_username(), startDate, endDate)\n QApplication.restoreOverrideCursor()\n \n # If there were any errors, print them out\n if(len(errors) > 0):\n self.printMessages(errors)\n else:\n # If there are no errors, format and plot out the data\n self.plotData = (dateList, scoreList, magnitudeList)\n self.tweetList = tweetList\n self.figure.clear()\n ax = self.figure.add_subplot(111)\n self.figure.subplots_adjust(top=0.88,\n bottom=0.255,\n left=0.17,\n right=0.9,\n hspace=0.2,\n wspace=0.2)\n\n ax.set_title(\"Sentiment Analysis of @{}'s tweets\".format(self.get_username(),)) \n ax.set_xlabel(\"Date\") \n ax.set_ylabel(\"Sentiment Value\") \n ax.xaxis.set_major_locator(plt.MaxNLocator(10))\n \n for tick in ax.get_xticklabels():\n tick.set_rotation(45)\n\n ax.plot(self.plotData[0],self.plotData[1],\"-bo\",label='Sentiment Score') \n ax.plot(self.plotData[0],self.plotData[2], \"-ro\",label='Sentiment Magnitude')\n ax.legend(loc=\"lower right\")\n self.canvas.draw()\n self.enableExport()\n\n\n '''\n Gets username from text field\n Input - self:Ui_Window\n Output - string\n '''\n def get_username(self):\n return (self.ui.usernameLineEdit.text())\n\n '''\n Gets start date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n '''\n def get_start_date(self):\n start_month = self.ui.startMonthSpinBox.value()\n start_day = self.ui.startDaySpinBox.value()\n start_year = self.ui.startYearSpinBox.value()\n \n try:\n startDate = datetime.datetime(start_year, start_month,start_day)\n except:\n self.printMessages(['Start date is improperly set. Check to see that the date is correct/exists.'])\n return None\n \n return startDate\n\n '''\n Gets end date from spin boxes\n Input - self:Ui_Window\n Output - datetime.datetime\n '''\n def get_end_date(self):\n end_month = self.ui.endMonthSpinBox.value()\n end_day = self.ui.endDaySpinBox.value()\n end_year = self.ui.endYearSpinBox.value()\n \n try:\n endDate = datetime.datetime(end_year, end_month,end_day)\n except:\n self.printMessages(['End date is improperly set. Check to see that the date is correct/exists.'])\n return None\n \n return endDate\n\n '''\n Toggles the export button.\n Input - self:Ui_Window\n Output - None\n '''\n def enableExport(self):\n self.ui.exportPushButton.setEnabled(True)\n\n '''\n Exports date, score/magntitude, and tweet text to csv and pops up a window when done\n Input - self:Ui_Window\n Output - None\n '''\n def exportValues(self):\n currentTimeDate = datetime.datetime.now()\n currentTimeDate = str(currentTimeDate.year)+'-'+str(currentTimeDate.month)+'-'+str(currentTimeDate.day)+'-'+str(currentTimeDate.hour)+'-'+str(currentTimeDate.minute)+'-'+str(currentTimeDate.second)\n\n with open(currentTimeDate+'_'+self.get_username()+'_score.csv', mode='w') as score_file:\n writer = csv.writer(score_file)\n for i in range(len(self.plotData[0])):\n writer.writerow( [ str(self.plotData[0][i]), self.plotData[1][i], \n self.tweetList[i].full_text.encode(encoding='UTF-8', errors='replace') ] )\n\n with open(currentTimeDate+'_'+self.get_username()+'_magnitude.csv', mode='w') as magnitude_file:\n writer = csv.writer(magnitude_file)\n for i in range(len(self.plotData[0])):\n writer.writerow( [ str(self.plotData[0][i]), self.plotData[2][i], \n self.tweetList[i].full_text.encode(encoding='UTF-8', errors='replace') ] )\n\n msgBox = QMessageBox()\n msgBox.setText('CSV files exported!')\n msgBox.exec()\n\n '''\n Prints out messages in a pop up window\n Input - self:Ui_Window\n Output - None\n '''\n def printMessages(self, messageList):\n msgBox = QMessageBox()\n msgBox.setIcon(QMessageBox.Critical)\n msgBox.setWindowTitle('Errors occured!')\n tempString = ''\n\n for message in messageList:\n tempString += (message + '\\n')\n msgBox.setText(tempString)\n msgBox.exec()\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n\n window = Ui_Window()\n window.show()\n\n sys.exit(app.exec_())",
"step-ids": [
9,
11,
12,
13,
14
]
}
|
[
9,
11,
12,
13,
14
] |
import dtw
import stats
import glob
import argparse
import matplotlib.pyplot as plt
GRAPH = False
PERCENTAGE = False
VERBOSE = False
def buildExpectations(queryPath, searchPatternPath):
"""
Based on SpeechCommand_v0.02 directory structure.
"""
expectations = []
currentDirectory = ""
queryFilename = queryPath.split("/")[-1]
queryDirectory = queryPath.split("/")[-2]
queryCode = queryFilename.split("_")[0]
searchFileList = sorted(glob.glob(searchPatternPath))
for searchFile in searchFileList:
searchFilename = searchFile.split("/")[-1]
searchDirectory = searchFile.split("/")[-2]
searchCode = searchFilename.split("_")[0]
if searchDirectory != currentDirectory:
currentDirectory = searchDirectory
if searchCode == queryCode:
if currentDirectory == queryDirectory:
expectations.append([[0, 1]])
else:
expectations.append([[0, 0]])
return expectations
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser(description='Dynamic Time Warping')
parser.add_argument('-g', '--graph', action='store_true', help='Enable graph display')
parser.add_argument('-t', '--threshold', type=float, default=0.4, help='Set score threshold')
parser.add_argument('query_path')
parser.add_argument('search_pattern_path')
printGroup = parser.add_mutually_exclusive_group()
printGroup.add_argument('-p', '--percentage', action='store_true', help='Enable percentage display')
printGroup.add_argument('-v', '--verbose', action='store_true', help='Enable verbose display')
args = parser.parse_args()
GRAPH = args.graph
PERCENTAGE = args.percentage
threshold = args.threshold
VERBOSE = args.verbose
queryPath = args.query_path
searchPatternPath = args.search_pattern_path
dtw.VERBOSE = VERBOSE
stats.VERBOSE = VERBOSE
labels, sweepList, bestList = dtw.runSearch(queryPath, searchPatternPath)
results = dtw.computeResultsPrecisely(sweepList, threshold, positiveOnly=True)
for i, result in enumerate(results):
print(labels[i] + ": ", end='')
for j, (hitIndex, _) in enumerate(result):
print(hitIndex * 3, end='')
if j < len(result) - 1:
print(" | ", end='')
print()
if GRAPH:
dtw.showSweeps(labels, sweepList, bestList)
plt.show()
|
normal
|
{
"blob_id": "03fb1cf0aac0c37858dd8163562a7139ed4e1179",
"index": 776,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef buildExpectations(queryPath, searchPatternPath):\n \"\"\"\n Based on SpeechCommand_v0.02 directory structure.\n \"\"\"\n expectations = []\n currentDirectory = ''\n queryFilename = queryPath.split('/')[-1]\n queryDirectory = queryPath.split('/')[-2]\n queryCode = queryFilename.split('_')[0]\n searchFileList = sorted(glob.glob(searchPatternPath))\n for searchFile in searchFileList:\n searchFilename = searchFile.split('/')[-1]\n searchDirectory = searchFile.split('/')[-2]\n searchCode = searchFilename.split('_')[0]\n if searchDirectory != currentDirectory:\n currentDirectory = searchDirectory\n if searchCode == queryCode:\n if currentDirectory == queryDirectory:\n expectations.append([[0, 1]])\n else:\n expectations.append([[0, 0]])\n return expectations\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Dynamic Time Warping')\n parser.add_argument('-g', '--graph', action='store_true', help=\n 'Enable graph display')\n parser.add_argument('-t', '--threshold', type=float, default=0.4, help=\n 'Set score threshold')\n parser.add_argument('query_path')\n parser.add_argument('search_pattern_path')\n printGroup = parser.add_mutually_exclusive_group()\n printGroup.add_argument('-p', '--percentage', action='store_true', help\n ='Enable percentage display')\n printGroup.add_argument('-v', '--verbose', action='store_true', help=\n 'Enable verbose display')\n args = parser.parse_args()\n GRAPH = args.graph\n PERCENTAGE = args.percentage\n threshold = args.threshold\n VERBOSE = args.verbose\n queryPath = args.query_path\n searchPatternPath = args.search_pattern_path\n dtw.VERBOSE = VERBOSE\n stats.VERBOSE = VERBOSE\n labels, sweepList, bestList = dtw.runSearch(queryPath, searchPatternPath)\n results = dtw.computeResultsPrecisely(sweepList, threshold,\n positiveOnly=True)\n for i, result in enumerate(results):\n print(labels[i] + ': ', end='')\n for j, (hitIndex, _) in enumerate(result):\n print(hitIndex * 3, end='')\n if j < len(result) - 1:\n print(' | ', end='')\n print()\n if GRAPH:\n dtw.showSweeps(labels, sweepList, bestList)\n plt.show()\n",
"step-3": "<mask token>\nGRAPH = False\nPERCENTAGE = False\nVERBOSE = False\n\n\ndef buildExpectations(queryPath, searchPatternPath):\n \"\"\"\n Based on SpeechCommand_v0.02 directory structure.\n \"\"\"\n expectations = []\n currentDirectory = ''\n queryFilename = queryPath.split('/')[-1]\n queryDirectory = queryPath.split('/')[-2]\n queryCode = queryFilename.split('_')[0]\n searchFileList = sorted(glob.glob(searchPatternPath))\n for searchFile in searchFileList:\n searchFilename = searchFile.split('/')[-1]\n searchDirectory = searchFile.split('/')[-2]\n searchCode = searchFilename.split('_')[0]\n if searchDirectory != currentDirectory:\n currentDirectory = searchDirectory\n if searchCode == queryCode:\n if currentDirectory == queryDirectory:\n expectations.append([[0, 1]])\n else:\n expectations.append([[0, 0]])\n return expectations\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Dynamic Time Warping')\n parser.add_argument('-g', '--graph', action='store_true', help=\n 'Enable graph display')\n parser.add_argument('-t', '--threshold', type=float, default=0.4, help=\n 'Set score threshold')\n parser.add_argument('query_path')\n parser.add_argument('search_pattern_path')\n printGroup = parser.add_mutually_exclusive_group()\n printGroup.add_argument('-p', '--percentage', action='store_true', help\n ='Enable percentage display')\n printGroup.add_argument('-v', '--verbose', action='store_true', help=\n 'Enable verbose display')\n args = parser.parse_args()\n GRAPH = args.graph\n PERCENTAGE = args.percentage\n threshold = args.threshold\n VERBOSE = args.verbose\n queryPath = args.query_path\n searchPatternPath = args.search_pattern_path\n dtw.VERBOSE = VERBOSE\n stats.VERBOSE = VERBOSE\n labels, sweepList, bestList = dtw.runSearch(queryPath, searchPatternPath)\n results = dtw.computeResultsPrecisely(sweepList, threshold,\n positiveOnly=True)\n for i, result in enumerate(results):\n print(labels[i] + ': ', end='')\n for j, (hitIndex, _) in enumerate(result):\n print(hitIndex * 3, end='')\n if j < len(result) - 1:\n print(' | ', end='')\n print()\n if GRAPH:\n dtw.showSweeps(labels, sweepList, bestList)\n plt.show()\n",
"step-4": "import dtw\nimport stats\nimport glob\nimport argparse\nimport matplotlib.pyplot as plt\nGRAPH = False\nPERCENTAGE = False\nVERBOSE = False\n\n\ndef buildExpectations(queryPath, searchPatternPath):\n \"\"\"\n Based on SpeechCommand_v0.02 directory structure.\n \"\"\"\n expectations = []\n currentDirectory = ''\n queryFilename = queryPath.split('/')[-1]\n queryDirectory = queryPath.split('/')[-2]\n queryCode = queryFilename.split('_')[0]\n searchFileList = sorted(glob.glob(searchPatternPath))\n for searchFile in searchFileList:\n searchFilename = searchFile.split('/')[-1]\n searchDirectory = searchFile.split('/')[-2]\n searchCode = searchFilename.split('_')[0]\n if searchDirectory != currentDirectory:\n currentDirectory = searchDirectory\n if searchCode == queryCode:\n if currentDirectory == queryDirectory:\n expectations.append([[0, 1]])\n else:\n expectations.append([[0, 0]])\n return expectations\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Dynamic Time Warping')\n parser.add_argument('-g', '--graph', action='store_true', help=\n 'Enable graph display')\n parser.add_argument('-t', '--threshold', type=float, default=0.4, help=\n 'Set score threshold')\n parser.add_argument('query_path')\n parser.add_argument('search_pattern_path')\n printGroup = parser.add_mutually_exclusive_group()\n printGroup.add_argument('-p', '--percentage', action='store_true', help\n ='Enable percentage display')\n printGroup.add_argument('-v', '--verbose', action='store_true', help=\n 'Enable verbose display')\n args = parser.parse_args()\n GRAPH = args.graph\n PERCENTAGE = args.percentage\n threshold = args.threshold\n VERBOSE = args.verbose\n queryPath = args.query_path\n searchPatternPath = args.search_pattern_path\n dtw.VERBOSE = VERBOSE\n stats.VERBOSE = VERBOSE\n labels, sweepList, bestList = dtw.runSearch(queryPath, searchPatternPath)\n results = dtw.computeResultsPrecisely(sweepList, threshold,\n positiveOnly=True)\n for i, result in enumerate(results):\n print(labels[i] + ': ', end='')\n for j, (hitIndex, _) in enumerate(result):\n print(hitIndex * 3, end='')\n if j < len(result) - 1:\n print(' | ', end='')\n print()\n if GRAPH:\n dtw.showSweeps(labels, sweepList, bestList)\n plt.show()\n",
"step-5": "import dtw\nimport stats\n\nimport glob\nimport argparse\nimport matplotlib.pyplot as plt\n\nGRAPH = False\nPERCENTAGE = False\nVERBOSE = False\n\ndef buildExpectations(queryPath, searchPatternPath):\n \"\"\"\n Based on SpeechCommand_v0.02 directory structure.\n \"\"\"\n expectations = []\n currentDirectory = \"\"\n queryFilename = queryPath.split(\"/\")[-1]\n queryDirectory = queryPath.split(\"/\")[-2]\n queryCode = queryFilename.split(\"_\")[0]\n searchFileList = sorted(glob.glob(searchPatternPath))\n for searchFile in searchFileList:\n searchFilename = searchFile.split(\"/\")[-1]\n searchDirectory = searchFile.split(\"/\")[-2]\n searchCode = searchFilename.split(\"_\")[0]\n if searchDirectory != currentDirectory:\n currentDirectory = searchDirectory\n if searchCode == queryCode:\n if currentDirectory == queryDirectory:\n expectations.append([[0, 1]])\n else:\n expectations.append([[0, 0]])\n return expectations\n\nif __name__ == \"__main__\":\n # Parse arguments\n parser = argparse.ArgumentParser(description='Dynamic Time Warping')\n parser.add_argument('-g', '--graph', action='store_true', help='Enable graph display')\n parser.add_argument('-t', '--threshold', type=float, default=0.4, help='Set score threshold')\n parser.add_argument('query_path')\n parser.add_argument('search_pattern_path')\n\n printGroup = parser.add_mutually_exclusive_group()\n printGroup.add_argument('-p', '--percentage', action='store_true', help='Enable percentage display')\n printGroup.add_argument('-v', '--verbose', action='store_true', help='Enable verbose display')\n\n args = parser.parse_args()\n\n GRAPH = args.graph\n PERCENTAGE = args.percentage\n threshold = args.threshold\n VERBOSE = args.verbose\n queryPath = args.query_path\n searchPatternPath = args.search_pattern_path\n\n dtw.VERBOSE = VERBOSE\n stats.VERBOSE = VERBOSE\n\n labels, sweepList, bestList = dtw.runSearch(queryPath, searchPatternPath)\n\n results = dtw.computeResultsPrecisely(sweepList, threshold, positiveOnly=True)\n for i, result in enumerate(results):\n print(labels[i] + \": \", end='')\n for j, (hitIndex, _) in enumerate(result):\n print(hitIndex * 3, end='')\n if j < len(result) - 1:\n print(\" | \", end='')\n print()\n\n if GRAPH:\n dtw.showSweeps(labels, sweepList, bestList)\n\n plt.show()\n",
"step-ids": [
0,
2,
3,
4,
5
]
}
|
[
0,
2,
3,
4,
5
] |
'''
文件读写的步骤
1.打开文件
2.处理数据
3.关闭文件
1.open函数：
fileobj = open(filename, mode)
fileobj是open()函数返回的文件对象
mode第一个字母指明文件类型和操作的字符串，第二个字母是文件类型：
t(可省略)文本类型，b二进制类型。
文件打开模式：r只读(默认)，w覆盖写(不存在则新创建)
a追加模式(不存在则创建)
2.read(size):从文件读取长度为size的字符串，若未给定或为负则读取所有内容
3.readline():读取整行返回字符串
4.readlines():读取所有行并返回列表
5.write(s):把字符串s的内容写入文件
'''
'''
#复制一个文件
fileobj1 = open("test1.txt", "r")
fileobj2 = open("test2.txt", "w")
s = fileobj1.read()
fileobj2.write(s)
fileobj1.close()
fileobj2.close()
'''
#多行文件读写
fileobj3 = open("lines.txt", "r")
for line in fileobj3.readlines():
print(line)
fileobj3.close()
|
normal
|
{
"blob_id": "25f3c9f48b779d2aec260d529529156ff3c508ca",
"index": 7719,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in fileobj3.readlines():\n print(line)\nfileobj3.close()\n",
"step-3": "<mask token>\nfileobj3 = open('lines.txt', 'r')\nfor line in fileobj3.readlines():\n print(line)\nfileobj3.close()\n",
"step-4": "'''\n文件读写的步骤\n 1.打开文件\n 2.处理数据\n 3.关闭文件\n1.open函数：\n fileobj = open(filename, mode)\n fileobj是open()函数返回的文件对象\n mode第一个字母指明文件类型和操作的字符串，第二个字母是文件类型：\n t(可省略)文本类型，b二进制类型。\n 文件打开模式：r只读(默认)，w覆盖写(不存在则新创建)\n a追加模式(不存在则创建)\n2.read(size):从文件读取长度为size的字符串，若未给定或为负则读取所有内容\n3.readline():读取整行返回字符串\n4.readlines():读取所有行并返回列表\n5.write(s):把字符串s的内容写入文件\n'''\n'''\n#复制一个文件\nfileobj1 = open(\"test1.txt\", \"r\")\nfileobj2 = open(\"test2.txt\", \"w\")\ns = fileobj1.read()\nfileobj2.write(s)\nfileobj1.close()\nfileobj2.close()\n'''\n\n#多行文件读写\nfileobj3 = open(\"lines.txt\", \"r\")\nfor line in fileobj3.readlines():\n print(line)\nfileobj3.close()",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class AFB_L1(nn.Module):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class AFB_L2(nn.Module):
def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):
super(AFB_L2, self).__init__()
self.n = n_l1
self.convs_ = nn.ModuleList()
for _ in range(n_l1):
self.convs_.append(AFB_L1(channels, 3, act))
self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(
channels * n_l1, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L3(nn.Module):
def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):
super(AFB_L3, self).__init__()
self.n = n_l2
self.convs_ = nn.ModuleList()
for _ in range(n_l2):
self.convs_.append(AFB_L2(channels, 4, act))
self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(
channels * n_l2, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFN(nn.Module):
def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=
nn.LeakyReLU(0.2, True)):
super(AFN, self).__init__()
self.head = conv_(in_c, n_feats)
self.n = n_l3
self.AFBs = nn.ModuleList()
for i in range(n_l3):
self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))
self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *
n_l3, n_feats, 1, padding=0, stride=1)])
self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,
kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,
out_c)])
def forward(self, x):
res = []
x = self.head(x)
for i in range(self.n):
x = self.AFBs[i](x)
res.append(x)
res = self.GFF(torch.cat(res, 1))
x = res + x
x = self.tail(x)
return x
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AFB_0(nn.Module):
def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):
super(AFB_0, self).__init__()
self.op = []
for _ in range(n_blocks):
self.op.append(conv_(channels, channels))
self.op.append(act)
self.op = nn.Sequential(*self.op)
<|reserved_special_token_0|>
class AFB_L1(nn.Module):
def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):
super(AFB_L1, self).__init__()
self.n = n_l0
self.convs_ = nn.ModuleList()
for _ in range(n_l0):
self.convs_.append(AFB_0(channels, 2, act))
self.LFF = nn.Sequential(SELayer(channels * n_l0, 16), nn.Conv2d(
channels * n_l0, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L2(nn.Module):
def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):
super(AFB_L2, self).__init__()
self.n = n_l1
self.convs_ = nn.ModuleList()
for _ in range(n_l1):
self.convs_.append(AFB_L1(channels, 3, act))
self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(
channels * n_l1, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L3(nn.Module):
def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):
super(AFB_L3, self).__init__()
self.n = n_l2
self.convs_ = nn.ModuleList()
for _ in range(n_l2):
self.convs_.append(AFB_L2(channels, 4, act))
self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(
channels * n_l2, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFN(nn.Module):
def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=
nn.LeakyReLU(0.2, True)):
super(AFN, self).__init__()
self.head = conv_(in_c, n_feats)
self.n = n_l3
self.AFBs = nn.ModuleList()
for i in range(n_l3):
self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))
self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *
n_l3, n_feats, 1, padding=0, stride=1)])
self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,
kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,
out_c)])
def forward(self, x):
res = []
x = self.head(x)
for i in range(self.n):
x = self.AFBs[i](x)
res.append(x)
res = self.GFF(torch.cat(res, 1))
x = res + x
x = self.tail(x)
return x
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def wrapper(args):
act = None
if args.act == 'relu':
act = nn.ReLU(True)
elif args.act == 'leak_relu':
act = nn.LeakyReLU(0.2, True)
elif args.act is None:
act = None
else:
raise NotImplementedError
return AFN(in_c=args.n_colors, out_c=args.n_colors, scale=args.scale,
n_feats=args.n_feats, act=act)
class AFB_0(nn.Module):
def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):
super(AFB_0, self).__init__()
self.op = []
for _ in range(n_blocks):
self.op.append(conv_(channels, channels))
self.op.append(act)
self.op = nn.Sequential(*self.op)
def forward(self, x):
x = x + self.op(x)
return x
class AFB_L1(nn.Module):
def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):
super(AFB_L1, self).__init__()
self.n = n_l0
self.convs_ = nn.ModuleList()
for _ in range(n_l0):
self.convs_.append(AFB_0(channels, 2, act))
self.LFF = nn.Sequential(SELayer(channels * n_l0, 16), nn.Conv2d(
channels * n_l0, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L2(nn.Module):
def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):
super(AFB_L2, self).__init__()
self.n = n_l1
self.convs_ = nn.ModuleList()
for _ in range(n_l1):
self.convs_.append(AFB_L1(channels, 3, act))
self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(
channels * n_l1, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L3(nn.Module):
def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):
super(AFB_L3, self).__init__()
self.n = n_l2
self.convs_ = nn.ModuleList()
for _ in range(n_l2):
self.convs_.append(AFB_L2(channels, 4, act))
self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(
channels * n_l2, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFN(nn.Module):
def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=
nn.LeakyReLU(0.2, True)):
super(AFN, self).__init__()
self.head = conv_(in_c, n_feats)
self.n = n_l3
self.AFBs = nn.ModuleList()
for i in range(n_l3):
self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))
self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *
n_l3, n_feats, 1, padding=0, stride=1)])
self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,
kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,
out_c)])
def forward(self, x):
res = []
x = self.head(x)
for i in range(self.n):
x = self.AFBs[i](x)
res.append(x)
res = self.GFF(torch.cat(res, 1))
x = res + x
x = self.tail(x)
return x
if __name__ == '__main__':
import numpy as np
import torch
import torchsummary
model = AFN(in_c=3, out_c=3, scale=8, n_feats=128, n_l3=3, act=nn.
LeakyReLU(0.2, True))
print(torchsummary.summary(model, (3, 24, 24), device='cpu'))
x = np.random.uniform(0, 1, [2, 3, 24, 24]).astype(np.float32)
x = torch.tensor(x)
with torch.autograd.profiler.profile(use_cuda=True) as prof:
y = model(x)
print(prof)
print(y.shape)
<|reserved_special_token_1|>
import torch
import torch.nn as nn
from model.common import UpsampleBlock, conv_, SELayer
def wrapper(args):
act = None
if args.act == 'relu':
act = nn.ReLU(True)
elif args.act == 'leak_relu':
act = nn.LeakyReLU(0.2, True)
elif args.act is None:
act = None
else:
raise NotImplementedError
return AFN(in_c=args.n_colors, out_c=args.n_colors, scale=args.scale,
n_feats=args.n_feats, act=act)
class AFB_0(nn.Module):
def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):
super(AFB_0, self).__init__()
self.op = []
for _ in range(n_blocks):
self.op.append(conv_(channels, channels))
self.op.append(act)
self.op = nn.Sequential(*self.op)
def forward(self, x):
x = x + self.op(x)
return x
class AFB_L1(nn.Module):
def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):
super(AFB_L1, self).__init__()
self.n = n_l0
self.convs_ = nn.ModuleList()
for _ in range(n_l0):
self.convs_.append(AFB_0(channels, 2, act))
self.LFF = nn.Sequential(SELayer(channels * n_l0, 16), nn.Conv2d(
channels * n_l0, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L2(nn.Module):
def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):
super(AFB_L2, self).__init__()
self.n = n_l1
self.convs_ = nn.ModuleList()
for _ in range(n_l1):
self.convs_.append(AFB_L1(channels, 3, act))
self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(
channels * n_l1, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L3(nn.Module):
def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):
super(AFB_L3, self).__init__()
self.n = n_l2
self.convs_ = nn.ModuleList()
for _ in range(n_l2):
self.convs_.append(AFB_L2(channels, 4, act))
self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(
channels * n_l2, channels, 1, padding=0, stride=1))
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFN(nn.Module):
def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=
nn.LeakyReLU(0.2, True)):
super(AFN, self).__init__()
self.head = conv_(in_c, n_feats)
self.n = n_l3
self.AFBs = nn.ModuleList()
for i in range(n_l3):
self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))
self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *
n_l3, n_feats, 1, padding=0, stride=1)])
self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,
kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,
out_c)])
def forward(self, x):
res = []
x = self.head(x)
for i in range(self.n):
x = self.AFBs[i](x)
res.append(x)
res = self.GFF(torch.cat(res, 1))
x = res + x
x = self.tail(x)
return x
if __name__ == '__main__':
import numpy as np
import torch
import torchsummary
model = AFN(in_c=3, out_c=3, scale=8, n_feats=128, n_l3=3, act=nn.
LeakyReLU(0.2, True))
print(torchsummary.summary(model, (3, 24, 24), device='cpu'))
x = np.random.uniform(0, 1, [2, 3, 24, 24]).astype(np.float32)
x = torch.tensor(x)
with torch.autograd.profiler.profile(use_cuda=True) as prof:
y = model(x)
print(prof)
print(y.shape)
<|reserved_special_token_1|>
import torch
import torch.nn as nn
from model.common import UpsampleBlock, conv_, SELayer
def wrapper(args):
act = None
if args.act == 'relu':
act = nn.ReLU(True)
elif args.act == 'leak_relu':
act = nn.LeakyReLU(0.2, True)
elif args.act is None:
act = None
else:
raise NotImplementedError
return AFN(in_c=args.n_colors, out_c=args.n_colors, scale=args.scale, n_feats=args.n_feats, act=act)
class AFB_0(nn.Module):
def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):
super(AFB_0, self).__init__()
self.op = []
for _ in range(n_blocks):
self.op.append(conv_(channels, channels))
self.op.append(act)
self.op = nn.Sequential(*self.op)
def forward(self, x):
x = x + self.op(x)
return x
class AFB_L1(nn.Module):
def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):
super(AFB_L1, self).__init__()
self.n = n_l0
self.convs_ = nn.ModuleList()
for _ in range(n_l0):
self.convs_.append(
AFB_0(channels, 2, act)
)
self.LFF = nn.Sequential(
SELayer(channels * n_l0, 16),
nn.Conv2d(channels * n_l0, channels, 1, padding=0, stride=1),
)
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L2(nn.Module):
def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):
super(AFB_L2, self).__init__()
self.n = n_l1
self.convs_ = nn.ModuleList()
for _ in range(n_l1):
self.convs_.append(
AFB_L1(channels, 3, act)
)
self.LFF = nn.Sequential(
SELayer(channels * n_l1, 16),
nn.Conv2d(channels * n_l1, channels, 1, padding=0, stride=1),
)
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFB_L3(nn.Module):
def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):
super(AFB_L3, self).__init__()
self.n = n_l2
self.convs_ = nn.ModuleList()
for _ in range(n_l2):
self.convs_.append(
AFB_L2(channels, 4, act)
)
self.LFF = nn.Sequential(
SELayer(channels * n_l2, 16),
nn.Conv2d(channels * n_l2, channels, 1, padding=0, stride=1),
)
def forward(self, x):
res = []
ox = x
for i in range(self.n):
x = self.convs_[i](x)
res.append(x)
res = self.LFF(torch.cat(res, 1))
x = res + ox
return x
class AFN(nn.Module):
def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=nn.LeakyReLU(0.2, True)):
super(AFN, self).__init__()
self.head = conv_(in_c, n_feats)
self.n = n_l3
self.AFBs = nn.ModuleList()
for i in range(n_l3):
self.AFBs.append(
AFB_L3(channels=n_feats, n_l2=4, act=act)
)
self.GFF = nn.Sequential(*[
SELayer(n_feats * n_l3),
conv_(n_feats * n_l3, n_feats, 1, padding=0, stride=1),
])
self.tail = nn.Sequential(*[
UpsampleBlock(scale, n_feats, kernel_size=3, stride=1, bias=True, act=act),
conv_(n_feats, out_c)
])
def forward(self, x):
res = []
x = self.head(x)
for i in range(self.n):
x = self.AFBs[i](x)
res.append(x)
res = self.GFF(torch.cat(res, 1))
x = res + x
x = self.tail(x)
return x
if __name__ == "__main__":
import numpy as np
import torch
import torchsummary
model = AFN(in_c=3, out_c=3, scale=8, n_feats=128, n_l3=3, act=nn.LeakyReLU(0.2, True))
print(torchsummary.summary(model, (3, 24, 24), device='cpu'))
x = np.random.uniform(0, 1, [2, 3, 24, 24]).astype(np.float32)
x = torch.tensor(x)
# loss = nn.L1Loss()
# Adam = torch.optim.Adam(model.parameters(), lr=1e-3, betas=(0.99, 0.999))
with torch.autograd.profiler.profile(use_cuda=True) as prof:
y = model(x)
print(prof)
print(y.shape)
|
flexible
|
{
"blob_id": "b2c0ef4a0af12b267a54a7ae3fed9edeab2fb879",
"index": 6570,
"step-1": "<mask token>\n\n\nclass AFB_L1(nn.Module):\n <mask token>\n <mask token>\n\n\nclass AFB_L2(nn.Module):\n\n def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):\n super(AFB_L2, self).__init__()\n self.n = n_l1\n self.convs_ = nn.ModuleList()\n for _ in range(n_l1):\n self.convs_.append(AFB_L1(channels, 3, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(\n channels * n_l1, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L3(nn.Module):\n\n def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):\n super(AFB_L3, self).__init__()\n self.n = n_l2\n self.convs_ = nn.ModuleList()\n for _ in range(n_l2):\n self.convs_.append(AFB_L2(channels, 4, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(\n channels * n_l2, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFN(nn.Module):\n\n def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=\n nn.LeakyReLU(0.2, True)):\n super(AFN, self).__init__()\n self.head = conv_(in_c, n_feats)\n self.n = n_l3\n self.AFBs = nn.ModuleList()\n for i in range(n_l3):\n self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))\n self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *\n n_l3, n_feats, 1, padding=0, stride=1)])\n self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,\n kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,\n out_c)])\n\n def forward(self, x):\n res = []\n x = self.head(x)\n for i in range(self.n):\n x = self.AFBs[i](x)\n res.append(x)\n res = self.GFF(torch.cat(res, 1))\n x = res + x\n x = self.tail(x)\n return x\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass AFB_0(nn.Module):\n\n def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):\n super(AFB_0, self).__init__()\n self.op = []\n for _ in range(n_blocks):\n self.op.append(conv_(channels, channels))\n self.op.append(act)\n self.op = nn.Sequential(*self.op)\n <mask token>\n\n\nclass AFB_L1(nn.Module):\n\n def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):\n super(AFB_L1, self).__init__()\n self.n = n_l0\n self.convs_ = nn.ModuleList()\n for _ in range(n_l0):\n self.convs_.append(AFB_0(channels, 2, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l0, 16), nn.Conv2d(\n channels * n_l0, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L2(nn.Module):\n\n def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):\n super(AFB_L2, self).__init__()\n self.n = n_l1\n self.convs_ = nn.ModuleList()\n for _ in range(n_l1):\n self.convs_.append(AFB_L1(channels, 3, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(\n channels * n_l1, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L3(nn.Module):\n\n def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):\n super(AFB_L3, self).__init__()\n self.n = n_l2\n self.convs_ = nn.ModuleList()\n for _ in range(n_l2):\n self.convs_.append(AFB_L2(channels, 4, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(\n channels * n_l2, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFN(nn.Module):\n\n def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=\n nn.LeakyReLU(0.2, True)):\n super(AFN, self).__init__()\n self.head = conv_(in_c, n_feats)\n self.n = n_l3\n self.AFBs = nn.ModuleList()\n for i in range(n_l3):\n self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))\n self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *\n n_l3, n_feats, 1, padding=0, stride=1)])\n self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,\n kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,\n out_c)])\n\n def forward(self, x):\n res = []\n x = self.head(x)\n for i in range(self.n):\n x = self.AFBs[i](x)\n res.append(x)\n res = self.GFF(torch.cat(res, 1))\n x = res + x\n x = self.tail(x)\n return x\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef wrapper(args):\n act = None\n if args.act == 'relu':\n act = nn.ReLU(True)\n elif args.act == 'leak_relu':\n act = nn.LeakyReLU(0.2, True)\n elif args.act is None:\n act = None\n else:\n raise NotImplementedError\n return AFN(in_c=args.n_colors, out_c=args.n_colors, scale=args.scale,\n n_feats=args.n_feats, act=act)\n\n\nclass AFB_0(nn.Module):\n\n def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):\n super(AFB_0, self).__init__()\n self.op = []\n for _ in range(n_blocks):\n self.op.append(conv_(channels, channels))\n self.op.append(act)\n self.op = nn.Sequential(*self.op)\n\n def forward(self, x):\n x = x + self.op(x)\n return x\n\n\nclass AFB_L1(nn.Module):\n\n def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):\n super(AFB_L1, self).__init__()\n self.n = n_l0\n self.convs_ = nn.ModuleList()\n for _ in range(n_l0):\n self.convs_.append(AFB_0(channels, 2, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l0, 16), nn.Conv2d(\n channels * n_l0, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L2(nn.Module):\n\n def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):\n super(AFB_L2, self).__init__()\n self.n = n_l1\n self.convs_ = nn.ModuleList()\n for _ in range(n_l1):\n self.convs_.append(AFB_L1(channels, 3, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(\n channels * n_l1, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L3(nn.Module):\n\n def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):\n super(AFB_L3, self).__init__()\n self.n = n_l2\n self.convs_ = nn.ModuleList()\n for _ in range(n_l2):\n self.convs_.append(AFB_L2(channels, 4, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(\n channels * n_l2, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFN(nn.Module):\n\n def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=\n nn.LeakyReLU(0.2, True)):\n super(AFN, self).__init__()\n self.head = conv_(in_c, n_feats)\n self.n = n_l3\n self.AFBs = nn.ModuleList()\n for i in range(n_l3):\n self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))\n self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *\n n_l3, n_feats, 1, padding=0, stride=1)])\n self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,\n kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,\n out_c)])\n\n def forward(self, x):\n res = []\n x = self.head(x)\n for i in range(self.n):\n x = self.AFBs[i](x)\n res.append(x)\n res = self.GFF(torch.cat(res, 1))\n x = res + x\n x = self.tail(x)\n return x\n\n\nif __name__ == '__main__':\n import numpy as np\n import torch\n import torchsummary\n model = AFN(in_c=3, out_c=3, scale=8, n_feats=128, n_l3=3, act=nn.\n LeakyReLU(0.2, True))\n print(torchsummary.summary(model, (3, 24, 24), device='cpu'))\n x = np.random.uniform(0, 1, [2, 3, 24, 24]).astype(np.float32)\n x = torch.tensor(x)\n with torch.autograd.profiler.profile(use_cuda=True) as prof:\n y = model(x)\n print(prof)\n print(y.shape)\n",
"step-4": "import torch\nimport torch.nn as nn\nfrom model.common import UpsampleBlock, conv_, SELayer\n\n\ndef wrapper(args):\n act = None\n if args.act == 'relu':\n act = nn.ReLU(True)\n elif args.act == 'leak_relu':\n act = nn.LeakyReLU(0.2, True)\n elif args.act is None:\n act = None\n else:\n raise NotImplementedError\n return AFN(in_c=args.n_colors, out_c=args.n_colors, scale=args.scale,\n n_feats=args.n_feats, act=act)\n\n\nclass AFB_0(nn.Module):\n\n def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):\n super(AFB_0, self).__init__()\n self.op = []\n for _ in range(n_blocks):\n self.op.append(conv_(channels, channels))\n self.op.append(act)\n self.op = nn.Sequential(*self.op)\n\n def forward(self, x):\n x = x + self.op(x)\n return x\n\n\nclass AFB_L1(nn.Module):\n\n def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):\n super(AFB_L1, self).__init__()\n self.n = n_l0\n self.convs_ = nn.ModuleList()\n for _ in range(n_l0):\n self.convs_.append(AFB_0(channels, 2, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l0, 16), nn.Conv2d(\n channels * n_l0, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L2(nn.Module):\n\n def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):\n super(AFB_L2, self).__init__()\n self.n = n_l1\n self.convs_ = nn.ModuleList()\n for _ in range(n_l1):\n self.convs_.append(AFB_L1(channels, 3, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l1, 16), nn.Conv2d(\n channels * n_l1, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L3(nn.Module):\n\n def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):\n super(AFB_L3, self).__init__()\n self.n = n_l2\n self.convs_ = nn.ModuleList()\n for _ in range(n_l2):\n self.convs_.append(AFB_L2(channels, 4, act))\n self.LFF = nn.Sequential(SELayer(channels * n_l2, 16), nn.Conv2d(\n channels * n_l2, channels, 1, padding=0, stride=1))\n\n def forward(self, x):\n res = []\n ox = x\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFN(nn.Module):\n\n def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=\n nn.LeakyReLU(0.2, True)):\n super(AFN, self).__init__()\n self.head = conv_(in_c, n_feats)\n self.n = n_l3\n self.AFBs = nn.ModuleList()\n for i in range(n_l3):\n self.AFBs.append(AFB_L3(channels=n_feats, n_l2=4, act=act))\n self.GFF = nn.Sequential(*[SELayer(n_feats * n_l3), conv_(n_feats *\n n_l3, n_feats, 1, padding=0, stride=1)])\n self.tail = nn.Sequential(*[UpsampleBlock(scale, n_feats,\n kernel_size=3, stride=1, bias=True, act=act), conv_(n_feats,\n out_c)])\n\n def forward(self, x):\n res = []\n x = self.head(x)\n for i in range(self.n):\n x = self.AFBs[i](x)\n res.append(x)\n res = self.GFF(torch.cat(res, 1))\n x = res + x\n x = self.tail(x)\n return x\n\n\nif __name__ == '__main__':\n import numpy as np\n import torch\n import torchsummary\n model = AFN(in_c=3, out_c=3, scale=8, n_feats=128, n_l3=3, act=nn.\n LeakyReLU(0.2, True))\n print(torchsummary.summary(model, (3, 24, 24), device='cpu'))\n x = np.random.uniform(0, 1, [2, 3, 24, 24]).astype(np.float32)\n x = torch.tensor(x)\n with torch.autograd.profiler.profile(use_cuda=True) as prof:\n y = model(x)\n print(prof)\n print(y.shape)\n",
"step-5": "import torch\nimport torch.nn as nn\nfrom model.common import UpsampleBlock, conv_, SELayer\n\ndef wrapper(args):\n act = None\n if args.act == 'relu':\n act = nn.ReLU(True)\n elif args.act == 'leak_relu':\n act = nn.LeakyReLU(0.2, True)\n elif args.act is None:\n act = None\n else:\n raise NotImplementedError\n\n return AFN(in_c=args.n_colors, out_c=args.n_colors, scale=args.scale, n_feats=args.n_feats, act=act)\n\nclass AFB_0(nn.Module):\n def __init__(self, channels, n_blocks=2, act=nn.ReLU(True)):\n super(AFB_0, self).__init__()\n self.op = []\n for _ in range(n_blocks):\n self.op.append(conv_(channels, channels))\n self.op.append(act)\n\n self.op = nn.Sequential(*self.op)\n\n def forward(self, x):\n x = x + self.op(x)\n return x\n\n\nclass AFB_L1(nn.Module):\n def __init__(self, channels, n_l0=3, act=nn.ReLU(True)):\n super(AFB_L1, self).__init__()\n\n self.n = n_l0\n self.convs_ = nn.ModuleList()\n for _ in range(n_l0):\n self.convs_.append(\n AFB_0(channels, 2, act)\n )\n\n self.LFF = nn.Sequential(\n SELayer(channels * n_l0, 16),\n nn.Conv2d(channels * n_l0, channels, 1, padding=0, stride=1),\n )\n\n def forward(self, x):\n res = []\n ox = x\n\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L2(nn.Module):\n def __init__(self, channels, n_l1=4, act=nn.ReLU(True)):\n super(AFB_L2, self).__init__()\n\n self.n = n_l1\n self.convs_ = nn.ModuleList()\n for _ in range(n_l1):\n self.convs_.append(\n AFB_L1(channels, 3, act)\n )\n\n self.LFF = nn.Sequential(\n SELayer(channels * n_l1, 16),\n nn.Conv2d(channels * n_l1, channels, 1, padding=0, stride=1),\n )\n\n def forward(self, x):\n res = []\n ox = x\n\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFB_L3(nn.Module):\n def __init__(self, channels, n_l2=4, act=nn.ReLU(True)):\n super(AFB_L3, self).__init__()\n\n self.n = n_l2\n self.convs_ = nn.ModuleList()\n for _ in range(n_l2):\n self.convs_.append(\n AFB_L2(channels, 4, act)\n )\n\n self.LFF = nn.Sequential(\n SELayer(channels * n_l2, 16),\n nn.Conv2d(channels * n_l2, channels, 1, padding=0, stride=1),\n )\n\n def forward(self, x):\n res = []\n ox = x\n\n for i in range(self.n):\n x = self.convs_[i](x)\n res.append(x)\n res = self.LFF(torch.cat(res, 1))\n x = res + ox\n return x\n\n\nclass AFN(nn.Module):\n def __init__(self, in_c=3, out_c=3, scale=4, n_feats=128, n_l3=3, act=nn.LeakyReLU(0.2, True)):\n super(AFN, self).__init__()\n\n self.head = conv_(in_c, n_feats)\n\n self.n = n_l3\n self.AFBs = nn.ModuleList()\n for i in range(n_l3):\n self.AFBs.append(\n AFB_L3(channels=n_feats, n_l2=4, act=act)\n )\n\n self.GFF = nn.Sequential(*[\n SELayer(n_feats * n_l3),\n conv_(n_feats * n_l3, n_feats, 1, padding=0, stride=1),\n ])\n\n self.tail = nn.Sequential(*[\n UpsampleBlock(scale, n_feats, kernel_size=3, stride=1, bias=True, act=act),\n conv_(n_feats, out_c)\n ])\n\n def forward(self, x):\n res = []\n x = self.head(x)\n\n for i in range(self.n):\n x = self.AFBs[i](x)\n res.append(x)\n\n res = self.GFF(torch.cat(res, 1))\n x = res + x\n\n x = self.tail(x)\n return x\n\nif __name__ == \"__main__\":\n import numpy as np\n import torch\n import torchsummary\n\n model = AFN(in_c=3, out_c=3, scale=8, n_feats=128, n_l3=3, act=nn.LeakyReLU(0.2, True))\n print(torchsummary.summary(model, (3, 24, 24), device='cpu'))\n\n x = np.random.uniform(0, 1, [2, 3, 24, 24]).astype(np.float32)\n x = torch.tensor(x)\n\n # loss = nn.L1Loss()\n # Adam = torch.optim.Adam(model.parameters(), lr=1e-3, betas=(0.99, 0.999))\n with torch.autograd.profiler.profile(use_cuda=True) as prof:\n y = model(x)\n print(prof)\n print(y.shape)\n",
"step-ids": [
10,
14,
17,
18,
19
]
}
|
[
10,
14,
17,
18,
19
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Order(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_1|>
<|reserved_special_token_0|>
class Order(models.Model):
subscription = models.OneToOneField(Subscription, on_delete=models.
CASCADE, related_name='order')
order_status = models.CharField(max_length=50, choices=OrderStatus.
Choices, default=OrderStatus.IN_PROGRESS)
created = models.DateTimeField(auto_now_add=True)
updated = models.DateTimeField(auto_now=True)
email = models.EmailField()
price = models.DecimalField(max_digits=10, decimal_places=2)
<|reserved_special_token_1|>
from django.db import models
from orders.constants import OrderStatus
from subscriptions.models import Subscription
class Order(models.Model):
subscription = models.OneToOneField(Subscription, on_delete=models.
CASCADE, related_name='order')
order_status = models.CharField(max_length=50, choices=OrderStatus.
Choices, default=OrderStatus.IN_PROGRESS)
created = models.DateTimeField(auto_now_add=True)
updated = models.DateTimeField(auto_now=True)
email = models.EmailField()
price = models.DecimalField(max_digits=10, decimal_places=2)
<|reserved_special_token_1|>
from django.db import models
from orders.constants import OrderStatus
from subscriptions.models import Subscription
class Order(models.Model):
subscription = models.OneToOneField(
Subscription,
on_delete=models.CASCADE,
related_name='order',
)
order_status = models.CharField(
max_length=50,
choices=OrderStatus.Choices,
default=OrderStatus.IN_PROGRESS,
)
created = models.DateTimeField(auto_now_add=True)
updated = models.DateTimeField(auto_now=True)
email = models.EmailField()
price = models.DecimalField(max_digits=10, decimal_places=2)
# def get_email(self):
# if self.email is None:
# self.email = Subscription.objects.get(client__email=...)
|
flexible
|
{
"blob_id": "78ddae64cc576ebaf7f2cfaa4553bddbabe474b7",
"index": 6918,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Order(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Order(models.Model):\n subscription = models.OneToOneField(Subscription, on_delete=models.\n CASCADE, related_name='order')\n order_status = models.CharField(max_length=50, choices=OrderStatus.\n Choices, default=OrderStatus.IN_PROGRESS)\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n email = models.EmailField()\n price = models.DecimalField(max_digits=10, decimal_places=2)\n",
"step-4": "from django.db import models\nfrom orders.constants import OrderStatus\nfrom subscriptions.models import Subscription\n\n\nclass Order(models.Model):\n subscription = models.OneToOneField(Subscription, on_delete=models.\n CASCADE, related_name='order')\n order_status = models.CharField(max_length=50, choices=OrderStatus.\n Choices, default=OrderStatus.IN_PROGRESS)\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n email = models.EmailField()\n price = models.DecimalField(max_digits=10, decimal_places=2)\n",
"step-5": "from django.db import models\n\nfrom orders.constants import OrderStatus\nfrom subscriptions.models import Subscription\n\n\nclass Order(models.Model):\n subscription = models.OneToOneField(\n Subscription,\n on_delete=models.CASCADE,\n related_name='order',\n )\n order_status = models.CharField(\n max_length=50,\n choices=OrderStatus.Choices,\n default=OrderStatus.IN_PROGRESS,\n )\n created = models.DateTimeField(auto_now_add=True)\n updated = models.DateTimeField(auto_now=True)\n email = models.EmailField()\n price = models.DecimalField(max_digits=10, decimal_places=2)\n\n # def get_email(self):\n # if self.email is None:\n # self.email = Subscription.objects.get(client__email=...)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
'''Given a range of 2 numbers (i.e) L and R count the number of prime numbers in the range (inclusive of L and R ).
Input Size : L <= R <= 100000(complexity O(n) read about Sieve of Eratosthenes)
Sample Testcase :
INPUT
2 5
OUTPUT
3'''
x,y=map(int,input().split())
count=0
for i in range(x,y+1):
if i>1:
for j in range(2,i):
if(i%j==0):
break
else:
count+=1
print(count)
|
normal
|
{
"blob_id": "06848ec0e327fed1da00446cec6392c6f42130af",
"index": 2158,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(x, y + 1):\n if i > 1:\n for j in range(2, i):\n if i % j == 0:\n break\n else:\n count += 1\nprint(count)\n",
"step-3": "<mask token>\nx, y = map(int, input().split())\ncount = 0\nfor i in range(x, y + 1):\n if i > 1:\n for j in range(2, i):\n if i % j == 0:\n break\n else:\n count += 1\nprint(count)\n",
"step-4": "'''Given a range of 2 numbers (i.e) L and R count the number of prime numbers in the range (inclusive of L and R ).\nInput Size : L <= R <= 100000(complexity O(n) read about Sieve of Eratosthenes)\nSample Testcase :\nINPUT\n2 5\nOUTPUT\n3'''\n\nx,y=map(int,input().split())\ncount=0\nfor i in range(x,y+1):\n if i>1:\n for j in range(2,i):\n if(i%j==0):\n break\n else:\n count+=1\nprint(count)\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|>
print(word[0])
<|reserved_special_token_0|>
print('こんにちわ、私の名前は {} です。'.format(name))
<|reserved_special_token_0|>
print('{}/{}/{}'.format(year, month, day))
for i in range(0, 5):
print('kamyu'[i])
print('aldous Huxley was born in 1894'.capitalize())
print('when? what? who?'.split())
<|reserved_special_token_0|>
print(word)
print('A screeming comes across the sky.'.replace('s', '$'))
print('Hemingway'.index('m'))
print('ケンシロウは言った"お前はもう死んでいる"とな')
print('アタタタ,' * 10 + 'オワッター！')
print('４月の晴れた寒い日で、時計がどれも十三時を打っていた。'.split('、')[0])
<|reserved_special_token_1|>
<|reserved_special_token_0|>
word = "what's up"
print(word[0])
name = 'lady gaga'
print('こんにちわ、私の名前は {} です。'.format(name))
<|reserved_special_token_0|>
year = 1990
month = 7
day = 11
print('{}/{}/{}'.format(year, month, day))
for i in range(0, 5):
print('kamyu'[i])
print('aldous Huxley was born in 1894'.capitalize())
print('when? what? who?'.split())
<|reserved_special_token_0|>
word = ['the', 'fox', 'jumped', 'over', 'the', 'fence', '.']
word = ' '.join(word)
word = word[0:-2] + '.'
print(word)
print('A screeming comes across the sky.'.replace('s', '$'))
print('Hemingway'.index('m'))
print('ケンシロウは言った"お前はもう死んでいる"とな')
print('アタタタ,' * 10 + 'オワッター！')
print('４月の晴れた寒い日で、時計がどれも十三時を打っていた。'.split('、')[0])
<|reserved_special_token_1|>
# python /Users/lawrie_6strings/be_professional_pythonist/control_string.py
# -*- coding: utf-8 -*-
# 文字列を３行で書いてみたい場合
"""
どないやねん。
最近の若いもんは、
ようやるやんけ。
"""
# 文字列の特定の文字を取得したい場合は,インデックスを指定してあげることでなんとかする。
word = "what's up"
print(word[0])
# 書式化
name = "lady gaga"
print("こんにちわ、私の名前は {} です。".format(name))
"複数の文字列を挿入することもできる。"
year = 1990
month = 7
day = 11
print("{}/{}/{}".format(year, month, day))
# チャレンジ
## １
for i in range(0, 5):
print("kamyu"[i])
## 2
# what = input("what:")
# who = input("who:")
# print("I write {},I send it to {}".format(what, who))
## 3
print("aldous Huxley was born in 1894".capitalize())
## 4
print("when? what? who?".split())
## 5
"最後のピリオドを再利用しようとしすぎて、詰まってしまった。"
word = ["the", "fox", "jumped", "over", "the", "fence", "."]
word = " ".join(word)
word = word[0:-2] + "."
print(word)
## 6
print("A screeming comes across the sky.".replace("s", "$"))
## 7
print("Hemingway".index("m"))
## 8 文字列の中にさらに文字列を入れたい時。
print("ケンシロウは言った\"お前はもう死んでいる\"とな")
## 9
print("アタタタ,"*10 + "オワッター！")
## 10
print("４月の晴れた寒い日で、時計がどれも十三時を打っていた。".split("、")[0])
|
flexible
|
{
"blob_id": "0e05eed2d6bc723fd8379e436621a6eba4aa5ab2",
"index": 1929,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(word[0])\n<mask token>\nprint('こんにちわ、私の名前は {} です。'.format(name))\n<mask token>\nprint('{}/{}/{}'.format(year, month, day))\nfor i in range(0, 5):\n print('kamyu'[i])\nprint('aldous Huxley was born in 1894'.capitalize())\nprint('when? what? who?'.split())\n<mask token>\nprint(word)\nprint('A screeming comes across the sky.'.replace('s', '$'))\nprint('Hemingway'.index('m'))\nprint('ケンシロウは言った\"お前はもう死んでいる\"とな')\nprint('アタタタ,' * 10 + 'オワッター！')\nprint('４月の晴れた寒い日で、時計がどれも十三時を打っていた。'.split('、')[0])\n",
"step-3": "<mask token>\nword = \"what's up\"\nprint(word[0])\nname = 'lady gaga'\nprint('こんにちわ、私の名前は {} です。'.format(name))\n<mask token>\nyear = 1990\nmonth = 7\nday = 11\nprint('{}/{}/{}'.format(year, month, day))\nfor i in range(0, 5):\n print('kamyu'[i])\nprint('aldous Huxley was born in 1894'.capitalize())\nprint('when? what? who?'.split())\n<mask token>\nword = ['the', 'fox', 'jumped', 'over', 'the', 'fence', '.']\nword = ' '.join(word)\nword = word[0:-2] + '.'\nprint(word)\nprint('A screeming comes across the sky.'.replace('s', '$'))\nprint('Hemingway'.index('m'))\nprint('ケンシロウは言った\"お前はもう死んでいる\"とな')\nprint('アタタタ,' * 10 + 'オワッター！')\nprint('４月の晴れた寒い日で、時計がどれも十三時を打っていた。'.split('、')[0])\n",
"step-4": "# python /Users/lawrie_6strings/be_professional_pythonist/control_string.py\n# -*- coding: utf-8 -*-\n# 文字列を３行で書いてみたい場合\n\"\"\"\nどないやねん。\n最近の若いもんは、\nようやるやんけ。\n\"\"\"\n\n# 文字列の特定の文字を取得したい場合は,インデックスを指定してあげることでなんとかする。\nword = \"what's up\"\nprint(word[0])\n\n# 書式化\nname = \"lady gaga\"\nprint(\"こんにちわ、私の名前は {} です。\".format(name))\n\n\"複数の文字列を挿入することもできる。\"\nyear = 1990\nmonth = 7\nday = 11\nprint(\"{}/{}/{}\".format(year, month, day))\n\n# チャレンジ\n## １\nfor i in range(0, 5):\n print(\"kamyu\"[i])\n\n## 2\n# what = input(\"what:\")\n# who = input(\"who:\")\n# print(\"I write {},I send it to {}\".format(what, who))\n\n## 3\nprint(\"aldous Huxley was born in 1894\".capitalize())\n\n## 4\nprint(\"when? what? who?\".split())\n\n## 5\n\"最後のピリオドを再利用しようとしすぎて、詰まってしまった。\"\nword = [\"the\", \"fox\", \"jumped\", \"over\", \"the\", \"fence\", \".\"]\nword = \" \".join(word)\nword = word[0:-2] + \".\"\nprint(word)\n\n## 6\nprint(\"A screeming comes across the sky.\".replace(\"s\", \"$\"))\n\n## 7\nprint(\"Hemingway\".index(\"m\"))\n\n## 8 文字列の中にさらに文字列を入れたい時。\nprint(\"ケンシロウは言った\\\"お前はもう死んでいる\\\"とな\")\n\n\n## 9\nprint(\"アタタタ,\"*10 + \"オワッター！\")\n\n## 10\nprint(\"４月の晴れた寒い日で、時計がどれも十三時を打っていた。\".split(\"、\")[0])",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
a= input("Enter number")
a= a.split()
b=[]
for x in a:
b.append(int(x))
print(b)
l=len(b)
c=0
s=0
for i in range(l):
s=len(b[:i])
for j in range(s):
if b[s]<b[j]:
c=b[s]
b.pop(s)
b.insert(b.index(b[j]),c)
print(b,b[:i],b[s])
|
normal
|
{
"blob_id": "24de4f486d4e976850e94a003f8d9cbe3e518402",
"index": 33,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor x in a:\n b.append(int(x))\nprint(b)\n<mask token>\nfor i in range(l):\n s = len(b[:i])\n for j in range(s):\n if b[s] < b[j]:\n c = b[s]\n b.pop(s)\n b.insert(b.index(b[j]), c)\n print(b, b[:i], b[s])\n",
"step-3": "a = input('Enter number')\na = a.split()\nb = []\nfor x in a:\n b.append(int(x))\nprint(b)\nl = len(b)\nc = 0\ns = 0\nfor i in range(l):\n s = len(b[:i])\n for j in range(s):\n if b[s] < b[j]:\n c = b[s]\n b.pop(s)\n b.insert(b.index(b[j]), c)\n print(b, b[:i], b[s])\n",
"step-4": "a= input(\"Enter number\")\r\na= a.split()\r\nb=[]\r\nfor x in a:\r\n b.append(int(x)) \r\n\r\nprint(b)\r\nl=len(b)\r\nc=0\r\ns=0\r\nfor i in range(l):\r\n s=len(b[:i])\r\n for j in range(s):\r\n \r\n if b[s]<b[j]:\r\n c=b[s]\r\n b.pop(s)\r\n b.insert(b.index(b[j]),c)\r\n print(b,b[:i],b[s])\r\n\r\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
import matplotlib.pyplot as plt
class Scatter:
def __init__(self, values, ylabel, title):
self.values = values
self.range = list(range(len(values)))
self.ylabel = ylabel
self.title = title
def plot(self):
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
ax.scatter(self.range, self.values, color='r', s=1)
ax.set_xlabel('Days')
ax.set_ylabel(self.ylabel)
ax.set_title(self.title)
plt.ylim(0, self.values[-1])
plt.show()
class Pie:
def __init__(self, values, labels, title):
self.style = "fivethirtyeight"
self.values = values
self.labels = labels
self.explode = [0 for i in range(len(values))]
self.title = title
def plot(self):
plt.style.use(self.style)
plt.pie(self.values, labels=self.labels, explode=self.explode, shadow=True,
startangle=90, autopct='%1.1f%%',
wedgeprops={'edgecolor': 'black'})
plt.title(self.title)
plt.tight_layout()
plt.show()
class Column:
pass
|
normal
|
{
"blob_id": "58385a7713a8f88925ced714d25f1522bc7e39d8",
"index": 1181,
"step-1": "<mask token>\n\n\nclass Scatter:\n <mask token>\n <mask token>\n\n\nclass Pie:\n\n def __init__(self, values, labels, title):\n self.style = 'fivethirtyeight'\n self.values = values\n self.labels = labels\n self.explode = [(0) for i in range(len(values))]\n self.title = title\n\n def plot(self):\n plt.style.use(self.style)\n plt.pie(self.values, labels=self.labels, explode=self.explode,\n shadow=True, startangle=90, autopct='%1.1f%%', wedgeprops={\n 'edgecolor': 'black'})\n plt.title(self.title)\n plt.tight_layout()\n plt.show()\n\n\nclass Column:\n pass\n",
"step-2": "<mask token>\n\n\nclass Scatter:\n\n def __init__(self, values, ylabel, title):\n self.values = values\n self.range = list(range(len(values)))\n self.ylabel = ylabel\n self.title = title\n <mask token>\n\n\nclass Pie:\n\n def __init__(self, values, labels, title):\n self.style = 'fivethirtyeight'\n self.values = values\n self.labels = labels\n self.explode = [(0) for i in range(len(values))]\n self.title = title\n\n def plot(self):\n plt.style.use(self.style)\n plt.pie(self.values, labels=self.labels, explode=self.explode,\n shadow=True, startangle=90, autopct='%1.1f%%', wedgeprops={\n 'edgecolor': 'black'})\n plt.title(self.title)\n plt.tight_layout()\n plt.show()\n\n\nclass Column:\n pass\n",
"step-3": "<mask token>\n\n\nclass Scatter:\n\n def __init__(self, values, ylabel, title):\n self.values = values\n self.range = list(range(len(values)))\n self.ylabel = ylabel\n self.title = title\n\n def plot(self):\n fig = plt.figure()\n ax = fig.add_axes([0, 0, 1, 1])\n ax.scatter(self.range, self.values, color='r', s=1)\n ax.set_xlabel('Days')\n ax.set_ylabel(self.ylabel)\n ax.set_title(self.title)\n plt.ylim(0, self.values[-1])\n plt.show()\n\n\nclass Pie:\n\n def __init__(self, values, labels, title):\n self.style = 'fivethirtyeight'\n self.values = values\n self.labels = labels\n self.explode = [(0) for i in range(len(values))]\n self.title = title\n\n def plot(self):\n plt.style.use(self.style)\n plt.pie(self.values, labels=self.labels, explode=self.explode,\n shadow=True, startangle=90, autopct='%1.1f%%', wedgeprops={\n 'edgecolor': 'black'})\n plt.title(self.title)\n plt.tight_layout()\n plt.show()\n\n\nclass Column:\n pass\n",
"step-4": "import matplotlib.pyplot as plt\n\n\nclass Scatter:\n\n def __init__(self, values, ylabel, title):\n self.values = values\n self.range = list(range(len(values)))\n self.ylabel = ylabel\n self.title = title\n\n def plot(self):\n fig = plt.figure()\n ax = fig.add_axes([0, 0, 1, 1])\n ax.scatter(self.range, self.values, color='r', s=1)\n ax.set_xlabel('Days')\n ax.set_ylabel(self.ylabel)\n ax.set_title(self.title)\n plt.ylim(0, self.values[-1])\n plt.show()\n\n\nclass Pie:\n\n def __init__(self, values, labels, title):\n self.style = 'fivethirtyeight'\n self.values = values\n self.labels = labels\n self.explode = [(0) for i in range(len(values))]\n self.title = title\n\n def plot(self):\n plt.style.use(self.style)\n plt.pie(self.values, labels=self.labels, explode=self.explode,\n shadow=True, startangle=90, autopct='%1.1f%%', wedgeprops={\n 'edgecolor': 'black'})\n plt.title(self.title)\n plt.tight_layout()\n plt.show()\n\n\nclass Column:\n pass\n",
"step-5": "import matplotlib.pyplot as plt\r\n\r\n\r\nclass Scatter:\r\n def __init__(self, values, ylabel, title):\r\n self.values = values\r\n self.range = list(range(len(values)))\r\n self.ylabel = ylabel\r\n self.title = title\r\n\r\n def plot(self):\r\n fig = plt.figure()\r\n ax = fig.add_axes([0, 0, 1, 1])\r\n ax.scatter(self.range, self.values, color='r', s=1)\r\n ax.set_xlabel('Days')\r\n ax.set_ylabel(self.ylabel)\r\n ax.set_title(self.title)\r\n plt.ylim(0, self.values[-1])\r\n plt.show()\r\n\r\n\r\nclass Pie:\r\n def __init__(self, values, labels, title):\r\n self.style = \"fivethirtyeight\"\r\n self.values = values\r\n self.labels = labels\r\n self.explode = [0 for i in range(len(values))]\r\n self.title = title\r\n\r\n def plot(self):\r\n plt.style.use(self.style)\r\n\r\n plt.pie(self.values, labels=self.labels, explode=self.explode, shadow=True,\r\n startangle=90, autopct='%1.1f%%',\r\n wedgeprops={'edgecolor': 'black'})\r\n\r\n plt.title(self.title)\r\n plt.tight_layout()\r\n\r\n plt.show()\r\n\r\n\r\nclass Column:\r\n pass",
"step-ids": [
5,
6,
7,
8,
9
]
}
|
[
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class Getter(object):
<|reserved_special_token_0|>
def __call__(self, url, **kwargs):
try:
return self._inner_call(url, **kwargs)
except (Timeout, ConnectionError, RequestException) as ex:
message = ex.response.reason if getattr(ex, 'response', None
) is not None else type(ex).__name__
raise GetterError(message, ex, not isinstance(ex, RequestException)
)
def _inner_call(self, url, **kwargs):
if 'timeout' not in kwargs:
kwargs['timeout'] = 20
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 401:
if self.login():
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 404:
return
result.raise_for_status()
return result
class GetterError(Exception):
def __init__(self, message, cause, connection_error):
super(GetterError, self).__init__()
self.message = message
self.cause = cause
self.connection_error = connection_error
self.request = getattr(cause, 'request', None)
self.response = getattr(cause, 'response', None)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Getter(object):
def __init__(self, contenttype=None, login=lambda : False, session=None):
self.session = session or retryable_session()
self.login = login
if contenttype:
self.session.headers['Accept'] = contenttype
def __call__(self, url, **kwargs):
try:
return self._inner_call(url, **kwargs)
except (Timeout, ConnectionError, RequestException) as ex:
message = ex.response.reason if getattr(ex, 'response', None
) is not None else type(ex).__name__
raise GetterError(message, ex, not isinstance(ex, RequestException)
)
def _inner_call(self, url, **kwargs):
if 'timeout' not in kwargs:
kwargs['timeout'] = 20
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 401:
if self.login():
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 404:
return
result.raise_for_status()
return result
class GetterError(Exception):
def __init__(self, message, cause, connection_error):
super(GetterError, self).__init__()
self.message = message
self.cause = cause
self.connection_error = connection_error
self.request = getattr(cause, 'request', None)
self.response = getattr(cause, 'response', None)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def retryable_session(retries=3, backoff_factor=0.5, status_forcelist=(500,
502, 504, 520), session=None):
session = session or requests.Session()
retry = Retry(total=retries, read=retries, connect=retries,
backoff_factor=backoff_factor, status_forcelist=status_forcelist)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)
return session
class Getter(object):
def __init__(self, contenttype=None, login=lambda : False, session=None):
self.session = session or retryable_session()
self.login = login
if contenttype:
self.session.headers['Accept'] = contenttype
def __call__(self, url, **kwargs):
try:
return self._inner_call(url, **kwargs)
except (Timeout, ConnectionError, RequestException) as ex:
message = ex.response.reason if getattr(ex, 'response', None
) is not None else type(ex).__name__
raise GetterError(message, ex, not isinstance(ex, RequestException)
)
def _inner_call(self, url, **kwargs):
if 'timeout' not in kwargs:
kwargs['timeout'] = 20
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 401:
if self.login():
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 404:
return
result.raise_for_status()
return result
class GetterError(Exception):
def __init__(self, message, cause, connection_error):
super(GetterError, self).__init__()
self.message = message
self.cause = cause
self.connection_error = connection_error
self.request = getattr(cause, 'request', None)
self.response = getattr(cause, 'response', None)
<|reserved_special_token_1|>
import requests
from requests.adapters import HTTPAdapter
from requests.exceptions import ConnectionError, Timeout, RequestException
from requests.packages.urllib3.util.retry import Retry
def retryable_session(retries=3, backoff_factor=0.5, status_forcelist=(500,
502, 504, 520), session=None):
session = session or requests.Session()
retry = Retry(total=retries, read=retries, connect=retries,
backoff_factor=backoff_factor, status_forcelist=status_forcelist)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)
return session
class Getter(object):
def __init__(self, contenttype=None, login=lambda : False, session=None):
self.session = session or retryable_session()
self.login = login
if contenttype:
self.session.headers['Accept'] = contenttype
def __call__(self, url, **kwargs):
try:
return self._inner_call(url, **kwargs)
except (Timeout, ConnectionError, RequestException) as ex:
message = ex.response.reason if getattr(ex, 'response', None
) is not None else type(ex).__name__
raise GetterError(message, ex, not isinstance(ex, RequestException)
)
def _inner_call(self, url, **kwargs):
if 'timeout' not in kwargs:
kwargs['timeout'] = 20
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 401:
if self.login():
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 404:
return
result.raise_for_status()
return result
class GetterError(Exception):
def __init__(self, message, cause, connection_error):
super(GetterError, self).__init__()
self.message = message
self.cause = cause
self.connection_error = connection_error
self.request = getattr(cause, 'request', None)
self.response = getattr(cause, 'response', None)
<|reserved_special_token_1|>
import requests
from requests.adapters import HTTPAdapter
from requests.exceptions import ConnectionError, Timeout, RequestException
# import from `requests` because Jarvis / some platforms still have old urllib3
from requests.packages.urllib3.util.retry import Retry
def retryable_session(retries=3, backoff_factor=0.5, status_forcelist=(500, 502, 504, 520), session=None):
# from https://www.peterbe.com/plog/best-practice-with-retries-with-requests
session = session or requests.Session()
# 'Retry-After' 413/503/529 headers are respected by default
retry = Retry(total=retries, read=retries, connect=retries,
backoff_factor=backoff_factor, status_forcelist=status_forcelist)
adapter = HTTPAdapter(max_retries=retry)
session.mount('http://', adapter)
session.mount('https://', adapter)
return session
class Getter(object):
def __init__(self, contenttype=None, login=lambda: False, session=None):
self.session = session or retryable_session()
self.login = login
if contenttype:
self.session.headers['Accept'] = contenttype
def __call__(self, url, **kwargs):
try:
return self._inner_call(url, **kwargs)
except (Timeout, ConnectionError, RequestException) as ex:
message = ex.response.reason if getattr(ex, 'response', None) is not None else type(ex).__name__
raise GetterError(message, ex, not isinstance(ex, RequestException))
def _inner_call(self, url, **kwargs):
if 'timeout' not in kwargs:
kwargs['timeout'] = 20
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 401:
if self.login():
result = self.session.get(url, **kwargs)
if result is None:
return
if result.status_code == 404:
return
result.raise_for_status()
return result
class GetterError(Exception):
def __init__(self, message, cause, connection_error):
super(GetterError, self).__init__()
self.message = message
self.cause = cause
self.connection_error = connection_error
self.request = getattr(cause, 'request', None)
self.response = getattr(cause, 'response', None)
|
flexible
|
{
"blob_id": "603708c830dadb6f1a3e5de00536d558f448b5fb",
"index": 1352,
"step-1": "<mask token>\n\n\nclass Getter(object):\n <mask token>\n\n def __call__(self, url, **kwargs):\n try:\n return self._inner_call(url, **kwargs)\n except (Timeout, ConnectionError, RequestException) as ex:\n message = ex.response.reason if getattr(ex, 'response', None\n ) is not None else type(ex).__name__\n raise GetterError(message, ex, not isinstance(ex, RequestException)\n )\n\n def _inner_call(self, url, **kwargs):\n if 'timeout' not in kwargs:\n kwargs['timeout'] = 20\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 401:\n if self.login():\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 404:\n return\n result.raise_for_status()\n return result\n\n\nclass GetterError(Exception):\n\n def __init__(self, message, cause, connection_error):\n super(GetterError, self).__init__()\n self.message = message\n self.cause = cause\n self.connection_error = connection_error\n self.request = getattr(cause, 'request', None)\n self.response = getattr(cause, 'response', None)\n",
"step-2": "<mask token>\n\n\nclass Getter(object):\n\n def __init__(self, contenttype=None, login=lambda : False, session=None):\n self.session = session or retryable_session()\n self.login = login\n if contenttype:\n self.session.headers['Accept'] = contenttype\n\n def __call__(self, url, **kwargs):\n try:\n return self._inner_call(url, **kwargs)\n except (Timeout, ConnectionError, RequestException) as ex:\n message = ex.response.reason if getattr(ex, 'response', None\n ) is not None else type(ex).__name__\n raise GetterError(message, ex, not isinstance(ex, RequestException)\n )\n\n def _inner_call(self, url, **kwargs):\n if 'timeout' not in kwargs:\n kwargs['timeout'] = 20\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 401:\n if self.login():\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 404:\n return\n result.raise_for_status()\n return result\n\n\nclass GetterError(Exception):\n\n def __init__(self, message, cause, connection_error):\n super(GetterError, self).__init__()\n self.message = message\n self.cause = cause\n self.connection_error = connection_error\n self.request = getattr(cause, 'request', None)\n self.response = getattr(cause, 'response', None)\n",
"step-3": "<mask token>\n\n\ndef retryable_session(retries=3, backoff_factor=0.5, status_forcelist=(500,\n 502, 504, 520), session=None):\n session = session or requests.Session()\n retry = Retry(total=retries, read=retries, connect=retries,\n backoff_factor=backoff_factor, status_forcelist=status_forcelist)\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n session.mount('https://', adapter)\n return session\n\n\nclass Getter(object):\n\n def __init__(self, contenttype=None, login=lambda : False, session=None):\n self.session = session or retryable_session()\n self.login = login\n if contenttype:\n self.session.headers['Accept'] = contenttype\n\n def __call__(self, url, **kwargs):\n try:\n return self._inner_call(url, **kwargs)\n except (Timeout, ConnectionError, RequestException) as ex:\n message = ex.response.reason if getattr(ex, 'response', None\n ) is not None else type(ex).__name__\n raise GetterError(message, ex, not isinstance(ex, RequestException)\n )\n\n def _inner_call(self, url, **kwargs):\n if 'timeout' not in kwargs:\n kwargs['timeout'] = 20\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 401:\n if self.login():\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 404:\n return\n result.raise_for_status()\n return result\n\n\nclass GetterError(Exception):\n\n def __init__(self, message, cause, connection_error):\n super(GetterError, self).__init__()\n self.message = message\n self.cause = cause\n self.connection_error = connection_error\n self.request = getattr(cause, 'request', None)\n self.response = getattr(cause, 'response', None)\n",
"step-4": "import requests\nfrom requests.adapters import HTTPAdapter\nfrom requests.exceptions import ConnectionError, Timeout, RequestException\nfrom requests.packages.urllib3.util.retry import Retry\n\n\ndef retryable_session(retries=3, backoff_factor=0.5, status_forcelist=(500,\n 502, 504, 520), session=None):\n session = session or requests.Session()\n retry = Retry(total=retries, read=retries, connect=retries,\n backoff_factor=backoff_factor, status_forcelist=status_forcelist)\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n session.mount('https://', adapter)\n return session\n\n\nclass Getter(object):\n\n def __init__(self, contenttype=None, login=lambda : False, session=None):\n self.session = session or retryable_session()\n self.login = login\n if contenttype:\n self.session.headers['Accept'] = contenttype\n\n def __call__(self, url, **kwargs):\n try:\n return self._inner_call(url, **kwargs)\n except (Timeout, ConnectionError, RequestException) as ex:\n message = ex.response.reason if getattr(ex, 'response', None\n ) is not None else type(ex).__name__\n raise GetterError(message, ex, not isinstance(ex, RequestException)\n )\n\n def _inner_call(self, url, **kwargs):\n if 'timeout' not in kwargs:\n kwargs['timeout'] = 20\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 401:\n if self.login():\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 404:\n return\n result.raise_for_status()\n return result\n\n\nclass GetterError(Exception):\n\n def __init__(self, message, cause, connection_error):\n super(GetterError, self).__init__()\n self.message = message\n self.cause = cause\n self.connection_error = connection_error\n self.request = getattr(cause, 'request', None)\n self.response = getattr(cause, 'response', None)\n",
"step-5": "import requests\nfrom requests.adapters import HTTPAdapter\nfrom requests.exceptions import ConnectionError, Timeout, RequestException\n# import from `requests` because Jarvis / some platforms still have old urllib3\nfrom requests.packages.urllib3.util.retry import Retry\n\ndef retryable_session(retries=3, backoff_factor=0.5, status_forcelist=(500, 502, 504, 520), session=None):\n # from https://www.peterbe.com/plog/best-practice-with-retries-with-requests\n session = session or requests.Session()\n # 'Retry-After' 413/503/529 headers are respected by default\n retry = Retry(total=retries, read=retries, connect=retries,\n backoff_factor=backoff_factor, status_forcelist=status_forcelist)\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n session.mount('https://', adapter)\n return session\n\nclass Getter(object):\n def __init__(self, contenttype=None, login=lambda: False, session=None):\n self.session = session or retryable_session()\n self.login = login\n if contenttype:\n self.session.headers['Accept'] = contenttype\n\n def __call__(self, url, **kwargs):\n try:\n return self._inner_call(url, **kwargs)\n except (Timeout, ConnectionError, RequestException) as ex:\n message = ex.response.reason if getattr(ex, 'response', None) is not None else type(ex).__name__\n raise GetterError(message, ex, not isinstance(ex, RequestException))\n\n def _inner_call(self, url, **kwargs):\n if 'timeout' not in kwargs:\n kwargs['timeout'] = 20\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n if result.status_code == 401:\n if self.login():\n result = self.session.get(url, **kwargs)\n if result is None:\n return\n\n if result.status_code == 404:\n return\n result.raise_for_status()\n return result\n\nclass GetterError(Exception):\n def __init__(self, message, cause, connection_error):\n super(GetterError, self).__init__()\n self.message = message\n self.cause = cause\n self.connection_error = connection_error\n self.request = getattr(cause, 'request', None)\n self.response = getattr(cause, 'response', None)\n",
"step-ids": [
5,
6,
7,
8,
9
]
}
|
[
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
__author__ = 'simsun'
|
flexible
|
{
"blob_id": "2b746d89d34435eb5f3a5b04da61c5cc88178852",
"index": 8784,
"step-1": "<mask token>\n",
"step-2": "__author__ = 'simsun'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
}
|
[
0,
1
] |
class UrlPath:
@staticmethod
def combine(*args):
result = ''
for path in args:
result += path if path.endswith('/') else '{}/'.format(path)
#result = result[:-1]
return result
|
normal
|
{
"blob_id": "aa579025cacd11486a101b2dc51b5ba4997bf84a",
"index": 95,
"step-1": "<mask token>\n",
"step-2": "class UrlPath:\n <mask token>\n",
"step-3": "class UrlPath:\n\n @staticmethod\n def combine(*args):\n result = ''\n for path in args:\n result += path if path.endswith('/') else '{}/'.format(path)\n return result\n",
"step-4": "class UrlPath:\n @staticmethod\n def combine(*args):\n result = ''\n for path in args:\n result += path if path.endswith('/') else '{}/'.format(path)\n #result = result[:-1]\n return result",
"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|>
urllib3.disable_warnings()
<|reserved_special_token_0|>
print(key.decode('ascii'))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urllib3.disable_warnings()
response = requests.get('https://freeaeskey.xyz', verify=False)
data = response.text.encode('utf-8')
key = data[data.index(b'<b>') + 3:data.index(b'</b>')]
print(key.decode('ascii'))
<|reserved_special_token_1|>
import requests
import urllib3
urllib3.disable_warnings()
response = requests.get('https://freeaeskey.xyz', verify=False)
data = response.text.encode('utf-8')
key = data[data.index(b'<b>') + 3:data.index(b'</b>')]
print(key.decode('ascii'))
<|reserved_special_token_1|>
#!/usr/bin/python3
import requests
import urllib3
urllib3.disable_warnings()
response = requests.get('https://freeaeskey.xyz', verify=False)
data = response.text.encode('utf-8')
key = data[data.index(b'<b>')+3:data.index(b'</b>')]
print(key.decode('ascii'))
|
flexible
|
{
"blob_id": "368e209f83cc0cade81791c8357e01e7e3f940c8",
"index": 97,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurllib3.disable_warnings()\n<mask token>\nprint(key.decode('ascii'))\n",
"step-3": "<mask token>\nurllib3.disable_warnings()\nresponse = requests.get('https://freeaeskey.xyz', verify=False)\ndata = response.text.encode('utf-8')\nkey = data[data.index(b'<b>') + 3:data.index(b'</b>')]\nprint(key.decode('ascii'))\n",
"step-4": "import requests\nimport urllib3\nurllib3.disable_warnings()\nresponse = requests.get('https://freeaeskey.xyz', verify=False)\ndata = response.text.encode('utf-8')\nkey = data[data.index(b'<b>') + 3:data.index(b'</b>')]\nprint(key.decode('ascii'))\n",
"step-5": "#!/usr/bin/python3\n\nimport requests\nimport urllib3\nurllib3.disable_warnings()\nresponse = requests.get('https://freeaeskey.xyz', verify=False)\ndata = response.text.encode('utf-8')\nkey = data[data.index(b'<b>')+3:data.index(b'</b>')]\nprint(key.decode('ascii'))\n\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
from django.shortcuts import render
from django.shortcuts import redirect
from block.models import Block
from .models import Article
from .forms import ArticleForm
from django.core.paginator import Paginator
from django.contrib.auth.decorators import login_required
def article_list(request, block_id):
block_id = int(block_id)
block = Block.objects.get(id=block_id)
all_articles = Article.objects.filter(block=block, article_status=0).order_by("-id")
ARTICLE_CNT_1PAGE = 2
p = Paginator(all_articles, ARTICLE_CNT_1PAGE)
page_no = int(request.GET.get("page_no", "1"))
page = p.page(page_no)
articles_objs = page.object_list
page_links = [i
for i in range(page_no - 2, page_no + 3) if i > 0 and i <= p.num_pages]
return render(request, "article_list.html",
{"articles": articles_objs, "b": block, "page_no": page_no, "page": page,
"page_links": page_links, "p": p})
@login_required
def article_create(request, block_id):
block_id = int(block_id)
block = Block.objects.get(id=block_id)
if request.method == "GET":
return render(request, "article_create.html", {"b": block})
else:
form = ArticleForm(request.POST)
if form.is_valid():
article = form.save(commit=False)
article.owner = request.user
article.block = block
article.article_status = 0
article.save()
return redirect("/article/list/%s" % block_id)
else:
return render(request, "article_create.html", {"b": block, "form": form})
def article_detail(request, article_id):
article = Article.objects.get(id=article_id)
return render(request, "article_detail.html", {"article": article})
|
normal
|
{
"blob_id": "0f94537fa64066bb29c5e9e97836b0a8ac01ac19",
"index": 9844,
"step-1": "<mask token>\n\n\n@login_required\ndef article_create(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n if request.method == 'GET':\n return render(request, 'article_create.html', {'b': block})\n else:\n form = ArticleForm(request.POST)\n if form.is_valid():\n article = form.save(commit=False)\n article.owner = request.user\n article.block = block\n article.article_status = 0\n article.save()\n return redirect('/article/list/%s' % block_id)\n else:\n return render(request, 'article_create.html', {'b': block,\n 'form': form})\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@login_required\ndef article_create(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n if request.method == 'GET':\n return render(request, 'article_create.html', {'b': block})\n else:\n form = ArticleForm(request.POST)\n if form.is_valid():\n article = form.save(commit=False)\n article.owner = request.user\n article.block = block\n article.article_status = 0\n article.save()\n return redirect('/article/list/%s' % block_id)\n else:\n return render(request, 'article_create.html', {'b': block,\n 'form': form})\n\n\ndef article_detail(request, article_id):\n article = Article.objects.get(id=article_id)\n return render(request, 'article_detail.html', {'article': article})\n",
"step-3": "<mask token>\n\n\ndef article_list(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n all_articles = Article.objects.filter(block=block, article_status=0\n ).order_by('-id')\n ARTICLE_CNT_1PAGE = 2\n p = Paginator(all_articles, ARTICLE_CNT_1PAGE)\n page_no = int(request.GET.get('page_no', '1'))\n page = p.page(page_no)\n articles_objs = page.object_list\n page_links = [i for i in range(page_no - 2, page_no + 3) if i > 0 and i <=\n p.num_pages]\n return render(request, 'article_list.html', {'articles': articles_objs,\n 'b': block, 'page_no': page_no, 'page': page, 'page_links':\n page_links, 'p': p})\n\n\n@login_required\ndef article_create(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n if request.method == 'GET':\n return render(request, 'article_create.html', {'b': block})\n else:\n form = ArticleForm(request.POST)\n if form.is_valid():\n article = form.save(commit=False)\n article.owner = request.user\n article.block = block\n article.article_status = 0\n article.save()\n return redirect('/article/list/%s' % block_id)\n else:\n return render(request, 'article_create.html', {'b': block,\n 'form': form})\n\n\ndef article_detail(request, article_id):\n article = Article.objects.get(id=article_id)\n return render(request, 'article_detail.html', {'article': article})\n",
"step-4": "from django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom block.models import Block\nfrom .models import Article\nfrom .forms import ArticleForm\nfrom django.core.paginator import Paginator\nfrom django.contrib.auth.decorators import login_required\n\n\ndef article_list(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n all_articles = Article.objects.filter(block=block, article_status=0\n ).order_by('-id')\n ARTICLE_CNT_1PAGE = 2\n p = Paginator(all_articles, ARTICLE_CNT_1PAGE)\n page_no = int(request.GET.get('page_no', '1'))\n page = p.page(page_no)\n articles_objs = page.object_list\n page_links = [i for i in range(page_no - 2, page_no + 3) if i > 0 and i <=\n p.num_pages]\n return render(request, 'article_list.html', {'articles': articles_objs,\n 'b': block, 'page_no': page_no, 'page': page, 'page_links':\n page_links, 'p': p})\n\n\n@login_required\ndef article_create(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n if request.method == 'GET':\n return render(request, 'article_create.html', {'b': block})\n else:\n form = ArticleForm(request.POST)\n if form.is_valid():\n article = form.save(commit=False)\n article.owner = request.user\n article.block = block\n article.article_status = 0\n article.save()\n return redirect('/article/list/%s' % block_id)\n else:\n return render(request, 'article_create.html', {'b': block,\n 'form': form})\n\n\ndef article_detail(request, article_id):\n article = Article.objects.get(id=article_id)\n return render(request, 'article_detail.html', {'article': article})\n",
"step-5": "from django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom block.models import Block\nfrom .models import Article\nfrom .forms import ArticleForm\nfrom django.core.paginator import Paginator\nfrom django.contrib.auth.decorators import login_required\n\n\ndef article_list(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n\n all_articles = Article.objects.filter(block=block, article_status=0).order_by(\"-id\")\n ARTICLE_CNT_1PAGE = 2\n p = Paginator(all_articles, ARTICLE_CNT_1PAGE)\n page_no = int(request.GET.get(\"page_no\", \"1\"))\n page = p.page(page_no)\n articles_objs = page.object_list\n\n page_links = [i\n for i in range(page_no - 2, page_no + 3) if i > 0 and i <= p.num_pages]\n\n return render(request, \"article_list.html\",\n {\"articles\": articles_objs, \"b\": block, \"page_no\": page_no, \"page\": page,\n \"page_links\": page_links, \"p\": p})\n\n@login_required\ndef article_create(request, block_id):\n block_id = int(block_id)\n block = Block.objects.get(id=block_id)\n if request.method == \"GET\":\n return render(request, \"article_create.html\", {\"b\": block})\n else:\n form = ArticleForm(request.POST)\n if form.is_valid():\n article = form.save(commit=False)\n article.owner = request.user\n article.block = block\n article.article_status = 0\n article.save()\n return redirect(\"/article/list/%s\" % block_id)\n else:\n return render(request, \"article_create.html\", {\"b\": block, \"form\": form})\n\n\ndef article_detail(request, article_id):\n article = Article.objects.get(id=article_id)\n return render(request, \"article_detail.html\", {\"article\": article})\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|>
class Song(models.Model):
<|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 Song(models.Model):
name = models.CharField(max_length=255)
filename = models.FileField(upload_to='canciones/')
album = models.ForeignKey(Albums)
def __unicode__(self):
return self.name
<|reserved_special_token_1|>
from django.db import models
from albums.models import Albums
class Song(models.Model):
name = models.CharField(max_length=255)
filename = models.FileField(upload_to='canciones/')
album = models.ForeignKey(Albums)
def __unicode__(self):
return self.name
<|reserved_special_token_1|>
from django.db import models
from albums.models import Albums
class Song(models.Model):
name = models.CharField(max_length=255)
filename = models.FileField(upload_to='canciones/')
album = models.ForeignKey(Albums)
def __unicode__(self,):
return self.name
|
flexible
|
{
"blob_id": "8ec18e259af1123fad7563aee3a363e095e30e8e",
"index": 1064,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Song(models.Model):\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 Song(models.Model):\n name = models.CharField(max_length=255)\n filename = models.FileField(upload_to='canciones/')\n album = models.ForeignKey(Albums)\n\n def __unicode__(self):\n return self.name\n",
"step-4": "from django.db import models\nfrom albums.models import Albums\n\n\nclass Song(models.Model):\n name = models.CharField(max_length=255)\n filename = models.FileField(upload_to='canciones/')\n album = models.ForeignKey(Albums)\n\n def __unicode__(self):\n return self.name\n",
"step-5": "from django.db import models\n\nfrom albums.models import Albums\n\nclass Song(models.Model):\n name = models.CharField(max_length=255)\n filename = models.FileField(upload_to='canciones/')\n album = models.ForeignKey(Albums)\n\n def __unicode__(self,):\n return self.name\n",
"step-ids": [
0,
2,
3,
4,
5
]
}
|
[
0,
2,
3,
4,
5
] |
#!/usr/bin/python
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM)
ledPin = 4
pinOn = False
GPIO.setup(ledPin, GPIO.OUT)
GPIO.output(ledPin, GPIO.LOW)
def print_pin_status(pin_number):
GPIO.setup(pin_number, GPIO.IN)
value = GPIO.input(pin_number)
print(f'Current Value of {pin_number} is {value}')
GPIO.setup(pin_number, GPIO.OUT)
while True:
print_pin_status(ledPin)
key = input("Action, press q to quit: ")
print(key)
if key == ' ':
print("space pushed")
if key == '1':
if pinOn:
print("turning led off")
GPIO.output(ledPin, GPIO.LOW)
pinOn = False
else:
print("turning led on")
GPIO.output(ledPin, GPIO.HIGH)
pinOn = True
if key == 'q':
print("Quiting. . .")
break
|
normal
|
{
"blob_id": "492c416becc44deaafef519eae8c9a82ac00cc0e",
"index": 8632,
"step-1": "<mask token>\n\n\ndef print_pin_status(pin_number):\n GPIO.setup(pin_number, GPIO.IN)\n value = GPIO.input(pin_number)\n print(f'Current Value of {pin_number} is {value}')\n GPIO.setup(pin_number, GPIO.OUT)\n\n\n<mask token>\n",
"step-2": "<mask token>\nGPIO.setmode(GPIO.BCM)\n<mask token>\nGPIO.setup(ledPin, GPIO.OUT)\nGPIO.output(ledPin, GPIO.LOW)\n\n\ndef print_pin_status(pin_number):\n GPIO.setup(pin_number, GPIO.IN)\n value = GPIO.input(pin_number)\n print(f'Current Value of {pin_number} is {value}')\n GPIO.setup(pin_number, GPIO.OUT)\n\n\nwhile True:\n print_pin_status(ledPin)\n key = input('Action, press q to quit: ')\n print(key)\n if key == ' ':\n print('space pushed')\n if key == '1':\n if pinOn:\n print('turning led off')\n GPIO.output(ledPin, GPIO.LOW)\n pinOn = False\n else:\n print('turning led on')\n GPIO.output(ledPin, GPIO.HIGH)\n pinOn = True\n if key == 'q':\n print('Quiting. . .')\n break\n",
"step-3": "<mask token>\nGPIO.setmode(GPIO.BCM)\nledPin = 4\npinOn = False\nGPIO.setup(ledPin, GPIO.OUT)\nGPIO.output(ledPin, GPIO.LOW)\n\n\ndef print_pin_status(pin_number):\n GPIO.setup(pin_number, GPIO.IN)\n value = GPIO.input(pin_number)\n print(f'Current Value of {pin_number} is {value}')\n GPIO.setup(pin_number, GPIO.OUT)\n\n\nwhile True:\n print_pin_status(ledPin)\n key = input('Action, press q to quit: ')\n print(key)\n if key == ' ':\n print('space pushed')\n if key == '1':\n if pinOn:\n print('turning led off')\n GPIO.output(ledPin, GPIO.LOW)\n pinOn = False\n else:\n print('turning led on')\n GPIO.output(ledPin, GPIO.HIGH)\n pinOn = True\n if key == 'q':\n print('Quiting. . .')\n break\n",
"step-4": "import RPi.GPIO as GPIO\nGPIO.setmode(GPIO.BCM)\nledPin = 4\npinOn = False\nGPIO.setup(ledPin, GPIO.OUT)\nGPIO.output(ledPin, GPIO.LOW)\n\n\ndef print_pin_status(pin_number):\n GPIO.setup(pin_number, GPIO.IN)\n value = GPIO.input(pin_number)\n print(f'Current Value of {pin_number} is {value}')\n GPIO.setup(pin_number, GPIO.OUT)\n\n\nwhile True:\n print_pin_status(ledPin)\n key = input('Action, press q to quit: ')\n print(key)\n if key == ' ':\n print('space pushed')\n if key == '1':\n if pinOn:\n print('turning led off')\n GPIO.output(ledPin, GPIO.LOW)\n pinOn = False\n else:\n print('turning led on')\n GPIO.output(ledPin, GPIO.HIGH)\n pinOn = True\n if key == 'q':\n print('Quiting. . .')\n break\n",
"step-5": "#!/usr/bin/python\n\nimport RPi.GPIO as GPIO\n\nGPIO.setmode(GPIO.BCM)\n\nledPin = 4\npinOn = False\n\nGPIO.setup(ledPin, GPIO.OUT)\nGPIO.output(ledPin, GPIO.LOW)\n\n\ndef print_pin_status(pin_number):\n GPIO.setup(pin_number, GPIO.IN)\n value = GPIO.input(pin_number)\n print(f'Current Value of {pin_number} is {value}')\n GPIO.setup(pin_number, GPIO.OUT)\n\n\nwhile True:\n print_pin_status(ledPin)\n\n key = input(\"Action, press q to quit: \")\n\n print(key)\n\n if key == ' ':\n print(\"space pushed\")\n\n if key == '1':\n\n if pinOn:\n print(\"turning led off\")\n GPIO.output(ledPin, GPIO.LOW)\n pinOn = False\n else:\n print(\"turning led on\")\n GPIO.output(ledPin, GPIO.HIGH)\n pinOn = True\n\n if key == 'q':\n print(\"Quiting. . .\")\n break\n\n\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
from pyathena import connect
from Config import config2
from Config import merchants
def get_mapped_sku(sku):
try:
cursor = connect(aws_access_key_id=config2["aws_access_key_id"],
aws_secret_access_key=config2["aws_secret_access_key"],
s3_staging_dir=config2["s3_staging_dir"],
region_name=config2["region_name"]).cursor()
cursor.execute("SELECT seller_sku, seller FROM optivations.master_product_list where sc_sku = %(sku)s ",
{"sku": str(sku)})
# print(cursor.description)
result = cursor.fetchall()
for row in result:
return {'Cross-Reference No': row[0], 'brand': row[1]}
except Exception as e:
print(e)
return {}
return {}
def get_sku(seller_sku, sc_sku, seller):
try:
cursor = connect(aws_access_key_id=config2["aws_access_key_id"],
aws_secret_access_key=config2["aws_secret_access_key"],
s3_staging_dir=config2["s3_staging_dir"],
region_name=config2["region_name"]).cursor()
cursor.execute("SELECT seller_sku FROM optivations.master_product_list where sc_sku = %(sku)s ",
{"sku": str(sku)})
# print(cursor.description)
# print(cursor.fetchall())
for row in cursor:
return (row[0])
except Exception as e:
print(e)
return False
return True
def add_sku(sc_sku, seller_sku, seller):
try:
cursor = connect(aws_access_key_id=config2["aws_access_key_id"],
aws_secret_access_key=config2["aws_secret_access_key"],
s3_staging_dir=config2["s3_staging_dir"],
region_name=config2["region_name"]).cursor()
cursor.execute("INSERT INTO optivations.master_product_list VALUES ( %(scsku)s, %(sellersku)s, %(seller)s )",
{"scsku": str(sc_sku), "sellersku": str(seller_sku), "seller": str(seller)})
return (cursor.description)
# print(cursor.fetchall())
# for row in cursor:
# return (row[0])
except Exception as e:
print(e)
return False
return True
# print(add_sku('test', 'test', 'Adean'))
# result = (get_mapped_sku('HDS-3571'))
# print(result['Cross-Reference No'])
|
normal
|
{
"blob_id": "6add599035573842475c7f9155c5dbbea6c96a8a",
"index": 3618,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_mapped_sku(sku):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'SELECT seller_sku, seller FROM optivations.master_product_list where sc_sku = %(sku)s '\n , {'sku': str(sku)})\n result = cursor.fetchall()\n for row in result:\n return {'Cross-Reference No': row[0], 'brand': row[1]}\n except Exception as e:\n print(e)\n return {}\n return {}\n\n\ndef get_sku(seller_sku, sc_sku, seller):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'SELECT seller_sku FROM optivations.master_product_list where sc_sku = %(sku)s '\n , {'sku': str(sku)})\n for row in cursor:\n return row[0]\n except Exception as e:\n print(e)\n return False\n return True\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef get_mapped_sku(sku):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'SELECT seller_sku, seller FROM optivations.master_product_list where sc_sku = %(sku)s '\n , {'sku': str(sku)})\n result = cursor.fetchall()\n for row in result:\n return {'Cross-Reference No': row[0], 'brand': row[1]}\n except Exception as e:\n print(e)\n return {}\n return {}\n\n\ndef get_sku(seller_sku, sc_sku, seller):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'SELECT seller_sku FROM optivations.master_product_list where sc_sku = %(sku)s '\n , {'sku': str(sku)})\n for row in cursor:\n return row[0]\n except Exception as e:\n print(e)\n return False\n return True\n\n\ndef add_sku(sc_sku, seller_sku, seller):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'INSERT INTO optivations.master_product_list VALUES ( %(scsku)s, %(sellersku)s, %(seller)s )'\n , {'scsku': str(sc_sku), 'sellersku': str(seller_sku), 'seller':\n str(seller)})\n return cursor.description\n except Exception as e:\n print(e)\n return False\n return True\n",
"step-4": "from pyathena import connect\nfrom Config import config2\nfrom Config import merchants\n\n\ndef get_mapped_sku(sku):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'SELECT seller_sku, seller FROM optivations.master_product_list where sc_sku = %(sku)s '\n , {'sku': str(sku)})\n result = cursor.fetchall()\n for row in result:\n return {'Cross-Reference No': row[0], 'brand': row[1]}\n except Exception as e:\n print(e)\n return {}\n return {}\n\n\ndef get_sku(seller_sku, sc_sku, seller):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'SELECT seller_sku FROM optivations.master_product_list where sc_sku = %(sku)s '\n , {'sku': str(sku)})\n for row in cursor:\n return row[0]\n except Exception as e:\n print(e)\n return False\n return True\n\n\ndef add_sku(sc_sku, seller_sku, seller):\n try:\n cursor = connect(aws_access_key_id=config2['aws_access_key_id'],\n aws_secret_access_key=config2['aws_secret_access_key'],\n s3_staging_dir=config2['s3_staging_dir'], region_name=config2[\n 'region_name']).cursor()\n cursor.execute(\n 'INSERT INTO optivations.master_product_list VALUES ( %(scsku)s, %(sellersku)s, %(seller)s )'\n , {'scsku': str(sc_sku), 'sellersku': str(seller_sku), 'seller':\n str(seller)})\n return cursor.description\n except Exception as e:\n print(e)\n return False\n return True\n",
"step-5": "from pyathena import connect\nfrom Config import config2\nfrom Config import merchants\n\n\ndef get_mapped_sku(sku):\n try:\n cursor = connect(aws_access_key_id=config2[\"aws_access_key_id\"],\n aws_secret_access_key=config2[\"aws_secret_access_key\"],\n s3_staging_dir=config2[\"s3_staging_dir\"],\n region_name=config2[\"region_name\"]).cursor()\n cursor.execute(\"SELECT seller_sku, seller FROM optivations.master_product_list where sc_sku = %(sku)s \",\n {\"sku\": str(sku)})\n\n # print(cursor.description)\n result = cursor.fetchall()\n for row in result:\n return {'Cross-Reference No': row[0], 'brand': row[1]}\n\n except Exception as e:\n print(e)\n return {}\n return {}\n\n\ndef get_sku(seller_sku, sc_sku, seller):\n try:\n cursor = connect(aws_access_key_id=config2[\"aws_access_key_id\"],\n aws_secret_access_key=config2[\"aws_secret_access_key\"],\n s3_staging_dir=config2[\"s3_staging_dir\"],\n region_name=config2[\"region_name\"]).cursor()\n cursor.execute(\"SELECT seller_sku FROM optivations.master_product_list where sc_sku = %(sku)s \",\n {\"sku\": str(sku)})\n\n # print(cursor.description)\n # print(cursor.fetchall())\n for row in cursor:\n return (row[0])\n except Exception as e:\n print(e)\n return False\n return True\n\n\ndef add_sku(sc_sku, seller_sku, seller):\n try:\n cursor = connect(aws_access_key_id=config2[\"aws_access_key_id\"],\n aws_secret_access_key=config2[\"aws_secret_access_key\"],\n s3_staging_dir=config2[\"s3_staging_dir\"],\n region_name=config2[\"region_name\"]).cursor()\n cursor.execute(\"INSERT INTO optivations.master_product_list VALUES ( %(scsku)s, %(sellersku)s, %(seller)s )\",\n {\"scsku\": str(sc_sku), \"sellersku\": str(seller_sku), \"seller\": str(seller)})\n\n return (cursor.description)\n # print(cursor.fetchall())\n # for row in cursor:\n # return (row[0])\n except Exception as e:\n print(e)\n return False\n return True\n# print(add_sku('test', 'test', 'Adean'))\n# result = (get_mapped_sku('HDS-3571'))\n# print(result['Cross-Reference No'])\n",
"step-ids": [
0,
2,
3,
4,
5
]
}
|
[
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class View(Renderable, ABC):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class View(Renderable, ABC):
@abstractmethod
def content_size(self, container_size: Size) ->Size:
pass
<|reserved_special_token_1|>
from abc import ABC, abstractmethod
from raspberry_home.view.geometry import *
from raspberry_home.view.renderable import Renderable
class View(Renderable, ABC):
@abstractmethod
def content_size(self, container_size: Size) ->Size:
pass
|
flexible
|
{
"blob_id": "913ff9b811d3abbe43bda0554e40a6a2c87053be",
"index": 4449,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass View(Renderable, ABC):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass View(Renderable, ABC):\n\n @abstractmethod\n def content_size(self, container_size: Size) ->Size:\n pass\n",
"step-4": "from abc import ABC, abstractmethod\nfrom raspberry_home.view.geometry import *\nfrom raspberry_home.view.renderable import Renderable\n\n\nclass View(Renderable, ABC):\n\n @abstractmethod\n def content_size(self, container_size: Size) ->Size:\n pass\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
from django import forms
from django.contrib.auth.models import User
from .models import TblPublish , TblSnippetTopics, TblSnippetData, TblLearnTopics, TblLearnData, TblBlog, TblBlogComments,TblLearnDataComments, TblBlogGvp, TblLearnDataGvp,TblSnippetDataGvp, TblHome, TblAbout, TblQueries
from django.contrib.auth.forms import UserCreationForm
class UsersigninForm(forms.Form):
username = forms.CharField(required = True, label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}))
password = forms.CharField(required = True, label = 'Password', max_length = 32, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))
class SignupForm(UserCreationForm):
email = forms.EmailField(max_length=200, help_text='Required')
class Meta:
model = User
fields = ('username', 'email', 'password1', 'password2')
def __init__(self, *args, **kwargs):
super(SignupForm, self).__init__(*args, **kwargs)
self.fields['username'].widget.attrs['placeholder'] = "Username"
self.fields['email'].widget.attrs['placeholder'] = "email"
self.fields['password1'].widget.attrs['placeholder'] ="password"
self.fields['password2'].widget.attrs['placeholder'] = "password Again"
class UserRegistrationForm(forms.Form):
username = forms.CharField(required = True, min_length=6,label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}) )
email = forms.EmailField(required = True, label = 'Email', max_length = 100, widget=forms.EmailInput(attrs={'placeholder': 'e.g. : email@gmail.com'}))
firstname = forms.CharField(required = True, label = 'First Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'First Name'}))
lastname = forms.CharField(required = True, label = 'Last Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Last Name'}))
password = forms.CharField(required = True, label = 'Password', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))
passwordagain = forms.CharField(required = True, label = 'Password (Again)', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password (Again)'}))
class TblPublishForm(forms.ModelForm):
class Meta():
model = TblPublish
fields = '__all__'
class TblSnippetDataForm(forms.ModelForm):
class Meta():
model = TblSnippetData
fields = ['snippet_topics','snippet_data_subject','snippet_data_description','snippet_data_keyword','snippet_data_code','snippet_data_datetime','snippet_data_added_by','snippet_topics','snippet_data_publish']
def clean_snippet_topics_added_by(self):
if not self.cleaned_data['snippet_topics_added_by']:
return User()
return self.cleaned_data['snippet_topics_added_by']
def __init__(self, *args, **kwargs):
super(TblSnippetDataForm, self).__init__(*args, **kwargs)
self.fields['snippet_data_datetime'].widget = forms.HiddenInput()
self.fields['snippet_data_added_by'].widget = forms.HiddenInput()
self.fields['snippet_topics'].widget = forms.HiddenInput()
self.fields['snippet_data_subject'].widget.attrs['placeholder'] = "Title/Topics"
self.fields['snippet_data_description'].widget.attrs['placeholder'] = "Brief Description"
self.fields['snippet_data_keyword'].widget.attrs['placeholder'] ="Keyword For Search"
self.fields['snippet_data_code'].widget.attrs['placeholder'] = "Snippet (Code)"
self.fields['snippet_data_publish'].widget.attrs['placeholder'] = "Ready-To-Publish"
self.fields['snippet_data_publish'].label = "Publish"
class TblBlogForm(forms.ModelForm):
class Meta():
model = TblBlog
fields = ['blog_title','blog_description','blog_keyword','blog_content','blog_pics','blog_publish','blog_datetime','blog_summary','blog_like','blog_added_by']
def __init__(self, *args, **kwargs):
super(TblBlogForm, self).__init__(*args, **kwargs)
self.fields['blog_datetime'].widget = forms.HiddenInput()
self.fields['blog_summary'].widget = forms.HiddenInput()
self.fields['blog_like'].widget = forms.HiddenInput()
self.fields['blog_added_by'].widget = forms.HiddenInput()
self.fields['blog_title'].widget.attrs['placeholder'] = "Title/Topics"
self.fields['blog_description'].widget.attrs['placeholder'] = "Brief Description"
self.fields['blog_content'].widget.attrs['placeholder'] = "Blog Content"
self.fields['blog_keyword'].widget.attrs['placeholder'] = "Keyword For Search"
self.fields['blog_pics'].widget.attrs['placeholder'] = "Upload Pics"
self.fields['blog_publish'].label = "Publish"
class TblBlogCommentsForm(forms.ModelForm):
class Meta():
model = TblBlogComments
fields = '__all__'
class TblLearnDataForm(forms.ModelForm):
class Meta():
model = TblLearnData
fields = ['learn_data','learn_data_keyword','learn_data_description','learn_data_publish','learn_data_datetime','learn_data_added_by','learn_topics','learn_data_like','learn_data_icon']
def __init__(self, *args, **kwargs):
super(TblLearnDataForm, self).__init__(*args, **kwargs)
self.fields['learn_data_datetime'].widget = forms.HiddenInput()
self.fields['learn_data_added_by'].widget = forms.HiddenInput()
self.fields['learn_topics'].widget = forms.HiddenInput()
self.fields['learn_data_like'].widget = forms.HiddenInput()
self.fields['learn_data_icon'].widget = forms.HiddenInput()
self.fields['learn_data'].widget.attrs['placeholder'] = "Title/Topics"
self.fields['learn_data_description'].widget.attrs['placeholder'] = "Brief Description"
self.fields['learn_data_keyword'].widget.attrs['placeholder'] = "Keyword For Search"
self.fields['learn_data_publish'].label = "Publish"
class TblLearnDataCommentsForm(forms.ModelForm):
class Meta():
model = TblLearnDataComments
fields = '__all__'
class TblBlogGvpForm(forms.ModelForm):
class Meta():
model = TblBlogGvp
fields = '__all__'
class TblLearnDataGvpForm(forms.ModelForm):
class Meta():
model = TblLearnDataGvp
fields = '__all__'
class TblHomeForm(forms.ModelForm):
class Meta():
model = TblHome
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblHomeForm, self).__init__(*args, **kwargs)
self.fields['home_datetime'].widget = forms.HiddenInput()
self.fields['home_added_by'].widget = forms.HiddenInput()
self.fields['home_pics'].widget.attrs['placeholder'] = "Upload Image"
self.fields['home_content'].widget.attrs['placeholder'] = "Content"
self.fields['home_content_description'].widget.attrs['placeholder'] = "Description"
self.fields['home_publish'].label = "Publish"
class TblAboutForm(forms.ModelForm):
class Meta():
model = TblAbout
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblAboutForm, self).__init__(*args, **kwargs)
self.fields['about_datetime'].widget = forms.HiddenInput()
self.fields['about_added_by'].widget = forms.HiddenInput()
self.fields['about_pics'].widget.attrs['placeholder'] = "Upload Image"
self.fields['about_content'].widget.attrs['placeholder'] = "Content"
self.fields['about_content_description'].widget.attrs['placeholder'] = "Description"
self.fields['about_publish'].label = "Publish"
class TblLearnTopicsForm(forms.ModelForm):
class Meta():
model = TblLearnTopics
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblLearnTopicsForm, self).__init__(*args, **kwargs)
self.fields['learn_topics_datetime'].widget = forms.HiddenInput()
# self.fields['learn_topics_added_by'].widget = forms.HiddenInput()
self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'
self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()
self.fields['learn_topics'].widget.attrs['placeholder'] = "Topics"
self.fields['learn_topics_description'].widget.attrs['placeholder'] = "Description"
self.fields['learn_topics_publish'].label = "Publish"
def clean_learn_topics_added_by(self):
if not self.cleaned_data['learn_topics_added_by']:
return User()
return self.cleaned_data['learn_topics_added_by']
class TblSnippetTopicsForm(forms.ModelForm):
class Meta():
model = TblSnippetTopics
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)
self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()
self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()
self.fields['snippet_topics_icon'].widget = forms.HiddenInput()
self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput()
self.fields['snippet_topics_expire'].widget = forms.HiddenInput()
self.fields['snippet_topics'].widget.attrs['placeholder'] = "Topics"
self.fields['snippet_topics_description'].widget.attrs['placeholder'] = "Description"
self.fields['snippet_topics_publish'].label = "Publish"
def clean_snippet_topics_added_by(self):
if not self.cleaned_data['snippet_topics_added_by']:
return User()
return self.cleaned_data['snippet_topics_added_by']
class TblQueriesForm(forms.ModelForm):
class Meta():
model = TblQueries
fields = '__all__'
def __init__(self, *args, **kwargs):
super(TblQueriesForm, self).__init__(*args, **kwargs)
self.fields['datetime'].widget = forms.HiddenInput()
self.fields['name'].widget.attrs['placeholder'] = "Name"
self.fields['email'].widget.attrs['placeholder'] = "Email"
self.fields['subject'].widget.attrs['placeholder'] = "Subject"
self.fields['message'].widget.attrs['placeholder'] = "Message"
|
normal
|
{
"blob_id": "9e02b1a90d61de6d794dd350b50417a2f7260df6",
"index": 5947,
"step-1": "<mask token>\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n <mask token>\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-2": "<mask token>\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['blog_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['blog_content'].widget.attrs['placeholder'\n ] = 'Blog Content'\n self.fields['blog_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['blog_pics'].widget.attrs['placeholder'] = 'Upload Pics'\n self.fields['blog_publish'].label = 'Publish'\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-3": "<mask token>\n\n\nclass TblSnippetDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetData\n fields = ['snippet_topics', 'snippet_data_subject',\n 'snippet_data_description', 'snippet_data_keyword',\n 'snippet_data_code', 'snippet_data_datetime',\n 'snippet_data_added_by', 'snippet_topics', 'snippet_data_publish']\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetDataForm, self).__init__(*args, **kwargs)\n self.fields['snippet_data_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_data_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget = forms.HiddenInput()\n self.fields['snippet_data_subject'].widget.attrs['placeholder'\n ] = 'Title/Topics'\n self.fields['snippet_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['snippet_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['snippet_data_code'].widget.attrs['placeholder'\n ] = 'Snippet (Code)'\n self.fields['snippet_data_publish'].widget.attrs['placeholder'\n ] = 'Ready-To-Publish'\n self.fields['snippet_data_publish'].label = 'Publish'\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['blog_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['blog_content'].widget.attrs['placeholder'\n ] = 'Blog Content'\n self.fields['blog_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['blog_pics'].widget.attrs['placeholder'] = 'Upload Pics'\n self.fields['blog_publish'].label = 'Publish'\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-4": "<mask token>\n\n\nclass SignupForm(UserCreationForm):\n <mask token>\n\n\n class Meta:\n model = User\n fields = 'username', 'email', 'password1', 'password2'\n <mask token>\n\n\nclass UserRegistrationForm(forms.Form):\n username = forms.CharField(required=True, min_length=6, label=\n 'Username', max_length=100, widget=forms.TextInput(attrs={\n 'placeholder': 'Username'}))\n email = forms.EmailField(required=True, label='Email', max_length=100,\n widget=forms.EmailInput(attrs={'placeholder':\n 'e.g. : email@gmail.com'}))\n firstname = forms.CharField(required=True, label='First Name',\n max_length=100, widget=forms.TextInput(attrs={'placeholder':\n 'First Name'}))\n lastname = forms.CharField(required=True, label='Last Name', max_length\n =100, widget=forms.TextInput(attrs={'placeholder': 'Last Name'}))\n password = forms.CharField(required=True, label='Password', max_length=\n 100, widget=forms.PasswordInput(attrs={'placeholder': 'Password'}))\n passwordagain = forms.CharField(required=True, label='Password (Again)',\n max_length=100, widget=forms.PasswordInput(attrs={'placeholder':\n 'Password (Again)'}))\n\n\nclass TblPublishForm(forms.ModelForm):\n\n\n class Meta:\n model = TblPublish\n fields = '__all__'\n\n\nclass TblSnippetDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetData\n fields = ['snippet_topics', 'snippet_data_subject',\n 'snippet_data_description', 'snippet_data_keyword',\n 'snippet_data_code', 'snippet_data_datetime',\n 'snippet_data_added_by', 'snippet_topics', 'snippet_data_publish']\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetDataForm, self).__init__(*args, **kwargs)\n self.fields['snippet_data_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_data_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget = forms.HiddenInput()\n self.fields['snippet_data_subject'].widget.attrs['placeholder'\n ] = 'Title/Topics'\n self.fields['snippet_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['snippet_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['snippet_data_code'].widget.attrs['placeholder'\n ] = 'Snippet (Code)'\n self.fields['snippet_data_publish'].widget.attrs['placeholder'\n ] = 'Ready-To-Publish'\n self.fields['snippet_data_publish'].label = 'Publish'\n\n\nclass TblBlogForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlog\n fields = ['blog_title', 'blog_description', 'blog_keyword',\n 'blog_content', 'blog_pics', 'blog_publish', 'blog_datetime',\n 'blog_summary', 'blog_like', 'blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['blog_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['blog_content'].widget.attrs['placeholder'\n ] = 'Blog Content'\n self.fields['blog_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['blog_pics'].widget.attrs['placeholder'] = 'Upload Pics'\n self.fields['blog_publish'].label = 'Publish'\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogComments\n fields = '__all__'\n\n\nclass TblLearnDataForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnData\n fields = ['learn_data', 'learn_data_keyword',\n 'learn_data_description', 'learn_data_publish',\n 'learn_data_datetime', 'learn_data_added_by', 'learn_topics',\n 'learn_data_like', 'learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = 'Title/Topics'\n self.fields['learn_data_description'].widget.attrs['placeholder'\n ] = 'Brief Description'\n self.fields['learn_data_keyword'].widget.attrs['placeholder'\n ] = 'Keyword For Search'\n self.fields['learn_data_publish'].label = 'Publish'\n\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataComments\n fields = '__all__'\n\n\nclass TblBlogGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblBlogGvp\n fields = '__all__'\n\n\nclass TblLearnDataGvpForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnDataGvp\n fields = '__all__'\n\n\nclass TblHomeForm(forms.ModelForm):\n\n\n class Meta:\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['home_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['home_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['home_publish'].label = 'Publish'\n\n\nclass TblAboutForm(forms.ModelForm):\n\n\n class Meta:\n model = TblAbout\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = 'Upload Image'\n self.fields['about_content'].widget.attrs['placeholder'] = 'Content'\n self.fields['about_content_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['about_publish'].label = 'Publish'\n\n\nclass TblLearnTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblLearnTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['learn_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['learn_topics_publish'].label = 'Publish'\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n\n\n class Meta:\n model = TblSnippetTopics\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput(\n )\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = 'Topics'\n self.fields['snippet_topics_description'].widget.attrs['placeholder'\n ] = 'Description'\n self.fields['snippet_topics_publish'].label = 'Publish'\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n\nclass TblQueriesForm(forms.ModelForm):\n\n\n class Meta:\n model = TblQueries\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = 'Name'\n self.fields['email'].widget.attrs['placeholder'] = 'Email'\n self.fields['subject'].widget.attrs['placeholder'] = 'Subject'\n self.fields['message'].widget.attrs['placeholder'] = 'Message'\n",
"step-5": "from django import forms\nfrom django.contrib.auth.models import User\nfrom .models import TblPublish , TblSnippetTopics, TblSnippetData, TblLearnTopics, TblLearnData, TblBlog, TblBlogComments,TblLearnDataComments, TblBlogGvp, TblLearnDataGvp,TblSnippetDataGvp, TblHome, TblAbout, TblQueries\nfrom django.contrib.auth.forms import UserCreationForm\n\nclass UsersigninForm(forms.Form):\n username = forms.CharField(required = True, label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}))\n password = forms.CharField(required = True, label = 'Password', max_length = 32, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))\n\nclass SignupForm(UserCreationForm):\n email = forms.EmailField(max_length=200, help_text='Required')\n class Meta:\n model = User\n fields = ('username', 'email', 'password1', 'password2')\n\n def __init__(self, *args, **kwargs):\n super(SignupForm, self).__init__(*args, **kwargs)\n self.fields['username'].widget.attrs['placeholder'] = \"Username\"\n self.fields['email'].widget.attrs['placeholder'] = \"email\"\n self.fields['password1'].widget.attrs['placeholder'] =\"password\"\n self.fields['password2'].widget.attrs['placeholder'] = \"password Again\"\n\nclass UserRegistrationForm(forms.Form):\n username = forms.CharField(required = True, min_length=6,label = 'Username', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Username'}) )\n email = forms.EmailField(required = True, label = 'Email', max_length = 100, widget=forms.EmailInput(attrs={'placeholder': 'e.g. : email@gmail.com'}))\n firstname = forms.CharField(required = True, label = 'First Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'First Name'}))\n lastname = forms.CharField(required = True, label = 'Last Name', max_length = 100, widget=forms.TextInput(attrs={'placeholder': 'Last Name'}))\n password = forms.CharField(required = True, label = 'Password', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password'}))\n passwordagain = forms.CharField(required = True, label = 'Password (Again)', max_length = 100, widget = forms.PasswordInput(attrs={'placeholder': 'Password (Again)'}))\n\nclass TblPublishForm(forms.ModelForm):\n class Meta():\n model = TblPublish\n fields = '__all__'\n\n\nclass TblSnippetDataForm(forms.ModelForm):\n class Meta():\n model = TblSnippetData\n fields = ['snippet_topics','snippet_data_subject','snippet_data_description','snippet_data_keyword','snippet_data_code','snippet_data_datetime','snippet_data_added_by','snippet_topics','snippet_data_publish']\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblSnippetDataForm, self).__init__(*args, **kwargs)\n self.fields['snippet_data_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_data_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget = forms.HiddenInput()\n self.fields['snippet_data_subject'].widget.attrs['placeholder'] = \"Title/Topics\"\n self.fields['snippet_data_description'].widget.attrs['placeholder'] = \"Brief Description\"\n self.fields['snippet_data_keyword'].widget.attrs['placeholder'] =\"Keyword For Search\"\n self.fields['snippet_data_code'].widget.attrs['placeholder'] = \"Snippet (Code)\"\n self.fields['snippet_data_publish'].widget.attrs['placeholder'] = \"Ready-To-Publish\"\n self.fields['snippet_data_publish'].label = \"Publish\"\n\nclass TblBlogForm(forms.ModelForm):\n class Meta():\n model = TblBlog\n fields = ['blog_title','blog_description','blog_keyword','blog_content','blog_pics','blog_publish','blog_datetime','blog_summary','blog_like','blog_added_by']\n\n def __init__(self, *args, **kwargs):\n super(TblBlogForm, self).__init__(*args, **kwargs)\n self.fields['blog_datetime'].widget = forms.HiddenInput()\n self.fields['blog_summary'].widget = forms.HiddenInput()\n self.fields['blog_like'].widget = forms.HiddenInput()\n self.fields['blog_added_by'].widget = forms.HiddenInput()\n self.fields['blog_title'].widget.attrs['placeholder'] = \"Title/Topics\"\n self.fields['blog_description'].widget.attrs['placeholder'] = \"Brief Description\"\n self.fields['blog_content'].widget.attrs['placeholder'] = \"Blog Content\"\n self.fields['blog_keyword'].widget.attrs['placeholder'] = \"Keyword For Search\"\n self.fields['blog_pics'].widget.attrs['placeholder'] = \"Upload Pics\"\n self.fields['blog_publish'].label = \"Publish\"\n\n\n\nclass TblBlogCommentsForm(forms.ModelForm):\n class Meta():\n model = TblBlogComments\n fields = '__all__'\n\nclass TblLearnDataForm(forms.ModelForm):\n class Meta():\n model = TblLearnData\n fields = ['learn_data','learn_data_keyword','learn_data_description','learn_data_publish','learn_data_datetime','learn_data_added_by','learn_topics','learn_data_like','learn_data_icon']\n\n def __init__(self, *args, **kwargs):\n super(TblLearnDataForm, self).__init__(*args, **kwargs)\n self.fields['learn_data_datetime'].widget = forms.HiddenInput()\n self.fields['learn_data_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget = forms.HiddenInput()\n self.fields['learn_data_like'].widget = forms.HiddenInput()\n self.fields['learn_data_icon'].widget = forms.HiddenInput()\n self.fields['learn_data'].widget.attrs['placeholder'] = \"Title/Topics\"\n self.fields['learn_data_description'].widget.attrs['placeholder'] = \"Brief Description\"\n self.fields['learn_data_keyword'].widget.attrs['placeholder'] = \"Keyword For Search\"\n self.fields['learn_data_publish'].label = \"Publish\"\n\nclass TblLearnDataCommentsForm(forms.ModelForm):\n class Meta():\n model = TblLearnDataComments\n fields = '__all__'\n\nclass TblBlogGvpForm(forms.ModelForm):\n class Meta():\n model = TblBlogGvp\n fields = '__all__'\nclass TblLearnDataGvpForm(forms.ModelForm):\n class Meta():\n model = TblLearnDataGvp\n fields = '__all__'\nclass TblHomeForm(forms.ModelForm):\n class Meta():\n model = TblHome\n fields = '__all__'\n\n def __init__(self, *args, **kwargs):\n super(TblHomeForm, self).__init__(*args, **kwargs)\n self.fields['home_datetime'].widget = forms.HiddenInput()\n self.fields['home_added_by'].widget = forms.HiddenInput()\n self.fields['home_pics'].widget.attrs['placeholder'] = \"Upload Image\"\n self.fields['home_content'].widget.attrs['placeholder'] = \"Content\"\n self.fields['home_content_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['home_publish'].label = \"Publish\"\n\n\nclass TblAboutForm(forms.ModelForm):\n class Meta():\n model = TblAbout\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblAboutForm, self).__init__(*args, **kwargs)\n self.fields['about_datetime'].widget = forms.HiddenInput()\n self.fields['about_added_by'].widget = forms.HiddenInput()\n self.fields['about_pics'].widget.attrs['placeholder'] = \"Upload Image\"\n self.fields['about_content'].widget.attrs['placeholder'] = \"Content\"\n self.fields['about_content_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['about_publish'].label = \"Publish\"\n\nclass TblLearnTopicsForm(forms.ModelForm):\n class Meta():\n model = TblLearnTopics\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblLearnTopicsForm, self).__init__(*args, **kwargs)\n self.fields['learn_topics_datetime'].widget = forms.HiddenInput()\n # self.fields['learn_topics_added_by'].widget = forms.HiddenInput()\n self.fields['learn_topics_icon'].widget.attrs['placeholder'] = 'Icon'\n self.fields['learn_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['learn_topics'].widget.attrs['placeholder'] = \"Topics\"\n self.fields['learn_topics_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['learn_topics_publish'].label = \"Publish\"\n\n\n\n def clean_learn_topics_added_by(self):\n if not self.cleaned_data['learn_topics_added_by']:\n return User()\n return self.cleaned_data['learn_topics_added_by']\n\nclass TblSnippetTopicsForm(forms.ModelForm):\n class Meta():\n model = TblSnippetTopics\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblSnippetTopicsForm, self).__init__(*args, **kwargs)\n self.fields['snippet_topics_datetime'].widget = forms.HiddenInput()\n self.fields['snippet_topics_added_by'].widget = forms.HiddenInput()\n self.fields['snippet_topics_icon'].widget = forms.HiddenInput()\n self.fields['snippet_topics_coverpage_img'].widget = forms.HiddenInput()\n self.fields['snippet_topics_expire'].widget = forms.HiddenInput()\n self.fields['snippet_topics'].widget.attrs['placeholder'] = \"Topics\"\n self.fields['snippet_topics_description'].widget.attrs['placeholder'] = \"Description\"\n self.fields['snippet_topics_publish'].label = \"Publish\"\n\n def clean_snippet_topics_added_by(self):\n if not self.cleaned_data['snippet_topics_added_by']:\n return User()\n return self.cleaned_data['snippet_topics_added_by']\n\nclass TblQueriesForm(forms.ModelForm):\n class Meta():\n model = TblQueries\n fields = '__all__'\n def __init__(self, *args, **kwargs):\n super(TblQueriesForm, self).__init__(*args, **kwargs)\n self.fields['datetime'].widget = forms.HiddenInput()\n self.fields['name'].widget.attrs['placeholder'] = \"Name\"\n self.fields['email'].widget.attrs['placeholder'] = \"Email\"\n self.fields['subject'].widget.attrs['placeholder'] = \"Subject\"\n self.fields['message'].widget.attrs['placeholder'] = \"Message\"\n",
"step-ids": [
19,
20,
22,
26,
32
]
}
|
[
19,
20,
22,
26,
32
] |
from pathlib import Path
from build_midi.appenders import *
from build_midi.converters import Converter
from build_midi.melody_builder import MelodyBuilder
from build_midi.sequences import *
from build_midi.tracks import *
from music_rules.instruments import Instruments
from music_rules.music_scale import MusicScale
from weather.weather_api import WeatherApi
class WeatherToMusicConverter:
PHRASE_LENGTH = 1200
OUTPUT_FILE_DIR = 'midi_out'
music_scales = MusicScale()
def weather_to_music(self, api_key, city) -> MidiFile:
api_handling = WeatherApi()
converter = Converter()
weather_forecast = api_handling.get_weather_forecast_from_api(city, api_key)
average_temperature = converter.average_temperature(weather_forecast.weather_timestamps)
ticks_per_beat = converter.average_temperature_to_ticks_per_beat(average_temperature)
outfile = MidiFile()
outfile.ticks_per_beat = ticks_per_beat
melody_builder = MelodyBuilder(outfile, self.PHRASE_LENGTH)
temperature = TemperatureTrack(1, Instruments.BrightAcousticPiano)
rain = RainTrack(2, Instruments.Celesta)
clouds = CloudsTrack(3, Instruments.TremoloStrings)
humidity = HumidityTrack(4, Instruments.ElectricGuitar_clean)
wind = WindTrack(5, Instruments.Seashore)
for track in [temperature, rain, clouds, humidity, wind]:
melody_builder.set_instrument(track.get_track(), track.get_channel(), track.get_instrument())
for entry in weather_forecast.weather_timestamps:
base_note = converter.temperature_to_base_note(entry.temperature.feels_like)
music_scale = self.music_scales.melodic_minor(base_note)
temperature_sequence = TemperatureSequence(entry.temperature, self.PHRASE_LENGTH, base_note, temperature.get_track())
temperature_appender = TemperatureAppender()
temperature_appender.append(melody_builder, temperature_sequence, temperature)
rain_sequence = RainSequence(entry.weather.rain, self.PHRASE_LENGTH, base_note, rain.get_track(), music_scale)
rain_appender = RainAppender()
rain_appender.append(melody_builder, rain_sequence, rain)
clouds_sequence = CloudsSequence(entry.weather.clouds, self.PHRASE_LENGTH, base_note, clouds.get_track())
clouds_appender = CloudsAppender()
clouds_appender.append(melody_builder, clouds_sequence, clouds)
humidity_sequence = HumiditySequence(entry.weather.humidity, self.PHRASE_LENGTH, base_note, humidity.get_track())
humidity_appender = HumidityAppender()
humidity_appender.append(melody_builder, humidity_sequence, humidity)
wind_sequence = WindSequence(entry.weather.wind_speed, self.PHRASE_LENGTH, base_note, wind.get_track())
wind_appender = WindAppender()
wind_appender.append(melody_builder, wind_sequence, wind)
for track in [temperature.get_track(), rain.get_track(), clouds.get_track(), humidity.get_track(), wind.get_track()]:
outfile.tracks.append(track)
file_name = 'weather_song_' + weather_forecast.city + '_' + weather_forecast.country + '_' + str(weather_forecast.weather_timestamps[0].timestamp)
self.save_file(outfile, self.OUTPUT_FILE_DIR, file_name)
return outfile
def save_file(self, outfile: MidiFile, file_dir: str, file_name: str) -> MidiFile:
Path(file_dir).mkdir(exist_ok=True)
file_path = file_dir + '/' + file_name + '.mid'
outfile.save(file_path)
print('file saved at ' + file_path)
return outfile
def get_midi_track_time(self, midi_track: MidiTrack):
sum = 0
for message in midi_track:
sum += message.time
return sum
|
normal
|
{
"blob_id": "c846c33ef13795d51c6d23ffa5a6b564b66e6a3c",
"index": 3438,
"step-1": "<mask token>\n\n\nclass WeatherToMusicConverter:\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 WeatherToMusicConverter:\n <mask token>\n <mask token>\n <mask token>\n\n def weather_to_music(self, api_key, city) ->MidiFile:\n api_handling = WeatherApi()\n converter = Converter()\n weather_forecast = api_handling.get_weather_forecast_from_api(city,\n api_key)\n average_temperature = converter.average_temperature(weather_forecast\n .weather_timestamps)\n ticks_per_beat = converter.average_temperature_to_ticks_per_beat(\n average_temperature)\n outfile = MidiFile()\n outfile.ticks_per_beat = ticks_per_beat\n melody_builder = MelodyBuilder(outfile, self.PHRASE_LENGTH)\n temperature = TemperatureTrack(1, Instruments.BrightAcousticPiano)\n rain = RainTrack(2, Instruments.Celesta)\n clouds = CloudsTrack(3, Instruments.TremoloStrings)\n humidity = HumidityTrack(4, Instruments.ElectricGuitar_clean)\n wind = WindTrack(5, Instruments.Seashore)\n for track in [temperature, rain, clouds, humidity, wind]:\n melody_builder.set_instrument(track.get_track(), track.\n get_channel(), track.get_instrument())\n for entry in weather_forecast.weather_timestamps:\n base_note = converter.temperature_to_base_note(entry.\n temperature.feels_like)\n music_scale = self.music_scales.melodic_minor(base_note)\n temperature_sequence = TemperatureSequence(entry.temperature,\n self.PHRASE_LENGTH, base_note, temperature.get_track())\n temperature_appender = TemperatureAppender()\n temperature_appender.append(melody_builder,\n temperature_sequence, temperature)\n rain_sequence = RainSequence(entry.weather.rain, self.\n PHRASE_LENGTH, base_note, rain.get_track(), music_scale)\n rain_appender = RainAppender()\n rain_appender.append(melody_builder, rain_sequence, rain)\n clouds_sequence = CloudsSequence(entry.weather.clouds, self.\n PHRASE_LENGTH, base_note, clouds.get_track())\n clouds_appender = CloudsAppender()\n clouds_appender.append(melody_builder, clouds_sequence, clouds)\n humidity_sequence = HumiditySequence(entry.weather.humidity,\n self.PHRASE_LENGTH, base_note, humidity.get_track())\n humidity_appender = HumidityAppender()\n humidity_appender.append(melody_builder, humidity_sequence,\n humidity)\n wind_sequence = WindSequence(entry.weather.wind_speed, self.\n PHRASE_LENGTH, base_note, wind.get_track())\n wind_appender = WindAppender()\n wind_appender.append(melody_builder, wind_sequence, wind)\n for track in [temperature.get_track(), rain.get_track(), clouds.\n get_track(), humidity.get_track(), wind.get_track()]:\n outfile.tracks.append(track)\n file_name = ('weather_song_' + weather_forecast.city + '_' +\n weather_forecast.country + '_' + str(weather_forecast.\n weather_timestamps[0].timestamp))\n self.save_file(outfile, self.OUTPUT_FILE_DIR, file_name)\n return outfile\n\n def save_file(self, outfile: MidiFile, file_dir: str, file_name: str\n ) ->MidiFile:\n Path(file_dir).mkdir(exist_ok=True)\n file_path = file_dir + '/' + file_name + '.mid'\n outfile.save(file_path)\n print('file saved at ' + file_path)\n return outfile\n\n def get_midi_track_time(self, midi_track: MidiTrack):\n sum = 0\n for message in midi_track:\n sum += message.time\n return sum\n",
"step-3": "<mask token>\n\n\nclass WeatherToMusicConverter:\n PHRASE_LENGTH = 1200\n OUTPUT_FILE_DIR = 'midi_out'\n music_scales = MusicScale()\n\n def weather_to_music(self, api_key, city) ->MidiFile:\n api_handling = WeatherApi()\n converter = Converter()\n weather_forecast = api_handling.get_weather_forecast_from_api(city,\n api_key)\n average_temperature = converter.average_temperature(weather_forecast\n .weather_timestamps)\n ticks_per_beat = converter.average_temperature_to_ticks_per_beat(\n average_temperature)\n outfile = MidiFile()\n outfile.ticks_per_beat = ticks_per_beat\n melody_builder = MelodyBuilder(outfile, self.PHRASE_LENGTH)\n temperature = TemperatureTrack(1, Instruments.BrightAcousticPiano)\n rain = RainTrack(2, Instruments.Celesta)\n clouds = CloudsTrack(3, Instruments.TremoloStrings)\n humidity = HumidityTrack(4, Instruments.ElectricGuitar_clean)\n wind = WindTrack(5, Instruments.Seashore)\n for track in [temperature, rain, clouds, humidity, wind]:\n melody_builder.set_instrument(track.get_track(), track.\n get_channel(), track.get_instrument())\n for entry in weather_forecast.weather_timestamps:\n base_note = converter.temperature_to_base_note(entry.\n temperature.feels_like)\n music_scale = self.music_scales.melodic_minor(base_note)\n temperature_sequence = TemperatureSequence(entry.temperature,\n self.PHRASE_LENGTH, base_note, temperature.get_track())\n temperature_appender = TemperatureAppender()\n temperature_appender.append(melody_builder,\n temperature_sequence, temperature)\n rain_sequence = RainSequence(entry.weather.rain, self.\n PHRASE_LENGTH, base_note, rain.get_track(), music_scale)\n rain_appender = RainAppender()\n rain_appender.append(melody_builder, rain_sequence, rain)\n clouds_sequence = CloudsSequence(entry.weather.clouds, self.\n PHRASE_LENGTH, base_note, clouds.get_track())\n clouds_appender = CloudsAppender()\n clouds_appender.append(melody_builder, clouds_sequence, clouds)\n humidity_sequence = HumiditySequence(entry.weather.humidity,\n self.PHRASE_LENGTH, base_note, humidity.get_track())\n humidity_appender = HumidityAppender()\n humidity_appender.append(melody_builder, humidity_sequence,\n humidity)\n wind_sequence = WindSequence(entry.weather.wind_speed, self.\n PHRASE_LENGTH, base_note, wind.get_track())\n wind_appender = WindAppender()\n wind_appender.append(melody_builder, wind_sequence, wind)\n for track in [temperature.get_track(), rain.get_track(), clouds.\n get_track(), humidity.get_track(), wind.get_track()]:\n outfile.tracks.append(track)\n file_name = ('weather_song_' + weather_forecast.city + '_' +\n weather_forecast.country + '_' + str(weather_forecast.\n weather_timestamps[0].timestamp))\n self.save_file(outfile, self.OUTPUT_FILE_DIR, file_name)\n return outfile\n\n def save_file(self, outfile: MidiFile, file_dir: str, file_name: str\n ) ->MidiFile:\n Path(file_dir).mkdir(exist_ok=True)\n file_path = file_dir + '/' + file_name + '.mid'\n outfile.save(file_path)\n print('file saved at ' + file_path)\n return outfile\n\n def get_midi_track_time(self, midi_track: MidiTrack):\n sum = 0\n for message in midi_track:\n sum += message.time\n return sum\n",
"step-4": "from pathlib import Path\nfrom build_midi.appenders import *\nfrom build_midi.converters import Converter\nfrom build_midi.melody_builder import MelodyBuilder\nfrom build_midi.sequences import *\nfrom build_midi.tracks import *\nfrom music_rules.instruments import Instruments\nfrom music_rules.music_scale import MusicScale\nfrom weather.weather_api import WeatherApi\n\n\nclass WeatherToMusicConverter:\n PHRASE_LENGTH = 1200\n OUTPUT_FILE_DIR = 'midi_out'\n music_scales = MusicScale()\n\n def weather_to_music(self, api_key, city) ->MidiFile:\n api_handling = WeatherApi()\n converter = Converter()\n weather_forecast = api_handling.get_weather_forecast_from_api(city,\n api_key)\n average_temperature = converter.average_temperature(weather_forecast\n .weather_timestamps)\n ticks_per_beat = converter.average_temperature_to_ticks_per_beat(\n average_temperature)\n outfile = MidiFile()\n outfile.ticks_per_beat = ticks_per_beat\n melody_builder = MelodyBuilder(outfile, self.PHRASE_LENGTH)\n temperature = TemperatureTrack(1, Instruments.BrightAcousticPiano)\n rain = RainTrack(2, Instruments.Celesta)\n clouds = CloudsTrack(3, Instruments.TremoloStrings)\n humidity = HumidityTrack(4, Instruments.ElectricGuitar_clean)\n wind = WindTrack(5, Instruments.Seashore)\n for track in [temperature, rain, clouds, humidity, wind]:\n melody_builder.set_instrument(track.get_track(), track.\n get_channel(), track.get_instrument())\n for entry in weather_forecast.weather_timestamps:\n base_note = converter.temperature_to_base_note(entry.\n temperature.feels_like)\n music_scale = self.music_scales.melodic_minor(base_note)\n temperature_sequence = TemperatureSequence(entry.temperature,\n self.PHRASE_LENGTH, base_note, temperature.get_track())\n temperature_appender = TemperatureAppender()\n temperature_appender.append(melody_builder,\n temperature_sequence, temperature)\n rain_sequence = RainSequence(entry.weather.rain, self.\n PHRASE_LENGTH, base_note, rain.get_track(), music_scale)\n rain_appender = RainAppender()\n rain_appender.append(melody_builder, rain_sequence, rain)\n clouds_sequence = CloudsSequence(entry.weather.clouds, self.\n PHRASE_LENGTH, base_note, clouds.get_track())\n clouds_appender = CloudsAppender()\n clouds_appender.append(melody_builder, clouds_sequence, clouds)\n humidity_sequence = HumiditySequence(entry.weather.humidity,\n self.PHRASE_LENGTH, base_note, humidity.get_track())\n humidity_appender = HumidityAppender()\n humidity_appender.append(melody_builder, humidity_sequence,\n humidity)\n wind_sequence = WindSequence(entry.weather.wind_speed, self.\n PHRASE_LENGTH, base_note, wind.get_track())\n wind_appender = WindAppender()\n wind_appender.append(melody_builder, wind_sequence, wind)\n for track in [temperature.get_track(), rain.get_track(), clouds.\n get_track(), humidity.get_track(), wind.get_track()]:\n outfile.tracks.append(track)\n file_name = ('weather_song_' + weather_forecast.city + '_' +\n weather_forecast.country + '_' + str(weather_forecast.\n weather_timestamps[0].timestamp))\n self.save_file(outfile, self.OUTPUT_FILE_DIR, file_name)\n return outfile\n\n def save_file(self, outfile: MidiFile, file_dir: str, file_name: str\n ) ->MidiFile:\n Path(file_dir).mkdir(exist_ok=True)\n file_path = file_dir + '/' + file_name + '.mid'\n outfile.save(file_path)\n print('file saved at ' + file_path)\n return outfile\n\n def get_midi_track_time(self, midi_track: MidiTrack):\n sum = 0\n for message in midi_track:\n sum += message.time\n return sum\n",
"step-5": "from pathlib import Path\n\nfrom build_midi.appenders import *\nfrom build_midi.converters import Converter\nfrom build_midi.melody_builder import MelodyBuilder\nfrom build_midi.sequences import *\nfrom build_midi.tracks import *\nfrom music_rules.instruments import Instruments\nfrom music_rules.music_scale import MusicScale\nfrom weather.weather_api import WeatherApi\n\n\nclass WeatherToMusicConverter:\n\n\tPHRASE_LENGTH = 1200\n\tOUTPUT_FILE_DIR = 'midi_out'\n\n\tmusic_scales = MusicScale()\n\n\tdef weather_to_music(self, api_key, city) -> MidiFile:\n\t\tapi_handling = WeatherApi()\n\t\tconverter = Converter()\n\n\t\tweather_forecast = api_handling.get_weather_forecast_from_api(city, api_key)\n\n\t\taverage_temperature = converter.average_temperature(weather_forecast.weather_timestamps)\n\t\tticks_per_beat = converter.average_temperature_to_ticks_per_beat(average_temperature)\n\n\t\toutfile = MidiFile()\n\t\toutfile.ticks_per_beat = ticks_per_beat\n\n\t\tmelody_builder = MelodyBuilder(outfile, self.PHRASE_LENGTH)\n\n\t\ttemperature = TemperatureTrack(1, Instruments.BrightAcousticPiano)\n\t\train = RainTrack(2, Instruments.Celesta)\n\t\tclouds = CloudsTrack(3, Instruments.TremoloStrings)\n\t\thumidity = HumidityTrack(4, Instruments.ElectricGuitar_clean)\n\t\twind = WindTrack(5, Instruments.Seashore)\n\n\t\tfor track in [temperature, rain, clouds, humidity, wind]:\n\t\t\tmelody_builder.set_instrument(track.get_track(), track.get_channel(), track.get_instrument())\n\n\t\tfor entry in weather_forecast.weather_timestamps:\n\n\t\t\tbase_note = converter.temperature_to_base_note(entry.temperature.feels_like)\n\t\t\tmusic_scale = self.music_scales.melodic_minor(base_note)\n\n\t\t\ttemperature_sequence = TemperatureSequence(entry.temperature, self.PHRASE_LENGTH, base_note, temperature.get_track())\n\t\t\ttemperature_appender = TemperatureAppender()\n\t\t\ttemperature_appender.append(melody_builder, temperature_sequence, temperature)\n\n\t\t\train_sequence = RainSequence(entry.weather.rain, self.PHRASE_LENGTH, base_note, rain.get_track(), music_scale)\n\t\t\train_appender = RainAppender()\n\t\t\train_appender.append(melody_builder, rain_sequence, rain)\n\n\t\t\tclouds_sequence = CloudsSequence(entry.weather.clouds, self.PHRASE_LENGTH, base_note, clouds.get_track())\n\t\t\tclouds_appender = CloudsAppender()\n\t\t\tclouds_appender.append(melody_builder, clouds_sequence, clouds)\n\n\t\t\thumidity_sequence = HumiditySequence(entry.weather.humidity, self.PHRASE_LENGTH, base_note, humidity.get_track())\n\t\t\thumidity_appender = HumidityAppender()\n\t\t\thumidity_appender.append(melody_builder, humidity_sequence, humidity)\n\n\t\t\twind_sequence = WindSequence(entry.weather.wind_speed, self.PHRASE_LENGTH, base_note, wind.get_track())\n\t\t\twind_appender = WindAppender()\n\t\t\twind_appender.append(melody_builder, wind_sequence, wind)\n\n\t\tfor track in [temperature.get_track(), rain.get_track(), clouds.get_track(), humidity.get_track(), wind.get_track()]:\n\t\t\toutfile.tracks.append(track)\n\n\t\tfile_name = 'weather_song_' + weather_forecast.city + '_' + weather_forecast.country + '_' + str(weather_forecast.weather_timestamps[0].timestamp)\n\t\tself.save_file(outfile, self.OUTPUT_FILE_DIR, file_name)\n\n\t\treturn outfile\n\n\tdef save_file(self, outfile: MidiFile, file_dir: str, file_name: str) -> MidiFile:\n\t\tPath(file_dir).mkdir(exist_ok=True)\n\t\tfile_path = file_dir + '/' + file_name + '.mid'\n\t\toutfile.save(file_path)\n\t\tprint('file saved at ' + file_path)\n\t\treturn outfile\n\n\tdef get_midi_track_time(self, midi_track: MidiTrack):\n\t\tsum = 0\n\t\tfor message in midi_track:\n\t\t\tsum += message.time\n\t\treturn sum\n\n",
"step-ids": [
1,
4,
5,
6,
7
]
}
|
[
1,
4,
5,
6,
7
] |
import xadmin
from xadmin import views
from .models import EmailVerifyRecord, Banner
class BaseMyAdminView(object):
'''
enable_themes 启动更改主题
use_bootswatch 启用网上主题
'''
enable_themes = True
use_bootswatch = True
class GlobalSettings(object):
'''
site_title 左上角名称
site_footer 底部名称
menu_style 更改左边样式
'''
site_title = "学习网后台管理系统"
site_footer = "学习网"
menu_style = "accordion"
class EmailVerifyRecordAdmin(object):
list_display = ['email', 'code', 'send_type', 'send_time']
search_fields = ['email', 'code', 'send_type']
list_filter = ['email', 'code', 'send_type', 'send_time']
class BannerAdmin(object):
list_disply = ['title', 'image', 'url', 'index', 'add_time']
search_fields = ['title', 'image', 'url', 'index']
list_filter = ['title', 'image', 'url', 'index', 'add_time']
xadmin.site.register(EmailVerifyRecord, EmailVerifyRecordAdmin)
xadmin.site.register(Banner, BannerAdmin)
xadmin.site.register(views.BaseAdminView, BaseMyAdminView)
xadmin.site.register(views.CommAdminView, GlobalSettings)
|
normal
|
{
"blob_id": "d7b830890400203ee45c9ec59611c0b20ab6bfc7",
"index": 8496,
"step-1": "<mask token>\n\n\nclass BaseMyAdminView(object):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass GlobalSettings(object):\n \"\"\"\n site_title 左上角名称\n site_footer 底部名称\n menu_style 更改左边样式\n \"\"\"\n site_title = '学习网后台管理系统'\n site_footer = '学习网'\n menu_style = 'accordion'\n\n\nclass EmailVerifyRecordAdmin(object):\n list_display = ['email', 'code', 'send_type', 'send_time']\n search_fields = ['email', 'code', 'send_type']\n list_filter = ['email', 'code', 'send_type', 'send_time']\n\n\nclass BannerAdmin(object):\n list_disply = ['title', 'image', 'url', 'index', 'add_time']\n search_fields = ['title', 'image', 'url', 'index']\n list_filter = ['title', 'image', 'url', 'index', 'add_time']\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass BaseMyAdminView(object):\n \"\"\"\n enable_themes 启动更改主题\n use_bootswatch 启用网上主题\n \"\"\"\n enable_themes = True\n use_bootswatch = True\n\n\nclass GlobalSettings(object):\n \"\"\"\n site_title 左上角名称\n site_footer 底部名称\n menu_style 更改左边样式\n \"\"\"\n site_title = '学习网后台管理系统'\n site_footer = '学习网'\n menu_style = 'accordion'\n\n\nclass EmailVerifyRecordAdmin(object):\n list_display = ['email', 'code', 'send_type', 'send_time']\n search_fields = ['email', 'code', 'send_type']\n list_filter = ['email', 'code', 'send_type', 'send_time']\n\n\nclass BannerAdmin(object):\n list_disply = ['title', 'image', 'url', 'index', 'add_time']\n search_fields = ['title', 'image', 'url', 'index']\n list_filter = ['title', 'image', 'url', 'index', 'add_time']\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass BaseMyAdminView(object):\n \"\"\"\n enable_themes 启动更改主题\n use_bootswatch 启用网上主题\n \"\"\"\n enable_themes = True\n use_bootswatch = True\n\n\nclass GlobalSettings(object):\n \"\"\"\n site_title 左上角名称\n site_footer 底部名称\n menu_style 更改左边样式\n \"\"\"\n site_title = '学习网后台管理系统'\n site_footer = '学习网'\n menu_style = 'accordion'\n\n\nclass EmailVerifyRecordAdmin(object):\n list_display = ['email', 'code', 'send_type', 'send_time']\n search_fields = ['email', 'code', 'send_type']\n list_filter = ['email', 'code', 'send_type', 'send_time']\n\n\nclass BannerAdmin(object):\n list_disply = ['title', 'image', 'url', 'index', 'add_time']\n search_fields = ['title', 'image', 'url', 'index']\n list_filter = ['title', 'image', 'url', 'index', 'add_time']\n\n\nxadmin.site.register(EmailVerifyRecord, EmailVerifyRecordAdmin)\nxadmin.site.register(Banner, BannerAdmin)\nxadmin.site.register(views.BaseAdminView, BaseMyAdminView)\nxadmin.site.register(views.CommAdminView, GlobalSettings)\n",
"step-4": "import xadmin\nfrom xadmin import views\nfrom .models import EmailVerifyRecord, Banner\n\n\nclass BaseMyAdminView(object):\n \"\"\"\n enable_themes 启动更改主题\n use_bootswatch 启用网上主题\n \"\"\"\n enable_themes = True\n use_bootswatch = True\n\n\nclass GlobalSettings(object):\n \"\"\"\n site_title 左上角名称\n site_footer 底部名称\n menu_style 更改左边样式\n \"\"\"\n site_title = '学习网后台管理系统'\n site_footer = '学习网'\n menu_style = 'accordion'\n\n\nclass EmailVerifyRecordAdmin(object):\n list_display = ['email', 'code', 'send_type', 'send_time']\n search_fields = ['email', 'code', 'send_type']\n list_filter = ['email', 'code', 'send_type', 'send_time']\n\n\nclass BannerAdmin(object):\n list_disply = ['title', 'image', 'url', 'index', 'add_time']\n search_fields = ['title', 'image', 'url', 'index']\n list_filter = ['title', 'image', 'url', 'index', 'add_time']\n\n\nxadmin.site.register(EmailVerifyRecord, EmailVerifyRecordAdmin)\nxadmin.site.register(Banner, BannerAdmin)\nxadmin.site.register(views.BaseAdminView, BaseMyAdminView)\nxadmin.site.register(views.CommAdminView, GlobalSettings)\n",
"step-5": "import xadmin\nfrom xadmin import views\n\nfrom .models import EmailVerifyRecord, Banner\n\n\nclass BaseMyAdminView(object):\n '''\n enable_themes 启动更改主题\n use_bootswatch 启用网上主题\n '''\n enable_themes = True\n use_bootswatch = True\n\n\nclass GlobalSettings(object):\n '''\n site_title 左上角名称\n site_footer 底部名称\n menu_style 更改左边样式\n '''\n site_title = \"学习网后台管理系统\"\n site_footer = \"学习网\"\n menu_style = \"accordion\"\n\n\nclass EmailVerifyRecordAdmin(object):\n list_display = ['email', 'code', 'send_type', 'send_time']\n search_fields = ['email', 'code', 'send_type']\n list_filter = ['email', 'code', 'send_type', 'send_time']\n\n\nclass BannerAdmin(object):\n list_disply = ['title', 'image', 'url', 'index', 'add_time']\n search_fields = ['title', 'image', 'url', 'index']\n list_filter = ['title', 'image', 'url', 'index', 'add_time']\n\n\nxadmin.site.register(EmailVerifyRecord, EmailVerifyRecordAdmin)\nxadmin.site.register(Banner, BannerAdmin)\nxadmin.site.register(views.BaseAdminView, BaseMyAdminView)\nxadmin.site.register(views.CommAdminView, GlobalSettings)",
"step-ids": [
8,
10,
11,
12,
13
]
}
|
[
8,
10,
11,
12,
13
] |
# Generated by Django 3.2.6 on 2021-08-19 16:17
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('crm', '0040_auto_20210819_1913'),
]
operations = [
migrations.RemoveField(
model_name='customer',
name='full_name',
),
migrations.RemoveField(
model_name='managercrm',
name='full_name',
),
]
|
normal
|
{
"blob_id": "42f021c728a88f34d09f94ea96d91abded8a29fb",
"index": 9553,
"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 = [('crm', '0040_auto_20210819_1913')]\n operations = [migrations.RemoveField(model_name='customer', name=\n 'full_name'), migrations.RemoveField(model_name='managercrm', name=\n 'full_name')]\n",
"step-4": "from django.db import migrations\n\n\nclass Migration(migrations.Migration):\n dependencies = [('crm', '0040_auto_20210819_1913')]\n operations = [migrations.RemoveField(model_name='customer', name=\n 'full_name'), migrations.RemoveField(model_name='managercrm', name=\n 'full_name')]\n",
"step-5": "# Generated by Django 3.2.6 on 2021-08-19 16:17\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('crm', '0040_auto_20210819_1913'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='customer',\n name='full_name',\n ),\n migrations.RemoveField(\n model_name='managercrm',\n name='full_name',\n ),\n ]\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|>
class WorkRequestForm(forms.ModelForm):
<|reserved_special_token_0|>
class Meta:
model = HhRequest
fields = 'profile', 'sphere', 'experience', 'work_request', 'resume'
widgets = {'profile': forms.Select(attrs={'id': 'profile',
'required': '', 'class': 'browser-default custom-select'}),
'sphere': forms.Select(attrs={'id': 'sphere', 'required': '',
'class': 'browser-default custom-select'}), 'experience': forms
.Select(attrs={'id': 'experience', 'required': '', 'class':
'browser-default custom-select'}), 'work_request': forms.Select
(attrs={'id': 'work_request', 'required': '', 'class':
'browser-default custom-select'}), 'resume': forms.FileInput(
attrs={'id': 'hh_resume', 'required': '', 'class':
'custom-file-input', 'lang': 'ru'})}
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class WorkRequestForm(forms.ModelForm):
"""Форма заявки на премию"""
class Meta:
model = HhRequest
fields = 'profile', 'sphere', 'experience', 'work_request', 'resume'
widgets = {'profile': forms.Select(attrs={'id': 'profile',
'required': '', 'class': 'browser-default custom-select'}),
'sphere': forms.Select(attrs={'id': 'sphere', 'required': '',
'class': 'browser-default custom-select'}), 'experience': forms
.Select(attrs={'id': 'experience', 'required': '', 'class':
'browser-default custom-select'}), 'work_request': forms.Select
(attrs={'id': 'work_request', 'required': '', 'class':
'browser-default custom-select'}), 'resume': forms.FileInput(
attrs={'id': 'hh_resume', 'required': '', 'class':
'custom-file-input', 'lang': 'ru'})}
<|reserved_special_token_1|>
from django import forms
from .models import HhRequest
class WorkRequestForm(forms.ModelForm):
"""Форма заявки на премию"""
class Meta:
model = HhRequest
fields = 'profile', 'sphere', 'experience', 'work_request', 'resume'
widgets = {'profile': forms.Select(attrs={'id': 'profile',
'required': '', 'class': 'browser-default custom-select'}),
'sphere': forms.Select(attrs={'id': 'sphere', 'required': '',
'class': 'browser-default custom-select'}), 'experience': forms
.Select(attrs={'id': 'experience', 'required': '', 'class':
'browser-default custom-select'}), 'work_request': forms.Select
(attrs={'id': 'work_request', 'required': '', 'class':
'browser-default custom-select'}), 'resume': forms.FileInput(
attrs={'id': 'hh_resume', 'required': '', 'class':
'custom-file-input', 'lang': 'ru'})}
<|reserved_special_token_1|>
from django import forms
from .models import HhRequest
class WorkRequestForm(forms.ModelForm):
"""Форма заявки на премию"""
class Meta:
model = HhRequest
fields = ('profile', 'sphere', 'experience', 'work_request', 'resume')
widgets = {
'profile': forms.Select(
attrs={
'id': 'profile',
'required': '',
'class': 'browser-default custom-select'
}
),
'sphere': forms.Select(
attrs={
'id': 'sphere',
'required': '',
'class': 'browser-default custom-select'
}
),
'experience': forms.Select(
attrs={
'id': 'experience',
'required': '',
'class': 'browser-default custom-select'
}
),
'work_request': forms.Select(
attrs={
'id': 'work_request',
'required': '',
'class': 'browser-default custom-select'
}
),
'resume': forms.FileInput(
attrs={
'id': 'hh_resume',
'required': '',
'class': 'custom-file-input',
'lang': 'ru'
}
),
}
|
flexible
|
{
"blob_id": "3887516e4222504defe439e62bd24b12db3cdd84",
"index": 695,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass WorkRequestForm(forms.ModelForm):\n <mask token>\n\n\n class Meta:\n model = HhRequest\n fields = 'profile', 'sphere', 'experience', 'work_request', 'resume'\n widgets = {'profile': forms.Select(attrs={'id': 'profile',\n 'required': '', 'class': 'browser-default custom-select'}),\n 'sphere': forms.Select(attrs={'id': 'sphere', 'required': '',\n 'class': 'browser-default custom-select'}), 'experience': forms\n .Select(attrs={'id': 'experience', 'required': '', 'class':\n 'browser-default custom-select'}), 'work_request': forms.Select\n (attrs={'id': 'work_request', 'required': '', 'class':\n 'browser-default custom-select'}), 'resume': forms.FileInput(\n attrs={'id': 'hh_resume', 'required': '', 'class':\n 'custom-file-input', 'lang': 'ru'})}\n",
"step-3": "<mask token>\n\n\nclass WorkRequestForm(forms.ModelForm):\n \"\"\"Форма заявки на премию\"\"\"\n\n\n class Meta:\n model = HhRequest\n fields = 'profile', 'sphere', 'experience', 'work_request', 'resume'\n widgets = {'profile': forms.Select(attrs={'id': 'profile',\n 'required': '', 'class': 'browser-default custom-select'}),\n 'sphere': forms.Select(attrs={'id': 'sphere', 'required': '',\n 'class': 'browser-default custom-select'}), 'experience': forms\n .Select(attrs={'id': 'experience', 'required': '', 'class':\n 'browser-default custom-select'}), 'work_request': forms.Select\n (attrs={'id': 'work_request', 'required': '', 'class':\n 'browser-default custom-select'}), 'resume': forms.FileInput(\n attrs={'id': 'hh_resume', 'required': '', 'class':\n 'custom-file-input', 'lang': 'ru'})}\n",
"step-4": "from django import forms\nfrom .models import HhRequest\n\n\nclass WorkRequestForm(forms.ModelForm):\n \"\"\"Форма заявки на премию\"\"\"\n\n\n class Meta:\n model = HhRequest\n fields = 'profile', 'sphere', 'experience', 'work_request', 'resume'\n widgets = {'profile': forms.Select(attrs={'id': 'profile',\n 'required': '', 'class': 'browser-default custom-select'}),\n 'sphere': forms.Select(attrs={'id': 'sphere', 'required': '',\n 'class': 'browser-default custom-select'}), 'experience': forms\n .Select(attrs={'id': 'experience', 'required': '', 'class':\n 'browser-default custom-select'}), 'work_request': forms.Select\n (attrs={'id': 'work_request', 'required': '', 'class':\n 'browser-default custom-select'}), 'resume': forms.FileInput(\n attrs={'id': 'hh_resume', 'required': '', 'class':\n 'custom-file-input', 'lang': 'ru'})}\n",
"step-5": "from django import forms\n\nfrom .models import HhRequest\n\n\nclass WorkRequestForm(forms.ModelForm):\n \"\"\"Форма заявки на премию\"\"\"\n class Meta:\n model = HhRequest\n fields = ('profile', 'sphere', 'experience', 'work_request', 'resume')\n\n widgets = {\n\n 'profile': forms.Select(\n attrs={\n 'id': 'profile',\n 'required': '',\n 'class': 'browser-default custom-select'\n }\n ),\n 'sphere': forms.Select(\n attrs={\n 'id': 'sphere',\n 'required': '',\n 'class': 'browser-default custom-select'\n }\n ),\n 'experience': forms.Select(\n attrs={\n 'id': 'experience',\n 'required': '',\n 'class': 'browser-default custom-select'\n }\n ),\n 'work_request': forms.Select(\n attrs={\n 'id': 'work_request',\n 'required': '',\n 'class': 'browser-default custom-select'\n }\n ),\n 'resume': forms.FileInput(\n attrs={\n 'id': 'hh_resume',\n 'required': '',\n 'class': 'custom-file-input',\n 'lang': 'ru'\n }\n ),\n\n }\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
#!env/bin/python3
from app import app
from config import config as cfg
app.run(debug=True, host=cfg.APP_HOST, port=cfg.APP_PORT)
|
normal
|
{
"blob_id": "f97150f60dfb3924cda2c969141d5bfe675725ef",
"index": 9150,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp.run(debug=True, host=cfg.APP_HOST, port=cfg.APP_PORT)\n",
"step-3": "from app import app\nfrom config import config as cfg\napp.run(debug=True, host=cfg.APP_HOST, port=cfg.APP_PORT)\n",
"step-4": "#!env/bin/python3\nfrom app import app\nfrom config import config as cfg\napp.run(debug=True, host=cfg.APP_HOST, port=cfg.APP_PORT)\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
<<<<<<< HEAD
"""Module docstring"""
import os
import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
=======
#!/usr/bin/python
"""Module docstring"""
import os
import numpy as np
from pickle_data_2 import Data
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
### classifier methods ###
def linear_discriminant_analysis(data):
"""Linear Discriminant Analysis"""
clf = LinearDiscriminantAnalysis()
clf.name = "LDA"
train_predict_and_results(data, clf)
def nearest_neighbors_classifier(data):
"""K Nearest neighbors classification"""
clf = KNeighborsClassifier(3, 'distance')
clf.name = "KNN"
train_predict_and_results(data, clf)
def support_vector_machine(data):
"""Support Vector Machines"""
clf = SVC()
clf.name = "SVC"
train_predict_and_results(data, clf)
def gaussian_naive_bayes(data):
"""Naive Bayes"""
clf = GaussianNB()
clf.name = "GaussNB"
train_predict_and_results(data, clf)
def logistic_regression(data):
"""Logistic Regression """
clf = LogisticRegression()
clf.name = "LoReg"
train_predict_and_results(data, clf)
def random_forest(data):
"""Random Forest"""
clf = RandomForestClassifier()
clf.name = "RNDForest"
train_predict_and_results(data, clf)
### End of classifier methods ###
>>>>>>> 05e11c3b88b3fb5313f29e74125ab6fdd8fffd84
def normalize(data):
"""Returns data with columns normalized
input: numpy array
output: numpy array
"""
# normalize data and return
# https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python
return (data - data.min(axis=0)) / data.ptp(axis=0)
<<<<<<< HEAD
def load_data():
"""Reads datafile and returns data as numpy array"""
# https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html
data = np.load("phase3-data/data_selected_1980_2010.npy").astype(float)
return normalize(data)
def load_target(column="label"):
"""Reads target labels and returns two columns: sum15 and label"""
columns = {"sum15": 0, "label": 1}
if column not in columns.keys():
raise ValueError("%s is not in target data" % column)
filepath = os.path.join("phase3-data", "target_1980_2010.npy")
target = np.load(filepath)
# lets normalize, sum15 might need it
target = normalize(target)
# return correct column
return target[:, columns[column]]
def concat_data(data, target):
'''Merge dataframe data with dataframe target and returns the final one '''
final_data = np.concatenate((data,target[:,None]), axis=1)
return final_data
=======
def load_ta_data():
"""Reads datafile and returns data as numpy array"""
# https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html
data = np.load("data/data_selected_1980_2010.npy").astype(float)
return normalize(data)
def load_ta_target():
"""Reads target labels and returns two columns: sum15 and label"""
filepath = os.path.join("data", "target_1980_2010.npy")
target = np.load(filepath)
return target[:, 1]
def load_own_data():
"""Loads data corresponding to selected features by custom saola algorithm"""
data = Data()
features = data.read_selected_features()
dataframe = data.get_dataframe_with(features)
return normalize(dataframe.values)
def load_own_target():
"""Loads target column as stored in our data files"""
data = Data()
target = data.get_label_col()
return target.values
>>>>>>> 05e11c3b88b3fb5313f29e74125ab6fdd8fffd84
def split_samples(data):
"""Splits data into training samples and test samples
input: numpy array
returns tuple (training_samples, test_samples)
both are numpy arrays
"""
training_samples = data[0:9497]
test_samples = data[9497:11300]
return training_samples, test_samples
<<<<<<< HEAD
def main():
"""The main method"""
feat_data = load_data()
label_data = load_target()
#final = concat_data(feat_data, label_data)
#print final
X_training, X_test = split_samples(feat_data)
Y_training, Y_test = split_samples(label_data)
#10- fold cross-validation
#knn = KNeighborsClassifier(n_neighbors=3)
lda = LinearDiscriminantAnalysis(n_components=3, priors=None, shrinkage=None,
solver='svd', store_covariance=False, tol=0.0001)
#folds = cross_val_score(lda, X_training, Y_training, cv=10)
#print folds
#kf = KFold(n_splits=10)
#print (kf.get_n_splits(X))
#for training_index, test_index in kf.split(X):
# print("TRAIN:", training_index, "TEST:", test_index)
# X_training, X_test = X[training_index], X[test_index]
# Y_training, Y_test = Y[training_index], Y[test_index]
#clf = LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,
# solver='svd', store_covariance=False, tol=0.0001)
lda.fit(X_training, Y_training)
predictions = lda.predict(X_test)
print predictions
print accuracy_score(Y_test, predictions)
=======
def prepare_data():
"""Prepare data for classifier to use"""
#data, label = load_ta_data(), load_ta_target()
data, label = load_own_data(), load_own_target()
tra_x, tst_x = split_samples(data)
tra_y, tst_y = split_samples(label)
return (tra_x, tst_x, tra_y, tst_y)
def train_predict_and_results(data, clf):
"""Perform training, calculate predictions and show results"""
tra_x, tst_x, tra_y, tst_y = data
clf.fit(tra_x, tra_y)
prd_y = clf.predict(tst_x)
cnf = confusion_matrix(tst_y, prd_y)
print ("Classifier: %s \tAccuracy score:%7.2f %%"
"\tTN:%5d FP:%5d FN:%5d TP:%5d"
% (clf.name, accuracy_score(tst_y, prd_y) * 100,
cnf[0][0], cnf[0][1], cnf[1][0], cnf[1][1]))
def main():
"""The main method"""
data = prepare_data()
linear_discriminant_analysis(data)
nearest_neighbors_classifier(data)
support_vector_machine(data)
gaussian_naive_bayes(data)
logistic_regression(data)
random_forest(data)
>>>>>>> 05e11c3b88b3fb5313f29e74125ab6fdd8fffd84
if __name__ == "__main__":
main()
|
normal
|
{
"blob_id": "2bce18354a53c49274f7dd017e1f65c9ff1327b9",
"index": 2264,
"step-1": "<<<<<<< HEAD\n\"\"\"Module docstring\"\"\"\nimport os\nimport numpy as np\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import accuracy_score\n\n=======\n#!/usr/bin/python\n\"\"\"Module docstring\"\"\"\nimport os\nimport numpy as np\nfrom pickle_data_2 import Data\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\n\n### classifier methods ###\n\ndef linear_discriminant_analysis(data):\n \"\"\"Linear Discriminant Analysis\"\"\"\n clf = LinearDiscriminantAnalysis()\n clf.name = \"LDA\"\n train_predict_and_results(data, clf)\n\ndef nearest_neighbors_classifier(data):\n \"\"\"K Nearest neighbors classification\"\"\"\n clf = KNeighborsClassifier(3, 'distance')\n clf.name = \"KNN\"\n train_predict_and_results(data, clf)\n\ndef support_vector_machine(data):\n \"\"\"Support Vector Machines\"\"\"\n clf = SVC()\n clf.name = \"SVC\"\n train_predict_and_results(data, clf)\n\ndef gaussian_naive_bayes(data):\n \"\"\"Naive Bayes\"\"\"\n clf = GaussianNB()\n clf.name = \"GaussNB\"\n train_predict_and_results(data, clf)\n\ndef logistic_regression(data):\n \"\"\"Logistic Regression \"\"\"\n clf = LogisticRegression()\n clf.name = \"LoReg\"\n train_predict_and_results(data, clf)\n\ndef random_forest(data):\n \"\"\"Random Forest\"\"\"\n clf = RandomForestClassifier()\n clf.name = \"RNDForest\"\n train_predict_and_results(data, clf)\n\n### End of classifier methods ###\n>>>>>>> 05e11c3b88b3fb5313f29e74125ab6fdd8fffd84\n\ndef normalize(data):\n \"\"\"Returns data with columns normalized\n input: numpy array\n output: numpy array\n \"\"\"\n # normalize data and return\n # https://stackoverflow.com/questions/29661574/normalize-numpy-array-columns-in-python\n return (data - data.min(axis=0)) / data.ptp(axis=0)\n\n<<<<<<< HEAD\ndef load_data():\n \"\"\"Reads datafile and returns data as numpy array\"\"\"\n\n # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html\n data = np.load(\"phase3-data/data_selected_1980_2010.npy\").astype(float)\n\n return normalize(data)\n\ndef load_target(column=\"label\"):\n \"\"\"Reads target labels and returns two columns: sum15 and label\"\"\"\n\n columns = {\"sum15\": 0, \"label\": 1}\n if column not in columns.keys():\n raise ValueError(\"%s is not in target data\" % column)\n\n filepath = os.path.join(\"phase3-data\", \"target_1980_2010.npy\")\n target = np.load(filepath)\n\n # lets normalize, sum15 might need it\n target = normalize(target)\n\n # return correct column\n return target[:, columns[column]]\n\ndef concat_data(data, target):\n '''Merge dataframe data with dataframe target and returns the final one '''\n \n final_data = np.concatenate((data,target[:,None]), axis=1)\n \n return final_data\n=======\ndef load_ta_data():\n \"\"\"Reads datafile and returns data as numpy array\"\"\"\n # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.astype.html\n data = np.load(\"data/data_selected_1980_2010.npy\").astype(float)\n return normalize(data)\n\ndef load_ta_target():\n \"\"\"Reads target labels and returns two columns: sum15 and label\"\"\"\n filepath = os.path.join(\"data\", \"target_1980_2010.npy\")\n target = np.load(filepath)\n return target[:, 1]\n\ndef load_own_data():\n \"\"\"Loads data corresponding to selected features by custom saola algorithm\"\"\"\n data = Data()\n features = data.read_selected_features()\n dataframe = data.get_dataframe_with(features)\n return normalize(dataframe.values)\n\ndef load_own_target():\n \"\"\"Loads target column as stored in our data files\"\"\"\n data = Data()\n target = data.get_label_col()\n return target.values\n>>>>>>> 05e11c3b88b3fb5313f29e74125ab6fdd8fffd84\n\ndef split_samples(data):\n \"\"\"Splits data into training samples and test samples\n input: numpy array\n\n returns tuple (training_samples, test_samples)\n both are numpy arrays\n \"\"\"\n\n training_samples = data[0:9497]\n test_samples = data[9497:11300]\n\n return training_samples, test_samples\n\n<<<<<<< HEAD\ndef main():\n \"\"\"The main method\"\"\"\n\n feat_data = load_data()\n label_data = load_target()\n #final = concat_data(feat_data, label_data)\n\n #print final\n X_training, X_test = split_samples(feat_data)\n Y_training, Y_test = split_samples(label_data)\n\n #10- fold cross-validation\n #knn = KNeighborsClassifier(n_neighbors=3)\n lda = LinearDiscriminantAnalysis(n_components=3, priors=None, shrinkage=None,\n solver='svd', store_covariance=False, tol=0.0001)\n #folds = cross_val_score(lda, X_training, Y_training, cv=10)\n #print folds\n\n #kf = KFold(n_splits=10)\n #print (kf.get_n_splits(X))\n #for training_index, test_index in kf.split(X):\n # print(\"TRAIN:\", training_index, \"TEST:\", test_index)\n # X_training, X_test = X[training_index], X[test_index]\n # Y_training, Y_test = Y[training_index], Y[test_index]\n \n \n #clf = LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n # solver='svd', store_covariance=False, tol=0.0001)\n lda.fit(X_training, Y_training)\n \n predictions = lda.predict(X_test)\n \n print predictions\n print accuracy_score(Y_test, predictions)\n=======\ndef prepare_data():\n \"\"\"Prepare data for classifier to use\"\"\"\n #data, label = load_ta_data(), load_ta_target()\n data, label = load_own_data(), load_own_target()\n tra_x, tst_x = split_samples(data)\n tra_y, tst_y = split_samples(label)\n return (tra_x, tst_x, tra_y, tst_y)\n\ndef train_predict_and_results(data, clf):\n \"\"\"Perform training, calculate predictions and show results\"\"\"\n tra_x, tst_x, tra_y, tst_y = data\n clf.fit(tra_x, tra_y)\n prd_y = clf.predict(tst_x)\n cnf = confusion_matrix(tst_y, prd_y)\n print (\"Classifier: %s \\tAccuracy score:%7.2f %%\"\n \"\\tTN:%5d FP:%5d FN:%5d TP:%5d\"\n % (clf.name, accuracy_score(tst_y, prd_y) * 100,\n cnf[0][0], cnf[0][1], cnf[1][0], cnf[1][1]))\n\ndef main():\n \"\"\"The main method\"\"\"\n data = prepare_data()\n linear_discriminant_analysis(data)\n nearest_neighbors_classifier(data)\n support_vector_machine(data)\n gaussian_naive_bayes(data)\n logistic_regression(data)\n random_forest(data)\n>>>>>>> 05e11c3b88b3fb5313f29e74125ab6fdd8fffd84\n\nif __name__ == \"__main__\":\n main()\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
<|reserved_special_token_0|>
class IBehaviourBase(Client):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class IBehaviourBase(Client):
<|reserved_special_token_0|>
def __init__(self, email, password, kwargs):
""""abstract class being parent of every user implemented behaviour;
it handles logging in and tasks on behaviour loader side"""
self.kwargs = kwargs
Client.__init__(self, email=email, password=password)
self.Run()
def Run(self):
print('behaviour base abstract method invoked error')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class IBehaviourBase(Client):
BreakFlag = False
def __init__(self, email, password, kwargs):
""""abstract class being parent of every user implemented behaviour;
it handles logging in and tasks on behaviour loader side"""
self.kwargs = kwargs
Client.__init__(self, email=email, password=password)
self.Run()
def Run(self):
print('behaviour base abstract method invoked error')
<|reserved_special_token_1|>
from fbchat import Client
class IBehaviourBase(Client):
BreakFlag = False
def __init__(self, email, password, kwargs):
""""abstract class being parent of every user implemented behaviour;
it handles logging in and tasks on behaviour loader side"""
self.kwargs = kwargs
Client.__init__(self, email=email, password=password)
self.Run()
def Run(self):
print('behaviour base abstract method invoked error')
<|reserved_special_token_1|>
from fbchat import Client
class IBehaviourBase(Client):
BreakFlag = False
def __init__(self,email,password, kwargs):
""""abstract class being parent of every user implemented behaviour;
it handles logging in and tasks on behaviour loader side"""
self.kwargs=kwargs
Client.__init__(self, email=email, password=password)
self.Run()
def Run(self):
print("behaviour base abstract method invoked error")
## todo add exception here
|
flexible
|
{
"blob_id": "e67f27eec53901f27ba5a7ee7e2a20bbb1e8f7f9",
"index": 2237,
"step-1": "<mask token>\n\n\nclass IBehaviourBase(Client):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass IBehaviourBase(Client):\n <mask token>\n\n def __init__(self, email, password, kwargs):\n \"\"\"\"abstract class being parent of every user implemented behaviour;\n it handles logging in and tasks on behaviour loader side\"\"\"\n self.kwargs = kwargs\n Client.__init__(self, email=email, password=password)\n self.Run()\n\n def Run(self):\n print('behaviour base abstract method invoked error')\n",
"step-3": "<mask token>\n\n\nclass IBehaviourBase(Client):\n BreakFlag = False\n\n def __init__(self, email, password, kwargs):\n \"\"\"\"abstract class being parent of every user implemented behaviour;\n it handles logging in and tasks on behaviour loader side\"\"\"\n self.kwargs = kwargs\n Client.__init__(self, email=email, password=password)\n self.Run()\n\n def Run(self):\n print('behaviour base abstract method invoked error')\n",
"step-4": "from fbchat import Client\n\n\nclass IBehaviourBase(Client):\n BreakFlag = False\n\n def __init__(self, email, password, kwargs):\n \"\"\"\"abstract class being parent of every user implemented behaviour;\n it handles logging in and tasks on behaviour loader side\"\"\"\n self.kwargs = kwargs\n Client.__init__(self, email=email, password=password)\n self.Run()\n\n def Run(self):\n print('behaviour base abstract method invoked error')\n",
"step-5": "from fbchat import Client\nclass IBehaviourBase(Client):\n BreakFlag = False\n def __init__(self,email,password, kwargs):\n \"\"\"\"abstract class being parent of every user implemented behaviour;\n it handles logging in and tasks on behaviour loader side\"\"\"\n self.kwargs=kwargs\n Client.__init__(self, email=email, password=password)\n\n self.Run()\n\n def Run(self):\n print(\"behaviour base abstract method invoked error\")\n ## todo add exception here\n\n",
"step-ids": [
1,
3,
4,
5,
6
]
}
|
[
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def tokenize_de(text):
return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]
def tokenize_en(text):
return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def tokenize_de(text):
return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]
def tokenize_en(text):
return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]
<|reserved_special_token_0|>
print(train.fields)
print(len(train))
print(vars(train[0]))
print(vars(train[100]))
DE.build_vocab(train.src, min_freq=3)
EN.build_vocab(train.trg, max_size=50000)
<|reserved_special_token_0|>
print(DE.vocab.freqs.most_common(10))
print(DE.vocab.size)
print(EN.vocab.freqs.most_common(10))
print(EN.vocab.size)
<|reserved_special_token_0|>
print(batch.src)
print(batch.trg)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
spacy_de = spacy.load('de')
spacy_en = spacy.load('en')
url = re.compile('(<url>.*</url>)')
def tokenize_de(text):
return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]
def tokenize_en(text):
return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]
DE = data.Field(tokenize=tokenize_de)
EN = data.Field(tokenize=tokenize_en)
train, val = datasets.TranslationDataset.splits(path='~/iwslt2016/de-en/',
train='train.tags.de-en', validation='IWSLT16.TED.tst2013.de-en', exts=
('.de', '.en'), fields=(DE, EN))
print(train.fields)
print(len(train))
print(vars(train[0]))
print(vars(train[100]))
DE.build_vocab(train.src, min_freq=3)
EN.build_vocab(train.trg, max_size=50000)
train_iter, val_iter = data.BucketIterator.splits((train, val), batch_size=
3, device=0)
print(DE.vocab.freqs.most_common(10))
print(DE.vocab.size)
print(EN.vocab.freqs.most_common(10))
print(EN.vocab.size)
batch = next(iter(train_iter))
print(batch.src)
print(batch.trg)
<|reserved_special_token_1|>
from torchtext import data
from torchtext import datasets
import re
import spacy
spacy_de = spacy.load('de')
spacy_en = spacy.load('en')
url = re.compile('(<url>.*</url>)')
def tokenize_de(text):
return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]
def tokenize_en(text):
return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]
DE = data.Field(tokenize=tokenize_de)
EN = data.Field(tokenize=tokenize_en)
train, val = datasets.TranslationDataset.splits(path='~/iwslt2016/de-en/',
train='train.tags.de-en', validation='IWSLT16.TED.tst2013.de-en', exts=
('.de', '.en'), fields=(DE, EN))
print(train.fields)
print(len(train))
print(vars(train[0]))
print(vars(train[100]))
DE.build_vocab(train.src, min_freq=3)
EN.build_vocab(train.trg, max_size=50000)
train_iter, val_iter = data.BucketIterator.splits((train, val), batch_size=
3, device=0)
print(DE.vocab.freqs.most_common(10))
print(DE.vocab.size)
print(EN.vocab.freqs.most_common(10))
print(EN.vocab.size)
batch = next(iter(train_iter))
print(batch.src)
print(batch.trg)
|
flexible
|
{
"blob_id": "4e715ccb4f95e7fe7e495a1181ad5df530f5a53f",
"index": 5773,
"step-1": "<mask token>\n\n\ndef tokenize_de(text):\n return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]\n\n\ndef tokenize_en(text):\n return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef tokenize_de(text):\n return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]\n\n\ndef tokenize_en(text):\n return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]\n\n\n<mask token>\nprint(train.fields)\nprint(len(train))\nprint(vars(train[0]))\nprint(vars(train[100]))\nDE.build_vocab(train.src, min_freq=3)\nEN.build_vocab(train.trg, max_size=50000)\n<mask token>\nprint(DE.vocab.freqs.most_common(10))\nprint(DE.vocab.size)\nprint(EN.vocab.freqs.most_common(10))\nprint(EN.vocab.size)\n<mask token>\nprint(batch.src)\nprint(batch.trg)\n",
"step-3": "<mask token>\nspacy_de = spacy.load('de')\nspacy_en = spacy.load('en')\nurl = re.compile('(<url>.*</url>)')\n\n\ndef tokenize_de(text):\n return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]\n\n\ndef tokenize_en(text):\n return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]\n\n\nDE = data.Field(tokenize=tokenize_de)\nEN = data.Field(tokenize=tokenize_en)\ntrain, val = datasets.TranslationDataset.splits(path='~/iwslt2016/de-en/',\n train='train.tags.de-en', validation='IWSLT16.TED.tst2013.de-en', exts=\n ('.de', '.en'), fields=(DE, EN))\nprint(train.fields)\nprint(len(train))\nprint(vars(train[0]))\nprint(vars(train[100]))\nDE.build_vocab(train.src, min_freq=3)\nEN.build_vocab(train.trg, max_size=50000)\ntrain_iter, val_iter = data.BucketIterator.splits((train, val), batch_size=\n 3, device=0)\nprint(DE.vocab.freqs.most_common(10))\nprint(DE.vocab.size)\nprint(EN.vocab.freqs.most_common(10))\nprint(EN.vocab.size)\nbatch = next(iter(train_iter))\nprint(batch.src)\nprint(batch.trg)\n",
"step-4": "from torchtext import data\nfrom torchtext import datasets\nimport re\nimport spacy\nspacy_de = spacy.load('de')\nspacy_en = spacy.load('en')\nurl = re.compile('(<url>.*</url>)')\n\n\ndef tokenize_de(text):\n return [tok.text for tok in spacy_de.tokenizer(url.sub('@URL@', text))]\n\n\ndef tokenize_en(text):\n return [tok.text for tok in spacy_en.tokenizer(url.sub('@URL@', text))]\n\n\nDE = data.Field(tokenize=tokenize_de)\nEN = data.Field(tokenize=tokenize_en)\ntrain, val = datasets.TranslationDataset.splits(path='~/iwslt2016/de-en/',\n train='train.tags.de-en', validation='IWSLT16.TED.tst2013.de-en', exts=\n ('.de', '.en'), fields=(DE, EN))\nprint(train.fields)\nprint(len(train))\nprint(vars(train[0]))\nprint(vars(train[100]))\nDE.build_vocab(train.src, min_freq=3)\nEN.build_vocab(train.trg, max_size=50000)\ntrain_iter, val_iter = data.BucketIterator.splits((train, val), batch_size=\n 3, device=0)\nprint(DE.vocab.freqs.most_common(10))\nprint(DE.vocab.size)\nprint(EN.vocab.freqs.most_common(10))\nprint(EN.vocab.size)\nbatch = next(iter(train_iter))\nprint(batch.src)\nprint(batch.trg)\n",
"step-5": null,
"step-ids": [
2,
3,
4,
5
]
}
|
[
2,
3,
4,
5
] |
# settings
import config
# various modules
import sys
import time
import multiprocessing
import threading
from queue import Queue
import time
import os
import signal
import db
import time
from random import randint
# telepot's msg loop & Bot
from telepot.loop import MessageLoop
from telepot import Bot
import asyncio
from handle_msg import handle_msg, get_image
# bot object
bot = Bot(config.token)
mgr = multiprocessing.Manager()
shared_dict = mgr.dict()
def thread(fork_process,thread_queue,shared_dict):
thread = threading.currentThread()
post = db.DB(config.username,config.password,config.dbname,config.host,config.port)
post.connect()
print('fork process - %s, thread - %s' % (fork_process,thread.getName()))
while 1:
msg = thread_queue.get()
print('received msg from fork_process - {}, thread - {}, msg - {}'.format(fork_process,thread.getName(),msg,post))
if 'forward' in shared_dict:
if shared_dict['forward'] != msg['chat']['id']:
bot.sendMessage(shared_dict['forward'],'{}'.format(msg))
if 'scheduler' not in msg:
handle_msg(msg,bot,shared_dict,post)
else:
if str(msg['chat_id']) + 'n' not in shared_dict:
shared_dict[str(msg['chat_id']) + 'n'] = 0
get_image(msg['chat_id'],msg['keyword'],shared_dict,post,bot,False)
def worker(parent_process,fork_queue,shared_dict):
fork_process = multiprocessing.current_process()
thread_queue = Queue()
for i in range(config.threads_qt):
t = threading.Thread(target=thread,args=(fork_process.name,thread_queue,shared_dict))
t.setDaemon(True)
t.start()
try:
#print 'Starting:',fork_process.name,fork_process.pid
while 1:
data = fork_queue.get()
thread_queue.put(data)
except KeyboardInterrupt as e:
pass
def handle(msg):
fork_queue.put(msg)
fork_queue = multiprocessing.Queue()
parent_process = os.getpid()
for i in range(config.forks_qt):
p = multiprocessing.Process(target=worker,args=(parent_process,fork_queue,shared_dict))
p.daemon = True
p.start()
@asyncio.coroutine
def scheduler():
while True:
yield None
for i in config.pida_groups:
time.sleep(int(str(3) + str(randint(100,999))))
bot.sendMessage(i,'kuku pidarugi')
fork_queue.put({'scheduler':1,'chat_id':i,'keyword':'victoria secret'})
@asyncio.coroutine
def telepuzik():
MessageLoop(bot,handle).run_as_thread()
yield None
if __name__ == "__main__":
try:
tasks = asyncio.gather(asyncio.async(telepuzik()),asyncio.async(scheduler()))
loop = asyncio.get_event_loop()
loop.run_forever()
except KeyboardInterrupt as e:
print("keyboard interrupted")
|
normal
|
{
"blob_id": "315fed1806999fed7cf1366ef0772318a0baa84d",
"index": 8789,
"step-1": "# settings\nimport config\n\n# various modules\nimport sys\nimport time\nimport multiprocessing\nimport threading\nfrom queue import Queue\nimport time\nimport os\nimport signal\nimport db\nimport time\nfrom random import randint\n\n# telepot's msg loop & Bot\nfrom telepot.loop import MessageLoop\nfrom telepot import Bot \nimport asyncio\nfrom handle_msg import handle_msg, get_image\n\n# bot object\nbot = Bot(config.token)\nmgr = multiprocessing.Manager()\nshared_dict = mgr.dict()\n\ndef thread(fork_process,thread_queue,shared_dict):\n thread = threading.currentThread()\n post = db.DB(config.username,config.password,config.dbname,config.host,config.port)\n post.connect()\n print('fork process - %s, thread - %s' % (fork_process,thread.getName()))\n while 1:\n msg = thread_queue.get()\n print('received msg from fork_process - {}, thread - {}, msg - {}'.format(fork_process,thread.getName(),msg,post))\n if 'forward' in shared_dict:\n if shared_dict['forward'] != msg['chat']['id']:\n bot.sendMessage(shared_dict['forward'],'{}'.format(msg))\n if 'scheduler' not in msg:\n handle_msg(msg,bot,shared_dict,post)\n else:\n if str(msg['chat_id']) + 'n' not in shared_dict:\n shared_dict[str(msg['chat_id']) + 'n'] = 0\n get_image(msg['chat_id'],msg['keyword'],shared_dict,post,bot,False)\n\ndef worker(parent_process,fork_queue,shared_dict):\n fork_process = multiprocessing.current_process()\n thread_queue = Queue()\n for i in range(config.threads_qt):\n t = threading.Thread(target=thread,args=(fork_process.name,thread_queue,shared_dict))\n t.setDaemon(True)\n t.start()\n try:\n #print 'Starting:',fork_process.name,fork_process.pid\n while 1:\n data = fork_queue.get()\n thread_queue.put(data)\n except KeyboardInterrupt as e:\n pass\n\ndef handle(msg):\n fork_queue.put(msg)\n\nfork_queue = multiprocessing.Queue()\nparent_process = os.getpid()\n\nfor i in range(config.forks_qt):\n p = multiprocessing.Process(target=worker,args=(parent_process,fork_queue,shared_dict))\n p.daemon = True\n p.start()\n\n@asyncio.coroutine\ndef scheduler():\n while True:\n yield None\n for i in config.pida_groups:\n time.sleep(int(str(3) + str(randint(100,999))))\n bot.sendMessage(i,'kuku pidarugi')\n fork_queue.put({'scheduler':1,'chat_id':i,'keyword':'victoria secret'})\n\n@asyncio.coroutine\ndef telepuzik():\n MessageLoop(bot,handle).run_as_thread()\n yield None\n\nif __name__ == \"__main__\":\n try:\n tasks = asyncio.gather(asyncio.async(telepuzik()),asyncio.async(scheduler()))\n loop = asyncio.get_event_loop()\n loop.run_forever()\n except KeyboardInterrupt as e:\n print(\"keyboard interrupted\")\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_instance(rest_url, params):
url = BASEURL + rest_url
print(url)
twitter = OAuth1Session(CK, CS, AT, AS)
return twitter.get(url, params=params)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
BASEURL = 'https://api.twitter.com/1.1/'
CK = '3rJOl1ODzm9yZy63FACdg'
CS = '5jPoQ5kQvMJFDYRNE8bQ4rHuds4xJqhvgNJM4awaE8'
AT = '333312023-6dTniMxvwlQG8bATKNYWBXaQkftz9t4ZjRBt7BWk'
AS = 'LQ8xXBTTN8F8CHQv9oDAqsGJFeexdnFf2DFzn3EzGH2L8'
def get_instance(rest_url, params):
url = BASEURL + rest_url
print(url)
twitter = OAuth1Session(CK, CS, AT, AS)
return twitter.get(url, params=params)
<|reserved_special_token_1|>
from requests_oauthlib import OAuth1Session
BASEURL = 'https://api.twitter.com/1.1/'
CK = '3rJOl1ODzm9yZy63FACdg'
CS = '5jPoQ5kQvMJFDYRNE8bQ4rHuds4xJqhvgNJM4awaE8'
AT = '333312023-6dTniMxvwlQG8bATKNYWBXaQkftz9t4ZjRBt7BWk'
AS = 'LQ8xXBTTN8F8CHQv9oDAqsGJFeexdnFf2DFzn3EzGH2L8'
def get_instance(rest_url, params):
url = BASEURL + rest_url
print(url)
twitter = OAuth1Session(CK, CS, AT, AS)
return twitter.get(url, params=params)
<|reserved_special_token_1|>
#! /usr/local/bin/python3
# -*- coding: utf-8 -*-
from requests_oauthlib import OAuth1Session
BASEURL = 'https://api.twitter.com/1.1/'
CK = '3rJOl1ODzm9yZy63FACdg'
CS = '5jPoQ5kQvMJFDYRNE8bQ4rHuds4xJqhvgNJM4awaE8'
AT = '333312023-6dTniMxvwlQG8bATKNYWBXaQkftz9t4ZjRBt7BWk'
AS = 'LQ8xXBTTN8F8CHQv9oDAqsGJFeexdnFf2DFzn3EzGH2L8'
def get_instance(rest_url, params):
url = BASEURL + rest_url
print(url)
twitter = OAuth1Session(CK, CS, AT, AS)
return twitter.get(url, params=params)
|
flexible
|
{
"blob_id": "63bfaa6e191e6090060877e737f4b003bed559cf",
"index": 9140,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_instance(rest_url, params):\n url = BASEURL + rest_url\n print(url)\n twitter = OAuth1Session(CK, CS, AT, AS)\n return twitter.get(url, params=params)\n",
"step-3": "<mask token>\nBASEURL = 'https://api.twitter.com/1.1/'\nCK = '3rJOl1ODzm9yZy63FACdg'\nCS = '5jPoQ5kQvMJFDYRNE8bQ4rHuds4xJqhvgNJM4awaE8'\nAT = '333312023-6dTniMxvwlQG8bATKNYWBXaQkftz9t4ZjRBt7BWk'\nAS = 'LQ8xXBTTN8F8CHQv9oDAqsGJFeexdnFf2DFzn3EzGH2L8'\n\n\ndef get_instance(rest_url, params):\n url = BASEURL + rest_url\n print(url)\n twitter = OAuth1Session(CK, CS, AT, AS)\n return twitter.get(url, params=params)\n",
"step-4": "from requests_oauthlib import OAuth1Session\nBASEURL = 'https://api.twitter.com/1.1/'\nCK = '3rJOl1ODzm9yZy63FACdg'\nCS = '5jPoQ5kQvMJFDYRNE8bQ4rHuds4xJqhvgNJM4awaE8'\nAT = '333312023-6dTniMxvwlQG8bATKNYWBXaQkftz9t4ZjRBt7BWk'\nAS = 'LQ8xXBTTN8F8CHQv9oDAqsGJFeexdnFf2DFzn3EzGH2L8'\n\n\ndef get_instance(rest_url, params):\n url = BASEURL + rest_url\n print(url)\n twitter = OAuth1Session(CK, CS, AT, AS)\n return twitter.get(url, params=params)\n",
"step-5": "#! /usr/local/bin/python3\n# -*- coding: utf-8 -*-\n\nfrom requests_oauthlib import OAuth1Session\n\nBASEURL = 'https://api.twitter.com/1.1/'\n\nCK = '3rJOl1ODzm9yZy63FACdg'\nCS = '5jPoQ5kQvMJFDYRNE8bQ4rHuds4xJqhvgNJM4awaE8'\nAT = '333312023-6dTniMxvwlQG8bATKNYWBXaQkftz9t4ZjRBt7BWk'\nAS = 'LQ8xXBTTN8F8CHQv9oDAqsGJFeexdnFf2DFzn3EzGH2L8'\n\n\ndef get_instance(rest_url, params):\n url = BASEURL + rest_url\n print(url)\n twitter = OAuth1Session(CK, CS, AT, AS)\n return twitter.get(url, params=params)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
K = input()
mat = "".join(raw_input() for i in xrange(4))
print ("YES", "NO")[max(mat.count(str(i)) for i in xrange(1, 10)) > K*2]
|
normal
|
{
"blob_id": "879f7503f7f427f92109024b4646d1dc7f15d63d",
"index": 2153,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('YES', 'NO')[max(mat.count(str(i)) for i in xrange(1, 10)) > K * 2]\n",
"step-3": "K = input()\nmat = ''.join(raw_input() for i in xrange(4))\nprint('YES', 'NO')[max(mat.count(str(i)) for i in xrange(1, 10)) > K * 2]\n",
"step-4": "K = input()\nmat = \"\".join(raw_input() for i in xrange(4))\nprint (\"YES\", \"NO\")[max(mat.count(str(i)) for i in xrange(1, 10)) > K*2]\n\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|>
class NameSearch(forms.Form):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class NameSearch(forms.Form):
name = forms.CharField(label='Search By Name')
<|reserved_special_token_1|>
from django import forms
from django.core import validators
class NameSearch(forms.Form):
name = forms.CharField(label='Search By Name')
|
flexible
|
{
"blob_id": "7620ff333422d0354cc41c2a66444c3e8a0c011f",
"index": 1606,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass NameSearch(forms.Form):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass NameSearch(forms.Form):\n name = forms.CharField(label='Search By Name')\n",
"step-4": "from django import forms\nfrom django.core import validators\n\n\nclass NameSearch(forms.Form):\n name = forms.CharField(label='Search By Name')\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
import csv
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
#funktion
def func(w,rc):
return 1/(np.sqrt(1+w**2*rc**2))
#daten einlesen
with open('data/phase.csv' ) as csvfile:
reader=csv.reader(csvfile, delimiter=',')
header_row=next(reader)
f, U, a, b = [], [], [], []
for row in reader:
f.append(row[0])
U.append(row[1])
a.append(row[2])
b.append(row[3])
f=np.array(f,dtype=float)
U=np.array(U,dtype=float)
a=np.array(a,dtype=float)
b=np.array(b,dtype=float)
#curvefit
U0=0.6
popt, pcov = curve_fit(func, f*2*np.pi, U/U0)
a1=popt[0]
#theoriewerte
R_th=11.01*10**3
C_th=93.3*10**(-9)
#plots
plt.xlabel(r'$f\, / \, Hz$')
plt.ylabel(r'$\frac{U_c}{U_0}$', fontsize=15)
plt.grid()
plt.semilogx(f,U/U0,'rx',label='Messwerte')
x=np.linspace(20,30000,10000)
plt.semilogx(x,func(x*2*np.pi,a1),'b-',label='Ausgleichsrechnung')
plt.semilogx(x,func(x*2*np.pi,R_th*C_th),'g-',label='Theoriekurve')
plt.legend()
plt.savefig('plotb.pdf')
plt.show()
#fehlerausgabe
uncertainties = np.sqrt(np.diag(pcov))
print('RC =',-a1,'+-',uncertainties[0])
print('Theoriewert:',11.01*1000*93.3*10**(-9))
print('Phase:',(a/b)*np.pi*2)
|
normal
|
{
"blob_id": "170d0560c40f3f642f319f6113b68ab8a6bea9ef",
"index": 468,
"step-1": "<mask token>\n\n\ndef func(w, rc):\n return 1 / np.sqrt(1 + w ** 2 * rc ** 2)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef func(w, rc):\n return 1 / np.sqrt(1 + w ** 2 * rc ** 2)\n\n\nwith open('data/phase.csv') as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n header_row = next(reader)\n f, U, a, b = [], [], [], []\n for row in reader:\n f.append(row[0])\n U.append(row[1])\n a.append(row[2])\n b.append(row[3])\n f = np.array(f, dtype=float)\n U = np.array(U, dtype=float)\n a = np.array(a, dtype=float)\n b = np.array(b, dtype=float)\n<mask token>\nplt.xlabel('$f\\\\, / \\\\, Hz$')\nplt.ylabel('$\\\\frac{U_c}{U_0}$', fontsize=15)\nplt.grid()\nplt.semilogx(f, U / U0, 'rx', label='Messwerte')\n<mask token>\nplt.semilogx(x, func(x * 2 * np.pi, a1), 'b-', label='Ausgleichsrechnung')\nplt.semilogx(x, func(x * 2 * np.pi, R_th * C_th), 'g-', label='Theoriekurve')\nplt.legend()\nplt.savefig('plotb.pdf')\nplt.show()\n<mask token>\nprint('RC =', -a1, '+-', uncertainties[0])\nprint('Theoriewert:', 11.01 * 1000 * 93.3 * 10 ** -9)\nprint('Phase:', a / b * np.pi * 2)\n",
"step-3": "<mask token>\n\n\ndef func(w, rc):\n return 1 / np.sqrt(1 + w ** 2 * rc ** 2)\n\n\nwith open('data/phase.csv') as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n header_row = next(reader)\n f, U, a, b = [], [], [], []\n for row in reader:\n f.append(row[0])\n U.append(row[1])\n a.append(row[2])\n b.append(row[3])\n f = np.array(f, dtype=float)\n U = np.array(U, dtype=float)\n a = np.array(a, dtype=float)\n b = np.array(b, dtype=float)\nU0 = 0.6\npopt, pcov = curve_fit(func, f * 2 * np.pi, U / U0)\na1 = popt[0]\nR_th = 11.01 * 10 ** 3\nC_th = 93.3 * 10 ** -9\nplt.xlabel('$f\\\\, / \\\\, Hz$')\nplt.ylabel('$\\\\frac{U_c}{U_0}$', fontsize=15)\nplt.grid()\nplt.semilogx(f, U / U0, 'rx', label='Messwerte')\nx = np.linspace(20, 30000, 10000)\nplt.semilogx(x, func(x * 2 * np.pi, a1), 'b-', label='Ausgleichsrechnung')\nplt.semilogx(x, func(x * 2 * np.pi, R_th * C_th), 'g-', label='Theoriekurve')\nplt.legend()\nplt.savefig('plotb.pdf')\nplt.show()\nuncertainties = np.sqrt(np.diag(pcov))\nprint('RC =', -a1, '+-', uncertainties[0])\nprint('Theoriewert:', 11.01 * 1000 * 93.3 * 10 ** -9)\nprint('Phase:', a / b * np.pi * 2)\n",
"step-4": "import csv\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.optimize import curve_fit\n\n\ndef func(w, rc):\n return 1 / np.sqrt(1 + w ** 2 * rc ** 2)\n\n\nwith open('data/phase.csv') as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n header_row = next(reader)\n f, U, a, b = [], [], [], []\n for row in reader:\n f.append(row[0])\n U.append(row[1])\n a.append(row[2])\n b.append(row[3])\n f = np.array(f, dtype=float)\n U = np.array(U, dtype=float)\n a = np.array(a, dtype=float)\n b = np.array(b, dtype=float)\nU0 = 0.6\npopt, pcov = curve_fit(func, f * 2 * np.pi, U / U0)\na1 = popt[0]\nR_th = 11.01 * 10 ** 3\nC_th = 93.3 * 10 ** -9\nplt.xlabel('$f\\\\, / \\\\, Hz$')\nplt.ylabel('$\\\\frac{U_c}{U_0}$', fontsize=15)\nplt.grid()\nplt.semilogx(f, U / U0, 'rx', label='Messwerte')\nx = np.linspace(20, 30000, 10000)\nplt.semilogx(x, func(x * 2 * np.pi, a1), 'b-', label='Ausgleichsrechnung')\nplt.semilogx(x, func(x * 2 * np.pi, R_th * C_th), 'g-', label='Theoriekurve')\nplt.legend()\nplt.savefig('plotb.pdf')\nplt.show()\nuncertainties = np.sqrt(np.diag(pcov))\nprint('RC =', -a1, '+-', uncertainties[0])\nprint('Theoriewert:', 11.01 * 1000 * 93.3 * 10 ** -9)\nprint('Phase:', a / b * np.pi * 2)\n",
"step-5": "import csv\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom scipy.optimize import curve_fit\r\n\r\n#funktion\r\ndef func(w,rc):\r\n return 1/(np.sqrt(1+w**2*rc**2))\r\n\r\n#daten einlesen\r\nwith open('data/phase.csv' ) as csvfile:\r\n reader=csv.reader(csvfile, delimiter=',')\r\n header_row=next(reader)\r\n f, U, a, b = [], [], [], []\r\n for row in reader:\r\n f.append(row[0])\r\n U.append(row[1])\r\n a.append(row[2])\r\n b.append(row[3])\r\n f=np.array(f,dtype=float)\r\n U=np.array(U,dtype=float)\r\n a=np.array(a,dtype=float)\r\n b=np.array(b,dtype=float)\r\n\r\n#curvefit\r\nU0=0.6\r\npopt, pcov = curve_fit(func, f*2*np.pi, U/U0)\r\na1=popt[0]\r\n\r\n#theoriewerte\r\nR_th=11.01*10**3\r\nC_th=93.3*10**(-9)\r\n\r\n#plots\r\nplt.xlabel(r'$f\\, / \\, Hz$')\r\nplt.ylabel(r'$\\frac{U_c}{U_0}$', fontsize=15)\r\nplt.grid()\r\nplt.semilogx(f,U/U0,'rx',label='Messwerte')\r\nx=np.linspace(20,30000,10000)\r\nplt.semilogx(x,func(x*2*np.pi,a1),'b-',label='Ausgleichsrechnung')\r\nplt.semilogx(x,func(x*2*np.pi,R_th*C_th),'g-',label='Theoriekurve')\r\nplt.legend()\r\nplt.savefig('plotb.pdf')\r\nplt.show()\r\n\r\n#fehlerausgabe\r\nuncertainties = np.sqrt(np.diag(pcov))\r\nprint('RC =',-a1,'+-',uncertainties[0])\r\nprint('Theoriewert:',11.01*1000*93.3*10**(-9))\r\nprint('Phase:',(a/b)*np.pi*2)",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
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