content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import graphlab as gl
from models import *
path = "s3://gl-demo-usw2/predictive_service/demolab/ps-1.6"
ps = gl.deploy.predictive_service.load(path)
# Define dependencies
state = {'details_filename': '../data/talks.json',
'speakers_filename': '../data/speakers.json',
'details_sf': '../data/talks.gl',
'speakers_sf': '../data/speakers.gl'}
# Data carpentry
details = parse_details(state['details_sf'])
speakers_dict, speakers = parse_speakers(state['speakers_sf'])
details = clean_timing(details)
details, talks_per_speaker = join_speaker_data_into_details(details, speakers)
details_dict, trimmed = create_details_dict(details)
# Create nearest neighbor model and get nearest items
nn_model, nearest = build_nn_model(details)
# Deploy models as a predictive service
upload_list_page(ps, trimmed)
upload_speaker(ps, talks_per_speaker)
upload_item_sim(ps, details, nn_model, nearest)
#########################################################
# Ad hoc testing
# Via Python client
print ps.query('stratanow_item_sim', input={'item_ids': ['43169'], 'how_many':5})
# Via Curl
# !curl -X POST -d '{"api_key": "b9b8dd75-a6d3-4903-b6a7-2dc691d060d8", "data":{"input": {"item_ids":["43750"], "how_many": 5}}}' stratanow-175425062.us-west-2.elb.amazonaws.com/data/item_sim
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# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import xlwt
#!gdown --id 1bb2irg5nFZhoFkpjWPQHJPBBr8FiK8l7
dataRestoran = pd.read_excel('restoran.xlsx')
print(dataRestoran)
# akan menghasilkan nilai kelayakan yang lebih bervariasi
titikPelayanan = [25, 37, 58, 65, 78, 89, 101]
titikMakanan = [3, 5, 8, 11]
grafikPelayanan()
grafikMakanan()
# print(fuzzificationPelayanan(dataRestoran))
# print(fuzzificationMakanan(dataRestoran))
dataFuzzyPelayanan = fuzzificationPelayanan(dataRestoran)
dataFuzzyMakanan = fuzzificationMakanan(dataRestoran)
# print(inference(dataFuzzyPelayanan, dataFuzzyMakanan))
arrx = [0, 30, 60, 99]
arry = [0, 1, 1, 1]
fig, ax = plt.subplots(nrows=1, figsize=(10, 4))
plt.xticks([30, 60, 99])
plt.yticks([0, 1])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.xlabel("Nilai Kelayakan skala [0,100]")
plt.ylabel("u")
plt.margins(y=0.17)
plt.title("FK singleton untuk Nilai Kelayakan")
plt.bar(arrx, arry, color=['red', 'red', 'orange', 'green'], width=[
0.4, 0.4, 0.4, 0.4], label="Runtime CycleSort")
rects = ax.patches
labels = ["", "rendah", "sedang", "tinggi"]
for rect, label in zip(rects, labels):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2, height+0.0088, label,
ha='center', va='bottom')
plt.show()
dataFuzzyRules = inference(dataFuzzyPelayanan, dataFuzzyMakanan)
hasilDefuzz = defuzzyfication(dataFuzzyRules)
dataRestoran["Result"] = hasilDefuzz
hasilAkhir = dataRestoran.sort_values(by="Result", ascending=False)[:10]
hasilAkhir
print("\nHasil Akhir:\n", hasilAkhir)
# Write Peringkat ke file xls.
# peringkat = xlwt.Workbook()
# ws = peringkat.add_sheet('Output')
# ws.write(0, 0, 'Record id')
# i = 1
# for j in hasilAkhir["id"]:
# ws.write(i, 0, j)
# i += 1
# peringkat.save('peringkat.xls')
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# The MIT License (MIT) - Copyright (c) 2021 xesscorp
"""
Categorized collections of circuits.
"""
import sys
import pint
# Create a shortcut name for "circuitsascode".
sys.modules["casc"] = sys.modules["circuitsascode"]
# For electrical units like ohms, volts, etc.
units = pint.UnitRegistry()
if sys.version_info[:2] >= (3, 8):
# TODO: Import directly (no need for conditional) when `python_requires = >= 3.8`
from importlib.metadata import PackageNotFoundError, version # pragma: no cover
else:
from importlib_metadata import PackageNotFoundError, version # pragma: no cover
try:
# Change here if project is renamed and does not equal the package name
dist_name = __name__
__version__ = version(dist_name)
except PackageNotFoundError: # pragma: no cover
__version__ = "unknown"
finally:
del version, PackageNotFoundError
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] | 3.149091 | 275 |
import pandas as pd
import numpy as np
import json
import datetime
import miasole_module_two as ps
import pvlib.pvsystem as pvsyst
#import shaded_miasole as ps
import interconnection as connect
import matplotlib.pyplot as plt
def align_yaxis(ax1, v1, ax2, v2):
"""adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1"""
_, y1 = ax1.transData.transform((0, v1))
_, y2 = ax2.transData.transform((0, v2))
inv = ax2.transData.inverted()
_, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
miny, maxy = ax2.get_ylim()
ax2.set_ylim(miny+dy, maxy+dy)
#This function finds the MPP for the measured data using the lists of I and V of the object
if __name__ == "__main__":
module_lookup_table_path = 'C:\Users\walkerl\Documents\MA_Local\Electrical_simulation\lookup\MIA_lookup.pkl'
lookup_table = pd.read_pickle(module_lookup_table_path)
lookup_table = lookup_table.astype('object')
number_of_subcells = 5
shading_string = 'completely shaded' #This variable does not change calculations but will app
irradiation_path = 'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\meas_irrad.xlsx'
time = datetime.datetime(2017,8,15,11,40,16)
temp_sensor_name = 'RTD3'
ambient_temperature = get_temperature(irradiation_path, time, temp_sensor_name)
if time.minute < 10:
measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \
str(time.hour)+ '_0' + str(time.minute) + '_' + str(time.second)
measurement_data_path = measurement_path + '.XLS'
shading_pattern_path = measurement_path + "_shading.json"
elif time.second < 10:
measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \
str(time.hour) + '_' + str(time.minute) + '_0' + str(time.second)
measurement_data_path = measurement_path + '.XLS'
shading_pattern_path = measurement_path + "_shading.json"
elif time.second < 10 and time.minute < 10:
measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \
str(time.hour) + '_0' + str(time.minute) + '_0' + str(time.second)
measurement_data_path = measurement_path + '.XLS'
shading_pattern_path = measurement_path + "_shading.json"
else:
measurement_path = r'C:\Users\walkerl\Documents\MA_Local\Versuche\Messungen_17_08_15\15-08-2017 ' + \
str(time.hour)+ '_' + str(time.minute) + '_' + str(time.second)
measurement_data_path = measurement_path + '.XLS'
shading_pattern_path = measurement_path + "_shading.json"
shading_pattern1 = get_shading_pattern(shading_pattern_path)
sensor_name1 = "Pyranometer 2 (W/m2)"
# sensor_name1 = "DNI (W/m2)"
database_path = r'C:\Users\walkerl\Documents\BIPV-planning-tool\BIPV-planning-tool\electrical_simulation\data\sam-library-cec-modules-2015-6-30.csv'
module_df = pvsyst.retrieve_sam(path=database_path)
irrad_value1 = get_irradiation_value(irradiation_path, time, sensor_name1)
irrad1 = create_irradiation_list(irrad_value1, shading_pattern1, partially_shaded_irrad=None)
irrad_on_sub_cells_ordered1 = rearrange_shading_pattern(irrad1,number_of_subcells)
i_module_sim1, v_module_sim1, lookup_table = ps.partial_shading(irrad_on_sub_cells_ordered1, temperature=ambient_temperature,
irrad_temp_lookup_df=lookup_table, module_df=module_df)
i_module_meas, v_module_meas = get_measured_iv_curves_from_excel(measurement_data_path)
mpp_measured = max(i_module_meas * v_module_meas)
mpp_simulated = max(i_module_sim1 * v_module_sim1)
print mpp_measured
print mpp_simulated
plt.plot(v_module_sim1, i_module_sim1, color='blue', linestyle='--')
plt.plot(v_module_meas, i_module_meas, color='blue' )
ax = plt.gca()
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels=['simulated IV ' , 'measured IV'],loc='upper left')
ax.set_title('Irradiation: ' + str(int(irrad_value1)) + ", T = " + str(ambient_temperature)+ u"\u00b0" + "C" + '\n Shaded cells: ' + shading_string)
ax.set_ylabel('Current I [A]')
ax.set_xlabel('Voltage V [V]')
ax.set_ylim(0,4)
ax.set_xlim(0,105)
ax2 = ax.twinx()
ax2.set_ylim(0, 50)
ax2.set_xlim(0,40)
ax2.set_ylabel("Power P [W]")
ax2.plot(v_module_sim1, v_module_sim1 * i_module_sim1, color='green', label='PV simulated', linestyle='--')
ax2.plot(v_module_meas, i_module_meas*v_module_meas, color='green', label='PV measured' )
handles, labels = ax2.get_legend_handles_labels()
ax2.legend(handles, labels=['simulated PV ', 'measured PV'])
align_yaxis(ax, 0, ax2, 0)
# plt.savefig("F:\Validation_final\Plots_MIA\single_module/" + shading_string + str(int(irrad_value1)) + '.png')
plt.show()
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"""PyZMQ and 0MQ version functions."""
# Copyright (C) PyZMQ Developers
# Distributed under the terms of the Modified BSD License.
from zmq.backend import zmq_version_info
VERSION_MAJOR = 16
VERSION_MINOR = 0
VERSION_PATCH = 4
VERSION_EXTRA = ""
__version__ = '%i.%i.%i' % (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH)
if VERSION_EXTRA:
__version__ = "%s.%s" % (__version__, VERSION_EXTRA)
version_info = (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH, float('inf'))
else:
version_info = (VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH)
__revision__ = ''
def pyzmq_version():
"""return the version of pyzmq as a string"""
if __revision__:
return '@'.join([__version__,__revision__[:6]])
else:
return __version__
def pyzmq_version_info():
"""return the pyzmq version as a tuple of at least three numbers
If pyzmq is a development version, `inf` will be appended after the third integer.
"""
return version_info
def zmq_version():
"""return the version of libzmq as a string"""
return "%i.%i.%i" % zmq_version_info()
__all__ = ['zmq_version', 'zmq_version_info',
'pyzmq_version','pyzmq_version_info',
'__version__', '__revision__'
]
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] | 2.437624 | 505 |
import json
import os
from tqdm import tqdm
def parse_book(book_data: dict) -> dict:
"""Parse book core data."""
info = {}
for param in ['isbn', 'title', 'onsale', 'price',
'language', 'pages', 'publisher']:
info[param] = book_data.get(param)
info['cover'] = f'https://images.randomhouse.com/cover/{info["isbn"]}'
info['format_family'] = book_data.get('formatFamily')
info['projected_minutes'] = book_data.get('projectedMinutes')
info['series_number'] = book_data.get('seriesNumber')
return info
def parse_authors(authors_data: list) -> list:
"""Extract information about contributors."""
authors = []
for author in authors_data:
authors.append({
'author_id': author.get('authorId'),
'first_name': author.get('first'),
'last_name': author.get('last'),
'company': author.get('company'),
'client_source_id': author.get('clientSourceId'),
'role': author.get('contribRoleCode')
})
return authors
def parse_categories(category_data: list) -> list:
"""Extract information about categories.
Since we downloaded data about categories separately,
keep here only category_id and the sequence.
"""
categories = []
for cat in category_data:
# Read PRH docs about sequencing
if cat.get('seq', 0) > 0:
categories.append({
'category_id': cat.get('catId'),
'seq': cat.get('seq')
})
return categories
def parse_series(series_data: list) -> list:
"""Extract information about series."""
series = []
for item in series_data:
series.append({
'series_id': item.get('seriesCode'),
'name': item.get('seriesName'),
'description': item.get('description'),
'series_count': item.get('seriesCount'),
'is_numbered': item.get('isNumbered'),
'is_kids': item.get('isKids')
})
return series
def parse_works(works_data: list) -> list:
"""Extract information about works."""
works = []
for work in works_data:
works.append({
'work_id': work.get('workId'),
'title': work.get('title'),
'author': work.get('author'),
'onsale': work.get('onsale'),
'language': work.get('language'),
'series_number': work.get('seriesNumber')
})
return works
def parse_content(content_data: dict) -> dict:
"""Extract long text data."""
content = {}
for param in ['flapcopy', 'excerpt']:
content[param] = content_data.get(param)
return content
if __name__ == '__main__':
# Paths
path_raw_books = os.path.join('..', 'data_raw', 'books.txt')
path_parsed_books = os.path.join('..', 'data_interm', 'books.txt')
# Parse the file line by line
with open(path_raw_books, 'r') as books_raw:
with open(path_parsed_books, 'w') as books_parsed:
for book in tqdm(books_raw):
book_data = json.loads(book)
# Get core book data
info = parse_book(book_data['titles'][0])
# Parse relative info
embeds = {}
for embed in book_data['_embeds']:
embeds.update(embed)
info['authors'] = parse_authors(embeds['authors'])
info['categories'] = parse_categories(embeds['categories'])
info['series'] = parse_series(embeds['series'])
info['works'] = parse_works(embeds['works'])
info.update(parse_content(embeds['content']))
# Save
data_string = json.dumps(info)
books_parsed.write(data_string)
books_parsed.write('\n')
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] | 2.221837 | 1,731 |
#!/usr/bin/python3
#
# 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.
"""Parses metadata from a .proto file
Parses metadata from a .proto file including various options and a list of top
level messages and enums declared within the proto file.
"""
import itertools
import re
import string
class ProtoMetadata:
"""Parses a proto file to extract options and other metadata."""
multiple_files = False
package = ''
java_package = ''
java_api_version = 2
java_alt_api_package = ''
outer_class = ''
optimize_for = 'SPEED'
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] | 3.584775 | 289 |
import operator
import pytest
from nettlesome.terms import ContextRegister, DuplicateTermError
from nettlesome.terms import Explanation, TermSequence, means
from nettlesome.entities import Entity
from nettlesome.groups import FactorGroup
from nettlesome.predicates import Predicate
from nettlesome.quantities import Comparison, Q_
from authorityspoke.facts import Fact, build_fact
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] | 3.64486 | 107 |
import _ast
import inspect
import re
import sys
import traceback
from . import closure_analyzer
from .code_emitter import CodeEmitter
DEBUG_CHECKS = True
BINOP_MAP = {
_ast.Add:"__add__",
_ast.Sub:"__sub__",
_ast.Mult:"__mul__",
_ast.BitOr:"__or__",
_ast.BitXor:"__xor__",
_ast.BitAnd:'__and__',
_ast.LShift:'__lshift__',
_ast.RShift:'__rshift__',
_ast.Mod:'__mod__',
_ast.Div:'__div__',
_ast.Pow:'__pow__',
}
COMPARE_MAP = {
_ast.Lt:"__lt__",
_ast.Gt:"__gt__",
_ast.LtE:"__le__",
_ast.GtE:"__ge__",
_ast.Eq:"__eq__",
_ast.NotEq:"__ne__",
_ast.In:"__contains__",
}
COMPARE_REFLECTIONS = {
_ast.Lt:_ast.Gt,
_ast.Gt:_ast.Lt,
_ast.LtE:_ast.GtE,
_ast.GtE:_ast.LtE,
_ast.Eq:_ast.Eq,
_ast.NotEq:_ast.NotEq,
}
# The same thing as an AttributeError, but for the compiled code rather than the compiler code
# The same thing as an TypeError, but for the compiled code rather than the compiler code
# AN error that you tried to instantiate an object that has no first-class representation (such as a polymorphic function, or module)
class AttributeAccessType(object):
""" An enum of the possible ways that an object attribute will
be generated. """
# A member variable of the object, that has a persistent memory location
FIELD = "field"
# A field that the object has implicitly, and is generated on access (such as __class__)
IMPLICIT_FIELD = "implicit_field"
# A class-level method of the object that doesn't change, which is instantiated into an instancemethod on access
CONST_METHOD = "attr_const_method"
# convert everything to IEEE754 format to make sure we don't lose any precision in serialization
_cached_templates = {}
_cached_ctemplates = {}
Variable._ok_code = list(func.func_code for name, func in inspect.getmembers(Variable, inspect.ismethod))
# All types default to raised types
Slice = singleton(SliceMT)
Slice.initialized = ("attrs", "write")
StrConstant = singleton(StrConstantMT)
StrConstant.initialized = ("attrs", "write")
None_ = singleton(NoneMT)
None_.initialized = ("attrs", "write")
Len = singleton(LenMT)
StrFunc = singleton(StrFuncMT)
ReprFunc = singleton(ReprFuncMT)
Nref = singleton(NrefMT)
TypeFunc = singleton(TypeFuncMT)
BoolFunc = singleton(BoolFuncMT)
Isinstance = singleton(IsinstanceMT)
Cast = singleton(CastMT)
StrFormat = singleton(StrFormatMT)
MapFunc = singleton(MapFuncMT)
ReduceFunc = singleton(ReduceFuncMT)
Enumerate = singleton(EnumerateMT)
MinFunc = MinFuncMT("min")
MaxFunc = MinFuncMT("max")
ListFunc = Parametric1ArgCtorFuncMT(ListMT.make_list, "append")
SetFunc = Parametric1ArgCtorFuncMT(SetMT.make_set, "add")
DequeFunc = Parametric1ArgCtorFuncMT(DequeMT.make_deque, "append")
DictFunc = singleton(DictFuncMT)
ObjectClass = ClassMT(None, "object", "object")
Object = ObjectClass._instance
IntClass = ClassMT(ObjectClass, "int", "int", llvm_type="i64")
Int = IntClass._instance
FloatClass = ClassMT(ObjectClass, "float", "float", llvm_type="double")
Float = FloatClass._instance
StrClass = ClassMT(ObjectClass, "str", "str", llvm_type="%string*")
Str = StrClass._instance
BoolClass = ClassMT(ObjectClass, "bool", "bool", "i1")
Bool = BoolClass._instance
TypeClass = ClassMT(ObjectClass, "type", "type")
Type = TypeClass._instance
FileClass = ClassMT(ObjectClass, "file", "file")
File = FileClass._instance
# TODO there is a lot of duplication between this and stuff like closures
class PtrMT(MT):
""" An MT to represent the type of a stored pointer to an object. They should only exist as a compiler implementation detail. """
Underlying = singleton(_UnderlyingMT)
_made_supertypes = {}
# Some type classes for stdlib stuff:
STDLIB_TYPES = []
_IntIterator, _IntIterable = _make_iterable(Int)
_FloatIterator, _FloatIterable = _make_iterable(Float)
_Boolable = BoxedMT([_FakeMT({
"__class__": (Type, AttributeAccessType.IMPLICIT_FIELD),
"__incref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD),
"__decref__": (CallableMT.make_callable([], 0, None_), AttributeAccessType.CONST_METHOD),
"__nonzero__": (CallableMT.make_callable([], 0, Bool), AttributeAccessType.CONST_METHOD),
})])
STDLIB_TYPES.append(_Boolable)
_BoolableIterator, _BoolableIterable = _make_iterable(_Boolable)
BUILTINS = {
"True":Variable(Bool, 1, 1, False),
"False":Variable(Bool, 0, 1, False),
"len":Variable(Len, (), 1, False),
"str":Variable(StrClass, (), 1, False),
"repr":Variable(ReprFunc, (), 1, False),
"type":Variable(TypeClass, (), 1, False),
"map":Variable(MapFunc, (), 1, False),
"reduce":Variable(ReduceFunc, (), 1, False),
"nrefs":Variable(Nref, (), 1, False),
"bool":Variable(BoolClass, (), 1, False),
"list":Variable(ListFunc, (), 1, False),
"dict":Variable(DictFunc, (), 1, False),
"set":Variable(SetFunc, (), 1, False),
"isinstance":Variable(Isinstance, (), 1, False),
"__cast__":Variable(Cast, (), 1, False),
"enumerate":Variable(Enumerate, (), 1, False),
"chr":Variable(UnboxedFunctionMT(None, None, [Int], Str), ("@chr", [], None), 1, False),
"ord":Variable(UnboxedFunctionMT(None, None, [Str], Int), ("@ord", [], None), 1, False),
# "open":Variable(UnboxedFunctionMT(None, None, [Str], File), ("@file_open", [], None), 1, True),
"open":Variable(UnboxedFunctionMT(None, None, [Str, Str], File, ndefaults=1), ("@file_open2", [Variable(Str, "@.str_r", 1, True)], None), 1, False),
"int":Variable(IntClass, (), 1, False),
"min":PolymorphicFunctionMT.make([
Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_min", [], None), 1, False),
Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_min", [], None), 1, False),
Variable(MinFunc, (), 1, False),
]),
"max":PolymorphicFunctionMT.make([
Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_max", [], None), 1, False),
Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_max", [], None), 1, False),
Variable(MaxFunc, (), 1, False),
]),
"float":Variable(FloatClass, (), 1, False),
"file":Variable(FileClass, (), 1, False),
"abs":PolymorphicFunctionMT.make([
Variable(UnboxedFunctionMT(None, None, [Int], Int), ("@int_abs", [], None), 1, False),
Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@float_abs", [], None), 1, False)]),
"None":Variable(None_, "null", 1, False),
"object":Variable(ObjectClass, (), 1, False),
"sum":PolymorphicFunctionMT.make([
Variable(UnboxedFunctionMT(None, None, [_IntIterable], Int), ("@sum_int", [], None), 1, False),
Variable(UnboxedFunctionMT(None, None, [_FloatIterable], Float), ("@sum_float", [], None), 1, False),
]),
"any":Variable(UnboxedFunctionMT(None, None, [_BoolableIterable], Bool), ("@any", [], None), 1, False),
}
BUILTIN_MODULES = {
"time":Variable(ModuleMT({
'time':Variable(UnboxedFunctionMT(None, None, [], Float), ("@time_time", [], None), 1, False),
'clock':Variable(UnboxedFunctionMT(None, None, [], Float), ("@time_clock", [], None), 1, False),
'sleep':Variable(UnboxedFunctionMT(None, None, [Float], None_), ("@time_sleep", [], None), 1, False),
}), 1, 1, False),
"sys":Variable(ModuleMT({
'stdin':Variable(File, "@sys_stdin", 1, False),
'stdout':Variable(File, "@sys_stdout", 1, False),
'stderr':Variable(File, "@sys_stderr", 1, False),
}), 1, 1, False),
"math":Variable(ModuleMT({
'sqrt':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@sqrt", [], None), 1, False),
'tan':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@tan", [], None), 1, False),
'sin':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@sin", [], None), 1, False),
'cos':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@cos", [], None), 1, False),
'ceil':Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@ceil", [], None), 1, False),
'pi':Variable(Float, format_float(3.141592653589793), 1, False),
}), 1, 1, False),
"collections":Variable(ModuleMT({
'deque':Variable(DequeFunc, (), 1, False),
}), 1, 1, False),
# Interopability library:
"hax":Variable(ModuleMT({
"ftoi":Variable(UnboxedFunctionMT(None, None, [Float], Int), ("@hax_ftoi", [], None), 1, False),
"itof":Variable(UnboxedFunctionMT(None, None, [Int], Float), ("@hax_itof", [], None), 1, False),
"min":Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_min", [], None), 1, False),
"max":Variable(UnboxedFunctionMT(None, None, [Int, Int], Int), ("@int_max", [], None), 1, False),
"fmin":Variable(UnboxedFunctionMT(None, None, [Float, Float], Float), ("@float_min", [], None), 1, False),
"abs":Variable(UnboxedFunctionMT(None, None, [Float], Float), ("@float_abs", [], None), 1, False),
"initvideo":Variable(UnboxedFunctionMT(None, None, [Int, Int], None_), ("@hax_initvideo", [], None), 1, False),
"plot":Variable(UnboxedFunctionMT(None, None, [Int, Int, Int, Int, Int], None_), ("@hax_plot", [], None), 1, False),
}), 1, 1, False),
}
SliceMT.setup_class_methods()
NoneMT.setup_class_methods()
setup_int()
setup_float()
setup_string()
setup_bool()
setup_type()
setup_file()
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] | 2.386863 | 4,141 |
import uuid
from sqlalchemy import Column, ForeignKey, String
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.orm import relationship
from .base import Base
from .mixins import DateFieldsMixins
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# Copyright (c) 2017 The Khronos Group Inc.
#
# 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.
#
# Imports
#
import os
from ...io.com.gltf2_io_debug import *
#
# Globals
#
#
# Functions
#
def get_material_requires_texcoords(glTF, index):
"""
Query function, if a material "needs" texture coordinates. This is the case, if a texture is present and used.
"""
if glTF.get('materials') is None:
return False
materials = glTF['materials']
if index < 0 or index >= len(materials):
return False
material = materials[index]
# General
if material.get('emissiveTexture') is not None:
return True
if material.get('normalTexture') is not None:
return True
if material.get('occlusionTexture') is not None:
return True
# Metallic roughness
if material.get('baseColorTexture') is not None:
return True
if material.get('metallicRoughnessTexture') is not None:
return True
# Specular glossiness
if material.get('diffuseTexture') is not None:
return True
if material.get('specularGlossinessTexture') is not None:
return True
# Unlit Material
if material.get('baseColorTexture') is not None:
return True
if material.get('diffuseTexture') is not None:
return True
# Displacement
if material.get('displacementTexture') is not None:
return True
return False
def get_material_requires_normals(glTF, index):
"""
Query function, if a material "needs" normals. This is the case, if a texture is present and used.
At point of writing, same function as for texture coordinates.
"""
return get_material_requires_texcoords(glTF, index)
def get_material_index(glTF, name):
"""
Return the material index in the glTF array.
"""
if name is None:
return -1
if glTF.get('materials') is None:
return -1
index = 0
for material in glTF['materials']:
if material['name'] == name:
return index
index += 1
return -1
def get_mesh_index(glTF, name):
"""
Return the mesh index in the glTF array.
"""
if glTF.get('meshes') is None:
return -1
index = 0
for mesh in glTF['meshes']:
if mesh['name'] == name:
return index
index += 1
return -1
def get_skin_index(glTF, name, index_offset):
"""
Return the skin index in the glTF array.
"""
if glTF.get('skins') is None:
return -1
skeleton = get_node_index(glTF, name)
index = 0
for skin in glTF['skins']:
if skin['skeleton'] == skeleton:
return index + index_offset
index += 1
return -1
def get_camera_index(glTF, name):
"""
Return the camera index in the glTF array.
"""
if glTF.get('cameras') is None:
return -1
index = 0
for camera in glTF['cameras']:
if camera['name'] == name:
return index
index += 1
return -1
def get_light_index(glTF, name):
"""
Return the light index in the glTF array.
"""
if glTF.get('extensions') is None:
return -1
extensions = glTF['extensions']
if extensions.get('KHR_lights_punctual') is None:
return -1
khr_lights_punctual = extensions['KHR_lights_punctual']
if khr_lights_punctual.get('lights') is None:
return -1
lights = khr_lights_punctual['lights']
index = 0
for light in lights:
if light['name'] == name:
return index
index += 1
return -1
def get_node_index(glTF, name):
"""
Return the node index in the glTF array.
"""
if glTF.get('nodes') is None:
return -1
index = 0
for node in glTF['nodes']:
if node['name'] == name:
return index
index += 1
return -1
def get_scene_index(glTF, name):
"""
Return the scene index in the glTF array.
"""
if glTF.get('scenes') is None:
return -1
index = 0
for scene in glTF['scenes']:
if scene['name'] == name:
return index
index += 1
return -1
def get_texture_index(glTF, filename):
"""
Return the texture index in the glTF array by a given filepath.
"""
if glTF.get('textures') is None:
return -1
image_index = get_image_index(glTF, filename)
if image_index == -1:
return -1
for texture_index, texture in enumerate(glTF['textures']):
if image_index == texture['source']:
return texture_index
return -1
def get_image_index(glTF, filename):
"""
Return the image index in the glTF array.
"""
if glTF.get('images') is None:
return -1
image_name = get_image_name(filename)
for index, current_image in enumerate(glTF['images']):
if image_name == current_image['name']:
return index
return -1
def get_image_name(filename):
"""
Return user-facing, extension-agnostic name for image.
"""
return os.path.splitext(filename)[0]
def get_scalar(default_value, init_value = 0.0):
"""
Return scalar with a given default/fallback value.
"""
return_value = init_value
if default_value is None:
return return_value
return_value = default_value
return return_value
def get_vec2(default_value, init_value = [0.0, 0.0]):
"""
Return vec2 with a given default/fallback value.
"""
return_value = init_value
if default_value is None or len(default_value) < 2:
return return_value
index = 0
for number in default_value:
return_value[index] = number
index += 1
if index == 2:
return return_value
return return_value
def get_vec3(default_value, init_value = [0.0, 0.0, 0.0]):
"""
Return vec3 with a given default/fallback value.
"""
return_value = init_value
if default_value is None or len(default_value) < 3:
return return_value
index = 0
for number in default_value:
return_value[index] = number
index += 1
if index == 3:
return return_value
return return_value
def get_vec4(default_value, init_value = [0.0, 0.0, 0.0, 1.0]):
"""
Return vec4 with a given default/fallback value.
"""
return_value = init_value
if default_value is None or len(default_value) < 4:
return return_value
index = 0
for number in default_value:
return_value[index] = number
index += 1
if index == 4:
return return_value
return return_value
def get_index(elements, name):
"""
Return index of a glTF element by a given name.
"""
if elements is None or name is None:
return -1
index = 0
for element in elements:
if element.get('name') is None:
return -1
if element['name'] == name:
return index
index += 1
return -1
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# Create dummy secrey key so we can use sessions
SECRET_KEY = '1234567890'
# Flask-Security config
SECURITY_URL_PREFIX = "/admin"
SECURITY_PASSWORD_HASH = "pbkdf2_sha256"
SECURITY_PASSWORD_SALT = "ATGUOHAELKiubahiughaerGOJAEGj"
SECURITY_USER_IDENTITY_ATTRIBUTES = ["name"]
# Flask-Security URLs, overridden because they don't put a / at the end
SECURITY_LOGIN_URL = "/login/"
SECURITY_LOGOUT_URL = "/logout/"
SECURITY_REGISTER_URL = "/register/"
SECURITY_POST_LOGIN_VIEW = "/admin/"
SECURITY_POST_LOGOUT_VIEW = "/admin/"
SECURITY_POST_REGISTER_VIEW = "/admin/"
# Flask-Security features
SECURITY_REGISTERABLE = True
SECURITY_SEND_REGISTER_EMAIL = False
SQLALCHEMY_TRACK_MODIFICATIONS = False | [
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"""Websocket API for mobile_app."""
import voluptuous as vol
from homeassistant.components.cloud import async_delete_cloudhook
from homeassistant.components.websocket_api import (
ActiveConnection,
async_register_command,
async_response,
error_message,
result_message,
websocket_command,
ws_require_user,
)
from homeassistant.components.websocket_api.const import (
ERR_INVALID_FORMAT,
ERR_NOT_FOUND,
ERR_UNAUTHORIZED,
)
from homeassistant.const import CONF_WEBHOOK_ID
from homeassistant.exceptions import HomeAssistantError
from homeassistant.helpers import config_validation as cv
from homeassistant.helpers.typing import HomeAssistantType
from .const import (
CONF_CLOUDHOOK_URL,
CONF_USER_ID,
DATA_CONFIG_ENTRIES,
DATA_DELETED_IDS,
DATA_STORE,
DOMAIN,
)
from .helpers import safe_registration, savable_state
def register_websocket_handlers(hass: HomeAssistantType) -> bool:
"""Register the websocket handlers."""
async_register_command(hass, websocket_get_user_registrations)
async_register_command(hass, websocket_delete_registration)
return True
@ws_require_user()
@async_response
@websocket_command(
{
vol.Required("type"): "mobile_app/get_user_registrations",
vol.Optional(CONF_USER_ID): cv.string,
}
)
async def websocket_get_user_registrations(
hass: HomeAssistantType, connection: ActiveConnection, msg: dict
) -> None:
"""Return all registrations or just registrations for given user ID."""
user_id = msg.get(CONF_USER_ID, connection.user.id)
if user_id != connection.user.id and not connection.user.is_admin:
# If user ID is provided and is not current user ID and current user
# isn't an admin user
connection.send_error(msg["id"], ERR_UNAUTHORIZED, "Unauthorized")
return
user_registrations = []
for config_entry in hass.config_entries.async_entries(domain=DOMAIN):
registration = config_entry.data
if connection.user.is_admin or registration[CONF_USER_ID] is user_id:
user_registrations.append(safe_registration(registration))
connection.send_message(result_message(msg["id"], user_registrations))
@ws_require_user()
@async_response
@websocket_command(
{
vol.Required("type"): "mobile_app/delete_registration",
vol.Required(CONF_WEBHOOK_ID): cv.string,
}
)
async def websocket_delete_registration(
hass: HomeAssistantType, connection: ActiveConnection, msg: dict
) -> None:
"""Delete the registration for the given webhook_id."""
user = connection.user
webhook_id = msg.get(CONF_WEBHOOK_ID)
if webhook_id is None:
connection.send_error(msg["id"], ERR_INVALID_FORMAT, "Webhook ID not provided")
return
config_entry = hass.data[DOMAIN][DATA_CONFIG_ENTRIES][webhook_id]
registration = config_entry.data
if registration is None:
connection.send_error(
msg["id"], ERR_NOT_FOUND, "Webhook ID not found in storage"
)
return
if registration[CONF_USER_ID] != user.id and not user.is_admin:
return error_message(
msg["id"], ERR_UNAUTHORIZED, "User is not registration owner"
)
await hass.config_entries.async_remove(config_entry.entry_id)
hass.data[DOMAIN][DATA_DELETED_IDS].append(webhook_id)
store = hass.data[DOMAIN][DATA_STORE]
try:
await store.async_save(savable_state(hass))
except HomeAssistantError:
return error_message(msg["id"], "internal_error", "Error deleting registration")
if CONF_CLOUDHOOK_URL in registration and "cloud" in hass.config.components:
await async_delete_cloudhook(hass, webhook_id)
connection.send_message(result_message(msg["id"], "ok"))
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] | 2.599588 | 1,456 |
#!/usr/bin/env python3
import pika
import time
import sys
import argparse
sys.path.append("lib")
from rabbitlock.mutex import Mutex
from rabbitlock.semaphore import Semaphore
# http://www.huyng.com/posts/python-performance-analysis/
parse_and_dispatch(sys.argv[1:])
| [
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] | 2.808081 | 99 |
# Generated by Django 3.0.1 on 2020-01-02 02:40
import datetime
from django.db import migrations, models
| [
2,
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] | 2.972222 | 36 |
# --------------------------------------------------------
# TAFSSL
# Copyright (c) 2019 IBM Corp
# Licensed under The Apache-2.0 License [see LICENSE for details]
# --------------------------------------------------------
import numpy as np
from utils.proto_msp import ProtoMSP
import time
import pickle
from utils.misc import print_params
from utils.misc import load_features
from utils.misc import print_msg
from utils.misc import avg, ci_95, parse_args
from utils.misc import create_episode, calc_acc
from utils.misc import get_color
from utils.misc import get_features
if __name__ == '__main__':
get_features()
n_query_exp()
n_query_exp_fig()
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13,
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1330,
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62,
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628,
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198,
361,
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3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
651,
62,
40890,
3419,
198,
220,
220,
220,
299,
62,
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62,
11201,
3419,
198,
220,
220,
220,
299,
62,
22766,
62,
11201,
62,
5647,
3419,
198
] | 3.455959 | 193 |
from .preprocess import * | [
6738,
764,
3866,
14681,
1330,
1635
] | 4.166667 | 6 |
#!/usr/bin/env python
# Returns obj_val function to be used in an optimizer
# A better and updated version of qaoa_obj.py
import networkx as nx
import numpy as np
# import matplotlib.pyplot as plt
from networkx.generators.classic import barbell_graph
import copy
import sys
import warnings
import qcommunity.modularity.graphs as gm
from qcommunity.utils.import_graph import generate_graph
from ibmqxbackend.ansatz import IBMQXVarForm
def get_obj(n_nodes,
B,
C=None,
obj_params='ndarray',
sign=1,
backend='IBMQX',
backend_params={'depth': 3},
return_x=False):
"""
:param obj_params: defines the signature of obj_val function. 'beta gamma' or 'ndarray' (added to support arbitrary number of steps and scipy.optimize.minimize.)
:return: obj_val function, number of variational parameters
:rtype: tuple
"""
if return_x:
all_x = []
all_vals = []
# TODO refactor, remove code duplication
if backend == 'IBMQX':
var_form = IBMQXVarForm(
num_qubits=n_nodes, depth=backend_params['depth'])
num_parameters = var_form.num_parameters
if obj_params == 'ndarray':
else:
raise ValueError(
"obj_params '{}' not compatible with backend '{}'".format(
obj_params, backend))
else:
raise ValueError("Unsupported backend: {}".format(backend))
if return_x:
return obj_val, num_parameters, all_x, all_vals
else:
return obj_val, num_parameters
if __name__ == "__main__":
x = np.array([2.1578616206475347, 0.1903995547630178])
obj_val, _ = get_obj_val("get_barbell_graph", 3, 3)
print(obj_val(x[0], x[1]))
obj_val, num_parameters = get_obj_val(
"get_barbell_graph", 3, 3, obj_params='ndarray', backend='IBMQX')
y = np.random.uniform(-np.pi, np.pi, num_parameters)
print(obj_val(y))
obj_val, num_parameters = get_obj_val(
"get_barbell_graph", 3, 3, obj_params='ndarray')
z = np.random.uniform(-np.pi, np.pi, num_parameters)
print(obj_val(z))
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62,
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11,
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62,
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366,
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62,
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62,
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11,
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11,
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1976,
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11,
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13,
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8,
198,
220,
220,
220,
3601,
7,
26801,
62,
2100,
7,
89,
4008,
198
] | 2.284188 | 936 |
import cv2
from PIL import Image
import numpy as np
import random
import pytest
from ggb import GGB, CVLib
from ggb.testing import ggb_test
from ggb.testing import get_random_image, get_filled_image
@ggb_test
@ggb_test
if __name__ == '__main__':
pytest.main([__file__])
| [
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13,
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26933,
834,
7753,
834,
12962,
198
] | 2.728155 | 103 |
# -*- coding: utf-8 -*-
"""
Tests `marc.marc_writer.py` module
"""
from contextlib import nullcontext as does_not_raise
import logging
import os
import pickle
from pymarc import Field, MARCReader, Record
import pytest
from nightshift import __title__, __version__
from nightshift.datastore import Resource
from nightshift.marc.marc_writer import BibEnhancer
| [
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] | 3.175439 | 114 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.13 on 2018-06-02 18:17
from __future__ import unicode_literals
from django.db import migrations, models
| [
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] | 2.754386 | 57 |
#!/usr/bin/env python3
# coding = utf-8
import os
import unittest as ut
from mykit.wien2k.utils import get_casename, find_complex_file, get_default_r0, get_default_rmt, get_z
if __name__ == '__main__':
ut.main() | [
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220,
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] | 2.444444 | 90 |
from unittest import TestCase
from degiro_pit.config import Currency
from degiro_pit.nbp_api import NbpApi
| [
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] | 3.114286 | 35 |
import copy
import json
import logging
import os
import time
from .rename import rename as rename_function
from django.apps import apps
from django.conf import settings
from django_rq import job
from mixmasta import mixmasta as mix
from utils.cache_helper import cache_get
# Load GADM3 from gadm app
if settings.CACHE_GADM:
gadm3 = apps.get_app_config("gadm").gadm3()
gadm2 = apps.get_app_config("gadm").gadm2()
else:
gadm3 = None
gadm2 = None
def dupe(annotations, rename_list, new_names):
"""annotations is a list of dictionaries, if entry["name"] is in rename_list copy over an entry for every name in new_names and rename the entry["name"] to the new name"""
added = []
new_list = []
rename_count = 0
for entry in annotations:
# if entry["name"] in primary_geo_renames: # RTK
if entry["name"] in rename_list:
# if not primary_geo_renamed: # RTK
if rename_count < len(rename_list):
rename_count += 1
for new_name in new_names:
# Don't add again, although duplicates are removed below.
if new_name in added:
continue
e = entry.copy()
e["name"] = new_name
e["display_name"] = new_name
e["type"] = new_name
new_list.append(e)
added.append(e["name"])
else:
if entry["name"] not in added:
new_list.append(entry)
added.append(entry["name"])
return new_list
def build_mapper(uuid):
"""
Description
-----------
Performs two functions:
(1) Build and return the mixmasta mapper.json from annotations.json.
(2) Return geo_select if "Geo_Select_Form" is annotated.
Returns
-------
ret: dictionary
geo, date, and feature keys for mixmasta process.
geo_select: string, default None
admin_level if set during annotation: country, admin1, admin2, admin3
"""
# Set default return value (None) for geo_select.
geo_select = None
fp = f"data/{uuid}/annotations.json"
with open(fp, "r") as f:
annotations = json.load(f)
conversion_names = {
"name": "display_name",
"geo": "geo_type",
"time": "date_type",
"format": "time_format",
"data_type": "feature_type",
"unit_description": "units_description",
"coord_pair_form": "is_geo_pair",
"qualifycolumn": "qualifies",
"string": "str",
}
ret = {"geo": [], "date": [], "feature": []}
for orig_name in annotations:
entry = {}
entry["name"] = orig_name
for x in annotations[orig_name].keys():
if x in ["redir_col"]:
continue
# Set geo_select if annotated.
if str(x).lower() == "geo_select_form":
geo_select = annotations[orig_name][x]
# Mixmasta expects "admin0" not "country".
if geo_select.lower() == "country":
geo_select = "admin0"
if x.lower() in conversion_names.keys():
new_col_name = conversion_names[x.lower()]
else:
new_col_name = x.lower()
if new_col_name != "display_name":
if new_col_name == "qualifies":
if type(annotations[orig_name][x]) == str:
annotations[orig_name][x] = [annotations[orig_name][x]]
if type(annotations[orig_name][x]) == str and new_col_name not in [
"is_geo_pair",
"qualifies",
"dateformat",
"time_format",
"description",
]:
entry[new_col_name] = annotations[orig_name][x].lower()
else:
entry[new_col_name] = annotations[orig_name][x]
else:
entry[new_col_name] = annotations[orig_name][x]
for x in ["dateassociate", "isgeopair", "qualify"]:
if x in entry.keys():
del entry[x]
ret[entry["type"]].append(entry)
for x in range(len(ret["date"])):
if "dateformat" in ret["date"][x]:
ret["date"][x]["time_format"] = ret["date"][x]["dateformat"]
del ret["date"][x]["dateformat"]
if ret["date"][x].get("primary_time", False):
ret["date"][x]["primary_date"] = True
del ret["date"][x]["primary_time"]
return ret, geo_select
@job("default", timeout=-1)
def post_mixmasta_annotation_processing(rename, context):
"""change annotations to reflect mixmasta's output"""
uuid = context["uuid"]
with open(context["mapper_fp"], "r") as f:
mixmasta_ready_annotations = json.load(f)
to_rename = {}
for k, x in rename.items():
for y in x:
to_rename[y] = k
mixmasta_ready_annotations = rename_function(mixmasta_ready_annotations, to_rename)
primary_date_renames = [
x["name"]
for x in mixmasta_ready_annotations["date"]
if x.get("primary_geo", False)
]
primary_geo_renames = [
x["name"]
for x in mixmasta_ready_annotations["geo"]
if x.get("primary_geo", False)
]
primary_geo_rename_count = 0 # RTK
mixmasta_ready_annotations["geo"] = dupe(
mixmasta_ready_annotations["geo"],
primary_geo_renames,
["admin1", "admin2", "admin3", "country", "lat", "lng"],
)
mixmasta_ready_annotations["date"] = dupe(
mixmasta_ready_annotations["date"], primary_date_renames, ["timestamp"]
)
json.dump(
mixmasta_ready_annotations,
open(f"data/{uuid}/mixmasta_ready_annotations.json", "w"),
)
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198,
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] | 2.072336 | 2,834 |
from .sensation import Sensation
from .train import Train
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] | 3.933333 | 15 |
import marisa_trie
import os
import gzip
from collections import defaultdict
from nltk.tokenize import RegexpTokenizer
from cuttsum.srilm import Client
from itertools import izip
import string
from ..geo import GeoQuery
import numpy as np
import pandas as pd
from nltk.corpus import wordnet as wn
import re
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] | 2.911504 | 113 |
import json
import os, errno
from typing import Dict
import numpy as np
import os
from sklearn.preprocessing import scale, minmax_scale
import logging
LOGGER = logging.getLogger(__name__)
def scale_features(input_folder: str, output_folder: str, op_conf: str, **kwargs):
"""
input_folder: folder which contains input audio files
output_folder: folder to store output numpy files
"""
optional_params = eval(op_conf)
LOGGER.info("kwargs ", optional_params)
for genre in list(os.listdir(input_folder)):
if os.path.isdir(f"{input_folder}/{genre}"):
genre_input_folder = f"{input_folder}/{genre}/"
genre_output_folder = f"{output_folder}/{genre}/"
try:
os.makedirs(genre_output_folder)
except OSError as e:
if e.errno != errno.EEXIST:
raise
for file_name in list(os.listdir(genre_input_folder)):
input_file_abs_path = f"{genre_input_folder}/{file_name}"
if os.path.isfile(f"{input_file_abs_path}") and file_name.endswith(
".npy"
):
LOGGER.info(
f"scale_features.task >>> INFO current file: {file_name}"
)
file_name_wo_ex = file_name[:-4]
# load np array
y = np.load(f"{input_file_abs_path}")
y_std_scaled = scale(y)
np.save(
f"{genre_output_folder}/{file_name_wo_ex}_standardcaler.npy",
y_std_scaled,
)
y_mm_scaled = minmax_scale(y)
np.save(
f"{genre_output_folder}/{file_name_wo_ex}_minmaxnormalizer.npy",
y_mm_scaled,
)
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] | 1.819942 | 1,033 |
import time
from functools import reduce
import distogram
import utils
if __name__ == '__main__':
bench_merge()
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# -*- coding: utf-8 -*-
# /!\/!\/!\/!\/!\/!\/!\/!\
# Note that this is just a sample code
# You need to add this file in __init__.py
# /!\/!\/!\/!\/!\/!\/!\/!\
from odoo import exceptions, models
from odoo.http import request
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] | 2.43617 | 94 |
F = fib(10) # 运行到这里没有任何反映
# print(next(F))
for i in F:
print(i)
"""
yield:
1. 保存运行状态-断点, 暂停执行将生成器挂起
2. 将yield后面表达式的值, 作为返回值返回
"""
# 使用yield实现协程
import time
if __name__ == '__main__':
main()
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] | 1.050228 | 219 |
import os
import json
import cv2
import numpy as np
import matplotlib.pyplot as plt
import torch
if __name__ == "__main__":
# json file contains the test images
test_json_path = './test.json'
# the folder to output density map and flow maps
output_folder = './plot'
with open(test_json_path, 'r') as outfile:
img_paths = json.load(outfile)
for i in range(2):
img_path = img_paths[i]
img_folder = os.path.dirname(img_path)
img_name = os.path.basename(img_path)
index = int(img_name.split('.')[0])
prev_index = int(max(1,index-5))
prev_img_path = os.path.join(img_folder,'%03d.jpg'%(prev_index))
c_img = cv2.imread(img_path)
c_img = cv2.resize(c_img, (640, 360))
c_prev_img = cv2.imread(prev_img_path)
c_prev_img = cv2.resize(c_prev_img, (640, 360))
hsv = OptFlow(c_prev_img, c_img)
rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
save_img = np.concatenate([c_prev_img, rgb], 0)
cv2.imwrite('opticalflow/opticalflow_{}.png'.format(i), save_img)
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23330,
27422,
11134,
4458,
18982,
7,
72,
828,
3613,
62,
9600,
8,
198
] | 2.097701 | 522 |
from torch import nn
from fairseq.modules import TransformerEncoderLayer, TransformerDecoderLayer
class Perceptron(nn.Module):
"""
1. 是否激活 通过是否有激活层来控制，最后一层都没有激活层
"""
class LogisticModel(nn.Module):
""" 两层感知机 """
def __init__(self, args, activation=None, dropout=0.1, contain_normalize=False, **unused):
""" 如果Logistic是模型的最后一层，contain_normalize=True; 否则，设置为False"""
super().__init__()
self.layers = nn.Sequential(
Perceptron(args.encoder_embed_dim, int(args.encoder_embed_dim / 2), drouput=dropout,
activation=activation),
Perceptron(int(args.encoder_embed_dim / 2), 1, drouput=dropout, activation=None)
)
self.activation = None
if contain_normalize:
self.activation = nn.Sigmoid()
"""
TODO: AT Decoder
"""
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# -*- coding: utf-8 -*-
""" flatten a 2 dimensional list into a 1 dimension list
by joining column items in a row into one item as single comma-separated string
This is useful for preparing data for a CSV writer function which requires a 1-dimensional list of rows with no columns
Alternatively, the join functional can be moved into the CSV writer so that the function can accept 2 dimensional lists
"""
list = [("A", "B", "C"), ("34", "32647", "43"), ("4556", "35235", "23623")]
str = map(lambda x: ",".join(x), list)
print str
"""
#output
['A,B,C',
'34,32647,43',
'4556,35235,23623']
"""
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from __future__ import print_function, division
from collections import OrderedDict as OD
import itertools
import os
import unittest
from astropy.io import fits # FITS file I/O
from astropy.table import Table # Used in converting to pandas DataFrame
import numpy as np
import pandas as pd
import NebulaBayes
from NebulaBayes import NB_Model, __version__
from NebulaBayes.NB1_Process_grids import RegularGridResampler
from NebulaBayes.NB3_Bayes import NB_nd_pdf
"""
Test suite to test NebulaBayes. Mostly functional and regression tests, with
some unit tests as well.
Works with Python 2 and Python 3.
To run only a particular test, type (e.g.):
python3 test_NB.py Test_real_data_with_dereddening
This test suite can be run in-place in NebulaBayes/tests under the NebulaBayes
installation directory (but use the correct python version for the
installation location).
Adam D. Thomas 2017
"""
clean_up = True # Delete test output files after running?
# Save test outputs in NebulaBayes/tests/test_outputs
THIS_FILE_DIR = os.path.dirname(os.path.realpath(__file__))
TEST_DIR = os.path.join(THIS_FILE_DIR, "test_outputs")
###############################################################################
# Helper functions
def build_grid(param_range_dict, line_peaks_dict, n_gridpts_list, std_frac=0.25):
"""
Initialise a grid - create a pandas DataFrame table. Fluxes for each
emission line form a Gaussian ball around a specified point.
param_range_dict: Ordered dict mapping parameter names to a tuple giving
the parameter minimum and maximum
line_peaks_dict: Ordered dict mapping line names to the location
(as a tuple) of the peak of the line flux in the grid, in
gridpoint index coordinates (from zero)
std_frac: Fraction of the range in each dimension used for the std
n_gridpts_list is a list of the number of gridpoints in each dimension.
"""
param_names = list(param_range_dict.keys())
param_val_arrs = [np.linspace(r[0], r[1], n) for r,n in zip(
param_range_dict.values(), n_gridpts_list)]
line_names = list(line_peaks_dict.keys())
std = np.array([(r[1] - r[0]) * std_frac for r in param_range_dict.values()])
line_peak_vals = {}
for line, peak_inds in line_peaks_dict.items():
line_peak = []
for p, peak_ind, val_arr in zip(param_names, peak_inds, param_val_arrs):
p_min, dp = val_arr[0], np.diff(val_arr)[0]
line_peak.append(p_min + peak_ind*dp)
line_peak_vals[line] = line_peak # An ND list corresponding to peak_inds
flux_fns = {}
for l,peak_tuple in line_peaks_dict.items():
peak = np.array(line_peak_vals[l]) # ND vector
flux_fns[l] = gaussian
# Make DataFrame table:
columns = param_names + line_names
n_gridpts = np.product(n_gridpts_list)
OD_for_DF = OD([(c, np.full(n_gridpts, np.nan)) for c in columns])
DF_grid = pd.DataFrame(OD_for_DF)
# Iterate over rows, filling in the table
for i, p_tuple in enumerate(itertools.product(*param_val_arrs)):
# Add parameter values into their columns:
for p,n in zip(p_tuple, param_names):
DF_grid.loc[i,n] = p
# Add "model" line fluxes into their columns:
for l in line_names:
DF_grid.loc[i,l] = flux_fns[l](np.array(p_tuple))
return DF_grid
def extract_grid_fluxes_i(DF, p_name_ind_map, line_names):
"""
Extract emission line fluxes from a grid (represented as a DataFrame) by
inputting gridpoint indices and taking the fluxes at the nearest gridpoint
"""
val_arrs = {p:np.unique(DF[p].values) for p in p_name_ind_map}
assert len(DF) == np.product([len(v) for v in val_arrs.values()])
where = np.full(len(DF), 1, dtype=bool)
for p,ind in p_name_ind_map.items():
where &= (DF.loc[:,p] == val_arrs[p][ind])
assert np.sum(where) == 1
return [DF[line].values[where][0] for line in line_names]
###############################################################################
# Helper class
class Base_2D_Grid_2_Lines(unittest.TestCase):
"""
Base class holding setup and cleanup methods to make a 2D grid with only 2
emission lines, and using a 2D Gaussian to make the grid. There are only
two lines, but one has fluxes set to all 1 and is just for normalisation.
"""
params = ["p1", "p2"]
param_range_dict = OD( [("p1", (-5, 3)), ("p2", (1.2e6, 15e6))] )
n_gridpts_list = (11, 9) # Number of gridpoints in each dimension
interpd_shape = (50, 45)
lines = ["L1", "L2"] # Line names
line_peaks = [8, 5] # Gridpoint indices from zero
@classmethod
def setUpClass(cls):
""" Make grid and run NebulaBayes to obtain the result object """
line_peaks_dict = OD([(l,cls.line_peaks) for l in cls.lines])
cls.DF = build_grid(cls.param_range_dict, line_peaks_dict, cls.n_gridpts_list)
cls.val_arrs = OD([(p,np.unique(cls.DF[p].values)) for p in cls.params])
cls.DF.loc[:,"L1"] = 1. # We'll normalise by this line
cls.grid_file = os.path.join(TEST_DIR, cls.__name__ + "_grid.csv")
cls.DF.to_csv(cls.grid_file, index=False)
cls.NB_Model_1 = NB_Model(cls.grid_file, cls.params, cls.lines,
interpd_grid_shape=cls.interpd_shape)
@classmethod
def tearDownClass(cls):
""" Remove the output when tests in this class have finished """
if clean_up:
os.remove(cls.grid_file)
if hasattr(cls, "posterior_plot"):
os.remove(cls.posterior_plot)
###############################################################################
class Test_Obs_from_Peak_Gridpoint_2D_Grid_2_Lines(Base_2D_Grid_2_Lines):
"""
Test for a grid from Base_2D_Grid_2_Lines: Take a gridpoint that is at
the peak of the Gaussian ball of emission line fluxes, and check that
treating these fluxes as observations leads to correct estimates from
NebulaBayes.
"""
test_gridpoint = [8, 5] # From zero. [11, 9] total gridpoints in each dim
@classmethod
def test_parameter_estimates(self):
""" Ensure the parameter estimates are as expected """
DF_est = self.Result.Posterior.DF_estimates
self.assertTrue(all(p in DF_est.index for p in self.params))
# Tolerance for distance between gridpoint we chose and the estimate:
grid_sep_frac = 0.1 # Allowed fraction of distance between gridpoints
for p, test_ind in zip(self.params, self.test_gridpoint):
tol = np.diff(self.val_arrs[p])[0] * grid_sep_frac
value = self.val_arrs[p][test_ind] # Expected parameter value
est = DF_est.loc[p, "Estimate"] # NebulaBayes estimate
self.assertTrue(np.isclose(est, value, atol=tol))
def test_raw_Grid_spec(self):
""" Ensure the raw grid spec is as expected """
RGrid_spec = self.NB_Model_1.Raw_grids
self.assertEqual(RGrid_spec.param_names, self.params)
self.assertEqual(RGrid_spec.ndim, len(self.params))
self.assertEqual(RGrid_spec.shape, self.n_gridpts_list)
self.assertEqual(RGrid_spec.n_gridpoints, np.product(self.n_gridpts_list))
for a1, a2 in zip(RGrid_spec.param_values_arrs, self.val_arrs.values()):
self.assertTrue(np.allclose(np.asarray(a1), np.asarray(a2)))
def test_interpolated_Grid_spec(self):
""" Ensure the interpolated grid spec is as expected """
IGrid_spec = self.Result.Grid_spec
self.assertEqual(IGrid_spec.param_names, self.params)
self.assertEqual(IGrid_spec.param_display_names, self.params)
self.assertEqual(IGrid_spec.shape, tuple(self.interpd_shape))
self.assertEqual(IGrid_spec.n_gridpoints, np.product(self.interpd_shape))
@classmethod
def tearDownClass(cls):
""" Remove the output files when tests in this class have finished """
super(Test_Obs_from_Peak_Gridpoint_2D_Grid_2_Lines,cls).tearDownClass()
if clean_up:
files = [os.path.join(TEST_DIR, l +
"_PDF_contributes_to_likelihood.pdf") for l in ["L1", "L2"]]
for file_i in files:
os.remove(file_i)
###############################################################################
class Test_Obs_from_nonPeak_Gridpoint_2D_Grid_2_Lines(Base_2D_Grid_2_Lines):
"""
Test for a grid from Base_2D_Grid_2_Lines: Take a gridpoint that is NOT at
the peak of the Gaussian ball of emission line fluxes.
Note that we don't check the values in the posterior or parameter
estimates - there isn't an obvious way to do this here.
We also test that a numpy array prior is accepted.
"""
longMessage = True # Append messages to existing message
test_gridpoint = [6, 4] # From zero. [11, 9] total gridpoints in each dim,
# the line peak is at line_peaks = [8, 5]
@classmethod
def test_parameters_in_output(self):
""" Check all parameters are found in output """
DF_est = self.Result.Posterior.DF_estimates
self.assertTrue(all(p in DF_est.index for p in self.params))
# Posterior is shaped like a donut. Check for a single local min?
###############################################################################
# Test the NebulaBayes ND linear interpolation
###############################################################################
class Test_1D_grid_and_public_attributes(unittest.TestCase):
"""
Test that a 1D grid works and gives expected results.
We use a gaussian 1D "grid", and input a point at the peak into NB to
ensure NB finds the correct point.
We also test that a DataFrame grid table is accepted.
"""
longMessage = True # Append messages to existing message
@classmethod
def test_parameter_estimate(self):
""" Ensure the single parameter estimate is as expected """
DF_est = self.Result.Posterior.DF_estimates
self.assertTrue("P0" in DF_est.index)
lower = self.p_vals[self.test_gridpoint - 1]
upper = self.p_vals[self.test_gridpoint + 1]
est = DF_est.loc["P0", "Estimate"]
self.assertTrue(lower < est < upper, msg="{0}, {1}, {2}".format(
lower, est, upper))
def test_NB_Model_attributes(self):
""" Check that the list of public attributes is what is documented """
public_attrs = sorted([a for a in dir(self.NB_Model_1)
if not a.startswith("_")])
expected_attrs = ["Interpd_grids", "Raw_grids"]
self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs))
def test_NB_Result_attributes(self):
""" Check that the list of public attributes is what is documented """
public_attrs = sorted([a for a in dir(self.Result)
if not a.startswith("_")])
expected_attrs = [
"DF_obs", "Grid_spec", "Likelihood", "Plot_Config", "Plotter",
"Posterior", "Prior", "deredden", "obs_flux_arrs",
"obs_flux_err_arrs", "propagate_dered_errors"]
self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs))
def test_NB_nd_pdf_attributes(self):
""" Check that the list of public attributes is what is documented """
public_attrs = sorted([a for a in dir(self.Result.Posterior)
if not a.startswith("_")])
expected_attrs = sorted(["DF_estimates", "Grid_spec", "best_model",
"marginalised_1D", "marginalised_2D", "name", "nd_pdf", "show"])
self.assertTrue(public_attrs == expected_attrs, msg=str(public_attrs))
def test_best_model_dict_keys(self):
""" Check that the list of best model keys is what is documented """
expected_keys = sorted(["table", "chi2", "extinction_Av_mag",
"grid_location"])
key_list = sorted(list(self.Result.Posterior.best_model.keys()))
self.assertEqual(key_list, expected_keys)
@classmethod
def tearDownClass(cls):
""" Remove the output when tests in this class have finished """
if clean_up:
if hasattr(cls, "posterior_plot"):
os.remove(cls.posterior_plot)
if hasattr(cls, "best_model_table"):
os.remove(cls.best_model_table)
###############################################################################
class Test_default_initialisation(unittest.TestCase):
"""
Test that we can initialise fully default HII and NLR NB models
"""
###############################################################################
class Test_real_data_with_dereddening(unittest.TestCase):
"""
Test some real data, from the S7 nuclear spectrum for NGC4691, a star-
forming galaxy. Include a line ratio prior and dereddening in NebulaBayes.
Test saving plots for all 3 Bayes Theorem PDFs.
"""
longMessage = True # Append messages to existing message
lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007",
"NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"]
obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942,
0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482]
obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012,
0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099]
obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8,
5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8]
@classmethod
def test_parameter_estimates(self):
"""
Regression check on parameter estimates.
"""
ests = self.Result.Posterior.DF_estimates["Estimate"] # pandas Series
self.assertTrue(np.isclose(ests["12 + log O/H"], 8.73615, atol=0.0001),
msg=str(ests["12 + log O/H"]))
self.assertTrue(np.isclose(ests["log P/k"], 6.82636, atol=0.0001),
msg=str(ests["log P/k"]))
self.assertTrue(np.isclose(ests["log U"], -2.84848, atol=0.0001),
msg=str(ests["log U"]))
def test_estimate_bounds_checks(self):
"""
Ensure that the "checking columns" in the estimate table are all
showing that the estimates are good.
"""
DF = self.Result.Posterior.DF_estimates # Parameter estimate table
for p in ["12 + log O/H", "log P/k", "log U"]:
for col in ["Est_in_CI68?", "Est_in_CI95?"]:
self.assertTrue(DF.loc[p,col] == "Y")
for col in ["Est_at_lower?", "Est_at_upper?", "P(lower)>50%?",
"P(upper)>50%?"]:
self.assertTrue(DF.loc[p,col] == "N")
self.assertTrue(DF.loc[p,"n_local_maxima"] == 1)
def test_chi2(self):
"""
Regression check that chi2 doesn't change
"""
chi2 = self.Result.Posterior.best_model["chi2"]
self.assertTrue(np.isclose(chi2, 2568.7, atol=0.2), msg=str(chi2))
def test_interp_order(self):
"""
Ensure the correct interpolation order (linear) is preserved
"""
self.assertTrue(self.NB_Model_1.Interpd_grids.interp_order == 1)
def test_all_zero_prior(self):
"""
We permit an all-zero prior - check that it works (a warning should
be printed).
"""
shape = self.NB_Model_1.Interpd_grids.shape
self.Result1 = self.NB_Model_1(self.obs_fluxes, self.obs_errs,
self.lines, prior=np.zeros(shape))
@classmethod
def tearDownClass(cls):
""" Remove the output files when tests in this class have finished """
if clean_up:
files = [cls.prior_plot, cls.likelihood_plot, cls.posterior_plot,
cls.estimate_table]
for file_i in files:
os.remove(file_i)
###############################################################################
class Test_real_data_with_cubic_interpolation(unittest.TestCase):
"""
Very similar to the previous test class, but we use cubic interpolation
instead of linear interpolation when interpolating model flux grids.
We also test resetting the logging level after using the "verbosity" kwarg.
"""
longMessage = True # Append messages to existing message
lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007",
"NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"]
obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942,
0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482]
obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012,
0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099]
obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8,
5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8]
@classmethod
def test_parameter_estimates(self):
"""
Regression check on parameter estimates. Estimates for P and U are
slightly different with the cubic interpolation.
"""
ests = self.Result.Posterior.DF_estimates["Estimate"] # pandas Series
self.assertTrue(np.isclose(ests["12 + log O/H"], 8.73615, atol=0.0001),
msg=str(ests["12 + log O/H"]))
self.assertTrue(np.isclose(ests["log P/k"], 6.86047, atol=0.0001),
msg=str(ests["log P/k"]))
self.assertTrue(np.isclose(ests["log U"], -2.82828, atol=0.0001),
msg=str(ests["log U"]))
def test_chi2(self):
"""
Regression check that chi2 doesn't change
"""
chi2 = self.Result.Posterior.best_model["chi2"]
self.assertTrue(np.isclose(chi2, 2522.7, atol=0.2), msg=str(chi2))
def test_interp_order(self):
"""
Ensure the correct interpolation order (cubic) is preserved
"""
self.assertTrue(self.NB_Model_1.Interpd_grids.interp_order == 3)
def test_resetting_log_level(self):
"""
Ensure that after using the verbosity keyword, the NB_logger
level is unchanged (i.e. was reset to its previous value)
"""
self.assertEqual(NebulaBayes.NB_logger.level, self.test_log_level)
def test_dereddening_result_attributes(self):
"""Ensure dereddening attributes added to Result object."""
self.assertTrue(self.Result.deredden)
self.assertTrue(self.Result.propagate_dered_errors)
@classmethod
def tearDownClass(cls):
""" Remove output files when tests in this class have finished,
and undo change to logging level. """
NebulaBayes.NB_logger.setLevel(cls.old_log_level)
if clean_up:
files = [cls.prior_plot, cls.likelihood_plot, cls.posterior_plot,
cls.estimate_table]
for file_i in files:
os.remove(file_i)
###############################################################################
class Test_upper_bounds_1D(unittest.TestCase):
"""
Test the treatment of upper bounds. We use a 1D grid.
"""
longMessage = True # Append messages to existing message
lines = ["line1", "line2", "line3", "line4", "line5", "line6"]
obs_fluxes = [ 1.0, 8.0, 10.2, -np.inf, -np.inf, -np.inf]
obs_errs = [ 0.05, 0.3, 3.1, 0.3, 0.4, 0.2]
pred_fluxes = [ 1.0, 5.0, 10.2, 0.1, 0.4, 0.4]
# The pred_fluxes are at the "peak" of the grid, that we'll input to NB.
@classmethod
def test_parameter_estimates(self):
"""
Regression test - check the parameter estimate is as expected.
"""
DF_est = self.Result.Posterior.DF_estimates # DataFrame
p0_est = DF_est.loc["p0", "Estimate"]
self.assertTrue(np.isclose(p0_est, self.expected_p0, atol=1))
@classmethod
def tearDownClass(cls):
""" Remove the output files when tests in this class have finished """
if clean_up:
files = [os.path.join(TEST_DIR, l +
"_PDF_contributes_to_likelihood.pdf") for l in cls.lines]
for file_i in files:
os.remove(file_i)
###############################################################################
class Test_all_zero_likelihood(unittest.TestCase):
"""
Test forcing a log_likelihood of all -inf, so the likelihood is all zero.
"""
longMessage = True # Append messages to existing message
lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"]
obs_fluxes = [ 1e250, 1, 1.2e250, 1.2e250, 1e250]
obs_errs = [ 0.004, 1, 0.005, 0.003, 0.002]
@classmethod
def test_likelihood_all_zero(self):
"""
Regression test - check likelihood is all zero.
"""
likelihood = self.Result.Likelihood.nd_pdf
self.assertTrue(np.all(likelihood == 0))
def test_posterior_all_zero(self):
"""
Regression test - check posterior is all zero.
"""
posterior = self.Result.Posterior.nd_pdf
self.assertTrue(np.all(posterior == 0))
###############################################################################
class Test_data_that_matches_models_poorly(unittest.TestCase):
"""
Test inputting fluxes and errors that are very poorly fit by the entire
model grid. In this case most of the likelihood is zero, and using a
reasonable-ish prior gives a posterior that is zero everywhere.
"""
longMessage = True # Append messages to existing message
lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"]
obs_fluxes = [ 3.1, 1, 1.8, 5.1, 1.2]
obs_errs = [ 0.01, 1, 0.01, 0.01, 0.01]
# Note the very small errors
@classmethod
def test_likelihood_mostly_zero(self):
"""
Regression test - check likelihood is mostly zero.
"""
likelihood = self.Result.Likelihood.nd_pdf
self.assertTrue(np.sum(likelihood != 0) < 65)
def test_posterior_all_zero(self):
"""
Regression test - check posterior is all zero.
"""
posterior = self.Result.Posterior.nd_pdf
self.assertTrue(np.all(posterior == 0))
###############################################################################
class Test_NB_nd_pdf(unittest.TestCase):
"""
Test the methods in the NB_nd_pdf class
"""
@classmethod
# We want to test NB_nd_pdf attributes; some of "DF_estimates", "Grid_spec",
# "best_model", marginalised_1D", "marginalised_2D", "name", "nd_pdf"
# "best_model" keys: "table", "chi2", "extinction_Av_mag", "grid_location"
def test_best_model_table(self):
""" Check that a single table value matches the desired gridpoint.
This test would fail on NebulaBayes 0.9.7 and earlier """
best_coords = (self.peak_ind_y, self.peak_ind_x)
normed_grids = self.NB_Model_1.Interpd_grids.grids["Hbeta_norm"]
model_OIII = normed_grids["OIII5007"][best_coords]
DF_best = self.NB_nd_pdf_1.best_model["table"]
table_model_OIII = DF_best.loc["OIII5007", "Model"]
self.assertEqual(table_model_OIII, model_OIII)
def test_marginalised_1D_pdf(self):
""" Check that the marginalised 1D pdfs are as expected """
m_1D = self.NB_nd_pdf_1.marginalised_1D
self.assertEqual(len(m_1D), 2)
# Scale the pdfs to compare despite the m_1D PDFs being normalised
m_1D["log U"] /= m_1D["log U"].max()
m_1D["12 + log O/H"] /= m_1D["12 + log O/H"].max()
expected_x_pdf = self.marginalised_x / self.marginalised_x.max()
expected_y_pdf = self.marginalised_y / self.marginalised_y.max()
self.assertTrue(np.allclose(m_1D["log U"], expected_x_pdf,
atol=1e-12, rtol=0))
self.assertTrue(np.allclose(m_1D["12 + log O/H"], expected_y_pdf,
atol=1e-12, rtol=0))
# May have swapped x and y, but it's all symmetric anyway...
def test_nd_pdf(self):
"""
Check that the normalised nd_pdf matches the input raw nd_pdf. We
avoid doing a proper normalisation by comparing with a simple scaling.
"""
pdf = self.NB_nd_pdf_1.nd_pdf
scaled_raw_nd_pdf = self.raw_pdf / self.raw_pdf.max()
self.assertTrue(np.array_equal(pdf / pdf.max(), scaled_raw_nd_pdf))
###############################################################################
class Test_dereddening_changes_results(unittest.TestCase):
"""
Test that using dereddening changes all three PDFs (when obs data are used
in the prior). There previously was a bug where the obs data in the line
ratio priors weren't dereddened.
Also test that PDFs change when errors from the Balmer decrement are
propagated into the dereddened line fluxes.
"""
@classmethod
def test_priors_differ(self):
""" Check that dereddened data was used in line ratio prior, when
requested. This test fails on NebulaBayes 0.9.7 """
pdf_dered1 = self.Result_dered1.Prior.nd_pdf
pdf_nodered = self.Result_nodered.Prior.nd_pdf
max_diff1 = np.max(np.abs(pdf_dered1 - pdf_nodered))
self.assertTrue(max_diff1 > 0.01, str(max_diff1))
# Test uncertainty propagation has an effect
pdf_dered2 = self.Result_dered2.Prior.nd_pdf
max_diff_u = np.max(np.abs(pdf_dered1 - pdf_dered2))
self.assertTrue(max_diff_u > 0.01, str(max_diff_u))
def test_propagate_dered_errors(self):
"""Check propagate_dered_errors values on Result object"""
# Checks default value of False
self.assertFalse(self.Result_dered1.propagate_dered_errors)
self.assertTrue(self.Result_dered2.propagate_dered_errors)
###############################################################################
class Test_likelihood_lines_keyword(unittest.TestCase):
"""
Test inputting fluxes and errors that aren't used in the likelihood, and
test that these lines may be used in a prior.
"""
longMessage = True # Append messages to existing message
lines = ["Halpha", "Hbeta", "OIII4363", "OIII5007", "NII6583"]
obs_fluxes = [ 3.1, 1, 1.8, 5.1, 1.2]
obs_errs = [ 0.01, 1, 0.01, 0.01, 0.01]
exclude_lines = ["Halpha", "OIII5007"]
@classmethod
def test_non_likelihood_lines_in_best_model_table(self):
"""
Regression test - lines not included in likelihood calculation should
still appear in the "best model" table.
"""
self.assertTrue(all(l in self.DF_best.index for l in self.exclude_lines))
def test_best_model_table_fields(self):
"""
Regression test - check fields of best model table (we test for the
case of no dereddening; field names are different with dereddening).
"""
correct_fields = ["In_lhood?", "Obs", "Model", "Resid_Stds", "Obs_S/N"]
t_fields = self.DF_best.columns.tolist()
self.assertTrue(t_fields == correct_fields, t_fields)
def test_In_lhood_field_in_best_model_table(self):
"""
Regression test - the "In_lhood?" field in the best model table should
correctly identify if a line was used in the likelihood.
"""
correct = [("N" if l in self.exclude_lines else "Y") for l in self.lines]
self.assertTrue(self.DF_best["In_lhood?"].values.tolist() == correct)
def test_permuting_input_line_order(self):
"""
Regression test - the order of the input lines should not affect the
results. There was a real bug introduced with the "likelihood_lines"
feature - this test fails on NB version 0.9.6 and 0.9.7!
"""
n = len(self.lines)
for i, ind_tuple in enumerate(itertools.permutations(range(n))):
# There are 5! = 120 permutations, so only check one in five:
if i % 5 != 2:
continue
obs_fluxes = [self.obs_fluxes[j] for j in ind_tuple]
obs_errs = [self.obs_errs[j] for j in ind_tuple]
lines = [self.lines[j] for j in ind_tuple]
Result_i = self.NB_Model_1(obs_fluxes, obs_errs, lines,
likelihood_lines=self.likelihood_lines, **self.kwargs)
P_i = Result_i.Posterior
estimate_Z_i = P_i.DF_estimates.loc["12 + log O/H", "Estimate"]
self.assertEqual(estimate_Z_i, self.estimate_Z)
###############################################################################
class Test_raising_errors(unittest.TestCase):
"""
Test raising errors on bad inputs
"""
@classmethod
def test_bad_grid_parameter_with_too_few_unique_values(self):
"""
Test correct error is raised if there are too few unique values for
a grid parameter.
"""
DF = pd.DataFrame({"p1": [4, 4, 4, 4], "p2": [1, 2, 3, 4],
"l2": [5, 6, 7, 8]})
self.assertRaisesRE(ValueError, "3 unique values are required",
NB_Model, DF, ["p1", "p2"])
###############################################################################
def interactive_plot_tests():
"""
This function needs to be called manually to test the interactive plotting.
from test_NB import interactive_plot_tests
interactive_plot_tests()
"""
lines = ["OII3726_29", "Hgamma", "OIII4363", "Hbeta", "OIII5007",
"NI5200", "OI6300", "Halpha", "NII6583", "SII6716", "SII6731"]
obs_fluxes = [1.22496, 0.3991, 0.00298, 1.0, 0.44942,
0.00766, 0.02923, 4.25103, 1.65312, 0.45598, 0.41482]
obs_errs = [0.00303, 0.00142, 0.00078, 0.0017, 0.0012,
0.00059, 0.00052, 0.00268, 0.00173, 0.00102, 0.00099]
obs_wavelengths = [3727.3, 4340.5, 4363.2, 4861.3, 5006.8,
5200.3, 6300.3, 6562.8, 6583.2, 6716.4, 6730.8]
NB_Model_1 = NB_Model("HII", grid_params=None, line_list=lines,
interpd_grid_shape=[50, 70, 50], grid_error=0.35)
kwargs = {"deredden": True, "propagate_dered_errors": True,
"obs_wavelengths": obs_wavelengths,
"prior":[("SII6716","SII6731")],
"plot_configs": [{"table_on_plot": True,
"legend_fontsize": 5}]*4,
}
Result = NB_Model_1(obs_fluxes, obs_errs, lines, **kwargs)
# Test both ways to make an interactive plot
Result.Plotter.interactive(Result.Posterior)
Result.Prior.show(Result.Plotter)
###############################################################################
# Ideas for more tests:
# Check that parameter estimates are inside the CIs, and check the flags for this
# Test normalising to different lines repeatedly, and checking that the
# unnecessary interpolated grids are deleted.
# Check coverage of the code, to see what isn't being run?
if __name__ == "__main__":
print("\nTesting NebulaBayes version {0} ...\n".format(__version__))
unittest.main(verbosity=2)
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"""
spider.distance.metricl.rfd sub-package
__init.py__
@author: david johnson
Primitive that learns and applies random-forest-based distance metric.
defines the module index
"""
from .rfd import RFD
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import pygame, sys
from pygame.locals import *
# Aclaraciones
# Requiere "pygame" para las graficas
#
# Se grafican las figuras para una mejor comprencion pero como son coordenadas tan pequenas no se muestran bien
# (aumentando las proporciones pude verse mejo)
# pero la orden del problema no lo permite.
#
# La solucion trate de buscarla matematicamente
# 1- para saber si es ciudadano o prisionero :
# comprobamos si el punto esta fuera o dentro de un poligono o en uno de sus vertices
#
# definiendo colores
NEGRO = (0, 0, 0)
ROJO = (255, 0, 0)
CAFE = (90, 50, 15)
BLANCO = (255, 255, 255)
AZUL = (0, 0, 255)
# Abriendo Fichero
infile = open('texto.txt', 'r')
for line in infile:
lista = line
pygame.init()
# Asignando dimenciones a la ventana
dimensiones = (500, 500)
pantalla = pygame.display.set_mode(dimensiones)
# asignando nombre de la ventana
pantalla.fill(BLANCO) # rellenando ventana
terminar = False
reloj = pygame.time.Clock()
while not terminar:
for Evento in pygame.event.get():
if Evento.type == pygame.QUIT:
terminar = True
# limpiando lista y declarando variables
lista = lista.replace(" ", ",").replace("|", ",")
lista_limpa = lista.split(",")
lista_x = []
lista_y = []
longitud = len(lista_limpa)
poligono = []
#separando las cordenadas X,Y y conformando el Poligono
i = 0
while i < longitud - 2:
cordenada_x = int(lista_limpa[i])
if (cordenada_x >= 0 and cordenada_x <= 10):
temp_x = int(lista_limpa[i]) # aca se puede aumentar las proporciones
lista_x.append(temp_x)
j = i + 1
cordenada_y = int(lista_limpa[j])
if (cordenada_y >= 0 and cordenada_y <= 10):
temp_y = int(lista_limpa[j]) # aca se puede aumentar las proporciones
lista_y.append(temp_y)
poligono.append((temp_x, temp_y))
i = i + 2
# Preparando las cordenadas para dibujar P (puntos de rectas ) D para las diagonales
# o rectas de cierre de la figura
i = 0
while i < len(lista_x):
px = int(lista_x[i])
py = int(lista_y[i])
pxx = int(lista_x[i + 1])
pyy = int(lista_y[i + 1])
if i == 0:
dx = int(lista_x[i])
dy = int(lista_y[i])
dxx = int(lista_x[i + 3])
dyy = int(lista_y[i + 3])
if i == 2:
dx = int(lista_x[i - 1])
dy = int(lista_y[i - 1])
dxx = int(lista_x[i])
dyy = int(lista_y[i])
#dibujando la figura
pygame.draw.line(pantalla, ROJO, [px, py], [pxx, pyy], 2)
pygame.draw.aaline(pantalla, ROJO, [dx, dy], [dxx, dyy], True)
i = i + 2
#campturando los puntos del usuario
punto_x = int(lista_limpa[len(lista_limpa) - 2])
punto_y = int(lista_limpa[len(lista_limpa) - 1])
#aplicando restricciones
if punto_y >= 3 and punto_y <= 12 and punto_x >= 3 and punto_x <= 12:
punto_x = punto_x
punto_y = punto_y
pygame.draw.circle(pantalla, CAFE, [punto_x, punto_y], 1) #dibujando el .
#metodo para definir si el punto esta dentro o fuera del poligono
if punto_en_poligono(punto_x, punto_y, poligono) == 2:
pygame.display.set_caption("Prisionero Estas en unode los vertice")
elif punto_en_poligono(punto_x, punto_y, poligono) == 1:
pygame.display.set_caption("Prisionero")
else:
pygame.display.set_caption("Ciudadano")
pygame.display.flip()
reloj.tick(20)
# Cerramos el fichero.
infile.close()
pygame.quit()
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] | 1.823529 | 2,210 |
#!/usr/bin/env python
import os
import sys
import json
from argparse import ArgumentParser
from mglib import obj_from_url, tab_to_matrix, AUTH_LIST, API_URL, biom_to_matrix, VERSION
prehelp = """
NAME
mg-compare-heatmap
VERSION
%s
SYNOPSIS
mg-compare-heatmap [ --help, --input <input file or stdin>, --output <output file or stdout>, --format <cv: 'text' or 'biom'>, --cluster <cv: ward, single, complete, mcquitty, median, centroid>, --distance <cv: bray-curtis, euclidean, maximum, manhattan, canberra, minkowski, difference>, --name <boolean>, --normalize <boolean> ]
DESCRIPTION
Retrieve Dendogram Heatmap from abundance profiles for multiple metagenomes.
"""
posthelp = """
Input
Tab-delimited table of abundance profiles, metagenomes in columns and annotation in rows.
OR
BIOM format of abundance profiles.
Output
JSON struct containing ordered distances for metagenomes and annotations, along with dendogram data.
EXAMPLES
mg-compare-taxa --ids "mgm4441679.3,mgm4441680.3,mgm4441681.3,mgm4441682.3" --level class --source RefSeq --format text | mg-compare-heatmap --input - --format text --cluster median --distance manhattan
SEE ALSO
-
AUTHORS
%s
"""
if __name__ == "__main__":
sys.exit(main(sys.argv))
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import settings
import cv2
from VideoTypes import imageframe, standardredditformat
import generatemovie
import generatorclient
import datetime
import os
import shutil
import videouploader
import random
import pickle
from time import sleep
videoscripts = []
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from tkinter import *
from main_window import MainWindow
if __name__ == "__main__":
root = Tk()
root.columnconfigure(0, weight=1)
root.columnconfigure(2, weight=1)
root.rowconfigure(0, weight=1)
m = MainWindow(root)
root.mainloop()
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# -*- coding: utf-8 -*-
"""Read and write parameters, results and metadata to the 'sim_db' database."""
# Copyright (C) 2017-2019 Håkon Austlid Taskén <[email protected]>
# Licenced under the MIT License.
import sim_db.src_command_line_tool.commands.helpers as helpers
import sqlite3
import argparse
import subprocess
import time
import hashlib
import threading
import os
import sys
class SimDB:
"""To interact with the **sim_db** database.
For an actuall simulation it should be initialised at the very start of the
simulation (with 'store_metadata' set to True) and closed with
:func:`~SimDB.close` at the very end of the simulation. This must be done
to add the corrrect metadata.
For multithreading/multiprocessing each thread/process MUST have its
own connection (instance of this class) and MUST provide it with its rank.
"""
def __init__(self, store_metadata=True, db_id=None, rank=None,
only_write_on_rank=0):
"""Connect to the **sim_db** database.
:param store_metadata: If False, no metadata is added to the database.
Typically used when postprocessing (visualizing) data from a
simulation.
:type store_metadata: bool
:param db_id: ID number of the simulation parameters in the **sim_db**
database. If it is 'None', then it is read from the argument passed
to the program after option '--id'.
:type db_id: int
:param rank: Number identifing the calling process and/or thread.
(Typically the MPI or OpenMP rank.) If provided, only the 'rank'
matching 'only_write_on_rank' will write to the database to avoid
too much concurrent writing to the database. Single process and
threaded programs may ignore this, while
multithreading/multiprocessing programs need to provide it.
:type rank: int
:param only_write_on_rank: Number identifing the only process/thread
that will write to the database. Only used if 'rank' is provided.
:type only_write_on_rank: int
"""
self.rank = rank
self.only_write_on_rank = only_write_on_rank
self.start_time = time.time()
self.store_metadata = store_metadata
self.id, self.path_proj_root = self.__read_from_command_line_arguments(
db_id)
self.db = helpers.connect_sim_db()
self.db_cursor = self.db.cursor()
self.column_names = []
self.column_types = []
if (self.store_metadata
and (self.rank == None or self.rank == self.only_write_on_rank)):
self.write('status', 'running')
self.write('time_started', self.__get_date_and_time_as_string())
if (self.store_metadata and self.__is_a_git_project()
and (self.rank == None or self.rank == self.only_write_on_rank)):
proc = subprocess.Popen(
[
'cd "{0}"; git rev-parse HEAD'.format(
self.path_proj_root)
],
stdout=subprocess.PIPE,
stderr=open(os.devnull, 'w'),
shell=True)
(out, err) = proc.communicate()
self.write(
column="git_hash",
value=out.decode('ascii', 'replace'))
proc = subprocess.Popen(
[
'cd "{0}"; git log -n 1 --format=%B HEAD'.format(
self.path_proj_root)
],
stdout=subprocess.PIPE,
stderr=open(os.devnull, 'w'),
shell=True)
(out, err) = proc.communicate()
self.write(
column="commit_message",
value=out.decode('ascii', 'replace'))
proc = subprocess.Popen(
[
'cd "{0}"; git diff HEAD --stat'.format(
self.path_proj_root)
],
stdout=subprocess.PIPE,
stderr=open(os.devnull, 'w'),
shell=True)
(out, err) = proc.communicate()
self.write(
column="git_diff_stat",
value=out.decode('ascii', 'replace'))
proc = subprocess.Popen(
['cd "{0}"; git diff HEAD'.format(self.path_proj_root)],
stdout=subprocess.PIPE,
stderr=open(os.devnull, 'w'),
shell=True)
(out, err) = proc.communicate()
out = out.decode('ascii', 'replace')
if len(out) > 3000:
warning = "WARNING: Diff limited to first 3000 characters.\n"
out = warning + '\n' + out[0:3000] + '\n\n' + warning
self.write(column="git_diff", value=out)
def read(self, column, check_type_is=''):
"""Read parameter in 'column' from the database.
Return None if parameter is empty.
:param column: Name of the column the parameter is read from.
:type column: str
:param check_type_is: Throws ValueError if type does not match
'check_type_is'.The valid types the strings 'int', 'float', 'bool',
'string' and 'int/float/bool/string array' or the types int, float,
bool, str and list.
:raises ColumnError: If column do not exists.
:raises ValueError: If return type does not match 'check_type_is'.
:raises sqlite3.OperationalError: Waited more than 5 seconds to read
from the database, because other threads/processes are busy writing
to it. Way too much concurrent writing is done and it indicates an
design error in the user program.
"""
if column not in self.column_names:
self.column_names, self.column_types = (
helpers.get_db_column_names_and_types(self.db_cursor))
if column not in self.column_names:
raise ColumnError("Column, {0}, is NOT a column in the "
"database.".format(column))
self.db_cursor.execute("SELECT {0} FROM runs WHERE id={1}".format(
column, self.id))
value = self.db_cursor.fetchone()
if value != None:
value = value[0]
value = self.__check_type(check_type_is, column, self.column_names,
self.column_types, value)
return value
def write(self, column, value, type_of_value='', only_if_empty=False):
"""Write value to 'column' in the database.
If 'column' does not exists, a new is added.
If value is None and type_of_value is not set, the entry under 'column'
is set to empty.
For multithreaded and multiprocess programs only a single will
process/thread write to the database to avoid too much concurrent
writing to the database. This is as long as the 'rank' was passed to
SimDB under initialisation.
:param column: Name of the column the parameter is read from.
:type column: str
:param value: New value of the specified entry in the database.
:param type_of_value: Needed if column does note exists or if
value is empty list. The valid types the strings 'int', 'float',
'bool', 'string' and 'int/float/bool/string array' or the types int,
float, bool and str.
:type type_of_value: str or type
:param only_if_empty: If True, it will only write to the database if the
simulation's entry under 'column' is empty.
:type only_if_empty: bool
:raises ValueError: If column exists, but type does not match, or
empty list is passed without type_of_value given.
"""
# For multithreaded/multiprocess programs only a single process/thread
# does any writing.
if self.rank != None and self.rank != self.only_write_on_rank:
return
self.__add_column_if_not_exists_and_check_type(column, type_of_value,
value)
value_string = self.__convert_to_value_string(value, type_of_value)
value_string = self.__escape_quote_with_two_quotes(value_string)
type_dict = dict(zip(self.column_names, self.column_types))
# 'and type(value != None) != bool' allow numpy arrays to be check
# without importing numpy and thereby relying on it being availble.
if (type_dict[column] == 'TEXT'
and (type(value != None) != bool or value != None)):
value_string = "'{0}'".format(value_string)
if only_if_empty and self.is_empty(column):
self.db_cursor.execute("UPDATE runs SET \"{0}\" = {1} WHERE \"id\" "
"= {2} AND {0} IS NULL".format(column, value_string, self.id))
self.db.commit()
else:
self.db_cursor.execute(
"UPDATE runs SET \"{0}\" = {1} WHERE id = {2}".format(
column, value_string, self.id))
self.db.commit()
def unique_results_dir(self, path_directory):
"""Get path to subdirectory in 'path_directory' unique to simulation.
The subdirectory will be named 'date_time_name_id' and is intended to
store results in. If 'results_dir' in the database is empty, a new and
unique directory is created and the path stored in 'results_dir'.
Otherwise the path in 'results_dir' is just returned.
:param path_directory: Path to directory of which to make a
subdirectory. If 'path_directory' starts with 'root/', that part
will be replaced by the full path of the root directory of the
project.
:type path_directory: str
:returns: Full path to new subdirectory.
:rtype: str
"""
results_dir = self.read("results_dir")
if results_dir == None:
if self.rank == None or self.rank == self.only_write_on_rank:
if (len(path_directory) >= 5
and path_directory[0:5] == 'root/'):
path_directory = os.path.join(self.path_proj_root,
path_directory[5:])
results_dir = os.path.join(path_directory,
self.__get_date_and_time_as_string())
results_dir += '_' + str(self.read('name')) + '_' + str(self.id)
results_dir = os.path.abspath(os.path.realpath(results_dir))
os.mkdir(results_dir)
self.write(column="results_dir", value=results_dir,
only_if_empty=False)
else:
while results_dir == None:
results_dir = self.read("results_dir")
return results_dir
def column_exists(self, column):
"""Return True if column is a column in the database.
:raises sqlite3.OperationalError: Waited more than 5 seconds to read
from the database, because other threads/processes are busy writing
to it. Way too much concurrent writing is done and it indicates an
design error in the user program.
"""
if column in self.column_names:
return True
else:
self.column_names, self.column_types = (
helpers.get_db_column_names_and_types(self.db_cursor))
if column in self.column_names:
return True
else:
return False
def is_empty(self, column):
"""Return True if entry in the database under 'column' is empty.
:raises sqlite3.OperationalError: Waited more than 5 seconds to read
from the database, because other threads/processes are busy writing
to it. Way too much concurrent writing is done and it indicates an
design error in the user program.
"""
value = self.read(column)
if value == None:
return True
else:
return False
def set_empty(self, column):
"""Set entry under 'column' in the database to empty."""
self.write(column, None)
def get_id(self):
"""Return 'ID' of the connected simulation."""
return self.id
def get_path_proj_root(self):
"""Return the path to the root directory of the project.
The project's root directory is assumed to be where the '.sim_db/'
directory is located.
"""
return self.path_proj_root
def update_sha1_executables(self, paths_executables):
"""Update the 'sha1_executable' column in the database.
Sets the entry to the sha1 of all the executables. The order will
affect the value.
:param paths_executables: List of full paths to executables.
:type paths_executables: [str]
:raises sqlite3.OperationalError: Waited more than 5 seconds to write
to the database, because other threads/processes are busy writing
to it. Way too much concurrent writing is done and it indicates an
design error in the user program.
"""
sha1 = hashlib.sha1()
for executable in executables:
with open(executable, 'r') as executable_file:
sha1.update(executable_file.read())
self.write('sha1_executables', sha1)
def delete_from_database(self):
"""Delete simulation from database.
:raises sqlite3.OperationalError: Waited more than 5 seconds to write
to the database, because other threads/processes are busy writing
to it. Way too much concurrent writing is done and it indicates an
design error in the user program.
"""
self.db_cursor.execute("DELETE FROM runs WHERE id = {0}".format(
self.id))
self.db.commit()
self.store_metadata = False
def close(self):
"""Closes connection to **sim_db** database and add metadata."""
if (self.store_metadata
and (self.rank == None or self.rank == self.only_write_on_rank)):
used_time = time.time() - self.start_time
used_walltime = "{0}h {1}m {2}s".format(
int(used_time / 3600), int(used_time / 60), used_time % 60)
self.write('used_walltime', used_walltime)
self.write('status', 'finished')
self.db_cursor.close()
self.db.close()
def __get_date_and_time_as_string(self):
"""Return data and time as 'Year-Month-Date_Hours-Minutes-Seconds'."""
return time.strftime("%Y-%b-%d_%H-%M-%S")
def add_empty_sim(store_metadata=False):
"""Add an empty entry into the database and SimDB connected to it.
:param store_metadata: If False, no metadata is added to the database.
Typically used when postprocessing (visualizing) data from a simulation.
:type store_metadata: bool
"""
db = helpers.connect_sim_db()
db_cursor = db.cursor()
default_db_columns = ""
for key in helpers.default_db_columns:
default_db_columns += key + " " + str(
helpers.default_db_columns[key]) + ", "
default_db_columns = default_db_columns[:-2]
db_cursor.execute("CREATE TABLE IF NOT EXISTS runs ({0});".format(
default_db_columns))
db_cursor.execute("INSERT INTO runs DEFAULT VALUES")
db_id = db_cursor.lastrowid
db.commit()
db_cursor.close()
db.close()
return SimDB(db_id=db_id, store_metadata=store_metadata)
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526,
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2,
15069,
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34,
8,
2177,
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367,
29090,
74,
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2517,
75,
312,
15941,
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1279,
43573,
261,
13,
35943,
268,
31,
14816,
13,
785,
29,
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2,
10483,
5864,
739,
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13789,
13,
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11748,
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37227,
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262,
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13,
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220,
220,
220,
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220,
220,
220,
27095,
973,
618,
1281,
36948,
357,
41464,
2890,
8,
1366,
422,
257,
198,
220,
220,
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220,
220,
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220,
220,
220,
220,
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18640,
13,
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1058,
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25,
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220,
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220,
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220,
1058,
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20613,
62,
312,
25,
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286,
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18640,
10007,
287,
262,
12429,
14323,
62,
9945,
1174,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6831,
13,
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318,
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3256,
788,
340,
318,
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422,
262,
4578,
3804,
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220,
220,
220,
220,
220,
220,
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284,
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220,
220,
220,
220,
220,
1058,
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20613,
62,
312,
25,
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220,
220,
220,
220,
220,
220,
220,
1058,
17143,
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25,
7913,
1852,
361,
278,
262,
4585,
1429,
290,
14,
273,
4704,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
49321,
262,
4904,
40,
393,
4946,
7378,
4279,
2014,
1002,
2810,
11,
691,
262,
705,
43027,
6,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12336,
705,
8807,
62,
13564,
62,
261,
62,
43027,
6,
481,
3551,
284,
262,
6831,
284,
3368,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1165,
881,
24580,
3597,
284,
262,
6831,
13,
14206,
1429,
290,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40945,
4056,
743,
8856,
428,
11,
981,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1963,
342,
25782,
14,
16680,
541,
305,
919,
278,
4056,
761,
284,
2148,
340,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
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25,
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220,
220,
220,
220,
220,
220,
220,
1058,
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691,
62,
13564,
62,
261,
62,
43027,
25,
7913,
1852,
361,
278,
262,
691,
1429,
14,
16663,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
326,
481,
3551,
284,
262,
6831,
13,
5514,
973,
611,
705,
43027,
6,
318,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
691,
62,
13564,
62,
261,
62,
43027,
25,
493,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
43027,
796,
4279,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
8807,
62,
13564,
62,
261,
62,
43027,
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691,
62,
13564,
62,
261,
62,
43027,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9688,
62,
2435,
796,
640,
13,
2435,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
8095,
62,
38993,
796,
3650,
62,
38993,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
312,
11,
2116,
13,
6978,
62,
1676,
73,
62,
15763,
796,
2116,
13,
834,
961,
62,
6738,
62,
21812,
62,
1370,
62,
853,
2886,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20613,
62,
312,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9945,
796,
49385,
13,
8443,
62,
14323,
62,
9945,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9945,
62,
66,
21471,
796,
2116,
13,
9945,
13,
66,
21471,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
28665,
62,
14933,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
28665,
62,
19199,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
611,
357,
944,
13,
8095,
62,
38993,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
357,
944,
13,
43027,
6624,
6045,
393,
2116,
13,
43027,
6624,
2116,
13,
8807,
62,
13564,
62,
261,
62,
43027,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
13564,
10786,
13376,
3256,
705,
20270,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
13564,
10786,
2435,
62,
46981,
3256,
2116,
13,
834,
1136,
62,
4475,
62,
392,
62,
2435,
62,
292,
62,
8841,
28955,
628,
220,
220,
220,
220,
220,
220,
220,
611,
357,
944,
13,
8095,
62,
38993,
290,
2116,
13,
834,
271,
62,
64,
62,
18300,
62,
16302,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
357,
944,
13,
43027,
6624,
6045,
393,
2116,
13,
43027,
6624,
2116,
13,
8807,
62,
13564,
62,
261,
62,
43027,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13834,
796,
850,
14681,
13,
47,
9654,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
10210,
45144,
15,
92,
8172,
17606,
2710,
12,
29572,
39837,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6978,
62,
1676,
73,
62,
15763,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16589,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14367,
448,
28,
7266,
14681,
13,
47,
4061,
36,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
336,
1082,
81,
28,
9654,
7,
418,
13,
7959,
8423,
11,
705,
86,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7582,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
448,
11,
11454,
8,
796,
13834,
13,
10709,
5344,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
13564,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5721,
2625,
18300,
62,
17831,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
28,
448,
13,
12501,
1098,
10786,
292,
979,
72,
3256,
705,
33491,
6,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13834,
796,
850,
14681,
13,
47,
9654,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
10210,
45144,
15,
92,
8172,
17606,
2604,
532,
77,
352,
1377,
18982,
28,
4,
33,
39837,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6978,
62,
1676,
73,
62,
15763,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16589,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14367,
448,
28,
7266,
14681,
13,
47,
4061,
36,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
336,
1082,
81,
28,
9654,
7,
418,
13,
7959,
8423,
11,
705,
86,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7582,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
448,
11,
11454,
8,
796,
13834,
13,
10709,
5344,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
13564,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5721,
2625,
41509,
62,
20500,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
28,
448,
13,
12501,
1098,
10786,
292,
979,
72,
3256,
705,
33491,
6,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13834,
796,
850,
14681,
13,
47,
9654,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
10210,
45144,
15,
92,
8172,
17606,
814,
39837,
1377,
14269,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6978,
62,
1676,
73,
62,
15763,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16589,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14367,
448,
28,
7266,
14681,
13,
47,
4061,
36,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
336,
1082,
81,
28,
9654,
7,
418,
13,
7959,
8423,
11,
705,
86,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7582,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
448,
11,
11454,
8,
796,
13834,
13,
10709,
5344,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
13564,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5721,
2625,
18300,
62,
26069,
62,
14269,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
28,
448,
13,
12501,
1098,
10786,
292,
979,
72,
3256,
705,
33491,
6,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13834,
796,
850,
14681,
13,
47,
9654,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37250,
10210,
45144,
15,
92,
8172,
17606,
814,
39837,
4458,
18982,
7,
944,
13,
6978,
62,
1676,
73,
62,
15763,
8,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14367,
448,
28,
7266,
14681,
13,
47,
4061,
36,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
336,
1082,
81,
28,
9654,
7,
418,
13,
7959,
8423,
11,
705,
86,
33809,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7582,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
448,
11,
11454,
8,
796,
13834,
13,
10709,
5344,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
796,
503,
13,
12501,
1098,
10786,
292,
979,
72,
3256,
705,
33491,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
448,
8,
1875,
20343,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6509,
796,
366,
31502,
25,
10631,
3614,
284,
717,
20343,
3435,
13,
59,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
796,
6509,
1343,
705,
59,
77,
6,
1343,
503,
58,
15,
25,
23924,
60,
1343,
705,
59,
77,
59,
77,
6,
1343,
6509,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
13564,
7,
28665,
2625,
18300,
62,
26069,
1600,
1988,
28,
448,
8,
628,
220,
220,
220,
825,
1100,
7,
944,
11,
5721,
11,
2198,
62,
4906,
62,
271,
28,
7061,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
5569,
11507,
287,
705,
28665,
6,
422,
262,
6831,
13,
628,
220,
220,
220,
220,
220,
220,
220,
8229,
6045,
611,
11507,
318,
6565,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
5721,
25,
6530,
286,
262,
5721,
262,
11507,
318,
1100,
422,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
5721,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
2198,
62,
4906,
62,
271,
25,
536,
8516,
11052,
12331,
611,
2099,
857,
407,
2872,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
9122,
62,
4906,
62,
271,
4458,
464,
4938,
3858,
262,
13042,
705,
600,
3256,
705,
22468,
3256,
705,
30388,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
8841,
6,
290,
705,
600,
14,
22468,
14,
30388,
14,
8841,
7177,
6,
393,
262,
3858,
493,
11,
12178,
11,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20512,
11,
965,
290,
1351,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
29201,
12331,
25,
1002,
5721,
466,
407,
7160,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
11052,
12331,
25,
1002,
1441,
2099,
857,
407,
2872,
705,
9122,
62,
4906,
62,
271,
4458,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
44161,
578,
18,
13,
18843,
864,
12331,
25,
15329,
863,
517,
621,
642,
4201,
284,
1100,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
262,
6831,
11,
780,
584,
14390,
14,
14681,
274,
389,
8179,
3597,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
340,
13,
6378,
1165,
881,
24580,
3597,
318,
1760,
290,
340,
9217,
281,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1486,
4049,
287,
262,
2836,
1430,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
611,
5721,
407,
287,
2116,
13,
28665,
62,
14933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
28665,
62,
14933,
11,
2116,
13,
28665,
62,
19199,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49385,
13,
1136,
62,
9945,
62,
28665,
62,
14933,
62,
392,
62,
19199,
7,
944,
13,
9945,
62,
66,
21471,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5721,
407,
287,
2116,
13,
28665,
62,
14933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
29201,
12331,
7203,
39470,
11,
1391,
15,
5512,
318,
5626,
257,
5721,
287,
262,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
48806,
526,
13,
18982,
7,
28665,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
9945,
62,
66,
21471,
13,
41049,
7203,
46506,
1391,
15,
92,
16034,
4539,
33411,
4686,
34758,
16,
92,
1911,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5721,
11,
2116,
13,
312,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
9945,
62,
66,
21471,
13,
69,
7569,
505,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
14512,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
1988,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
834,
9122,
62,
4906,
7,
9122,
62,
4906,
62,
271,
11,
5721,
11,
2116,
13,
28665,
62,
14933,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
28665,
62,
19199,
11,
1988,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
825,
3551,
7,
944,
11,
5721,
11,
1988,
11,
2099,
62,
1659,
62,
8367,
11639,
3256,
691,
62,
361,
62,
28920,
28,
25101,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
16594,
1988,
284,
705,
28665,
6,
287,
262,
6831,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1002,
705,
28665,
6,
857,
407,
7160,
11,
257,
649,
318,
2087,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1002,
1988,
318,
6045,
290,
2099,
62,
1659,
62,
8367,
318,
407,
900,
11,
262,
5726,
739,
705,
28665,
6,
198,
220,
220,
220,
220,
220,
220,
220,
318,
900,
284,
6565,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1114,
1963,
342,
961,
276,
290,
18540,
305,
919,
4056,
691,
257,
2060,
481,
198,
220,
220,
220,
220,
220,
220,
220,
1429,
14,
16663,
3551,
284,
262,
6831,
284,
3368,
1165,
881,
24580,
220,
198,
220,
220,
220,
220,
220,
220,
220,
3597,
284,
262,
6831,
13,
770,
318,
355,
890,
355,
262,
705,
43027,
6,
373,
3804,
284,
220,
198,
220,
220,
220,
220,
220,
220,
220,
3184,
11012,
739,
4238,
5612,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
5721,
25,
6530,
286,
262,
5721,
262,
11507,
318,
1100,
422,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
5721,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
1988,
25,
968,
1988,
286,
262,
7368,
5726,
287,
262,
6831,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
2099,
62,
1659,
62,
8367,
25,
10664,
276,
611,
5721,
857,
3465,
7160,
393,
611,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
318,
6565,
1351,
13,
383,
4938,
3858,
262,
13042,
705,
600,
3256,
705,
22468,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
30388,
3256,
705,
8841,
6,
290,
705,
600,
14,
22468,
14,
30388,
14,
8841,
7177,
6,
393,
262,
3858,
493,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12178,
11,
20512,
290,
965,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
2099,
62,
1659,
62,
8367,
25,
965,
393,
2099,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
17143,
691,
62,
361,
62,
28920,
25,
1002,
6407,
11,
340,
481,
691,
3551,
284,
262,
6831,
611,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18640,
338,
5726,
739,
705,
28665,
6,
318,
6565,
13,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
691,
62,
361,
62,
28920,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
11052,
12331,
25,
1002,
5721,
7160,
11,
475,
2099,
857,
407,
2872,
11,
393,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6565,
1351,
318,
3804,
1231,
2099,
62,
1659,
62,
8367,
1813,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
1114,
1963,
342,
961,
276,
14,
16680,
541,
305,
919,
4056,
691,
257,
2060,
1429,
14,
16663,
220,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
857,
597,
3597,
13,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
43027,
14512,
6045,
290,
2116,
13,
43027,
14512,
2116,
13,
8807,
62,
13564,
62,
261,
62,
43027,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
834,
2860,
62,
28665,
62,
361,
62,
1662,
62,
1069,
1023,
62,
392,
62,
9122,
62,
4906,
7,
28665,
11,
2099,
62,
1659,
62,
8367,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
8,
628,
220,
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5721,
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383,
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220,
220,
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6,
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7,
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13,
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10786,
3672,
6,
4008,
1343,
705,
62,
6,
1343,
965,
7,
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13,
312,
8,
198,
220,
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220,
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2482,
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13,
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2625,
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1988,
28,
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198,
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691,
62,
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28,
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2073,
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220,
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220,
220,
220,
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981,
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62,
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6624,
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25,
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220,
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7,
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37227,
13615,
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13,
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1058,
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18,
13,
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25,
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863,
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14,
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274,
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3597,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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284,
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13,
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318,
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290,
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9217,
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198,
220,
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220,
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1486,
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13,
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220,
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220,
37227,
198,
220,
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220,
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220,
611,
5721,
287,
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13,
28665,
62,
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25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
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198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
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220,
220,
220,
220,
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220,
220,
220,
220,
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2116,
13,
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11,
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357,
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220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
49385,
13,
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62,
9945,
62,
28665,
62,
14933,
62,
392,
62,
19199,
7,
944,
13,
9945,
62,
66,
21471,
4008,
198,
220,
220,
220,
220,
220,
220,
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220,
220,
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220,
611,
5721,
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13,
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62,
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25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
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220,
220,
220,
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220,
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220,
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220,
220,
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1441,
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220,
220,
825,
318,
62,
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7,
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11,
5721,
2599,
198,
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220,
220,
220,
220,
220,
220,
37227,
13615,
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5726,
287,
262,
6831,
739,
705,
28665,
6,
318,
6565,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1058,
430,
2696,
44161,
578,
18,
13,
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864,
12331,
25,
15329,
863,
517,
621,
642,
4201,
284,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
262,
6831,
11,
780,
584,
14390,
14,
14681,
274,
389,
8179,
3597,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
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13,
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9217,
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37227,
198,
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220,
220,
220,
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1988,
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13,
961,
7,
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8,
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220,
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1988,
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25,
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1441,
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825,
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62,
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7,
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11,
5721,
2599,
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220,
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220,
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37227,
7248,
5726,
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6,
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825,
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62,
312,
7,
944,
2599,
198,
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220,
220,
220,
220,
220,
220,
37227,
13615,
705,
2389,
6,
286,
262,
5884,
18640,
526,
15931,
198,
220,
220,
220,
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] | 2.209911 | 7,184 |
from tensorflow.python.keras.models import Input, Model
from tensorflow.python.keras.layers import Dense, Reshape, Activation, Conv2D, Conv2DTranspose
from tensorflow.python.keras.layers import BatchNormalization, Add, Embedding, Concatenate
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import backend as K
from gan.utils import glorot_init, resblock, dcblock, get_m_group
from gan.layers.coloring import ConditionalConv11, ConditionalCenterScale, CenterScale, FactorizedConv11
from gan.layers.normalization import DecorelationNormalization
from gan.layers.misc import Split
from layers.spectral_normalized_layers import SNConv2D, SNConditionalConv11, SNDense, SNEmbeding, SNFactorizedConv11
from functools import partial
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85,
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198,
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1257,
310,
10141,
1330,
13027,
628,
628
] | 3.194915 | 236 |
# Copyright 2017 LinkedIn Corporation. All rights reserved. Licensed under the BSD-2 Clause license.
# See LICENSE in the project root for license information.
import os
from fossor.checks.check import Check
class LoadAvg(Check):
'''this Check will compare the current load average summaries against the count of CPU cores
in play, and will alert the user if there are more processes waiting'''
if __name__ == '__main__':
l = LoadAvg()
print(l.run({}))
| [
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] | 3.618321 | 131 |
from requests import HTTPError
from urllib.parse import parse_qs
from requests.exceptions import ConnectTimeout, ReadTimeout
import pytest
import requests_mock
from app.clients.sms.firetext import get_firetext_responses, SmsClientResponseException, FiretextClientResponseException
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62,
76,
735,
198,
198,
6738,
598,
13,
565,
2334,
13,
82,
907,
13,
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62,
6495,
5239,
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311,
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31077,
16922,
11,
3764,
5239,
11792,
31077,
16922,
628,
628,
628,
628,
628,
628
] | 3.769231 | 78 |
import random
# names = ['Alex', 'Beth', 'Carol', 'Dave', 'Kim', 'Sam', 'Heather', 'Hank']
# students_scores = {student:random.randint(1, 100) for student in names}
# passed_students = {student:score for (student, score) in students_scores.items() if score > 59}
# print(students_scores)
# print(passed_students)
# sentence = "What is the Airspeed Velocity of an Unladden Swallow?"
# result = {word:len(word) for word in sentence.split(' ')}
# print(result)
# weather_c = {
# 'Monday': 12,
# 'Tuesday': 14,
# 'Wednesday': 15,
# 'Thursday': 14,
# 'Friday': 21,
# 'Saturday': 22,
# 'Sunday': 24
# }
# weather_f = {day: temp * 9 / 5 + 32 for (day, temp) in weather_c.items()}
# print(weather_f)
# import pandas
#
# student_dict = {
# 'student': ['Mary', 'Andy', 'Peter'],
# 'score': [56, 76, 98]
# }
# student_data_frame = pandas.DataFrame(student_dict)
# print(student_data_frame)
# for (index, row) in student_data_frame.iterrows():
# if row.student == 'Mary':
# print(row.score)
import pandas
letter_dict = {r.letter:r.code for (i, r) in pandas.read_csv('nato_phonetic_alphabet.csv').iterrows()}
generate_phonetic() | [
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8,
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292,
201,
198,
201,
198,
9291,
62,
11600,
796,
1391,
81,
13,
9291,
25,
81,
13,
8189,
329,
357,
72,
11,
374,
8,
287,
19798,
292,
13,
961,
62,
40664,
10786,
77,
5549,
62,
746,
261,
5139,
62,
17307,
8380,
13,
40664,
27691,
2676,
8516,
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92,
201,
198,
201,
198,
201,
198,
8612,
378,
62,
746,
261,
5139,
3419
] | 2.386588 | 507 |
import pyeccodes.accessors as _
| [
11748,
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628
] | 3 | 11 |
"""Prepare OpenGL commands for use in templates."""
from enum import auto, Enum
from typing import Iterable, Mapping, Optional, Union
import attr
from gladiator.parse.command import Command, Type
from gladiator.prepare.enum import PreparedEnum
from gladiator.prepare.style import transform_symbol
from gladiator.optional import OptionalValue
from gladiator.options import Options
from gladiator.resources import read_resource_file
@attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True)
@attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True)
@attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True)
@attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True)
@attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True)
@attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True)
@attr.s(auto_attribs=True, kw_only=True, slots=True, frozen=True)
_TYPE_TRANSLATIONS = dict(
t.split(",") for t in read_resource_file("data/type_translations").split("\n") if t
)
# TODO: take options such as casing, style and namespace
# TODO: generate special wrappers for generators and deleters
def prepare_commands(
commands: Iterable[Command],
prepared_enums: Mapping[str, PreparedEnum],
options: Options,
):
"""Prepare the given commands for use as references and in templates. The
given enums are used to construct type references. Yields tuples mapping the
original command name to the prepared command.
"""
for command in commands:
yield command.name, PreparedCommand(
original=command,
type_=CommandType.DEFAULT,
implementation=_make_default_implementation(command, prepared_enums),
name=transform_symbol(
command.name, options.function_case, options.omit_prefix
),
)
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7,
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220,
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220,
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220,
220,
220,
220,
220,
220,
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3141,
13,
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11,
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13,
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10612,
198,
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] | 2.787425 | 668 |
"""
This file contains all the functions used in the notebooks
under the Binary Quadratic Model section.
Prepared by Akash Narayanan B
"""
from dimod import BinaryQuadraticModel
# Task 3
linear = {'x1': 3, 'x2': -1, 'x3': 10, 'x4': 7}
quadratic = {('x1', 'x2'): 2, ('x1', 'x3'): -5, ('x2', 'x3'): 3, ('x3', 'x4'): 11}
offset = 8
vartype = 'BINARY' | [
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# -*- coding: utf-8 -*-
# Generated by Django 1.10.8 on 2017-09-22 13:19
from __future__ import unicode_literals
from django.db import migrations, models
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] | 2.736842 | 57 |
"""
ScanObjectNN download: http://103.24.77.34/scanobjectnn/h5_files.zip
"""
import os
import sys
import glob
import h5py
import numpy as np
from torch.utils.data import Dataset
os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
if __name__ == '__main__':
train = ScanObjectNN(1024)
test = ScanObjectNN(1024, 'test')
for data, label in train:
print(data.shape)
print(label)
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] | 2.484663 | 163 |
default_app_config = "sgi.recursos_humanos.apps.RecursosHumanosConfig"
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import re
import hashlib
import time
import StringIO
__version__ = '0.8'
#GNTP/<version> <messagetype> <encryptionAlgorithmID>[:<ivValue>][ <keyHashAlgorithmID>:<keyHash>.<salt>]
GNTP_INFO_LINE = re.compile(
'GNTP/(?P<version>\d+\.\d+) (?P<messagetype>REGISTER|NOTIFY|SUBSCRIBE|\-OK|\-ERROR)' +
' (?P<encryptionAlgorithmID>[A-Z0-9]+(:(?P<ivValue>[A-F0-9]+))?) ?' +
'((?P<keyHashAlgorithmID>[A-Z0-9]+):(?P<keyHash>[A-F0-9]+).(?P<salt>[A-F0-9]+))?\r\n',
re.IGNORECASE
)
GNTP_INFO_LINE_SHORT = re.compile(
'GNTP/(?P<version>\d+\.\d+) (?P<messagetype>REGISTER|NOTIFY|SUBSCRIBE|\-OK|\-ERROR)',
re.IGNORECASE
)
GNTP_HEADER = re.compile('([\w-]+):(.+)')
GNTP_EOL = '\r\n'
class _GNTPBuffer(StringIO.StringIO):
"""GNTP Buffer class"""
def writefmt(self, message="", *args):
"""Shortcut function for writing GNTP Headers"""
self.write((message % args).encode('utf8', 'replace'))
self.write(GNTP_EOL)
class _GNTPBase(object):
"""Base initilization
:param string messagetype: GNTP Message type
:param string version: GNTP Protocol version
:param string encription: Encryption protocol
"""
def _parse_info(self, data):
"""Parse the first line of a GNTP message to get security and other info values
:param string data: GNTP Message
:return dict: Parsed GNTP Info line
"""
match = GNTP_INFO_LINE.match(data)
if not match:
raise ParseError('ERROR_PARSING_INFO_LINE')
info = match.groupdict()
if info['encryptionAlgorithmID'] == 'NONE':
info['encryptionAlgorithmID'] = None
return info
def set_password(self, password, encryptAlgo='MD5'):
"""Set a password for a GNTP Message
:param string password: Null to clear password
:param string encryptAlgo: Supports MD5, SHA1, SHA256, SHA512
"""
hash = {
'MD5': hashlib.md5,
'SHA1': hashlib.sha1,
'SHA256': hashlib.sha256,
'SHA512': hashlib.sha512,
}
self.password = password
self.encryptAlgo = encryptAlgo.upper()
if not password:
self.info['encryptionAlgorithmID'] = None
self.info['keyHashAlgorithm'] = None
return
if not self.encryptAlgo in hash.keys():
raise UnsupportedError('INVALID HASH "%s"' % self.encryptAlgo)
hashfunction = hash.get(self.encryptAlgo)
password = password.encode('utf8')
seed = time.ctime()
salt = hashfunction(seed).hexdigest()
saltHash = hashfunction(seed).digest()
keyBasis = password + saltHash
key = hashfunction(keyBasis).digest()
keyHash = hashfunction(key).hexdigest()
self.info['keyHashAlgorithmID'] = self.encryptAlgo
self.info['keyHash'] = keyHash.upper()
self.info['salt'] = salt.upper()
def _decode_hex(self, value):
"""Helper function to decode hex string to `proper` hex string
:param string value: Human readable hex string
:return string: Hex string
"""
result = ''
for i in range(0, len(value), 2):
tmp = int(value[i:i + 2], 16)
result += chr(tmp)
return result
def _validate_password(self, password):
"""Validate GNTP Message against stored password"""
self.password = password
if password == None:
raise AuthError('Missing password')
keyHash = self.info.get('keyHash', None)
if keyHash is None and self.password is None:
return True
if keyHash is None:
raise AuthError('Invalid keyHash')
if self.password is None:
raise AuthError('Missing password')
password = self.password.encode('utf8')
saltHash = self._decode_hex(self.info['salt'])
keyBasis = password + saltHash
key = hashlib.md5(keyBasis).digest()
keyHash = hashlib.md5(key).hexdigest()
if not keyHash.upper() == self.info['keyHash'].upper():
raise AuthError('Invalid Hash')
return True
def validate(self):
"""Verify required headers"""
for header in self._requiredHeaders:
if not self.headers.get(header, False):
raise ParseError('Missing Notification Header: ' + header)
def _format_info(self):
"""Generate info line for GNTP Message
:return string:
"""
info = u'GNTP/%s %s' % (
self.info.get('version'),
self.info.get('messagetype'),
)
if self.info.get('encryptionAlgorithmID', None):
info += ' %s:%s' % (
self.info.get('encryptionAlgorithmID'),
self.info.get('ivValue'),
)
else:
info += ' NONE'
if self.info.get('keyHashAlgorithmID', None):
info += ' %s:%s.%s' % (
self.info.get('keyHashAlgorithmID'),
self.info.get('keyHash'),
self.info.get('salt')
)
return info
def _parse_dict(self, data):
"""Helper function to parse blocks of GNTP headers into a dictionary
:param string data:
:return dict:
"""
dict = {}
for line in data.split('\r\n'):
match = GNTP_HEADER.match(line)
if not match:
continue
key = unicode(match.group(1).strip(), 'utf8', 'replace')
val = unicode(match.group(2).strip(), 'utf8', 'replace')
dict[key] = val
return dict
def add_resource(self, data):
"""Add binary resource
:param string data: Binary Data
"""
identifier = hashlib.md5(data).hexdigest()
self.resources[identifier] = data
return 'x-growl-resource://%s' % identifier
def decode(self, data, password=None):
"""Decode GNTP Message
:param string data:
"""
self.password = password
self.raw = data
parts = self.raw.split('\r\n\r\n')
self.info = self._parse_info(data)
self.headers = self._parse_dict(parts[0])
def encode(self):
"""Encode a generic GNTP Message
:return string: GNTP Message ready to be sent
"""
buffer = _GNTPBuffer()
buffer.writefmt(self._format_info())
#Headers
for k, v in self.headers.iteritems():
buffer.writefmt('%s: %s', k, v)
buffer.writefmt()
#Resources
for resource, data in self.resources.iteritems():
buffer.writefmt('Identifier: %s', resource)
buffer.writefmt('Length: %d', len(data))
buffer.writefmt()
buffer.write(data)
buffer.writefmt()
buffer.writefmt()
return buffer.getvalue()
class GNTPRegister(_GNTPBase):
"""Represents a GNTP Registration Command
:param string data: (Optional) See decode()
:param string password: (Optional) Password to use while encoding/decoding messages
"""
_requiredHeaders = [
'Application-Name',
'Notifications-Count'
]
_requiredNotificationHeaders = ['Notification-Name']
def validate(self):
'''Validate required headers and validate notification headers'''
for header in self._requiredHeaders:
if not self.headers.get(header, False):
raise ParseError('Missing Registration Header: ' + header)
for notice in self.notifications:
for header in self._requiredNotificationHeaders:
if not notice.get(header, False):
raise ParseError('Missing Notification Header: ' + header)
def decode(self, data, password):
"""Decode existing GNTP Registration message
:param string data: Message to decode
"""
self.raw = data
parts = self.raw.split('\r\n\r\n')
self.info = self._parse_info(data)
self._validate_password(password)
self.headers = self._parse_dict(parts[0])
for i, part in enumerate(parts):
if i == 0:
continue # Skip Header
if part.strip() == '':
continue
notice = self._parse_dict(part)
if notice.get('Notification-Name', False):
self.notifications.append(notice)
elif notice.get('Identifier', False):
notice['Data'] = self._decode_binary(part, notice)
#open('register.png','wblol').write(notice['Data'])
self.resources[notice.get('Identifier')] = notice
def add_notification(self, name, enabled=True):
"""Add new Notification to Registration message
:param string name: Notification Name
:param boolean enabled: Enable this notification by default
"""
notice = {}
notice['Notification-Name'] = u'%s' % name
notice['Notification-Enabled'] = u'%s' % enabled
self.notifications.append(notice)
self.add_header('Notifications-Count', len(self.notifications))
def encode(self):
"""Encode a GNTP Registration Message
:return string: Encoded GNTP Registration message
"""
buffer = _GNTPBuffer()
buffer.writefmt(self._format_info())
#Headers
for k, v in self.headers.iteritems():
buffer.writefmt('%s: %s', k, v)
buffer.writefmt()
#Notifications
if len(self.notifications) > 0:
for notice in self.notifications:
for k, v in notice.iteritems():
buffer.writefmt('%s: %s', k, v)
buffer.writefmt()
#Resources
for resource, data in self.resources.iteritems():
buffer.writefmt('Identifier: %s', resource)
buffer.writefmt('Length: %d', len(data))
buffer.writefmt()
buffer.write(data)
buffer.writefmt()
buffer.writefmt()
return buffer.getvalue()
class GNTPNotice(_GNTPBase):
"""Represents a GNTP Notification Command
:param string data: (Optional) See decode()
:param string app: (Optional) Set Application-Name
:param string name: (Optional) Set Notification-Name
:param string title: (Optional) Set Notification Title
:param string password: (Optional) Password to use while encoding/decoding messages
"""
_requiredHeaders = [
'Application-Name',
'Notification-Name',
'Notification-Title'
]
def decode(self, data, password):
"""Decode existing GNTP Notification message
:param string data: Message to decode.
"""
self.raw = data
parts = self.raw.split('\r\n\r\n')
self.info = self._parse_info(data)
self._validate_password(password)
self.headers = self._parse_dict(parts[0])
for i, part in enumerate(parts):
if i == 0:
continue # Skip Header
if part.strip() == '':
continue
notice = self._parse_dict(part)
if notice.get('Identifier', False):
notice['Data'] = self._decode_binary(part, notice)
#open('notice.png','wblol').write(notice['Data'])
self.resources[notice.get('Identifier')] = notice
class GNTPSubscribe(_GNTPBase):
"""Represents a GNTP Subscribe Command
:param string data: (Optional) See decode()
:param string password: (Optional) Password to use while encoding/decoding messages
"""
_requiredHeaders = [
'Subscriber-ID',
'Subscriber-Name',
]
class GNTPOK(_GNTPBase):
"""Represents a GNTP OK Response
:param string data: (Optional) See _GNTPResponse.decode()
:param string action: (Optional) Set type of action the OK Response is for
"""
_requiredHeaders = ['Response-Action']
class GNTPError(_GNTPBase):
"""Represents a GNTP Error response
:param string data: (Optional) See _GNTPResponse.decode()
:param string errorcode: (Optional) Error code
:param string errordesc: (Optional) Error Description
"""
_requiredHeaders = ['Error-Code', 'Error-Description']
def parse_gntp(data, password=None):
"""Attempt to parse a message as a GNTP message
:param string data: Message to be parsed
:param string password: Optional password to be used to verify the message
"""
match = GNTP_INFO_LINE_SHORT.match(data)
if not match:
raise ParseError('INVALID_GNTP_INFO')
info = match.groupdict()
if info['messagetype'] == 'REGISTER':
return GNTPRegister(data, password=password)
elif info['messagetype'] == 'NOTIFY':
return GNTPNotice(data, password=password)
elif info['messagetype'] == 'SUBSCRIBE':
return GNTPSubscribe(data, password=password)
elif info['messagetype'] == '-OK':
return GNTPOK(data)
elif info['messagetype'] == '-ERROR':
return GNTPError(data)
raise ParseError('INVALID_GNTP_MESSAGE')
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] | 2.707618 | 4,135 |
from .backend import *
from .commands import *
check_config()
if not DB_PATH.is_file():
init_db()
init_firewall()
| [
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] | 2.530612 | 49 |
"""Copyright 2022 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
# [START drive_recover_team_drives]
from __future__ import print_function
import google.auth
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
def recover_team_drives(real_user):
"""Finds all Team Drives without an organizer and add one
Args:
real_user:User ID for the new organizer.
Returns:
team drives_object.
Load pre-authorized user credentials from the environment.
TODO(developer) - See https://developers.google.com/identity
for guides on implementing OAuth2 for the application.
"""
creds, _ = google.auth.default()
try:
# call drive api client
service = build('drive', 'v3', credentials=creds)
# pylint: disable=maybe-no-member
team_drives = []
page_token = None
new_organizer_permission = {'type': 'user',
'role': 'organizer',
'value': '[email protected]'}
new_organizer_permission['emailAddress'] = real_user
while True:
response = service.teamdrives().list(q='organizerCount = 0',
fields='nextPageToken, '
'teamDrives(id, '
'name)',
useDomainAdminAccess=True,
pageToken=page_token
).execute()
for team_drive in response.get('teamDrives', []):
print('Found Team Drive without organizer: {team_drive.get('
'"title")},{team_drive.get("id")}')
permission = service.permissions().create(
fileId=team_drive.get('id'),
body=new_organizer_permission, useDomainAdminAccess=True,
supportsTeamDrives=True, fields='id').execute()
print(F'Added organizer permission:{permission.get("id")}')
team_drives.extend(response.get('teamDrives', []))
page_token = response.get('nextPageToken', None)
if page_token is None:
break
except HttpError as error:
print(F'An error occurred: {error}')
team_drives = None
print(team_drives)
if __name__ == '__main__':
recover_team_drives(real_user='[email protected]')
# [END drive_recover_team_drives]
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] | 2.190071 | 1,410 |
import json
import numpy as np
import pandas as pd
from sklearn.externals import joblib
from bld.project_paths import project_paths_join as ppj
# This list is ordered according to the item table in our paper.
PERCEIVED_CONTROL = [
"LOC_LIFES_COURSE",
"LOC_ACHIEVED_DESERVE",
"LOC_LUCK",
"LOC_OTHERS",
"LOC_DOUBT",
"LOC_POSSIBILITIES",
"LOC_LITTLE_CONTROL",
]
LOC_VALUES = {
"[1] Trifft ueberhaupt nicht zu": 1,
"[2] [2/10]": 2,
"[3] [3/10]": 3,
"[4] [4/10]": 4,
"[5] [5/10]": 5,
"[6] [6/10]": 6,
"[7] Trifft voll zu": 7,
}
def invert_items(df):
"""This function inverts the scale of some items of LoC so that for all
items higher numbers reflect greater feelings of control."""
inverted_items = [
"LOC_ACHIEVED_DESERVE",
"LOC_LUCK",
"LOC_OTHERS",
"LOC_DOUBT",
"LOC_POSSIBILITIES",
"LOC_ABILITIES",
"LOC_LITTLE_CONTROL",
]
for item in inverted_items:
df[item].replace(
to_replace=[1, 2, 3, 4, 5, 6, 7], value=[7, 6, 5, 4, 3, 2, 1], inplace=True
)
return df
def create_index(df):
"""This function creates and index which is the average over all LoC
items."""
df["LOC_INDEX"] = df[PERCEIVED_CONTROL].mean(axis="columns")
return df
if __name__ == "__main__":
# Load dataset
df = pd.read_pickle(ppj("OUT_DATA", "loc_raw.pkl"))
# Clean the data
df = clean_variables(df)
# Invert items so that higher numbers indicate greater feelings of control
df = invert_items(df)
# Calculate Cronbach's alpha for the whole scale
data = df[[i for i in df if "LOC" in i]].values.T
cronbachs_alpha_ten = calculate_cronbachs_alpha(data)
# Restrict to seven item scale proposed by Specht et al (2013)
df = df[["ID", "YEAR"] + PERCEIVED_CONTROL]
# Create an index as the average of LoC items
df = create_index(df)
# Calculate Cronbach's Alpha for seven item scale. First, reshape the data
# to n (items) * p (observations)
data = df[PERCEIVED_CONTROL].values.T
cronbachs_alpha_seven = calculate_cronbachs_alpha(data)
# Create container
container = {}
container["data"] = df
# Save numbers to json
with open(ppj("OUT_TABLES", "cronbachs_alphas.json"), "w") as file:
file.write(
json.dumps(
{"ca_seven": cronbachs_alpha_seven, "ca_ten": cronbachs_alpha_ten}
)
)
# Save data for PCA and FA
joblib.dump(container, ppj("OUT_DATA", "loc_container.pkl"))
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1600,
366,
17946,
62,
34924,
13,
79,
41582,
48774,
198
] | 2.263204 | 1,136 |
import os
import sys
import time
import shutil
import argparse
import traceback
import subprocess
from . import __version__
| [
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] | 3.90625 | 32 |
import os
import json
import shlex
from .cli_bash_operator import CliBashOperator
from ..config import OPEN_BUS_PIPELINES_ROOTDIR
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] | 3 | 44 |
#!/usr/bin/env python
# _*_ coding: utf-8 _*_
__author__: 'Patrick Wang'
__date__: '2019/2/28 14:52'
from scrapy.cmdline import execute
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
# execute(["scrapy", "crawl", "jobbole"])
execute(["scrapy", "crawl", "zhihu"])
# execute(["scrapy", "crawl", "lagou"])
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] | 2.368056 | 144 |
# 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.
"""
This module defines exceptions for Presto operations. It follows the structure
defined in pep-0249.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import random
import time
import prestodb.logging
logger = prestodb.logging.get_logger(__name__)
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62,
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1362,
7,
834,
3672,
834,
8,
628,
628,
628,
628,
628,
628
] | 3.697095 | 241 |
from os import remove
import shlex
from os.path import isfile, join, split, splitext
from prody.tests import TestCase, skipIf, skipUnless
from numpy.testing import *
try:
import numpy.testing.decorators as dec
except ImportError:
from numpy.testing import dec
from prody.tests.datafiles import TEMPDIR, pathDatafile
from prody.apps import prody_parser
from prody.tests import MATPLOTLIB, NOPRODYCMD, WINDOWS
| [
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] | 3.065693 | 137 |
from __future__ import absolute_import
from datetime import datetime
import factory
from . import models
from talks.users.models import Collection, CollectionItem, CollectedDepartment
| [
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] | 4.266667 | 45 |
from __future__ import unicode_literals
from django.apps import AppConfig
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] | 3.619048 | 21 |
#!/usr/bin/env python3
#
# setup.py
# From the stagger project: http://code.google.com/p/stagger/
#
# Copyright (c) 2009-2011 Karoly Lorentey <[email protected]>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# - Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# - Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the
# distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import distribute_setup
distribute_setup.use_setuptools()
from setuptools import setup;
setup(
name="stagger",
version="0.4.2",
url="http://code.google.com/p/stagger",
author="Karoly Lorentey",
author_email="[email protected]",
packages=["stagger"],
entry_points = {
'console_scripts': ['stagger = stagger.commandline:main']
},
test_suite = "test.alltests.suite",
license="BSD",
description="ID3v1/ID3v2 tag manipulation package in pure Python 3",
long_description="""
The ID3v2 tag format is notorious for its useless specification
documents and its quirky, mutually incompatible
part-implementations. Stagger is to provide a robust tagging package
that is able to handle all the various badly formatted tags out there
and allow you to convert them to a consensus format.
""",
classifiers=[
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3",
"License :: OSI Approved :: BSD License",
"Operating System :: OS Independent",
"Topic :: Multimedia :: Sound/Audio"
],
)
| [
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] | 3.11071 | 831 |
#!/usr/bin/env python3
"""Calculate the value of pi using multiprocessing in Python"""
from datetime import datetime
from multiprocessing import Pool
import os
from sys import argv
if __name__ == "__main__":
main()
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] | 3.15493 | 71 |
# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""squad_question_generation dataset."""
import os
import tensorflow as tf
import tensorflow_datasets.public_api as tfds
from tensorflow_datasets.question_answering import qa_utils
_CITATION = """\
@article{zhou2017neural,
title={Neural Question Generation from Text: A Preliminary Study},
author={Zhou, Qingyu and Yang, Nan and Wei, Furu and Tan, Chuanqi and Bao, Hangbo and Zhou, Ming},
journal={arXiv preprint arXiv:1704.01792},
year={2017}
}
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
"""
_DESCRIPTION = """\
Question generation using squad dataset and data split described in
'Neural Question Generation from Text: A Preliminary Study'.
"""
_URLS = {
"train":
"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json",
"dev":
"https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json",
"mapping":
"https://res.qyzhou.me/qas_id_in_squad.zip",
}
_HOMEPAGE_URL = "https://github.com/magic282/NQG"
class SquadQuestionGeneration(tfds.core.GeneratorBasedBuilder):
"""DatasetBuilder for squad_question_generation dataset."""
VERSION = tfds.core.Version("1.0.0")
RELEASE_NOTES = {
"1.0.0": "Initial build.",
}
def _info(self):
"""Returns the dataset metadata."""
features_dict = qa_utils.SQUADLIKE_FEATURES
return tfds.core.DatasetInfo(
builder=self,
description=_DESCRIPTION,
features=features_dict,
supervised_keys=("context", "question"),
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_paths = dl_manager.download_and_extract(_URLS)
mapping_dir = os.path.join(dl_paths["mapping"], "qas_id_in_squad")
return {
tfds.Split.TRAIN:
self._generate_examples(dl_paths["train"],
os.path.join(mapping_dir, "train.txt.id")),
tfds.Split.VALIDATION:
self._generate_examples(
dl_paths["dev"],
os.path.join(mapping_dir, "dev.txt.shuffle.dev.id")),
tfds.Split.TEST:
self._generate_examples(
dl_paths["dev"],
os.path.join(mapping_dir, "dev.txt.shuffle.test.id")),
}
def _generate_examples(self, data_path: str, mapping_path: str):
"""Yields examples."""
with tf.io.gfile.GFile(mapping_path, "r") as f:
ids = set(f.read().splitlines())
for k, ex in qa_utils.generate_squadlike_examples(data_path):
if k in ids:
yield k, ex
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220,
220,
220,
220,
220,
220,
220,
7800,
479,
11,
409,
198
] | 2.388776 | 1,479 |
import sys
from ujson import loads, dumps
for line in sys.stdin:
obj = loads(line)
sys.stdout.write(dumps(obj['actor']))
sys.stdout.write('\n')
| [
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] | 2.446154 | 65 |
from azure.storage.queue import (
QueueClient,
TextBase64EncodePolicy,
TextBase64DecodePolicy
)
import os, uuid, time, json
import mysql.connector
from datetime import datetime
connect_str = "DefaultEndpointsProtocol=https;AccountName=replace;AccountKey=replacewithyours;EndpointSuffix=core.windows.net"
queue_name = "name of queue"
mySql_dbName = "sensordata"
mySql_tableName = "temperature"
queue_client = QueueClient.from_connection_string(conn_str=connect_str, queue_name=queue_name, message_decode_policy=TextBase64DecodePolicy())
messages = queue_client.receive_messages(messages_per_page=5)
db = mysql.connector.connect(
host="db",
user="root",
passwd="example",
database=mySql_dbName
)
cursor = db.cursor()
for message in messages:
processMessage()
queue_client.delete_message(message.id, message.pop_receipt)
time.sleep(0.1)
print("All Done")
| [
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15,
13,
16,
8,
198,
198,
4798,
7203,
3237,
24429,
4943,
628
] | 2.714715 | 333 |
from .controllers.twitter import search | [
6738,
764,
3642,
36667,
13,
6956,
1330,
2989
] | 4.875 | 8 |
while True:
try:
print("a")
finally:
continue | [
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220,
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2555
] | 2.375 | 24 |
import copy
if __name__ == '__main__':
epss = np.logspace(-10, -1, 30)
baseline_objective = augmented_objective(x0)
xis = []
for eps in epss:
xi = copy.copy(x0)
xi[4] += eps
xis.append(xi)
objs = [augmented_objective(xi) for xi in xis]
# pool = mp.Pool(mp.cpu_count())
# objs = pool.map(augmented_objective, xis)
# pool.close()
objs = np.array(objs)
derivs = (objs - baseline_objective) / epss
fig, ax = plt.subplots(1, 1, figsize=(6.4, 4.8), dpi=200)
plt.loglog(epss, np.abs(derivs), ".-")
plt.show() | [
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366,
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4943,
198,
220,
220,
220,
458,
83,
13,
12860,
3419
] | 2 | 291 |
# Copyright (c) 2016 Ansible, Inc.
# All Rights Reserved.
import logging
from django.db import models
from django.utils.translation import ugettext_lazy as _
from awx.main.fields import JSONBField
__all__ = ('Fact',)
logger = logging.getLogger('awx.main.models.fact')
class Fact(models.Model):
"""A model representing a fact returned from Ansible.
Facts are stored as JSON dictionaries.
"""
host = models.ForeignKey(
'Host',
related_name='facts',
db_index=True,
on_delete=models.CASCADE,
help_text=_('Host for the facts that the fact scan captured.'),
)
timestamp = models.DateTimeField(
default=None,
editable=False,
help_text=_('Date and time of the corresponding fact scan gathering time.')
)
module = models.CharField(max_length=128)
facts = JSONBField(blank=True, default={}, help_text=_('Arbitrary JSON structure of module facts captured at timestamp for a single host.'))
@staticmethod
@staticmethod
@staticmethod
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62,
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28,
62,
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3163,
2545,
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286,
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2488,
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24396,
628,
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24396,
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] | 2.776596 | 376 |
# import multiprocessing
pidfile = 'flask_app.pid'
workers = 2
# workers = multiprocessing.cpu_count() * 2 + 1
bind = '0.0.0.0:80'
accesslog = './logs/access.log'
errorlog = './logs/error.log'
#certfile = './certs/local.cer'
#keyfile = './certs/local.key'
# user = 'ubuntu'
# group = 'ubuntu' | [
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] | 2.401639 | 122 |
from django.core.management.base import BaseCommand
from django.contrib.contenttypes.models import ContentType
from django.db.models.functions import Length
from user.models import User
from discussion.models import Thread
import uuid
from utils.siftscience import decisions_api, events_api
| [
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] | 3.805195 | 77 |
#
# Copyright 2018 Analytics Zoo Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import ray
from ray import tune
from copy import deepcopy
import os
from zoo.automl.search.abstract import *
from zoo.automl.common.util import *
from zoo.automl.common.metrics import Evaluator
from zoo.automl.impute.impute import *
from ray.tune import Trainable
import ray.tune.track
from zoo.automl.logger import TensorboardXLogger
from zoo.automl.model.model_builder import ModelBuilder
from zoo.automl.feature.identity_transformer import IdentityTransformer
from zoo.automl.search.tune_utils import (create_searcher,
create_scheduler)
SEARCH_ALG_ALLOWED = ("variant_generator", "skopt", "bayesopt")
class RayTuneSearchEngine(SearchEngine):
"""
Tune driver
"""
def __init__(self,
logs_dir="",
resources_per_trial=None,
name="",
remote_dir=None,
):
"""
Constructor
:param resources_per_trial: resources for each trial
"""
self.pipeline = None
self.train_func = None
self.trainable_class = None
self.resources_per_trail = resources_per_trial
self.trials = None
self.remote_dir = remote_dir
self.name = name
self.logs_dir = os.path.abspath(os.path.expanduser(logs_dir))
def compile(self,
data,
model_create_func,
recipe,
search_space=None,
search_alg=None,
search_alg_params=None,
scheduler=None,
scheduler_params=None,
feature_transformers=None,
mc=False,
metric="mse"):
"""
Do necessary preparations for the engine
:param input_df:
:param search_space:
:param num_samples:
:param stop:
:param search_algorithm:
:param search_algorithm_params:
:param fixed_params:
:param feature_transformers:
:param model:
:param validation_df:
:param metric:
:return:
"""
# data mode detection
assert isinstance(data, dict), 'ERROR: Argument \'data\' should be a dictionary.'
data_mode = None # data_mode can only be 'dataframe' or 'ndarray'
data_schema = set(data.keys())
if set(["df"]).issubset(data_schema):
data_mode = 'dataframe'
if set(["x", "y"]).issubset(data_schema):
data_mode = 'ndarray'
assert data_mode in ['dataframe', 'ndarray'],\
'ERROR: Argument \'data\' should fit either \
dataframe schema (include \'df\' in keys) or\
ndarray (include \'x\' and \'y\' in keys) schema.'
# data extract
if data_mode == 'dataframe':
input_df = data['df']
feature_cols = data.get("feature_cols", None)
target_col = data.get("target_col", None)
validation_df = data.get("val_df", None)
else:
if data["x"].ndim == 1:
data["x"] = data["x"].reshape(-1, 1)
if data["y"].ndim == 1:
data["y"] = data["y"].reshape(-1, 1)
if "val_x" in data.keys() and data["val_x"].ndim == 1:
data["val_x"] = data["val_x"].reshape(-1, 1)
if "val_y" in data.keys() and data["val_y"].ndim == 1:
data["val_y"] = data["val_y"].reshape(-1, 1)
input_data = {"x": data["x"], "y": data["y"]}
if 'val_x' in data.keys():
validation_data = {"x": data["val_x"], "y": data["val_y"]}
else:
validation_data = None
# prepare parameters for search engine
runtime_params = recipe.runtime_params()
self.num_samples = runtime_params['num_samples']
stop = dict(runtime_params)
del stop['num_samples']
self.stop_criteria = stop
if search_space is None:
search_space = recipe.search_space(all_available_features=None)
self._search_alg = RayTuneSearchEngine._set_search_alg(search_alg, search_alg_params,
recipe, search_space)
self._scheduler = RayTuneSearchEngine._set_scheduler(scheduler, scheduler_params)
self.search_space = self._prepare_tune_config(search_space)
if feature_transformers is None and data_mode == 'dataframe':
feature_transformers = IdentityTransformer(feature_cols, target_col)
if data_mode == 'dataframe':
self.train_func = self._prepare_train_func(input_data=input_df,
model_create_func=model_create_func,
feature_transformers=feature_transformers,
validation_data=validation_df,
metric=metric,
mc=mc,
remote_dir=self.remote_dir,
numpy_format=False
)
else:
self.train_func = self._prepare_train_func(input_data=input_data,
model_create_func=model_create_func,
feature_transformers=None,
validation_data=validation_data,
metric=metric,
mc=mc,
remote_dir=self.remote_dir,
numpy_format=True
)
# self.trainable_class = self._prepare_trainable_class(input_df,
# feature_transformers,
# # model,
# future_seq_len,
# validation_df,
# metric_op,
# self.remote_dir)
@staticmethod
@staticmethod
def run(self):
"""
Run trials
:return: trials result
"""
analysis = tune.run(
self.train_func,
local_dir=self.logs_dir,
name=self.name,
stop=self.stop_criteria,
config=self.search_space,
search_alg=self._search_alg,
num_samples=self.num_samples,
scheduler=self._scheduler,
resources_per_trial=self.resources_per_trail,
verbose=1,
reuse_actors=True
)
self.trials = analysis.trials
# Visualization code for ray (leaderboard)
# catch the ImportError Since it has been processed in TensorboardXLogger
tf_config, tf_metric = self._log_adapt(analysis)
self.logger = TensorboardXLogger(os.path.join(self.logs_dir, self.name+"_leaderboard"))
self.logger.run(tf_config, tf_metric)
self.logger.close()
return analysis
@staticmethod
def _get_best_trial(trial_list, metric):
"""Retrieve the best trial."""
return max(trial_list, key=lambda trial: trial.last_result.get(metric, 0))
@staticmethod
@staticmethod
def _get_best_result(trial_list, metric):
"""Retrieve the last result from the best trial."""
return {metric: RayTuneSearchEngine._get_best_trial(trial_list, metric).last_result[metric]}
@staticmethod
@staticmethod
def _prepare_train_func(input_data,
model_create_func,
feature_transformers,
metric,
validation_data=None,
mc=False,
remote_dir=None,
numpy_format=False,
):
"""
Prepare the train function for ray tune
:param input_df: input dataframe
:param feature_transformers: feature transformers
:param model: model or model selector
:param validation_df: validation dataframe
:param metric: the rewarding metric
:return: the train function
"""
numpy_format_id = ray.put(numpy_format)
input_data_id = ray.put(input_data)
ft_id = ray.put(feature_transformers)
# model_id = ray.put(model)
# validation data processing
df_not_empty = isinstance(validation_data, dict) or\
(isinstance(validation_data, pd.DataFrame) and not validation_data.empty)
df_list_not_empty = isinstance(validation_data, dict) or\
(isinstance(validation_data, list) and validation_data
and not all([d.empty for d in validation_data]))
if validation_data is not None and (df_not_empty or df_list_not_empty):
validation_data_id = ray.put(validation_data)
is_val_valid = True
else:
is_val_valid = False
return train_func
@staticmethod
def _prepare_trainable_class(input_df,
feature_transformers,
future_seq_len,
metric,
validation_df=None,
mc=False,
remote_dir=None
):
"""
Prepare the train function for ray tune
:param input_df: input dataframe
:param feature_transformers: feature transformers
:param model: model or model selector
:param validation_df: validation dataframe
:param metric: the rewarding metric
:return: the train function
"""
input_df_id = ray.put(input_df)
ft_id = ray.put(feature_transformers)
# model_id = ray.put(model)
df_not_empty = isinstance(validation_df, pd.DataFrame) and not validation_df.empty
df_list_not_empty = isinstance(validation_df, list) and validation_df \
and not all([d.empty for d in validation_df])
if validation_df is not None and (df_not_empty or df_list_not_empty):
validation_df_id = ray.put(validation_df)
is_val_df_valid = True
else:
is_val_df_valid = False
return TrainableClass
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220,
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220,
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220,
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1441,
16835,
540,
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198
] | 1.901993 | 6,020 |
from .custom import *
@DATASETS.register_module(force=True) | [
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8
] | 2.857143 | 21 |
import os
from utilidades.consola import *
from CriptografiaModerna.menuCM import menuCM
from CriptografiaClasica.menuCC import menuCC
#DEFINICIÓN DE VARIABLES
#DEFINICIÓN DE FUNCIONES
limpiarPantalla()
iniciarMenu()
despedida()
input('')
limpiarPantalla() | [
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] | 2.243902 | 123 |
#!/usr/bin/env python
# coding=utf-8
# Stan 2012-03-12
from __future__ import (division, absolute_import,
print_function, unicode_literals)
import sys
import os
import logging
from importlib import import_module
from .core.types23 import *
from .core.db import getDbUri, openDbUri
from .core.recorder import Recorder
from . import __version__ as index_version
from .base import proceed
from .base.parse import parse_files
from .base.models import Base, Error
| [
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62,
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198,
6738,
764,
8692,
13,
27530,
1330,
7308,
11,
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628
] | 2.97561 | 164 |
##############################################################################
##############################################################################
##############################################################################
##############################################################################
| [
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] | 6.444444 | 54 |
import torch.utils.data as data
from PIL import Image
import os
import os.path
import random
def _make_dataset(dir):
"""
Creates a 2D list of all the frames in N clips containing
M frames each.
2D List Structure:
[[frame00, frame01,...frameM] <-- clip0
[frame00, frame01,...frameM] <-- clip0
:
[frame00, frame01,...frameM]] <-- clipN
Parameters
----------
dir : string
root directory containing clips.
Returns
-------
list
2D list described above.
"""
framesPath = []
# Find and loop over all the clips in root `dir`.
for index, folder in enumerate(os.listdir(dir)):
clipsFolderPath = os.path.join(dir, folder)
# Skip items which are not folders.
if not (os.path.isdir(clipsFolderPath)):
continue
framesPath.append([])
# Find and loop over all the frames inside the clip.
for image in sorted(os.listdir(clipsFolderPath)):
# Add path to list.
framesPath[index].append(os.path.join(clipsFolderPath, image))
return framesPath
def _make_video_dataset(dir):
"""
Creates a 1D list of all the frames.
1D List Structure:
[frame0, frame1,...frameN]
Parameters
----------
dir : string
root directory containing frames.
Returns
-------
list
1D list described above.
"""
framesPath = []
# Find and loop over all the frames in root `dir`.
for image in sorted(os.listdir(dir)):
# Add path to list.
framesPath.append(os.path.join(dir, image))
return framesPath
def _pil_loader(path, cropArea=None, resizeDim=None, frameFlip=0):
"""
Opens image at `path` using pil and applies data augmentation.
Parameters
----------
path : string
path of the image.
cropArea : tuple, optional
coordinates for cropping image. Default: None
resizeDim : tuple, optional
dimensions for resizing image. Default: None
frameFlip : int, optional
Non zero to flip image horizontally. Default: 0
Returns
-------
list
2D list described above.
"""
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
img = Image.open(f)
# Resize image if specified.
resized_img = img.resize(resizeDim, Image.ANTIALIAS) if (resizeDim != None) else img
# Crop image if crop area specified.
cropped_img = img.crop(cropArea) if (cropArea != None) else resized_img
# Flip image horizontally if specified.
flipped_img = cropped_img.transpose(Image.FLIP_LEFT_RIGHT) if frameFlip else cropped_img
return flipped_img.convert('RGB')
class SuperSloMo(data.Dataset):
"""
A dataloader for loading N samples arranged in this way:
|-- clip0
|-- frame00
|-- frame01
:
|-- frame11
|-- frame12
|-- clip1
|-- frame00
|-- frame01
:
|-- frame11
|-- frame12
:
:
|-- clipN
|-- frame00
|-- frame01
:
|-- frame11
|-- frame12
...
Attributes
----------
framesPath : list
List of frames' path in the dataset.
Methods
-------
__getitem__(index)
Returns the sample corresponding to `index` from dataset.
__len__()
Returns the size of dataset. Invoked as len(datasetObj).
__repr__()
Returns printable representation of the dataset object.
"""
def __init__(self, root, transform=None, dim=(640, 360), randomCropSize=(352, 352), train=True):
"""
Parameters
----------
root : string
Root directory path.
transform : callable, optional
A function/transform that takes in
a sample and returns a transformed version.
E.g, ``transforms.RandomCrop`` for images.
dim : tuple, optional
Dimensions of images in dataset. Default: (640, 360)
randomCropSize : tuple, optional
Dimensions of random crop to be applied. Default: (352, 352)
train : boolean, optional
Specifies if the dataset is for training or testing/validation.
`True` returns samples with data augmentation like random
flipping, random cropping, etc. while `False` returns the
samples without randomization. Default: True
"""
# Populate the list with image paths for all the
# frame in `root`.
framesPath = _make_dataset(root)
# Raise error if no images found in root.
if len(framesPath) == 0:
raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n"))
self.randomCropSize = randomCropSize
self.cropX0 = dim[0] - randomCropSize[0]
self.cropY0 = dim[1] - randomCropSize[1]
self.root = root
self.transform = transform
self.train = train
self.framesPath = framesPath
def __getitem__(self, index):
"""
Returns the sample corresponding to `index` from dataset.
The sample consists of two reference frames - I0 and I1 -
and a random frame chosen from the 7 intermediate frames
available between I0 and I1 along with it's relative index.
Parameters
----------
index : int
Index
Returns
-------
tuple
(sample, returnIndex) where sample is
[I0, intermediate_frame, I1] and returnIndex is
the position of `random_intermediate_frame`.
e.g.- `returnIndex` of frame next to I0 would be 0 and
frame before I1 would be 6.
"""
sample = []
if (self.train):
### Data Augmentation ###
# To select random 9 frames from 12 frames in a clip
firstFrame = random.randint(0, 3)
# Apply random crop on the 9 input frames
cropX = random.randint(0, self.cropX0)
cropY = random.randint(0, self.cropY0)
cropArea = (cropX, cropY, cropX + self.randomCropSize[0], cropY + self.randomCropSize[1])
# Random reverse frame
#frameRange = range(firstFrame, firstFrame + 9) if (random.randint(0, 1)) else range(firstFrame + 8, firstFrame - 1, -1)
IFrameIndex = random.randint(firstFrame + 1, firstFrame + 7)
if (random.randint(0, 1)):
frameRange = [firstFrame, IFrameIndex, firstFrame + 8]
returnIndex = IFrameIndex - firstFrame - 1
else:
frameRange = [firstFrame + 8, IFrameIndex, firstFrame]
returnIndex = firstFrame - IFrameIndex + 7
# Random flip frame
randomFrameFlip = random.randint(0, 1)
else:
# Fixed settings to return same samples every epoch.
# For validation/test sets.
firstFrame = 0
cropArea = (0, 0, self.randomCropSize[0], self.randomCropSize[1])
IFrameIndex = ((index) % 7 + 1)
returnIndex = IFrameIndex - 1
frameRange = [0, IFrameIndex, 8]
randomFrameFlip = 0
# Loop over for all frames corresponding to the `index`.
for frameIndex in frameRange:
# Open image using pil and augment the image.
image = _pil_loader(self.framesPath[index][frameIndex], cropArea=cropArea, frameFlip=randomFrameFlip)
# Apply transformation if specified.
if self.transform is not None:
image = self.transform(image)
sample.append(image)
return sample, returnIndex
def __len__(self):
"""
Returns the size of dataset. Invoked as len(datasetObj).
Returns
-------
int
number of samples.
"""
return len(self.framesPath)
def __repr__(self):
"""
Returns printable representation of the dataset object.
Returns
-------
string
info.
"""
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
class UCI101Test(data.Dataset):
"""
A dataloader for loading N samples arranged in this way:
|-- clip0
|-- frame00
|-- frame01
|-- frame02
|-- clip1
|-- frame00
|-- frame01
|-- frame02
:
:
|-- clipN
|-- frame00
|-- frame01
|-- frame02
...
Attributes
----------
framesPath : list
List of frames' path in the dataset.
Methods
-------
__getitem__(index)
Returns the sample corresponding to `index` from dataset.
__len__()
Returns the size of dataset. Invoked as len(datasetObj).
__repr__()
Returns printable representation of the dataset object.
"""
def __init__(self, root, transform=None):
"""
Parameters
----------
root : string
Root directory path.
transform : callable, optional
A function/transform that takes in
a sample and returns a transformed version.
E.g, ``transforms.RandomCrop`` for images.
"""
# Populate the list with image paths for all the
# frame in `root`.
framesPath = _make_dataset(root)
# Raise error if no images found in root.
if len(framesPath) == 0:
raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n"))
self.root = root
self.framesPath = framesPath
self.transform = transform
def __getitem__(self, index):
"""
Returns the sample corresponding to `index` from dataset.
The sample consists of two reference frames - I0 and I1 -
and a intermediate frame between I0 and I1.
Parameters
----------
index : int
Index
Returns
-------
tuple
(sample, returnIndex) where sample is
[I0, intermediate_frame, I1] and returnIndex is
the position of `intermediate_frame`.
The returnIndex is always 3 and is being returned
to maintain compatibility with the `SuperSloMo`
dataloader where 3 corresponds to the middle frame.
"""
sample = []
# Loop over for all frames corresponding to the `index`.
for framePath in self.framesPath[index]:
# Open image using pil.
image = _pil_loader(framePath)
# Apply transformation if specified.
if self.transform is not None:
image = self.transform(image)
sample.append(image)
return sample, 3
def __len__(self):
"""
Returns the size of dataset. Invoked as len(datasetObj).
Returns
-------
int
number of samples.
"""
return len(self.framesPath)
def __repr__(self):
"""
Returns printable representation of the dataset object.
Returns
-------
string
info.
"""
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
class Video(data.Dataset):
"""
A dataloader for loading all video frames in a folder:
|-- frame0
|-- frame1
:
:
|-- frameN
...
Attributes
----------
framesPath : list
List of frames' path in the dataset.
origDim : tuple
original dimensions of the video.
dim : tuple
resized dimensions of the video (for CNN).
Methods
-------
__getitem__(index)
Returns the sample corresponding to `index` from dataset.
__len__()
Returns the size of dataset. Invoked as len(datasetObj).
__repr__()
Returns printable representation of the dataset object.
"""
def __init__(self, root, transform=None):
"""
Parameters
----------
root : string
Root directory path.
transform : callable, optional
A function/transform that takes in
a sample and returns a transformed version.
E.g, ``transforms.RandomCrop`` for images.
"""
# Populate the list with image paths for all the
# frame in `root`.
framesPath = _make_video_dataset(root)
# Get dimensions of frames
frame = _pil_loader(framesPath[0])
self.origDim = frame.size
self.dim = int(self.origDim[0] / 32) * 32, int(self.origDim[1] / 32) * 32
# Raise error if no images found in root.
if len(framesPath) == 0:
raise(RuntimeError("Found 0 files in: " + root + "\n"))
self.root = root
self.framesPath = framesPath
self.transform = transform
def __getitem__(self, index):
"""
Returns the sample corresponding to `index` from dataset.
The sample consists of two reference frames - I0 and I1.
Parameters
----------
index : int
Index
Returns
-------
list
sample is [I0, I1] where I0 is the frame with index
`index` and I1 is the next frame.
"""
sample = []
# Loop over for all frames corresponding to the `index`.
for framePath in [self.framesPath[index], self.framesPath[index + 1]]:
# Open image using pil.
image = _pil_loader(framePath, resizeDim=self.dim)
# Apply transformation if specified.
if self.transform is not None:
image = self.transform(image)
sample.append(image)
return sample
def __len__(self):
"""
Returns the size of dataset. Invoked as len(datasetObj).
Returns
-------
int
number of samples.
"""
# Using `-1` so that dataloader accesses only upto
# frames [N-1, N] and not [N, N+1] which because frame
# N+1 doesn't exist.
return len(self.framesPath) - 1
def __repr__(self):
"""
Returns printable representation of the dataset object.
Returns
-------
string
info.
"""
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str
class Images(data.Dataset):
"""
A dataloader for loading all video frames in a folder:
|-- frame0
|-- frame1
:
:
|-- frameN
...
Attributes
----------
framesPath : list
List of frames' path in the dataset.
origDim : tuple
original dimensions of the video.
dim : tuple
resized dimensions of the video (for CNN).
Methods
-------
__getitem__(index)
Returns the sample corresponding to `index` from dataset.
__len__()
Returns the size of dataset. Invoked as len(datasetObj).
__repr__()
Returns printable representation of the dataset object.
"""
def __init__(self, frame0, frame1, transform=None):
"""
Parameters
----------
frame0 : string
Input image 1
frame1: string
Input image 2
transform : callable, optional
A function/transform that takes in
a sample and returns a transformed version.
E.g, ``transforms.RandomCrop`` for images.
"""
# Populate the list with image paths for all the
# frame in `root`.
framesPath = [frame0, frame1]
# Get dimensions of frames
frame = _pil_loader(frame0)
self.origDim = frame.size
self.dim = int(self.origDim[0] / 32) * 32, int(self.origDim[1] / 32) * 32
# Raise error if no images found in root.
if len(framesPath) == 0:
raise(RuntimeError("Found 0 files in: " + root + "\n"))
self.framesPath = framesPath
self.transform = transform
def __getitem__(self, index):
"""
Returns the sample corresponding to `index` from dataset.
The sample consists of two reference frames - I0 and I1.
Parameters
----------
index : int
Index
Returns
-------
list
sample is [I0, I1] where I0 is the frame with index
`index` and I1 is the next frame.
"""
sample = []
# Loop over for all frames corresponding to the `index`.
for framePath in [self.framesPath[index], self.framesPath[index + 1]]:
# Open image using pil.
image = _pil_loader(framePath, resizeDim=self.dim)
# Apply transformation if specified.
if self.transform is not None:
image = self.transform(image)
sample.append(image)
return sample
def __len__(self):
"""
Returns the size of dataset. Invoked as len(datasetObj).
Returns
-------
int
number of samples.
"""
# Using `-1` so that dataloader accesses only upto
# frames [N-1, N] and not [N, N+1] which because frame
# N+1 doesn't exist.
return len(self.framesPath) - 1
def __repr__(self):
"""
Returns printable representation of the dataset object.
Returns
-------
string
info.
"""
fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
fmt_str += ' Root Location: {}\n'.format(self.root)
tmp = ' Transforms (if any): '
fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
return fmt_str | [
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318,
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257,
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2656,
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2008,
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46545,
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220,
220,
220,
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581,
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40117,
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20410,
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198,
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11,
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198,
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1303,
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13,
198,
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] | 2.183961 | 8,953 |
from typing import Optional
from botocore.client import BaseClient
from typing import Dict
from botocore.paginate import Paginator
from botocore.waiter import Waiter
from typing import Union
from typing import List
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] | 4 | 54 |
# -*- coding: utf-8 -*-
__author__ = "R. Bauer"
__copyright__ = "MedPhyDO - Machbarkeitsstudien des Instituts für Medizinische Strahlenphysik und Strahlenschutz am Klinikum Dortmund im Rahmen von Bachelor und Masterarbeiten an der TU-Dortmund / FH-Dortmund"
__credits__ = ["R.Bauer", "K.Loot"]
__license__ = "MIT"
__version__ = "0.1.2"
__status__ = "Prototype"
from dotmap import DotMap
import os.path as osp
from pathlib import Path
import pandas as pd
import numpy as np
import json
from datetime import date
from isp.dicom import ispDicom
from isp.config import dict_merge
from app.config import infoFields
from app.aria import ariaClass
#from app.dicom import dicomClass
from app.results import ispResults
from app.qa.mlc import checkMlc
from app.qa.field import checkField
from app.qa.wl import checkWL
from app.qa.vmat import checkVMAT
import logging
logger = logging.getLogger( "MQTT" )
class ariaDicomClass( ariaClass, ispDicom ):
'''Zentrale Klasse
Attributes
----------
config : Dot
konfigurations Daten
variables :
Metadaten aus config.variables
infoFields:
Infofelder aus config
dicomfiles: dict
geladene Dicom dateien
pd_results: pd
testergebnisse als Pandas tabelle
resultfile
Datei mit Ergebnissen als panda File
lastSQL: str
die letzte durchgeführte sql Abfrage
'''
def __init__( self, database=None, server="VMSDBD", config=None ):
"""Klasse sowie ariaClass und dicomClass initialisieren
"""
# Klassen defaults setzen und übergaben
self.config = config
self.variables = self.config.variables
self.infoFields = infoFields
self.dicomfiles: dict = {}
self.pd_results = None
self.resultfile = None
self.lastSQL = ""
# ariaClass initialisieren
ariaClass.__init__( self, database )
# dicomClass initialisieren. Der Erfolg kann über dicomClass.initialized abgefragt werden
ispDicom.__init__( self, server, self.config )
# Datei mit Ergebnissen als pandas laden
self.resultfile = osp.join( self.config.get("resultsPath", ".."), self.config.get("database.gqa.name", "gqa.json") )
self.pd_results = ispResults( self.config, self.resultfile )
def initResultsPath(self, AcquisitionYear=None ):
'''Den Ablegeort zu den PDF Dateien bestimmen
in variables.path befindet sich jetzt der resultsPath ggf. mit angehängten AcquisitionYear
Parameters
----------
AcquisitionYear : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
dirname : str
der aktuelle PDF Pfad (auch in self.variables["path"] )
'''
paths = [ ]
# ist der Pfad relativ angegeben ab base path verwenden
if self.config["resultsPath"][0] == ".":
paths.append( self.config["BASE_DIR"] )
paths.append( self.config["resultsPath"] )
else:
paths.append( self.config["resultsPath"] )
# zusätzlich noch das AcquisitionYear anfügen
if AcquisitionYear:
paths.append( str(AcquisitionYear) )
# den Pfad in variables["path"] ablegen
dirname = osp.abspath( osp.join( *paths ) )
self.variables["path"] = dirname
return dirname
def getAllGQA(self, pids=None, testTags:list=None, year:int=None, month:int=None, day:int=None, withInfo=True, withResult=False ):
'''Holt für die angegebenen PatientenIds aus allen Courses
die Felder mit Angaben in [Radiation].[Comment] und wertet sie entsprechend aus
Parameters
----------
pids : list, optional
DESCRIPTION. The default is None.
testTags : list, optional
DESCRIPTION. The default is None.
year : int, optional
DESCRIPTION. The default is None.
month : int, optional
DESCRIPTION. The default is None.
day : int, optional
DESCRIPTION. The default is None.
withInfo : TYPE, optional
DESCRIPTION. The default is True.
withResult : TYPE, optional
DESCRIPTION. The default is False.
Returns
-------
gqa : dict
Aufbau::
units: dict
<unit>: dict
<infoType>: dict
ready: dict
all: int
<energy> : int
gqa: dict
fields: int
energyFields: int
counts: dict
all: int
<energy> : int
pdf: dict,
items: dict
<energy>: dict
<SliceUID>: {info} -> dies wird bei run in ein DataFrame umgewandelt
series: [],
'''
if not pids:
return {}
if type(pids) == str:
pids = pids.split(",")
if not type(pids) == list:
pids = [pids]
if not pids or len(pids) == 0:
return {}
# filter zusammenstellen
where = "LEN([Radiation].[Comment]) > 0 "
subSql = []
for pid in pids:
subSql.append( "[Patient].[PatientId]='{}'".format( pid.strip() ) )
if len( subSql ) > 0:
where += " AND (" + " OR ".join( subSql ) + ")"
images, sql = self.getImages(
addWhere=where,
AcquisitionYear=year,
AcquisitionMonth=month,
AcquisitionDay=day,
testTags=testTags
)
self.lastSQL = sql
# Pfad für die PDF Dateien
self.initResultsPath( year )
return self.prepareGQA( images, year=year, withInfo=withInfo, withResult=withResult )
def prepareGQA(self, imagedatas=[], year:int=0, withInfo=True, withResult=False, withDicomData:bool=False ):
"""Auswertung für GQA vorbereiten zusätzlich noch Ergebnisse aus der Datenbank einfügen
Benötig config.GQA und config.units
- units: ["Linac-1", "Linac-2"],
- gqa : dict
<testId>: dict
<unit>: dict
fields: int
energyFields: int
Parameters
----------
imagedatas : list, optional
Auflistungen von Bildinformationen aus der Aria Datenbank. The default is [].
year : int, optional
DESCRIPTION. The default is 0.
withInfo : TYPE, optional
alle ImageInfos mit hinzufügen. The default is True.
withResult : boolean, optional
Testergebnisse mit ausgeben. The default is False.
withDicomData : boolean, optional
Info pro gerät in dicomfiles ablegen. The default is False.
Returns
-------
gqa : dict
# alles aus config.gqa dabei die Unites mit Daten füllen
<testname>
info:
inaktiv
tip
anleitung
options:
TODO:
tolerance:
<energy>
<unit-n>
fields: int
energyFields: int
energy: list
"""
# dicom gerät , name , infos
self.dicomfiles = {}
units = self.config.units
# Dateien im Pfad
pdfFiles = []
if osp.exists( self.variables["path"] ):
p = Path( self.variables["path"] )
pdfFiles = [i.name for i in p.glob( '*.pdf' )]
# files = os.listdir( self.variables["path"] )
data = {
"GQA" : self.config.get("GQA").toDict(),
"units" : units,
"testTags" : {},
"testIds": {}
}
# nur das gesuchte Jahr, ohne index
df_results = self.pd_results.gqa[ self.pd_results.gqa['year'] == year ].reset_index()
result_fields = [ "acceptance", "group" ]
if withResult:
result_fields.append("data")
# neuen index setzen
# Das geht nur bei daten in df_results
if len(df_results.index) > 0:
df_results.set_index( df_results.apply(lambda x: f"{x['year']}|{x['unit']}|{x['test']}|{x['energy']}|{x['month']}", axis=1), inplace=True )
data["results"] = df_results[ result_fields ].to_dict( orient="split" )
else:
data["results"] = {
"columns":result_fields,
"data":[],
"index":[]
}
# tags und gqa ids bestimmen
for testid, item in self.config.GQA.items():
if "tag" in item:
data["testTags"][ item["tag"] ] = testid
data["testIds"][ testid ] = item["tag"]
tagNotFound = {}
inactiv = []
testNotFound = []
for imagedata in imagedatas:
# bereitetet die Datenbank Informationen auf
info = self.getImageInfos( imagedata )
unit = info["unit"]
energy = info["energy"]
#
# zusätzlich die Daten in self.dicomfiles ablegen
#
if withDicomData:
if not unit in self.dicomfiles:
self.dicomfiles[ unit ] = {}
# zusätzlich in dicomfiles ablegen
self.dicomfiles[ unit ][ info["id"] ] = info
# Felder zuordnen, eine Aufnahme kann für mehrere tests verwendet werden
# tag für die Datenbank, testid für das PDF
for testTag in info["testTags"]:
# nur wenn es auch einen test gibt
if not testTag in data["testTags"]:
tagNotFound[ testTag ] = testTag
continue
testId = data["testTags"][testTag]
# ist der test in gqa nicht erlaubt überspringen
# inaktive kann auch einen Text enthalten der beschreibt warum
# FIXME: inaktive
t = "GQA.{}.info.inaktiv".format( testId )
if not self.config.get(t, False) == False:
inactiv.append( self.config.get(t) )
continue
# gibt es in GQA passend zum Test dem Gerät und der Energie einen Eintrag
t = "GQA.{}.{}.energyFields.{}".format( testId, unit, energy )
energyFields = self.config.get(t, False)
if energyFields == False:
testNotFound.append( t )
continue
# Art des tests MT|JT
tagArt = testId[0:2]
if tagArt == "JT":
dateFlag = "0"
else:
dateFlag = str( info["AcquisitionMonth"] )
#
test_unit = data["GQA"][testId][unit]
if not dateFlag in test_unit:
test_unit[ dateFlag ] = {}
if not energy in test_unit[ dateFlag ]:
test_unit[ dateFlag ][energy] = {
"counts": 0,
"ready": False,
"pdfName" : "",
"pdf": False,
"acceptance" : {}
}
# Anzahl der Felder für das Datumsflag der jeweiligen Energie erhöhen (counts)
test_unit[ dateFlag ][ energy ][ "counts" ] += 1
# auf mid Anzahl prüfen
if test_unit[ dateFlag ][ energy ][ "counts" ] >= energyFields:
test_unit[ dateFlag ][ energy ][ "ready" ] = True
# PDF Dateiname zusammenstellen
pdfName = self.config.render_template(
self.config["templates"][ "PDF-" + tagArt + "-filename"],
{
"AcquisitionYear": info["AcquisitionYear"],
"AcquisitionMonth": info["AcquisitionMonth"],
"unit": unit,
"energy": energy,
"testId": testId
}
)
if pdfName in pdfFiles:
test_unit[ dateFlag ][ energy ][ "pdfName" ] = pdfName
test_unit[ dateFlag ][ energy ][ "pdf" ] = True
# nicht gefundene Tags
data["inactiv"] = inactiv
data["tagNotFound"] = tagNotFound
data["testNotFound"] = testNotFound
return data
# ---------------------- einfache Ausgaben
def getTagging(self, art:str="full", pid:list=[], output_format="json" ):
"""alle Tags in Comment Feldern als html Tabelle zurückgeben
Parameters
----------
art : str, optional
Art der Tagging Tabellen (). The default is "full".
* full
* sum
* test
* tags
pid : list, optional
Angabe von PatientsIds für die Tags bestimmt werden sollen. The default is [].
output_format: str
Format der Ausgabe [ json, html ]
Returns
-------
str|dict
html Tags code oder dict.
"""
style = """
<style>
.gqa-tagging {
}
.gqa-tagging table {
color: #333;
font-family: Helvetica, Arial, sans-serif;
min-width: 100px;
border-collapse: collapse;
border-spacing: 0;
font-size: 10px;
}
.gqa-tagging table td, .gqa-tagging table th {
border: 1px solid gray;
text-align: center;
vertical-align: middle;
}
.gqa-tagging table th {
font-weight: bold;
}
.gqa-tagging table thead th, .gqa-tagging table tbody th {
background-color: #F7F7F7;
}
.gqa-tagging table td {
background-color: white;
}
.gqa-tagging table th, .gqa-tagging table td, .gqa-tagging table caption {
padding: 2px 2px 2px 2px;
}
</style>
"""
split = True
if art == "tags":
# bei tags conmment nicht splitten
split = False
tags = self.getTags( pid, split )
if output_format == "json":
return tags
if not tags or len(tags) == 0:
return "getTagging: keine Daten gefunden"
html = '<div class="gqa-tagging flex-1">'
html += '<h1 class="m-0 p-1 text-white bg-secondary">Art: ' + art + '</h2>'
# Pandas erzeugen
df = pd.DataFrame( tags )
if art == "full":
table = pd.pivot_table( df,
index=['Comment', 'CourseId', 'PlanSetupId', 'Energy', 'DoseRate', 'RadiationId'],
columns='PatientId',
values= "nummer",
fill_value=0
)
elif art == "sum":
table = pd.pivot_table( df,
index=['Comment', 'CourseId', 'PlanSetupId','Energy', 'DoseRate'],
columns=['PatientId'],
values= 'nummer',
aggfunc=[np.sum],
fill_value=0
)
elif art == "test":
table = pd.pivot_table( df,
index=['Comment', 'CourseId', 'Energy', 'DoseRate'],
columns=[ 'PlanSetupId', 'PatientId'],
values= 'nummer',
aggfunc=[np.sum],
fill_value=0
)
elif art == "tags":
table = pd.pivot_table( df,
index=['Comment'],
columns=['PatientId'],
values= 'nummer',
fill_value=0
#aggfunc=[np.sum]
)
# tags zurückgeben als einfache Tabelle
#table = df[ ["Comment"] ].groupby( "Comment" ).first().reset_index()
# table.fillna('', inplace=True)
html += (table.style
.applymap( highlight_fifty )
.set_table_attributes('class="gqa-tagging-table"')
#.float_format()
.render()
)
html += '</div>'
return style + html
def getMatrix( self, output_format="json", params:dict={} ):
"""Gibt eine Liste alle Testbeschreibungen (config) mit Anleitungen
Parameters
----------
output_format: str
Format der Ausgabe [ json, html ]
params: dict
Aufrufparameter mit year und month
Returns
-------
str|dict
html matrix code oder dict.
"""
# jahr und Monat bei 0 mit dem aktuellen belegen
today = date.today()
if params["year"] == 0:
params["year"] = today.year
if params["month"] == 0:
params["month"] = today.month
# pdf wird zum laden der Texte verwendet
from isp.mpdf import PdfGenerator as ispPdf
pdf = ispPdf()
html_jt = ""
html_mt = ""
html_nn = ""
data_dict = {}
for key, content in self.config.GQA.items():
data = {
"key" : key,
"tip" : "",
"need" : "",
"anleitung" : "",
"chips" : ""
}
chips = []
# units und energy
for unit_key, unit in self.config.units.items():
if unit in content:
for energy in content[ unit ].energy:
chips.append( { "class": "badge badge-pill badge-info mr-1", "content": "{} - {}".format( unit_key, energy ) } )
# info bestimmen
info = content.info
data["tip"] = info.get("tip", "")
need = info.get("need", "")
if type(need) == str and need != "":
chips.append( { "class": "badge badge-pill badge-success", "content": 'benötigt: ' + need } )
# Anleitung
anleitung_filename = info.get("anleitung", "")
data["anleitung"] = '<p class="badge badge-pill badge-primary">Anleitung fehlt!</p>'
if anleitung_filename != "":
anleitung = pdf.textFile(anleitung_filename, render = False)
if anleitung:
data["anleitung"] = anleitung
# Toleranz
tolerance = content.info.get("tolerance", False)
if tolerance:
data["anleitung"] += "<h6>Toleranz</h6>"
# ggf formel erstellen
for e, item in tolerance.items():
self.prepare_tolerance(key, e)
pass
# toleranz einfügen
data["anleitung"] += '<pre class="toleranz bg-light text-monospace ">' + json.dumps( tolerance, indent=2 ) + '</pre>'
# ist der test als inaktiv Hinweis ausgeben
inaktiv = content.info.get('inaktiv', False)
if inaktiv != False:
chips.append( { "class": "inaktiv", "content": 'Inaktiv: ' + inaktiv } )
# gibt es optional Angaben
optional = content.info.get('optional', [])
if len(optional) > 0:
for item in optional:
chips.append( { "class": "badge badge-pill badge-primary", "content": 'Optional wenn: ' + item + ' OK' } )
# TODO
todo = content.info.get("TODO", False)
if todo and len(todo) > 0:
data["anleitung"] += "TODO"
data["anleitung"] += '<pre class="p-1 bg-warning">'
for t in todo:
data["anleitung"] += "* " + t + "\n"
data["anleitung"] += '</pre>'
# markierungen zusammenstellen
for chip in chips:
data["chips"] += '<div class="{class}">{content}</div>'.format(**chip)
data_dict[ key ] = content.toDict()
data_dict[ key ][ "anleitung" ] = anleitung
card = """
<div class="card m-3" >
<div class="card-header">
<span class="font-weight-bolder">{key}</span>
<span class="pl-3">{tip}</span>
<div class="float-right">{chips}</div>
</div>
<div class="card-body p-1">
{anleitung}
</div>
</div>
""".format( **data )
if key[0:2] == "JT":
html_jt += card
elif key[0:2] == "MT":
html_mt += card
else:
html_nn += card
if output_format == "json":
return data_dict
style = """
<style>
/* Anpassung pdf text */
.gqa_matrix h2 {
font-size: 1.1667em;
font-weight: bold;
line-height: 1.286em;
margin-top: 0.5em;
margin-bottom: 0.5em;
}
.gqa_matrix .card-body p::first-of-type {
background-color: #FFFFFFAA;
}
</style>
"""
html = '''
<div class="gqa_matrix">
<h1 class="m-0 p-1 text-white bg-secondary" >Angaben für: {month}/{year}</h1>
<content class="p-1 d-flex flex-row" >
<div class="w-50">{jt}</div>
<div class="w-50">{mt}</div>
<div class="">{nn}</div>
</content>
</div>
'''.format( jt=html_jt, mt=html_mt, nn=html_nn, **params )
return style + html
def prepare_tolerance(self, testid:str="", energy=None):
"""Prüft ob es in conig eine tolerance Angabe für die testid und die Energie gibt
Stellt wenn f nicht angegeben wurde eine Formel in f zusammen
Gibt es eine GQA.<testid>.info.tolerance.default Angabe, so wird diese als Grundlage für alle Energien verwendet
Zweig in config::
GQA.<testid>.info.tolerance.<energy>
{
name: {
f: formel mit {value}
value: wert
range: [min, max]
operator: [ eq, ne, lt, gt, le, ge]
}
}
Parameters
----------
testid : str, optional
id des zu verarbeitenden tolerance Bereichs
energy : str, optional
Augabe der Energie für die Info. The default is None.
Ohne Angabe wird nur der Parameter info zurückgegeben
Returns
-------
info : dict
Parameter info mit zusätzlichen Angaben für die Energie.
Beispiel::
"default": {
"warning" : { "f":"abs({value}) > 1.0", "unit": "%" },
"error" : { "f":"abs({value}) > 2.0", "unit": "%" },
"check" : { "field": "diff", "query":"ME == 100" }
},
"MU_20": {
"warning" : { "f":"abs({value}) > 1.0", "unit": "%" },
"error" : { "f":"abs({value}) > 2.5", "unit": "%" },
"check" : { "field": "diff", "query":"ME == 20" }
},
"""
info = self.config.get( ["GQA", testid, "info" ] )
default = info.tolerance.get( "default", False )
tolerance = info.tolerance.get( energy, False )
if not tolerance and not default:
return DotMap()
if not default:
default = DotMap()
if tolerance:
tolerance = dict_merge( default, tolerance)
else:
tolerance = default
#print("prepare_tolerance tolerance", tolerance )
import functools
# alle Angaben durchgehen
for name in tolerance:
if not isinstance( tolerance.get(name), dict ):
continue
for artName, art in tolerance.get(name).items():
# überspringen wenn art = soll oder f schon vorhanden
if artName == "soll" or art.get("f", None):
continue
# gibt es keine formel dann erstellen
# wurde ein wert angegeben
_value = art.get("value", None)
_range = art.get("range", None)
if _value:
#zuerst den operator festlegen
operator = art.get("operator", "gt")
# [ eq, ne, lt, gt, le, ge]
operator = functools.reduce(lambda a, b: a.replace(*b)
, [('eq','=='),('ne','!='),('lt', '<'),( 'gt', '>'),( 'le','<='),( 'ge', '>=')] #iterable of pairs: (oldval, newval)
, operator #The string from which to replace values
)
tolerance[name][artName]["f"] = "abs({}) {} {}".format( "{value}", operator, _value )
# wurde ein Bereich angegeben
elif art.get("range", None) and len(_range) >= 2:
tolerance[name][artName]["f"] = "{} <= {} >= {}".format( _range[0], "{value}", _range[1] )
return tolerance
# ---------------------- Test durchführung
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567,
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17635,
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12315,
6,
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1303,
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1976,
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585,
38572,
11,
304,
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317,
3046,
40909,
1326,
479,
1236,
277,
25151,
502,
71,
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260,
5254,
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86,
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316,
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263,
6559,
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60,
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5031,
549,
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82,
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220,
220,
3084,
796,
279,
67,
13,
79,
45785,
62,
11487,
7,
47764,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
17816,
21357,
3256,
705,
49046,
7390,
3256,
705,
20854,
40786,
7390,
3256,
705,
28925,
3256,
705,
35,
577,
32184,
3256,
705,
15546,
3920,
7390,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
11639,
12130,
1153,
7390,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
28,
366,
22510,
647,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6070,
62,
8367,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1242,
6624,
366,
16345,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3084,
796,
279,
67,
13,
79,
45785,
62,
11487,
7,
47764,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
17816,
21357,
3256,
705,
49046,
7390,
3256,
705,
20854,
40786,
7390,
41707,
28925,
3256,
705,
35,
577,
32184,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
17816,
12130,
1153,
7390,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
28,
705,
22510,
647,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4194,
20786,
41888,
37659,
13,
16345,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6070,
62,
8367,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1242,
6624,
366,
9288,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3084,
796,
279,
67,
13,
79,
45785,
62,
11487,
7,
47764,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
17816,
21357,
3256,
705,
49046,
7390,
3256,
705,
28925,
3256,
705,
35,
577,
32184,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
41888,
705,
20854,
40786,
7390,
3256,
705,
12130,
1153,
7390,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
28,
705,
22510,
647,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4194,
20786,
41888,
37659,
13,
16345,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6070,
62,
8367,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1242,
6624,
366,
31499,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3084,
796,
279,
67,
13,
79,
45785,
62,
11487,
7,
47764,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
17816,
21357,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
17816,
12130,
1153,
7390,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
28,
705,
22510,
647,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6070,
62,
8367,
28,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9460,
20786,
41888,
37659,
13,
16345,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
15940,
1976,
333,
9116,
694,
469,
11722,
435,
82,
304,
10745,
4891,
309,
9608,
293,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
11487,
796,
47764,
58,
14631,
21357,
8973,
20740,
8094,
1525,
7,
366,
21357,
1,
6739,
11085,
22446,
42503,
62,
9630,
3419,
628,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3084,
13,
20797,
2616,
10786,
3256,
287,
5372,
28,
17821,
8,
628,
220,
220,
220,
220,
220,
220,
220,
27711,
15853,
357,
11487,
13,
7635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
764,
39014,
8899,
7,
7238,
62,
69,
24905,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
764,
2617,
62,
11487,
62,
1078,
7657,
10786,
4871,
2625,
70,
20402,
12,
12985,
2667,
12,
11487,
1,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13,
22468,
62,
18982,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
764,
13287,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
27711,
15853,
705,
3556,
7146,
29,
6,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
3918,
1343,
27711,
628,
198,
220,
220,
220,
825,
651,
46912,
7,
2116,
11,
5072,
62,
18982,
2625,
17752,
1600,
42287,
25,
11600,
34758,
92,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
38,
571,
83,
304,
500,
7343,
68,
28654,
6208,
12636,
354,
260,
571,
2150,
268,
357,
11250,
8,
10255,
1052,
293,
270,
2150,
268,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
62,
18982,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18980,
4587,
27545,
70,
11231,
685,
33918,
11,
27711,
2361,
198,
220,
220,
220,
220,
220,
220,
220,
42287,
25,
8633,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
3046,
622,
69,
17143,
2357,
10255,
614,
3318,
1227,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
965,
91,
11600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
27711,
17593,
2438,
267,
1082,
8633,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
474,
993,
81,
3318,
2892,
265,
307,
72,
657,
10255,
1357,
257,
21841,
518,
297,
268,
307,
1455,
268,
198,
220,
220,
220,
220,
220,
220,
220,
1909,
796,
3128,
13,
40838,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
611,
42287,
14692,
1941,
8973,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
14692,
1941,
8973,
796,
1909,
13,
1941,
198,
220,
220,
220,
220,
220,
220,
220,
611,
42287,
14692,
8424,
8973,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
42287,
14692,
8424,
8973,
796,
1909,
13,
8424,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
37124,
266,
1447,
1976,
388,
9717,
268,
4587,
3567,
660,
3326,
86,
437,
316,
198,
220,
220,
220,
220,
220,
220,
220,
422,
318,
79,
13,
3149,
7568,
1330,
350,
7568,
8645,
1352,
355,
318,
79,
47,
7568,
198,
220,
220,
220,
220,
220,
220,
220,
37124,
796,
318,
79,
47,
7568,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
27711,
62,
73,
83,
796,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
27711,
62,
16762,
796,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
27711,
62,
20471,
796,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
11600,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
2695,
287,
2116,
13,
11250,
13,
38,
48,
32,
13,
23814,
33529,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2539,
1,
1058,
1994,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
22504,
1,
1058,
366,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
31227,
1,
1058,
366,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
272,
293,
270,
2150,
1,
1058,
366,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
354,
2419,
1,
1058,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12014,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4991,
3318,
2568,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
4326,
62,
2539,
11,
4326,
287,
2116,
13,
11250,
13,
41667,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4326,
287,
2695,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
2568,
287,
2695,
58,
4326,
20740,
22554,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12014,
13,
33295,
7,
1391,
366,
4871,
1298,
366,
14774,
469,
23009,
12,
27215,
23009,
12,
10951,
285,
81,
12,
16,
1600,
366,
11299,
1298,
45144,
92,
532,
23884,
1911,
18982,
7,
4326,
62,
2539,
11,
2568,
1267,
220,
1782,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
7508,
1266,
320,
3653,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7508,
796,
2695,
13,
10951,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
22504,
8973,
796,
7508,
13,
1136,
7203,
22504,
1600,
366,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
761,
796,
7508,
13,
1136,
7203,
31227,
1600,
366,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2099,
7,
31227,
8,
6624,
965,
290,
761,
14512,
366,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12014,
13,
33295,
7,
1391,
366,
4871,
1298,
366,
14774,
469,
23009,
12,
27215,
23009,
12,
13138,
1600,
366,
11299,
1298,
705,
11722,
9101,
83,
328,
83,
25,
705,
1343,
761,
220,
1782,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1052,
293,
270,
2150,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
281,
293,
270,
2150,
62,
34345,
796,
7508,
13,
1136,
7203,
272,
293,
270,
2150,
1600,
366,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
796,
705,
27,
79,
1398,
2625,
14774,
469,
23009,
12,
27215,
23009,
12,
39754,
5320,
2025,
293,
270,
2150,
730,
71,
2528,
0,
3556,
79,
29,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
281,
293,
270,
2150,
62,
34345,
14512,
366,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
281,
293,
270,
2150,
796,
37124,
13,
5239,
8979,
7,
272,
293,
270,
2150,
62,
34345,
11,
8543,
796,
10352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
281,
293,
270,
2150,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
796,
281,
293,
270,
2150,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
309,
13625,
35410,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15621,
796,
2695,
13,
10951,
13,
1136,
7203,
83,
37668,
1600,
10352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
15621,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
15853,
33490,
71,
21,
29,
51,
13625,
35410,
3556,
71,
21,
24618,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
308,
70,
69,
1296,
417,
1931,
301,
40635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
304,
11,
2378,
287,
15621,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
46012,
533,
62,
83,
37668,
7,
2539,
11,
304,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8214,
35410,
304,
10745,
9116,
5235,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
15853,
705,
27,
3866,
1398,
2625,
83,
13625,
35410,
275,
70,
12,
2971,
2420,
12,
2144,
24912,
366,
29,
6,
1343,
33918,
13,
67,
8142,
7,
15621,
11,
33793,
28,
17,
1267,
1343,
705,
3556,
3866,
29,
6,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
318,
83,
4587,
1332,
435,
82,
287,
461,
83,
452,
29094,
732,
271,
220,
257,
385,
469,
11722,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
461,
83,
452,
796,
2695,
13,
10951,
13,
1136,
10786,
259,
461,
83,
452,
3256,
10352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
287,
461,
83,
452,
14512,
10352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12014,
13,
33295,
7,
1391,
366,
4871,
1298,
366,
259,
461,
83,
452,
1600,
366,
11299,
1298,
705,
818,
461,
83,
452,
25,
705,
1343,
287,
461,
83,
452,
1782,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
46795,
83,
1658,
11902,
2895,
397,
268,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
796,
2695,
13,
10951,
13,
1136,
10786,
25968,
3256,
685,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
25968,
8,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
2378,
287,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12014,
13,
33295,
7,
1391,
366,
4871,
1298,
366,
14774,
469,
23009,
12,
27215,
23009,
12,
39754,
1600,
366,
11299,
1298,
705,
30719,
266,
1697,
25,
705,
1343,
2378,
1343,
705,
7477,
6,
1782,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
16926,
46,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
4598,
796,
2695,
13,
10951,
13,
1136,
7203,
51,
3727,
46,
1600,
10352,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
284,
4598,
290,
18896,
7,
83,
24313,
8,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
15853,
366,
51,
3727,
46,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
15853,
705,
27,
3866,
1398,
2625,
79,
12,
16,
275,
70,
12,
43917,
5320,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
256,
287,
284,
4598,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
15853,
366,
9,
366,
1343,
256,
1343,
37082,
77,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
272,
293,
270,
2150,
8973,
15853,
705,
3556,
3866,
29,
6,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1317,
959,
2150,
268,
1976,
385,
321,
3653,
301,
40635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
11594,
287,
12014,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
14692,
354,
2419,
8973,
15853,
705,
27,
7146,
1398,
2625,
90,
4871,
92,
5320,
90,
11299,
92,
3556,
7146,
29,
4458,
18982,
7,
1174,
35902,
8,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
11600,
58,
1994,
2361,
796,
2695,
13,
1462,
35,
713,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
62,
11600,
58,
1994,
41832,
366,
272,
293,
270,
2150,
1,
2361,
796,
281,
293,
270,
2150,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2657,
796,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
2625,
9517,
285,
12,
18,
1,
1875,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
2625,
9517,
12,
25677,
5320,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1279,
12626,
1398,
2625,
10331,
12,
6551,
12,
65,
19892,
5320,
90,
2539,
92,
3556,
12626,
29,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1279,
12626,
1398,
2625,
489,
12,
18,
5320,
90,
22504,
92,
3556,
12626,
29,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
2625,
22468,
12,
3506,
5320,
90,
354,
2419,
92,
3556,
7146,
29,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7359,
7146,
29,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
2625,
9517,
12,
2618,
279,
12,
16,
5320,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
272,
293,
270,
2150,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7359,
7146,
29,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7359,
7146,
29,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13538,
1911,
18982,
7,
12429,
7890,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
58,
15,
25,
17,
60,
6624,
366,
41,
51,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
27711,
62,
73,
83,
15853,
2657,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
1994,
58,
15,
25,
17,
60,
6624,
366,
13752,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
27711,
62,
16762,
15853,
2657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
27711,
62,
20471,
15853,
2657,
628,
220,
220,
220,
220,
220,
220,
220,
611,
5072,
62,
18982,
6624,
366,
17752,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
62,
11600,
628,
220,
220,
220,
220,
220,
220,
220,
3918,
796,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1279,
7635,
29,
198,
220,
220,
220,
220,
220,
220,
220,
11900,
1052,
6603,
2150,
37124,
2420,
9466,
198,
220,
220,
220,
220,
220,
220,
220,
764,
70,
20402,
62,
6759,
8609,
289,
17,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10369,
12,
7857,
25,
352,
13,
1433,
3134,
368,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10369,
12,
6551,
25,
10758,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1627,
12,
17015,
25,
352,
13,
27033,
368,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10330,
12,
4852,
25,
657,
13,
20,
368,
26,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10330,
12,
22487,
25,
657,
13,
20,
368,
26,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
764,
70,
20402,
62,
6759,
8609,
764,
9517,
12,
2618,
279,
3712,
11085,
12,
1659,
12,
4906,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4469,
12,
8043,
25,
1303,
29312,
5777,
3838,
26,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
7359,
7635,
29,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
27711,
796,
705,
7061,
198,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
2625,
70,
20402,
62,
6759,
8609,
5320,
198,
220,
220,
220,
220,
220,
220,
220,
1279,
71,
16,
1398,
2625,
76,
12,
15,
279,
12,
16,
2420,
12,
11186,
275,
70,
12,
38238,
1,
1875,
13450,
397,
268,
277,
25151,
25,
1391,
8424,
92,
14,
90,
1941,
92,
3556,
71,
16,
29,
198,
220,
220,
220,
220,
220,
220,
220,
1279,
11299,
1398,
2625,
79,
12,
16,
288,
12,
32880,
7059,
12,
808,
1,
1875,
198,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
2625,
86,
12,
1120,
5320,
90,
73,
83,
92,
3556,
7146,
29,
198,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
2625,
86,
12,
1120,
5320,
90,
16762,
92,
3556,
7146,
29,
198,
220,
220,
220,
220,
220,
220,
220,
1279,
7146,
1398,
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9116,
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2150,
628
] | 1.882654 | 13,473 |
#!/usr/bin/env python
## evoware/py -- python modules for Evoware scripting
## Copyright 2014 - 2019 Raik Gruenberg
##
## 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.
"""Reset a (worklist) file to empty; or create an empty file."""
import sys, os
import evoware.fileutil as F
import evoware.dialogs as D
###########################
# MAIN
###########################
if __name__ == '__main__':
f = ''
try:
if len(sys.argv) < 2:
_use()
f = F.absfile(sys.argv[1])
h = open(f, 'w')
h.close()
except Exception as why:
D.lastException('Error resetting file %r' % f)
| [
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2393,
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81,
6,
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8,
198
] | 2.726651 | 439 |
# test.py
# a list of attributes a component should have for sure
basic = [
'merit_tag',
'styling',
'dof',
'lower',
'upper',
'lifetime',
'capex',
'opex',
'variable_cost',
'variable_income',
]
power_control = [
'positive',
'negative',
]
| [
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] | 2.224806 | 129 |
import tensorflow as tf
#from tensorflow import keras
#from tensorflow.keras import backend as K
import numpy as np
#import matplotlib.pyplot as plt
from time import sleep
#=======================================================================================#
class SOMLayer(tf.keras.layers.Layer):
"""
Self-Organizing Map layer class with rectangular topology
# Example
```
model.add(SOMLayer(map_size=(10,10)))
```
# Arguments
map_size: Tuple representing the size of the rectangular map. Number of prototypes is map_size[0]*map_size[1].
prototypes: Numpy array with shape `(n_prototypes, latent_dim)` witch represents the initial cluster centers
# Input shape
2D tensor with shape: `(n_samples, latent_dim)`
# Output shape
2D tensor with shape: `(n_samples, n_prototypes)`
"""
def call(self, inputs, **kwargs):
"""
Calculate pairwise squared euclidean distances between inputs and prototype vectors
Arguments:
inputs: the variable containing data, Tensor with shape `(n_samples, latent_dim)`
Return:
d: distances between inputs and prototypes, Tensor with shape `(n_samples, n_prototypes)`
"""
# Note: (tf.expand_dims(inputs, axis=1) - self.prototypes) has shape (n_samples, n_prototypes, latent_dim)
d = tf.reduce_sum(tf.square(tf.expand_dims(inputs, axis=1) - self.prototypes), axis=2)
return d
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62,
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7,
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11,
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28,
16,
8,
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13,
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8,
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11,
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62,
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8,
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220,
220,
220,
220,
220,
220,
220,
288,
796,
48700,
13,
445,
7234,
62,
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7,
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13,
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7,
27110,
13,
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392,
62,
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7,
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82,
11,
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28,
16,
8,
532,
2116,
13,
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13567,
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16488,
28,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
288,
628
] | 2.699634 | 546 |
# 034_Aumentos_multiplos.py
print()
salario = float(input("Salário atual: R$"))
print()
# if (salario <= 1250):
# salario *= 1.15 # -> salario = salario * 1.15
# else:
# salario *= 1.10 # -> salario = salario * 1.10
salario = salario * 1.15 if salario<= 1250 else salario * 1.10
print(f"Seu novo salário será: {salario:.2f}")
print()
| [
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3419,
198
] | 2.269737 | 152 |
#
# Copyright 2020 Two Sigma Open Source, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""This program should exit within a second or two if CTRL+C is pressed (SIGINT)."""
import time
import uberjob
if __name__ == "__main__":
main()
| [
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628,
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361,
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834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
3419,
198
] | 3.631068 | 206 |
from collections import defaultdict
import numpy as np
r, c, k = map(int, input().split())
items = np.zeros((r, c), np.int32)
for _ in range(k):
y, x, v = map(int, input().split())
items[y - 1, x - 1] = v
dp = np.zeros((r, c, 4), np.int64)
for y in range(r):
for x in range(c):
dp[y, x, 0] = max(dp[y - 1, x]) if y != 0 else 0
dp[y, x, 1] = max(dp[y - 1, x]) + items[y, x] if y != 0 else 0
dp[y, x, 0] = max(dp[y, x, 0], dp[y, x - 1, 0]) if x != 0 else dp[y, x, 0]
if x != 0:
for k in range(3):
dp[y, x, k + 1] = max(dp[y, x - 1, k] + items[y, x], dp[y, x - 1, k + 1], dp[y, x, k + 1])
else:
dp[y, x, 1] = dp[y, x, 0] + items[y, x]
#print(*dp, sep="\n")
print(max(dp[-1, -1])) | [
6738,
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] | 1.773243 | 441 |
#!/usr/bin/python2 -utt
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import numpy as np
import sys
import os
import time
from PIL import Image
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import torch.optim as optim
from tqdm import tqdm
import math
import torch.nn.functional as F
from copy import deepcopy
from SparseImgRepresenter import ScaleSpaceAffinePatchExtractor
from LAF import denormalizeLAFs, LAFs2ellT, abc2A
from Utils import line_prepender
from architectures import AffNetFast
from HandCraftedModules import AffineShapeEstimator
USE_CUDA = False
try:
input_img_fname = sys.argv[1]
output_fname = sys.argv[2]
nfeats = int(sys.argv[3])
except:
print "Wrong input format. Try python hesaffBaum.py imgs/cat.png cat.txt 2000"
sys.exit(1)
img = Image.open(input_img_fname).convert('RGB')
img = np.mean(np.array(img), axis = 2)
var_image = torch.autograd.Variable(torch.from_numpy(img.astype(np.float32)), volatile = True)
var_image_reshape = var_image.view(1, 1, var_image.size(0),var_image.size(1))
HA = ScaleSpaceAffinePatchExtractor( mrSize = 5.192, num_features = nfeats, border = 5, num_Baum_iters = 16, AffNet = AffineShapeEstimator(patch_size=19))
if USE_CUDA:
HA = HA.cuda()
var_image_reshape = var_image_reshape.cuda()
LAFs, resp = HA(var_image_reshape)
ells = LAFs2ellT(LAFs.cpu()).cpu().numpy()
np.savetxt(output_fname, ells, delimiter=' ', fmt='%10.10f')
line_prepender(output_fname, str(len(ells)))
line_prepender(output_fname, '1.0')
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] | 2.585859 | 594 |
# -*- coding: utf-8 -*-
#
# michael a.g. aïvázis
# orthologue
# (c) 1998-2020 all rights reserved
#
# externals
import os
# superclass
from .String import String
# declaration
class EnvVar(String):
"""
A type declarator for strings whose default values are associated with an environment variable
"""
# constants
typename = 'envvar' # the name of my type
# meta-methods
# end of file
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] | 2.836735 | 147 |
from ..share.cal import parse_abs_from_rel_date
from .streams import Stream
__all__ = ["load_stream"]
def load_stream(
programme="Today",
station="r4",
broadcaster="bbc",
ymd=None,
ymd_ago=None,
**stream_opts,
):
"""
Create a `Stream` for a specific episode of a radio programme from the named
arguments and pass `stream_opts` through.
`ymd` and `ymd_ago` are options to specify either an absolute
or relative date as `(year, month, day)` tuple of 3 integers in both cases.
`ymd` defaults to today's date and `ymd_ago` defaults to `(0,0,0)`.
`stream_opts` include:
- `transcribe=False` to determine whether the `Stream.transcribe`
method is called upon initialisation
- `reload=False` to control whether to reload the stream from disk
- `min_s=5.`/`max_s=50.` to control the min./max. audio segment length.
If `reload` is True, do not pull/preprocess/transcribe: the transcripts are expected
to already exist on disk, so just load them from there and recreate the `Stream`.
"""
if broadcaster != "bbc":
raise NotImplementedError("Only currently supporting BBC stations")
date = parse_abs_from_rel_date(ymd=ymd, ymd_ago=ymd_ago)
ymd = (date.year, date.month, date.day)
stream = Stream(programme, station, broadcaster, ymd, **stream_opts)
return stream
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] | 2.818557 | 485 |
# -*- coding: utf-8 -*-
# ------------------------------------------------------------------------------
#
# Copyright 2022 Valory AG
# Copyright 2018-2021 Fetch.AI Limited
#
# 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.
#
# ------------------------------------------------------------------------------
"""This module contains the tests of the dialogue classes of the generic seller skill."""
from pathlib import Path
from typing import cast
import pytest
from aea.exceptions import AEAEnforceError
from aea.helpers.transaction.base import Terms
from aea.protocols.dialogue.base import DialogueLabel
from aea.test_tools.test_skill import BaseSkillTestCase, COUNTERPARTY_AGENT_ADDRESS
from packages.fetchai.protocols.default.message import DefaultMessage
from packages.fetchai.protocols.fipa.message import FipaMessage
from packages.fetchai.protocols.ledger_api.message import LedgerApiMessage
from packages.fetchai.protocols.oef_search.message import OefSearchMessage
from packages.fetchai.skills.generic_seller.dialogues import (
DefaultDialogue,
DefaultDialogues,
FipaDialogue,
FipaDialogues,
LedgerApiDialogue,
LedgerApiDialogues,
OefSearchDialogue,
OefSearchDialogues,
)
from tests.conftest import ROOT_DIR
class TestDialogues(BaseSkillTestCase):
"""Test dialogue classes of generic seller."""
path_to_skill = Path(ROOT_DIR, "packages", "fetchai", "skills", "generic_seller")
@classmethod
def setup(cls):
"""Setup the test class."""
super().setup()
cls.default_dialogues = cast(
DefaultDialogues, cls._skill.skill_context.default_dialogues
)
cls.fipa_dialogues = cast(
FipaDialogues, cls._skill.skill_context.fipa_dialogues
)
cls.ledger_api_dialogues = cast(
LedgerApiDialogues, cls._skill.skill_context.ledger_api_dialogues
)
cls.oef_search_dialogues = cast(
OefSearchDialogues, cls._skill.skill_context.oef_search_dialogues
)
def test_default_dialogues(self):
"""Test the DefaultDialogues class."""
_, dialogue = self.default_dialogues.create(
counterparty=COUNTERPARTY_AGENT_ADDRESS,
performative=DefaultMessage.Performative.BYTES,
content=b"some_content",
)
assert dialogue.role == DefaultDialogue.Role.AGENT
assert dialogue.self_address == self.skill.skill_context.agent_address
def test_fipa_dialogue(self):
"""Test the FipaDialogue class."""
fipa_dialogue = FipaDialogue(
DialogueLabel(
("", ""),
COUNTERPARTY_AGENT_ADDRESS,
self.skill.skill_context.agent_address,
),
self.skill.skill_context.agent_address,
role=DefaultDialogue.Role.AGENT,
)
# terms
with pytest.raises(AEAEnforceError, match="Terms not set!"):
assert fipa_dialogue.terms
terms = Terms(
"some_ledger_id",
self.skill.skill_context.agent_address,
"counterprty",
{"currency_id": 50},
{"good_id": -10},
"some_nonce",
)
fipa_dialogue.terms = terms
with pytest.raises(AEAEnforceError, match="Terms already set!"):
fipa_dialogue.terms = terms
assert fipa_dialogue.terms == terms
def test_fipa_dialogues(self):
"""Test the FipaDialogues class."""
_, dialogue = self.fipa_dialogues.create(
counterparty=COUNTERPARTY_AGENT_ADDRESS,
performative=FipaMessage.Performative.CFP,
query="some_query",
)
assert dialogue.role == FipaDialogue.Role.SELLER
assert dialogue.self_address == self.skill.skill_context.agent_address
def test_ledger_api_dialogue(self):
"""Test the LedgerApiDialogue class."""
ledger_api_dialogue = LedgerApiDialogue(
DialogueLabel(
("", ""),
COUNTERPARTY_AGENT_ADDRESS,
self.skill.skill_context.agent_address,
),
self.skill.skill_context.agent_address,
role=LedgerApiDialogue.Role.AGENT,
)
# associated_fipa_dialogue
with pytest.raises(AEAEnforceError, match="FipaDialogue not set!"):
assert ledger_api_dialogue.associated_fipa_dialogue
fipa_dialogue = FipaDialogue(
DialogueLabel(
("", ""),
COUNTERPARTY_AGENT_ADDRESS,
self.skill.skill_context.agent_address,
),
self.skill.skill_context.agent_address,
role=FipaDialogue.Role.BUYER,
)
ledger_api_dialogue.associated_fipa_dialogue = fipa_dialogue
with pytest.raises(AEAEnforceError, match="FipaDialogue already set!"):
ledger_api_dialogue.associated_fipa_dialogue = fipa_dialogue
assert ledger_api_dialogue.associated_fipa_dialogue == fipa_dialogue
def test_ledger_api_dialogues(self):
"""Test the LedgerApiDialogues class."""
_, dialogue = self.ledger_api_dialogues.create(
counterparty=COUNTERPARTY_AGENT_ADDRESS,
performative=LedgerApiMessage.Performative.GET_BALANCE,
ledger_id="some_ledger_id",
address="some_address",
)
assert dialogue.role == LedgerApiDialogue.Role.AGENT
assert dialogue.self_address == str(self.skill.skill_context.skill_id)
def test_oef_search_dialogues(self):
"""Test the OefSearchDialogues class."""
_, dialogue = self.oef_search_dialogues.create(
counterparty=COUNTERPARTY_AGENT_ADDRESS,
performative=OefSearchMessage.Performative.SEARCH_SERVICES,
query="some_query",
)
assert dialogue.role == OefSearchDialogue.Role.AGENT
assert dialogue.self_address == str(self.skill.skill_context.skill_id)
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] | 2.337302 | 2,772 |
import numpy as np
import matplotlib.pyplot as plt
import sys
S = read_instance(sys.argv[1])
plt.hist(S.flatten(), bins=np.linspace(0, 1, 200))
plt.title("Histogram of similarity values")
plt.xlabel("Similarity")
plt.ylabel("Frequency")
plt.savefig(sys.argv[1]+"_viz2.pdf", dpi=400)
plt.close()
n = len(S)
x = np.arange(n)
S[(x,x)] = 0.5
S = S - 0.5
m = np.quantile(np.abs(S), 0.99)
S = S / m / 2
S = S + 0.5
S[(x,x)] = 1
#print(S)
plt.imshow(S, vmin=0, vmax=1, cmap='RdBu_r')
plt.colorbar()
plt.title("Similarity matrix")
plt.savefig(sys.argv[1]+"_viz1.pdf", dpi=400)
plt.close()
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] | 2 | 293 |
import logging
import os
import sys
cwd = os.getcwd()
if cwd not in sys.path:
sys.path.append( cwd )
new_path = [ os.path.join( cwd, "lib" ) ]
if new_path not in sys.path:
new_path.extend( sys.path )
sys.path = new_path
from galaxy.util import parse_xml
log = logging.getLogger(__name__)
# Set a 10 minute timeout for repository installation.
repository_installation_timeout = 600
def get_installed_repository_info( elem, last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision, tool_path ):
"""
Return the GALAXY_TEST_FILE_DIR, the containing repository name and the
change set revision for the tool elem. This only happens when testing
tools installed from the tool shed.
"""
tool_config_path = elem.get( 'file' )
installed_tool_path_items = tool_config_path.split( '/repos/' )
sans_shed = installed_tool_path_items[ 1 ]
path_items = sans_shed.split( '/' )
repository_owner = path_items[ 0 ]
repository_name = path_items[ 1 ]
changeset_revision = path_items[ 2 ]
if repository_name != last_tested_repository_name or changeset_revision != last_tested_changeset_revision:
# Locate the test-data directory.
installed_tool_path = os.path.join( installed_tool_path_items[ 0 ], 'repos', repository_owner, repository_name, changeset_revision )
for root, dirs, files in os.walk( os.path.join(tool_path, installed_tool_path )):
if '.' in dirs:
dirs.remove( '.hg' )
if 'test-data' in dirs:
return os.path.join( root, 'test-data' ), repository_name, changeset_revision
return None, repository_name, changeset_revision
return last_galaxy_test_file_dir, last_tested_repository_name, last_tested_changeset_revision
def parse_tool_panel_config( config, shed_tools_dict ):
"""
Parse a shed-related tool panel config to generate the shed_tools_dict. This only happens when testing tools installed from the tool shed.
"""
last_galaxy_test_file_dir = None
last_tested_repository_name = None
last_tested_changeset_revision = None
tool_path = None
has_test_data = False
tree = parse_xml( config )
root = tree.getroot()
tool_path = root.get('tool_path')
for elem in root:
if elem.tag == 'tool':
galaxy_test_file_dir, \
last_tested_repository_name, \
last_tested_changeset_revision = get_installed_repository_info( elem,
last_galaxy_test_file_dir,
last_tested_repository_name,
last_tested_changeset_revision,
tool_path )
if galaxy_test_file_dir:
if not has_test_data:
has_test_data = True
if galaxy_test_file_dir != last_galaxy_test_file_dir:
if not os.path.isabs( galaxy_test_file_dir ):
galaxy_test_file_dir = os.path.join( os.getcwd(), galaxy_test_file_dir )
guid = elem.get( 'guid' )
shed_tools_dict[ guid ] = galaxy_test_file_dir
last_galaxy_test_file_dir = galaxy_test_file_dir
elif elem.tag == 'section':
for section_elem in elem:
if section_elem.tag == 'tool':
galaxy_test_file_dir, \
last_tested_repository_name, \
last_tested_changeset_revision = get_installed_repository_info( section_elem,
last_galaxy_test_file_dir,
last_tested_repository_name,
last_tested_changeset_revision,
tool_path )
if galaxy_test_file_dir:
if not has_test_data:
has_test_data = True
if galaxy_test_file_dir != last_galaxy_test_file_dir:
if not os.path.isabs( galaxy_test_file_dir ):
galaxy_test_file_dir = os.path.join( os.getcwd(), galaxy_test_file_dir )
guid = section_elem.get( 'guid' )
shed_tools_dict[ guid ] = galaxy_test_file_dir
last_galaxy_test_file_dir = galaxy_test_file_dir
return has_test_data, shed_tools_dict
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] | 1.894531 | 2,560 |
largura = float(input('Qual a largura da parede em metros? '))
altura = float(input('Qual a altura da parede em metros? '))
area = largura * altura
print(f'A área dessa parede é: {area}m². ')
tinta = area / 2
print(f'Será usado {tinta}L de tinta para cada metro quadrado.') | [
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from django.db import models
class Profile(models.Model):
"""Profile model."""
user = models.OneToOneField('user.User', on_delete=models.CASCADE)
picture = models.ImageField(
'profile picture',
upload_to='user/pictures/',
blank=True,
null=True
)
biography = models.TextField(max_length=500, blank=True)
movies_create = models.PositiveIntegerField(default=0)
movies_recomment = models.PositiveIntegerField(default=0)
| [
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] | 2.642857 | 182 |
def resolve():
'''
code here
求めるものは
k番目のボールを除いた N−1個のボールから、書かれている整数が等しいような異なる2つのボールを選び出す方法
言い換えて
①同じ数から2個選ぶ組み合わせの和
②k番目のボールを除いた N−1個のボールから、K番目のボールと同じ数を選ぶ数
※選ぶボールとペアになっていた個数を数え上げて引く
①-②
'''
import collections
N = int(input())
A_list = [int(item) for item in input().split()]
origin_dict = collections.Counter(A_list)
twopair_in_N = 0
for i in origin_dict.values():
twopair_in_N += i*(i-1)//2
for j in range(N):
deff = origin_dict[A_list[j]] -1
print(twopair_in_N - deff)
if __name__ == "__main__":
resolve()
| [
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] | 1.324895 | 474 |
import os
import pandas as pd
def runtime_input_folder(scenario, datasheet_name):
"""
Creates a SyncroSim Datasheet input folder.
Parameters
----------
scenario : Scenario
Scenario class instance.
datasheet_name : String
Name of SyncroSim Datasheet.
Returns
-------
String
Path to input folder.
"""
_validate_environment()
parent_folder = _environment.input_directory.item()
return _create_scenario_folder(scenario, parent_folder, datasheet_name)
def runtime_output_folder(scenario, datasheet_name):
"""
Creates a SyncroSim Datasheet output folder.
Parameters
----------
scenario : Scenario
Scenario class instance.
datasheet_name : String
Name of SyncroSim Datasheet.
Returns
-------
String
Path to ouput folder.
"""
_validate_environment()
parent_folder = _environment.output_directory.item()
return _create_scenario_folder(scenario, parent_folder, datasheet_name)
def runtime_temp_folder(folder_name):
"""
Creates a SyncroSim Datasheet temporary folder.
Parameters
----------
folder_name : String
Name of temporary folder.
Returns
-------
String
Path to temporary folder.
"""
_validate_environment()
return _create_temp_folder(folder_name)
def progress_bar(report_type="step", iteration=None, timestep=None,
total_steps=None, message=None):
"""
Begins, steps, ends, and reports progress for a SyncroSim simulation.
Parameters
----------
report_type : String, optional
Directive to "begin", "end", "report", "message", or "step" the
simulation. The default is "step".
iteration : Int, optional
Number of iterations. The default is None.
timestep : Int, optional
Number of timesteps. The default is None.
total_steps : Int, optional
Number of total steps in the simulation. The default is None.
message : String, optional
A message to print to the progress bar status. The default is None.
Raises
------
TypeError
If iteration, timestep, or total_steps are not Integers.
ValueError
If report_type is not "begin", "end", "step", "report", or "message".
Returns
-------
None.
"""
_validate_environment()
# Begin progress bar tracking
if report_type == "begin":
try:
assert total_steps % 1 == 0
total_steps = int(total_steps)
print("ssim-task-start=%d\r\n" % total_steps, flush=True)
except AssertionError or TypeError:
raise TypeError("total_steps must be an Integer")
# End progress bar tracking
elif report_type == "end":
print("ssim-task-end=True\r\n", flush=True)
# Step progress bar
elif report_type == "step":
print("ssim-task-step=1\r\n", flush=True)
# Report iteration and timestep
elif report_type == "report":
try:
assert iteration % 1 == 0
assert timestep % 1 == 0
print(
f"ssim-task-status=Simulating -> Iteration is {iteration}" +
" - Timestep is {timestep}\r\n",
flush=True)
except AssertionError or TypeError:
raise TypeError("iteration and timestep must be Integers")
# Print arbitrary message
elif report_type == "message":
print(
"ssim-task-status=" + str(message) + "\r\n",
flush=True)
else:
raise ValueError("Invalid report_type")
def update_run_log(*message, sep=""):
"""
Begins, steps, ends, and reports progress for a SyncroSim simulation.
Parameters
----------
*message : String
Message to write to the run log. Can be provided as multiple arguments
that will be concatenated together using sep.
sep : String, optional
String to use if concatenating multiple message arguments. The default
is an empty String.
Raises
------
ValueError
If no message is provided.
Returns
-------
None.
"""
_validate_environment()
# Check that a message is provided
if len(message) == 0:
raise ValueError("Please include a message to send to the run log.")
# Initialize the message
final_message = "ssim-task-log=" + str(message[0])
# Concatenate additional message pieces
if len(message) > 1:
for m in message[1:]:
final_message = final_message + str(sep) + str(m)
# Finalize message
final_message = final_message + "\r\n"
print(final_message, flush=True)
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] | 2.525027 | 1,878 |
#! /usr/bin/env python3
# The MIT License (MIT)
#
# Copyright(c) 2021, Damien Feneyrou <[email protected]>
#
# 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.
# Palanteer: Generate an external strings lookup from C++ code
# and/or from a binary (for the automatic instrumentation case)
#
# What it does for source code filenames:
# - loop on all the provided C/C++ files
# - identify Palanteer calls and corresponding parameters + file basenames
# - compute and display the tuple <key> <string> on stdout
#
# What it does for a provided binary:
# - calls "nm" on the Linux elf binary
# - collect all symbols from the text section
# - format and display the tuples <key> <string> on stdout (2 per function: filename and functio
import sys
if sys.version_info.major < 3:
print("ERROR: This tool requires python3 (not python2)", file=sys.stderr)
sys.exit(1)
import os
import os.path
import re
import subprocess
# Constants
# =========
# Regexp to detect if a word which starts with pl[g] (so that it looks like a command) followed with a parenthesis
MATCH_DETECT = re.compile(".*?(^|[^a-zA-Z\d])pl(g?)([a-zA-Z]*)\s*\((.*)")
# Regexp to extract the symbols from the text section, and also the weak ones
MATCH_INFO_LINE = re.compile("^([0-9a-z]+)\s+[TW]\s(.*?)\s(\S+):(\d+)$", re.IGNORECASE)
# Commands whose parameters shall be processed. Associated values are: 0=convert only strings 1=convert all parameters
PL_COMMANDS_TYPE = {
"Assert": 1,
"Begin": 0,
"End": 0,
"Data": 0,
"Text": 0,
"DeclareThread": 0,
"LockNotify": 0,
"LockNotifyDyn": 0,
"LockWait": 0,
"LockWaitDyn": 0,
"LockState": 0,
"LockStateDyn": 0,
"LockScopeState": 0,
"LockScopeStateDyn": 0,
"MakeString": 0,
"Marker": 0,
"MarkerDyn": 0,
"MemPush": 0,
"RegisterCli": 1,
"Scope": 0,
"Text": 0,
"Var": 1,
}
# Helpers
# =======
# Main entry
# ==========
# Bootstrap
if __name__ == "__main__":
main(sys.argv)
# Unit test
# =========
# ./extStringCppParser.py extStringCppParser.py shall give "good" followed by a sequential numbers, and no "BAD"
"""
plBegin aa("BAD0");
plBegin("good01");
plBegin ("good02");
plBegin("good03", "good04");
plgBegin(BAD1, "good05");
plBegin("good06",
"good07") "BAD2";
plBegin("good08", // "BAD3"
"good09"); // "BAD4"
plVar(good10, good11);
plgVar (BAD5, good12,
good13);
plgVar (BAD6, good14, // BAD7
good15);
plAssert(good16);
plgAssert(BAD8, good17,
good18);
plAssert(good19(a,b()), good20("content("), good21);
not at start of the line plMakeString("good22"),plMakeString ("good23" ) , plMakeString ( "good24")
plBegin("good25 <<< last one"); // Easy one at the end so it is easy to detect non sequential "goods"
"""
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340,
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284,
4886,
1729,
35582,
366,
11274,
82,
1,
198,
37811,
198
] | 2.776642 | 1,370 |
# Generated by Django 3.2.3 on 2021-05-28 10:52
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
513,
13,
17,
13,
18,
319,
33448,
12,
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12,
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25,
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198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
s = input()
m, n = s.split(" ")
m = int(m)
n = int(n)
for ri in range(m):
for ci in range(n):
v = ( ri + 1 ) * ( ci + 1 )
print(v, end=" ")
print()
| [
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# !/usr/bin/python
# _ _ ____ _ _ ____ _ _ _
# | | (_) ___ ___ _ __ ___ ___ | _ \| | __ _| |_ ___ | _ \ ___ ___ ___ __ _ _ __ (_) |_(_) ___ _ __
# | | | |/ __/ _ \ '_ \/ __|/ _ \ | |_) | |/ _` | __/ _ \ | |_) / _ \/ __/ _ \ / _` | '_ \| | __| |/ _ \| '_ \
# | |___| | (__ __/ | | \__ \ __/ | __/| | (_| | |_ __/ | _ < __/ (__ (_) | (_| | | | | | |_| | (_) | | | |
# |_____|_|\___\___|_| |_|___/\___| |_| |_|\__,_|\__\___| |_| \_\___|\___\___/ \__, |_| |_|_|\__|_|\___/|_| |_|
# |___/
# (c) Shahar Gino, July-2017, [email protected]
# ---------------------------------------------------------------------------------------------------------------
class PossiblePlate:
""" Class for representing a (possible) license-plate object """
# -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- .. -- ..
def __init__(self):
""" Constructor """
self.imgPlate = None
self.imgGrayscale = None
self.imgThresh = None
self.rrLocationOfPlateInScene = None
self.rrLocationOfPlateInSceneGbl = None
self.strChars = ""
self.rectFind = False
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