hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e22ce0744b5992da29b3bb2c5f852c9d7b69cb4e
| 210
|
py
|
Python
|
iris_sdk/models/data/ord/city_search_order.py
|
NumberAI/python-bandwidth-iris
|
0e05f79d68b244812afb97e00fd65b3f46d00aa3
|
[
"MIT"
] | 2
|
2020-04-13T13:47:59.000Z
|
2022-02-23T20:32:41.000Z
|
iris_sdk/models/data/ord/city_search_order.py
|
bandwidthcom/python-bandwidth-iris
|
dbcb30569631395041b92917252d913166f7d3c9
|
[
"MIT"
] | 5
|
2020-09-18T20:59:24.000Z
|
2021-08-25T16:51:42.000Z
|
iris_sdk/models/data/ord/city_search_order.py
|
bandwidthcom/python-bandwidth-iris
|
dbcb30569631395041b92917252d913166f7d3c9
|
[
"MIT"
] | 5
|
2018-12-12T14:39:50.000Z
|
2020-11-17T21:42:29.000Z
|
#!/usr/bin/env python
from iris_sdk.models.base_resource import BaseData
from iris_sdk.models.maps.ord.city_search_order import CitySearchOrderMap
class CitySearchOrder(CitySearchOrderMap, BaseData):
pass
| 30
| 73
| 0.838095
| 28
| 210
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| 0.75
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| 0.128655
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| 0.090476
| 210
| 7
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| 30
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| true
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| 1
| 0
|
0
| 7
|
e268687fa1c123ed2c84de4abe445ff07847470e
| 16,999
|
py
|
Python
|
tests/ut/python/fallback/syntax/test_isinstance.py
|
httpsgithu/mindspore
|
c29d6bb764e233b427319cb89ba79e420f1e2c64
|
[
"Apache-2.0"
] | 1
|
2022-03-30T03:43:29.000Z
|
2022-03-30T03:43:29.000Z
|
tests/ut/python/fallback/syntax/test_isinstance.py
|
949144093/mindspore
|
c29d6bb764e233b427319cb89ba79e420f1e2c64
|
[
"Apache-2.0"
] | null | null | null |
tests/ut/python/fallback/syntax/test_isinstance.py
|
949144093/mindspore
|
c29d6bb764e233b427319cb89ba79e420f1e2c64
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""test graph is_instance"""
import pytest
import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, CSRTensor, COOTensor, RowTensor, ms_function, ms_class, context
from mindspore.ops import Primitive
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.variable import Variable
context.set_context(mode=context.GRAPH_MODE)
class Net1(nn.Cell):
def construct(self, x):
return x
class Net2(nn.Cell):
def construct(self, x):
return x + 1
class NetCombine(Net1, Net2):
def construct(self, x):
return x + 2
@ms_class
class MSClass1:
def __init__(self):
self.num1 = Tensor(1)
@ms_class
class MSClass2:
def __init__(self):
self.num2 = Tensor(2)
@ms_class
class MSCombine(MSClass1, MSClass2):
def __init__(self):
super(MSCombine, self).__init__()
self.num3 = Tensor(3)
def test_isinstance_x_cell_obj_base_type_cell():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be cell.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, nn.Cell)
assert foo()
def test_isinstance_x_cell_obj_base_type_cell_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be cell.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, Net1)
assert foo()
def test_isinstance_x_cell_obj_base_type_cell_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be cell.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, Net2)
assert not foo()
def test_isinstance_x_cell_obj_base_type_cell_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be cell.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, NetCombine)
assert not foo()
def test_isinstance_x_cell_obj_base_type_csr_tensor():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be CSRTensor.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, CSRTensor)
assert not foo()
def test_isinstance_x_cell_obj_base_type_tuple():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be tuple.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, (Net1, int, np.ndarray, MSCombine))
assert foo()
def test_isinstance_x_cell_obj_base_type_tuple_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be tuple.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, (Net2, int, np.ndarray))
assert not foo()
def test_isinstance_x_cell_obj_base_type_tuple_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be tuple.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, (int, float, (CSRTensor, (Net1, nn.Cell))))
assert foo()
def test_isinstance_x_cell_obj_base_type_tuple_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be cell object and base type to be tuple.
Expectation: No exception.
"""
net = Net1()
@ms_function
def foo():
return isinstance(net, (int, float, (CSRTensor, (Net2, Primitive), MSClass1)))
assert not foo()
def test_isinstance_x_ms_class_obj():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be ms_class object.
Expectation: No exception.
"""
ms_obj = MSClass1()
@ms_function
def foo():
return isinstance(ms_obj, (int, float, (MSClass1,)))
assert foo()
def test_isinstance_x_ms_class_obj_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be ms_class object.
Expectation: No exception.
"""
ms_obj = MSClass1()
@ms_function
def foo():
return isinstance(ms_obj, (int, float, (MSClass2,)))
assert not foo()
def test_isinstance_x_ms_class_obj_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be ms_class object.
Expectation: No exception.
"""
ms_obj = MSCombine()
@ms_function
def foo():
return isinstance(ms_obj, (int, float, (MSClass1, MSCombine)))
assert foo()
def test_isinstance_x_ms_class_obj_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be ms_class object.
Expectation: No exception.
"""
ms_obj = MSClass2()
@ms_function
def foo():
return isinstance(ms_obj, (int, float, (MSClass1, MSCombine)))
assert not foo()
def test_isinstance_x_ms_class_obj_5():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be ms_class object.
Expectation: No exception.
"""
ms_obj = MSClass1()
@ms_function
def foo():
return isinstance(ms_obj, (int, float, (MSClass2(), MSCombine)))
with pytest.raises(TypeError) as err:
foo()
assert "isinstance() arg 2 must be a type or tuple of types" in str(err.value)
def test_isinstance_x_primitive_obj_base_type_primitive():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be primitive object and base type to be primitive.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(P.Add(), Primitive) and isinstance(P.Add(), P.Add)
assert foo()
def test_isinstance_x_primitive_obj_base_type_primitive_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be primitive object and base type to be primitive.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(P.Add(), (Primitive, np.ndarray, int)) and isinstance(P.Add(), (P.Add, Net2, float))
assert foo()
def test_isinstance_x_primitive_obj_base_type_primitive_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be primitive object and base type to be primitive.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Primitive("test"), Primitive)
assert foo()
def test_isinstance_x_primitive_obj_base_type_primitive_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be primitive object and base type to be primitive.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Primitive("test"), (Primitive, CSRTensor, MSCombine))
assert foo()
def test_isinstance_x_primitive_obj_base_type_primitive_5():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be primitive object and base type to be primitive.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Primitive("test"), (int, list, (Primitive,), CSRTensor))
assert foo()
def test_isinstance_x_external_obj():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be external object which need to use fallback.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(np.array(1), np.ndarray)
assert foo()
def test_isinstance_x_external_obj_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be external object which need to use fallback.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(np.array(1), list)
assert not foo()
def test_isinstance_x_external_obj_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be external object which need to use fallback.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(np.array(1), (list, np.ndarray, Tensor, CSRTensor))
assert foo()
def test_isinstance_x_external_obj_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be external object which need to use fallback.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(np.array(1), ((int, list), np.ndarray, Tensor, CSRTensor, MSClass2))
assert foo()
def test_isinstance_x_tensor():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be tensor.
Expectation: No exception.
"""
@ms_function
def foo(x):
return isinstance(x, (list, np.ndarray, Tensor, CSRTensor))
x = Tensor(1)
assert foo(x)
def test_isinstance_x_tensor_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be tensor.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Tensor(1), (list, np.ndarray, Tensor, CSRTensor))
assert foo()
def test_isinstance_x_tensor_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be tensor.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Tensor(1), (list, np.ndarray, CSRTensor))
assert not foo()
def test_isinstance_x_tensor_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be tensor.
Expectation: No exception.
"""
@ms_function
def foo():
a = Tensor(np.array([4.0, 5.0, 6.0]).astype(np.float32))
return isinstance(F.sin(a), (list, np.ndarray, Tensor, CSRTensor, MSClass1))
assert foo()
def test_isinstance_x_coo_tensor():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be COOTensor.
Expectation: No exception.
"""
@ms_function
def foo():
indices = Tensor([[0, 1], [1, 2]])
values = Tensor([1, 2])
shape = (3, 4)
x = COOTensor(indices, values, shape)
return isinstance(x, (int, list, (Tensor, COOTensor)))
assert foo()
def test_isinstance_x_coo_tensor_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be COOTensor.
Expectation: No exception.
"""
@ms_function
def foo():
indices = Tensor([[0, 1], [1, 2]])
values = Tensor([1, 2])
shape = (3, 4)
x = COOTensor(indices, values, shape)
return isinstance(x, (int, list, (Tensor, nn.Cell, CSRTensor)))
assert not foo()
def test_isinstance_x_csr_tensor():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be CSRTensor.
Expectation: No exception.
"""
@ms_function
def foo():
indptr = Tensor([0, 1, 2])
indices = Tensor([0, 1])
values = Tensor([1, 2])
shape = (2, 4)
x = CSRTensor(indptr, indices, values, shape)
return isinstance(x, (int, tuple, (CSRTensor, COOTensor), nn.Cell))
assert foo()
def test_isinstance_x_csr_tensor_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be CSRTensor.
Expectation: No exception.
"""
@ms_function
def foo():
indptr = Tensor([0, 1, 2])
indices = Tensor([0, 1])
values = Tensor([1, 2])
shape = (2, 4)
x = CSRTensor(indptr, indices, values, shape)
return isinstance(x, (int, tuple, (Tensor, COOTensor), nn.Cell))
assert not foo()
def test_isinstance_x_row_tensor():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be RowTensor.
Expectation: No exception.
"""
@ms_function
def foo():
indices = Tensor([0])
values = Tensor([[1, 2]])
shape = (3, 2)
x = RowTensor(indices, values, shape)
return isinstance(x, (Tensor, COOTensor, (list, tuple), RowTensor, MSClass2))
assert foo()
def test_isinstance_x_row_tensor_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance support x to be RowTensor.
Expectation: No exception.
"""
@ms_function
def foo():
indices = Tensor([0])
values = Tensor([[1, 2]])
shape = (3, 2)
x = RowTensor(indices, values, shape)
return isinstance(x, (Tensor, COOTensor, (list, tuple)))
assert not foo()
def test_isinstance_wrong_cmp_input():
"""
Feature: Graph isinstance.
Description: Graph isinstance when cmp input is wrong.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Tensor(1), (np.array([1, 2, 3], Tensor)))
with pytest.raises(TypeError) as err:
foo()
assert "isinstance() arg 2 must be a type or tuple of types" in str(err.value)
def test_isinstance_wrong_cmp_input_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance when cmp input is wrong.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Tensor(1), Tensor(1))
with pytest.raises(TypeError) as err:
foo()
assert "isinstance() arg 2 must be a type or tuple of types" in str(err.value)
def test_isinstance_wrong_cmp_input_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance when cmp input is wrong.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Tensor(1), [list, tuple])
with pytest.raises(TypeError) as err:
foo()
assert "isinstance() arg 2 must be a type or tuple of types" in str(err.value)
def test_isinstance_wrong_cmp_input_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance when cmp input is wrong.
Expectation: No exception.
"""
@ms_function
def foo():
return isinstance(Tensor(1), [1, tuple])
with pytest.raises(TypeError) as err:
foo()
assert "isinstance() arg 2 must be a type or tuple of types" in str(err.value)
@pytest.mark.skip(reason='Variable feature not support scalar input')
def test_isinstance_x_variable():
"""
Feature: Graph isinstance.
Description: Graph isinstance when x is variable.
Expectation: No exception.
"""
@ms_function
def foo(x):
return isinstance(x, (int, tuple))
x = Variable(2)
assert foo(x)
@pytest.mark.skip(reason='Variable feature not support scalar input')
def test_isinstance_x_variable_2():
"""
Feature: Graph isinstance.
Description: Graph isinstance when x is variable.
Expectation: No exception.
"""
@ms_function
def foo(x):
return isinstance(x, (int, tuple))
x = Variable(True)
assert foo(x)
@pytest.mark.skip(reason='Variable feature not support scalar input')
def test_isinstance_x_variable_3():
"""
Feature: Graph isinstance.
Description: Graph isinstance when x is variable.
Expectation: No exception.
"""
@ms_function
def foo(x):
return isinstance(x, (int, tuple))
x = Variable([1, 2, 3, 4])
assert not foo(x)
def test_isinstance_x_variable_4():
"""
Feature: Graph isinstance.
Description: Graph isinstance when x is variable.
Expectation: No exception.
"""
@ms_function
def foo(x):
return isinstance(x, (int, list))
x = Variable([Tensor(1), Tensor(2), Tensor(3)])
assert foo(x)
def test_isinstance_x_variable_5():
"""
Feature: Graph isinstance.
Description: Graph isinstance when x is variable.
Expectation: No exception.
"""
@ms_function
def foo(x):
return isinstance(x, (int, tuple))
x = Variable([Tensor(1), Tensor(2), Tensor(3)])
assert not foo(x)
| 24.353868
| 110
| 0.651862
| 2,171
| 16,999
| 4.957623
| 0.077384
| 0.117068
| 0.066338
| 0.128775
| 0.878008
| 0.869832
| 0.858404
| 0.833504
| 0.807396
| 0.784725
| 0
| 0.012944
| 0.241014
| 16,999
| 697
| 111
| 24.388809
| 0.821268
| 0.345197
| 0
| 0.644518
| 0
| 0
| 0.038439
| 0
| 0
| 0
| 0
| 0
| 0.139535
| 1
| 0.299003
| false
| 0
| 0.026578
| 0.126246
| 0.495017
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 8
|
e27bcb77e6176e1a8fc47192a0c1968674e1f99a
| 196
|
py
|
Python
|
python_iugu/request/__init__.py
|
guiflemes/python_iugu
|
e7efca84e76ebd5b99773f4e57a14f991fbcb520
|
[
"MIT"
] | 2
|
2021-01-07T14:31:16.000Z
|
2021-07-16T11:49:18.000Z
|
python_iugu/request/__init__.py
|
guiflemes/python_iugu
|
e7efca84e76ebd5b99773f4e57a14f991fbcb520
|
[
"MIT"
] | 1
|
2021-08-05T00:51:40.000Z
|
2021-08-06T17:14:42.000Z
|
python_iugu/request/__init__.py
|
guiflemes/python_iugu
|
e7efca84e76ebd5b99773f4e57a14f991fbcb520
|
[
"MIT"
] | 1
|
2021-08-24T23:49:08.000Z
|
2021-08-24T23:49:08.000Z
|
from python_iugu.request import subscription_request
from python_iugu.request import customer_request
from python_iugu.request import invoice_request
from python_iugu.request import utils_request
| 39.2
| 52
| 0.897959
| 28
| 196
| 6
| 0.321429
| 0.238095
| 0.333333
| 0.5
| 0.767857
| 0.607143
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081633
| 196
| 4
| 53
| 49
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
e29dad435564d2828da36adfc12de58704504d87
| 1,231
|
py
|
Python
|
wsgi/iportalen_django/speaker_list/utils.py
|
I-sektionen/i-portalen
|
1713e5814d40c0da1bf3278d60a561e7d3df3550
|
[
"MIT"
] | 4
|
2016-09-21T17:06:01.000Z
|
2018-02-06T16:36:44.000Z
|
wsgi/iportalen_django/speaker_list/utils.py
|
I-sektionen/i-portalen
|
1713e5814d40c0da1bf3278d60a561e7d3df3550
|
[
"MIT"
] | 149
|
2016-03-07T23:50:47.000Z
|
2022-03-11T23:16:33.000Z
|
wsgi/iportalen_django/speaker_list/utils.py
|
I-sektionen/i-portalen
|
1713e5814d40c0da1bf3278d60a561e7d3df3550
|
[
"MIT"
] | 1
|
2016-03-07T23:02:06.000Z
|
2016-03-07T23:02:06.000Z
|
from django.http import JsonResponse
from .models import SpeakerList
from .exceptions import SpeakerListException
from django.utils.translation import ugettext as _
def add_to_speakerlist(event, speech_nr):
try:
user = event.entryasparticipant_set.get(speech_nr=speech_nr).user
SpeakerList.objects.add(event=event, user=user)
return JsonResponse({'status': 'ok'})
except SpeakerListException as e:
return JsonResponse({"status": str(e.reason)})
except:
return JsonResponse({"status": _("Ingen användare med det talarnummret.")})
def next_speaker(event):
try:
SpeakerList.objects.next(event=event)
return JsonResponse({'status': 'ok'})
except SpeakerListException as e:
return JsonResponse({"status": str(e.reason)})
def remove_from_speakerlist(event, speech_nr):
try:
user = event.entryasparticipant_set.get(speech_nr=speech_nr).user
SpeakerList.objects.remove(event=event, user=user)
return JsonResponse({'status': 'ok'})
except SpeakerListException as e:
return JsonResponse({"status": str(e.reason)})
except:
return JsonResponse({"status": _("Ingen användare med det talarnummret.")})
| 34.194444
| 83
| 0.701868
| 140
| 1,231
| 6.057143
| 0.285714
| 0.169811
| 0.226415
| 0.091981
| 0.73467
| 0.73467
| 0.73467
| 0.73467
| 0.73467
| 0.73467
| 0
| 0
| 0.186028
| 1,231
| 35
| 84
| 35.171429
| 0.846307
| 0
| 0
| 0.642857
| 0
| 0
| 0.104065
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.107143
| false
| 0
| 0.142857
| 0
| 0.535714
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
2ca5cb37e2db615334da65a6d7b9c96ef1817571
| 89
|
py
|
Python
|
tests/utils/test_helper.py
|
rafaelsdellama/nnga
|
9c04fd0c5f68644345d197db44b1bc6f6c780a30
|
[
"MIT"
] | 1
|
2021-12-28T23:40:41.000Z
|
2021-12-28T23:40:41.000Z
|
tests/utils/test_helper.py
|
rafaelsdellama/nnga
|
9c04fd0c5f68644345d197db44b1bc6f6c780a30
|
[
"MIT"
] | null | null | null |
tests/utils/test_helper.py
|
rafaelsdellama/nnga
|
9c04fd0c5f68644345d197db44b1bc6f6c780a30
|
[
"MIT"
] | null | null | null |
from nnga.utils.helper import dump_tensors
def test_dump_tensors():
dump_tensors()
| 14.833333
| 42
| 0.775281
| 13
| 89
| 5
| 0.692308
| 0.507692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146067
| 89
| 5
| 43
| 17.8
| 0.855263
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
2cc0a85a7a1e42f83e49c6a6c1eeadbcb3ea34b6
| 302
|
py
|
Python
|
peek_plugin_base/agent/PeekAgentPlatformHookABC.py
|
Synerty/peek_plugin_base
|
aa8a09a52bd5e1f95e4b61907f5de3b661fc9780
|
[
"MIT"
] | null | null | null |
peek_plugin_base/agent/PeekAgentPlatformHookABC.py
|
Synerty/peek_plugin_base
|
aa8a09a52bd5e1f95e4b61907f5de3b661fc9780
|
[
"MIT"
] | null | null | null |
peek_plugin_base/agent/PeekAgentPlatformHookABC.py
|
Synerty/peek_plugin_base
|
aa8a09a52bd5e1f95e4b61907f5de3b661fc9780
|
[
"MIT"
] | 1
|
2016-12-12T15:05:18.000Z
|
2016-12-12T15:05:18.000Z
|
from peek_plugin_base.PeekPlatformCommonHookABC import PeekPlatformCommonHookABC
from peek_plugin_base.PeekPlatformServerInfoHookABC import PeekPlatformServerInfoHookABC
class PeekAgentPlatformHookABC(PeekPlatformCommonHookABC,
PeekPlatformServerInfoHookABC):
pass
| 37.75
| 88
| 0.817881
| 19
| 302
| 12.789474
| 0.526316
| 0.065844
| 0.115226
| 0.148148
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.162252
| 302
| 7
| 89
| 43.142857
| 0.960474
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.2
| 0.4
| 0
| 0.6
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
e2c3ef687c80c154eda06d7c0a59c2cb28e1b79d
| 5,232
|
py
|
Python
|
pysdrc/tests/test_decoder.py
|
Flameeyes/pysdrc
|
ae8b8a7214c3844cdfb5b9ca6ffe7ae0abc7d845
|
[
"MIT-0",
"MIT"
] | 1
|
2021-04-03T12:17:28.000Z
|
2021-04-03T12:17:28.000Z
|
pysdrc/tests/test_decoder.py
|
Flameeyes/pysirc
|
ae8b8a7214c3844cdfb5b9ca6ffe7ae0abc7d845
|
[
"MIT-0",
"MIT"
] | null | null | null |
pysdrc/tests/test_decoder.py
|
Flameeyes/pysirc
|
ae8b8a7214c3844cdfb5b9ca6ffe7ae0abc7d845
|
[
"MIT-0",
"MIT"
] | null | null | null |
# SPDX-FileCopyrightText: 2020 Diego Elio Pettenò
#
# SPDX-License-Identifier: MIT
import unittest
from pysdrc import decoder
class SIRCDecoderTest(unittest.TestCase):
def test_simple(self):
pulses = [
2442,
560,
623,
575,
1221,
577,
617,
582,
622,
577,
1219,
579,
625,
574,
620,
578,
1218,
580,
624,
575,
619,
581,
623,
575,
618,
]
self.assertEqual(decoder.decode_sirc(pulses), (18, 1))
def test_ideal(self):
pulses = [
2400,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
600,
600,
600,
]
self.assertEqual(decoder.decode_sirc(pulses), (18, 1))
def test_bad_header(self):
pulses = [
200000,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
600,
600,
600,
]
with self.assertRaises(decoder.DecodeException):
decoder.decode_sirc(pulses)
def test_short(self):
pulses = [
2400,
600,
600,
]
with self.assertRaises(decoder.DecodeException):
decoder.decode_sirc(pulses)
def test_bad_length(self):
pulses = [
2400,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
1200,
600,
600,
600,
600,
600,
]
with self.assertRaises(decoder.DecodeException):
decoder.decode_sirc(pulses)
class NECDecoderTest(unittest.TestCase):
def test_simple(self):
pulses = [
9155,
4505,
604,
536,
596,
548,
595,
541,
601,
539,
604,
536,
606,
534,
598,
542,
600,
1650,
603,
1648,
596,
1653,
600,
1650,
603,
1647,
596,
1654,
600,
1649,
604,
1646,
597,
543,
599,
1651,
601,
538,
605,
1646,
597,
542,
600,
540,
603,
537,
604,
536,
608,
533,
597,
543,
600,
1650,
603,
537,
605,
1645,
599,
1651,
603,
1648,
604,
1644,
600,
1650,
602,
]
self.assertEqual(decoder.decode_nec(pulses), (128, 5))
def test_invalid(self):
pulses = [
9155,
4505,
604,
536,
596,
548,
595,
541,
601,
539,
604,
536,
606,
534,
598,
542,
600,
1650,
603,
1648,
596,
1653,
600,
1650,
603,
1647,
596,
1654,
600,
1649,
604,
1646,
597,
543,
599,
1651,
601,
538,
605,
1646,
597,
542,
600,
540,
603,
537,
604,
536,
608,
533,
597,
543,
600,
1650,
603,
537,
605,
1645,
599,
1651,
603,
1648,
604,
600,
600,
1650,
602,
]
with self.assertRaises(decoder.NECDecodeException):
decoder.decode_nec(pulses)
| 18.229965
| 62
| 0.285933
| 355
| 5,232
| 4.169014
| 0.284507
| 0.202703
| 0.218919
| 0.194595
| 0.720946
| 0.711486
| 0.711486
| 0.665541
| 0.665541
| 0.665541
| 0
| 0.416216
| 0.646407
| 5,232
| 286
| 63
| 18.293706
| 0.383784
| 0.014526
| 0
| 0.855556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025926
| 1
| 0.025926
| false
| 0
| 0.007407
| 0
| 0.040741
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
391b14e9e7188190e26e5213c69211d3981c08a0
| 13,639
|
py
|
Python
|
src/tests/test_resolver.py
|
go-tk/jroh
|
b91d69c2348b74cf5b6894040b9b2a451b7c72df
|
[
"MIT"
] | 15
|
2021-08-09T04:24:03.000Z
|
2022-01-22T06:23:41.000Z
|
src/tests/test_resolver.py
|
go-tk/jroh
|
b91d69c2348b74cf5b6894040b9b2a451b7c72df
|
[
"MIT"
] | null | null | null |
src/tests/test_resolver.py
|
go-tk/jroh
|
b91d69c2348b74cf5b6894040b9b2a451b7c72df
|
[
"MIT"
] | null | null | null |
import unittest
from ..jroh.resolver import InvalidSpecError
from . import common
class TestResolver(unittest.TestCase):
def test_duplicate_id(self):
test_data_list = [
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Foo:
version: 1.1.1
""",
"foo2.yaml": """
services:
Foo:
version: 1.1.1
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: duplicate service id; node_uri1='foo2\.yaml#/services/Foo' node_uri2='foo\.yaml#/services/Foo'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
methods:
Hello:
service_id: World
""",
"foo2.yaml": """
methods:
Hello:
service_id: World
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: duplicate method id; node_uri1='foo2\.yaml#/methods/Hello' node_uri2='foo\.yaml#/methods/Hello'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
models:
Test:
type: struct
""",
"foo2.yaml": """
models:
Test:
type: enum
underlying_type: int32
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: duplicate model id; node_uri1='foo2\.yaml#/models/Test' node_uri2='foo\.yaml#/models/Test'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
models:
EEE:
type: enum
underlying_type: int32
constants:
Apple:
value: 100
""",
"foo2.yaml": """
models:
TTT:
type: enum
underlying_type: string
constants:
Apple:
value: "apple"
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: duplicate constant id; node_uri1='foo2\.yaml#/models/TTT/constants/Apple' node_uri2='foo\.yaml#/models/EEE/constants/Apple'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
errors:
Err:
code: 1000
status_code: 400
""",
"foo2.yaml": """
errors:
Err:
code: 2000
status_code: 400
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: duplicate error id; node_uri1='foo2\.yaml#/errors/Err' node_uri2='foo\.yaml#/errors/Err'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
World:
version: 1.1.1
methods:
Hello:
service_id: World
models:
Test:
type: struct
errors:
Err:
code: 1000
status_code: 400
""",
"foo2.yaml": """
namespace: New
services:
World:
version: 1.1.1
methods:
Hello:
service_id: World
models:
Test:
type: struct
errors:
Err:
code: 1000
status_code: 400
""",
},
),
]
common.test(self, test_data_list)
def test_duplicate_error_code(self):
test_data_list = [
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
errors:
Foo:
code: 1000
status_code: 400
""",
"foo2.yaml": """
errors:
Bar:
code: 1000
status_code: 400
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: duplicate error code; node_uri1='foo2\.yaml#/errors/Bar/code' node_uri2='foo\.yaml#/errors/Foo/code' error_code=1",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
namespace: New
errors:
Foo:
code: 1000
status_code: 400
""",
"foo2.yaml": """
errors:
Bar:
code: 1000
status_code: 400
""",
},
),
]
common.test(self, test_data_list)
def test_error_code_conflict(self):
test_data_list = [
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
Foo: {}
New.Bar: {}
errors:
Foo:
code: 1000
status_code: 400
""",
"foo2.yaml": """
namespace: New
errors:
Bar:
code: 1000
status_code: 400
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: error code conflict; node_uri1='foo\.yaml#/methods/World/error_cases/New\.Bar' node_uri2='foo\.yaml#/methods/World/error_cases/Foo' error_code=1000",
),
]
common.test(self, test_data_list)
def test_service_ref(self):
test_data_list = [
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
methods:
World:
service_id: Hello1
"""
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: service not found; node_uri='foo\.yaml#/methods/World/service_ids\[0\]' service_id='Hello1'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
"""
},
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_ids:
- Hello
- Hello1
"""
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: service not found; node_uri='foo\.yaml#/methods/World/service_ids\[1\]' service_id='Hello1'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
namespace: New
services:
Hello:
version: 1.1.1
""",
"bar.yaml": """
methods:
World:
service_id: Hello
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: service not found; node_uri='bar\.yaml#/methods/World/service_ids\[0\]' service_id='Hello'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
World:
version: 1.1.2
""",
"bar.yaml": """
methods:
World:
service_ids:
- Hello
- World
""",
},
),
]
common.test(self, test_data_list)
def test_error_ref(self):
test_data_list = [
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
New.Something-Wrong: {}
"""
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: error not found; node_uri='foo\.yaml#/methods/World/error_cases/New.Something-Wrong' namespace='New' error_id='Something-Wrong'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
Something-Wrong: {}
errors:
Something-Wrong:
code: 1000
status_code: 403
"""
},
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
namespace: New
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
Something-Wrong: {}
""",
"bar.yaml": """
errors:
Something-Wrong:
code: 1000
status_code: 403
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: error not found; node_uri='foo\.yaml#/methods/World/error_cases/Something-Wrong' namespace='New' error_id='Something-Wrong'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
Something-Wrong: {}
""",
"bar.yaml": """
namespace: New
errors:
Something-Wrong:
code: 1000
status_code: 403
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: error not found; node_uri='foo\.yaml#/methods/World/error_cases/Something-Wrong' namespace='Default' error_id='Something-Wrong'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
Something-Wrong: {}
""",
"bar.yaml": """
errors:
Something-Wrong:
code: 1000
status_code: 403
""",
},
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
namespace: New
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
Default.Something-Wrong: {}
""",
"bar.yaml": """
errors:
Something-Wrong:
code: 1000
status_code: 403
""",
},
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
error_cases:
New.Something-Wrong: {}
""",
"bar.yaml": """
namespace: New
errors:
Something-Wrong:
code: 1000
status_code: 403
""",
},
),
]
common.test(self, test_data_list)
def test_field_ref(self):
test_data_list = [
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
params:
Bar:
type: Bar
"""
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: model not found; node_uri='foo\.yaml#/methods/World/params/Bar/type' namespace='Default' model_id='Bar'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
params:
Bar:
type: Bar
models:
Bar:
type: struct
"""
},
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
namespace: New
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
results:
Bar:
type: Bar
I:
type: int32
""",
"bar.yaml": """
models:
Bar:
type: struct
""",
},
out_exception_type=InvalidSpecError,
out_exception_re=r"invalid spec: model not found; node_uri='foo\.yaml#/methods/World/results/Bar/type' namespace='New' model_id='Bar'",
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
params:
Bar:
type: Bar
""",
"bar.yaml": """
models:
Bar:
type: struct
""",
},
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
namespace: New
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
params:
Bar:
type: Default.Bar
""",
"bar.yaml": """
models:
Bar:
type: struct
""",
},
),
common.TestData(
in_file_path_2_file_data={
"foo.yaml": """
services:
Hello:
version: 1.1.1
methods:
World:
service_id: Hello
params:
Bar:
type: New.Bar
""",
"bar.yaml": """
namespace: New
models:
Bar:
type: struct
fields:
Baz:
type: Baz
Baz:
type: struct
""",
},
),
]
common.test(self, test_data_list)
def test_unused_node(self):
test_data_list = [
common.TestData(
in_file_path_2_file_data={
"foo1.yaml": """
services:
Hello:
version: 1.1.1
""",
"foo2.yaml": """
models:
Foo:
type: enum
underlying_type: int32
""",
"foo3.yaml": """
errors:
Wrong:
code: 1000
status_code: 403
""",
},
out_unused_node_uris={
"foo1.yaml#/services/Hello",
"foo2.yaml#/models/Foo",
"foo3.yaml#/errors/Wrong",
},
),
]
common.test(self, test_data_list)
if __name__ == "__main__":
unittest.main()
| 22.581126
| 198
| 0.505682
| 1,417
| 13,639
| 4.622442
| 0.066337
| 0.014351
| 0.068397
| 0.085496
| 0.875725
| 0.822137
| 0.788244
| 0.76855
| 0.74916
| 0.726107
| 0
| 0.032375
| 0.372681
| 13,639
| 603
| 199
| 22.618574
| 0.73317
| 0
| 0
| 0.846284
| 0
| 0.025338
| 0.485446
| 0.087103
| 0
| 0
| 0
| 0
| 0
| 1
| 0.011824
| false
| 0
| 0.005068
| 0
| 0.018581
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3924481fb30cc0ec841ff9cd2bc1d8457f971ab8
| 1,035
|
py
|
Python
|
tests/test_classroom.py
|
thiagosalvatore/poo-exercise
|
ab897d9b17b3aa63252c4fa7334f624f6d380d9a
|
[
"Apache-2.0"
] | null | null | null |
tests/test_classroom.py
|
thiagosalvatore/poo-exercise
|
ab897d9b17b3aa63252c4fa7334f624f6d380d9a
|
[
"Apache-2.0"
] | null | null | null |
tests/test_classroom.py
|
thiagosalvatore/poo-exercise
|
ab897d9b17b3aa63252c4fa7334f624f6d380d9a
|
[
"Apache-2.0"
] | null | null | null |
from poo_exercise.models import Classroom, Student
def test_create_classroom(tst):
cr = Classroom(tst.teacher, 2018, 1)
assert len(cr.students) == 0
assert cr.teacher.name == tst.teacher.name
assert cr.semester == 1
assert cr.year == 2018
def test_add_student_to_classroom(tst):
cr = Classroom(tst.teacher, 2018, 1)
student = Student(name='Calvin Bond')
assert len(cr.students) == 0
assert cr.teacher.name == tst.teacher.name
assert cr.semester == 1
assert cr.year == 2018
cr.add_student(student)
assert len(cr.students) == 1
assert cr.students[0].name == student.name
def test_add_student_to_classroom_twice(tst):
cr = Classroom(tst.teacher, 2018, 1)
student = Student(name='Calvin Bond')
assert len(cr.students) == 0
assert cr.teacher.name == tst.teacher.name
assert cr.semester == 1
assert cr.year == 2018
cr.add_student(student)
cr.add_student(student)
assert len(cr.students) == 1
assert cr.students[0].name == student.name
| 26.538462
| 50
| 0.679227
| 151
| 1,035
| 4.556291
| 0.178808
| 0.127907
| 0.079942
| 0.138081
| 0.911337
| 0.911337
| 0.843023
| 0.843023
| 0.774709
| 0.774709
| 0
| 0.044686
| 0.2
| 1,035
| 38
| 51
| 27.236842
| 0.786232
| 0
| 0
| 0.857143
| 0
| 0
| 0.021256
| 0
| 0
| 0
| 0
| 0
| 0.571429
| 1
| 0.107143
| false
| 0
| 0.035714
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
1a2fdcd2a8cab6de6db5933a0757a8beb063ac74
| 147,063
|
py
|
Python
|
boto3_type_annotations_with_docs/boto3_type_annotations/pinpoint_email/client.py
|
cowboygneox/boto3_type_annotations
|
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
|
[
"MIT"
] | 119
|
2018-12-01T18:20:57.000Z
|
2022-02-02T10:31:29.000Z
|
boto3_type_annotations_with_docs/boto3_type_annotations/pinpoint_email/client.py
|
cowboygneox/boto3_type_annotations
|
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
|
[
"MIT"
] | 15
|
2018-11-16T00:16:44.000Z
|
2021-11-13T03:44:18.000Z
|
boto3_type_annotations_with_docs/boto3_type_annotations/pinpoint_email/client.py
|
cowboygneox/boto3_type_annotations
|
450dce1de4e066b939de7eac2ec560ed1a7ddaa2
|
[
"MIT"
] | 11
|
2019-05-06T05:26:51.000Z
|
2021-09-28T15:27:59.000Z
|
from typing import Optional
from botocore.client import BaseClient
from typing import Dict
from botocore.paginate import Paginator
from datetime import datetime
from botocore.waiter import Waiter
from typing import Union
from typing import List
class Client(BaseClient):
def can_paginate(self, operation_name: str = None):
"""
Check if an operation can be paginated.
:type operation_name: string
:param operation_name: The operation name. This is the same name
as the method name on the client. For example, if the
method name is ``create_foo``, and you\'d normally invoke the
operation as ``client.create_foo(**kwargs)``, if the
``create_foo`` operation can be paginated, you can use the
call ``client.get_paginator(\"create_foo\")``.
:return: ``True`` if the operation can be paginated,
``False`` otherwise.
"""
pass
def create_configuration_set(self, ConfigurationSetName: str = None, TrackingOptions: Dict = None, DeliveryOptions: Dict = None, ReputationOptions: Dict = None, SendingOptions: Dict = None, Tags: List = None) -> Dict:
"""
Create a configuration set. *Configuration sets* are groups of rules that you can apply to the emails you send using Amazon Pinpoint. You apply a configuration set to an email by including a reference to the configuration set in the headers of the email. When you apply a configuration set to an email, all of the rules in that configuration set are applied to the email.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/CreateConfigurationSet>`_
**Request Syntax**
::
response = client.create_configuration_set(
ConfigurationSetName='string',
TrackingOptions={
'CustomRedirectDomain': 'string'
},
DeliveryOptions={
'SendingPoolName': 'string'
},
ReputationOptions={
'ReputationMetricsEnabled': True|False,
'LastFreshStart': datetime(2015, 1, 1)
},
SendingOptions={
'SendingEnabled': True|False
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName:
The name of the configuration set.
:type TrackingOptions: dict
:param TrackingOptions:
An object that defines the open and click tracking options for emails that you send using the configuration set.
- **CustomRedirectDomain** *(string) --* **[REQUIRED]**
The domain that you want to use for tracking open and click events.
:type DeliveryOptions: dict
:param DeliveryOptions:
An object that defines the dedicated IP pool that is used to send emails that you send using the configuration set.
- **SendingPoolName** *(string) --*
The name of the dedicated IP pool that you want to associate with the configuration set.
:type ReputationOptions: dict
:param ReputationOptions:
An object that defines whether or not Amazon Pinpoint collects reputation metrics for the emails that you send that use the configuration set.
- **ReputationMetricsEnabled** *(boolean) --*
If ``true`` , tracking of reputation metrics is enabled for the configuration set. If ``false`` , tracking of reputation metrics is disabled for the configuration set.
- **LastFreshStart** *(datetime) --*
The date and time (in Unix time) when the reputation metrics were last given a fresh start. When your account is given a fresh start, your reputation metrics are calculated starting from the date of the fresh start.
:type SendingOptions: dict
:param SendingOptions:
An object that defines whether or not Amazon Pinpoint can send email that you send using the configuration set.
- **SendingEnabled** *(boolean) --*
If ``true`` , email sending is enabled for the configuration set. If ``false`` , email sending is disabled for the configuration set.
:type Tags: list
:param Tags:
An object that defines the tags (keys and values) that you want to associate with the configuration set.
- *(dict) --*
An object that defines the tags that are associated with a resource. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Tags can help you categorize and manage resources in different ways, such as by purpose, owner, environment, or other criteria. A resource can have as many as 50 tags.
Each tag consists of a required *tag key* and an associated *tag value* , both of which you define. A tag key is a general label that acts as a category for a more specific tag value. A tag value acts as a descriptor within a tag key. For example, if you have two versions of an Amazon Pinpoint project, one for internal testing and another for external use, you might assign a ``Stack`` tag key to both projects. The value of the ``Stack`` tag key might be ``Test`` for one project and ``Production`` for the other project.
A tag key can contain as many as 128 characters. A tag value can contain as many as 256 characters. The characters can be Unicode letters, digits, white space, or one of the following symbols: _ . : / = + -. The following additional restrictions apply to tags:
* Tag keys and values are case sensitive.
* For each associated resource, each tag key must be unique and it can have only one value.
* The ``aws:`` prefix is reserved for use by AWS; you can’t use it in any tag keys or values that you define. In addition, you can\'t edit or remove tag keys or values that use this prefix. Tags that use this prefix don’t count against the limit of 50 tags per resource.
* You can associate tags with public or shared resources, but the tags are available only for your AWS account, not any other accounts that share the resource. In addition, the tags are available only for resources that are located in the specified AWS Region for your AWS account.
- **Key** *(string) --* **[REQUIRED]**
One part of a key-value pair that defines a tag. The maximum length of a tag key is 128 characters. The minimum length is 1 character.
- **Value** *(string) --* **[REQUIRED]**
The optional part of a key-value pair that defines a tag. The maximum length of a tag value is 256 characters. The minimum length is 0 characters. If you don’t want a resource to have a specific tag value, don’t specify a value for this parameter. Amazon Pinpoint will set the value to an empty string.
:rtype: dict
:returns:
"""
pass
def create_configuration_set_event_destination(self, ConfigurationSetName: str, EventDestinationName: str, EventDestination: Dict) -> Dict:
"""
Create an event destination. In Amazon Pinpoint, *events* include message sends, deliveries, opens, clicks, bounces, and complaints. *Event destinations* are places that you can send information about these events to. For example, you can send event data to Amazon SNS to receive notifications when you receive bounces or complaints, or you can use Amazon Kinesis Data Firehose to stream data to Amazon S3 for long-term storage.
A single configuration set can include more than one event destination.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/CreateConfigurationSetEventDestination>`_
**Request Syntax**
::
response = client.create_configuration_set_event_destination(
ConfigurationSetName='string',
EventDestinationName='string',
EventDestination={
'Enabled': True|False,
'MatchingEventTypes': [
'SEND'|'REJECT'|'BOUNCE'|'COMPLAINT'|'DELIVERY'|'OPEN'|'CLICK'|'RENDERING_FAILURE',
],
'KinesisFirehoseDestination': {
'IamRoleArn': 'string',
'DeliveryStreamArn': 'string'
},
'CloudWatchDestination': {
'DimensionConfigurations': [
{
'DimensionName': 'string',
'DimensionValueSource': 'MESSAGE_TAG'|'EMAIL_HEADER'|'LINK_TAG',
'DefaultDimensionValue': 'string'
},
]
},
'SnsDestination': {
'TopicArn': 'string'
},
'PinpointDestination': {
'ApplicationArn': 'string'
}
}
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that you want to add an event destination to.
:type EventDestinationName: string
:param EventDestinationName: **[REQUIRED]**
A name that identifies the event destination within the configuration set.
:type EventDestination: dict
:param EventDestination: **[REQUIRED]**
An object that defines the event destination.
- **Enabled** *(boolean) --*
If ``true`` , the event destination is enabled. When the event destination is enabled, the specified event types are sent to the destinations in this ``EventDestinationDefinition`` .
If ``false`` , the event destination is disabled. When the event destination is disabled, events aren\'t sent to the specified destinations.
- **MatchingEventTypes** *(list) --*
An array that specifies which events Amazon Pinpoint should send to the destinations in this ``EventDestinationDefinition`` .
- *(string) --*
An email sending event type. For example, email sends, opens, and bounces are all email events.
- **KinesisFirehoseDestination** *(dict) --*
An object that defines an Amazon Kinesis Data Firehose destination for email events. You can use Amazon Kinesis Data Firehose to stream data to other services, such as Amazon S3 and Amazon Redshift.
- **IamRoleArn** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the IAM role that Amazon Pinpoint uses when sending email events to the Amazon Kinesis Data Firehose stream.
- **DeliveryStreamArn** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the Amazon Kinesis Data Firehose stream that Amazon Pinpoint sends email events to.
- **CloudWatchDestination** *(dict) --*
An object that defines an Amazon CloudWatch destination for email events. You can use Amazon CloudWatch to monitor and gain insights on your email sending metrics.
- **DimensionConfigurations** *(list) --* **[REQUIRED]**
An array of objects that define the dimensions to use when you send email events to Amazon CloudWatch.
- *(dict) --*
An object that defines the dimension configuration to use when you send Amazon Pinpoint email events to Amazon CloudWatch.
- **DimensionName** *(string) --* **[REQUIRED]**
The name of an Amazon CloudWatch dimension associated with an email sending metric. The name has to meet the following criteria:
* It can only contain ASCII letters (a-z, A-Z), numbers (0-9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
- **DimensionValueSource** *(string) --* **[REQUIRED]**
The location where Amazon Pinpoint finds the value of a dimension to publish to Amazon CloudWatch. If you want Amazon Pinpoint to use the message tags that you specify using an X-SES-MESSAGE-TAGS header or a parameter to the SendEmail/SendRawEmail API, choose ``messageTag`` . If you want Amazon Pinpoint to use your own email headers, choose ``emailHeader`` . If you want Amazon Pinpoint to use link tags, choose ``linkTags`` .
- **DefaultDimensionValue** *(string) --* **[REQUIRED]**
The default value of the dimension that is published to Amazon CloudWatch if you don\'t provide the value of the dimension when you send an email. This value has to meet the following criteria:
* It can only contain ASCII letters (a-z, A-Z), numbers (0-9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
- **SnsDestination** *(dict) --*
An object that defines an Amazon SNS destination for email events. You can use Amazon SNS to send notification when certain email events occur.
- **TopicArn** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the Amazon SNS topic that you want to publish email events to. For more information about Amazon SNS topics, see the `Amazon SNS Developer Guide <https://docs.aws.amazon.com/sns/latest/dg/CreateTopic.html>`__ .
- **PinpointDestination** *(dict) --*
An object that defines a Amazon Pinpoint destination for email events. You can use Amazon Pinpoint events to create attributes in Amazon Pinpoint projects. You can use these attributes to create segments for your campaigns.
- **ApplicationArn** *(string) --*
The Amazon Resource Name (ARN) of the Amazon Pinpoint project that you want to send email events to.
:rtype: dict
:returns:
"""
pass
def create_dedicated_ip_pool(self, PoolName: str, Tags: List = None) -> Dict:
"""
Create a new pool of dedicated IP addresses. A pool can include one or more dedicated IP addresses that are associated with your Amazon Pinpoint account. You can associate a pool with a configuration set. When you send an email that uses that configuration set, Amazon Pinpoint sends it using only the IP addresses in the associated pool.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/CreateDedicatedIpPool>`_
**Request Syntax**
::
response = client.create_dedicated_ip_pool(
PoolName='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type PoolName: string
:param PoolName: **[REQUIRED]**
The name of the dedicated IP pool.
:type Tags: list
:param Tags:
An object that defines the tags (keys and values) that you want to associate with the pool.
- *(dict) --*
An object that defines the tags that are associated with a resource. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Tags can help you categorize and manage resources in different ways, such as by purpose, owner, environment, or other criteria. A resource can have as many as 50 tags.
Each tag consists of a required *tag key* and an associated *tag value* , both of which you define. A tag key is a general label that acts as a category for a more specific tag value. A tag value acts as a descriptor within a tag key. For example, if you have two versions of an Amazon Pinpoint project, one for internal testing and another for external use, you might assign a ``Stack`` tag key to both projects. The value of the ``Stack`` tag key might be ``Test`` for one project and ``Production`` for the other project.
A tag key can contain as many as 128 characters. A tag value can contain as many as 256 characters. The characters can be Unicode letters, digits, white space, or one of the following symbols: _ . : / = + -. The following additional restrictions apply to tags:
* Tag keys and values are case sensitive.
* For each associated resource, each tag key must be unique and it can have only one value.
* The ``aws:`` prefix is reserved for use by AWS; you can’t use it in any tag keys or values that you define. In addition, you can\'t edit or remove tag keys or values that use this prefix. Tags that use this prefix don’t count against the limit of 50 tags per resource.
* You can associate tags with public or shared resources, but the tags are available only for your AWS account, not any other accounts that share the resource. In addition, the tags are available only for resources that are located in the specified AWS Region for your AWS account.
- **Key** *(string) --* **[REQUIRED]**
One part of a key-value pair that defines a tag. The maximum length of a tag key is 128 characters. The minimum length is 1 character.
- **Value** *(string) --* **[REQUIRED]**
The optional part of a key-value pair that defines a tag. The maximum length of a tag value is 256 characters. The minimum length is 0 characters. If you don’t want a resource to have a specific tag value, don’t specify a value for this parameter. Amazon Pinpoint will set the value to an empty string.
:rtype: dict
:returns:
"""
pass
def create_deliverability_test_report(self, FromEmailAddress: str, Content: Dict, ReportName: str = None, Tags: List = None) -> Dict:
"""
Create a new predictive inbox placement test. Predictive inbox placement tests can help you predict how your messages will be handled by various email providers around the world. When you perform a predictive inbox placement test, you provide a sample message that contains the content that you plan to send to your customers. Amazon Pinpoint then sends that message to special email addresses spread across several major email providers. After about 24 hours, the test is complete, and you can use the ``GetDeliverabilityTestReport`` operation to view the results of the test.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/CreateDeliverabilityTestReport>`_
**Request Syntax**
::
response = client.create_deliverability_test_report(
ReportName='string',
FromEmailAddress='string',
Content={
'Simple': {
'Subject': {
'Data': 'string',
'Charset': 'string'
},
'Body': {
'Text': {
'Data': 'string',
'Charset': 'string'
},
'Html': {
'Data': 'string',
'Charset': 'string'
}
}
},
'Raw': {
'Data': b'bytes'
}
},
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
**Response Syntax**
::
{
'ReportId': 'string',
'DeliverabilityTestStatus': 'IN_PROGRESS'|'COMPLETED'
}
**Response Structure**
- *(dict) --*
Information about the predictive inbox placement test that you created.
- **ReportId** *(string) --*
A unique string that identifies the predictive inbox placement test.
- **DeliverabilityTestStatus** *(string) --*
The status of the predictive inbox placement test. If the status is ``IN_PROGRESS`` , then the predictive inbox placement test is currently running. Predictive inbox placement tests are usually complete within 24 hours of creating the test. If the status is ``COMPLETE`` , then the test is finished, and you can use the ``GetDeliverabilityTestReport`` to view the results of the test.
:type ReportName: string
:param ReportName:
A unique name that helps you to identify the predictive inbox placement test when you retrieve the results.
:type FromEmailAddress: string
:param FromEmailAddress: **[REQUIRED]**
The email address that the predictive inbox placement test email was sent from.
:type Content: dict
:param Content: **[REQUIRED]**
The HTML body of the message that you sent when you performed the predictive inbox placement test.
- **Simple** *(dict) --*
The simple email message. The message consists of a subject and a message body.
- **Subject** *(dict) --* **[REQUIRED]**
The subject line of the email. The subject line can only contain 7-bit ASCII characters. However, you can specify non-ASCII characters in the subject line by using encoded-word syntax, as described in `RFC 2047 <https://tools.ietf.org/html/rfc2047>`__ .
- **Data** *(string) --* **[REQUIRED]**
The content of the message itself.
- **Charset** *(string) --*
The character set for the content. Because of the constraints of the SMTP protocol, Amazon Pinpoint uses 7-bit ASCII by default. If the text includes characters outside of the ASCII range, you have to specify a character set. For example, you could specify ``UTF-8`` , ``ISO-8859-1`` , or ``Shift_JIS`` .
- **Body** *(dict) --* **[REQUIRED]**
The body of the message. You can specify an HTML version of the message, a text-only version of the message, or both.
- **Text** *(dict) --*
An object that represents the version of the message that is displayed in email clients that don\'t support HTML, or clients where the recipient has disabled HTML rendering.
- **Data** *(string) --* **[REQUIRED]**
The content of the message itself.
- **Charset** *(string) --*
The character set for the content. Because of the constraints of the SMTP protocol, Amazon Pinpoint uses 7-bit ASCII by default. If the text includes characters outside of the ASCII range, you have to specify a character set. For example, you could specify ``UTF-8`` , ``ISO-8859-1`` , or ``Shift_JIS`` .
- **Html** *(dict) --*
An object that represents the version of the message that is displayed in email clients that support HTML. HTML messages can include formatted text, hyperlinks, images, and more.
- **Data** *(string) --* **[REQUIRED]**
The content of the message itself.
- **Charset** *(string) --*
The character set for the content. Because of the constraints of the SMTP protocol, Amazon Pinpoint uses 7-bit ASCII by default. If the text includes characters outside of the ASCII range, you have to specify a character set. For example, you could specify ``UTF-8`` , ``ISO-8859-1`` , or ``Shift_JIS`` .
- **Raw** *(dict) --*
The raw email message. The message has to meet the following criteria:
* The message has to contain a header and a body, separated by one blank line.
* All of the required header fields must be present in the message.
* Each part of a multipart MIME message must be formatted properly.
* If you include attachments, they must be in a file format that Amazon Pinpoint supports.
* The entire message must be Base64 encoded.
* If any of the MIME parts in your message contain content that is outside of the 7-bit ASCII character range, you should encode that content to ensure that recipients\' email clients render the message properly.
* The length of any single line of text in the message can\'t exceed 1,000 characters. This restriction is defined in `RFC 5321 <https://tools.ietf.org/html/rfc5321>`__ .
- **Data** *(bytes) --* **[REQUIRED]**
The raw email message. The message has to meet the following criteria:
* The message has to contain a header and a body, separated by one blank line.
* All of the required header fields must be present in the message.
* Each part of a multipart MIME message must be formatted properly.
* Attachments must be in a file format that Amazon Pinpoint supports.
* The entire message must be Base64 encoded.
* If any of the MIME parts in your message contain content that is outside of the 7-bit ASCII character range, you should encode that content to ensure that recipients\' email clients render the message properly.
* The length of any single line of text in the message can\'t exceed 1,000 characters. This restriction is defined in `RFC 5321 <https://tools.ietf.org/html/rfc5321>`__ .
:type Tags: list
:param Tags:
An object that defines the tags (keys and values) that you want to associate with the predictive inbox placement test.
- *(dict) --*
An object that defines the tags that are associated with a resource. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Tags can help you categorize and manage resources in different ways, such as by purpose, owner, environment, or other criteria. A resource can have as many as 50 tags.
Each tag consists of a required *tag key* and an associated *tag value* , both of which you define. A tag key is a general label that acts as a category for a more specific tag value. A tag value acts as a descriptor within a tag key. For example, if you have two versions of an Amazon Pinpoint project, one for internal testing and another for external use, you might assign a ``Stack`` tag key to both projects. The value of the ``Stack`` tag key might be ``Test`` for one project and ``Production`` for the other project.
A tag key can contain as many as 128 characters. A tag value can contain as many as 256 characters. The characters can be Unicode letters, digits, white space, or one of the following symbols: _ . : / = + -. The following additional restrictions apply to tags:
* Tag keys and values are case sensitive.
* For each associated resource, each tag key must be unique and it can have only one value.
* The ``aws:`` prefix is reserved for use by AWS; you can’t use it in any tag keys or values that you define. In addition, you can\'t edit or remove tag keys or values that use this prefix. Tags that use this prefix don’t count against the limit of 50 tags per resource.
* You can associate tags with public or shared resources, but the tags are available only for your AWS account, not any other accounts that share the resource. In addition, the tags are available only for resources that are located in the specified AWS Region for your AWS account.
- **Key** *(string) --* **[REQUIRED]**
One part of a key-value pair that defines a tag. The maximum length of a tag key is 128 characters. The minimum length is 1 character.
- **Value** *(string) --* **[REQUIRED]**
The optional part of a key-value pair that defines a tag. The maximum length of a tag value is 256 characters. The minimum length is 0 characters. If you don’t want a resource to have a specific tag value, don’t specify a value for this parameter. Amazon Pinpoint will set the value to an empty string.
:rtype: dict
:returns:
"""
pass
def create_email_identity(self, EmailIdentity: str, Tags: List = None) -> Dict:
"""
Verifies an email identity for use with Amazon Pinpoint. In Amazon Pinpoint, an identity is an email address or domain that you use when you send email. Before you can use an identity to send email with Amazon Pinpoint, you first have to verify it. By verifying an address, you demonstrate that you're the owner of the address, and that you've given Amazon Pinpoint permission to send email from the address.
When you verify an email address, Amazon Pinpoint sends an email to the address. Your email address is verified as soon as you follow the link in the verification email.
When you verify a domain, this operation provides a set of DKIM tokens, which you can convert into CNAME tokens. You add these CNAME tokens to the DNS configuration for your domain. Your domain is verified when Amazon Pinpoint detects these records in the DNS configuration for your domain. It usually takes around 72 hours to complete the domain verification process.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/CreateEmailIdentity>`_
**Request Syntax**
::
response = client.create_email_identity(
EmailIdentity='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
**Response Syntax**
::
{
'IdentityType': 'EMAIL_ADDRESS'|'DOMAIN'|'MANAGED_DOMAIN',
'VerifiedForSendingStatus': True|False,
'DkimAttributes': {
'SigningEnabled': True|False,
'Status': 'PENDING'|'SUCCESS'|'FAILED'|'TEMPORARY_FAILURE'|'NOT_STARTED',
'Tokens': [
'string',
]
}
}
**Response Structure**
- *(dict) --*
If the email identity is a domain, this object contains tokens that you can use to create a set of CNAME records. To sucessfully verify your domain, you have to add these records to the DNS configuration for your domain.
If the email identity is an email address, this object is empty.
- **IdentityType** *(string) --*
The email identity type.
- **VerifiedForSendingStatus** *(boolean) --*
Specifies whether or not the identity is verified. In Amazon Pinpoint, you can only send email from verified email addresses or domains. For more information about verifying identities, see the `Amazon Pinpoint User Guide <https://docs.aws.amazon.com/pinpoint/latest/userguide/channels-email-manage-verify.html>`__ .
- **DkimAttributes** *(dict) --*
An object that contains information about the DKIM attributes for the identity. This object includes the tokens that you use to create the CNAME records that are required to complete the DKIM verification process.
- **SigningEnabled** *(boolean) --*
If the value is ``true`` , then the messages that Amazon Pinpoint sends from the identity are DKIM-signed. If the value is ``false`` , then the messages that Amazon Pinpoint sends from the identity aren't DKIM-signed.
- **Status** *(string) --*
Describes whether or not Amazon Pinpoint has successfully located the DKIM records in the DNS records for the domain. The status can be one of the following:
* ``PENDING`` – Amazon Pinpoint hasn't yet located the DKIM records in the DNS configuration for the domain, but will continue to attempt to locate them.
* ``SUCCESS`` – Amazon Pinpoint located the DKIM records in the DNS configuration for the domain and determined that they're correct. Amazon Pinpoint can now send DKIM-signed email from the identity.
* ``FAILED`` – Amazon Pinpoint was unable to locate the DKIM records in the DNS settings for the domain, and won't continue to search for them.
* ``TEMPORARY_FAILURE`` – A temporary issue occurred, which prevented Amazon Pinpoint from determining the DKIM status for the domain.
* ``NOT_STARTED`` – Amazon Pinpoint hasn't yet started searching for the DKIM records in the DKIM records for the domain.
- **Tokens** *(list) --*
A set of unique strings that you use to create a set of CNAME records that you add to the DNS configuration for your domain. When Amazon Pinpoint detects these records in the DNS configuration for your domain, the DKIM authentication process is complete. Amazon Pinpoint usually detects these records within about 72 hours of adding them to the DNS configuration for your domain.
- *(string) --*
:type EmailIdentity: string
:param EmailIdentity: **[REQUIRED]**
The email address or domain that you want to verify.
:type Tags: list
:param Tags:
An object that defines the tags (keys and values) that you want to associate with the email identity.
- *(dict) --*
An object that defines the tags that are associated with a resource. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Tags can help you categorize and manage resources in different ways, such as by purpose, owner, environment, or other criteria. A resource can have as many as 50 tags.
Each tag consists of a required *tag key* and an associated *tag value* , both of which you define. A tag key is a general label that acts as a category for a more specific tag value. A tag value acts as a descriptor within a tag key. For example, if you have two versions of an Amazon Pinpoint project, one for internal testing and another for external use, you might assign a ``Stack`` tag key to both projects. The value of the ``Stack`` tag key might be ``Test`` for one project and ``Production`` for the other project.
A tag key can contain as many as 128 characters. A tag value can contain as many as 256 characters. The characters can be Unicode letters, digits, white space, or one of the following symbols: _ . : / = + -. The following additional restrictions apply to tags:
* Tag keys and values are case sensitive.
* For each associated resource, each tag key must be unique and it can have only one value.
* The ``aws:`` prefix is reserved for use by AWS; you can’t use it in any tag keys or values that you define. In addition, you can\'t edit or remove tag keys or values that use this prefix. Tags that use this prefix don’t count against the limit of 50 tags per resource.
* You can associate tags with public or shared resources, but the tags are available only for your AWS account, not any other accounts that share the resource. In addition, the tags are available only for resources that are located in the specified AWS Region for your AWS account.
- **Key** *(string) --* **[REQUIRED]**
One part of a key-value pair that defines a tag. The maximum length of a tag key is 128 characters. The minimum length is 1 character.
- **Value** *(string) --* **[REQUIRED]**
The optional part of a key-value pair that defines a tag. The maximum length of a tag value is 256 characters. The minimum length is 0 characters. If you don’t want a resource to have a specific tag value, don’t specify a value for this parameter. Amazon Pinpoint will set the value to an empty string.
:rtype: dict
:returns:
"""
pass
def delete_configuration_set(self, ConfigurationSetName: str) -> Dict:
"""
Delete an existing configuration set.
In Amazon Pinpoint, *configuration sets* are groups of rules that you can apply to the emails you send. You apply a configuration set to an email by including a reference to the configuration set in the headers of the email. When you apply a configuration set to an email, all of the rules in that configuration set are applied to the email.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/DeleteConfigurationSet>`_
**Request Syntax**
::
response = client.delete_configuration_set(
ConfigurationSetName='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that you want to delete.
:rtype: dict
:returns:
"""
pass
def delete_configuration_set_event_destination(self, ConfigurationSetName: str, EventDestinationName: str) -> Dict:
"""
Delete an event destination.
In Amazon Pinpoint, *events* include message sends, deliveries, opens, clicks, bounces, and complaints. *Event destinations* are places that you can send information about these events to. For example, you can send event data to Amazon SNS to receive notifications when you receive bounces or complaints, or you can use Amazon Kinesis Data Firehose to stream data to Amazon S3 for long-term storage.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/DeleteConfigurationSetEventDestination>`_
**Request Syntax**
::
response = client.delete_configuration_set_event_destination(
ConfigurationSetName='string',
EventDestinationName='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that contains the event destination that you want to delete.
:type EventDestinationName: string
:param EventDestinationName: **[REQUIRED]**
The name of the event destination that you want to delete.
:rtype: dict
:returns:
"""
pass
def delete_dedicated_ip_pool(self, PoolName: str) -> Dict:
"""
Delete a dedicated IP pool.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/DeleteDedicatedIpPool>`_
**Request Syntax**
::
response = client.delete_dedicated_ip_pool(
PoolName='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type PoolName: string
:param PoolName: **[REQUIRED]**
The name of the dedicated IP pool that you want to delete.
:rtype: dict
:returns:
"""
pass
def delete_email_identity(self, EmailIdentity: str) -> Dict:
"""
Deletes an email identity that you previously verified for use with Amazon Pinpoint. An identity can be either an email address or a domain name.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/DeleteEmailIdentity>`_
**Request Syntax**
::
response = client.delete_email_identity(
EmailIdentity='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type EmailIdentity: string
:param EmailIdentity: **[REQUIRED]**
The identity (that is, the email address or domain) that you want to delete from your Amazon Pinpoint account.
:rtype: dict
:returns:
"""
pass
def generate_presigned_url(self, ClientMethod: str = None, Params: Dict = None, ExpiresIn: int = None, HttpMethod: str = None):
"""
Generate a presigned url given a client, its method, and arguments
:type ClientMethod: string
:param ClientMethod: The client method to presign for
:type Params: dict
:param Params: The parameters normally passed to
``ClientMethod``.
:type ExpiresIn: int
:param ExpiresIn: The number of seconds the presigned url is valid
for. By default it expires in an hour (3600 seconds)
:type HttpMethod: string
:param HttpMethod: The http method to use on the generated url. By
default, the http method is whatever is used in the method\'s model.
:returns: The presigned url
"""
pass
def get_account(self) -> Dict:
"""
Obtain information about the email-sending status and capabilities of your Amazon Pinpoint account in the current AWS Region.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetAccount>`_
**Request Syntax**
::
response = client.get_account()
**Response Syntax**
::
{
'SendQuota': {
'Max24HourSend': 123.0,
'MaxSendRate': 123.0,
'SentLast24Hours': 123.0
},
'SendingEnabled': True|False,
'DedicatedIpAutoWarmupEnabled': True|False,
'EnforcementStatus': 'string',
'ProductionAccessEnabled': True|False
}
**Response Structure**
- *(dict) --*
A list of details about the email-sending capabilities of your Amazon Pinpoint account in the current AWS Region.
- **SendQuota** *(dict) --*
An object that contains information about the per-day and per-second sending limits for your Amazon Pinpoint account in the current AWS Region.
- **Max24HourSend** *(float) --*
The maximum number of emails that you can send in the current AWS Region over a 24-hour period. This value is also called your *sending quota* .
- **MaxSendRate** *(float) --*
The maximum number of emails that you can send per second in the current AWS Region. This value is also called your *maximum sending rate* or your *maximum TPS (transactions per second) rate* .
- **SentLast24Hours** *(float) --*
The number of emails sent from your Amazon Pinpoint account in the current AWS Region over the past 24 hours.
- **SendingEnabled** *(boolean) --*
Indicates whether or not email sending is enabled for your Amazon Pinpoint account in the current AWS Region.
- **DedicatedIpAutoWarmupEnabled** *(boolean) --*
Indicates whether or not the automatic warm-up feature is enabled for dedicated IP addresses that are associated with your account.
- **EnforcementStatus** *(string) --*
The reputation status of your Amazon Pinpoint account. The status can be one of the following:
* ``HEALTHY`` – There are no reputation-related issues that currently impact your account.
* ``PROBATION`` – We've identified some issues with your Amazon Pinpoint account. We're placing your account under review while you work on correcting these issues.
* ``SHUTDOWN`` – Your account's ability to send email is currently paused because of an issue with the email sent from your account. When you correct the issue, you can contact us and request that your account's ability to send email is resumed.
- **ProductionAccessEnabled** *(boolean) --*
Indicates whether or not your account has production access in the current AWS Region.
If the value is ``false`` , then your account is in the *sandbox* . When your account is in the sandbox, you can only send email to verified identities. Additionally, the maximum number of emails you can send in a 24-hour period (your sending quota) is 200, and the maximum number of emails you can send per second (your maximum sending rate) is 1.
If the value is ``true`` , then your account has production access. When your account has production access, you can send email to any address. The sending quota and maximum sending rate for your account vary based on your specific use case.
:rtype: dict
:returns:
"""
pass
def get_blacklist_reports(self, BlacklistItemNames: List) -> Dict:
"""
Retrieve a list of the blacklists that your dedicated IP addresses appear on.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetBlacklistReports>`_
**Request Syntax**
::
response = client.get_blacklist_reports(
BlacklistItemNames=[
'string',
]
)
**Response Syntax**
::
{
'BlacklistReport': {
'string': [
{
'RblName': 'string',
'ListingTime': datetime(2015, 1, 1),
'Description': 'string'
},
]
}
}
**Response Structure**
- *(dict) --*
An object that contains information about blacklist events.
- **BlacklistReport** *(dict) --*
An object that contains information about a blacklist that one of your dedicated IP addresses appears on.
- *(string) --*
An IP address that you want to obtain blacklist information for.
- *(list) --*
- *(dict) --*
An object that contains information about a blacklisting event that impacts one of the dedicated IP addresses that is associated with your account.
- **RblName** *(string) --*
The name of the blacklist that the IP address appears on.
- **ListingTime** *(datetime) --*
The time when the blacklisting event occurred, shown in Unix time format.
- **Description** *(string) --*
Additional information about the blacklisting event, as provided by the blacklist maintainer.
:type BlacklistItemNames: list
:param BlacklistItemNames: **[REQUIRED]**
A list of IP addresses that you want to retrieve blacklist information about. You can only specify the dedicated IP addresses that you use to send email using Amazon Pinpoint or Amazon SES.
- *(string) --*
An IP address that you want to obtain blacklist information for.
:rtype: dict
:returns:
"""
pass
def get_configuration_set(self, ConfigurationSetName: str) -> Dict:
"""
Get information about an existing configuration set, including the dedicated IP pool that it's associated with, whether or not it's enabled for sending email, and more.
In Amazon Pinpoint, *configuration sets* are groups of rules that you can apply to the emails you send. You apply a configuration set to an email by including a reference to the configuration set in the headers of the email. When you apply a configuration set to an email, all of the rules in that configuration set are applied to the email.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetConfigurationSet>`_
**Request Syntax**
::
response = client.get_configuration_set(
ConfigurationSetName='string'
)
**Response Syntax**
::
{
'ConfigurationSetName': 'string',
'TrackingOptions': {
'CustomRedirectDomain': 'string'
},
'DeliveryOptions': {
'SendingPoolName': 'string'
},
'ReputationOptions': {
'ReputationMetricsEnabled': True|False,
'LastFreshStart': datetime(2015, 1, 1)
},
'SendingOptions': {
'SendingEnabled': True|False
}
}
**Response Structure**
- *(dict) --*
Information about a configuration set.
- **ConfigurationSetName** *(string) --*
The name of the configuration set.
- **TrackingOptions** *(dict) --*
An object that defines the open and click tracking options for emails that you send using the configuration set.
- **CustomRedirectDomain** *(string) --*
The domain that you want to use for tracking open and click events.
- **DeliveryOptions** *(dict) --*
An object that defines the dedicated IP pool that is used to send emails that you send using the configuration set.
- **SendingPoolName** *(string) --*
The name of the dedicated IP pool that you want to associate with the configuration set.
- **ReputationOptions** *(dict) --*
An object that defines whether or not Amazon Pinpoint collects reputation metrics for the emails that you send that use the configuration set.
- **ReputationMetricsEnabled** *(boolean) --*
If ``true`` , tracking of reputation metrics is enabled for the configuration set. If ``false`` , tracking of reputation metrics is disabled for the configuration set.
- **LastFreshStart** *(datetime) --*
The date and time (in Unix time) when the reputation metrics were last given a fresh start. When your account is given a fresh start, your reputation metrics are calculated starting from the date of the fresh start.
- **SendingOptions** *(dict) --*
An object that defines whether or not Amazon Pinpoint can send email that you send using the configuration set.
- **SendingEnabled** *(boolean) --*
If ``true`` , email sending is enabled for the configuration set. If ``false`` , email sending is disabled for the configuration set.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that you want to obtain more information about.
:rtype: dict
:returns:
"""
pass
def get_configuration_set_event_destinations(self, ConfigurationSetName: str) -> Dict:
"""
Retrieve a list of event destinations that are associated with a configuration set.
In Amazon Pinpoint, *events* include message sends, deliveries, opens, clicks, bounces, and complaints. *Event destinations* are places that you can send information about these events to. For example, you can send event data to Amazon SNS to receive notifications when you receive bounces or complaints, or you can use Amazon Kinesis Data Firehose to stream data to Amazon S3 for long-term storage.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetConfigurationSetEventDestinations>`_
**Request Syntax**
::
response = client.get_configuration_set_event_destinations(
ConfigurationSetName='string'
)
**Response Syntax**
::
{
'EventDestinations': [
{
'Name': 'string',
'Enabled': True|False,
'MatchingEventTypes': [
'SEND'|'REJECT'|'BOUNCE'|'COMPLAINT'|'DELIVERY'|'OPEN'|'CLICK'|'RENDERING_FAILURE',
],
'KinesisFirehoseDestination': {
'IamRoleArn': 'string',
'DeliveryStreamArn': 'string'
},
'CloudWatchDestination': {
'DimensionConfigurations': [
{
'DimensionName': 'string',
'DimensionValueSource': 'MESSAGE_TAG'|'EMAIL_HEADER'|'LINK_TAG',
'DefaultDimensionValue': 'string'
},
]
},
'SnsDestination': {
'TopicArn': 'string'
},
'PinpointDestination': {
'ApplicationArn': 'string'
}
},
]
}
**Response Structure**
- *(dict) --*
Information about an event destination for a configuration set.
- **EventDestinations** *(list) --*
An array that includes all of the events destinations that have been configured for the configuration set.
- *(dict) --*
In Amazon Pinpoint, *events* include message sends, deliveries, opens, clicks, bounces, and complaints. *Event destinations* are places that you can send information about these events to. For example, you can send event data to Amazon SNS to receive notifications when you receive bounces or complaints, or you can use Amazon Kinesis Data Firehose to stream data to Amazon S3 for long-term storage.
- **Name** *(string) --*
A name that identifies the event destination.
- **Enabled** *(boolean) --*
If ``true`` , the event destination is enabled. When the event destination is enabled, the specified event types are sent to the destinations in this ``EventDestinationDefinition`` .
If ``false`` , the event destination is disabled. When the event destination is disabled, events aren't sent to the specified destinations.
- **MatchingEventTypes** *(list) --*
The types of events that Amazon Pinpoint sends to the specified event destinations.
- *(string) --*
An email sending event type. For example, email sends, opens, and bounces are all email events.
- **KinesisFirehoseDestination** *(dict) --*
An object that defines an Amazon Kinesis Data Firehose destination for email events. You can use Amazon Kinesis Data Firehose to stream data to other services, such as Amazon S3 and Amazon Redshift.
- **IamRoleArn** *(string) --*
The Amazon Resource Name (ARN) of the IAM role that Amazon Pinpoint uses when sending email events to the Amazon Kinesis Data Firehose stream.
- **DeliveryStreamArn** *(string) --*
The Amazon Resource Name (ARN) of the Amazon Kinesis Data Firehose stream that Amazon Pinpoint sends email events to.
- **CloudWatchDestination** *(dict) --*
An object that defines an Amazon CloudWatch destination for email events. You can use Amazon CloudWatch to monitor and gain insights on your email sending metrics.
- **DimensionConfigurations** *(list) --*
An array of objects that define the dimensions to use when you send email events to Amazon CloudWatch.
- *(dict) --*
An object that defines the dimension configuration to use when you send Amazon Pinpoint email events to Amazon CloudWatch.
- **DimensionName** *(string) --*
The name of an Amazon CloudWatch dimension associated with an email sending metric. The name has to meet the following criteria:
* It can only contain ASCII letters (a-z, A-Z), numbers (0-9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
- **DimensionValueSource** *(string) --*
The location where Amazon Pinpoint finds the value of a dimension to publish to Amazon CloudWatch. If you want Amazon Pinpoint to use the message tags that you specify using an X-SES-MESSAGE-TAGS header or a parameter to the SendEmail/SendRawEmail API, choose ``messageTag`` . If you want Amazon Pinpoint to use your own email headers, choose ``emailHeader`` . If you want Amazon Pinpoint to use link tags, choose ``linkTags`` .
- **DefaultDimensionValue** *(string) --*
The default value of the dimension that is published to Amazon CloudWatch if you don't provide the value of the dimension when you send an email. This value has to meet the following criteria:
* It can only contain ASCII letters (a-z, A-Z), numbers (0-9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
- **SnsDestination** *(dict) --*
An object that defines an Amazon SNS destination for email events. You can use Amazon SNS to send notification when certain email events occur.
- **TopicArn** *(string) --*
The Amazon Resource Name (ARN) of the Amazon SNS topic that you want to publish email events to. For more information about Amazon SNS topics, see the `Amazon SNS Developer Guide <https://docs.aws.amazon.com/sns/latest/dg/CreateTopic.html>`__ .
- **PinpointDestination** *(dict) --*
An object that defines a Amazon Pinpoint destination for email events. You can use Amazon Pinpoint events to create attributes in Amazon Pinpoint projects. You can use these attributes to create segments for your campaigns.
- **ApplicationArn** *(string) --*
The Amazon Resource Name (ARN) of the Amazon Pinpoint project that you want to send email events to.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that contains the event destination.
:rtype: dict
:returns:
"""
pass
def get_dedicated_ip(self, Ip: str) -> Dict:
"""
Get information about a dedicated IP address, including the name of the dedicated IP pool that it's associated with, as well information about the automatic warm-up process for the address.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetDedicatedIp>`_
**Request Syntax**
::
response = client.get_dedicated_ip(
Ip='string'
)
**Response Syntax**
::
{
'DedicatedIp': {
'Ip': 'string',
'WarmupStatus': 'IN_PROGRESS'|'DONE',
'WarmupPercentage': 123,
'PoolName': 'string'
}
}
**Response Structure**
- *(dict) --*
Information about a dedicated IP address.
- **DedicatedIp** *(dict) --*
An object that contains information about a dedicated IP address.
- **Ip** *(string) --*
An IP address that is reserved for use by your Amazon Pinpoint account.
- **WarmupStatus** *(string) --*
The warm-up status of a dedicated IP address. The status can have one of the following values:
* ``IN_PROGRESS`` – The IP address isn't ready to use because the dedicated IP warm-up process is ongoing.
* ``DONE`` – The dedicated IP warm-up process is complete, and the IP address is ready to use.
- **WarmupPercentage** *(integer) --*
Indicates how complete the dedicated IP warm-up process is. When this value equals 1, the address has completed the warm-up process and is ready for use.
- **PoolName** *(string) --*
The name of the dedicated IP pool that the IP address is associated with.
:type Ip: string
:param Ip: **[REQUIRED]**
The IP address that you want to obtain more information about. The value you specify has to be a dedicated IP address that\'s assocaited with your Amazon Pinpoint account.
:rtype: dict
:returns:
"""
pass
def get_dedicated_ips(self, PoolName: str = None, NextToken: str = None, PageSize: int = None) -> Dict:
"""
List the dedicated IP addresses that are associated with your Amazon Pinpoint account.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetDedicatedIps>`_
**Request Syntax**
::
response = client.get_dedicated_ips(
PoolName='string',
NextToken='string',
PageSize=123
)
**Response Syntax**
::
{
'DedicatedIps': [
{
'Ip': 'string',
'WarmupStatus': 'IN_PROGRESS'|'DONE',
'WarmupPercentage': 123,
'PoolName': 'string'
},
],
'NextToken': 'string'
}
**Response Structure**
- *(dict) --*
Information about the dedicated IP addresses that are associated with your Amazon Pinpoint account.
- **DedicatedIps** *(list) --*
A list of dedicated IP addresses that are reserved for use by your Amazon Pinpoint account.
- *(dict) --*
Contains information about a dedicated IP address that is associated with your Amazon Pinpoint account.
- **Ip** *(string) --*
An IP address that is reserved for use by your Amazon Pinpoint account.
- **WarmupStatus** *(string) --*
The warm-up status of a dedicated IP address. The status can have one of the following values:
* ``IN_PROGRESS`` – The IP address isn't ready to use because the dedicated IP warm-up process is ongoing.
* ``DONE`` – The dedicated IP warm-up process is complete, and the IP address is ready to use.
- **WarmupPercentage** *(integer) --*
Indicates how complete the dedicated IP warm-up process is. When this value equals 1, the address has completed the warm-up process and is ready for use.
- **PoolName** *(string) --*
The name of the dedicated IP pool that the IP address is associated with.
- **NextToken** *(string) --*
A token that indicates that there are additional dedicated IP addresses to list. To view additional addresses, issue another request to ``GetDedicatedIps`` , passing this token in the ``NextToken`` parameter.
:type PoolName: string
:param PoolName:
The name of the IP pool that the dedicated IP address is associated with.
:type NextToken: string
:param NextToken:
A token returned from a previous call to ``GetDedicatedIps`` to indicate the position of the dedicated IP pool in the list of IP pools.
:type PageSize: integer
:param PageSize:
The number of results to show in a single call to ``GetDedicatedIpsRequest`` . If the number of results is larger than the number you specified in this parameter, then the response includes a ``NextToken`` element, which you can use to obtain additional results.
:rtype: dict
:returns:
"""
pass
def get_deliverability_dashboard_options(self) -> Dict:
"""
Show the status of the Deliverability dashboard. When the Deliverability dashboard is enabled, you gain access to reputation metrics for the domains that you use to send email using Amazon Pinpoint. You also gain the ability to perform predictive inbox placement tests.
When you use the Deliverability dashboard, you pay a monthly charge of USD$1,250.00, in addition to any other fees that you accrue by using Amazon Pinpoint. If you enable the Deliverability dashboard after the first day of a calendar month, AWS prorates the monthly charge based on how many days have elapsed in the current calendar month.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetDeliverabilityDashboardOptions>`_
**Request Syntax**
::
response = client.get_deliverability_dashboard_options()
**Response Syntax**
::
{
'DashboardEnabled': True|False
}
**Response Structure**
- *(dict) --*
An object that shows the status of the Deliverability dashboard for your Amazon Pinpoint account.
- **DashboardEnabled** *(boolean) --*
Indicates whether the Deliverability dashboard is enabled. If the value is ``true`` , then the dashboard is enabled.
:rtype: dict
:returns:
"""
pass
def get_deliverability_test_report(self, ReportId: str) -> Dict:
"""
Retrieve the results of a predictive inbox placement test.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetDeliverabilityTestReport>`_
**Request Syntax**
::
response = client.get_deliverability_test_report(
ReportId='string'
)
**Response Syntax**
::
{
'DeliverabilityTestReport': {
'ReportId': 'string',
'ReportName': 'string',
'Subject': 'string',
'FromEmailAddress': 'string',
'CreateDate': datetime(2015, 1, 1),
'DeliverabilityTestStatus': 'IN_PROGRESS'|'COMPLETED'
},
'OverallPlacement': {
'InboxPercentage': 123.0,
'SpamPercentage': 123.0,
'MissingPercentage': 123.0,
'SpfPercentage': 123.0,
'DkimPercentage': 123.0
},
'IspPlacements': [
{
'IspName': 'string',
'PlacementStatistics': {
'InboxPercentage': 123.0,
'SpamPercentage': 123.0,
'MissingPercentage': 123.0,
'SpfPercentage': 123.0,
'DkimPercentage': 123.0
}
},
],
'Message': 'string'
}
**Response Structure**
- *(dict) --*
The results of the predictive inbox placement test.
- **DeliverabilityTestReport** *(dict) --*
An object that contains the results of the predictive inbox placement test.
- **ReportId** *(string) --*
A unique string that identifies the predictive inbox placement test.
- **ReportName** *(string) --*
A name that helps you identify a predictive inbox placement test report.
- **Subject** *(string) --*
The subject line for an email that you submitted in a predictive inbox placement test.
- **FromEmailAddress** *(string) --*
The sender address that you specified for the predictive inbox placement test.
- **CreateDate** *(datetime) --*
The date and time when the predictive inbox placement test was created, in Unix time format.
- **DeliverabilityTestStatus** *(string) --*
The status of the predictive inbox placement test. If the status is ``IN_PROGRESS`` , then the predictive inbox placement test is currently running. Predictive inbox placement tests are usually complete within 24 hours of creating the test. If the status is ``COMPLETE`` , then the test is finished, and you can use the ``GetDeliverabilityTestReport`` to view the results of the test.
- **OverallPlacement** *(dict) --*
An object that specifies how many test messages that were sent during the predictive inbox placement test were delivered to recipients' inboxes, how many were sent to recipients' spam folders, and how many weren't delivered.
- **InboxPercentage** *(float) --*
The percentage of emails that arrived in recipients' inboxes during the predictive inbox placement test.
- **SpamPercentage** *(float) --*
The percentage of emails that arrived in recipients' spam or junk mail folders during the predictive inbox placement test.
- **MissingPercentage** *(float) --*
The percentage of emails that didn't arrive in recipients' inboxes at all during the predictive inbox placement test.
- **SpfPercentage** *(float) --*
The percentage of emails that were authenticated by using Sender Policy Framework (SPF) during the predictive inbox placement test.
- **DkimPercentage** *(float) --*
The percentage of emails that were authenticated by using DomainKeys Identified Mail (DKIM) during the predictive inbox placement test.
- **IspPlacements** *(list) --*
An object that describes how the test email was handled by several email providers, including Gmail, Hotmail, Yahoo, AOL, and others.
- *(dict) --*
An object that describes how email sent during the predictive inbox placement test was handled by a certain email provider.
- **IspName** *(string) --*
The name of the email provider that the inbox placement data applies to.
- **PlacementStatistics** *(dict) --*
An object that contains inbox placement metrics for a specific email provider.
- **InboxPercentage** *(float) --*
The percentage of emails that arrived in recipients' inboxes during the predictive inbox placement test.
- **SpamPercentage** *(float) --*
The percentage of emails that arrived in recipients' spam or junk mail folders during the predictive inbox placement test.
- **MissingPercentage** *(float) --*
The percentage of emails that didn't arrive in recipients' inboxes at all during the predictive inbox placement test.
- **SpfPercentage** *(float) --*
The percentage of emails that were authenticated by using Sender Policy Framework (SPF) during the predictive inbox placement test.
- **DkimPercentage** *(float) --*
The percentage of emails that were authenticated by using DomainKeys Identified Mail (DKIM) during the predictive inbox placement test.
- **Message** *(string) --*
An object that contains the message that you sent when you performed this predictive inbox placement test.
:type ReportId: string
:param ReportId: **[REQUIRED]**
A unique string that identifies the predictive inbox placement test.
:rtype: dict
:returns:
"""
pass
def get_domain_statistics_report(self, Domain: str, StartDate: datetime, EndDate: datetime) -> Dict:
"""
Retrieve inbox placement and engagement rates for the domains that you use to send email.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetDomainStatisticsReport>`_
**Request Syntax**
::
response = client.get_domain_statistics_report(
Domain='string',
StartDate=datetime(2015, 1, 1),
EndDate=datetime(2015, 1, 1)
)
**Response Syntax**
::
{
'OverallVolume': {
'VolumeStatistics': {
'InboxRawCount': 123,
'SpamRawCount': 123,
'ProjectedInbox': 123,
'ProjectedSpam': 123
},
'ReadRatePercent': 123.0,
'DomainIspPlacements': [
{
'IspName': 'string',
'InboxRawCount': 123,
'SpamRawCount': 123,
'InboxPercentage': 123.0,
'SpamPercentage': 123.0
},
]
},
'DailyVolumes': [
{
'StartDate': datetime(2015, 1, 1),
'VolumeStatistics': {
'InboxRawCount': 123,
'SpamRawCount': 123,
'ProjectedInbox': 123,
'ProjectedSpam': 123
},
'DomainIspPlacements': [
{
'IspName': 'string',
'InboxRawCount': 123,
'SpamRawCount': 123,
'InboxPercentage': 123.0,
'SpamPercentage': 123.0
},
]
},
]
}
**Response Structure**
- *(dict) --*
An object that includes statistics that are related to the domain that you specified.
- **OverallVolume** *(dict) --*
An object that contains deliverability metrics for the domain that you specified. The data in this object is a summary of all of the data that was collected from the ``StartDate`` to the ``EndDate`` .
- **VolumeStatistics** *(dict) --*
An object that contains information about the numbers of messages that arrived in recipients' inboxes and junk mail folders.
- **InboxRawCount** *(integer) --*
The total number of emails that arrived in recipients' inboxes.
- **SpamRawCount** *(integer) --*
The total number of emails that arrived in recipients' spam or junk mail folders.
- **ProjectedInbox** *(integer) --*
An estimate of the percentage of emails sent from the current domain that will arrive in recipients' inboxes.
- **ProjectedSpam** *(integer) --*
An estimate of the percentage of emails sent from the current domain that will arrive in recipients' spam or junk mail folders.
- **ReadRatePercent** *(float) --*
The percentage of emails that were sent from the domain that were read by their recipients.
- **DomainIspPlacements** *(list) --*
An object that contains inbox and junk mail placement metrics for individual email providers.
- *(dict) --*
An object that contains inbox placement data for email sent from one of your email domains to a specific email provider.
- **IspName** *(string) --*
The name of the email provider that the inbox placement data applies to.
- **InboxRawCount** *(integer) --*
The total number of messages that were sent from the selected domain to the specified email provider that arrived in recipients' inboxes.
- **SpamRawCount** *(integer) --*
The total number of messages that were sent from the selected domain to the specified email provider that arrived in recipients' spam or junk mail folders.
- **InboxPercentage** *(float) --*
The percentage of messages that were sent from the selected domain to the specified email provider that arrived in recipients' inboxes.
- **SpamPercentage** *(float) --*
The percentage of messages that were sent from the selected domain to the specified email provider that arrived in recipients' spam or junk mail folders.
- **DailyVolumes** *(list) --*
An object that contains deliverability metrics for the domain that you specified. This object contains data for each day, starting on the ``StartDate`` and ending on the ``EndDate`` .
- *(dict) --*
An object that contains information about the volume of email sent on each day of the analysis period.
- **StartDate** *(datetime) --*
The date that the DailyVolume metrics apply to, in Unix time.
- **VolumeStatistics** *(dict) --*
An object that contains inbox placement metrics for a specific day in the analysis period.
- **InboxRawCount** *(integer) --*
The total number of emails that arrived in recipients' inboxes.
- **SpamRawCount** *(integer) --*
The total number of emails that arrived in recipients' spam or junk mail folders.
- **ProjectedInbox** *(integer) --*
An estimate of the percentage of emails sent from the current domain that will arrive in recipients' inboxes.
- **ProjectedSpam** *(integer) --*
An estimate of the percentage of emails sent from the current domain that will arrive in recipients' spam or junk mail folders.
- **DomainIspPlacements** *(list) --*
An object that contains inbox placement metrics for a specifid day in the analysis period, broken out by the recipient's email provider.
- *(dict) --*
An object that contains inbox placement data for email sent from one of your email domains to a specific email provider.
- **IspName** *(string) --*
The name of the email provider that the inbox placement data applies to.
- **InboxRawCount** *(integer) --*
The total number of messages that were sent from the selected domain to the specified email provider that arrived in recipients' inboxes.
- **SpamRawCount** *(integer) --*
The total number of messages that were sent from the selected domain to the specified email provider that arrived in recipients' spam or junk mail folders.
- **InboxPercentage** *(float) --*
The percentage of messages that were sent from the selected domain to the specified email provider that arrived in recipients' inboxes.
- **SpamPercentage** *(float) --*
The percentage of messages that were sent from the selected domain to the specified email provider that arrived in recipients' spam or junk mail folders.
:type Domain: string
:param Domain: **[REQUIRED]**
The domain that you want to obtain deliverability metrics for.
:type StartDate: datetime
:param StartDate: **[REQUIRED]**
The first day (in Unix time) that you want to obtain domain deliverability metrics for.
:type EndDate: datetime
:param EndDate: **[REQUIRED]**
The last day (in Unix time) that you want to obtain domain deliverability metrics for. The ``EndDate`` that you specify has to be less than or equal to 30 days after the ``StartDate`` .
:rtype: dict
:returns:
"""
pass
def get_email_identity(self, EmailIdentity: str) -> Dict:
"""
Provides information about a specific identity associated with your Amazon Pinpoint account, including the identity's verification status, its DKIM authentication status, and its custom Mail-From settings.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/GetEmailIdentity>`_
**Request Syntax**
::
response = client.get_email_identity(
EmailIdentity='string'
)
**Response Syntax**
::
{
'IdentityType': 'EMAIL_ADDRESS'|'DOMAIN'|'MANAGED_DOMAIN',
'FeedbackForwardingStatus': True|False,
'VerifiedForSendingStatus': True|False,
'DkimAttributes': {
'SigningEnabled': True|False,
'Status': 'PENDING'|'SUCCESS'|'FAILED'|'TEMPORARY_FAILURE'|'NOT_STARTED',
'Tokens': [
'string',
]
},
'MailFromAttributes': {
'MailFromDomain': 'string',
'MailFromDomainStatus': 'PENDING'|'SUCCESS'|'FAILED'|'TEMPORARY_FAILURE',
'BehaviorOnMxFailure': 'USE_DEFAULT_VALUE'|'REJECT_MESSAGE'
}
}
**Response Structure**
- *(dict) --*
Details about an email identity.
- **IdentityType** *(string) --*
The email identity type.
- **FeedbackForwardingStatus** *(boolean) --*
The feedback forwarding configuration for the identity.
If the value is ``true`` , Amazon Pinpoint sends you email notifications when bounce or complaint events occur. Amazon Pinpoint sends this notification to the address that you specified in the Return-Path header of the original email.
When you set this value to ``false`` , Amazon Pinpoint sends notifications through other mechanisms, such as by notifying an Amazon SNS topic or another event destination. You're required to have a method of tracking bounces and complaints. If you haven't set up another mechanism for receiving bounce or complaint notifications, Amazon Pinpoint sends an email notification when these events occur (even if this setting is disabled).
- **VerifiedForSendingStatus** *(boolean) --*
Specifies whether or not the identity is verified. In Amazon Pinpoint, you can only send email from verified email addresses or domains. For more information about verifying identities, see the `Amazon Pinpoint User Guide <https://docs.aws.amazon.com/pinpoint/latest/userguide/channels-email-manage-verify.html>`__ .
- **DkimAttributes** *(dict) --*
An object that contains information about the DKIM attributes for the identity. This object includes the tokens that you use to create the CNAME records that are required to complete the DKIM verification process.
- **SigningEnabled** *(boolean) --*
If the value is ``true`` , then the messages that Amazon Pinpoint sends from the identity are DKIM-signed. If the value is ``false`` , then the messages that Amazon Pinpoint sends from the identity aren't DKIM-signed.
- **Status** *(string) --*
Describes whether or not Amazon Pinpoint has successfully located the DKIM records in the DNS records for the domain. The status can be one of the following:
* ``PENDING`` – Amazon Pinpoint hasn't yet located the DKIM records in the DNS configuration for the domain, but will continue to attempt to locate them.
* ``SUCCESS`` – Amazon Pinpoint located the DKIM records in the DNS configuration for the domain and determined that they're correct. Amazon Pinpoint can now send DKIM-signed email from the identity.
* ``FAILED`` – Amazon Pinpoint was unable to locate the DKIM records in the DNS settings for the domain, and won't continue to search for them.
* ``TEMPORARY_FAILURE`` – A temporary issue occurred, which prevented Amazon Pinpoint from determining the DKIM status for the domain.
* ``NOT_STARTED`` – Amazon Pinpoint hasn't yet started searching for the DKIM records in the DKIM records for the domain.
- **Tokens** *(list) --*
A set of unique strings that you use to create a set of CNAME records that you add to the DNS configuration for your domain. When Amazon Pinpoint detects these records in the DNS configuration for your domain, the DKIM authentication process is complete. Amazon Pinpoint usually detects these records within about 72 hours of adding them to the DNS configuration for your domain.
- *(string) --*
- **MailFromAttributes** *(dict) --*
An object that contains information about the Mail-From attributes for the email identity.
- **MailFromDomain** *(string) --*
The name of a domain that an email identity uses as a custom MAIL FROM domain.
- **MailFromDomainStatus** *(string) --*
The status of the MAIL FROM domain. This status can have the following values:
* ``PENDING`` – Amazon Pinpoint hasn't started searching for the MX record yet.
* ``SUCCESS`` – Amazon Pinpoint detected the required MX record for the MAIL FROM domain.
* ``FAILED`` – Amazon Pinpoint can't find the required MX record, or the record no longer exists.
* ``TEMPORARY_FAILURE`` – A temporary issue occurred, which prevented Amazon Pinpoint from determining the status of the MAIL FROM domain.
- **BehaviorOnMxFailure** *(string) --*
The action that Amazon Pinpoint to takes if it can't read the required MX record for a custom MAIL FROM domain. When you set this value to ``UseDefaultValue`` , Amazon Pinpoint uses *amazonses.com* as the MAIL FROM domain. When you set this value to ``RejectMessage`` , Amazon Pinpoint returns a ``MailFromDomainNotVerified`` error, and doesn't attempt to deliver the email.
These behaviors are taken when the custom MAIL FROM domain configuration is in the ``Pending`` , ``Failed`` , and ``TemporaryFailure`` states.
:type EmailIdentity: string
:param EmailIdentity: **[REQUIRED]**
The email identity that you want to retrieve details for.
:rtype: dict
:returns:
"""
pass
def get_paginator(self, operation_name: str = None) -> Paginator:
"""
Create a paginator for an operation.
:type operation_name: string
:param operation_name: The operation name. This is the same name
as the method name on the client. For example, if the
method name is ``create_foo``, and you\'d normally invoke the
operation as ``client.create_foo(**kwargs)``, if the
``create_foo`` operation can be paginated, you can use the
call ``client.get_paginator(\"create_foo\")``.
:raise OperationNotPageableError: Raised if the operation is not
pageable. You can use the ``client.can_paginate`` method to
check if an operation is pageable.
:rtype: L{botocore.paginate.Paginator}
:return: A paginator object.
"""
pass
def get_waiter(self, waiter_name: str = None) -> Waiter:
"""
Returns an object that can wait for some condition.
:type waiter_name: str
:param waiter_name: The name of the waiter to get. See the waiters
section of the service docs for a list of available waiters.
:returns: The specified waiter object.
:rtype: botocore.waiter.Waiter
"""
pass
def list_configuration_sets(self, NextToken: str = None, PageSize: int = None) -> Dict:
"""
List all of the configuration sets associated with your Amazon Pinpoint account in the current region.
In Amazon Pinpoint, *configuration sets* are groups of rules that you can apply to the emails you send. You apply a configuration set to an email by including a reference to the configuration set in the headers of the email. When you apply a configuration set to an email, all of the rules in that configuration set are applied to the email.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/ListConfigurationSets>`_
**Request Syntax**
::
response = client.list_configuration_sets(
NextToken='string',
PageSize=123
)
**Response Syntax**
::
{
'ConfigurationSets': [
'string',
],
'NextToken': 'string'
}
**Response Structure**
- *(dict) --*
A list of configuration sets in your Amazon Pinpoint account in the current AWS Region.
- **ConfigurationSets** *(list) --*
An array that contains all of the configuration sets in your Amazon Pinpoint account in the current AWS Region.
- *(string) --*
The name of a configuration set.
In Amazon Pinpoint, *configuration sets* are groups of rules that you can apply to the emails you send. You apply a configuration set to an email by including a reference to the configuration set in the headers of the email. When you apply a configuration set to an email, all of the rules in that configuration set are applied to the email.
- **NextToken** *(string) --*
A token that indicates that there are additional configuration sets to list. To view additional configuration sets, issue another request to ``ListConfigurationSets`` , and pass this token in the ``NextToken`` parameter.
:type NextToken: string
:param NextToken:
A token returned from a previous call to ``ListConfigurationSets`` to indicate the position in the list of configuration sets.
:type PageSize: integer
:param PageSize:
The number of results to show in a single call to ``ListConfigurationSets`` . If the number of results is larger than the number you specified in this parameter, then the response includes a ``NextToken`` element, which you can use to obtain additional results.
:rtype: dict
:returns:
"""
pass
def list_dedicated_ip_pools(self, NextToken: str = None, PageSize: int = None) -> Dict:
"""
List all of the dedicated IP pools that exist in your Amazon Pinpoint account in the current AWS Region.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/ListDedicatedIpPools>`_
**Request Syntax**
::
response = client.list_dedicated_ip_pools(
NextToken='string',
PageSize=123
)
**Response Syntax**
::
{
'DedicatedIpPools': [
'string',
],
'NextToken': 'string'
}
**Response Structure**
- *(dict) --*
A list of dedicated IP pools.
- **DedicatedIpPools** *(list) --*
A list of all of the dedicated IP pools that are associated with your Amazon Pinpoint account.
- *(string) --*
The name of a dedicated IP pool.
- **NextToken** *(string) --*
A token that indicates that there are additional IP pools to list. To view additional IP pools, issue another request to ``ListDedicatedIpPools`` , passing this token in the ``NextToken`` parameter.
:type NextToken: string
:param NextToken:
A token returned from a previous call to ``ListDedicatedIpPools`` to indicate the position in the list of dedicated IP pools.
:type PageSize: integer
:param PageSize:
The number of results to show in a single call to ``ListDedicatedIpPools`` . If the number of results is larger than the number you specified in this parameter, then the response includes a ``NextToken`` element, which you can use to obtain additional results.
:rtype: dict
:returns:
"""
pass
def list_deliverability_test_reports(self, NextToken: str = None, PageSize: int = None) -> Dict:
"""
Show a list of the predictive inbox placement tests that you've performed, regardless of their statuses. For predictive inbox placement tests that are complete, you can use the ``GetDeliverabilityTestReport`` operation to view the results.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/ListDeliverabilityTestReports>`_
**Request Syntax**
::
response = client.list_deliverability_test_reports(
NextToken='string',
PageSize=123
)
**Response Syntax**
::
{
'DeliverabilityTestReports': [
{
'ReportId': 'string',
'ReportName': 'string',
'Subject': 'string',
'FromEmailAddress': 'string',
'CreateDate': datetime(2015, 1, 1),
'DeliverabilityTestStatus': 'IN_PROGRESS'|'COMPLETED'
},
],
'NextToken': 'string'
}
**Response Structure**
- *(dict) --*
A list of the predictive inbox placement test reports that are available for your account, regardless of whether or not those tests are complete.
- **DeliverabilityTestReports** *(list) --*
An object that contains a lists of predictive inbox placement tests that you've performed.
- *(dict) --*
An object that contains metadata related to a predictive inbox placement test.
- **ReportId** *(string) --*
A unique string that identifies the predictive inbox placement test.
- **ReportName** *(string) --*
A name that helps you identify a predictive inbox placement test report.
- **Subject** *(string) --*
The subject line for an email that you submitted in a predictive inbox placement test.
- **FromEmailAddress** *(string) --*
The sender address that you specified for the predictive inbox placement test.
- **CreateDate** *(datetime) --*
The date and time when the predictive inbox placement test was created, in Unix time format.
- **DeliverabilityTestStatus** *(string) --*
The status of the predictive inbox placement test. If the status is ``IN_PROGRESS`` , then the predictive inbox placement test is currently running. Predictive inbox placement tests are usually complete within 24 hours of creating the test. If the status is ``COMPLETE`` , then the test is finished, and you can use the ``GetDeliverabilityTestReport`` to view the results of the test.
- **NextToken** *(string) --*
A token that indicates that there are additional predictive inbox placement tests to list. To view additional predictive inbox placement tests, issue another request to ``ListDeliverabilityTestReports`` , and pass this token in the ``NextToken`` parameter.
:type NextToken: string
:param NextToken:
A token returned from a previous call to ``ListDeliverabilityTestReports`` to indicate the position in the list of predictive inbox placement tests.
:type PageSize: integer
:param PageSize:
The number of results to show in a single call to ``ListDeliverabilityTestReports`` . If the number of results is larger than the number you specified in this parameter, then the response includes a ``NextToken`` element, which you can use to obtain additional results.
The value you specify has to be at least 0, and can be no more than 1000.
:rtype: dict
:returns:
"""
pass
def list_email_identities(self, NextToken: str = None, PageSize: int = None) -> Dict:
"""
Returns a list of all of the email identities that are associated with your Amazon Pinpoint account. An identity can be either an email address or a domain. This operation returns identities that are verified as well as those that aren't.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/ListEmailIdentities>`_
**Request Syntax**
::
response = client.list_email_identities(
NextToken='string',
PageSize=123
)
**Response Syntax**
::
{
'EmailIdentities': [
{
'IdentityType': 'EMAIL_ADDRESS'|'DOMAIN'|'MANAGED_DOMAIN',
'IdentityName': 'string',
'SendingEnabled': True|False
},
],
'NextToken': 'string'
}
**Response Structure**
- *(dict) --*
A list of all of the identities that you've attempted to verify for use with Amazon Pinpoint, regardless of whether or not those identities were successfully verified.
- **EmailIdentities** *(list) --*
An array that includes all of the identities associated with your Amazon Pinpoint account.
- *(dict) --*
Information about an email identity.
- **IdentityType** *(string) --*
The email identity type. The identity type can be one of the following:
* ``EMAIL_ADDRESS`` – The identity is an email address.
* ``DOMAIN`` – The identity is a domain.
* ``MANAGED_DOMAIN`` – The identity is a domain that is managed by AWS.
- **IdentityName** *(string) --*
The address or domain of the identity.
- **SendingEnabled** *(boolean) --*
Indicates whether or not you can send email from the identity.
In Amazon Pinpoint, an identity is an email address or domain that you send email from. Before you can send email from an identity, you have to demostrate that you own the identity, and that you authorize Amazon Pinpoint to send email from that identity.
- **NextToken** *(string) --*
A token that indicates that there are additional configuration sets to list. To view additional configuration sets, issue another request to ``ListEmailIdentities`` , and pass this token in the ``NextToken`` parameter.
:type NextToken: string
:param NextToken:
A token returned from a previous call to ``ListEmailIdentities`` to indicate the position in the list of identities.
:type PageSize: integer
:param PageSize:
The number of results to show in a single call to ``ListEmailIdentities`` . If the number of results is larger than the number you specified in this parameter, then the response includes a ``NextToken`` element, which you can use to obtain additional results.
The value you specify has to be at least 0, and can be no more than 1000.
:rtype: dict
:returns:
"""
pass
def list_tags_for_resource(self, ResourceArn: str) -> Dict:
"""
Retrieve a list of the tags (keys and values) that are associated with a specific resource. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Each tag consists of a required *tag key* and an optional associated *tag value* . A tag key is a general label that acts as a category for more specific tag values. A tag value acts as a descriptor within a tag key.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/ListTagsForResource>`_
**Request Syntax**
::
response = client.list_tags_for_resource(
ResourceArn='string'
)
**Response Syntax**
::
{
'Tags': [
{
'Key': 'string',
'Value': 'string'
},
]
}
**Response Structure**
- *(dict) --*
- **Tags** *(list) --*
An array that lists all the tags that are associated with the resource. Each tag consists of a required tag key (``Key`` ) and an associated tag value (``Value`` )
- *(dict) --*
An object that defines the tags that are associated with a resource. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Tags can help you categorize and manage resources in different ways, such as by purpose, owner, environment, or other criteria. A resource can have as many as 50 tags.
Each tag consists of a required *tag key* and an associated *tag value* , both of which you define. A tag key is a general label that acts as a category for a more specific tag value. A tag value acts as a descriptor within a tag key. For example, if you have two versions of an Amazon Pinpoint project, one for internal testing and another for external use, you might assign a ``Stack`` tag key to both projects. The value of the ``Stack`` tag key might be ``Test`` for one project and ``Production`` for the other project.
A tag key can contain as many as 128 characters. A tag value can contain as many as 256 characters. The characters can be Unicode letters, digits, white space, or one of the following symbols: _ . : / = + -. The following additional restrictions apply to tags:
* Tag keys and values are case sensitive.
* For each associated resource, each tag key must be unique and it can have only one value.
* The ``aws:`` prefix is reserved for use by AWS; you can’t use it in any tag keys or values that you define. In addition, you can't edit or remove tag keys or values that use this prefix. Tags that use this prefix don’t count against the limit of 50 tags per resource.
* You can associate tags with public or shared resources, but the tags are available only for your AWS account, not any other accounts that share the resource. In addition, the tags are available only for resources that are located in the specified AWS Region for your AWS account.
- **Key** *(string) --*
One part of a key-value pair that defines a tag. The maximum length of a tag key is 128 characters. The minimum length is 1 character.
- **Value** *(string) --*
The optional part of a key-value pair that defines a tag. The maximum length of a tag value is 256 characters. The minimum length is 0 characters. If you don’t want a resource to have a specific tag value, don’t specify a value for this parameter. Amazon Pinpoint will set the value to an empty string.
:type ResourceArn: string
:param ResourceArn: **[REQUIRED]**
The Amazon Resource Name (ARN) of the resource that you want to retrieve tag information for.
:rtype: dict
:returns:
"""
pass
def put_account_dedicated_ip_warmup_attributes(self, AutoWarmupEnabled: bool = None) -> Dict:
"""
Enable or disable the automatic warm-up feature for dedicated IP addresses.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutAccountDedicatedIpWarmupAttributes>`_
**Request Syntax**
::
response = client.put_account_dedicated_ip_warmup_attributes(
AutoWarmupEnabled=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type AutoWarmupEnabled: boolean
:param AutoWarmupEnabled:
Enables or disables the automatic warm-up feature for dedicated IP addresses that are associated with your Amazon Pinpoint account in the current AWS Region. Set to ``true`` to enable the automatic warm-up feature, or set to ``false`` to disable it.
:rtype: dict
:returns:
"""
pass
def put_account_sending_attributes(self, SendingEnabled: bool = None) -> Dict:
"""
Enable or disable the ability of your account to send email.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutAccountSendingAttributes>`_
**Request Syntax**
::
response = client.put_account_sending_attributes(
SendingEnabled=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type SendingEnabled: boolean
:param SendingEnabled:
Enables or disables your account\'s ability to send email. Set to ``true`` to enable email sending, or set to ``false`` to disable email sending.
.. note::
If AWS paused your account\'s ability to send email, you can\'t use this operation to resume your account\'s ability to send email.
:rtype: dict
:returns:
"""
pass
def put_configuration_set_delivery_options(self, ConfigurationSetName: str, SendingPoolName: str = None) -> Dict:
"""
Associate a configuration set with a dedicated IP pool. You can use dedicated IP pools to create groups of dedicated IP addresses for sending specific types of email.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutConfigurationSetDeliveryOptions>`_
**Request Syntax**
::
response = client.put_configuration_set_delivery_options(
ConfigurationSetName='string',
SendingPoolName='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that you want to associate with a dedicated IP pool.
:type SendingPoolName: string
:param SendingPoolName:
The name of the dedicated IP pool that you want to associate with the configuration set.
:rtype: dict
:returns:
"""
pass
def put_configuration_set_reputation_options(self, ConfigurationSetName: str, ReputationMetricsEnabled: bool = None) -> Dict:
"""
Enable or disable collection of reputation metrics for emails that you send using a particular configuration set in a specific AWS Region.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutConfigurationSetReputationOptions>`_
**Request Syntax**
::
response = client.put_configuration_set_reputation_options(
ConfigurationSetName='string',
ReputationMetricsEnabled=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that you want to enable or disable reputation metric tracking for.
:type ReputationMetricsEnabled: boolean
:param ReputationMetricsEnabled:
If ``true`` , tracking of reputation metrics is enabled for the configuration set. If ``false`` , tracking of reputation metrics is disabled for the configuration set.
:rtype: dict
:returns:
"""
pass
def put_configuration_set_sending_options(self, ConfigurationSetName: str, SendingEnabled: bool = None) -> Dict:
"""
Enable or disable email sending for messages that use a particular configuration set in a specific AWS Region.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutConfigurationSetSendingOptions>`_
**Request Syntax**
::
response = client.put_configuration_set_sending_options(
ConfigurationSetName='string',
SendingEnabled=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that you want to enable or disable email sending for.
:type SendingEnabled: boolean
:param SendingEnabled:
If ``true`` , email sending is enabled for the configuration set. If ``false`` , email sending is disabled for the configuration set.
:rtype: dict
:returns:
"""
pass
def put_configuration_set_tracking_options(self, ConfigurationSetName: str, CustomRedirectDomain: str = None) -> Dict:
"""
Specify a custom domain to use for open and click tracking elements in email that you send using Amazon Pinpoint.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutConfigurationSetTrackingOptions>`_
**Request Syntax**
::
response = client.put_configuration_set_tracking_options(
ConfigurationSetName='string',
CustomRedirectDomain='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that you want to add a custom tracking domain to.
:type CustomRedirectDomain: string
:param CustomRedirectDomain:
The domain that you want to use to track open and click events.
:rtype: dict
:returns:
"""
pass
def put_dedicated_ip_in_pool(self, Ip: str, DestinationPoolName: str) -> Dict:
"""
Move a dedicated IP address to an existing dedicated IP pool.
.. note::
The dedicated IP address that you specify must already exist, and must be associated with your Amazon Pinpoint account.
The dedicated IP pool you specify must already exist. You can create a new pool by using the ``CreateDedicatedIpPool`` operation.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutDedicatedIpInPool>`_
**Request Syntax**
::
response = client.put_dedicated_ip_in_pool(
Ip='string',
DestinationPoolName='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type Ip: string
:param Ip: **[REQUIRED]**
The IP address that you want to move to the dedicated IP pool. The value you specify has to be a dedicated IP address that\'s associated with your Amazon Pinpoint account.
:type DestinationPoolName: string
:param DestinationPoolName: **[REQUIRED]**
The name of the IP pool that you want to add the dedicated IP address to. You have to specify an IP pool that already exists.
:rtype: dict
:returns:
"""
pass
def put_dedicated_ip_warmup_attributes(self, Ip: str, WarmupPercentage: int) -> Dict:
"""
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutDedicatedIpWarmupAttributes>`_
**Request Syntax**
::
response = client.put_dedicated_ip_warmup_attributes(
Ip='string',
WarmupPercentage=123
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type Ip: string
:param Ip: **[REQUIRED]**
The dedicated IP address that you want to update the warm-up attributes for.
:type WarmupPercentage: integer
:param WarmupPercentage: **[REQUIRED]**
The warm-up percentage that you want to associate with the dedicated IP address.
:rtype: dict
:returns:
"""
pass
def put_deliverability_dashboard_option(self, DashboardEnabled: bool) -> Dict:
"""
Enable or disable the Deliverability dashboard. When you enable the Deliverability dashboard, you gain access to reputation metrics for the domains that you use to send email using Amazon Pinpoint. You also gain the ability to perform predictive inbox placement tests.
When you use the Deliverability dashboard, you pay a monthly charge of USD$1,250.00, in addition to any other fees that you accrue by using Amazon Pinpoint. If you enable the Deliverability dashboard after the first day of a calendar month, we prorate the monthly charge based on how many days have elapsed in the current calendar month.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutDeliverabilityDashboardOption>`_
**Request Syntax**
::
response = client.put_deliverability_dashboard_option(
DashboardEnabled=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
A response that indicates whether the Deliverability dashboard is enabled for your Amazon Pinpoint account.
:type DashboardEnabled: boolean
:param DashboardEnabled: **[REQUIRED]**
Indicates whether the Deliverability dashboard is enabled. If the value is ``true`` , then the dashboard is enabled.
:rtype: dict
:returns:
"""
pass
def put_email_identity_dkim_attributes(self, EmailIdentity: str, SigningEnabled: bool = None) -> Dict:
"""
Used to enable or disable DKIM authentication for an email identity.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutEmailIdentityDkimAttributes>`_
**Request Syntax**
::
response = client.put_email_identity_dkim_attributes(
EmailIdentity='string',
SigningEnabled=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type EmailIdentity: string
:param EmailIdentity: **[REQUIRED]**
The email identity that you want to change the DKIM settings for.
:type SigningEnabled: boolean
:param SigningEnabled:
Sets the DKIM signing configuration for the identity.
When you set this value ``true`` , then the messages that Amazon Pinpoint sends from the identity are DKIM-signed. When you set this value to ``false`` , then the messages that Amazon Pinpoint sends from the identity aren\'t DKIM-signed.
:rtype: dict
:returns:
"""
pass
def put_email_identity_feedback_attributes(self, EmailIdentity: str, EmailForwardingEnabled: bool = None) -> Dict:
"""
Used to enable or disable feedback forwarding for an identity. This setting determines what happens when an identity is used to send an email that results in a bounce or complaint event.
When you enable feedback forwarding, Amazon Pinpoint sends you email notifications when bounce or complaint events occur. Amazon Pinpoint sends this notification to the address that you specified in the Return-Path header of the original email.
When you disable feedback forwarding, Amazon Pinpoint sends notifications through other mechanisms, such as by notifying an Amazon SNS topic. You're required to have a method of tracking bounces and complaints. If you haven't set up another mechanism for receiving bounce or complaint notifications, Amazon Pinpoint sends an email notification when these events occur (even if this setting is disabled).
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutEmailIdentityFeedbackAttributes>`_
**Request Syntax**
::
response = client.put_email_identity_feedback_attributes(
EmailIdentity='string',
EmailForwardingEnabled=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type EmailIdentity: string
:param EmailIdentity: **[REQUIRED]**
The email identity that you want to configure bounce and complaint feedback forwarding for.
:type EmailForwardingEnabled: boolean
:param EmailForwardingEnabled:
Sets the feedback forwarding configuration for the identity.
If the value is ``true`` , Amazon Pinpoint sends you email notifications when bounce or complaint events occur. Amazon Pinpoint sends this notification to the address that you specified in the Return-Path header of the original email.
When you set this value to ``false`` , Amazon Pinpoint sends notifications through other mechanisms, such as by notifying an Amazon SNS topic or another event destination. You\'re required to have a method of tracking bounces and complaints. If you haven\'t set up another mechanism for receiving bounce or complaint notifications, Amazon Pinpoint sends an email notification when these events occur (even if this setting is disabled).
:rtype: dict
:returns:
"""
pass
def put_email_identity_mail_from_attributes(self, EmailIdentity: str, MailFromDomain: str = None, BehaviorOnMxFailure: str = None) -> Dict:
"""
Used to enable or disable the custom Mail-From domain configuration for an email identity.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/PutEmailIdentityMailFromAttributes>`_
**Request Syntax**
::
response = client.put_email_identity_mail_from_attributes(
EmailIdentity='string',
MailFromDomain='string',
BehaviorOnMxFailure='USE_DEFAULT_VALUE'|'REJECT_MESSAGE'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type EmailIdentity: string
:param EmailIdentity: **[REQUIRED]**
The verified email identity that you want to set up the custom MAIL FROM domain for.
:type MailFromDomain: string
:param MailFromDomain:
The custom MAIL FROM domain that you want the verified identity to use. The MAIL FROM domain must meet the following criteria:
* It has to be a subdomain of the verified identity.
* It can\'t be used to receive email.
* It can\'t be used in a \"From\" address if the MAIL FROM domain is a destination for feedback forwarding emails.
:type BehaviorOnMxFailure: string
:param BehaviorOnMxFailure:
The action that you want Amazon Pinpoint to take if it can\'t read the required MX record when you send an email. When you set this value to ``UseDefaultValue`` , Amazon Pinpoint uses *amazonses.com* as the MAIL FROM domain. When you set this value to ``RejectMessage`` , Amazon Pinpoint returns a ``MailFromDomainNotVerified`` error, and doesn\'t attempt to deliver the email.
These behaviors are taken when the custom MAIL FROM domain configuration is in the ``Pending`` , ``Failed`` , and ``TemporaryFailure`` states.
:rtype: dict
:returns:
"""
pass
def send_email(self, Destination: Dict, Content: Dict, FromEmailAddress: str = None, ReplyToAddresses: List = None, FeedbackForwardingEmailAddress: str = None, EmailTags: List = None, ConfigurationSetName: str = None) -> Dict:
"""
Sends an email message. You can use the Amazon Pinpoint Email API to send two types of messages:
* **Simple** – A standard email message. When you create this type of message, you specify the sender, the recipient, and the message body, and Amazon Pinpoint assembles the message for you.
* **Raw** – A raw, MIME-formatted email message. When you send this type of email, you have to specify all of the message headers, as well as the message body. You can use this message type to send messages that contain attachments. The message that you specify has to be a valid MIME message.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/SendEmail>`_
**Request Syntax**
::
response = client.send_email(
FromEmailAddress='string',
Destination={
'ToAddresses': [
'string',
],
'CcAddresses': [
'string',
],
'BccAddresses': [
'string',
]
},
ReplyToAddresses=[
'string',
],
FeedbackForwardingEmailAddress='string',
Content={
'Simple': {
'Subject': {
'Data': 'string',
'Charset': 'string'
},
'Body': {
'Text': {
'Data': 'string',
'Charset': 'string'
},
'Html': {
'Data': 'string',
'Charset': 'string'
}
}
},
'Raw': {
'Data': b'bytes'
}
},
EmailTags=[
{
'Name': 'string',
'Value': 'string'
},
],
ConfigurationSetName='string'
)
**Response Syntax**
::
{
'MessageId': 'string'
}
**Response Structure**
- *(dict) --*
A unique message ID that you receive when Amazon Pinpoint accepts an email for sending.
- **MessageId** *(string) --*
A unique identifier for the message that is generated when Amazon Pinpoint accepts the message.
.. note::
It is possible for Amazon Pinpoint to accept a message without sending it. This can happen when the message you're trying to send has an attachment doesn't pass a virus check, or when you send a templated email that contains invalid personalization content, for example.
:type FromEmailAddress: string
:param FromEmailAddress:
The email address that you want to use as the \"From\" address for the email. The address that you specify has to be verified.
:type Destination: dict
:param Destination: **[REQUIRED]**
An object that contains the recipients of the email message.
- **ToAddresses** *(list) --*
An array that contains the email addresses of the \"To\" recipients for the email.
- *(string) --*
- **CcAddresses** *(list) --*
An array that contains the email addresses of the \"CC\" (carbon copy) recipients for the email.
- *(string) --*
- **BccAddresses** *(list) --*
An array that contains the email addresses of the \"BCC\" (blind carbon copy) recipients for the email.
- *(string) --*
:type ReplyToAddresses: list
:param ReplyToAddresses:
The \"Reply-to\" email addresses for the message. When the recipient replies to the message, each Reply-to address receives the reply.
- *(string) --*
:type FeedbackForwardingEmailAddress: string
:param FeedbackForwardingEmailAddress:
The address that Amazon Pinpoint should send bounce and complaint notifications to.
:type Content: dict
:param Content: **[REQUIRED]**
An object that contains the body of the message. You can send either a Simple message or a Raw message.
- **Simple** *(dict) --*
The simple email message. The message consists of a subject and a message body.
- **Subject** *(dict) --* **[REQUIRED]**
The subject line of the email. The subject line can only contain 7-bit ASCII characters. However, you can specify non-ASCII characters in the subject line by using encoded-word syntax, as described in `RFC 2047 <https://tools.ietf.org/html/rfc2047>`__ .
- **Data** *(string) --* **[REQUIRED]**
The content of the message itself.
- **Charset** *(string) --*
The character set for the content. Because of the constraints of the SMTP protocol, Amazon Pinpoint uses 7-bit ASCII by default. If the text includes characters outside of the ASCII range, you have to specify a character set. For example, you could specify ``UTF-8`` , ``ISO-8859-1`` , or ``Shift_JIS`` .
- **Body** *(dict) --* **[REQUIRED]**
The body of the message. You can specify an HTML version of the message, a text-only version of the message, or both.
- **Text** *(dict) --*
An object that represents the version of the message that is displayed in email clients that don\'t support HTML, or clients where the recipient has disabled HTML rendering.
- **Data** *(string) --* **[REQUIRED]**
The content of the message itself.
- **Charset** *(string) --*
The character set for the content. Because of the constraints of the SMTP protocol, Amazon Pinpoint uses 7-bit ASCII by default. If the text includes characters outside of the ASCII range, you have to specify a character set. For example, you could specify ``UTF-8`` , ``ISO-8859-1`` , or ``Shift_JIS`` .
- **Html** *(dict) --*
An object that represents the version of the message that is displayed in email clients that support HTML. HTML messages can include formatted text, hyperlinks, images, and more.
- **Data** *(string) --* **[REQUIRED]**
The content of the message itself.
- **Charset** *(string) --*
The character set for the content. Because of the constraints of the SMTP protocol, Amazon Pinpoint uses 7-bit ASCII by default. If the text includes characters outside of the ASCII range, you have to specify a character set. For example, you could specify ``UTF-8`` , ``ISO-8859-1`` , or ``Shift_JIS`` .
- **Raw** *(dict) --*
The raw email message. The message has to meet the following criteria:
* The message has to contain a header and a body, separated by one blank line.
* All of the required header fields must be present in the message.
* Each part of a multipart MIME message must be formatted properly.
* If you include attachments, they must be in a file format that Amazon Pinpoint supports.
* The entire message must be Base64 encoded.
* If any of the MIME parts in your message contain content that is outside of the 7-bit ASCII character range, you should encode that content to ensure that recipients\' email clients render the message properly.
* The length of any single line of text in the message can\'t exceed 1,000 characters. This restriction is defined in `RFC 5321 <https://tools.ietf.org/html/rfc5321>`__ .
- **Data** *(bytes) --* **[REQUIRED]**
The raw email message. The message has to meet the following criteria:
* The message has to contain a header and a body, separated by one blank line.
* All of the required header fields must be present in the message.
* Each part of a multipart MIME message must be formatted properly.
* Attachments must be in a file format that Amazon Pinpoint supports.
* The entire message must be Base64 encoded.
* If any of the MIME parts in your message contain content that is outside of the 7-bit ASCII character range, you should encode that content to ensure that recipients\' email clients render the message properly.
* The length of any single line of text in the message can\'t exceed 1,000 characters. This restriction is defined in `RFC 5321 <https://tools.ietf.org/html/rfc5321>`__ .
:type EmailTags: list
:param EmailTags:
A list of tags, in the form of name/value pairs, to apply to an email that you send using the ``SendEmail`` operation. Tags correspond to characteristics of the email that you define, so that you can publish email sending events.
- *(dict) --*
Contains the name and value of a tag that you apply to an email. You can use message tags when you publish email sending events.
- **Name** *(string) --* **[REQUIRED]**
The name of the message tag. The message tag name has to meet the following criteria:
* It can only contain ASCII letters (a–z, A–Z), numbers (0–9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
- **Value** *(string) --* **[REQUIRED]**
The value of the message tag. The message tag value has to meet the following criteria:
* It can only contain ASCII letters (a–z, A–Z), numbers (0–9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
:type ConfigurationSetName: string
:param ConfigurationSetName:
The name of the configuration set that you want to use when sending the email.
:rtype: dict
:returns:
"""
pass
def tag_resource(self, ResourceArn: str, Tags: List) -> Dict:
"""
Add one or more tags (keys and values) to one or more specified resources. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Tags can help you categorize and manage resources in different ways, such as by purpose, owner, environment, or other criteria. A resource can have as many as 50 tags.
Each tag consists of a required *tag key* and an associated *tag value* , both of which you define. A tag key is a general label that acts as a category for more specific tag values. A tag value acts as a descriptor within a tag key.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/TagResource>`_
**Request Syntax**
::
response = client.tag_resource(
ResourceArn='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
]
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type ResourceArn: string
:param ResourceArn: **[REQUIRED]**
The Amazon Resource Name (ARN) of the resource that you want to add one or more tags to.
:type Tags: list
:param Tags: **[REQUIRED]**
A list of the tags that you want to add to the resource. A tag consists of a required tag key (``Key`` ) and an associated tag value (``Value`` ). The maximum length of a tag key is 128 characters. The maximum length of a tag value is 256 characters.
- *(dict) --*
An object that defines the tags that are associated with a resource. A *tag* is a label that you optionally define and associate with a resource in Amazon Pinpoint. Tags can help you categorize and manage resources in different ways, such as by purpose, owner, environment, or other criteria. A resource can have as many as 50 tags.
Each tag consists of a required *tag key* and an associated *tag value* , both of which you define. A tag key is a general label that acts as a category for a more specific tag value. A tag value acts as a descriptor within a tag key. For example, if you have two versions of an Amazon Pinpoint project, one for internal testing and another for external use, you might assign a ``Stack`` tag key to both projects. The value of the ``Stack`` tag key might be ``Test`` for one project and ``Production`` for the other project.
A tag key can contain as many as 128 characters. A tag value can contain as many as 256 characters. The characters can be Unicode letters, digits, white space, or one of the following symbols: _ . : / = + -. The following additional restrictions apply to tags:
* Tag keys and values are case sensitive.
* For each associated resource, each tag key must be unique and it can have only one value.
* The ``aws:`` prefix is reserved for use by AWS; you can’t use it in any tag keys or values that you define. In addition, you can\'t edit or remove tag keys or values that use this prefix. Tags that use this prefix don’t count against the limit of 50 tags per resource.
* You can associate tags with public or shared resources, but the tags are available only for your AWS account, not any other accounts that share the resource. In addition, the tags are available only for resources that are located in the specified AWS Region for your AWS account.
- **Key** *(string) --* **[REQUIRED]**
One part of a key-value pair that defines a tag. The maximum length of a tag key is 128 characters. The minimum length is 1 character.
- **Value** *(string) --* **[REQUIRED]**
The optional part of a key-value pair that defines a tag. The maximum length of a tag value is 256 characters. The minimum length is 0 characters. If you don’t want a resource to have a specific tag value, don’t specify a value for this parameter. Amazon Pinpoint will set the value to an empty string.
:rtype: dict
:returns:
"""
pass
def untag_resource(self, ResourceArn: str, TagKeys: List) -> Dict:
"""
Remove one or more tags (keys and values) from a specified resource.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/UntagResource>`_
**Request Syntax**
::
response = client.untag_resource(
ResourceArn='string',
TagKeys=[
'string',
]
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type ResourceArn: string
:param ResourceArn: **[REQUIRED]**
The Amazon Resource Name (ARN) of the resource that you want to remove one or more tags from.
:type TagKeys: list
:param TagKeys: **[REQUIRED]**
The tags (tag keys) that you want to remove from the resource. When you specify a tag key, the action removes both that key and its associated tag value.
To remove more than one tag from the resource, append the ``TagKeys`` parameter and argument for each additional tag to remove, separated by an ampersand. For example: ``/v1/email/tags?ResourceArn=ResourceArn&TagKeys=Key1&TagKeys=Key2``
- *(string) --*
:rtype: dict
:returns:
"""
pass
def update_configuration_set_event_destination(self, ConfigurationSetName: str, EventDestinationName: str, EventDestination: Dict) -> Dict:
"""
Update the configuration of an event destination for a configuration set.
In Amazon Pinpoint, *events* include message sends, deliveries, opens, clicks, bounces, and complaints. *Event destinations* are places that you can send information about these events to. For example, you can send event data to Amazon SNS to receive notifications when you receive bounces or complaints, or you can use Amazon Kinesis Data Firehose to stream data to Amazon S3 for long-term storage.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/pinpoint-email-2018-07-26/UpdateConfigurationSetEventDestination>`_
**Request Syntax**
::
response = client.update_configuration_set_event_destination(
ConfigurationSetName='string',
EventDestinationName='string',
EventDestination={
'Enabled': True|False,
'MatchingEventTypes': [
'SEND'|'REJECT'|'BOUNCE'|'COMPLAINT'|'DELIVERY'|'OPEN'|'CLICK'|'RENDERING_FAILURE',
],
'KinesisFirehoseDestination': {
'IamRoleArn': 'string',
'DeliveryStreamArn': 'string'
},
'CloudWatchDestination': {
'DimensionConfigurations': [
{
'DimensionName': 'string',
'DimensionValueSource': 'MESSAGE_TAG'|'EMAIL_HEADER'|'LINK_TAG',
'DefaultDimensionValue': 'string'
},
]
},
'SnsDestination': {
'TopicArn': 'string'
},
'PinpointDestination': {
'ApplicationArn': 'string'
}
}
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
An HTTP 200 response if the request succeeds, or an error message if the request fails.
:type ConfigurationSetName: string
:param ConfigurationSetName: **[REQUIRED]**
The name of the configuration set that contains the event destination that you want to modify.
:type EventDestinationName: string
:param EventDestinationName: **[REQUIRED]**
The name of the event destination that you want to modify.
:type EventDestination: dict
:param EventDestination: **[REQUIRED]**
An object that defines the event destination.
- **Enabled** *(boolean) --*
If ``true`` , the event destination is enabled. When the event destination is enabled, the specified event types are sent to the destinations in this ``EventDestinationDefinition`` .
If ``false`` , the event destination is disabled. When the event destination is disabled, events aren\'t sent to the specified destinations.
- **MatchingEventTypes** *(list) --*
An array that specifies which events Amazon Pinpoint should send to the destinations in this ``EventDestinationDefinition`` .
- *(string) --*
An email sending event type. For example, email sends, opens, and bounces are all email events.
- **KinesisFirehoseDestination** *(dict) --*
An object that defines an Amazon Kinesis Data Firehose destination for email events. You can use Amazon Kinesis Data Firehose to stream data to other services, such as Amazon S3 and Amazon Redshift.
- **IamRoleArn** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the IAM role that Amazon Pinpoint uses when sending email events to the Amazon Kinesis Data Firehose stream.
- **DeliveryStreamArn** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the Amazon Kinesis Data Firehose stream that Amazon Pinpoint sends email events to.
- **CloudWatchDestination** *(dict) --*
An object that defines an Amazon CloudWatch destination for email events. You can use Amazon CloudWatch to monitor and gain insights on your email sending metrics.
- **DimensionConfigurations** *(list) --* **[REQUIRED]**
An array of objects that define the dimensions to use when you send email events to Amazon CloudWatch.
- *(dict) --*
An object that defines the dimension configuration to use when you send Amazon Pinpoint email events to Amazon CloudWatch.
- **DimensionName** *(string) --* **[REQUIRED]**
The name of an Amazon CloudWatch dimension associated with an email sending metric. The name has to meet the following criteria:
* It can only contain ASCII letters (a-z, A-Z), numbers (0-9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
- **DimensionValueSource** *(string) --* **[REQUIRED]**
The location where Amazon Pinpoint finds the value of a dimension to publish to Amazon CloudWatch. If you want Amazon Pinpoint to use the message tags that you specify using an X-SES-MESSAGE-TAGS header or a parameter to the SendEmail/SendRawEmail API, choose ``messageTag`` . If you want Amazon Pinpoint to use your own email headers, choose ``emailHeader`` . If you want Amazon Pinpoint to use link tags, choose ``linkTags`` .
- **DefaultDimensionValue** *(string) --* **[REQUIRED]**
The default value of the dimension that is published to Amazon CloudWatch if you don\'t provide the value of the dimension when you send an email. This value has to meet the following criteria:
* It can only contain ASCII letters (a-z, A-Z), numbers (0-9), underscores (_), or dashes (-).
* It can contain no more than 256 characters.
- **SnsDestination** *(dict) --*
An object that defines an Amazon SNS destination for email events. You can use Amazon SNS to send notification when certain email events occur.
- **TopicArn** *(string) --* **[REQUIRED]**
The Amazon Resource Name (ARN) of the Amazon SNS topic that you want to publish email events to. For more information about Amazon SNS topics, see the `Amazon SNS Developer Guide <https://docs.aws.amazon.com/sns/latest/dg/CreateTopic.html>`__ .
- **PinpointDestination** *(dict) --*
An object that defines a Amazon Pinpoint destination for email events. You can use Amazon Pinpoint events to create attributes in Amazon Pinpoint projects. You can use these attributes to create segments for your campaigns.
- **ApplicationArn** *(string) --*
The Amazon Resource Name (ARN) of the Amazon Pinpoint project that you want to send email events to.
:rtype: dict
:returns:
"""
pass
| 65.129761
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0
| 9
|
1a880a76392cf5b92896589675433ee884c6d98c
| 62,734
|
py
|
Python
|
tests/system/test_v3.py
|
epenet/simplisafe-python
|
1ac074788e3560ca2ba568e12b411537c23275d4
|
[
"MIT"
] | null | null | null |
tests/system/test_v3.py
|
epenet/simplisafe-python
|
1ac074788e3560ca2ba568e12b411537c23275d4
|
[
"MIT"
] | null | null | null |
tests/system/test_v3.py
|
epenet/simplisafe-python
|
1ac074788e3560ca2ba568e12b411537c23275d4
|
[
"MIT"
] | null | null | null |
"""Define tests for v3 System objects."""
# pylint: disable=protected-access,too-many-arguments,unused-argument
from datetime import datetime, timedelta
import logging
import aiohttp
import pytest
import pytz
from simplipy import API
from simplipy.errors import (
EndpointUnavailableError,
InvalidCredentialsError,
PinError,
RequestError,
SimplipyError,
)
from simplipy.system import SystemStates
from simplipy.system.v3 import Volume
from tests.common import (
TEST_AUTHORIZATION_CODE,
TEST_CODE_VERIFIER,
TEST_SUBSCRIPTION_ID,
TEST_SYSTEM_ID,
TEST_SYSTEM_SERIAL_NO,
TEST_USER_ID,
)
@pytest.mark.asyncio
async def test_as_dict(aresponses, v3_server):
"""Test dumping the system as a dict."""
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
assert system.as_dict() == {
"address": "1234 Main Street",
"alarm_going_off": False,
"connection_type": "wifi",
"notifications": [
{
"notification_id": "xxxxxxxxxxxxxxxxxxxxxxxx",
"text": "Power Outage - Backup battery in use.",
"category": "error",
"code": "2000",
"timestamp": 1581823228,
"link": "http://link.to.info",
"link_label": "More Info",
}
],
"serial": "1234ABCD",
"state": 10,
"system_id": 12345,
"temperature": 67,
"version": 3,
"sensors": [
{
"name": "Fire Door",
"serial": "825",
"type": 5,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Front Door",
"serial": "14",
"type": 5,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Patio Door",
"serial": "185",
"type": 5,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": True,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": True,
"triggered": False,
},
{
"name": "Basement",
"serial": "236",
"type": 13,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"alarmVolume": 3,
"doorChime": 0,
"exitBeeps": 0,
"entryBeeps": 2,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Front Door",
"serial": "789",
"type": 3,
"error": False,
"low_battery": False,
"offline": False,
"settings": {"alarm": 1},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Master BR",
"serial": "822",
"type": 3,
"error": False,
"low_battery": False,
"offline": False,
"settings": {"alarm": 1},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Kitchen",
"serial": "972",
"type": 1,
"error": False,
"low_battery": False,
"offline": False,
"settings": {"lowPowerMode": False, "alarm": 1},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Upstairs",
"serial": "93",
"type": 8,
"error": False,
"low_battery": False,
"offline": False,
"settings": {},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Downstairs",
"serial": "650",
"type": 8,
"error": False,
"low_battery": False,
"offline": False,
"settings": {},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Basement N",
"serial": "491",
"type": 6,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Mud Counter",
"serial": "280",
"type": 6,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Basement S",
"serial": "430",
"type": 6,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Laundry",
"serial": "129",
"type": 9,
"error": False,
"low_battery": False,
"offline": False,
"settings": {"alarm": 1},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Basement",
"serial": "975",
"type": 9,
"error": False,
"low_battery": False,
"offline": False,
"settings": {"alarm": 1},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Fridge",
"serial": "382",
"type": 9,
"error": False,
"low_battery": False,
"offline": False,
"settings": {"alarm": 1},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Basement",
"serial": "320",
"type": 10,
"error": False,
"low_battery": False,
"offline": False,
"settings": {"highTemp": 95, "lowTemp": 41, "alarm": 1},
"trigger_instantly": False,
"triggered": False,
"temperature": 67,
},
{
"name": "Upstairs",
"serial": "785",
"type": 4,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 0,
"home": 0,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Downstairs",
"serial": "934",
"type": 4,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 0,
"home": 0,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Landing",
"serial": "634",
"type": 6,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Living Room",
"serial": "801",
"type": 6,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Eating Area",
"serial": "946",
"type": 6,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"instantTrigger": False,
"away2": 1,
"away": 1,
"home2": 1,
"home": 1,
"off": 0,
},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Front Door",
"serial": "987a",
"type": 253,
"error": False,
"low_battery": False,
"offline": False,
"settings": {},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Front Door",
"serial": "654a",
"type": 253,
"error": False,
"low_battery": False,
"offline": False,
"settings": {},
"trigger_instantly": False,
"triggered": False,
},
{
"name": "Front Door",
"serial": "321a",
"type": 253,
"error": False,
"low_battery": False,
"offline": False,
"settings": {},
"trigger_instantly": False,
"triggered": False,
},
],
"alarm_duration": 240,
"alarm_volume": 3,
"battery_backup_power_level": 5293,
"cameras": [
{
"camera_settings": {
"cameraName": "Camera",
"pictureQuality": "720p",
"nightVision": "auto",
"statusLight": "off",
"micSensitivity": 100,
"micEnable": True,
"speakerVolume": 75,
"motionSensitivity": 0,
"shutterHome": "closedAlarmOnly",
"shutterAway": "open",
"shutterOff": "closedAlarmOnly",
"wifiSsid": "",
"canStream": False,
"canRecord": False,
"pirEnable": True,
"vaEnable": True,
"notificationsEnable": False,
"enableDoorbellNotification": True,
"doorbellChimeVolume": "off",
"privacyEnable": False,
"hdr": False,
"vaZoningEnable": False,
"vaZoningRows": 0,
"vaZoningCols": 0,
"vaZoningMask": [],
"maxDigitalZoom": 10,
"supportedResolutions": ["480p", "720p"],
"admin": {
"IRLED": 0,
"pirSens": 0,
"statusLEDState": 1,
"lux": "lowLux",
"motionDetectionEnabled": False,
"motionThresholdZero": 0,
"motionThresholdOne": 10000,
"levelChangeDelayZero": 30,
"levelChangeDelayOne": 10,
"audioDetectionEnabled": False,
"audioChannelNum": 2,
"audioSampleRate": 16000,
"audioChunkBytes": 2048,
"audioSampleFormat": 3,
"audioSensitivity": 50,
"audioThreshold": 50,
"audioDirection": 0,
"bitRate": 284,
"longPress": 2000,
"kframe": 1,
"gopLength": 40,
"idr": 1,
"fps": 20,
"firmwareVersion": "2.6.1.107",
"netConfigVersion": "",
"camAgentVersion": "",
"lastLogin": 1600639997,
"lastLogout": 1600639944,
"pirSampleRateMs": 800,
"pirHysteresisHigh": 2,
"pirHysteresisLow": 10,
"pirFilterCoefficient": 1,
"logEnabled": True,
"logLevel": 3,
"logQDepth": 20,
"firmwareGroup": "public",
"irOpenThreshold": 445,
"irCloseThreshold": 840,
"irOpenDelay": 3,
"irCloseDelay": 3,
"irThreshold1x": 388,
"irThreshold2x": 335,
"irThreshold3x": 260,
"rssi": [[1600935204, -43]],
"battery": [],
"dbm": 0,
"vmUse": 161592,
"resSet": 10540,
"uptime": 810043.74,
"wifiDisconnects": 1,
"wifiDriverReloads": 1,
"statsPeriod": 3600000,
"sarlaccDebugLogTypes": 0,
"odProcessingFps": 8,
"odObjectMinWidthPercent": 6,
"odObjectMinHeightPercent": 24,
"odEnableObjectDetection": True,
"odClassificationMask": 2,
"odClassificationConfidenceThreshold": 0.95,
"odEnableOverlay": False,
"odAnalyticsLib": 2,
"odSensitivity": 85,
"odEventObjectMask": 2,
"odLuxThreshold": 445,
"odLuxHysteresisHigh": 4,
"odLuxHysteresisLow": 4,
"odLuxSamplingFrequency": 30,
"odFGExtractorMode": 2,
"odVideoScaleFactor": 1,
"odSceneType": 1,
"odCameraView": 3,
"odCameraFOV": 2,
"odBackgroundLearnStationary": True,
"odBackgroundLearnStationarySpeed": 15,
"odClassifierQualityProfile": 1,
"odEnableVideoAnalyticsWhileStreaming": False,
"wlanMac": "XX:XX:XX:XX:XX:XX",
"region": "us-east-1",
"enableWifiAnalyticsLib": False,
"ivLicense": "",
},
"pirLevel": "medium",
"odLevel": "medium",
},
"camera_type": 0,
"name": "Camera",
"serial": "1234567890",
"shutter_open_when_away": True,
"shutter_open_when_home": False,
"shutter_open_when_off": False,
"status": "online",
"subscription_enabled": True,
},
{
"camera_settings": {
"cameraName": "Doorbell",
"pictureQuality": "720p",
"nightVision": "auto",
"statusLight": "off",
"micSensitivity": 100,
"micEnable": True,
"speakerVolume": 75,
"motionSensitivity": 0,
"shutterHome": "closedAlarmOnly",
"shutterAway": "open",
"shutterOff": "closedAlarmOnly",
"wifiSsid": "",
"canStream": False,
"canRecord": False,
"pirEnable": True,
"vaEnable": True,
"notificationsEnable": False,
"enableDoorbellNotification": True,
"doorbellChimeVolume": "off",
"privacyEnable": False,
"hdr": False,
"vaZoningEnable": False,
"vaZoningRows": 0,
"vaZoningCols": 0,
"vaZoningMask": [],
"maxDigitalZoom": 10,
"supportedResolutions": ["480p", "720p"],
"admin": {
"IRLED": 0,
"pirSens": 0,
"statusLEDState": 1,
"lux": "lowLux",
"motionDetectionEnabled": False,
"motionThresholdZero": 0,
"motionThresholdOne": 10000,
"levelChangeDelayZero": 30,
"levelChangeDelayOne": 10,
"audioDetectionEnabled": False,
"audioChannelNum": 2,
"audioSampleRate": 16000,
"audioChunkBytes": 2048,
"audioSampleFormat": 3,
"audioSensitivity": 50,
"audioThreshold": 50,
"audioDirection": 0,
"bitRate": 284,
"longPress": 2000,
"kframe": 1,
"gopLength": 40,
"idr": 1,
"fps": 20,
"firmwareVersion": "2.6.1.107",
"netConfigVersion": "",
"camAgentVersion": "",
"lastLogin": 1600639997,
"lastLogout": 1600639944,
"pirSampleRateMs": 800,
"pirHysteresisHigh": 2,
"pirHysteresisLow": 10,
"pirFilterCoefficient": 1,
"logEnabled": True,
"logLevel": 3,
"logQDepth": 20,
"firmwareGroup": "public",
"irOpenThreshold": 445,
"irCloseThreshold": 840,
"irOpenDelay": 3,
"irCloseDelay": 3,
"irThreshold1x": 388,
"irThreshold2x": 335,
"irThreshold3x": 260,
"rssi": [[1600935204, -43]],
"battery": [],
"dbm": 0,
"vmUse": 161592,
"resSet": 10540,
"uptime": 810043.74,
"wifiDisconnects": 1,
"wifiDriverReloads": 1,
"statsPeriod": 3600000,
"sarlaccDebugLogTypes": 0,
"odProcessingFps": 8,
"odObjectMinWidthPercent": 6,
"odObjectMinHeightPercent": 24,
"odEnableObjectDetection": True,
"odClassificationMask": 2,
"odClassificationConfidenceThreshold": 0.95,
"odEnableOverlay": False,
"odAnalyticsLib": 2,
"odSensitivity": 85,
"odEventObjectMask": 2,
"odLuxThreshold": 445,
"odLuxHysteresisHigh": 4,
"odLuxHysteresisLow": 4,
"odLuxSamplingFrequency": 30,
"odFGExtractorMode": 2,
"odVideoScaleFactor": 1,
"odSceneType": 1,
"odCameraView": 3,
"odCameraFOV": 2,
"odBackgroundLearnStationary": True,
"odBackgroundLearnStationarySpeed": 15,
"odClassifierQualityProfile": 1,
"odEnableVideoAnalyticsWhileStreaming": False,
"wlanMac": "XX:XX:XX:XX:XX:XX",
"region": "us-east-1",
"enableWifiAnalyticsLib": False,
"ivLicense": "",
},
"pirLevel": "medium",
"odLevel": "medium",
},
"camera_type": 1,
"name": "Doorbell",
"serial": "1234567892",
"shutter_open_when_away": True,
"shutter_open_when_home": False,
"shutter_open_when_off": False,
"status": "online",
"subscription_enabled": True,
},
{
"camera_settings": {
"cameraName": "Unknown Camera",
"pictureQuality": "720p",
"nightVision": "auto",
"statusLight": "off",
"micSensitivity": 100,
"micEnable": True,
"speakerVolume": 75,
"motionSensitivity": 0,
"shutterHome": "closedAlarmOnly",
"shutterAway": "open",
"shutterOff": "closedAlarmOnly",
"wifiSsid": "",
"canStream": False,
"canRecord": False,
"pirEnable": True,
"vaEnable": True,
"notificationsEnable": False,
"enableDoorbellNotification": True,
"doorbellChimeVolume": "off",
"privacyEnable": False,
"hdr": False,
"vaZoningEnable": False,
"vaZoningRows": 0,
"vaZoningCols": 0,
"vaZoningMask": [],
"maxDigitalZoom": 10,
"supportedResolutions": ["480p", "720p"],
"admin": {
"IRLED": 0,
"pirSens": 0,
"statusLEDState": 1,
"lux": "lowLux",
"motionDetectionEnabled": False,
"motionThresholdZero": 0,
"motionThresholdOne": 10000,
"levelChangeDelayZero": 30,
"levelChangeDelayOne": 10,
"audioDetectionEnabled": False,
"audioChannelNum": 2,
"audioSampleRate": 16000,
"audioChunkBytes": 2048,
"audioSampleFormat": 3,
"audioSensitivity": 50,
"audioThreshold": 50,
"audioDirection": 0,
"bitRate": 284,
"longPress": 2000,
"kframe": 1,
"gopLength": 40,
"idr": 1,
"fps": 20,
"firmwareVersion": "2.6.1.107",
"netConfigVersion": "",
"camAgentVersion": "",
"lastLogin": 1600639997,
"lastLogout": 1600639944,
"pirSampleRateMs": 800,
"pirHysteresisHigh": 2,
"pirHysteresisLow": 10,
"pirFilterCoefficient": 1,
"logEnabled": True,
"logLevel": 3,
"logQDepth": 20,
"firmwareGroup": "public",
"irOpenThreshold": 445,
"irCloseThreshold": 840,
"irOpenDelay": 3,
"irCloseDelay": 3,
"irThreshold1x": 388,
"irThreshold2x": 335,
"irThreshold3x": 260,
"rssi": [[1600935204, -43]],
"battery": [],
"dbm": 0,
"vmUse": 161592,
"resSet": 10540,
"uptime": 810043.74,
"wifiDisconnects": 1,
"wifiDriverReloads": 1,
"statsPeriod": 3600000,
"sarlaccDebugLogTypes": 0,
"odProcessingFps": 8,
"odObjectMinWidthPercent": 6,
"odObjectMinHeightPercent": 24,
"odEnableObjectDetection": True,
"odClassificationMask": 2,
"odClassificationConfidenceThreshold": 0.95,
"odEnableOverlay": False,
"odAnalyticsLib": 2,
"odSensitivity": 85,
"odEventObjectMask": 2,
"odLuxThreshold": 445,
"odLuxHysteresisHigh": 4,
"odLuxHysteresisLow": 4,
"odLuxSamplingFrequency": 30,
"odFGExtractorMode": 2,
"odVideoScaleFactor": 1,
"odSceneType": 1,
"odCameraView": 3,
"odCameraFOV": 2,
"odBackgroundLearnStationary": True,
"odBackgroundLearnStationarySpeed": 15,
"odClassifierQualityProfile": 1,
"odEnableVideoAnalyticsWhileStreaming": False,
"wlanMac": "XX:XX:XX:XX:XX:XX",
"region": "us-east-1",
"enableWifiAnalyticsLib": False,
"ivLicense": "",
},
"pirLevel": "medium",
"odLevel": "medium",
},
"camera_type": 99,
"name": "Unknown Camera",
"serial": "1234567891",
"shutter_open_when_away": True,
"shutter_open_when_home": False,
"shutter_open_when_off": False,
"status": "online",
"subscription_enabled": True,
},
],
"chime_volume": 2,
"entry_delay_away": 30,
"entry_delay_home": 30,
"exit_delay_away": 60,
"exit_delay_home": 0,
"gsm_strength": -73,
"light": True,
"locks": [
{
"name": "Front Door",
"serial": "987",
"type": 16,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"autoLock": 3,
"away": 1,
"home": 1,
"awayToOff": 0,
"homeToOff": 1,
},
"disabled": False,
"lock_low_battery": False,
"pin_pad_low_battery": False,
"pin_pad_offline": False,
"state": 1,
},
{
"name": "Back Door",
"serial": "654",
"type": 16,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"autoLock": 3,
"away": 1,
"home": 1,
"awayToOff": 0,
"homeToOff": 1,
},
"disabled": False,
"lock_low_battery": False,
"pin_pad_low_battery": False,
"pin_pad_offline": False,
"state": 2,
},
{
"name": "Side Door",
"serial": "321",
"type": 16,
"error": False,
"low_battery": False,
"offline": False,
"settings": {
"autoLock": 3,
"away": 1,
"home": 1,
"awayToOff": 0,
"homeToOff": 1,
},
"disabled": False,
"lock_low_battery": False,
"pin_pad_low_battery": False,
"pin_pad_offline": False,
"state": 99,
},
],
"offline": False,
"power_outage": False,
"rf_jamming": False,
"voice_prompt_volume": 2,
"wall_power_level": 5933,
"wifi_ssid": "MY_WIFI",
"wifi_strength": -49,
}
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_alarm_state(aresponses, v3_server):
"""Test that we can get the alarm state."""
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
assert system.state == SystemStates.OFF
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_clear_notifications(aresponses, v3_server, v3_settings_response):
"""Test clearing all active notifications."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/messages",
"delete",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_clear_notifications()
assert system.notifications == []
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_get_last_event(aresponses, latest_event_response, v3_server):
"""Test getting the latest event."""
v3_server.add(
"api.simplisafe.com",
f"/v1/subscriptions/{TEST_SUBSCRIPTION_ID}/events",
"get",
response=aiohttp.web_response.json_response(latest_event_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
latest_event = await system.async_get_latest_event()
assert latest_event["eventId"] == 1234567890
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_get_pins(aresponses, v3_server, v3_settings_response):
"""Test getting PINs associated with a V3 system."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
pins = await system.async_get_pins()
assert len(pins) == 4
assert pins["master"] == "1234"
assert pins["duress"] == "9876"
assert pins["Test 1"] == "3456"
assert pins["Test 2"] == "5423"
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_async_get_systems(aresponses, v3_server):
"""Test the ability to get systems attached to a v3 account."""
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
assert len(systems) == 1
system = systems[TEST_SYSTEM_ID]
assert system.serial == TEST_SYSTEM_SERIAL_NO
assert system.system_id == TEST_SYSTEM_ID
assert len(system.sensors) == 24
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_empty_events(aresponses, events_response, v3_server):
"""Test that an empty events structure is handled correctly."""
events_response["events"] = []
v3_server.add(
"api.simplisafe.com",
f"/v1/subscriptions/{TEST_SUBSCRIPTION_ID}/events",
"get",
response=aiohttp.web_response.json_response(events_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
# Test the events key existing, but being empty:
with pytest.raises(SimplipyError):
_ = await system.async_get_latest_event()
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_lock_state_update_bug(aresponses, caplog, v3_server, v3_state_response):
"""Test halting updates within a 15-second window from arming/disarming."""
caplog.set_level(logging.INFO)
v3_state_response["state"] = "AWAY"
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/state/away",
"post",
response=aiohttp.web_response.json_response(v3_state_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_set_away()
assert system.state == SystemStates.AWAY
await system.async_update()
assert any("Skipping system update" in e.message for e in caplog.records)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_missing_events(aresponses, events_response, v3_server):
"""Test that an altogether-missing events structure is handled correctly."""
events_response.pop("events")
v3_server.add(
"api.simplisafe.com",
f"/v1/subscriptions/{TEST_SUBSCRIPTION_ID}/events",
"get",
response=aiohttp.web_response.json_response(events_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
# Test the events key existing, but being empty:
with pytest.raises(SimplipyError):
_ = await system.async_get_latest_event()
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_no_state_change_on_failure(aresponses, v3_server):
"""Test that the system doesn't change state on an error."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/state/away",
"post",
response=aresponses.Response(text="Unauthorized", status=401),
)
v3_server.add(
"auth.simplisafe.com",
"/oauth/token",
"post",
response=aresponses.Response(text="Unauthorized", status=401),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
# Manually set the expiration datetime to force a refresh token flow:
simplisafe._token_last_refreshed = datetime.utcnow() - timedelta(seconds=30)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
assert system.state == SystemStates.OFF
with pytest.raises(InvalidCredentialsError):
await system.async_set_away()
assert system.state == SystemStates.OFF
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_properties(aresponses, v3_server, v3_settings_response):
"""Test that v3 system properties are available."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"post",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
assert system.alarm_duration == 240
assert system.alarm_volume == Volume.HIGH
assert system.battery_backup_power_level == 5293
assert system.chime_volume == Volume.MEDIUM
assert system.connection_type == "wifi"
assert system.entry_delay_away == 30
assert system.entry_delay_home == 30
assert system.exit_delay_away == 60
assert system.exit_delay_home == 0
assert system.gsm_strength == -73
assert system.light is True
assert system.offline is False
assert system.power_outage is False
assert system.rf_jamming is False
assert system.voice_prompt_volume == Volume.MEDIUM
assert system.wall_power_level == 5933
assert system.wifi_ssid == "MY_WIFI"
assert system.wifi_strength == -49
# Test "setting" various system properties by overriding their values, then
# calling the update functions:
system.settings_data["settings"]["normal"]["alarmDuration"] = 0
system.settings_data["settings"]["normal"]["alarmVolume"] = 0
system.settings_data["settings"]["normal"]["doorChime"] = 0
system.settings_data["settings"]["normal"]["entryDelayAway"] = 0
system.settings_data["settings"]["normal"]["entryDelayHome"] = 0
system.settings_data["settings"]["normal"]["exitDelayAway"] = 0
system.settings_data["settings"]["normal"]["exitDelayHome"] = 1000
system.settings_data["settings"]["normal"]["light"] = False
system.settings_data["settings"]["normal"]["voicePrompts"] = 0
await system.async_set_properties(
{
"alarm_duration": 240,
"alarm_volume": Volume.HIGH,
"chime_volume": Volume.MEDIUM,
"entry_delay_away": 30,
"entry_delay_home": 30,
"exit_delay_away": 60,
"exit_delay_home": 0,
"light": True,
"voice_prompt_volume": Volume.MEDIUM,
}
)
assert system.alarm_duration == 240
assert system.alarm_volume == Volume.HIGH
assert system.chime_volume == Volume.MEDIUM
assert system.entry_delay_away == 30
assert system.entry_delay_home == 30
assert system.exit_delay_away == 60
assert system.exit_delay_home == 0
assert system.light is True
assert system.voice_prompt_volume == Volume.MEDIUM
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_remove_nonexistent_pin(aresponses, v3_server, v3_settings_response):
"""Test throwing an error when removing a nonexistent PIN."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
with pytest.raises(PinError) as err:
await system.async_remove_pin("0000")
assert "Refusing to delete nonexistent PIN" in str(err)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_remove_pin(aresponses, v3_server, v3_settings_response):
"""Test removing a PIN in a V3 system."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_settings_response["settings"]["pins"]["users"][1]["pin"] = ""
v3_settings_response["settings"]["pins"]["users"][1]["name"] = ""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/pins",
"post",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
latest_pins = await system.async_get_pins()
assert len(latest_pins) == 4
await system.async_remove_pin("Test 2")
latest_pins = await system.async_get_pins()
assert len(latest_pins) == 3
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_remove_reserved_pin(aresponses, v3_server, v3_settings_response):
"""Test throwing an error when removing a reserved PIN."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
with pytest.raises(PinError) as err:
await system.async_remove_pin("master")
assert "Refusing to delete reserved PIN" in str(err)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_set_duplicate_pin(aresponses, v3_server, v3_settings_response):
"""Test throwing an error when setting a duplicate PIN."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/pins",
"post",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
with pytest.raises(PinError) as err:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_set_pin("whatever", "1234")
assert "Refusing to create duplicate PIN" in str(err)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_set_invalid_property(aresponses, v3_server, v3_settings_response):
"""Test that setting an invalid property raises a ValueError."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"post",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
with pytest.raises(ValueError):
await system.async_set_properties({"Fake": "News"})
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_set_max_user_pins(
aresponses,
subscriptions_response,
v3_server,
v3_settings_response,
):
"""Test throwing an error when setting too many user PINs."""
v3_settings_response["settings"]["pins"]["users"] = [
{
"_id": "1271279d966212121124c6",
"pin": "1234",
"name": "Test 1",
},
{
"_id": "1271279d966212121124c7",
"pin": "5678",
"name": "Test 2",
},
{
"_id": "1271279d966212121124c8",
"pin": "9012",
"name": "Test 3",
},
{
"_id": "1271279d966212121124c9",
"pin": "3456",
"name": "Test 4",
},
]
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/pins",
"post",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
with pytest.raises(PinError) as err:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_set_pin("whatever", "8121")
assert "Refusing to create more than" in str(err)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_set_pin(aresponses, v3_server, v3_settings_response):
"""Test setting a PIN in a V3 system."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_settings_response["settings"]["pins"]["users"][2]["pin"] = "1274"
v3_settings_response["settings"]["pins"]["users"][2]["name"] = "whatever"
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/pins",
"post",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
latest_pins = await system.async_get_pins()
assert len(latest_pins) == 4
await system.async_set_pin("whatever", "1274")
latest_pins = await system.async_get_pins()
assert len(latest_pins) == 5
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_set_pin_wrong_chars(aresponses, v3_server):
"""Test throwing an error when setting a PIN with non-digits."""
async with aiohttp.ClientSession() as session:
with pytest.raises(PinError) as err:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_set_pin("whatever", "abcd")
assert "PINs can only contain numbers" in str(err)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_set_pin_wrong_length(aresponses, v3_server):
"""Test throwing an error when setting a PIN with the wrong length."""
async with aiohttp.ClientSession() as session:
with pytest.raises(PinError) as err:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_set_pin("whatever", "1122334455")
assert "digits long" in str(err)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_set_states(aresponses, v3_server, v3_state_response):
"""Test the ability to set the state of the system."""
v3_state_response["state"] = "AWAY"
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/state/away",
"post",
response=aiohttp.web_response.json_response(v3_state_response, status=200),
)
v3_state_response["state"] = "HOME"
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/state/home",
"post",
response=aiohttp.web_response.json_response(v3_state_response, status=200),
)
v3_state_response["state"] = "OFF"
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/state/off",
"post",
response=aiohttp.web_response.json_response(v3_state_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_set_away()
assert system.state == SystemStates.AWAY
await system.async_set_home()
assert system.state == SystemStates.HOME
await system.async_set_off()
assert system.state == SystemStates.OFF
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_system_notifications(aresponses, v3_server):
"""Test getting system notifications."""
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
assert len(system.notifications) == 1
notification1 = system.notifications[0]
assert notification1.notification_id == "xxxxxxxxxxxxxxxxxxxxxxxx"
assert notification1.text == "Power Outage - Backup battery in use."
assert notification1.category == "error"
assert notification1.code == "2000"
assert notification1.received_dt == datetime(
2020, 2, 16, 3, 20, 28, tzinfo=pytz.UTC
)
assert notification1.link == "http://link.to.info"
assert notification1.link_label == "More Info"
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_unavailable_endpoint(
aresponses, unavailable_endpoint_response, v3_server
):
"""Test that an unavailable endpoint logs a message."""
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(
unavailable_endpoint_response, status=403
),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
with pytest.raises(EndpointUnavailableError):
await system.async_update(include_subscription=False, include_devices=False)
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_update_system_data(
aresponses,
subscriptions_response,
v3_sensors_response,
v3_server,
v3_settings_response,
):
"""Test getting updated data for a v3 system."""
v3_server.add(
"api.simplisafe.com",
f"/v1/users/{TEST_USER_ID}/subscriptions",
"get",
response=aiohttp.web_response.json_response(subscriptions_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/sensors",
"get",
response=aiohttp.web_response.json_response(v3_sensors_response, status=200),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE, TEST_CODE_VERIFIER, session=session
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
await system.async_update()
assert system.serial == TEST_SYSTEM_SERIAL_NO
assert system.system_id == TEST_SYSTEM_ID
assert len(system.sensors) == 24
aresponses.assert_plan_strictly_followed()
@pytest.mark.asyncio
async def test_update_error(
aresponses,
subscriptions_response,
v3_sensors_response,
v3_server,
v3_settings_response,
):
"""Test handling a generic error during update."""
v3_server.add(
"api.simplisafe.com",
f"/v1/users/{TEST_USER_ID}/subscriptions",
"get",
response=aiohttp.web_response.json_response(subscriptions_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/settings/normal",
"get",
response=aiohttp.web_response.json_response(v3_settings_response, status=200),
)
v3_server.add(
"api.simplisafe.com",
f"/v1/ss3/subscriptions/{TEST_SUBSCRIPTION_ID}/sensors",
"get",
response=aresponses.Response(text="Server Error", status=500),
)
async with aiohttp.ClientSession() as session:
simplisafe = await API.async_from_auth(
TEST_AUTHORIZATION_CODE,
TEST_CODE_VERIFIER,
session=session,
# Set so that our tests don't take too long:
request_retries=1,
)
systems = await simplisafe.async_get_systems()
system = systems[TEST_SYSTEM_ID]
with pytest.raises(RequestError):
await system.async_update()
aresponses.assert_plan_strictly_followed()
| 38.416412
| 88
| 0.475803
| 4,967
| 62,734
| 5.80753
| 0.111133
| 0.016363
| 0.023088
| 0.016016
| 0.849546
| 0.834119
| 0.819143
| 0.798759
| 0.7773
| 0.771303
| 0
| 0.039222
| 0.423295
| 62,734
| 1,632
| 89
| 38.439951
| 0.758092
| 0.006583
| 0
| 0.722793
| 0
| 0
| 0.206495
| 0.05274
| 0
| 0
| 0
| 0
| 0.063655
| 1
| 0
| false
| 0
| 0.006845
| 0
| 0.006845
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
|
0
| 7
|
1aa7f08f84df240963f83c90b6e911567668ce7c
| 15,388
|
py
|
Python
|
quimb/tensor/geometry.py
|
mattorourke17/quimb
|
81f553da3c26e7dcf2d3d9f6816806dd6b78db9b
|
[
"Apache-2.0"
] | 264
|
2018-01-30T10:06:09.000Z
|
2022-03-28T01:39:40.000Z
|
quimb/tensor/geometry.py
|
adamcallison/quimb
|
a06fb9fc58976936bf7d631179ece0f832bf96a9
|
[
"Apache-2.0"
] | 114
|
2017-10-24T22:37:15.000Z
|
2022-03-31T18:58:55.000Z
|
quimb/tensor/geometry.py
|
adamcallison/quimb
|
a06fb9fc58976936bf7d631179ece0f832bf96a9
|
[
"Apache-2.0"
] | 60
|
2017-10-24T22:28:43.000Z
|
2022-02-28T13:54:20.000Z
|
import itertools
def sort_unique(edges):
"""Make sure there are no duplicate edges and that for each
``coo_a < coo_b``.
"""
return tuple(sorted(
tuple(sorted(edge))
for edge in set(map(frozenset, edges))
))
# ----------------------------------- 2D ------------------------------------ #
def check_2d(coo, Lx, Ly, cyclic):
"""Check ``coo`` in inbounds for a maybe cyclic 2D lattice.
"""
x, y = coo
if (not cyclic) and not ((0 <= x < Lx) and (0 <= y < Ly)):
return
return (x % Lx, y % Ly)
def edges_2d_square(Lx, Ly, cyclic=False, cells=None):
"""Return the graph edges of a finite 2D square lattice. The nodes
(sites) are labelled like ``(i, j)``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly))``.
Returns
-------
edges : list[((int, int), (int, int))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly))
edges = []
for i, j in cells:
for coob in [(i, j + 1), (i + 1, j)]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j), coob))
return sort_unique(edges)
def edges_2d_hexagonal(Lx, Ly, cyclic=False, cells=None):
"""Return the graph edges of a finite 2D hexagonal lattice. There are two
sites per cell, and note the cells do not form a square tiling. The nodes
(sites) are labelled like ``(i, j, s)`` for ``s`` in ``'AB'``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly))``.
Returns
-------
edges : list[((int, int, str), (int, int, str))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly))
edges = []
for i, j in cells:
for *coob, lbl in [
(i, j, 'B'),
(i, j - 1, 'B'),
(i - 1, j, 'B'),
]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j, 'A'), (*coob, lbl)))
for *coob, lbl in [
(i, j, 'A'),
(i, j + 1, 'A'),
(i + 1, j, 'A'),
]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j, 'B'), (*coob, lbl)))
return sort_unique(edges)
def edges_2d_triangular(Lx, Ly, cyclic=False, cells=None):
"""Return the graph edges of a finite 2D triangular lattice. There is a
single site per cell, and note the cells do not form a square tiling.
The nodes (sites) are labelled like ``(i, j)``.
Parameters
----------
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly))``.
Returns
-------
edges : list[((int, int), (int, int))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly))
edges = []
for i, j in cells:
for coob in [(i, j + 1), (i + 1, j), (i + 1, j - 1)]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j), coob))
return sort_unique(edges)
def edges_2d_triangular_rectangular(Lx, Ly, cyclic=False, cells=None):
"""Return the graph edges of a finite 2D triangular lattice tiled in a
rectangular geometry. There are two sites per rectangular cell. The nodes
(sites) are labelled like ``(i, j, s)`` for ``s`` in ``'AB'``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly))``.
Returns
-------
edges : list[((int, int, s), (int, int, s))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly))
edges = []
for i, j in cells:
for *coob, lbl in [
(i, j, 'B'),
(i, j - 1, 'B'),
(i, j + 1, 'A'),
]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j, 'A'), (*coob, lbl)))
for *coob, lbl in [
(i + 1, j, 'A'),
(i, j + 1, 'B'),
(i + 1, j + 1, 'A'),
]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j, 'B'), (*coob, lbl)))
return sort_unique(edges)
def edges_2d_kagome(Lx, Ly, cyclic=False, cells=None):
"""Return the graph edges of a finite 2D kagome lattice. There are
three sites per cell, and note the cells do not form a square tiling. The
nodes (sites) are labelled like ``(i, j, s)`` for ``s`` in ``'ABC'``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly))``.
Returns
-------
edges : list[((int, int, str), (int, int, str))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly))
edges = []
for i, j in cells:
for *coob, lbl in [
(i, j, 'B'),
(i, j - 1, 'B'),
(i, j, 'C'),
(i - 1, j, 'C')
]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j, 'A'), (*coob, lbl)))
for *coob, lbl in [
(i, j, 'C'),
(i - 1, j + 1, 'C'),
(i, j, 'A'),
(i, j + 1, 'A')
]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j, 'B'), (*coob, lbl)))
for *coob, lbl in [
(i, j, 'A'),
(i + 1, j, 'A'),
(i, j, 'B'),
(i + 1, j - 1, 'B')
]:
coob = check_2d(coob, Lx, Ly, cyclic)
if coob:
edges.append(((i, j, 'C'), (*coob, lbl)))
return sort_unique(edges)
# ----------------------------------- 3D ------------------------------------ #
def check_3d(coo, Lx, Ly, Lz, cyclic):
"""Check ``coo`` in inbounds for a maybe cyclic 3D lattice.
"""
x, y, z = coo
OBC = not cyclic
inbounds = (0 <= x < Lx) and (0 <= y < Ly) and (0 <= z < Lz)
if OBC and not inbounds:
return
return (x % Lx, y % Ly, z % Lz)
def edges_3d_cubic(Lx, Ly, Lz, cyclic=False, cells=None):
"""Return the graph edges of a finite 3D cubic lattice. The nodes
(sites) are labelled like ``(i, j, k)``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
Lz : int
The number of cells along the z-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly), range(Lz))``.
Returns
-------
edges : list[((int, int, int), (int, int, int))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly), range(Lz))
edges = []
for i, j, k in cells:
for coob in [(i, j, k + 1), (i, j + 1, k), (i + 1, j, k)]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k), coob))
return sort_unique(edges)
def edges_3d_pyrochlore(Lx, Ly, Lz, cyclic=False, cells=None):
"""Return the graph edges of a finite 3D pyorchlore lattice. There are
four sites per cell, and note the cells do not form a cubic tiling. The
nodes (sites) are labelled like ``(i, j, k, s)`` for ``s`` in ``'ABCD'``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
Lz : int
The number of cells along the z-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly), range(Lz))``.
Returns
-------
edges : list[((int, int, int, str), (int, int, int, str))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly), range(Lz))
edges = []
for i, j, k in cells:
for *coob, lbl in [
(i, j, k, 'B'),
(i, j - 1, k, 'B'),
(i, j, k, 'C'),
(i - 1, j, k, 'C'),
(i, j, k, 'D'),
(i, j, k - 1, 'D'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'A'), (*coob, lbl)))
for *coob, lbl in [
(i, j, k, 'C'),
(i - 1, j + 1, k, 'C'),
(i, j, k, 'D'),
(i, j + 1, k - 1, 'D'),
(i, j, k, 'A'),
(i, j + 1, k, 'A'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'B'), (*coob, lbl)))
for *coob, lbl in [
(i, j, k, 'D'),
(i + 1, j, k - 1, 'D'),
(i, j, k, 'A'),
(i + 1, j, k, 'A'),
(i, j, k, 'B'),
(i + 1, j - 1, k, 'B'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'C'), (*coob, lbl)))
for *coob, lbl in [
(i, j, k, 'A'),
(i, j, k + 1, 'A'),
(i, j, k, 'B'),
(i, j - 1, k + 1, 'B'),
(i, j, k, 'C'),
(i - 1, j, k + 1, 'C'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'D'), (*coob, lbl)))
return sort_unique(edges)
def edges_3d_diamond(Lx, Ly, Lz, cyclic=False, cells=None):
"""Return the graph edges of a finite 3D diamond lattice. There are
two sites per cell, and note the cells do not form a cubic tiling. The
nodes (sites) are labelled like ``(i, j, k, s)`` for ``s`` in ``'AB'``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
Lz : int
The number of cells along the z-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly), range(Lz))``.
Returns
-------
edges : list[((int, int, int, str), (int, int, int, str))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly), range(Lz))
edges = []
for i, j, k in cells:
for *coob, lbl in [
(i, j, k, 'B'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'A'), (*coob, lbl)))
for *coob, lbl in [
(i, j, k + 1, 'A'),
(i, j + 1, k, 'A'),
(i + 1, j, k, 'A'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'B'), (*coob, lbl)))
return sort_unique(edges)
def edges_3d_diamond_cubic(Lx, Ly, Lz, cyclic=False, cells=None):
"""Return the graph edges of a finite 3D diamond lattice tiled in a cubic
geometry. There are eight sites per cubic cell. The nodes (sites) are
labelled like ``(i, j, k, s)`` for ``s`` in ``'ABCDEFGH'``.
Parameters
----------
Lx : int
The number of cells along the x-direction.
Ly : int
The number of cells along the y-direction.
Lz : int
The number of cells along the z-direction.
cyclic : bool, optional
Whether to use periodic boundary conditions.
cells : list, optional
A list of cells to use. If not given the cells used are
``itertools.product(range(Lx), range(Ly), range(Lz))``.
Returns
-------
edges : list[((int, int, int, str), (int, int, int, str))]
"""
if cells is None:
cells = itertools.product(range(Lx), range(Ly), range(Lz))
edges = []
for i, j, k in cells:
for *coob, lbl in [
(i, j, k, 'E'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'A'), (*coob, lbl)))
for *coob, lbl in [
(i, j, k, 'E'),
(i, j, k, 'F'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'B'), (*coob, lbl)))
for *coob, lbl in [
(i, j, k, 'E'),
(i, j, k, 'G'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'C'), (*coob, lbl)))
for *coob, lbl in [
(i, j, k, 'E'),
(i, j, k, 'H'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'D'), (*coob, lbl)))
for *coob, lbl in []:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'E'), (*coob, lbl)))
for *coob, lbl in [
(i, j + 1, k, 'C'),
(i + 1, j, k, 'D'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'F'), (*coob, lbl)))
for *coob, lbl in [
(i + 1, j, k + 1, 'A'),
(i, j, k + 1, 'B'),
(i + 1, j, k, 'D'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'G'), (*coob, lbl)))
for *coob, lbl in [
(i, j + 1, k + 1, 'A'),
(i, j, k + 1, 'B'),
(i, j + 1, k, 'C'),
]:
coob = check_3d(coob, Lx, Ly, Lz, cyclic)
if coob:
edges.append(((i, j, k, 'H'), (*coob, lbl)))
return sort_unique(edges)
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0
| 7
|
1ab10b4ef315606af33faf5902ab6259b6d5c906
| 156,500
|
py
|
Python
|
searchpubchem/searchpubchem/xsd_schema/pug_view.py
|
coreymhudson/searchpubchem
|
b70272dc3d402a288ceb12acea7d41abddf43989
|
[
"BSD-2-Clause"
] | 1
|
2021-04-05T23:41:32.000Z
|
2021-04-05T23:41:32.000Z
|
searchpubchem/searchpubchem/xsd_schema/pug_view.py
|
coreymhudson/searchpubchem
|
b70272dc3d402a288ceb12acea7d41abddf43989
|
[
"BSD-2-Clause"
] | null | null | null |
searchpubchem/searchpubchem/xsd_schema/pug_view.py
|
coreymhudson/searchpubchem
|
b70272dc3d402a288ceb12acea7d41abddf43989
|
[
"BSD-2-Clause"
] | 1
|
2019-03-20T22:14:47.000Z
|
2019-03-20T22:14:47.000Z
|
# ./pug_view.py
# -*- coding: utf-8 -*-
# PyXB bindings for NM:ab0c9708514235065268b3a38971dc1c51967c4a
# Generated 2016-02-27 01:23:31.783845 by PyXB version 1.2.4 using Python 2.7.5.final.0
# Namespace http://pubchem.ncbi.nlm.nih.gov/pug_view
from __future__ import unicode_literals
import pyxb
import pyxb.binding
import pyxb.binding.saxer
import io
import pyxb.utils.utility
import pyxb.utils.domutils
import sys
import pyxb.utils.six as _six
# Unique identifier for bindings created at the same time
_GenerationUID = pyxb.utils.utility.UniqueIdentifier('urn:uuid:c4a6d4de-dd33-11e5-991d-542696d54607')
# Version of PyXB used to generate the bindings
_PyXBVersion = '1.2.4'
# Generated bindings are not compatible across PyXB versions
if pyxb.__version__ != _PyXBVersion:
raise pyxb.PyXBVersionError(_PyXBVersion)
# Import bindings for namespaces imported into schema
import pyxb.binding.datatypes
# NOTE: All namespace declarations are reserved within the binding
Namespace = pyxb.namespace.NamespaceForURI('http://pubchem.ncbi.nlm.nih.gov/pug_view', create_if_missing=True)
Namespace.configureCategories(['typeBinding', 'elementBinding'])
def CreateFromDocument (xml_text, default_namespace=None, location_base=None):
"""Parse the given XML and use the document element to create a
Python instance.
@param xml_text An XML document. This should be data (Python 2
str or Python 3 bytes), or a text (Python 2 unicode or Python 3
str) in the L{pyxb._InputEncoding} encoding.
@keyword default_namespace The L{pyxb.Namespace} instance to use as the
default namespace where there is no default namespace in scope.
If unspecified or C{None}, the namespace of the module containing
this function will be used.
@keyword location_base: An object to be recorded as the base of all
L{pyxb.utils.utility.Location} instances associated with events and
objects handled by the parser. You might pass the URI from which
the document was obtained.
"""
if pyxb.XMLStyle_saxer != pyxb._XMLStyle:
dom = pyxb.utils.domutils.StringToDOM(xml_text)
return CreateFromDOM(dom.documentElement, default_namespace=default_namespace)
if default_namespace is None:
default_namespace = Namespace.fallbackNamespace()
saxer = pyxb.binding.saxer.make_parser(fallback_namespace=default_namespace, location_base=location_base)
handler = saxer.getContentHandler()
xmld = xml_text
if isinstance(xmld, _six.text_type):
xmld = xmld.encode(pyxb._InputEncoding)
saxer.parse(io.BytesIO(xmld))
instance = handler.rootObject()
return instance
def CreateFromDOM (node, default_namespace=None):
"""Create a Python instance from the given DOM node.
The node tag must correspond to an element declaration in this module.
@deprecated: Forcing use of DOM interface is unnecessary; use L{CreateFromDocument}."""
if default_namespace is None:
default_namespace = Namespace.fallbackNamespace()
return pyxb.binding.basis.element.AnyCreateFromDOM(node, default_namespace)
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 47, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Code uses Python identifier Code
__Code = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Code'), 'Code', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_httppubchem_ncbi_nlm_nih_govpug_viewCode', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 49, 6), )
Code = property(__Code.value, __Code.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Message uses Python identifier Message
__Message = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Message'), 'Message', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_httppubchem_ncbi_nlm_nih_govpug_viewMessage', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 50, 6), )
Message = property(__Message.value, __Message.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Details uses Python identifier Details
__Details = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Details'), 'Details', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_httppubchem_ncbi_nlm_nih_govpug_viewDetails', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 51, 6), )
Details = property(__Details.value, __Details.set, None, None)
_ElementMap.update({
__Code.name() : __Code,
__Message.name() : __Message,
__Details.name() : __Details
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_ (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 57, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Record uses Python identifier Record
__Record = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Record'), 'Record', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON__httppubchem_ncbi_nlm_nih_govpug_viewRecord', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 64, 0), )
Record = property(__Record.value, __Record.set, None, None)
_ElementMap.update({
__Record.name() : __Record
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_2 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 65, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordType uses Python identifier RecordType
__RecordType = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), 'RecordType', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_2_httppubchem_ncbi_nlm_nih_govpug_viewRecordType', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 67, 6), )
RecordType = property(__RecordType.value, __RecordType.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordNumber uses Python identifier RecordNumber
__RecordNumber = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), 'RecordNumber', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_2_httppubchem_ncbi_nlm_nih_govpug_viewRecordNumber', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 68, 6), )
RecordNumber = property(__RecordNumber.value, __RecordNumber.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}AvailableViews uses Python identifier AvailableViews
__AvailableViews = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'AvailableViews'), 'AvailableViews', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_2_httppubchem_ncbi_nlm_nih_govpug_viewAvailableViews', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 69, 6), )
AvailableViews = property(__AvailableViews.value, __AvailableViews.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Section uses Python identifier Section
__Section = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Section'), 'Section', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_2_httppubchem_ncbi_nlm_nih_govpug_viewSection', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 77, 0), )
Section = property(__Section.value, __Section.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Information uses Python identifier Information
__Information = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Information'), 'Information', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_2_httppubchem_ncbi_nlm_nih_govpug_viewInformation', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 94, 0), )
Information = property(__Information.value, __Information.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Reference uses Python identifier Reference
__Reference = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Reference'), 'Reference', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_2_httppubchem_ncbi_nlm_nih_govpug_viewReference', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 122, 0), )
Reference = property(__Reference.value, __Reference.set, None, None)
_ElementMap.update({
__RecordType.name() : __RecordType,
__RecordNumber.name() : __RecordNumber,
__AvailableViews.name() : __AvailableViews,
__Section.name() : __Section,
__Information.name() : __Information,
__Reference.name() : __Reference
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_3 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 78, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Section uses Python identifier Section
__Section = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Section'), 'Section', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewSection', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 77, 0), )
Section = property(__Section.value, __Section.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}TOCHeading uses Python identifier TOCHeading
__TOCHeading = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'TOCHeading'), 'TOCHeading', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewTOCHeading', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 81, 8), )
TOCHeading = property(__TOCHeading.value, __TOCHeading.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}TOCID uses Python identifier TOCID
__TOCID = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'TOCID'), 'TOCID', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewTOCID', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 82, 8), )
TOCID = property(__TOCID.value, __TOCID.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Description uses Python identifier Description
__Description = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Description'), 'Description', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewDescription', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 84, 6), )
Description = property(__Description.value, __Description.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Views uses Python identifier Views
__Views = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Views'), 'Views', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewViews', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 85, 6), )
Views = property(__Views.value, __Views.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}HintGroupSubsectionsByReference uses Python identifier HintGroupSubsectionsByReference
__HintGroupSubsectionsByReference = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'HintGroupSubsectionsByReference'), 'HintGroupSubsectionsByReference', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewHintGroupSubsectionsByReference', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 86, 6), )
HintGroupSubsectionsByReference = property(__HintGroupSubsectionsByReference.value, __HintGroupSubsectionsByReference.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}HintEmbeddedHTML uses Python identifier HintEmbeddedHTML
__HintEmbeddedHTML = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'HintEmbeddedHTML'), 'HintEmbeddedHTML', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewHintEmbeddedHTML', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 87, 6), )
HintEmbeddedHTML = property(__HintEmbeddedHTML.value, __HintEmbeddedHTML.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Information uses Python identifier Information
__Information = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Information'), 'Information', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_3_httppubchem_ncbi_nlm_nih_govpug_viewInformation', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 94, 0), )
Information = property(__Information.value, __Information.set, None, None)
_ElementMap.update({
__Section.name() : __Section,
__TOCHeading.name() : __TOCHeading,
__TOCID.name() : __TOCID,
__Description.name() : __Description,
__Views.name() : __Views,
__HintGroupSubsectionsByReference.name() : __HintGroupSubsectionsByReference,
__HintEmbeddedHTML.name() : __HintEmbeddedHTML,
__Information.name() : __Information
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_4 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 95, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ReferenceNumber uses Python identifier ReferenceNumber
__ReferenceNumber = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ReferenceNumber'), 'ReferenceNumber', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewReferenceNumber', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 97, 6), )
ReferenceNumber = property(__ReferenceNumber.value, __ReferenceNumber.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Name uses Python identifier Name
__Name = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Name'), 'Name', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewName', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 98, 6), )
Name = property(__Name.value, __Name.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Description uses Python identifier Description
__Description = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Description'), 'Description', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewDescription', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 99, 6), )
Description = property(__Description.value, __Description.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Reference uses Python identifier Reference
__Reference = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Reference'), 'Reference', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewReference', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 100, 6), )
Reference = property(__Reference.value, __Reference.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}URL uses Python identifier URL
__URL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'URL'), 'URL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 101, 6), )
URL = property(__URL.value, __URL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}NumValue uses Python identifier NumValue
__NumValue = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'NumValue'), 'NumValue', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewNumValue', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 103, 8), )
NumValue = property(__NumValue.value, __NumValue.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}NumValueList uses Python identifier NumValueList
__NumValueList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'NumValueList'), 'NumValueList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewNumValueList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 104, 8), )
NumValueList = property(__NumValueList.value, __NumValueList.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}DateValue uses Python identifier DateValue
__DateValue = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'DateValue'), 'DateValue', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewDateValue', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 105, 8), )
DateValue = property(__DateValue.value, __DateValue.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}DateValueList uses Python identifier DateValueList
__DateValueList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'DateValueList'), 'DateValueList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewDateValueList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 106, 8), )
DateValueList = property(__DateValueList.value, __DateValueList.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}BoolValue uses Python identifier BoolValue
__BoolValue = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'BoolValue'), 'BoolValue', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewBoolValue', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 107, 8), )
BoolValue = property(__BoolValue.value, __BoolValue.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}BoolValueList uses Python identifier BoolValueList
__BoolValueList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'BoolValueList'), 'BoolValueList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewBoolValueList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 108, 8), )
BoolValueList = property(__BoolValueList.value, __BoolValueList.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}StringValue uses Python identifier StringValue
__StringValue = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'StringValue'), 'StringValue', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewStringValue', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 109, 8), )
StringValue = property(__StringValue.value, __StringValue.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}StringValueList uses Python identifier StringValueList
__StringValueList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'StringValueList'), 'StringValueList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewStringValueList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 110, 8), )
StringValueList = property(__StringValueList.value, __StringValueList.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}BinaryValue uses Python identifier BinaryValue
__BinaryValue = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'BinaryValue'), 'BinaryValue', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewBinaryValue', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 111, 8), )
BinaryValue = property(__BinaryValue.value, __BinaryValue.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}BinaryValueList uses Python identifier BinaryValueList
__BinaryValueList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'BinaryValueList'), 'BinaryValueList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewBinaryValueList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 112, 8), )
BinaryValueList = property(__BinaryValueList.value, __BinaryValueList.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ExternalDataURL uses Python identifier ExternalDataURL
__ExternalDataURL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataURL'), 'ExternalDataURL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewExternalDataURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 113, 8), )
ExternalDataURL = property(__ExternalDataURL.value, __ExternalDataURL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ExternalDataURLList uses Python identifier ExternalDataURLList
__ExternalDataURLList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataURLList'), 'ExternalDataURLList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewExternalDataURLList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 114, 8), )
ExternalDataURLList = property(__ExternalDataURLList.value, __ExternalDataURLList.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ValueUnit uses Python identifier ValueUnit
__ValueUnit = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ValueUnit'), 'ValueUnit', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewValueUnit', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 116, 6), )
ValueUnit = property(__ValueUnit.value, __ValueUnit.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ExternalDataMimeType uses Python identifier ExternalDataMimeType
__ExternalDataMimeType = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataMimeType'), 'ExternalDataMimeType', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_4_httppubchem_ncbi_nlm_nih_govpug_viewExternalDataMimeType', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 117, 6), )
ExternalDataMimeType = property(__ExternalDataMimeType.value, __ExternalDataMimeType.set, None, None)
_ElementMap.update({
__ReferenceNumber.name() : __ReferenceNumber,
__Name.name() : __Name,
__Description.name() : __Description,
__Reference.name() : __Reference,
__URL.name() : __URL,
__NumValue.name() : __NumValue,
__NumValueList.name() : __NumValueList,
__DateValue.name() : __DateValue,
__DateValueList.name() : __DateValueList,
__BoolValue.name() : __BoolValue,
__BoolValueList.name() : __BoolValueList,
__StringValue.name() : __StringValue,
__StringValueList.name() : __StringValueList,
__BinaryValue.name() : __BinaryValue,
__BinaryValueList.name() : __BinaryValueList,
__ExternalDataURL.name() : __ExternalDataURL,
__ExternalDataURLList.name() : __ExternalDataURLList,
__ValueUnit.name() : __ValueUnit,
__ExternalDataMimeType.name() : __ExternalDataMimeType
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_5 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 123, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ReferenceNumber uses Python identifier ReferenceNumber
__ReferenceNumber = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ReferenceNumber'), 'ReferenceNumber', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_5_httppubchem_ncbi_nlm_nih_govpug_viewReferenceNumber', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 125, 6), )
ReferenceNumber = property(__ReferenceNumber.value, __ReferenceNumber.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}SourceName uses Python identifier SourceName
__SourceName = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'SourceName'), 'SourceName', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_5_httppubchem_ncbi_nlm_nih_govpug_viewSourceName', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 126, 6), )
SourceName = property(__SourceName.value, __SourceName.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}SourceID uses Python identifier SourceID
__SourceID = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'SourceID'), 'SourceID', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_5_httppubchem_ncbi_nlm_nih_govpug_viewSourceID', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 127, 6), )
SourceID = property(__SourceID.value, __SourceID.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Name uses Python identifier Name
__Name = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Name'), 'Name', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_5_httppubchem_ncbi_nlm_nih_govpug_viewName', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 128, 6), )
Name = property(__Name.value, __Name.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Description uses Python identifier Description
__Description = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Description'), 'Description', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_5_httppubchem_ncbi_nlm_nih_govpug_viewDescription', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 129, 6), )
Description = property(__Description.value, __Description.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}URL uses Python identifier URL
__URL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'URL'), 'URL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_5_httppubchem_ncbi_nlm_nih_govpug_viewURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 130, 6), )
URL = property(__URL.value, __URL.set, None, None)
_ElementMap.update({
__ReferenceNumber.name() : __ReferenceNumber,
__SourceName.name() : __SourceName,
__SourceID.name() : __SourceID,
__Name.name() : __Name,
__Description.name() : __Description,
__URL.name() : __URL
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_6 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 138, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordType uses Python identifier RecordType
__RecordType = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), 'RecordType', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_6_httppubchem_ncbi_nlm_nih_govpug_viewRecordType', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 140, 6), )
RecordType = property(__RecordType.value, __RecordType.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordNumber uses Python identifier RecordNumber
__RecordNumber = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), 'RecordNumber', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_6_httppubchem_ncbi_nlm_nih_govpug_viewRecordNumber', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 141, 6), )
RecordNumber = property(__RecordNumber.value, __RecordNumber.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}NeighborsOfType uses Python identifier NeighborsOfType
__NeighborsOfType = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'NeighborsOfType'), 'NeighborsOfType', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_6_httppubchem_ncbi_nlm_nih_govpug_viewNeighborsOfType', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 142, 6), )
NeighborsOfType = property(__NeighborsOfType.value, __NeighborsOfType.set, None, None)
_ElementMap.update({
__RecordType.name() : __RecordType,
__RecordNumber.name() : __RecordNumber,
__NeighborsOfType.name() : __NeighborsOfType
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_7 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 143, 8)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Type uses Python identifier Type
__Type = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Type'), 'Type', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_7_httppubchem_ncbi_nlm_nih_govpug_viewType', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 145, 12), )
Type = property(__Type.value, __Type.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}IDList uses Python identifier IDList
__IDList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'IDList'), 'IDList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_7_httppubchem_ncbi_nlm_nih_govpug_viewIDList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 146, 12), )
IDList = property(__IDList.value, __IDList.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}NameList uses Python identifier NameList
__NameList = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'NameList'), 'NameList', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_7_httppubchem_ncbi_nlm_nih_govpug_viewNameList', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 147, 12), )
NameList = property(__NameList.value, __NameList.set, None, None)
_ElementMap.update({
__Type.name() : __Type,
__IDList.name() : __IDList,
__NameList.name() : __NameList
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_8 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 156, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordType uses Python identifier RecordType
__RecordType = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), 'RecordType', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_8_httppubchem_ncbi_nlm_nih_govpug_viewRecordType', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 158, 6), )
RecordType = property(__RecordType.value, __RecordType.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordNumber uses Python identifier RecordNumber
__RecordNumber = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), 'RecordNumber', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_8_httppubchem_ncbi_nlm_nih_govpug_viewRecordNumber', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 159, 6), )
RecordNumber = property(__RecordNumber.value, __RecordNumber.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}URL uses Python identifier URL
__URL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'URL'), 'URL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_8_httppubchem_ncbi_nlm_nih_govpug_viewURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 160, 3), )
URL = property(__URL.value, __URL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}NumberOfStructures uses Python identifier NumberOfStructures
__NumberOfStructures = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'NumberOfStructures'), 'NumberOfStructures', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_8_httppubchem_ncbi_nlm_nih_govpug_viewNumberOfStructures', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 161, 3), )
NumberOfStructures = property(__NumberOfStructures.value, __NumberOfStructures.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Structures uses Python identifier Structures
__Structures = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Structures'), 'Structures', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_8_httppubchem_ncbi_nlm_nih_govpug_viewStructures', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 162, 6), )
Structures = property(__Structures.value, __Structures.set, None, None)
_ElementMap.update({
__RecordType.name() : __RecordType,
__RecordNumber.name() : __RecordNumber,
__URL.name() : __URL,
__NumberOfStructures.name() : __NumberOfStructures,
__Structures.name() : __Structures
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_9 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 163, 8)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}MMDB_ID uses Python identifier MMDB_ID
__MMDB_ID = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'MMDB_ID'), 'MMDB_ID', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_9_httppubchem_ncbi_nlm_nih_govpug_viewMMDB_ID', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 165, 12), )
MMDB_ID = property(__MMDB_ID.value, __MMDB_ID.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}PDB_ID uses Python identifier PDB_ID
__PDB_ID = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'PDB_ID'), 'PDB_ID', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_9_httppubchem_ncbi_nlm_nih_govpug_viewPDB_ID', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 166, 12), )
PDB_ID = property(__PDB_ID.value, __PDB_ID.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}URL uses Python identifier URL
__URL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'URL'), 'URL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_9_httppubchem_ncbi_nlm_nih_govpug_viewURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 167, 12), )
URL = property(__URL.value, __URL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ImageURL uses Python identifier ImageURL
__ImageURL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ImageURL'), 'ImageURL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_9_httppubchem_ncbi_nlm_nih_govpug_viewImageURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 168, 12), )
ImageURL = property(__ImageURL.value, __ImageURL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Description uses Python identifier Description
__Description = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Description'), 'Description', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_9_httppubchem_ncbi_nlm_nih_govpug_viewDescription', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 169, 12), )
Description = property(__Description.value, __Description.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Taxonomy uses Python identifier Taxonomy
__Taxonomy = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Taxonomy'), 'Taxonomy', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_9_httppubchem_ncbi_nlm_nih_govpug_viewTaxonomy', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 170, 12), )
Taxonomy = property(__Taxonomy.value, __Taxonomy.set, None, None)
_ElementMap.update({
__MMDB_ID.name() : __MMDB_ID,
__PDB_ID.name() : __PDB_ID,
__URL.name() : __URL,
__ImageURL.name() : __ImageURL,
__Description.name() : __Description,
__Taxonomy.name() : __Taxonomy
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_10 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 171, 14)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}ID uses Python identifier ID
__ID = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'ID'), 'ID', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_10_httppubchem_ncbi_nlm_nih_govpug_viewID', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 173, 18), )
ID = property(__ID.value, __ID.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Name uses Python identifier Name
__Name = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Name'), 'Name', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_10_httppubchem_ncbi_nlm_nih_govpug_viewName', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 174, 18), )
Name = property(__Name.value, __Name.set, None, None)
_ElementMap.update({
__ID.name() : __ID,
__Name.name() : __Name
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_11 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 186, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordType uses Python identifier RecordType
__RecordType = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), 'RecordType', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_11_httppubchem_ncbi_nlm_nih_govpug_viewRecordType', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 188, 6), )
RecordType = property(__RecordType.value, __RecordType.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordNumber uses Python identifier RecordNumber
__RecordNumber = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), 'RecordNumber', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_11_httppubchem_ncbi_nlm_nih_govpug_viewRecordNumber', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 189, 6), )
RecordNumber = property(__RecordNumber.value, __RecordNumber.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Categories uses Python identifier Categories
__Categories = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Categories'), 'Categories', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_11_httppubchem_ncbi_nlm_nih_govpug_viewCategories', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 190, 6), )
Categories = property(__Categories.value, __Categories.set, None, None)
_ElementMap.update({
__RecordType.name() : __RecordType,
__RecordNumber.name() : __RecordNumber,
__Categories.name() : __Categories
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_12 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 191, 8)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Category uses Python identifier Category
__Category = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Category'), 'Category', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_12_httppubchem_ncbi_nlm_nih_govpug_viewCategory', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 193, 12), )
Category = property(__Category.value, __Category.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}URL uses Python identifier URL
__URL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'URL'), 'URL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_12_httppubchem_ncbi_nlm_nih_govpug_viewURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 194, 12), )
URL = property(__URL.value, __URL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Sources uses Python identifier Sources
__Sources = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Sources'), 'Sources', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_12_httppubchem_ncbi_nlm_nih_govpug_viewSources', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 195, 12), )
Sources = property(__Sources.value, __Sources.set, None, None)
_ElementMap.update({
__Category.name() : __Category,
__URL.name() : __URL,
__Sources.name() : __Sources
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_13 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 196, 14)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}SID uses Python identifier SID
__SID = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'SID'), 'SID', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_13_httppubchem_ncbi_nlm_nih_govpug_viewSID', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 198, 18), )
SID = property(__SID.value, __SID.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}SourceName uses Python identifier SourceName
__SourceName = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'SourceName'), 'SourceName', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_13_httppubchem_ncbi_nlm_nih_govpug_viewSourceName', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 199, 18), )
SourceName = property(__SourceName.value, __SourceName.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}SourceURL uses Python identifier SourceURL
__SourceURL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'SourceURL'), 'SourceURL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_13_httppubchem_ncbi_nlm_nih_govpug_viewSourceURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 200, 18), )
SourceURL = property(__SourceURL.value, __SourceURL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RegistryID uses Python identifier RegistryID
__RegistryID = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RegistryID'), 'RegistryID', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_13_httppubchem_ncbi_nlm_nih_govpug_viewRegistryID', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 201, 18), )
RegistryID = property(__RegistryID.value, __RegistryID.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}SourceRecordURL uses Python identifier SourceRecordURL
__SourceRecordURL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'SourceRecordURL'), 'SourceRecordURL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_13_httppubchem_ncbi_nlm_nih_govpug_viewSourceRecordURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 202, 18), )
SourceRecordURL = property(__SourceRecordURL.value, __SourceRecordURL.set, None, None)
_ElementMap.update({
__SID.name() : __SID,
__SourceName.name() : __SourceName,
__SourceURL.name() : __SourceURL,
__RegistryID.name() : __RegistryID,
__SourceRecordURL.name() : __SourceRecordURL
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_14 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 214, 2)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordType uses Python identifier RecordType
__RecordType = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), 'RecordType', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_14_httppubchem_ncbi_nlm_nih_govpug_viewRecordType', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 216, 6), )
RecordType = property(__RecordType.value, __RecordType.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}RecordNumber uses Python identifier RecordNumber
__RecordNumber = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), 'RecordNumber', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_14_httppubchem_ncbi_nlm_nih_govpug_viewRecordNumber', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 217, 6), )
RecordNumber = property(__RecordNumber.value, __RecordNumber.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}AllURL uses Python identifier AllURL
__AllURL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'AllURL'), 'AllURL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_14_httppubchem_ncbi_nlm_nih_govpug_viewAllURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 218, 6), )
AllURL = property(__AllURL.value, __AllURL.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Subheadings uses Python identifier Subheadings
__Subheadings = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Subheadings'), 'Subheadings', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_14_httppubchem_ncbi_nlm_nih_govpug_viewSubheadings', True, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 219, 6), )
Subheadings = property(__Subheadings.value, __Subheadings.set, None, None)
_ElementMap.update({
__RecordType.name() : __RecordType,
__RecordNumber.name() : __RecordNumber,
__AllURL.name() : __AllURL,
__Subheadings.name() : __Subheadings
})
_AttributeMap.update({
})
# Complex type [anonymous] with content type ELEMENT_ONLY
class CTD_ANON_15 (pyxb.binding.basis.complexTypeDefinition):
"""Complex type [anonymous] with content type ELEMENT_ONLY"""
_TypeDefinition = None
_ContentTypeTag = pyxb.binding.basis.complexTypeDefinition._CT_ELEMENT_ONLY
_Abstract = False
_ExpandedName = None
_XSDLocation = pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 220, 8)
_ElementMap = {}
_AttributeMap = {}
# Base type is pyxb.binding.datatypes.anyType
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}Subheading uses Python identifier Subheading
__Subheading = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'Subheading'), 'Subheading', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_15_httppubchem_ncbi_nlm_nih_govpug_viewSubheading', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 222, 12), )
Subheading = property(__Subheading.value, __Subheading.set, None, None)
# Element {http://pubchem.ncbi.nlm.nih.gov/pug_view}SubheadingURL uses Python identifier SubheadingURL
__SubheadingURL = pyxb.binding.content.ElementDeclaration(pyxb.namespace.ExpandedName(Namespace, 'SubheadingURL'), 'SubheadingURL', '__httppubchem_ncbi_nlm_nih_govpug_view_CTD_ANON_15_httppubchem_ncbi_nlm_nih_govpug_viewSubheadingURL', False, pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 223, 12), )
SubheadingURL = property(__SubheadingURL.value, __SubheadingURL.set, None, None)
_ElementMap.update({
__Subheading.name() : __Subheading,
__SubheadingURL.name() : __SubheadingURL
})
_AttributeMap.update({
})
Fault = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Fault'), CTD_ANON, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 46, 0))
Namespace.addCategoryObject('elementBinding', Fault.name().localName(), Fault)
Records = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Records'), CTD_ANON_, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 56, 0))
Namespace.addCategoryObject('elementBinding', Records.name().localName(), Records)
Record = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Record'), CTD_ANON_2, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 64, 0))
Namespace.addCategoryObject('elementBinding', Record.name().localName(), Record)
Section = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Section'), CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 77, 0))
Namespace.addCategoryObject('elementBinding', Section.name().localName(), Section)
Information = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Information'), CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 94, 0))
Namespace.addCategoryObject('elementBinding', Information.name().localName(), Information)
Reference = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Reference'), CTD_ANON_5, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 122, 0))
Namespace.addCategoryObject('elementBinding', Reference.name().localName(), Reference)
Neighbors = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Neighbors'), CTD_ANON_6, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 137, 0))
Namespace.addCategoryObject('elementBinding', Neighbors.name().localName(), Neighbors)
Structure = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Structure'), CTD_ANON_8, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 155, 0))
Namespace.addCategoryObject('elementBinding', Structure.name().localName(), Structure)
SourceCategories = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SourceCategories'), CTD_ANON_11, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 185, 0))
Namespace.addCategoryObject('elementBinding', SourceCategories.name().localName(), SourceCategories)
Literature = pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Literature'), CTD_ANON_14, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 213, 0))
Namespace.addCategoryObject('elementBinding', Literature.name().localName(), Literature)
CTD_ANON._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Code'), pyxb.binding.datatypes.string, scope=CTD_ANON, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 49, 6)))
CTD_ANON._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Message'), pyxb.binding.datatypes.string, scope=CTD_ANON, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 50, 6)))
CTD_ANON._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Details'), pyxb.binding.datatypes.string, scope=CTD_ANON, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 51, 6)))
def _BuildAutomaton ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton
del _BuildAutomaton
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 51, 6))
counters.add(cc_0)
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Code')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 49, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Message')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 50, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_0, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Details')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 51, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_0, True) ]))
st_2._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON._Automaton = _BuildAutomaton()
CTD_ANON_._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Record'), CTD_ANON_2, scope=CTD_ANON_, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 64, 0)))
def _BuildAutomaton_ ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_
del _BuildAutomaton_
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Record')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 59, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
transitions = []
transitions.append(fac.Transition(st_0, [
]))
st_0._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_._Automaton = _BuildAutomaton_()
CTD_ANON_2._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), pyxb.binding.datatypes.string, scope=CTD_ANON_2, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 67, 6)))
CTD_ANON_2._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), pyxb.binding.datatypes.int, scope=CTD_ANON_2, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 68, 6)))
CTD_ANON_2._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'AvailableViews'), pyxb.binding.datatypes.string, scope=CTD_ANON_2, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 69, 6)))
CTD_ANON_2._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Section'), CTD_ANON_3, scope=CTD_ANON_2, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 77, 0)))
CTD_ANON_2._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Information'), CTD_ANON_4, scope=CTD_ANON_2, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 94, 0)))
CTD_ANON_2._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Reference'), CTD_ANON_5, scope=CTD_ANON_2, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 122, 0)))
def _BuildAutomaton_2 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_2
del _BuildAutomaton_2
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 69, 6))
counters.add(cc_0)
cc_1 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 70, 6))
counters.add(cc_1)
cc_2 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 71, 6))
counters.add(cc_2)
cc_3 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 72, 6))
counters.add(cc_3)
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_2._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordType')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 67, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_2._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 68, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_0, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_2._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'AvailableViews')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 69, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_1, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_2._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Information')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 70, 6))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_2, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_2._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Section')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 71, 6))
st_4 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_4)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_3, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_2._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Reference')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 72, 6))
st_5 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_5)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
transitions.append(fac.Transition(st_3, [
]))
transitions.append(fac.Transition(st_4, [
]))
transitions.append(fac.Transition(st_5, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_0, True) ]))
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_0, False) ]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_1, True) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_1, False) ]))
st_3._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_2, True) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_2, False) ]))
st_4._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_3, True) ]))
st_5._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_2._Automaton = _BuildAutomaton_2()
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Section'), CTD_ANON_3, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 77, 0)))
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'TOCHeading'), pyxb.binding.datatypes.string, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 81, 8)))
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'TOCID'), pyxb.binding.datatypes.int, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 82, 8)))
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Description'), pyxb.binding.datatypes.string, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 84, 6)))
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Views'), pyxb.binding.datatypes.string, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 85, 6)))
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'HintGroupSubsectionsByReference'), pyxb.binding.datatypes.boolean, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 86, 6)))
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'HintEmbeddedHTML'), pyxb.binding.datatypes.boolean, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 87, 6)))
CTD_ANON_3._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Information'), CTD_ANON_4, scope=CTD_ANON_3, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 94, 0)))
def _BuildAutomaton_3 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_3
del _BuildAutomaton_3
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 84, 6))
counters.add(cc_0)
cc_1 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 85, 6))
counters.add(cc_1)
cc_2 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 86, 6))
counters.add(cc_2)
cc_3 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 87, 6))
counters.add(cc_3)
cc_4 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 88, 6))
counters.add(cc_4)
cc_5 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 89, 6))
counters.add(cc_5)
states = []
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'TOCHeading')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 81, 8))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'TOCID')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 82, 8))
st_1 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_0, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Description')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 84, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_1, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Views')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 85, 6))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_2, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'HintGroupSubsectionsByReference')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 86, 6))
st_4 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_4)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_3, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'HintEmbeddedHTML')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 87, 6))
st_5 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_5)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Information')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 88, 6))
st_6 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_6)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_5, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_3._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Section')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 89, 6))
st_7 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_7)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
transitions.append(fac.Transition(st_3, [
]))
transitions.append(fac.Transition(st_4, [
]))
transitions.append(fac.Transition(st_5, [
]))
transitions.append(fac.Transition(st_6, [
]))
transitions.append(fac.Transition(st_7, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
transitions.append(fac.Transition(st_3, [
]))
transitions.append(fac.Transition(st_4, [
]))
transitions.append(fac.Transition(st_5, [
]))
transitions.append(fac.Transition(st_6, [
]))
transitions.append(fac.Transition(st_7, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_0, True) ]))
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_0, False) ]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_1, True) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_1, False) ]))
st_3._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_2, True) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_2, False) ]))
st_4._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_3, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_3, False) ]))
st_5._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, False) ]))
st_6._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_5, True) ]))
st_7._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_3._Automaton = _BuildAutomaton_3()
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ReferenceNumber'), pyxb.binding.datatypes.int, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 97, 6)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Name'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 98, 6)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Description'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 99, 6)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Reference'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 100, 6)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'URL'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 101, 6)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'NumValue'), pyxb.binding.datatypes.double, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 103, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'NumValueList'), pyxb.binding.datatypes.double, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 104, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'DateValue'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 105, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'DateValueList'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 106, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'BoolValue'), pyxb.binding.datatypes.boolean, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 107, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'BoolValueList'), pyxb.binding.datatypes.boolean, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 108, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'StringValue'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 109, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'StringValueList'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 110, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'BinaryValue'), pyxb.binding.datatypes.base64Binary, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 111, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'BinaryValueList'), pyxb.binding.datatypes.base64Binary, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 112, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataURL'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 113, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataURLList'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 114, 8)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ValueUnit'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 116, 6)))
CTD_ANON_4._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataMimeType'), pyxb.binding.datatypes.string, scope=CTD_ANON_4, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 117, 6)))
def _BuildAutomaton_4 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_4
del _BuildAutomaton_4
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 98, 6))
counters.add(cc_0)
cc_1 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 99, 6))
counters.add(cc_1)
cc_2 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 100, 6))
counters.add(cc_2)
cc_3 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 101, 6))
counters.add(cc_3)
cc_4 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 102, 6))
counters.add(cc_4)
cc_5 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 116, 6))
counters.add(cc_5)
cc_6 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 117, 6))
counters.add(cc_6)
states = []
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ReferenceNumber')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 97, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_0, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Name')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 98, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_1, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Description')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 99, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_2, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Reference')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 100, 6))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_3, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'URL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 101, 6))
st_4 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_4)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'NumValue')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 103, 8))
st_5 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_5)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'NumValueList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 104, 8))
st_6 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_6)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'DateValue')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 105, 8))
st_7 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_7)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'DateValueList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 106, 8))
st_8 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_8)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'BoolValue')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 107, 8))
st_9 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_9)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'BoolValueList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 108, 8))
st_10 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_10)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'StringValue')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 109, 8))
st_11 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_11)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'StringValueList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 110, 8))
st_12 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_12)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'BinaryValue')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 111, 8))
st_13 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_13)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'BinaryValueList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 112, 8))
st_14 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_14)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataURL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 113, 8))
st_15 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_15)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_4, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataURLList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 114, 8))
st_16 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_16)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_5, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ValueUnit')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 116, 6))
st_17 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_17)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_6, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_4._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ExternalDataMimeType')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 117, 6))
st_18 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_18)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
transitions.append(fac.Transition(st_2, [
]))
transitions.append(fac.Transition(st_3, [
]))
transitions.append(fac.Transition(st_4, [
]))
transitions.append(fac.Transition(st_5, [
]))
transitions.append(fac.Transition(st_6, [
]))
transitions.append(fac.Transition(st_7, [
]))
transitions.append(fac.Transition(st_8, [
]))
transitions.append(fac.Transition(st_9, [
]))
transitions.append(fac.Transition(st_10, [
]))
transitions.append(fac.Transition(st_11, [
]))
transitions.append(fac.Transition(st_12, [
]))
transitions.append(fac.Transition(st_13, [
]))
transitions.append(fac.Transition(st_14, [
]))
transitions.append(fac.Transition(st_15, [
]))
transitions.append(fac.Transition(st_16, [
]))
transitions.append(fac.Transition(st_17, [
]))
transitions.append(fac.Transition(st_18, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_1, [
fac.UpdateInstruction(cc_0, True) ]))
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_0, False) ]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_1, True) ]))
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_1, False) ]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_2, True) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_2, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_2, False) ]))
st_3._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_3, True) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_3, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_3, False) ]))
st_4._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_5._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_6._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_7._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_8._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_9._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_10._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_11._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_12._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_13._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_14._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_15._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_6, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_7, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_8, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_9, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_10, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_11, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_12, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_13, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_14, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_15, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_16, [
]))
transitions.append(fac.Transition(st_16, [
fac.UpdateInstruction(cc_4, True) ]))
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_4, False) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_4, False) ]))
st_16._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_17, [
fac.UpdateInstruction(cc_5, True) ]))
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_5, False) ]))
st_17._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_18, [
fac.UpdateInstruction(cc_6, True) ]))
st_18._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_4._Automaton = _BuildAutomaton_4()
CTD_ANON_5._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ReferenceNumber'), pyxb.binding.datatypes.int, scope=CTD_ANON_5, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 125, 6)))
CTD_ANON_5._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SourceName'), pyxb.binding.datatypes.string, scope=CTD_ANON_5, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 126, 6)))
CTD_ANON_5._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SourceID'), pyxb.binding.datatypes.string, scope=CTD_ANON_5, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 127, 6)))
CTD_ANON_5._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Name'), pyxb.binding.datatypes.string, scope=CTD_ANON_5, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 128, 6)))
CTD_ANON_5._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Description'), pyxb.binding.datatypes.string, scope=CTD_ANON_5, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 129, 6)))
CTD_ANON_5._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'URL'), pyxb.binding.datatypes.string, scope=CTD_ANON_5, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 130, 6)))
def _BuildAutomaton_5 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_5
del _BuildAutomaton_5
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 127, 6))
counters.add(cc_0)
cc_1 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 128, 6))
counters.add(cc_1)
cc_2 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 129, 6))
counters.add(cc_2)
cc_3 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 130, 6))
counters.add(cc_3)
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_5._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ReferenceNumber')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 125, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_5._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'SourceName')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 126, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_0, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_5._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'SourceID')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 127, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_1, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_5._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Name')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 128, 6))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_2, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_5._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Description')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 129, 6))
st_4 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_4)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_3, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_5._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'URL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 130, 6))
st_5 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_5)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
transitions.append(fac.Transition(st_3, [
]))
transitions.append(fac.Transition(st_4, [
]))
transitions.append(fac.Transition(st_5, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_0, True) ]))
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_0, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_0, False) ]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_1, True) ]))
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_1, False) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_1, False) ]))
st_3._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_2, True) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_2, False) ]))
st_4._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_3, True) ]))
st_5._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_5._Automaton = _BuildAutomaton_5()
CTD_ANON_6._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), pyxb.binding.datatypes.string, scope=CTD_ANON_6, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 140, 6)))
CTD_ANON_6._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), pyxb.binding.datatypes.int, scope=CTD_ANON_6, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 141, 6)))
CTD_ANON_6._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'NeighborsOfType'), CTD_ANON_7, scope=CTD_ANON_6, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 142, 6)))
def _BuildAutomaton_6 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_6
del _BuildAutomaton_6
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_6._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordType')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 140, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_6._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 141, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_6._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'NeighborsOfType')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 142, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_2._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_6._Automaton = _BuildAutomaton_6()
CTD_ANON_7._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Type'), pyxb.binding.datatypes.string, scope=CTD_ANON_7, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 145, 12)))
CTD_ANON_7._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'IDList'), pyxb.binding.datatypes.int, scope=CTD_ANON_7, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 146, 12)))
CTD_ANON_7._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'NameList'), pyxb.binding.datatypes.string, scope=CTD_ANON_7, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 147, 12)))
def _BuildAutomaton_7 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_7
del _BuildAutomaton_7
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_7._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Type')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 145, 12))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_7._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'IDList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 146, 12))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_7._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'NameList')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 147, 12))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
transitions.append(fac.Transition(st_2, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_2._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_7._Automaton = _BuildAutomaton_7()
CTD_ANON_8._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), pyxb.binding.datatypes.string, scope=CTD_ANON_8, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 158, 6)))
CTD_ANON_8._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), pyxb.binding.datatypes.int, scope=CTD_ANON_8, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 159, 6)))
CTD_ANON_8._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'URL'), pyxb.binding.datatypes.string, scope=CTD_ANON_8, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 160, 3)))
CTD_ANON_8._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'NumberOfStructures'), pyxb.binding.datatypes.int, scope=CTD_ANON_8, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 161, 3)))
CTD_ANON_8._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Structures'), CTD_ANON_9, scope=CTD_ANON_8, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 162, 6)))
def _BuildAutomaton_8 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_8
del _BuildAutomaton_8
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_8._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordType')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 158, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_8._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 159, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_8._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'URL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 160, 3))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_8._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'NumberOfStructures')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 161, 3))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_8._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Structures')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 162, 6))
st_4 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_4)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
]))
st_3._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
]))
st_4._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_8._Automaton = _BuildAutomaton_8()
CTD_ANON_9._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'MMDB_ID'), pyxb.binding.datatypes.int, scope=CTD_ANON_9, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 165, 12)))
CTD_ANON_9._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'PDB_ID'), pyxb.binding.datatypes.string, scope=CTD_ANON_9, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 166, 12)))
CTD_ANON_9._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'URL'), pyxb.binding.datatypes.string, scope=CTD_ANON_9, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 167, 12)))
CTD_ANON_9._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ImageURL'), pyxb.binding.datatypes.string, scope=CTD_ANON_9, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 168, 12)))
CTD_ANON_9._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Description'), pyxb.binding.datatypes.string, scope=CTD_ANON_9, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 169, 12)))
CTD_ANON_9._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Taxonomy'), CTD_ANON_10, scope=CTD_ANON_9, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 170, 12)))
def _BuildAutomaton_9 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_9
del _BuildAutomaton_9
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 166, 12))
counters.add(cc_0)
cc_1 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 169, 12))
counters.add(cc_1)
cc_2 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 170, 12))
counters.add(cc_2)
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_9._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'MMDB_ID')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 165, 12))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_9._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'PDB_ID')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 166, 12))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_9._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'URL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 167, 12))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_9._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ImageURL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 168, 12))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_1, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_9._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Description')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 169, 12))
st_4 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_4)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_2, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_9._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Taxonomy')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 170, 12))
st_5 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_5)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
transitions.append(fac.Transition(st_2, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_1, [
fac.UpdateInstruction(cc_0, True) ]))
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_0, False) ]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
]))
transitions.append(fac.Transition(st_5, [
]))
st_3._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_1, True) ]))
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_1, False) ]))
st_4._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_5, [
fac.UpdateInstruction(cc_2, True) ]))
st_5._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_9._Automaton = _BuildAutomaton_9()
CTD_ANON_10._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'ID'), pyxb.binding.datatypes.int, scope=CTD_ANON_10, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 173, 18)))
CTD_ANON_10._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Name'), pyxb.binding.datatypes.string, scope=CTD_ANON_10, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 174, 18)))
def _BuildAutomaton_10 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_10
del _BuildAutomaton_10
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_10._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'ID')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 173, 18))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_10._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Name')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 174, 18))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
st_1._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_10._Automaton = _BuildAutomaton_10()
CTD_ANON_11._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), pyxb.binding.datatypes.string, scope=CTD_ANON_11, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 188, 6)))
CTD_ANON_11._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), pyxb.binding.datatypes.int, scope=CTD_ANON_11, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 189, 6)))
CTD_ANON_11._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Categories'), CTD_ANON_12, scope=CTD_ANON_11, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 190, 6)))
def _BuildAutomaton_11 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_11
del _BuildAutomaton_11
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_11._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordType')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 188, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_11._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 189, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_11._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Categories')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 190, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_2._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_11._Automaton = _BuildAutomaton_11()
CTD_ANON_12._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Category'), pyxb.binding.datatypes.string, scope=CTD_ANON_12, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 193, 12)))
CTD_ANON_12._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'URL'), pyxb.binding.datatypes.string, scope=CTD_ANON_12, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 194, 12)))
CTD_ANON_12._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Sources'), CTD_ANON_13, scope=CTD_ANON_12, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 195, 12)))
def _BuildAutomaton_12 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_12
del _BuildAutomaton_12
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_12._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Category')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 193, 12))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_12._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'URL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 194, 12))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_12._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Sources')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 195, 12))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_2._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_12._Automaton = _BuildAutomaton_12()
CTD_ANON_13._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SID'), pyxb.binding.datatypes.int, scope=CTD_ANON_13, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 198, 18)))
CTD_ANON_13._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SourceName'), pyxb.binding.datatypes.string, scope=CTD_ANON_13, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 199, 18)))
CTD_ANON_13._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SourceURL'), pyxb.binding.datatypes.string, scope=CTD_ANON_13, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 200, 18)))
CTD_ANON_13._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RegistryID'), pyxb.binding.datatypes.string, scope=CTD_ANON_13, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 201, 18)))
CTD_ANON_13._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SourceRecordURL'), pyxb.binding.datatypes.string, scope=CTD_ANON_13, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 202, 18)))
def _BuildAutomaton_13 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_13
del _BuildAutomaton_13
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 200, 18))
counters.add(cc_0)
cc_1 = fac.CounterCondition(min=0, max=1, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 202, 18))
counters.add(cc_1)
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_13._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'SID')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 198, 18))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_13._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'SourceName')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 199, 18))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_13._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'SourceURL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 200, 18))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_13._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RegistryID')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 201, 18))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_1, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_13._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'SourceRecordURL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 202, 18))
st_4 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_4)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
transitions.append(fac.Transition(st_3, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
fac.UpdateInstruction(cc_0, True) ]))
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_0, False) ]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
]))
st_3._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_4, [
fac.UpdateInstruction(cc_1, True) ]))
st_4._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_13._Automaton = _BuildAutomaton_13()
CTD_ANON_14._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordType'), pyxb.binding.datatypes.string, scope=CTD_ANON_14, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 216, 6)))
CTD_ANON_14._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber'), pyxb.binding.datatypes.int, scope=CTD_ANON_14, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 217, 6)))
CTD_ANON_14._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'AllURL'), pyxb.binding.datatypes.string, scope=CTD_ANON_14, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 218, 6)))
CTD_ANON_14._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Subheadings'), CTD_ANON_15, scope=CTD_ANON_14, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 219, 6)))
def _BuildAutomaton_14 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_14
del _BuildAutomaton_14
import pyxb.utils.fac as fac
counters = set()
cc_0 = fac.CounterCondition(min=0, max=None, metadata=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 219, 6))
counters.add(cc_0)
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_14._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordType')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 216, 6))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_14._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'RecordNumber')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 217, 6))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_14._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'AllURL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 218, 6))
st_2 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_2)
final_update = set()
final_update.add(fac.UpdateInstruction(cc_0, False))
symbol = pyxb.binding.content.ElementUse(CTD_ANON_14._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Subheadings')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 219, 6))
st_3 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_3)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_2, [
]))
st_1._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
]))
st_2._set_transitionSet(transitions)
transitions = []
transitions.append(fac.Transition(st_3, [
fac.UpdateInstruction(cc_0, True) ]))
st_3._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_14._Automaton = _BuildAutomaton_14()
CTD_ANON_15._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'Subheading'), pyxb.binding.datatypes.string, scope=CTD_ANON_15, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 222, 12)))
CTD_ANON_15._AddElement(pyxb.binding.basis.element(pyxb.namespace.ExpandedName(Namespace, 'SubheadingURL'), pyxb.binding.datatypes.string, scope=CTD_ANON_15, location=pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 223, 12)))
def _BuildAutomaton_15 ():
# Remove this helper function from the namespace after it is invoked
global _BuildAutomaton_15
del _BuildAutomaton_15
import pyxb.utils.fac as fac
counters = set()
states = []
final_update = None
symbol = pyxb.binding.content.ElementUse(CTD_ANON_15._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'Subheading')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 222, 12))
st_0 = fac.State(symbol, is_initial=True, final_update=final_update, is_unordered_catenation=False)
states.append(st_0)
final_update = set()
symbol = pyxb.binding.content.ElementUse(CTD_ANON_15._UseForTag(pyxb.namespace.ExpandedName(Namespace, 'SubheadingURL')), pyxb.utils.utility.Location('/Users/cmhudso/Desktop/SRAnt/NCBI/pug_view.xsd', 223, 12))
st_1 = fac.State(symbol, is_initial=False, final_update=final_update, is_unordered_catenation=False)
states.append(st_1)
transitions = []
transitions.append(fac.Transition(st_1, [
]))
st_0._set_transitionSet(transitions)
transitions = []
st_1._set_transitionSet(transitions)
return fac.Automaton(states, counters, False, containing_state=None)
CTD_ANON_15._Automaton = _BuildAutomaton_15()
| 57.368035
| 405
| 0.750518
| 19,784
| 156,500
| 5.671199
| 0.024717
| 0.023271
| 0.065776
| 0.098664
| 0.900035
| 0.892281
| 0.883421
| 0.882637
| 0.849116
| 0.842877
| 0
| 0.021736
| 0.123073
| 156,500
| 2,727
| 406
| 57.389072
| 0.795809
| 0.079668
| 0
| 0.724311
| 1
| 0
| 0.169685
| 0.147012
| 0
| 0
| 0
| 0
| 0
| 1
| 0.009023
| false
| 0
| 0.013033
| 0
| 0.174937
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
2043e2b971ae583a802696636bc273ed34f55a70
| 2,419
|
py
|
Python
|
staff/migrations/0003_auto_20200310_2228.py
|
mamalmaleki/maktab-community
|
8ce25053ea0f6f0a6c082617c9ff306d1ada9707
|
[
"MIT"
] | null | null | null |
staff/migrations/0003_auto_20200310_2228.py
|
mamalmaleki/maktab-community
|
8ce25053ea0f6f0a6c082617c9ff306d1ada9707
|
[
"MIT"
] | null | null | null |
staff/migrations/0003_auto_20200310_2228.py
|
mamalmaleki/maktab-community
|
8ce25053ea0f6f0a6c082617c9ff306d1ada9707
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.0.3 on 2020-03-10 22:28
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('staff', '0002_auto_20200310_2151'),
]
operations = [
migrations.RemoveField(
model_name='instructor',
name='created_by',
),
migrations.RemoveField(
model_name='instructor',
name='creation_time',
),
migrations.RemoveField(
model_name='instructor',
name='delete_time',
),
migrations.RemoveField(
model_name='instructor',
name='deleted_by',
),
migrations.RemoveField(
model_name='instructor',
name='integration_code',
),
migrations.RemoveField(
model_name='instructor',
name='last_update',
),
migrations.RemoveField(
model_name='instructor',
name='status',
),
migrations.RemoveField(
model_name='instructor',
name='unique_id',
),
migrations.RemoveField(
model_name='instructor',
name='updated_by',
),
migrations.RemoveField(
model_name='instructor',
name='version',
),
migrations.RemoveField(
model_name='student',
name='created_by',
),
migrations.RemoveField(
model_name='student',
name='creation_time',
),
migrations.RemoveField(
model_name='student',
name='delete_time',
),
migrations.RemoveField(
model_name='student',
name='deleted_by',
),
migrations.RemoveField(
model_name='student',
name='integration_code',
),
migrations.RemoveField(
model_name='student',
name='last_update',
),
migrations.RemoveField(
model_name='student',
name='status',
),
migrations.RemoveField(
model_name='student',
name='unique_id',
),
migrations.RemoveField(
model_name='student',
name='updated_by',
),
migrations.RemoveField(
model_name='student',
name='version',
),
]
| 25.734043
| 47
| 0.504754
| 184
| 2,419
| 6.423913
| 0.233696
| 0.35533
| 0.439932
| 0.507614
| 0.86802
| 0.86802
| 0.677665
| 0
| 0
| 0
| 0
| 0.020861
| 0.385697
| 2,419
| 93
| 48
| 26.010753
| 0.774563
| 0.018603
| 0
| 0.91954
| 1
| 0
| 0.17032
| 0.009696
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.011494
| 0
| 0.045977
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
64cfa91aa88f4ce083a251bb333cab82872c5280
| 3,010
|
py
|
Python
|
model/aster/builders/bidirectional_rnn_builder_test.py
|
yyttmonster/text-flask
|
2a63c3bf6859bd2feac87d619650dae4b6b8af6e
|
[
"MIT"
] | 7
|
2019-05-13T17:59:53.000Z
|
2022-03-05T11:49:50.000Z
|
model/aster/builders/bidirectional_rnn_builder_test.py
|
yyttmonster/text-flask
|
2a63c3bf6859bd2feac87d619650dae4b6b8af6e
|
[
"MIT"
] | null | null | null |
model/aster/builders/bidirectional_rnn_builder_test.py
|
yyttmonster/text-flask
|
2a63c3bf6859bd2feac87d619650dae4b6b8af6e
|
[
"MIT"
] | 1
|
2020-04-03T19:23:41.000Z
|
2020-04-03T19:23:41.000Z
|
import tensorflow as tf
from google.protobuf import text_format
from model.aster.builders import bidirectional_rnn_builder
from model.aster.protos import bidirectional_rnn_pb2
class BidirectionalRnnBuilderTest(tf.test.TestCase):
def test_bidirectional_rnn(self):
text_proto = """
static: true
fw_bw_rnn_cell {
lstm_cell {
num_units: 32
forget_bias: 1.0
initializer { orthogonal_initializer {} }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
num_output_units: 31
fc_hyperparams {
op: FC
activation: RELU
initializer { variance_scaling_initializer { } }
regularizer { l2_regularizer { weight: 1e-4 } }
}
"""
config = bidirectional_rnn_pb2.BidirectionalRnn()
text_format.Merge(text_proto, config)
brnn_object = bidirectional_rnn_builder.build(config, True)
test_input = tf.random_uniform([2, 5, 32], dtype=tf.float32)
test_output = brnn_object.predict(test_input)
with self.test_session() as sess:
tf.global_variables_initializer().run()
sess_outputs = sess.run({'outputs': test_output})
self.assertAllEqual(sess_outputs['outputs'].shape, [2, 5, 31])
def test_dynamic_bidirectional_rnn(self):
text_proto = """
static: false
fw_bw_rnn_cell {
lstm_cell {
num_units: 32
forget_bias: 1.0
initializer { orthogonal_initializer {} }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
num_output_units: 31
fc_hyperparams {
op: FC
activation: RELU
initializer { variance_scaling_initializer { } }
regularizer { l2_regularizer { weight: 1e-4 } }
}
"""
config = bidirectional_rnn_pb2.BidirectionalRnn()
text_format.Merge(text_proto, config)
brnn_object = bidirectional_rnn_builder.build(config, True)
test_input = tf.random_uniform([2, 5, 32], dtype=tf.float32)
test_output = brnn_object.predict(test_input)
with self.test_session() as sess:
tf.global_variables_initializer().run()
sess_outputs = sess.run({'outputs': test_output})
self.assertAllEqual(sess_outputs['outputs'].shape, [2, 5, 31])
def test_bidirectional_rnn_nofc(self):
text_proto = """
static: true
fw_bw_rnn_cell {
lstm_cell {
num_units: 32
forget_bias: 1.0
initializer { orthogonal_initializer {} }
}
}
rnn_regularizer { l2_regularizer { weight: 1e-4 } }
"""
config = bidirectional_rnn_pb2.BidirectionalRnn()
text_format.Merge(text_proto, config)
brnn_object = bidirectional_rnn_builder.build(config, True)
test_input = tf.random_uniform([2, 5, 32], dtype=tf.float32)
test_output = brnn_object.predict(test_input)
with self.test_session() as sess:
tf.global_variables_initializer().run()
sess_outputs = sess.run({'outputs': test_output})
self.assertAllEqual(sess_outputs['outputs'].shape, [2, 5, 64])
if __name__ == '__main__':
tf.test.main()
| 30.714286
| 68
| 0.678738
| 367
| 3,010
| 5.247956
| 0.226158
| 0.091381
| 0.062305
| 0.077882
| 0.866563
| 0.866563
| 0.840083
| 0.840083
| 0.840083
| 0.840083
| 0
| 0.027589
| 0.217276
| 3,010
| 97
| 69
| 31.030928
| 0.789898
| 0
| 0
| 0.761905
| 0
| 0
| 0.375083
| 0.040532
| 0
| 0
| 0
| 0
| 0.035714
| 1
| 0.035714
| false
| 0
| 0.047619
| 0
| 0.095238
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b3b649c417156b7c9b50aade8ad4fe0120ea01bf
| 3,681
|
py
|
Python
|
tests/unit/test_translate_data_structure_service.py
|
harvard-lts/drs-translation-service
|
8b448422c460c735860d3a64cf2ddc01f6e16eb3
|
[
"Apache-2.0"
] | null | null | null |
tests/unit/test_translate_data_structure_service.py
|
harvard-lts/drs-translation-service
|
8b448422c460c735860d3a64cf2ddc01f6e16eb3
|
[
"Apache-2.0"
] | 3
|
2022-03-21T23:40:33.000Z
|
2022-03-28T01:35:01.000Z
|
tests/unit/test_translate_data_structure_service.py
|
harvard-lts/drs-translation-service
|
8b448422c460c735860d3a64cf2ddc01f6e16eb3
|
[
"Apache-2.0"
] | null | null | null |
import pytest, sys, os.path, shutil
sys.path.append('app')
import translation_service.translate_data_structure_service as translate_data_structure_service
def test_translate_data_structure():
'''Formats the directory and verifies that all files ended up where they should be'''
loc = "/home/appuser/tests/data/doi-translation-service-test"
expected_batch_dir = os.path.join(loc, os.path.basename(loc) + "-batch")
batch_dir = translate_data_structure_service.translate_data_structure(loc)
assert(expected_batch_dir == batch_dir)
obj_dir = os.path.join(batch_dir, os.path.basename(loc))
obj_aux_dir= os.path.join(loc, "_aux", os.path.basename(loc) + "-batch", os.path.basename(loc))
assert os.path.exists(obj_dir)
assert os.path.exists(obj_aux_dir)
#Check that all files are where they are expected to be
assert os.path.exists(os.path.join(obj_dir, "content", "test1.Bin"))
assert os.path.exists(os.path.join(obj_dir, "content", "test2.Bin"))
assert os.path.exists(os.path.join(obj_dir, "content", "test3.Bin"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "bag-info.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "bagit.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "manifest-md5.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "datacite.xml"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "oai-ore.jsonld"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "pid-mapping.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "doi-translation-service-test_datacite.v1.0.xml"))
cleanup_batch_dirs(batch_dir, os.path.join(loc, "_aux"), os.path.join(loc, "project.conf"))
def test_translate_data_structure_doc_only():
'''Formats the directory and verifies that all files ended up where they should be'''
loc = "/home/appuser/tests/data/doi-translation-service-test-doc-only"
expected_batch_dir = os.path.join(loc, os.path.basename(loc) + "-batch")
batch_dir = translate_data_structure_service.translate_data_structure(loc)
assert(expected_batch_dir == batch_dir)
obj_dir = os.path.join(batch_dir, os.path.basename(loc))
obj_aux_dir= os.path.join(loc, "_aux", os.path.basename(loc) + "-batch", os.path.basename(loc))
assert os.path.exists(obj_dir)
assert os.path.exists(obj_aux_dir)
#Check that all files are where they are expected to be
assert not os.path.exists(os.path.join(obj_dir, "content"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "bag-info.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "bagit.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "manifest-md5.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "datacite.xml"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "oai-ore.jsonld"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "pid-mapping.txt"))
assert os.path.exists(os.path.join(obj_dir, "documentation", "doi-translation-service-test_datacite.v1.0.xml"))
cleanup_batch_dirs(batch_dir, os.path.join(loc, "_aux"), os.path.join(loc, "project.conf"))
def cleanup_batch_dirs(batch_path, aux_dir, project_conf):
'''Removes the newly created batch folders'''
try:
shutil.rmtree(batch_path)
shutil.rmtree(aux_dir)
os.remove(project_conf)
shutil.rmtree(os.path.join(os.getenv("DROPBOX_PATH"), os.path.basename(batch_path)))
except OSError as e:
print("Error in cleanup: %s" % (e.strerror))
| 56.630769
| 115
| 0.718283
| 563
| 3,681
| 4.534636
| 0.16341
| 0.143361
| 0.113592
| 0.148061
| 0.841363
| 0.81982
| 0.81982
| 0.81982
| 0.81982
| 0.80611
| 0
| 0.002818
| 0.132301
| 3,681
| 65
| 116
| 56.630769
| 0.796493
| 0.083673
| 0
| 0.612245
| 0
| 0
| 0.208222
| 0.061662
| 0
| 0
| 0
| 0
| 0.489796
| 1
| 0.061224
| false
| 0
| 0.040816
| 0
| 0.102041
| 0.020408
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
37ca8d06497ce272b1b2366db988bc69db913411
| 16
|
py
|
Python
|
examples/list.py
|
LayneInNL/py2flows
|
5ecb555c64350cb13c3885a78fe89a40994e9d0e
|
[
"Apache-2.0"
] | 3
|
2022-03-21T12:10:37.000Z
|
2022-03-24T13:31:19.000Z
|
examples/list.py
|
Robin199412/py2flows
|
52e5e5bdbd83ede4a994f2e429dac770a7926032
|
[
"Apache-2.0"
] | 1
|
2022-03-17T02:09:37.000Z
|
2022-03-17T10:08:14.000Z
|
examples/list.py
|
Robin199412/py2flows
|
52e5e5bdbd83ede4a994f2e429dac770a7926032
|
[
"Apache-2.0"
] | 1
|
2022-03-23T01:12:56.000Z
|
2022-03-23T01:12:56.000Z
|
[1, 2, (x + 1)]
| 8
| 15
| 0.25
| 4
| 16
| 1
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.272727
| 0.3125
| 16
| 1
| 16
| 16
| 0.090909
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
807b18db7dc469ac00a1bf9f95588d5cb2cbb01c
| 47
|
py
|
Python
|
fibonacci.py
|
sonmezbaris/python-exercises
|
949934bde2a78a718fbac76906e888ca24ae4103
|
[
"Apache-2.0"
] | null | null | null |
fibonacci.py
|
sonmezbaris/python-exercises
|
949934bde2a78a718fbac76906e888ca24ae4103
|
[
"Apache-2.0"
] | null | null | null |
fibonacci.py
|
sonmezbaris/python-exercises
|
949934bde2a78a718fbac76906e888ca24ae4103
|
[
"Apache-2.0"
] | null | null | null |
a,b=0,1
while b<10:
print(b)
a,b=b,a+b
| 9.4
| 13
| 0.489362
| 14
| 47
| 1.642857
| 0.5
| 0.26087
| 0.26087
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 0.276596
| 47
| 4
| 14
| 11.75
| 0.558824
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.25
| 1
| 1
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
807f396af25b0c34325934c511059dc76848cb04
| 31,625
|
py
|
Python
|
sdk/python/pulumi_gcp/compute/target_ssl_proxy.py
|
sisisin/pulumi-gcp
|
af6681d70ea457843409110c1324817fe55f68ad
|
[
"ECL-2.0",
"Apache-2.0"
] | 121
|
2018-06-18T19:16:42.000Z
|
2022-03-31T06:06:48.000Z
|
sdk/python/pulumi_gcp/compute/target_ssl_proxy.py
|
sisisin/pulumi-gcp
|
af6681d70ea457843409110c1324817fe55f68ad
|
[
"ECL-2.0",
"Apache-2.0"
] | 492
|
2018-06-22T19:41:03.000Z
|
2022-03-31T15:33:53.000Z
|
sdk/python/pulumi_gcp/compute/target_ssl_proxy.py
|
sisisin/pulumi-gcp
|
af6681d70ea457843409110c1324817fe55f68ad
|
[
"ECL-2.0",
"Apache-2.0"
] | 43
|
2018-06-19T01:43:13.000Z
|
2022-03-23T22:43:37.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['TargetSSLProxyArgs', 'TargetSSLProxy']
@pulumi.input_type
class TargetSSLProxyArgs:
def __init__(__self__, *,
backend_service: pulumi.Input[str],
ssl_certificates: pulumi.Input[Sequence[pulumi.Input[str]]],
description: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
proxy_header: Optional[pulumi.Input[str]] = None,
ssl_policy: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a TargetSSLProxy resource.
:param pulumi.Input[str] backend_service: A reference to the BackendService resource.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ssl_certificates: A list of SslCertificate resources that are used to authenticate
connections between users and the load balancer. At least one
SSL certificate must be specified.
:param pulumi.Input[str] description: An optional description of this resource.
:param pulumi.Input[str] name: Name of the resource. Provided by the client when the resource is
created. The name must be 1-63 characters long, and comply with
RFC1035. Specifically, the name must be 1-63 characters long and match
the regular expression `a-z?` which means the
first character must be a lowercase letter, and all following
characters must be a dash, lowercase letter, or digit, except the last
character, which cannot be a dash.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] proxy_header: Specifies the type of proxy header to append before sending data to
the backend.
Default value is `NONE`.
Possible values are `NONE` and `PROXY_V1`.
:param pulumi.Input[str] ssl_policy: A reference to the SslPolicy resource that will be associated with
the TargetSslProxy resource. If not set, the TargetSslProxy
resource will not have any SSL policy configured.
"""
pulumi.set(__self__, "backend_service", backend_service)
pulumi.set(__self__, "ssl_certificates", ssl_certificates)
if description is not None:
pulumi.set(__self__, "description", description)
if name is not None:
pulumi.set(__self__, "name", name)
if project is not None:
pulumi.set(__self__, "project", project)
if proxy_header is not None:
pulumi.set(__self__, "proxy_header", proxy_header)
if ssl_policy is not None:
pulumi.set(__self__, "ssl_policy", ssl_policy)
@property
@pulumi.getter(name="backendService")
def backend_service(self) -> pulumi.Input[str]:
"""
A reference to the BackendService resource.
"""
return pulumi.get(self, "backend_service")
@backend_service.setter
def backend_service(self, value: pulumi.Input[str]):
pulumi.set(self, "backend_service", value)
@property
@pulumi.getter(name="sslCertificates")
def ssl_certificates(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
"""
A list of SslCertificate resources that are used to authenticate
connections between users and the load balancer. At least one
SSL certificate must be specified.
"""
return pulumi.get(self, "ssl_certificates")
@ssl_certificates.setter
def ssl_certificates(self, value: pulumi.Input[Sequence[pulumi.Input[str]]]):
pulumi.set(self, "ssl_certificates", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
An optional description of this resource.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of the resource. Provided by the client when the resource is
created. The name must be 1-63 characters long, and comply with
RFC1035. Specifically, the name must be 1-63 characters long and match
the regular expression `a-z?` which means the
first character must be a lowercase letter, and all following
characters must be a dash, lowercase letter, or digit, except the last
character, which cannot be a dash.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def project(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@project.setter
def project(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "project", value)
@property
@pulumi.getter(name="proxyHeader")
def proxy_header(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the type of proxy header to append before sending data to
the backend.
Default value is `NONE`.
Possible values are `NONE` and `PROXY_V1`.
"""
return pulumi.get(self, "proxy_header")
@proxy_header.setter
def proxy_header(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "proxy_header", value)
@property
@pulumi.getter(name="sslPolicy")
def ssl_policy(self) -> Optional[pulumi.Input[str]]:
"""
A reference to the SslPolicy resource that will be associated with
the TargetSslProxy resource. If not set, the TargetSslProxy
resource will not have any SSL policy configured.
"""
return pulumi.get(self, "ssl_policy")
@ssl_policy.setter
def ssl_policy(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ssl_policy", value)
@pulumi.input_type
class _TargetSSLProxyState:
def __init__(__self__, *,
backend_service: Optional[pulumi.Input[str]] = None,
creation_timestamp: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
proxy_header: Optional[pulumi.Input[str]] = None,
proxy_id: Optional[pulumi.Input[int]] = None,
self_link: Optional[pulumi.Input[str]] = None,
ssl_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
ssl_policy: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering TargetSSLProxy resources.
:param pulumi.Input[str] backend_service: A reference to the BackendService resource.
:param pulumi.Input[str] creation_timestamp: Creation timestamp in RFC3339 text format.
:param pulumi.Input[str] description: An optional description of this resource.
:param pulumi.Input[str] name: Name of the resource. Provided by the client when the resource is
created. The name must be 1-63 characters long, and comply with
RFC1035. Specifically, the name must be 1-63 characters long and match
the regular expression `a-z?` which means the
first character must be a lowercase letter, and all following
characters must be a dash, lowercase letter, or digit, except the last
character, which cannot be a dash.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] proxy_header: Specifies the type of proxy header to append before sending data to
the backend.
Default value is `NONE`.
Possible values are `NONE` and `PROXY_V1`.
:param pulumi.Input[int] proxy_id: The unique identifier for the resource.
:param pulumi.Input[str] self_link: The URI of the created resource.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ssl_certificates: A list of SslCertificate resources that are used to authenticate
connections between users and the load balancer. At least one
SSL certificate must be specified.
:param pulumi.Input[str] ssl_policy: A reference to the SslPolicy resource that will be associated with
the TargetSslProxy resource. If not set, the TargetSslProxy
resource will not have any SSL policy configured.
"""
if backend_service is not None:
pulumi.set(__self__, "backend_service", backend_service)
if creation_timestamp is not None:
pulumi.set(__self__, "creation_timestamp", creation_timestamp)
if description is not None:
pulumi.set(__self__, "description", description)
if name is not None:
pulumi.set(__self__, "name", name)
if project is not None:
pulumi.set(__self__, "project", project)
if proxy_header is not None:
pulumi.set(__self__, "proxy_header", proxy_header)
if proxy_id is not None:
pulumi.set(__self__, "proxy_id", proxy_id)
if self_link is not None:
pulumi.set(__self__, "self_link", self_link)
if ssl_certificates is not None:
pulumi.set(__self__, "ssl_certificates", ssl_certificates)
if ssl_policy is not None:
pulumi.set(__self__, "ssl_policy", ssl_policy)
@property
@pulumi.getter(name="backendService")
def backend_service(self) -> Optional[pulumi.Input[str]]:
"""
A reference to the BackendService resource.
"""
return pulumi.get(self, "backend_service")
@backend_service.setter
def backend_service(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "backend_service", value)
@property
@pulumi.getter(name="creationTimestamp")
def creation_timestamp(self) -> Optional[pulumi.Input[str]]:
"""
Creation timestamp in RFC3339 text format.
"""
return pulumi.get(self, "creation_timestamp")
@creation_timestamp.setter
def creation_timestamp(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "creation_timestamp", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
An optional description of this resource.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of the resource. Provided by the client when the resource is
created. The name must be 1-63 characters long, and comply with
RFC1035. Specifically, the name must be 1-63 characters long and match
the regular expression `a-z?` which means the
first character must be a lowercase letter, and all following
characters must be a dash, lowercase letter, or digit, except the last
character, which cannot be a dash.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def project(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@project.setter
def project(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "project", value)
@property
@pulumi.getter(name="proxyHeader")
def proxy_header(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the type of proxy header to append before sending data to
the backend.
Default value is `NONE`.
Possible values are `NONE` and `PROXY_V1`.
"""
return pulumi.get(self, "proxy_header")
@proxy_header.setter
def proxy_header(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "proxy_header", value)
@property
@pulumi.getter(name="proxyId")
def proxy_id(self) -> Optional[pulumi.Input[int]]:
"""
The unique identifier for the resource.
"""
return pulumi.get(self, "proxy_id")
@proxy_id.setter
def proxy_id(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "proxy_id", value)
@property
@pulumi.getter(name="selfLink")
def self_link(self) -> Optional[pulumi.Input[str]]:
"""
The URI of the created resource.
"""
return pulumi.get(self, "self_link")
@self_link.setter
def self_link(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "self_link", value)
@property
@pulumi.getter(name="sslCertificates")
def ssl_certificates(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
A list of SslCertificate resources that are used to authenticate
connections between users and the load balancer. At least one
SSL certificate must be specified.
"""
return pulumi.get(self, "ssl_certificates")
@ssl_certificates.setter
def ssl_certificates(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "ssl_certificates", value)
@property
@pulumi.getter(name="sslPolicy")
def ssl_policy(self) -> Optional[pulumi.Input[str]]:
"""
A reference to the SslPolicy resource that will be associated with
the TargetSslProxy resource. If not set, the TargetSslProxy
resource will not have any SSL policy configured.
"""
return pulumi.get(self, "ssl_policy")
@ssl_policy.setter
def ssl_policy(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ssl_policy", value)
class TargetSSLProxy(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
backend_service: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
proxy_header: Optional[pulumi.Input[str]] = None,
ssl_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
ssl_policy: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Represents a TargetSslProxy resource, which is used by one or more
global forwarding rule to route incoming SSL requests to a backend
service.
To get more information about TargetSslProxy, see:
* [API documentation](https://cloud.google.com/compute/docs/reference/v1/targetSslProxies)
* How-to Guides
* [Setting Up SSL proxy for Google Cloud Load Balancing](https://cloud.google.com/compute/docs/load-balancing/tcp-ssl/)
## Example Usage
### Target Ssl Proxy Basic
```python
import pulumi
import pulumi_gcp as gcp
default_ssl_certificate = gcp.compute.SSLCertificate("defaultSSLCertificate",
private_key=(lambda path: open(path).read())("path/to/private.key"),
certificate=(lambda path: open(path).read())("path/to/certificate.crt"))
default_health_check = gcp.compute.HealthCheck("defaultHealthCheck",
check_interval_sec=1,
timeout_sec=1,
tcp_health_check=gcp.compute.HealthCheckTcpHealthCheckArgs(
port=443,
))
default_backend_service = gcp.compute.BackendService("defaultBackendService",
protocol="SSL",
health_checks=[default_health_check.id])
default_target_ssl_proxy = gcp.compute.TargetSSLProxy("defaultTargetSSLProxy",
backend_service=default_backend_service.id,
ssl_certificates=[default_ssl_certificate.id])
```
## Import
TargetSslProxy can be imported using any of these accepted formats
```sh
$ pulumi import gcp:compute/targetSSLProxy:TargetSSLProxy default projects/{{project}}/global/targetSslProxies/{{name}}
```
```sh
$ pulumi import gcp:compute/targetSSLProxy:TargetSSLProxy default {{project}}/{{name}}
```
```sh
$ pulumi import gcp:compute/targetSSLProxy:TargetSSLProxy default {{name}}
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] backend_service: A reference to the BackendService resource.
:param pulumi.Input[str] description: An optional description of this resource.
:param pulumi.Input[str] name: Name of the resource. Provided by the client when the resource is
created. The name must be 1-63 characters long, and comply with
RFC1035. Specifically, the name must be 1-63 characters long and match
the regular expression `a-z?` which means the
first character must be a lowercase letter, and all following
characters must be a dash, lowercase letter, or digit, except the last
character, which cannot be a dash.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] proxy_header: Specifies the type of proxy header to append before sending data to
the backend.
Default value is `NONE`.
Possible values are `NONE` and `PROXY_V1`.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ssl_certificates: A list of SslCertificate resources that are used to authenticate
connections between users and the load balancer. At least one
SSL certificate must be specified.
:param pulumi.Input[str] ssl_policy: A reference to the SslPolicy resource that will be associated with
the TargetSslProxy resource. If not set, the TargetSslProxy
resource will not have any SSL policy configured.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: TargetSSLProxyArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Represents a TargetSslProxy resource, which is used by one or more
global forwarding rule to route incoming SSL requests to a backend
service.
To get more information about TargetSslProxy, see:
* [API documentation](https://cloud.google.com/compute/docs/reference/v1/targetSslProxies)
* How-to Guides
* [Setting Up SSL proxy for Google Cloud Load Balancing](https://cloud.google.com/compute/docs/load-balancing/tcp-ssl/)
## Example Usage
### Target Ssl Proxy Basic
```python
import pulumi
import pulumi_gcp as gcp
default_ssl_certificate = gcp.compute.SSLCertificate("defaultSSLCertificate",
private_key=(lambda path: open(path).read())("path/to/private.key"),
certificate=(lambda path: open(path).read())("path/to/certificate.crt"))
default_health_check = gcp.compute.HealthCheck("defaultHealthCheck",
check_interval_sec=1,
timeout_sec=1,
tcp_health_check=gcp.compute.HealthCheckTcpHealthCheckArgs(
port=443,
))
default_backend_service = gcp.compute.BackendService("defaultBackendService",
protocol="SSL",
health_checks=[default_health_check.id])
default_target_ssl_proxy = gcp.compute.TargetSSLProxy("defaultTargetSSLProxy",
backend_service=default_backend_service.id,
ssl_certificates=[default_ssl_certificate.id])
```
## Import
TargetSslProxy can be imported using any of these accepted formats
```sh
$ pulumi import gcp:compute/targetSSLProxy:TargetSSLProxy default projects/{{project}}/global/targetSslProxies/{{name}}
```
```sh
$ pulumi import gcp:compute/targetSSLProxy:TargetSSLProxy default {{project}}/{{name}}
```
```sh
$ pulumi import gcp:compute/targetSSLProxy:TargetSSLProxy default {{name}}
```
:param str resource_name: The name of the resource.
:param TargetSSLProxyArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(TargetSSLProxyArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
backend_service: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
proxy_header: Optional[pulumi.Input[str]] = None,
ssl_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
ssl_policy: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = TargetSSLProxyArgs.__new__(TargetSSLProxyArgs)
if backend_service is None and not opts.urn:
raise TypeError("Missing required property 'backend_service'")
__props__.__dict__["backend_service"] = backend_service
__props__.__dict__["description"] = description
__props__.__dict__["name"] = name
__props__.__dict__["project"] = project
__props__.__dict__["proxy_header"] = proxy_header
if ssl_certificates is None and not opts.urn:
raise TypeError("Missing required property 'ssl_certificates'")
__props__.__dict__["ssl_certificates"] = ssl_certificates
__props__.__dict__["ssl_policy"] = ssl_policy
__props__.__dict__["creation_timestamp"] = None
__props__.__dict__["proxy_id"] = None
__props__.__dict__["self_link"] = None
super(TargetSSLProxy, __self__).__init__(
'gcp:compute/targetSSLProxy:TargetSSLProxy',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
backend_service: Optional[pulumi.Input[str]] = None,
creation_timestamp: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
proxy_header: Optional[pulumi.Input[str]] = None,
proxy_id: Optional[pulumi.Input[int]] = None,
self_link: Optional[pulumi.Input[str]] = None,
ssl_certificates: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
ssl_policy: Optional[pulumi.Input[str]] = None) -> 'TargetSSLProxy':
"""
Get an existing TargetSSLProxy resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] backend_service: A reference to the BackendService resource.
:param pulumi.Input[str] creation_timestamp: Creation timestamp in RFC3339 text format.
:param pulumi.Input[str] description: An optional description of this resource.
:param pulumi.Input[str] name: Name of the resource. Provided by the client when the resource is
created. The name must be 1-63 characters long, and comply with
RFC1035. Specifically, the name must be 1-63 characters long and match
the regular expression `a-z?` which means the
first character must be a lowercase letter, and all following
characters must be a dash, lowercase letter, or digit, except the last
character, which cannot be a dash.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[str] proxy_header: Specifies the type of proxy header to append before sending data to
the backend.
Default value is `NONE`.
Possible values are `NONE` and `PROXY_V1`.
:param pulumi.Input[int] proxy_id: The unique identifier for the resource.
:param pulumi.Input[str] self_link: The URI of the created resource.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ssl_certificates: A list of SslCertificate resources that are used to authenticate
connections between users and the load balancer. At least one
SSL certificate must be specified.
:param pulumi.Input[str] ssl_policy: A reference to the SslPolicy resource that will be associated with
the TargetSslProxy resource. If not set, the TargetSslProxy
resource will not have any SSL policy configured.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _TargetSSLProxyState.__new__(_TargetSSLProxyState)
__props__.__dict__["backend_service"] = backend_service
__props__.__dict__["creation_timestamp"] = creation_timestamp
__props__.__dict__["description"] = description
__props__.__dict__["name"] = name
__props__.__dict__["project"] = project
__props__.__dict__["proxy_header"] = proxy_header
__props__.__dict__["proxy_id"] = proxy_id
__props__.__dict__["self_link"] = self_link
__props__.__dict__["ssl_certificates"] = ssl_certificates
__props__.__dict__["ssl_policy"] = ssl_policy
return TargetSSLProxy(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="backendService")
def backend_service(self) -> pulumi.Output[str]:
"""
A reference to the BackendService resource.
"""
return pulumi.get(self, "backend_service")
@property
@pulumi.getter(name="creationTimestamp")
def creation_timestamp(self) -> pulumi.Output[str]:
"""
Creation timestamp in RFC3339 text format.
"""
return pulumi.get(self, "creation_timestamp")
@property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
"""
An optional description of this resource.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Name of the resource. Provided by the client when the resource is
created. The name must be 1-63 characters long, and comply with
RFC1035. Specifically, the name must be 1-63 characters long and match
the regular expression `a-z?` which means the
first character must be a lowercase letter, and all following
characters must be a dash, lowercase letter, or digit, except the last
character, which cannot be a dash.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def project(self) -> pulumi.Output[str]:
"""
The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@property
@pulumi.getter(name="proxyHeader")
def proxy_header(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the type of proxy header to append before sending data to
the backend.
Default value is `NONE`.
Possible values are `NONE` and `PROXY_V1`.
"""
return pulumi.get(self, "proxy_header")
@property
@pulumi.getter(name="proxyId")
def proxy_id(self) -> pulumi.Output[int]:
"""
The unique identifier for the resource.
"""
return pulumi.get(self, "proxy_id")
@property
@pulumi.getter(name="selfLink")
def self_link(self) -> pulumi.Output[str]:
"""
The URI of the created resource.
"""
return pulumi.get(self, "self_link")
@property
@pulumi.getter(name="sslCertificates")
def ssl_certificates(self) -> pulumi.Output[Sequence[str]]:
"""
A list of SslCertificate resources that are used to authenticate
connections between users and the load balancer. At least one
SSL certificate must be specified.
"""
return pulumi.get(self, "ssl_certificates")
@property
@pulumi.getter(name="sslPolicy")
def ssl_policy(self) -> pulumi.Output[Optional[str]]:
"""
A reference to the SslPolicy resource that will be associated with
the TargetSslProxy resource. If not set, the TargetSslProxy
resource will not have any SSL policy configured.
"""
return pulumi.get(self, "ssl_policy")
| 44.542254
| 139
| 0.644617
| 3,725
| 31,625
| 5.309799
| 0.071678
| 0.070074
| 0.074321
| 0.065625
| 0.898933
| 0.874109
| 0.86334
| 0.852571
| 0.841448
| 0.817584
| 0
| 0.004561
| 0.26517
| 31,625
| 709
| 140
| 44.605078
| 0.846551
| 0.45034
| 0
| 0.71246
| 1
| 0
| 0.095727
| 0.00275
| 0
| 0
| 0
| 0
| 0
| 1
| 0.162939
| false
| 0.003195
| 0.015974
| 0
| 0.277955
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
80842c35c42b1ba24fd39100a11e2209e740b458
| 216
|
py
|
Python
|
salesforce/tests/__init__.py
|
Catchoom/django-salesforce
|
1c2f24a5d27a2d46549835bada7d0525bf7511a9
|
[
"MIT"
] | 1
|
2017-07-05T10:29:36.000Z
|
2017-07-05T10:29:36.000Z
|
salesforce/tests/__init__.py
|
Catchoom/django-salesforce
|
1c2f24a5d27a2d46549835bada7d0525bf7511a9
|
[
"MIT"
] | null | null | null |
salesforce/tests/__init__.py
|
Catchoom/django-salesforce
|
1c2f24a5d27a2d46549835bada7d0525bf7511a9
|
[
"MIT"
] | null | null | null |
from salesforce.tests.test_auth import *
from salesforce.tests.test_integration import *
from salesforce.tests.test_browser import *
from salesforce.tests.test_unit import *
from salesforce.tests.test_utils import *
| 36
| 47
| 0.837963
| 30
| 216
| 5.866667
| 0.333333
| 0.397727
| 0.539773
| 0.653409
| 0.659091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.092593
| 216
| 5
| 48
| 43.2
| 0.897959
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
03b3f7a7224e076180d8e21ea1c876bc56a19d99
| 3,715
|
py
|
Python
|
web/pipeline/migrations/0048_auto_20200804_2138.py
|
stevenstuber/CIT
|
8c485e72084c06da6db45da1cb402bac26411ec2
|
[
"Apache-2.0"
] | 10
|
2020-11-12T15:13:40.000Z
|
2022-03-05T22:33:08.000Z
|
web/pipeline/migrations/0048_auto_20200804_2138.py
|
stevenstuber/CIT
|
8c485e72084c06da6db45da1cb402bac26411ec2
|
[
"Apache-2.0"
] | 28
|
2020-07-17T16:33:55.000Z
|
2022-03-21T16:24:25.000Z
|
web/pipeline/migrations/0048_auto_20200804_2138.py
|
stevenstuber/CIT
|
8c485e72084c06da6db45da1cb402bac26411ec2
|
[
"Apache-2.0"
] | 5
|
2020-11-02T23:39:53.000Z
|
2022-03-01T19:09:45.000Z
|
# Generated by Django 2.2.13 on 2020-08-04 21:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pipeline', '0047_auto_20200726_2207'),
]
operations = [
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_0_4',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_100',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_10_14',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_15_19',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_20_24',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_25_29',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_30_34',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_35_39',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_40_44',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_45_49',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_50_54',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_55_59',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_5_9',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_60_64',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_65_69',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_70_74',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_75_79',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_80_84',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_85_89',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_90_94',
field=models.FloatField(null=True),
),
migrations.AddField(
model_name='censussubdivision',
name='pop_pct_95_99',
field=models.FloatField(null=True),
),
]
| 31.218487
| 48
| 0.554509
| 343
| 3,715
| 5.755102
| 0.221574
| 0.191489
| 0.244681
| 0.287234
| 0.882979
| 0.868288
| 0.868288
| 0.868288
| 0.840932
| 0.840932
| 0
| 0.045049
| 0.336743
| 3,715
| 118
| 49
| 31.483051
| 0.756088
| 0.012382
| 0
| 0.75
| 1
| 0
| 0.17862
| 0.006272
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.008929
| 0
| 0.035714
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
03de56027780c953e68c6504cc7b0fe104ab53c9
| 44,985
|
py
|
Python
|
test/kb_blast_server_test.py
|
kbaseapps/kb_blast
|
dd1fb01a9d1448514cd047e4fce1e8fb6b43ff0d
|
[
"MIT"
] | null | null | null |
test/kb_blast_server_test.py
|
kbaseapps/kb_blast
|
dd1fb01a9d1448514cd047e4fce1e8fb6b43ff0d
|
[
"MIT"
] | 25
|
2017-03-21T22:11:49.000Z
|
2021-08-05T18:09:58.000Z
|
test/kb_blast_server_test.py
|
kbaseapps/kb_blast
|
dd1fb01a9d1448514cd047e4fce1e8fb6b43ff0d
|
[
"MIT"
] | 8
|
2017-03-18T22:00:42.000Z
|
2020-03-18T17:25:00.000Z
|
# -*- coding: utf-8 -*-
import json # noqa: F401
import os # noqa: F401
import time
import shutil
from pprint import pprint
import unittest
from configparser import ConfigParser # py3
from os import environ
from installed_clients.WorkspaceClient import Workspace as workspaceService
from installed_clients.GenomeFileUtilClient import GenomeFileUtil
from kb_blast.authclient import KBaseAuth as _KBaseAuth
from kb_blast.kb_blastImpl import kb_blast
from kb_blast.kb_blastServer import MethodContext
class kb_blastTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
token = environ.get('KB_AUTH_TOKEN', None)
config_file = environ.get('KB_DEPLOYMENT_CONFIG', None)
cls.cfg = {}
config = ConfigParser()
config.read(config_file)
for nameval in config.items('kb_blast'):
cls.cfg[nameval[0]] = nameval[1]
# Getting username from Auth profile for token
authServiceUrl = cls.cfg['auth-service-url']
auth_client = _KBaseAuth(authServiceUrl)
user_id = auth_client.get_user(token)
# WARNING: don't call any logging methods on the context object,
# it'll result in a NoneType error
cls.ctx = MethodContext(None)
cls.ctx.update({'token': token,
'user_id': user_id,
'provenance': [
{'service': 'kb_blast',
'method': 'please_never_use_it_in_production',
'method_params': []
}],
'authenticated': 1})
cls.wsURL = cls.cfg['workspace-url']
cls.wsClient = workspaceService(cls.wsURL)
cls.serviceImpl = kb_blast(cls.cfg)
cls.scratch = cls.cfg['scratch']
cls.callback_url = os.environ['SDK_CALLBACK_URL']
@classmethod
def tearDownClass(cls):
if hasattr(cls, 'wsName'):
cls.wsClient.delete_workspace({'workspace': cls.wsName})
print('Test workspace was deleted')
def getWsClient(self):
return self.__class__.wsClient
def getWsName(self):
if hasattr(self.__class__, 'wsName'):
return self.__class__.wsName
suffix = int(time.time() * 1000)
wsName = "test_kb_blast_" + str(suffix)
ret = self.getWsClient().create_workspace({'workspace': wsName}) # noqa
self.__class__.wsName = wsName
return wsName
def getImpl(self):
return self.__class__.serviceImpl
def getContext(self):
return self.__class__.ctx
# get obj_ref in form D/D/D
def get_obj_ref_from_obj_info (self, obj_info):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
return '/'.join([str(obj_info[WSID_I]), str(obj_info[OBJID_I]), str(obj_info[VERSION_I])])
# retrieve stored obj info
def _get_stored_obj_info (self, obj_type, obj_name, item_i=0):
infoAttr = obj_type + 'Info_list' # e.g. 'ama' or 'genome'
nameAttr = obj_type + 'Name_list'
if hasattr(self.__class__, infoAttr):
try:
info_list = getattr(self.__class__, infoAttr)
name_list = getattr(self.__class__, nameAttr)
info = info_list[item_i]
name = name_list[item_i]
if info != None:
if name != obj_name:
info_list[item_i] = None
name_list[item_i] = None
setattr (self.__class__, infoAttr, info_list)
setattr (self.__class__, nameAttr, name_list)
else:
return info
except:
pass
return None
# save stored obj info
def _save_stored_obj_info (self, obj_type, obj_info, obj_name, item_i=0):
infoAttr = obj_type + 'Info_list' # e.g. 'ama' or 'genome'
nameAttr = obj_type + 'Name_list'
if not hasattr(self.__class__, infoAttr):
setattr (self.__class__, infoAttr, [])
setattr (self.__class__, nameAttr, [])
info_list = getattr(self.__class__, infoAttr)
name_list = getattr(self.__class__, nameAttr)
for i in range(item_i+1):
try:
assigned = info_list[i]
except:
info_list.append(None)
name_list.append(None)
info_list[item_i] = obj_info
name_list[item_i] = obj_name
setattr (self.__class__, infoAttr, info_list)
setattr (self.__class__, nameAttr, name_list)
return
# call this method to get the WS object info of a Genome
# (will upload the example data if this is the first time the method is called during tests)
def getGenomeInfo(self, genome_basename, item_i=0):
info = self._get_stored_obj_info ('genome', genome_basename, item_i)
if info != None:
return info
# 1) transform genbank to kbase genome object and upload to ws
shared_dir = "/kb/module/work/tmp"
genome_data_file = 'data/genomes/'+genome_basename+'.gbff.gz'
genome_file = os.path.join(shared_dir, os.path.basename(genome_data_file))
shutil.copy(genome_data_file, genome_file)
SERVICE_VER = 'release'
GFU = GenomeFileUtil(os.environ['SDK_CALLBACK_URL'],
token=self.getContext()['token'],
service_ver=SERVICE_VER
)
print ("UPLOADING genome: "+genome_basename+" to WORKSPACE "+self.getWsName()+" ...")
genome_upload_result = GFU.genbank_to_genome({'file': {'path': genome_file },
'workspace_name': self.getWsName(),
'genome_name': genome_basename
})
pprint(genome_upload_result)
genome_ref = genome_upload_result['genome_ref']
new_obj_info = self.getWsClient().get_object_info_new({'objects': [{'ref': genome_ref}]})[0]
# 2) store it
self._save_stored_obj_info ('genome', new_obj_info, genome_basename, item_i)
return new_obj_info
# call this method to get the WS object info of an AnnotatedMetagenomeAssembly
# (will upload the example data if this is the first time the method is called during tests)
def getAMAInfo(self, ama_basename, item_i=0):
info = self._get_stored_obj_info ('ama', ama_basename, item_i)
if info != None:
return info
# 1) transform GFF+FNA to kbase AMA object and upload to ws
shared_dir = "/kb/module/work/tmp"
ama_gff_srcfile = 'data/amas/'+ama_basename+'.gff'
ama_fna_srcfile = 'data/amas/'+ama_basename+'.fa'
ama_gff_dstfile = os.path.join(shared_dir, os.path.basename(ama_gff_srcfile))
ama_fna_dstfile = os.path.join(shared_dir, os.path.basename(ama_fna_srcfile))
shutil.copy(ama_gff_srcfile, ama_gff_dstfile)
shutil.copy(ama_fna_srcfile, ama_fna_dstfile)
try:
SERVICE_VER = 'release'
GFU = GenomeFileUtil(os.environ['SDK_CALLBACK_URL'],
token=self.getContext()['token'],
service_ver=SERVICE_VER
)
except:
raise ValueError ("unable to obtain GenomeFileUtil client")
print ("UPLOADING AMA: "+ama_basename+" to WORKSPACE "+self.getWsName()+" ...")
ama_upload_params = {
"workspace_name": self.getWsName(),
"genome_name": ama_basename,
"fasta_file": {"path": ama_fna_dstfile},
"gff_file": {"path": ama_gff_dstfile},
"source": "GFF",
"scientific_name": "TEST AMA",
"generate_missing_genes": "True"
}
try:
ama_upload_result = GFU.fasta_gff_to_metagenome(ama_upload_params)
except:
raise ValueError("unable to upload test AMA data object")
print ("AMA UPLOADED")
pprint(ama_upload_result)
ama_ref = ama_upload_result['metagenome_ref']
new_obj_info = self.getWsClient().get_object_info_new({'objects': [{'ref': ama_ref}]})[0]
# 2) store it
self._save_stored_obj_info ('ama', new_obj_info, ama_basename, item_i)
return new_obj_info
#
# NOTE: According to Python unittest naming rules test method names should start from 'test'. # noqa
#
# Test BLASTn: Single Genome target
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTn_Search_01")
def test_kb_blast_BLASTn_Search_01(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
obj_basename = 'BLASTn'
obj_out_name = obj_basename+".test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
expected_hit_cnt = 1
genomeInfo_0 = self.getGenomeInfo('GCF_001566335.1_ASM156633v1_genomic', 0) # E. coli K-12 MG1655
genome_ref_0 = self.get_obj_ref_from_obj_info(genomeInfo_0)
# E. coli K-12 MG1655 dnaA
query_seq_nuc = 'GTGTCACTTTCGCTTTGGCAGCAGTGTCTTGCCCGATTGCAGGATGAGTTACCAGCCACAGAATTCAGTATGTGGATACGCCCATTGCAGGCGGAACTGAGCGATAACACGCTGGCCCTGTACGCGCCAAACCGTTTTGTCCTCGATTGGGTACGGGACAAGTACCTTAATAATATCAATGGACTGCTAACCAGTTTCTGCGGAGCGGATGCCCCACAGCTGCGTTTTGAAGTCGGCACCAAACCGGTGACGCAAACGCCACAAGCGGCAGTGACGAGCAACGTCGCGGCCCCTGCACAGGTGGCGCAAACGCAGCCGCAACGTGCTGCGCCTTCTACGCGCTCAGGTTGGGATAACGTCCCGGCCCCGGCAGAACCGACCTATCGTTCTAACGTAAACGTCAAACACACGTTTGATAACTTCGTTGAAGGTAAATCTAACCAACTGGCGCGCGCGGCGGCTCGCCAGGTGGCGGATAACCCTGGCGGTGCCTATAACCCGTTGTTCCTTTATGGCGGCACGGGTCTGGGTAAAACTCACCTGCTGCATGCGGTGGGTAACGGCATTATGGCGCGCAAGCCGAATGCCAAAGTGGTTTATATGCACTCCGAGCGCTTTGTTCAGGACATGGTTAAAGCCCTGCAAAACAACGCGATCGAAGAGTTTAAACGCTACTACCGTTCCGTAGATGCACTGCTGATCGACGATATTCAGTTTTTTGCTAATAAAGAACGATCTCAGGAAGAGTTTTTCCACACCTTCAACGCCCTGCTGGAAGGTAATCAACAGATCATTCTCACCTCGGATCGCTATCCGAAAGAGATCAACGGCGTTGAGGATCGTTTGAAATCCCGCTTCGGTTGGGGACTGACTGTGGCGATCGAACCGCCAGAGCTGGAAACCCGTGTGGCGATCCTGATGAAAAAGGCCGACGAAAACGACATTCGTTTGCCGGGCGAAGTGGCGTTCTTTATCGCCAAGCGTCTACGATCTAACGTACGTGAGCTGGAAGGGGCGCTGAACCGCGTCATTGCCAATGCCAACTTTACCGGACGGGCGATCACCATCGACTTCGTGCGTGAGGCGCTGCGCGACTTGCTGGCATTGCAGGAAAAACTGGTCACCATCGACAATATTCAGAAGACGGTGGCGGAGTACTACAAGATCAAAGTCGCGGATCTCCTTTCCAAGCGTCGATCCCGCTCGGTGGCGCGTCCGCGCCAGATGGCGATGGCGCTGGCGAAAGAGCTGACTAACCACAGTCTGCCGGAGATTGGCGATGCGTTTGGTGGCCGTGACCACACGACGGTGCTTCATGCCTGCCGTAAGATCGAGCAGTTGCGTGAAGAGAGCCACGATATCAAAGAAGATTTTTCAAATTTAATCAGAACATTGTCATCGTAA'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_nuc,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [genome_ref_0],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'obj_name',
'e_value': ".001",
'bitscore': "50",
'ident_thresh': "97.0",
'overlap_fraction': "50.0",
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTn_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
# Test BLASTn: GenomeSet target
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTn_Search_01")
def test_kb_blast_BLASTn_Search_01(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
obj_basename = 'BLASTn'
obj_out_name = obj_basename+".test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
genomeSet_name = 'test_genomeSet.BLASTn.GenomeSet'
expected_hit_cnt = 3
load_genomes = [
{ 'file': 'GCF_001566335.1_ASM156633v1_genomic',
'sciname': 'E. coli K-12 MG1655'
},
{ 'file': 'GCF_000021385.1_ASM2138v1_genomic',
'sciname': 'D. vulgaris str. Miyazaki F'
},
{ 'file': 'GCF_001721825.1_ASM172182v1_genomic',
'sciname': 'Pseudomonas aeruginosa'
},
{ 'file': 'GCF_002950035.1_ASM295003v1_genomic',
'sciname': 'Shigella boydii'
},
{ 'file': 'GCF_000512125.1_ASM51212v1_genomic',
'sciname': 'Escherichia albertii KF1'
}
]
for genome_i,genome in enumerate(load_genomes):
load_genomes[genome_i]['ref'] = self.get_obj_ref_from_obj_info(self.getGenomeInfo(genome['file'], genome_i))
# create GenomeSet
testGS = {
'description': 'five genomes',
'elements': dict()
}
for genome_i,genome in enumerate(load_genomes):
testGS['elements'][genome['sciname']] = { 'ref': genome['ref'] }
obj_info = self.getWsClient().save_objects({'workspace': self.getWsName(),
'objects': [
{
'type':'KBaseSearch.GenomeSet',
'data':testGS,
'name':genomeSet_name,
'meta':{},
'provenance':[
{
'service':'kb_blast',
'method':'BLASTn_Search'
}
]
}]
})[0]
#pprint(obj_info)
target_genomeSet_ref = self.get_obj_ref_from_obj_info(obj_info)
# E. coli K-12 MG1655 dnaA
query_seq_nuc = 'GTGTCACTTTCGCTTTGGCAGCAGTGTCTTGCCCGATTGCAGGATGAGTTACCAGCCACAGAATTCAGTATGTGGATACGCCCATTGCAGGCGGAACTGAGCGATAACACGCTGGCCCTGTACGCGCCAAACCGTTTTGTCCTCGATTGGGTACGGGACAAGTACCTTAATAATATCAATGGACTGCTAACCAGTTTCTGCGGAGCGGATGCCCCACAGCTGCGTTTTGAAGTCGGCACCAAACCGGTGACGCAAACGCCACAAGCGGCAGTGACGAGCAACGTCGCGGCCCCTGCACAGGTGGCGCAAACGCAGCCGCAACGTGCTGCGCCTTCTACGCGCTCAGGTTGGGATAACGTCCCGGCCCCGGCAGAACCGACCTATCGTTCTAACGTAAACGTCAAACACACGTTTGATAACTTCGTTGAAGGTAAATCTAACCAACTGGCGCGCGCGGCGGCTCGCCAGGTGGCGGATAACCCTGGCGGTGCCTATAACCCGTTGTTCCTTTATGGCGGCACGGGTCTGGGTAAAACTCACCTGCTGCATGCGGTGGGTAACGGCATTATGGCGCGCAAGCCGAATGCCAAAGTGGTTTATATGCACTCCGAGCGCTTTGTTCAGGACATGGTTAAAGCCCTGCAAAACAACGCGATCGAAGAGTTTAAACGCTACTACCGTTCCGTAGATGCACTGCTGATCGACGATATTCAGTTTTTTGCTAATAAAGAACGATCTCAGGAAGAGTTTTTCCACACCTTCAACGCCCTGCTGGAAGGTAATCAACAGATCATTCTCACCTCGGATCGCTATCCGAAAGAGATCAACGGCGTTGAGGATCGTTTGAAATCCCGCTTCGGTTGGGGACTGACTGTGGCGATCGAACCGCCAGAGCTGGAAACCCGTGTGGCGATCCTGATGAAAAAGGCCGACGAAAACGACATTCGTTTGCCGGGCGAAGTGGCGTTCTTTATCGCCAAGCGTCTACGATCTAACGTACGTGAGCTGGAAGGGGCGCTGAACCGCGTCATTGCCAATGCCAACTTTACCGGACGGGCGATCACCATCGACTTCGTGCGTGAGGCGCTGCGCGACTTGCTGGCATTGCAGGAAAAACTGGTCACCATCGACAATATTCAGAAGACGGTGGCGGAGTACTACAAGATCAAAGTCGCGGATCTCCTTTCCAAGCGTCGATCCCGCTCGGTGGCGCGTCCGCGCCAGATGGCGATGGCGCTGGCGAAAGAGCTGACTAACCACAGTCTGCCGGAGATTGGCGATGCGTTTGGTGGCCGTGACCACACGACGGTGCTTCATGCCTGCCGTAAGATCGAGCAGTTGCGTGAAGAGAGCCACGATATCAAAGAAGATTTTTCAAATTTAATCAGAACATTGTCATCGTAA'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_nuc,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [target_genomeSet_ref],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'obj_name',
#'e_value': ".001",
#'bitscore': "50",
#'ident_thresh': "10.0",
#'overlap_fraction': "50.0",
'e_value': ".1",
'bitscore': "10",
'ident_thresh': "10.0",
'overlap_fraction': "10.0",
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTn_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
# Test BLASTp: Single Genome target
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTp_Search_01_Genome")
def test_kb_blast_BLASTp_Search_01_Genome(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
obj_basename = 'BLASTp_Genome'
obj_out_name = obj_basename+".test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
expected_hit_cnt = 1
genomeInfo_0 = self.getGenomeInfo('GCF_001566335.1_ASM156633v1_genomic', 0) # E. coli K-12 MG1655
genome_ref_0 = self.get_obj_ref_from_obj_info(genomeInfo_0)
# E. coli K-12 MG1655 dnaA
query_seq_prot = 'MSLSLWQQCLARLQDELPATEFSMWIRPLQAELSDNTLALYAPNRFVLDWVRDKYLNNINGLLTSFCGADAPQLRFEVGTKPVTQTPQAAVTSNVAAPAQVAQTQPQRAAPSTRSGWDNVPAPAEPTYRSNVNVKHTFDNFVEGKSNQLARAAARQVADNPGGAYNPLFLYGGTGLGKTHLLHAVGNGIMARKPNAKVVYMHSERFVQDMVKALQNNAIEEFKRYYRSVDALLIDDIQFFANKERSQEEFFHTFNALLEGNQQIILTSDRYPKEINGVEDRLKSRFGWGLTVAIEPPELETRVAILMKKADENDIRLPGEVAFFIAKRLRSNVRELEGALNRVIANANFTGRAITIDFVREALRDLLALQEKLVTIDNIQKTVAEYYKIKVADLLSKRRSRSVARPRQMAMALAKELTNHSLPEIGDAFGGRDHTTVLHACRKIEQLREESHDIKEDFSNLIRTLSS'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_prot,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [genome_ref_0],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'sci_name',
'e_value': ".001",
'bitscore': "50",
'ident_thresh': "40.0",
'overlap_fraction': "50.0",
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTp_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
# Test BLASTp: GenomeSet target
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTp_Search_02_GenomeSet")
def test_kb_blast_BLASTp_Search_02_GenomeSet(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = range(11) # object_info tuple
obj_basename = 'BLASTp_GenomeSet'
obj_out_name = obj_basename+".test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
genomeSet_name = 'test_genomeSet.BLASTp.GenomeSet'
expected_hit_cnt = 2
load_genomes = [
{ 'file': 'GCF_001566335.1_ASM156633v1_genomic',
'sciname': 'E. coli K-12 MG1655'
},
{ 'file': 'GCF_000021385.1_ASM2138v1_genomic',
'sciname': 'D. vulgaris str. Miyazaki F'
},
{ 'file': 'GCF_001721825.1_ASM172182v1_genomic',
'sciname': 'Pseudomonas aeruginosa'
},
]
for genome_i,genome in enumerate(load_genomes):
load_genomes[genome_i]['ref'] = self.get_obj_ref_from_obj_info(self.getGenomeInfo(genome['file'], genome_i))
# create GenomeSet
testGS = {
'description': 'three genomes',
'elements': dict()
}
for genome_i,genome in enumerate(load_genomes):
testGS['elements'][genome['sciname']] = { 'ref': genome['ref'] }
obj_info = self.getWsClient().save_objects({'workspace': self.getWsName(),
'objects': [
{
'type':'KBaseSearch.GenomeSet',
'data':testGS,
'name':genomeSet_name,
'meta':{},
'provenance':[
{
'service':'kb_blast',
'method':'BLASTp_Search'
}
]
}]
})[0]
#pprint(obj_info)
target_genomeSet_ref = self.get_obj_ref_from_obj_info(obj_info)
# E. coli K-12 MG1655 dnaA
query_seq_prot = 'MSLSLWQQCLARLQDELPATEFSMWIRPLQAELSDNTLALYAPNRFVLDWVRDKYLNNINGLLTSFCGADAPQLRFEVGTKPVTQTPQAAVTSNVAAPAQVAQTQPQRAAPSTRSGWDNVPAPAEPTYRSNVNVKHTFDNFVEGKSNQLARAAARQVADNPGGAYNPLFLYGGTGLGKTHLLHAVGNGIMARKPNAKVVYMHSERFVQDMVKALQNNAIEEFKRYYRSVDALLIDDIQFFANKERSQEEFFHTFNALLEGNQQIILTSDRYPKEINGVEDRLKSRFGWGLTVAIEPPELETRVAILMKKADENDIRLPGEVAFFIAKRLRSNVRELEGALNRVIANANFTGRAITIDFVREALRDLLALQEKLVTIDNIQKTVAEYYKIKVADLLSKRRSRSVARPRQMAMALAKELTNHSLPEIGDAFGGRDHTTVLHACRKIEQLREESHDIKEDFSNLIRTLSS'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_prot,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [target_genomeSet_ref],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'obj_name_sci_name',
'e_value': ".001",
'bitscore': "50",
'ident_thresh': "40.0",
'overlap_fraction': "50.0",
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTp_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
# Test BLASTp: FeatureSet
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTp_Search_03_FeatureSet")
def test_kb_blast_BLASTp_Search_03_FeatureSet(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = range(11) # object_info tuple
obj_basename = 'BLASTp_FeatureSet'
obj_out_name = obj_basename+".test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
target_1 = obj_basename+'.test_FeatureSet'
expected_hit_cnt = 2
load_genomes = [
{ 'file': 'GCF_001566335.1_ASM156633v1_genomic',
'sciname': 'E. coli K-12 MG1655'
},
{ 'file': 'GCF_000021385.1_ASM2138v1_genomic',
'sciname': 'D. vulgaris str. Miyazaki F'
},
{ 'file': 'GCF_001721825.1_ASM172182v1_genomic',
'sciname': 'Pseudomonas aeruginosa'
},
]
for genome_i,genome in enumerate(load_genomes):
load_genomes[genome_i]['ref'] = self.get_obj_ref_from_obj_info(self.getGenomeInfo(genome['file'], genome_i))
# build FeatureSet obj
feature_ids = [ [ 'AWN69_RS07145', # dnaA
'AWN69_RS00105'
],
[ 'DVMF_RS00005', # dnaA
'DVMF_RS00075',
],
[ 'A6701_RS00005', # dnaA
'A6701_RS00105'
]
]
testFS = {
'description': 'a few features',
'elements': { feature_ids[0][0]: [load_genomes[0]['ref']],
feature_ids[0][1]: [load_genomes[0]['ref']],
feature_ids[1][0]: [load_genomes[1]['ref']],
feature_ids[1][1]: [load_genomes[1]['ref']],
feature_ids[2][0]: [load_genomes[2]['ref']],
feature_ids[2][1]: [load_genomes[2]['ref']]
}
}
obj_info = self.getWsClient().save_objects({'workspace': self.getWsName(),
'objects': [
{
'type':'KBaseCollections.FeatureSet',
'data':testFS,
'name':obj_basename+'.test_FeatureSet',
'meta':{},
'provenance':[
{
'service':'kb_blast',
'method':'BLASTp_Search'
}
]
}]
})[0]
#pprint(obj_info)
target_featureSet_ref = self.get_obj_ref_from_obj_info(obj_info)
# E. coli K-12 MG1655 dnaA
query_seq_prot = 'MSLSLWQQCLARLQDELPATEFSMWIRPLQAELSDNTLALYAPNRFVLDWVRDKYLNNINGLLTSFCGADAPQLRFEVGTKPVTQTPQAAVTSNVAAPAQVAQTQPQRAAPSTRSGWDNVPAPAEPTYRSNVNVKHTFDNFVEGKSNQLARAAARQVADNPGGAYNPLFLYGGTGLGKTHLLHAVGNGIMARKPNAKVVYMHSERFVQDMVKALQNNAIEEFKRYYRSVDALLIDDIQFFANKERSQEEFFHTFNALLEGNQQIILTSDRYPKEINGVEDRLKSRFGWGLTVAIEPPELETRVAILMKKADENDIRLPGEVAFFIAKRLRSNVRELEGALNRVIANANFTGRAITIDFVREALRDLLALQEKLVTIDNIQKTVAEYYKIKVADLLSKRRSRSVARPRQMAMALAKELTNHSLPEIGDAFGGRDHTTVLHACRKIEQLREESHDIKEDFSNLIRTLSS'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_prot,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [target_featureSet_ref],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'obj_name_ver_sci_name',
'e_value': ".001",
'bitscore': "50",
'ident_thresh': "40.0",
'overlap_fraction': "50.0",
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTp_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
# Test BLASTp: AnnotatedMetagenomeAssembly Target
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTp_Search_04_AnnotatedMetagenomeAssembly")
def test_kb_blast_BLASTp_Search_04_AnnotatedMetagenomeAssembly(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
obj_basename = 'BLASTp_AnnotatedMetagenomeAssembly'
obj_out_name = obj_basename+".test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
expected_hit_cnt = 1
# upload test AMA
amaInfo_0 = self.getAMAInfo("test_ama", 0)
ama_ref_1 = self.get_obj_ref_from_obj_info(amaInfo_0)
# gene 5_267 from test_ama.AMA
query_seq_prot = 'MDRDALTKLVTDLVSIPSVNPLEGPVGNGRGEAELAAFIHSRLTEAGVVCELKEALPGRPNIIARLPGQSEEMIWFDAHMDTVSGEGMAFPPFEPLIEGDRLLGRGSSDNKGSIATMMAALMEVAKSGERPPLTVVFTATADEEYMMRGMLSLFEAGLTAKAGIVAEPTALEIVIAHKGVARFKISTTGKAAHSSRPEEGVNAIYRMGKVLGAIEAYAKRGVGRETHPLLGKGTLSVGIIRGGEYVNVVPDQCEVDVDRRLLPGEDPRRAVSDVRDYLSNALQEEVGLKVSGPTLTVPGLAVSAESPLVQAVAAAVREVTGKAPLTGMQGATHAGQMAAVDIPALVFGPGQMGQAHTATEELDLTQLERAAAVYERLMRTGL'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_prot,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [ama_ref_1],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'obj_name_ver_sci_name',
'e_value': ".001",
'bitscore': "50",
'ident_thresh': "40.0",
'overlap_fraction': "50.0",
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTp_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
# Test BLASTp: Multiple targets of different types
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTp_Search_05_MultipleTargets")
def test_kb_blast_BLASTp_Search_05_MultipleTargets(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
obj_basename = 'BLASTp_MultipleTargets'
obj_out_name = obj_basename+".test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
genomeSet_name = 'test_genomeSet_multiple.BLASTp.GenomeSet'
expected_hit_cnt = 10
load_genomes = [
{ 'file': 'GCF_001566335.1_ASM156633v1_genomic',
'sciname': 'E. coli K-12 MG1655'
},
{ 'file': 'GCF_000021385.1_ASM2138v1_genomic',
'sciname': 'D. vulgaris str. Miyazaki F'
},
{ 'file': 'GCF_001721825.1_ASM172182v1_genomic',
'sciname': 'Pseudomonas aeruginosa'
},
]
for genome_i,genome in enumerate(load_genomes):
load_genomes[genome_i]['ref'] = self.get_obj_ref_from_obj_info(self.getGenomeInfo(genome['file'], genome_i))
# create GenomeSet
testGS = {
'description': 'three genomes',
'elements': dict()
}
for genome_i,genome in enumerate(load_genomes):
testGS['elements'][genome['sciname']] = { 'ref': genome['ref'] }
obj_info = self.getWsClient().save_objects({'workspace': self.getWsName(),
'objects': [
{
'type':'KBaseSearch.GenomeSet',
'data':testGS,
'name':genomeSet_name,
'meta':{},
'provenance':[
{
'service':'kb_blast',
'method':'BLASTp_Search'
}
]
}]
})[0]
#pprint(obj_info)
target_genomeSet_ref = self.get_obj_ref_from_obj_info(obj_info)
# upload test AMA
amaInfo_0 = self.getAMAInfo("test_ama", 0)
ama_ref_1 = self.get_obj_ref_from_obj_info(amaInfo_0)
ama_name = amaInfo_0[NAME_I]
# gene 5_267 from ama_test.AMA
query_seq_prot = 'MDRDALTKLVTDLVSIPSVNPLEGPVGNGRGEAELAAFIHSRLTEAGVVCELKEALPGRPNIIARLPGQSEEMIWFDAHMDTVSGEGMAFPPFEPLIEGDRLLGRGSSDNKGSIATMMAALMEVAKSGERPPLTVVFTATADEEYMMRGMLSLFEAGLTAKAGIVAEPTALEIVIAHKGVARFKISTTGKAAHSSRPEEGVNAIYRMGKVLGAIEAYAKRGVGRETHPLLGKGTLSVGIIRGGEYVNVVPDQCEVDVDRRLLPGEDPRRAVSDVRDYLSNALQEEVGLKVSGPTLTVPGLAVSAESPLVQAVAAAVREVTGKAPLTGMQGATHAGQMAAVDIPALVFGPGQMGQAHTATEELDLTQLERAAAVYERLMRTGL'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_prot,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [ama_ref_1, target_genomeSet_ref],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'obj_name_ver_sci_name',
'e_value': ".001",
'bitscore': "50",
'ident_thresh': "10.0",
'overlap_fraction': "25.0",
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTp_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
created_obj_1_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][1]['ref']}]})[0]
self.assertEqual(created_obj_1_info[NAME_I], obj_out_name+'-'+genomeSet_name)
self.assertEqual(created_obj_1_info[TYPE_I].split('-')[0], obj_out_type)
created_obj_2_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][2]['ref']}]})[0]
self.assertEqual(created_obj_2_info[NAME_I], obj_out_name+'-'+ama_name)
self.assertEqual(created_obj_2_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
# Test BLASTx: Single Genome target
#
# Uncomment to skip this test
# HIDE @unittest.skip("skipped test_kb_blast_BLASTx_Search_01")
def test_kb_blast_BLASTx_Search_01(self):
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
obj_basename = 'BLASTx'
obj_out_name = obj_basename+'.'+"test_output.FS"
obj_out_type = "KBaseCollections.FeatureSet"
expected_hit_cnt = 1
genomeInfo_0 = self.getGenomeInfo('GCF_001566335.1_ASM156633v1_genomic', 0) # E. coli K-12 MG1655
genome_ref_0 = self.get_obj_ref_from_obj_info(genomeInfo_0)
# E. coli K-12 MG1655 dnaA
query_seq_nuc = 'GTGTCACTTTCGCTTTGGCAGCAGTGTCTTGCCCGATTGCAGGATGAGTTACCAGCCACAGAATTCAGTATGTGGATACGCCCATTGCAGGCGGAACTGAGCGATAACACGCTGGCCCTGTACGCGCCAAACCGTTTTGTCCTCGATTGGGTACGGGACAAGTACCTTAATAATATCAATGGACTGCTAACCAGTTTCTGCGGAGCGGATGCCCCACAGCTGCGTTTTGAAGTCGGCACCAAACCGGTGACGCAAACGCCACAAGCGGCAGTGACGAGCAACGTCGCGGCCCCTGCACAGGTGGCGCAAACGCAGCCGCAACGTGCTGCGCCTTCTACGCGCTCAGGTTGGGATAACGTCCCGGCCCCGGCAGAACCGACCTATCGTTCTAACGTAAACGTCAAACACACGTTTGATAACTTCGTTGAAGGTAAATCTAACCAACTGGCGCGCGCGGCGGCTCGCCAGGTGGCGGATAACCCTGGCGGTGCCTATAACCCGTTGTTCCTTTATGGCGGCACGGGTCTGGGTAAAACTCACCTGCTGCATGCGGTGGGTAACGGCATTATGGCGCGCAAGCCGAATGCCAAAGTGGTTTATATGCACTCCGAGCGCTTTGTTCAGGACATGGTTAAAGCCCTGCAAAACAACGCGATCGAAGAGTTTAAACGCTACTACCGTTCCGTAGATGCACTGCTGATCGACGATATTCAGTTTTTTGCTAATAAAGAACGATCTCAGGAAGAGTTTTTCCACACCTTCAACGCCCTGCTGGAAGGTAATCAACAGATCATTCTCACCTCGGATCGCTATCCGAAAGAGATCAACGGCGTTGAGGATCGTTTGAAATCCCGCTTCGGTTGGGGACTGACTGTGGCGATCGAACCGCCAGAGCTGGAAACCCGTGTGGCGATCCTGATGAAAAAGGCCGACGAAAACGACATTCGTTTGCCGGGCGAAGTGGCGTTCTTTATCGCCAAGCGTCTACGATCTAACGTACGTGAGCTGGAAGGGGCGCTGAACCGCGTCATTGCCAATGCCAACTTTACCGGACGGGCGATCACCATCGACTTCGTGCGTGAGGCGCTGCGCGACTTGCTGGCATTGCAGGAAAAACTGGTCACCATCGACAATATTCAGAAGACGGTGGCGGAGTACTACAAGATCAAAGTCGCGGATCTCCTTTCCAAGCGTCGATCCCGCTCGGTGGCGCGTCCGCGCCAGATGGCGATGGCGCTGGCGAAAGAGCTGACTAACCACAGTCTGCCGGAGATTGGCGATGCGTTTGGTGGCCGTGACCACACGACGGTGCTTCATGCCTGCCGTAAGATCGAGCAGTTGCGTGAAGAGAGCCACGATATCAAAGAAGATTTTTCAAATTTAATCAGAACATTGTCATCGTAA'
parameters = { 'workspace_name': self.getWsName(),
'input_one_sequence': query_seq_nuc,
#'input_one_ref': "",
'output_one_name': obj_basename+'.'+"test_query.SS",
'input_many_refs': [genome_ref_0],
'output_filtered_name': obj_out_name,
'genome_disp_name_config': 'obj_name_ver',
'e_value': ".001",
'bitscore': "50",
'ident_thresh': "40.0",
'overlap_fraction': "25.0", # 50.0 is too high for this query
'maxaccepts': "1000",
'output_extra_format': "none"
}
ret = self.getImpl().BLASTx_Search(self.getContext(), parameters)[0]
self.assertIsNotNone(ret['report_ref'])
# check created obj
#report_obj = self.getWsClient().get_objects2({'objects':[{'ref':ret['report_ref']}]})[0]['data']
report_obj = self.getWsClient().get_objects([{'ref':ret['report_ref']}])[0]['data']
self.assertIsNotNone(report_obj['objects_created'][0]['ref'])
created_obj_0_info = self.getWsClient().get_object_info_new({'objects':[{'ref':report_obj['objects_created'][0]['ref']}]})[0]
[OBJID_I, NAME_I, TYPE_I, SAVE_DATE_I, VERSION_I, SAVED_BY_I, WSID_I, WORKSPACE_I, CHSUM_I, SIZE_I, META_I] = list(range(11)) # object_info tuple
self.assertEqual(created_obj_0_info[NAME_I], obj_out_name)
self.assertEqual(created_obj_0_info[TYPE_I].split('-')[0], obj_out_type)
# check number of hits in featureSet output
featureSet_out_obj = self.getWsClient().get_objects([{'ref':report_obj['objects_created'][0]['ref']}])[0]['data']
self.assertEqual(expected_hit_cnt, len(featureSet_out_obj['element_ordering']))
pass
| 55.743494
| 1,430
| 0.605357
| 4,132
| 44,985
| 6.232575
| 0.086157
| 0.012503
| 0.025162
| 0.023221
| 0.859473
| 0.845766
| 0.82802
| 0.81241
| 0.811129
| 0.809226
| 0
| 0.024989
| 0.294565
| 44,985
| 806
| 1,431
| 55.812655
| 0.786538
| 0.091164
| 0
| 0.606838
| 0
| 0
| 0.302859
| 0.189645
| 0
| 1
| 0
| 0
| 0.075214
| 1
| 0.032479
| false
| 0.015385
| 0.022222
| 0.005128
| 0.078632
| 0.011966
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
03fb14a6200bdfc732a8d9a7967ad531cc3c55ea
| 41,154
|
py
|
Python
|
src/genie/libs/parser/iosxe/show_l2route.py
|
eneiford-forks/genieparser
|
0914dd5dd3a977913688d5083a3e88e22c1fdd05
|
[
"Apache-2.0"
] | 1
|
2021-10-01T05:41:06.000Z
|
2021-10-01T05:41:06.000Z
|
src/genie/libs/parser/iosxe/show_l2route.py
|
eneiford-forks/genieparser
|
0914dd5dd3a977913688d5083a3e88e22c1fdd05
|
[
"Apache-2.0"
] | null | null | null |
src/genie/libs/parser/iosxe/show_l2route.py
|
eneiford-forks/genieparser
|
0914dd5dd3a977913688d5083a3e88e22c1fdd05
|
[
"Apache-2.0"
] | null | null | null |
''' show_l2route.py
IOS parsers for the following show commands:
* show l2route evpn mac ip detail
* show l2route evpn mac ip host-ip <ip> detail
* show l2route evpn mac ip host-ip <ip> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> next-hop <next_hop> detail
* show l2route evpn mac ip host-ip <ip> next-hop <next_hop> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> next-hop <next_hop> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> producer <producer> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> producer <producer> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> producer <producer> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> topology <evi> producer <producer> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> producer <producer> mac-address <mac_addr> detail
* show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> producer <producer> mac-address <mac_addr> esi <esi> detail
* show l2route evpn imet detail
* show l2route evpn imet origin-rtr <origin-ip> detail
* show l2route evpn imet producer <prod> detail
* show l2route evpn imet producer <prod> origin-rtr <origin-ip> detail
* show l2route evpn imet topology <evi>:<etag> detail
* show l2route evpn imet topology <evi>:<etag> producer <prod> detail
* show l2route evpn imet topology <evi>:<etag> origin-rtr <origin-ip> detail
* show l2route evpn imet topology <evi>:<etag> producer <prod> origin-rtr <origin-ip> detail
* show l2route evpn imet topology <evi> detail
* show l2route evpn imet topology <evi> producer <prod> detail
* show l2route evpn imet topology <evi> origin-rtr <origin-ip> detail
* show l2route evpn imet topology <evi> producer <prod> origin-rtr <origin-ip> detail
* show l2route evpn mac ip
* show l2route evpn mac ip esi <esi>
* show l2route evpn mac ip mac-address <macaddr>
* show l2route evpn mac ip mac-address <macaddr> esi <esi>
* show l2route evpn mac ip next-hop <next-hop>
* show l2route evpn mac ip next-hop <next-hop> mac-address <macaddr>
* show l2route evpn mac ip next-hop <next-hop> mac-address <macaddr> esi <esi>
* show l2route evpn mac ip producer <prod>
* show l2route evpn mac ip producer <prod> next-hop <next-hop>
* show l2route evpn mac ip producer <prod> next-hop <next-hop> mac-address <macaddr>
* show l2route evpn mac ip producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
* show l2route evpn mac ip topology <evi:etag>
* show l2route evpn mac ip topology <evi:etag> producer <prod>
* show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop>
* show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop> mac-address <macaddr>
* show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
* show l2route evpn mac ip topology <evi>
* show l2route evpn mac ip topology <evi> producer <prod>
* show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop>
* show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop> mac-address <macaddr>
* show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
Copyright (c) 2021 by Cisco Systems, Inc.
All rights reserved.
'''
import re
# genie
from genie.metaparser import MetaParser
from genie.metaparser.util.schemaengine import Any, ListOf
# =============================================
# Schema for 'show l2route evpn mac ip detail'
# =============================================
class ShowL2routeEvpnMacIpDetailSchema(MetaParser):
""" Schema for show l2route evpn mac ip detail
show l2route evpn mac ip host-ip <ip> detail
show l2route evpn mac ip host-ip <ip> esi <esi> detail
show l2route evpn mac ip host-ip <ip> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> esi <esi> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> producer <producer> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> producer <producer> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> producer <producer> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi> producer <producer> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> producer <producer> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> producer <producer> mac-address <mac_addr> esi <esi> detail
"""
schema = {
'evi': {
Any(): {
'producer': {
Any(): {
'host_ips': {
Any(): {
'eth_tag': int,
'mac_addr': str,
'seq_number': int,
'label_2': int,
'esi': str,
'mac_rt_flags': str,
'next_hops': ListOf(
{
'next_hop': str
}
)
}
}
}
}
}
}
}
# =============================================
# Parser for 'show l2route evpn mac ip detail''
# =============================================
class ShowL2routeEvpnMacIpDetail(ShowL2routeEvpnMacIpDetailSchema):
""" Parser for show l2route evpn mac ip detail
show l2route evpn mac ip host-ip <ip> detail
show l2route evpn mac ip host-ip <ip> esi <esi> detail
show l2route evpn mac ip host-ip <ip> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> esi <esi> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> producer <producer> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> producer <producer> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi> producer <producer> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi> producer <producer> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> next-hop <next_hop> mac-address <mac_addr> esi <esi> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> producer <producer> mac-address <mac_addr> detail
show l2route evpn mac ip host-ip <ip> topology <evi>:<etag> producer <producer> mac-address <mac_addr> esi <esi> detail
"""
cli_command = [
'show l2route evpn mac ip detail',
'show l2route evpn mac ip host-ip {ip} detail',
'show l2route evpn mac ip host-ip {ip} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} next-hop {next_hop} detail',
'show l2route evpn mac ip host-ip {ip} next-hop {next_hop} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} next-hop {next_hop} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} next-hop {next_hop} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} producer {producer} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} producer {producer} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} next-hop {next_hop} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} next-hop {next_hop} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} next-hop {next_hop} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} next-hop {next_hop} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} producer {producer} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi} producer {producer} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} next-hop {next_hop} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} next-hop {next_hop} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} next-hop {next_hop} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} next-hop {next_hop} mac-address {mac_addr} esi {esi} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} producer {producer} mac-address {mac_addr} detail',
'show l2route evpn mac ip host-ip {ip} topology {evi_etag} producer {producer} mac-address {mac_addr} esi {esi} detail',
]
def cli(self, output=None, ip=None, esi=None, mac_addr=None, next_hop=None, producer=None, evi=None, etag=None):
if not output:
cli_cmd = 'show l2route evpn mac ip'
if ip:
cli_cmd += ' host-ip {ip}'.format(ip=ip)
if evi:
if etag:
evi_etag = "{}:{}".format(evi,etag)
cli_cmd += ' topology {evi_etag}'.format(evi_etag=evi_etag)
else:
cli_cmd += ' topology {evi}'.format(evi=evi)
if producer:
cli_cmd += ' producer {producer}'.format(producer=producer)
if next_hop:
cli_cmd += ' next-hop {next_hop}'.format(next_hop=next_hop)
if mac_addr:
cli_cmd += ' mac-address {mac_addr}'.format(mac_addr=mac_addr)
if esi:
cli_cmd += ' esi {esi}'.format(esi=esi)
cli_cmd += ' detail'
cli_output = self.device.execute(cli_cmd)
else:
cli_output = output
# EVPN Instance: 2
p1 = re.compile(r'^EVPN Instance:\s+(?P<evi>\d+)$')
# Ethernet Tag: 0
p2 = re.compile(r'^Ethernet Tag:\s+(?P<eth_tag>\d+)$')
# Producer Name: BGP
p3 = re.compile(r'^Producer Name:\s+(?P<producer>\w+)$')
# MAC Address: 0012.0012.0012
p4 = re.compile(r'^MAC Address:\s+(?P<mac_addr>[0-9a-fA-F\.]+)$')
# Host IP: 192.168.12.254
p5 = re.compile(r'^Host IP:\s+(?P<host_ip>[0-9a-fA-F\.:]+)$')
# Sequence Number: 0
p6 = re.compile(r'^Sequence Number:\s+(?P<seq_number>\d+)$')
# Label 2: 0
p7 = re.compile(r'^Label 2:\s+(?P<label_2>\d+)$')
# ESI: 0000.0000.0000.0000.0000
p8 = re.compile(r'^ESI:\s+(?P<esi>[0-9a-fA-F\.]+)$')
# MAC Route Flags: BInt(Brm)Dgr
p9 = re.compile(r'^MAC Route Flags:\s+(?P<mac_rt_flags>[\w()]+)$')
# Next Hop(s): L:16 2.2.2.1
p10 = re.compile(r'^[Next Hop\(s\):]*\s*(?P<next_hop>[\w\d\s.:()/]+)$')
parser_dict = {}
for line in cli_output.splitlines():
line = line.strip()
if not line:
continue
# EVPN Instance: 2
m = p1.match(line)
if m:
group = m.groupdict()
evi = int(group['evi'])
evi_list = parser_dict.setdefault('evi', {})
evis = evi_list.setdefault(evi, {})
continue
# Ethernet Tag: 0
m = p2.match(line)
if m:
group = m.groupdict()
eth_tag = int(group['eth_tag'])
continue
# Producer Name: BGP
m = p3.match(line)
if m:
group = m.groupdict()
producer_list = evis.setdefault('producer', {})
producers = producer_list.setdefault(group['producer'], {})
continue
# MAC Address: 0012.0012.0012
m = p4.match(line)
if m:
group = m.groupdict()
mac_addr = group['mac_addr']
continue
# Host IP: 192.168.12.254
m = p5.match(line)
if m:
group = m.groupdict()
host_ips = producers.setdefault('host_ips', {})
routes = host_ips.setdefault(group['host_ip'], {})
next_hops = routes.setdefault('next_hops', [])
routes.update({'eth_tag': eth_tag})
routes.update({'mac_addr': mac_addr})
continue
# Sequence Number: 0
m = p6.match(line)
if m:
group = m.groupdict()
routes.update({'seq_number': int(group['seq_number'])})
continue
# Label 2: 0
m = p7.match(line)
if m:
group = m.groupdict()
routes.update({'label_2': int(group['label_2'])})
continue
# ESI: 0000.0000.0000.0000.0000
m = p8.match(line)
if m:
group = m.groupdict()
routes.update({'esi': group['esi']})
continue
# MAC Route Flags: BInt(Brm)Dgr
m = p9.match(line)
if m:
group = m.groupdict()
routes.update({'mac_rt_flags': group['mac_rt_flags']})
continue
# Next Hop(s): L:16 2.2.2.1
m = p10.match(line)
if m:
group = m.groupdict()
next_hops_dict= {}
next_hops_dict.update({'next_hop': group['next_hop']})
next_hops.append(next_hops_dict)
continue
return parser_dict
# =============================================
# Schema for 'show l2route evpn imet detail'
# =============================================
class ShowL2routeEvpnImetDetailSchema(MetaParser):
""" Schema for show l2route evpn imet detail
show l2route evpn imet origin-rtr <origin-ip> detail
show l2route evpn imet producer <prod> detail
show l2route evpn imet producer <prod> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi>:<etag> detail
show l2route evpn imet topology <evi>:<etag> producer <prod> detail
show l2route evpn imet topology <evi>:<etag> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi>:<etag> producer <prod> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi> detail
show l2route evpn imet topology <evi> producer <prod> detail
show l2route evpn imet topology <evi> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi> producer <prod> origin-rtr <origin-ip> detail
"""
schema = {
'evi': {
Any(): {
'producer': {
Any(): {
'origin_router_ip': {
Any(): {
'eth_tag': int,
'router_eth_tag': int,
'tunnel_id': {
Any(): {
'tunnel_flags': int,
'tunnel_type': str,
'tunnel_labels': int,
}
},
'multi_proxy': str,
'next_hops':ListOf(
{
'next_hop': str
}
)
}
}
}
}
}
}
}
# =============================================
# Parser for 'show l2route evpn imet detail'
# =============================================
class ShowL2routeEvpnImetDetail(ShowL2routeEvpnImetDetailSchema):
""" Parser for show l2route evpn imet detail
show l2route evpn imet origin-rtr <origin-ip> detail
show l2route evpn imet producer <prod> detail
show l2route evpn imet producer <prod> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi>:<etag> detail
show l2route evpn imet topology <evi>:<etag> producer <prod> detail
show l2route evpn imet topology <evi>:<etag> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi>:<etag> producer <prod> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi> detail
show l2route evpn imet topology <evi> producer <prod> detail
show l2route evpn imet topology <evi> origin-rtr <origin-ip> detail
show l2route evpn imet topology <evi> producer <prod> origin-rtr <origin-ip> detail
"""
cli_command = [
'show l2route evpn imet detail',
'show l2route evpn imet origin-rtr {origin_ip} detail',
'show l2route evpn imet producer {prod} detail',
'show l2route evpn imet producer {prod} origin-rtr {origin_ip} detail',
'show l2route evpn imet topology {evi_etag} detail',
'show l2route evpn imet topology {evi_etag} producer {prod} detail',
'show l2route evpn imet topology {evi_etag} origin-rtr {origin_ip} detail',
'show l2route evpn imet topology {evi_etag} producer {prod} origin-rtr {origin_ip} detail',
'show l2route evpn imet topology {evi} detail',
'show l2route evpn imet topology {evi} producer {prod} detail',
'show l2route evpn imet topology {evi} origin-rtr {origin_ip} detail',
'show l2route evpn imet topology {evi} producer {prod} origin-rtr {origin_ip} detail'
]
def cli(self, output=None, origin_ip=None, prod=None, evi=None, etag=None):
if not output:
cli_command = 'show l2route evpn imet'
if evi:
if etag:
evi_etag = "{}:{}".format(evi,etag)
cli_command += ' topology {evi_etag}'.format(evi_etag=evi_etag)
else:
cli_command += ' topology {evi}'.format(evi=evi)
if prod:
cli_command += ' producer {prod}'.format(prod=prod)
if origin_ip:
cli_command += ' origin-rtr {origin_ip}'.format(origin_ip=origin_ip)
cli_command += ' detail'
cli_output = self.device.execute(cli_command)
else:
cli_output = output
# start regex statement complies
#EVPN Instance: 1
p1 = re.compile(r'^EVPN Instance:\s+(?P<evi>\d+)$')
#Ethernet Tag: 0
p2 = re.compile(r'^Ethernet Tag:\s+(?P<eth_tag>\d+)$')
#Producer Name: BGP
p3 = re.compile(r'^Producer Name:\s+(?P<producer>\w+)$')
#Router IP Addr: 3.3.3.2
p4 = re.compile(r'^Router IP Addr:\s+(?P<origin_ip>[0-9a-fA-F\.:]+)$')
#Route Ethernet Tag: 0
p5 = re.compile(r'^Route Ethernet Tag:\s+(?P<r_eth_tag>\d+)$')
#Tunnel Flags: 0
p6 = re.compile(r'^Tunnel Flags:\s+(?P<tun_flag>\d+)$')
#Tunnel Type: Ingress Replication
p7 = re.compile(r'^Tunnel Type:\s+(?P<tun_type>.*)$')
#Tunnel Labels: 20011
p8 = re.compile(r'^Tunnel Labels:\s+(?P<tun_labels>\d+)$')
#Tunnel ID: 3.3.3.2
p9 = re.compile(r'^Tunnel ID:\s+(?P<tun_id>[0-9a-fA-F\.:]+)$')
#Multicast Proxy: IGMP
p10 = re.compile(r'^Multicast Proxy:\s+(?P<multi_proxy>\w+)$')
#Next Hop(s): V:20011 3.3.3.2
#Next Hop(s): N/A
p11 = re.compile(r'^[Next Hop\(s\):]+\s+(?P<next_hop>[\w\d\s.:()/]+)$')
parser_dict = {}
for line in cli_output.splitlines():
line = line.strip()
if not line:
continue
#EVPN Instance
m = p1.match(line)
if m:
group = m.groupdict()
evi = int(group['evi'])
evi_list = parser_dict.setdefault('evi',{})
evis = evi_list.setdefault(evi,{})
continue
#Ethernet Tag
m = p2.match(line)
if m:
group = m.groupdict()
eth_tag = int(group['eth_tag'])
continue
#Producer Name
m = p3.match(line)
if m:
group = m.groupdict()
producer_list = evis.setdefault('producer', {})
producers = producer_list.setdefault(group['producer'], {})
continue
#Router IP Addr
m = p4.match(line)
if m:
group = m.groupdict()
router_ips = producers.setdefault('origin_router_ip', {})
origins_rtr_ip = router_ips.setdefault(group['origin_ip'], {})
next_hops = origins_rtr_ip.setdefault('next_hops', [])
origins_rtr_ip.update({'eth_tag': eth_tag})
continue
#Route Ethernet Tag:
m = p5.match(line)
if m:
group = m.groupdict()
origins_rtr_ip.update({'router_eth_tag': int(group['r_eth_tag'] ) } )
continue
#Tunnel Flags:
m = p6.match(line)
if m:
group = m.groupdict()
tun_flag = int(group['tun_flag'])
continue
#Tunnel Type:
m = p7.match(line)
if m:
group = m.groupdict()
tun_type = str(group['tun_type'])
continue
#Tunnel Labels:
m = p8.match(line)
if m:
group = m.groupdict()
tun_labels = int(group['tun_labels'])
continue
#Tunnel ID:
m = p9.match(line)
if m:
group = m.groupdict()
tun_id = group['tun_id']
tunnel_info = origins_rtr_ip.setdefault('tunnel_id', {})
tunnel_id = tunnel_info.setdefault(tun_id, {})
tunnel_id.update({'tunnel_flags': tun_flag})
tunnel_id.update({'tunnel_type': tun_type})
tunnel_id.update({'tunnel_labels': tun_labels})
continue
#Multicast Proxy:
m = p10.match(line)
if m:
group = m.groupdict()
origins_rtr_ip.update({'multi_proxy': str(group['multi_proxy'])})
continue
#Next Hop(s):
m = p11.match(line)
if m:
group = m.groupdict()
next_hops_dict = {}
next_hops_dict.update({'next_hop': group['next_hop']})
next_hops.append(next_hops_dict)
continue
return (parser_dict)
# =============================================
# Schema for 'show l2route evpn mac ip'
# =============================================
class ShowL2routeEvpnMacIpSchema(MetaParser):
""" Schema for show l2route evpn mac ip
show l2route evpn mac ip esi <esi>
show l2route evpn mac ip mac-address <macaddr>
show l2route evpn mac ip mac-address <macaddr> esi <esi>
show l2route evpn mac ip next-hop <next-hop>
show l2route evpn mac ip next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip next-hop <next-hop> mac-address <macaddr> esi <esi>
show l2route evpn mac ip producer <prod>
show l2route evpn mac ip producer <prod> next-hop <next-hop>
show l2route evpn mac ip producer <prod> next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
show l2route evpn mac ip topology <evi:etag>
show l2route evpn mac ip topology <evi:etag> producer <prod>
show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop>
show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
show l2route evpn mac ip topology <evi>
show l2route evpn mac ip topology <evi> producer <prod>
show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop>
show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
"""
schema = {
'evi': {
Any(): {
'producer': {
Any(): {
'host_ip': {
Any(): {
'eth_tag': int,
'mac_addr': str,
'next_hops': ListOf(
str
)
}
}
}
}
}
}
}
# =============================================
# Parser for 'show l2route evpn mac ip'
# =============================================
class ShowL2routeEvpnMacIp(ShowL2routeEvpnMacIpSchema):
""" Parser for show l2route evpn mac ip
show l2route evpn mac ip esi <esi>
show l2route evpn mac ip mac-address <macaddr>
show l2route evpn mac ip mac-address <macaddr> esi <esi>
show l2route evpn mac ip next-hop <next-hop>
show l2route evpn mac ip next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip next-hop <next-hop> mac-address <macaddr> esi <esi>
show l2route evpn mac ip producer <prod>
show l2route evpn mac ip producer <prod> next-hop <next-hop>
show l2route evpn mac ip producer <prod> next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
show l2route evpn mac ip topology <evi:etag>
show l2route evpn mac ip topology <evi:etag> producer <prod>
show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop>
show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip topology <evi:etag> producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
show l2route evpn mac ip topology <evi>
show l2route evpn mac ip topology <evi> producer <prod>
show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop>
show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop> mac-address <macaddr>
show l2route evpn mac ip topology <evi> producer <prod> next-hop <next-hop> mac-address <macaddr> esi <esi>
"""
cli_command = [
'show l2route evpn mac ip',
'show l2route evpn mac ip esi {esi}',
'show l2route evpn mac ip mac-address {macaddr}',
'show l2route evpn mac ip mac-address {macaddr} esi {esi}',
'show l2route evpn mac ip next-hop {next_hop}',
'show l2route evpn mac ip next-hop {next_hop} mac-address {macaddr}',
'show l2route evpn mac ip next-hop {next_hop} mac-address {macaddr} esi {esi}',
'show l2route evpn mac ip producer {prod}',
'show l2route evpn mac ip producer {prod} next-hop {next_hop}',
'show l2route evpn mac ip producer {prod} next-hop {next_hop} mac-address {macaddr}',
'show l2route evpn mac ip producer {prod} next-hop {next_hop} mac-address {macaddr} esi {esi}',
'show l2route evpn mac ip topology {evi_etag}',
'show l2route evpn mac ip topology {evi_etag} producer {prod}',
'show l2route evpn mac ip topology {evi_etag} producer {prod} next-hop {next_hop}',
'show l2route evpn mac ip topology {evi_etag} producer {prod} next-hop {next_hop} mac-address {macaddr}',
'show l2route evpn mac ip topology {evi_etag} producer {prod} next-hop {next_hop} mac-address {macaddr} esi {esi}',
'show l2route evpn mac ip topology {evi}',
'show l2route evpn mac ip topology {evi} producer {prod}',
'show l2route evpn mac ip topology {evi} producer {prod} next-hop {next_hop}',
'show l2route evpn mac ip topology {evi} producer {prod} next-hop {next_hop} mac-address {macaddr}',
'show l2route evpn mac ip topology {evi} producer {prod} next-hop {next_hop} mac-address {macaddr} esi {esi}'
]
def cli (self, output=None, esi=None, macaddr=None, next_hop=None, prod=None, evi=None, etag=None):
if not output:
cli_command = 'show l2route evpn mac ip'
if esi:
cli_command += ' esi {esi}'.format(esi=esi)
if macaddr:
cli_command += ' mac-address {macaddr}'.format(macaddr=macaddr)
if next_hop:
cli_command += ' next-hop {next_hop}'.format(next_hop=next_hop)
if prod:
cli_command += ' producer {prod}'.format(prod=prod)
if evi:
if etag:
cli_command += ' topology {evi}:{etag}'.format(evi,etag)
else:
cli_command += ' topology {evi}'.format(evi=evi)
cli_output = self.device.execute(cli_command)
else:
cli_output = output
# EVI ETag Prod Mac Address Host IP Next Hop(s)
p1 = re.compile(r'^EVI\s+ETag\s+Prod\s+Mac Address\s+Host IP\s+Next Hop\(s\)$')
# 1 0 BGP 0011.0011.0011 192.168.11.254 V:20011 3.3.3.2
# 1 0 L2VPN aabb.0011.0011 FE80::A8BB:FF:FE11:11 \
p2 = re.compile(r'^(?P<evi>\d+)\s+(?P<eth_tag>\d+)\s+(?P<producer>\w+)\s+(?P<mac_addr>[0-9a-fA-F.]+)'
r'\s+(?P<host_ip>[0-9a-fA-F.:]+)\s+(?P<next_hop>.+)$')
# Et0/1:11
p3 = re.compile(r'^(?P<next_hop>.+)$')
parser_dict = {}
header_found = False
next_hop_in_newline = False
for line in cli_output.splitlines():
line = line.strip()
if not line:
continue
# test for the correct header
m = p1.match(line)
if m:
header_found = True
continue
# 1 0 BGP 0011.0011.0011 192.168.11.254 V:20011 3.3.3.2
# 1 0 L2VPN aabb.0011.0011 FE80::A8BB:FF:FE11:11 \
m = p2.match(line)
if m:
group = m.groupdict()
evis = parser_dict.setdefault('evi', {})
evi_list = evis.setdefault(int(group['evi']), {})
producers = evi_list.setdefault('producer', {})
producers_list = producers.setdefault( group['producer'], {} )
host_ip = producers_list.setdefault( 'host_ip' , {} )
host_ip_list = host_ip.setdefault( group['host_ip'], {} )
if group['next_hop'] != '\\':
host_ip_list.update({
'eth_tag': int(group['eth_tag']),
'mac_addr': group['mac_addr']
})
next_hops = host_ip_list.setdefault('next_hops', [])
next_hops.append(group['next_hop'])
else:
eth_tag = int(group['eth_tag'])
mac_addr = group['mac_addr']
next_hop_in_newline = True
continue
# Et0/1:11
m = p3.match(line)
if m:
group = m.groupdict()
if next_hop_in_newline:
host_ip_list.update({
'eth_tag': eth_tag,
'mac_addr': mac_addr
})
next_hops = host_ip_list.setdefault('next_hops', [])
next_hops.append(group['next_hop'])
next_hop_in_newline = False
continue
if not header_found:
return ({})
return (parser_dict)
| 52.159696
| 139
| 0.537323
| 5,186
| 41,154
| 4.180486
| 0.034323
| 0.134963
| 0.183349
| 0.177675
| 0.861301
| 0.847371
| 0.829889
| 0.82191
| 0.812869
| 0.787316
| 0
| 0.022179
| 0.344851
| 41,154
| 788
| 140
| 52.225888
| 0.781915
| 0.426447
| 0
| 0.504739
| 0
| 0.075829
| 0.326773
| 0.037706
| 0
| 0
| 0
| 0
| 0
| 1
| 0.007109
| false
| 0
| 0.007109
| 0
| 0.052133
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
ff0df382fa8fc76915882ce92745036004b6cefe
| 268
|
py
|
Python
|
moha/system/basis_set/__init__.py
|
ZhaoYilin/moha
|
d701fd921839474380982db1478e66f0dc8cbd98
|
[
"MIT"
] | 12
|
2019-12-07T18:37:34.000Z
|
2022-03-30T14:23:38.000Z
|
moha/system/basis_set/__init__.py
|
ZhaoYilin/moha
|
d701fd921839474380982db1478e66f0dc8cbd98
|
[
"MIT"
] | null | null | null |
moha/system/basis_set/__init__.py
|
ZhaoYilin/moha
|
d701fd921839474380982db1478e66f0dc8cbd98
|
[
"MIT"
] | 2
|
2019-12-08T05:48:47.000Z
|
2021-10-31T21:40:21.000Z
|
from __future__ import division, print_function
from __future__ import absolute_import
from moha.system.basis_set.abc import *
from moha.system.basis_set.base import *
from moha.system.basis_set.hf_basis_set import *
#from moha.system.basis_set.ci_basis_set import *
| 33.5
| 49
| 0.835821
| 43
| 268
| 4.790698
| 0.348837
| 0.23301
| 0.271845
| 0.38835
| 0.543689
| 0.543689
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097015
| 268
| 7
| 50
| 38.285714
| 0.85124
| 0.179104
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0.2
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
2095c16dbddcae70da48f1f992d369f9acb25a82
| 200
|
py
|
Python
|
HMQ/datasets/__init__.py
|
UniSerj/ai-research
|
79f0093c93408cc5dd7d3f56aafd7dc1f901421c
|
[
"Apache-2.0"
] | 46
|
2020-07-15T08:55:31.000Z
|
2022-03-29T08:34:30.000Z
|
HMQ/datasets/__init__.py
|
UniSerj/ai-research
|
79f0093c93408cc5dd7d3f56aafd7dc1f901421c
|
[
"Apache-2.0"
] | 2
|
2021-02-09T06:53:50.000Z
|
2021-09-12T09:28:22.000Z
|
HMQ/datasets/__init__.py
|
UniSerj/ai-research
|
79f0093c93408cc5dd7d3f56aafd7dc1f901421c
|
[
"Apache-2.0"
] | 13
|
2020-07-14T07:43:17.000Z
|
2022-01-17T14:44:47.000Z
|
from datasets.dataset_loader import get_dataset, Dataset
from datasets.data_augmentation import get_augmentation, Augmentation
from datasets.data_preprocessing import get_preprocessing, PreProcessing
| 50
| 72
| 0.895
| 24
| 200
| 7.208333
| 0.375
| 0.208092
| 0.184971
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075
| 200
| 3
| 73
| 66.666667
| 0.935135
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
20c91cf45c4c72fec9d713dc556b61e89a67308c
| 711
|
py
|
Python
|
deeppavlov_agent/state_formatters/output_formatters.py
|
deepmipt/dp-agent
|
74b5b993a451ac54efd9a5086ff507a9520ad0c6
|
[
"Apache-2.0"
] | 54
|
2019-05-16T12:58:54.000Z
|
2022-01-09T06:09:01.000Z
|
deeppavlov_agent/state_formatters/output_formatters.py
|
deepmipt/dp-agent
|
74b5b993a451ac54efd9a5086ff507a9520ad0c6
|
[
"Apache-2.0"
] | 27
|
2019-06-13T12:31:42.000Z
|
2021-09-10T16:37:59.000Z
|
deeppavlov_agent/state_formatters/output_formatters.py
|
deepmipt/dp-agent
|
74b5b993a451ac54efd9a5086ff507a9520ad0c6
|
[
"Apache-2.0"
] | 25
|
2019-06-08T03:30:48.000Z
|
2022-03-17T15:47:27.000Z
|
from typing import Dict
def http_api_output_formatter(payload: Dict):
return {
'dialog_id': payload['dialog_id'],
'utt_id': payload['utterances'][-1]['utt_id'],
'user_id': payload['human']['user_external_id'],
'response': payload['utterances'][-1]['text'],
}
def http_debug_output_formatter(payload: Dict):
return {
'dialog_id': payload['dialog_id'],
'utt_id': payload['utterances'][-1]['utt_id'],
'user_id': payload['human']['user_external_id'],
'response': payload['utterances'][-1]['text'],
'active_skill': payload['utterances'][-1]['active_skill'],
'debug_output': payload['utterances'][-2]['hypotheses']
}
| 32.318182
| 66
| 0.610408
| 81
| 711
| 5.074074
| 0.320988
| 0.131387
| 0.218978
| 0.126521
| 0.705596
| 0.705596
| 0.705596
| 0.705596
| 0.705596
| 0.705596
| 0
| 0.010435
| 0.19128
| 711
| 21
| 67
| 33.857143
| 0.704348
| 0
| 0
| 0.588235
| 0
| 0
| 0.345992
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.117647
| false
| 0
| 0.058824
| 0.117647
| 0.294118
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 7
|
455d34c973e3d796d24b8d0000011d5380fc9c7c
| 937
|
py
|
Python
|
private_storage/permissions.py
|
zang3tsu/django-private-storage
|
f2a968b515e4bfe401e149d264ba802359a9452a
|
[
"Apache-2.0"
] | 322
|
2016-10-06T19:10:41.000Z
|
2022-03-28T19:52:30.000Z
|
private_storage/permissions.py
|
zang3tsu/django-private-storage
|
f2a968b515e4bfe401e149d264ba802359a9452a
|
[
"Apache-2.0"
] | 72
|
2017-01-08T00:14:12.000Z
|
2022-01-31T12:15:44.000Z
|
private_storage/permissions.py
|
zang3tsu/django-private-storage
|
f2a968b515e4bfe401e149d264ba802359a9452a
|
[
"Apache-2.0"
] | 61
|
2017-01-07T19:36:03.000Z
|
2022-02-07T10:49:08.000Z
|
"""
Possible functions for the ``PRIVATE_STORAGE_AUTH_FUNCTION`` setting.
"""
import django
if django.VERSION >= (1, 10):
def allow_authenticated(private_file):
return private_file.request.user.is_authenticated
def allow_staff(private_file):
request = private_file.request
return request.user.is_authenticated and request.user.is_staff
def allow_superuser(private_file):
request = private_file.request
return request.user.is_authenticated and request.user.is_superuser
else:
def allow_authenticated(private_file):
return private_file.request.user.is_authenticated()
def allow_staff(private_file):
request = private_file.request
return request.user.is_authenticated() and request.user.is_staff
def allow_superuser(private_file):
request = private_file.request
return request.user.is_authenticated() and request.user.is_superuser
| 33.464286
| 76
| 0.741729
| 117
| 937
| 5.675214
| 0.230769
| 0.198795
| 0.271084
| 0.23494
| 0.864458
| 0.864458
| 0.864458
| 0.864458
| 0.864458
| 0.864458
| 0
| 0.003906
| 0.180363
| 937
| 27
| 77
| 34.703704
| 0.860677
| 0.073639
| 0
| 0.526316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.315789
| false
| 0
| 0.052632
| 0.105263
| 0.684211
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 10
|
4582ab75a03d0bed5195bd2c8bcf3a15c0a98063
| 16,118
|
py
|
Python
|
downunder/test/python/run_MT_app.py
|
markendr/esys-escript.github.io
|
0023eab09cd71f830ab098cb3a468e6139191e8d
|
[
"Apache-2.0"
] | null | null | null |
downunder/test/python/run_MT_app.py
|
markendr/esys-escript.github.io
|
0023eab09cd71f830ab098cb3a468e6139191e8d
|
[
"Apache-2.0"
] | null | null | null |
downunder/test/python/run_MT_app.py
|
markendr/esys-escript.github.io
|
0023eab09cd71f830ab098cb3a468e6139191e8d
|
[
"Apache-2.0"
] | null | null | null |
__copyright__ = "Copyright (c) 2020 by University of Queensland http://www.uq.edu.au"
__license__ = "Licensed under the Apache License, version 2.0 http://www.apache.org/licenses/LICENSE-2.0"
__credits__ = "Lutz Gross"
# import unittest
import sys
import numpy as np
from esys.escript import *
from esys.escript.pdetools import Locator
from esys.ripley import Rectangle
from esys.downunder.apps import MT2DTEModel, MT2DTMModel
import esys.escriptcore.utestselect as unittest
from esys.escriptcore.testing import *
NO_TRILINOS = not hasFeature("trilinos")
class TestMT2DTE(unittest.TestCase):
DEPTH=3000.
WIDTH=1000.
SIGMA0=1./100.
NEX=100
NEZ=300
STATION_OFFSET=500.
TRUE_PHASE=45.
TRUE_RHO=100.
USEFASTSOLVER=False # True works not yet
def setUp(self):
self.domain=Rectangle(self.NEX,self.NEZ,l0=self.WIDTH,l1=self.DEPTH)
self.loc=Locator(ReducedFunction(self.domain), [ (self.STATION_OFFSET,self.DEPTH-self.DEPTH/self.NEZ/2)] )
def tearDown(self):
del self.domain
del self.loc
def runModel(self, model, PERIODS, TOL):
for frq in PERIODS:
Zxy = model.getImpedance(f=frq)
self.assertIsInstance(Zxy, Data)
self.assertEqual(Zxy.getShape(), ())
self.assertEqual(Zxy.getFunctionSpace(), ReducedFunction(self.domain))
rho=model.getApparentResitivity(frq, Zxy)
self.assertIsInstance(rho, Data)
self.assertEqual(rho.getShape(), ())
self.assertEqual(rho.getFunctionSpace(), ReducedFunction(self.domain))
phi=model.getPhase(frq, Zxy)
self.assertIsInstance(phi, Data)
self.assertEqual(phi.getShape(), ())
self.assertEqual(phi.getFunctionSpace(), ReducedFunction(self.domain))
#print(frq, self.loc(rho)[0], self.loc(phi)[0])
self.assertAlmostEqual(self.loc(rho)[0], self.TRUE_RHO, delta=TOL*self.TRUE_RHO)
self.assertAlmostEqual(self.loc(phi)[0], self.TRUE_PHASE, delta=TOL*self.TRUE_PHASE)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaFloatSigmaBFloat(self):
"""
conductivity set as float, radiation condition
"""
PERIODS=np.logspace(-2, 2, num=5, endpoint=True, base=10.0, dtype=float)
model=MT2DTEModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
model.setConductivity(self.SIGMA0, sigma_boundary=self.SIGMA0)
self.runModel(model, PERIODS, TOL=1.e-3)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaFloatNoSigmaB(self):
"""
conductivity set as float, no value on boundary, radiation condition
"""
PERIODS=np.logspace(-2, 2, num=5, endpoint=True, base=10.0, dtype=float)
model=MT2DTEModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
model.setConductivity(self.SIGMA0)
self.runModel(model, PERIODS, TOL=1.e-3)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaDataNoSigmaB(self):
"""
conductivity set as Scalar, no value on boundary, radiation condition
"""
PERIODS=np.logspace(-2, 2, num=5, endpoint=True, base=10.0, dtype=float)
model=MT2DTEModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
sigma=Scalar(self.SIGMA0, ContinuousFunction(self.domain))
model.setConductivity(sigma)
self.runModel(model, PERIODS, TOL=1.e-3)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaDataNoSigmaBFails(self):
"""
conductivity set as Scalar, no value on boundary but interpolation is not possible
"""
model=MT2DTEModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
sigma=Scalar(self.SIGMA0, Function(self.domain))
self.assertRaises(RuntimeError, model.setConductivity, *(sigma, ))
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaFloatSigmaBFloatFixedButtom(self):
"""
conductivity set as float, fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTEModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
model.setConductivity(self.SIGMA0, sigma_boundary=self.SIGMA0)
self.runModel(model, PERIODS, TOL=1.e-1)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaFloatNoSigmaBFixedButtom(self):
"""
conductivity set as float, no value on boundary, fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTEModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
model.setConductivity(self.SIGMA0)
self.runModel(model, PERIODS, TOL=1.e-1)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaDataNoSigmaBFixedButtom(self):
"""
conductivity set as Scalar, no value on boundary, fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTEModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
sigma=Scalar(self.SIGMA0, ContinuousFunction(self.domain))
model.setConductivity(sigma)
self.runModel(model, PERIODS, TOL=1.e-1)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_SigmaDataNoSigmaBFixedButtom2(self):
"""
conductivity set as Scalar, no value on boundary, interpolation is not possible but fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTEModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
sigma=Scalar(self.SIGMA0, Function(self.domain))
model.setConductivity(sigma)
self.runModel(model, PERIODS, TOL=1.e-1)
class TestMT2DTMNoAirLayer(unittest.TestCase):
DEPTH=3000.
WIDTH=1000.
RHO0=100.
NEX=100
NEZ=300
STATION_OFFSET=500.
TRUE_PHASE=45.
TRUE_RHO=100.
USEFASTSOLVER=False # True works not yet
def setUp(self):
self.domain=Rectangle(self.NEX,self.NEZ,l0=self.WIDTH,l1=self.DEPTH)
self.loc=Locator(ReducedFunction(self.domain), [ (self.STATION_OFFSET,self.DEPTH-self.DEPTH/self.NEZ/2)] )
def tearDown(self):
del self.domain
del self.loc
def runModel(self, model, PERIODS, TOL):
for frq in PERIODS:
Zyx = model.getImpedance(f=frq)
self.assertIsInstance(Zyx, Data)
self.assertEqual(Zyx.getShape(), ())
self.assertEqual(Zyx.getFunctionSpace(), ReducedFunction(self.domain))
rho=model.getApparentResitivity(frq, Zyx)
self.assertIsInstance(rho, Data)
self.assertEqual(rho.getShape(), ())
self.assertEqual(rho.getFunctionSpace(), ReducedFunction(self.domain))
phi=model.getPhase(frq, Zyx)
self.assertIsInstance(phi, Data)
self.assertEqual(phi.getShape(), ())
self.assertEqual(phi.getFunctionSpace(), ReducedFunction(self.domain))
#print(frq, self.loc(rho)[0], self.loc(phi)[0])
self.assertAlmostEqual(self.loc(rho)[0], self.TRUE_RHO, delta=TOL*self.TRUE_RHO)
self.assertAlmostEqual(self.loc(phi)[0], self.TRUE_PHASE, delta=TOL*self.TRUE_PHASE)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoFloatRhoBFloat(self):
"""
resistivity set as float, radiation condition
"""
PERIODS=np.logspace(-2, 2, num=5, endpoint=True, base=10.0, dtype=float)
model=MT2DTMModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
model.setResistivity(self.RHO0, rho_boundary=self.RHO0)
self.runModel(model, PERIODS, TOL=1.e-3)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoFloatNoRhoB(self):
"""
resistivity set as float, no value on boundary, radiation condition
"""
PERIODS=np.logspace(-2, 2, num=5, endpoint=True, base=10.0, dtype=float)
model=MT2DTMModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
model.setResistivity(self.RHO0)
self.runModel(model, PERIODS, TOL=1.e-3)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoDataNoRhoB(self):
"""
resistivity set as Scalar, no value on boundary, radiation condition
"""
PERIODS=np.logspace(-2, 2, num=5, endpoint=True, base=10.0, dtype=float)
model=MT2DTMModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
rho=Scalar(self.RHO0, ContinuousFunction(self.domain))
model.setResistivity(rho)
self.runModel(model, PERIODS, TOL=1.e-3)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoDataNoRhoBFails(self):
"""
resistivity set as Scalar, no value on boundary but interpolation is not possible
"""
model=MT2DTMModel(self.domain, fixBottom=False, useFastSolver=self.USEFASTSOLVER)
rho=Scalar(self.RHO0, Function(self.domain))
self.assertRaises(RuntimeError, model.setResistivity, *(rho, ))
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoFloatRhoBFloatFixedButtom(self):
"""
resistivity set as float, fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTMModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
model.setResistivity(self.RHO0, rho_boundary=self.RHO0)
self.runModel(model, PERIODS, TOL=1.e-1)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoFloatNoRhoBFixedButtom(self):
"""
resistivity set as float, no value on boundary, fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTMModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
model.setResistivity(self.RHO0)
self.runModel(model, PERIODS, TOL=1.e-1)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoDataNoRhoBFixedButtom(self):
"""
resistivity set as Scalar, no value on boundary, fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTMModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
rho=Scalar(self.RHO0, ContinuousFunction(self.domain))
model.setResistivity(rho)
self.runModel(model, PERIODS, TOL=1.e-1)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoDataNoRhoBFixedButtom2(self):
"""
resistivity set as Scalar, no value on boundary, interpolation is not possible but fixed bottom -> TRUE values are not matched exactly
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
model=MT2DTMModel(self.domain, fixBottom=True, useFastSolver=self.USEFASTSOLVER)
rho=Scalar(self.RHO0, Function(self.domain))
model.setResistivity(rho)
self.runModel(model, PERIODS, TOL=1.e-1)
class TestMT2DTMWithAirLayer(unittest.TestCase):
DEPTH=3000.
WIDTH=1000.
RHO0=100.
NEX=100
NEZ=300
STATION_OFFSET=1000.
AIR_LAYER=300.
TRUE_PHASE=45.
TRUE_RHO=100.
USEFASTSOLVER=False # True works not yet
def setUp(self):
self.domain=Rectangle(self.NEX,self.NEZ,l0=self.WIDTH,l1=self.DEPTH)
self.loc=Locator(ReducedFunction(self.domain), [ (self.STATION_OFFSET,self.DEPTH-self.AIR_LAYER-self.DEPTH/self.NEZ/2)] )
self.airLayerMask=whereNonNegative(self.domain.getX()[1]-self.DEPTH+self.AIR_LAYER)
self.airLayerMaskCenter=whereNonNegative(ReducedFunction(self.domain).getX()[1]-self.DEPTH+self.AIR_LAYER)
def tearDown(self):
del self.domain
del self.loc
def runModel(self, model, PERIODS, TOL):
for frq in PERIODS:
Zyx = model.getImpedance(f=frq)
self.assertIsInstance(Zyx, Data)
self.assertEqual(Zyx.getShape(), ())
self.assertEqual(Zyx.getFunctionSpace(), ReducedFunction(self.domain))
rho=model.getApparentResitivity(frq, Zyx)
self.assertIsInstance(rho, Data)
self.assertEqual(rho.getShape(), ())
self.assertEqual(rho.getFunctionSpace(), ReducedFunction(self.domain))
phi=model.getPhase(frq, Zyx)
self.assertIsInstance(phi, Data)
self.assertEqual(phi.getShape(), ())
self.assertEqual(phi.getFunctionSpace(), ReducedFunction(self.domain))
#print(frq, self.loc(rho)[0], self.loc(phi)[0])
self.assertAlmostEqual(self.loc(rho)[0], self.TRUE_RHO, delta=TOL*self.TRUE_RHO)
self.assertAlmostEqual(self.loc(phi)[0], self.TRUE_PHASE, delta=TOL*self.TRUE_PHASE)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoBFloat(self):
"""
resistivity set on elements, radiation condition
"""
PERIODS=np.logspace(-2, 2, num=5, endpoint=True, base=10.0, dtype=float)
rho=self.airLayerMaskCenter*9999999+(1-self.airLayerMaskCenter)*self.RHO0
model=MT2DTMModel(self.domain, fixBottom=False, airLayer=self.DEPTH-self.AIR_LAYER, useFastSolver=self.USEFASTSOLVER)
self.assertEqual(0, Lsup(model.airLayer-self.airLayerMask))
model.setResistivity(rho, rho_boundary=self.RHO0)
self.runModel(model, PERIODS, TOL=1.e-3)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoBDataFixedButtom(self):
"""
resistivity set on elements, no value on boundary but interpolation is not possible fixed bottom
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
rho=self.airLayerMaskCenter*9999999+(1-self.airLayerMaskCenter)*self.RHO0
model=MT2DTMModel(self.domain, fixBottom=True, airLayer=self.DEPTH-self.AIR_LAYER, useFastSolver=self.USEFASTSOLVER)
self.assertEqual(0, Lsup(model.airLayer-self.airLayerMask))
model.setResistivity(rho)
self.runModel(model, PERIODS, TOL=1.e-1)
@unittest.skipIf(NO_TRILINOS, "requires Trilinos")
def test_RhoBDataFailed(self):
"""
resistivity set on elements, no value on boundary but interpolation is not possible fixed bottom
"""
PERIODS=np.logspace(2, 3, num=3, endpoint=True, base=10.0, dtype=float)
rho=self.airLayerMaskCenter*9999999+(1-self.airLayerMaskCenter)*self.RHO0
model=MT2DTMModel(self.domain, fixBottom=False, airLayer=self.DEPTH-self.AIR_LAYER, useFastSolver=self.USEFASTSOLVER)
self.assertEqual(0, Lsup(model.airLayer-self.airLayerMask))
self.assertRaises(RuntimeError, model.setResistivity, *(rho, ))
if __name__ == '__main__':
mySuite=unittest.TestSuite()
mySuite.addTest(unittest.makeSuite(TestMT2DTE))
mySuite.addTest(unittest.makeSuite(TestMT2DTMNoAirLayer))
mySuite.addTest(unittest.makeSuite(TestMT2DTMWithAirLayer))
runner=unittest.TextTestRunner(verbosity=2)
runner.run(mySuite)
| 44.648199
| 143
| 0.657526
| 1,864
| 16,118
| 5.634657
| 0.100858
| 0.044749
| 0.027135
| 0.043416
| 0.875464
| 0.865657
| 0.850233
| 0.843949
| 0.828049
| 0.802533
| 0
| 0.026466
| 0.22875
| 16,118
| 360
| 144
| 44.772222
| 0.818438
| 0.113289
| 0
| 0.767635
| 0
| 0.004149
| 0.036467
| 0
| 0
| 0
| 0
| 0
| 0.161826
| 1
| 0.116183
| false
| 0
| 0.033195
| 0
| 0.278008
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
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| null | 0
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| 0
| 0
|
0
| 7
|
459eddec905c4b7010f3c4f2c523ed3a49d9d142
| 5,595
|
py
|
Python
|
dtech_instagram/InstagramAPI/src/Constants.py
|
hideki-saito/InstagramAPP_Flask
|
c3ee6f10d35edb74f0f82f4370faca8f0c25200c
|
[
"MIT"
] | 126
|
2016-05-18T19:20:32.000Z
|
2022-02-12T10:30:50.000Z
|
dtech_instagram/InstagramAPI/src/Constants.py
|
hideki-saito/InstagramAPP_Flask
|
c3ee6f10d35edb74f0f82f4370faca8f0c25200c
|
[
"MIT"
] | 41
|
2016-08-07T17:32:37.000Z
|
2022-01-13T00:25:31.000Z
|
dtech_instagram/InstagramAPI/src/Constants.py
|
hideki-saito/InstagramAPP_Flask
|
c3ee6f10d35edb74f0f82f4370faca8f0c25200c
|
[
"MIT"
] | 61
|
2016-07-07T14:18:38.000Z
|
2021-03-28T12:48:26.000Z
|
class Constants(object):
"""
Constant declarations.
"""
API_URL = 'https://i.instagram.com/api/v1/'
VERSION = '9.2.0'
IG_SIG_KEY = '012a54f51c49aa8c5c322416ab1410909add32c966bbaa0fe3dc58ac43fd7ede'
EXPERIMENTS = 'ig_android_ad_holdout_16m5_universe,ig_android_progressive_jpeg,ig_creation_growth_holdout,ig_android_oppo_app_badging,ig_android_ad_remove_username_from_caption_universe,ig_android_enable_share_to_whatsapp,ig_android_direct_drawing_in_quick_cam_universe,ig_android_ad_always_send_ad_attribution_id_universe,ig_android_universe_video_production,ig_android_direct_plus_button,ig_android_ads_heatmap_overlay_universe,ig_android_http_stack_experiment_2016,ig_android_infinite_scrolling,ig_fbns_blocked,ig_android_post_auto_retry_v7_21,ig_fbns_push,ig_android_video_playback_bandwidth_threshold,ig_android_direct_link_preview,ig_android_direct_typing_indicator,ig_android_preview_capture,ig_android_feed_pill,ig_android_profile_link_iab,ig_android_story_caption,ig_android_network_cancellation,ig_android_histogram_reporter,ig_android_anrwatchdog,ig_android_search_client_matching,ig_android_follow_request_text_buttons,ig_android_feed_zoom,ig_android_drafts_universe,ig_android_disable_comment,ig_android_user_detail_endpoint,ig_android_os_version_blocking,ig_android_blocked_list,ig_android_event_creation,ig_android_high_res_upload_2,ig_android_2fac,ig_android_mark_reel_seen_on_Swipe_forward,ig_android_comment_redesign,ig_android_ad_sponsored_label_universe,ig_android_mentions_dismiss_rule,ig_android_disable_chroma_subsampling,ig_android_share_spinner,ig_android_video_reuse_surface,ig_explore_v3_android_universe,ig_android_media_favorites,ig_android_nux_holdout,ig_android_insta_video_universe,ig_android_search_null_state,ig_android_universe_reel_video_production,liger_instagram_android_univ,ig_android_direct_emoji_picker,ig_feed_holdout_universe,ig_android_direct_send_auto_retry_universe,ig_android_samsung_app_badging,ig_android_disk_usage,ig_android_business_promotion,ig_android_direct_swipe_to_inbox,ig_android_feed_reshare_button_nux,ig_android_react_native_boost_post,ig_android_boomerang_feed_attribution,ig_fbns_shared,ig_fbns_dump_ids,ig_android_react_native_universe,ig_show_promote_button_in_feed,ig_android_ad_metadata_behavior_universe,ig_android_video_loopcount_int,ig_android_inline_gallery_backoff_hours_universe,ig_android_rendering_controls,ig_android_profile_photo_as_media,ig_android_async_stack_image_cache,ig_video_max_duration_qe_preuniverse,ig_video_copyright_whitelist,ig_android_render_stories_with_content_override,ig_android_ad_intent_to_highlight_universe,ig_android_swipe_navigation_x_angle_universe,ig_android_disable_comment_public_test,ig_android_profile,ig_android_direct_blue_tab,ig_android_enable_share_to_messenger,ig_android_fetch_reel_tray_on_resume_universe,ig_android_promote_again,ig_feed_event_landing_page_channel,ig_ranking_following,ig_android_pending_request_search_bar,ig_android_feed_ufi_redesign,ig_android_pending_edits_dialog_universe,ig_android_business_conversion_flow_universe,ig_android_show_your_story_when_empty_universe,ig_android_ad_drop_cookie_early,ig_android_app_start_config,ig_android_fix_ise_two_phase,ig_android_ppage_toggle_universe,ig_android_pbia_normal_weight_universe,ig_android_profanity_filter,ig_ios_su_activity_feed,ig_android_search,ig_android_boomerang_entry,ig_android_mute_story,ig_android_inline_gallery_universe,ig_android_ad_remove_one_tap_indicator_universe,ig_android_view_count_decouple_likes_universe,ig_android_contact_button_redesign_v2,ig_android_periodic_analytics_upload_v2,ig_android_send_direct_typing_indicator,ig_android_ad_holdout_16h2m1_universe,ig_android_react_native_comment_moderation_settings,ig_video_use_sve_universe,ig_android_inline_gallery_no_backoff_on_launch_universe,ig_android_immersive_viewer,ig_android_discover_people_icon,ig_android_profile_follow_back_button,is_android_feed_seen_state,ig_android_dense_feed_unit_cards,ig_android_drafts_video_universe,ig_android_exoplayer,ig_android_add_to_last_post,ig_android_ad_remove_cta_chevron_universe,ig_android_ad_comment_cta_universe,ig_android_ad_chevron_universe,ig_android_ad_comment_cta_universe,ig_android_search_event_icon,ig_android_channels_home,ig_android_feed,ig_android_dv2_realtime_private_share,ig_android_non_square_first,ig_android_video_interleaved_v2,ig_android_video_cache_policy,ig_android_react_native_universe_kill_switch,ig_android_video_captions_universe,ig_android_follow_search_bar,ig_android_last_edits,ig_android_two_step_capture_flow,ig_android_video_download_logging,ig_android_share_link_to_whatsapp,ig_android_facebook_twitter_profile_photos,ig_android_swipeable_filters_blacklist,ig_android_ad_pbia_profile_tap_universe,ig_android_use_software_layer_for_kc_drawing_universe,ig_android_react_native_ota,ig_android_direct_mutually_exclusive_experiment_universe,ig_android_following_follower_social_context'
LOGIN_EXPERIMENTS = 'ig_android_reg_login_btn_active_state,ig_android_ci_opt_in_at_reg,ig_android_one_click_in_old_flow,ig_android_merge_fb_and_ci_friends_page,ig_android_non_fb_sso,ig_android_mandatory_full_name,ig_android_reg_enable_login_password_btn,ig_android_reg_phone_email_active_state,ig_android_analytics_data_loss,ig_fbns_blocked,ig_android_contact_point_triage,ig_android_reg_next_btn_active_state,ig_android_prefill_phone_number,ig_android_show_fb_social_context_in_nux,ig_android_one_tap_login_upsell,ig_fbns_push,ig_android_phoneid_sync_interval'
SIG_KEY_VERSION = '4'
ANDROID_VERSION = 18
ANDROID_RELEASE = '4.3'
| 399.642857
| 4,726
| 0.950849
| 888
| 5,595
| 5.20045
| 0.372748
| 0.274794
| 0.132525
| 0.024686
| 0.110871
| 0.022954
| 0.022954
| 0.022954
| 0.022954
| 0.022954
| 0
| 0.011957
| 0.013405
| 5,595
| 13
| 4,727
| 430.384615
| 0.824638
| 0.003932
| 0
| 0
| 0
| 0.222222
| 0.96257
| 0.955372
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.111111
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 8
|
b30d3f30306b25e9a716b8a7f3ca863762e54218
| 3,216
|
py
|
Python
|
sms_sdk_renderer_python/lib/helpers.py
|
SymphonyPlatformSolutions/sms_sdk_renderer_python
|
17743486a7482d04f3acf876cf88606c3eb60393
|
[
"MIT"
] | null | null | null |
sms_sdk_renderer_python/lib/helpers.py
|
SymphonyPlatformSolutions/sms_sdk_renderer_python
|
17743486a7482d04f3acf876cf88606c3eb60393
|
[
"MIT"
] | null | null | null |
sms_sdk_renderer_python/lib/helpers.py
|
SymphonyPlatformSolutions/sms_sdk_renderer_python
|
17743486a7482d04f3acf876cf88606c3eb60393
|
[
"MIT"
] | null | null | null |
import pybars
def _header(this, options, items):
result = [u'<tr>']
if items['select']['position'] == 'left':
if items['select']['type'] == 'button':
result.append(u'<td>Select</td>')
else:
result.append(u'<td><input type="checkbox" name="tablesel-header" /></td>')
for item in items['header_list']:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
else:
if items['select']['type'] == 'button':
for item in items['header_list']:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
result.append(u'<td>Select</td>')
else:
for item in items['header_list']:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
result.append(u'<td><input type="checkbox" name="tablesel-header" /></td>')
result.append(u'</tr>')
return result
def _body(this, options, items):
result = []
if items['select']['position'] == 'left':
for row in items['body']:
result.append(u'<tr>')
if items['select']['type'] == 'button':
result.append(u'<td><button type="action" name="tablesel-row-button">SELECT</button></td>')
else:
result.append(u'<td><input type="checkbox" name="tablesel-header" /></td>')
for item in row:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
result.append(u'</tr>')
else:
for row in items['body']:
result.append(u'<tr>')
for item in row:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
if items['select']['type'] == 'button':
result.append(u'<td><button type="action" name="tablesel-row-button">SELECT</button></td>')
else:
result.append(u'<td><input type="checkbox" name="tablesel-header" /></td>')
result.append(u'</tr>')
return result
def _footer(this, options, items):
result = [u'<tr>']
if items['select']['position'] == 'left':
if items['select']['type'] == 'button':
result.append(u'<td>Select</td>')
else:
result.append(u'<td><input type="checkbox" name="tablesel-header" /></td>')
for item in items['footer_list']:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
else:
if items['select']['type'] == 'button':
for item in items['footer_list']:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
result.append(u'<td>Select</td>')
else:
for item in items['footer_list']:
result.append(u'<td>')
result.append(item)
result.append(u'</td>')
result.append(u'<td><input type="checkbox" name="tablesel-header" /></td>')
result.append(u'</tr>')
return result
| 38.285714
| 107
| 0.497201
| 368
| 3,216
| 4.320652
| 0.086957
| 0.316981
| 0.277987
| 0.264151
| 0.964151
| 0.94717
| 0.94717
| 0.94717
| 0.94717
| 0.906918
| 0
| 0
| 0.317475
| 3,216
| 83
| 108
| 38.746988
| 0.724374
| 0
| 0
| 0.9375
| 0
| 0.025
| 0.273632
| 0.070274
| 0
| 0
| 0
| 0
| 0
| 1
| 0.0375
| false
| 0
| 0.0125
| 0
| 0.0875
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
b32485e7ad2992489ddc4119b290c2af6342d4d1
| 2,092
|
py
|
Python
|
test/src/Syntax/Statements/if/nested-conditionals.py
|
milliburn/llvmPy
|
d6fa3002e823fae00cf33d9b2ea480604681376c
|
[
"MIT"
] | 1
|
2019-01-22T02:58:04.000Z
|
2019-01-22T02:58:04.000Z
|
test/src/Syntax/Statements/if/nested-conditionals.py
|
roberth-k/llvmPy
|
d6fa3002e823fae00cf33d9b2ea480604681376c
|
[
"MIT"
] | null | null | null |
test/src/Syntax/Statements/if/nested-conditionals.py
|
roberth-k/llvmPy
|
d6fa3002e823fae00cf33d9b2ea480604681376c
|
[
"MIT"
] | null | null | null |
# RUN: test-parser.sh %s
# RUN: test-output.sh %s
x = 1 # PARSER-LABEL:x = 1i
y = 2 # PARSER-NEXT:y = 2i
print("Start") # PARSER-NEXT:print("Start")
# OUTPUT-LABEL: Start
if x == 1: # PARSER-NEXT:if (x == 1i):
if y == 3: # PARSER-NEXT: if (y == 3i):
print("A") # PARSER-NEXT: print("A")
else: # PARSER-NEXT:else:
print("C") # PARSER-NEXT: print("C")
print("D") # PARSER-NEXT:print("D")
# OUTPUT-NEXT: D
if x == 1: # PARSER-NEXT:if (x == 1i):
if y == 3: # PARSER-NEXT: if (y == 3i):
print("A") # PARSER-NEXT: print("A")
else: # PARSER-NEXT: else:
print("B") # PARSER-NEXT: print("B")
else: # PARSER-NEXT:else:
print("C") # PARSER-NEXT: print("C")
print("D") # PARSER-NEXT:print("D")
# OUTPUT-NEXT: B
# OUTPUT-NEXT: D
if x == 1: # PARSER-NEXT:if (x == 1i):
if y == 3: # PARSER-NEXT: if (y == 3i):
print("A") # PARSER-NEXT: print("A")
else: # PARSER-NEXT: else:
print("B") # PARSER-NEXT: print("B")
print("D") # PARSER-NEXT:print("D")
# OUTPUT-NEXT: B
# OUTPUT-NEXT: D
if x == 1: # PARSER-NEXT:if (x == 1i):
if y == 3: # PARSER-NEXT: if (y == 3i):
print("A") # PARSER-NEXT: print("A")
else: # PARSER-NEXT: else:
print("X") # PARSER-NEXT: print("X")
if y == 2: # PARSER-NEXT: if (y == 2i):
print("B") # PARSER-NEXT: print("B")
else: # PARSER-NEXT: else:
print("E") # PARSER-NEXT: print("E")
else: # PARSER-NEXT:else:
print("C") # PARSER-NEXT: print("C")
print("D") # PARSER-NEXT:print("D")
# OUTPUT-NEXT: X
# OUTPUT-NEXT: B
# OUTPUT-NEXT: D
| 36.701754
| 61
| 0.404398
| 251
| 2,092
| 3.370518
| 0.099602
| 0.401891
| 0.301418
| 0.148936
| 0.807329
| 0.807329
| 0.781324
| 0.781324
| 0.781324
| 0.781324
| 0
| 0.018122
| 0.419694
| 2,092
| 56
| 62
| 37.357143
| 0.678748
| 0.549235
| 0
| 0.828571
| 0
| 0
| 0.023438
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.485714
| 0
| 0
| 0
| null | 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 10
|
2fbb38b0238badaf624ebda4cde9b6e737dfba64
| 163
|
py
|
Python
|
app/tests/accounts/test_models.py
|
aalmazan/circleci-hello
|
8b4d92f12ccdab5cd6de9c7a7f4bdc824f2efc23
|
[
"MIT"
] | null | null | null |
app/tests/accounts/test_models.py
|
aalmazan/circleci-hello
|
8b4d92f12ccdab5cd6de9c7a7f4bdc824f2efc23
|
[
"MIT"
] | 3
|
2020-02-12T03:02:05.000Z
|
2021-06-10T21:41:33.000Z
|
app/tests/accounts/test_models.py
|
aalmazan/circleci-hello
|
8b4d92f12ccdab5cd6de9c7a7f4bdc824f2efc23
|
[
"MIT"
] | null | null | null |
import pytest
from django.conf import settings
def test_ensure_correct_settings():
"""Test if TEST_SETTING exists."""
assert settings.TEST_SETTING == 1
| 18.111111
| 38
| 0.748466
| 22
| 163
| 5.318182
| 0.681818
| 0.205128
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007353
| 0.165644
| 163
| 8
| 39
| 20.375
| 0.852941
| 0.171779
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0.25
| true
| 0
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
44073d61870b2effe1cc976ce510ba9fa81005c7
| 159
|
py
|
Python
|
app/exceptions.py
|
Anupam02/tic-tac-toe
|
8fac66a5b0cd119f02193fdd92240171f52f0992
|
[
"MIT"
] | null | null | null |
app/exceptions.py
|
Anupam02/tic-tac-toe
|
8fac66a5b0cd119f02193fdd92240171f52f0992
|
[
"MIT"
] | null | null | null |
app/exceptions.py
|
Anupam02/tic-tac-toe
|
8fac66a5b0cd119f02193fdd92240171f52f0992
|
[
"MIT"
] | null | null | null |
class InvalidSymbolException(Exception):
pass
class InvalidMoveException(Exception):
pass
class InvalidCoordinateInputException(Exception):
pass
| 17.666667
| 49
| 0.798742
| 12
| 159
| 10.583333
| 0.5
| 0.307087
| 0.283465
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.144654
| 159
| 8
| 50
| 19.875
| 0.933824
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
441d94f2ec636bc737a8679984a704cf6eb5704c
| 5,423
|
py
|
Python
|
robocorp-code/tests/robocorp_code_tests/test_compute_launch.py
|
avaissi/robotframework-lsp
|
0e7de8368a09cba08f97975b5d491cf044957d93
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
robocorp-code/tests/robocorp_code_tests/test_compute_launch.py
|
avaissi/robotframework-lsp
|
0e7de8368a09cba08f97975b5d491cf044957d93
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
robocorp-code/tests/robocorp_code_tests/test_compute_launch.py
|
avaissi/robotframework-lsp
|
0e7de8368a09cba08f97975b5d491cf044957d93
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
def test_compute_launch_01(tmpdir):
import os
from robocorp_code import compute_launch
robot_yaml = tmpdir.join("robot.yaml")
tmpdir.join("task.py").write("foo")
robot_yaml.write(
"""
tasks:
Default:
command:
- python
- task.py
condaConfigFile: conda.yaml
artifactsDir: output
PATH:
- .
PYTHONPATH:
- .
ignoreFiles:
- .gitignore
"""
)
additional_pythonpath_entries = []
launch = compute_launch.compute_robot_launch_from_robocorp_code_launch(
"Launch name",
"launch",
"Default",
str(robot_yaml),
additional_pythonpath_entries,
None,
None,
)
cwd = str(tmpdir)
assert launch == {
"success": True,
"message": None,
"result": {
"type": "python",
"name": "Launch name",
"request": "launch",
"cwd": cwd,
"args": [],
"pythonArgs": [],
"console": "internalConsole",
"program": os.path.join(cwd, "task.py"),
},
}
def test_compute_launch_02(tmpdir):
import os
from robocorp_code import compute_launch
robot_yaml = tmpdir.join("robot.yaml")
tmpdir.join("task.py").write("foo")
robot_yaml.write(
"""
tasks:
Default:
command:
- python
- task.py
- arg1
condaConfigFile: conda.yaml
artifactsDir: output
PATH:
- .
PYTHONPATH:
- .
ignoreFiles:
- .gitignore
"""
)
additional_pythonpath_entries = []
launch = compute_launch.compute_robot_launch_from_robocorp_code_launch(
"Launch name",
"launch",
"Default",
str(robot_yaml),
additional_pythonpath_entries,
None,
"python_executable.exe",
)
cwd = str(tmpdir)
assert launch == {
"success": True,
"message": None,
"result": {
"type": "python",
"name": "Launch name",
"request": "launch",
"cwd": cwd,
"args": ["arg1"],
"pythonArgs": [],
"console": "internalConsole",
"program": os.path.join(cwd, "task.py"),
"pythonPath": "python_executable.exe",
},
}
def test_compute_launch_03(tmpdir):
from robocorp_code import compute_launch
robot_yaml = tmpdir.join("robot.yaml")
robot_yaml.write(
"""
tasks:
Default:
command:
- python
- -c
- print('something')
- arg1
condaConfigFile: conda.yaml
artifactsDir: output
PATH:
- .
PYTHONPATH:
- .
ignoreFiles:
- .gitignore
"""
)
additional_pythonpath_entries = []
launch = compute_launch.compute_robot_launch_from_robocorp_code_launch(
"Launch name",
"launch",
"Default",
str(robot_yaml),
additional_pythonpath_entries,
None,
None,
)
assert not launch["success"]
assert launch["message"] == "Unable to deal with running with python '-c' flag."
def test_compute_launch_04(tmpdir):
import os
from robocorp_code import compute_launch
robot_yaml = tmpdir.join("robot.yaml")
tmpdir.join("task.py").write("foo")
robot_yaml.write(
"""
tasks:
Default:
command:
- python
- -u
- -m
- module_name
- arg1
condaConfigFile: conda.yaml
artifactsDir: output
PATH:
- .
PYTHONPATH:
- .
ignoreFiles:
- .gitignore
"""
)
additional_pythonpath_entries = []
launch = compute_launch.compute_robot_launch_from_robocorp_code_launch(
"Launch name",
"launch",
"Default",
str(robot_yaml),
additional_pythonpath_entries,
None,
"python_executable.exe",
)
cwd = str(tmpdir)
assert launch == {
"success": True,
"message": None,
"result": {
"type": "python",
"name": "Launch name",
"request": "launch",
"cwd": cwd,
"args": ["arg1"],
"pythonArgs": ["-u"],
"console": "internalConsole",
"module": "module_name",
"pythonPath": "python_executable.exe",
},
}
def test_compute_launch_05(tmpdir):
import os
from robocorp_code import compute_launch
robot_yaml = tmpdir.join("robot.yaml")
tmpdir.join("task.py").write("foo")
robot_yaml.write(
"""
tasks:
Default:
command:
- python
- -u
- -m
- module_name
- arg1
condaConfigFile: conda.yaml
artifactsDir: output
PATH:
- .
PYTHONPATH:
- .
ignoreFiles:
- .gitignore
"""
)
additional_pythonpath_entries = []
launch = compute_launch.compute_robot_launch_from_robocorp_code_launch(
"Launch name",
"launch",
"", # Don't provide task name: should be ok if only 1 task is there.
str(robot_yaml),
additional_pythonpath_entries,
None,
"python_executable.exe",
)
cwd = str(tmpdir)
assert launch == {
"success": True,
"message": None,
"result": {
"type": "python",
"name": "Launch name",
"request": "launch",
"cwd": cwd,
"args": ["arg1"],
"pythonArgs": ["-u"],
"console": "internalConsole",
"module": "module_name",
"pythonPath": "python_executable.exe",
},
}
| 20.777778
| 84
| 0.550065
| 510
| 5,423
| 5.643137
| 0.154902
| 0.062543
| 0.055594
| 0.059416
| 0.9246
| 0.9246
| 0.9246
| 0.911049
| 0.887074
| 0.887074
| 0
| 0.004893
| 0.321593
| 5,423
| 260
| 85
| 20.857692
| 0.777385
| 0.011433
| 0
| 0.817568
| 0
| 0
| 0.204933
| 0.029046
| 0
| 0
| 0
| 0
| 0.040541
| 1
| 0.033784
| false
| 0
| 0.060811
| 0
| 0.094595
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2bdf0038bab92af38e7990c3307a4412f0359b3b
| 78,679
|
py
|
Python
|
src/training_modules/unused_modules/train_waymo_model.py
|
petergroth/trajectory_forecasting
|
35bcf1e60d818cc1aaff746c3818ff56c574e854
|
[
"MIT"
] | 1
|
2022-01-26T11:54:46.000Z
|
2022-01-26T11:54:46.000Z
|
src/training_modules/unused_modules/train_waymo_model.py
|
petergroth/trajectory_forecasting
|
35bcf1e60d818cc1aaff746c3818ff56c574e854
|
[
"MIT"
] | null | null | null |
src/training_modules/unused_modules/train_waymo_model.py
|
petergroth/trajectory_forecasting
|
35bcf1e60d818cc1aaff746c3818ff56c574e854
|
[
"MIT"
] | 1
|
2022-03-18T03:13:01.000Z
|
2022-03-18T03:13:01.000Z
|
import argparse
import math
import os
import random
from typing import Union
import hydra
import pytorch_lightning as pl
import torch
import torch_geometric.nn
import torchmetrics
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning.callbacks import RichProgressBar
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.utilities.seed import seed_everything
from torch_geometric.data import Batch
from src.data.dataset_waymo import (OneStepWaymoDataModule,
SequentialWaymoDataModule)
from src.models.model import *
class OneStepModule(pl.LightningModule):
def __init__(
self,
model_type: Union[None, str],
model_dict: Union[None, dict],
noise: Union[None, float] = None,
lr: float = 1e-4,
weight_decay: float = 0.0,
edge_type: str = "knn",
min_dist: int = 0,
n_neighbours: int = 30,
fully_connected: bool = True,
edge_weight: bool = False,
self_loop: bool = True,
undirected: bool = False,
out_features: int = 6,
normalise: bool = True,
node_features: int = 9,
edge_features: int = 1,
):
super().__init__()
# Verify inputs
assert edge_type in ["knn", "distance"]
if edge_type == "distance":
assert min_dist > 0.0
# Instantiate model
self.model_type = model_type
self.model = eval(model_type)(**model_dict)
# Setup metrics
self.train_pos_loss = torchmetrics.MeanSquaredError()
self.train_vel_loss = torchmetrics.MeanSquaredError()
self.train_yaw_loss = torchmetrics.MeanSquaredError()
self.train_difference_loss = torchmetrics.MeanSquaredError()
self.val_ade_loss = torchmetrics.MeanSquaredError()
self.val_fde_loss = torchmetrics.MeanSquaredError()
self.val_vel_loss = torchmetrics.MeanSquaredError()
self.val_yaw_loss = torchmetrics.MeanSquaredError()
self.val_fde_ttp_loss = torchmetrics.MeanSquaredError()
self.val_ade_ttp_loss = torchmetrics.MeanSquaredError()
# Learning parameters
self.normalise = normalise
self.global_scale = 8.025897979736328
# self.global_scale = 1
self.noise = noise
self.lr = lr
self.weight_decay = weight_decay
# Model parameters
self.out_features = out_features
self.edge_features = edge_features
self.node_features = node_features
# Graph parameters
self.edge_type = edge_type
self.min_dist = min_dist
self.fully_connected = fully_connected
self.n_neighbours = 128 if fully_connected else n_neighbours
self.edge_weight = edge_weight
self.self_loop = self_loop
self.undirected = undirected
self.save_hyperparameters()
def training_step(self, batch: Batch, batch_idx: int):
# CARS
type_mask = batch.x[:, 11] == 1
batch.x = batch.x[type_mask]
batch.y = batch.y[type_mask]
batch.batch = batch.batch[type_mask]
# Remove type from data
batch.x = batch.x[:, :10]
# Extract node features
x = batch.x
edge_attr = None
######################
# Graph construction #
######################
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x[:, :2], k=self.n_neighbours, batch=batch.batch, loop=self.self_loop
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x[:, :2],
r=self.min_dist,
batch=batch.batch,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, _ = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
self.log("train_edges_per_node", edge_index.shape[1] / x.shape[0])
# Determine whether to add random noise to dynamic states
if self.noise is not None:
x[:, : self.out_features] += self.noise * torch.randn_like(
x[:, : self.out_features]
)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x[row, :2] - x[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = edge_attr.type_as(batch.x)
######################
# Training 1/1 #
######################
# Obtain target delta dynamic nodes
y_target = batch.y[:, : self.out_features] - x[:, : self.out_features]
y_target = y_target.type_as(batch.x)
if self.normalise:
if edge_attr is None:
# Center node positions
x[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
else:
# Center node positions
x[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain predicted delta dynamics
delta_x = self.model(
x=x, edge_index=edge_index, edge_attr=edge_attr, batch=batch.batch
)
# Process predicted yaw values
yaw_pred = torch.tanh(delta_x[:, 5:9])
yaw_targ = torch.vstack(
[
torch.sin(y_target[:, 5]),
torch.cos(y_target[:, 5]),
torch.sin(y_target[:, 6]),
torch.cos(y_target[:, 6]),
]
).T
# Compute new positions using old velocities (in normalised space)
# pos_expected = batch.x[:, [0, 1]] + 0.1 * batch.x[:, [3, 4]]
# # Compute new positions by updating old position with new (normalised) delta dynamics
# pos_new = delta_x[:, [0, 1]] / self.global_scale + batch.x[:, [0, 1]]
# Compute and log loss
pos_loss = self.train_pos_loss(delta_x[:, :3], y_target[:, :3])
vel_loss = self.train_vel_loss(delta_x[:, 3:5], y_target[:, 3:5])
yaw_loss = self.train_yaw_loss(yaw_pred, yaw_targ)
# pos_diff = self.train_difference_loss(pos_new, pos_expected)
self.log("train_pos_loss", pos_loss, on_step=True, on_epoch=True)
self.log("train_vel_loss", vel_loss, on_step=True, on_epoch=True)
self.log("train_yaw_loss", yaw_loss, on_step=True, on_epoch=True)
# self.log("position_difference", pos_diff, on_step=True, on_epoch=True)
loss = pos_loss + vel_loss + yaw_loss # + pos_diff
self.log("train_total_loss", loss, on_step=True, on_epoch=True)
return loss
def validation_step(self, batch: Batch, batch_idx: int):
######################
# Initialisation #
######################
# Validate on sequential dataset. First 11 observations are used to prime the model.
# Loss is computed on remaining 80 samples using rollout.
# Determine valid initialisations at t=11
mask = batch.x[:, :, -1]
valid_mask = mask[:, 10] > 0
# Discard non-valid nodes as no initial trajectories will be known
batch.x = batch.x[valid_mask]
batch.batch = batch.batch[valid_mask]
batch.tracks_to_predict = batch.tracks_to_predict[valid_mask]
batch.type = batch.type[valid_mask]
# CARS
type_mask = batch.type[:, 1] == 1
batch.x = batch.x[type_mask]
batch.batch = batch.batch[type_mask]
batch.tracks_to_predict = batch.tracks_to_predict[type_mask]
batch.type = batch.type[type_mask]
# Update mask
mask = batch.x[:, :, -1].bool()
# Allocate target/prediction tensors
n_nodes = batch.num_nodes
y_hat = torch.zeros((80, n_nodes, self.out_features))
y_target = torch.zeros((80, n_nodes, self.out_features))
# Ensure device placement
y_hat = y_hat.type_as(batch.x)
y_target = y_target.type_as(batch.x)
# Discard mask from features and extract static features
batch.x = batch.x[:, :, :-1]
# static_features = torch.cat(
# [batch.x[:, 10, self.out_features :], batch.type], dim=1
# )
static_features = torch.cat([batch.x[:, 10, self.out_features :]], dim=1)
static_features = static_features.type_as(batch.x)
edge_attr = None
######################
# History #
######################
for t in range(11):
######################
# Graph construction #
######################
mask_t = mask[:, t]
# x_t = torch.cat([batch.x[mask_t, t, :], batch.type[mask_t]], dim=1)
x_t = batch.x[mask_t, t, :].clone()
batch_t = batch.batch[mask_t]
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch_t,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch_t,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, _ = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
self.log(
"val_history_edges_per_node",
edge_index.shape[1] / x_t.shape[0],
on_step=True,
on_epoch=True,
)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = edge_attr.type_as(batch.x)
######################
# Validation 1/2 #
######################
# Normalise input graph
if self.normalise:
if edge_attr is None:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][mask_t][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
else:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][mask_t][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain predicted delta dynamics
delta_x = self.model(
x=x_t, edge_index=edge_index, edge_attr=edge_attr, batch=batch_t
)
# Transform yaw
bbox_yaw = torch.atan2(
torch.tanh(delta_x[:, 5]), torch.tanh(delta_x[:, 6])
).unsqueeze(1)
vel_yaw = torch.atan2(
torch.tanh(delta_x[:, 7]), torch.tanh(delta_x[:, 8])
).unsqueeze(1)
tmp = torch.cat([delta_x[:, 0:5], bbox_yaw, vel_yaw], dim=1)
delta_x = tmp
# Add deltas to input graph
predicted_graph = torch.cat(
(
batch.x[mask_t, t, : self.out_features] + delta_x,
static_features[mask_t],
),
dim=-1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Process yaw values to ensure [-pi, pi] interval
yaws = predicted_graph[:, [5, 6]]
yaws[yaws > 0] = (
torch.fmod(yaws[yaws > 0] + math.pi, torch.tensor(2 * math.pi))
- math.pi
)
yaws[yaws < 0] = (
torch.fmod(yaws[yaws < 0] - math.pi, torch.tensor(2 * math.pi))
+ math.pi
)
predicted_graph[:, [5, 6]] = yaws
# Save first prediction and target
y_hat[0, mask_t, :] = predicted_graph[:, : self.out_features]
y_target[0, mask_t, :] = batch.x[mask_t, 11, : self.out_features]
######################
# Future #
######################
for t in range(11, 90):
######################
# Graph construction #
######################
# Latest prediction as input
x_t = predicted_graph.clone()
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch.batch,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch.batch,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, _ = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
self.log(
"val_future_edges_per_node",
edge_index.shape[1] / x_t.shape[0],
on_step=True,
on_epoch=True,
)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = edge_attr.type_as(batch.x)
######################
# Validation 2/2 #
######################
# Normalise input graph
if self.normalise:
if edge_attr is None:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
else:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain predicted delta dynamics
delta_x = self.model(
x=x_t, edge_index=edge_index, edge_attr=edge_attr, batch=batch.batch
)
# Transform yaw
bbox_yaw = torch.atan2(
torch.tanh(delta_x[:, 5]), torch.tanh(delta_x[:, 6])
).unsqueeze(1)
vel_yaw = torch.atan2(
torch.tanh(delta_x[:, 7]), torch.tanh(delta_x[:, 8])
).unsqueeze(1)
tmp = torch.cat([delta_x[:, 0:5], bbox_yaw, vel_yaw], dim=1)
delta_x = tmp
# Add deltas to input graph
predicted_graph = torch.cat(
[
predicted_graph[:, : self.out_features] + delta_x,
predicted_graph[:, self.out_features :],
],
dim=1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Process yaw values to ensure [-pi, pi] interval
yaws = predicted_graph[:, [5, 6]]
yaws[yaws > 0] = (
torch.fmod(yaws[yaws > 0] + math.pi, torch.tensor(2 * math.pi))
- math.pi
)
yaws[yaws < 0] = (
torch.fmod(yaws[yaws < 0] - math.pi, torch.tensor(2 * math.pi))
+ math.pi
)
predicted_graph[:, [5, 6]] = yaws
# Save prediction alongside true value (next time step state)
y_hat[t - 10, :, :] = predicted_graph[:, : self.out_features]
y_target[t - 10, :, :] = batch.x[:, t + 1, : self.out_features]
fde_mask = mask[:, -1]
val_mask = mask[:, 11:].permute(1, 0)
# Compute and log loss
fde_loss = self.val_fde_loss(
y_hat[-1, fde_mask, :3], y_target[-1, fde_mask, :3]
)
ade_loss = self.val_ade_loss(
y_hat[:, :, 0:3][val_mask], y_target[:, :, 0:3][val_mask]
)
vel_loss = self.val_vel_loss(
y_hat[:, :, 3:5][val_mask], y_target[:, :, 3:5][val_mask]
)
yaw_loss = self.val_yaw_loss(
y_hat[:, :, 5:7][val_mask], y_target[:, :, 5:7][val_mask]
)
# Compute losses on "tracks_to_predict"
fde_ttp_mask = torch.logical_and(fde_mask, batch.tracks_to_predict)
fde_ttp_loss = self.val_fde_ttp_loss(
y_hat[-1, fde_ttp_mask, :3], y_target[-1, fde_ttp_mask, :3]
)
ade_ttp_mask = torch.logical_and(
val_mask, batch.tracks_to_predict.expand((80, mask.size(0)))
)
ade_ttp_loss = self.val_ade_loss(
y_hat[:, :, 0:3][ade_ttp_mask], y_target[:, :, 0:3][ade_ttp_mask]
)
######################
# Logging #
######################
self.log("val_ade_loss", ade_loss)
self.log("val_fde_loss", fde_loss)
self.log("val_vel_loss", vel_loss)
self.log("val_yaw_loss", yaw_loss)
self.log("val_total_loss", (ade_loss + vel_loss + yaw_loss) / 3)
self.log("val_fde_ttp_loss", fde_ttp_loss)
self.log("val_ade_ttp_loss", ade_ttp_loss)
return (ade_loss + vel_loss + yaw_loss) / 3
def predict_step(self, batch, batch_idx=None):
######################
# Initialisation #
######################
# Determine valid initialisations at t=11
mask = batch.x[:, :, -1]
valid_mask = mask[:, 10] > 0
# Discard non-valid nodes as no initial trajectories will be known
batch.x = batch.x[valid_mask]
batch.batch = batch.batch[valid_mask]
batch.tracks_to_predict = batch.tracks_to_predict[valid_mask]
batch.type = batch.type[valid_mask]
# CARS
type_mask = batch.type[:, 1] == 1
batch.x = batch.x[type_mask]
batch.batch = batch.batch[type_mask]
batch.tracks_to_predict = batch.tracks_to_predict[type_mask]
batch.type = batch.type[type_mask]
# Update mask
mask = batch.x[:, :, -1].bool()
# Allocate target/prediction tensors
n_nodes = batch.num_nodes
y_hat = torch.zeros((90, n_nodes, self.node_features))
y_target = torch.zeros((90, n_nodes, self.node_features))
# Ensure device placement
y_hat = y_hat.type_as(batch.x)
y_target = y_target.type_as(batch.x)
batch.x = batch.x[:, :, :-1]
# static_features = torch.cat(
# [batch.x[:, 10, self.out_features :], batch.type], dim=1
# )
static_features = torch.cat([batch.x[:, 10, self.out_features :]], dim=1)
edge_attr = None
######################
# History #
######################
for t in range(11):
######################
# Graph construction #
######################
mask_t = mask[:, t]
# x_t = torch.cat([batch.x[mask_t, t, :], batch.type[mask_t]], dim=1)
x_t = batch.x[mask_t, t, :].clone()
batch_t = batch.batch[mask_t]
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch_t,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch_t,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = edge_attr.type_as(batch.x)
######################
# Prediction 1/2 #
######################
# Normalise input graph
if self.normalise:
if edge_attr is None:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][mask_t][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
else:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][mask_t][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain predicted delta dynamics
delta_x = self.model(
x=x_t, edge_index=edge_index, edge_attr=edge_attr, batch=batch_t
)
# Transform yaw
bbox_yaw = torch.atan2(
torch.tanh(delta_x[:, 5]), torch.tanh(delta_x[:, 6])
).unsqueeze(1)
vel_yaw = torch.atan2(
torch.tanh(delta_x[:, 7]), torch.tanh(delta_x[:, 8])
).unsqueeze(1)
tmp = torch.cat([delta_x[:, 0:5], bbox_yaw, vel_yaw], dim=1)
delta_x = tmp
# Add deltas to input graph
predicted_graph = torch.cat(
(
batch.x[mask_t, t, : self.out_features] + delta_x,
static_features[mask_t],
),
dim=-1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Process yaw values to ensure [-pi, pi] interval
yaws = predicted_graph[:, [5, 6]]
yaws[yaws > 0] = (
torch.fmod(yaws[yaws > 0] + math.pi, torch.tensor(2 * math.pi))
- math.pi
)
yaws[yaws < 0] = (
torch.fmod(yaws[yaws < 0] - math.pi, torch.tensor(2 * math.pi))
+ math.pi
)
predicted_graph[:, [5, 6]] = yaws
# Save first prediction and target
y_hat[t, mask_t, :] = predicted_graph
# y_target[t, mask_t, :] = torch.cat(
# [batch.x[mask_t, t + 1, :], batch.type[mask_t]], dim=-1
# )
y_target[t, mask_t, :] = batch.x[mask_t, t + 1, :].clone()
######################
# Future #
######################
for t in range(11, 90):
######################
# Graph construction #
######################
# Latest prediction as input
x_t = predicted_graph.clone()
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch.batch,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch.batch,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = edge_attr.type_as(batch.x)
######################
# Prediction 2/2 #
######################
# Normalise input graph
if self.normalise:
if edge_attr is None:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
else:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, [0, 1, 2, 3, 4, 7, 8, 9]] /= self.global_scale
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain predicted delta dynamics
delta_x = self.model(
x=x_t, edge_index=edge_index, edge_attr=edge_attr, batch=batch.batch
)
# Transform yaw
bbox_yaw = torch.atan2(
torch.tanh(delta_x[:, 5]), torch.tanh(delta_x[:, 6])
).unsqueeze(1)
vel_yaw = torch.atan2(
torch.tanh(delta_x[:, 7]), torch.tanh(delta_x[:, 8])
).unsqueeze(1)
tmp = torch.cat([delta_x[:, 0:5], bbox_yaw, vel_yaw], dim=1)
delta_x = tmp
# Add deltas to input graph
predicted_graph = torch.cat(
[
predicted_graph[:, : self.out_features] + delta_x,
predicted_graph[:, self.out_features :],
],
dim=1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Process yaw values to ensure [-pi, pi] interval
yaws = predicted_graph[:, [5, 6]]
yaws[yaws > 0] = (
torch.fmod(yaws[yaws > 0] + math.pi, torch.tensor(2 * math.pi))
- math.pi
)
yaws[yaws < 0] = (
torch.fmod(yaws[yaws < 0] - math.pi, torch.tensor(2 * math.pi))
+ math.pi
)
predicted_graph[:, [5, 6]] = yaws
# Save prediction alongside true value (next time step state)
y_hat[t, :, :] = predicted_graph
# y_target[t, :, :] = torch.cat([batch.x[:, t + 1], batch.type], dim=-1)
y_target[t, :, :] = batch.x[:, t + 1].clone()
return y_hat, y_target, mask
def configure_optimizers(self):
return torch.optim.Adam(
self.parameters(), lr=self.lr, weight_decay=self.weight_decay
)
class SequentialModule(pl.LightningModule):
def __init__(
self,
model_type: Union[None, str],
model_dict: Union[None, dict],
lr: float = 1e-4,
weight_decay: float = 0.0,
noise: Union[None, float] = None,
teacher_forcing: bool = False,
teacher_forcing_ratio: float = 0.3,
min_dist: int = 0,
n_neighbours: int = 30,
fully_connected: bool = True,
edge_weight: bool = False,
edge_type: str = "knn",
self_loop: bool = True,
undirected: bool = False,
out_features: int = 6,
node_features: int = 9,
edge_features: int = 1,
normalise: bool = True,
training_horizon: int = 90,
):
super().__init__()
# Verify inputs
assert edge_type in ["knn", "distance"]
if edge_type == "distance":
assert min_dist > 0.0
assert out_features == 9
assert node_features == 12
# Set up metrics
self.train_ade_loss = torchmetrics.MeanSquaredError()
self.train_fde_loss = torchmetrics.MeanSquaredError()
self.train_vel_loss = torchmetrics.MeanSquaredError()
self.train_yaw_loss = torchmetrics.MeanAbsoluteError()
self.val_ade_loss = torchmetrics.MeanSquaredError()
self.val_fde_loss = torchmetrics.MeanSquaredError()
self.val_vel_loss = torchmetrics.MeanSquaredError()
self.val_yaw_loss = torchmetrics.MeanAbsoluteError()
self.val_fde_ttp_loss = torchmetrics.MeanSquaredError()
self.val_ade_ttp_loss = torchmetrics.MeanSquaredError()
# Instantiate model
self.model_type = model_type
self.model = eval(model_type)(**model_dict)
# Learning parameters
self.normalise = normalise
self.global_scale = 8.025897979736328
self.noise = noise
self.lr = lr
self.weight_decay = weight_decay
self.teacher_forcing = teacher_forcing
self.teacher_forcing_ratio = teacher_forcing_ratio
self.training_horizon = training_horizon
self.norm_index = [0, 1, 2, 3, 4, 9, 10, 11] # Don't normalise sines/cosines
# Model parameters
self.rnn_type = (
model_dict["rnn_type"] if "rnn_type" in model_dict.keys() else None
)
self.out_features = out_features
self.edge_features = edge_features
self.node_features = node_features
# Graph parameters
self.edge_type = edge_type
self.min_dist = min_dist
self.fully_connected = fully_connected
self.n_neighbours = 128 if fully_connected else n_neighbours
self.edge_weight = edge_weight
self.self_loop = self_loop
self.undirected = undirected
self.save_hyperparameters()
def training_step(self, batch: Batch, batch_idx: int):
######################
# Initialisation #
######################
# Determine valid initialisations at t=11
mask = batch.x[:, :, -1]
valid_mask = mask[:, 10] > 0
# Discard non-valid nodes as no initial trajectories will be known
batch.x = batch.x[valid_mask]
batch.batch = batch.batch[valid_mask]
batch.tracks_to_predict = batch.tracks_to_predict[valid_mask]
batch.type = batch.type[valid_mask]
# CARS
type_mask = batch.type[:, 1] == 1
batch.x = batch.x[type_mask]
batch.batch = batch.batch[type_mask]
batch.tracks_to_predict = batch.tracks_to_predict[type_mask]
batch.type = batch.type[type_mask]
# Discard future values not used for training
batch.x = batch.x[:, : (self.training_horizon + 1)]
# Update mask
mask = batch.x[:, :, -1].bool()
# Discard mask and extract static features
batch.x = batch.x[:, :, :-1]
# static_features = torch.cat(
# [batch.x[:, 10, self.out_features :], batch.type], dim=1
# )
static_features = batch.x[:, 10, (self.out_features - 2) :]
static_features = static_features.type_as(batch.x)
edge_attr = None
# Extract dimensions and allocate predictions
n_nodes = batch.num_nodes
y_predictions = torch.zeros((n_nodes, self.training_horizon, self.out_features))
y_predictions = y_predictions.type_as(batch.x)
# Decompose angular attributes into sines/cosines
sines = torch.sin(batch.x[:, :, [5, 6]])
cosines = torch.cos(batch.x[:, :, [5, 6]])
# Replace yaws with sine/cosine pairs
batch.x = torch.cat(
[
batch.x[:, :, [0, 1, 2, 3, 4]],
sines[:, :, 0].unsqueeze(2),
cosines[:, :, 0].unsqueeze(2),
sines[:, :, 1].unsqueeze(2),
cosines[:, :, 1].unsqueeze(2),
batch.x[:, :, (self.out_features - 2) :],
],
dim=-1,
)
# Define target values
y_target = batch.x[:, 1 : (self.training_horizon + 1), : self.out_features]
y_target = y_target.type_as(batch.x)
assert y_target.shape == y_predictions.shape
# Initial hidden state
if self.rnn_type == "GRU":
h = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
h = h.type_as(batch.x)
c = None
elif self.rnn_type == "LSTM":
h = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
h = h.type_as(batch.x)
c = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
c = c.type_as(batch.x)
else:
h, c = None, None
######################
# History #
######################
for t in range(11):
# Extract current input
mask_t = mask[:, t]
# x_t = torch.cat([batch.x[mask_t, t, :], batch.type[mask_t]], dim=1)
x_t = batch.x[mask_t, t, :]
x_t = x_t.type_as(batch.x)
# Add noise if specified
if self.noise is not None:
x_t[:, : self.out_features] += self.noise * torch.randn_like(
x_t[:, : self.out_features]
)
######################
# Graph construction #
######################
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch.batch[mask_t],
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch.batch[mask_t],
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = 1 / edge_attr
edge_attr = torch.nan_to_num(edge_attr, nan=0, posinf=0, neginf=0)
edge_attr = edge_attr.type_as(batch.x)
#######################
# Training 1/2 #
#######################
# Normalise input graph
if self.normalise:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][mask_t][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, self.norm_index] /= self.global_scale
if edge_attr is not None:
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain predicted delta dynamics
if h is None:
delta_x = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
)
elif self.rnn_type == "GRU":
delta_x, h_t = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
hidden=h[:, mask_t],
)
# Update hidden states
h[:, mask_t] = h_t
else: # LSTM
delta_x, (h_t, c_t) = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
hidden=(h[:, mask_t], c[:, mask_t]),
)
h[:, mask_t] = h_t
c[:, mask_t] = c_t
# Process predicted yaw values into sine/cosines via tanh
yaw_pred = torch.tanh(delta_x[:, [5, 6, 7, 8]])
# Add deltas to input graph and update yaw values directly
x_t = torch.cat(
[
batch.x[mask_t, t, : (self.out_features - 4)] + delta_x[:, :5],
yaw_pred,
static_features[mask_t],
],
dim=-1,
)
# Save predictions
y_predictions[mask_t, t, :] = x_t[:, : self.out_features]
# If using teacher_forcing, draw sample and accept <teach_forcing_ratio*100> % of the time. Else, deny.
use_groundtruth = random.random() < self.teacher_forcing_ratio
######################
# Future #
######################
for t in range(11, self.training_horizon):
# Use groundtruth 'teacher_forcing_ratio' % of the time
if use_groundtruth:
# x_t = torch.cat([batch.x[:, t, :], batch.type], dim=1)
x_t = batch.x[:, t, :].clone()
x_prev = x_t.clone()
######################
# Graph construction #
######################
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch.batch,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch.batch,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = 1 / edge_attr
edge_attr = torch.nan_to_num(edge_attr, nan=0, posinf=0, neginf=0)
edge_attr = edge_attr.type_as(batch.x)
#######################
# Training 2/2 #
#######################
# Normalise input graph
if self.normalise:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, self.norm_index] /= self.global_scale
if edge_attr is not None:
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain normalised predicted delta dynamics
if h is None:
delta_x = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
)
elif self.rnn_type == "GRU":
delta_x, h = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
hidden=h,
)
else:
delta_x, (h, c) = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
hidden=(h, c),
)
# Process predicted yaw values
yaw_pred = torch.tanh(delta_x[:, 5:9])
# Add deltas to input graph. Input for next timestep
x_t = torch.cat(
(
x_prev[:, : (self.out_features - 4)] + delta_x[:, :5],
yaw_pred,
x_prev[:, self.out_features :],
),
dim=-1,
)
# Save predictions
y_predictions[:, t, :] = x_t[:, : self.out_features]
# Determine valid input and target pairs. Compute loss mask as their intersection
loss_mask_target = mask[:, 1 : (self.training_horizon + 1)]
loss_mask_input = mask[:, 0 : self.training_horizon]
loss_mask = torch.logical_and(loss_mask_input, loss_mask_target)
# Determine valid end-points
fde_mask_target = mask[:, -1]
fde_mask_input = mask[:, -2]
fde_mask = torch.logical_and(fde_mask_input, fde_mask_target)
assert (y_target[:, :, [0, 1]][loss_mask] == 0).sum() == 0
assert (y_predictions[:, :, [0, 1]][loss_mask] == 0).sum() == 0
# Compute and log loss
fde_loss = self.train_fde_loss(
y_predictions[fde_mask, -1][:, [0, 1]], y_target[fde_mask, -1][:, [0, 1]]
)
ade_loss = self.train_ade_loss(
y_predictions[:, :, [0, 1]][loss_mask], y_target[:, :, [0, 1]][loss_mask]
)
vel_loss = self.train_vel_loss(
y_predictions[:, :, [2, 3]][loss_mask], y_target[:, :, [2, 3]][loss_mask]
)
yaw_loss = self.train_yaw_loss(
y_predictions[:, :, [5, 6, 7, 8]][loss_mask],
y_target[:, :, [5, 6, 7, 8]][loss_mask],
)
self.log(
"train_fde_loss",
fde_loss,
on_step=True,
on_epoch=True,
batch_size=fde_mask.sum().item(),
)
self.log(
"train_ade_loss",
ade_loss,
on_step=True,
on_epoch=True,
batch_size=loss_mask.sum().item(),
)
self.log(
"train_vel_loss",
vel_loss,
on_step=True,
on_epoch=True,
batch_size=loss_mask.sum().item(),
)
self.log(
"train_yaw_loss",
yaw_loss,
on_step=True,
on_epoch=True,
batch_size=loss_mask.sum().item(),
)
loss = ade_loss + vel_loss + yaw_loss + fde_loss
self.log(
"train_total_loss",
loss,
on_step=True,
on_epoch=True,
batch_size=loss_mask.sum().item(),
)
return loss
def validation_step(self, batch: Batch, batch_idx: int):
######################
# Initialisation #
######################
# Validate on sequential dataset. First 11 observations are used to prime the model.
# Loss is computed on remaining 80 samples using rollout.
# Determine valid initialisations at t=11
mask = batch.x[:, :, -1]
valid_mask = mask[:, 10] > 0
# Discard non-valid nodes as no initial trajectories will be known
batch.x = batch.x[valid_mask]
batch.batch = batch.batch[valid_mask]
batch.tracks_to_predict = batch.tracks_to_predict[valid_mask]
batch.type = batch.type[valid_mask]
# CARS
type_mask = batch.type[:, 1] == 1
batch.x = batch.x[type_mask]
batch.batch = batch.batch[type_mask]
batch.tracks_to_predict = batch.tracks_to_predict[type_mask]
batch.type = batch.type[type_mask]
# Update mask
mask = batch.x[:, :, -1].bool()
# Allocate target/prediction tensors
n_nodes = batch.num_nodes
y_hat = torch.zeros((80, n_nodes, self.out_features))
y_hat = y_hat.type_as(batch.x)
batch.x = batch.x[:, :, :-1]
# static_features = torch.cat(
# [batch.x[:, 10, self.out_features :], batch.type], dim=1
# )
static_features = batch.x[:, 10, (self.out_features - 2) :]
static_features = static_features.type_as(batch.x)
edge_attr = None
# Decompose angular attributes into sines/cosines
sines = torch.sin(batch.x[:, :, [5, 6]])
cosines = torch.cos(batch.x[:, :, [5, 6]])
# Replace yaws with sine/cosine pairs
batch.x = torch.cat(
[
batch.x[:, :, [0, 1, 2, 3, 4]],
sines[:, :, 0].unsqueeze(2),
cosines[:, :, 0].unsqueeze(2),
sines[:, :, 1].unsqueeze(2),
cosines[:, :, 1].unsqueeze(2),
batch.x[:, :, (self.out_features - 2) :],
],
dim=-1,
)
# Define target values
y_target = batch.x[:, 11:, : self.out_features]
y_target = y_target.type_as(batch.x)
y_target = y_target.permute(1, 0, 2)
assert y_target.shape == y_hat.shape
# Initial hidden state
if self.rnn_type == "GRU":
h = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
h = h.type_as(batch.x)
c = None
elif self.rnn_type == "LSTM":
h = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
h = h.type_as(batch.x)
c = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
c = c.type_as(batch.x)
else:
h, c = None, None
######################
# History #
######################
for t in range(11):
######################
# Graph construction #
######################
mask_t = mask[:, t]
# x_t = torch.cat([batch.x[mask_t, t, :], batch.type[mask_t]], dim=1)
x_t = batch.x[mask_t, t, :]
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch.batch[mask_t],
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch.batch[mask_t],
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = 1 / edge_attr
edge_attr = edge_attr.type_as(batch.x)
edge_attr = torch.nan_to_num(edge_attr, nan=0, posinf=0, neginf=0)
######################
# Validation 1/2 #
######################
# Normalise input graph
if self.normalise:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][mask_t][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, self.norm_index] /= self.global_scale
if edge_attr is not None:
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain normalised predicted delta dynamics
if h is None:
delta_x = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
)
elif self.rnn_type == "GRU":
delta_x, h_t = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
hidden=h[:, mask_t],
)
# Update hidden state
h[:, mask_t] = h_t
else: # LSTM
delta_x, (h_t, c_t) = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
hidden=(h[:, mask_t], c[:, mask_t]),
)
# Update hidden state
h[:, mask_t] = h_t
c[:, mask_t] = c_t
if t == 10:
# Process predicted yaw values into sine/cosines via tanh
yaw_pred = torch.tanh(delta_x[:, [5, 6, 7, 8]])
# Add deltas to input graph and update yaw values directly
predicted_graph = torch.cat(
[
batch.x[mask_t, t, : (self.out_features - 4)] + delta_x[:, :5],
yaw_pred,
static_features[mask_t],
],
dim=-1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Save first prediction and target
y_hat[0, mask_t, :] = predicted_graph[:, : self.out_features]
######################
# Future #
######################
for t in range(11, 90):
######################
# Graph construction #
######################
# Latest prediction as input
x_t = predicted_graph.clone()
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch.batch,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch.batch,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = edge_attr.type_as(batch.x)
######################
# Validation 2/2 #
######################
# Normalise input graph
if self.normalise:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, self.norm_index] /= self.global_scale
if edge_attr is not None:
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain normalised predicted delta dynamics
if h is None:
delta_x = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
)
elif self.rnn_type == "GRU":
delta_x, h = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
hidden=h,
)
else:
delta_x, (h, c) = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
hidden=(h, c),
)
# Process predicted yaw values
yaw_pred = torch.tanh(delta_x[:, 5:9])
# Add deltas to input graph. Input for next timestep
predicted_graph = torch.cat(
(
predicted_graph[:, : (self.out_features - 4)] + delta_x[:, :5],
yaw_pred,
predicted_graph[:, self.out_features :],
),
dim=-1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Save prediction alongside true value (next time step state)
y_hat[t - 10, :, :] = predicted_graph[:, : self.out_features]
fde_mask = mask[:, -1]
val_mask = mask[:, 11:].permute(1, 0)
# Compute and log loss
fde_loss = self.val_fde_loss(
y_hat[-1, fde_mask][:, [0, 1, 2]], y_target[-1, fde_mask][:, [0, 1, 2]]
)
ade_loss = self.val_ade_loss(
y_hat[:, :, [0, 1, 2]][val_mask], y_target[:, :, [0, 1, 2]][val_mask]
)
vel_loss = self.val_vel_loss(
y_hat[:, :, [3, 4]][val_mask], y_target[:, :, [3, 4]][val_mask]
)
yaw_loss = self.val_yaw_loss(
y_hat[:, :, [5, 6, 7, 8]][val_mask], y_target[:, :, [5, 6, 7, 8]][val_mask]
)
# Compute losses on "tracks_to_predict"
fde_ttp_mask = torch.logical_and(fde_mask, batch.tracks_to_predict)
fde_ttp_loss = self.val_fde_ttp_loss(
y_hat[-1, fde_ttp_mask, :3], y_target[-1, fde_ttp_mask, :3]
)
ade_ttp_mask = torch.logical_and(
val_mask, batch.tracks_to_predict.expand((80, mask.size(0)))
)
ade_ttp_loss = self.val_ade_loss(
y_hat[:, :, 0:3][ade_ttp_mask], y_target[:, :, 0:3][ade_ttp_mask]
)
######################
# Logging #
######################
self.log("val_ade_loss", ade_loss, batch_size=val_mask.sum().item())
self.log("val_fde_loss", fde_loss, batch_size=fde_mask.sum().item())
self.log("val_vel_loss", vel_loss, batch_size=val_mask.sum().item())
self.log("val_yaw_loss", yaw_loss, batch_size=val_mask.sum().item())
self.log(
"val_total_loss",
ade_loss + vel_loss + yaw_loss + fde_loss,
batch_size=val_mask.sum().item(),
)
self.log("val_fde_ttp_loss", fde_ttp_loss, batch_size=fde_ttp_mask.sum().item())
self.log("val_ade_ttp_loss", ade_ttp_loss, batch_size=ade_ttp_mask.sum().item())
return ade_loss + vel_loss + yaw_loss + fde_loss
def predict_step(self, batch, batch_idx=None):
######################
# Initialisation #
######################
# Determine valid initialisations at t=11
mask = batch.x[:, :, -1]
valid_mask = mask[:, 10] > 0
# Discard non-valid nodes as no initial trajectories will be known
batch.x = batch.x[valid_mask]
batch.batch = batch.batch[valid_mask]
batch.tracks_to_predict = batch.tracks_to_predict[valid_mask]
batch.type = batch.type[valid_mask]
# CARS
type_mask = batch.type[:, 1] == 1
batch.x = batch.x[type_mask]
batch.batch = batch.batch[type_mask]
batch.tracks_to_predict = batch.tracks_to_predict[type_mask]
batch.type = batch.type[type_mask]
# Update mask
mask = batch.x[:, :, -1].bool()
# Allocate target/prediction tensors
n_nodes = batch.num_nodes
y_hat = torch.zeros((90, n_nodes, self.node_features))
y_target = torch.zeros((90, n_nodes, self.node_features))
# Ensure device placement
y_hat = y_hat.type_as(batch.x)
y_target = y_target.type_as(batch.x)
batch.x = batch.x[:, :, :-1]
# static_features = torch.cat(
# [batch.x[:, 10, self.out_features :], batch.type], dim=1
# )
static_features = batch.x[:, 10, (self.out_features - 2) :]
edge_attr = None
# Decompose angular attributes into sines/cosines
sines = torch.sin(batch.x[:, :, [5, 6]])
cosines = torch.cos(batch.x[:, :, [5, 6]])
# Replace yaws with sine/cosine pairs
batch.x = torch.cat(
[
batch.x[:, :, [0, 1, 2, 3, 4]],
sines[:, :, 0].unsqueeze(2),
cosines[:, :, 0].unsqueeze(2),
sines[:, :, 1].unsqueeze(2),
cosines[:, :, 1].unsqueeze(2),
batch.x[:, :, (self.out_features - 2) :],
],
dim=-1,
)
# Initial hidden state
if self.rnn_type == "GRU":
h = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
h = h.type_as(batch.x)
c = None
elif self.rnn_type == "LSTM":
h = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
h = h.type_as(batch.x)
c = torch.zeros((self.model.num_layers, n_nodes, self.model.rnn_size))
c = c.type_as(batch.x)
else:
h, c = None, None
######################
# History #
######################
for t in range(11):
######################
# Graph construction #
######################
mask_t = mask[:, t]
# x_t = torch.cat([batch.x[mask_t, t, :], batch.type[mask_t]], dim=1)
x_t = batch.x[mask_t, t, :].clone()
batch_t = batch.batch[mask_t]
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch_t,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch_t,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = 1 / edge_attr
edge_attr = edge_attr.type_as(batch.x)
edge_attr = torch.nan_to_num(edge_attr, nan=0, posinf=0, neginf=0)
######################
# Predictions 1/2 #
######################
# Normalise input graph
if self.normalise:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][mask_t][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, self.norm_index] /= self.global_scale
if edge_attr is not None:
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain normalised predicted delta dynamics
if h is None:
delta_x = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
)
elif self.rnn_type == "GRU":
delta_x, h_t = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
hidden=h[:, mask_t],
)
# Update hidden state
h[:, mask_t] = h_t
else: # LSTM
delta_x, (h_t, c_t) = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch[mask_t],
hidden=(h[:, mask_t], c[:, mask_t]),
)
# Update hidden state
h[:, mask_t] = h_t
c[:, mask_t] = c_t
# Process predicted yaw values into sine/cosines via tanh
yaw_pred = torch.tanh(delta_x[:, [5, 6, 7, 8]])
# Add deltas to input graph and update yaw values directly
predicted_graph = torch.cat(
[
batch.x[mask_t, t, : (self.out_features - 4)] + delta_x[:, :5],
yaw_pred,
static_features[mask_t],
],
dim=-1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Save predictions and targets
y_hat[t, mask_t, :] = predicted_graph
# y_target[t, mask_t, :] = torch.cat(
# [batch.x[mask_t, t + 1, :], batch.type[mask_t]], dim=1
# )
y_target[t, mask_t, :] = batch.x[mask_t, t + 1, :]
######################
# Future #
######################
for t in range(11, 90):
######################
# Graph construction #
######################
x_t = predicted_graph.clone()
# Construct edges
if self.edge_type == "knn":
# Neighbour-based graph
edge_index = torch_geometric.nn.knn_graph(
x=x_t[:, :2],
k=self.n_neighbours,
batch=batch.batch,
loop=self.self_loop,
)
else:
# Distance-based graph
edge_index = torch_geometric.nn.radius_graph(
x=x_t[:, :2],
r=self.min_dist,
batch=batch.batch,
loop=self.self_loop,
max_num_neighbors=self.n_neighbours,
flow="source_to_target",
)
if self.undirected:
edge_index, edge_attr = torch_geometric.utils.to_undirected(edge_index)
# Remove duplicates and sort
edge_index = torch_geometric.utils.coalesce(edge_index)
# Create edge_attr if specified
if self.edge_weight:
# Encode distance between nodes as edge_attr
row, col = edge_index
edge_attr = (x_t[row, :2] - x_t[col, :2]).norm(dim=-1).unsqueeze(1)
edge_attr = 1 / edge_attr
edge_attr = edge_attr.type_as(batch.x)
edge_attr = torch.nan_to_num(edge_attr, nan=0, posinf=0, neginf=0)
######################
# Predictions 2/2 #
######################
# Normalise input graph
if self.normalise:
# Center node positions
x_t[:, [0, 1, 2]] -= batch.loc[batch.batch][:, [0, 1, 2]]
# Scale all features (except yaws) with global scaler
x_t[:, self.norm_index] /= self.global_scale
if edge_attr is not None:
# Scale edge attributes
edge_attr /= self.global_scale
# Obtain normalised predicted delta dynamics
if h is None:
delta_x = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
)
elif self.rnn_type == "GRU":
delta_x, h = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batc,
hidden=h,
)
else: # LSTM
delta_x, (h, c) = self.model(
x=x_t,
edge_index=edge_index,
edge_attr=edge_attr,
batch=batch.batch,
hidden=(h, c),
)
# Process predicted yaw values into sine/cosines via tanh
yaw_pred = torch.tanh(delta_x[:, [5, 6, 7, 8]])
# Add deltas to input graph and update yaw values directly
predicted_graph = torch.cat(
[
predicted_graph[:, t, : (self.out_features - 4)] + delta_x[:, :5],
yaw_pred,
predicted_graph[:, self.out_features :],
],
dim=-1,
)
predicted_graph = predicted_graph.type_as(batch.x)
# Save prediction alongside true value (next time step state)
y_hat[t, :, :] = predicted_graph
# y_target[t, :, :] = torch.cat([batch.x[:, t + 1, :], batch.type], dim=1)
y_target[t, :, :] = batch.x[:, t + 1, :]
# Process sine/cosines into angular values
yaws = y_target[:, :, [5, 6, 7, 8]]
bbox_yaws = torch.atan2(yaws[:, :, 0], yaws[:, :, 1])
vel_yaws = torch.atan2(yaws[:, :, 2], yaws[:, :, 3])
y_target = torch.cat(
[
y_target[:, :, [0, 1, 2, 3, 4]],
bbox_yaws.unsqueeze(2),
vel_yaws.unsqueeze(2),
y_target[:, :, [9, 10, 11]],
],
dim=-1,
)
yaws = y_hat[:, :, [5, 6, 7, 8]]
bbox_yaws = torch.atan2(yaws[:, :, 0], yaws[:, :, 1])
vel_yaws = torch.atan2(yaws[:, :, 2], yaws[:, :, 3])
y_hat = torch.cat(
[
y_hat[:, :, [0, 1, 2, 3, 4]],
bbox_yaws.unsqueeze(2),
vel_yaws.unsqueeze(2),
y_hat[:, :, [9, 10, 11]],
],
dim=-1,
)
return y_hat, y_target, mask
def configure_optimizers(self):
return torch.optim.Adam(
self.parameters(), lr=self.lr, weight_decay=self.weight_decay
)
class ConstantPhysicalBaselineModule(pl.LightningModule):
def __init__(self, out_features: int = 6, prediction_horizon: int = 91, **kwargs):
super().__init__()
self.val_ade_loss = torchmetrics.MeanSquaredError()
self.val_fde_loss = torchmetrics.MeanSquaredError()
self.val_yaw_loss = torchmetrics.MeanSquaredError()
self.val_vel_loss = torchmetrics.MeanSquaredError()
self.val_fde_ttp_loss = torchmetrics.MeanSquaredError()
self.val_ade_ttp_loss = torchmetrics.MeanSquaredError()
self.prediction_horizon = prediction_horizon
self.out_features = out_features
self.save_hyperparameters()
def training_step(self, batch: Batch, batch_idx: int):
pass
def validation_step(self, batch: Batch, batch_idx: int):
######################
# Initialisation #
######################
# Validate on sequential dataset. First 11 observations are used to prime the model.
# Loss is computed on remaining 80 samples using rollout.
# Determine valid initialisations at t=11
mask = batch.x[:, :, -1]
valid_mask = mask[:, 10] > 0
# Discard non-valid nodes as no initial trajectories will be known
batch.x = batch.x[valid_mask]
batch.batch = batch.batch[valid_mask]
batch.tracks_to_predict = batch.tracks_to_predict[valid_mask]
batch.type = batch.type[valid_mask]
# CARS
type_mask = batch.type[:, 1] == 1
batch.x = batch.x[type_mask]
batch.batch = batch.batch[type_mask]
batch.tracks_to_predict = batch.tracks_to_predict[type_mask]
batch.type = batch.type[type_mask]
# Update input using prediction horizon
batch.x = batch.x[:, : self.prediction_horizon]
# Limit to x, y, x_vel, y_vel
batch.x = batch.x[:, :, [0, 1, 3, 4, 10]]
# Update mask
mask = batch.x[:, :, -1].bool()
# Allocate target/prediction tensors
n_nodes = batch.num_nodes
y_hat = torch.zeros((self.prediction_horizon - 11, n_nodes, self.out_features))
y_target = torch.zeros(
(self.prediction_horizon - 11, n_nodes, self.out_features)
)
# Remove valid flag from features
batch.x = batch.x[:, :, :-1]
# Find valid agents at time t=11
initial_mask = mask[:, 10]
# Extract final dynamic states to use for predictions
last_pos = batch.x[initial_mask, 10][:, [0, 1]]
last_vel = batch.x[initial_mask, 10][:, [2, 3]]
# Constant change in positions
delta_pos = last_vel * 0.1
# First updated position
predicted_pos = last_pos + delta_pos
predicted_graph = torch.cat([predicted_pos, last_vel], dim=1)
# Save first prediction and target
y_hat[0, :, :] = predicted_graph[:, : self.out_features]
y_target[0, :, :] = batch.x[:, 11, : self.out_features]
for t in range(11, self.prediction_horizon - 1):
predicted_pos += delta_pos
predicted_graph = torch.cat([predicted_pos, last_vel], dim=1)
y_hat[t - 10, :, :] = predicted_graph[:, : self.out_features]
y_target[t - 10, :, :] = batch.x[:, t + 1, : self.out_features]
# Extract loss mask
fde_mask = mask[:, -1]
val_mask = mask[:, 11:].permute(1, 0)
# Compute and log loss
fde_loss = self.val_fde_loss(
y_hat[-1, fde_mask][:, [0, 1]], y_target[-1, fde_mask][:, [0, 1]]
)
ade_loss = self.val_ade_loss(
y_hat[:, :, [0, 1]][val_mask], y_target[:, :, [0, 1]][val_mask]
)
vel_loss = self.val_vel_loss(
y_hat[:, :, [2, 3]][val_mask], y_target[:, :, [2, 3]][val_mask]
)
# Compute losses on "tracks_to_predict"
fde_ttp_mask = torch.logical_and(fde_mask, batch.tracks_to_predict)
fde_ttp_loss = self.val_fde_ttp_loss(
y_hat[-1, fde_ttp_mask][:, [0, 1]], y_target[-1, fde_ttp_mask][:, [0, 1]]
)
ade_ttp_mask = torch.logical_and(
val_mask,
batch.tracks_to_predict.expand(
(self.prediction_horizon - 11, mask.size(0))
),
)
ade_ttp_loss = self.val_ade_loss(
y_hat[:, :, [0, 1]][ade_ttp_mask], y_target[:, :, [0, 1]][ade_ttp_mask]
)
######################
# Logging #
######################
self.log("val_ade_loss", ade_loss)
self.log("val_fde_loss", fde_loss)
self.log("val_vel_loss", vel_loss)
loss = ade_loss
self.log("val_total_loss", loss)
self.log("val_fde_ttp_loss", fde_ttp_loss)
self.log("val_ade_ttp_loss", ade_ttp_loss)
return loss
def predict_step(self, batch, batch_idx=None):
######################
# Initialisation #
######################
# Determine valid initialisations at t=11
mask = batch.x[:, :, -1]
valid_mask = mask[:, 10] > 0
# Discard non-valid nodes as no initial trajectories will be known
batch.x = batch.x[valid_mask]
batch.batch = batch.batch[valid_mask]
batch.tracks_to_predict = batch.tracks_to_predict[valid_mask]
batch.type = batch.type[valid_mask]
# CARS
type_mask = batch.type[:, 1] == 1
batch.x = batch.x[type_mask]
batch.batch = batch.batch[type_mask]
batch.tracks_to_predict = batch.tracks_to_predict[type_mask]
batch.type = batch.type[type_mask]
# Update input using prediction horizon
batch.x = batch.x[:, : self.prediction_horizon]
# Limit to x, y, x_vel, y_vel
batch.x = batch.x[:, :, [0, 1, 2, 3]]
# Update mask
mask = batch.x[:, :, -1].bool()
# Allocate target/prediction tensors
n_nodes = batch.num_nodes
y_hat = torch.zeros((self.prediction_horizon - 1, n_nodes, 4))
# Remove valid flag from features
batch.x = batch.x[:, :, :-1]
# Fill in targets
y_target = batch.x[:, 1:]
y_target = y_target.permute(1, 0, 2)
for t in range(11):
mask_t = mask[:, t]
last_pos = batch.x[mask_t, t, 0:2]
last_vel = batch.x[mask_t, t, 3:5]
delta_pos = last_vel * 0.1
predicted_pos = last_pos + delta_pos
predicted_graph = torch.cat([predicted_pos, last_vel], dim=1)
y_hat[t, mask_t, :] = predicted_graph
for t in range(11, 90):
last_pos = predicted_pos
predicted_pos = last_pos + delta_pos
predicted_graph = torch.cat([predicted_pos, last_vel], dim=1)
y_hat[t, :, :] = predicted_graph
return y_hat, y_target, mask
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=1e-4)
@hydra.main(config_path="../../../configs/waymo/", config_name="config")
def main(config):
# Print configuration for online monitoring
print(OmegaConf.to_yaml(config))
# Save complete yaml file for logging and reproducibility
log_dir = f"logs/{config.logger.project}/{config.logger.version}"
os.makedirs(log_dir, exist_ok=True)
yaml_path = f"{log_dir}/{config.logger.version}.yaml"
OmegaConf.save(config, f=yaml_path)
# Seed for reproducibility
seed_everything(config["misc"]["seed"], workers=True)
# Load data, model, and regressor
datamodule = eval(config["misc"]["dm_type"])(**config["datamodule"])
# Define model
if config["misc"]["model_type"] != "ConstantModel":
model_dict = dict(config["model"])
model_type = config["misc"]["model_type"]
else:
model_dict, model_type = None, None
# Define LightningModule
regressor = eval(config["misc"]["regressor_type"])(
model_type=model_type, model_dict=model_dict, **config["regressor"]
)
# Setup logging (using saved yaml file)
wandb_logger = WandbLogger(
entity="petergroth",
config=OmegaConf.to_container(config, resolve=True),
**config["logger"],
)
wandb_logger.watch(regressor, log_freq=config["misc"]["log_freq"], log_graph=False)
# Add default dir for logs
# Setup callbacks
checkpoint_callback = pl.callbacks.ModelCheckpoint(
filename=config["logger"]["version"], monitor="val_total_loss", save_last=True
)
# Create trainer, fit, and validate
trainer = pl.Trainer(
logger=wandb_logger, **config["trainer"], callbacks=[checkpoint_callback]
)
if config["misc"]["train"]:
trainer.fit(model=regressor, datamodule=datamodule)
trainer.validate(regressor, datamodule=datamodule)
if __name__ == "__main__":
main()
| 36.765888
| 111
| 0.498685
| 9,133
| 78,679
| 4.065258
| 0.042812
| 0.028927
| 0.029089
| 0.013898
| 0.881841
| 0.865438
| 0.845777
| 0.838262
| 0.827327
| 0.815907
| 0
| 0.020858
| 0.370544
| 78,679
| 2,139
| 112
| 36.783076
| 0.728824
| 0.147625
| 0
| 0.752899
| 0
| 0
| 0.016652
| 0.002531
| 0
| 0
| 0
| 0
| 0.007246
| 1
| 0.011594
| false
| 0.000725
| 0.012319
| 0.002174
| 0.034058
| 0.000725
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
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| null | 0
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|
0
| 7
|
ecff1cd6cf37a0cc34207f72b05693043936a8ab
| 12,154
|
py
|
Python
|
quanttrader/gui/ui_risk_menu.py
|
qalpha/quanttrader
|
e5c407f76c9d0beeccaa8735052a7e7717f0bed6
|
[
"Apache-2.0"
] | 135
|
2020-09-07T01:07:18.000Z
|
2022-03-31T23:04:40.000Z
|
quanttrader/gui/ui_risk_menu.py
|
qalpha/quanttrader
|
e5c407f76c9d0beeccaa8735052a7e7717f0bed6
|
[
"Apache-2.0"
] | 4
|
2021-04-09T22:13:48.000Z
|
2021-12-23T02:10:37.000Z
|
quanttrader/gui/ui_risk_menu.py
|
qalpha/quanttrader
|
e5c407f76c9d0beeccaa8735052a7e7717f0bed6
|
[
"Apache-2.0"
] | 51
|
2020-09-08T00:18:45.000Z
|
2022-03-28T19:42:44.000Z
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from PyQt5 import QtCore, QtWidgets, QtGui
import logging
_logger = logging.getLogger(__name__)
class RiskMenu(QtWidgets.QWidget):
def __init__(self, strategy_manager):
super(RiskMenu, self).__init__()
self.strategy_manager = strategy_manager
self.init_ui()
def init_ui(self):
self.setWindowTitle('Risk Manager')
self.setWindowIcon(QtGui.QIcon("gui/image/logo.ico"))
self.resize(800, 500)
hbox = QtWidgets.QHBoxLayout()
top = QtWidgets.QFrame()
top.setFrameShape(QtWidgets.QFrame.StyledPanel)
control_layout = QtWidgets.QHBoxLayout()
self.strategy_List = QtWidgets.QComboBox()
self.strategy_List.addItems([str(i) for i in range(len(self.strategy_manager._strategy_dict)+1)])
control_layout.addWidget(self.strategy_List)
self.btn_load = QtWidgets.QPushButton('Load')
self.btn_load.clicked.connect(self.load_config)
control_layout.addWidget(self.btn_load)
top.setLayout(control_layout)
bottom = QtWidgets.QWidget()
bottom_layout = QtWidgets.QFormLayout()
self.order_start_time = QtWidgets.QLineEdit()
self.order_end_time = QtWidgets.QLineEdit()
self.single_trade_limit = QtWidgets.QLineEdit()
self.total_trade_limit = QtWidgets.QLineEdit()
self.total_cancel_limit = QtWidgets.QLineEdit()
self.total_active_limit = QtWidgets.QLineEdit()
self.total_loss_limit = QtWidgets.QLineEdit()
self.btn_save = QtWidgets.QPushButton('Save')
self.btn_save.clicked.connect(self.save_config)
bottom_layout.addRow('order_start_time', self.order_start_time)
bottom_layout.addRow('order_end_time', self.order_end_time)
bottom_layout.addRow('single_trade_limit', self.single_trade_limit)
bottom_layout.addRow('total_trade_limit', self.total_trade_limit)
bottom_layout.addRow('total_cancel_limit', self.total_cancel_limit)
bottom_layout.addRow('total_active_limit', self.total_active_limit)
bottom_layout.addRow('total_loss_limit', self.total_loss_limit)
bottom_layout.addRow(self.btn_save)
bottom.setLayout(bottom_layout)
splitter1 = QtWidgets.QSplitter(QtCore.Qt.Vertical)
splitter1.addWidget(top)
splitter1.addWidget(bottom)
hbox.addWidget(splitter1)
self.setLayout(hbox)
def load_config(self):
sid = self.strategy_List.currentIndex()
if sid == 0:
self.order_start_time.setText('')
self.order_end_time.setText('')
self.single_trade_limit.setText('')
if 'total_trade_limit' in self.strategy_manager._config.keys():
if self.strategy_manager._config['total_trade_limit'] is not None:
self.total_trade_limit.setText(str(self.strategy_manager._config['total_trade_limit']))
else:
self.total_trade_limit.setText('')
else:
self.total_trade_limit.setText('')
if 'total_cancel_limit' in self.strategy_manager._config.keys():
if self.strategy_manager._config['total_cancel_limit'] is not None:
self.total_cancel_limit.setText(str(self.strategy_manager._config['total_cancel_limit']))
else:
self.total_cancel_limit.setText('')
else:
self.total_cancel_limit.setText('')
if 'total_active_limit' in self.strategy_manager._config.keys():
if self.strategy_manager._config['total_active_limit'] is not None:
self.total_active_limit.setText(str(self.strategy_manager._config['total_active_limit']))
else:
self.total_active_limit.setText('')
else:
self.total_active_limit.setText('')
if 'total_loss_limit' in self.strategy_manager._config.keys():
if self.strategy_manager._config['total_loss_limit'] is not None:
self.total_loss_limit.setText(str(self.strategy_manager._config['total_loss_limit']))
else:
self.total_loss_limit.setText('')
else:
self.total_loss_limit.setText('')
else:
if 'order_start_time' in self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name].keys():
if self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_start_time'] is not None:
self.order_start_time.setText(self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_start_time'])
else:
self.order_start_time.setText('')
else:
self.order_start_time.setText('')
if 'order_end_time' in self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name].keys():
if self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_end_time'] is not None:
self.order_end_time.setText(self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_end_time'])
else:
self.order_end_time.setText('')
else:
self.order_end_time.setText('')
if 'single_trade_limit' in self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name].keys():
if self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['single_trade_limit'] is not None:
self.single_trade_limit.setText(str(self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['single_trade_limit']))
else:
self.single_trade_limit.setText('')
else:
self.single_trade_limit.setText('')
if 'total_trade_limit' in self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name].keys():
if self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_trade_limit'] is not None:
self.total_trade_limit.setText(str(self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_trade_limit']))
else:
self.total_trade_limit.setText('')
else:
self.total_trade_limit.setText('')
if 'total_cancel_limit' in self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name].keys():
if self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_cancel_limit'] is not None:
self.total_cancel_limit.setText(str(self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_cancel_limit']))
else:
self.total_cancel_limit.setText('')
else:
self.total_cancel_limit.setText('')
if 'total_active_limit' in self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name].keys():
if self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_active_limit'] is not None:
self.total_active_limit.setText(str(self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_active_limit']))
else:
self.total_active_limit.setText('')
else:
self.total_active_limit.setText('')
if 'total_loss_limit' in self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name].keys():
if self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_loss_limit'] is not None:
self.total_loss_limit.setText(str(self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_loss_limit']))
else:
self.total_loss_limit.setText('')
else:
self.total_loss_limit.setText('')
def save_config(self):
sid = self.strategy_List.currentIndex()
if sid == 0:
if not self.total_trade_limit.text():
self.strategy_manager._config['total_trade_limit'] = None
else:
self.strategy_manager._config['total_trade_limit'] = int(self.total_trade_limit.text())
if not self.total_cancel_limit.text():
self.strategy_manager._config['total_cancel_limit'] = None
else:
self.strategy_manager._config['total_cancel_limit'] = int(self.total_cancel_limit.text())
if not self.total_active_limit.text():
self.strategy_manager._config['total_active_limit'] = None
else:
self.strategy_manager._config['total_active_limit'] = int(self.total_active_limit.text())
if not self.total_loss_limit.text():
self.strategy_manager._config['total_loss_limit'] = None
else:
self.strategy_manager._config['total_loss_limit'] = float(self.total_loss_limit.text())
else:
if not self.order_start_time.text():
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_start_time'] = None
else:
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_start_time'] = self.order_start_time.text()
if not self.order_end_time.text():
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_end_time'] = None
else:
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['order_end_time'] = self.order_end_time.text()
if not self.single_trade_limit.text():
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['single_trade_limit'] = None
else:
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['single_trade_limit'] = int(self.single_trade_limit.text())
if not self.total_trade_limit.text():
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_trade_limit'] = None
else:
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_trade_limit'] = int(self.total_trade_limit.text())
if not self.total_cancel_limit.text():
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_cancel_limit'] = None
else:
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_cancel_limit'] = int(self.total_cancel_limit.text())
if not self.total_active_limit.text():
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_active_limit'] = None
else:
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_active_limit'] = int(self.total_active_limit.text())
if not self.total_loss_limit.text():
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_loss_limit'] = None
else:
self.strategy_manager._config['strategy'][self.strategy_manager._strategy_dict[sid].name]['total_loss_limit'] = float(self.total_loss_limit.text())
| 56.794393
| 169
| 0.660523
| 1,430
| 12,154
| 5.241259
| 0.068531
| 0.156905
| 0.235757
| 0.183456
| 0.82028
| 0.790794
| 0.738492
| 0.723549
| 0.723549
| 0.688726
| 0
| 0.001589
| 0.223548
| 12,154
| 214
| 170
| 56.794393
| 0.792625
| 0.003456
| 0
| 0.4
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| 0
| 0.112707
| 0
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| 0
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| 1
| 0.022222
| false
| 0
| 0.011111
| 0
| 0.038889
| 0
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| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a65e664d42c9621863aad77ff9252683c6eec881
| 92
|
py
|
Python
|
augustine_text/__init__.py
|
kajuberdut/augustine-text
|
3ac2e307d1ecc3c80218ebf24dce1189fe889fbf
|
[
"MIT"
] | null | null | null |
augustine_text/__init__.py
|
kajuberdut/augustine-text
|
3ac2e307d1ecc3c80218ebf24dce1189fe889fbf
|
[
"MIT"
] | null | null | null |
augustine_text/__init__.py
|
kajuberdut/augustine-text
|
3ac2e307d1ecc3c80218ebf24dce1189fe889fbf
|
[
"MIT"
] | null | null | null |
from augustine_text.__version__ import __version__
from augustine_text.markov import Markov
| 30.666667
| 50
| 0.891304
| 12
| 92
| 6
| 0.5
| 0.361111
| 0.472222
| 0
| 0
| 0
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| 0
| 0
| 0
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| 0
| 0.086957
| 92
| 2
| 51
| 46
| 0.857143
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| true
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| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
a680a5fd039061a056aeb1ecba35301fbb784fc1
| 3,603
|
py
|
Python
|
PEtab_problems/Code/Liver/Liver_PEtab.py
|
EmadAlamoudi/FMC_paper
|
fc318407dfee11f373f766222dd37879500d90ce
|
[
"MIT"
] | null | null | null |
PEtab_problems/Code/Liver/Liver_PEtab.py
|
EmadAlamoudi/FMC_paper
|
fc318407dfee11f373f766222dd37879500d90ce
|
[
"MIT"
] | null | null | null |
PEtab_problems/Code/Liver/Liver_PEtab.py
|
EmadAlamoudi/FMC_paper
|
fc318407dfee11f373f766222dd37879500d90ce
|
[
"MIT"
] | null | null | null |
import petab_MS
from fitmulticell.PEtab.base import PetabImporter
from fitmulticell.model import MorpheusModel as morpheus_model
from fitmulticell.model import MorpheusModels as morpheus_models
from pyabc.sampler import RedisEvalParallelSampler
from pyabc.sampler import MulticoreEvalParallelSampler
from pyabc import QuantileEpsilon
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import math
import pyabc
import matplotlib.pylab as plt
from pathlib import Path
import os
import tempfile
# import pathlib
#
# os.chdir('/home/emad/PycharmProjects/PEtab_FMC_extension')
# print(pathlib.Path().resolve())
# petab_problem_path = "/home/emad/Insync/blackhand.3@gmail.com/Google_Drive/Bonn/Github/FMC_paper" + '/PEtab_problems' + '/Liver_regeneration' + '/Meyer_MolSystBiol_2020.yaml'
# petab_problem = petab_MS.Problem.from_yaml(petab_problem_path)
# importer = PetabImporter(petab_problem)
# model = importer.import_petab_problem()
# petab_problem_path = "/home/emad/Insync/blackhand.3@gmail.com/Google_Drive/Bonn/Github/FMC_paper" + '/PEtab_problems' + '/Liver_regeneration' + '/Meyer_MolSystBiol_2020.yaml'
# petab_problem = petab_MS.Problem.from_yaml(petab_problem_path)
# importer = PetabImporter(petab_problem)
# PEtab_prior = importer.create_prior()
# par_map_imported = importer.get_par_map()
# obs_pars_imported = petab_problem.get_x_nominal_dict(scaled=True)
# PEtab_par_scale = petab_problem.get_optimization_parameter_scales()
# dict_data_imported = petab_problem.get_measurement_dict()
# PEtab_model = importer.create_model()
# PEtab_model.timeout = 900
# PEtab_model.ignore_list = ["cell.id", "Tension", "time"]
#
# PEtab_tryjectory = PEtab_model.sample(obs_pars_imported)
# model_dir = "/home/emad/Insync/blackhand.3@gmail.com/Google_Drive/Bonn/Github/FMC_paper" + '/PEtab_problems' + '/Liver_regeneration' + '/YAP_Signaling_Liver_Regeneration_Model_reparametrized_further.xml'
#
# abc = pyabc.ABCSMC(PEtab_model, PEtab_prior, eucl_dist, population_size=2,
# eps=QuantileEpsilon(alpha=0.3), all_accepted=False)
#
# db_path = ("sqlite:///" +
# os.path.join(tempfile.gettempdir(), "test.db"))
# history = abc.new(db_path, dict_data_imported)
# abc.run(max_nr_populations=2)
########################### NEW IMPLEMINTATION ################################
petab_problem_path = "/home/emad/Insync/blackhand.3@gmail.com/Google_Drive/Bonn/Github/FMC_paper" + '/PEtab_problems' + '/Liver_regeneration' + '/Meyer_MolSystBiol_2020.yaml'
petab_problem = petab_MS.Problem.from_yaml(petab_problem_path)
importer = PetabImporter(petab_problem)
PEtab_prior = importer.create_prior()
par_map_imported = importer.get_par_map()
obs_pars_imported = petab_problem.get_x_nominal_dict(scaled=True)
PEtab_par_scale = petab_problem.get_optimization_parameter_scales()
dict_data_imported = petab_problem.get_measurement_dict()
PEtab_model = importer.create_model()
PEtab_model.timeout = 900
PEtab_model.ignore_list = ["cell.id", "Tension", "time"]
Petab_obj_func = importer.get_objective_function()
PEtab_tryjectory = PEtab_model.sample(obs_pars_imported)
model_dir = "/home/emad/Insync/blackhand.3@gmail.com/Google_Drive/Bonn/Github/FMC_paper" + '/PEtab_problems' + '/Liver_regeneration' + '/YAP_Signaling_Liver_Regeneration_Model_reparametrized_further.xml'
abc = pyabc.ABCSMC(PEtab_model, PEtab_prior, Petab_obj_func, population_size=2,
eps=QuantileEpsilon(alpha=0.3))
db_path = ("sqlite:///" +
os.path.join(tempfile.gettempdir(), "test.db"))
history = abc.new(db_path, dict_data_imported)
abc.run(max_nr_populations=2)
| 45.607595
| 205
| 0.780738
| 481
| 3,603
| 5.513514
| 0.253638
| 0.085973
| 0.036199
| 0.043364
| 0.756787
| 0.756787
| 0.756787
| 0.756787
| 0.726621
| 0.726621
| 0
| 0.009474
| 0.091868
| 3,603
| 78
| 206
| 46.192308
| 0.801039
| 0.466833
| 0
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| 0.055556
| 0.188628
| 0.132313
| 0.055556
| 0
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| 0
| 0
| 1
| 0
| false
| 0
| 0.694444
| 0
| 0.694444
| 0
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| 0
| null | 0
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| 1
| 1
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| 1
| 0
| 1
| 0
|
0
| 7
|
a68e3afb357fc90603845f43b1107f1d5a1ab656
| 31,277
|
py
|
Python
|
tests/connection/provider/test_server.py
|
workfloworchestrator/SuPA
|
75c34a446e7133ac3f9378810db749a7df2c21a3
|
[
"Apache-2.0"
] | null | null | null |
tests/connection/provider/test_server.py
|
workfloworchestrator/SuPA
|
75c34a446e7133ac3f9378810db749a7df2c21a3
|
[
"Apache-2.0"
] | 6
|
2021-12-01T13:05:28.000Z
|
2022-03-07T12:40:10.000Z
|
tests/connection/provider/test_server.py
|
workfloworchestrator/SuPA
|
75c34a446e7133ac3f9378810db749a7df2c21a3
|
[
"Apache-2.0"
] | null | null | null |
import unittest.mock
from datetime import datetime, timedelta, timezone
from json import dumps
from typing import Any
from uuid import uuid4
import pytest
from google.protobuf.json_format import Parse
from grpc import ServicerContext
from sqlalchemy import Column
from supa import const
from supa.connection.provider.server import ConnectionProviderService
from supa.db.model import Reservation
from supa.grpc_nsi.connection_common_pb2 import Header, Schedule
from supa.grpc_nsi.connection_provider_pb2 import (
ProvisionRequest,
ReleaseRequest,
ReservationRequestCriteria,
ReserveAbortRequest,
ReserveCommitRequest,
ReserveRequest,
TerminateRequest,
)
from supa.grpc_nsi.services_pb2 import PointToPointService
from supa.util.timestamp import EPOCH
@pytest.fixture()
def pb_header() -> Header:
"""Create protobuf header with unique correlation_id."""
return Parse(
dumps(
{
"protocol_version": "application/vnd.ogf.nsi.cs.v2.provider+soap",
"correlation_id": uuid4().urn,
"requester_nsa": "urn:ogf:network:surf.nl:2020:onsaclient",
"provider_nsa": "urn:ogf:network:example.domain:2001:supa",
"reply_to": "http://127.0.0.1:7080/NSI/services/RequesterService2",
}
),
Header(),
)
@pytest.fixture()
def pb_schedule() -> Schedule:
"""Create protobuf schedule with start time now+1hour and end time now+2hours."""
schedule = Schedule()
schedule.start_time.FromDatetime(datetime.now(timezone.utc) + timedelta(hours=1))
schedule.end_time.FromDatetime(datetime.now(timezone.utc) + timedelta(hours=2))
return schedule
@pytest.fixture()
def pb_ptps() -> PointToPointService:
"""Create protobuf point-to-point-service with standard STPs."""
ptps = PointToPointService()
ptps.capacity = 10
ptps.symmetric_path = True
ptps.source_stp = "urn:ogf:network:netherlight.net:2013:production8:port1?vlan=1783"
ptps.dest_stp = "urn:ogf:network:netherlight.net:2013:production8:port2?vlan=1783"
# The initial version didn't have to support Explicit Routing Objects.
# for param in reservation.parameters:
# pb_ptps.parameters[param.key] = param.value
return ptps
@pytest.fixture()
def pb_reservation_request_criteria(pb_schedule: Schedule, pb_ptps: PointToPointService) -> ReservationRequestCriteria:
"""Create protobuf criteria with filled in schedule and point-to-point-service."""
reservation_request_criteria = ReservationRequestCriteria()
reservation_request_criteria.schedule.CopyFrom(pb_schedule)
reservation_request_criteria.service_type = const.SERVICE_TYPE
reservation_request_criteria.ptps.CopyFrom(pb_ptps)
return reservation_request_criteria
@pytest.fixture()
def pb_reserve_request(
pb_header: Header, pb_reservation_request_criteria: ReservationRequestCriteria
) -> ReserveRequest:
"""Create protobuf reserve request with filled in header and criteria."""
pb_request = ReserveRequest()
pb_request.header.CopyFrom(pb_header)
pb_request.description = "reserve request"
pb_request.criteria.CopyFrom(pb_reservation_request_criteria)
# pb_request.connection_id = ""
return pb_request
@pytest.fixture()
def pb_reserve_request_end_time_before_start_time(pb_reserve_request: ReserveRequest) -> ReserveRequest:
"""Modify schedule of reserve request so that end time is before start time."""
pb_reserve_request.criteria.schedule.start_time.FromDatetime(datetime.now(timezone.utc) + timedelta(hours=2))
pb_reserve_request.criteria.schedule.end_time.FromDatetime(datetime.now(timezone.utc) + timedelta(hours=1))
return pb_reserve_request
@pytest.fixture()
def pb_reserve_request_end_time_in_past(pb_reserve_request: ReserveRequest) -> ReserveRequest:
"""Modify schedule of reserve request so that end time is in the past."""
pb_reserve_request.criteria.schedule.start_time.FromDatetime(EPOCH)
pb_reserve_request.criteria.schedule.end_time.FromDatetime(datetime.now(timezone.utc) - timedelta(hours=1))
return pb_reserve_request
@pytest.fixture()
def pb_reserve_commit_request(pb_header: Header, connection_id: Column) -> ReserveCommitRequest:
"""Create protobuf reserve commit request for connection_id."""
pb_request = ReserveCommitRequest()
pb_request.header.CopyFrom(pb_header)
pb_request.connection_id = str(connection_id)
return pb_request
@pytest.fixture()
def pb_reserve_abort_request(pb_header: Header, connection_id: Column) -> ReserveAbortRequest:
"""Create protobuf reserve abort request for connection_id."""
pb_request = ReserveAbortRequest()
pb_request.header.CopyFrom(pb_header)
pb_request.connection_id = str(connection_id)
return pb_request
@pytest.fixture()
def pb_provision_request(pb_header: Header, connection_id: Column) -> ProvisionRequest:
"""Create protobuf provision request for connection_id."""
pb_request = ProvisionRequest()
pb_request.header.CopyFrom(pb_header)
pb_request.connection_id = str(connection_id)
return pb_request
@pytest.fixture()
def pb_release_request(pb_header: Header, connection_id: Column) -> ReleaseRequest:
"""Create protobuf release request for connection_id."""
pb_request = ReleaseRequest()
pb_request.header.CopyFrom(pb_header)
pb_request.connection_id = str(connection_id)
return pb_request
@pytest.fixture()
def pb_terminate_request(pb_header: Header, connection_id: Column) -> TerminateRequest:
"""Create protobuf terminate request for connection_id."""
pb_request = TerminateRequest()
pb_request.header.CopyFrom(pb_header)
pb_request.connection_id = str(connection_id)
return pb_request
def test_reserve_request(pb_reserve_request: ReserveRequest, caplog: Any) -> None:
"""Test the connection provider Reserve happy path returns connection id."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
request_correlation_id = pb_reserve_request.header.correlation_id
reserve_response = service.Reserve(pb_reserve_request, mock_context)
assert request_correlation_id == reserve_response.header.correlation_id
assert not reserve_response.header.reply_to
assert reserve_response.connection_id
assert not reserve_response.HasField("service_exception")
assert 'Added job "ReserveJob" to job store' in caplog.text
assert 'Added job "ReserveTimeoutJob" to job store' in caplog.text
def test_reserve_request_end_time_before_start_time(
pb_reserve_request_end_time_before_start_time: ReserveRequest, caplog: Any
) -> None:
"""Test the connection provider Reserve returns service exception when end time before start time."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
reserve_response = service.Reserve(pb_reserve_request_end_time_before_start_time, mock_context)
assert pb_reserve_request_end_time_before_start_time.header.correlation_id == reserve_response.header.correlation_id
assert not reserve_response.header.reply_to
assert not reserve_response.connection_id
assert reserve_response.HasField("service_exception")
assert reserve_response.service_exception.error_id == "00101"
assert "End time cannot come before start time" in caplog.text
def test_reserve_request_end_time_in_past(pb_reserve_request_end_time_in_past: ReserveRequest, caplog: Any) -> None:
"""Test the connection provider Reserve returns service exception when end time before start time."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
reserve_response = service.Reserve(pb_reserve_request_end_time_in_past, mock_context)
assert pb_reserve_request_end_time_in_past.header.correlation_id == reserve_response.header.correlation_id
assert not reserve_response.header.reply_to
assert not reserve_response.connection_id
assert reserve_response.HasField("service_exception")
assert reserve_response.service_exception.error_id == "00101"
assert "End time lies in the past" in caplog.text
def test_reserve_commit(pb_reserve_commit_request: ReserveCommitRequest, reserve_held: None, caplog: Any) -> None:
"""Test the connection provider ReserveCommit happy path."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
reserve_commit_response = service.ReserveCommit(pb_reserve_commit_request, mock_context)
assert pb_reserve_commit_request.header.correlation_id == reserve_commit_response.header.correlation_id
assert not reserve_commit_response.header.reply_to
assert not reserve_commit_response.HasField("service_exception")
assert "Canceled reservation timeout timer" in caplog.text
assert 'Added job "ReserveCommitJob" to job store' in caplog.text
def test_reserve_commit_random_connection_id(pb_reserve_commit_request: ReserveCommitRequest, caplog: Any) -> None:
"""Test the connection provider ReserveCommit returns service exception for random connection id."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
# overwrite connection id from reservation in db with random UUID
pb_reserve_commit_request.connection_id = str(uuid4())
reserve_commit_response = service.ReserveCommit(pb_reserve_commit_request, mock_context)
assert pb_reserve_commit_request.header.correlation_id == reserve_commit_response.header.correlation_id
assert pb_reserve_commit_request.connection_id == reserve_commit_response.service_exception.connection_id
assert not reserve_commit_response.header.reply_to
assert reserve_commit_response.HasField("service_exception")
assert reserve_commit_response.service_exception.error_id == "00203"
assert len(reserve_commit_response.service_exception.variables) == 1
assert reserve_commit_response.service_exception.variables[0].type == "connectionId"
assert reserve_commit_response.service_exception.variables[0].value == pb_reserve_commit_request.connection_id
assert "Connection ID does not exist" in caplog.text
def test_reserve_commit_invalid_transition(
pb_reserve_commit_request: ReserveCommitRequest, reserve_committing: None, caplog: Any
) -> None:
"""Test the connection provider ReserveCommit returns service exception when in invalid state for request."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
reserve_commit_response = service.ReserveCommit(pb_reserve_commit_request, mock_context)
assert pb_reserve_commit_request.header.correlation_id == reserve_commit_response.header.correlation_id
assert pb_reserve_commit_request.connection_id == reserve_commit_response.service_exception.connection_id
assert not reserve_commit_response.header.reply_to
assert reserve_commit_response.HasField("service_exception")
assert reserve_commit_response.service_exception.error_id == "00201"
assert len(reserve_commit_response.service_exception.variables) == 2
assert reserve_commit_response.service_exception.variables[0].type == "connectionId"
assert reserve_commit_response.service_exception.variables[0].value == pb_reserve_commit_request.connection_id
assert reserve_commit_response.service_exception.variables[1].type == "reservationState"
assert reserve_commit_response.service_exception.variables[1].value == "RESERVE_COMMITTING"
assert "Not scheduling ReserveCommitJob" in caplog.text
def test_reserve_commit_timed_out(
pb_reserve_commit_request: ReserveCommitRequest, reserve_held: None, flag_reservation_timeout: None, caplog: Any
) -> None:
"""Test connection provider ReserveCommit returns service exception when reservation was flagged as timed out."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
reserve_commit_response = service.ReserveCommit(pb_reserve_commit_request, mock_context)
assert pb_reserve_commit_request.header.correlation_id == reserve_commit_response.header.correlation_id
assert pb_reserve_commit_request.connection_id == reserve_commit_response.service_exception.connection_id
assert not reserve_commit_response.header.reply_to
assert reserve_commit_response.HasField("service_exception")
assert reserve_commit_response.service_exception.error_id == "00700"
assert len(reserve_commit_response.service_exception.variables) == 1
assert reserve_commit_response.service_exception.variables[0].type == "connectionId"
assert reserve_commit_response.service_exception.variables[0].value == pb_reserve_commit_request.connection_id
assert "Cannot commit a timed out reservation" in caplog.text
def test_reserve_abort(pb_reserve_abort_request: ReserveAbortRequest, reserve_held: None, caplog: Any) -> None:
"""Test the connection provider ReserveAbort happy path."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
reserve_abort_response = service.ReserveAbort(pb_reserve_abort_request, mock_context)
assert pb_reserve_abort_request.header.correlation_id == reserve_abort_response.header.correlation_id
assert not reserve_abort_response.header.reply_to
assert not reserve_abort_response.HasField("service_exception")
assert 'Added job "ReserveAbortJob" to job store' in caplog.text
def test_reserve_abort_random_connection_id(pb_reserve_abort_request: ReserveAbortRequest, caplog: Any) -> None:
"""Test the connection provider ReserveAbort returns service exception for random connection id."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
# overwrite connection id from reservation in db with random UUID
pb_reserve_abort_request.connection_id = str(uuid4())
reserve_abort_response = service.ReserveAbort(pb_reserve_abort_request, mock_context)
assert pb_reserve_abort_request.header.correlation_id == reserve_abort_response.header.correlation_id
assert pb_reserve_abort_request.connection_id == reserve_abort_response.service_exception.connection_id
assert not reserve_abort_response.header.reply_to
assert reserve_abort_response.HasField("service_exception")
assert reserve_abort_response.service_exception.error_id == "00203"
assert len(reserve_abort_response.service_exception.variables) == 1
assert reserve_abort_response.service_exception.variables[0].type == "connectionId"
assert reserve_abort_response.service_exception.variables[0].value == pb_reserve_abort_request.connection_id
assert "Connection ID does not exist" in caplog.text
def test_reserve_abort_invalid_transition(
pb_reserve_abort_request: ReserveAbortRequest, reserve_aborting: None, caplog: Any
) -> None:
"""Test the connection provider ReserveAbort returns service exception when in invalid state for request."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
reserve_abort_response = service.ReserveAbort(pb_reserve_abort_request, mock_context)
assert pb_reserve_abort_request.header.correlation_id == reserve_abort_response.header.correlation_id
assert pb_reserve_abort_request.connection_id == reserve_abort_response.service_exception.connection_id
assert not reserve_abort_response.header.reply_to
assert reserve_abort_response.HasField("service_exception")
assert reserve_abort_response.service_exception.error_id == "00201"
assert len(reserve_abort_response.service_exception.variables) == 2
assert reserve_abort_response.service_exception.variables[0].type == "connectionId"
assert reserve_abort_response.service_exception.variables[0].value == pb_reserve_abort_request.connection_id
assert reserve_abort_response.service_exception.variables[1].type == "reservationState"
assert reserve_abort_response.service_exception.variables[1].value == "RESERVE_ABORTING"
assert "Not scheduling ReserveAbortJob" in caplog.text
def test_provision(pb_provision_request: ProvisionRequest, released: None, caplog: Any) -> None:
"""Test the connection provider Provision happy path."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
provision_response = service.Provision(pb_provision_request, mock_context)
assert pb_provision_request.header.correlation_id == provision_response.header.correlation_id
assert not provision_response.header.reply_to
assert not provision_response.HasField("service_exception")
assert 'Added job "ProvisionJob" to job store' in caplog.text
def test_provision_random_connection_id(pb_provision_request: ProvisionRequest, caplog: Any) -> None:
"""Test the connection provider Provision returns service exception for random connection id."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
# overwrite connection id from reservation in db with random UUID
pb_provision_request.connection_id = str(uuid4())
provision_response = service.Provision(pb_provision_request, mock_context)
assert pb_provision_request.header.correlation_id == provision_response.header.correlation_id
assert pb_provision_request.connection_id == provision_response.service_exception.connection_id
assert not provision_response.header.reply_to
assert provision_response.HasField("service_exception")
assert provision_response.service_exception.error_id == "00203"
assert pb_provision_request.connection_id == provision_response.service_exception.connection_id
assert len(provision_response.service_exception.variables) == 1
assert provision_response.service_exception.variables[0].type == "connectionId"
assert provision_response.service_exception.variables[0].value == pb_provision_request.connection_id
assert "Connection ID does not exist" in caplog.text
def test_provision_invalid_transition(pb_provision_request: ProvisionRequest, provisioned: None, caplog: Any) -> None:
"""Test the connection provider Provision returns service exception when in invalid state for request."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
provision_response = service.Provision(pb_provision_request, mock_context)
assert pb_provision_request.header.correlation_id == provision_response.header.correlation_id
assert pb_provision_request.connection_id == provision_response.service_exception.connection_id
assert not provision_response.header.reply_to
assert provision_response.HasField("service_exception")
assert provision_response.service_exception.error_id == "00201"
assert pb_provision_request.connection_id == provision_response.service_exception.connection_id
assert len(provision_response.service_exception.variables) == 2
assert provision_response.service_exception.variables[0].type == "connectionId"
assert provision_response.service_exception.variables[0].value == pb_provision_request.connection_id
assert provision_response.service_exception.variables[1].type == "provisionState"
assert provision_response.service_exception.variables[1].value == "PROVISIONED"
assert "Not scheduling ProvisionJob" in caplog.text
def test_provision_not_committed(pb_provision_request: ProvisionRequest, reserve_held: None, caplog: Any) -> None:
"""Test the connection provider Provision returns service exception when reservation not committed yet."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
provision_response = service.Provision(pb_provision_request, mock_context)
assert pb_provision_request.header.correlation_id == provision_response.header.correlation_id
assert pb_provision_request.connection_id == provision_response.service_exception.connection_id
assert not provision_response.header.reply_to
assert provision_response.HasField("service_exception")
assert provision_response.service_exception.error_id == "00201"
assert len(provision_response.service_exception.variables) == 2
assert provision_response.service_exception.variables[0].type == "connectionId"
assert provision_response.service_exception.variables[0].value == pb_provision_request.connection_id
assert provision_response.service_exception.variables[1].type == "reservationState"
assert provision_response.service_exception.variables[1].value == "RESERVE_HELD"
assert "First version of reservation not committed yet" in caplog.text
def test_provision_passed_end_time(
pb_provision_request: ProvisionRequest, connection_id: Column, released: None, caplog: Any
) -> None:
"""Test the connection provider Provision returns service exception when reservation is passed end time."""
from supa.db.session import db_session
with db_session() as session:
reservation = session.query(Reservation).filter(Reservation.connection_id == connection_id).one()
reservation.start_time = datetime.now(timezone.utc) - timedelta(hours=2)
reservation.end_time = datetime.now(timezone.utc) - timedelta(hours=1)
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
provision_response = service.Provision(pb_provision_request, mock_context)
assert pb_provision_request.header.correlation_id == provision_response.header.correlation_id
assert pb_provision_request.connection_id == provision_response.service_exception.connection_id
assert not provision_response.header.reply_to
assert provision_response.HasField("service_exception")
assert provision_response.service_exception.error_id == "00700"
assert len(provision_response.service_exception.variables) == 1
assert provision_response.service_exception.variables[0].type == "connectionId"
assert provision_response.service_exception.variables[0].value == pb_provision_request.connection_id
assert "Cannot provision a reservation that is passed end time" in caplog.text
def test_release(pb_release_request: ReleaseRequest, provisioned: None, caplog: Any) -> None:
"""Test the connection provider Release happy path."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
release_response = service.Release(pb_release_request, mock_context)
assert pb_release_request.header.correlation_id == release_response.header.correlation_id
assert not release_response.header.reply_to
assert not release_response.HasField("service_exception")
assert 'Added job "ReleaseJob" to job store' in caplog.text
def test_release_random_connection_id(pb_release_request: ReleaseRequest, caplog: Any) -> None:
"""Test the connection provider Release returns service exception for random connection id."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
# overwrite connection id from reservation in db with random UUID
pb_release_request.connection_id = str(uuid4())
release_response = service.Release(pb_release_request, mock_context)
assert pb_release_request.header.correlation_id == release_response.header.correlation_id
assert pb_release_request.connection_id == release_response.service_exception.connection_id
assert not release_response.header.reply_to
assert release_response.HasField("service_exception")
assert release_response.service_exception.error_id == "00203"
assert len(release_response.service_exception.variables) == 1
assert release_response.service_exception.variables[0].type == "connectionId"
assert release_response.service_exception.variables[0].value == pb_release_request.connection_id
assert "Connection ID does not exist" in caplog.text
def test_release_invalid_transition(pb_release_request: ReleaseRequest, released: None, caplog: Any) -> None:
"""Test the connection provider release returns service exception when in invalid state for request."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
release_response = service.Release(pb_release_request, mock_context)
assert pb_release_request.header.correlation_id == release_response.header.correlation_id
assert pb_release_request.connection_id == release_response.service_exception.connection_id
assert not release_response.header.reply_to
assert release_response.HasField("service_exception")
assert release_response.service_exception.error_id == "00201"
assert len(release_response.service_exception.variables) == 2
assert release_response.service_exception.variables[0].type == "connectionId"
assert release_response.service_exception.variables[0].value == pb_release_request.connection_id
assert release_response.service_exception.variables[1].type == "provisionState"
assert release_response.service_exception.variables[1].value == "RELEASED"
assert "Not scheduling ReleaseJob" in caplog.text
def test_release_not_committed(pb_release_request: ReleaseRequest, reserve_held: None, caplog: Any) -> None:
"""Test the connection provider Release returns service exception when reservation not committed yet."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
release_response = service.Release(pb_release_request, mock_context)
assert pb_release_request.header.correlation_id == release_response.header.correlation_id
assert pb_release_request.connection_id == release_response.service_exception.connection_id
assert not release_response.header.reply_to
assert release_response.HasField("service_exception")
assert release_response.service_exception.error_id == "00201"
assert len(release_response.service_exception.variables) == 2
assert release_response.service_exception.variables[0].type == "connectionId"
assert release_response.service_exception.variables[0].value == pb_release_request.connection_id
assert release_response.service_exception.variables[1].type == "reservationState"
assert release_response.service_exception.variables[1].value == "RESERVE_HELD"
assert "First version of reservation not committed yet" in caplog.text
def test_release_passed_end_time(
pb_release_request: ReleaseRequest, connection_id: Column, provisioned: None, caplog: Any
) -> None:
"""Test the connection provider Release returns service exception when reservation is passed end time."""
from supa.db.session import db_session
with db_session() as session:
reservation = session.query(Reservation).filter(Reservation.connection_id == connection_id).one()
reservation.start_time = datetime.now(timezone.utc) - timedelta(hours=2)
reservation.end_time = datetime.now(timezone.utc) - timedelta(hours=1)
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
release_response = service.Release(pb_release_request, mock_context)
assert pb_release_request.header.correlation_id == release_response.header.correlation_id
assert pb_release_request.connection_id == release_response.service_exception.connection_id
assert not release_response.header.reply_to
assert release_response.HasField("service_exception")
assert release_response.service_exception.error_id == "00700"
assert len(release_response.service_exception.variables) == 1
assert release_response.service_exception.variables[0].type == "connectionId"
assert release_response.service_exception.variables[0].value == pb_release_request.connection_id
assert "Cannot release a reservation that is passed end time" in caplog.text
def test_terminate(pb_terminate_request: TerminateRequest, provisioned: None, caplog: Any) -> None:
"""Test the connection provider Terminate happy path."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
terminate_response = service.Terminate(pb_terminate_request, mock_context)
assert pb_terminate_request.header.correlation_id == terminate_response.header.correlation_id
assert not terminate_response.header.reply_to
assert not terminate_response.HasField("service_exception")
assert 'Added job "TerminateJob" to job store' in caplog.text
def test_terminate_random_connection_id(pb_terminate_request: TerminateRequest, caplog: Any) -> None:
"""Test the connection provider Release returns service exception for random connection id."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
# overwrite connection id from reservation in db with random UUID
pb_terminate_request.connection_id = str(uuid4())
terminate_response = service.Terminate(pb_terminate_request, mock_context)
assert pb_terminate_request.header.correlation_id == terminate_response.header.correlation_id
assert pb_terminate_request.connection_id == terminate_response.service_exception.connection_id
assert not terminate_response.header.reply_to
assert terminate_response.HasField("service_exception")
assert terminate_response.service_exception.error_id == "00203"
assert len(terminate_response.service_exception.variables) == 1
assert terminate_response.service_exception.variables[0].type == "connectionId"
assert terminate_response.service_exception.variables[0].value == pb_terminate_request.connection_id
assert "Connection ID does not exist" in caplog.text
def test_terminate_invalid_transition(pb_terminate_request: TerminateRequest, terminated: None, caplog: Any) -> None:
"""Test the connection provider Release returns service exception for random connection id."""
service = ConnectionProviderService()
mock_context = unittest.mock.create_autospec(spec=ServicerContext)
terminate_response = service.Terminate(pb_terminate_request, mock_context)
assert pb_terminate_request.header.correlation_id == terminate_response.header.correlation_id
assert pb_terminate_request.connection_id == terminate_response.service_exception.connection_id
assert not terminate_response.header.reply_to
assert terminate_response.HasField("service_exception")
assert terminate_response.service_exception.error_id == "00201"
assert len(terminate_response.service_exception.variables) == 2
assert terminate_response.service_exception.variables[0].type == "connectionId"
assert terminate_response.service_exception.variables[0].value == pb_terminate_request.connection_id
assert terminate_response.service_exception.variables[1].type == "lifecycleState"
assert terminate_response.service_exception.variables[1].value == "TERMINATED"
assert "Not scheduling TerminateJob" in caplog.text
| 58.243948
| 120
| 0.802219
| 3,790
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| 0.764939
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| 0.696009
| 0
| 0.007531
| 0.121207
| 31,277
| 536
| 121
| 58.352612
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| 0.075664
| 0.008973
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| 0
| 0.454545
| 1
| 0.083732
| false
| 0.009569
| 0.043062
| 0
| 0.155502
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
a6d9eb02fa54616d7a7a592a14d492e3f1cf4247
| 856
|
py
|
Python
|
apiConDjango/administrador/models.py
|
fernandopacco71/PROYECT-API-DJANGO
|
fdf95c381ca9dc3e503b19d30c716100a0714018
|
[
"MIT"
] | null | null | null |
apiConDjango/administrador/models.py
|
fernandopacco71/PROYECT-API-DJANGO
|
fdf95c381ca9dc3e503b19d30c716100a0714018
|
[
"MIT"
] | null | null | null |
apiConDjango/administrador/models.py
|
fernandopacco71/PROYECT-API-DJANGO
|
fdf95c381ca9dc3e503b19d30c716100a0714018
|
[
"MIT"
] | null | null | null |
from django.db import models
# Create your models here.
#class Personal(models.Model):
# Nombres = models.CharField(max_length=30, null=False)
# Apellidos = models.CharField(max_length=30)
# DNI = models.IntegerField()
# Direccion = models.CharField(max_length=100)
# Sexo = models.CharField(max_length=40)
# Cargo = models.CharField(max_length=50)
# Telefono = models.IntegerField()
# Correo = models.CharField(max_length=50)
class Personal(models.Model):
Nombres = models.CharField(max_length=30, null=False)
Apellidos = models.CharField(max_length=30)
DNI = models.CharField(max_length=8)
Direccion = models.CharField(max_length=100)
Sexo = models.CharField(max_length=40)
Cargo = models.CharField(max_length=50)
Telefono = models.CharField(max_length=9)
Correo = models.CharField(max_length=50)
| 38.909091
| 58
| 0.731308
| 111
| 856
| 5.513514
| 0.279279
| 0.343137
| 0.411765
| 0.54902
| 0.826797
| 0.826797
| 0.722222
| 0.722222
| 0.722222
| 0.722222
| 0
| 0.038462
| 0.149533
| 856
| 22
| 59
| 38.909091
| 0.802198
| 0.457944
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
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| 0
| 1
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| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 10
|
4702887a2015ec3abf015e4cfd513a623891ceb7
| 2,381
|
py
|
Python
|
notebooks/autoencoders/CIFAR10/classifier.py
|
tayden/NoveltyDetection
|
797de85b7543e4d0f118295b31a36ec17126d459
|
[
"MIT"
] | null | null | null |
notebooks/autoencoders/CIFAR10/classifier.py
|
tayden/NoveltyDetection
|
797de85b7543e4d0f118295b31a36ec17126d459
|
[
"MIT"
] | null | null | null |
notebooks/autoencoders/CIFAR10/classifier.py
|
tayden/NoveltyDetection
|
797de85b7543e4d0f118295b31a36ec17126d459
|
[
"MIT"
] | null | null | null |
from keras.layers import Input, Conv2D, MaxPool2D, Dense
from keras.layers import Activation, Flatten, Dropout, BatchNormalization
from keras import regularizers
from keras.models import Model
def create_model():
image = Input(shape=(32, 32, 3))
x = Conv2D(64, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(image)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.3)(x)
x = Conv2D(64, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = MaxPool2D(padding='same')(x)
x = Conv2D(128, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.4)(x)
x = Conv2D(128, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = MaxPool2D(padding='same')(x)
x = Conv2D(256, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.4)(x)
x = Conv2D(256, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = MaxPool2D(padding='same')(x)
x = Conv2D(512, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.4)(x)
x = Conv2D(512, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = MaxPool2D(padding='same')(x)
x = Conv2D(512, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.4)(x)
x = Conv2D(512, 3, padding='same', kernel_regularizer=regularizers.l2(5e-4))(x)
x = Activation('relu')(x)
x = BatchNormalization()(x)
x = MaxPool2D(padding='same')(x)
x = Flatten()(x)
x = Dropout(0.5)(x)
x = Dense(4096, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Dense(4096, activation='relu')(x)
x = BatchNormalization()(x)
x = Dense(10)(x)
x = Activation('softmax')(x)
classifier = Model(inputs=image, outputs=x)
return Model(image, x)
| 31.328947
| 86
| 0.631667
| 333
| 2,381
| 4.483483
| 0.129129
| 0.06296
| 0.026122
| 0.1286
| 0.8071
| 0.8071
| 0.8071
| 0.8071
| 0.8071
| 0.8071
| 0
| 0.058763
| 0.185216
| 2,381
| 75
| 87
| 31.746667
| 0.710825
| 0
| 0
| 0.754386
| 0
| 0
| 0.048299
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.017544
| false
| 0
| 0.070175
| 0
| 0.105263
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
47091f9d2c4224d5220caaf70b7aaceebb001d41
| 8,931
|
py
|
Python
|
api/services/v1/dataset_object_service_pb2_grpc.py
|
cpfaff/py.nfdi.core.storage
|
306cff52c32517df3747533bd4a2850d15ad0956
|
[
"Apache-2.0"
] | null | null | null |
api/services/v1/dataset_object_service_pb2_grpc.py
|
cpfaff/py.nfdi.core.storage
|
306cff52c32517df3747533bd4a2850d15ad0956
|
[
"Apache-2.0"
] | null | null | null |
api/services/v1/dataset_object_service_pb2_grpc.py
|
cpfaff/py.nfdi.core.storage
|
306cff52c32517df3747533bd4a2850d15ad0956
|
[
"Apache-2.0"
] | null | null | null |
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from api.services.v1 import dataset_object_service_models_pb2 as api_dot_services_dot_v1_dot_dataset__object__service__models__pb2
class DatasetObjectsServiceStub(object):
"""Missing associated documentation comment in .proto file."""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.CreateObjectGroup = channel.unary_unary(
'/api.services.v1.DatasetObjectsService/CreateObjectGroup',
request_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.CreateObjectGroupRequest.SerializeToString,
response_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.CreateObjectGroupResponse.FromString,
)
self.GetObjectGroup = channel.unary_unary(
'/api.services.v1.DatasetObjectsService/GetObjectGroup',
request_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.GetObjectGroupRequest.SerializeToString,
response_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.GetObjectGroupResponse.FromString,
)
self.FinishObjectUpload = channel.unary_unary(
'/api.services.v1.DatasetObjectsService/FinishObjectUpload',
request_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.FinishObjectUploadRequest.SerializeToString,
response_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.FinishObjectUploadResponse.FromString,
)
self.DeleteObjectGroup = channel.unary_unary(
'/api.services.v1.DatasetObjectsService/DeleteObjectGroup',
request_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.DeleteObjectGroupRequest.SerializeToString,
response_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.DeleteObjectGroupResponse.FromString,
)
class DatasetObjectsServiceServicer(object):
"""Missing associated documentation comment in .proto file."""
def CreateObjectGroup(self, request, context):
"""CreateObjectGroup Creates a new object group
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def GetObjectGroup(self, request, context):
"""GetObjectGroup Returns the object group with the given ID
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def FinishObjectUpload(self, request, context):
"""FinishObjectUpload Finishes the upload process for an object
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def DeleteObjectGroup(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_DatasetObjectsServiceServicer_to_server(servicer, server):
rpc_method_handlers = {
'CreateObjectGroup': grpc.unary_unary_rpc_method_handler(
servicer.CreateObjectGroup,
request_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.CreateObjectGroupRequest.FromString,
response_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.CreateObjectGroupResponse.SerializeToString,
),
'GetObjectGroup': grpc.unary_unary_rpc_method_handler(
servicer.GetObjectGroup,
request_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.GetObjectGroupRequest.FromString,
response_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.GetObjectGroupResponse.SerializeToString,
),
'FinishObjectUpload': grpc.unary_unary_rpc_method_handler(
servicer.FinishObjectUpload,
request_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.FinishObjectUploadRequest.FromString,
response_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.FinishObjectUploadResponse.SerializeToString,
),
'DeleteObjectGroup': grpc.unary_unary_rpc_method_handler(
servicer.DeleteObjectGroup,
request_deserializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.DeleteObjectGroupRequest.FromString,
response_serializer=api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.DeleteObjectGroupResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'api.services.v1.DatasetObjectsService', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class DatasetObjectsService(object):
"""Missing associated documentation comment in .proto file."""
@staticmethod
def CreateObjectGroup(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/api.services.v1.DatasetObjectsService/CreateObjectGroup',
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.CreateObjectGroupRequest.SerializeToString,
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.CreateObjectGroupResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def GetObjectGroup(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/api.services.v1.DatasetObjectsService/GetObjectGroup',
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.GetObjectGroupRequest.SerializeToString,
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.GetObjectGroupResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def FinishObjectUpload(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/api.services.v1.DatasetObjectsService/FinishObjectUpload',
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.FinishObjectUploadRequest.SerializeToString,
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.FinishObjectUploadResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def DeleteObjectGroup(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/api.services.v1.DatasetObjectsService/DeleteObjectGroup',
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.DeleteObjectGroupRequest.SerializeToString,
api_dot_services_dot_v1_dot_dataset__object__service__models__pb2.DeleteObjectGroupResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
| 52.846154
| 151
| 0.725563
| 853
| 8,931
| 7.065651
| 0.134818
| 0.056081
| 0.086278
| 0.112162
| 0.80521
| 0.778497
| 0.778497
| 0.711465
| 0.702505
| 0.683591
| 0
| 0.00868
| 0.213078
| 8,931
| 168
| 152
| 53.160714
| 0.84889
| 0.072668
| 0
| 0.484848
| 1
| 0
| 0.089038
| 0.058587
| 0
| 0
| 0
| 0
| 0
| 1
| 0.075758
| false
| 0
| 0.015152
| 0.030303
| 0.143939
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
5b2a9472870dacb18ed71dc4b6d97e11785a1fdc
| 1,613
|
py
|
Python
|
windows/crypto/dpapi.py
|
IMULMUL/PythonForWindows
|
61e027a678d5b87aa64fcf8a37a6661a86236589
|
[
"BSD-3-Clause"
] | 479
|
2016-01-08T00:53:34.000Z
|
2022-03-22T10:28:19.000Z
|
windows/crypto/dpapi.py
|
IMULMUL/PythonForWindows
|
61e027a678d5b87aa64fcf8a37a6661a86236589
|
[
"BSD-3-Clause"
] | 38
|
2017-12-29T17:09:04.000Z
|
2022-01-31T08:27:47.000Z
|
windows/crypto/dpapi.py
|
IMULMUL/PythonForWindows
|
61e027a678d5b87aa64fcf8a37a6661a86236589
|
[
"BSD-3-Clause"
] | 103
|
2016-01-10T01:32:17.000Z
|
2021-12-24T17:21:06.000Z
|
from windows import winproxy
import windows.generated_def as gdef
__all__ = ["protect", "unprotect"]
def protect(data, entropy=None, flags=gdef.CRYPTPROTECT_UI_FORBIDDEN):
in_blob = gdef.DATA_BLOB.from_string(data)
out_blob = gdef.DATA_BLOB()
if entropy is not None:
entropy = gdef.DATA_BLOB.from_string(entropy)
winproxy.CryptProtectData(in_blob, pOptionalEntropy=entropy, dwFlags=flags, pDataOut=out_blob)
encrypted_data = bytes(out_blob.data)
# https://docs.microsoft.com/en-us/windows/win32/api/dpapi/nf-dpapi-cryptprotectdata
# pDataOut: A pointer to a DATA_BLOB structure that receives the encrypted data.
# When you have finished using the DATA_BLOB structure, free its pbData member by calling the LocalFree function.
winproxy.LocalFree(out_blob.pbData)
del out_blob
return encrypted_data
def unprotect(data, entropy=None, flags=gdef.CRYPTPROTECT_UI_FORBIDDEN):
in_blob = gdef.DATA_BLOB.from_string(data)
out_blob = gdef.DATA_BLOB()
if entropy is not None:
entropy = gdef.DATA_BLOB.from_string(entropy)
winproxy.CryptUnprotectData(in_blob, pOptionalEntropy=entropy, dwFlags=flags, pDataOut=out_blob)
decrypted_data = bytes(out_blob.data)
# https://docs.microsoft.com/en-us/windows/win32/api/dpapi/nf-dpapi-cryptprotectdata
# pDataOut: A pointer to a DATA_BLOB structure that receives the encrypted data.
# When you have finished using the DATA_BLOB structure, free its pbData member by calling the LocalFree function.
winproxy.LocalFree(out_blob.pbData)
del out_blob
return decrypted_data
| 47.441176
| 117
| 0.761314
| 227
| 1,613
| 5.229075
| 0.286344
| 0.067397
| 0.060657
| 0.053917
| 0.85257
| 0.85257
| 0.85257
| 0.85257
| 0.85257
| 0.758214
| 0
| 0.002943
| 0.157471
| 1,613
| 34
| 118
| 47.441176
| 0.870493
| 0.344079
| 0
| 0.521739
| 1
| 0
| 0.015209
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.086957
| false
| 0
| 0.086957
| 0
| 0.26087
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
5b691188e643f57bf3d54cd77835b3db4e5ca2f8
| 238
|
py
|
Python
|
provisioner/__init__.py
|
lander2k2/silenus-provisioner
|
3c0c722ac553be53964e0ffb3530d8ae18ab7f1c
|
[
"Apache-2.0"
] | null | null | null |
provisioner/__init__.py
|
lander2k2/silenus-provisioner
|
3c0c722ac553be53964e0ffb3530d8ae18ab7f1c
|
[
"Apache-2.0"
] | null | null | null |
provisioner/__init__.py
|
lander2k2/silenus-provisioner
|
3c0c722ac553be53964e0ffb3530d8ae18ab7f1c
|
[
"Apache-2.0"
] | null | null | null |
import os
from provisioner.database import Database
db = Database(os.environ.get('DB_HOST'),
os.environ.get('POSTGRES_DB'),
os.environ.get('POSTGRES_USER'),
os.environ.get('POSTGRES_PASSWORD'))
| 26.444444
| 50
| 0.634454
| 29
| 238
| 5.068966
| 0.413793
| 0.244898
| 0.326531
| 0.408163
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.235294
| 238
| 8
| 51
| 29.75
| 0.807692
| 0
| 0
| 0
| 0
| 0
| 0.202532
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.166667
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
5bcdc59a05a6f007039422aec645504c7467e82b
| 4,682
|
py
|
Python
|
year_2017/day_02.py
|
Alex-Diez/python-advent-of-code
|
d53f13a1b960559da64a2e87746000c78775d584
|
[
"MIT"
] | null | null | null |
year_2017/day_02.py
|
Alex-Diez/python-advent-of-code
|
d53f13a1b960559da64a2e87746000c78775d584
|
[
"MIT"
] | null | null | null |
year_2017/day_02.py
|
Alex-Diez/python-advent-of-code
|
d53f13a1b960559da64a2e87746000c78775d584
|
[
"MIT"
] | null | null | null |
import unittest
def checksum(spreed_sheet):
check_sum = 0
for row in spreed_sheet.split('\n'):
numbers = row.split('\t')
max_num = int(numbers[0])
min_num = int(numbers[0])
for index in range(1, len(numbers)):
num = int(numbers[index])
if max_num < num:
max_num = num
if min_num > num:
min_num = num
check_sum += max_num - min_num
return check_sum
def sum_of_divisible(spreed_sheet):
check_sum = 0
for row in spreed_sheet.split('\n'):
numbers = row.split('\t')
check_sum += _check_sum_of_division(numbers)
return check_sum
def _check_sum_of_division(numbers):
for i in range(len(numbers)):
dividend = int(numbers[i])
for j in range(len(numbers)):
if i != j:
divisor = int(numbers[j])
if dividend % divisor == 0:
return dividend / divisor
return 0
class CheckSumTest(unittest.TestCase):
def testCheckSumOfOnes(self):
self.assertEqual(0, checksum('1\t1\t1\n1\t1\t1\t1\n1\t1\t1'))
def testCheckSumOfSingleDiff(self):
self.assertEqual(1, checksum('1\t1\t1\n1\t2\t1\t1\n1\t1\t1'))
def testTaskInput(self):
task_input = """5\t1\t9\t5
7\t5\t3
2\t4\t6\t8"""
self.assertEqual(18, checksum(task_input))
def testPrintSolution(self):
test_input = """116\t1259\t1045\t679\t1334\t157\t277\t1217\t218\t641\t1089\t136\t247\t1195\t239\t834
269\t1751\t732\t3016\t260\t6440\t5773\t4677\t306\t230\t6928\t7182\t231\t2942\t2738\t3617
644\t128\t89\t361\t530\t97\t35\t604\t535\t297\t599\t121\t567\t106\t114\t480
105\t408\t120\t363\t430\t102\t137\t283\t123\t258\t19\t101\t181\t477\t463\t279
873\t116\t840\t105\t285\t238\t540\t22\t117\t125\t699\t953\t920\t106\t113\t259
3695\t161\t186\t2188\t3611\t2802\t157\t2154\t3394\t145\t2725\t1327\t3741\t2493\t3607\t4041
140\t1401\t110\t119\t112\t1586\t125\t937\t1469\t1015\t879\t1798\t122\t1151\t100\t926
2401\t191\t219\t607\t267\t2362\t932\t2283\t889\t2567\t2171\t2409\t1078\t2247\t2441\t245
928\t1142\t957\t1155\t922\t1039\t452\t285\t467\t305\t506\t221\t281\t59\t667\t232
3882\t1698\t170\t5796\t2557\t173\t1228\t4630\t174\t3508\t5629\t4395\t180\t5100\t2814\t2247
396\t311\t223\t227\t340\t313\t355\t469\t229\t162\t107\t76\t363\t132\t453\t161
627\t1331\t1143\t1572\t966\t388\t198\t2068\t201\t239\t176\t1805\t1506\t1890\t1980\t1887
3390\t5336\t1730\t4072\t5342\t216\t3823\t85\t5408\t5774\t247\t5308\t232\t256\t5214\t787
176\t1694\t1787\t1586\t3798\t4243\t157\t4224\t3603\t2121\t3733\t851\t2493\t4136\t148\t153
2432\t4030\t3397\t4032\t3952\t2727\t157\t3284\t3450\t3229\t4169\t3471\t4255\t155\t127\t186
919\t615\t335\t816\t138\t97\t881\t790\t855\t89\t451\t789\t423\t108\t95\t116"""
print(checksum(test_input))
class SumOfEvenlyDivisibleValuesTest(unittest.TestCase):
def testNoDivisibleValues(self):
self.assertEqual(0, sum_of_divisible('3\t5'))
def testDividendAndDivisor(self):
self.assertEqual(2, sum_of_divisible('2\t4'))
def testTaskInput(self):
task_input = """5\t9\t2\t8
9\t4\t7\t3
3\t8\t6\t5"""
self.assertEqual(9, sum_of_divisible(task_input))
def testPrintSolution(self):
test_input = """116\t1259\t1045\t679\t1334\t157\t277\t1217\t218\t641\t1089\t136\t247\t1195\t239\t834
269\t1751\t732\t3016\t260\t6440\t5773\t4677\t306\t230\t6928\t7182\t231\t2942\t2738\t3617
644\t128\t89\t361\t530\t97\t35\t604\t535\t297\t599\t121\t567\t106\t114\t480
105\t408\t120\t363\t430\t102\t137\t283\t123\t258\t19\t101\t181\t477\t463\t279
873\t116\t840\t105\t285\t238\t540\t22\t117\t125\t699\t953\t920\t106\t113\t259
3695\t161\t186\t2188\t3611\t2802\t157\t2154\t3394\t145\t2725\t1327\t3741\t2493\t3607\t4041
140\t1401\t110\t119\t112\t1586\t125\t937\t1469\t1015\t879\t1798\t122\t1151\t100\t926
2401\t191\t219\t607\t267\t2362\t932\t2283\t889\t2567\t2171\t2409\t1078\t2247\t2441\t245
928\t1142\t957\t1155\t922\t1039\t452\t285\t467\t305\t506\t221\t281\t59\t667\t232
3882\t1698\t170\t5796\t2557\t173\t1228\t4630\t174\t3508\t5629\t4395\t180\t5100\t2814\t2247
396\t311\t223\t227\t340\t313\t355\t469\t229\t162\t107\t76\t363\t132\t453\t161
627\t1331\t1143\t1572\t966\t388\t198\t2068\t201\t239\t176\t1805\t1506\t1890\t1980\t1887
3390\t5336\t1730\t4072\t5342\t216\t3823\t85\t5408\t5774\t247\t5308\t232\t256\t5214\t787
176\t1694\t1787\t1586\t3798\t4243\t157\t4224\t3603\t2121\t3733\t851\t2493\t4136\t148\t153
2432\t4030\t3397\t4032\t3952\t2727\t157\t3284\t3450\t3229\t4169\t3471\t4255\t155\t127\t186
919\t615\t335\t816\t138\t97\t881\t790\t855\t89\t451\t789\t423\t108\t95\t116"""
print(sum_of_divisible(test_input))
| 45.456311
| 108
| 0.724904
| 775
| 4,682
| 4.322581
| 0.390968
| 0.019104
| 0.020896
| 0.007164
| 0.768657
| 0.744179
| 0.719403
| 0.719403
| 0.719403
| 0.719403
| 0
| 0.430407
| 0.11918
| 4,682
| 102
| 109
| 45.901961
| 0.381911
| 0
| 0
| 0.511628
| 0
| 0.372093
| 0.605938
| 0.583084
| 0
| 0
| 0
| 0
| 0.069767
| 1
| 0.127907
| false
| 0
| 0.011628
| 0
| 0.209302
| 0.023256
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
f334e935c233a5f2d07953818712b0c79c94c877
| 14,500
|
py
|
Python
|
src/third_party/beaengine/tests/0f01.py
|
CrackerCat/rp
|
5fe693c26d76b514efaedb4084f6e37d820db023
|
[
"MIT"
] | 1
|
2022-01-17T17:40:29.000Z
|
2022-01-17T17:40:29.000Z
|
src/third_party/beaengine/tests/0f01.py
|
CrackerCat/rp
|
5fe693c26d76b514efaedb4084f6e37d820db023
|
[
"MIT"
] | null | null | null |
src/third_party/beaengine/tests/0f01.py
|
CrackerCat/rp
|
5fe693c26d76b514efaedb4084f6e37d820db023
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>
#
# @author : beaengine@gmail.com
from headers.BeaEnginePython import *
from nose.tools import *
class TestSuite:
def check_np(self, data):
Buffer = bytes.fromhex(f'66{data}')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.repr(), '???')
Buffer = bytes.fromhex(f'f2{data}')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.repr(), '???')
Buffer = bytes.fromhex(f'f3{data}')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.repr(), '???')
def test(self):
# 66 0F 01 CF
# SEAMCALL
Buffer = bytes.fromhex('660f01cf')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, VM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'seamcall')
assert_equal(myDisasm.repr(), 'seamcall')
# 66 0F 01 CE
# SEAMOPS
Buffer = bytes.fromhex('660f01ce')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, VM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'seamops')
assert_equal(myDisasm.infos.Operand1.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand1.Registers.gpr, REG0)
assert_equal(myDisasm.infos.Operand1.OpSize, 64)
assert_equal(myDisasm.infos.Operand1.AccessMode, READ)
assert_equal(myDisasm.repr(), 'seamops')
# 66 0F 01 CD
# SEAMRET
Buffer = bytes.fromhex('660f01cd')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, VM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'seamret')
assert_equal(myDisasm.repr(), 'seamret')
# 66 0F 01 CC
# TDCALL
Buffer = bytes.fromhex('660f01cc')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, VM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'tdcall')
assert_equal(myDisasm.repr(), 'tdcall')
# NP 0F 01 CA
# CLAC
Buffer = bytes.fromhex('0f01ca')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'clac')
self.check_np('0f01ca')
# NP 0F 01 CB
# STAC
Buffer = bytes.fromhex('0f01cb')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'stac')
self.check_np('0f01cb')
# NP 0F 01 C5
# PCONFIG
# #UD If any of the LOCK/REP/OSIZE/VEX prefixes are used.
Buffer = bytes.fromhex('0f01c5')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'pconfig')
Buffer = bytes.fromhex('f00f01c5')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'pconfig')
assert_equal(myDisasm.infos.Reserved_.ERROR_OPCODE, UD_)
Buffer = bytes.fromhex('f20f01c5')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'pconfig')
assert_equal(myDisasm.infos.Reserved_.ERROR_OPCODE, UD_)
Buffer = bytes.fromhex('f30f01c5')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'pconfig')
assert_equal(myDisasm.infos.Reserved_.ERROR_OPCODE, UD_)
# NP 0F 01 C0
# ENCLV
Buffer = bytes.fromhex('0f01c0')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'enclv')
self.check_np('0f01c0')
# NP 0F 01 D7
# ENCLU
Buffer = bytes.fromhex('0f01d7')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'enclu')
self.check_np('0f01d7')
# NP 0F 01 CF
# ENCLS
Buffer = bytes.fromhex('0f01cf')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'encls')
# F3 0F 01 EA (mod=11, /5, RM=010)
# SAVEPREVSSP
Buffer = bytes.fromhex('f30f01ea')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Reserved_.REGOPCODE, 5)
assert_equal(myDisasm.infos.Reserved_.MOD_, 3)
assert_equal(myDisasm.infos.Reserved_.RM_, 2)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'saveprevssp')
# F3 0F 01 EC
# UIRET
Buffer = bytes.fromhex('f30f01ec')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'uiret')
assert_equal(myDisasm.infos.Instruction.Category, UINTR_INSTRUCTION + CONTROL_TRANSFER)
assert_equal(myDisasm.infos.Instruction.BranchType, RetType)
# F3 0F 01 ED
# TESTUI
Buffer = bytes.fromhex('f30f01ed')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, UINTR_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'testui')
assert_equal(myDisasm.infos.Operand1.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand1.Registers.special, REG0)
assert_equal(myDisasm.infos.Operand1.OpSize, 1)
assert_equal(myDisasm.infos.Operand1.AccessMode, WRITE)
assert_equal(myDisasm.infos.Operand2.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand2.Registers.special, REG4)
assert_equal(myDisasm.infos.Operand2.OpSize, 1)
assert_equal(myDisasm.infos.Operand2.AccessMode, READ)
# F3 0F 01 EE
# CLUI
Buffer = bytes.fromhex('f30f01ee')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, UINTR_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'clui')
assert_equal(myDisasm.infos.Operand1.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand1.Registers.special, REG4)
assert_equal(myDisasm.infos.Operand1.OpSize, 1)
assert_equal(myDisasm.infos.Operand1.AccessMode, WRITE)
# F3 0F 01 EF
# STUI
Buffer = bytes.fromhex('f30f01ef')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, UINTR_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'stui')
assert_equal(myDisasm.infos.Operand1.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand1.Registers.special, REG4)
assert_equal(myDisasm.infos.Operand1.OpSize, 1)
assert_equal(myDisasm.infos.Operand1.AccessMode, WRITE)
# 0F 01 EF
# WRPKRU
Buffer = bytes.fromhex('0f01ef')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'wrpkru')
assert_equal(myDisasm.infos.Operand1.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand1.Registers.special, REG3)
assert_equal(myDisasm.infos.Operand1.OpSize, 32)
assert_equal(myDisasm.infos.Operand1.AccessMode, WRITE)
assert_equal(myDisasm.infos.Operand2.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand2.Registers.gpr, REG0)
assert_equal(myDisasm.infos.Operand2.OpSize, 32)
assert_equal(myDisasm.infos.Operand2.AccessMode, READ)
Buffer = bytes.fromhex('f00f01ef')
myDisasm = Disasm(Buffer)
myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'wrpkru')
assert_equal(myDisasm.infos.Reserved_.ERROR_OPCODE, UD_)
# NP 0F 01 D4 (reg = 2, mod = 3, rm = 4)
# VMFUNC
Buffer = bytes.fromhex('0f01d4')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Category, VM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'vmfunc')
assert_equal(myDisasm.infos.Operand1.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand1.Registers.gpr, REG0)
assert_equal(myDisasm.infos.Operand1.OpSize, 32)
assert_equal(myDisasm.infos.Operand1.AccessMode, READ)
self.check_np('0f01d4')
# NP 0F 01 D5 (reg = 2, mod = 3, rm = 5)
# XEND
Buffer = bytes.fromhex('0f01d5')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'xend')
assert_equal(myDisasm.infos.Instruction.Category, VM_INSTRUCTION)
assert_equal(myDisasm.infos.Operand1.OpType, REGISTER_TYPE)
assert_equal(myDisasm.infos.Operand1.Registers.gpr, REG0)
assert_equal(myDisasm.infos.Operand1.OpSize, 32)
assert_equal(myDisasm.infos.Operand1.AccessMode, WRITE)
self.check_np('0f01d5')
# NP 0F 01 D6
# XTEST
Buffer = bytes.fromhex('0f01d6')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'xtest')
assert_equal(myDisasm.infos.Instruction.Category, VM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.Flags.OF_, RE_)
assert_equal(myDisasm.infos.Instruction.Flags.SF_, RE_)
assert_equal(myDisasm.infos.Instruction.Flags.ZF_, MO_)
assert_equal(myDisasm.infos.Instruction.Flags.AF_, RE_)
assert_equal(myDisasm.infos.Instruction.Flags.PF_, RE_)
assert_equal(myDisasm.infos.Instruction.Flags.CF_, RE_)
assert_equal(myDisasm.infos.Instruction.ImplicitModifiedRegs.type, SPECIAL_REG)
assert_equal(myDisasm.infos.Instruction.ImplicitModifiedRegs.special, REG0)
self.check_np('0f01d6')
# NP 0F 01 EE (reg = 5, mod = 3, rm = 6)
# RDPKRU
Buffer = bytes.fromhex('0f01ee')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'rdpkru')
assert_equal(myDisasm.infos.Instruction.Category, SYSTEM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.ImplicitModifiedRegs.type, GENERAL_REG)
assert_equal(myDisasm.infos.Instruction.ImplicitModifiedRegs.gpr, REG0 | REG2)
# 0F 01 F9 (reg = 7, mod = 3, rm = 1)
# RDTSCP
Buffer = bytes.fromhex('0f01f9')
myDisasm = Disasm(Buffer)
length = myDisasm.read()
assert_equal(myDisasm.infos.Instruction.Opcode, 0xf01)
assert_equal(myDisasm.length, len(Buffer))
assert_equal(myDisasm.infos.Instruction.Mnemonic, b'rdtscp')
assert_equal(myDisasm.infos.Instruction.Category, SYSTEM_INSTRUCTION)
assert_equal(myDisasm.infos.Instruction.ImplicitModifiedRegs.type, GENERAL_REG)
assert_equal(myDisasm.infos.Instruction.ImplicitModifiedRegs.gpr, REG0 | REG1 | REG2)
# IA32_TIME_STAMP_COUNTER
assert_equal(myDisasm.infos.Instruction.ImplicitUsedRegs.type, SPECIAL_REG)
assert_equal(myDisasm.infos.Instruction.ImplicitUsedRegs.special & REG5, REG5)
# IA32_TSC_AUX
assert_equal(myDisasm.infos.Instruction.ImplicitUsedRegs.type, SPECIAL_REG)
assert_equal(myDisasm.infos.Instruction.ImplicitUsedRegs.special & REG6, REG6)
| 39.944904
| 95
| 0.675379
| 1,641
| 14,500
| 5.837904
| 0.146862
| 0.164196
| 0.283612
| 0.308142
| 0.794363
| 0.775992
| 0.762004
| 0.709916
| 0.698643
| 0.698643
| 0
| 0.034171
| 0.220966
| 14,500
| 362
| 96
| 40.055249
| 0.813916
| 0.090759
| 0
| 0.635983
| 0
| 0
| 0.032541
| 0
| 0
| 0
| 0.009526
| 0
| 0.598326
| 1
| 0.008368
| false
| 0
| 0.008368
| 0
| 0.020921
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
f3a95db6dcbb9e277a0f851e3f04ae7a342899a2
| 7,168
|
py
|
Python
|
gen_data.py
|
monolive/hive-examples
|
07b4e168142856129abbaf4334c581464fcfc001
|
[
"Apache-2.0"
] | 1
|
2015-07-10T13:17:24.000Z
|
2015-07-10T13:17:24.000Z
|
gen_data.py
|
monolive/hive-examples
|
07b4e168142856129abbaf4334c581464fcfc001
|
[
"Apache-2.0"
] | null | null | null |
gen_data.py
|
monolive/hive-examples
|
07b4e168142856129abbaf4334c581464fcfc001
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python
import logging
import argparse
import random
import sys
def main():
output = open('dataset_1.txt', 'w')
for i in xrange(1, 3000000000):
toto = '{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11},{12},{13},{14},{15},{16},{17},{18},{19},{20},{21},{22},{23},{24},{25},{26},{27},{28},{29},{30},{31},{32},{33},{34},{35}\n'\
.format(str(i).zfill(10),\
str('%08x' % random.randrange(16**8)),str('%08x' % random.randrange(16**8)),\
str('%08x' % random.randrange(16**8)),str('%08x' % random.randrange(16**8)),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99)).zfill(2),str(random.randint(0,99)).zfill(2),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8))
output.write(str(toto))
output.close()
output = open('dataset_2.txt', 'w')
gen_file_2(1,500000000,166666666,output)
gen_file_2(500000001,1000000000,166666666,output)
gen_file_2(1000000001,1500000000,166666666,output)
gen_file_2(1500000001,2000000000,166666666,output)
gen_file_2(2000000001,2500000000,166666666,output)
gen_file_2(2500000001,3000000000,166666670,output)
output.close()
def gen_file_2(min,max,amount,output_file):
for i in random.sample(xrange(min,max),amount):
toto = ('{0},{1},{2},{3},{4},{5},{6},{7},{8},{9},{10},{11},{12},{13},{14},{15},{16},{17},{18},{19},{20},{21},{22},{23},{24},{25},{26},{27},{28},{29},{30},{31},{32},{33},{34},{35}' +
'{36},{37},{38},{38},{39},{40},{41},{42},{43},{44},{45},{46},{47},{48},{49},{50},{51},{52},{53},{54},{55},{56},{57},{58},{59},{60},{61},{62},{63},{64},{65},{66},{67},{68},{69},' +
'{70},{71},{72},{73},{74},{75},{76},{77},{78},{79},{80},{81},{82},{83},{84},{85},{86},{87}\n')\
.format(str(i).zfill(10),\
'%08x' % random.randrange(16**8),'%08x' % random.randrange(16**8),\
'%08x' % random.randrange(16**8),'%08x' % random.randrange(16**8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str(random.randint(0,99999999)).zfill(8),str(random.randint(0,99999999)).zfill(8),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip(),\
str('%08x' % random.randrange(16**20)).strip(),str('%08x' % random.randrange(16**20)).strip())
output_file.write(str(toto))
if __name__ == "__main__":
main()
#logger.info('Finished data generation successfully')
sys.exit(0)
| 70.970297
| 183
| 0.635324
| 1,126
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| 0.840989
| 0.840989
| 0
| 0.189981
| 0.061523
| 7,168
| 101
| 184
| 70.970297
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| 0
| 0.043478
| 0.132131
| 0.085364
| 0
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| 0.021739
| false
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| 0.043478
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| 0.065217
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| null | 0
| 1
| 1
| 1
| 1
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0
| 10
|
345039ae6f06b33ecd9fb5ae94fe90e6947b1a9b
| 116,233
|
py
|
Python
|
epa/tri/lookup_table.py
|
MAPC/epa_python
|
6ee7ed562a70071dbed845bf04494c7fef0ab840
|
[
"BSD-3-Clause"
] | 3
|
2015-10-12T18:05:14.000Z
|
2021-03-07T22:58:03.000Z
|
epa/tri/lookup_table.py
|
MAPC/epa_python
|
6ee7ed562a70071dbed845bf04494c7fef0ab840
|
[
"BSD-3-Clause"
] | 1
|
2017-05-12T15:57:14.000Z
|
2019-02-03T13:49:09.000Z
|
epa/tri/lookup_table.py
|
MAPC/epa_python
|
6ee7ed562a70071dbed845bf04494c7fef0ab840
|
[
"BSD-3-Clause"
] | 3
|
2019-02-02T21:08:10.000Z
|
2021-04-17T15:05:10.000Z
|
lookup_table = {
"TRI_FACILITY_NPDES": {
"ASGN_NPDES_IND": "Indicates that the associated NPDES_NUM represents the principal NPDES permit number as assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"NPDES_NUM": "The permit number of a specific discharge to a water body under the National Pollutant Discharge Elimination System (NPDES) of the Clean Water Act (CWA). Not all facilities will have a NPDES permit number. A facility may have multiple NPDES permit numbers. The NPDES permit number may not pertain to the toxic chemical reported to TRI."
},
"TRI_OFF_SITE_TRANSFER_LOCATION": {
"PROVINCE": "The province of the location to which the toxic chemical in wastes is transferred. A facility may transfer toxic chemicals in waste to off-site locations that are outside of the United States. The province field gives a facility the flexibility needed to enter a correct off-site location address that is outside the United States.",
"TRANSFER_LOC_NUM": "The sequence in which an off-site transfer is reported on a Form R submission.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"CONTROLLED_LOC": "Indicator that shows whether the off-site location to which toxic chemicals are transferred in wastes is owned or controlled by the facility or the parent company. Values: 1 = 'Yes', 0 = 'No', 2 = blank or not entered.",
"OFF_SITE_STREET_ADDRESS": "The street address for the physical location of the entity receiving the toxic chemical.",
"COUNTRY_CODE": "The country code where the entity receiving the toxic chemical is located.",
"COUNTY_NAME": "The standardized name of the county where the facility is located.",
"CITY_NAME": "The city where the facility or establishment is physically located.",
"OFF_SITE_NAME": "The name of the entity receiving the toxic chemical.",
"RCRA_NUM": "The number assigned to the facility by EPA for purposes of the Resource Conservation and Recovery Act (RCRA). Not all facilities will have a RCRA Identification Number. A facility will only have a RCRA Identification Number if it manages RCRA regulated hazardous waste. Some facilities may have more than one RCRA Identification Number.",
"STATE_ABBR": "The state abbreviation where the facility or establishment is physically located.",
"ZIP_CODE": "The Zone Improvement Plan (ZIP) code assigned by the U.S. Postal Service as part of the address of a facility."
},
"TRI_TRANSFER_QTY": {
"TRANSFER_LOC_NUM": "The sequence in which an off-site transfer is reported on a Form R submission.",
"TRANSFER_BASIS_EST_CODE": "The code representing the technique used to develop the estimate of the release amount reported in the 'Total Transfers' box (TOTAL_TRANSFER). The values are as follows:",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"TOTAL_TRANSFER": "The total amount (in pounds) of the toxic chemical transferred from the facility to Publicly Owned Treatment Works (POTW) or to an off-site location (non-POTW) during the calendar year (January 1 - December 31). POTW refers to a municipal sewage treatment plant. The most common transfers will be conveyances of the toxic chemical in facility wastewater through underground sewage pipes, however, trucked or other direct shipments to a POTW are also included in this estimate.",
"TRANSFER_RANGE_CODE": "Code that corresponds to the amount of toxic chemical released annually by the reporting facility, reported as a range for releases less than 1,000 pounds. When a facility uses a range code, the amount reported to TRI is the midpoint of the range. On Form R, letter codes are used to represent ranges: A = 1-10 pounds, B = 11-499 pounds, and C = 500-999 pounds. The letters are converted to numbers for storage in the TRIS database where '1' represents range 'A', '3' represents range 'B', and'4' represents range 'C'. The historical value '2' = 1-499 pounds.",
"OFF_SITE_AMOUNT_SEQUENCE": "Sequence in which an off-site transfer amount is reported on a submission.",
"TRANSFER_EST_NA": "Indicates that 'NA' (Not Applicable) was entered on Form R when a facility does not discharge wastewater containing the toxic chemical to Publicly Owned Treatment Works (Section 6.1.B_) or in wastes to other off-site facilities (section 6.2_). Values: 1 = 'Yes', 0 = 'No'.",
"TYPE_OF_WASTE_MANAGEMENT": "The type of waste treatment, disposal, recycling, or energy recovery methods the off-site location uses to manage the toxic chemical. A two-digit code is used to indicate the type of waste management activity employed. This refers to the ultimate disposition of the toxic chemical, not the intermediate activities used for the waste stream. (In Envirofacts, the code 'P91' indicates a transfer to a POTW. All other codes refer to off-site transfers.)"
},
"TRI_SOURCE_REDUCT_METHOD": {
"SOURCE_REDUCT_METHOD_1": "Indicates the method or methods used at the facility to identify the possibility for a source reduction activity implementation at the facility. This does not include all source reduction activities ongoing at the facility but only those activities related to the reported toxic chemical. An example of a method used to identify source reduction opportunities would be an internal pollution prevention audit.",
"SOURCE_REDUCT_METHOD_2": "Indicates the method or methods used at the facility to identify the possibility for a source reduction activity implementation at the facility. This does not include all source reduction activities ongoing at the facility but only those activities related to the reported toxic chemical. An example of a method used to identify source reduction opportunities would be an internal pollution prevention audit.",
"SOURCE_REDUCT_METHOD_3": "Indicates the method or methods used at the facility to identify the possibility for a source reduction activity implementation at the facility. This does not include all source reduction activities ongoing at the facility but only those activities related to the reported toxic chemical. An example of a method used to identify source reduction opportunities would be an internal pollution prevention audit.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"SOURCE_REDUCT_ACTIVITY": "Indicates the type of source reduction activity implemented at the facility during the reporting year. This does not include all source reduction activities ongoing at the facility but only those activities related to the reported toxic chemical. An example of a source reduction activity would include a spill and leak prevention program such as the installation of a vapor recovery system.",
"REDUCTION_SEQUENCE_NUM": "Sequence in which a source reduction method is reported on a submission."
},
"TRI_POTW_LOCATION": {
"POTW_NAME": "The name of the publicly owned treatment works (POTW) receiving the toxic chemical.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"POTW_STREET_ADDRESS": "The street address for the physical location of the publicly owned treatment works (POTW) receiving the toxic chemical.",
"STATE_ABBR": "The state abbreviation where the facility or establishment is physically located.",
"COUNTY_NAME": "The standardized name of the county where the facility is located.",
"CITY_NAME": "The city where the facility or establishment is physically located.",
"POTW_LOC_NUM": "The sequence in which an POTW transfer is reported on a Form R submission.",
"ZIP_CODE": "The Zone Improvement Plan (ZIP) code assigned by the U.S. Postal Service as part of the address of a facility."
},
"TRI_TABLE_ID_NAME": {
"TABLE_ID": "A designation for a related group of permissible values. The name that identifies this group is located in TRI_TABLE_ID_NAME.",
"TABLE_NAME": "The table description for the TRI_CODE_DESC.TABLE_ID ."
},
"TRI_CODE_DESC": {
"DESCRIPT": "The text description of a permissible value contained in CODE.",
"CODE": "The permissible values for a column.",
"TABLE_ID": "A designation for a related group of permissible values. The name that identifies this group is located in TRI_TABLE_ID_NAME."
},
"TRI_FACILITY_NPDES_HISTORY": {
"ASGN_NPDES_IND": "Indicates that the associated NPDES_NUM represents the principal NPDES permit number as assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"NPDES_NUM": "The permit number of a specific discharge to a water body under the National Pollutant Discharge Elimination System (NPDES) of the Clean Water Act (CWA). Not all facilities will have a NPDES permit number. A facility may have multiple NPDES permit numbers. The NPDES permit number may not pertain to the toxic chemical reported to TRI.",
"REPORTING_YEAR": "The year for which the form was submitted. This is not the year in which the form was filed but rather it is the calendar year (January 1 - December 31) during which the toxic chemical was, manufactured, processed and/or otherwise used and released or otherwise managed as a waste."
},
"TRI_ZIP_CODE": {
"TRI_CENTROID_LAT": "The assigned centroid latitude based on zip code.",
"REGION": "The EPA region in which the facility is located.",
"CITY_NAME": "The city where the facility or establishment is physically located.",
"STATE_ABBR": "The state abbreviation where the facility or establishment is physically located.",
"TRI_CENTROID_LONG": "The assigned centroid longitude based on zip code.",
"COUNTRY_NAME": "The country where the facility is located, if outside the United States.",
"ZIP_CODE": "The Zone Improvement Plan (ZIP) code assigned by the U.S. Postal Service as part of the address of a facility."
},
"TRI_FACILITY_RCRA_HISTORY": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"ASGN_RCRA_IND": "Indicates that the associated RCRA_NUM represents the principal RCRA Identification Number as assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"REPORTING_YEAR": "The year for which the form was submitted. This is not the year in which the form was filed but rather it is the calendar year (January 1 - December 31) during which the toxic chemical was, manufactured, processed and/or otherwise used and released or otherwise managed as a waste.",
"RCRA_NUM": "The number assigned to the facility by EPA for purposes of the Resource Conservation and Recovery Act (RCRA). Not all facilities will have a RCRA Identification Number. A facility will only have a RCRA Identification Number if it manages RCRA regulated hazardous waste. Some facilities may have more than one RCRA Identification Number."
},
"TRI_REPORTING_FORM": {
"PRODUCTION_RATIO_NA": "Indicator that shows whether 'NA' was entered in Section 8.9, Production Ratio or Activity Index (PRODUCTION_RATIO). Values: 1 = 'Yes', 0 = 'No'.",
"PUBLIC_CONTACT_PHONE": "The phone number to reach the person identified in the Public Contact Name box (PUBLIC_CONTACT_PERSON).",
"FEDERAL_FAC_IND": "Indicates whether the 'Federal' box was checked on the submission. A Federal facility is a facility owned or operated by the Federal government. This includes facilities that are operated by contractors to the Federal government (i.e., a facility where the land is owned by the Federal government but a private company is under contract to run the facility's operations). The types of Federal facilities that report to TRI are broader than the types of private sector facilities that report to TRI (e.g., DOD military bases). Values: 1 = box checked, 0 = box not checked.",
"TRADE_SECRET_IND": "Indicator that shows whether the identity of the toxic chemical has been claimed a trade secret. If the facility has indicated that the chemical name is a trade secret, the chemical name will not be released to the public. Values: 1 = 'Trade Secret' box checked, 0 = 'Trade Secret' box not checked.",
"MAX_AMOUNT_OF_CHEM": "The two digit code indicating a range for the maximum amount of the chemical present at the facility at any one time during the calendar year (January 1 - December 31) for which the report was submitted.",
"CERTIF_NAME": "The name of the owner, operator, or senior management official who is certifying that the information provided is true and complete and that the values reported are accurate based on reasonable estimates. This individual has management responsibility for the person or persons completing the report.",
"CERTIF_OFFICIAL_TITLE": "The title of the owner, operator, or senior management official who is certifying that the information provided is true and complete and that the values reported are accurate based on reasonable estimates. This individual has management responsibility for the person or persons completing the report.",
"ONE_TIME_RELEASE_QTY": "The total amount (in pounds) of the toxic chemical released directly to the environment or sent offsite for recycling, energy recovery, treatment, or disposal during the reporting year due to remedial actions, catastrophic events such as earthquakes or floods, and one-time events not associated with normal or routine production processes. These amounts are not included in the amounts reported in sections 8.1-8.7 (TRI_SOURCE_REDUCTION_QTY).",
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"ORIG_RECEIVED": "The original received date for a submission for this chemical from this facility and this reporting year.",
"REVISION_NA": "Indicator that shows whether the submission 'Revision' box on form R was checked by the submitter. Values: 1 = box checked, 0 = box not checked.",
"PUBLIC_CONTACT_PERSON": "The name of the individual who may be contacted by the general public with questions regarding the information reported to TRI on this chemical. This person may or may not be familiar with the information provided in the form but has been designated by the facility or establishment to handle public inquiries.",
"ENTIRE_FAC": "Indicates that only one Form R was filed for this chemical for the entire facility. Values: 1 = Form R 'Entire' box check, 0 = box not checked.",
"ACTIVE_STATUS": "Indicates the status of the submitted Form R. Value: 1 = 'Active submission'.",
"POSTMARK_DATE": "The most recent postmark date for a submission for this chemical from this facility and this reporting year . The date may represent a revised submission or be the same as the ORIG_POSTMARK.",
"DIOXIN_DISTRIBUTION_2": "Indicates the distribution (percentage) of 1,2,3,4,7,8,9-Heptachlorodibenzofuran (CAS Number: 55673-89-7) in the reported dioxin or dioxin-like compounds.",
"REPORTING_YEAR": "The year for which the form was submitted. This is not the year in which the form was filed but rather it is the calendar year (January 1 - December 31) during which the toxic chemical was, manufactured, processed and/or otherwise used and released or otherwise managed as a waste.",
"RECEIVED_DATE": "The date the submission was received at the EPCRA Reporting Center.",
"DIOXIN_DISTRIBUTION_14": "Indicates the distribution (percentage) of 2,3,4,7,8-Pentachlorodibenzofuran (CAS Number: 57117-31-4) in the reported dioxin or dioxin-like compounds.",
"DIOXIN_DISTRIBUTION_15": "Indicates the distribution (percentage) of 1,2,3,7,8-Pentachlorodibenzo- p-dioxin (CAS Number: 40321-76-4) in the reported dioxin or dioxin-like compounds.",
"ORIG_POSTMARK": "The original postmark date for a submission for this chemical from this facility and this reporting year.",
"DIOXIN_DISTRIBUTION_17": "Indicates the distribution (percentage) of 2,3,7,8-Tetrachlorodibenzo- p-dioxin (CAS Number: 01746-01-6) in the reported dioxin or dioxin-like compounds.",
"CERTIF_SIGNATURE": "Indicator for the signature of the individual who is certifying that the information being provided in the form is true and complete and that the values reported are accurate based on reasonable estimates.",
"DIOXIN_DISTRIBUTION_11": "Indicates the distribution (percentage) of 1,2,3,4,6,7,8,9-Octachlorodibenzofuran (CAS Number: 39001-02-0) in the reported dioxin or dioxin-like compounds.",
"DIOXIN_DISTRIBUTION_12": "Indicates the distribution (percentage) of 1,2,3,4,6,7,8,9-Octachlorodibenzo- p-dioxin (CAS Number: 03268-87-9) in the reported dioxin or dioxin-like compounds.",
"ADDITIONAL_DATA_IND": "For reporting years beginning in 1991, the indicator that shows whether additional optional information on source reduction, pollution control, or recycling activities implemented during the reporting year or prior years has been attached to the submission. For reporting years 1987 through 1990, the indicator shows whether waste minimization data was reported on Form R and has since been archived. Values: 1 = 'Yes', 0 = 'No'', 2 = blank or not entered.",
"CAS_CHEM_NAME": "The official name of the toxic chemical, toxic chemical mixture, (e.g., xylene mixed isomers), or chemical category as it appears on the EPCRA Section 313 list. ) or 2.1 . This space will be empty if a trade secret was claimed for the toxic chemical and information is provided in Section 1.3 (MIXTURE_NAME) or 2.1 (GENERIC_CHEM_NAME).",
"DIOXIN_DISTRIBUTION_8": "Indicates the distribution (percentage) of 1,2,3,6,7,8-Hexachlorodibenzo- p-dioxin (CAS Number: 57653-85-7) in the reported dioxin or dioxin-like compounds.",
"DIOXIN_DISTRIBUTION_9": "Indicates the distribution (percentage) of 1,2,3,7,8,9-Hexachlorodibenzo- p-dioxin (CAS Number: 19408-74-3) in the reported dioxin or dioxin-like compounds.",
"GENERIC_CHEM_NAME": "The generic, structurally descriptive term used in place of the toxic chemical name when a trade secret was claimed for the toxic chemical. The name must appear on both sanitized and unsanitized Form Rs and be the same as that used on the substantiation form. Section 1.3 will be 'NA' or blank if information is provided in Sections 1.1 (TRI_CHEM_ID) and 1.2 (CAS_CHEM_NAME), or 2.1 (MIXTURE_NAME). Note: Only Sanitized Trade Secret submissions are stored in the TRIS database.",
"DIOXIN_DISTRIBUTION_3": "Indicates the distribution (percentage) of 1,2,3,4,7,8-Hexachlorodibenzofuran (CAS Number: 70648-26-9) in the reported dioxin or dioxin-like compounds.",
"MIXTURE_NAME": "The generic term used in place of the toxic chemical name when a trade secret was claimed for the toxic chemical by the supplier of the toxic chemical. This is generally used when the supplier of a chemical formulation wishes to keep the identity of a particular ingredient in the formulation a secret. It is only used when the supplier, not the reporting facility, is claiming the trade secret. If the reporting facility is claiming a trade secret for the toxic chemical, the generic name is provided in Section 1.3 (GENERIC_CHEM_NAME) and this section (MIXTURE_NAME) is left blank. This space will also be left blank if a trade secret is not being claimed for the toxic chemical.",
"DIOXIN_DISTRIBUTION_1": "Indicates the distribution (percentage) of 1,2,3,4,6,7,8-Heptachlorodibenzofuran (CAS Number: 67562-39-4) in the reported dioxin or dioxin-like compounds.",
"DIOXIN_DISTRIBUTION_6": "Indicates the distribution (percentage) of 2,3,4,6,7,8-Hexachlorodibenzofuran (CAS Number: 60851-34-5) in the reported dioxin or dioxin-like compounds.",
"DIOXIN_DISTRIBUTION_7": "Indicates the distribution (percentage) of 1,2,3,4,7,8-Hexachlorodibenzo- p-dioxin (CAS Number: 39227-28-6) in the reported dioxin or dioxin-like compounds.",
"DIOXIN_DISTRIBUTION_4": "Indicates the distribution (percentage) of 1,2,3,6,7,8-Hextachlorodibenzofuran (CAS Number: 57117-44-9) in the reported dioxin or dioxin-like compounds.",
"DIOXIN_DISTRIBUTION_5": "Indicates the distribution (percentage) of 1,2,3,7,8,9-Hexachlorodibenzofuran (CAS Number: 72918-21-9) in the reported dioxin or dioxin-like compounds.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"PUBLIC_CONTACT_EMAIL": "The Email address of the PUBLIC_CONTACT_PERSON.",
"PARTIAL_FAC": "Indicates that the facility has chosen to report by establishment or groups of establishments. Therefore, there may be other reports filed for this chemical by other establishments of the facility. Values: 1 = Form R 'Partial' box checked, 0 = box not checked.",
"REVISION_CODE": "Facilities that filed a Form R and/or a Form A Certification Statement under EPCRA section 313 may submit a request to the revise the data. The REVISION_CODE is a code indicating the current form is a revision of a previous form and the reason it was revised. Added in reporting year 2007, the data element can have the following values:",
"DIOXIN_DISTRIBUTION_16": "Indicates the distribution (percentage) of 2,3,7,8-Tetrachlorodibenzofuran (CAS Number: 51207-31-9) in the reported dioxin or dioxin-like compounds.",
"ONE_TIME_RELEASE_QTY_NA": "Indicator that shows whether 'NA' was entered in Section 8.8, Quantity Released to the Environment as Result of Remedial Actions, Catastrophic Events, or One-Time Events Not Associated with Production Process (ONE_TIME_RELEASE_QTY). Values: 1 = 'Yes', 0 = 'No'.",
"CERTIF_DATE_SIGNED": "The date that the senior management official signed the certification statement.",
"DIOXIN_DISTRIBUTION_NA": "Indicates whether 'NA' (Not Applicable) was entered on the Form R for the Distribution of Each Member of the Dioxin and Dioxin-like Compounds Category. The Form R asks facilities to report a distribution of chemicals included in the Dioxin and Dioxin-like compounds category. There are 17 individual chemicals listed in the Dioxin and Dioxin-like compounds category. A value of '1' for this variable indicates that the facility did not have the speciation (distribution) information available.",
"DIOXIN_DISTRIBUTION_10": "Indicates the distribution (percentage) of 1,2,3,4,6,7,8-Hexachlorodibenzo- p-dioxin (CAS Number: 35822-46-9) in the reported dioxin or dioxin-like compounds.",
"FORM_TYPE_IND": "Indicates the type of form received. Values: L = Form R, S = Form A.",
"GOCO_FLAG": "Indicates whether the 'GOCO' box was checked on the submission. A GOCO facility is a Government-Owned, Contractor-Operated facility. Values: 1= box checked, 0= box not checked.",
"TRI_CHEM_ID": "The number assigned to chemicals regulated under Section 313 of the Emergency Planning and Community Right-to-Know Act (EPCRA). For most toxic chemicals or mixture of chemicals (e.g., xylene mixed isomers), the TRI_CHEM_ID is the Chemical Abstract Service Registry (CAS) number. A given listed toxic chemical or mixture may be known by many names but it will have only one CAS number. For example, methyl ethyl ketone and 2-butanone are synonyms for the same toxic chemical and thus have only one CAS number (78-93-3). For categories of chemicals for which CAS Registry numbers have not been assigned, a four-character category code, asssigned by TRI, is included in TRI_CHEM_ID. Form R section 1.1 will be empty if a trade secret was claimed for the toxic chemical and information is provided in Section 1.3 or 2.1.",
"SANITIZED_IND": "Indicator that shows whether the submission 'Sanitized Trade Secret' box was checked by the submitter. Note: Only Sanitized Trade Secret submissions are stored in the TRIS database. Values: 1 = box checked, 0 = box not checked.",
"DIOXIN_DISTRIBUTION_13": "Indicates the distribution (percentage) of 1,2,3,7,8-Pentachlorodibenzofuran (CAS Number: 57117-41-6) in the reported dioxin or dioxin-like compounds.",
"PRODUCTION_RATIO": "Indicates the level of increase or decrease from the previous year, of the production process or other activity in which the toxic chemical is used. This number is usually around 1.0. For example, a production ratio or activity index of 1.5 would indicate that production associated with the use of the toxic chemical has increased by about 50 percent. Conversely, a production ratio or activity index of 0.3 would indicate that production associated with the use of the toxic chemical has decreased by about 70 percent."
},
"TRI_FACILITY_UIC_HISTORY": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"ASGN_UIC_IND": "Indicates that the associated UIC_NUM represents the principal underground injection code identification number (UIC ID) as assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"UIC_NUM": "The unique number assigned to a specific underground injection well under the Safe Drinking Water Act (SDWA). A facility with multiple injection wells will have multiple underground injection code identification number (UIC ID) Numbers. If the facility does not have an underground injection well regulated by the SDWA, it will not have a UIC ID number.",
"REPORTING_YEAR": "The year for which the form was submitted. This is not the year in which the form was filed but rather it is the calendar year (January 1 - December 31) during which the toxic chemical was, manufactured, processed and/or otherwise used and released or otherwise managed as a waste."
},
"TRI_CHEM_INFO": {
"CAAC_IND": "Indicates whether the chemical is reportable under the Clean Air Act. Values: 1 = 'Yes', 0 = 'No'.",
"CARC_IND": "Indicates whether the chemical is reportable as a carcinogen under the CARC. Values: 1 = 'Yes', 0 = 'No'.",
"UNIT_OF_MEASURE": "Indicates the unit of measure used to quantify the chemical. Values: {Pounds, Grams}",
"CLASSIFICATION": "Indicates the classification of the chemical. Chemicals can be classified as either a Dioxin or Dioxin-like compounds, a PBT (Persistent, Bioaccumulative and Toxic) chemical or a general EPCRA Section 313 chemical. Values: 0=TRI, 1=PBT, 2=Dioxin",
"FEDS_IND": "Indicates whether the chemical is a non-Section 313 chemical submitted by a federal facility under Executive Order 12856. Values: 1 = 'Yes', 0 = 'No'.",
"METAL_IND": "Indicates whether the chemical is a metal or metal compound. Values: 1 = 'Yes', 0 = 'No'.",
"NO_DECIMALS": "Indicates the maximum number of decimals that can be used to quantify a chemical. This measurement applies to release, transfer and source reduction quantities. PBT (Persistent, Bioaccumulative and Toxic) chemicals, including Dioxins and Dioxin-like Compounds, can be quantified using numbers to the right of the decimal point. The measurement expresses the maximum number of positions to the right of the decimal point that a PBT chemical can be expressed in. All other Non-PBT chemicals are reported as whole numbers.",
"R3350_IND": "Indicates whether the chemical is reportable under Regulation 3350. Values: 1 = 'Yes', 0 = 'No'.",
"TRI_CHEM_ID": "The number assigned to chemicals regulated under Section 313 of the Emergency Planning and Community Right-to-Know Act (EPCRA). For most toxic chemicals or mixture of chemicals (e.g., xylene mixed isomers), the TRI_CHEM_ID is the Chemical Abstract Service Registry (CAS) number. A given listed toxic chemical or mixture may be known by many names but it will have only one CAS number. For example, methyl ethyl ketone and 2-butanone are synonyms for the same toxic chemical and thus have only one CAS number (78-93-3). For categories of chemicals for which CAS Registry numbers have not been assigned, a four-character category code, asssigned by TRI, is included in TRI_CHEM_ID. Form R section 1.1 will be empty if a trade secret was claimed for the toxic chemical and information is provided in Section 1.3 or 2.1.",
"ACTIVE_DATE": "First year that this chemical must be reported to TRI.",
"INACTIVE_DATE": "Final year that this chemical must be reported to TRI.",
"PBT_END_YEAR": "Indicates the year that a PBT (Persistent, Bioaccumulative and Toxic) chemical was dropped as an EPCRA Section 313 PBT Chemical, Toxics Release Inventory.",
"PBT_START_YEAR": "Indicates the year that a PBT (Persistent, Bioaccumulative and Toxic) chemical was designated as an EPCRA Section 313 PBT Chemical, Toxics Release Inventory.",
"CHEM_NAME": "The official name of the toxic chemical, toxic chemical mixture, (e.g., xylene mixed isomers), or chemical category as it appears on the EPCRA Section 313 list."
},
"TRI_FACILITY_UIC": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"ASGN_UIC_IND": "Indicates that the associated UIC_NUM represents the principal underground injection code identification number (UIC ID) as assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"UIC_NUM": "The unique number assigned to a specific underground injection well under the Safe Drinking Water Act (SDWA). A facility with multiple injection wells will have multiple underground injection code identification number (UIC ID) Numbers. If the facility does not have an underground injection well regulated by the SDWA, it will not have a UIC ID number."
},
"TRI_FACILITY": {
"PREF_DESC_CATEGORY": "The EPA's preferred geographic coordinate description category. Describes the category of feature referenced by the latitude and longitude.",
"ASGN_PARTIAL_IND": "Indicates that the facility reports by establishment or groups of establishments as assigned by TRI from Form R submisions. Partial facilities may have more than one submission for the same chemical in one reporting year. Values: 0 = 'Entire facility', 1 = 'Partial facility'.",
"FACILITY_NAME": "The name of the facility or establishment for which the form was submitted. For purposes of TRI a \"facility\" is generally considered to be all buildings and equipment owned or operated by a company on a single piece of property. The facility may be only one building in an industrial park or it may be a large complex covering many acres. At some larger facilities there may be several different businesses that are all run by the same company. These different businesses are referred to as \"establishments.\" Generally, a company will submit one Form R for the entire facility. A facility may choose, however, to submit a Form R for each establishment separately. The name in this section will either be the name used for the entire facility or the name of the specific establishment, depending on how the facility chooses to report.",
"STATE_COUNTY_FIPS_CODE": "Combination of the two-letter state abbreviation and the county code.",
"MAIL_STATE_ABBR": "The state abbreviation the facility or establishment uses to receive mail. This may or may not be the same as the information reported in the State box.",
"MAIL_ZIP_CODE": "The zip code the facility or establishment uses to receive mail. This may or may not be the same as the information reported in the Zip Code box.",
"CITY_NAME": "The city where the facility or establishment is physically located.",
"MAIL_COUNTRY": "The country the facility or establishment uses to receive mail.",
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"PREF_HORIZONTAL_DATUM": "The EPA's preferred geographic coordinate horizontal datum. Reference datum of the latitude and longitude.",
"FAC_CLOSED_IND": "A flag that indicates whether a facility is open (value =' 0'), closed (value = '1'), or inactive for TRI (value = '2').",
"FAC_LONGITUDE": "The series of numbers which identifies the exact physical location of the facility as a measure of the arc or portion of the earth's equator between the meridian of the center of the facility and the prime meridian. The right-justified value is stored as degrees, minutes and seconds (0DDDMMSS). Tenths of seconds are not stored. The value is negative for locations in the Western hemisphere.",
"MAIL_STREET_ADDRESS": "The address the facility or establishment uses for receiving mail. Form R instructs the submitter to enter the address used for mail only if different than in the Street box. The TRIS database stores the address from the Street box (STREET_ADDRESS) in MAILING_STREET_ADDRESS even when the facility Mailing address is not different.",
"STATE_ABBR": "The state abbreviation where the facility or establishment is physically located.",
"COUNTY_NAME": "The standardized name of the county where the facility is located.",
"FAC_LATITUDE": "The series of numbers that identifies the exact physical location of the facility as a measure of the angular distance north form the earth's equator to the center of the facility. The value is stored as degrees, minutes and seconds (0DDMMSS), and the first position is zero-filled. The value is positive for locations north of the equator.",
"PREF_LATITUDE": "The EPA's preferred geographic latitude estimation of the reporting facility. Value for latitude is in decimal degrees. This is a signed field.",
"PREF_COLLECT_METH": "The EPA's preferred geographic coordinate collection method code for the reporting facility. Method used to determine the latitude and longitude.",
"ASGN_PUBLIC_PHONE": "The phone number to reach the person identified in the Public Contact Name box (PUBLIC_CONTACT_PERSON), as assigned by TRI from Form R submissions.",
"PREF_ACCURACY": "The EPA's preferred geographic coordinate accuracy estimation for the reporting facility. Describes the accuracy value as a range (+/) in meters of the latitude and longitude.",
"ASGN_FEDERAL_IND": "An identifier that indicates the ownership status of a facility. A Federal facility is a facility owned or operated by the Federal government. This includes facilities that are operated by contractors to the Federal government (i.e., a facility where the land is owned by the Federal government but a private company is under contract to run the facility's operations). The types of Federal facilities that report to TRI are broader than the types of private sector facilities that report to TRI (e.g., DOD military bases). Values: C = 'Commercial', F = 'Federal facility', and G = 'Government owned/contractor operated' (GOCO).",
"ASGN_AGENCY": "An abbreviation for the name of the agency supported by a federal or Government Owned/Contractor Operated (GOCO) reporting site.",
"MAIL_PROVINCE": "The province the facility or establishment uses to receive mail. A facility may receive mail at an address outside of the United States. The province field gives a facility the flexibility needed to enter a correct mailing address outside the United States.",
"PREF_LONGITUDE": "The EPA's preferred geographic longitude estimation of the reporting facility. Value for longitude is in decimal degrees. This is a signed field.",
"STREET_ADDRESS": "The street address for the physical location of the facility or establishment.",
"ZIP_CODE": "The Zone Improvement Plan (ZIP) code assigned by the U.S. Postal Service as part of the address of a facility.",
"MAIL_NAME": "The name which the facility or establishment uses for receiving mail if the address used for mail is different than in the Street box. This may or may not be the same as the name listed in the Facility or Establishment Name box.",
"PREF_SOURCE_SCALE": "The EPA's preferred geographic coordinate source map scale code. This is the scale of the source used to determine the latitude and longitude.",
"MAIL_CITY": "The city the facility or establishment uses to receive mail. This may or may not be the same as the information reported in the City box.",
"PARENT_CO_NAME": "Name of the corporation or other business company that is the ultimate parent company, located in the United States, of the facility or establishment submitting the data. The parent company is the company that directly owns at least 50 percent of the voting stock of the reporting company. This does not include foreign parent companies. 'NA' indicates that the facility does not have a parent company.",
"PREF_QA_CODE": "Contains the results of four quality assurance tests (Test 1 through Test 4 below) used to determine facility location. \"ZIP Code Bounding Box\" is a rectangle generated from the ZIP Code boundaries, which is defined by the extreme north-south latitude and east-west longitudes, plus 1 kilometer (km) in each direction. The quality assurance tests are:",
"FRS_ID": "A unique code used to identify the facility in the Facility Registry System (FRS). Note: The column will be populated in the future when values have been established.",
"PARENT_CO_DB_NUM": "The number which has been assigned to the parent company by Dun & Bradstreet. Dun & Bradstreet is a private financial tracking and accounting firm. Not all parent companies will have a Dun & Bradstreet number. 'NA' indicates that the facility or establishment's parent company does not have a Dun & Bradstreet number.",
"ASGN_PUBLIC_CONTACT": "The name of the individual who may be contacted by the general public with questions regarding the company and the information reported to TRI as assigned by TRI from Form R submissions.. This person may or may not be familiar with the information provided in the form but has been designated by the facility or establishment to handle public inquiries.",
"REGION": "The EPA region in which the facility is located."
},
"TRI_SUBMISSION_SIC": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"SIC_SEQUENCE_NUM": "The sequence of the facility's Standard Industrial Classification (SIC) code as entered on Form R or Form A.",
"SIC_CODE": "The Standard Industrial Classification (SIC) code or codes which best describes the activities conducted at the facility. SIC codes are 4 digit numbers used by the Bureau of Census as part of a system to categorize and track the types of business activities conducted in the United States. The first two digits of the code represent the major industry group (e.g., SIC code 25XX indicates Furniture and Fixtures) and the second two digits represent the specific subset of that group (e.g., 2511 indicates wood household furniture). EPA instructs facilities to enter their primary SIC code first. Many facilities do not report their primary SIC code first.",
"PRIMARY_IND": "Indicates whether the associated SIC_CODE/NAICS_CODE represents the facility's primary business activity as entered by the submitter. EPA instructs facilities to enter their primary SIC/NAICS on the Form R or Form A in part I, section 4.5, box a. Values: 1 = 'Yes', 0 = 'No'."
},
"TRI_RECYCLING_PROCESS": {
"ONSITE_RECYCLING_PROC_CODE": "Indicates the specific on-site recycling method or methods applied to the toxic chemical. Similar to section 7B and unlike section 7A, on-site recycling under section 7C refers only to recycling activities directed at the specific toxic chemical being reported, not all recycling methods applied to the waste stream. Section 7C is not completed unless the specific toxic chemical being reported is recovered from the waste stream for reuse.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit."
},
"TRI_ENERGY_RECOVERY": {
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"ONSITE_ENERGY_PROC_CODE": "Code for the specific energy recovery method applied to the toxic chemical. Unlike section 7A which includes all treatment methods applied to the waste stream, the energy recovery must be directed at the specific toxic chemical being reported. This means that the toxic chemical must have significant heating value. Section 7B should not be used for chemicals that do not have significant heating values such as metals. Values: U01 = Industrial Kiln, U02 = Industrial Furnace, U03 = Industrial Boiler, U09 = Other Energy Recovery Methods, NA = not applicable, no on-site energy recovery applied to the toxic chemical."
},
"TRI_FACILITY_DB": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"ASGN_DB_IND": "Indicates that the associated DB_NUM represents the principal Dun & Bradstreet number assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"DB_NUM": "The number or numbers which have been assigned to the facility by Dun & Bradstreet. Dun & Bradstreet is a private financial tracking and accounting firm. Not all facilities will have Dun & Bradstreet numbers."
},
"TRI_CHEM_ACTIVITY": {
"REACTANT": "Indicates the toxic chemical is used in chemical reactions to create another chemical substance or product that is then sold or otherwise distributed to other facilities. Some examples of reactants include feedstocks, raw materials, intermediates, and initiators. Values: 1 = 'Yes', 0 = 'No'.",
"MANUFACTURE_AID": "Indicates the toxic chemical is used to aid in the manufacturing process but does not come into contact with the product during manufacture. Some examples include valve lubricants, refrigerants, metalworking fluids, coolants, and hydraulic fluids. Values: 1 = 'Yes', 0 = 'No'.",
"IMPORTED": "Indicates the toxic chemical was imported into the Customs Territory of the United States by the facility. This includes the facility directly importing the toxic chemical or specifically requesting a broker or other party to obtain the toxic chemical from a foreign source. The Customs Territory of the United States includes the 50 States, Guam, Puerto Rico, American Samoa, and the U.S. Virgin Islands. Values: 1 = 'Yes', 0 = 'No'.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"USED_PROCESSED": "Indicates the toxic chemical was produced or imported by the facility and then further processed or otherwise used at the same facility. If this box is checked, at least one box in section 3.2 or section 3.3 will be checked. Values: 1 = 'Yes', 0 = 'No'.",
"PRODUCE": "Indicates the toxic chemical was created by the facility. A toxic chemical is considered manufactured even if the toxic chemical is created unintentionally or exists only for a short period of time. Values: 1 = 'Yes', 0 = 'No'.",
"FORMULATION_COMPONENT": "Indicates the toxic chemical is used as an ingredient in a product mixture to enhance performance of the product during its use, such as dyes in ink, solvents in paint, additions, reaction diluents, initiators, inhibitors, emulsifiers, surfactants, lubricants, flame retardants, and rheological modifiers. Values: 1 = 'Yes', 0 = 'No'.",
"MANUFACTURE_IMPURITY": "Indicator that shows whether the facility produces the reported chemical as a result of the manufacture, processing, or otherwise use of another chemical, but does not separate the chemical and it remains primarily in the mixture or product with that other chemical. Values: 1 = 'Yes', 0 = 'No'.",
"CHEM_PROCESSING_AID": "Indicates the toxic chemical is used to aid in the manufacture or synthesis of another chemical substance such that it comes into contact with the product during manufacture, but is not intended to remain with or become part of the final product or mixture. Some examples of chemical processing aids are process solvents, catalysts, solution buffers, inhibitors, and reaction terminators. Values: 1 = 'Yes', 0 = 'No'.",
"BYPRODUCT": "Indicates the toxic chemical is produced coincidentally during the manufacture, process, or otherwise use of another chemical substance or mixture and, following its production, is separated from that other chemical substance or mixture. This includes toxic chemicals that may be created as the result of waste management. Values: 1 = 'Yes', 0 = 'No'.",
"ANCILLARY": "Indicates the toxic chemical is used at the facility for purposes other than as a manufacturing aid or chemical processing aid, such as cleaners, degreasers, lubricants, fuels, toxic chemicals used for treating wastes, and toxic chemicals used to treat water at the facility. Values: 1 = 'Yes', 0 = 'No'.",
"REPACKAGING": "Indicates the toxic chemical has been received by the facility and subsequently prepared for distribution into commerce in a different form, state, or quantity than it was received, such as petroleum being transferred from a storage tank to tanker trucks. Values: 1 = 'Yes', 0 = 'No'.",
"ARTICLE_COMPONENT": "Indicates the toxic chemical becomes an integral part of an article distributed into commerce, such as copper in wire or resins in a plastic pen, or the pigment components of paint applied to a chair that is sold. Values: 1 = 'Yes', 0 = 'No'.",
"PROCESS_IMPURITY": "Indicator that shows whether the facility processed the reported chemical but did not separate it and it remains as an impurity in the primary the mixture or trade name product. Values: 1 = 'Yes', 0 = 'No'.",
"SALE_DISTRIBUTION": "Indicates the toxic chemical was produced or imported by the facility specifically to be sold or distributed to other outside facilities. Values: 1 = 'Yes', 0 = 'No'."
},
"TRI_COUNTY": {
"COUNTY_NAME": "The standardized name of the county where the facility is located.",
"ZIP_CODE": "The Zone Improvement Plan (ZIP) code assigned by the U.S. Postal Service as part of the address of a facility."
},
"TRI_FACILITY_HISTORY": {
"PREF_DESC_CATEGORY": "The EPA's preferred geographic coordinate description category. Describes the category of feature referenced by the latitude and longitude.",
"ASGN_PARTIAL_IND": "Indicates that the facility reports by establishment or groups of establishments as assigned by TRI from Form R submisions. Partial facilities may have more than one submission for the same chemical in one reporting year. Values: 0 = 'Entire facility', 1 = 'Partial facility'.",
"FACILITY_NAME": "The name of the facility or establishment for which the form was submitted. For purposes of TRI a \"facility\" is generally considered to be all buildings and equipment owned or operated by a company on a single piece of property. The facility may be only one building in an industrial park or it may be a large complex covering many acres. At some larger facilities there may be several different businesses that are all run by the same company. These different businesses are referred to as \"establishments.\" Generally, a company will submit one Form R for the entire facility. A facility may choose, however, to submit a Form R for each establishment separately. The name in this section will either be the name used for the entire facility or the name of the specific establishment, depending on how the facility chooses to report.",
"STATE_COUNTY_FIPS_CODE": "Combination of the two-letter state abbreviation and the county code.",
"MAIL_STATE_ABBR": "The state abbreviation the facility or establishment uses to receive mail. This may or may not be the same as the information reported in the State box.",
"MAIL_ZIP_CODE": "The zip code the facility or establishment uses to receive mail. This may or may not be the same as the information reported in the Zip Code box.",
"CITY_NAME": "The city where the facility or establishment is physically located.",
"MAIL_COUNTRY": "The country the facility or establishment uses to receive mail.",
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"PREF_HORIZONTAL_DATUM": "The EPA's preferred geographic coordinate horizontal datum. Reference datum of the latitude and longitude.",
"FAC_LONGITUDE": "The series of numbers which identifies the exact physical location of the facility as a measure of the arc or portion of the earth's equator between the meridian of the center of the facility and the prime meridian. The right-justified value is stored as degrees, minutes and seconds (0DDDMMSS). Tenths of seconds are not stored. The value is negative for locations in the Western hemisphere.",
"MAIL_STREET_ADDRESS": "The address the facility or establishment uses for receiving mail. Form R instructs the submitter to enter the address used for mail only if different than in the Street box. The TRIS database stores the address from the Street box (STREET_ADDRESS) in MAILING_STREET_ADDRESS even when the facility Mailing address is not different.",
"REPORTING_YEAR": "The year for which the form was submitted. This is not the year in which the form was filed but rather it is the calendar year (January 1 - December 31) during which the toxic chemical was, manufactured, processed and/or otherwise used and released or otherwise managed as a waste.",
"STATE_ABBR": "The state abbreviation where the facility or establishment is physically located.",
"COUNTY_NAME": "The standardized name of the county where the facility is located.",
"FAC_LATITUDE": "The series of numbers that identifies the exact physical location of the facility as a measure of the angular distance north form the earth's equator to the center of the facility. The value is stored as degrees, minutes and seconds (0DDMMSS), and the first position is zero-filled. The value is positive for locations north of the equator.",
"PREF_LATITUDE": "The EPA's preferred geographic latitude estimation of the reporting facility. Value for latitude is in decimal degrees. This is a signed field.",
"PREF_COLLECT_METH": "The EPA's preferred geographic coordinate collection method code for the reporting facility. Method used to determine the latitude and longitude.",
"ASGN_PUBLIC_PHONE": "The phone number to reach the person identified in the Public Contact Name box (PUBLIC_CONTACT_PERSON), as assigned by TRI from Form R submissions.",
"PREF_ACCURACY": "The EPA's preferred geographic coordinate accuracy estimation for the reporting facility. Describes the accuracy value as a range (+/) in meters of the latitude and longitude.",
"ASGN_FEDERAL_IND": "An identifier that indicates the ownership status of a facility. A Federal facility is a facility owned or operated by the Federal government. This includes facilities that are operated by contractors to the Federal government (i.e., a facility where the land is owned by the Federal government but a private company is under contract to run the facility's operations). The types of Federal facilities that report to TRI are broader than the types of private sector facilities that report to TRI (e.g., DOD military bases). Values: C = 'Commercial', F = 'Federal facility', and G = 'Government owned/contractor operated' (GOCO).",
"ASGN_AGENCY": "An abbreviation for the name of the agency supported by a federal or Government Owned/Contractor Operated (GOCO) reporting site.",
"MAIL_PROVINCE": "The province the facility or establishment uses to receive mail. A facility may receive mail at an address outside of the United States. The province field gives a facility the flexibility needed to enter a correct mailing address outside the United States.",
"PREF_LONGITUDE": "The EPA's preferred geographic longitude estimation of the reporting facility. Value for longitude is in decimal degrees. This is a signed field.",
"STREET_ADDRESS": "The street address for the physical location of the facility or establishment.",
"ZIP_CODE": "The Zone Improvement Plan (ZIP) code assigned by the U.S. Postal Service as part of the address of a facility.",
"MAIL_NAME": "The name which the facility or establishment uses for receiving mail if the address used for mail is different than in the Street box. This may or may not be the same as the name listed in the Facility or Establishment Name box.",
"PREF_SOURCE_SCALE": "The EPA's preferred geographic coordinate source map scale code. This is the scale of the source used to determine the latitude and longitude.",
"MAIL_CITY": "The city the facility or establishment uses to receive mail. This may or may not be the same as the information reported in the City box.",
"PARENT_CO_NAME": "Name of the corporation or other business company that is the ultimate parent company, located in the United States, of the facility or establishment submitting the data. The parent company is the company that directly owns at least 50 percent of the voting stock of the reporting company. This does not include foreign parent companies. 'NA' indicates that the facility does not have a parent company.",
"PREF_QA_CODE": "Contains the results of four quality assurance tests (Test 1 through Test 4 below) used to determine facility location. \"ZIP Code Bounding Box\" is a rectangle generated from the ZIP Code boundaries, which is defined by the extreme north-south latitude and east-west longitudes, plus 1 kilometer (km) in each direction. The quality assurance tests are:",
"PARENT_CO_DB_NUM": "The number which has been assigned to the parent company by Dun & Bradstreet. Dun & Bradstreet is a private financial tracking and accounting firm. Not all parent companies will have a Dun & Bradstreet number. 'NA' indicates that the facility or establishment's parent company does not have a Dun & Bradstreet number.",
"ASGN_PUBLIC_CONTACT": "The name of the individual who may be contacted by the general public with questions regarding the company and the information reported to TRI as assigned by TRI from Form R submissions.. This person may or may not be familiar with the information provided in the form but has been designated by the facility or establishment to handle public inquiries.",
"REGION": "The EPA region in which the facility is located."
},
"TRI_SUBMISSION_NAICS": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"NAICS_CODE": "The North American Industry Classification System (NAICS) Codes(s) that best describe the business activities conducted ata facility or establishment. NAICS codes are 6 digit numbers used by the Bureau of Census as part of a system to categorizeand track the types of business activities conducted in the United States. ",
"NAICS_SEQUENCE_NUM": "The sequence of the facility's North American Industry Classification System (NAICS) code as entered in section 4.5 of part I of the Form R or Form A.",
"PRIMARY_IND": "Indicates whether the associated SIC_CODE/NAICS_CODE represents the facility's primary business activity as entered by the submitter. EPA instructs facilities to enter their primary SIC/NAICS on the Form R or Form A in part I, section 4.5, box a. Values: 1 = 'Yes', 0 = 'No'."
},
"TRI_WATER_STREAM": {
"WATER_SEQUENCE_NUM": "Sequence in which a release to water is reported on a Form R submission.",
"STREAM_NAME": "The name of the stream, river, lake, or other water body to which the chemical is discharged. The name is listed as it appears on the NPDES permit, or, if the facility does not have a NPDES permit, as the water body is publicly known. This is not a list of all streams through which the toxic chemical flows but is a list of direct discharges. If more than one name is listed on form R, the facility has a separate discharge to each water body listed.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"STORM_WATER_PERCENT": "The amount of the release, by weight percent, to water bodies, that came from stormwater runoff. This figure is only required when data are available.",
"STORM_WATER_NA": "Indicates that 'NA' (Not Applicable) was entered on Form R for the percent of a release that came from stormwater runoff. Values: 1 = 'Yes', 0 = 'No'."
},
"TRI_SOURCE_REDUCT_QTY": {
"ENERGY_OFFSITE_CURR_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste sent offsite to be burned for energy recovery during the calendar year (January 1 - December 31) for which the report was submitted. This includes all amounts of the toxic chemical that were intended to be recovered for energy and were sent offsite for that purpose. This figure includes all transfers offsite reported in section 6.2 which are classified with an energy recovery code. This does not include quantities of the toxic chemical that are combusted for energy recovery offsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81A_CURR_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.A, on-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_PREV_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the released previous year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_ONSITE_FOLL_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste expected to be burned for energy recovery onsite during the calendar year (January 1 - December 31) following the year for which the report was submitted. This should not include quantities of the toxic chemical that will be combusted for energy recovery onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"RECYC_OFFSITE_FOLL_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be sent offsite for recycling during the calendar year (January 1 - December 31) following the year for which the report was submitted. This amount does not include quantities of the toxic chemical that will be transferred offsite for recycling as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"RECYC_ONSITE_PREV_YR_QTY": "The total amount (in pounds) of the toxic chemical recycled onsite during the calendar year (January 1 - December 31) prior to the year for which the report was submitted. This includes only the amount of the toxic chemical actually recovered for reuse, not the total amount of the toxic chemical in the wastestream entering recycling units onsite. This amount does not include quantities of the toxic chemical that were recycled onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"RECYC_ONSITE_SECD_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be recycled onsite during the calendar year (January 1 - December 31) two years following the year for which the report was submitted. This amount does not include quantities of the toxic chemical that will be recycled onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81A_PREV_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.A, prior year on-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_FOLL_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be released by the facility to all environmental media both on and off site during the calendar year (January 1 - December 31) following the year for which the report was submitted. This includes air emissions, discharges to water bodies, underground injection, and land disposal on site (all releases reported in section 5). It also includes transfers of the toxic chemical offsite for disposal (transfers reported in section 6.2 which are classified with a disposal waste management code) and amounts of metals transferred to POTWs (metals reported in 6.1).",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"ENERGY_OFFSITE_CURR_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery offsite current year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"RECYC_OFFSITE_CURR_YR_QTY": "The total amount (in pounds) of the toxic chemical sent offsite for recycling during the calendar year (January 1 - December 31) for which the report was submitted. This includes all amounts of the toxic chemical intended to be recycled, not just the amount of the toxic chemical actually recovered. This figure includes all transfers offsite reported in section 6.2 which are classified with an recycling code. This amount does not include quantities of the toxic chemical that were transferred offsite for recycling as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"RECYC_ONSITE_CURR_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled on-site current year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"TREATED_ONSITE_CURR_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated onsite current year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_CURR_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the released current year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"RECYC_ONSITE_CURR_YR_QTY": "The total amount (in pounds) of the toxic chemical recycled onsite during the calendar year (January 1 - December 31) for which the report was submitted. This includes only the amount of the toxic chemical actually recovered, not the total amount of the toxic chemical in the wastestream sent for recycling activities. This amount does not include quantities of the toxic chemical that were recycled onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81C_CURR_YR_QTY": "The total amount of the toxic chemical released off-site due to production related events by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the calendar year (January 1 - December 31). This total does not include off-site releases or disposal due to catastrophic events.",
"REL_81C_SECD_YR_QTY": "The total amount of the toxic chemical expected to be released off-site by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the second following calendar year (January 1 - December 31). This total does not include off-site releases or disposal due to catastrophic events.",
"REL_81B_SECD_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.B, second following year on-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_81D_FOLL_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.D, following year off-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_81C_FOLL_YR_QTY": "The total amount of the toxic chemical expected to be released off-site by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the following calendar year (January 1 - December 31). This total does not include off-site releases or disposal due to catastrophic events.",
"REL_81B_CURR_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.B, on-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"TREATED_OFFSITE_CURR_YR_QTY": "The total amount (in pounds) of the toxic chemical sent for treatment offsite during the calendar year (January 1 - December 31) for which the report was submitted. This includes the total amount of the toxic chemical intended to be treated (destroyed) and sent offsite for that purpose, not the amount of the toxic chemical actually treated (destroyed) by offsite processes. This figure includes all transfers offsite reported in section 6.2 which are classified with treatment waste management codes and most transfers to POTWs reported in section 6.1, except for metals. This does not include transfers of metals to publicly owned treatment works (POTWs) because metals cannot be treated (destroyed) and will ultimately be disposed. Transfers of metals to POTWs are included in section 8.1. This amount also does not include quantities of the toxic chemical that were transferred off-site for treatment as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81C_PREV_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.C, prior year off-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_PREV_YR_QTY": "The total amount (in pounds) of the toxic chemical released due to production related events by the facility to all environmental media both on and off site during the calendar year (January 1 - December 31) prior to the year for which the report was submitted. This includes air emissions, discharges to water bodies, underground injection, and land disposal on site (all releases reported in section 5). It also includes transfers of the toxic chemical offsite for disposal (transfers reported in section 6.2 which are classified with a disposal waste management code) and amounts of metals transferred to POTWs (metals reported in 6.1).",
"TREATED_OFFSITE_CURR_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated offsite current year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_OFFSITE_FOLL_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste expected to be sent offsite to be burned for energy recovery during the calendar year (January 1 - December 31) following the year for which the report was submitted. This does not include quantities of the toxic chemical that will be combusted for energy recovery offsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"RECYC_ONSITE_FOLL_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be recycled onsite during the calendar year (January 1 - December 31) following the year for which the report was submitted. This amount does not include quantities of the toxic chemical that will be recycled onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81B_SECD_YR_QTY": "The total amount of the toxic chemical expected to be released on-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the second following calendar year (January 1 - December 31). These mediums include fugitive and stack air emissions, discharges to water bodies, underground injection to class II-V wells, land treatment/application farming, RCRA subtitle C surface impoundments, Other surface Impoundments and Other disposals. This total does not include on-site releases or disposal due to catastrophic events.",
"REL_81B_FOLL_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.B, following year on-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_81D_CURR_YR_QTY": "The total amount of the toxic chemical released off-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the calendar year (January 1 - December 31). These off-site mediums include Storage Only, Solidification/Stabilization (for metals only), Wastewater Treatment (Excluding POTWs) (for metals only), Subtitle C Surface Impoundment, Other Surface Impoundment, Land Treatment, Other Land Disposal, Underground Injection to Class II-V Wells, Other off-site Management, Transfers to Waste brokers for Disposal and Unknown. This total does not include off-site releases or disposal due to catastrophic events.",
"REL_81C_SECD_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.C, second following year off-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_81C_CURR_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.C, off-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"ENERGY_OFFSITE_PREV_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste sent offsite to be burned for energy recovery during the calendar year (January 1 - December 31) prior to the year for which the report was submitted. This includes all amounts of the toxic chemical that were intended to be recovered for energy and were sent offsite for that purpose. This figure includes all transfers offsite reported in section 6.2 which are classified with an energy recovery code. This does not include quantities of the toxic chemical that are combusted for energy recovery offsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"TREATED_ONSITE_FOLL_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated onsite following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81B_PREV_YR_QTY": "The total amount of the toxic chemical released on-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the prior calendar year (January 1 - December 31). These mediums include fugitive and stack air emissions, discharges to water bodies, underground injection to class II-V wells, land treatment/application farming, RCRA subtitle C surface impoundments, Other surface Impoundments and Other disposals. This total does not include on-site releases or disposal due to catastrophic events.",
"RECYC_ONSITE_PREV_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled on-site previous year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"RECYC_OFFSITE_PREV_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled off-site previous year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_SECD_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the released second following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81D_CURR_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.D, off-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"ENERGY_ONSITE_CURR_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery onsite current year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81A_CURR_YR_QTY": "The total amount of the toxic chemical released on-site due to production related events by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the calendar year (January 1 - December 31). This total does not include on-site releases or disposal due to catastrophic events.",
"RECYC_OFFSITE_SECD_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be sent offsite for recycling during the calendar year (January 1 - December 31) two years following the year for which the report was submitted. This amount does not include quantities of the toxic chemical that will be transferred offsite for recycling as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"TREATED_OFFSITE_SECD_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be sent for treatment offsite during the calendar year (January 1 - December 31) two years following the year for which the report was submitted. This does not include expected transfers of metals to publicly owned treatment works (POTWs) because metals cannot be treated (destroyed) and will ultimately be disposed. Expected transfers of metals to POTWs are included in section 8.1. This amount also does not include quantities of the toxic chemical that will be transferred off-site for treatment as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81D_SECD_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.D, second following year off-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"RECYC_OFFSITE_CURR_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled off-site current year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"TREATED_OFFSITE_PREV_YR_QTY": "The total amount (in pounds) of the toxic chemical sent for treatment offsite during the calendar year (January 1 - December 31) prior to the year for which the report was submitted. This includes the total amount of the toxic chemical intended to be treated (destroyed) and sent offsite for that purpose, not the amount of the toxic chemical actually treated (destroyed) by offsite processes. This figure includes all transfers offsite reported in section 6.2 which are classified with treatment waste management codes and most transfers to POTWs reported in section 6.1, except for metals. This does not include transfers of metals to publicly owned treatment works (POTWs) because metals cannot be treated (destroyed) and will ultimately be disposed. Transfers of metals to POTWs are included in section 8.1. This amount also does not include quantities of the toxic chemical that were transferred off-site for treatment as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"TREATED_OFFSITE_FOLL_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated offsite following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_OFFSITE_SECD_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery offsite second following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_CURR_YR_QTY": "The total amount (in pounds) of the toxic chemical released due to production related events by the facility to all environmental media both on and off site during the calendar year (January 1 - December 31) for which the report was submitted. This includes both fugitive and stack air emissions, discharges to water bodies, underground injection, and land disposal on site (all releases reported in section 5). It also includes transfers of the toxic chemical offsite for disposal (transfers reported in section 6.2 which are classified with a disposal waste management code) and amounts of metals transferred to POTWs, because metals cannot be treated (destroyed) and will ultimately be disposed (metals reported in 6.1).",
"TREATED_ONSITE_CURR_YR_QTY": "The total amount (in pounds) of the toxic chemical treated onsite during the calendar year (January 1 - December 31) for which the report was submitted. This includes only the amount of the toxic chemical actually treated (destroyed) by processes at the facility, not the total amount of the toxic chemical present in wastestreams sent to those processes. This amount does not include quantities of the toxic chemical that were treated for destruction onsite as the result of a catastrophic event,remedial action or other, one-time event not associated with production.",
"TREATED_ONSITE_SECD_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated onsite second following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81B_FOLL_YR_QTY": "The total amount of the toxic chemical expected to be released on-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the following calendar year (January 1 - December 31). These mediums include fugitive and stack air emissions, discharges to water bodies, underground injection to class II-V wells, land treatment/application farming, RCRA subtitle C surface impoundments, Other surface Impoundments and Other disposals. This total does not include on-site releases or disposal due to catastrophic events.",
"ENERGY_OFFSITE_PREV_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery offsite previous year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81A_FOLL_YR_QTY": "The total amount of the toxic chemical expected to be released on-site by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the following calendar year (January 1 - December 31). This total does not include on-site releases or disposal due to catastrophic events.",
"RECYC_OFFSITE_SECD_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled off-site second following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81D_PREV_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.D, prior year off-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"RECYC_OFFSITE_PREV_YR_QTY": "The total amount (in pounds) of the toxic chemical sent offsite for recycling during the calendar year (January 1 - December 31) prior to the year for which the report was submitted. This includes all amounts of the toxic chemical intended to be recycled and sent offsite for that purpose, not just the amount of the toxic chemical actually recovered. This figure includes all transfers offsite reported in section 6.2 which are classified with a recycling code. This amount does not include quantities of the toxic chemical that were transferred offsite for recycling as the result of a catastrophic event, remedial action or other, one-time event not associated with production",
"RECYC_OFFSITE_FOLL_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled off-site following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81D_SECD_YR_QTY": "The total amount of the toxic chemical expected to be released off-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the second following calendar year (January 1 - December 31). These off-site mediums include Storage Only, Solidification/Stabilization (for metals only), Wastewater Treatment (Excluding POTWs) (for metals only), Subtitle C Surface Impoundment, Other Surface Impoundment, Land Treatment, Other Land Disposal, Underground Injection to Class II-V Wells, Other off-site Management, Transfers to Waste brokers for Disposal and Unknown. This total does not include off-site releases or disposal due to catastrophic events.",
"REL_SECD_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be released by the facility to all environmental media both on and off site during the calendar year (January 1 - December 31) two years following the year for which the report was submitted. This includes air emissions, discharges to water bodies, underground injection, and land disposal on site (all releases reported in section 5). It also includes transfers of the toxic chemical offsite for disposal (transfers reported in section 6.2 which are classified with a disposal waste management code) and amounts of metals transferred to POTWs (metals reported in 6.1).",
"REL_81D_PREV_YR_QTY": "The total amount of the toxic chemical released off-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the prior calendar year (January 1 - December 31). These off-site mediums include Storage Only, Solidification/Stabilization (for metals only), Wastewater Treatment (Excluding POTWs) (for metals only), Subtitle C Surface Impoundment, Other Surface Impoundment, Land Treatment, Other Land Disposal, Underground Injection to Class II-V Wells, Other off-site Management, Transfers to Waste brokers for Disposal and Unknown. This total does not include off-site releases or disposal due to catastrophic events.",
"TREATED_OFFSITE_FOLL_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be sent for treatment offsite during the calendar year (January 1 - December 31) following the year for which the report was submitted. This does not include expected transfers of metals to publicly owned treatment works (POTWs) because metals cannot be treated (destroyed) and will ultimately be disposed. Expected transfers of metals to POTWs are included in section 8.1. This amount also does not include quantities of the toxic chemical that will be transferred off-site for treatment as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81B_CURR_YR_QTY": "The total amount of the toxic chemical released on-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the calendar year (January 1 - December 31). These mediums include fugitive and stack air emissions, discharges to water bodies, underground injection to class II-V wells, land treatment/application farming, RCRA subtitle C surface impoundments, Other surface Impoundments and Other disposals. This total does not include on-site releases or disposal due to catastrophic events.",
"TREATED_OFFSITE_SECD_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated offsite second following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81C_FOLL_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.C, following year off-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_81B_PREV_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.B, prior year on-site releases to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"RECYC_ONSITE_SECD_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled on-site second following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_OFFSITE_FOLL_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery offsite following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_ONSITE_SECD_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery onsite second following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_ONSITE_FOLL_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery onsite following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81A_SECD_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.A, second following year on-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"REL_81D_FOLL_YR_QTY": "The total amount of the toxic chemical expected to be released off-site due to production related events by the facility to mediums other than Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the following calendar year (January 1 - December 31). These off-site mediums include Storage Only, Solidification/Stabilization (for metals only), Wastewater Treatment (Excluding POTWs) (for metals only), Subtitle C Surface Impoundment, Other Surface Impoundment, Land Treatment, Other Land Disposal, Underground Injection to Class II-V Wells, Other off-site Management, Transfers to Waste brokers for Disposal and Unknown. This total does not include off-site releases or disposal due to catastrophic events.",
"ENERGY_ONSITE_SECD_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste expected to be burned for energy recovery onsite during the calendar year (January 1 - December 31) two years following the year for which the report was submitted. This should not include quantities of the toxic chemical that will be combusted for energy recovery onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81A_SECD_YR_QTY": "The total amount of the toxic chemical expected to be released on-site by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the second following calendar year (January 1 - December 31). This total does not include on-site releases or disposal due to catastrophic events.",
"REL_FOLL_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the released following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"REL_81A_FOLL_YR_NA": "Indicates if 'NA' ('not applicable') was entered for Section 8.1.A, following year on-site releases to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills. Values: 1 = 'NA'; 0 = '0' (zero) or not 'NA'.",
"ENERGY_ONSITE_PREV_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste burned for energy recovery onsite during the calendar year (January 1 - December 31) prior to the year for which the report was submitted. This includes only the amount of the toxic chemical actually combusted in the unit, not the total amount of the toxic chemical in the wastestream sent for energy recovery. This also does not include quantities of the toxic chemical that are combusted for energy recovery onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"ENERGY_ONSITE_CURR_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste burned for energy recovery onsite during the calendar year (January 1 - December 31) for which the report was submitted. This includes only the amount of the toxic chemical actually combusted in the unit, not the total amount of the toxic chemical in the wastestream sent for energy recovery. This also does not include quantities of the toxic chemical that are combusted for energy recovery onsite as the result of a catastrophic event,remedial action or other, one-time event not associated with production.",
"TREATED_ONSITE_PREV_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated onsite previous year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"TREATED_ONSITE_PREV_YR_QTY": "The total amount (in pounds) of the toxic chemical treated onsite during the calendar year (January 1 - December 31) prior to the year for which the report was submitted. This includes only the amount of the toxic chemical actually treated (destroyed) by processes at the facility, not the total amount of the toxic chemical present in wastestreams sent to those processes. This amount does not include quantities of the toxic chemical that were treated for destruction onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81A_PREV_YR_QTY": "The total amount of the toxic chemical released on-site due to production related events by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the prior calendar year (January 1 - December 31). This total does not include on-site releases or disposal due to catastrophic events.",
"TREATED_ONSITE_FOLL_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be treated onsite during the calendar year (January 1 - December 31) following the year for which the report was submitted. This amount does not include quantities of the toxic chemical that will be treated for destruction onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"REL_81C_PREV_YR_QTY": "The total amount of the toxic chemical released off-site due to production related events by the facility to Class I Underground Injection Wells, RCRA Subtitle C landfills, and other landfills during the prior calendar year (January 1 - December 31). This total does not include off-site releases or disposal due to catastrophic events.",
"RECYC_ONSITE_FOLL_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the recycled on-site following year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"TREATED_ONSITE_SECD_YR_QTY": "The total amount (in pounds) of the toxic chemical expected to be treated onsite during the calendar year (January 1 - December 31) two years following the year for which the report was submitted. This amount does not include quantities of the toxic chemical that will be treated for destruction onsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production.",
"TREATED_OFFSITE_PREV_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the treated offsite previous year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_ONSITE_PREV_YR_NA": "Indicates if '0' (zero) or 'NA' ('not applicable') was entered for the energy recovery onsite previous year quantity. Values: 1 = 'NA', 0 = '0' (zero) or not 'NA'.",
"ENERGY_OFFSITE_SECD_YR_QTY": "The total amount (in pounds) of the toxic chemical in waste expected to be sent offsite to be burned for energy recovery during the calendar year (January 1 - December 31) two years following the year for which the report was submitted. This does not include quantities of the toxic chemical that will be combusted for energy recovery offsite as the result of a catastrophic event, remedial action or other, one-time event not associated with production."
},
"TRI_RELEASE_QTY": {
"RELEASE_BASIS_EST_CODE": "The code representing the technique used to develop theestimate of releases reported in the 'Total Release' box (TOTAL_RELEASE). Thevalues are as follows:",
"WATER_SEQUENCE_NUM": "Sequence in which a release to water is reported on a Form R submission.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"TOTAL_RELEASE": "The total amount (in pounds) of the toxic chemical released to air, water, land, and underground injection wells during the calendar year (January 1 - December 31). Release amounts may be reported as specific numbers or as ranges (RELEASE_RANGE_CODE). Descriptions by Form R Section number for each environmental medium follow.",
"RELEASE_NA": "Indicates whether 'NA' (Not Applicable) was entered on Form R for the release estimate. Values: 1 = 'Yes', 0 = 'No'. Descriptions by Form R Section number for each environmental medium follow.",
"RELEASE_RANGE_CODE": "The code that corresponds to the amount of toxic chemical released annually by the reporting facility, reported as a range for releases less than 1,000 pounds. When a facility uses a range code, the amount reported to TRI is the midpoint of the range. On Form R, letter codes are used to represent ranges: A = 1-10 pounds, B = 11-499 pounds, and C = 500-999 pounds. The letters are converted to numbers for storage in the TRIS database where '1' represents range 'A', '3' represents range 'B', and '4' represents range 'C'. The historical value '2' = 1-499 pounds.",
"ENVIRONMENTAL_MEDIUM": "Code indicating the environmental medium to which the toxic chemical is released from the facility."
},
"TRI_ONSITE_WASTESTREAM": {
"OPERATING_DATA_IND": "Indicates if the waste treatment efficiency estimate (TREATMENT_EFFCIENCY_EST) is based on actual operating data, such as monitoring influent and effluent toxic chemical levels in the waste stream; or, indicates if TREATMENT_EFFCIENCY_EST is not based on actual operating or monitoring data, but rather some other technique, such as published data for similar processes or the equipment supplier's literature. Values: 1 = 'Yes', 0 = 'No'', 2 = blank or not entered.",
"WASTESTREAM_CODE": "Indicates the general waste stream type containing the toxic chemical. The four codes used to indicate the general waste stream types are: A = Gaseous (gases, vapors, airborne particles), W = Wastewater (aqueous waste), L = Liquid (non-aqueous, liquid waste), and S = Solid (including sludges and slurries).",
"WASTESTREAM_SEQ_NUM": "Sequence in which an on-site waste treatment process is reported on a Form R submission.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"INFLUENT_CONC_RANGE": "Indicates the range of concentration of the toxic chemical in the waste stream as it typically enters the waste treatment step or sequence. The concentration is based on the amount or mass of the toxic chemical in the waste stream as compared to the total amount or mass of the waste stream and is determined prior to the application of any waste management methods. Facilities report using one of the following five codes:",
"TREATMENT_EFFICIENCY_EST_NA": "Indicates whether 'NA' (Not Applicable) was entered on Form R for the waste treatment efficiency estimate. Values: 1 = 'Yes', 0 = 'No'.",
"SEQUENTIAL_TREAT_87_90": "Indicator that shows whether treatment steps were used in sequence, for Reporting Years 1987 through 1990, to estimate treatment efficiency of the overall treatment process.",
"TREATMENT_EFFICIENCY_EST": "The percentage of the toxic chemical removed from the waste stream through destruction, biological degradation, chemical conversion, or physical removal. This estimate represents the overall percentage of the toxic chemical destroyed or removed (based on amount or mass) throughout all waste management methods, not merely changes in volume or concentration and not merely the efficiency of one method in a sequence of activities. This also does not represent the waste treatment efficiency for the entire waste stream but only the removal or destruction of this specific toxic chemical in that waste stream. This does not include energy recovery or recycling activities. Energy recovery and recycling activities are reported in sections 7B and 7C, respectively. The value is calculated as follows: ((I - E)/1) * 100, where I equals the amount of toxic chemical in the influent waste stream, and E equals the amount of the toxic chemical in the effluent waste stream.",
"EFFICIENCY_RANGE_CODE": "The range code representing the percentage of the toxic chemical removed from the waste stream through destruction, biological degradation, chemical conversion, or physical removal. This range code represents the overall percentage of the toxic chemical destroyed or removed (based on amount or mass) throughout all waste management methods, not merely changes in volume or concentration and not merely the efficiency of one method in a sequence of activities. This also does not represent the waste treatment efficiency for the entire waste stream but only the removal or destruction of this specific toxic chemical in that waste stream. This does not include energy recovery or recycling activities. Energy recovery and recycling activities are reported in sections 7B and 7C, respectively. "
},
"TRI_FACILITY_RCRA": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"ASGN_RCRA_IND": "Indicates that the associated RCRA_NUM represents the principal RCRA Identification Number as assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"RCRA_NUM": "The number assigned to the facility by EPA for purposes of the Resource Conservation and Recovery Act (RCRA). Not all facilities will have a RCRA Identification Number. A facility will only have a RCRA Identification Number if it manages RCRA regulated hazardous waste. Some facilities may have more than one RCRA Identification Number."
},
"TRI_FACILITY_DB_HISTORY": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"ASGN_DB_IND": "Indicates that the associated DB_NUM represents the principal Dun & Bradstreet number assigned to the facility by TRI from Form R or Form A submissions. Values: 1 = 'Yes', 0 = 'No'.",
"REPORTING_YEAR": "The year for which the form was submitted. This is not the year in which the form was filed but rather it is the calendar year (January 1 - December 31) during which the toxic chemical was, manufactured, processed and/or otherwise used and released or otherwise managed as a waste.",
"DB_NUM": "The number or numbers which have been assigned to the facility by Dun & Bradstreet. Dun & Bradstreet is a private financial tracking and accounting firm. Not all facilities will have Dun & Bradstreet numbers."
},
"TRI_FACILITY_SIC": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"SIC_CODE": "The Standard Industrial Classification (SIC) code or codes which best describes the activities conducted at the facility. SIC codes are 4 digit numbers used by the Bureau of Census as part of a system to categorize and track the types of business activities conducted in the United States. The first two digits of the code represent the major industry group (e.g., SIC code 25XX indicates Furniture and Fixtures) and the second two digits represent the specific subset of that group (e.g., 2511 indicates wood household furniture). EPA instructs facilities to enter their primary SIC code first. Many facilities do not report their primary SIC code first.",
"PRIMARY_IND": "Indicates whether the associated SIC_CODE/NAICS_CODE represents the facility's primary business activity as entered by the submitter. EPA instructs facilities to enter their primary SIC/NAICS on the Form R or Form A in part I, section 4.5, box a. Values: 1 = 'Yes', 0 = 'No'."
},
"TRI_ONSITE_WASTE_TREATMENT_MET": {
"WASTESTREAM_SEQ_NUM": "Sequence in which an on-site waste treatment process is reported on a Form R submission.",
"DOC_CTRL_NUM": "DOC_CTRL_NUM is a unique identification number assigned to each submission. The format is TTYYNNNNNNNNN, where TT = document type, YY = reporting year, and NNNNNNNNN = assigned number with a check digit.",
"TREATMENT_METHOD_CODE": "The on-site waste treatment activity that is applied to the waste stream containing the toxic chemical. This includes all waste treatment methods through which the toxic chemical passes as part of that waste stream, regardless of whether or not the method has, or is intended to have, any effect on the toxic chemical. If the waste stream moves through a series of waste treatment activities, each method will be listed sequentially.",
"TREATMENT_SEQUENCE": "Sequence in which a TREATMENT_METHOD_CODE is entered on a Form R submission, and indicates the on-site order of treatment."
},
"TRI_FACILITY_SIC_HISTORY": {
"TRI_FACILITY_ID": "The unique number assigned to each facility for purposes of the TRI program. Usually, only one number is assigned to each facility and the number is for the entire facility. One company may have multiple TRI Facility Identification (ID) numbers if they have multiple facilities. One facility with many establishments will usually have only one TRI Facility ID number. They will then use this number for all of their Form Rs even if they are submitting a Form R for different establishments with different names. In a few instances different establishments of the same facility will have different TRI Facility ID numbers. The format is ZZZZZNNNNNSSSSS, where ZZZZZ = ZIP code, NNNNN = the first 5 consonants of the name, and SSSSS = the first 5 non-blank non-special characters in the street address.",
"REPORTING_YEAR": "The year for which the form was submitted. This is not the year in which the form was filed but rather it is the calendar year (January 1 - December 31) during which the toxic chemical was, manufactured, processed and/or otherwise used and released or otherwise managed as a waste.",
"SIC_CODE": "The Standard Industrial Classification (SIC) code or codes which best describes the activities conducted at the facility. SIC codes are 4 digit numbers used by the Bureau of Census as part of a system to categorize and track the types of business activities conducted in the United States. The first two digits of the code represent the major industry group (e.g., SIC code 25XX indicates Furniture and Fixtures) and the second two digits represent the specific subset of that group (e.g., 2511 indicates wood household furniture). EPA instructs facilities to enter their primary SIC code first. Many facilities do not report their primary SIC code first.",
"PRIMARY_IND": "Indicates whether the associated SIC_CODE/NAICS_CODE represents the facility's primary business activity as entered by the submitter. EPA instructs facilities to enter their primary SIC/NAICS on the Form R or Form A in part I, section 4.5, box a. Values: 1 = 'Yes', 0 = 'No'."
}
}
| 271.57243
| 1,079
| 0.773722
| 18,292
| 116,233
| 4.864367
| 0.058988
| 0.014891
| 0.02949
| 0.023062
| 0.853224
| 0.835658
| 0.823689
| 0.814113
| 0.805595
| 0.797739
| 0
| 0.011685
| 0.170184
| 116,233
| 427
| 1,080
| 272.208431
| 0.910836
| 0
| 0
| 0.327869
| 0
| 0.721311
| 0.955262
| 0.02988
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.002342
| 0.007026
| 0
| 0.007026
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
3452de38a50313c8a2953faa40954c90ad14c915
| 1,129
|
py
|
Python
|
temboo/core/Library/LastFm/Album/__init__.py
|
jordanemedlock/psychtruths
|
52e09033ade9608bd5143129f8a1bfac22d634dd
|
[
"Apache-2.0"
] | 7
|
2016-03-07T02:07:21.000Z
|
2022-01-21T02:22:41.000Z
|
temboo/core/Library/LastFm/Album/__init__.py
|
jordanemedlock/psychtruths
|
52e09033ade9608bd5143129f8a1bfac22d634dd
|
[
"Apache-2.0"
] | null | null | null |
temboo/core/Library/LastFm/Album/__init__.py
|
jordanemedlock/psychtruths
|
52e09033ade9608bd5143129f8a1bfac22d634dd
|
[
"Apache-2.0"
] | 8
|
2016-06-14T06:01:11.000Z
|
2020-04-22T09:21:44.000Z
|
from temboo.Library.LastFm.Album.AddTags import AddTags, AddTagsInputSet, AddTagsResultSet, AddTagsChoreographyExecution
from temboo.Library.LastFm.Album.GetBuyLinks import GetBuyLinks, GetBuyLinksInputSet, GetBuyLinksResultSet, GetBuyLinksChoreographyExecution
from temboo.Library.LastFm.Album.GetInfo import GetInfo, GetInfoInputSet, GetInfoResultSet, GetInfoChoreographyExecution
from temboo.Library.LastFm.Album.GetShouts import GetShouts, GetShoutsInputSet, GetShoutsResultSet, GetShoutsChoreographyExecution
from temboo.Library.LastFm.Album.GetTags import GetTags, GetTagsInputSet, GetTagsResultSet, GetTagsChoreographyExecution
from temboo.Library.LastFm.Album.GetTopTags import GetTopTags, GetTopTagsInputSet, GetTopTagsResultSet, GetTopTagsChoreographyExecution
from temboo.Library.LastFm.Album.RemoveTag import RemoveTag, RemoveTagInputSet, RemoveTagResultSet, RemoveTagChoreographyExecution
from temboo.Library.LastFm.Album.Search import Search, SearchInputSet, SearchResultSet, SearchChoreographyExecution
from temboo.Library.LastFm.Album.Share import Share, ShareInputSet, ShareResultSet, ShareChoreographyExecution
| 112.9
| 140
| 0.888397
| 99
| 1,129
| 10.131313
| 0.424242
| 0.089731
| 0.152542
| 0.206381
| 0.251246
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.055802
| 1,129
| 9
| 141
| 125.444444
| 0.940901
| 0
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| 0
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| 0
| 0
| 0
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| 0
| 1
| 0
| true
| 0
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| 1
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| null | 0
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| 0
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| 0
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 7
|
345788758edf74716928f3967880b9505f28ed02
| 4,993
|
py
|
Python
|
src/rest.py
|
huangbop/tko
|
62339d51349d4c03e9e3261bca367eb0ce3f74ff
|
[
"MIT"
] | null | null | null |
src/rest.py
|
huangbop/tko
|
62339d51349d4c03e9e3261bca367eb0ce3f74ff
|
[
"MIT"
] | null | null | null |
src/rest.py
|
huangbop/tko
|
62339d51349d4c03e9e3261bca367eb0ce3f74ff
|
[
"MIT"
] | null | null | null |
"""*
"""
import requests
import json
RAW_HEADERS = {
"X-Bmob-Application-Id": "e9c72808f8555c8d7846a13a50e907a6",
"X-Bmob-REST-API-Key": "9632a84d440c4faa818f033355cb9bf3",
}
JSON_HEADERS = {
"X-Bmob-Application-Id": "e9c72808f8555c8d7846a13a50e907a6",
"X-Bmob-REST-API-Key": "9632a84d440c4faa818f033355cb9bf3",
"Content-Type": "application/json",
}
JPG_HEADERS = {
"X-Bmob-Application-Id": "e9c72808f8555c8d7846a13a50e907a6",
"X-Bmob-REST-API-Key": "9632a84d440c4faa818f033355cb9bf3",
"Content-Type": "image/jpeg",
}
PNG_HEADERS = {
"X-Bmob-Application-Id": "e9c72808f8555c8d7846a13a50e907a6",
"X-Bmob-REST-API-Key": "9632a84d440c4faa818f033355cb9bf3",
"Content-Type": "image/png",
}
CLASSES_BASE_URL = 'https://api.bmob.cn/1/classes/'
FILES_BASE_URL = 'https://api.bmob.cn/1/files/'
def add_image(info):
"""*
"""
import pdb; pdb.set_trace()
image_headers = JPG_HEADERS
if info['type'] == 'png':
image_headers = PNG_HEADERS
res = requests.post(FILES_BASE_URL + info['name'], headers=image_headers,
data=info['bin'])
if res.status_code == 201:
file_desc = json.loads(res.content.decode())
file_desc['__type'] = "File"
info['file'] = file_desc
del info['bin']
res = requests.post('%s%s' % (CLASSES_BASE_URL, 'images'),
headers=JSON_HEADERS, data=json.dumps(info))
print(res)
def add_product(info):
"""*
"""
res = requests.post('%s%s' % (CLASSES_BASE_URL, 'products'),
headers=JSON_HEADERS, data=json.dumps(info))
print(res)
def modify_image(info):
"""*
"""
# get objectid
import pdb; pdb.set_trace()
params = 'where={"name": "%s"}' % info['name']
res = requests.get(CLASSES_BASE_URL + 'images', headers=RAW_HEADERS,
params=params)
if res.status_code == 200:
record = json.loads(res.content.decode())
objectid = record['results'][0]['objectId']
# load file first
image_headers = JPG_HEADERS
if info['type'] == 'png':
image_headers = PNG_HEADERS
res = requests.post(FILES_BASE_URL + info['name'],
headers=image_headers, data=info['bin'])
if res.status_code == 201:
file_desc = json.loads(res.content.decode())
file_desc['__type'] = "File"
info['file'] = file_desc
del info['bin']
del info['name']
res = requests.put('%simages/%s' % (CLASSES_BASE_URL, objectid),
headers=JSON_HEADERS, data=json.dumps(info))
print(res)
def modify_product(info):
"""*
"""
import pdb; pdb.set_trace()
# get objectid
res = requests.get(CLASSES_BASE_URL + 'products', headers=RAW_HEADERS,
data="where={'name': '%s'}" % info['name'])
if res.status_code == 200:
record = json.loads(res.content.decode())
objectid = record['results'][0]['objectId']
# modify special record
del info['name']
res = requests.put('%sproducts/%s' % (CLASSES_BASE_URL, objectid),
headers=JSON_HEADERS, data=json.dumps(info))
print(res)
def delete_image(info):
"""*
"""
# get objectid
import pdb; pdb.set_trace()
params = 'where={"name": "%s"}' % info['name']
res = requests.get(CLASSES_BASE_URL + 'images', headers=RAW_HEADERS,
params=params)
if res.status_code == 200:
record = json.loads(res.content.decode())
try:
objectid = record['results'][0]['objectId']
except:
print('No objectid found with %s' % info['name'])
return
# delete record
res = requests.delete('%simages/%s' % (CLASSES_BASE_URL, objectid),
headers=RAW_HEADERS)
if res.status_code == 200:
print('Delete %s OK.' % info['name'])
else:
print('Delete %s failed.' % info['name'])
def delete_product(info):
"""*
"""
# get objectid
import pdb; pdb.set_trace()
params = 'where={"name": "%s"}' % info['name']
res = requests.get(CLASSES_BASE_URL + 'products', headers=RAW_HEADERS,
params=params)
if res.status_code == 200:
record = json.loads(res.content.decode())
try:
objectid = record['results'][0]['objectId']
except:
print('No objectid found with %s' % info['name'])
return
# delete record
res = requests.delete('%sproducts/%s' % (CLASSES_BASE_URL, objectid),
headers=RAW_HEADERS)
if res.status_code == 200:
print('Delete %s OK.' % info['name'])
else:
print('Delete %s failed.' % info['name'])
else:
print('Get objectid of %s failed.' % info['name'])
| 32.212903
| 77
| 0.565191
| 554
| 4,993
| 4.947653
| 0.142599
| 0.04378
| 0.056184
| 0.04378
| 0.919372
| 0.907698
| 0.871945
| 0.844217
| 0.821598
| 0.821598
| 0
| 0.057478
| 0.282195
| 4,993
| 154
| 78
| 32.422078
| 0.70731
| 0.032445
| 0
| 0.707965
| 0
| 0
| 0.215793
| 0.071026
| 0
| 0
| 0
| 0
| 0
| 1
| 0.053097
| false
| 0
| 0.061947
| 0
| 0.132743
| 0.097345
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ca9e06e4f2ed1b2f9e2beceb41bb40aa08909d83
| 77
|
py
|
Python
|
forum/tests/__init__.py
|
kraft99/forum
|
59b35bd102da3bdeb0d6bc104de77572158992b3
|
[
"MIT"
] | 8
|
2015-12-25T06:33:20.000Z
|
2021-01-04T22:37:56.000Z
|
forum/tests/__init__.py
|
insin/forum
|
59b35bd102da3bdeb0d6bc104de77572158992b3
|
[
"MIT"
] | null | null | null |
forum/tests/__init__.py
|
insin/forum
|
59b35bd102da3bdeb0d6bc104de77572158992b3
|
[
"MIT"
] | 7
|
2015-07-18T17:52:52.000Z
|
2020-04-09T10:46:47.000Z
|
import forum
from forum.tests.auth import *
from forum.tests.models import *
| 19.25
| 32
| 0.792208
| 12
| 77
| 5.083333
| 0.5
| 0.295082
| 0.459016
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12987
| 77
| 4
| 32
| 19.25
| 0.910448
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| true
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 7
|
caabfff293660db0ea0850f1e34afaf2bd2e2b57
| 140
|
py
|
Python
|
packages/Python/lldbsuite/test/repl/array/TestREPLArray.py
|
xiaobai/swift-lldb
|
9238527ce430e6837108a16d2a91b147551fb83c
|
[
"Apache-2.0"
] | 765
|
2015-12-03T16:44:59.000Z
|
2022-03-07T12:41:10.000Z
|
packages/Python/lldbsuite/test/repl/array/TestREPLArray.py
|
xiaobai/swift-lldb
|
9238527ce430e6837108a16d2a91b147551fb83c
|
[
"Apache-2.0"
] | 1,815
|
2015-12-11T23:56:05.000Z
|
2020-01-10T19:28:43.000Z
|
packages/Python/lldbsuite/test/repl/array/TestREPLArray.py
|
xiaobai/swift-lldb
|
9238527ce430e6837108a16d2a91b147551fb83c
|
[
"Apache-2.0"
] | 284
|
2015-12-03T16:47:25.000Z
|
2022-03-12T05:39:48.000Z
|
import lldbsuite.test.lldbinrepl as lldbinrepl
import lldbsuite.test.lldbtest as lldbtest
lldbinrepl.MakeREPLTest(__file__, globals(), [])
| 28
| 48
| 0.821429
| 16
| 140
| 6.9375
| 0.5625
| 0.27027
| 0.342342
| 0
| 0
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| 0
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| 0
| 0
| 0.085714
| 140
| 4
| 49
| 35
| 0.867188
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| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
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| null | 1
| 1
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| null | 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
cac1508c2be7a6948fdef7cd52e79cf9f2fdc45f
| 42,397
|
py
|
Python
|
pria_lifechem/analysis/all_models_loader.py
|
chao1224/pria_lifechem
|
1fd892505a45695c6197f8d711a8a37589cd7097
|
[
"MIT"
] | 5
|
2018-05-14T10:15:13.000Z
|
2021-03-15T17:18:10.000Z
|
pria_lifechem/analysis/all_models_loader.py
|
chao1224/pria_lifechem
|
1fd892505a45695c6197f8d711a8a37589cd7097
|
[
"MIT"
] | 5
|
2018-05-05T21:04:11.000Z
|
2019-06-24T22:05:35.000Z
|
pria_lifechem/analysis/all_models_loader.py
|
chao1224/pria_lifechem
|
1fd892505a45695c6197f8d711a8a37589cd7097
|
[
"MIT"
] | 2
|
2019-10-18T23:42:27.000Z
|
2020-07-08T19:46:14.000Z
|
from evaluation import *
import argparse
import pandas as pd
import csv
import numpy as np
import json
import sys
sys.path.insert(0, '..') # Add path from parent folder
sys.path.insert(0, '.') # Add path from current folder
from function import *
import os, glob
import deepchem as dc
from sklearn.externals import joblib
from deepchem.trans import undo_transforms
from sklearn.externals import joblib
from sklearn.ensemble import RandomForestClassifier
from shutil import copy2
import copy
from models.deep_classification import *
from models.deep_regression import *
from models.vanilla_lstm import *
"""
Function that loads all models for each class:
1- random_forest
2- irv
4- neural_networks
returns a dict with following hierarchy:
class_name -> model_name -> fold_# -> labels, y_train, y_val, y_test, y_pred_on_train, y_pred_on_val, y_pred_on_test
"""
def stage_1_results(model_directory, data_directory):
#define folders for each class
class_dirs = [model_directory+'/random_forest/stage_1/',
model_directory+'/irv/stage_1/',
model_directory+'/neural_networks/stage_1/',
model_directory+'/docking/stage_1/']
stage_1_dict = {'random_forest' : get_rf_results_stage_1(class_dirs[0], data_directory),
'irv' : get_irv_results_stage_1(class_dirs[1], data_directory),
'neural_networks' : get_nn_results_stage_1(class_dirs[3], data_directory),
'docking' : get_docking_results_stage_1(class_dirs[4], data_directory)
}
return stage_1_dict
def stage_2_results(model_directory, data_directory, held_out_data_file):
#define folders for each class
class_dirs = [model_directory+'/random_forest/stage_2/',
model_directory+'/irv/stage_2/',
model_directory+'/neural_networks/stage_2/',
model_directory+'/docking/stage_2/',
model_directory+'/baseline/stage_2/']
stage_2_dict = {'random_forest' : get_rf_results_stage_2(class_dirs[0], data_directory, held_out_data_file),
'irv' : get_irv_results_stage_2(class_dirs[1], data_directory, held_out_data_file),
'neural_networks' : get_nn_results_stage_2(class_dirs[3], data_directory, held_out_data_file),
'docking' : get_docking_results_stage_2(class_dirs[4], data_directory, held_out_data_file),
'baseline' : get_baseline_results_stage_2(class_dirs[5], data_directory, held_out_data_file)
}
return stage_2_dict
"""
Loads random_forest model results as a list:
model_name -> fold_# -> labels, y_train, y_val, y_test, y_pred_on_train, y_pred_on_val, y_pred_on_test
"""
def get_rf_results_stage_1(model_directory, data_directory, k=5):
model_list = {}
if not os.path.exists(model_directory):
return model_list
model_names = os.listdir(model_directory)
#load data
file_list = []
for i in range(k):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
for m_name in model_names:
model_list[m_name] = {}
for i in range(k):
fold_dir = model_directory+'/'+m_name+'/fold_'+str(i)
csv_file_list = output_file_list[:]
test_pd = read_merged_data([csv_file_list[i]])
csv_file_list.pop(i)
val_pd = read_merged_data([csv_file_list[i%len(csv_file_list)]])
csv_file_list.pop(i%len(csv_file_list))
train_pd = read_merged_data(csv_file_list)
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
# extract data, and split training data into training and val
X_train, y_train = extract_feature_and_label(train_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_val, y_val = extract_feature_and_label(val_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_test, y_test = extract_feature_and_label(test_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_train = np.concatenate((X_train, X_val))
y_train = np.concatenate((y_train, y_val))
#load model and predict
y_pred_on_train = np.zeros(shape=y_train.shape)
y_pred_on_test = np.zeros(shape=y_test.shape)
for j, label in zip(range(len(labels)), labels):
model = joblib.load(fold_dir+'/rf_clf_'+label+'.pkl')
y_pred_on_train[:,j] = model.predict_proba(X_train)[:,1]
y_pred_on_test[:,j] = model.predict_proba(X_test)[:,1]
model_list[m_name]['fold_'+str(i)] = (labels, y_train, np.nan, y_test,
y_pred_on_train, np.nan, y_pred_on_test)
return model_list
def get_rf_results_stage_2(model_directory, data_directory, held_out_data_file, k=5):
model_list = {}
if not os.path.exists(model_directory):
return model_list
model_names = os.listdir(model_directory)
#load data
file_list = []
for i in range(k):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
for m_name in model_names:
model_list[m_name] = {}
for i in range(1):
fold_dir = model_directory+'/'+m_name+'/fold_'+str(i)
train_pd = read_merged_data(output_file_list)
test_pd = read_merged_data([held_out_data_file])
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
# extract data, and split training data into training and val
X_train, y_train = extract_feature_and_label(train_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_test, y_test = extract_feature_and_label(test_pd,
feature_name='Fingerprints',
label_name_list=labels)
#load model and predict
y_pred_on_train = np.zeros(shape=y_train.shape)
y_pred_on_test = np.zeros(shape=y_test.shape)
for j, label in zip(range(len(labels)), labels):
model = joblib.load(fold_dir+'/rf_clf_'+label+'.pkl')
y_pred_on_train[:,j] = model.predict_proba(X_train)[:,1]
y_pred_on_test[:,j] = model.predict_proba(X_test)[:,1]
model_list[m_name] = (labels, y_train, np.nan, y_test,
y_pred_on_train, np.nan, y_pred_on_test)
return model_list
"""
Loads irv model results as a list:
model_name -> fold_# -> labels, y_train, y_val, y_test, y_pred_on_train, y_pred_on_val, y_pred_on_test
"""
def get_irv_results_stage_1(model_directory, data_directory, k=5):
model_list = {}
if not os.path.exists(model_directory):
return model_list
model_names = os.listdir(model_directory)
#load data
file_list = []
for i in range(k):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
for m_name in model_names:
K_neighbors = int(m_name.split('_')[-1])
model_list[m_name] = {}
for ti in range(k):
for vi in [(ti+1)%k]:
i=ti
fold_dir = model_directory+'/'+m_name+'/fold_'+str(i)+'/'
logdir = model_directory+'/'+m_name+'/tf_checkpoints/'
csv_file_list = output_file_list[:]
test_files = [csv_file_list[ti]]
val_files = [csv_file_list[vi]]
model_dict = {}
train_files = [q for j, q in enumerate(csv_file_list) if j not in [ti, vi]]
train_data = []
val_data = []
test_data = []
bal_train_data = []
featurizer='MorganFP'
if featurizer == 'MorganFP':
featurizer_func = dc.feat.CircularFingerprint(size=1024)
elif featurizer == 'GraphConv':
featurizer_func = dc.feat.ConvMolFeaturizer()
for label_index in range(len(labels)):
loader = dc.data.CSVLoader(tasks=[labels[label_index]],
smiles_field="SMILES",
featurizer=featurizer_func,
verbose=False)
# extract data, and split training data into training and val
bal_train_data.append(loader.featurize(train_files, shard_size=2**15))
train_data.append(loader.featurize(train_files, shard_size=2**15))
val_data.append(loader.featurize(val_files, shard_size=2**15))
test_data.append(loader.featurize(test_files, shard_size=2**15))
bal_train_data[label_index] = dc.data.NumpyDataset(bal_train_data[label_index].X, bal_train_data[label_index].y,
bal_train_data[label_index].w, bal_train_data[label_index].ids)
train_data[label_index] = dc.data.NumpyDataset(train_data[label_index].X, train_data[label_index].y,
train_data[label_index].w, train_data[label_index].ids)
val_data[label_index] = dc.data.NumpyDataset(val_data[label_index].X, val_data[label_index].y,
val_data[label_index].w, val_data[label_index].ids)
test_data[label_index] = dc.data.NumpyDataset(test_data[label_index].X, test_data[label_index].y,
test_data[label_index].w, test_data[label_index].ids)
bal_train_data[label_index] = dc.trans.BalancingTransformer(transform_w=True, dataset=bal_train_data[label_index]).transform(bal_train_data[label_index])
transformers = [dc.trans.IRVTransformer(K_neighbors, 1, bal_train_data[label_index])]
for transformer in transformers:
train_data[label_index] = transformer.transform(train_data[label_index])
val_data[label_index] = transformer.transform(val_data[label_index])
test_data[label_index] = transformer.transform(test_data[label_index])
model_dict[labels[label_index]] = dc.models.TensorflowMultiTaskIRVClassifier(
1,
K=K_neighbors,
learning_rate=0.01,
penalty=0.05,
batch_size=8192,
fit_transformers=[],
logdir=logdir+'/'+labels[label_index]+'/',
verbose=False)
curr_ckpt_file = logdir+'/'+labels[label_index]+'/model.ckpt-2'
best_ckpt_file = fold_dir+'/'+labels[label_index]+'/best.ckpt'
copy2(best_ckpt_file+'.data-00000-of-00001', curr_ckpt_file+'.data-00000-of-00001')
copy2(best_ckpt_file+'.index', curr_ckpt_file+'.index')
copy2(best_ckpt_file+'.meta', curr_ckpt_file+'.meta')
if label_index == 0:
y_pred_on_train = np.zeros(shape=(train_data[label_index].y.shape[0], 3))
y_pred_on_val = np.zeros(shape=(val_data[label_index].y.shape[0], 3))
y_pred_on_test = np.zeros(shape=(test_data[label_index].y.shape[0], 3))
y_train = np.zeros(shape=(train_data[label_index].y.shape[0], 3))
w_train = np.zeros(shape=(train_data[label_index].y.shape[0], 3))
y_val = np.zeros(shape=(val_data[label_index].y.shape[0], 3))
w_val = np.zeros(shape=(val_data[label_index].y.shape[0], 3))
y_test = np.zeros(shape=(test_data[label_index].y.shape[0], 3))
w_test = np.zeros(shape=(test_data[label_index].y.shape[0], 3))
y_pred_on_train[:,label_index] = model_dict[labels[label_index]].predict_proba(train_data[label_index])[:,:,1][:,0]
y_pred_on_val[:,label_index] = model_dict[labels[label_index]].predict_proba(val_data[label_index])[:,:,1][:,0]
y_pred_on_test[:,label_index] = model_dict[labels[label_index]].predict_proba(test_data[label_index])[:,:,1][:,0]
y_train[:,label_index] = copy.deepcopy(train_data[label_index].y[:,0])
w_train[:,label_index] = copy.deepcopy(train_data[label_index].w[:,0])
y_val[:,label_index] = copy.deepcopy(val_data[label_index].y[:,0])
w_val[:,label_index] = copy.deepcopy(val_data[label_index].w[:,0])
y_test[:,label_index] = copy.deepcopy(test_data[label_index].y[:,0])
w_test[:,label_index] = copy.deepcopy(test_data[label_index].w[:,0])
for j, label in zip(range(len(labels)), labels):
y_train[np.where(w_train[:,j] == 0)[0],j] = np.nan
y_val[np.where(w_val[:,j] == 0)[0],j] = np.nan
y_test[np.where(w_test[:,j] == 0)[0],j] = np.nan
model_list[m_name]['fold_'+str(i)] = (labels, y_train, y_val, y_test,
y_pred_on_train, y_pred_on_val, y_pred_on_test)
return model_list
def get_irv_results_stage_2(model_directory, data_directory, held_out_data_file, k=5):
model_list = {}
if not os.path.exists(model_directory):
return model_list
model_names = os.listdir(model_directory)
#load data
file_list = []
for i in range(k):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
for m_name in model_names:
K_neighbors = int(m_name.split('_')[-1])
model_list[m_name] = {}
vi = 0
i = 1
csv_file_list = output_file_list[:]
val_files = [csv_file_list[vi]]
test_files = [held_out_data_file]
train_files = [q for j, q in enumerate(csv_file_list) if j not in [vi]]
fold_dir = model_directory+'/'+m_name+'/fold_'+str(i)+'/'
logdir = model_directory+'/'+m_name+'/tf_checkpoints/'
model_dict = {}
train_data = []
val_data = []
test_data = []
bal_train_data = []
featurizer='MorganFP'
if featurizer == 'MorganFP':
featurizer_func = dc.feat.CircularFingerprint(size=1024)
elif featurizer == 'GraphConv':
featurizer_func = dc.feat.ConvMolFeaturizer()
for label_index in range(len(labels)):
loader = dc.data.CSVLoader(tasks=[labels[label_index]],
smiles_field="SMILES",
featurizer=featurizer_func,
verbose=False)
# extract data, and split training data into training and val
bal_train_data.append(loader.featurize(train_files, shard_size=2**15))
train_data.append(loader.featurize(train_files, shard_size=2**15))
val_data.append(loader.featurize(val_files, shard_size=2**15))
test_data.append(loader.featurize(test_files, shard_size=2**15))
bal_train_data[label_index] = dc.data.NumpyDataset(bal_train_data[label_index].X, bal_train_data[label_index].y,
bal_train_data[label_index].w, bal_train_data[label_index].ids)
train_data[label_index] = dc.data.NumpyDataset(train_data[label_index].X, train_data[label_index].y,
train_data[label_index].w, train_data[label_index].ids)
val_data[label_index] = dc.data.NumpyDataset(val_data[label_index].X, val_data[label_index].y,
val_data[label_index].w, val_data[label_index].ids)
test_data[label_index] = dc.data.NumpyDataset(test_data[label_index].X, test_data[label_index].y,
test_data[label_index].w, test_data[label_index].ids)
bal_train_data[label_index] = dc.trans.BalancingTransformer(transform_w=True, dataset=bal_train_data[label_index]).transform(bal_train_data[label_index])
transformers = [dc.trans.IRVTransformer(K_neighbors, 1, bal_train_data[label_index])]
for transformer in transformers:
train_data[label_index] = transformer.transform(train_data[label_index])
val_data[label_index] = transformer.transform(val_data[label_index])
test_data[label_index] = transformer.transform(test_data[label_index])
model_dict[labels[label_index]] = dc.models.TensorflowMultiTaskIRVClassifier(
1,
K=K_neighbors,
learning_rate=0.01,
penalty=0.05,
batch_size=8192,
fit_transformers=[],
logdir=logdir+'/'+labels[label_index]+'/',
verbose=False)
curr_ckpt_file = logdir+'/'+labels[label_index]+'/model.ckpt-2'
best_ckpt_file = fold_dir+'/'+labels[label_index]+'/best.ckpt'
copy2(best_ckpt_file+'.data-00000-of-00001', curr_ckpt_file+'.data-00000-of-00001')
copy2(best_ckpt_file+'.index', curr_ckpt_file+'.index')
copy2(best_ckpt_file+'.meta', curr_ckpt_file+'.meta')
if label_index == 0:
y_pred_on_train = np.zeros(shape=(train_data[label_index].y.shape[0], 3))
y_pred_on_val = np.zeros(shape=(val_data[label_index].y.shape[0], 3))
y_pred_on_test = np.zeros(shape=(test_data[label_index].y.shape[0], 3))
y_train = np.zeros(shape=(train_data[label_index].y.shape[0], 3))
w_train = np.zeros(shape=(train_data[label_index].y.shape[0], 3))
y_val = np.zeros(shape=(val_data[label_index].y.shape[0], 3))
w_val = np.zeros(shape=(val_data[label_index].y.shape[0], 3))
y_test = np.zeros(shape=(test_data[label_index].y.shape[0], 3))
w_test = np.zeros(shape=(test_data[label_index].y.shape[0], 3))
y_pred_on_train[:,label_index] = model_dict[labels[label_index]].predict_proba(train_data[label_index])[:,:,1][:,0]
y_pred_on_val[:,label_index] = model_dict[labels[label_index]].predict_proba(val_data[label_index])[:,:,1][:,0]
y_pred_on_test[:,label_index] = model_dict[labels[label_index]].predict_proba(test_data[label_index])[:,:,1][:,0]
y_train[:,label_index] = copy.deepcopy(train_data[label_index].y[:,0])
w_train[:,label_index] = copy.deepcopy(train_data[label_index].w[:,0])
y_val[:,label_index] = copy.deepcopy(val_data[label_index].y[:,0])
w_val[:,label_index] = copy.deepcopy(val_data[label_index].w[:,0])
y_test[:,label_index] = copy.deepcopy(test_data[label_index].y[:,0])
w_test[:,label_index] = copy.deepcopy(test_data[label_index].w[:,0])
for j, label in zip(range(len(labels)), labels):
y_train[np.where(w_train[:,j] == 0)[0],j] = np.nan
y_val[np.where(w_val[:,j] == 0)[0],j] = np.nan
y_test[np.where(w_test[:,j] == 0)[0],j] = np.nan
model_list[m_name] = (labels, y_train, y_val, y_test,
y_pred_on_train, y_pred_on_val, y_pred_on_test)
return model_list
"""
Results from baseline method using similarity measure.
"""
def get_baseline_results_stage_2(model_directory, data_directory, held_out_data_file, k=5):
model_list = {}
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
model_names = os.listdir(model_directory)
for m_name in model_names:
model_list['baseline'] = {}
for i in range(1):
test_pd = read_merged_data([held_out_data_file])
_, y_test = extract_feature_and_label(test_pd,
feature_name='Fingerprints',
label_name_list=labels)
y_pred_on_test = np.zeros(shape=(y_test.shape[0], 3))
y_pred_on_test[:,0] = np.array(pd.read_csv(model_directory+'/'+m_name)['Keck_Pria_AS_Retest'], dtype=float)
y_pred_on_test[:,1] = np.nan
y_pred_on_test[:,2] = np.nan
model_list['baseline'] = (labels, np.nan, np.nan, y_test,
np.nan, np.nan, y_pred_on_test)
return model_list
"""
Loads neural_network model results as a list:
model_name -> fold_# -> labels, y_train, y_val, y_test, y_pred_on_train, y_pred_on_val, y_pred_on_test
"""
def get_nn_results_stage_1(model_directory, data_directory, k=20):
model_list = {}
if not os.path.exists(model_directory):
return model_list
model_names = []
label_dirs = [model_directory + ldir + '/' for ldir in ['cross_validation_Keck_Pria_Retest',
'cross_validation_Keck_FP',
'cross_validation_RMI']]
for l_dir in label_dirs:
model_names.extend([f.rstrip('.json') for f in os.listdir(l_dir) if f.endswith('.json')])
model_names = list(set(model_names))
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
#load data
file_list = []
for i in range(5):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
output_file_list = np.array(output_file_list)
for m_name in model_names:
model_list[m_name] = {}
for running_index in range(k):
i=running_index
test_index = running_index // 4
val_index = running_index % 4 + (running_index % 4 >= test_index)
complete_index = np.arange(5)
train_index = np.where((complete_index != test_index) & (complete_index != val_index))[0]
train_file_list = output_file_list[train_index]
val_file_list = output_file_list[val_index:val_index+1]
test_file_list = output_file_list[test_index:test_index+1]
test_pd = read_merged_data(test_file_list)
val_pd = read_merged_data(val_file_list)
train_pd = read_merged_data(train_file_list)
# extract data, and split training data into training and val
X_train, y_train = extract_feature_and_label(train_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_val, y_val = extract_feature_and_label(val_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_test, y_test = extract_feature_and_label(test_pd,
feature_name='Fingerprints',
label_name_list=labels)
y_pred_on_train = np.zeros(shape=y_train.shape)
y_pred_on_val = np.zeros(shape=y_val.shape)
y_pred_on_test = np.zeros(shape=y_test.shape)
for j, label in zip(range(len(labels)), labels):
m_name_dir = label_dirs[j]+m_name+'/'
if os.path.exists(m_name_dir):
fold_dir = [m_name_dir+f+'/' for f in os.listdir(m_name_dir)
if str.isdigit(f)][0]
with open(label_dirs[j]+m_name+'.json', 'r') as f:
conf = json.load(f)
model = None
if 'single_classification' in m_name:
model = SingleClassification(conf=conf)
elif 'multi_classification' in m_name:
model = MultiClassification(conf=conf)
elif 'single_regression' in m_name:
model = SingleRegression(conf=conf)
elif 'vanilla_lstm' in m_name:
model = VanillaLSTM(conf=conf)
SMILES_mapping_json_file=model_directory+'SMILES_mapping.json'
X_train, _ = extract_SMILES_and_label(train_pd,
feature_name='SMILES',
label_name_list=labels,
SMILES_mapping_json_file=SMILES_mapping_json_file)
X_val, _ = extract_SMILES_and_label(val_pd,
feature_name='SMILES',
label_name_list=labels,
SMILES_mapping_json_file=SMILES_mapping_json_file)
X_test, _ = extract_SMILES_and_label(test_pd,
feature_name='SMILES',
label_name_list=labels,
SMILES_mapping_json_file=SMILES_mapping_json_file)
X_train = sequence.pad_sequences(X_train, maxlen=model.padding_length)
X_val = sequence.pad_sequences(X_val, maxlen=model.padding_length)
X_test = sequence.pad_sequences(X_test, maxlen=model.padding_length)
weight_file = fold_dir+str(i)+'.weight'
if os.path.exists(weight_file):
model = model.setup_model()
model.load_weights(weight_file)
y_pred_on_train[:,j] = model.predict(X_train)[:,-1]
y_pred_on_val[:,j] = model.predict(X_val)[:,-1]
y_pred_on_test[:,j] = model.predict(X_test)[:,-1]
else:
y_pred_on_train[:,j] = np.nan
y_pred_on_val[:,j] = np.nan
y_pred_on_test[:,j] = np.nan
else:
y_pred_on_train[:,j] = np.nan
y_pred_on_val[:,j] = np.nan
y_pred_on_test[:,j] = np.nan
#load model and predict
model_list[m_name]['fold_'+str(i)] = (labels, y_train, y_val, y_test,
y_pred_on_train, y_pred_on_val, y_pred_on_test)
return model_list
def get_nn_results_stage_2(model_directory, data_directory, held_out_data_file, k=20):
model_list = {}
if not os.path.exists(model_directory):
return model_list
model_names = []
label_dirs = [model_directory + ldir + '/' for ldir in ['Keck_Pria_AS_Retest',
'Keck_FP',
'RMI']]
for l_dir in label_dirs:
model_names.extend([f.rstrip('.json') for f in os.listdir(l_dir) if f.endswith('.json')])
model_names = list(set(model_names))
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
#load data
file_list = []
for i in range(5):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
output_file_list = np.array(output_file_list)
for m_name in model_names:
model_list[m_name] = {}
i=0
val_index = 0
train_index = [1, 2, 3, 4]
train_file_list = output_file_list[train_index]
val_file_list = output_file_list[val_index:val_index+1]
test_pd = read_merged_data([held_out_data_file])
val_pd = read_merged_data(val_file_list)
train_pd = read_merged_data(train_file_list)
# extract data, and split training data into training and val
X_train, y_train = extract_feature_and_label(train_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_val, y_val = extract_feature_and_label(val_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_test, y_test = extract_feature_and_label(test_pd,
feature_name='Fingerprints',
label_name_list=labels)
y_pred_on_train = np.zeros(shape=y_train.shape)
y_pred_on_val = np.zeros(shape=y_val.shape)
y_pred_on_test = np.zeros(shape=y_test.shape)
for j, label in zip(range(len(labels)), labels):
m_name_dir = label_dirs[j]+m_name+'/'
if os.path.exists(m_name_dir):
with open(label_dirs[j]+m_name+'.json', 'r') as f:
conf = json.load(f)
model = None
if 'single_classification' in m_name:
model = SingleClassification(conf=conf)
elif 'multi_classification' in m_name:
model = MultiClassification(conf=conf)
elif 'single_regression' in m_name:
model = SingleRegression(conf=conf)
elif 'vanilla_lstm' in m_name:
model = VanillaLSTM(conf=conf)
SMILES_mapping_json_file=model_directory+'SMILES_mapping.json'
X_train, _ = extract_SMILES_and_label(train_pd,
feature_name='SMILES',
label_name_list=labels,
SMILES_mapping_json_file=SMILES_mapping_json_file)
X_val, _ = extract_SMILES_and_label(val_pd,
feature_name='SMILES',
label_name_list=labels,
SMILES_mapping_json_file=SMILES_mapping_json_file)
X_test, _ = extract_SMILES_and_label(test_pd,
feature_name='SMILES',
label_name_list=labels,
SMILES_mapping_json_file=SMILES_mapping_json_file)
X_train = sequence.pad_sequences(X_train, maxlen=model.padding_length)
X_val = sequence.pad_sequences(X_val, maxlen=model.padding_length)
X_test = sequence.pad_sequences(X_test, maxlen=model.padding_length)
weight_file = m_name_dir+m_name+'.weight'
if os.path.exists(weight_file):
model = model.setup_model()
model.load_weights(weight_file)
y_pred_on_train[:,j] = model.predict(X_train)[:,-1]
y_pred_on_val[:,j] = model.predict(X_val)[:,-1]
y_pred_on_test[:,j] = model.predict(X_test)[:,-1]
else:
y_pred_on_train[:,j] = np.nan
y_pred_on_val[:,j] = np.nan
y_pred_on_test[:,j] = np.nan
else:
y_pred_on_train[:,j] = np.nan
y_pred_on_val[:,j] = np.nan
y_pred_on_test[:,j] = np.nan
#load model and predict
model_list[m_name] = (labels, y_train, y_val, y_test,
y_pred_on_train, y_pred_on_val, y_pred_on_test)
return model_list
"""
Loads docking model results as a list:
model_name -> fold_# -> labels, y_train, y_val, y_test, y_pred_on_train, y_pred_on_val, y_pred_on_test
"""
def get_docking_results_stage_1(model_directory, data_directory, k=5):
model_list = {}
if not os.path.exists(model_directory):
return model_list
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
#load data
file_list = []
for i in range(k):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
pria_df = pd.read_csv(model_directory+'/lc123_rmi_all_docking_scores_complete.csv.gz')
rmi_df = pd.read_csv(model_directory+'/lc123_pria_all_docking_scores_complete.csv.gz')
pria_df = pria_df.rename(columns={'molid': 'Molecule'})
rmi_df = rmi_df.rename(columns={'molid': 'Molecule'})
model_names = pria_df.columns.values[1:]
for m_name in model_names:
model_list[m_name] = {}
for i in range(k):
csv_file_list = output_file_list[:]
test_pd = read_merged_data([csv_file_list[i]])
csv_file_list.pop(i)
val_pd = read_merged_data([csv_file_list[i%len(csv_file_list)]])
csv_file_list.pop(i%len(csv_file_list))
train_pd = read_merged_data(csv_file_list)
# extract data, and split training data into training and val
train_pd = train_pd.merge(pria_df, how='inner', on='Molecule')
val_pd = val_pd.merge(pria_df, how='inner', on='Molecule')
test_pd = test_pd.merge(pria_df, how='inner', on='Molecule')
train_pd = train_pd.merge(rmi_df, how='inner', on='Molecule', suffixes=('_pria', '_rmi'))
val_pd = val_pd.merge(rmi_df, how='inner', on='Molecule', suffixes=('_pria', '_rmi'))
test_pd = test_pd.merge(rmi_df, how='inner', on='Molecule', suffixes=('_pria', '_rmi'))
X_train, y_train = extract_feature_and_label(train_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_val, y_val = extract_feature_and_label(val_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_test, y_test = extract_feature_and_label(test_pd,
feature_name='Fingerprints',
label_name_list=labels)
for m_name in model_names:
y_pred_on_train = np.array(pd.concat((train_pd[m_name+'_pria'],train_pd[m_name+'_pria'],
train_pd[m_name+'_rmi']),axis=1))
y_pred_on_val = np.array(pd.concat((val_pd[m_name+'_pria'],val_pd[m_name+'_pria'],
val_pd[m_name+'_rmi']),axis=1))
y_pred_on_test = np.array(pd.concat((test_pd[m_name+'_pria'],test_pd[m_name+'_pria'],
test_pd[m_name+'_rmi']),axis=1))
#load model and predict
model_list[m_name]['fold_'+str(i)] = (labels, y_train, y_val, y_test,
y_pred_on_train, y_pred_on_val, y_pred_on_test)
return model_list
def get_docking_results_stage_2(model_directory, data_directory, held_out_data_file, k=5):
model_list = {}
if not os.path.exists(model_directory):
return model_list
labels = ["Keck_Pria_AS_Retest", "Keck_Pria_FP_data", "Keck_RMI_cdd"]
#load data
file_list = []
for i in range(k):
file_list.append('file_{}.csv'.format(i))
output_file_list = [data_directory + f_ for f_ in file_list]
pria_df = pd.read_csv(model_directory+'/lc123_rmi_all_docking_scores_complete.csv.gz')
rmi_df = pd.read_csv(model_directory+'/lc123_pria_all_docking_scores_complete.csv.gz')
pria_df = pria_df.rename(columns={'molid': 'Molecule'})
rmi_df = rmi_df.rename(columns={'molid': 'Molecule'})
test_pria_df = pd.read_csv(model_directory+'/lc4_all_docking_scores.csv')
test_rmi_df = pd.read_csv(model_directory+'/lc4_rmi_all_docking_scores.csv')
test_pria_df = test_pria_df.rename(columns={'molid': 'Molecule'})
test_rmi_df = test_rmi_df.rename(columns={'molid': 'Molecule'})
model_names = pria_df.columns.values[1:]
for m_name in model_names:
model_list[m_name] = {}
for i in range(1):
csv_file_list = output_file_list[:]
val_pd = read_merged_data([csv_file_list[i%len(csv_file_list)]])
csv_file_list.pop(i%len(csv_file_list))
train_pd = read_merged_data(csv_file_list)
test_pd = read_merged_data([held_out_data_file])
# extract data, and split training data into training and val
train_pd = train_pd.merge(pria_df, how='inner', on='Molecule')
val_pd = val_pd.merge(pria_df, how='inner', on='Molecule')
train_pd = train_pd.merge(rmi_df, how='inner', on='Molecule', suffixes=('_pria', '_rmi'))
val_pd = val_pd.merge(rmi_df, how='inner', on='Molecule', suffixes=('_pria', '_rmi'))
test_pd = test_pd.merge(test_pria_df, how='inner', on='Molecule')
test_pd = test_pd.merge(test_rmi_df, how='inner', on='Molecule', suffixes=('_pria', '_rmi'))
X_train, y_train = extract_feature_and_label(train_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_val, y_val = extract_feature_and_label(val_pd,
feature_name='Fingerprints',
label_name_list=labels)
X_test, y_test = extract_feature_and_label(test_pd,
feature_name='Fingerprints',
label_name_list=labels)
for m_name in model_names:
y_pred_on_train = np.array(pd.concat((train_pd[m_name+'_pria'],train_pd[m_name+'_pria'],
train_pd[m_name+'_rmi']),axis=1))
y_pred_on_val = np.array(pd.concat((val_pd[m_name+'_pria'],val_pd[m_name+'_pria'],
val_pd[m_name+'_rmi']),axis=1))
y_pred_on_test = np.array(pd.concat((test_pd[m_name+'_pria'],test_pd[m_name+'_pria'],
test_pd[m_name+'_rmi']),axis=1))
#load model and predict
model_list[m_name] = (labels, y_train, y_val, y_test,
y_pred_on_train, y_pred_on_val, y_pred_on_test)
return model_list
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| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1b079141f7d5b13e30ae3d40d16d57ee78c4f772
| 10,569
|
py
|
Python
|
tests/vns3ms/test_vns3_management_api.py
|
cohesive/python-cohesivenet-sdk
|
5620acfa669ff97c94d9aa04a16facda37d648c1
|
[
"MIT"
] | null | null | null |
tests/vns3ms/test_vns3_management_api.py
|
cohesive/python-cohesivenet-sdk
|
5620acfa669ff97c94d9aa04a16facda37d648c1
|
[
"MIT"
] | null | null | null |
tests/vns3ms/test_vns3_management_api.py
|
cohesive/python-cohesivenet-sdk
|
5620acfa669ff97c94d9aa04a16facda37d648c1
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
VNS3:ms API
Cohesive networks VNS3 API providing complete control of your network's addresses, routes, rules and edge # noqa: E501
Contact: solutions@cohesive.net
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import pytest
import cohesivenet
from cohesivenet.api.vns3ms import vns3_management_api # noqa: E501
from cohesivenet.rest import ApiException
from tests.openapi import generate_method_test
from tests.vns3ms.stub_data import Vns3ManagementApiData
class TestMSVNS3ManagementApi(object):
"""VNS3:ms VNS3 Management API unit tests"""
def test_get_vns3_snapshots(self, rest_mocker, ms_client, ms_api_schema: dict):
"""Test case for get_vns3_snapshots"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/snapshots",
rest_mocker,
mock_request_from_schema=True,
mock_response=Vns3ManagementApiData.Vns3SnapshotsList,
)(vns3_management_api.get_vns3_snapshots)
def test_post_create_vns3_snapshot(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for post_create_vns3_snapshot"""
generate_method_test(
ms_client,
ms_api_schema,
"POST",
"/snapshots",
rest_mocker,
mock_request_from_schema=True,
mock_response={"response_type": "success", "response": "Snapshot created"},
)(vns3_management_api.post_create_vns3_snapshot)
def test_delete_vns3_snapshots(self, rest_mocker, ms_client, ms_api_schema: dict):
"""Test case for delete_vns3_snapshots"""
generate_method_test(
ms_client,
ms_api_schema,
"DELETE",
"/snapshots",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.delete_vns3_snapshots)
def test_get_download_vns3_snapshot(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for get_download_vns3_snapshot"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/snapshots/download",
rest_mocker,
mock_request_from_schema=True,
mock_response="imafileimafileimafileimafileimafileimafileimafile",
)(vns3_management_api.get_download_vns3_snapshot)
def test_get_controller_report(self, rest_mocker, ms_client, ms_api_schema: dict):
"""Test case for get_controller_report"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/system/controller_report",
rest_mocker,
mock_request_from_schema=True,
mock_response=Vns3ManagementApiData.ControllerReport,
)(vns3_management_api.get_controller_report)
def test_get_vns3_controllers(self, rest_mocker, ms_client, ms_api_schema: dict):
"""Test case for get_vns3_controllers"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/vns3_controllers",
rest_mocker,
mock_request_from_schema=True,
mock_response=Vns3ManagementApiData.Vns3ControllerList,
)(vns3_management_api.get_vns3_controllers)
def test_post_add_vns3_controller(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for post_add_vns3_controller"""
generate_method_test(
ms_client,
ms_api_schema,
"POST",
"/vns3_controllers",
rest_mocker,
mock_request_from_schema=True,
mock_response=Vns3ManagementApiData.CreateVns3ControllerResponse,
)(vns3_management_api.post_add_vns3_controller)
def test_get_vns3_controller(self, rest_mocker, ms_client, ms_api_schema: dict):
"""Test case for get_vns3_controller"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/vns3_controllers/{vns3_controller_id}",
rest_mocker,
mock_request_from_schema=True,
mock_response=Vns3ManagementApiData.Vns3ControllerDetail,
)(vns3_management_api.get_vns3_controller)
def test_put_update_vns3_controller(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for put_update_vns3_controller"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.put_update_vns3_controller)
def test_delete_vns3_controller(self, rest_mocker, ms_client, ms_api_schema: dict):
"""Test case for delete_vns3_controller"""
generate_method_test(
ms_client,
ms_api_schema,
"DELETE",
"/vns3_controllers/{vns3_controller_id}",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.delete_vns3_controller)
def test_get_vns3_controller_status(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for get_vns3_controller_status"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/vns3_controllers/{vns3_controller_id}/status",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.get_vns3_controller_status)
def test_put_vns3_controller_api_password(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for put_vns3_controller_api_password"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}/update_api_password",
rest_mocker,
mock_request_from_schema=True,
mock_response={
"response_type": "success",
"response": "API password updated",
},
)(vns3_management_api.put_vns3_controller_api_password)
def test_put_vns3_controller_ui(self, rest_mocker, ms_client, ms_api_schema: dict):
"""Test case for put_vns3_controller_ui"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}/update_ui",
rest_mocker,
mock_request_from_schema=True,
mock_response={
"response_type": "success",
"response": "UI details updated",
},
)(vns3_management_api.put_vns3_controller_ui)
def test_get_vns3_controller_ha_details(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for get_vns3_controller_ha_details"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/vns3_controllers/{vns3_controller_id}/ha",
rest_mocker,
mock_request_from_schema=True,
mock_response=Vns3ManagementApiData.Vns3ControllerHaDetail,
)(vns3_management_api.get_vns3_controller_ha_details)
def test_put_update_vns3_controller_ha(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for put_update_vns3_controller_ha"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}/ha",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.put_update_vns3_controller_ha)
def test_put_validate_vns3_controller_ha(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for put_validate_vns3_controller_ha"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}/ha/validate",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.put_validate_vns3_controller_ha)
def test_put_init_vns3_controller_ha(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for put_init_vns3_controller_ha"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}/ha/initialise",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.put_init_vns3_controller_ha)
def test_put_sync_vns3_controller_ha(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for put_sync_vns3_controller_ha"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}/ha/sync",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.put_sync_vns3_controller_ha)
def test_put_activate_vns3_controller_ha(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for put_activate_vns3_controller_ha"""
generate_method_test(
ms_client,
ms_api_schema,
"PUT",
"/vns3_controllers/{vns3_controller_id}/ha/activate",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.put_activate_vns3_controller_ha)
def test_get_vns3_controller_ha_status(
self, rest_mocker, ms_client, ms_api_schema: dict
):
"""Test case for get_vns3_controller_ha_status"""
generate_method_test(
ms_client,
ms_api_schema,
"GET",
"/vns3_controllers/{vns3_controller_id}/ha/activate",
rest_mocker,
mock_request_from_schema=True,
mock_response_from_schema=True,
)(vns3_management_api.get_vns3_controller_ha_status)
| 35.347826
| 123
| 0.639323
| 1,186
| 10,569
| 5.188027
| 0.084317
| 0.125142
| 0.065009
| 0.084512
| 0.817812
| 0.778157
| 0.744352
| 0.715423
| 0.715423
| 0.698033
| 0
| 0.017497
| 0.286214
| 10,569
| 298
| 124
| 35.466443
| 0.798118
| 0.10228
| 0
| 0.716667
| 0
| 0
| 0.102372
| 0.07149
| 0
| 0
| 0
| 0
| 0
| 1
| 0.083333
| false
| 0.016667
| 0.029167
| 0
| 0.116667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1b1eefb801007320ffbceeb312b0625dc7363971
| 61,120
|
py
|
Python
|
tests/unit/test_type_inferencer.py
|
paulross/typin
|
113224d868c95e93b9ae724b0a9d9cfe3e3c78f8
|
[
"MIT"
] | 7
|
2017-11-12T21:29:18.000Z
|
2019-01-30T01:50:47.000Z
|
tests/unit/test_type_inferencer.py
|
paulross/typin
|
113224d868c95e93b9ae724b0a9d9cfe3e3c78f8
|
[
"MIT"
] | null | null | null |
tests/unit/test_type_inferencer.py
|
paulross/typin
|
113224d868c95e93b9ae724b0a9d9cfe3e3c78f8
|
[
"MIT"
] | null | null | null |
'''
Created on 22 Jun 2017
@author: paulross
WARNING: Declaring multiple classes of the same name in different functions
might mess up the finding of bases due the garbage collector not releasing
previous definitions.
Earlier multiple class A definitions ware causing a
subtle problem in this regard. See test_class_multiple_inheritance_unsorted().
'''
import collections # To test problematic named tuple
import base64 # Just used as an example of stdlib usage
import inspect
import io
import os
import pprint
# import sys
import pytest
from typin import type_inferencer
def test_creation():
t = type_inferencer.TypeInferencer()
assert t is not None
def test_RE_TEMPORARY_FILE():
ti = type_inferencer.TypeInferencer()
assert ti.is_temporary_file('<string>')
assert ti.is_temporary_file('/Users/USER/Documents/workspace/typin/src/typin/<frozen importlib._bootstrap>')
assert not ti.is_temporary_file('/Users/USER/Documents/workspace/typin/src/typin/typin_cli.py')
@pytest.mark.parametrize('string', [
'@property',
' @property',
' @property',
'@property ',
'@property ',
' @property ',
' @property ',
])
def test_RE_DECORATOR(string):
regex = type_inferencer.RE_DECORATOR
assert regex.match(string) is not None
assert regex.match(string).group(1) == 'property'
@pytest.mark.parametrize('string, result', [
('def foo():', ('foo', '):')),
('def foo(): ', ('foo', '): ')),
(' def foo():', ('foo', '):')),
(' def foo():', ('foo', '):')),
(' def foo(): ', ('foo', '): ')),
('def foo(a, b, c):', ('foo', 'a, b, c):')),
(' def foo(\n', ('foo', '')),
])
def test_RE_FUNCTION(string, result):
regex = type_inferencer.RE_FUNCTION
assert regex.match(string) is not None
assert regex.match(string).group(1) == result[0]
assert regex.match(string).group(2) == result[1]
@pytest.mark.parametrize('string, result', [
('def foo(self):', ('foo', '):')),
('def foo(self): ', ('foo', '): ')),
(' def foo(self):', ('foo', '):')),
(' def foo(self):', ('foo', '):')),
(' def foo(self): ', ('foo', '): ')),
('def foo(self, a, b, c):', ('foo', ', a, b, c):')),
('def foo(self,a, b, c):', ('foo', ',a, b, c):')),
(' def foo(self,\n', ('foo', ',')),
])
def test_RE_METHOD(string, result):
regex = type_inferencer.RE_METHOD
assert regex.match(string) is not None
assert regex.match(string).group(1) == result[0]
assert regex.match(string).group(2) == result[1]
# def _pretty_print(ti):
# print(ti.pretty_format())
# def _pretty_format(ti, file=None):
# str_list = []
# if file is not None:
# for function_name in sorted(ti.function_map[file]):
# str_list.append('def {:s}{:s}'.format(
# function_name,
# ti.function_map[file][function_name].stub_file_str()
# )
# )
# else:
# # {file_path : { function_name : FunctionTypes, ...}
# for file_path in sorted(ti.function_map):
# str_list.append('File: {:s}'.format(file_path))
# for function_name in sorted(ti.function_map[file_path]):
# str_list.append('def {:s}{:s}'.format(
# function_name,
# ti.function_map[file_path][function_name].stub_file_str()
# )
# )
# return '\n'.join(str_list)
def test_a_couple_of_functions():
def func_single_arg_no_return(arg):
pass
def func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
func_single_arg_no_return('string')
func_single_arg_return_arg('string')
# print()
# print('test_single_function()')
# _pretty_print(ti)
# pprint.pprint(ti.function_map)
expected = [
'def func_single_arg_no_return(arg: str) -> None: ...',
'def func_single_arg_return_arg(arg: str) -> str: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_a_couple_of_functions_with_line_numbers():
line_fn_1 = inspect.currentframe().f_lineno + 1
def func_single_arg_no_return(arg):
pass
line_fn_2 = inspect.currentframe().f_lineno + 1
def func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
func_single_arg_no_return('string')
func_single_arg_return_arg('string')
expected = [
'def func_single_arg_no_return(arg: str) -> None: ...#{:d}'.format(line_fn_1),
'def func_single_arg_return_arg(arg: str) -> str: ...#{:d}'.format(line_fn_2),
]
assert ti.pretty_format(__file__, add_line_number_as_comment=True) == '\n'.join(expected)
def test_single_function_that_raises():
line_raises = inspect.currentframe().f_lineno + 2
def func_that_raises():
raise ValueError('Error message')
with type_inferencer.TypeInferencer() as ti:
try:
func_that_raises()
except ValueError:
pass
# print()
# print('test_single_function_that_raises()')
# _pretty_print(ti)
# pprint.pprint(ti.function_map)
expected = [
'def func_that_raises() -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
fts = ti.function_types(__file__, '', 'func_that_raises')
assert fts.exception_type_strings == {line_raises : {'ValueError'}}
def test_single_function_that_raises_and_catches():
line_raises = inspect.currentframe().f_lineno + 3
def func_that_raises_and_catches():
try:
raise ValueError('Error message')
except ValueError as _err:
pass
return 'OK'
with type_inferencer.TypeInferencer() as ti:
func_that_raises_and_catches()
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
expected = [
'def func_that_raises_and_catches() -> str: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
fts = ti.function_types(__file__, '', 'func_that_raises_and_catches')
# No exception recorded.
assert fts.exception_type_strings == {}
def test_nested_function_that_raises():
func_no_catch_line = inspect.currentframe().f_lineno + 2
def func_no_catch():
func_that_raises()
func_that_raises_line = inspect.currentframe().f_lineno + 2
def func_that_raises():
raise ValueError('Error message')
with type_inferencer.TypeInferencer() as ti:
try:
func_no_catch()
except ValueError:
pass
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
expected = [
'def func_no_catch() -> None: ...',
'def func_that_raises() -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
fts = ti.function_types(__file__, '', 'func_that_raises')
assert fts.exception_type_strings == {func_that_raises_line : {'ValueError'}}
fts = ti.function_types(__file__, '', 'func_no_catch')
assert fts.exception_type_strings == {func_no_catch_line : {'ValueError'}}
def test_nested_functions_some_that_raises():
# line_func_that_catches = inspect.currentframe().f_lineno + 1
def func_that_catches():
try:
func_no_catch()
except ValueError:
pass
line_func_no_catch = inspect.currentframe().f_lineno + 2
def func_no_catch():
func_that_raises()
line_func_that_raises = inspect.currentframe().f_lineno + 2
def func_that_raises():
raise ValueError('Error message')
with type_inferencer.TypeInferencer() as ti:
func_that_catches()
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
expected = [
'def func_no_catch() -> None: ...',
'def func_that_catches() -> None: ...',
'def func_that_raises() -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
fts = ti.function_types(__file__, '', 'func_that_catches')
assert fts.exception_type_strings == {}
fts = ti.function_types(__file__, '', 'func_no_catch')
assert fts.exception_type_strings == {line_func_no_catch : {'ValueError'}}
fts = ti.function_types(__file__, '', 'func_that_raises')
assert fts.exception_type_strings == {line_func_that_raises : {'ValueError'}}
@pytest.mark.xfail(reason='Not quite sure what we should do here.')
def test_function_within_function_that_raises():
print('TRACE:')
func_no_catch_line = inspect.currentframe().f_lineno + 2
func_that_raises_line = inspect.currentframe().f_lineno + 2
def func_no_catch():
def func_that_raises():
raise ValueError('Error message')
func_that_raises()
with type_inferencer.TypeInferencer() as ti:
try:
func_no_catch()
except ValueError:
pass
# print()
# print(' test_function_within_function_that_raises() '.center(75, '-'))
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
# print(ti.function_map)
# for filename in sorted(ti.function_map.keys()):
# print('File:', filename)
# for namespace in sorted(ti.function_map[filename].keys()):
# print('Namespace: "{:s}"'.format(namespace))
# for function in ti.function_map[filename][namespace]:
# print('Function: {:s}: {!r:s}'.format(
# function,
# ti.function_map[filename][namespace][function]
# ))
# print()
# print('Class bases:')
# pprint.pprint(ti.class_bases)
# print(' DONE: test_function_within_function_that_raises() '.center(75, '-'))
expected = [
'def func_no_catch() -> None: ...',
' def func_that_raises() -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected) # Fails
fts = ti.function_types(__file__, '', 'func_that_raises')
assert fts.exception_type_strings == {func_that_raises_line : {'ValueError'}}
fts = ti.function_types(__file__, '', 'func_no_catch')
assert fts.exception_type_strings == {func_no_catch_line : {'ValueError'}}
@pytest.mark.xfail(reason='Not quite sure what we should do here.')
def test_function_within_function_in_class_that_raises():
print('TRACE:')
func_no_catch_line = inspect.currentframe().f_lineno + 2
func_that_raises_line = inspect.currentframe().f_lineno + 2
class FunctionsWithinFunctions:
def func_no_catch(self):
def func_that_raises():
raise ValueError('Error message')
func_that_raises()
with type_inferencer.TypeInferencer() as ti:
obj = FunctionsWithinFunctions()
try:
obj.func_no_catch()
except ValueError:
pass
# print()
# print(' test_function_within_function_that_raises() '.center(75, '-'))
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
# print(ti.function_map)
# for filename in sorted(ti.function_map.keys()):
# print('File:', filename)
# for namespace in sorted(ti.function_map[filename].keys()):
# print('Namespace: "{:s}"'.format(namespace))
# for function in ti.function_map[filename][namespace]:
# print('Function: {:s}: {!r:s}'.format(
# function,
# ti.function_map[filename][namespace][function]
# ))
# print()
# print('Class bases:')
# pprint.pprint(ti.class_bases)
# print(' DONE: test_function_within_function_that_raises() '.center(75, '-'))
expected = [
'class FunctionsWithinFunctions:',
' def func_no_catch(self) -> None: ...',
' def func_that_raises() -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected) # Fails
fts = ti.function_types(__file__, '', 'func_that_raises')
assert fts.exception_type_strings == {func_that_raises_line : {'ValueError'}}
fts = ti.function_types(__file__, '', 'func_no_catch')
assert fts.exception_type_strings == {func_no_catch_line : {'ValueError'}}
def test_single_function_that_might_raises_does_not_return_none():
"""Functions that raise will also be seen to return None from the same
line even if they never return None."""
def may_raise(v):
if v == 0:
raise ValueError('Value can not be zero')
return 1.0 / v
with type_inferencer.TypeInferencer() as ti:
may_raise(1)
try:
may_raise(0)
except ValueError:
pass
expected = [
'def may_raise(v: int) -> float: ...',
]
# print()
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_single_function_that_might_raises_does_return_none():
"""Functions that raise will also be seen to return None from the same
line. This function could also specifically return None."""
def may_raise(v):
if v == 0:
raise ValueError('Value can not be zero')
if v > 0:
return 1.0 / v
return None
with type_inferencer.TypeInferencer() as ti:
may_raise(1)
may_raise(-1)
try:
may_raise(0)
except ValueError:
pass
expected = [
'def may_raise(v: int) -> Union[None, float]: ...',
]
# print()
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_context_manager_external():
def function(v): pass
ti = type_inferencer.TypeInferencer()
with ti:
function('string')
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
expected = [
'def function(v: str) -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_context_manager_nested():
def outer(v):
pass
def inner(v):
pass
def other(v):
pass
with type_inferencer.TypeInferencer() as ti_outer:
outer('string')
with type_inferencer.TypeInferencer() as ti_inner:
inner(42)
other(4.2)
# print()
# print(ti_outer.pretty_format(__file__))
# print(ti_inner.pretty_format(__file__))
expected_outer = [
'def other(v: float) -> None: ...',
'def outer(v: str) -> None: ...',
]
assert ti_outer.pretty_format(__file__) == '\n'.join(expected_outer)
expected_inner = [
'def inner(v: int) -> None: ...',
]
assert ti_inner.pretty_format(__file__) == '\n'.join(expected_inner)
def test_context_manager_nested_external():
def outer(v):
pass
def inner(v):
pass
def other(v):
pass
ti = type_inferencer.TypeInferencer()
with ti:
outer('string')
with ti:
inner(42)
other(4.2)
# print()
# print(ti_outer.pretty_format(__file__))
# print(ti_inner.pretty_format(__file__))
expected_outer = [
'def inner(v: int) -> None: ...',
'def other(v: float) -> None: ...',
'def outer(v: str) -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected_outer)
# ==== Test some functions that take basic builtins ====
# ---- Test numbers ---
def test_typeshed_integers():
"""Tests functions that take and return integers."""
def one(i): return i
def one_None(i): return None
def many(i, j, k): return i * j * j
with type_inferencer.TypeInferencer() as ti:
one(4)
one_None(4)
many(1, 2, 3)
expected = [
'def many(i: int, j: int, k: int) -> int: ...',
'def one(i: int) -> int: ...',
'def one_None(i: int) -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_typeshed_float():
"""Tests functions that take and return floats."""
def one(i): return i
def one_None(i): return None
def many(i, j, k): return i * j * j
with type_inferencer.TypeInferencer() as ti:
one(4.)
one_None(4.)
many(1., 2., 3.)
expected = [
'def many(i: float, j: float, k: float) -> float: ...',
'def one(i: float) -> float: ...',
'def one_None(i: float) -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_typeshed_complex():
"""Tests functions that take and return complex."""
def one(i): return i
def one_None(i): return None
def many(i, j, k): return i * j * j
with type_inferencer.TypeInferencer() as ti:
one((4. + 0j))
one_None((4. + 0j))
many((1. + 0j), (2. + 0j), (3. + 0j))
expected = [
'def many(i: complex, j: complex, k: complex) -> complex: ...',
'def one(i: complex) -> complex: ...',
'def one_None(i: complex) -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_typeshed_mixed_union():
"""Tests functions that take and return complex."""
def one(i): return i
with type_inferencer.TypeInferencer() as ti:
one(4)
one(4.)
one((4. + 0j))
expected = [
'def one(i: complex, float, int) -> Union[complex, float, int]: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
# ---- END:Test numbers ---
# ==== END: Test some functions that take basic builtins ====
# def test_typeshed_base64():
# """From the typeshed: https://github.com/python/typeshed/blob/master/stdlib/2and3/base64.pyi
#
# def b64decode(s: _decodable, altchars: bytes = ...,
# validate: bool = ...) -> bytes: ...
# def b64encode(s: _encodable, altchars: bytes = ...) -> bytes: ...
#
# def decode(input: IO[bytes], output: IO[bytes]) -> None: ...
# def decodebytes(s: bytes) -> bytes: ...
# def decodestring(s: bytes) -> bytes: ...
# def encode(input: IO[bytes], output: IO[bytes]) -> None: ...
# def encodebytes(s: bytes) -> bytes: ...
# def encodestring(s: bytes) -> bytes: ...
# """
# with type_inferencer.TypeInferencer() as ti:
# base64.b64encode(b'')
# base64.b64decode(b'')
# # stream_in = io.StringIO()
# # stream_out = io.StringIO()
# # base64.encode(stream_in, stream_out)
# # base64.decode(stream_in, stream_out)
# # base64.encodebytes(b'')
# # base64.decodebytes(b'')
# # base64.decode(stream_in, stream_out)
# # encoded = base64.encodestring(b'')
# # decoded = base64.encodestring(encoded)
# print()
# print('test_typeshed_base64()')
# _pretty_print(ti)
# # def _input_type_check(s: 'bytes') -> NoneType: ...
# # def decode(input: '_io.StringIO', output: '_io.StringIO') -> NoneType: ...
# # def decodebytes(s: 'bytes') -> bytes: ...
# # def encode(input: '_io.StringIO', output: '_io.StringIO') -> NoneType: ...
# # def encodebytes(s: 'bytes') -> bytes: ...
# # def encodestring(s: 'bytes') -> bytes: ...
#
# # pprint.pprint(ti.function_map)
def test_typeshed_io_StringIO():
"""Based on base64 from the typeshed:
https://github.com/python/typeshed/blob/master/stdlib/2and3/base64.pyi
Should see stub file of:
def decode(input: IO[bytes], output: IO[bytes]) -> None: ...
def encode(input: IO[bytes], output: IO[bytes]) -> None: ...
"""
def decode(input, output):
pass
def encode(input, output):
pass
with type_inferencer.TypeInferencer() as ti:
stream_in = io.StringIO()
stream_out = io.StringIO()
encode(stream_in, stream_out)
decode(stream_in, stream_out)
expected = [
'def decode(input: IO[bytes], output: IO[bytes]) -> None: ...',
'def encode(input: IO[bytes], output: IO[bytes]) -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_typeshed_encode_bytes():
"""Based on base64 from the typeshed:
https://github.com/python/typeshed/blob/master/stdlib/2and3/base64.pyi
Should see stub file of:
def decodebytes(s: 'bytes') -> bytes: ...
def encodebytes(s: 'bytes') -> bytes: ...
"""
def decodebytes(s):
return s
def encodebytes(s):
return s
with type_inferencer.TypeInferencer() as ti:
encodebytes(b'')
decodebytes(b'')
expected = [
'def decodebytes(s: bytes) -> bytes: ...',
'def encodebytes(s: bytes) -> bytes: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_simple_class():
"""A simple class with a constructor and a single method."""
class Simple:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
s = Simple('First', 'Last')
s.name()
expected = [
'class Simple:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_in_class():
"""A class in a class and a single method."""
class Outer:
class Inner:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
s = Outer.Inner('First', 'Last')
s.name()
expected = [
'class Outer:',
' class Inner:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# pprint.pprint(ti.function_map)
# print()
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_in_class_both_have_methods():
"""A simple class with a constructor and a single method."""
class Outer:
def z_function(self, s):
return s
class Inner:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
o = Outer()
o.z_function(b'')
s = Outer.Inner('First', 'Last')
s.name()
expected = [
'class Outer:',
' def z_function(self, s: bytes) -> bytes: ...',
' class Inner:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# pprint.pprint(ti.function_map)
# print()
# print(ti.pretty_format(__file__))
# print('\n'.join(expected))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_three_nested_classes_only_inner_has_methods():
"""Three nested classes and a single method."""
class A:
class B:
class C:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
s = A.B.C('First', 'Last')
s.name()
expected = [
'class A:',
' class B:',
' class C:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# pprint.pprint(ti.function_map)
# print()
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_three_nested_classes_only_inner_has_methods_rev_names():
"""Three nested classes and a single method, names reversed."""
class C:
class B:
class A:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
s = C.B.A('First', 'Last')
s.name()
expected = [
'class C:',
' class B:',
' class A:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# pprint.pprint(ti.function_map)
# print()
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_three_nested_classes_middle_and_inner_has_methods():
"""Three nested classes and a single method."""
class A:
class B:
class C:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
def some_function(self, byt): return byt
with type_inferencer.TypeInferencer() as ti:
c = A.B.C('First', 'Last')
c.name()
b = A.B()
b.some_function(b'')
expected = [
'class A:',
' class B:',
' def some_function(self, byt: bytes) -> bytes: ...',
' class C:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# pprint.pprint(ti.function_map)
# print()
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_four_nested_classes_A_and_inner_has_methods():
class A:
def some_function(self, byt): return byt
class B:
class C:
class D:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
d = A.B.C.D('First', 'Last')
d.name()
a = A()
a.some_function(b'')
expected = [
'class A:',
' def some_function(self, byt: bytes) -> bytes: ...',
' class B:',
' class C:',
' class D:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# pprint.pprint(ti.function_map)
# print()
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_four_nested_classes_B_and_inner_has_methods():
class A:
class B:
def some_function(self, byt): return byt
class C:
class D:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
d = A.B.C.D('First', 'Last')
d.name()
b = A.B()
b.some_function(b'')
expected = [
'class A:',
' class B:',
' def some_function(self, byt: bytes) -> bytes: ...',
' class C:',
' class D:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map[__file__])
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_four_nested_classes_C_and_inner_has_methods():
class A:
class B:
class C:
def some_function(self, byt): return byt
class D:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
d = A.B.C.D('First', 'Last')
d.name()
c = A.B.C()
c.some_function(b'')
expected = [
'class A:',
' class B:',
' class C:',
' def some_function(self, byt: bytes) -> bytes: ...',
' class D:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map[__file__])
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_four_nested_classes_all_have_methods():
class A:
def some_function(self, byt): return byt
class B:
def some_function(self, byt): return byt
class C:
def some_function(self, byt): return byt
class D:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
d = A.B.C.D('First', 'Last')
d.name()
c = A.B.C()
c.some_function(b'')
b = A.B()
b.some_function(b'')
a = A()
a.some_function(b'')
expected = [
'class A:',
' def some_function(self, byt: bytes) -> bytes: ...',
' class B:',
' def some_function(self, byt: bytes) -> bytes: ...',
' class C:',
' def some_function(self, byt: bytes) -> bytes: ...',
' class D:',
' def __init__(self, first_name: str, last_name: str) -> None: ...',
' def name(self) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map[__file__])
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_inheritance():
"""From the calendar module stubs file:
class IllegalMonthError(ValueError):
def __init__(self, month: int) -> None: ...
def __str__(self) -> str: ...
"""
class IllegalMonthError(ValueError):
def __init__(self, month): pass
def __str__(self):
return ''
with type_inferencer.TypeInferencer() as ti:
obj = IllegalMonthError(4)
str(obj)
expected = [
'class IllegalMonthError(ValueError):',
' def __init__(self, month: int) -> None: ...',
' def __str__(self) -> str: ...',
]
# print()
# print(ti.pretty_format(__file__))
# print(ti.class_bases)
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_multiple_inheritance_unsorted():
"""Tests that the base names do not get sorted."""
class X: pass
class Y: pass
class Z: pass
class AZXY(Z, X, Y):
def __str__(self):
return ''
with type_inferencer.TypeInferencer() as ti:
obj = AZXY()
assert str(obj) == ''
expected = [
'class AZXY(Z, X, Y):',
' def __str__(self) -> str: ...',
]
# print()
# print(ti.pretty_format(__file__))
# print(ti.class_bases)
assert ti.pretty_format(__file__) == '\n'.join(expected)
# Used for line number and ranges
line_first = inspect.currentframe().f_lineno + 1
def function_lineno(v):
if v == 0:
return inspect.currentframe().f_lineno
elif v == 1:
raise ValueError()
elif v == 2:
return inspect.currentframe().f_lineno
return inspect.currentframe().f_lineno
line_last = inspect.currentframe().f_lineno - 1
@pytest.mark.parametrize('value, expected_increment', [
(0, 2), # Partial, return
(1, 4), # Partial, raises
(2, 6), # Partial, return
(3, 7), # Full, return
])
def test_single_function_line_range(value, expected_increment):
with type_inferencer.TypeInferencer() as ti:
try:
function_lineno(value)
except ValueError:
pass
fts = ti.function_types(__file__, '', 'function_lineno')
assert fts.line_range == (line_first, line_first + expected_increment)
assert fts.call_line_numbers == [line_first,]
# Generators and line numbers.
# Used for line number and ranges
line_first_gen = inspect.currentframe().f_lineno + 1
def function_gen_lineno():
for _i in range(3):
yield inspect.currentframe().f_lineno
for _i in range(3):
yield inspect.currentframe().f_lineno
line_last_gen = inspect.currentframe().f_lineno - 1
def test_generator_line_number_range():
"""Tests the line numbers of """
lines_yielded = []
with type_inferencer.TypeInferencer() as ti:
for line_no in function_gen_lineno():
lines_yielded.append(line_no - line_first_gen)
assert lines_yielded == [2, 2, 2, 4, 4, 4,]
fts = ti.function_types(__file__, '', 'function_gen_lineno')
assert fts.line_range == (line_first_gen, line_last_gen)
assert fts.call_line_numbers == [
line_first_gen,
line_first_gen + 2,
line_first_gen + 4,
]
def test_file_filtering():
"""This exercises code in the stdlib, 3rd party libraries and local
functions and extracts just the local data."""
def function(v):
"""Local function."""
pass
with type_inferencer.TypeInferencer() as ti:
function('string') # Local function
# Stdlib
base64.b64encode(b'')
base64.b64decode(b'')
stream_in = io.StringIO()
stream_out = io.StringIO()
base64.encode(stream_in, stream_out)
base64.decode(stream_in, stream_out)
base64.encodebytes(b'')
base64.decodebytes(b'')
base64.decode(stream_in, stream_out)
encoded = base64.encodestring(b'')
_decoded = base64.encodestring(encoded)
# 3rd Party
with pytest.raises(ValueError):
raise ValueError
# print()
# pprint.pprint(ti.function_map)
# print(os.getcwd())
# pprint.pprint(sorted(ti.function_map.keys()))
# for file_path in sorted(ti.function_map.keys()):
# print(file_path)
# pprint.pprint(ti.function_map[file_path])
# print(ti.pretty_format(__file__))
expected = [
'def function(v: str) -> None: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
assert ti.file_paths_filtered(
file_path_prefix=os.path.dirname(__file__),
relative=True) == [(__file__, os.path.basename(__file__))]
# assert ti.file_paths_cwd(relative=True) == [(__file__, 'tests/unit/test_type_inferencer.py')]
def test_class_semie_private_methods():
class A:
pass
class B(A):
def public(self, value):
return self._semie_private(value)
def _semie_private(self, value):
return '{!r:s}'.format(value)
with type_inferencer.TypeInferencer() as ti:
b = B()
b.public(14)
expected = [
'class B(A):',
' def _semie_private(self, value: int) -> str: ...',
' def public(self, value: int) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_private_methods():
class A:
pass
class B(A):
def public(self, value):
return self._semie_private(value)
def _semie_private(self, value):
return self.__private(value)
def __private(self, value):
return '{!r:s}'.format(value)
with type_inferencer.TypeInferencer() as ti:
b = B()
b.public(14)
expected = [
'class B(A):',
' def __private(self, value: int) -> str: ...',
' def _semie_private(self, value: int) -> str: ...',
' def public(self, value: int) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_private_methods_trailing_underscore():
class A:
pass
class B(A):
def public(self, value):
return self._semie_private(value)
def _semie_private(self, value):
return self.__private_(value)
def __private_(self, value):
# One trailing underscore allowed.
return '{!r:s}'.format(value)
with type_inferencer.TypeInferencer() as ti:
b = B()
b.public(14)
expected = [
'class B(A):',
' def __private_(self, value: int) -> str: ...',
' def _semie_private(self, value: int) -> str: ...',
' def public(self, value: int) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_private_methods_nested_class():
class A:
pass
class B(A):
class C:
def public(self, value):
return self._semie_private(value)
def _semie_private(self, value):
return self.__private(value)
def __private(self, value):
return '{!r:s}'.format(value)
with type_inferencer.TypeInferencer() as ti:
b = B.C()
b.public(14)
expected = [
# NOTE: No inheritance of A by B
'class B:',
' class C:',
' def __private(self, value: int) -> str: ...',
' def _semie_private(self, value: int) -> str: ...',
' def public(self, value: int) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_private_methods_nested_class_inherits():
class A:
def __init__(self):
pass
class B(A):
def __init__(self):
super().__init__()
class C:
def public(self, value):
return self._semie_private(value)
def _semie_private(self, value):
return self.__private(value)
def __private(self, value):
return '{!r:s}'.format(value)
with type_inferencer.TypeInferencer() as ti:
b = B()
c = b.C()
c.public(14)
expected = [
# NOTE: Inheritance of A by B
'class A:',
' def __init__(self) -> None: ...',
'class B(A):',
' def __init__(self) -> None: ...',
' class C:',
' def __private(self, value: int) -> str: ...',
' def _semie_private(self, value: int) -> str: ...',
' def public(self, value: int) -> str: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_properties():
class A:
@property
def get(self):
return id(self)
with type_inferencer.TypeInferencer() as ti:
b = A()
b.get
expected = [
'class A:',
' def get(self) -> int: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_properties():
class A:
@property
def get(self):
return id(self)
with type_inferencer.TypeInferencer() as ti:
b = A()
b.get
expected = [
'class A:',
' def get(self) -> int: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_properties_get_set():
class A:
def __init__(self, value):
self._value = value
@property
def value(self):
return self._value
@value.setter
def value(self, value):
self._value = value
with type_inferencer.TypeInferencer() as ti:
b = A(21)
assert b.value == 21
b.value = 42
assert b.value == 42
expected = [
'class A:',
' def __init__(self, value: int) -> None: ...',
' def value(self, value: int) -> Union[None, int]: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_class_properties_get_set_calls_reversed():
class A:
def __init__(self, value):
self._value = value
@property
def value(self):
return self._value
@value.setter
def value(self, value):
self._value = value
with type_inferencer.TypeInferencer() as ti:
b = A(21)
b.value = 42
assert b.value == 42
expected = [
'class A:',
' def __init__(self, value: int) -> None: ...',
' def value(self, value: int) -> Union[None, int]: ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_named_tuple():
"""Named tuples are weird."""
MyNT = collections.namedtuple('MyNT', 'a b c')
with type_inferencer.TypeInferencer() as ti:
nt = MyNT(1, 'B', b'C')
nt.a
nt.b
nt.c
nt.count(1)
# print()
# print(dir(MyNT))
# print(MyNT.__module__)
# print(dir(MyNT.__new__))
# print(locals())
# print(nt._fields)
expected = [
'class TypeInferencer:',
' def __exit__(self, exc_type: None, exc_value: None, traceback: None) -> None: ...',
]
# print()
# pprint.pprint(ti.function_map)
# for filename in ti.function_map.keys():
# print(filename)
# print(ti.pretty_format(filename))
# No function called in the scope of ti
assert __file__ not in ti.file_paths()
def _test_named_tuple_subclass():
"""Named tuples are a bit awkward as they are equivelent to mere tuples."""
class Dim(collections.namedtuple('Dim', 'value units',)):
"""Represents a dimension as an engineering value i.e. a number and units."""
__slots__ = ()
def scale(self, factor):
"""Returns a new Dim() scaled by a factor, units are unchanged."""
return self._replace(value=self.value*factor)
with type_inferencer.TypeInferencer() as ti:
d = Dim(12, 'kilometers')
d.scale(10) # 120 kilometers
print()
# print(type(d))
# print(d._fields)
e = type(d)(*d)
print(e)
print(e._fields)
print(e.value)
print(e.units)
e.scale(.1)
expected = [
'class Dim(tests.unit.test_type_inferencer.Dim):',
' def scale(self, factor: int) -> tuple([int, str]): ...',
]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
assert ti.pretty_format(__file__) == '\n'.join(expected)
def test_list_comprehension_ignored():
with type_inferencer.TypeInferencer() as ti:
[i for i in range(8)]
# print()
# pprint.pprint(ti.function_map)
assert __file__ not in ti.function_map
def test_dict_comprehension_ignored():
with type_inferencer.TypeInferencer() as ti:
{i : i for i in range(8)}
# print()
# pprint.pprint(ti.function_map)
assert __file__ not in ti.function_map
def test_set_comprehension_ignored():
with type_inferencer.TypeInferencer() as ti:
{i for i in range(8)}
# print()
# pprint.pprint(ti.function_map)
assert __file__ not in ti.function_map
def test_generator():
# Generator events should not be recorded as exceptions
line_raises = inspect.currentframe().f_lineno + 2
def gen():
for i in range(3):
yield i
with type_inferencer.TypeInferencer() as ti:
result = []
for i in gen():
result.append(i)
assert result == [0, 1, 2]
# print()
# pprint.pprint(ti.function_map)
expected = [
'def gen() -> Union[None, int]: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
fts = ti.function_types(__file__, '', 'gen')
assert fts.exception_type_strings == {}
def test_generator_in_generator():
# Generator events should not be recorded as exceptions
line_raises = inspect.currentframe().f_lineno + 2
def gen_outer():
for i in gen_inner(3):
yield i
def gen_inner(num):
for i in range(num):
yield i
with type_inferencer.TypeInferencer() as ti:
result = []
for i in gen_outer():
result.append(i)
assert result == [0, 1, 2]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
expected = [
'def gen_inner(num: int) -> Union[None, int]: ...',
'def gen_outer() -> Union[None, int]: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
fts = ti.function_types(__file__, '', 'gen_inner')
assert fts.exception_type_strings == {}
fts = ti.function_types(__file__, '', 'gen_outer')
assert fts.exception_type_strings == {}
def test_generator_in_generator_that_raises():
# Generator events should not be recorded as exceptions
def gen_outer():
try:
for i in gen_inner(3):
yield i
except RuntimeError:
pass
line_raises = inspect.currentframe().f_lineno + 4
def gen_inner(num):
for i in range(num):
if i % 2 == 1:
raise RuntimeError()
yield i
with type_inferencer.TypeInferencer() as ti:
result = []
for i in gen_outer():
result.append(i)
assert result == [0,]
# print()
# pprint.pprint(ti.function_map)
# print(ti.pretty_format(__file__))
expected = [
'def gen_inner(num: int) -> int: ...',
'def gen_outer() -> Union[None, int]: ...',
]
assert ti.pretty_format(__file__) == '\n'.join(expected)
fts = ti.function_types(__file__, '', 'gen_inner')
assert fts.exception_type_strings == {line_raises: {'RuntimeError'}}
fts = ti.function_types(__file__, '', 'gen_outer')
assert fts.exception_type_strings == {}
def test_find_docstring_insertion_line_number_simple():
src = """# Line 1
# Line 2
def foo(): # Line 3 <---
"""
src_lines = src.split('\n')
ti = type_inferencer.TypeInferencer()
assert ti.find_docstring_insertion_line_number('foo', src_lines, 3) == 3
def test_find_docstring_insertion_line_number_properties():
src = """# Line 1
# Line 2
@foo # Line 3
@bar # Line 4
def foo(): # Line 5 <---
"""
src_lines = src.split('\n')
ti = type_inferencer.TypeInferencer()
assert ti.find_docstring_insertion_line_number('foo', src_lines, 3) == 5
def test_find_docstring_insertion_line_number_lambda():
src = """# Line 1
# Line 2
lambda x, y: x + y # Line 3
"""
src_lines = src.split('\n')
ti = type_inferencer.TypeInferencer()
assert ti.find_docstring_insertion_line_number('foo', src_lines, 3) == 0
def test_find_docstring_insertion_line_number_multi_line():
src = """# Line 1
# Line 2
def foo(a, # Line 3
b, # Line 4
c): # Line 5 <---
"""
src_lines = src.split('\n')
ti = type_inferencer.TypeInferencer()
assert ti.find_docstring_insertion_line_number('foo', src_lines, 3) == 5
def test_find_docstring_insertion_line_number_multi_line_alt():
src = """# Line 1
# Line 2
def foo(a, # Line 3
b, # Line 4
c, # Line 5
): # Line 6 <---
"""
src_lines = src.split('\n')
ti = type_inferencer.TypeInferencer()
assert ti.find_docstring_insertion_line_number('foo', src_lines, 3) == 6
def test_find_docstring_insertion_line_number_multi_line_overrun():
src = """# Line 1
# Line 2
def foo(a, # Line 3
b, # Line 4
c, # Line 5, missing close function declaration
"""
src_lines = src.split('\n')
ti = type_inferencer.TypeInferencer()
with pytest.raises(IndexError):
ti.find_docstring_insertion_line_number('foo', src_lines, 3)
def test_insert_docstrings_simple_function():
start_lineno = inspect.currentframe().f_lineno + 1
def func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
func_single_arg_return_arg('string')
src_lines = ['\n'] * (start_lineno - 1)
src_lines += [
'def func_single_arg_return_arg(arg):\n',
' return arg\n',
]
exp_lines = ['\n'] * (start_lineno - 1)
exp_lines += [
'def func_single_arg_return_arg(arg):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :param arg: <insert_documentation_for_argument>\n',
' :type arg: ``str``\n',
' \n',
' :returns: ``str`` -- <insert_documentation_for_return_values>\n',
' """\n',
' return arg\n',
]
new_src_lines = ti.insert_docstrings(__file__, src_lines, style='sphinx')
# print()
# print(ti.docstring_map(__file__, style='sphinx'))
# src_start = list(ti.docstring_map(__file__, style='sphinx').keys())[0]
# print(src_lines[src_start-1:])
# print(new_src_lines[src_start-1:])
assert new_src_lines == exp_lines
def test_num_docstrings_to_insert_simple_function():
start_lineno = inspect.currentframe().f_lineno + 1
def func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
func_single_arg_return_arg('string')
assert ti.num_docstrings_to_insert(__file__) == 1
def test_insert_docstrings_two_functions():
start_lineno = inspect.currentframe().f_lineno + 1
def func_single_arg_return_arg(arg):
return arg
def another_func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
# NOTE: Reverse calling order
another_func_single_arg_return_arg(b'bytes')
func_single_arg_return_arg('string')
src_lines = ['\n'] * (start_lineno - 1)
src_lines += [
'def func_single_arg_return_arg(arg):\n',
' return arg\n',
'\n',
'def another_func_single_arg_return_arg(arg):\n',
' return arg\n',
]
exp_lines = ['\n'] * (start_lineno - 1)
exp_lines += [
'def func_single_arg_return_arg(arg):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :param arg: <insert_documentation_for_argument>\n',
' :type arg: ``str``\n',
' \n',
' :returns: ``str`` -- <insert_documentation_for_return_values>\n',
' """\n',
' return arg\n',
'\n',
'def another_func_single_arg_return_arg(arg):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :param arg: <insert_documentation_for_argument>\n',
' :type arg: ``bytes``\n',
' \n',
' :returns: ``bytes`` -- <insert_documentation_for_return_values>\n',
' """\n',
' return arg\n',
]
new_src_lines = ti.insert_docstrings(__file__, src_lines, style='sphinx')
# print()
# print(ti.docstring_map(__file__, style='sphinx'))
# src_start = list(ti.docstring_map(__file__, style='sphinx').keys())[0]
# print(src_lines[src_start-1:])
# print(new_src_lines[src_start-1:])
assert new_src_lines == exp_lines
def test_num_docstrings_to_insert_functions():
def func_single_arg_return_arg(arg):
return arg
def another_func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
another_func_single_arg_return_arg(b'bytes')
func_single_arg_return_arg('string')
assert ti.num_docstrings_to_insert(__file__) == 2
def test_insert_docstrings_simple_class():
start_lineno = inspect.currentframe().f_lineno + 1
class Simple:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
s = Simple('First', 'Last')
s.name()
src_lines = ['\n'] * (start_lineno - 1)
src_lines += [
'class Simple:\n',
' def __init__(self, first_name, last_name):\n',
' self.first_name = first_name\n',
' self.last_name = last_name\n',
' \n',
' def name(self):\n',
' return \'{:s}, {:s}\'.format(self.last_name, self.first_name)\n',
]
exp_lines = ['\n'] * (start_lineno - 1)
exp_lines += [
'class Simple:\n',
' def __init__(self, first_name, last_name):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :param first_name: <insert_documentation_for_argument>\n',
' :type first_name: ``str``\n',
' \n',
' :param last_name: <insert_documentation_for_argument>\n',
' :type last_name: ``str``\n',
' """\n',
' self.first_name = first_name\n',
' self.last_name = last_name\n',
' \n',
' def name(self):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :returns: ``str`` -- <insert_documentation_for_return_values>\n',
' """\n',
" return '{:s}, {:s}'.format(self.last_name, self.first_name)\n"
]
new_src_lines = ti.insert_docstrings(__file__, src_lines, style='sphinx')
# print()
# print(ti.docstring_map(__file__, style='sphinx'))
# src_start = list(ti.docstring_map(__file__, style='sphinx').keys())[0]
# print(src_lines[src_start-1:])
# pprint.pprint(new_src_lines[src_start-2:])
assert new_src_lines == exp_lines
def test_insert_docstrings_simple_class_with_property():
start_lineno = inspect.currentframe().f_lineno + 1
class Simple:
def __init__(self, first_name, last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
s = Simple('First', 'Last')
s.name()
src_lines = ['\n'] * (start_lineno - 1)
src_lines += [
'class Simple:\n',
' def __init__(self, first_name, last_name):\n',
' self.first_name = first_name\n',
' self.last_name = last_name\n',
' \n',
' @property\n',
' def name(self):\n',
' return \'{:s}, {:s}\'.format(self.last_name, self.first_name)\n',
]
exp_lines = ['\n'] * (start_lineno - 1)
exp_lines += [
'class Simple:\n',
' def __init__(self, first_name, last_name):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :param first_name: <insert_documentation_for_argument>\n',
' :type first_name: ``str``\n',
' \n',
' :param last_name: <insert_documentation_for_argument>\n',
' :type last_name: ``str``\n',
' """\n',
' self.first_name = first_name\n',
' self.last_name = last_name\n',
' \n',
' @property\n',
' def name(self):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :returns: ``str`` -- <insert_documentation_for_return_values>\n',
' """\n',
" return '{:s}, {:s}'.format(self.last_name, self.first_name)\n"
]
new_src_lines = ti.insert_docstrings(__file__, src_lines, style='sphinx')
# print()
# print(ti.docstring_map(__file__, style='sphinx'))
# src_start = list(ti.docstring_map(__file__, style='sphinx').keys())[0]
# print(src_lines[src_start-1:])
# pprint.pprint(new_src_lines[src_start-2:])
assert new_src_lines == exp_lines
def test_insert_docstrings_simple_class_multiline_arguments():
start_lineno = inspect.currentframe().f_lineno + 1
class Simple:
def __init__(self,
first_name,
last_name):
self.first_name = first_name
self.last_name = last_name
def name(self):
return '{:s}, {:s}'.format(self.last_name, self.first_name)
with type_inferencer.TypeInferencer() as ti:
s = Simple('First', 'Last')
s.name()
src_lines = ['\n'] * (start_lineno - 1)
src_lines += [
'class Simple:\n',
' def __init__(self,\n',
' first_name,\n',
' last_name):',
' self.first_name = first_name\n',
' self.last_name = last_name\n',
' \n',
' def name(self):\n',
' return \'{:s}, {:s}\'.format(self.last_name, self.first_name)\n',
]
exp_lines = ['\n'] * (start_lineno - 1)
exp_lines += [
'class Simple:\n',
' def __init__(self,\n',
' first_name,\n',
' last_name):',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :param first_name: <insert_documentation_for_argument>\n',
' :type first_name: ``str``\n',
' \n',
' :param last_name: <insert_documentation_for_argument>\n',
' :type last_name: ``str``\n',
' """\n',
' self.first_name = first_name\n',
' self.last_name = last_name\n',
' \n',
' def name(self):\n',
' """\n',
' <insert_documentation_for_function>\n',
' \n',
' :returns: ``str`` -- <insert_documentation_for_return_values>\n',
' """\n',
" return '{:s}, {:s}'.format(self.last_name, self.first_name)\n"
]
new_src_lines = ti.insert_docstrings(__file__, src_lines, style='sphinx')
# print()
# print(ti.docstring_map(__file__, style='sphinx'))
# src_start = list(ti.docstring_map(__file__, style='sphinx').keys())[0]
# print(src_lines[src_start-1:])
# pprint.pprint(new_src_lines[src_start-2:])
assert new_src_lines == exp_lines
def test_event_count_on_a_couple_of_functions():
def func_single_arg_no_return(arg):
pass
def func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
func_single_arg_no_return('string')
func_single_arg_return_arg('string')
assert ti.eventno == 8
def test_event_counter_on_a_couple_of_functions():
def func_single_arg_no_return(arg):
pass
def func_single_arg_return_arg(arg):
return arg
with type_inferencer.TypeInferencer() as ti:
func_single_arg_no_return('string')
func_single_arg_return_arg('string')
# print()
# print(ti.event_counter)
# Counter({'call': 3, 'line': 3, 'return': 2})
assert sorted(ti.event_counter.keys()) == ['call', 'line', 'return']
assert ti.event_counter['call'] == 3
assert ti.event_counter['line'] == 3
assert ti.event_counter['return'] == 2
| 33.767956
| 112
| 0.57662
| 7,384
| 61,120
| 4.447996
| 0.056744
| 0.02521
| 0.040434
| 0.042199
| 0.860127
| 0.835343
| 0.800664
| 0.763884
| 0.742875
| 0.722628
| 0
| 0.007102
| 0.281201
| 61,120
| 1,809
| 113
| 33.786622
| 0.740491
| 0.19501
| 0
| 0.728736
| 0
| 0.001533
| 0.233426
| 0.034953
| 0
| 0
| 0
| 0
| 0.082759
| 1
| 0.144828
| false
| 0.025287
| 0.006897
| 0.048276
| 0.236782
| 0.00613
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
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0
| 7
|
84a082fce59fb7052d07e827422a778d0c638d8d
| 17,083
|
py
|
Python
|
varsom_avalanche_client/api/avalanche_warning_by_region_api.py
|
NVE/python-varsom-avalanche-client
|
c7787bf070d8ea91efd3a2a9e7782eedd4961528
|
[
"MIT"
] | null | null | null |
varsom_avalanche_client/api/avalanche_warning_by_region_api.py
|
NVE/python-varsom-avalanche-client
|
c7787bf070d8ea91efd3a2a9e7782eedd4961528
|
[
"MIT"
] | null | null | null |
varsom_avalanche_client/api/avalanche_warning_by_region_api.py
|
NVE/python-varsom-avalanche-client
|
c7787bf070d8ea91efd3a2a9e7782eedd4961528
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
Snøskredvarsel API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: v5.0.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from varsom_avalanche_client.api_client import ApiClient
class AvalancheWarningByRegionApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def avalanche_warning_by_region_detail(self, regionid, langkey, startdate, enddate, **kwargs): # noqa: E501
"""avalanche_warning_by_region_detail # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.avalanche_warning_by_region_detail(regionid, langkey, startdate, enddate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int regionid: (required)
:param int langkey: (required)
:param datetime startdate: (required)
:param datetime enddate: (required)
:return: list[AvalancheWarningDetail]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.avalanche_warning_by_region_detail_with_http_info(regionid, langkey, startdate, enddate, **kwargs) # noqa: E501
else:
(data) = self.avalanche_warning_by_region_detail_with_http_info(regionid, langkey, startdate, enddate, **kwargs) # noqa: E501
return data
def avalanche_warning_by_region_detail_with_http_info(self, regionid, langkey, startdate, enddate, **kwargs): # noqa: E501
"""avalanche_warning_by_region_detail # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.avalanche_warning_by_region_detail_with_http_info(regionid, langkey, startdate, enddate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int regionid: (required)
:param int langkey: (required)
:param datetime startdate: (required)
:param datetime enddate: (required)
:return: list[AvalancheWarningDetail]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['regionid', 'langkey', 'startdate', 'enddate'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method avalanche_warning_by_region_detail" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'regionid' is set
if ('regionid' not in params or
params['regionid'] is None):
raise ValueError("Missing the required parameter `regionid` when calling `avalanche_warning_by_region_detail`") # noqa: E501
# verify the required parameter 'langkey' is set
if ('langkey' not in params or
params['langkey'] is None):
raise ValueError("Missing the required parameter `langkey` when calling `avalanche_warning_by_region_detail`") # noqa: E501
# verify the required parameter 'startdate' is set
if ('startdate' not in params or
params['startdate'] is None):
raise ValueError("Missing the required parameter `startdate` when calling `avalanche_warning_by_region_detail`") # noqa: E501
# verify the required parameter 'enddate' is set
if ('enddate' not in params or
params['enddate'] is None):
raise ValueError("Missing the required parameter `enddate` when calling `avalanche_warning_by_region_detail`") # noqa: E501
collection_formats = {}
path_params = {}
if 'regionid' in params:
path_params['regionid'] = params['regionid'] # noqa: E501
if 'langkey' in params:
path_params['langkey'] = params['langkey'] # noqa: E501
if 'startdate' in params:
path_params['startdate'] = params['startdate'] # noqa: E501
if 'enddate' in params:
path_params['enddate'] = params['enddate'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/api/AvalancheWarningByRegion/Detail/{regionid}/{langkey}/{startdate}/{enddate}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[AvalancheWarningDetail]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def avalanche_warning_by_region_obs(self, regionid, langkey, startdate, enddate, **kwargs): # noqa: E501
"""avalanche_warning_by_region_obs # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.avalanche_warning_by_region_obs(regionid, langkey, startdate, enddate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int regionid: (required)
:param int langkey: (required)
:param datetime startdate: (required)
:param datetime enddate: (required)
:return: list[ObsWarning]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.avalanche_warning_by_region_obs_with_http_info(regionid, langkey, startdate, enddate, **kwargs) # noqa: E501
else:
(data) = self.avalanche_warning_by_region_obs_with_http_info(regionid, langkey, startdate, enddate, **kwargs) # noqa: E501
return data
def avalanche_warning_by_region_obs_with_http_info(self, regionid, langkey, startdate, enddate, **kwargs): # noqa: E501
"""avalanche_warning_by_region_obs # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.avalanche_warning_by_region_obs_with_http_info(regionid, langkey, startdate, enddate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int regionid: (required)
:param int langkey: (required)
:param datetime startdate: (required)
:param datetime enddate: (required)
:return: list[ObsWarning]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['regionid', 'langkey', 'startdate', 'enddate'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method avalanche_warning_by_region_obs" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'regionid' is set
if ('regionid' not in params or
params['regionid'] is None):
raise ValueError("Missing the required parameter `regionid` when calling `avalanche_warning_by_region_obs`") # noqa: E501
# verify the required parameter 'langkey' is set
if ('langkey' not in params or
params['langkey'] is None):
raise ValueError("Missing the required parameter `langkey` when calling `avalanche_warning_by_region_obs`") # noqa: E501
# verify the required parameter 'startdate' is set
if ('startdate' not in params or
params['startdate'] is None):
raise ValueError("Missing the required parameter `startdate` when calling `avalanche_warning_by_region_obs`") # noqa: E501
# verify the required parameter 'enddate' is set
if ('enddate' not in params or
params['enddate'] is None):
raise ValueError("Missing the required parameter `enddate` when calling `avalanche_warning_by_region_obs`") # noqa: E501
collection_formats = {}
path_params = {}
if 'regionid' in params:
path_params['regionid'] = params['regionid'] # noqa: E501
if 'langkey' in params:
path_params['langkey'] = params['langkey'] # noqa: E501
if 'startdate' in params:
path_params['startdate'] = params['startdate'] # noqa: E501
if 'enddate' in params:
path_params['enddate'] = params['enddate'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/api/AvalancheWarningByRegion/Obs/{regionid}/{langkey}/{startdate}/{enddate}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[ObsWarning]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def avalanche_warning_by_region_simple(self, regionid, langkey, startdate, enddate, **kwargs): # noqa: E501
"""avalanche_warning_by_region_simple # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.avalanche_warning_by_region_simple(regionid, langkey, startdate, enddate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int regionid: (required)
:param int langkey: (required)
:param datetime startdate: (required)
:param datetime enddate: (required)
:return: list[AvalancheWarningSimple]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.avalanche_warning_by_region_simple_with_http_info(regionid, langkey, startdate, enddate, **kwargs) # noqa: E501
else:
(data) = self.avalanche_warning_by_region_simple_with_http_info(regionid, langkey, startdate, enddate, **kwargs) # noqa: E501
return data
def avalanche_warning_by_region_simple_with_http_info(self, regionid, langkey, startdate, enddate, **kwargs): # noqa: E501
"""avalanche_warning_by_region_simple # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.avalanche_warning_by_region_simple_with_http_info(regionid, langkey, startdate, enddate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int regionid: (required)
:param int langkey: (required)
:param datetime startdate: (required)
:param datetime enddate: (required)
:return: list[AvalancheWarningSimple]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['regionid', 'langkey', 'startdate', 'enddate'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method avalanche_warning_by_region_simple" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'regionid' is set
if ('regionid' not in params or
params['regionid'] is None):
raise ValueError("Missing the required parameter `regionid` when calling `avalanche_warning_by_region_simple`") # noqa: E501
# verify the required parameter 'langkey' is set
if ('langkey' not in params or
params['langkey'] is None):
raise ValueError("Missing the required parameter `langkey` when calling `avalanche_warning_by_region_simple`") # noqa: E501
# verify the required parameter 'startdate' is set
if ('startdate' not in params or
params['startdate'] is None):
raise ValueError("Missing the required parameter `startdate` when calling `avalanche_warning_by_region_simple`") # noqa: E501
# verify the required parameter 'enddate' is set
if ('enddate' not in params or
params['enddate'] is None):
raise ValueError("Missing the required parameter `enddate` when calling `avalanche_warning_by_region_simple`") # noqa: E501
collection_formats = {}
path_params = {}
if 'regionid' in params:
path_params['regionid'] = params['regionid'] # noqa: E501
if 'langkey' in params:
path_params['langkey'] = params['langkey'] # noqa: E501
if 'startdate' in params:
path_params['startdate'] = params['startdate'] # noqa: E501
if 'enddate' in params:
path_params['enddate'] = params['enddate'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'text/json', 'application/xml', 'text/xml']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/api/AvalancheWarningByRegion/Simple/{regionid}/{langkey}/{startdate}/{enddate}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[AvalancheWarningSimple]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 44.371429
| 138
| 0.633729
| 1,905
| 17,083
| 5.453543
| 0.085039
| 0.042352
| 0.067571
| 0.090095
| 0.950043
| 0.947156
| 0.943113
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| 0.930696
| 0.929252
| 0
| 0.014013
| 0.273137
| 17,083
| 384
| 139
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| 0.822662
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| 1
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| 0.259008
| 0.090942
| 0
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| 0
| 0
| 0
| 1
| 0.033333
| false
| 0
| 0.019048
| 0
| 0.1
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| null | 0
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|
0
| 8
|
ca29b560779c36ea5e3c04e66627d1cf22dfbd7c
| 16,684
|
py
|
Python
|
exoctk/contam_visibility/f_visibilityPeriods.py
|
bourque/exoctk
|
1d2f8e7b9c00e74033626d81593b1f879b7df6ad
|
[
"BSD-3-Clause"
] | null | null | null |
exoctk/contam_visibility/f_visibilityPeriods.py
|
bourque/exoctk
|
1d2f8e7b9c00e74033626d81593b1f879b7df6ad
|
[
"BSD-3-Clause"
] | null | null | null |
exoctk/contam_visibility/f_visibilityPeriods.py
|
bourque/exoctk
|
1d2f8e7b9c00e74033626d81593b1f879b7df6ad
|
[
"BSD-3-Clause"
] | 1
|
2021-10-05T17:07:46.000Z
|
2021-10-05T17:07:46.000Z
|
"""
Series of functions to compute the visibility periods for a given (RA,DEC)
with in some cases the possibility to select a PA value.
Functions derived from the code of Wayne Kinzel provided by Jeff Valenti
Extract from the e-mail of Wayne Kinzel:
As before, the code is not officially tested, nor is it an official STScI
product. Users should be warned that the apparent position of the Sun changes
~+/-0.2 degrees epending upon where JWST is in its orbit.
So do not rely strongly on these results if the target is within ~0.2 degrees
of |ecliptic latitude| 45 degrees or 85 degrees.
For example if a target is at 84.9 degrees latitude and the tool says it is
CVZ, it may not be with the operational orbit.
"""
import math
D2R = math.pi / 180. # degrees to radians
R2D = 180. / math.pi # radians to degrees
PI2 = 2. * math.pi # 2 pi
def f_computeVisibilityPeriods(ephemeris, mjdmin, mjdmax, ra, dec):
"""Returns two lists containing the start end end of each
visibility period and a list containing a status flag
flag = 0 visibility period fully in the search interval
flag = -1 start of the visibility period truncated by
the start of the search interval
flag = -2 end of the visibility period truncated by
the end of the search interval
flag = +1 the search interval is fully included in
the visibility period
Parameters
----------
ephemeris: Ephemeris
The input ephemeris object.
mjdmin: float
The beginning of the search interval (modified
Julian date). It must be covered by the ephemeris.
mjdmax: float
The end of the search interval (modified
Julian date). It must be covered by the ephemeris.
ra: float
The input RA coordinate (equatorial coordinate, in rad).
dec: float
The input DEC coordinate (equatorial coordinate, in rad).
Example
-------
>>> f_computeVisibilityPeriods(ephemeris, mjdmin, mjdmax, ra, dec)
Returns
-------
tuple
The lists of visibility period starts and ends with flags.
"""
if (ephemeris.amin > mjdmin):
print("""f_computeVisibilityPeriods(): the start of the search\
interval is not covered by the ephemeris.""")
print("""Ephemeris start date (modified Julian date):\
{:8.5f}""".format(ephemeris.amin))
print("""Search interval start date (modified Julian date):\
{:8.5f}""".format(mjdmin))
raise ValueError
if (ephemeris.amax < mjdmax):
print("""f_computeVisibilityPeriods(): the end of the search interval\
is not covered by the ephemeris.""")
print("""Ephemeris end date (modified Julian date):\
{:8.5f}""".format(ephemeris.amax))
print("""Search interval end date (modified Julian date):\
{:8.5f}""".format(mjdmax))
raise ValueError
# ===========================================================
# Scanning the search period
# ===========================================================
# Flag used to track the beginning and the end of a visibility period
iflip = False
wstart = mjdmin
startList = []
endList = []
statusList = []
# Scannning step size (must be small enough to make sure that
# it cannot contain a full vsibility period (we would miss it)
scanningStepSize = 0.1
span = int((mjdmax - mjdmin) / scanningStepSize)
# Initialisation (first step of the scan is outside from the loop
iflag_old = ephemeris.in_FOR(mjdmin, ra, dec)
for i in range(span):
# Current date (the last step may be partial to remain
# within the search interval
currentdate = mjdmin + (i + 1) * scanningStepSize
if (currentdate >= mjdmax):
currentdate = mjdmax
iflag = ephemeris.in_FOR(currentdate, ra, dec)
# Checking if we are reaching the beginning or the end of a
# visibility period (in which case the iflag value will change)
if iflag != iflag_old:
# Setting the iflip flag to True to keep track of the change
# (in order to detect CVZ object which are permanenetly visible)
# If iflag = True we are starting a visibility period and use
# a bisection method to find the exact transition date
# This assumes that there is a single transition in the
# interval => it looks like a step size of 0.1 day is
# sufficient to ensure that.
if (iflag):
step = currentdate - scanningStepSize
wstart = ephemeris.bisect_by_FOR(currentdate, step, ra, dec)
# IF iflag = False we are reaching the end of a visibility period.
# Like for the previous case a bisection method is used to locate
# accurately the end of the visibility period.
else:
step = currentdate - scanningStepSize
wend = ephemeris.bisect_by_FOR(step, currentdate, ra, dec)
startList.append(wstart)
endList.append(wend)
if (iflip):
statusList.append(0)
else:
statusList.append(-1)
iflip = True
iflag_old = iflag
# If there was a transition and we end up with a valid date, we close the
# interval with the end of the search interval
if (iflag and iflip):
startList.append(wstart)
endList.append(currentdate)
statusList.append(-2)
# There is also the case were the visibility period covers the complete
# search interval
if (iflag and (not iflip)):
startList.append(mjdmin)
endList.append(mjdmax)
statusList.append(1)
# End of the function
return startList, endList, statusList
def f_computeVisibilityPeriodsWithPA(ephemeris, mjdmin, mjdmax, ra, dec, pa):
"""Returns two lists containing the start end end of each
visibility period and a list containing a status flag
flag = 0 visibility period fully in the search interval
flag = -1 start of the visibility period truncated by
the start of the search interval
flag = -2 end of the visibility period truncated by
the end of the search interval
flag = +1 the search interval is fully included in
the visibility period
Parameters
----------
ephemeris: Ephemeris
The input ephemeris object.
mjdmin: float
The beginning of the search interval (modified
Julian date). It must be covered by the ephemeris.
mjdmax: float
The end of the search interval (modified
Julian date). It must be covered by the ephemeris.
ra: float
The input RA coordinate (equatorial coordinate, in rad).
dec: float
The input DEC coordinate (equatorial coordinate, in rad).
pa: float
The position angle.
Example
-------
>>> f_computeVisibilityPeriodsWithPA(ephemeris, mjdmin, mjdmax, ra, dec,
pa)
Returns
-------
tuple
The lists of visibility period starts and ends with flags
"""
if (ephemeris.amin > mjdmin):
print("""f_computeVisibilityPeriodsWithPA(): the start of the search\
interval is not covered by the ephemeris.""")
print("""Ephemeris start date (modified Julian date):\
{:8.5f}""".format(ephemeris.amin))
print("""Search interval start date (modified Julian date):\
{:8.5f}""".format(mjdmin))
raise ValueError
if (ephemeris.amax < mjdmax):
print("""f_computeVisibilityPeriodsWithPA(): the end of the search\
interval is not covered by the ephemeris.""")
print("""Ephemeris end date (modified Julian date):\
{:8.5f}""".format(ephemeris.amax))
print("""Search interval end date (modified Julian date):\
{:8.5f}""".format(mjdmax))
raise ValueError
# ===========================================================
# Scanning the search period
# ===========================================================
# Flag used to track the beginning and the end of a
# visibility period
iflip = False
wstart = mjdmin
startList = []
endList = []
statusList = []
# Scannning step size (must be small enough to make sure that
# it cannot contain a full vsibility period (we would miss
# it)
scanningStepSize = 0.1
span = int((mjdmax - mjdmin) / scanningStepSize)
# Initialisation (first step of the scan is outside from the
# loop
iflag_old = ephemeris.is_valid(mjdmin, ra, dec, pa)
for i in range(span):
# Current date (the last step may be partial to remain
# within the search interval
currentdate = mjdmin + (i + 1) * scanningStepSize
if (currentdate >= mjdmax):
currentdate = mjdmax
iflag = ephemeris.is_valid(currentdate, ra, dec, pa)
# Checking if we are reaching the beginning or the end of a
# visibility period
# (in which case the iflag value will change)
if iflag != iflag_old:
# Setting the iflip flag to True to keep track of the change
# (in order to
# detect CVZ object which are permanenetly visible)
# If iflag = True we are starting a visibility period and use
# a bisection method
# to find the exact transition date. This assumes that there
# is a single
# transition in the interval => it looks like a step size of
# 0.1 day is
# sufficient to ensure that.
if (iflag):
step = currentdate - scanningStepSize
wstart = ephemeris.bisect_by_attitude(currentdate, step,
ra, dec, pa)
# IF iflag = False we are reaching the end of a visibility period.
# Like for the previous case a bisection method is used to locate
# accurately the end of the visibility period.
else:
step = currentdate - scanningStepSize
wend = ephemeris.bisect_by_attitude(step, currentdate,
ra, dec, pa)
startList.append(wstart)
endList.append(wend)
if (iflip):
statusList.append(0)
else:
statusList.append(-1)
iflip = True
iflag_old = iflag
# If there was a transition and we end up with a valid date, we close
# the interval with the end of the search interval
if (iflag and iflip):
startList.append(wstart)
endList.append(currentdate)
statusList.append(-2)
# There is also the case were the visibility period covers the
# complete search interval
if (iflag and (not iflip)):
startList.append(mjdmin)
endList.append(mjdmax)
statusList.append(1)
# End of the function
return startList, endList, statusList
def f_computeDurationOfVisibilityPeriodWithPA(ephemeris, mjdmin, mjdmax,
ra, dec, pa, mjdc):
"""Computes the duration of a specific visibility period associated to a
given (RA,DEC), a given PA and given date
flag = 0 visibility period fully in the search interval
flag = -1 start of the visibility period truncated by
the start of the search interval
flag = -2 end of the visibility period truncated by
the end of the search interval
flag = +1 the search interval is fully included in
the visibility period
Parameters
----------
ephemeris: Ephemeris
The input ephemeris object.
mjdmin: float
The beginning of the search interval (modified
Julian date). It must be covered by the ephemeris.
mjdmax: float
The end of the search interval (modified
Julian date). It must be covered by the ephemeris.
ra: float
The input RA coordinate (equatorial coordinate, in rad).
dec: float
The input DEC coordinate (equatorial coordinate, in rad).
pa: float
The position angle.
Example
-------
>>> f_computeDurationOfVisibilityPeriodWithPA(ephemeris, mjdmin, mjdmax,
ra, dec, pa, mjdc)
Returns
-------
tuple
The lists of visibility period starts and ends with flags.
"""
if (ephemeris.amin > mjdmin):
print("""f_computeDurationOfVisibilityPeriodWithPA(): the start of\
thesearch interval is not covered by the ephemeris.""")
print("""Ephemeris start date (modified Julian date):\
{:8.5f}""".format(ephemeris.amin))
print("""Search interval start date (modified Julian date):\
{:8.5f}""".format(mjdmin))
raise ValueError
if (ephemeris.amax < mjdmax):
print("""f_computeDurationOfVisibilityPeriodWithPA(): the end of the\
search interval is not covered by the ephemeris.""")
print("""Ephemeris end date (modified Julian date):\
{:8.5f}""".format(ephemeris.amax))
print("""Search interval end date (modified Julian date):\
{:8.5f}""".format(mjdmax))
raise ValueError
if (mjdmin > mjdc):
print("""f_computeDurationOfVisibilityPeriodWithPA():\
initial date is not included in the search interval.""")
print("""Search interval start date (modified Julian date):\
{:8.5f}""".format(mjdmin))
print("Initial date (modified Julian date): {:8.5f}".format(mjdc))
raise ValueError
if (mjdmax < mjdc):
print("""f_computeDurationOfVisibilityPeriodWithPA(): initial date is\
not included in the search interval.""")
print("""Search interval end date (modified Julian date):\
{:8.5f}""".format(mjdmax))
print("Initial date (modified Julian date): {:8.5f}".format(mjdc))
raise ValueError
iflag = ephemeris.is_valid(mjdc, ra, dec, pa)
if (not iflag):
print("""f_computeDurationOfVisibilityPeriodWithPA(): invalid date\
(not in a vsibility period).""")
print("Date (modified Julian date): {:8.5f}".format(mjdc))
raise ValueError
# ===========================================================
# Looking for the start of the visibility period
# ===========================================================
scanningStepSize = 0.1
iflipLeft = False
currentmjd = mjdc
continueFlag = True
boundaryFlag = False
while (continueFlag):
currentmjd -= scanningStepSize
if (currentmjd < mjdmin):
currentmjd = mjdmin
boundaryFlag = True
continueFlag = False
iflag = ephemeris.is_valid(currentmjd, ra, dec, pa)
if (not iflag):
wstart = ephemeris.bisect_by_attitude(
currentmjd, currentmjd + scanningStepSize, ra, dec, pa)
iflipLeft = True
continueFlag = False
elif (boundaryFlag):
wstart = mjdmin
iflipRight = False
currentmjd = mjdc
boundaryFlag = False
continueFlag = True
while (continueFlag):
currentmjd += scanningStepSize
if (currentmjd > mjdmax):
currentmjd = mjdmax
boundaryFlag = True
continueFlag = False
iflag = ephemeris.is_valid(currentmjd, ra, dec, pa)
if (not iflag):
wend = ephemeris.bisect_by_attitude(currentmjd - scanningStepSize,
currentmjd, ra, dec, pa)
iflipRight = True
continueFlag = False
elif (boundaryFlag):
wend = mjdmax
if ((not iflipLeft) and (not iflipRight)):
status = 1
elif (not iflipLeft):
status = -1
elif (not iflipRight):
status = -2
else:
status = 0
# End of the function
return wstart, wend, status
| 38.354023
| 79
| 0.582234
| 1,887
| 16,684
| 5.126656
| 0.133015
| 0.05644
| 0.050961
| 0.037317
| 0.82944
| 0.806905
| 0.793777
| 0.793777
| 0.783233
| 0.756564
| 0
| 0.008153
| 0.323663
| 16,684
| 434
| 80
| 38.442396
| 0.849167
| 0.415668
| 0
| 0.707071
| 0
| 0
| 0.247172
| 0.03948
| 0
| 0
| 0
| 0
| 0
| 1
| 0.015152
| false
| 0
| 0.005051
| 0
| 0.035354
| 0.131313
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ca34b897b49303eb78529e3433d4049bbd251815
| 108
|
py
|
Python
|
metagym/metamaze/envs/__init__.py
|
WorldEditors/MetaGym
|
ad7263fcc80abd6831965ab6b556d54f75e17315
|
[
"Apache-2.0"
] | null | null | null |
metagym/metamaze/envs/__init__.py
|
WorldEditors/MetaGym
|
ad7263fcc80abd6831965ab6b556d54f75e17315
|
[
"Apache-2.0"
] | null | null | null |
metagym/metamaze/envs/__init__.py
|
WorldEditors/MetaGym
|
ad7263fcc80abd6831965ab6b556d54f75e17315
|
[
"Apache-2.0"
] | null | null | null |
from metagym.metamaze.envs.maze_env import MetaMaze3D
from metagym.metamaze.envs.maze_env import MetaMaze2D
| 36
| 53
| 0.87037
| 16
| 108
| 5.75
| 0.5625
| 0.23913
| 0.413043
| 0.5
| 0.782609
| 0.782609
| 0.782609
| 0
| 0
| 0
| 0
| 0.02
| 0.074074
| 108
| 2
| 54
| 54
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 10
|
ca6ae7cf69f01afed5d35751edc4734c14b224b7
| 1,354
|
py
|
Python
|
ProjectEuler/08_largest_product_in_a_series.py
|
lucianofg/Exercises
|
fd408d18e23fda33e24c529d666cda208f1139f3
|
[
"MIT"
] | null | null | null |
ProjectEuler/08_largest_product_in_a_series.py
|
lucianofg/Exercises
|
fd408d18e23fda33e24c529d666cda208f1139f3
|
[
"MIT"
] | null | null | null |
ProjectEuler/08_largest_product_in_a_series.py
|
lucianofg/Exercises
|
fd408d18e23fda33e24c529d666cda208f1139f3
|
[
"MIT"
] | null | null | null |
# Find the thirteen adjacent digits in the 1000-digit number that have the
# greatest product. What is the value of this product?
# Problem taken from https://projecteuler.net/problem=8
number = "7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450"
products = set()
for i in range(len(number) - 13):
p = 1
for j in number[i:i+13]:
p *= int(j)
products.add(p)
print(max(products))
| 84.625
| 1,011
| 0.912851
| 59
| 1,354
| 20.949153
| 0.677966
| 0.004854
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.790916
| 0.056869
| 1,354
| 15
| 1,012
| 90.266667
| 0.176977
| 0.132201
| 0
| 0
| 0
| 0
| 0.854701
| 0.854701
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.125
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
04af0b097b7d940c9b7b40d9d4b658cff482b9be
| 2,114
|
py
|
Python
|
project/editorial/migrations/0008_auto_20160319_2059.py
|
cojennin/facet
|
230e65316134b3399a35d40034728e61ba63cb2a
|
[
"MIT"
] | 25
|
2015-07-13T22:16:36.000Z
|
2021-11-11T02:45:32.000Z
|
project/editorial/migrations/0008_auto_20160319_2059.py
|
cojennin/facet
|
230e65316134b3399a35d40034728e61ba63cb2a
|
[
"MIT"
] | 74
|
2015-12-01T18:57:47.000Z
|
2022-03-11T23:25:47.000Z
|
project/editorial/migrations/0008_auto_20160319_2059.py
|
cojennin/facet
|
230e65316134b3399a35d40034728e61ba63cb2a
|
[
"MIT"
] | 6
|
2016-01-08T21:12:43.000Z
|
2019-05-20T16:07:56.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('editorial', '0007_auto_20160319_2007'),
]
operations = [
migrations.AddField(
model_name='audiofacet',
name='document_assets',
field=models.ManyToManyField(to='editorial.DocumentAsset', blank=True),
),
migrations.AddField(
model_name='audiofacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', blank=True),
),
migrations.AddField(
model_name='historicalaudiofacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', blank=True),
),
migrations.AddField(
model_name='historicalprintfacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', blank=True),
),
migrations.AddField(
model_name='historicalvideofacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', blank=True),
),
migrations.AddField(
model_name='printfacet',
name='document_assets',
field=models.ManyToManyField(to='editorial.DocumentAsset', blank=True),
),
migrations.AddField(
model_name='printfacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', blank=True),
),
migrations.AddField(
model_name='videofacet',
name='document_assets',
field=models.ManyToManyField(to='editorial.DocumentAsset', blank=True),
),
migrations.AddField(
model_name='videofacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', blank=True),
),
]
| 35.233333
| 97
| 0.60123
| 211
| 2,114
| 5.872038
| 0.232227
| 0.130751
| 0.16707
| 0.196126
| 0.823245
| 0.823245
| 0.785311
| 0.785311
| 0.753027
| 0.753027
| 0
| 0.011326
| 0.289972
| 2,114
| 59
| 98
| 35.830508
| 0.814124
| 0.009934
| 0
| 0.792453
| 0
| 0
| 0.259206
| 0.043998
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.037736
| 0
| 0.09434
| 0.056604
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
04b5e4a7899a1724827ca6a4c12f986495827227
| 3,117
|
py
|
Python
|
stubs/micropython-v1_18-rp2/uasyncio/stream.py
|
mattytrentini/micropython-stubs
|
4d596273823b69e9e5bcf5fa67f249c374ee0bbc
|
[
"MIT"
] | null | null | null |
stubs/micropython-v1_18-rp2/uasyncio/stream.py
|
mattytrentini/micropython-stubs
|
4d596273823b69e9e5bcf5fa67f249c374ee0bbc
|
[
"MIT"
] | null | null | null |
stubs/micropython-v1_18-rp2/uasyncio/stream.py
|
mattytrentini/micropython-stubs
|
4d596273823b69e9e5bcf5fa67f249c374ee0bbc
|
[
"MIT"
] | null | null | null |
"""
Module: 'uasyncio.stream' on micropython-v1.18-rp2
"""
# MCU: {'family': 'micropython', 'sysname': 'rp2', 'version': '1.18.0', 'build': '', 'mpy': 5637, 'port': 'rp2', 'platform': 'rp2', 'name': 'micropython', 'arch': 'armv7m', 'machine': 'Arduino Nano RP2040 Connect with RP2040', 'nodename': 'rp2', 'ver': 'v1.18', 'release': '1.18.0'}
# Stubber: 1.5.3
from typing import Any
class Stream():
''
def __init__(self, *argv, **kwargs) -> None:
''
...
def close(self, *args, **kwargs) -> Any:
...
read : Any ## <class 'generator'> = <generator>
readinto : Any ## <class 'generator'> = <generator>
readline : Any ## <class 'generator'> = <generator>
def write(self, *args, **kwargs) -> Any:
...
wait_closed : Any ## <class 'generator'> = <generator>
aclose : Any ## <class 'generator'> = <generator>
awrite : Any ## <class 'generator'> = <generator>
awritestr : Any ## <class 'generator'> = <generator>
def get_extra_info(self, *args, **kwargs) -> Any:
...
readexactly : Any ## <class 'generator'> = <generator>
drain : Any ## <class 'generator'> = <generator>
class StreamReader():
''
def __init__(self, *argv, **kwargs) -> None:
''
...
def close(self, *args, **kwargs) -> Any:
...
read : Any ## <class 'generator'> = <generator>
readinto : Any ## <class 'generator'> = <generator>
readline : Any ## <class 'generator'> = <generator>
def write(self, *args, **kwargs) -> Any:
...
wait_closed : Any ## <class 'generator'> = <generator>
aclose : Any ## <class 'generator'> = <generator>
awrite : Any ## <class 'generator'> = <generator>
awritestr : Any ## <class 'generator'> = <generator>
def get_extra_info(self, *args, **kwargs) -> Any:
...
readexactly : Any ## <class 'generator'> = <generator>
drain : Any ## <class 'generator'> = <generator>
class StreamWriter():
''
def __init__(self, *argv, **kwargs) -> None:
''
...
def close(self, *args, **kwargs) -> Any:
...
read : Any ## <class 'generator'> = <generator>
readinto : Any ## <class 'generator'> = <generator>
readline : Any ## <class 'generator'> = <generator>
def write(self, *args, **kwargs) -> Any:
...
wait_closed : Any ## <class 'generator'> = <generator>
aclose : Any ## <class 'generator'> = <generator>
awrite : Any ## <class 'generator'> = <generator>
awritestr : Any ## <class 'generator'> = <generator>
def get_extra_info(self, *args, **kwargs) -> Any:
...
readexactly : Any ## <class 'generator'> = <generator>
drain : Any ## <class 'generator'> = <generator>
open_connection : Any ## <class 'generator'> = <generator>
class Server():
''
def __init__(self, *argv, **kwargs) -> None:
''
...
def close(self, *args, **kwargs) -> Any:
...
wait_closed : Any ## <class 'generator'> = <generator>
start_server : Any ## <class 'generator'> = <generator>
stream_awrite : Any ## <class 'generator'> = <generator>
| 33.516129
| 282
| 0.559833
| 312
| 3,117
| 5.5
| 0.217949
| 0.149184
| 0.30711
| 0.469697
| 0.805944
| 0.769231
| 0.769231
| 0.769231
| 0.769231
| 0.769231
| 0
| 0.014793
| 0.240937
| 3,117
| 92
| 283
| 33.880435
| 0.710482
| 0.449471
| 0
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.194444
| false
| 0
| 0.013889
| 0
| 0.652778
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 10
|
04ce66bf9e5f1bdaa2a8d7c4ab32230df5ff18a4
| 2,005
|
py
|
Python
|
apps/preferences/views.py
|
godetaph/uresearch
|
fb23cb0fe07f8b434b9c46f80b5b43030a3d5323
|
[
"MIT"
] | null | null | null |
apps/preferences/views.py
|
godetaph/uresearch
|
fb23cb0fe07f8b434b9c46f80b5b43030a3d5323
|
[
"MIT"
] | null | null | null |
apps/preferences/views.py
|
godetaph/uresearch
|
fb23cb0fe07f8b434b9c46f80b5b43030a3d5323
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
from rest_framework import status
from rest_framework.response import Response
from rest_framework import viewsets, permissions
# from rest_framework.generics import ListCreateAPIView, RetrieveUpdateDestroyAPIView
from .serializers import UnitSerializer, Unit1Serializer, Unit2Serializer, SemSySerializer
from .models import Unit, Unit1, Unit2, SemSy
# class UnitListAPIView(ListCreateAPIView):
# serializer_class = UnitSerializer
# queryset = Unit.objects.all()
# permisstion_classes = [permissions.IsAuthenticated,]
# class UnitDetailAPIView(RetrieveUpdateDestroyAPIView):
# serializer_class = UnitSerializer
# queryset = Unit.objects.all()
# permisstion_classes = [permissions.IsAuthenticated,]
# lookup_field = 'id'
class Unit1ViewSet(viewsets.ModelViewSet):
serializer_class = Unit1Serializer
queryset = Unit1.objects.all()
permission_classes = (permissions.IsAuthenticated,)
class Unit2ViewSet(viewsets.ModelViewSet):
serializer_class = Unit2Serializer
queryset = Unit2.objects.all()
permission_classes = (permissions.IsAuthenticated,)
class SemSyViewSet(viewsets.ModelViewSet):
serializer_class = SemSySerializer
queryset = SemSy.objects.all()
permission_classes = (permissions.IsAuthenticated,)
class UnitViewSet(viewsets.ModelViewSet):
serializer_class = UnitSerializer
queryset = Unit.objects.all()
permission_classes = (permissions.IsAuthenticated,)
class Unit1ViewSet(viewsets.ModelViewSet):
serializer_class = Unit1Serializer
queryset = Unit1.objects.all()
permission_classes = (permissions.IsAuthenticated,)
class Unit2ViewSet(viewsets.ModelViewSet):
serializer_class = Unit2Serializer
queryset = Unit2.objects.all()
permission_classes = (permissions.IsAuthenticated,)
class SemSyViewSet(viewsets.ModelViewSet):
serializer_class = SemSySerializer
queryset = SemSy.objects.all()
permission_classes = (permissions.IsAuthenticated,)
| 34.568966
| 90
| 0.784539
| 179
| 2,005
| 8.659218
| 0.240223
| 0.087097
| 0.191613
| 0.171613
| 0.70129
| 0.70129
| 0.70129
| 0.637419
| 0.637419
| 0.637419
| 0
| 0.009238
| 0.13616
| 2,005
| 57
| 91
| 35.175439
| 0.885681
| 0.230424
| 0
| 0.735294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.176471
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
b6c95e613e639c309923be9bada31c2615a075ab
| 130
|
py
|
Python
|
swig-2.0.4/Examples/python/docstrings/runme.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | 1
|
2016-04-09T02:58:13.000Z
|
2016-04-09T02:58:13.000Z
|
swig-2.0.4/Examples/python/docstrings/runme.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | null | null | null |
swig-2.0.4/Examples/python/docstrings/runme.py
|
vidkidz/crossbridge
|
ba0bf94aee0ce6cf7eb5be882382e52bc57ba396
|
[
"MIT"
] | null | null | null |
# file: runme.py
import example
print "example.Foo.bar.__doc__ =", repr(example.Foo.bar.__doc__), "(Should be 'No comment')"
| 18.571429
| 93
| 0.692308
| 19
| 130
| 4.315789
| 0.736842
| 0.243902
| 0.317073
| 0.390244
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138462
| 130
| 6
| 94
| 21.666667
| 0.732143
| 0.107692
| 0
| 0
| 0
| 0
| 0.442478
| 0.20354
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.5
| null | null | 0.5
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
0
| 8
|
f3d0600c7706101cae58c457979fa66602a8202e
| 667
|
py
|
Python
|
regseq/deprecated/do_many.py
|
RPGroup-PBoC/RegSeq
|
d1df8727f27bd3a48f297974a4f9c5f170a34de4
|
[
"MIT"
] | 3
|
2020-04-01T21:17:38.000Z
|
2022-03-08T00:54:42.000Z
|
regseq/deprecated/do_many.py
|
RPGroup-PBoC/RegSeq
|
d1df8727f27bd3a48f297974a4f9c5f170a34de4
|
[
"MIT"
] | 19
|
2020-03-30T21:02:28.000Z
|
2020-06-23T18:32:39.000Z
|
regseq/deprecated/do_many.py
|
RPGroup-PBoC/RegSeq
|
d1df8727f27bd3a48f297974a4f9c5f170a34de4
|
[
"MIT"
] | 2
|
2020-06-25T02:00:59.000Z
|
2020-10-05T06:58:06.000Z
|
import os
import glob
ournames = ['tarphoPdataset_alldone_with_large']
os.system('/home/ubuntu/anaconda2/bin/mpathic learn_model -i /home/ubuntu/tarphoPdataset_alldone_with_large -o /home/ubuntu/results/tarphoPdataset_alldone_with_largeMCMC194 -lm IM --iteration 300000 --thin 60 -db /home/ubuntu/results/tarphoPdataset_alldone_with_largeMCMC194_0 --initialize rand')
os.system('/home/ubuntu/anaconda2/bin/mpathic learn_model -i /home/ubuntu/tarphoPdataset_alldone_with_large -o /home/ubuntu/results/tarphoPdataset_alldone_with_largeMCMC195 -lm IM --iteration 300000 --thin 60 -db /home/ubuntu/results/tarphoPdataset_alldone_with_largeMCMC195_0 --initialize rand')
| 111.166667
| 297
| 0.838081
| 91
| 667
| 5.868132
| 0.340659
| 0.149813
| 0.327715
| 0.23221
| 0.838951
| 0.838951
| 0.838951
| 0.749064
| 0.749064
| 0.749064
| 0
| 0.051118
| 0.061469
| 667
| 5
| 298
| 133.4
| 0.801917
| 0
| 0
| 0
| 0
| 0.4
| 0.898051
| 0.661169
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 14
|
eddba2bb61a799edb2fc7d35aed4632ed54168d9
| 6,876
|
py
|
Python
|
ilpyt/nets/net2d.py
|
mitre/ilpyt
|
6aecbe414f0032514ffb4206200596b8c3860b58
|
[
"Apache-2.0"
] | 6
|
2021-09-20T20:25:11.000Z
|
2022-01-05T16:04:04.000Z
|
ilpyt/nets/net2d.py
|
mitre/ilpyt
|
6aecbe414f0032514ffb4206200596b8c3860b58
|
[
"Apache-2.0"
] | null | null | null |
ilpyt/nets/net2d.py
|
mitre/ilpyt
|
6aecbe414f0032514ffb4206200596b8c3860b58
|
[
"Apache-2.0"
] | null | null | null |
"""
These 2D networks are suited for 3D inputs (h,w,c).
"""
from typing import Tuple, Union
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical, Distribution, Normal
from ilpyt.nets.base_net import BaseNetwork, get_activation_layer
class DiscreteNetwork2D(BaseNetwork):
def initialize(
self,
input_shape: tuple,
output_shape: int,
activation: str = 'relu',
with_action_shape: int = 0,
) -> None:
"""
2D network for discrete outputs.
Parameters
----------
input_shape: tuple
shape of input to network
output_shape: int
shape of output of network
activation: str, default='relu'
activation layer to add after hidden layers of network
with_action_shape: int, default=0
if specified, action will be incorporated into the net forward pass
Raises
------
AssertionError:
if length of `input_shape` is not 3
"""
assert len(input_shape) == 3
activation_layer = get_activation_layer(activation)
self.input_shape = (
input_shape[2],
input_shape[0],
input_shape[1],
) # HWC to CHW
in_channels = self.input_shape[0]
self.features = nn.Sequential(
nn.Conv2d(in_channels, 16, 8, 4),
activation_layer,
nn.Conv2d(16, 32, 4, 2),
activation_layer,
nn.Conv2d(32, 32, 4, 2),
activation_layer,
)
self.with_action_shape = with_action_shape
self.fc = nn.Sequential(
nn.Linear(self.feature_size(), 512),
activation_layer,
nn.Linear(512, output_shape),
nn.ReLU(),
)
def forward(
self, x: torch.Tensor, a: Union[torch.Tensor, None] = None
) -> torch.Tensor:
"""
Forward pass for network.
Parameters
----------
x: torch.Tensor
input state tensor to network
a: torch.Tensor, default=None
optional; input action tensor
Returns
-------
torch.Tensor:
output tensor of forward pass
"""
x = x.permute(0, 3, 1, 2)
x = x / 127.5 - 1
x = self.features(x)
x = x.reshape(x.size(0), -1)
if a is not None and self.with_action_shape:
a = F.one_hot(
a.to(torch.int64), num_classes=self.with_action_shape
)
xa = torch.cat([x, a], dim=-1)
return self.fc(xa)
return self.fc(x)
def get_action(self, x: torch.Tensor) -> Tuple[Distribution, torch.Tensor]:
"""
Select an action by drawing from a distribution.
Parameters
----------
x: torch.Tensor
input state tensor to network
Returns
-------
torch.distributions.Distribution:
distribution to sample actions from
torch.Tensor:
action tensor, sampled from distribution
"""
logits = self.forward(x)
probs = F.softmax(logits, dim=-1)
dist = Categorical(probs)
actions = dist.sample()
return dist, actions
def feature_size(self):
return (
self.features(autograd.Variable(torch.zeros(1, *self.input_shape)))
.view(1, -1)
.size(1)
+ self.with_action_shape
)
class ContinuousNetwork2D(BaseNetwork):
def initialize(
self,
input_shape: tuple,
output_shape: int,
activation: str = 'relu',
with_action_shape: int = 0,
) -> None:
"""
2D network for discrete outputs.
Parameters
----------
input_shape: tuple
shape of input to network
output_shape: int
shape of output of network
activation: str, default='relu'
activation layer to add after hidden layers of network
with_action_shape: int, default=0
if specified, action will be incorporated into the net forward pass
Raises
------
AssertionError:
if length of `input_shape` is not 3
"""
assert len(input_shape) == 3
activation_layer = get_activation_layer(activation)
self.input_shape = (
input_shape[2],
input_shape[0],
input_shape[1],
) # HWC to CHW
in_channels = self.input_shape[0]
self.features = nn.Sequential(
nn.Conv2d(in_channels, 32, 8, 4),
activation_layer,
nn.Conv2d(32, 64, 4, 2),
activation_layer,
nn.Conv2d(64, 64, 3, 1),
activation_layer,
)
self.with_action_shape = with_action_shape
self.fc = nn.Sequential(
nn.Linear(self.feature_size(), 512),
activation_layer,
nn.Linear(512, output_shape),
nn.ReLU(),
)
# Standard deviation
log_std = 0.5 * torch.ones(output_shape)
self.log_std = torch.nn.Parameter(log_std)
def forward(
self, x: torch.Tensor, a: Union[torch.Tensor, None] = None
) -> torch.Tensor:
"""
Forward pass for network.
Parameters
----------
x: torch.Tensor
input state tensor to network
a: torch.Tensor, default=None
optional; input action tensor
Returns
-------
torch.Tensor:
output tensor of forward pass
"""
# Normalize
x = x.permute(0, 3, 1, 2)
x = x / 127.5 - 1
# Forward
x = self.features(x)
x = x.reshape(x.size(0), -1)
if a is not None and self.with_action_shape:
xa = torch.cat([x, a], dim=-1)
return self.fc(xa)
return self.fc(x)
def get_action(self, x: torch.Tensor) -> Tuple[Distribution, torch.Tensor]:
"""
Select an action by drawing from a distribution.
Parameters
----------
x: torch.Tensor
input state tensor to network
Returns
-------
torch.distributions.Distribution:
distribution to sample actions from
torch.Tensor:
action tensor, sampled from distribution
"""
mu = self.forward(x)
std = torch.exp(self.log_std)
dist = Normal(mu, std)
actions = dist.sample()
return dist, actions
def feature_size(self):
return (
self.features(autograd.Variable(torch.zeros(1, *self.input_shape)))
.view(1, -1)
.size(1)
+ self.with_action_shape
)
| 27.504
| 79
| 0.539703
| 784
| 6,876
| 4.623724
| 0.178571
| 0.055172
| 0.053793
| 0.03669
| 0.847172
| 0.844414
| 0.816828
| 0.816828
| 0.816828
| 0.816828
| 0
| 0.024807
| 0.360966
| 6,876
| 249
| 80
| 27.614458
| 0.800182
| 0.284468
| 0
| 0.721311
| 0
| 0
| 0.001893
| 0
| 0
| 0
| 0
| 0
| 0.016393
| 1
| 0.065574
| false
| 0
| 0.057377
| 0.016393
| 0.204918
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
edf0ba50d3243fc6a279484bb1ad8f1637d8696a
| 12,864
|
py
|
Python
|
examples/images.py
|
slightlynybbled/stringify
|
fa8b14a084fa269cc43a01fbbce609ac57fcea9f
|
[
"MIT"
] | 5
|
2019-01-05T03:35:45.000Z
|
2022-03-01T10:08:20.000Z
|
examples/images.py
|
slightlynybbled/stringify
|
fa8b14a084fa269cc43a01fbbce609ac57fcea9f
|
[
"MIT"
] | null | null | null |
examples/images.py
|
slightlynybbled/stringify
|
fa8b14a084fa269cc43a01fbbce609ac57fcea9f
|
[
"MIT"
] | null | null | null |
# flake8: noqa
yellow_dot = 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0
| 8
|
b6165dae0128dfff8d0eb68f24b50c84259e930d
| 632
|
py
|
Python
|
tests/parse_host_string_doctest.py
|
lincheney/ssh-forward-proxy
|
cf8e0dda4448c3ba16b509570ee0920380aa9c1c
|
[
"MIT"
] | 7
|
2016-08-04T15:25:21.000Z
|
2021-08-25T00:02:48.000Z
|
tests/parse_host_string_doctest.py
|
lincheney/ssh-forward-proxy
|
cf8e0dda4448c3ba16b509570ee0920380aa9c1c
|
[
"MIT"
] | 1
|
2016-02-16T13:14:26.000Z
|
2016-02-16T13:14:26.000Z
|
tests/parse_host_string_doctest.py
|
lincheney/ssh-forward-proxy
|
cf8e0dda4448c3ba16b509570ee0920380aa9c1c
|
[
"MIT"
] | 2
|
2015-05-15T10:38:39.000Z
|
2020-01-14T01:47:16.000Z
|
"""
Tests for ssh_forward_proxy.parse_host_string
No user or port
>>> parse_host_string('host')
(None, 'host', 22)
With a user
>>> parse_host_string('user@host')
('user', 'host', 22)
>>> parse_host_string('@host')
(None, 'host', 22)
With a port
>>> parse_host_string('host:1234')
(None, 'host', 1234)
With an invalid port
>>> parse_host_string('host:')
(None, 'host:', 22)
>>> parse_host_string('host:abcd')
(None, 'host:abcd', 22)
With both user and port
>>> parse_host_string('user@host:1234')
('user', 'host', 1234)
"""
from ssh_forward_proxy import parse_host_string
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0
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fcd0fc12dbd19d3f44fdd900412a13e4b62cb2f1
| 13
|
py
|
Python
|
revision/ch-02.py
|
victortheleon/python_classes
|
3435948a4c989b7de4e4d7f05eb8aed8a5420592
|
[
"MIT"
] | null | null | null |
revision/ch-02.py
|
victortheleon/python_classes
|
3435948a4c989b7de4e4d7f05eb8aed8a5420592
|
[
"MIT"
] | null | null | null |
revision/ch-02.py
|
victortheleon/python_classes
|
3435948a4c989b7de4e4d7f05eb8aed8a5420592
|
[
"MIT"
] | null | null | null |
5
x = 5
x + 1
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| 5
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0
| 8
|
fcef80e423a1daf9d69b63e71ec7937da68229ac
| 193
|
py
|
Python
|
illud/exceptions/unexpected_input_exception.py
|
AustinScola/illud
|
a6aca1de38bbe9d5a795aaa084bcbd6731767d18
|
[
"MIT"
] | 1
|
2020-12-05T00:59:15.000Z
|
2020-12-05T00:59:15.000Z
|
illud/exceptions/unexpected_input_exception.py
|
AustinScola/illud
|
a6aca1de38bbe9d5a795aaa084bcbd6731767d18
|
[
"MIT"
] | 112
|
2021-01-15T21:42:27.000Z
|
2021-04-17T19:11:21.000Z
|
illud/exceptions/unexpected_input_exception.py
|
AustinScola/illud
|
a6aca1de38bbe9d5a795aaa084bcbd6731767d18
|
[
"MIT"
] | null | null | null |
"""Raised when there is an unexpected input."""
from illud.exception import IlludException
class UnexpectedInputException(IlludException):
"""Raised when there is an unexpected input."""
| 27.571429
| 51
| 0.772021
| 22
| 193
| 6.772727
| 0.636364
| 0.134228
| 0.201342
| 0.228188
| 0.456376
| 0.456376
| 0.456376
| 0
| 0
| 0
| 0
| 0
| 0.134715
| 193
| 6
| 52
| 32.166667
| 0.892216
| 0.430052
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
1e8f913fe5f0ceb6d5c01c391921ecf9e08fc36c
| 153
|
py
|
Python
|
ApisCore/views.py
|
HaruHuey/HaruHueyApis
|
3223ded277bb7012d4d81624feaa6a72c2c36245
|
[
"Apache-2.0"
] | 1
|
2019-04-22T09:29:14.000Z
|
2019-04-22T09:29:14.000Z
|
ApisCore/views.py
|
HaruHuey/HaruHueyApis
|
3223ded277bb7012d4d81624feaa6a72c2c36245
|
[
"Apache-2.0"
] | 7
|
2019-10-21T19:39:22.000Z
|
2021-06-10T21:22:35.000Z
|
ApisCore/views.py
|
HaruHuey/HaruHueyApis
|
3223ded277bb7012d4d81624feaa6a72c2c36245
|
[
"Apache-2.0"
] | null | null | null |
from django.http import HttpResponse
from django.shortcuts import render
# Create your views here.
# 변경
def test():
return HttpResponse("ApisCore")
| 19.125
| 36
| 0.764706
| 20
| 153
| 5.85
| 0.8
| 0.17094
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.156863
| 153
| 8
| 37
| 19.125
| 0.906977
| 0.169935
| 0
| 0
| 0
| 0
| 0.064
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.5
| 0.25
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
94ac64bb0e1bbf2af73eb8ba772af55398cc8297
| 3,249
|
py
|
Python
|
tests/test_logical_names.py
|
rackerlabs/tuvok
|
8c5d2171f156f35fd6570b2a199ab75ff9dc286f
|
[
"Apache-2.0"
] | 19
|
2018-10-26T18:42:30.000Z
|
2020-05-24T10:44:55.000Z
|
tests/test_logical_names.py
|
rackerlabs/tuvok
|
8c5d2171f156f35fd6570b2a199ab75ff9dc286f
|
[
"Apache-2.0"
] | 11
|
2018-10-26T17:06:54.000Z
|
2020-01-04T02:46:06.000Z
|
tests/test_logical_names.py
|
rackerlabs/tuvok
|
8c5d2171f156f35fd6570b2a199ab75ff9dc286f
|
[
"Apache-2.0"
] | 3
|
2018-12-29T06:58:06.000Z
|
2020-04-24T19:48:19.000Z
|
from tuvok import cli
from helpers.wrap import Wrap
class TestOutput(object):
def setup(self):
self.main = cli.main
def teardown(self):
self.main = None
def test_fails_if_variable_not_using_snake_case(self, caplog):
file = 'tests/test_logical_names/bad/variables.tf'
with Wrap(self, [file], expect_exit=False):
assert ('[FAIL] name_snake_case:Logical names should use snake_case:variable:FooBar:{}'.format(file)) in caplog.text
assert ('[FAIL] name_snake_case:Logical names should use snake_case:variable:foo-bar:{}'.format(file)) in caplog.text
def test_passes_if_variable_using_snake_case(self, caplog):
file = 'tests/test_logical_names/good/variables.tf'
with Wrap(self, [file], expect_exit=False):
assert ('[PASS] name_snake_case:Logical names should use snake_case:{}'.format(file)) in caplog.text
def test_fails_if_output_not_using_snake_case(self, caplog):
file = 'tests/test_logical_names/bad/outputs.tf'
with Wrap(self, [file], expect_exit=False):
assert ('[FAIL] name_snake_case:Logical names should use snake_case:output:FooBar:{}'.format(file)) in caplog.text
assert ('[FAIL] name_snake_case:Logical names should use snake_case:output:foo-bar:{}'.format(file)) in caplog.text
def test_passes_if_output_uses_snake_case(self, caplog, capsys):
file = 'tests/test_logical_names/good/outputs.tf'
with Wrap(self, [file], expect_exit=False):
assert ('[PASS] name_snake_case:Logical names should use snake_case:{}'.format(file)) in caplog.text
err = capsys.readouterr().err
assert err == ''
def test_fails_if_resource_not_using_snake_case(self, caplog):
file = 'tests/test_logical_names/bad/main.tf'
with Wrap(self, [file], expect_exit=False):
assert ('[FAIL] resource_name_snake_case:Logical names should use snake_case:resource:aws_iam_role:FooBar:{}'.format(file)) in caplog.text
assert ('[FAIL] resource_name_snake_case:Logical names should use snake_case:resource:aws_iam_role:foo-bar:{}'.format(file)) in caplog.text
assert ('[FAIL] resource_name_snake_case:Logical names should use snake_case:resource:aws_iam_policy:FooBaz:{}'.format(file)) in caplog.text
assert ('[FAIL] resource_name_snake_case:Logical names should use snake_case:resource:aws_iam_role_policy_attachment:foo-baz:{}'.format(file)) in caplog.text
assert ('[FAIL] name_snake_case:Logical names should use snake_case:module:FooBin:{}'.format(file)) in caplog.text
assert ('[FAIL] name_snake_case:Logical names should use snake_case:module:foo-bin:{}'.format(file)) in caplog.text
def test_passes_if_resource_using_snake_case(self, caplog, capsys):
file = 'tests/test_logical_names/good/main.tf'
with Wrap(self, [file], expect_exit=False):
assert ('[PASS] name_snake_case:Logical names should use snake_case:{}'.format(file)) in caplog.text
assert ('[PASS] resource_name_snake_case:Logical names should use snake_case:{}'.format(file)) in caplog.text
err = capsys.readouterr().err
assert err == ''
| 55.067797
| 169
| 0.701139
| 460
| 3,249
| 4.704348
| 0.143478
| 0.141405
| 0.084104
| 0.12939
| 0.884011
| 0.881701
| 0.877079
| 0.871072
| 0.868299
| 0.851201
| 0
| 0
| 0.183749
| 3,249
| 58
| 170
| 56.017241
| 0.815988
| 0
| 0
| 0.302326
| 0
| 0
| 0.419514
| 0.295783
| 0
| 0
| 0
| 0
| 0.372093
| 1
| 0.186047
| false
| 0.162791
| 0.046512
| 0
| 0.255814
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
94d91139178389299aa6efe9ac6ab267e5a7043f
| 1,582
|
py
|
Python
|
python/linked-list-insertions/tests/test_linked-list-insertions.py
|
mohmmadnoorjebreen/data-structures-and-algorithms
|
ab69cd9dc48e8508947a6f3f316cb44a96c99c42
|
[
"MIT"
] | null | null | null |
python/linked-list-insertions/tests/test_linked-list-insertions.py
|
mohmmadnoorjebreen/data-structures-and-algorithms
|
ab69cd9dc48e8508947a6f3f316cb44a96c99c42
|
[
"MIT"
] | 18
|
2021-07-29T19:52:28.000Z
|
2021-09-11T11:22:43.000Z
|
python/linked-list-insertions/tests/test_linked-list-insertions.py
|
mohmmadnoorjebreen/data-structures-and-algorithms
|
ab69cd9dc48e8508947a6f3f316cb44a96c99c42
|
[
"MIT"
] | null | null | null |
from linked_list_insertions.linked_list_insertions import Node , LinkedList
def test_add_node_to_end():
node = LinkedList()
node.insert(2)
node.insert(5)
node.append(10)
excepted = '5->2->10->NULL'
actual = str(node)
assert actual == excepted
def test_add_multiple_node_to_end():
node = LinkedList()
node.insert(2)
node.insert(5)
node.append(10)
node.append(11)
excepted = '5->2->10->11->NULL'
actual = str(node)
assert actual == excepted
def test_insert_node_before_the_middle_node():
node = LinkedList()
node.insert(2)
node.insert(5)
node.append(10)
node.append(11)
node.insert_before(10,8)
excepted = '5->2->8->10->11->NULL'
actual = str(node)
assert actual == excepted
def test_insert_node_before_the_first_node():
node = LinkedList()
node.insert(2)
node.insert(5)
node.append(10)
node.append(11)
node.insert_before(5,3)
excepted = '3->5->2->10->11->NULL'
actual = str(node)
assert actual == excepted
def test_insert_node_after_the_middle_node():
node = LinkedList()
node.insert(2)
node.insert(5)
node.append(10)
node.append(11)
node.insert_after(2,4)
excepted = '5->2->4->10->11->NULL'
actual = str(node)
assert actual == excepted
def test_insert_node_after_the_last_node():
node = LinkedList()
node.insert(2)
node.insert(5)
node.append(10)
node.append(11)
node.insert_after(11,20)
excepted = '5->2->10->11->20->NULL'
actual = str(node)
assert actual == excepted
| 23.264706
| 76
| 0.644753
| 231
| 1,582
| 4.238095
| 0.147186
| 0.163432
| 0.110317
| 0.147089
| 0.846782
| 0.824311
| 0.824311
| 0.786517
| 0.786517
| 0.741573
| 0
| 0.06747
| 0.213021
| 1,582
| 67
| 77
| 23.61194
| 0.718876
| 0
| 0
| 0.706897
| 0
| 0
| 0.074004
| 0.053763
| 0
| 0
| 0
| 0
| 0.103448
| 1
| 0.103448
| false
| 0
| 0.017241
| 0
| 0.12069
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
94f54409394dfc8e2839e6f3610c68f9d2099c3a
| 6,786
|
py
|
Python
|
tests/scripts/orders_codegen_test.py
|
zhangted/tda-api
|
1169c87129b80c120217d420e4996a439c5903dc
|
[
"MIT"
] | 986
|
2020-04-14T21:50:03.000Z
|
2022-03-29T19:09:31.000Z
|
tests/scripts/orders_codegen_test.py
|
zhangted/tda-api
|
1169c87129b80c120217d420e4996a439c5903dc
|
[
"MIT"
] | 243
|
2020-04-26T14:05:34.000Z
|
2022-03-12T13:02:51.000Z
|
tests/scripts/orders_codegen_test.py
|
zhangted/tda-api
|
1169c87129b80c120217d420e4996a439c5903dc
|
[
"MIT"
] | 286
|
2020-04-14T22:17:04.000Z
|
2022-03-27T07:30:15.000Z
|
import unittest
from unittest.mock import call, MagicMock, patch
from ..utils import AnyStringWith, no_duplicates
from tda.scripts.orders_codegen import latest_order_main
class LatestOrderTest(unittest.TestCase):
def setUp(self):
self.args = []
def add_arg(self, arg):
self.args.append(arg)
def main(self):
return latest_order_main(self.args)
@no_duplicates
@patch('builtins.print')
@patch('tda.scripts.orders_codegen.client_from_token_file')
@patch('tda.scripts.orders_codegen.construct_repeat_order')
@patch('tda.scripts.orders_codegen.code_for_builder')
def test_success_no_account_id(
self,
mock_code_for_builder,
mock_construct_repeat_order,
mock_client_from_token_file,
mock_print):
self.add_arg('--token_file')
self.add_arg('filename.json')
self.add_arg('--api_key')
self.add_arg('api-key')
orders = [
{'orderId': 201},
{'orderId': 101},
{'orderId': 301},
{'orderId': 401},
]
mock_client = MagicMock()
mock_client_from_token_file.return_value = mock_client
mock_client.get_orders_by_query.return_value.json.return_value = orders
self.assertEqual(self.main(), 0)
mock_construct_repeat_order.assert_called_once_with(orders[3])
mock_print.assert_has_calls([
call('# Order ID', 401),
call(mock_code_for_builder.return_value)])
@no_duplicates
@patch('builtins.print')
@patch('tda.scripts.orders_codegen.client_from_token_file')
@patch('tda.scripts.orders_codegen.construct_repeat_order')
@patch('tda.scripts.orders_codegen.code_for_builder')
def test_no_account_id_no_such_order(
self,
mock_code_for_builder,
mock_construct_repeat_order,
mock_client_from_token_file,
mock_print):
self.add_arg('--token_file')
self.add_arg('filename.json')
self.add_arg('--api_key')
self.add_arg('api-key')
orders = []
mock_client = MagicMock()
mock_client_from_token_file.return_value = mock_client
mock_client.get_orders_by_query.return_value.json.return_value = orders
self.assertEqual(self.main(), 0)
mock_construct_repeat_order.assert_not_called()
mock_print.assert_called_once_with('No recent orders found')
@no_duplicates
@patch('builtins.print')
@patch('tda.scripts.orders_codegen.client_from_token_file')
@patch('tda.scripts.orders_codegen.construct_repeat_order')
@patch('tda.scripts.orders_codegen.code_for_builder')
def test_no_account_error(
self,
mock_code_for_builder,
mock_construct_repeat_order,
mock_client_from_token_file,
mock_print):
self.add_arg('--token_file')
self.add_arg('filename.json')
self.add_arg('--api_key')
self.add_arg('api-key')
orders = {'error': 'invalid'}
mock_client = MagicMock()
mock_client_from_token_file.return_value = mock_client
mock_client.get_orders_by_query.return_value.json.return_value = orders
self.assertEqual(self.main(), -1)
mock_construct_repeat_order.assert_not_called()
mock_print.assert_called_once_with(
AnyStringWith('TDA returned error: "invalid"'))
@no_duplicates
@patch('builtins.print')
@patch('tda.scripts.orders_codegen.client_from_token_file')
@patch('tda.scripts.orders_codegen.construct_repeat_order')
@patch('tda.scripts.orders_codegen.code_for_builder')
def test_success_account_id(
self,
mock_code_for_builder,
mock_construct_repeat_order,
mock_client_from_token_file,
mock_print):
self.add_arg('--token_file')
self.add_arg('filename.json')
self.add_arg('--api_key')
self.add_arg('api-key')
self.add_arg('--account_id')
self.add_arg('123456')
orders = [
{'orderId': 201},
{'orderId': 101},
{'orderId': 301},
{'orderId': 401},
]
mock_client = MagicMock()
mock_client_from_token_file.return_value = mock_client
mock_client.get_orders_by_path.return_value.json.return_value = orders
self.assertEqual(self.main(), 0)
mock_construct_repeat_order.assert_called_once_with(orders[3])
mock_print.assert_has_calls([
call('# Order ID', 401),
call(mock_code_for_builder.return_value)])
@no_duplicates
@patch('builtins.print')
@patch('tda.scripts.orders_codegen.client_from_token_file')
@patch('tda.scripts.orders_codegen.construct_repeat_order')
@patch('tda.scripts.orders_codegen.code_for_builder')
def test_account_id_no_orders(
self,
mock_code_for_builder,
mock_construct_repeat_order,
mock_client_from_token_file,
mock_print):
self.add_arg('--token_file')
self.add_arg('filename.json')
self.add_arg('--api_key')
self.add_arg('api-key')
self.add_arg('--account_id')
self.add_arg('123456')
orders = []
mock_client = MagicMock()
mock_client_from_token_file.return_value = mock_client
mock_client.get_orders_by_path.return_value.json.return_value = orders
self.assertEqual(self.main(), 0)
mock_construct_repeat_order.assert_not_called
mock_print.assert_called_once_with('No recent orders found')
@no_duplicates
@patch('builtins.print')
@patch('tda.scripts.orders_codegen.client_from_token_file')
@patch('tda.scripts.orders_codegen.construct_repeat_order')
@patch('tda.scripts.orders_codegen.code_for_builder')
def test_account_id_error(
self,
mock_code_for_builder,
mock_construct_repeat_order,
mock_client_from_token_file,
mock_print):
self.add_arg('--token_file')
self.add_arg('filename.json')
self.add_arg('--api_key')
self.add_arg('api-key')
self.add_arg('--account_id')
self.add_arg('123456')
orders = {'error': 'invalid'}
mock_client = MagicMock()
mock_client_from_token_file.return_value = mock_client
mock_client.get_orders_by_path.return_value.json.return_value = orders
self.assertEqual(self.main(), -1)
mock_construct_repeat_order.assert_not_called
mock_print.assert_called_once_with(
AnyStringWith('TDA returned error: "invalid"'))
| 32.941748
| 79
| 0.649131
| 831
| 6,786
| 4.883273
| 0.090253
| 0.045835
| 0.073928
| 0.107689
| 0.927551
| 0.927551
| 0.927551
| 0.927551
| 0.927551
| 0.927551
| 0
| 0.010946
| 0.246095
| 6,786
| 205
| 80
| 33.102439
| 0.782252
| 0
| 0
| 0.884848
| 0
| 0
| 0.211023
| 0.124668
| 0
| 0
| 0
| 0
| 0.109091
| 1
| 0.054545
| false
| 0
| 0.024242
| 0.006061
| 0.090909
| 0.109091
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
94fdab2444952ea4ac9a0fd39c516376339d7676
| 203
|
py
|
Python
|
torchero/models/text/nn/__init__.py
|
juancruzsosa/torchero
|
d1440b7a9c3ab2c1d3abbb282abb9ee1ea240797
|
[
"MIT"
] | 10
|
2020-07-06T13:35:26.000Z
|
2021-08-10T09:46:53.000Z
|
torchero/models/text/nn/__init__.py
|
juancruzsosa/torchero
|
d1440b7a9c3ab2c1d3abbb282abb9ee1ea240797
|
[
"MIT"
] | 6
|
2020-07-07T20:52:16.000Z
|
2020-07-14T04:05:02.000Z
|
torchero/models/text/nn/__init__.py
|
juancruzsosa/torchero
|
d1440b7a9c3ab2c1d3abbb282abb9ee1ea240797
|
[
"MIT"
] | 1
|
2021-06-28T17:56:11.000Z
|
2021-06-28T17:56:11.000Z
|
from torchero.models.text.nn.linear import LinearModel
from torchero.models.text.nn.lstm import LSTMForTextClassification
from torchero.models.text.nn.transformer import TransformerForTextClassification
| 50.75
| 80
| 0.881773
| 24
| 203
| 7.458333
| 0.5
| 0.201117
| 0.301676
| 0.368715
| 0.402235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.059113
| 203
| 3
| 81
| 67.666667
| 0.937173
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 8
|
bf7466a770cf59fc0d947412af0514de406b6a29
| 33,052
|
py
|
Python
|
applications/MultiScaleApplication/python_scripts/TK_Rve_V2_MT.py
|
AndreaVoltan/MyKratos7.0
|
e977752722e8ef1b606f25618c4bf8fd04c434cc
|
[
"BSD-4-Clause"
] | 2
|
2020-04-30T19:13:08.000Z
|
2021-04-14T19:40:47.000Z
|
applications/MultiScaleApplication/python_scripts/TK_Rve_V2_MT.py
|
Jacklwln/Kratos
|
12ffe332622d7e8ea3e4a10bc061beb9d8e6e8de
|
[
"BSD-4-Clause"
] | 1
|
2020-04-30T19:19:09.000Z
|
2020-05-02T14:22:36.000Z
|
applications/MultiScaleApplication/python_scripts/TK_Rve_V2_MT.py
|
Jacklwln/Kratos
|
12ffe332622d7e8ea3e4a10bc061beb9d8e6e8de
|
[
"BSD-4-Clause"
] | 1
|
2020-06-12T08:51:24.000Z
|
2020-06-12T08:51:24.000Z
|
## @package Rve
# This module contains classes (Rve Modelers) that are used
# to handle the generation, assignment and tracking of
# RveConstitutiveLaws.
#
# More details
from __future__ import print_function, absolute_import, division #makes KratosMultiphysics backward compatible with python 2.6 and 2.7
import datetime
import time
from KratosMultiphysics import *
from KratosMultiphysics.ExternalSolversApplication import *
from KratosMultiphysics.MultiScaleApplication import *
from KratosMultiphysics.SolidMechanicsApplication import *
from KratosMultiphysics.StructuralMechanicsApplication import *
import TK_Props
CheckForPreviousImport()
## RVEPropertyMap
#
# Detailed description...
class RVEPropertyMap:
## Constructor
def __init__(self,
PropertyID,
Values):
self.PropertyID = PropertyID
self.Values = Values
## RVEModelPartPrototype
#
# Detailed description...
class RVEModelPartPrototype:
## Constructor
def __init__(self,
ModelName,
NodalVariables = ([
DISPLACEMENT,
REACTION,
]),
DOFs = ([
(DISPLACEMENT_X, REACTION_X),
(DISPLACEMENT_Y, REACTION_Y),
(DISPLACEMENT_Z, REACTION_Z),
]),
BufferSize = 2,
RVEPropertyMapList = None):
# create the model part
self.Model = ModelPart(ModelName)
# add nodal variables
for ivar in NodalVariables:
self.Model.AddNodalSolutionStepVariable(ivar)
# read the model part
model_part_io = ModelPartIO(ModelName)
model_part_io.ReadModelPart(self.Model)
# add all degrees of freedom
for inode in self.Model.Nodes:
for idof in DOFs:
inode.AddDof(idof[0], idof[1])
# set buffer size
self.Model.SetBufferSize(BufferSize)
# set up all the properties
for ipmap in RVEPropertyMapList:
TK_Props.Property(
Pro = self.Model.Properties[ipmap.PropertyID],
Values = ipmap.Values
)
class RVEModelPartPrototype_2Phisics:
## Constructor
def __init__(self,
ModelNameA,
ModelNameB,
NodalVariables = ([
DISPLACEMENT,
REACTION,
]),
DOFs = ([
(DISPLACEMENT_X, REACTION_X),
(DISPLACEMENT_Y, REACTION_Y),
(DISPLACEMENT_Z, REACTION_Z),
]),
BufferSize = 2,
RVEPropertyMapListA = None,
RVEPropertyMapListB = None):
# create the model part
self.ModelA = ModelPart(ModelNameA)
self.ModelB = ModelPart(ModelNameB)
# add nodal variables
for ivar in NodalVariables:
self.ModelA.AddNodalSolutionStepVariable(ivar)
# read the model part
model_part_io = ModelPartIO(ModelNameA)
model_part_io.ReadModelPart(self.ModelA)
# add all degrees of freedom
for inode in self.ModelA.Nodes:
for idof in DOFs:
inode.AddDof(idof[0], idof[1])
# set buffer size
self.ModelA.SetBufferSize(BufferSize)
self.ModelB.SetBufferSize(BufferSize)
# preserve connectivities
model_part_io_conn_preserv = ModelPartIOConnPreserver(ModelNameB, self.ModelA)
model_part_io_conn_preserv.ReadModelPart(self.ModelB)
# set up all the properties
for ipmap in RVEPropertyMapListA:
TK_Props.Property(
Pro = self.ModelA.Properties[ipmap.PropertyID],
Values = ipmap.Values
)
# set up all the properties
for ipmap in RVEPropertyMapListB:
TK_Props.Property(
Pro = self.ModelB.Properties[ipmap.PropertyID],
Values = ipmap.Values
)
## RVEStrainSize
#
# Detailed description...
class RVEStrainSize:
RVE_PLANE_STRESS = 0
RVE_PLANE_STRAIN = 1
RVE_3D = 2
RVE_THERMAL_PLANE_STRESS = 3
RVE_THERMAL_3D = 4
## The RveModeler for continuum elements.
#
# This class is a specialized RveModeler for continuum elements in 1 Physics.
class RVEModelerSolid:
## Constructor.
def __init__(self,
MicroModelPart, # Actual Physics
MicroModelPartPrototype, # Database Of 2 Physics (In 1 Physics MicroModelPartPrototype.Model = MicroModelPartPrototype)
StrainSize,
ResultsIOClass,
ResultsOnNodes = [],
ResultsOnGaussPoints = [],
RveConstraintHandlerClass = RveConstraintHandler_ZBF_SD,
RveHomogenizerClass = RveHomogenizer,
SchemeClass = RveStaticScheme,
LinearSolverClass = SuperLUSolver,
MaxIterations = 10,
CalculateReactions = False,
ReformDofSetAtEachIteration = False,
MoveMesh = False,
ConvergenceCriteriaClass = ResidualNormCriteria,
ConvergenceRelativeTolerance = 1.0E-6,
ConvergenceAbsoluteTolerance = 1.0E-9,
ConvergenceIsVerbose = False,
TargetElementList = [],
OutputElementList = [],
BoundingPolygonNodesID = None,
# NEW
SecondaryRveModeler = None,
IsSecondary = False):
self.MicroModelPart = MicroModelPart
self.MicroModelPartPrototype = MicroModelPartPrototype
self.StrainSize = StrainSize
self.BoundingPolygonNodesID = BoundingPolygonNodesID
self.IsSecondary = IsSecondary
if(self.StrainSize == RVEStrainSize.RVE_THERMAL_PLANE_STRESS):
self.RveAdapterClass = RveThermal2DAdapterV2
self.RveMaterialClass = RveConstitutiveLawV2Thermal2D
elif(self.StrainSize == RVEStrainSize.RVE_THERMAL_3D):
self.RveAdapterClass = RveThermal3DAdapterV2
self.RveMaterialClass = RveConstitutiveLawV2Thermal3D
elif(self.StrainSize == RVEStrainSize.RVE_PLANE_STRESS):
self.RveAdapterClass = RvePlaneStressAdapterV2
self.RveMaterialClass = RveConstitutiveLawV2PlaneStress
elif(self.StrainSize == RVEStrainSize.RVE_PLANE_STRAIN):
raise Exception("Rve Plane Strain Not Yet Implemented")
else: # RVEStrainSize.RVE_3D):
self.RveAdapterClass = Rve3DAdapterV2
self.RveMaterialClass = RveConstitutiveLawV23D
self.RveGeometryDescr = None
self.ResultsIOClass = ResultsIOClass
self.ResultsOnNodes = ResultsOnNodes
self.ResultsOnGaussPoints = ResultsOnGaussPoints
self.RveConstraintHandlerClass = RveConstraintHandlerClass
self.RveHomogenizerClass = RveHomogenizerClass
self.SchemeClass = SchemeClass
self.LinearSolverClass = LinearSolverClass
self.MaxIterations = MaxIterations
self.CalculateReactions = CalculateReactions
self.ReformDofSetAtEachIteration = ReformDofSetAtEachIteration
self.MoveMesh = MoveMesh
self.ConvergenceCriteriaClass = ConvergenceCriteriaClass
self.ConvergenceRelativeTolerance = ConvergenceRelativeTolerance
self.ConvergenceAbsoluteTolerance = ConvergenceAbsoluteTolerance
self.ConvergenceIsVerbose = ConvergenceIsVerbose
self.TargetElementList = TargetElementList
self.OutputElementList = OutputElementList
self.TrackList = {}
self.Initialized = False
# NEW <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
self.SecondaryRveModeler = SecondaryRveModeler
self.IsSecondary = IsSecondary
# if(self.IsSecondary == False):
# if(self.SecondaryRveModeler is None):
# raise exeption(" -- RVEModelerSolid is the first physic and need SecondaryRveModeler for add RVE_Clone_ModelPart_List -- ")
# NEW <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
## Initialize
#
# called at the very beginning of the analysis history to
# perform all initializations. This method should be called
# only once.
def Initialize(self, Model):
if(self.Initialized == False):
# initialize the geometry descriptor
self.RveGeometryDescr = RveGeometryDescriptor()
if(self.BoundingPolygonNodesID is not None):
self.RveGeometryDescr.SetUserCornerNodes(self.BoundingPolygonNodesID)
self.RveGeometryDescr.Build(self.MicroModelPart.Model)
#print(self.RveGeometryDescr)
# generate,assign and track all required rve's
if(self.IsSecondary == True):
# il primario ha generato la lista di cloni
# ho bisogno di sapere in quale id mi trovo della lista
self.clone_list_counter = 0
for elem_id in self.TargetElementList:
elem = Model.Elements[elem_id]
dummy = self.__assign_rve_constitutive_law(elem)
self.clone_list_counter = 0 # non necessario ma per sicurazzo lo riazzeriamo
else:
# se sono il primario genero una lista di [nelem*ngauss] di rve clones...
self.stored_rvemdpa_clones=[]
for elem_id in self.TargetElementList:
elem = Model.Elements[elem_id]
elem_rvemdpa_clone_list = self.__assign_rve_constitutive_law(elem)
for iclone in elem_rvemdpa_clone_list:
self.stored_rvemdpa_clones.append(iclone)
# ... e la copio nel modeler secondario (che non dovra generarla!!!!!)
if(self.SecondaryRveModeler is not None):
self.SecondaryRveModeler.stored_rvemdpa_clones = self.stored_rvemdpa_clones
# initialize the output
self.__initialize_output()
# set initialization flag
self.Initialized = True
## OnBeforeSolutionStage
#
# called before each solutions stage
def OnBeforeSolutionStage(self, Model):
pass
## OnSolutionStageCompleted
#
# called after each solutions stage
def OnSolutionStageCompleted(self, Model):
pass
## OnBeforeSolutionStep
#
# called before each solutions steps is solved
def OnBeforeSolutionStep(self, Model):
pass
## OnSolutionStepCompleted
#
# called after each solutions steps is solved
def OnSolutionStepCompleted(self, Model):
# write the output for this time step
self.__write_output(Model.ProcessInfo[TIME])
## Finalize
#
# called at the end of the analysis history to
# perform all finalizations. This method should be called
# only once.
def Finalize(self, Model):
if(self.Initialized == True):
# finalize the output
self.__finalize_output()
# private methods *******************************************************************************************
## __generate_rve_constitutive_law
#
# This method generates a new rve constitutive law
# by cloning the rve model part prototype and creating
# a new rve constitutive law out of it.
# This method is meant to be private, do NOT call it explicitly
def __generate_rve_constitutive_law(self):
if(self.IsSecondary == True):
current_rve_primary_clone = self.stored_rvemdpa_clones[ self.clone_list_counter ]
modelPartClone = ModelPart(self.MicroModelPart.Model.Name + "_RVE")
RveCloneModelPart2Physics(self.MicroModelPart.Model, current_rve_primary_clone, modelPartClone) # clone the model part prototype
self.clone_list_counter = self.clone_list_counter + 1
else:
modelPartClone = ModelPart(self.MicroModelPart.Model.Name + "_RVE")
RveCloneModelPart(self.MicroModelPart.Model, modelPartClone) # clone the model part prototype
msData = RveMacroscaleData()
linSolver = self.LinearSolverClass()
timeScheme = self.SchemeClass()
timeScheme.Check(modelPartClone)
convCriteria = self.ConvergenceCriteriaClass(
self.ConvergenceRelativeTolerance,
self.ConvergenceAbsoluteTolerance,
self.ConvergenceIsVerbose,
)
constraint_handler = self.RveConstraintHandlerClass()
homogenizer = self.RveHomogenizerClass()
adapter = self.RveAdapterClass() # generate the rve adapter
adapter.SetRveData(
modelPartClone,
msData,
self.RveGeometryDescr,
constraint_handler,
RveLinearSystemOfEquations(linSolver),
homogenizer,
timeScheme,
convCriteria
) # set all data (just for testing...)
rveLaw = self.RveMaterialClass(adapter) # finally generate the constitutive law adapter
if (self.IsSecondary == False):
for i in range(modelPartClone.GetBufferSize()):
modelPartClone.CloneTimeStep(0.0)
return (rveLaw,modelPartClone) # return a tuple
## __track_rve_constitutive_law
#
# This method tracks a rve constitutive law
# at a given element in a given gauss point.
# This method is meant to be private, do NOT call it explicitly
def __track_rve_constitutive_law(self, rveLaw, elemID, gpID):
elInfo = SolidElementInfo(elemID, gpID)
if( next((x for x in self.OutputElementList if x == elemID), None) is not None ):
outputFileName = self.MicroModelPart.Model.Name + "__" + elInfo.GetStringExtension()
rveLawIO = self.ResultsIOClass(rveLaw.GetModelPart(), outputFileName, self.ResultsOnNodes, self.ResultsOnGaussPoints)
# if (self.IsSecondary == False):
# print ("ResultsIOClass Mechanical Mdpa")
# rveLawIO = self.ResultsIOClass(rveLaw.GetModelPart(), self.MicroModelPartB.Model, outputFileName, self.ResultsOnNodes, self.ResultsOnGaussPoints_ModA, self.ResultsOnGaussPoints_ModB)
self.TrackList[elInfo] = (rveLaw, rveLawIO)
else:
self.TrackList[elInfo] = (rveLaw, None)
## __assign_rve_constitutive_law
#
# This method assignes a rve constitutive law
# at a given element.
# This method is meant to be private, do NOT call it explicitly
def __assign_rve_constitutive_law(self, Element):
# list of generated rve mdpa clones
rve_mdpa_clones = []
# get the number of integration points
elemIntPoints = Element.GetIntegrationPoints()
num_gp = len(elemIntPoints)
elem_id = Element.Id
# get a reference to the process into
pinfo = self.MicroModelPart.Model.ProcessInfo
# prepare the list of constitutive laws for the element
constitutiveLaws = []
# for each element integration point ...
for gp_id in range(num_gp):
# generate a new rve constitutive law
rve_law__rve_mdpa__tuple = self.__generate_rve_constitutive_law()
aRveLaw = rve_law__rve_mdpa__tuple[0]
constitutiveLaws.append(aRveLaw)
# TODO: check what rve law to track...
# for the moment let's track them all
self.__track_rve_constitutive_law(aRveLaw, elem_id, gp_id)
# store the rve mdpa clone
rve_mdpa_clones.append(rve_law__rve_mdpa__tuple[1])
# assign the list of constitutive laws
Element.SetValuesOnIntegrationPoints(CONSTITUTIVE_LAW_POINTER, constitutiveLaws, pinfo)
return rve_mdpa_clones
## Initializes the output for the tracked rves (only if required)
def __initialize_output(self):
for key, value in self.TrackList.items():
rveIO = value[1]
if(rveIO is not None):
rveIO.Initialize()
## Writes the output for the tracked rves (only if required)
def __write_output(self, currentTime):
for key, value in self.TrackList.items():
rveIO = value[1]
if(rveIO is not None):
rveIO.Write(currentTime)
## Finalizes the output for the tracked rves (only if required)
def __finalize_output(self):
for key, value in self.TrackList.items():
rveIO = value[1]
if(rveIO is not None):
rveIO.Finalize()
def GENERATE_RVE_LAW(self):
return self.__generate_rve_constitutive_law()
def TRACK_RVE_LAW(self,rveLaw,elemID,gpID):
return self.__track_rve_constitutive_law(rveLaw,elemID,gpID)
def INIT_OUTPUT(self):
return self.__initialize_output()
def WRITE_OUTPUT(self,time):
return self.__write_output(time)
def FIN_OUTPUT(self):
return self.__finalize_output()
## Prints an extensive description of this object
def __print_info(self):
print ("")
print ("====================================================")
print ("RveModelerShell - Info:")
print ("====================================================")
print ("MODEL PART - PROTOTYPE:")
print (self.MicroModelPart.Model)
print ("====================================================")
print ("TRACK LIST:")
ii = 0
print ("+--------------------------------------------------------+")
for key, value in self.TrackList.items():
print ("AT[", ii, "]")
print ("Info:")
print (key)
print ("(RveMaterial, IO)")
print (value)
print ("Micro Model Clone:")
micro = value[0].GetModelPart()
print (hex(id(micro)))
print (micro)
print ("+--------------------------------------------------------+")
ii+=1
#
class RVEModelerSolidPredictor:
## Constructor.
def __init__(self,
MicroModelPart, # Actual Physics
MicroModelPartPrototype, # Database Of 2 Physics (In 1 Physics MicroModelPartPrototype.Model = MicroModelPartPrototype)
StrainSize,
ResultsIOClass,
ResultsOnNodes = [],
ResultsOnGaussPoints = [],
RveConstraintHandlerClass = RveConstraintHandler_ZBF_SD,
RveHomogenizerClass = RveHomogenizer,
SchemeClass = RveStaticScheme,
LinearSolverClass = SuperLUSolver,
MaxIterations = 10,
CalculateReactions = False,
ReformDofSetAtEachIteration = False,
MoveMesh = False,
ConvergenceCriteriaClass = ResidualNormCriteria,
ConvergenceRelativeTolerance = 1.0E-6,
ConvergenceAbsoluteTolerance = 1.0E-9,
ConvergenceIsVerbose = False,
TargetElementList = [],
OutputElementList = [],
BoundingPolygonNodesID = None,
# NEW
C0FileName = [],
HashTagFileName = [],
JsonFileName = [],
SecondaryRveModeler = None,
IsSecondary = False):
self.MicroModelPart = MicroModelPart
self.MicroModelPartPrototype = MicroModelPartPrototype
self.StrainSize = StrainSize
self.BoundingPolygonNodesID = BoundingPolygonNodesID
self.IsSecondary = IsSecondary
self.C0FileName = C0FileName
self.HashTagFileName = HashTagFileName
self.JsonFileName = JsonFileName
if(self.StrainSize == RVEStrainSize.RVE_THERMAL_PLANE_STRESS):
self.RveAdapterClass = RveThermal2DAdapterV2
self.RveMaterialClass = RveConstitutiveLawV2Thermal2D
elif(self.StrainSize == RVEStrainSize.RVE_THERMAL_3D):
self.RveAdapterClass = RveThermal3DAdapterV2
self.RveMaterialClass = RveConstitutiveLawV2Thermal3D
elif(self.StrainSize == RVEStrainSize.RVE_PLANE_STRESS):
self.RveAdapterClass = RvePlaneStressAdapterV2
self.RveMaterialClass = RveConstitutiveLawV2PlaneStress
elif(self.StrainSize == RVEStrainSize.RVE_PLANE_STRAIN):
raise Exception("Rve Plane Strain Not Yet Implemented")
else: # RVEStrainSize.RVE_3D):
self.RveAdapterClass = Rve3DAdapterV2
self.RveMaterialClass = RveConstitutiveLawV23D
self.RveGeometryDescr = None
self.RvePredictorCalc = None
self.ResultsIOClass = ResultsIOClass
self.ResultsOnNodes = ResultsOnNodes
self.ResultsOnGaussPoints = ResultsOnGaussPoints
self.RveConstraintHandlerClass = RveConstraintHandlerClass
self.RveHomogenizerClass = RveHomogenizerClass
self.SchemeClass = SchemeClass
self.LinearSolverClass = LinearSolverClass
self.MaxIterations = MaxIterations
self.CalculateReactions = CalculateReactions
self.ReformDofSetAtEachIteration = ReformDofSetAtEachIteration
self.MoveMesh = MoveMesh
self.ConvergenceCriteriaClass = ConvergenceCriteriaClass
self.ConvergenceRelativeTolerance = ConvergenceRelativeTolerance
self.ConvergenceAbsoluteTolerance = ConvergenceAbsoluteTolerance
self.ConvergenceIsVerbose = ConvergenceIsVerbose
self.TargetElementList = TargetElementList
self.OutputElementList = OutputElementList
self.ActiveElementList = []
self.AdapterDict = {}
self.RveGenReq = False
self.TrackList = {}
self.Initialized = False
# NEW <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
self.SecondaryRveModeler = SecondaryRveModeler
self.IsSecondary = IsSecondary
# if(self.IsSecondary == False):
# if(self.SecondaryRveModeler is None):
# raise exeption(" -- RVEModelerSolid is the first physic and need SecondaryRveModeler for add RVE_Clone_ModelPart_List -- ")
# NEW <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
## Initialize
#
# called at the very beginning of the analysis history to
# perform all initializations. This method should be called
# only once.
def Initialize(self, Model):
if(self.Initialized == False):
# initialize the geometry descriptor
self.RveGeometryDescr = RveGeometryDescriptor()
if(self.BoundingPolygonNodesID is not None):
self.RveGeometryDescr.SetUserCornerNodes(self.BoundingPolygonNodesID)
self.RveGeometryDescr.Build(self.MicroModelPart.Model)
#print(self.RveGeometryDescr)
# initialize the Predictor with HashTag in orter to create the maps
if(self.IsSecondary == False):
# print("self.HashTagFileName: ",self.HashTagFileName[0])
self.RvePredictorCalc = RvePredictorCalculator(self.C0FileName[0],self.HashTagFileName[0],self.JsonFileName[0])
else:
self.RvePredictorCalc = RvePredictorCalculator("ThermalAnalysis"," do not need"," Prediction")
#print(self.RvePredictorCalc)
# generate,assign and track all required rve's
if(self.IsSecondary == True):
# il primario ha generato la lista di cloni
# ho bisogno di sapere in quale id mi trovo della lista
self.clone_list_counter = 0
for elem_id in self.TargetElementList:
elem = Model.Elements[elem_id]
dummy = self.__assign_rve_constitutive_law(elem)
self.clone_list_counter = 0 # non necessario ma per sicurezza lo riazzeriamo
else:
# se sono il primario genero una lista di [nelem*ngauss] di rve clones...
self.stored_rvemdpa_clones=[]
for elem_id in self.TargetElementList:
elem = Model.Elements[elem_id]
elem_rvemdpa_clone_list = self.__assign_rve_constitutive_law(elem)
for iclone in elem_rvemdpa_clone_list:
self.stored_rvemdpa_clones.append(iclone)
# ... e la copio nel modeler secondario (che non dovra generarla!!!!!)
self.SecondaryRveModeler.stored_rvemdpa_clones = self.stored_rvemdpa_clones
# initialize the output
# self.__initialize_output()
# set initialization flag
self.Initialized = True
## OnBeforeSolutionStep
#
# called before each solutions steps is solved
def OnBeforeSolutionStep(self, Model):
print(' OnBeforeSolutionStep...')
# generate,assign and track all required rve's
if(self.IsSecondary == False):
# print('1. Mechanical')
# se sono il meccanico genero una lista di [nelem*ngauss] di rve clones...
for elem_id in self.TargetElementList:
#Take information of Elements that needs Rve
non_linear_flags = []
non_linear_flags = Model.Elements[elem_id].GetValuesOnIntegrationPoints(RVE_NON_LINEAR_FLAG,Model.ProcessInfo)
for i_non_linear_flag in non_linear_flags:
if Model.ProcessInfo[RVE_PREDICTION_FLAG] == -1:
i_non_linear_flag[0] = 1.0
# print('i_non_linear_flag: ',i_non_linear_flag)
if i_non_linear_flag[0] == 1.0:
# print(' RVE_NON_LINEAR_FLAG != 0.0 for the element ', elem_id)
if self.ActiveElementList.count(elem_id) == 0:
self.RveGenReq = True
# set initialization flag
self.ActiveElementList.append(elem_id)
elem = Model.Elements[elem_id]
self.__assign_rve_constitutive_law_from_adapter(elem)
if (len(self.OutputElementList) <= 0):
self.OutputElementList = self.ActiveElementList
# self.OutputElementList = self.ActiveElementList
# initialize the output
self.__initialize_output()
print(' Number of Active Elements: ',len(self.ActiveElementList), ' - Over ', len(self.TargetElementList), ' total elements')
# pass
## OnSolutionStepCompleted
#
# called after each solutions steps is solved
def OnSolutionStepCompleted(self, Model):
# write the output for this time step
self.__write_output(Model.ProcessInfo[TIME])
## Finalize
#
# called at the end of the analysis history to
# perform all finalizations. This method should be called
# only once.
def Finalize(self, Model):
if(self.Initialized == True):
# finalize the output
self.__finalize_output()
# private methods *******************************************************************************************
## __generate_rve_constitutive_law
#
# This method generates a new rve constitutive law
# by cloning the rve model part prototype and creating
# a new rve constitutive law out of it.
# This method is meant to be private, do NOT call it explicitly
def __generate_rve_constitutive_law(self):
if(self.IsSecondary == True):
current_rve_primary_clone = self.stored_rvemdpa_clones[ self.clone_list_counter ]
modelPartClone = ModelPart(self.MicroModelPart.Model.Name + "_RVE")
RveCloneModelPart2Physics(self.MicroModelPart.Model, current_rve_primary_clone, modelPartClone) # clone the model part prototype
self.clone_list_counter = self.clone_list_counter + 1
else:
modelPartClone = ModelPart(self.MicroModelPart.Model.Name + "_RVE")
RveCloneModelPart(self.MicroModelPart.Model, modelPartClone) # clone the model part prototype
msData = RveMacroscaleData()
linSolver = self.LinearSolverClass()
timeScheme = self.SchemeClass()
timeScheme.Check(modelPartClone)
convCriteria = self.ConvergenceCriteriaClass(
self.ConvergenceRelativeTolerance,
self.ConvergenceAbsoluteTolerance,
self.ConvergenceIsVerbose,
)
constraint_handler = self.RveConstraintHandlerClass()
homogenizer = self.RveHomogenizerClass()
adapter = self.RveAdapterClass() # generate the rve adapter
# RveGenReq = adapter.RveGenerationRequested()
# print('RveGenReq: ',self.RveGenReq)
if (self.IsSecondary == False):
if (self.RveGenReq == False):
# print('SetPredictorData...')
adapter.SetPredictorData(
modelPartClone,
self.RvePredictorCalc
)
else:
adapter.SetRveDataAfterPredictor(
# modelPartClone,
msData,
self.RveGeometryDescr,
constraint_handler,
RveLinearSystemOfEquations(linSolver),
homogenizer,
timeScheme,
convCriteria
) # set all data (just for testing...)
else:
adapter.SetPredictorData(
modelPartClone,
self.RvePredictorCalc
)
adapter.SetRveDataAfterPredictor(
# modelPartClone,
msData,
self.RveGeometryDescr,
constraint_handler,
RveLinearSystemOfEquations(linSolver),
homogenizer,
timeScheme,
convCriteria
) # set all data (just for testing...)
rveLaw = self.RveMaterialClass(adapter) # finally generate the constitutive law adapter
if (self.IsSecondary == False):
for i in range(modelPartClone.GetBufferSize()):
modelPartClone.CloneTimeStep(0.0)
return (rveLaw,modelPartClone,adapter) # occhio return a tuple <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
## __generate_rve_constitutive_law_from_adapter
#
# This method generates a new rve constitutive law
# by cloning the rve model part prototype and creating
# a new rve constitutive law out of it.
# This method is meant to be private, do NOT call it explicitly
# ONLY MECHANICAL MODELPART USE IT
def __generate_rve_constitutive_law_from_adapter(self, Adapter):
# [0] [1] [2]
# Adapter is a tuple: {aRveLaw,modelPartClone,adapter}
modelPartClone = Adapter[1]
msData = RveMacroscaleData()
linSolver = self.LinearSolverClass()
timeScheme = self.SchemeClass()
timeScheme.Check(modelPartClone)
convCriteria = self.ConvergenceCriteriaClass(
self.ConvergenceRelativeTolerance,
self.ConvergenceAbsoluteTolerance,
self.ConvergenceIsVerbose,
)
constraint_handler = self.RveConstraintHandlerClass()
homogenizer = self.RveHomogenizerClass()
adapt = Adapter[2]
adapt.SetRveDataAfterPredictor(
# modelPartClone,
msData,
self.RveGeometryDescr,
constraint_handler,
RveLinearSystemOfEquations(linSolver),
homogenizer,
timeScheme,
convCriteria
) # set all data (just for testing...)
return
## __track_rve_constitutive_law
#
# This method tracks a rve constitutive law
# at a given element in a given gauss point.
# This method is meant to be private, do NOT call it explicitly
def __track_rve_constitutive_law(self, rveLaw, elemID, gpID):
elInfo = SolidElementInfo(elemID, gpID)
if( next((x for x in self.OutputElementList if x == elemID), None) is not None ):
outputFileName = self.MicroModelPart.Model.Name + "__" + elInfo.GetStringExtension()
rveLawIO = self.ResultsIOClass(rveLaw.GetModelPart(), outputFileName, self.ResultsOnNodes, self.ResultsOnGaussPoints)
# if (self.IsSecondary == False):
# print ("ResultsIOClass Mechanical Mdpa")
# rveLawIO = self.ResultsIOClass(rveLaw.GetModelPart(), self.MicroModelPartB.Model, outputFileName, self.ResultsOnNodes, self.ResultsOnGaussPoints_ModA, self.ResultsOnGaussPoints_ModB)
self.TrackList[elInfo] = (rveLaw, rveLawIO)
else:
self.TrackList[elInfo] = (rveLaw, None)
## __assign_rve_constitutive_law
#
# This method assignes a rve constitutive law
# at a given element.
# This method is meant to be private, do NOT call it explicitly
def __assign_rve_constitutive_law(self, Element):
# list of generated rve mdpa clones
rve_mdpa_clones = []
# get the number of integration points
elemIntPoints = Element.GetIntegrationPoints()
num_gp = len(elemIntPoints)
elem_id = Element.Id
# get a reference to the process into
pinfo = self.MicroModelPart.Model.ProcessInfo
# prepare the list of constitutive laws for the element
constitutiveLaws = []
# for each element integration point ...
for gp_id in range(num_gp):
# generate a new rve constitutive law
rve_law__rve_mdpa__tuple = self.__generate_rve_constitutive_law()
aRveLaw = rve_law__rve_mdpa__tuple[0]
constitutiveLaws.append(aRveLaw)
# TODO: check what rve law to track...
# for the moment let's track them all
self.__track_rve_constitutive_law(aRveLaw, elem_id, gp_id)
# store the rve mdpa clone
rve_mdpa_clones.append(rve_law__rve_mdpa__tuple[1])
#Fill Adapter Dict
adapt = rve_law__rve_mdpa__tuple[2]
self.AdapterDict[elem_id,gp_id] = (rve_law__rve_mdpa__tuple)
# assign the list of constitutive laws
Element.SetValuesOnIntegrationPoints(CONSTITUTIVE_LAW_POINTER, constitutiveLaws, pinfo)
return rve_mdpa_clones
## __assign_rve_constitutive_law_from_adapter
#
# This method assignes a rve constitutive law
# at a given element.
# This method is meant to be private, do NOT call it explicitly
def __assign_rve_constitutive_law_from_adapter(self, Element):
# get the number of integration points
elemIntPoints = Element.GetIntegrationPoints()
num_gp = len(elemIntPoints)
elem_id = Element.Id
# for each element integration point ...
for gp_id in range(num_gp):
# generate a new rve constitutive law
self.__generate_rve_constitutive_law_from_adapter(self.AdapterDict[elem_id,gp_id])
aRveLaw = self.AdapterDict[elem_id,gp_id][0]
# TODO: check what rve law to track...
# for the moment let's track them all
self.__track_rve_constitutive_law(aRveLaw, elem_id, gp_id)
return
## Initializes the output for the tracked rves (only if required)
def __initialize_output(self):
for key, value in self.TrackList.items():
rveIO = value[1]
if(rveIO is not None):
rveIO.Initialize()
## Writes the output for the tracked rves (only if required)
def __write_output(self, currentTime):
for key, value in self.TrackList.items():
rveIO = value[1]
if(rveIO is not None):
rveIO.Write(currentTime)
## Finalizes the output for the tracked rves (only if required)
def __finalize_output(self):
for key, value in self.TrackList.items():
rveIO = value[1]
if(rveIO is not None):
rveIO.Finalize()
def GENERATE_RVE_LAW(self):
return self.__generate_rve_constitutive_law()
def TRACK_RVE_LAW(self,rveLaw,elemID,gpID):
return self.__track_rve_constitutive_law(rveLaw,elemID,gpID)
def INIT_OUTPUT(self):
return self.__initialize_output()
def WRITE_OUTPUT(self,time):
return self.__write_output(time)
def FIN_OUTPUT(self):
return self.__finalize_output()
## Prints an extensive description of this object
def __print_info(self):
print ("")
print ("====================================================")
print ("RveModelerShell - Info:")
print ("====================================================")
print ("MODEL PART - PROTOTYPE:")
print (self.MicroModelPart.Model)
print ("====================================================")
print ("TRACK LIST:")
ii = 0
print ("+--------------------------------------------------------+")
for key, value in self.TrackList.items():
print ("AT[", ii, "]")
print ("Info:")
print (key)
print ("(RveMaterial, IO)")
print (value)
print ("Micro Model Clone:")
micro = value[0].GetModelPart()
print (hex(id(micro)))
print (micro)
print ("+--------------------------------------------------------+")
ii+=1
| 34.074227
| 189
| 0.695026
| 3,414
| 33,052
| 6.565612
| 0.125952
| 0.032791
| 0.036137
| 0.011778
| 0.85532
| 0.838055
| 0.821102
| 0.813429
| 0.803391
| 0.798483
| 0
| 0.004756
| 0.198384
| 33,052
| 970
| 190
| 34.074227
| 0.841253
| 0.262495
| 0
| 0.816467
| 0
| 0
| 0.041115
| 0.024514
| 0
| 0
| 0
| 0.001031
| 0
| 1
| 0.070326
| false
| 0.005146
| 0.017153
| 0.017153
| 0.133791
| 0.070326
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
bfa64eef07e190189683725f80f3f45e233f6176
| 418
|
py
|
Python
|
remove_old_files.py
|
rahulporuri/trains
|
54572af1ea39a58c1761929d585d7b67f0949a6c
|
[
"MIT"
] | null | null | null |
remove_old_files.py
|
rahulporuri/trains
|
54572af1ea39a58c1761929d585d7b67f0949a6c
|
[
"MIT"
] | null | null | null |
remove_old_files.py
|
rahulporuri/trains
|
54572af1ea39a58c1761929d585d7b67f0949a6c
|
[
"MIT"
] | null | null | null |
import glob
import os
list_of_files = glob.glob('del/*-10-*')
for filename in list_of_files:
os.remove(filename)
list_of_files = glob.glob('del/*-9-*')
for filename in list_of_files:
os.remove(filename)
list_of_files = glob.glob('del/*_1-11-*')
for filename in list_of_files:
os.remove(filename)
list_of_files = glob.glob('del/*_2-11-*')
for filename in list_of_files:
os.remove(filename)
| 14.928571
| 41
| 0.700957
| 70
| 418
| 3.928571
| 0.228571
| 0.174545
| 0.32
| 0.218182
| 0.916364
| 0.916364
| 0.836364
| 0.836364
| 0.836364
| 0.836364
| 0
| 0.025496
| 0.155502
| 418
| 27
| 42
| 15.481481
| 0.753541
| 0
| 0
| 0.571429
| 0
| 0
| 0.102871
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.142857
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
78331ed66738591db0f74133b5921140c66f1b47
| 2,809
|
py
|
Python
|
handlers/watch.py
|
byn9826/Thousand-Day
|
cd1396cfb6110d1560aea705f2e24fec0b72d258
|
[
"BSD-3-Clause"
] | 2
|
2017-04-30T02:39:13.000Z
|
2017-05-05T13:11:54.000Z
|
handlers/watch.py
|
byn9826/Thousand-Day
|
cd1396cfb6110d1560aea705f2e24fec0b72d258
|
[
"BSD-3-Clause"
] | null | null | null |
handlers/watch.py
|
byn9826/Thousand-Day
|
cd1396cfb6110d1560aea705f2e24fec0b72d258
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import mysql.connector
#Create watch relation between user and pet
#return 0 for error
#return 1 for success
def createWatch(userId, petId, cnx):
watchQuery = 'INSERT INTO pet_watch (pet_id, user_id) VALUES (%s, %s)'
try:
watchCursor = cnx.cursor()
watchCursor.execute(watchQuery, (petId, userId))
cnx.commit()
return '1'
except mysql.connector.Error as err:
cnx.rollback()
print('Something went wrong: {}'.format(err))
return '0'
finally:
watchCursor.close()
#delete watch relation between user and pet
#return 0 for error
#return 1 for success
def deleteWatch(userId, petId, cnx):
watchQuery = 'DELETE FROM pet_watch WHERE pet_id = %s AND user_id = %s'
try:
watchCursor = cnx.cursor()
watchCursor.execute(watchQuery, (petId, userId))
cnx.commit()
return '1'
except mysql.connector.Error as err:
cnx.rollback()
print('Something went wrong: {}'.format(err))
return '0'
finally:
watchCursor.close()
#get all pet ids on one users watch list
#return 0 for error
def allWatch(userId, cnx):
watchQuery = 'SELECT pet_id FROM pet_watch WHERE user_id = %s'
try:
watchCursor = cnx.cursor()
watchCursor.execute(watchQuery, (userId, ))
watchRaw = watchCursor.fetchall()
#get array store all watcher id
return [x[0] for x in watchRaw]
#return 0 for db error
except mysql.connector.Error as err:
print('Something went wrong: {}'.format(err))
return '0'
finally:
watchCursor.close()
#search 20 pet ids of one user
#return 0 for error
#return pet id list for success
def userWatch(userId, pin, cnx):
watchQuery = 'SELECT pet_id FROM pet_watch WHERE user_id = %s LIMIT %s, 20'
try:
watchCursor = cnx.cursor()
watchCursor.execute(watchQuery, (userId, pin))
watchRaw = watchCursor.fetchall()
#get array store all watcher id
return [x[0] for x in watchRaw]
#return 0 for db error
except mysql.connector.Error as err:
print('Something went wrong: {}'.format(err))
return '0'
finally:
watchCursor.close()
#search all watcher ids of one pet
#return 0 for error
def searchWatch(petId, cnx):
watchQuery = 'SELECT user_id FROM pet_watch WHERE pet_id = %s'
try:
watchCursor = cnx.cursor()
watchCursor.execute(watchQuery, (petId, ))
watchRaw = watchCursor.fetchall()
#get array store all watcher id
return [x[0] for x in watchRaw]
#return 0 for db error
except mysql.connector.Error as err:
print('Something went wrong: {}'.format(err))
return '0'
finally:
watchCursor.close()
| 31.211111
| 79
| 0.636881
| 367
| 2,809
| 4.833787
| 0.209809
| 0.051297
| 0.045096
| 0.042277
| 0.808906
| 0.773957
| 0.773957
| 0.749718
| 0.714205
| 0.714205
| 0
| 0.012042
| 0.260947
| 2,809
| 89
| 80
| 31.561798
| 0.842486
| 0.190815
| 0
| 0.777778
| 0
| 0
| 0.174068
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.079365
| false
| 0
| 0.015873
| 0
| 0.253968
| 0.079365
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
7866c80aef551bbd6c843c11fafba7bfdaf2b6b9
| 186
|
py
|
Python
|
codewars/7kyu/doha22/max_number/max_number.py
|
doha22/Training_one
|
0cd7cf86c7da0f6175834146296b763d1841766b
|
[
"MIT"
] | null | null | null |
codewars/7kyu/doha22/max_number/max_number.py
|
doha22/Training_one
|
0cd7cf86c7da0f6175834146296b763d1841766b
|
[
"MIT"
] | 2
|
2019-01-22T10:53:42.000Z
|
2019-01-31T08:02:48.000Z
|
codewars/7kyu/doha22/max_number/max_number.py
|
doha22/Training_one
|
0cd7cf86c7da0f6175834146296b763d1841766b
|
[
"MIT"
] | 13
|
2019-01-22T10:37:42.000Z
|
2019-01-25T13:30:43.000Z
|
def max_number(n):
sorting ="".join(sorted(str(n), reverse = True))
s =int(sorting)
return s
def max_number2(n):
return int(''.join(sorted(str(n), reverse=True)))
| 20.666667
| 53
| 0.607527
| 28
| 186
| 3.964286
| 0.5
| 0.108108
| 0.234234
| 0.252252
| 0.45045
| 0.45045
| 0
| 0
| 0
| 0
| 0
| 0.006803
| 0.209677
| 186
| 8
| 54
| 23.25
| 0.748299
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.166667
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
787451aa886b2ccce6b386c2ab16bf27544954ba
| 16,755
|
py
|
Python
|
proto/fyrmesh_pb2_grpc.py
|
fyrwatch/fyrmesh
|
3c383d542af0c82026561bc347482949aa571340
|
[
"MIT"
] | 1
|
2021-02-27T19:48:20.000Z
|
2021-02-27T19:48:20.000Z
|
proto/fyrmesh_pb2_grpc.py
|
fyrwatch/fyrmesh
|
3c383d542af0c82026561bc347482949aa571340
|
[
"MIT"
] | 22
|
2021-05-16T18:32:06.000Z
|
2021-05-31T13:04:01.000Z
|
proto/fyrmesh_pb2_grpc.py
|
fyrwatch/fyrmesh
|
3c383d542af0c82026561bc347482949aa571340
|
[
"MIT"
] | 1
|
2021-05-15T22:04:39.000Z
|
2021-05-15T22:04:39.000Z
|
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from proto import fyrmesh_pb2 as proto_dot_fyrmesh__pb2
class InterfaceStub(object):
"""Missing associated documentation comment in .proto file."""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.Read = channel.unary_stream(
'/main.Interface/Read',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.ComplexLog.FromString,
)
self.Write = channel.unary_unary(
'/main.Interface/Write',
request_serializer=proto_dot_fyrmesh__pb2.ControlCommand.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.Acknowledge.FromString,
)
class InterfaceServicer(object):
"""Missing associated documentation comment in .proto file."""
def Read(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Write(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_InterfaceServicer_to_server(servicer, server):
rpc_method_handlers = {
'Read': grpc.unary_stream_rpc_method_handler(
servicer.Read,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.ComplexLog.SerializeToString,
),
'Write': grpc.unary_unary_rpc_method_handler(
servicer.Write,
request_deserializer=proto_dot_fyrmesh__pb2.ControlCommand.FromString,
response_serializer=proto_dot_fyrmesh__pb2.Acknowledge.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'main.Interface', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class Interface(object):
"""Missing associated documentation comment in .proto file."""
@staticmethod
def Read(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_stream(request, target, '/main.Interface/Read',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.ComplexLog.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Write(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Interface/Write',
proto_dot_fyrmesh__pb2.ControlCommand.SerializeToString,
proto_dot_fyrmesh__pb2.Acknowledge.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
class OrchestratorStub(object):
"""Missing associated documentation comment in .proto file."""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.Status = channel.unary_unary(
'/main.Orchestrator/Status',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.MeshOrchStatus.FromString,
)
self.Connection = channel.unary_unary(
'/main.Orchestrator/Connection',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.Acknowledge.FromString,
)
self.Observe = channel.unary_stream(
'/main.Orchestrator/Observe',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.SimpleLog.FromString,
)
self.Ping = channel.unary_unary(
'/main.Orchestrator/Ping',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.Acknowledge.FromString,
)
self.Nodelist = channel.unary_unary(
'/main.Orchestrator/Nodelist',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.NodeList.FromString,
)
self.Command = channel.unary_unary(
'/main.Orchestrator/Command',
request_serializer=proto_dot_fyrmesh__pb2.ControlCommand.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.Acknowledge.FromString,
)
self.SchedulerToggle = channel.unary_unary(
'/main.Orchestrator/SchedulerToggle',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.Acknowledge.FromString,
)
self.Simulate = channel.unary_unary(
'/main.Orchestrator/Simulate',
request_serializer=proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
response_deserializer=proto_dot_fyrmesh__pb2.Acknowledge.FromString,
)
class OrchestratorServicer(object):
"""Missing associated documentation comment in .proto file."""
def Status(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Connection(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Observe(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Ping(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Nodelist(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Command(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def SchedulerToggle(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Simulate(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_OrchestratorServicer_to_server(servicer, server):
rpc_method_handlers = {
'Status': grpc.unary_unary_rpc_method_handler(
servicer.Status,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.MeshOrchStatus.SerializeToString,
),
'Connection': grpc.unary_unary_rpc_method_handler(
servicer.Connection,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.Acknowledge.SerializeToString,
),
'Observe': grpc.unary_stream_rpc_method_handler(
servicer.Observe,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.SimpleLog.SerializeToString,
),
'Ping': grpc.unary_unary_rpc_method_handler(
servicer.Ping,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.Acknowledge.SerializeToString,
),
'Nodelist': grpc.unary_unary_rpc_method_handler(
servicer.Nodelist,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.NodeList.SerializeToString,
),
'Command': grpc.unary_unary_rpc_method_handler(
servicer.Command,
request_deserializer=proto_dot_fyrmesh__pb2.ControlCommand.FromString,
response_serializer=proto_dot_fyrmesh__pb2.Acknowledge.SerializeToString,
),
'SchedulerToggle': grpc.unary_unary_rpc_method_handler(
servicer.SchedulerToggle,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.Acknowledge.SerializeToString,
),
'Simulate': grpc.unary_unary_rpc_method_handler(
servicer.Simulate,
request_deserializer=proto_dot_fyrmesh__pb2.Trigger.FromString,
response_serializer=proto_dot_fyrmesh__pb2.Acknowledge.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'main.Orchestrator', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class Orchestrator(object):
"""Missing associated documentation comment in .proto file."""
@staticmethod
def Status(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Orchestrator/Status',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.MeshOrchStatus.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Connection(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Orchestrator/Connection',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.Acknowledge.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Observe(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_stream(request, target, '/main.Orchestrator/Observe',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.SimpleLog.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Ping(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Orchestrator/Ping',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.Acknowledge.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Nodelist(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Orchestrator/Nodelist',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.NodeList.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Command(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Orchestrator/Command',
proto_dot_fyrmesh__pb2.ControlCommand.SerializeToString,
proto_dot_fyrmesh__pb2.Acknowledge.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def SchedulerToggle(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Orchestrator/SchedulerToggle',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.Acknowledge.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Simulate(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/main.Orchestrator/Simulate',
proto_dot_fyrmesh__pb2.Trigger.SerializeToString,
proto_dot_fyrmesh__pb2.Acknowledge.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
| 42.742347
| 99
| 0.65085
| 1,546
| 16,755
| 6.741268
| 0.071798
| 0.05949
| 0.087795
| 0.105354
| 0.89906
| 0.872961
| 0.861255
| 0.817118
| 0.817118
| 0.806179
| 0
| 0.005087
| 0.272575
| 16,755
| 391
| 100
| 42.851662
| 0.850016
| 0.07365
| 0
| 0.670807
| 1
| 0
| 0.070318
| 0.030963
| 0
| 0
| 0
| 0
| 0
| 1
| 0.074534
| false
| 0
| 0.006211
| 0.031056
| 0.130435
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
78be267dda39cc4b64b79f79400fac9f638887f0
| 213
|
py
|
Python
|
models/modules/__init__.py
|
zhigangjiang/LGT-Net
|
d9a619158b2dc66a50c100e7fa7e491f1df16fd7
|
[
"MIT"
] | 11
|
2022-03-03T17:49:33.000Z
|
2022-03-25T11:23:11.000Z
|
models/modules/__init__.py
|
zhigangjiang/LGT-Net
|
d9a619158b2dc66a50c100e7fa7e491f1df16fd7
|
[
"MIT"
] | null | null | null |
models/modules/__init__.py
|
zhigangjiang/LGT-Net
|
d9a619158b2dc66a50c100e7fa7e491f1df16fd7
|
[
"MIT"
] | 1
|
2022-03-04T06:39:50.000Z
|
2022-03-04T06:39:50.000Z
|
"""
@Date: 2021/09/01
@description:
"""
from models.modules.swin_transformer import Swin_Transformer
from models.modules.swg_transformer import SWG_Transformer
from models.modules.transformer import Transformer
| 23.666667
| 60
| 0.826291
| 27
| 213
| 6.37037
| 0.444444
| 0.174419
| 0.296512
| 0.325581
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| 0.089202
| 213
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| 0
|
0
| 7
|
15550e7c50e7ed0824214ffd50a0b16d7d9a9eab
| 20,132
|
py
|
Python
|
ccinput/tests/test_xtb.py
|
geem-lab/ccinput
|
53dfd233e19f25fb17e50f8b2529ce6c50c737a0
|
[
"BSD-3-Clause"
] | 1
|
2022-01-21T17:34:55.000Z
|
2022-01-21T17:34:55.000Z
|
ccinput/tests/test_xtb.py
|
geem-lab/ccinput
|
53dfd233e19f25fb17e50f8b2529ce6c50c737a0
|
[
"BSD-3-Clause"
] | null | null | null |
ccinput/tests/test_xtb.py
|
geem-lab/ccinput
|
53dfd233e19f25fb17e50f8b2529ce6c50c737a0
|
[
"BSD-3-Clause"
] | null | null | null |
from ccinput.tests.testing_utilities import InputTests
from ccinput.packages.xtb import XtbCalculation
from ccinput.exceptions import InvalidParameter, ImpossibleCalculation
class XtbTests(InputTests):
def test_sp_basic(self):
params = {
"type": "Single-Point Energy",
"file": "ethanol.xyz",
"software": "xtb",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_sp_charge(self):
params = {
"type": "Single-Point Energy",
"file": "Cl.xyz",
"software": "xtb",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --chrg -1"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_sp_multiplicity(self):
params = {
"type": "Single-Point Energy",
"file": "Cl.xyz",
"software": "xtb",
"multiplicity": "2",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --uhf 2"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_sp_charge_multiplicity(self):
params = {
"type": "Single-Point Energy",
"file": "Cl.xyz",
"software": "xtb",
"multiplicity": "3",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --chrg -1 --uhf 3"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_opt_charge(self):
params = {
"type": "Geometrical Optimisation",
"file": "Cl.xyz",
"software": "xtb",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --opt tight --chrg -1"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_freq_charge(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --hess --chrg -1"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_opt_freq(self):
params = {
"type": "Opt+Freq",
"file": "Cl.xyz",
"software": "xtb",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --ohess --chrg -1"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_solvent(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"solvation_model": "GBSA",
"solvent": "chcl3",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --hess -g chcl3 --chrg -1"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_solvent_ALPB(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"solvent": "chcl3",
"solvation_model": "ALPB",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --hess --alpb chcl3 --chrg -1"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_solvent_invalid_PCM(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"solvent": "chcl3",
"solvation_model": "PCM",
"charge": "-1",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_solvent_change_solvation_radii(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"solvent": "chcl3",
"solvation_model": "ALPB",
"solvation_radii": "PCM",
"charge": "-1",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_solvent_custom_solvation_radii(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"solvent": "chcl3",
"solvation_model": "ALPB",
"custom_solvation_radii": "I=3.00",
"charge": "-1",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_solvent_synonym(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"solvent": "chloroform",
"solvation_model": "GBSA",
"charge": "-1",
}
xtb = self.generate_calculation(**params)
REF = "xtb Cl.xyz --hess -g chcl3 --chrg -1"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertTrue(self.is_equivalent("", xtb.input_file))
def test_solvent_invalid(self):
params = {
"type": "Frequency Calculation",
"file": "Cl.xyz",
"software": "xtb",
"solvent": "octanol",
"solvation_model": "GBSA",
"charge": "-1",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_scan(self):
params = {
"type": "Constrained Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Scan_9_1.4_10/1_2;",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --opt tight --input input"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=1.0
distance: 1, 2, auto
$scan
1: 9.0, 1.4, 10
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_constrained_opt_no_constraint(self):
params = {
"type": "Constrained Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_freeze(self):
params = {
"type": "Constrained Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/1_2;",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --opt tight --input input"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=1.0
distance: 1, 2, auto
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_constraint_overlap(self):
params = {
"type": "Constrained Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/1_2;Freeze/2_3;",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --opt tight --input input"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=1.0
distance: 1, 2, auto
distance: 2, 3, auto
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_freeze_soft(self):
params = {
"type": "Constrained Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/1_2;",
"specifications": "--forceconstant 0.1",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --opt tight --input input"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=0.1
distance: 1, 2, auto
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_duplicate_specifications(self):
params = {
"type": "Constrained Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/1_2;",
"specifications": "--forceconstant 0.1 --forceconstant 0.2",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --opt tight --input input"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=0.2
distance: 1, 2, auto
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_conformational_search(self):
params = {
"type": "Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -rthr 0.6 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertEqual("", xtb.input_file)
def test_conformational_search_specs(self):
params = {
"type": "Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--rthr 0.8 --ewin 8",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -rthr 0.8 -ewin 8"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertEqual("", xtb.input_file)
def test_conformational_search_nci(self):
params = {
"type": "Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--rthr 0.8 --nci",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -nci -rthr 0.8 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
self.assertEqual("", xtb.input_file)
def test_constrained_conformational_search1(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/1_2;",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -cinp input -rthr 0.6 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=1.0
reference=ethanol.xyz
distance: 1, 2, auto
atoms: 1-2
$metadyn
atoms: 3-9
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_constrained_conformational_search2(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/1_4;Freeze/6_8;",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -cinp input -rthr 0.6 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=1.0
reference=ethanol.xyz
distance: 1, 4, auto
distance: 6, 8, auto
atoms: 1,4,6,8
$metadyn
atoms: 2-3,5,7,9
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_constrained_conformational_search3(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/2_3;",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -cinp input -rthr 0.6 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=1.0
reference=ethanol.xyz
distance: 2, 3, auto
atoms: 2-3
$metadyn
atoms: 1,4-9
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_constrained_conformational_search4(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/2_3;",
"specifications": "--force_constant 2.0",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -cinp input -rthr 0.6 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=2.0
reference=ethanol.xyz
distance: 2, 3, auto
atoms: 2-3
$metadyn
atoms: 1,4-9
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_constrained_conformational_search5(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/2_3;Freeze/3_4;",
"specifications": "--force_constant 2.0",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -cinp input -rthr 0.6 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=2.0
reference=ethanol.xyz
distance: 2, 3, auto
distance: 3, 4, auto
atoms: 2-4
$metadyn
atoms: 1,5-9
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_constrained_conformational_search_no_constraint(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--force_constant 2.0",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_constrained_conformational_search_equals(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/2_3;",
"specifications": "--force_constant=2.0",
}
xtb = self.generate_calculation(**params)
REF = "crest ethanol.xyz -cinp input -rthr 0.6 -ewin 6"
self.assertTrue(self.is_equivalent(REF, xtb.command))
INPUT = """$constrain
force constant=2.0
reference=ethanol.xyz
distance: 2, 3, auto
atoms: 2-3
$metadyn
atoms: 1,4-9
"""
self.assertTrue(self.is_equivalent(INPUT, xtb.input_file))
def test_invalid_specification(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/2_3;",
"specifications": "--force 2.0",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_invalid_specification2(self):
params = {
"type": "Constrained Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"constraints": "Freeze/2_3;",
"specifications": "-force_constant 2.0",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_invalid_specification3(self):
params = {
"type": "Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "-rthr 0.8 --ewin 8",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_invalid_specification4(self):
params = {
"type": "Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--rthr abc",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_invalid_specification_for_type1(self):
params = {
"type": "Geometrical Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--rthr 0.8",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_invalid_specification_for_type2(self):
params = {
"type": "Geometrical Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--ewin 8",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_unknown_specification(self):
params = {
"type": "Conformational Search",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--abc",
}
with self.assertRaises(InvalidParameter):
xtb = self.generate_calculation(**params)
def test_gfn0(self):
params = {
"type": "Single-Point Energy",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--gfn 0",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --gfn 0"
self.assertTrue(self.is_equivalent(REF, xtb.command))
def test_gfn0_2(self):
params = {
"type": "Single-Point Energy",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--gfn0",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --gfn 0"
self.assertTrue(self.is_equivalent(REF, xtb.command))
def test_gfn1(self):
params = {
"type": "Single-Point Energy",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--gfn 0",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --gfn 0"
self.assertTrue(self.is_equivalent(REF, xtb.command))
def test_gfn2(self):
params = {
"type": "Single-Point Energy",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--gfn 2",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz"
self.assertTrue(self.is_equivalent(REF, xtb.command))
def test_gfnff(self):
params = {
"type": "Single-Point Energy",
"file": "ethanol.xyz",
"software": "xtb",
"specifications": "--gfnff",
}
xtb = self.generate_calculation(**params)
REF = "xtb ethanol.xyz --gfnff"
self.assertTrue(self.is_equivalent(REF, xtb.command))
def test_unavailable_calc_type(self):
params = {
"type": "TS Optimisation",
"file": "ethanol.xyz",
"software": "xtb",
}
with self.assertRaises(ImpossibleCalculation):
xtb = self.generate_calculation(**params)
| 29.134588
| 72
| 0.542221
| 2,008
| 20,132
| 5.308765
| 0.065239
| 0.052533
| 0.084428
| 0.093809
| 0.920919
| 0.910507
| 0.904784
| 0.900563
| 0.900469
| 0.880675
| 0
| 0.017543
| 0.320435
| 20,132
| 690
| 73
| 29.176812
| 0.76164
| 0
| 0
| 0.735568
| 0
| 0
| 0.296692
| 0.01063
| 0
| 0
| 0
| 0
| 0.124767
| 1
| 0.080074
| false
| 0
| 0.005587
| 0
| 0.087523
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1592d00d5f2ac35cca4184060c5e06666e186135
| 9,617
|
py
|
Python
|
test_we.py
|
MrArna/BetweenessCentrality
|
6c20161854b31a5e446f8e20af3367ed76099f23
|
[
"Apache-2.0"
] | null | null | null |
test_we.py
|
MrArna/BetweenessCentrality
|
6c20161854b31a5e446f8e20af3367ed76099f23
|
[
"Apache-2.0"
] | null | null | null |
test_we.py
|
MrArna/BetweenessCentrality
|
6c20161854b31a5e446f8e20af3367ed76099f23
|
[
"Apache-2.0"
] | null | null | null |
import os
import subprocess
import sys
test_output = open("output_we.txt", "w")
#
# Delaunay_n14
#
test_output.write("delaunay_n14\n")
we = subprocess.Popen(['./we_parallel', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
we = subprocess.Popen(['./we_parallel', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./we_parallel', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./we_parallel', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./we_parallel', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
#
# Delaunay_n16
#
test_output.write("delaunay_n16\n")
we = subprocess.Popen(['./we_parallel', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
we = subprocess.Popen(['./we_parallel', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./we_parallel', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./we_parallel', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./we_parallel', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
#
# Luxembourg_osm
#
test_output.write("luxembourg_osm\n")
we = subprocess.Popen(['./we_parallel', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
we = subprocess.Popen(['./we_parallel', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./we_parallel', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./we_parallel', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./we_parallel', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
#
# fb-wosn-friends.edges
#
test_output.write("fb-wosn-friends.edges\n")
we = subprocess.Popen(['./we_parallel', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
we = subprocess.Popen(['./we_parallel', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./we_parallel', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./we_parallel', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./we_parallel', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
test_output.close()
#
#
# edge parallel
#
#
test_output = open("output_ep.txt", "w")
#
# Delaunay_n14
#
test_output.write("delaunay_n14\n")
we = subprocess.Popen(['./ep', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
test_output.flush()
we = subprocess.Popen(['./ep', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./ep', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./ep', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./ep', 'delaunay_n14.mtx'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
#
# Delaunay_n16
#
test_output.write("delaunay_n16\n")
we = subprocess.Popen(['./ep', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
we = subprocess.Popen(['./ep', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./ep', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./ep', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./ep', 'delaunay_n16.mtx'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
#
# Luxembourg_osm
#
test_output.write("luxembourg_osm\n")
we = subprocess.Popen(['./ep', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
we = subprocess.Popen(['./ep', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./ep', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./ep', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./ep', 'luxembourg_osm.mtx'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
#
# fb-wosn-friends.edges
#
test_output.write("fb-wosn-friends.edges\n")
we = subprocess.Popen(['./ep', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '16'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo1"
we = subprocess.Popen(['./ep', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '8'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo2"
we = subprocess.Popen(['./ep', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '4'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo3"
we = subprocess.Popen(['./ep', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '2'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo4"
we = subprocess.Popen(['./ep', 'fb-wosn-friends.edges'], env={'OMP_NUM_THREADS' : '1'}, stderr=subprocess.PIPE)
for line in we.stderr:
test_output.write(line)
#print line
if we.poll() != None:
break
print "pippo5"
test_output.close()
| 22.057339
| 121
| 0.689092
| 1,453
| 9,617
| 4.425327
| 0.035788
| 0.082426
| 0.111975
| 0.099533
| 0.983826
| 0.983826
| 0.983826
| 0.983826
| 0.983826
| 0.983826
| 0
| 0.017219
| 0.130394
| 9,617
| 435
| 122
| 22.108046
| 0.751644
| 0.056047
| 0
| 0.820313
| 0
| 0
| 0.233178
| 0.028425
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.011719
| null | null | 0.15625
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
eca9c31e5f7a2a6a071896c66e3581685d3af99d
| 83,538
|
py
|
Python
|
Password-Manager-2.0v.py
|
ANIRUDHADITHYA/Password-Manager
|
06953051668f1bf069b10ac8b427394051ae6668
|
[
"MIT"
] | null | null | null |
Password-Manager-2.0v.py
|
ANIRUDHADITHYA/Password-Manager
|
06953051668f1bf069b10ac8b427394051ae6668
|
[
"MIT"
] | null | null | null |
Password-Manager-2.0v.py
|
ANIRUDHADITHYA/Password-Manager
|
06953051668f1bf069b10ac8b427394051ae6668
|
[
"MIT"
] | null | null | null |
import os
import stdiomask
import datetime
from tabulate import tabulate
import mysql.connector
print("*************************************************************************************************************************************************************************")
print("* ********************************************************************************************************************************************************************* *")
print("* ******** |*******| |**| |*******| |*******| |*| |*|*| |*| |****| |*******| |******| ******** *")
print("* ******* |*| |*| |*||*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| ******* *")
print("* ****** |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| ****** *")
print("* ***** |*******| |********| |*******| |*******| |*| |*| |*| |*| |*| |****| |*| |*| ***** *")
print("* **** |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| **** *")
print("* *** |*| |*| |*| |*| |*| |*| |*| |*| |*|*| |*| |*| |*| |*| |*| |*| |*| *** *")
print("* ** |*| |*| |*| |*******| |*******| |**| |**| |****| |*| |*| |******| ** *")
print("* * * *")
print("* ** |*| |*| |**| |*| \ |*| |**| |******| |********| |*******| ** *")
print("* *** |*|\ /|*| |*||*| |*|\ \ |*| |*||*| |*| |*| |*| |*| |*| *** *")
print("* **** |*| \/ |*| |*| |*| |*| \ \ |*| |*| |*| |*| |*| |*| |*| **** *")
print("* ***** |*| |*| |********| |*| \ \ |*| |********| |*| |*****| |********| |*****| ***** *")
print("* ****** |*| |*| |*| |*| |*| \ \ |*| |*| |*| |*| |*| |*| |*| |*| ****** *")
print("* ******* |*| |*| |*| |*| |*| \ \|*| |*| |*| |*| |*| |*| |*| |*| ******* *")
print("* ******** |*| |*| |*| |*| |*| \ |*| |*| |*| |******| |********| |*| |*| ******** *")
print("* ********************************************************************************************************************************************************************* *")
print("*************************************************************************************************************************************************************************")
while True:
os.system("cls")
print("*************************************************************************************************************************************************************************")
print("* ********************************************************************************************************************************************************************* *")
print("* ******** |*******| |**| |*******| |*******| |*| |*|*| |*| |****| |*******| |******| ******** *")
print("* ******* |*| |*| |*||*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| ******* *")
print("* ****** |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| ****** *")
print("* ***** |*******| |********| |*******| |*******| |*| |*| |*| |*| |*| |****| |*| |*| ***** *")
print("* **** |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| **** *")
print("* *** |*| |*| |*| |*| |*| |*| |*| |*| |*|*| |*| |*| |*| |*| |*| |*| |*| *** *")
print("* ** |*| |*| |*| |*******| |*******| |**| |**| |****| |*| |*| |******| ** *")
print("* * * *")
print("* ** |*| |*| |**| |*| \ |*| |**| |******| |********| |*******| ** *")
print("* *** |*|\ /|*| |*||*| |*|\ \ |*| |*||*| |*| |*| |*| |*| |*| *** *")
print("* **** |*| \/ |*| |*| |*| |*| \ \ |*| |*| |*| |*| |*| |*| |*| **** *")
print("* ***** |*| |*| |********| |*| \ \ |*| |********| |*| |*****| |********| |*****| ***** *")
print("* ****** |*| |*| |*| |*| |*| \ \ |*| |*| |*| |*| |*| |*| |*| |*| ****** *")
print("* ******* |*| |*| |*| |*| |*| \ \|*| |*| |*| |*| |*| |*| |*| |*| ******* *")
print("* ******** |*| |*| |*| |*| |*| \ |*| |*| |*| |******| |********| |*| |*| ******** *")
print("* ********************************************************************************************************************************************************************* *")
print("*************************************************************************************************************************************************************************")
try:
sql_pass=stdiomask.getpass(prompt='Enter your MYSQL Password: ', mask='*')
host=input("Enter your Host: ")
connection=mysql.connector.connect(user="root",password=""+sql_pass+"",host=""+host+"")
mycur=connection.cursor()
break
except mysql.connector.Error:
input("Incorrect Password or Host!!! \nPress Any Key to Try Again")
continue
while True:
os.system("cls")
print("*************************************************************************************************************************************************************************")
print("* ********************************************************************************************************************************************************************* *")
print("* ******** |*******| |**| |*******| |*******| |*| |*|*| |*| |****| |*******| |******| ******** *")
print("* ******* |*| |*| |*||*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| ******* *")
print("* ****** |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| ****** *")
print("* ***** |*******| |********| |*******| |*******| |*| |*| |*| |*| |*| |****| |*| |*| ***** *")
print("* **** |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| |*| **** *")
print("* *** |*| |*| |*| |*| |*| |*| |*| |*| |*|*| |*| |*| |*| |*| |*| |*| |*| *** *")
print("* ** |*| |*| |*| |*******| |*******| |**| |**| |****| |*| |*| |******| ** *")
print("* * * *")
print("* ** |*| |*| |**| |*| \ |*| |**| |******| |********| |*******| ** *")
print("* *** |*|\ /|*| |*||*| |*|\ \ |*| |*||*| |*| |*| |*| |*| |*| *** *")
print("* **** |*| \/ |*| |*| |*| |*| \ \ |*| |*| |*| |*| |*| |*| |*| **** *")
print("* ***** |*| |*| |********| |*| \ \ |*| |********| |*| |*****| |********| |*****| ***** *")
print("* ****** |*| |*| |*| |*| |*| \ \ |*| |*| |*| |*| |*| |*| |*| |*| ****** *")
print("* ******* |*| |*| |*| |*| |*| \ \|*| |*| |*| |*| |*| |*| |*| |*| ******* *")
print("* ******** |*| |*| |*| |*| |*| \ |*| |*| |*| |******| |********| |*| |*| ******** *")
print("* ********************************************************************************************************************************************************************* *")
print("*************************************************************************************************************************************************************************")
try:
cre_acc=int(input("1.Login \n2.Register your Account\nEnter Your Choice: "))
if cre_acc==1:
database=input("Enter Account ID: ")
try:
connection=mysql.connector.connect(user="root",password=""+sql_pass+"",host="localhost",database=""+database+"")
mycur=connection.cursor()
password=stdiomask.getpass(prompt='Enter the Password: ', mask='*')
pass_db=("""INSERT INTO pass_db(user_choice, password) values (3,'%s')"""%password)
mycur.execute(pass_db)
ori_pass=("select password from pass_db where user_choice=1")
check_pass=("select password from pass_db where user_choice=3")
mycur.execute(check_pass)
data1=mycur.fetchall()
mycur.execute(ori_pass)
data=mycur.fetchall()
if data==data1:
print("Sucessfully Loged In")
mycur.execute("delete from pass_db where user_choice=3")
break
else:
mycur.execute("delete from pass_db where user_choice=3")
input("Invalid Password !!! \nPress Any Key to Continue")
except mysql.connector.Error:
input("Invalid or Account can't be Found !!! \nPress Any Key to Continue")
elif cre_acc==2:
try:
database=input("Create your Account ID: ")
try:
connection=mysql.connector.connect(user="root",password=""+sql_pass+"",host="localhost",database=""+database+"")
print("Account Already Exists")
input("Press Any Key to Continue")
continue
except mysql.connector.Error:
mycur.execute("create database "+database)
connection=mysql.connector.connect(user="root",password=""+sql_pass+"",host="localhost",database=""+database+"")
mycur=connection.cursor()
mycur.execute("create table pass_db(user_choice varchar(30), password varchar(30))")
mycur.execute("create table social_media_management(sno varchar(30), name varchar(30), username varchar(30), password varchar(30), 2fa varchar(30), notes varchar(30))")
mycur.execute("create table inter_banking(sno varchar(30), bname varchar(30), username varchar(30), password varchar(30), transaction_password varchar(30), register_mobile varchar(30), notes varchar(30))")
mycur.execute("create table bank_details(sno varchar(30), bname varchar(30), acc_holder varchar(30), acc_number varchar(30), ifsc_number varchar(30), branch_name varchar(30), register_mobile varchar(30), register_email varchar(30), customer_number varchar(30), cif_number varchar(30), notes varchar(30))")
mycur.execute("create table save_cards(sno varchar(30), card_provider varchar(30), card_holder varchar(30), card_type varchar(30), card_number varchar(30), card_expiry varchar(30), card_cvv varchar(30), card_register_mobile varchar(30), notes varchar(30))")
create_password=stdiomask.getpass(prompt='Create your Password: ', mask='*')
pass_db=("""INSERT INTO pass_db(user_choice, password) values (1,'%s')"""%create_password)
mycur.execute(pass_db)
connection.commit()
print("Thank You for Choosing Password Manager\nYour Account for Password Manager has been Created Sucessfully :) ")
input("Press Any Key to Return Home")
except mysql.connector.Error:
input("Warning!! Please Don't use Special Character \nPress Any Key to Continue")
else:
input("Invalid Option\nPress Any Key to Continue")
except ValueError:
input("Enter Only Integer, Invalid Option\nPress Any Key to Continue")
#######MAIN MENU#########
while True:
os.system("cls")
print("***Welcome To Password Manager***\n")
print("Main Menu")
print("\n1.Account Settings")
print("2.Add your Passwords and Datas")
print("3.View/Update/Delete your Password")
print("4.Save Datas to Text File")
print("5.Info about Password Manager")
print("6.Help Desk")
print("7.Exit")
try:
user_choice=int(input("Enter your Choice: "))
#Main
#Main Choice1
if user_choice==1:
mycur.execute("select *from pass_db")
pre_check=mycur.fetchall()
pre_check_data=len(pre_check)
if pre_check_data==0:
print("Sorry !!! Something Went Wrong :(")
print("\n########################################################################")
input("Press Enter To Continue")
print("\n########################################################################")
else:
password=stdiomask.getpass(prompt='Enter the Password: ', mask='*')
pass_db=("""INSERT INTO pass_db(user_choice, password) values (3,'%s')"""%password)
mycur.execute(pass_db)
ori_pass=("select password from pass_db where user_choice=1")
check_pass=("select password from pass_db where user_choice=3")
mycur.execute(check_pass)
data1=mycur.fetchall()
mycur.execute(ori_pass)
data=mycur.fetchall()
if data==data1:
print("Sucessfully Loged In")
mycur.execute("delete from pass_db where user_choice=3")
while True:
os.system("cls")
print("Settings\n")
print("1.Update your Password")
print("2.Backup and Reset your Database")
print("3.Delete your Account")
print("4.Back")
try:
set_choice=int(input("Please Enter Your Choice "))
if set_choice==1:
new_pass=stdiomask.getpass(prompt='Enter your New Password: ', mask='*')
mycur.execute("update pass_db set password='"+new_pass+"' where user_choice=1")
connection.commit()
input("Sucessfully Updated :)")
elif set_choice==2:
input("Press Enter to Continue\n***Note Your Password of Your Account will be Deleted Completly\n and Automatically your Passwords will be Saved in Text File")
print("bakuping your datas........\ncopying datas.........")
time=datetime.datetime.now()
table=('\n'+tabulate([[1, 'Social Media Accounts'], [2, 'Internet Banking Accounts'], [3, 'Bank Details'], [4, 'Saved Cards']],
headers=['Number of Tables', 'Tables you can use'], tablefmt='psql'))
mycur.execute("select name, username, password, 2fa, notes from social_media_management")
social_media_data=mycur.fetchall()
social_data=((tabulate(social_media_data, headers=["Social Media Name","Username","Password","2FA","Notes"], tablefmt="psql")))
mycur.execute("select bname, username, password, transaction_password, register_mobile, notes from inter_banking")
inter_bank_data=mycur.fetchall()
inter_data=((tabulate(inter_bank_data, headers=["Bank Name","Username","Password","Transaction Password","Mobile Number","Notes"], tablefmt="psql")))
mycur.execute("select bname, acc_holder, acc_number, ifsc_number, branch_name, register_mobile, register_email, customer_number, cif_number, notes from bank_details")
bank_data=mycur.fetchall()
bank_details=((tabulate(bank_data, headers=["Bank Name","Acc Holder Name","Acc Number","IFSC Code","Branch Name","Mobile Number","Email ID","Customer Number","CIF Number","Notes"], tablefmt="psql")))
mycur.execute("select card_provider, card_holder, card_type, card_number, card_expiry, card_cvv, card_register_mobile, notes from save_cards")
card_data=mycur.fetchall()
save_cards=((tabulate(card_data, headers=["Provider Name","Holder Name","Card Type","Card Number","Expiry Data","CVV Number","Mobile Number","Notes"], tablefmt="psql")))
test_data=open("Password Manager Backup "+time.strftime("%d %B %Y")+".txt","w")
test_data.write("============================================================================TABLE===================================================================\n")
test_data.writelines(table)
test_data.writelines("\n==============================================================SOCIAL MEDIA ACCOUNTS=================================================================\n")
test_data.writelines(social_data)
test_data.writelines("\n============================================================INTERNET BANKING ACCOUNTS===============================================================\n")
test_data.writelines(inter_data)
test_data.writelines("\n==================================================================BANK DETAILS======================================================================\n")
test_data.writelines(bank_details)
test_data.writelines("\n===================================================================SAVED CARDS======================================================================\n")
test_data.writelines(save_cards)
test_data.close()
print("Backuped your Datas to Text File ('Password Manager Backup"+time.strftime("%d %B %Y")+".txt') Sucessfully,")
print("Reseting your tables...." )
mycur.execute("drop table social_media_management")
print("Reseting your tables...." )
mycur.execute("drop table inter_banking")
print("Reseting your tables...." )
mycur.execute("drop table bank_details")
print("Reseting your tables...." )
mycur.execute("drop table save_cards")
print("creating tables")
print("creating table social_media_management...........")
mycur.execute("create table social_media_management(sno varchar(30), name varchar(30), username varchar(30), password varchar(30), 2fa varchar(30), notes varchar(30))")
print("creating table inter_banking...........")
mycur.execute("create table inter_banking(sno varchar(30), bname varchar(30), username varchar(30), password varchar(30), transaction_password varchar(30), register_mobile varchar(30), notes varchar(30))")
print("creating table bank_details...........")
mycur.execute("create table bank_details(sno varchar(30), bname varchar(30), acc_holder varchar(30), acc_number varchar(30), ifsc_number varchar(30), branch_name varchar(30), register_mobile varchar(30), register_email varchar(30), customer_number varchar(30), cif_number varchar(30), notes varchar(30))")
print("creating table save_cards...........")
mycur.execute("create table save_cards(sno varchar(30), card_provider varchar(30), card_holder varchar(30), card_type varchar(30), card_number varchar(30), card_expiry varchar(30), card_cvv varchar(30), card_register_mobile varchar(30), notes varchar(30))")
print("Table Created Sucessfully")
print("Reseted Sucessfully")
input("Press Enter to Continue")
elif set_choice==3:
connection1=mysql.connector.connect(user="root",password=""+sql_pass+"",host="localhost")
mycur1=connection1.cursor()
mycur1.execute("drop database "+database)
input("Sucessfully Deleted\nPress Any Enter to Log Out and Quit")
quit()
elif set_choice==4:
break
else:
print("Wrong Option :(")
input("Press Enter To Continue")
except ValueError:
input("Invalid Option, Enter only Integers\n Press Any Key to Continue")
else:
print("Sorry!!Access Denied")
mycur.execute("delete from pass_db where user_choice=3")
connection.commit()
#connection.close()
input("Press Enter To Continue")
elif user_choice==2:
mycur.execute("select *from pass_db")
pre_check=mycur.fetchall()
pre_check_data=len(pre_check)
if pre_check_data==0:
print("Sorry !!! Something Went Wrong :(")
print("\n########################################################################")
input("Press Enter To Continue")
print("\n########################################################################")
else:
password=stdiomask.getpass(prompt='Enter the Password: ', mask='*')
pass_db=("""INSERT INTO pass_db(user_choice, password) values (3,'%s')"""%password)
mycur.execute(pass_db)
ori_pass=("select password from pass_db where user_choice=1")
check_pass=("select password from pass_db where user_choice=3")
mycur.execute(check_pass)
data1=mycur.fetchall()
mycur.execute(ori_pass)
data=mycur.fetchall()
if data==data1:
print("Sucessfully Loged In")
mycur.execute("delete from pass_db where user_choice=3")
#After Log In
while True:
os.system("cls")
print("1.Add your Passwords of Social Media")
print("2.Add your Passwords of Internet Banking Accounts")
print("3.Add your Bank Details")
print("4.Add your Purchase Cards")
print("5.Back")
try:
add_choice=int(input("Select your option: "))
##SUBCHOICE1
if add_choice==1:
while True:
os.system("cls")
try:
repeat=int(input("Enter How Many Accounts to be Added in Social Media Management\n(Only 5 Times per run Recommanded)"))
print("Fill Each Option with Correct Information ")
print("########################################################################")
for i in range(repeat):
mycur.execute("select *from social_media_management")
row_no=mycur.fetchall()
sno=len(row_no)+1
sname=input("Enter Your Social Media Name: ")
susername=input("Enter Your "+sname+" Username: ")
spassword=input("Enter Your "+sname+" Password: ")
s2fa=input("If 2FA Authendication is Enabled (Optional): ")
snotes=input("You can add Aditional Information if any (optional): ")
print("########################################################################")
##SQl CONNECTION##
add_social=("""INSERT INTO social_media_management(sno, name, username, password, 2fa, notes) values ('%s','%s','%s','%s','%s','%s')"""%(sno, sname, susername, spassword, s2fa, snotes))
mycur.execute(add_social)
connection.commit()
#connection.close()
print("Your Password has been Sucessfully saved in Database as given,\nThank You Have a Nice Day :)")
input("Press Enter To Continue")
print("########################################################################")
break
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To Continue")
##SUBCHOICE2
elif add_choice==2:
while True:
os.system("cls")
try:
repeat1=int(input("Enter How Many Accounts to be Added in Internet Banking Management\n(Only 5 Times per run Recommanded)"))
print("Fill Each Option with Correct Information ")
print("########################################################################")
for i in range(repeat1):
mycur.execute("select *from inter_banking")
row_no1=mycur.fetchall()
sno=len(row_no1)+1
bname=input("Enter Your Bank Name: ")
busername=input("Enter Your "+bname+" Username: ")
bpassword=input("Enter Your "+bname+" Password: ")
btrans_pass=input("Enter Your "+bname+" Transaction Password (Optional): ")
while True:
try:
breg_mob=int(input("Enter Your "+bname+" Registered Mobile Number (Optional): "))
break
except ValueError:
print("Enter Only Integer")
input("Press Any Key to ReEnter")
bnotes=input("You can add Aditional Information if any (optional): ")
print("########################################################################")
##SQl CONNECTION##
add_inter_bank=("""INSERT INTO inter_banking(sno, bname, username, password, transaction_password, register_mobile, notes) values ('%s','%s','%s','%s','%s','%s','%s')"""
%(sno, bname, busername, bpassword, btrans_pass, str(breg_mob), bnotes))
mycur.execute(add_inter_bank)
connection.commit()
#connection.close()
print("Your Password has been Sucessfully saved in Database as given,\nThank You Have a Nice Day :)")
input("Press Enter To Continue")
print("\n########################################################################")
break
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To Continue")
##SUBCHOICE3
elif add_choice==3:
while True:
os.system("cls")
try:
repeat2=int(input("Enter How Many Accounts to be Added in Bank Details Management\n(Only 5 Times per run Recommanded)"))
print("Fill Each Option with Correct Information ")
print("########################################################################")
for i in range(repeat2):
mycur.execute("select *from bank_details")
row_no2=mycur.fetchall()
sno=len(row_no2)+1
b_name=input("Enter Bank Name: ")
b_hold=input("Enter Account Holder Name: ")
while True:
try:
b_number=int(input("Enter "+b_name+" Account Number: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
b_ifsc=input("Enter "+b_name+" IFSC Number: ")
b_branch=input("Enter "+b_name+" Branch Name: ")
while True:
try:
b_regmob=int(input("Enter "+b_name+" Registered Mobile Number: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
b_regmail=input("Enter "+b_name+" Registered Email Id (Optional): ")
while True:
try:
b_cusno=int(input("Enter "+b_hold+"'s Customer Number (Optional): "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
b_cif=input("Enter "+b_hold+"'s CIF Number (Optional): ")
b_notes=input("You can add Aditional Information if any (optional): ")
print("########################################################################")
##SQl CONNECTION##
add_bank_details=("""INSERT INTO bank_details
(sno, bname, acc_holder, acc_number, ifsc_number, branch_name, register_mobile, register_email, customer_number, cif_number, notes) values ('%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s')"""
%(sno, b_name, b_hold, str(b_number), b_ifsc, b_branch, str(b_regmob), b_regmail, str(b_cusno), b_cif, b_notes))
mycur.execute(add_bank_details)
connection.commit()
#connection.close()
print("Your Bank Account Details has been Sucessfully saved in Database as given,\nThank You Have a Nice Day :)")
input("Press Enter To Continue")
print("\n########################################################################")
break
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To Continue")
##SUBCHOICE4
elif add_choice==4:
while True:
os.system("cls")
try:
repeat3=int(input("Enter How Many Cards to be Added in Cards Management\n(Only 5 Times per run Recommanded)"))
print("Fill Each Option with Correct Information ")
print("########################################################################")
for i in range(repeat3):
mycur.execute("select *from save_cards")
row_no3=mycur.fetchall()
sno=len(row_no3)+1
cpname=input("Enter Your Card Provider Name: ")
chold=input("Enter Card Holder Name: ")
ctype=input("Enter Card Type (Visa/MasterCard/Rupay/GiftCard/Other): ")
while True:
try:
cnumber=int(input("Enter Card Number: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
cexpiry=input("Enter Card Expiry Date (optional): ")
while True:
try:
ccvv=int(input("Enter Card CVV NO (optional): "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
while True:
try:
cregmob=int(input("Enter Card Registered Mobile Number (optional): "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
cnotes=input("You can add Aditional Information if any(optional): ")
print("########################################################################")
##SQl CONNECTION##
save_cards=("""INSERT INTO save_cards
(sno, card_provider, card_holder, card_type, card_number, card_expiry, card_cvv, card_register_mobile, notes) values ('%s','%s','%s','%s','%s','%s','%s','%s','%s')"""
%(sno, cpname, chold, ctype, str(cnumber), cexpiry, str(ccvv), str(cregmob), cnotes))
mycur.execute(save_cards)
connection.commit()
#connection.close()
print("Your Cards has been Sucessfully saved in Database as given,\nThank You Have a Nice Day :)")
input("Press Enter To Continue")
break
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To Continue")
elif add_choice==5:
break
else:
print("Wrong Option :( ")
input("Press Enter To Continue")
print("\n########################################################################")
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To continue")
else:
print("Sorry!!Access Denied")
mycur.execute("delete from pass_db where user_choice=3")
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
#Main Choice3
elif user_choice==3:
mycur.execute("select *from pass_db")
pre_check=mycur.fetchall()
pre_check_data=len(pre_check)
if pre_check_data==0:
print("Something went wrong :( !!!")
print("\n########################################################################")
input("Press Enter To Continue")
print("\n########################################################################")
else:
password=stdiomask.getpass(prompt='Enter the Password: ', mask='*')
pass_db=("""INSERT INTO pass_db(user_choice, password) values (3,'%s')"""%password)
mycur.execute(pass_db)
ori_pass=("select password from pass_db where user_choice=1")
check_pass=("select password from pass_db where user_choice=3")
mycur.execute(check_pass)
data1=mycur.fetchall()
mycur.execute(ori_pass)
data=mycur.fetchall()
if data==data1:
print("Sucessfully Loged In")
mycur.execute("delete from pass_db where user_choice=3")
while True:
os.system("cls")
print("1.View\n2.Update Datas\n3.Delete Accounts\n4.Back")
try:
up_view=int(input("Select your Operation: "))
if up_view==1:
while True:
os.system("cls")
print('1.Social Media Accounts\n2.Internet Banking Accounts\n3.Bank Details\n4.Saved Cards\n5.Back')
try:
tab_sele=int(input("Choose the Table that you want to View: "))
##view_table_section###
if tab_sele==1:
mycur.execute("select name, username, password, 2fa, notes from social_media_management")
social_media_data=mycur.fetchall()
print((tabulate(social_media_data, headers=["Social Media Name","Username","Password","2FA","Notes"], tablefmt="psql")))
input("Press Enter To Continue")
elif tab_sele==2:
mycur.execute("select bname, username, password, transaction_password, register_mobile, notes from inter_banking")
inter_bank_data=mycur.fetchall()
print((tabulate(inter_bank_data, headers=["Bank Name","Username","Password","Transaction Password","Mobile Number","Notes"], tablefmt="psql")))
input("Press Enter To Continue")
elif tab_sele==3:
mycur.execute("select bname, acc_holder, acc_number, ifsc_number, branch_name, register_mobile, register_email, customer_number, cif_number, notes from bank_details")
bank_data=mycur.fetchall()
print((tabulate(bank_data, headers=
["Bank Name","Acc Holder Name","Acc Number","IFSC Code","Branch Name","Mobile Number","Email ID","Customer Number","CIF Number","Notes"], tablefmt="psql")))
input("Press Enter To Continue")
elif tab_sele==4:
mycur.execute("select card_provider, card_holder, card_type, card_number, card_expiry, card_cvv, card_register_mobile, notes from save_cards")
card_data=mycur.fetchall()
print((tabulate(card_data, headers=
["Provider Name","Holder Name","Card Type","Card Number","Expiry Data","CVV Number","Mobile Number","Notes"], tablefmt="psql")))
input("Press Enter To Continue")
elif tab_sele==5:
break
else:
print("Wrong Option :(")
input("Press Enter To Continue")
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To continue")
##update_table_section###
elif up_view==2:
while True:
os.system("cls")
print('1.Social Media Accounts\n2.Internet Banking Accounts\n3.Bank Details\n4.Saved Cards\n5.Back')
try:
tab_sele=int(input("Choose the Table that you want to Replace Datas: "))
if tab_sele==1:
mycur.execute("select *from social_media_management")
social_media_data=mycur.fetchall()
print((tabulate(social_media_data, headers=["Altering\nNumber","name","username","password","2fa","notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many updates do you want to perform?:"))
for i in range(user_col):
col_name=input("Enter Column Name (*Note Case Sensitive): ")
while True:
try:
alter_col=int(input("Enter Altering Number of your Updating Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
update_to=input("Enter data what has to be Updated: ")
rep=("update social_media_management set "+col_name+"='"+update_to+"' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("\n########################################################################")
print("Sucessfully Updated in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select name, username, password, 2fa, notes from social_media_management")
social_media_data=mycur.fetchall()
print((tabulate(social_media_data, headers=["Social Media Name","Username","Password","2FA","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
elif tab_sele==2:
mycur.execute("select *from inter_banking")
inter_bank_data=mycur.fetchall()
print((tabulate(inter_bank_data, headers=["Altering\nNumber","bname","username","password","transaction_password","register_mobile","notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many updates do you want to perform?:"))
for i in range(user_col):
col_name=input("Enter Column Name (*Note Case Sensitive): ")
while True:
try:
alter_col=int(input("Enter Altering Number of your Updating Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
update_to=input("Enter data what has to be Updated: ")
rep=("update inter_banking set "+col_name+"='"+update_to+"' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("\n########################################################################")
print("Sucessfully Updated in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select bname, username, password, transaction_password, register_mobile, notes from inter_banking")
inter_bank_data=mycur.fetchall()
print((tabulate(inter_bank_data, headers=["Bank Name","Username","Password","Transaction Password","Mobile Number","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
elif tab_sele==3:
mycur.execute("select *from bank_details")
bank_data=mycur.fetchall()
print((tabulate(bank_data, headers=
["Altering\nNumber","bname","acc_holder","acc_number","ifsc_number","branch_name","register_mobile","register_email","customer_number","cif_number","notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many updates do you want to perform?:"))
for i in range(user_col):
col_name=input("Enter Column Name (*Note Case Sensitive): ")
while True:
try:
alter_col=int(input("Enter Altering Number of your Updating Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
update_to=input("Enter data what has to be Updated: ")
rep=("update bank_details set "+col_name+"='"+update_to+"' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("\n########################################################################")
print("Sucessfully Updated in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select bname, acc_holder, acc_number, ifsc_number, branch_name, register_mobile, register_email, customer_number, cif_number, notes from bank_details")
bank_data=mycur.fetchall()
print((tabulate(bank_data, headers=
["Bank Name","Acc Holder Name","Acc Number","IFSC Code","Branch Name","Mobile Number","Email ID","Customer Number","CIF Number","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
elif tab_sele==4:
mycur.execute("select *from save_cards")
card_data=mycur.fetchall()
print((tabulate(card_data, headers=
["Altering\nNumber","card_provider","card_holder","card_type","card_number","card_expiry","card_cvv","card_register_mobile","notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many updates do you want to perform?:"))
for i in range(user_col):
col_name=input("Enter Column Name (*Note Case Sensitive): ")
while True:
try:
alter_col=int(input("Enter Altering Number of your Updating Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
update_to=input("Enter data what has to be Updated: ")
rep=("update save_cards set "+col_name+"='"+update_to+"' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("\n########################################################################")
print("Sucessfully Updated in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select card_provider, card_holder, card_type, card_number, card_expiry, card_cvv, card_register_mobile, notes from save_cards")
card_data=mycur.fetchall()
print((tabulate(card_data, headers=
["Provider Name","Holder Name","Card Type","Card Number","Expiry Data","CVV Number","Mobile Number","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
elif tab_sele==5:
break
else:
print("Wrong Option :(")
input("Press Enter To Continue")
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To continue")
##delete_row_section##
elif up_view==3:
while True:
os.system("cls")
print('1.Social Media Accounts\n2.Internet Banking Accounts\n3.Bank Details\n4.Saved Cards\n5.Back')
try:
tab_sele=int(input("Choose the Table that you want to Delete Datas: "))
if tab_sele==1:
mycur.execute("select *from social_media_management")
social_media_data=mycur.fetchall()
print((tabulate(social_media_data, headers=["Altering\nNumber","Social Media Name","Username","Password","2FA","Notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many Accounts to be Deleted?:"))
for i in range(user_col):
while True:
try:
alter_col=int(input("Enter Altering Number of your Deleting Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
rep=("update social_media_management set name='NULL', username='NULL', password='NULL', 2fa='NULL', notes='NULL' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("########################################################################")
print("Sucessfully Deleted in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select name, username, password, 2fa, notes from social_media_management")
social_media_data=mycur.fetchall()
print((tabulate(social_media_data, headers=["Social Media Name","Username","Password","2FA","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
elif tab_sele==2:
mycur.execute("select *from inter_banking")
inter_bank_data=mycur.fetchall()
print((tabulate(inter_bank_data, headers=["Altering\nNumber","Bank Name","Username","Password","Transaction Password","Mobile Number","Notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many Accounts to be Deleted?:"))
for i in range(user_col):
while True:
try:
alter_col=int(input("Enter Altering Number of your Deleting Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
rep=("update inter_banking set bname='NULL', username='NULL', password='NULL', transaction_password='NULL', register_mobile='NULL', notes='NULL' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("########################################################################")
print("Sucessfully Deleted in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select bname, username, password, transaction_password, register_mobile, notes from inter_banking")
inter_bank_data=mycur.fetchall()
print((tabulate(inter_bank_data, headers=["Bank Name","Username","Password","Transaction Password","Mobile Number","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
elif tab_sele==3:
mycur.execute("select *from bank_details")
bank_data=mycur.fetchall()
print((tabulate(bank_data, headers=
["Altering\nNumber","Bank Name","Acc Holder Name","Acc Number","IFSC Code","Branch Name","Mobile Number","Email ID","Customer Number","CIF Number","Notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many Accounts to be Deleted?:"))
for i in range(user_col):
while True:
try:
alter_col=int(input("Enter Altering Number of your Deleting Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
rep=("update bank_details set bname='NULL', acc_holder='NULL', acc_number='NULL', ifsc_number='NULL', branch_name='NULL', register_mobile='NULL', register_email='NULL', customer_number='NULL', cif_number='NULL', notes='NULL' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("########################################################################")
print("Sucessfully Deleted in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select bname, acc_holder, acc_number, ifsc_number, branch_name, register_mobile, register_email, customer_number, cif_number, notes from bank_details")
bank_data=mycur.fetchall()
print((tabulate(bank_data, headers=
["Bank Name","Acc Holder Name","Acc Number","IFSC Code","Branch Name","Mobile Number","Email ID","Customer Number","CIF Number","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
elif tab_sele==4:
mycur.execute("select *from save_cards")
card_data=mycur.fetchall()
print((tabulate(card_data, headers=
["Altering\nNumber","Provider Name","Holder Name","Card Type","Card Number","Expiry Data","CVV Number","Mobile Number","Notes"], tablefmt="psql")))
print("\n########################################################################")
user_col=int(input("How many Accounts to be Deleted?:"))
for i in range(user_col):
while True:
try:
alter_col=int(input("Enter Altering Number of your Deleting Row: "))
break
except ValueError:
print("Enter Only Integer, Invalid Input")
input("Press Enter To Continue")
rep=("update save_cards set card_provider='NULL', card_holder='NULL', card_type='NULL', card_number='NULL', card_expiry='NULL', card_cvv='NULL', card_register_mobile='NULL', notes='NULL' where sno="+str(alter_col)+";")
mycur.execute(rep)
print("########################################################################")
print("Sucessfully Deleted in Database as given,\nThank You Have a Nice Day :)")
print("########################################################################")
print("\n=======New Table=======")
mycur.execute("select card_provider, card_holder, card_type, card_number, card_expiry, card_cvv, card_register_mobile, notes from save_cards")
card_data=mycur.fetchall()
print((tabulate(card_data, headers=
["Provider Name","Holder Name","Card Type","Card Number","Expiry Data","CVV Number","Mobile Number","Notes"], tablefmt="psql")))
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
elif tab_sele==5:
break
else:
print("Wrong Option :(")
input("Press Enter To Continue")
print("\n########################################################################")
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To continue")
elif up_view==4:
break
else:
print("Wrong Option :(")
input("Press Enter To Continue")
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To continue")
else:
print("Sorry!!Access Denied")
mycur.execute("delete from pass_db where user_choice=3")
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
##Main Choice4
elif user_choice==4:
mycur.execute("select *from pass_db")
pre_check=mycur.fetchall()
pre_check_data=len(pre_check)
if pre_check_data==0:
print("Something Went Wrong :( !!!")
print("\n########################################################################")
input("Press Enter To Continue")
print("\n########################################################################")
else:
password=stdiomask.getpass(prompt='Enter the Password: ', mask='*')
pass_db=("""INSERT INTO pass_db(user_choice, password) values (3,'%s')"""%password)
mycur.execute(pass_db)
ori_pass=("select password from pass_db where user_choice=1")
check_pass=("select password from pass_db where user_choice=3")
mycur.execute(check_pass)
data1=mycur.fetchall()
mycur.execute(ori_pass)
data=mycur.fetchall()
if data==data1:
print("Sucessfully Loged In")
mycur.execute("delete from pass_db where user_choice=3")
print("\n########################################################################")
print("copying datas.........")
time=datetime.datetime.now()
table=('\n'+tabulate([[1, 'Social Media Accounts'], [2, 'Internet Banking Accounts'], [3, 'Bank Details'], [4, 'Saved Cards']],
headers=['Number of Tables', 'Tables you can use'], tablefmt='psql'))
mycur.execute("select name, username, password, 2fa, notes from social_media_management")
social_media_data=mycur.fetchall()
social_data=((tabulate(social_media_data, headers=["Social Media Name","Username","Password","2FA","Notes"], tablefmt="psql")))
mycur.execute("select bname, username, password, transaction_password, register_mobile, notes from inter_banking")
inter_bank_data=mycur.fetchall()
inter_data=((tabulate(inter_bank_data, headers=["Bank Name","Username","Password","Transaction Password","Mobile Number","Notes"], tablefmt="psql")))
mycur.execute("select bname, acc_holder, acc_number, ifsc_number, branch_name, register_mobile, register_email, customer_number, cif_number, notes from bank_details")
bank_data=mycur.fetchall()
bank_details=((tabulate(bank_data, headers=["Bank Name","Acc Holder Name","Acc Number","IFSC Code","Branch Name","Mobile Number","Email ID","Customer Number","CIF Number","Notes"], tablefmt="psql")))
mycur.execute("select card_provider, card_holder, card_type, card_number, card_expiry, card_cvv, card_register_mobile, notes from save_cards")
card_data=mycur.fetchall()
save_cards=((tabulate(card_data, headers=
["Provider Name","Holder Name","Card Type","Card Number","Expiry Data","CVV Number","Mobile Number","Notes"], tablefmt="psql")))
test_data=open("Password Manager "+time.strftime("%d %B %Y")+".txt","w")
test_data.write("============================================================================TABLE===================================================================\n")
test_data.writelines(table)
test_data.writelines("\n==============================================================SOCIAL MEDIA ACCOUNTS=================================================================\n")
test_data.writelines(social_data)
test_data.writelines("\n============================================================INTERNET BANKING ACCOUNTS===============================================================\n")
test_data.writelines(inter_data)
test_data.writelines("\n==================================================================BANK DETAILS======================================================================\n")
test_data.writelines(bank_details)
test_data.writelines("\n===================================================================SAVED CARDS======================================================================\n")
test_data.writelines(save_cards)
test_data.close()
print("Copied your Datas to Text File ('Password Manager "+time.strftime("%d %B %Y")+".txt') Sucessfully,\nThank You Have a Nice Day :)")
input("Press Enter To Continue")
print("\n########################################################################")
else:
print("Sorry!!Access Denied")
mycur.execute("delete from pass_db where user_choice=3")
connection.commit()
#connection.close()
input("Press Enter To Continue")
print("\n########################################################################")
##Main Choice5
elif user_choice==5:
print("We Provide you Secure Database \n\nwhere you can Manage Your\n-->Internet Banking\n-->Manage Your Bank Account Details\n-->Save Your Debit/Credit/Other Cards\n-->Your Social Media Accounts")
input("Press Enter To Continue")
##Main Choice6
elif user_choice==6:
print("Under Maintenance")
input("Press Enter To Continue")
print("\n########################################################################")
##Main Choice7
elif user_choice==7:
quit()
else:
print("Invalid Option")
input("Press Enter to Continue")
except ValueError:
print("Enter Only Integer, Invalid Option")
input("Press Enter To continue")
| 81.421053
| 338
| 0.324738
| 5,351
| 83,538
| 4.9473
| 0.062605
| 0.040343
| 0.033997
| 0.03853
| 0.84165
| 0.810599
| 0.79277
| 0.783402
| 0.768103
| 0.765988
| 0
| 0.007209
| 0.4819
| 83,538
| 1,025
| 339
| 81.500488
| 0.604445
| 0.006524
| 0
| 0.785134
| 0
| 0.033682
| 0.422209
| 0.107723
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.132404
| 0.005807
| 0
| 0.005807
| 0.285714
| 0
| 0
| 0
| null | 0
| 0
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| 1
| 1
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|
0
| 8
|
ecbc7902241c2548ca1c47bd76a49f04d71ab442
| 20,094
|
py
|
Python
|
openmdao/components/tests/test_eq_constraint_comp.py
|
onodip/OpenMDAO
|
96a99806fb3a547b881d2ad3da2733bca9978567
|
[
"Apache-2.0"
] | 1
|
2016-05-10T17:01:17.000Z
|
2016-05-10T17:01:17.000Z
|
openmdao/components/tests/test_eq_constraint_comp.py
|
gsoxley/OpenMDAO
|
709401e535cf6933215abd942d4b4d49dbf61b2b
|
[
"Apache-2.0"
] | 3
|
2016-05-10T16:55:46.000Z
|
2018-10-22T23:28:52.000Z
|
openmdao/components/tests/test_eq_constraint_comp.py
|
gsoxley/OpenMDAO
|
709401e535cf6933215abd942d4b4d49dbf61b2b
|
[
"Apache-2.0"
] | 2
|
2018-04-05T15:53:54.000Z
|
2018-10-22T22:48:00.000Z
|
import unittest
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, ExecComp, \
EQConstraintComp, ScipyOptimizeDriver
from openmdao.test_suite.components.sellar import \
SellarDis1withDerivatives, SellarDis2withDerivatives
from openmdao.utils.assert_utils import assert_rel_error, assert_check_partials
from numpy.testing import assert_almost_equal
class SellarIDF(Group):
"""
Individual Design Feasible (IDF) architecture for the Sellar problem.
"""
def setup(self):
# construct the Sellar model with `y1` and `y2` as independent variables
dv = IndepVarComp()
dv.add_output('x', 5.)
dv.add_output('y1', 5.)
dv.add_output('y2', 5.)
dv.add_output('z', np.array([2., 0.]))
self.add_subsystem('dv', dv)
self.add_subsystem('d1', SellarDis1withDerivatives())
self.add_subsystem('d2', SellarDis2withDerivatives())
self.add_subsystem('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
x=0., z=np.array([0., 0.])))
self.add_subsystem('con_cmp1', ExecComp('con1 = 3.16 - y1'))
self.add_subsystem('con_cmp2', ExecComp('con2 = y2 - 24.0'))
self.connect('dv.x', ['d1.x', 'obj_cmp.x'])
self.connect('dv.y1', ['d2.y1', 'obj_cmp.y1', 'con_cmp1.y1'])
self.connect('dv.y2', ['d1.y2', 'obj_cmp.y2', 'con_cmp2.y2'])
self.connect('dv.z', ['d1.z', 'd2.z', 'obj_cmp.z'])
# rather than create a cycle by connecting d1.y1 to d2.y1 and d2.y2 to d1.y2
# we will constrain y1 and y2 to be equal for the two disciplines
equal = EQConstraintComp()
self.add_subsystem('equal', equal)
equal.add_eq_output('y1', add_constraint=True)
equal.add_eq_output('y2', add_constraint=True)
self.connect('dv.y1', 'equal.lhs:y1')
self.connect('d1.y1', 'equal.rhs:y1')
self.connect('dv.y2', 'equal.lhs:y2')
self.connect('d2.y2', 'equal.rhs:y2')
# the driver will effectively solve the cycle
# by satisfying the equality constraints
self.add_design_var('dv.x', lower=0., upper=5.)
self.add_design_var('dv.y1', lower=0., upper=5.)
self.add_design_var('dv.y2', lower=0., upper=5.)
self.add_design_var('dv.z', lower=np.array([-5., 0.]), upper=np.array([5., 5.]))
self.add_objective('obj_cmp.obj')
self.add_constraint('con_cmp1.con1', upper=0.)
self.add_constraint('con_cmp2.con2', upper=0.)
class TestEQConstraintComp(unittest.TestCase):
def test_sellar_idf(self):
prob = Problem(SellarIDF())
prob.driver = ScipyOptimizeDriver(optimizer='SLSQP', disp=False)
prob.setup()
# check derivatives
prob['dv.y1'] = 100
prob['equal.rhs:y1'] = 1
prob.run_model()
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
# check results
prob.run_driver()
assert_rel_error(self, prob['dv.x'], 0., 1e-5)
assert_rel_error(self, prob['dv.z'], [1.977639, 0.], 1e-5)
assert_rel_error(self, prob['obj_cmp.obj'], 3.18339395045, 1e-5)
assert_almost_equal(prob['dv.y1'], 3.16)
assert_almost_equal(prob['d1.y1'], 3.16)
assert_almost_equal(prob['dv.y2'], 3.7552778)
assert_almost_equal(prob['d2.y2'], 3.7552778)
assert_almost_equal(prob['equal.y1'], 0.0)
assert_almost_equal(prob['equal.y2'], 0.0)
def test_create_on_init(self):
prob = Problem()
model = prob.model
# find intersection of two non-parallel lines
model.add_subsystem('indep', IndepVarComp('x', val=0.))
model.add_subsystem('f', ExecComp('y=3*x-3', x=0.))
model.add_subsystem('g', ExecComp('y=2.3*x+4', x=0.))
model.add_subsystem('equal', EQConstraintComp('y', val=11.))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'g.x')
model.connect('f.y', 'equal.lhs:y')
model.connect('g.y', 'equal.rhs:y')
model.add_design_var('indep.x', lower=0., upper=20.)
model.add_objective('f.y')
prob.setup(mode='fwd')
# verify that the output variable has been initialized
self.assertEqual(prob['equal.y'], 11.)
# verify that the constraint has not been added
self.assertFalse('equal.y' in model.get_constraints())
# manually add the constraint
model.add_constraint('equal.y', equals=0.)
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_almost_equal(prob['equal.y'], 0.)
assert_almost_equal(prob['indep.x'], 10.)
assert_almost_equal(prob['f.y'], 27.)
assert_almost_equal(prob['g.y'], 27.)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_create_on_init_add_constraint(self):
prob = Problem()
model = prob.model
# find intersection of two non-parallel lines
model.add_subsystem('indep', IndepVarComp('x', val=0.))
model.add_subsystem('f', ExecComp('y=3*x-3', x=0.))
model.add_subsystem('g', ExecComp('y=2.3*x+4', x=0.))
model.add_subsystem('equal', EQConstraintComp('y', add_constraint=True))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'g.x')
model.connect('f.y', 'equal.lhs:y')
model.connect('g.y', 'equal.rhs:y')
model.add_design_var('indep.x', lower=0., upper=20.)
model.add_objective('f.y')
prob.setup(mode='fwd')
# verify that the constraint has been added as requested
self.assertTrue('equal.y' in model.get_constraints())
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_almost_equal(prob['equal.y'], 0.)
assert_almost_equal(prob['indep.x'], 10.)
assert_almost_equal(prob['f.y'], 27.)
assert_almost_equal(prob['g.y'], 27.)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_create_on_init_add_constraint_no_normalization(self):
prob = Problem()
model = prob.model
# find intersection of two non-parallel lines
model.add_subsystem('indep', IndepVarComp('x', val=-2.0))
model.add_subsystem('f', ExecComp('y=3*x-3', x=0.))
model.add_subsystem('g', ExecComp('y=2.3*x+4', x=0.))
model.add_subsystem('equal', EQConstraintComp('y', add_constraint=True, normalize=False,
ref0=0, ref=100.0))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'g.x')
model.connect('f.y', 'equal.lhs:y')
model.connect('g.y', 'equal.rhs:y')
model.add_design_var('indep.x', lower=0., upper=20.)
model.add_objective('f.y')
prob.setup(mode='fwd')
# verify that the constraint has been added as requested
self.assertTrue('equal.y' in model.get_constraints())
# verify that the output is not being normalized
prob.run_model()
lhs = prob['f.y']
rhs = prob['g.y']
diff = lhs - rhs
assert_rel_error(self, prob['equal.y'], diff)
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_almost_equal(prob['equal.y'], 0.)
assert_almost_equal(prob['indep.x'], 10.)
assert_almost_equal(prob['f.y'], 27.)
assert_almost_equal(prob['g.y'], 27.)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_vectorized(self):
prob = Problem()
model = prob.model
n = 100
# find intersection of two non-parallel lines, vectorized
model.add_subsystem('indep', IndepVarComp('x', val=np.ones(n)))
model.add_subsystem('f', ExecComp('y=3*x-3', x=np.ones(n), y=np.ones(n)))
model.add_subsystem('g', ExecComp('y=2.3*x+4', x=np.ones(n), y=np.ones(n)))
model.add_subsystem('equal', EQConstraintComp('y', val=np.ones(n), add_constraint=True))
model.add_subsystem('obj_cmp', ExecComp('obj=sum(y)', y=np.zeros(n)))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'g.x')
model.connect('f.y', 'equal.lhs:y')
model.connect('g.y', 'equal.rhs:y')
model.connect('f.y', 'obj_cmp.y')
model.add_design_var('indep.x', lower=np.zeros(n), upper=20.*np.ones(n))
model.add_objective('obj_cmp.obj')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_almost_equal(prob['equal.y'], np.zeros(n))
assert_almost_equal(prob['indep.x'], np.ones(n)*10.)
assert_almost_equal(prob['f.y'], np.ones(n)*27.)
assert_almost_equal(prob['g.y'], np.ones(n)*27.)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_vectorized_no_normalization(self):
prob = Problem()
model = prob.model
n = 100
# find intersection of two non-parallel lines, vectorized
model.add_subsystem('indep', IndepVarComp('x', val=-2.0*np.ones(n)))
model.add_subsystem('f', ExecComp('y=3*x-3', x=np.ones(n), y=np.ones(n)))
model.add_subsystem('g', ExecComp('y=2.3*x+4', x=np.ones(n), y=np.ones(n)))
model.add_subsystem('equal', EQConstraintComp('y', val=np.ones(n), add_constraint=True,
normalize=False))
model.add_subsystem('obj_cmp', ExecComp('obj=sum(y)', y=np.zeros(n)))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'g.x')
model.connect('f.y', 'equal.lhs:y')
model.connect('g.y', 'equal.rhs:y')
model.connect('f.y', 'obj_cmp.y')
model.add_design_var('indep.x', lower=np.zeros(n), upper=20.*np.ones(n))
model.add_objective('obj_cmp.obj')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
# verify that the output is not being normalized
prob.run_model()
lhs = prob['f.y']
rhs = prob['g.y']
diff = lhs - rhs
assert_rel_error(self, prob['equal.y'], diff)
prob.run_driver()
assert_almost_equal(prob['equal.y'], np.zeros(n))
assert_almost_equal(prob['indep.x'], np.ones(n)*10.)
assert_almost_equal(prob['f.y'], np.ones(n)*27.)
assert_almost_equal(prob['g.y'], np.ones(n)*27.)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_scalar_with_mult(self):
prob = Problem()
model = prob.model
# find where 2*x == x^2
model.add_subsystem('indep', IndepVarComp('x', val=1.))
model.add_subsystem('multx', IndepVarComp('m', val=2.))
model.add_subsystem('f', ExecComp('y=x**2', x=1.))
model.add_subsystem('equal', EQConstraintComp('y', use_mult=True))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'equal.lhs:y')
model.connect('multx.m', 'equal.mult:y')
model.connect('f.y', 'equal.rhs:y')
model.add_design_var('indep.x', lower=0., upper=10.)
model.add_constraint('equal.y', equals=0.)
model.add_objective('f.y')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_rel_error(self, prob['equal.y'], 0., 1e-6)
assert_rel_error(self, prob['indep.x'], 2., 1e-6)
assert_rel_error(self, prob['f.y'], 4., 1e-6)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_vectorized_with_mult(self):
prob = Problem()
model = prob.model
n = 100
# find where 2*x == x^2, vectorized
model.add_subsystem('indep', IndepVarComp('x', val=np.ones(n)))
model.add_subsystem('multx', IndepVarComp('m', val=np.ones(n)*2.))
model.add_subsystem('f', ExecComp('y=x**2', x=np.ones(n), y=np.ones(n)))
model.add_subsystem('equal', EQConstraintComp('y', val=np.ones(n),
use_mult=True, add_constraint=True))
model.add_subsystem('obj_cmp', ExecComp('obj=sum(y)', y=np.zeros(n)))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'equal.lhs:y')
model.connect('multx.m', 'equal.mult:y')
model.connect('f.y', 'equal.rhs:y')
model.connect('f.y', 'obj_cmp.y')
model.add_design_var('indep.x', lower=np.zeros(n), upper=np.ones(n)*10.)
model.add_objective('obj_cmp.obj')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_rel_error(self, prob['equal.y'], np.zeros(n), 1e-6)
assert_rel_error(self, prob['indep.x'], np.ones(n)*2., 1e-6)
assert_rel_error(self, prob['f.y'], np.ones(n)*4., 1e-6)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_vectorized_with_default_mult(self):
prob = Problem()
model = prob.model
n = 100
# find where 2*x == x^2, vectorized
model.add_subsystem('indep', IndepVarComp('x', val=np.ones(n)))
model.add_subsystem('f', ExecComp('y=x**2', x=np.ones(n), y=np.ones(n)))
model.add_subsystem('equal', EQConstraintComp('y', val=np.ones(n),
use_mult=True, mult_val=2., add_constraint=True))
model.add_subsystem('obj_cmp', ExecComp('obj=sum(y)', y=np.zeros(n)))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'equal.lhs:y')
model.connect('f.y', 'equal.rhs:y')
model.connect('f.y', 'obj_cmp.y')
model.add_design_var('indep.x', lower=np.zeros(n), upper=np.ones(n)*10.)
model.add_objective('obj_cmp.obj')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_rel_error(self, prob['equal.y'], np.zeros(n), 1e-6)
assert_rel_error(self, prob['indep.x'], np.ones(n)*2., 1e-6)
assert_rel_error(self, prob['f.y'], np.ones(n)*4., 1e-6)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_rhs_val(self):
prob = Problem()
model = prob.model
# find where x^2 == 4
model.add_subsystem('indep', IndepVarComp('x', val=1.))
model.add_subsystem('f', ExecComp('y=x**2', x=1.))
model.add_subsystem('equal', EQConstraintComp('y', rhs_val=4.))
model.connect('indep.x', 'f.x')
model.connect('f.y', 'equal.lhs:y')
model.add_design_var('indep.x', lower=0., upper=10.)
model.add_constraint('equal.y', equals=0.)
model.add_objective('f.y')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_rel_error(self, prob['equal.y'], 0., 1e-6)
assert_rel_error(self, prob['indep.x'], 2., 1e-6)
assert_rel_error(self, prob['f.y'], 4., 1e-6)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
def test_vectorized_rhs_val(self):
prob = Problem()
model = prob.model
n = 100
# find where x^2 == 4, vectorized
model.add_subsystem('indep', IndepVarComp('x', val=np.ones(n)))
model.add_subsystem('f', ExecComp('y=x**2', x=np.ones(n), y=np.ones(n)))
model.add_subsystem('equal', EQConstraintComp('y', val=np.ones(n),
rhs_val=np.ones(n)*4., use_mult=True, mult_val=2.))
model.add_subsystem('obj_cmp', ExecComp('obj=sum(y)', y=np.zeros(n)))
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'equal.lhs:y')
model.connect('f.y', 'obj_cmp.y')
model.add_design_var('indep.x', lower=np.zeros(n), upper=np.ones(n)*10.)
model.add_constraint('equal.y', equals=0.)
model.add_objective('obj_cmp.obj')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_rel_error(self, prob['equal.y'], np.zeros(n), 1e-6)
assert_rel_error(self, prob['indep.x'], np.ones(n)*2., 1e-6)
assert_rel_error(self, prob['f.y'], np.ones(n)*4., 1e-6)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
def test_renamed_vars(self):
prob = Problem()
model = prob.model
# find intersection of two non-parallel lines, fx_y and gx_y
equal = EQConstraintComp('y', lhs_name='fx_y', rhs_name='gx_y',
add_constraint=True)
model.add_subsystem('indep', IndepVarComp('x', val=0.))
model.add_subsystem('f', ExecComp('y=3*x-3', x=0.))
model.add_subsystem('g', ExecComp('y=2.3*x+4', x=0.))
model.add_subsystem('equal', equal)
model.connect('indep.x', 'f.x')
model.connect('indep.x', 'g.x')
model.connect('f.y', 'equal.fx_y')
model.connect('g.y', 'equal.gx_y')
model.add_design_var('indep.x', lower=0., upper=20.)
model.add_objective('f.y')
prob.setup(mode='fwd')
prob.driver = ScipyOptimizeDriver(disp=False)
prob.run_driver()
assert_almost_equal(prob['equal.y'], 0.)
assert_almost_equal(prob['indep.x'], 10.)
assert_almost_equal(prob['f.y'], 27.)
assert_almost_equal(prob['g.y'], 27.)
cpd = prob.check_partials(out_stream=None)
for (of, wrt) in cpd['equal']:
assert_almost_equal(cpd['equal'][of, wrt]['abs error'], 0.0, decimal=5)
assert_check_partials(cpd, atol=1e-5, rtol=1e-5)
class TestFeatureEQConstraintComp(unittest.TestCase):
def test_feature_sellar_idf(self):
prob = Problem(model=SellarIDF())
prob.driver = ScipyOptimizeDriver(optimizer='SLSQP', disp=True)
prob.setup()
prob.run_driver()
assert_rel_error(self, prob['dv.x'], 0., 1e-5)
assert_rel_error(self, [prob['dv.y1'], prob['d1.y1']], [[3.16], [3.16]], 1e-5)
assert_rel_error(self, [prob['dv.y2'], prob['d2.y2']], [[3.7552778], [3.7552778]], 1e-5)
assert_rel_error(self, prob['dv.z'], [1.977639, 0.], 1e-5)
assert_rel_error(self, prob['obj_cmp.obj'], 3.18339395045, 1e-5)
if __name__ == '__main__': # pragma: no cover
unittest.main()
| 35.818182
| 96
| 0.595252
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0
| 7
|
ecd84c307637926f1b7f031f8fecb3d5ea27ed4e
| 178
|
py
|
Python
|
data_selection/__init__.py
|
christian-5-28/aimlx-demos
|
ba63edb80f37b1a8ced70d5e29038eafa3b48b91
|
[
"MIT"
] | 6
|
2017-06-28T10:50:21.000Z
|
2022-01-05T18:28:39.000Z
|
data_selection/__init__.py
|
christian-5-28/aimlx-demos
|
ba63edb80f37b1a8ced70d5e29038eafa3b48b91
|
[
"MIT"
] | 3
|
2017-12-07T16:02:13.000Z
|
2018-09-06T11:39:36.000Z
|
data_selection/__init__.py
|
christian-5-28/aimlx-demos
|
ba63edb80f37b1a8ced70d5e29038eafa3b48b91
|
[
"MIT"
] | 23
|
2017-08-08T09:31:16.000Z
|
2018-10-24T14:31:36.000Z
|
from flask import Blueprint
data_selection = Blueprint('data_selection', __name__, template_folder='templates', static_folder='static')
from . import data_selection_controller
| 29.666667
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0
| 7
|
01d562b39d248347397d8eed67c45fb54aae31b2
| 49,381
|
py
|
Python
|
startup/SST/HW/energy.py
|
NSLS-II-SST/profile_collection
|
e2c9d1fce421e7ed8a60fb744c34a770f7780803
|
[
"BSD-3-Clause"
] | null | null | null |
startup/SST/HW/energy.py
|
NSLS-II-SST/profile_collection
|
e2c9d1fce421e7ed8a60fb744c34a770f7780803
|
[
"BSD-3-Clause"
] | 12
|
2019-05-30T15:08:15.000Z
|
2021-04-29T03:24:10.000Z
|
startup/SST/HW/energy.py
|
NSLS-II-SST/profile_collection
|
e2c9d1fce421e7ed8a60fb744c34a770f7780803
|
[
"BSD-3-Clause"
] | 2
|
2019-05-23T17:13:04.000Z
|
2019-10-20T14:52:05.000Z
|
from ophyd import (
PVPositioner,
EpicsSignalRO,
PseudoPositioner,SoftPositioner,
PVPositionerPC,
PseudoSingle,
EpicsMotor,
EpicsSignal,
Signal,
)
from ophyd import Component as Cpt
import bluesky.plan_stubs as bps
from ophyd.pseudopos import pseudo_position_argument, real_position_argument
import pathlib
import numpy as np
import xarray as xr
from ..CommonFunctions.functions import boxed_text, colored, run_report
from ..Base.motors import PrettyMotorFMBO
from ..Base.mirrors import FMBHexapodMirrorAxisStandAlonePitch
from ..HW.shutters import psh4
from ..HW.motors import grating, mirror2
from ..HW.mirrors import mir3
run_report(__file__)
class UndulatorMotor(EpicsMotor):
user_setpoint = Cpt(EpicsSignal, "-SP", limits=True)
done = Cpt(EpicsSignalRO, ".MOVN")
done_value = 0
class EpuMode(PVPositionerPC):
setpoint = Cpt(EpicsSignal,"-SP", kind="normal")
readback = Cpt(EpicsSignal,"-RB", kind="normal")
#epu_mode = EpicsSignal(
# "SR:C07-ID:G1A{SST1:1-Ax:Phase}Phs:Mode-SP", name="EPU 60 Mode", kind="normal"
#)
class FMB_Mono_Grating_Type(PVPositioner):
setpoint = Cpt(EpicsSignal,'_TYPE_SP',string=True)
readback = Cpt(EpicsSignal,'_TYPE_MON',string=True)
actuate = Cpt(EpicsSignal,'_DCPL_CALC.PROC')
enable = Cpt(EpicsSignal,'_ENA_CMD.PROC')
kill = Cpt(EpicsSignal,'_KILL_CMD.PROC')
home = Cpt(EpicsSignal,'_HOME_CMD.PROC')
clear_encoder_loss = Cpt(EpicsSignal,'_ENC_LSS_CLR_CMD.PROC')
done = Cpt(EpicsSignal,'_AXIS_STS')
class Monochromator(PVPositioner):
setpoint = Cpt(EpicsSignal, ":ENERGY_SP", kind="normal")
readback = Cpt(EpicsSignalRO, ":ENERGY_MON", kind="hinted")
grating = Cpt(PrettyMotorFMBO, "GrtP}Mtr", name="Mono Grating", kind="normal")
mirror2 = Cpt(PrettyMotorFMBO, "MirP}Mtr", name="Mono Mirror", kind="normal")
cff = Cpt(EpicsSignal, ":CFF_SP", name="Mono CFF", kind="normal", auto_monitor=True)
vls = Cpt(EpicsSignal, ":VLS_B2.A", name="Mono VLS", kind="normal", auto_monitor=True)
gratingx = Cpt(FMB_Mono_Grating_Type,"GrtX}Mtr",kind="normal")
mirror2x = Cpt(FMB_Mono_Grating_Type,"MirX}Mtr",kind="normal")
Scan_Start_ev = Cpt(EpicsSignal,":EVSTART_SP", name="MONO scan start energy", kind="normal")
Scan_Stop_ev = Cpt(EpicsSignal,":EVSTOP_SP", name="MONO scan stop energy", kind="normal")
Scan_Speed_ev = Cpt(EpicsSignal,":EVVELO_SP", name="MONO scan speed", kind="normal")
Scan_Start = Cpt(EpicsSignal,":START_CMD.PROC",name="MONO scan start command",kind="normal")
Scan_Stop = Cpt(EpicsSignal,":ENERGY_ST_CMD.PROC",name="MONO scan start command",kind="normal")
scanlock = Cpt(Signal,value=0,name='lock flag for during scans')
done = Cpt(EpicsSignalRO, ":ERDY_STS")
done_value = 1
stop_signal = Cpt(EpicsSignal, ":ENERGY_ST_CMD")
def _setup_move(self, position):
"""Move and do not wait until motion is complete (asynchronous)"""
self.log.debug("%s.setpoint = %s", self.name, position)
# copy from pv_positioner, with wait changed to false
# possible problem with IOC not returning from a set
self.setpoint.put(position, wait=False)
if self.actuate is not None:
self.log.debug("%s.actuate = %s", self.name, self.actuate_value)
self.actuate.put(self.actuate_value, wait=False)
# mono_en= Monochromator('XF:07ID1-OP{Mono:PGM1-Ax:', name='Monochromator Energy',kind='normal')
class EnPos(PseudoPositioner):
"""Energy pseudopositioner class.
Parameters:
-----------
"""
# synthetic axis
energy = Cpt(PseudoSingle, kind="hinted", limits=(71, 2250), name="Beamline Energy")
polarization = Cpt(
PseudoSingle, kind="hinted", limits=(-1, 180), name="X-ray Polarization"
)
sample_polarization = Cpt(
PseudoSingle, kind="hinted", name="Sample X-ray polarization"
)
# real motors
monoen = Cpt(
Monochromator, "XF:07ID1-OP{Mono:PGM1-Ax:", kind="hinted", name="Mono Energy"
)
epugap = Cpt(
UndulatorMotor,
"SR:C07-ID:G1A{SST1:1-Ax:Gap}-Mtr",
kind="normal",
name="EPU Gap",
)
epuphase = Cpt(
UndulatorMotor,
"SR:C07-ID:G1A{SST1:1-Ax:Phase}-Mtr",
kind="normal",
name="EPU Phase",
)
mir3Pitch = Cpt(
FMBHexapodMirrorAxisStandAlonePitch,
"XF:07ID1-OP{Mir:M3ABC",
kind="normal",
name="M3Pitch",
)
epumode = Cpt(EpuMode,'SR:C07-ID:G1A{SST1:1-Ax:Phase}Phs:Mode',
name='EPU Mode', kind='normal')
sim_epu_mode = Cpt(Signal,value=0,name='dont interact with the real EPU',kind='config')
scanlock = Cpt(Signal,value=0,name="Lock Harmonic, Pitch, Grating for scan",kind='config')
harmonic = Cpt(Signal, value=1, name="EPU Harmonic",kind='config')
m3offset = Cpt(Signal, value=7.91, name="EPU Harmonic",kind='config')
rotation_motor = None
@pseudo_position_argument
def forward(self, pseudo_pos):
"""Run a forward (pseudo -> real) calculation"""
# print('In forward')
ret = self.RealPosition(
epugap=self.gap(pseudo_pos.energy, pseudo_pos.polarization,self.scanlock.get(),self.sim_epu_mode.get()),
monoen=pseudo_pos.energy,
epuphase=abs(self.phase(pseudo_pos.energy, pseudo_pos.polarization,self.sim_epu_mode.get())),
mir3Pitch=self.m3pitchcalc(pseudo_pos.energy,self.scanlock.get()),
epumode=self.mode(pseudo_pos.polarization,self.sim_epu_mode.get()),
#harmonic=self.choose_harmonic(pseudo_pos.energy,pseudo_pos.polarization,self.scanlock.get())
)
# print('finished forward')
return ret
@real_position_argument
def inverse(self, real_pos):
"""Run an inverse (real -> pseudo) calculation"""
# print('in Inverse')
ret = self.PseudoPosition(
energy=real_pos.monoen,
polarization=self.pol(real_pos.epuphase, real_pos.epumode),
sample_polarization=self.sample_pol(
self.pol(real_pos.epuphase, real_pos.epumode)
),
)
# print('Finished inverse')
return ret
def where_sp(self):
return (
"Beamline Energy Setpoint : {}"
"\nMonochromator Readback : {}"
"\nEPU Gap Setpoint : {}"
"\nEPU Gap Readback : {}"
"\nEPU Phase Setpoint : {}"
"\nEPU Phase Readback : {}"
"\nEPU Mode Setpoint : {}"
"\nEPU Mode Readback : {}"
"\nGrating Setpoint : {}"
"\nGrating Readback : {}"
"\nGratingx Setpoint : {}"
"\nGratingx Readback : {}"
"\nMirror2 Setpoint : {}"
"\nMirror2 Readback : {}"
"\nMirror2x Setpoint : {}"
"\nMirror2x Readback : {}"
"\nCFF : {}"
"\nVLS : {}"
).format(
colored(
"{:.2f}".format(self.monoen.setpoint.get()).rstrip("0").rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.readback.get()).rstrip("0").rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epugap.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epugap.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epuphase.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epuphase.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epumode.setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epumode.readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.grating.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.grating.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(self.monoen.gratingx.setpoint.get(),
"yellow",
),
colored(self.monoen.gratingx.readback.get(),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.mirror2.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.mirror2.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(self.monoen.mirror2x.setpoint.get(),
"yellow",
),
colored(self.monoen.mirror2x.readback.get(),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.cff.get()).rstrip("0").rstrip("."), "yellow"
),
colored(
"{:.2f}".format(self.monoen.vls.get()).rstrip("0").rstrip("."), "yellow"
),
)
def where(self):
return (
"Beamline Energy : {}\nPolarization : {}\nSample Polarization : {}"
).format(
colored(
"{:.2f}".format(self.monoen.readback.get()).rstrip("0").rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.polarization.readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.sample_polarization.readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
)
def wh(self):
boxed_text(self.name + " location", self.where_sp(), "green", shrink=True)
def _sequential_move(self, real_pos, timeout=None, **kwargs):
raise Exception("nope")
# end class methods, begin internal methods
# begin LUT Functions
def __init__(
self,
a,
rotation_motor=None,
configpath=pathlib.Path(__file__).parent.absolute() / "config",
**kwargs,
):
super().__init__(a, **kwargs)
self.gap_fit = np.zeros((10, 10))
self.gap_fit[0][:] = [889.981, 222.966, -0.945368, 0.00290731, -5.87973e-06, 7.80556e-09, -6.69661e-12,
3.56679e-15, -1.07195e-18, 1.39775e-22]
self.gap_fit[1][:] = [-51.6545, -1.60757, 0.00914746, -2.65003e-05, 4.46303e-08, -4.8934e-11, 3.51531e-14,
-1.4802e-17, 2.70647e-21, 0]
self.gap_fit[2][:] = [9.74128, 0.0528884, -0.000270428, 6.71135e-07, -6.68204e-10, 2.71974e-13, -2.82766e-17,
-3.77566e-21, 0, 0]
self.gap_fit[3][:] = [-2.94165, -0.00110173, 3.13309e-06, -1.21787e-08, 1.21638e-11, -4.27216e-15, 3.59552e-19,
0, 0, 0]
self.gap_fit[4][:] = [0.19242, 2.19545e-05, 6.11159e-08, 4.21707e-11, -6.84942e-14, 1.84302e-17, 0, 0, 0, 0]
self.gap_fit[5][:] = [-0.00615458, -9.55015e-07, -1.28929e-09, 4.28363e-13, 3.26302e-17, 0, 0, 0, 0, 0]
self.gap_fit[6][:] = [0.000113341, 1.90112e-08, 6.92088e-12, -1.87659e-15, 0, 0, 0, 0, 0, 0]
self.gap_fit[7][:] = [-1.22095e-06, -1.5686e-10, -1.09857e-14, 0, 0, 0, 0, 0, 0, 0]
self.gap_fit[8][:] = [7.13593e-09, 4.69949e-13, 0, 0, 0, 0, 0, 0, 0, 0]
self.gap_fit[9][:] = [-1.74622e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.polphase = xr.load_dataarray(configpath / "polphase.nc")
self.phasepol = xr.DataArray(
data=self.polphase.pol,
coords={"phase": self.polphase.values},
dims={"phase"},
)
self.rotation_motor = rotation_motor
def gap(
self,
energy,
pol,
locked,
sim=0,
):
if(sim):
return self.epugap.get() # never move the gap if we are in simulated gap mode
# this might cause problems if someone else is moving the gap, we might move it back
# but I think this is not a common reason for this mode
self.harmonic.set(self.choose_harmonic(energy, pol, locked))
energy = energy / self.harmonic.get()
if (pol == -1):
encalc = energy - 105.002
gap = 13979.0
gap += 82.857 * encalc ** 1
gap += -0.26294 * encalc ** 2
gap += 0.00090199 * encalc ** 3
gap += -2.3176e-06 * encalc ** 4
gap += 4.205e-09 * encalc ** 5
gap += -5.139e-12 * encalc ** 6
gap += 4.0034e-15 * encalc ** 7
gap += -1.7862e-18 * encalc ** 8
gap += 3.4687e-22 * encalc ** 9
return max(14000.0,min(100000.0, gap))
elif (pol == -0.5):
encalc = energy - 104.996
gap = 14013.0
gap += 82.76 * encalc ** 1
gap += -0.26128 * encalc ** 2
gap += 0.00088353 * encalc ** 3
gap += -2.2149e-06 * encalc ** 4
gap += 3.8919e-09 * encalc ** 5
gap += -4.5887e-12 * encalc ** 6
gap += 3.4467e-15 * encalc ** 7
gap += -1.4851e-18 * encalc ** 8
gap += 2.795e-22 * encalc ** 9
return max(14000.0,min(100000.0, gap))
elif 0 <= pol <= 90:
return max(14000.0,min(100000.0, self.epu_gap(energy,pol)))
elif 90 < pol <= 180:
return max(14000.0,min(100000.0, self.epu_gap(energy,180.0-pol)))
else:
return np.nan
def epu_gap(self, en, pol):
"""
calculate the epu gap from the energy and polarization, using a 2D polynomial fit
@param en: energy (valid between ~70 and 1300
@param pol: polarization (valid between 0 and 90)
@return: gap in microns
"""
x = float(en)
y = float(pol)
z = 0.0
for i in np.arange(self.gap_fit.shape[0]):
for j in np.arange(self.gap_fit.shape[1]):
z += self.gap_fit[j, i] * (x ** i) * (y ** j)
return z
def phase(self, en, pol,sim=0):
if(sim):
return self.epuphase.get() # never move the gap if we are in simulated gap mode
# this might cause problems if someone else is moving the gap, we might move it back
# but I think this is not a common reason for this mode
if pol == -1:
return 15000
elif pol == -0.5:
return 15000
elif 90 < pol <= 180:
return -min(29500.0, max(0.0,float(self.polphase.interp(pol=180 - pol, method="cubic"))))
else:
return min(29500.0, max(0.0, float(self.polphase.interp(pol=pol, method="cubic"))))
def pol(self, phase, mode):
if mode == 0:
return -1
elif mode == 1:
return -0.5
elif mode == 2:
return float(self.phasepol.interp(phase=np.abs(phase), method="cubic"))
elif mode == 3:
return 180 - float(
self.phasepol.interp(phase=np.abs(phase), method="cubic")
)
def mode(self, pol,sim=0):
"""
@param pol:
@return:
"""
if(sim):
return self.epumode.get() # never move the gap if we are in simulated gap mode
# this might cause problems if someone else is moving the gap, we might move it back
# but I think this is not a common reason for this mode
if pol == -1:
return 0
elif pol == -0.5:
return 1
elif 90 < pol <= 180:
return 3
else:
return 2
def sample_pol(self, pol):
th = self.rotation_motor.user_setpoint.get()
return (
np.arccos(np.cos(pol * np.pi / 180) * np.sin(th * np.pi / 180))
* 180
/ np.pi
)
def m3pitchcalc(self,energy,locked):
pitch = self.mir3Pitch.setpoint.get()
if locked:
return pitch
elif "1200" in self.monoen.gratingx.readback.get():
pitch = self.m3offset.get()+0.038807*np.exp(-(energy-100)/91.942)+0.050123*np.exp(-(energy-100)/1188.9)
elif "250" in self.monoen.gratingx.readback.get():
pitch = self.m3offset.get()+0.022665*np.exp(-(energy-90)/37.746)+0.024897*np.exp(-(energy-90)/450.9)
return round(100*pitch)/100
def choose_harmonic(self,energy,pol,locked):
if locked:
return self.harmonic.get()
elif energy < 1200:
return 1
else:
return 3
def base_set_polarization(pol, en):
yield from bps.mv(en.polarization, pol)
return 0
def base_grating_to_250(mono_en, en):
type = mono_en.gratingx.readback.get()
if '250' in type:
print("the grating is already at 250 l/mm")
return 0 # the grating is already here
print("Moving the grating to 250 l/mm. This will take a minute...")
yield from psh4.close_plan()
yield from bps.abs_set(mono_en.gratingx, 2, wait=True)
#yield from bps.sleep(60)
yield from bps.mv(mirror2.user_offset, 0.04) #0.0315)
yield from bps.mv(grating.user_offset, -0.0874)#-0.0959)
yield from bps.mv(mono_en.cff, 1.385)
yield from bps.mv(en, 270)
yield from psh4.open_plan()
print("the grating is now at 250 l/mm")
return 1
def base_grating_to_1200(mono_en, en):
type = mono_en.gratingx.readback.get()
if '1200' in type:
print("the grating is already at 1200 l/mm")
return 0 # the grating is already here
print("Moving the grating to 1200 l/mm. This will take a minute...")
yield from psh4.close_plan()
yield from bps.abs_set(mono_en.gratingx, 9, wait=True)
#yield from bps.sleep(60)
yield from bps.mv(mirror2.user_offset, 0.2044) #0.1962) #0.2052) # 0.1745) # 8.1264)
yield from bps.mv(grating.user_offset, 0.0769) #0.0687) # 0.0777) # 0.047) # 7.2964) # 7.2948)#7.2956
yield from bps.mv(mono_en.cff, 1.7)
yield from bps.mv(en, 270)
yield from psh4.open_plan()
print("the grating is now at 1200 l/mm")
return 1
def epugap_from_en_pol(energy, polarization):
gap = None
if polarization == 190: # vertical polarization (29500 phase)
if 145.212 <= energy < 1100:
enoff = energy - 145.212
gap = (
(enoff ** 0) * 14012.9679723399
+ (enoff ** 1) * 50.90077784479197
+ (enoff ** 2) * -0.151128059295173
+ (enoff ** 3) * 0.0007380466942855418
+ (enoff ** 4) * -2.88796126025716e-06
+ (enoff ** 5) * 7.334088791503296e-09
+ (enoff ** 6) * -1.138174337292876e-11
+ (enoff ** 7) * 1.043317214147193e-14
+ (enoff ** 8) * -5.190019656736424e-18
+ (enoff ** 9) * 1.081963010325867e-21
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 145.212
gap = (
(enoff ** 0) * 14012.9679723399
+ (enoff ** 1) * 50.90077784479197
+ (enoff ** 2) * -0.151128059295173
+ (enoff ** 3) * 0.0007380466942855418
+ (enoff ** 4) * -2.88796126025716e-06
+ (enoff ** 5) * 7.334088791503296e-09
+ (enoff ** 6) * -1.138174337292876e-11
+ (enoff ** 7) * 1.043317214147193e-14
+ (enoff ** 8) * -5.190019656736424e-18
+ (enoff ** 9) * 1.081963010325867e-21
)
else:
gap = None
elif polarization == 126: # 26000 phase
if 159.381 <= energy < 1100:
enoff = energy - 159.381
gap = (
(enoff ** 0) * 14016.21086765142
+ (enoff ** 1) * 47.07181476458327
+ (enoff ** 2) * -0.1300551161025656
+ (enoff ** 3) * 0.0006150285348211382
+ (enoff ** 4) * -2.293881944658508e-06
+ (enoff ** 5) * 5.587375098889097e-09
+ (enoff ** 6) * -8.43630153398218e-12
+ (enoff ** 7) * 7.633856981759912e-15
+ (enoff ** 8) * -3.794296038862279e-18
+ (enoff ** 9) * 7.983637046811202e-22
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 159.381
gap = (
(enoff ** 0) * 14016.21086765142
+ (enoff ** 1) * 47.07181476458327
+ (enoff ** 2) * -0.1300551161025656
+ (enoff ** 3) * 0.0006150285348211382
+ (enoff ** 4) * -2.293881944658508e-06
+ (enoff ** 5) * 5.587375098889097e-09
+ (enoff ** 6) * -8.43630153398218e-12
+ (enoff ** 7) * 7.633856981759912e-15
+ (enoff ** 8) * -3.794296038862279e-18
+ (enoff ** 9) * 7.983637046811202e-22
)
else:
gap = None
elif polarization == 123: # 23000 phase
if 182.5 <= energy < 1100:
enoff = energy - 182.5
gap = (
(enoff ** 0) * 14003.31346237464
+ (enoff ** 1) * 40.94577604418467
+ (enoff ** 2) * -0.06267710555062726
+ (enoff ** 3) * 0.0001737842192174001
+ (enoff ** 4) * -7.357701847539232e-07
+ (enoff ** 5) * 2.558819479531793e-09
+ (enoff ** 6) * -5.240182651164082e-12
+ (enoff ** 7) * 6.024494955600835e-15
+ (enoff ** 8) * -3.616738308743303e-18
+ (enoff ** 9) * 8.848652101678885e-22
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 182.5
gap = (
(enoff ** 0) * 14003.31346237464
+ (enoff ** 1) * 40.94577604418467
+ (enoff ** 2) * -0.06267710555062726
+ (enoff ** 3) * 0.0001737842192174001
+ (enoff ** 4) * -7.357701847539232e-07
+ (enoff ** 5) * 2.558819479531793e-09
+ (enoff ** 6) * -5.240182651164082e-12
+ (enoff ** 7) * 6.024494955600835e-15
+ (enoff ** 8) * -3.616738308743303e-18
+ (enoff ** 9) * 8.848652101678885e-22
)
else:
gap = None
elif polarization == 121: # 21000 phase
if 198.751 <= energy < 1100:
enoff = energy - 198.751
gap = (
(enoff ** 0) * 14036.87876588605
+ (enoff ** 1) * 36.26534721487319
+ (enoff ** 2) * -0.02493769623114209
+ (enoff ** 3) * 7.394536103134409e-05
+ (enoff ** 4) * -7.431387500375352e-07
+ (enoff ** 5) * 3.111643242754014e-09
+ (enoff ** 6) * -6.397457929818655e-12
+ (enoff ** 7) * 7.103146460443289e-15
+ (enoff ** 8) * -4.1024632494443e-18
+ (enoff ** 9) * 9.715673261754361e-22
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 198.751
gap = (
(enoff ** 0) * 14036.87876588605
+ (enoff ** 1) * 36.26534721487319
+ (enoff ** 2) * -0.02493769623114209
+ (enoff ** 3) * 7.394536103134409e-05
+ (enoff ** 4) * -7.431387500375352e-07
+ (enoff ** 5) * 3.111643242754014e-09
+ (enoff ** 6) * -6.397457929818655e-12
+ (enoff ** 7) * 7.103146460443289e-15
+ (enoff ** 8) * -4.1024632494443e-18
+ (enoff ** 9) * 9.715673261754361e-22
)
else:
gap = None
elif polarization == 118: # 18000 phase
if 207.503 <= energy < 1100:
enoff = energy - 207.503
gap = (
(enoff ** 0) * 14026.99244058688
+ (enoff ** 1) * 41.45793369967348
+ (enoff ** 2) * -0.05393526187293287
+ (enoff ** 3) * 0.000143951535786684
+ (enoff ** 4) * -3.934262835746608e-07
+ (enoff ** 5) * 6.627045869131144e-10
+ (enoff ** 6) * -4.544338541442881e-13
+ (enoff ** 7) * -8.922084434570775e-17
+ (enoff ** 8) * 2.598052818031009e-19
+ (enoff ** 9) * -8.57226301371417e-23
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 207.503
gap = (
(enoff ** 0) * 14026.99244058688
+ (enoff ** 1) * 41.45793369967348
+ (enoff ** 2) * -0.05393526187293287
+ (enoff ** 3) * 0.000143951535786684
+ (enoff ** 4) * -3.934262835746608e-07
+ (enoff ** 5) * 6.627045869131144e-10
+ (enoff ** 6) * -4.544338541442881e-13
+ (enoff ** 7) * -8.922084434570775e-17
+ (enoff ** 8) * 2.598052818031009e-19
+ (enoff ** 9) * -8.57226301371417e-23
)
else:
gap = None
elif polarization == 115: # 15000 phase
if 182.504 <= energy < 1100:
enoff = energy - 182.504
gap = (
(enoff ** 0) * 13992.18828384784
+ (enoff ** 1) * 53.60817055119084
+ (enoff ** 2) * -0.1051753524422272
+ (enoff ** 3) * 0.0003593146854690839
+ (enoff ** 4) * -1.31756627781552e-06
+ (enoff ** 5) * 3.797812404620049e-09
+ (enoff ** 6) * -7.051992603620334e-12
+ (enoff ** 7) * 7.780656762625199e-15
+ (enoff ** 8) * -4.613775121707344e-18
+ (enoff ** 9) * 1.130384721733557e-21
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 182.504
gap = (
(enoff ** 0) * 13992.18828384784
+ (enoff ** 1) * 53.60817055119084
+ (enoff ** 2) * -0.1051753524422272
+ (enoff ** 3) * 0.0003593146854690839
+ (enoff ** 4) * -1.31756627781552e-06
+ (enoff ** 5) * 3.797812404620049e-09
+ (enoff ** 6) * -7.051992603620334e-12
+ (enoff ** 7) * 7.780656762625199e-15
+ (enoff ** 8) * -4.613775121707344e-18
+ (enoff ** 9) * 1.130384721733557e-21
)
else:
gap = None
elif polarization == 112: # 12000 phase
if 144.997 <= energy < 1100:
enoff = energy - 144.997
gap = (
(enoff ** 0) * 13989.91908871217
+ (enoff ** 1) * 79.52996467926575
+ (enoff ** 2) * -0.397042588553584
+ (enoff ** 3) * 0.002410883165646499
+ (enoff ** 4) * -9.116960991617411e-06
+ (enoff ** 5) * 2.050737514265884e-08
+ (enoff ** 6) * -2.757338309663122e-11
+ (enoff ** 7) * 2.170984724060052e-14
+ (enoff ** 8) * -9.195312153413385e-18
+ (enoff ** 9) * 1.610241510211877e-21
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 144.997
gap = (
(enoff ** 0) * 13989.91908871217
+ (enoff ** 1) * 79.52996467926575
+ (enoff ** 2) * -0.397042588553584
+ (enoff ** 3) * 0.002410883165646499
+ (enoff ** 4) * -9.116960991617411e-06
+ (enoff ** 5) * 2.050737514265884e-08
+ (enoff ** 6) * -2.757338309663122e-11
+ (enoff ** 7) * 2.170984724060052e-14
+ (enoff ** 8) * -9.195312153413385e-18
+ (enoff ** 9) * 1.610241510211877e-21
)
else:
gap = None
elif polarization == 108: # 8000 phase
if 130.875 <= energy < 1040:
enoff = energy - 130.875
gap = (
(enoff ** 0) * 16104.67059771744
+ (enoff ** 1) * 98.54001020289179
+ (enoff ** 2) * -0.5947064552024715
+ (enoff ** 3) * 0.004033533429002568
+ (enoff ** 4) * -1.782124825808961e-05
+ (enoff ** 5) * 4.847183095095359e-08
+ (enoff ** 6) * -8.068283751628014e-11
+ (enoff ** 7) * 8.010337241397708e-14
+ (enoff ** 8) * -4.353748003371495e-17
+ (enoff ** 9) * 9.967428321189753e-21
)
elif 1040 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 130.875
gap = (
(enoff ** 0) * 16104.67059771744
+ (enoff ** 1) * 98.54001020289179
+ (enoff ** 2) * -0.5947064552024715
+ (enoff ** 3) * 0.004033533429002568
+ (enoff ** 4) * -1.782124825808961e-05
+ (enoff ** 5) * 4.847183095095359e-08
+ (enoff ** 6) * -8.068283751628014e-11
+ (enoff ** 7) * 8.010337241397708e-14
+ (enoff ** 8) * -4.353748003371495e-17
+ (enoff ** 9) * 9.967428321189753e-21
)
else:
gap = None
elif polarization == 104: # 4000 phase
if 129.248 <= energy < 986:
enoff = energy - 129.248
gap = (
(enoff ** 0) * 18071.42451568721
+ (enoff ** 1) * 105.3373080773754
+ (enoff ** 2) * -0.6939864876005439
+ (enoff ** 3) * 0.004579806360258253
+ (enoff ** 4) * -1.899845179678045e-05
+ (enoff ** 5) * 4.885880764915016e-08
+ (enoff ** 6) * -7.850671908438762e-11
+ (enoff ** 7) * 7.704987833035725e-14
+ (enoff ** 8) * -4.23491772565011e-17
+ (enoff ** 9) * 1.000126057875859e-20
)
elif 986 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 129.248
gap = (
(enoff ** 0) * 18071.42451568721
+ (enoff ** 1) * 105.3373080773754
+ (enoff ** 2) * -0.6939864876005439
+ (enoff ** 3) * 0.004579806360258253
+ (enoff ** 4) * -1.899845179678045e-05
+ (enoff ** 5) * 4.885880764915016e-08
+ (enoff ** 6) * -7.850671908438762e-11
+ (enoff ** 7) * 7.704987833035725e-14
+ (enoff ** 8) * -4.23491772565011e-17
+ (enoff ** 9) * 1.000126057875859e-20
)
else:
gap = None
elif polarization == 1: # circular polarization
if 233.736 <= energy < 1800:
enoff = energy - 233.736
gap = (
(enoff ** 0) * 15007.3400729319
+ (enoff ** 1) * 40.66671812653791
+ (enoff ** 2) * -0.07652786157391561
+ (enoff ** 3) * 0.0002182211302350642
+ (enoff ** 4) * -5.623344130428556e-07
+ (enoff ** 5) * 1.006174849255388e-09
+ (enoff ** 6) * -1.118133612192828e-12
+ (enoff ** 7) * 7.294270087002236e-16
+ (enoff ** 8) * -2.546331275323855e-19
+ (enoff ** 9) * 3.661307542366029e-23
)
else:
# polarization is 100: # horizontal polarization - default
if 80.0586 <= energy < 1100:
enoff = energy - 80.0586
gap = (
(enoff ** 0) * 13999.72137152461
+ (enoff ** 1) * 123.5660983013199
+ (enoff ** 2) * -0.5357230317064841
+ (enoff ** 3) * 0.00207025419625126
+ (enoff ** 4) * -5.279184665398675e-06
+ (enoff ** 5) * 8.561167840842576e-09
+ (enoff ** 6) * -8.648473125484471e-12
+ (enoff ** 7) * 5.239890404156463e-15
+ (enoff ** 8) * -1.734402937759189e-18
+ (enoff ** 9) * 2.419654287110562e-22
)
elif 1100 <= energy < 2200: # third harmonic
enoff = (energy / 3) - 80.0586
gap = (
(enoff ** 0) * 13999.72137152461
+ (enoff ** 1) * 123.5660983013199
+ (enoff ** 2) * -0.5357230317064841
+ (enoff ** 3) * 0.00207025419625126
+ (enoff ** 4) * -5.279184665398675e-06
+ (enoff ** 5) * 8.561167840842576e-09
+ (enoff ** 6) * -8.648473125484471e-12
+ (enoff ** 7) * 5.239890404156463e-15
+ (enoff ** 8) * -1.734402937759189e-18
+ (enoff ** 9) * 2.419654287110562e-22
)
else:
gap = None
return gap
class EnSimEPUPos(PseudoPositioner):
"""Energy pseudopositioner class.
Parameters:
-----------
"""
# synthetic axis
energy = Cpt(PseudoSingle, kind="hinted", limits=(71, 2250), name="Beamline Energy")
polarization = Cpt(
PseudoSingle, kind="hinted", limits=(-1, 180), name="X-ray Polarization"
)
sample_polarization = Cpt(
PseudoSingle, kind="hinted", name="Sample X-ray polarization"
)
# real motors
monoen = Cpt(
Monochromator, "XF:07ID1-OP{Mono:PGM1-Ax:", kind="hinted", name="Mono Energy"
)
epugap = Cpt(
SoftPositioner,
kind="normal",
name="Fake EPU Gap",
)
epuphase = Cpt(
SoftPositioner,
kind="normal",
name="Fake EPU Phase",
)
mir3Pitch = Cpt(
FMBHexapodMirrorAxisStandAlonePitch,
"XF:07ID1-OP{Mir:M3ABC",
kind="normal",
name="M3Pitch",
)
epumode = Cpt(SoftPositioner,
name='Fake EPU Mode', kind='normal')
sim_epu_mode = Cpt(Signal,value=0,name='dont interact with the real EPU',kind='config')
scanlock = Cpt(Signal,value=0,name="Lock Harmonic, Pitch, Grating for scan",kind='config')
harmonic = Cpt(Signal, value=1, name="EPU Harmonic",kind='config')
m3offset = Cpt(Signal, value=7.91, name="EPU Harmonic",kind='config')
rotation_motor = None
@pseudo_position_argument
def forward(self, pseudo_pos):
"""Run a forward (pseudo -> real) calculation"""
# print('In forward')
ret = self.RealPosition(
epugap=self.gap(pseudo_pos.energy, pseudo_pos.polarization,self.scanlock.get(),self.sim_epu_mode.get()),
monoen=pseudo_pos.energy,
epuphase=abs(self.phase(pseudo_pos.energy, pseudo_pos.polarization,self.sim_epu_mode.get())),
mir3Pitch=self.m3pitchcalc(pseudo_pos.energy,self.scanlock.get()),
epumode=self.mode(pseudo_pos.polarization,self.sim_epu_mode.get()),
#harmonic=self.choose_harmonic(pseudo_pos.energy,pseudo_pos.polarization,self.scanlock.get())
)
# print('finished forward')
return ret
@real_position_argument
def inverse(self, real_pos):
"""Run an inverse (real -> pseudo) calculation"""
# print('in Inverse')
ret = self.PseudoPosition(
energy=real_pos.monoen,
polarization=self.pol(real_pos.epuphase, real_pos.epumode),
sample_polarization=self.sample_pol(
self.pol(real_pos.epuphase, real_pos.epumode)
),
)
# print('Finished inverse')
return ret
def where_sp(self):
return (
"Beamline Energy Setpoint : {}"
"\nMonochromator Readback : {}"
"\nEPU Gap Setpoint : {}"
"\nEPU Gap Readback : {}"
"\nEPU Phase Setpoint : {}"
"\nEPU Phase Readback : {}"
"\nEPU Mode Setpoint : {}"
"\nEPU Mode Readback : {}"
"\nGrating Setpoint : {}"
"\nGrating Readback : {}"
"\nGratingx Setpoint : {}"
"\nGratingx Readback : {}"
"\nMirror2 Setpoint : {}"
"\nMirror2 Readback : {}"
"\nMirror2x Setpoint : {}"
"\nMirror2x Readback : {}"
"\nCFF : {}"
"\nVLS : {}"
).format(
colored(
"{:.2f}".format(self.monoen.setpoint.get()).rstrip("0").rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.readback.get()).rstrip("0").rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epugap.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epugap.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epuphase.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epuphase.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epumode.setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.epumode.readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.grating.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.grating.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(self.monoen.gratingx.setpoint.get(),
"yellow",
),
colored(self.monoen.gratingx.readback.get(),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.mirror2.user_setpoint.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.mirror2.user_readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(self.monoen.mirror2x.setpoint.get(),
"yellow",
),
colored(self.monoen.mirror2x.readback.get(),
"yellow",
),
colored(
"{:.2f}".format(self.monoen.cff.get()).rstrip("0").rstrip("."), "yellow"
),
colored(
"{:.2f}".format(self.monoen.vls.get()).rstrip("0").rstrip("."), "yellow"
),
)
def where(self):
return (
"Beamline Energy : {}\nPolarization : {}\nSample Polarization : {}"
).format(
colored(
"{:.2f}".format(self.monoen.readback.get()).rstrip("0").rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.polarization.readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
colored(
"{:.2f}".format(self.sample_polarization.readback.get())
.rstrip("0")
.rstrip("."),
"yellow",
),
)
def wh(self):
boxed_text(self.name + " location", self.where_sp(), "green", shrink=True)
def _sequential_move(self, real_pos, timeout=None, **kwargs):
raise Exception("nope")
# end class methods, begin internal methods
# begin LUT Functions
def __init__(
self,
a,
rotation_motor=None,
configpath=pathlib.Path(__file__).parent.absolute() / "config",
**kwargs,
):
super().__init__(a, **kwargs)
self.gap_fit = np.zeros((10, 10))
self.gap_fit[0][:] = [889.981, 222.966, -0.945368, 0.00290731, -5.87973e-06, 7.80556e-09, -6.69661e-12,
3.56679e-15, -1.07195e-18, 1.39775e-22]
self.gap_fit[1][:] = [-51.6545, -1.60757, 0.00914746, -2.65003e-05, 4.46303e-08, -4.8934e-11, 3.51531e-14,
-1.4802e-17, 2.70647e-21, 0]
self.gap_fit[2][:] = [9.74128, 0.0528884, -0.000270428, 6.71135e-07, -6.68204e-10, 2.71974e-13, -2.82766e-17,
-3.77566e-21, 0, 0]
self.gap_fit[3][:] = [-2.94165, -0.00110173, 3.13309e-06, -1.21787e-08, 1.21638e-11, -4.27216e-15, 3.59552e-19,
0, 0, 0]
self.gap_fit[4][:] = [0.19242, 2.19545e-05, 6.11159e-08, 4.21707e-11, -6.84942e-14, 1.84302e-17, 0, 0, 0, 0]
self.gap_fit[5][:] = [-0.00615458, -9.55015e-07, -1.28929e-09, 4.28363e-13, 3.26302e-17, 0, 0, 0, 0, 0]
self.gap_fit[6][:] = [0.000113341, 1.90112e-08, 6.92088e-12, -1.87659e-15, 0, 0, 0, 0, 0, 0]
self.gap_fit[7][:] = [-1.22095e-06, -1.5686e-10, -1.09857e-14, 0, 0, 0, 0, 0, 0, 0]
self.gap_fit[8][:] = [7.13593e-09, 4.69949e-13, 0, 0, 0, 0, 0, 0, 0, 0]
self.gap_fit[9][:] = [-1.74622e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.polphase = xr.load_dataarray(configpath / "polphase.nc")
self.phasepol = xr.DataArray(
data=self.polphase.pol,
coords={"phase": self.polphase.values},
dims={"phase"},
)
self.rotation_motor = rotation_motor
def gap(
self,
energy,
pol,
locked,
sim=0,
):
if(sim):
return self.epugap.get() # never move the gap if we are in simulated gap mode
# this might cause problems if someone else is moving the gap, we might move it back
# but I think this is not a common reason for this mode
self.harmonic.set(self.choose_harmonic(energy, pol, locked))
energy = energy / self.harmonic.get()
if (pol == -1):
encalc = energy - 105.002
gap = 13979.0
gap += 82.857 * encalc ** 1
gap += -0.26294 * encalc ** 2
gap += 0.00090199 * encalc ** 3
gap += -2.3176e-06 * encalc ** 4
gap += 4.205e-09 * encalc ** 5
gap += -5.139e-12 * encalc ** 6
gap += 4.0034e-15 * encalc ** 7
gap += -1.7862e-18 * encalc ** 8
gap += 3.4687e-22 * encalc ** 9
return max(14000.0,min(100000.0, gap))
elif (pol == -0.5):
encalc = energy - 104.996
gap = 14013.0
gap += 82.76 * encalc ** 1
gap += -0.26128 * encalc ** 2
gap += 0.00088353 * encalc ** 3
gap += -2.2149e-06 * encalc ** 4
gap += 3.8919e-09 * encalc ** 5
gap += -4.5887e-12 * encalc ** 6
gap += 3.4467e-15 * encalc ** 7
gap += -1.4851e-18 * encalc ** 8
gap += 2.795e-22 * encalc ** 9
return max(14000.0,min(100000.0, gap))
elif 0 <= pol <= 90:
return max(14000.0,min(100000.0, self.epu_gap(energy,pol)))
elif 90 < pol <= 180:
return max(14000.0,min(100000.0, self.epu_gap(energy,180.0-pol)))
else:
return np.nan
def epu_gap(self, en, pol):
"""
calculate the epu gap from the energy and polarization, using a 2D polynomial fit
@param en: energy (valid between ~70 and 1300
@param pol: polarization (valid between 0 and 90)
@return: gap in microns
"""
x = float(en)
y = float(pol)
z = 0.0
for i in np.arange(self.gap_fit.shape[0]):
for j in np.arange(self.gap_fit.shape[1]):
z += self.gap_fit[j, i] * (x ** i) * (y ** j)
return z
def phase(self, en, pol,sim=0):
if(sim):
return self.epuphase.get() # never move the gap if we are in simulated gap mode
# this might cause problems if someone else is moving the gap, we might move it back
# but I think this is not a common reason for this mode
if pol == -1:
return 15000
elif pol == -0.5:
return 15000
elif 90 < pol <= 180:
return -min(29500.0, max(0.0,float(self.polphase.interp(pol=180 - pol, method="cubic"))))
else:
return min(29500.0, max(0.0, float(self.polphase.interp(pol=pol, method="cubic"))))
def pol(self, phase, mode):
if mode == 0:
return -1
elif mode == 1:
return -0.5
elif mode == 2:
return float(self.phasepol.interp(phase=np.abs(phase), method="cubic"))
elif mode == 3:
return 180 - float(
self.phasepol.interp(phase=np.abs(phase), method="cubic")
)
def mode(self, pol,sim=0):
"""
@param pol:
@return:
"""
if(sim):
return self.epumode.get() # never move the gap if we are in simulated gap mode
# this might cause problems if someone else is moving the gap, we might move it back
# but I think this is not a common reason for this mode
if pol == -1:
return 0
elif pol == -0.5:
return 1
elif 90 < pol <= 180:
return 3
else:
return 2
def sample_pol(self, pol):
th = self.rotation_motor.user_setpoint.get()
return (
np.arccos(np.cos(pol * np.pi / 180) * np.sin(th * np.pi / 180))
* 180
/ np.pi
)
def m3pitchcalc(self,energy,locked):
pitch = self.mir3Pitch.setpoint.get()
if locked:
return pitch
elif "1200" in self.monoen.gratingx.readback.get():
pitch = self.m3offset.get()+0.038807*np.exp(-(energy-100)/91.942)+0.050123*np.exp(-(energy-100)/1188.9)
elif "250" in self.monoen.gratingx.readback.get():
pitch = self.m3offset.get()+0.022665*np.exp(-(energy-90)/37.746)+0.024897*np.exp(-(energy-90)/450.9)
return round(100*pitch)/100
def choose_harmonic(self,energy,pol,locked):
if locked:
return self.harmonic.get()
elif energy < 1200:
return 1
else:
return 3
| 39.759259
| 123
| 0.47822
| 5,206
| 49,381
| 4.483096
| 0.124856
| 0.006684
| 0.007198
| 0.007198
| 0.866447
| 0.850422
| 0.842324
| 0.833626
| 0.825871
| 0.81773
| 0
| 0.209081
| 0.382738
| 49,381
| 1,241
| 124
| 39.791297
| 0.556609
| 0.067475
| 0
| 0.815642
| 0
| 0
| 0.069559
| 0.004748
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032588
| false
| 0
| 0.012104
| 0.003724
| 0.165736
| 0.005587
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
01e72ca4c6d73140e712493516cfcb686ef6c18a
| 8,200
|
py
|
Python
|
tests/test_match_Match.py
|
basbloemsaat/dartsense
|
3114a3b73861baf9cf0019a9a2454d7f38e67af1
|
[
"MIT"
] | null | null | null |
tests/test_match_Match.py
|
basbloemsaat/dartsense
|
3114a3b73861baf9cf0019a9a2454d7f38e67af1
|
[
"MIT"
] | 5
|
2018-03-16T09:59:05.000Z
|
2019-02-10T21:55:03.000Z
|
tests/test_match_Match.py
|
basbloemsaat/dartsense
|
3114a3b73861baf9cf0019a9a2454d7f38e67af1
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
import pytest
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "../lib"))
import dartsense.event
import dartsense.match
import dartsense.player
from pprint import pprint
def test_match_Match_init():
match = dartsense.match.Match()
assert isinstance(match, dartsense.match.Match)
assert hasattr(match, 'id')
assert match.id == None
assert hasattr(match, 'player_1')
assert match.player_1 == None
assert hasattr(match, 'player_1')
assert match.player_2 == None
assert hasattr(match, 'event')
assert match.event == None
assert hasattr(match, 'player_1_score')
assert match.player_1_score == 0
assert hasattr(match, 'player_2_score')
assert match.player_2_score == 0
assert hasattr(match, 'player_1_180s')
assert match.player_1_180s == 0
assert hasattr(match, 'player_2_180s')
assert match.player_2_180s == 0
assert hasattr(match, 'player_1_lollies')
assert match.player_1_lollies == 0
assert hasattr(match, 'player_2_lollies')
assert match.player_2_lollies == 0
assert hasattr(match, 'player_1_finishes')
assert match.player_1_finishes == []
assert hasattr(match, 'player_2_finishes')
assert match.player_2_finishes == []
def test_match_Match_load(setup_db):
match = dartsense.match.Match(id=pytest.setup_vars['match1_id'])
assert isinstance(match, dartsense.match.Match)
assert hasattr(match, 'id')
assert match.id == pytest.setup_vars['match1_id']
assert hasattr(match, 'player_1')
assert isinstance(match.player_1, dartsense.player.Player)
assert match.player_1.id == pytest.setup_vars['player1_id']
assert hasattr(match, 'player_2')
assert isinstance(match.player_2, dartsense.player.Player)
assert match.player_2.id == pytest.setup_vars['player2_id']
assert isinstance(match.event, dartsense.event.Event)
assert match.event.id == pytest.setup_vars['testcompetition1_round1_id']
assert hasattr(match, 'round')
assert match.round == '1'
assert hasattr(match, 'player_1_score')
assert match.player_1_score == 1
assert hasattr(match, 'player_2_score')
assert match.player_2_score == 2
assert hasattr(match, 'player_1_180s')
assert match.player_1_180s == 3
assert hasattr(match, 'player_2_180s')
assert match.player_2_180s == 4
assert hasattr(match, 'player_1_lollies')
assert match.player_1_lollies == 5
assert hasattr(match, 'player_2_lollies')
assert match.player_2_lollies == 6
assert hasattr(match, 'player_1_finishes')
assert match.player_1_finishes == []
assert hasattr(match, 'player_2_finishes')
assert match.player_2_finishes == []
def test_match_Match_new_empty(setup_db):
match = dartsense.match.Match()
assert match.id == None
assert hasattr(match, 'save')
assert match.save() == None
assert match.id == None
assert match.player_1 == None
assert match.player_2 == None
assert match.event == None
player1 = dartsense.player.Player(id=pytest.setup_vars['player1_id'])
player2 = dartsense.player.Player(id=pytest.setup_vars['player2_id'])
event = dartsense.event.Event(id=pytest.setup_vars['testcompetition1_round2_id'])
match.player_1 = player1
match.player_2 = player2
assert isinstance(match.player_1, dartsense.player.Player)
assert match.player_1.id == pytest.setup_vars['player1_id']
assert isinstance(match.player_2, dartsense.player.Player)
assert match.player_2.id == pytest.setup_vars['player2_id']
match.event = event
assert isinstance(match.event, dartsense.event.Event)
assert match.event.id == pytest.setup_vars['testcompetition1_round2_id']
match = dartsense.match.Match()
assert match.id == None
assert hasattr(match, 'save')
assert match.save() == None
assert match.id == None
assert match.player_1 == None
assert match.player_2 == None
assert match.event == None
match.player_1 = pytest.setup_vars['player1_id']
match.player_2 = pytest.setup_vars['player2_id']
assert match.player_1.id == pytest.setup_vars['player1_id']
assert match.player_2.id == pytest.setup_vars['player2_id']
match.event = pytest.setup_vars['testcompetition1_round2_id']
assert isinstance(match.event, dartsense.event.Event)
assert match.event.id == pytest.setup_vars['testcompetition1_round2_id']
new_id = match.save()
assert isinstance(new_id, int)
assert new_id > 0
def test_match_Match_new_construct(setup_db):
player1 = dartsense.player.Player(id=pytest.setup_vars['player1_id'])
player2 = dartsense.player.Player(id=pytest.setup_vars['player2_id'])
event = dartsense.event.Event(id=pytest.setup_vars['testcompetition1_round2_id'])
match = dartsense.match.Match(
player_1=player1,
player_2=player2,
event=event,
)
assert isinstance(match.player_1, dartsense.player.Player)
assert match.player_1.id == pytest.setup_vars['player1_id']
assert isinstance(match.player_2, dartsense.player.Player)
assert match.player_2.id == pytest.setup_vars['player2_id']
assert isinstance(match.event, dartsense.event.Event)
assert match.event.id == pytest.setup_vars['testcompetition1_round2_id']
new_id = match.save()
assert isinstance(new_id, int)
assert new_id > 0
def test_match_Match_update(setup_db):
match = dartsense.match.Match(id=pytest.setup_vars['match1_id'])
# check before update
assert match.player_1.id == pytest.setup_vars['player1_id']
assert match.player_2.id == pytest.setup_vars['player2_id']
assert match.event.id == pytest.setup_vars['testcompetition1_round1_id']
assert match.round == '1'
assert match.type == 'bo3games'
assert match.player_1_score == 1
assert match.player_1_180s == 3
assert match.player_1_lollies == 5
assert match.player_2_score == 2
assert match.player_2_180s == 4
assert match.player_2_lollies == 6
# update
match.player_1 = pytest.setup_vars['player4_id']
match.player_2 = pytest.setup_vars['player3_id']
match.event = pytest.setup_vars['testcompetition1_round2_id']
match.round = '2'
match.type = 'bo5games'
match.player_1_score = 8
match.player_1_180s = 9
match.player_1_lollies = 10
match.player_2_score = 11
match.player_2_180s = 12
match.player_2_lollies = 13
# check before save
assert match.player_1.id == pytest.setup_vars['player4_id']
assert match.player_2.id == pytest.setup_vars['player3_id']
assert match.event.id == pytest.setup_vars['testcompetition1_round2_id']
assert match.round == '2'
assert match.type == 'bo5games'
assert match.player_1_score == 8
assert match.player_1_180s == 9
assert match.player_1_lollies == 10
assert match.player_2_score == 11
assert match.player_2_180s == 12
assert match.player_2_lollies == 13
assert match.save() == pytest.setup_vars['match1_id']
# check before save
assert match.player_1.id == pytest.setup_vars['player4_id']
assert match.player_2.id == pytest.setup_vars['player3_id']
assert match.event.id == pytest.setup_vars['testcompetition1_round2_id']
assert match.round == '2'
assert match.type == 'bo5games'
assert match.player_1_score == 8
assert match.player_1_180s == 9
assert match.player_1_lollies == 10
assert match.player_2_score == 11
assert match.player_2_180s == 12
assert match.player_2_lollies == 13
match = None
match = dartsense.match.Match(
id=pytest.setup_vars['match1_id']
)
# check after reload
assert match.player_1.id == pytest.setup_vars['player4_id']
assert match.player_2.id == pytest.setup_vars['player3_id']
assert match.event.id == pytest.setup_vars['testcompetition1_round2_id']
assert match.round == '2'
assert match.type == 'bo5games'
assert match.player_1_score == 8
assert match.player_1_180s == 9
assert match.player_1_lollies == 10
assert match.player_2_score == 11
assert match.player_2_180s == 12
assert match.player_2_lollies == 13
#
| 32.539683
| 85
| 0.713049
| 1,147
| 8,200
| 4.845684
| 0.061901
| 0.199892
| 0.189637
| 0.103994
| 0.924253
| 0.882332
| 0.860202
| 0.81846
| 0.813242
| 0.780856
| 0
| 0.042733
| 0.175244
| 8,200
| 251
| 86
| 32.669323
| 0.779092
| 0.012561
| 0
| 0.745946
| 0
| 0
| 0.116564
| 0.038566
| 0
| 0
| 0
| 0
| 0.718919
| 1
| 0.027027
| false
| 0
| 0.037838
| 0
| 0.064865
| 0.005405
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
bf1798c9719252d7daa88f3d2cfca117fc8d0b18
| 218
|
py
|
Python
|
moyasar/resource.py
|
nuhamozaini/moyasar-python-1
|
89100f9bac91809e8bbfe1cba0e165ffc9b7b979
|
[
"MIT"
] | null | null | null |
moyasar/resource.py
|
nuhamozaini/moyasar-python-1
|
89100f9bac91809e8bbfe1cba0e165ffc9b7b979
|
[
"MIT"
] | null | null | null |
moyasar/resource.py
|
nuhamozaini/moyasar-python-1
|
89100f9bac91809e8bbfe1cba0e165ffc9b7b979
|
[
"MIT"
] | null | null | null |
from moyasar.actions.fetch import Fetch
from moyasar.actions.list import List
from moyasar.actions.create import Create
from moyasar.actions.update import Update
class Resource(Fetch, List, Create, Update):
pass
| 24.222222
| 44
| 0.807339
| 31
| 218
| 5.677419
| 0.354839
| 0.25
| 0.409091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12844
| 218
| 8
| 45
| 27.25
| 0.926316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.166667
| 0.666667
| 0
| 0.833333
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
bf3334de6dc103065c9e5b83a1c86bd723773f91
| 68,382
|
py
|
Python
|
tests/test_dataflow/conftest.py
|
alexandreMayerowitz/playground-plums
|
a6be79e4c30c7abcbade5581f052a4e8035a2057
|
[
"MIT"
] | null | null | null |
tests/test_dataflow/conftest.py
|
alexandreMayerowitz/playground-plums
|
a6be79e4c30c7abcbade5581f052a4e8035a2057
|
[
"MIT"
] | null | null | null |
tests/test_dataflow/conftest.py
|
alexandreMayerowitz/playground-plums
|
a6be79e4c30c7abcbade5581f052a4e8035a2057
|
[
"MIT"
] | 2
|
2021-02-03T12:37:53.000Z
|
2022-03-09T03:48:12.000Z
|
import os
import shutil
import pytest
from appdirs import user_cache_dir
from plums.commons.path import Path
_tree_complex = {
'metadata.csv': '',
'tree.json': '',
'data': {
'images.json': '',
'images': {
'dataset_1': {
'aoi_0': {
'labeled': {
'tile_00.jpg': '',
'tile_01.jpg': '',
'tile_02.jpg': '',
'tile_03.jpg': '',
'tile_04.jpg': '',
'tile_05.jpg': '',
'tile_06.jpg': '',
'tile_07.jpg': '',
},
'simulated': {
'tile_aa.jpg': '',
'tile_ab.jpg': '',
'tile_ac.jpg': '',
'tile_ad.jpg': '',
'tile_ae.jpg': '',
'tile_af.jpg': '',
}},
'aoi_3': {
'labeled': {
'tile_8.jpg': '',
'tile_9.jpg': '',
'tile_10.jpg': '',
'tile_11.jpg': '',
'tile_12.jpg': '',
'tile_13.jpg': '',
'tile_14.jpg': '',
'tile_15.jpg': '',
},
'simulated': {
'tile_ba.jpg': '',
'tile_bb.jpg': '',
'tile_bc.jpg': '',
'tile_bd.jpg': '',
'tile_be.jpg': '',
'tile_bf.jpg': '',
}
}
},
'dataset_0': {
'labeled': {
'tile_20.jpg': '',
'tile_21.jpg': '',
'tile_22.jpg': '',
'tile_23.jpg': '',
'tile_24.jpg': '',
'tile_25.jpg': '',
'tile_26.jpg': '',
'tile_27.jpg': '',
}
},
'dataset_3': {
'tile_30.jpg': '',
'tile_31.jpg': '',
'tile_32.jpg': '',
'tile_33.jpg': '',
'tile_34.jpg': '',
'added': {
'tile_ca.jpg': '',
'tile_cb.jpg': '',
'tile_cc.jpg': '',
}
}
}
}
}
_tree_pattern_loose = {
'metadata.csv': '',
'tree.json': '',
'data': {
'images.json': '',
'images': {
'dataset_1': {
'aoi_0': {
'labeled': {
'tile_00.jpg': '',
'tile_01.jpg': '',
'tile_00.json': '',
'tile_01.geojson': '',
},
'simulated': {
'tile_00.jpg': '',
'tile_01.jpg': '',
}
},
'aoi_3': {
'labeled': {
'tile_00.jpg': '',
'tile_01.jpg': '',
},
'simulated': {
'tile_00.jpg': '',
'tile_01.jpg': '',
}
}
},
'dataset_0': {
'labeled': {
'prior': {
'tile_00.jpg': '',
'tile_01.jpg': '',
},
'posterior': {
'tile_00.jpg': '',
'tile_01.jpg': '',
}
},
'labels': {
'tile_00.json': '',
'tile_01.json': '',
}
}
},
'labels': {
'dataset_1': {
'aoi_0': {
'simulated': {
'tile_00.json': '',
'tile_01.json': '',
}
},
'aoi_3': {
'labeled': {
'tile_00.json': '',
'tile_01.json': '',
},
'simulated': {
'tile_00.json': '',
'tile_01.json': '',
}
}
},
'dataset_0': {
'labeled': {
'tile_00.json': '',
'tile_01.json': '',
}
}
}
}
}
_tree_pattern_strict = {
'metadata.csv': '',
'tree.json': '',
'data': {
'images.json': '',
'images': {
'dataset_1': {
'aoi_0': {
'labeled': {
'tile_00.jpg': '',
'tile_01.jpg': '',
},
'simulated': {
'tile_00.jpg': '',
'tile_01.jpg': '',
}},
'aoi_3': {
'labeled': {
'tile_00.jpg': '',
'tile_01.jpg': '',
},
'simulated': {
'tile_00.jpg': '',
'tile_01.jpg': '',
}
}
},
'dataset_0': {
'labeled': {
'tile_00.jpg': '',
'tile_01.jpg': '',
},
'labels': {
'tile_00.json': '',
'tile_01.json': '',
}
}
},
'labels': {
'dataset_1': {
'aoi_0': {
'labeled': {
'tile_00.json': '',
'tile_01.json': '',
},
'simulated': {
'tile_00.json': '',
'tile_01.json': '',
}},
'aoi_3': {
'labeled': {
'tile_00.json': '',
'tile_01.json': '',
},
'simulated': {
'tile_00.json': '',
'tile_01.json': '',
}
}
},
'dataset_0': {
'labeled': {
'tile_00.json': '',
'tile_01.json': '',
},
'images': {
'tile_00.jpg': '',
'tile_01.jpg': '',
}
}
},
}
}
with open(str(Path(__file__)[:-1] / 'test_io' / 'test_tile' / '_data' / 'test_jpg.jpg'), 'rb') as f:
_mona_lisa = f.read()
_feature_collection = """
{
"type": "FeatureCollection",
"features": [
{
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
92.7,
256
],
[
92.8,
253.6
],
[
86,
253.4
],
[
85.9,
256
],
[
92.7,
256
]
]
]
},
"type": "Feature",
"properties": {
"last_modifier_id": "35e372a9-6b76-40c6-a3d5-1ee7183c3dc7",
"comment": null,
"orientation": -178.522,
"tags": [
"tag",
"class"
],
"surface": 64.2146176930851,
"kept_percentage": 0.09569366018406641,
"image_id": "9ad5b20165e2873321bbc1f979c6669cdc451014",
"dataset_id": "f16fff43-2535-4e34-afec-6404dcdcd545",
"id": "record.6e73eff2-06f3-11ea-976a-b24c6cdc2bc0",
"zone_id": "10187fa3-30df-4eb4-a1e9-6b1dcdc79951",
"confidence": null,
"angle": 2.16567500171822,
"job_id": null,
"created_at": "2019-11-14T15:28:38.813332",
"modified_at": "2019-11-14T15:28:38.805320",
"length": 15.8988469989003,
"width": 4.06052237034644,
"state": "ADDED",
"record_id": "6e73eff2-06f3-11ea-976a-b2cdca212bc0",
"image_2_id": null,
"owner_id": "35e370a9-6b76-4ac6-a3d5-1eeb983c3dc7"
}
},
{
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
0,
0
],
[
0,
256
],
[
256,
256
],
[
256,
0
],
[
0,
0
]
]
]
},
"type": "Feature",
"properties": {
"kept_percentage": 1,
"mask": true
}
}
]
}
""".encode('utf8')
_taxonomy = """
{
"tag": {
"class": {}
}
}
""".encode('utf8')
_taxonomy_conflict = """
{
"tags": {
"class": {}
}
}
""".encode('utf8')
_dataset_1_summary = """
{
"targetZoom": 18,
"datasetName": "Test PGML 1",
"imageIds": [
["f9525e3bfbd081cd545261b3b5414eb88f689005",
"75ad128196254e711ef7c9b129d1c59153098b18"],
["S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548",
"088dbf07-2879-4b23-af06-b3f4189fcae6"]
],
"zoneIds": [
"fa719db8-31e9-49d1-9344-d4608ef6417e",
"b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7"
],
"creationDate": "2021-12-31T23:59:59.676066",
"datasetId": "63d0da07-0a4b-4ffd-844f-af75c02288e0"
}
""".encode('utf8')
_dataset_2_summary = """
{
"targetZoom": 14,
"datasetName": "Test PGML 2",
"imageIds": [
["d51636c2-e94d-422c-a034-82e4ff8fa7aa"],
["4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed",
"5562b632-72c3-4c21-b24e-e0536d8b20c8"]
],
"zoneIds": [
"c3e8b68b-f862-41bd-848c-6e2df28e4dd8",
"2411dbb6-e7bf-41fd-8898-83325a9c6e5a"
],
"creationDate": "2021-12-31T23:59:59.676066",
"datasetId": "1af6c4c5-278d-40ae-9e32-dc8192f8402a"
}
""".encode('utf8')
_tree_playground = {
'63d0da07-0a4b-4ffd-844f-af75c02288e0': {
'taxonomy.json': _taxonomy,
'dataset_summary.json': _dataset_1_summary,
'samples': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': { # uuid-1
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'88aff0a92b21b86460bfd4474ab1626a.jpg': _mona_lisa
},
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
}
}, # ----------------------------------------------------------------------------------------------------------
'labels': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
'0cc175b9c0f1b6a831c399e269772661.json': _feature_collection
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': {}, # uuid-1
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection,
'88aff0a92b21b86460bfd4474ab1626a.json': _feature_collection
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
}
}, # ----------------------------------------------------------------------------------------------------------
},
'1af6c4c5-278d-40ae-9e32-dc8192f8402a': {
'taxonomy.json': _taxonomy,
'dataset_summary.json': _dataset_2_summary,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection
}
}
},
# --------------------------------------------------- Out ----------------------------------------------------------
'd53187c6-4d99-11ea-92ec-a0481c91ddca': { # uuid-1
'taxonomy.json': _taxonomy_conflict,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
}
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
'bb959eb8-692f-3225-9506-e885ac3770bf': { # uuid-3
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
}
},
'6e9a3589-d6c8-534e-ada1-b769aeec2fe2': { # uuid-5
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
# ------------------------------------------------------------------------------------------------------------------
}
_tree_playground_summary_missing_image = {
'63d0da07-0a4b-4ffd-844f-af75c02288e0': {
'taxonomy.json': _taxonomy,
'dataset_summary.json': _dataset_1_summary,
'samples': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128156ef4e711ef7c9b129d1c59153098b18': { # Spot -> Missing from summary
'7c47df10ef5649278c052e93e1d1903a.jpg': _mona_lisa
},
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': { # uuid-1
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'88aff0a92b21b86460bfd4474ab1626a.jpg': _mona_lisa
},
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
}
}, # ----------------------------------------------------------------------------------------------------------
'labels': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection,
'7c47df10ef5649278c052e93e1d1903a.json': _feature_collection # -> Missing from summary
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
'0cc175b9c0f1b6a831c399e269772661.json': _feature_collection
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': {}, # uuid-1
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection,
'88aff0a92b21b86460bfd4474ab1626a.json': _feature_collection
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
}
}, # ----------------------------------------------------------------------------------------------------------
},
'1af6c4c5-278d-40ae-9e32-dc8192f8402a': {
'taxonomy.json': _taxonomy,
'dataset_summary.json': _dataset_2_summary,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection
}
}
},
# --------------------------------------------------- Out ----------------------------------------------------------
'd53187c6-4d99-11ea-92ec-a0481c91ddca': { # uuid-1
'taxonomy.json': _taxonomy_conflict,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
}
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
'bb959eb8-692f-3225-9506-e885ac3770bf': { # uuid-3
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
}
},
'6e9a3589-d6c8-534e-ada1-b769aeec2fe2': { # uuid-5
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
# ------------------------------------------------------------------------------------------------------------------
}
_tree_playground_summary_missing_zone = {
'63d0da07-0a4b-4ffd-844f-af75c02288e0': {
'taxonomy.json': _taxonomy,
'dataset_summary.json': _dataset_1_summary,
'samples': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': { # uuid-1
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'88aff0a92b21b86460bfd4474ab1626a.jpg': _mona_lisa
},
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
}
}, # ----------------------------------------------------------------------------------------------------------
'labels': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
'0cc175b9c0f1b6a831c399e269772661.json': _feature_collection
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': {}, # uuid-1
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection,
'88aff0a92b21b86460bfd4474ab1626a.json': _feature_collection
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
}
}, # ----------------------------------------------------------------------------------------------------------
},
'1af6c4c5-278d-40ae-9e32-dc8192f8402a': {
'taxonomy.json': _taxonomy,
'dataset_summary.json': _dataset_2_summary,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa
},
},
'2411ef56-e7bf-41fd-8898-83325a9c6e5a': { # -> Missing from summary
'4e1ef5a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'4a8a08f0ef56b73795649038408b5f33.jpg': _mona_lisa,
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection
},
'2411ef56-e7bf-41fd-8898-83325a9c6e5a': { # -> Missing from summary
'4a8a08f0ef56b73795649038408b5f33.json': _feature_collection,
}
}
},
# --------------------------------------------------- Out ----------------------------------------------------------
'd53187c6-4d99-11ea-92ec-a0481c91ddca': { # uuid-1
'taxonomy.json': _taxonomy_conflict,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
}
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
'bb959eb8-692f-3225-9506-e885ac3770bf': { # uuid-3
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
}
},
'6e9a3589-d6c8-534e-ada1-b769aeec2fe2': { # uuid-5
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
# ------------------------------------------------------------------------------------------------------------------
}
_tree_playground_summary_missing_dataset = {
'63d0da07-0a4b-4ffd-844f-af75c02288e0': {
'taxonomy.json': _taxonomy,
'dataset_summary.json': _dataset_1_summary,
'samples': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': { # uuid-1
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'88aff0a92b21b86460bfd4474ab1626a.jpg': _mona_lisa
},
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
}
}, # ----------------------------------------------------------------------------------------------------------
'labels': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
'0cc175b9c0f1b6a831c399e269772661.json': _feature_collection
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': {}, # uuid-1
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection,
'88aff0a92b21b86460bfd4474ab1626a.json': _feature_collection
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
}
}, # ----------------------------------------------------------------------------------------------------------
},
'1af6c4c5-278d-40ae-9e32-dc8192f8402a': {
'taxonomy.json': _taxonomy,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection
}
}
},
# --------------------------------------------------- Out ----------------------------------------------------------
'd53187c6-4d99-11ea-92ec-a0481c91ddca': { # uuid-1
'taxonomy.json': _taxonomy_conflict,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
}
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
'bb959eb8-692f-3225-9506-e885ac3770bf': { # uuid-3
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
}
},
'6e9a3589-d6c8-534e-ada1-b769aeec2fe2': { # uuid-5
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
# ------------------------------------------------------------------------------------------------------------------
}
_tree_playground_summary_missing_summaries = {
'63d0da07-0a4b-4ffd-844f-af75c02288e0': {
'taxonomy.json': _taxonomy,
'samples': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': { # uuid-1
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'88aff0a92b21b86460bfd4474ab1626a.jpg': _mona_lisa
},
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
}
}, # ----------------------------------------------------------------------------------------------------------
'labels': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
'0cc175b9c0f1b6a831c399e269772661.json': _feature_collection
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': {}, # uuid-1
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection,
'88aff0a92b21b86460bfd4474ab1626a.json': _feature_collection
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
}
}, # ----------------------------------------------------------------------------------------------------------
},
'1af6c4c5-278d-40ae-9e32-dc8192f8402a': {
'taxonomy.json': _taxonomy,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection
}
}
},
# --------------------------------------------------- Out ----------------------------------------------------------
'd53187c6-4d99-11ea-92ec-a0481c91ddca': { # uuid-1
'taxonomy.json': _taxonomy_conflict,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
}
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
'bb959eb8-692f-3225-9506-e885ac3770bf': { # uuid-3
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
}
},
'6e9a3589-d6c8-534e-ada1-b769aeec2fe2': { # uuid-5
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
# ------------------------------------------------------------------------------------------------------------------
}
_tree_playground_conflict = {
'63d0da07-0a4b-4ffd-844f-af75c02288e0': {
'taxonomy.json': _taxonomy,
'samples': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': { # uuid-1
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
'088dbf07-2879-4b23-af06-b3f4189fcae6': { # AERIAL
'0cc175b9c0f1b6a831c399e269772661.jpg': _mona_lisa
},
},
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'f9525e3bfbd081cd545261b3b5414eb88f689005': { # Pleiades
'7c47df1097b349278c052e93e1d1903a.jpg': _mona_lisa
},
'75ad128196254e711ef7c9b129d1c59153098b18': { # Spot
'88aff0a92b21b86460bfd4474ab1626a.jpg': _mona_lisa
},
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'S2B_MSIL1C_20200212T025609_N0209_R003_T47DMH_20200212T054548': { # Sentinel-2
'453e41d218e071ccfb2d1c99ce23906a.jpg': _mona_lisa,
'453e41d218e071ccfb2d1c99ce23906a.jgw': '',
},
}
}, # ----------------------------------------------------------------------------------------------------------
'labels': {
'fa719db8-31e9-49d1-9344-d4608ef6417e': {
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection
},
'b4d9ffe3-ab2d-4f18-b1c5-b4c3d9b2f6f7': {
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
'0cc175b9c0f1b6a831c399e269772661.json': _feature_collection
},
# ---------------------------------------------- Out -------------------------------------------------------
'99aa890e-4d9a-11ea-92ec-a0481c91ddca': {}, # uuid-1
'732af79d-f68d-393b-b2f8-9239bcd62a27': { # uuid-3
'7c47df1097b349278c052e93e1d1903a.json': _feature_collection,
'88aff0a92b21b86460bfd4474ab1626a.json': _feature_collection
},
'f9a071b2-7c2d-5987-b251-f386a554e28a': { # uuid-5
'453e41d218e071ccfb2d1c99ce23906a.json': _feature_collection,
}
}, # ----------------------------------------------------------------------------------------------------------
},
'1af6c4c5-278d-40ae-9e32-dc8192f8402a': {
'taxonomy.json': _taxonomy_conflict,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection
}
}
},
# --------------------------------------------------- Out ----------------------------------------------------------
'd53187c6-4d99-11ea-92ec-a0481c91ddca': { # uuid-1
'taxonomy.json': _taxonomy_conflict,
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
}
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
'bb959eb8-692f-3225-9506-e885ac3770bf': { # uuid-3
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
}
},
'6e9a3589-d6c8-534e-ada1-b769aeec2fe2': { # uuid-5
'samples': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'd51636c2-e94d-422c-a034-82e4ff8fa7aa': { # Deimos-2
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.jpg': _mona_lisa
},
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'4e15b4a3-ee52-4382-b8a8-7d492fb1a6ed': { # Vision1
'04a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
'4a8a08f09d37b73795649038408b5f33.jpg': _mona_lisa,
},
'5562b632-72c3-4c21-b24e-e0536d8b20c8': { # TerraSAR-X
'8277e0910d750195b448797616e091ad.jpg': _mona_lisa
},
}
},
'labels': {
'c3e8b68b-f862-41bd-848c-6e2df28e4dd8': {
'92eb5ffee6ae2fec3ad71c777531578f': '',
'92eb5ffee6ae2fec3ad71c777531578b.json': _feature_collection
},
'2411dbb6-e7bf-41fd-8898-83325a9c6e5a': {
'04a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'4a8a08f09d37b73795649038408b5f33.json': _feature_collection,
'8277e0910d750195b448797616e091ad.json': _feature_collection
}
}
},
# ------------------------------------------------------------------------------------------------------------------
}
def _make_tree_level_from_dict(root, tree):
pointer = Path(root)
for name, value in tree.items():
if isinstance(value, dict):
(pointer / name).mkdir(exist_ok=True)
_make_tree_level_from_dict(pointer / name, value)
else:
with open(str(pointer / name), 'wb') as f:
if isinstance(value, str):
value = value.encode('utf8')
f.write(value)
def _make_tree_path_list_from_dict(root, tree):
pointer = Path(root)
for name, value in tree.items():
if isinstance(value, dict):
yield from _make_tree_path_list_from_dict(pointer / name, value)
else:
yield pointer / name
def _clean_up(path):
for root, dirs, files in path.walk():
for f in files:
os.unlink(str(root / f))
for d in dirs:
shutil.rmtree(str(root / d))
@pytest.fixture()
def json_feature_collection():
return _feature_collection.decode('utf8')
@pytest.fixture(scope="session", autouse=True)
def cache():
cache_dir = Path(user_cache_dir('plums')) / 'pattern'
_clean_up(cache_dir)
yield
_clean_up(cache_dir)
@pytest.fixture(scope='session')
def complex_tree(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('complex_tree')
_make_tree_level_from_dict(tmp_path, _tree_complex)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_complex))
@pytest.fixture(scope='session')
def loose_pattern_tree(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('loose_pattern_tree')
_make_tree_level_from_dict(tmp_path, _tree_pattern_loose)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_pattern_loose))
@pytest.fixture(scope='session')
def strict_pattern_tree(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('strict_pattern_tree')
_make_tree_level_from_dict(tmp_path, _tree_pattern_strict)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_pattern_strict))
@pytest.fixture(scope='session')
def playground_tree(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('playground_tree')
_make_tree_level_from_dict(tmp_path, _tree_playground)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_playground))
@pytest.fixture(scope='session')
def playground_tree_conflict(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('playground_tree_conflict')
_make_tree_level_from_dict(tmp_path, _tree_playground_conflict)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_playground_conflict))
@pytest.fixture(scope='session')
def playground_tree_summary_missing_image(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('playground_tree_summary_missing_image')
_make_tree_level_from_dict(tmp_path, _tree_playground_summary_missing_image)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_playground_summary_missing_image))
@pytest.fixture(scope='session')
def playground_tree_summary_missing_zone(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('playground_tree_summary_missing_zone')
_make_tree_level_from_dict(tmp_path, _tree_playground_summary_missing_zone)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_playground_summary_missing_zone))
@pytest.fixture(scope='session')
def playground_tree_summary_missing_dataset(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('playground_tree_summary_missing_dataset')
_make_tree_level_from_dict(tmp_path, _tree_playground_summary_missing_dataset)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_playground_summary_missing_dataset))
@pytest.fixture(scope='session')
def playground_tree_summary_missing_summaries(tmp_path_factory):
tmp_path = tmp_path_factory.mktemp('playground_tree_summary_missing_summaries')
_make_tree_level_from_dict(tmp_path, _tree_playground_summary_missing_summaries)
return Path(tmp_path), list(_make_tree_path_list_from_dict(tmp_path, _tree_playground_summary_missing_summaries))
| 42.926554
| 120
| 0.497733
| 4,349
| 68,382
| 7.553691
| 0.079559
| 0.037259
| 0.050896
| 0.032876
| 0.927582
| 0.909683
| 0.904447
| 0.899212
| 0.893611
| 0.881617
| 0
| 0.319628
| 0.342722
| 68,382
| 1,592
| 121
| 42.953518
| 0.411271
| 0.081249
| 0
| 0.577836
| 0
| 0
| 0.456488
| 0.382515
| 0
| 0
| 0
| 0
| 0
| 1
| 0.009235
| false
| 0
| 0.003298
| 0.00066
| 0.019129
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
bd83beb388f6151aadf29ba6b2018f8432244850
| 179
|
py
|
Python
|
agagd/agagd/settings/__init__.py
|
leeschumacher/agagd
|
3f23c7b8702c4dcf1b9f4f8a248ee5c5c0b81b9b
|
[
"MIT"
] | null | null | null |
agagd/agagd/settings/__init__.py
|
leeschumacher/agagd
|
3f23c7b8702c4dcf1b9f4f8a248ee5c5c0b81b9b
|
[
"MIT"
] | null | null | null |
agagd/agagd/settings/__init__.py
|
leeschumacher/agagd
|
3f23c7b8702c4dcf1b9f4f8a248ee5c5c0b81b9b
|
[
"MIT"
] | null | null | null |
# local_settings not required, but makes for a friendlier way to set stage
try:
from local_settings import *
except ImportError:
print "local_settings not found"
pass
| 25.571429
| 74
| 0.75419
| 26
| 179
| 5.076923
| 0.807692
| 0.295455
| 0.242424
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.206704
| 179
| 6
| 75
| 29.833333
| 0.929577
| 0.402235
| 0
| 0
| 0
| 0
| 0.228571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.2
| 0.4
| null | null | 0.2
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
bd8ca74a5cc221d730c767197d2a5529654ebce7
| 382,018
|
pyt
|
Python
|
eran/NNet/nnet/ACASXU_run2a_4_7_batch_2000_16bit.pyt
|
pauls658/ReluDiff-ICSE2020-Artifact
|
212854fe04f482183c239e5dfec70106a9a83df8
|
[
"Apache-2.0"
] | 7
|
2020-01-27T21:25:49.000Z
|
2022-01-07T04:37:37.000Z
|
eran/NNet/nnet/ACASXU_run2a_4_7_batch_2000_16bit.pyt
|
yqtianust/ReluDiff-ICSE2020-Artifact
|
149f6efe4799602db749faa576980c36921a07c7
|
[
"Apache-2.0"
] | 1
|
2022-01-25T17:41:54.000Z
|
2022-01-26T02:27:51.000Z
|
eran/NNet/nnet/ACASXU_run2a_4_7_batch_2000_16bit.pyt
|
yqtianust/ReluDiff-ICSE2020-Artifact
|
149f6efe4799602db749faa576980c36921a07c7
|
[
"Apache-2.0"
] | 3
|
2020-03-14T17:12:17.000Z
|
2022-03-16T09:50:46.000Z
|
ReLU
[[-0.426116, -1.36682, 1.82537, -0.359894, 0.781783], [0.0141898, -1.74668, 2.10078, 0.199607, -0.625995], [0.236925, -0.387744, 0.335238, -0.0708328, -0.872241], [0.189836, 1.80645, 0.228501, 1.39699, -1.70341], [3.36428, -0.00959754, -0.02829, -0.00182374, -0.00321689], [-0.00116646, -1.30479, -0.646313, 0.384975, -0.172187], [0.059504, -1.04445, 1.15267, -0.0491381, 0.274068], [-0.0885259, -0.600468, -0.168808, 0.320975, -0.19985], [-0.360641, 0.723289, -1.27592, 0.0365215, -0.00916054], [0.0174352, -0.377868, -0.115205, 0.397747, -0.457395], [0.119381, -1.40394, 1.73194, 0.214848, -0.5308], [0.510919, -0.0145736, 0.0727201, -0.272957, -0.58318], [0.0892236, -0.0912425, -0.536878, -0.193087, 0.259249], [-0.106965, 0.292023, -0.281651, -0.379942, 0.351947], [0.313856, -0.841286, -0.235311, 0.0131289, 0.0961032], [-0.00807936, -0.000563769, -0.00712403, 0.00185073, 0.00615519], [0.142517, 0.0997765, -0.234522, 0.0641164, 0.0173711], [0.120767, -0.621179, 0.840395, -0.0966762, 0.738722], [-0.120072, -0.251172, 2.6124, 0.154528, 0.378212], [0.171677, 2.17068, -0.949588, 0.292661, 0.130445], [-0.118201, 0.905917, 0.0613212, -0.508156, 0.641137], [0.198566, -1.69141, 0.635503, 0.444259, -0.64184], [-0.0835745, 0.0201827, 0.131306, -0.465076, -0.0931595], [-0.00889577, 1.47801, -1.7606, -0.273348, 0.685236], [-0.99453, 0.223245, 0.0780545, -0.00929289, -0.256348], [0.00638617, -0.191506, 0.514769, -0.852697, 0.848115], [-0.297726, -0.242926, 0.274442, 0.999968, -1.1845], [-0.0484978, 0.898421, 0.947518, 0.280254, -0.0864377], [-0.0178505, -0.184101, -0.222805, 0.117964, 0.154774], [0.0333232, 1.36975, -1.6105, -0.107663, 0.495742], [0.98863, 0.416651, 0.00679299, 0.268351, 0.181104], [0.000498813, 0.357345, 1.12966, -0.224096, 0.202364], [0.0563342, 0.365707, 0.477882, -0.373783, 0.145331], [0.0852371, -1.11661, -0.296353, 0.385203, -0.172235], [0.00101593, 0.971395, 0.792908, 0.348388, -0.210291], [0.00250681, 1.24018, -1.09202, -0.273149, 0.162231], [-0.135747, -0.901627, -0.581315, -0.278672, 0.223302], [-0.0149513, 0.016501, 0.000874109, 0.00829948, 0.0108247], [0.129122, -1.53063, 1.5018, 0.11217, 0.243414], [0.10337, 0.0582161, -0.0713023, -0.15957, -2.17338], [-0.0108974, 0.0136867, 0.00199981, -0.00192423, -0.00166569], [0.300335, 1.40717, -0.85673, 0.227232, -0.162827], [0.0199644, -2.69606, 0.983252, 0.515997, -0.76219], [0.0206727, -0.476237, -0.122935, -0.166794, -0.02558], [-0.0372325, -1.68642, -0.0342497, 0.0462046, -0.703736], [0.0919305, 1.05597, -1.07173, -0.341911, 0.411172], [0.355212, 1.22531, 0.438395, 0.765531, -0.377483], [-2.08839, -0.0338612, 0.00263981, 0.326788, 0.19074], [0.014666, 0.575711, -0.750375, 1.29255, -1.5435], [-0.197753, 2.14334, -2.00387, 0.0774846, -0.612016], [-0.426, -1.367, 1.825, -0.3599, 0.7817], [0.01419, -1.747, 2.102, 0.1996, -0.626], [0.2369, -0.3877, 0.3352, -0.07086, -0.872], [0.1898, 1.807, 0.2285, 1.397, -1.703], [3.365, -0.0096, -0.02829, -0.001823, -0.003218], [-0.001166, -1.305, -0.6465, 0.385, -0.1722], [0.0595, -1.045, 1.152, -0.04913, 0.2742], [-0.0885, -0.6006, -0.1688, 0.321, -0.1998], [-0.3606, 0.723, -1.276, 0.03653, -0.00916], [0.01744, -0.378, -0.11523, 0.3977, -0.4573], [0.1194, -1.404, 1.732, 0.2148, -0.531], [0.5107, -0.01457, 0.0727, -0.273, -0.583], [0.08923, -0.09125, -0.537, -0.1931, 0.2593], [-0.107, 0.292, -0.2817, -0.38, 0.352], [0.314, -0.8413, -0.2354, 0.01313, 0.0961], [-0.00808, -0.0005636, -0.007126, 0.001851, 0.006157], [0.1425, 0.0998, -0.2345, 0.0641, 0.01736], [0.1208, -0.621, 0.8403, -0.0967, 0.739], [-0.12006, -0.2512, 2.613, 0.1545, 0.3782], [0.1716, 2.17, -0.9497, 0.2927, 0.1305], [-0.1182, 0.906, 0.0613, -0.5083, 0.641], [0.1986, -1.691, 0.6357, 0.4443, -0.6416], [-0.08356, 0.02019, 0.1313, -0.465, -0.09314], [-0.008896, 1.478, -1.761, -0.2734, 0.685], [-0.9946, 0.2233, 0.07806, -0.00929, -0.2563], [0.006386, -0.1915, 0.5146, -0.8525, 0.848], [-0.2976, -0.2429, 0.2744, 1.0, -1.185], [-0.0485, 0.8984, 0.9478, 0.2803, -0.0864], [-0.01785, -0.1841, -0.2228, 0.118, 0.1548], [0.03333, 1.37, -1.61, -0.10767, 0.4958], [0.989, 0.4167, 0.006794, 0.2683, 0.1812], [0.000499, 0.3574, 1.13, -0.2241, 0.2024], [0.05634, 0.3657, 0.4778, -0.3738, 0.1454], [0.08527, -1.116, -0.2964, 0.3853, -0.1722], [0.001016, 0.971, 0.793, 0.3484, -0.2103], [0.002506, 1.24, -1.092, -0.2732, 0.1622], [-0.1357, -0.902, -0.5815, -0.2786, 0.2233], [-0.01495, 0.0165, 0.000874, 0.0083, 0.010826], [0.1292, -1.53, 1.502, 0.1122, 0.2434], [0.1034, 0.05823, -0.0713, -0.1595, -2.174], [-0.010895, 0.01369, 0.001999, -0.0019245, -0.001666], [0.3003, 1.407, -0.857, 0.2272, -0.1628], [0.01996, -2.695, 0.9834, 0.516, -0.762], [0.02068, -0.4763, -0.1229, -0.1667, -0.02557], [-0.03723, -1.687, -0.03424, 0.0462, -0.7036], [0.0919, 1.056, -1.071, -0.3418, 0.4111], [0.3552, 1.226, 0.4385, 0.7656, -0.3774], [-2.088, -0.03387, 0.00264, 0.327, 0.1908], [0.01466, 0.5757, -0.7505, 1.293, -1.544], [-0.1978, 2.143, -2.004, 0.0775, -0.612]]
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ReLU
[[-0.0257284, -0.0236522, 0.00533226, -0.00479611, -0.0435576, -0.024969, -0.0148108, 0.000334024, -0.0550894, 0.0356814, -0.0163375, 0.0301327, -0.025592, -0.00017491, 0.0443188, -0.0219289, -0.0149489, 0.0178137, 0.00772056, -0.0270143, -0.046169, 0.0251348, -0.0361942, -0.0561861, -0.0178958, -0.0570119, -0.0574254, -0.0500563, -0.0256907, 0.027076, -0.0153894, -0.00949664, -0.0400265, -0.0510351, -0.0160994, 0.0018803, -0.0481571, 0.00733657, -0.0131476, -0.0495243, 0.0169441, -0.0178048, 0.0365598, 0.00279935, 0.036292, -0.0157837, -0.00707921, -0.0120638, 0.0316389, 0.0237082, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-0.218614, 0.911912, -0.84065, -0.191406, -0.0275153, -0.717804, 2.22887, 0.0544677, -0.136135, 1.22709, 1.12116, -0.398822, -4.32472, 1.31661, -0.249738, 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ReLU
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ReLU
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ReLU
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ReLU
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0
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da4a0fcccdd9cdc3d7e411d6d39890239f410363
| 6,729
|
py
|
Python
|
cmd_test_pipe_dnd.py
|
guochengqian/TENet
|
a8685e81d64fc90ca676ae32d3c9fc8751acef3e
|
[
"MIT"
] | 211
|
2019-05-13T17:56:17.000Z
|
2022-03-24T07:16:33.000Z
|
cmd_test_pipe_dnd.py
|
guochengqian/TENet
|
a8685e81d64fc90ca676ae32d3c9fc8751acef3e
|
[
"MIT"
] | 13
|
2019-05-14T09:37:22.000Z
|
2022-03-14T09:45:00.000Z
|
cmd_test_pipe_dnd.py
|
guochengqian/TENet
|
a8685e81d64fc90ca676ae32d3c9fc8751acef3e
|
[
"MIT"
] | 30
|
2019-06-06T08:28:32.000Z
|
2021-11-26T03:54:22.000Z
|
"""
The testing file for rethinking the pipeline
Instruction:
# in_type: current test in_type
# out_type: current test out_type
# model: current model
# pre_in_type: previous test in_type
# pre_out_type: previous test out_type
# pre_model: previous model
# intermidiate intermidiate state default false (false to read from dnd , true to read from previous)
"""
import os
import argparse
cmd = "git --version"
return_value = os.system(cmd)
print('returned value :', return_value)
parser = argparse.ArgumentParser(description='PyTorch implementation of ISP-Net')
parser.add_argument('--pipeline', type=int, required=True,
help='which pipeline to evaluate'
'DN-> DM->SR: 0'
'DN-> SR->DM: 1'
'DM -> DN -> SR: 2'
'DM -> SR -> DN: 3'
'SR -> DM -> DN: 4'
'SR -> DN -> DM: 5'
'')
parser.add_argument('--pretrain_dir', type=str, required=True)
parser.add_argument('--save_dir', type=str, default=None)
parser.add_argument('--test_data', type=str, required=True)
parser.add_argument('--scale', type=int, default=2)
args = parser.parse_args()
pipeline = args.pipeline
if args.save_dir is None:
args.save_dir = os.path.join(args.pretrain_dir, 'pipe_result')
if pipeline == 0:
# cmd1 denoise
# cmd2 demosaic
# cmd3 super resolution
# DN-> DM->SR
cmd1 = "python test_pipe_dnd.py --phase test --in_type noisy_raw --out_type raw --pre_out_type raw --model resnet --pretrain_dir {} --save_dir {}/dndmsr --test_data {}".format(args.pretrain_dir, args.save_dir, args.test_data)
cmd2 = "python test_pipe_dnd.py --phase test --in_type raw --out_type linrgb --pre_in_type noisy_raw --pre_out_type raw --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dndmsr".format(args.pretrain_dir, args.save_dir)
cmd3 = "python test_pipe_dnd.py --phase test --in_type lr_linrgb --out_type linrgb --pre_in_type raw --pre_out_type linrgb --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dndmsr --scale {}".format(args.pretrain_dir, args.save_dir, args.scale)
elif pipeline == 1:
# cmd1 denoise
# cmd2 super resolution
# cmd3 demosaic
# DN-> SR->DM
cmd1 = "python test_pipe_dnd.py --phase test --in_type noisy_raw --out_type raw --pre_out_type raw --model resnet --pretrain_dir {} --save_dir {}/dnsrdm --test_data {}".format(args.pretrain_dir, args.save_dir, args.test_data)
cmd2 = "python test_pipe_dnd.py --phase test --in_type lr_raw --out_type raw --pre_in_type noisy_raw --pre_out_type raw --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dnsrdm --scale {}".format(args.pretrain_dir, args.save_dir, args.scale)
cmd3 = "python test_pipe_dnd.py --phase test --in_type raw --out_type linrgb --pre_in_type lr_raw --pre_out_type raw --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dnsrdm".format(args.pretrain_dir, args.save_dir)
elif pipeline == 2:
# cmd1 demosaic
# cmd2 denoise
# cmd3 super resolution
# DM -> DN -> SR
cmd1 = "python test_pipe_dnd.py --phase test --in_type raw --out_type linrgb --pre_out_type raw --model resnet --pretrain_dir {} --save_dir {}/dmdnsr --test_data {}".format(args.pretrain_dir, args.save_dir, args.test_data)
cmd2 = "python test_pipe_dnd.py --phase test --in_type noisy_linrgb --out_type linrgb --pre_in_type raw --pre_out_type linrgb --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dmdnsr".format(args.pretrain_dir, args.save_dir)
cmd3 = "python test_pipe_dnd.py --phase test --in_type lr_linrgb --out_type linrgb --pre_in_type noisy_rgb --pre_out_type rgb --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dmdnsr --scale {}".format(args.pretrain_dir, args.save_dir, args.scale)
elif pipeline == 3:
# cmd1 demosaic
# cmd2 super resolution
# cmd3 denoise
# DM -> SR -> DN
cmd1 = "python test_pipe_dnd.py --phase test --in_type raw --out_type linrgb --pre_out_type raw --model resnet --pretrain_dir {} --save_dir {}/dmsrdn --test_data {}".format(args.pretrain_dir, args.save_dir, args.test_data)
cmd2 = "python test_pipe_dnd.py --phase test --in_type lr_linrgb --out_type linrgb --pre_in_type raw --pre_out_type linrgb --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dmsrdn --scale {}".format(args.pretrain_dir, args.save_dir, args.scale)
cmd3 = "python test_pipe_dnd.py --phase test --in_type noisy_linrgb --out_type linrgb --pre_in_type noisy_rgb --pre_out_type linrgb --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/dmsrdn".format(args.pretrain_dir, args.save_dir)
elif pipeline == 4:
# cmd1 super resolution
# cmd2 demosaic
# cmd3 denoise
# SR -> DM -> DN
cmd1 = "python test_pipe_dnd.py --phase test --in_type lr_raw --out_type raw --pre_out_type raw --model resnet --pretrain_dir {} --save_dir {}/srdmdn --test_data {} --scale {}".format(args.pretrain_dir, args.save_dir, args.test_data, args.scale)
cmd2 = "python test_pipe_dnd.py --phase test --in_type raw --out_type linrgb --pre_in_type lr_raw --pre_out_type raw --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/srdmdn".format(args.pretrain_dir, args.save_dir)
cmd3 = "python test_pipe_dnd.py --phase test --in_type noisy_linrgb --out_type linrgb --pre_in_type raw --pre_out_type linrgb --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/srdmdn".format(args.pretrain_dir, args.save_dir)
elif pipeline == 5:
# cmd1 super resolution
# cmd2 denoise
# cmd3 demosaic
# SR -> DN -> DM
cmd1 = "python test_pipe_dnd.py --phase test --in_type lr_raw --out_type raw --pre_out_type raw --model resnet --pretrain_dir {} --save_dir {}/srdndm --test_data {} --scale {}".format(args.pretrain_dir, args.save_dir, args.test_data, args.scale)
cmd2 = "python test_pipe_dnd.py --phase test --in_type noisy_raw --out_type raw --pre_in_type lr_raw --pre_out_type raw --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/srdndm".format(args.pretrain_dir, args.save_dir)
cmd3 = "python test_pipe_dnd.py --phase test --in_type raw --out_type linrgb --pre_in_type noisy_raw --pre_out_type raw --model resnet --pre_model resnet --intermediate 1 --pretrain_dir {} --save_dir {}/srdndm".format(args.pretrain_dir, args.save_dir)
return_value = os.system(cmd1)
# return_value = os.system(cmd2)
# return_value = os.system(cmd3)
| 70.09375
| 287
| 0.697726
| 1,023
| 6,729
| 4.318671
| 0.096774
| 0.063377
| 0.045269
| 0.069262
| 0.749887
| 0.741738
| 0.741738
| 0.725441
| 0.725441
| 0.702807
| 0
| 0.011412
| 0.166592
| 6,729
| 95
| 288
| 70.831579
| 0.776391
| 0.118591
| 0
| 0
| 0
| 0.375
| 0.631106
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.041667
| 0
| 0.041667
| 0.020833
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
da69d412905efd49dacbc91f385f290691d3cace
| 5,457
|
py
|
Python
|
coop_cms/apps/newsletters/tests/test_unregister.py
|
ljean/coop_cms
|
531f65ceb9ad82c113597d15b764dbcf51264794
|
[
"BSD-3-Clause"
] | 3
|
2016-01-29T10:55:09.000Z
|
2022-03-08T16:02:12.000Z
|
coop_cms/apps/newsletters/tests/test_unregister.py
|
ljean/coop_cms
|
531f65ceb9ad82c113597d15b764dbcf51264794
|
[
"BSD-3-Clause"
] | 11
|
2015-03-07T17:30:24.000Z
|
2016-07-13T09:40:43.000Z
|
coop_cms/apps/newsletters/tests/test_unregister.py
|
ljean/coop_cms
|
531f65ceb9ad82c113597d15b764dbcf51264794
|
[
"BSD-3-Clause"
] | 5
|
2018-08-30T09:03:22.000Z
|
2019-09-10T13:01:56.000Z
|
# -*- coding: utf-8 -*-
"""test email sending"""
from django.contrib.sites.models import Site
from django.urls import reverse
from coop_cms.tests import BaseTestCase
from model_mommy import mommy
from .. import models
class UnregisterTest(BaseTestCase):
def test_unregister_mailinglist(self):
site1 = Site.objects.get_current()
newsletter_subscription = mommy.make(models.SubscriptionType, name="newsletter", site=site1)
third_party_subscription = mommy.make(models.SubscriptionType, name="3rd_party", site=site1)
contact = mommy.make(models.Contact, email='toto@toto.fr', last_name='Toto')
subscription1 = mommy.make(
models.Subscription,
subscription_type=newsletter_subscription,
contact=contact,
accept_subscription=True
)
subscription2 = mommy.make(
models.Subscription,
subscription_type=third_party_subscription,
contact=contact,
accept_subscription=True
)
emailing = mommy.make(models.Emailing, subscription_type=newsletter_subscription)
emailing.sent_to.add(contact)
emailing.save()
url = reverse('newsletters:unregister', args=[emailing.id, contact.uuid])
response = self.client.get(url)
self.assertEqual(200, response.status_code)
response = self.client.post(url, data={'unregister': True})
self.assertEqual(200, response.status_code)
subscription1 = models.Subscription.objects.get(id=subscription1.id)
self.assertEqual(subscription1.accept_subscription, False)
self.assertEqual(subscription1.contact, contact)
subscription2 = models.Subscription.objects.get(id=subscription2.id)
self.assertEqual(subscription2.accept_subscription, True)
self.assertEqual(subscription2.contact, contact)
self.assertEqual(emailing.unsub.count(), 1)
self.assertEqual(emailing.unsub.all()[0], contact)
def test_unregister_mailinglist_dont_exist(self):
site1 = Site.objects.get_current()
newsletter_subscription = mommy.make(models.SubscriptionType, name="newsletter", site=site1)
contact = mommy.make(models.Contact, email='toto@toto.fr', last_name='Toto')
emailing = mommy.make(models.Emailing, subscription_type=newsletter_subscription)
emailing.sent_to.add(contact)
emailing.save()
url = reverse('newsletters:unregister', args=[emailing.id, contact.uuid])
response = self.client.get(url)
self.assertEqual(200, response.status_code)
response = self.client.post(url, data={'unregister': True})
self.assertEqual(200, response.status_code)
subscription1 = models.Subscription.objects.get(subscription_type=newsletter_subscription)
self.assertEqual(subscription1.accept_subscription, False)
self.assertEqual(subscription1.contact, contact)
self.assertEqual(emailing.unsub.count(), 1)
self.assertEqual(list(emailing.unsub.all()), [contact])
def test_unregister_mailinglist_twice(self):
site1 = Site.objects.get_current()
newsletter_subscription = mommy.make(models.SubscriptionType, name="newsletter", site=site1)
contact = mommy.make(models.Contact, email='toto@toto.fr', last_name='Toto')
subscription1 = mommy.make(
models.Subscription,
subscription_type=newsletter_subscription,
contact=contact,
accept_subscription=False
)
emailing = mommy.make(models.Emailing, subscription_type=newsletter_subscription)
emailing.sent_to.add(contact)
emailing.save()
url = reverse('newsletters:unregister', args=[emailing.id, contact.uuid])
response = self.client.get(url)
self.assertEqual(200, response.status_code)
response = self.client.post(url, data={'unregister': True})
self.assertEqual(200, response.status_code)
subscription1 = models.Subscription.objects.get(id=subscription1.id)
self.assertEqual(subscription1.accept_subscription, False)
self.assertEqual(subscription1.contact, contact)
self.assertEqual(emailing.unsub.count(), 1)
self.assertEqual(list(emailing.unsub.all()), [contact])
def test_unregister_mailinglist_notfound_emailing(self):
site1 = Site.objects.get_current()
contact = mommy.make(models.Contact, email='toto@toto.fr')
url = reverse('newsletters:unregister', args=[1, contact.uuid])
response = self.client.get(url)
self.assertEqual(404, response.status_code)
response = self.client.post(url, data={'unregister': True})
self.assertEqual(404, response.status_code)
self.assertEqual(models.Emailing.objects.count(), 0)
def test_unregister_mailinglist_not_found_contact(self):
site1 = Site.objects.get_current()
newsletter_subscription = mommy.make(models.SubscriptionType, name="newsletter", site=site1)
emailing = mommy.make(models.Emailing, subscription_type=newsletter_subscription)
url = reverse('newsletters:unregister', args=[emailing.id, "aaaa"])
response = self.client.get(url)
self.assertEqual(404, response.status_code)
response = self.client.post(url, data={'unregister': True})
self.assertEqual(404, response.status_code)
self.assertEqual(emailing.unsub.count(), 0)
| 49.162162
| 100
| 0.69727
| 587
| 5,457
| 6.359455
| 0.13799
| 0.104474
| 0.064291
| 0.071256
| 0.85588
| 0.82427
| 0.778194
| 0.76614
| 0.76614
| 0.732923
| 0
| 0.015196
| 0.192047
| 5,457
| 110
| 101
| 49.609091
| 0.831481
| 0.007513
| 0
| 0.714286
| 0
| 0
| 0.050462
| 0.020333
| 0
| 0
| 0
| 0
| 0.265306
| 1
| 0.05102
| false
| 0
| 0.05102
| 0
| 0.112245
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
da8be490062e282f10f95e437986a2f20a4cee5a
| 39
|
py
|
Python
|
ListQues/__init__.py
|
ylyzty/leecode_study
|
8ea3c378a8d06f9f376ec57a553cf61592e3e1f5
|
[
"MIT"
] | null | null | null |
ListQues/__init__.py
|
ylyzty/leecode_study
|
8ea3c378a8d06f9f376ec57a553cf61592e3e1f5
|
[
"MIT"
] | null | null | null |
ListQues/__init__.py
|
ylyzty/leecode_study
|
8ea3c378a8d06f9f376ec57a553cf61592e3e1f5
|
[
"MIT"
] | null | null | null |
# Author: LZY
# Time: 2022/3/3 20:18
| 13
| 23
| 0.589744
| 8
| 39
| 2.875
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 0.230769
| 39
| 2
| 24
| 19.5
| 0.433333
| 0.820513
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e53de8b47653601de6f20af95339ffc7e03d5f8e
| 1,326
|
py
|
Python
|
Gmail-HACKING.py
|
Obito-indo/gmail-hacking
|
f81efb5a6240c5846ec39a7b6c98c1154507c566
|
[
"Apache-2.0"
] | 1
|
2020-12-27T22:47:11.000Z
|
2020-12-27T22:47:11.000Z
|
Gmail-HACKING.py
|
Obito-indo/gmail-hacking
|
f81efb5a6240c5846ec39a7b6c98c1154507c566
|
[
"Apache-2.0"
] | null | null | null |
Gmail-HACKING.py
|
Obito-indo/gmail-hacking
|
f81efb5a6240c5846ec39a7b6c98c1154507c566
|
[
"Apache-2.0"
] | null | null | null |
import marshal,zlib,base64
exec(marshal.loads(zlib.decompress(base64.b64decode("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"))))
| 663
| 1,298
| 0.956259
| 49
| 1,326
| 25.877551
| 0.938776
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130763
| 0.002262
| 1,326
| 2
| 1,298
| 663
| 0.827664
| 0
| 0
| 0
| 0
| 0.5
| 0.935143
| 0.935143
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 10
|
e57504b3886167fc288ee414a27ccc93ee34415a
| 73,126
|
py
|
Python
|
decoder/audio_decoders/config/audio_decoders.py
|
Microchip-MPLAB-Harmony/audio
|
0aef4f742c3a0e6a79d179019e257712b84df467
|
[
"0BSD"
] | 10
|
2019-03-19T23:00:12.000Z
|
2021-03-18T07:43:33.000Z
|
decoder/audio_decoders/config/audio_decoders.py
|
Microchip-MPLAB-Harmony/audio
|
0aef4f742c3a0e6a79d179019e257712b84df467
|
[
"0BSD"
] | 6
|
2019-11-06T19:22:17.000Z
|
2021-11-24T12:35:40.000Z
|
decoder/audio_decoders/config/audio_decoders.py
|
Microchip-MPLAB-Harmony/audio
|
0aef4f742c3a0e6a79d179019e257712b84df467
|
[
"0BSD"
] | 4
|
2019-06-12T05:57:31.000Z
|
2021-05-23T08:38:32.000Z
|
# coding: utf-8
##############################################################################
# Copyright (C) 2018 Microchip Technology Inc. and its subsidiaries.
#
# Subject to your compliance with these terms, you may use Microchip software
# and any derivatives exclusively with Microchip products. It is your
# responsibility to comply with third party license terms applicable to your
# use of third party software (including open source software) that may
# accompany Microchip software.
#
# THIS SOFTWARE IS SUPPLIED BY MICROCHIP "AS IS". NO WARRANTIES, WHETHER
# EXPRESS, IMPLIED OR STATUTORY, APPLY TO THIS SOFTWARE, INCLUDING ANY IMPLIED
# WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A
# PARTICULAR PURPOSE.
#
# IN NO EVENT WILL MICROCHIP BE LIABLE FOR ANY INDIRECT, SPECIAL, PUNITIVE,
# INCIDENTAL OR CONSEQUENTIAL LOSS, DAMAGE, COST OR EXPENSE OF ANY KIND
# WHATSOEVER RELATED TO THE SOFTWARE, HOWEVER CAUSED, EVEN IF MICROCHIP HAS
# BEEN ADVISED OF THE POSSIBILITY OR THE DAMAGES ARE FORESEEABLE. TO THE
# FULLEST EXTENT ALLOWED BY LAW, MICROCHIP'S TOTAL LIABILITY ON ALL CLAIMS IN
# ANY WAY RELATED TO THIS SOFTWARE WILL NOT EXCEED THE AMOUNT OF FEES, IF ANY,
# THAT YOU HAVE PAID DIRECTLY TO MICROCHIP FOR THIS SOFTWARE.
##############################################################################
import os
import sys
dbg = 0
src_ext = ('.c')
hdr_ext = ('.h')
lib_ext = ('.a')
imp_ext = ('.gpl', '.xiph')
wavTable = [("LIB_", "wav/", "wav_dec.h", "audio/decoder/audio_decoders/wav"),
("LIB_", "wav/", "wav_dec.c", "audio/decoder/audio_decoders/wav")]
adpcmTable = [("LIB_", "adpcm/", "adpcm_dec.h", "audio/decoder/audio_decoders/adpcm"),
("LIB_", "adpcm/", "adpcm_dec.c", "audio/decoder/audio_decoders/adpcm")]
mp3Table = [("LIB_", "mp3/", "mp3_dec.h", "audio/decoder/audio_decoders/mp3"),
("LIB_", "mp3/", "mp3_dec.c", "audio/decoder/audio_decoders/mp3"),
("LIB_", "../../../helix_mp3/fixpnt/", "mp3dec.c", "audio/decoder/audio_decoders/mp3/fixpnt"),
("LIB_", "../../../helix_mp3/fixpnt/", "mp3tabs.c", "audio/decoder/audio_decoders/mp3/fixpnt"),
("LIB_", "../../../helix_mp3/fixpnt/pub/", "mp3common.h", "audio/decoder/audio_decoders/mp3/fixpnt/pub"),
("LIB_", "../../../helix_mp3/fixpnt/pub/", "mp3dec.h", "audio/decoder/audio_decoders/mp3/fixpnt/pub"),
("LIB_", "../../../helix_mp3/fixpnt/pub/", "mpadecobjfixpt.h", "audio/decoder/audio_decoders/mp3/fixpnt/pub"),
("LIB_", "../../../helix_mp3/fixpnt/pub/", "statname.h", "audio/decoder/audio_decoders/mp3/fixpnt/pub"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "assembly.h", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "coder.h", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "bitstream.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "buffers.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "dct32.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "dequant.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "dqchan.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "huffman.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "hufftabs.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "imdct.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "polyphase.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "scalfact.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "stproc.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "subband.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "../../../helix_mp3/fixpnt/real/", "trigtabs.c", "audio/decoder/audio_decoders/mp3/fixpnt/real"),
("LIB_", "mp3/id3/", "id3.c", "audio/decoder/audio_decoders/mp3/id3"),
("LIB_", "mp3/id3/", "id3.h", "audio/decoder/audio_decoders/mp3/id3"),
("LIB_", "mp3/id3/", "utils.c", "audio/decoder/audio_decoders/mp3/id3"),
("LIB_", "mp3/id3/", "utils.h", "audio/decoder/audio_decoders/mp3/id3")]
flacTable = [("LIB_", "flac/", "flac_dec.h", "audio/decoder/audio_decoders/flac"),
("LIB_", "flac/", "flac_dec.c", "audio/decoder/audio_decoders/flac"),
# ("LIB_", "flac/src/", "bitmath.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "bitreader.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "cpu.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "crc.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "fixed.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "float.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "format.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "lpc.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "main.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "md5.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "memory.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/src/", "stream_decoder.c", "audio/decoder/audio_decoders/flac/src"),
# ("LIB_", "flac/include/", "config.h", "audio/decoder/audio_decoders/flac/include"),
# ("LIB_", "flac/include/FLAC/", "all.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "assert.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "callback.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "export.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "format.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "metadata.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "ordinals.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "stream_decoder.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/FLAC/", "stream_encoder.h", "audio/decoder/audio_decoders/flac/include/FLAC"),
# ("LIB_", "flac/include/private/", "all.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "bitmath.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "bitreader.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "bitwriter.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "cpu.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "crc.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "fixed.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "float.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "format.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "lpc.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "macros.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "md5.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "memory.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "metadata.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "ogg_decoder_aspect.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "ogg_encoder_aspect.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "ogg_helper.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "ogg_mapping.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "stream_encoder.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "stream_encoder_framing.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/private/", "window.h", "audio/decoder/audio_decoders/flac/include/private"),
# ("LIB_", "flac/include/protected/", "all.h", "audio/decoder/audio_decoders/flac/include/protected"),
# ("LIB_", "flac/include/protected/", "stream_decoder.h", "audio/decoder/audio_decoders/flac/include/protected"),
# ("LIB_", "flac/include/protected/", "stream_encoder.h", "audio/decoder/audio_decoders/flac/include/protected"),
# ("LIB_", "flac/include/share/", "alloc.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "compat.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "endswap.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "getopt.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "grabbag.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "macros.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "private.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "replaygain_analysis.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "replaygain_synthesis.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "safe_str.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "utf8.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/", "win_utf8_io.h", "audio/decoder/audio_decoders/flac/include/share"),
# ("LIB_", "flac/include/share/grabbag/", "cuesheet.h", "audio/decoder/audio_decoders/flac/include/share/grabbag"),
# ("LIB_", "flac/include/share/grabbag/", "file.h", "audio/decoder/audio_decoders/flac/include/share/grabbag"),
# ("LIB_", "flac/include/share/grabbag/", "picture.h", "audio/decoder/audio_decoders/flac/include/share/grabbag"),
# ("LIB_", "flac/include/share/grabbag/", "replaygain.h", "audio/decoder/audio_decoders/flac/include/share/grabbag"),
# ("LIB_", "flac/include/share/grabbag/", "seektable.h", "audio/decoder/audio_decoders/flac/include/share/grabbag"),
]
opusTable = [("LIB_", "opus/", "opus_dec.h", "audio/decoder/audio_decoders/opus"),
("LIB_", "opus/", "opus_dec.c", "audio/decoder/audio_decoders/opus"),
# ("LIB_", "opus/src/src/", "analysis.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "mlp.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "mlp_data.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_compare.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_decoder.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_demo.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_encoder.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_multistream.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_multistream_decoder.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_multistream_encoder.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "repacketizer.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "repacketizer_demo.c", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "analysis.h", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "mlp.h", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "opus_private.h", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/src/", "tansig_table.h", "audio/decoder/audio_decoders/opus/src/src"),
# ("LIB_", "opus/src/silk/", "A2NLSF.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "ana_filt_bank_1.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "API.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "biquad_alt.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "bwexpander.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "bwexpander_32.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "check_control_input.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "CNG.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "code_signs.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "control.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "control_audio_bandwidth.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "control_codec.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "control_SNR.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "debug.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "debug.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "decoder_set_fs.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "decode_core.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "decode_frame.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "decode_indices.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "decode_parameters.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "decode_pitch.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "decode_pulses.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "dec_API.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "define.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "encode_indices.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "encode_pulses.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "enc_API.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "errors.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "gain_quant.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "HP_variable_cutoff.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "init_decoder.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "init_encoder.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "Inlines.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "inner_prod_aligned.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "interpolate.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "lin2log.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "log2lin.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "LPC_analysis_filter.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "LPC_inv_pred_gain.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "LP_variable_cutoff.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "MacroCount.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "MacroDebug.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "macros.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "main.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF2A.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF_decode.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF_del_dec_quant.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF_encode.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF_stabilize.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF_unpack.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF_VQ.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NLSF_VQ_weights_laroia.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NSQ.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "NSQ_del_dec.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "pitch_est_defines.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "pitch_est_tables.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "PLC.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "PLC.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "process_NLSFs.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "quant_LTP_gains.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_down2.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_down2_3.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_private.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_private_AR2.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_private_down_FIR.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_private_IIR_FIR.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_private_up2_HQ.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_rom.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_rom.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "resampler_structs.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "shell_coder.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "sigm_Q15.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "SigProc_FIX.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "sort.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "stereo_decode_pred.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "stereo_encode_pred.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "stereo_find_predictor.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "stereo_LR_to_MS.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "stereo_MS_to_LR.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "stereo_quant_pred.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "structs.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "sum_sqr_shift.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables_gain.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables_LTP.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables_NLSF_CB_NB_MB.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables_NLSF_CB_WB.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables_other.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables_pitch_lag.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tables_pulses_per_block.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "table_LSF_cos.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "tuning_parameters.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "typedef.h", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "VAD.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/", "VQ_WMat_EC.c", "audio/decoder/audio_decoders/opus/src/silk"),
# ("LIB_", "opus/src/silk/x86/", "main_sse.h", "audio/decoder/audio_decoders/opus/src/silk/x86"),
# ("LIB_", "opus/src/silk/x86/", "NSQ_del_dec_sse.c", "audio/decoder/audio_decoders/opus/src/silk/x86"),
# ("LIB_", "opus/src/silk/x86/", "NSQ_sse.c", "audio/decoder/audio_decoders/opus/src/silk/x86"),
# ("LIB_", "opus/src/silk/x86/", "SigProc_FIX_sse.h", "audio/decoder/audio_decoders/opus/src/silk/x86"),
# ("LIB_", "opus/src/silk/x86/", "VAD_sse.c", "audio/decoder/audio_decoders/opus/src/silk/x86"),
# ("LIB_", "opus/src/silk/x86/", "VQ_WMat_EC_sse.c", "audio/decoder/audio_decoders/opus/src/silk/x86"),
# ("LIB_", "opus/src/silk/x86/", "x86_silk_map.c", "audio/decoder/audio_decoders/opus/src/silk/x86"),
# ("LIB_", "opus/src/silk/mips/", "macros_mipsr1.h", "audio/decoder/audio_decoders/opus/src/silk/mips"),
# ("LIB_", "opus/src/silk/mips/", "NSQ_del_dec_mipsr1.h", "audio/decoder/audio_decoders/opus/src/silk/mips"),
# ("LIB_", "opus/src/silk/mips/", "sigproc_fix_mipsr1.h", "audio/decoder/audio_decoders/opus/src/silk/mips"),
# ("LIB_", "opus/src/silk/float/", "apply_sine_window_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "autocorrelation_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "burg_modified_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "bwexpander_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "corrMatrix_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "encode_frame_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "energy_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "find_LPC_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "find_LTP_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "find_pitch_lags_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "find_pred_coefs_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "inner_product_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "k2a_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "levinsondurbin_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "LPC_analysis_filter_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "LPC_inv_pred_gain_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "LTP_analysis_filter_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "LTP_scale_ctrl_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "main_FLP.h", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "noise_shape_analysis_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "pitch_analysis_core_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "prefilter_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "process_gains_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "regularize_correlations_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "residual_energy_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "scale_copy_vector_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "scale_vector_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "schur_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "SigProc_FLP.h", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "solve_LS_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "sort_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "structs_FLP.h", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "warped_autocorrelation_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/float/", "wrappers_FLP.c", "audio/decoder/audio_decoders/opus/src/silk/float"),
# ("LIB_", "opus/src/silk/fixed/", "apply_sine_window_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "autocorr_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "burg_modified_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "corrMatrix_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "encode_frame_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "find_LPC_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "find_LTP_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "find_pitch_lags_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "find_pred_coefs_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "k2a_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "k2a_Q16_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "LTP_analysis_filter_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "LTP_scale_ctrl_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "main_FIX.h", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "noise_shape_analysis_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "pitch_analysis_core_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "prefilter_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "process_gains_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "regularize_correlations_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "residual_energy16_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "residual_energy_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "schur64_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "schur_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "solve_LS_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "structs_FIX.h", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "vector_ops_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/", "warped_autocorrelation_FIX.c", "audio/decoder/audio_decoders/opus/src/silk/fixed"),
# ("LIB_", "opus/src/silk/fixed/x86/", "burg_modified_FIX_sse.c", "audio/decoder/audio_decoders/opus/src/silk/fixed/x86"),
# ("LIB_", "opus/src/silk/fixed/x86/", "prefilter_FIX_sse.c", "audio/decoder/audio_decoders/opus/src/silk/fixed/x86"),
# ("LIB_", "opus/src/silk/fixed/x86/", "vector_ops_FIX_sse.c", "audio/decoder/audio_decoders/opus/src/silk/fixed/x86"),
# ("LIB_", "opus/src/silk/fixed/mips/", "noise_shape_analysis_FIX_mipsr1.h", "audio/decoder/audio_decoders/opus/src/silk/fixed/mips"),
# ("LIB_", "opus/src/silk/fixed/mips/", "prefilter_FIX_mipsr1.h", "audio/decoder/audio_decoders/opus/src/silk/fixed/mips"),
# ("LIB_", "opus/src/silk/fixed/mips/", "warped_autocorrelation_FIX_mipsr1.h", "audio/decoder/audio_decoders/opus/src/silk/fixed/mips"),
# ("LIB_", "opus/src/silk/arm/", "macros_armv4.h", "audio/decoder/audio_decoders/opus/src/silk/arm"),
# ("LIB_", "opus/src/silk/arm/", "macros_armv5e.h", "audio/decoder/audio_decoders/opus/src/silk/arm"),
# ("LIB_", "opus/src/silk/arm/", "SigProc_FIX_armv4.h", "audio/decoder/audio_decoders/opus/src/silk/arm"),
# ("LIB_", "opus/src/silk/arm/", "SigProc_FIX_armv5e.h", "audio/decoder/audio_decoders/opus/src/silk/arm"),
# ("LIB_", "opus/src/celt/", "arch.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "bands.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "bands.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "celt.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "celt.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "celt_decoder.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "celt_encoder.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "celt_lpc.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "celt_lpc.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "cpu_support.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "cwrs.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "cwrs.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "ecintrin.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "entcode.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "entcode.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "entdec.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "entdec.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "entenc.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "entenc.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "fixed_debug.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "fixed_generic.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "float_cast.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "kiss_fft.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "kiss_fft.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "laplace.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "laplace.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "mathops.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "mathops.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "mdct.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "mdct.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "mfrngcod.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "modes.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "modes.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "opus_custom_demo.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "os_support.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "pitch.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "pitch.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "quant_bands.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "quant_bands.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "rate.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "rate.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "stack_alloc.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "static_modes_fixed.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "static_modes_fixed_arm_ne10.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "static_modes_float.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "static_modes_float_arm_ne10.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "vq.c", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "vq.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/", "_kiss_fft_guts.h", "audio/decoder/audio_decoders/opus/src/celt"),
# ("LIB_", "opus/src/celt/x86/", "celt_lpc_sse.c", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "celt_lpc_sse.h", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "pitch_sse.c", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "pitch_sse.h", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "pitch_sse2.c", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "pitch_sse4_1.c", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "x86cpu.c", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "x86cpu.h", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/x86/", "x86_celt_map.c", "audio/decoder/audio_decoders/opus/src/celt/x86"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_cwrs32.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_dft.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_entropy.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_laplace.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_mathops.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_mdct.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_rotation.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/tests/", "test_unit_types.c", "audio/decoder/audio_decoders/opus/src/celt/tests"),
# ("LIB_", "opus/src/celt/mips/", "celt_mipsr1.h", "audio/decoder/audio_decoders/opus/src/celt/mips"),
# ("LIB_", "opus/src/celt/mips/", "fixed_generic_mipsr1.h", "audio/decoder/audio_decoders/opus/src/celt/mips"),
# ("LIB_", "opus/src/celt/mips/", "kiss_fft_mipsr1.h", "audio/decoder/audio_decoders/opus/src/celt/mips"),
# ("LIB_", "opus/src/celt/mips/", "mdct_mipsr1.h", "audio/decoder/audio_decoders/opus/src/celt/mips"),
# ("LIB_", "opus/src/celt/mips/", "pitch_mipsr1.h", "audio/decoder/audio_decoders/opus/src/celt/mips"),
# ("LIB_", "opus/src/celt/mips/", "vq_mipsr1.h", "audio/decoder/audio_decoders/opus/src/celt/mips"),
# ("LIB_", "opus/src/celt/arm/", "armcpu.c", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "armcpu.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "arm_celt_map.c", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "celt_ne10_fft.c", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "celt_ne10_mdct.c", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "celt_neon_intr.c", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "fft_arm.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "fixed_armv4.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "fixed_armv5e.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "kiss_fft_armv4.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "kiss_fft_armv5e.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "mdct_arm.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/src/celt/arm/", "pitch_arm.h", "audio/decoder/audio_decoders/opus/src/celt/arm"),
# ("LIB_", "opus/include/", "opus.h", "audio/decoder/audio_decoders/opus/include"),
# ("LIB_", "opus/include/", "opus_custom.h", "audio/decoder/audio_decoders/opus/include"),
# ("LIB_", "opus/include/", "opus_defines.h", "audio/decoder/audio_decoders/opus/include"),
# ("LIB_", "opus/include/", "opus_multistream.h", "audio/decoder/audio_decoders/opus/include"),
# ("LIB_", "opus/include/", "opus_types.h", "audio/decoder/audio_decoders/opus/include"),
]
speexTable = [("LIB_", "speex/", "speex_dec.c", "audio/decoder/audio_decoders/speex"),
("LIB_", "speex/", "speex_dec.h", "audio/decoder/audio_decoders/speex"),
# ("LIB_", "speex/libspeex/", "arch.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "bits.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "cb_search.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "cb_search.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "cb_search_arm4.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "cb_search_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "cb_search_sse.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "exc_10_16_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "exc_10_32_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "exc_20_32_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "exc_5_256_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "exc_5_64_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "exc_8_128_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "fftwrap.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "filters.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "filters.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "filters_arm4.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "filters_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "filters_sse.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "fixed_arm4.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "fixed_arm5e.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "fixed_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "fixed_debug.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "fixed_generic.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "gain_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "gain_table_lbr.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "hexc_10_32_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "hexc_table.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "high_lsp_tables.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "kiss_fft.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "kiss_fft.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "kiss_fftr.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "kiss_fftr.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "lpc.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "lpc.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "lpc_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "lsp.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "lsp.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "lsp_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "lsp_tables_nb.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "ltp.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "ltp.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "ltp_arm4.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "ltp_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "ltp_sse.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "math_approx.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "misc_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "modes.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "modes.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "modes_wb.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "nb_celp.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "nb_celp.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "os_support.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "quant_lsp.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "quant_lsp.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "quant_lsp_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "sb_celp.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "sb_celp.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "smallft.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "smallft.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "speex.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "speex_callbacks.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "speex_header.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "stack_alloc.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "stereo.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "testenc.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "testenc_uwb.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "testenc_wb.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vbr.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vbr.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vorbis_psy.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vorbis_psy.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vq.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vq.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vq_arm4.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vq_bfin.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "vq_sse.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "window.c", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/libspeex/", "_kiss_fft_guts.h", "audio/decoder/audio_decoders/speex/libspeex"),
# ("LIB_", "speex/include/speex/", "speex.h", "audio/decoder/audio_decoders/speex/include/speex"),
# ("LIB_", "speex/include/speex/", "speex_bits.h", "audio/decoder/audio_decoders/speex/include/speex"),
# ("LIB_", "speex/include/speex/", "speex_callbacks.h", "audio/decoder/audio_decoders/speex/include/speex"),
# ("LIB_", "speex/include/speex/", "speex_config.h", "audio/decoder/audio_decoders/speex/include/speex"),
# ("LIB_", "speex/include/speex/", "speex_config_types.h", "audio/decoder/audio_decoders/speex/include/speex"),
# ("LIB_", "speex/include/speex/", "speex_header.h", "audio/decoder/audio_decoders/speex/include/speex"),
# ("LIB_", "speex/include/speex/", "speex_stereo.h", "audio/decoder/audio_decoders/speex/include/speex"),
# ("LIB_", "speex/include/speex/", "speex_types.h", "audio/decoder/audio_decoders/speex/include/speex")
]
utilsTable = [("LIB_", "utils/", "utils.c", "audio/decoder/audio_decoders/utils"),
("LIB_", "utils/", "utils.h", "audio/decoder/audio_decoders/utils")]
oggTable = [("LIB_", "ogg/", "ogg.h", "audio/decoder/audio_decoders/ogg")]
ftlTable = [("LIB_", "../templates/", "audio_decoders.h.ftl", "audio/decoder"),
("LIB_", "../templates/", "audio_decoders_config.h.ftl", "audio/decoder")]
# Wav
def enableWavDecoderFiles(component, enable):
for fileSymbol, srcPath, file, destPath in wavTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Generate file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec("component.getSymbolByID(\"" + symbol + "\").setEnabled(enable)")
def enableWavDecoder(symbol, event):
enableWavDecoderFiles(symbol.getComponent(), event["value"]==True)
# ADPCM
def enableAdpcmDecoderFiles(component, enable):
for fileSymbol, srcPath, file, destPath in adpcmTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Generate file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec("component.getSymbolByID(\"" + symbol + "\").setEnabled(enable)")
def enableAdpcmDecoder(symbol, event):
enableAdpcmDecoderFiles(symbol.getComponent(), event["value"]==True)
# MP3
def enableMP3DecoderFiles(component, enable):
for fileSymbol, srcPath, file, destPath in mp3Table:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Generate file symbol
modifiedSrcPath = srcPath.replace("../../../", "")
symbol = fileSymbol + modifiedSrcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec("component.getSymbolByID(\"" + symbol + "\").setEnabled(enable)")
def enableMP3Decoder(symbol, event):
enableMP3DecoderFiles(symbol.getComponent(), event["value"]==True)
# FLAC
def enableFlacDecoderFiles(component, enable):
for fileSymbol, srcPath, file, destPath in flacTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Generate file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec("component.getSymbolByID(\"" + symbol + "\").setEnabled(enable)")
def enableFlacDecoder(symbol, event):
enableFlacDecoderFiles(symbol.getComponent(), event["value"]==True)
# SPEEX
def enableSpeexDecoderFiles(component, enable):
for fileSymbol, srcPath, file, destPath in speexTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Generate file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec("component.getSymbolByID(\"" + symbol + "\").setEnabled(enable)")
def enableSpeexDecoder(symbol, event):
enableSpeexDecoderFiles(symbol.getComponent(), event["value"]==True)
# OPUS
def enableOpusDecoderFiles(component, enable):
for fileSymbol, srcPath, file, destPath in opusTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Generate file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec("component.getSymbolByID(\"" + symbol + "\").setEnabled(enable)")
for fileSymbol, srcPath, file, destPath in oggTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Generate file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec("component.getSymbolByID(\"" + symbol + "\").setEnabled(enable)")
def enableOpusDecoder(symbol, event):
enableOpusDecoderFiles(symbol.getComponent(), event["value"]==True)
def instantiateComponent(audioDecoderComponent):
global CONFIG_USE_WAV_STREAMING
global CONFIG_USE_ADPCM_STREAMING
global CONFIG_USE_MP3_DECODER
CONFIG_USE_WAV_STREAMING = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_WAV_STREAMING", None)
CONFIG_USE_WAV_STREAMING.setVisible(True)
CONFIG_USE_WAV_STREAMING.setLabel("Enable WAV Decoder")
CONFIG_USE_WAV_STREAMING.setDefaultValue(True)
CONFIG_USE_WAV_STREAMING.setDependencies(enableWavDecoder, ["CONFIG_USE_WAV_STREAMING"])
CONFIG_USE_ADPCM_STREAMING = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_ADPCM_STREAMING", None)
CONFIG_USE_ADPCM_STREAMING.setVisible(True)
CONFIG_USE_ADPCM_STREAMING.setLabel("Enable ADPCM Decoder")
CONFIG_USE_ADPCM_STREAMING.setDefaultValue(False)
CONFIG_USE_ADPCM_STREAMING.setDependencies(enableAdpcmDecoder, ["CONFIG_USE_ADPCM_STREAMING"])
CONFIG_USE_MP3_DECODER = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_MP3_DECODER", None)
CONFIG_USE_MP3_DECODER.setVisible(True)
CONFIG_USE_MP3_DECODER.setLabel("Enable MP3 Decoder")
CONFIG_USE_MP3_DECODER.setDefaultValue(False)
CONFIG_USE_MP3_DECODER.setDependencies(enableMP3Decoder, ["CONFIG_USE_MP3_DECODER"])
CONFIG_USE_FLAC_DECODER = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_FLAC_DECODER", None)
CONFIG_USE_FLAC_DECODER.setVisible(False)
CONFIG_USE_FLAC_DECODER.setLabel("Enable FLAC Decoder")
CONFIG_USE_FLAC_DECODER.setDefaultValue(False)
CONFIG_USE_FLAC_DECODER.setDependencies(enableFlacDecoder, ["CONFIG_USE_FLAC_DECODER"])
CONFIG_USE_OGG_SPEEX_DECODER = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_OGG_SPEEX_DECODER", None)
CONFIG_USE_OGG_SPEEX_DECODER.setVisible(False)
CONFIG_USE_OGG_SPEEX_DECODER.setLabel("Enable SPEEX Decoder")
CONFIG_USE_OGG_SPEEX_DECODER.setDefaultValue(False)
CONFIG_USE_OGG_SPEEX_DECODER.setDependencies(enableSpeexDecoder, ["CONFIG_USE_OGG_SPEEX_DECODER"])
CONFIG_USE_OGG_OPUS_DECODER = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_OGG_OPUS_DECODER", None)
CONFIG_USE_OGG_OPUS_DECODER.setVisible(False)
CONFIG_USE_OGG_OPUS_DECODER.setLabel("Enable OPUS Decoder")
CONFIG_USE_OGG_OPUS_DECODER.setDefaultValue(False)
CONFIG_USE_OGG_OPUS_DECODER.setDependencies(enableOpusDecoder, ["CONFIG_USE_OGG_OPUS_DECODER"])
CONFIG_USE_WMA_DECODER = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_WMA_DECODER", None)
CONFIG_USE_WMA_DECODER.setVisible(False)
CONFIG_USE_WMA_DECODER.setLabel("Enable WMA Decoder")
CONFIG_USE_WMA_DECODER.setDefaultValue(False)
CONFIG_USE_WMA_DECODER.setDependencies(enableOpusDecoder, ["CONFIG_USE_WMA_DECODER"])
CONFIG_USE_AAC_DECODER = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_AAC_DECODER", None)
CONFIG_USE_AAC_DECODER.setVisible(False)
CONFIG_USE_AAC_DECODER.setLabel("Enable AAC Decoder")
CONFIG_USE_AAC_DECODER.setDefaultValue(False)
# CONFIG_USE_AAC_DECODER.setDependencies(enableAacDecoder, ["CONFIG_USE_AAC_DECODER"])
CONFIG_USE_JPEG_DECODER = audioDecoderComponent.createBooleanSymbol("CONFIG_USE_JPEG_DECODER", None)
CONFIG_USE_JPEG_DECODER.setVisible(False)
CONFIG_USE_JPEG_DECODER.setLabel("Enable JPEG Decoder")
CONFIG_USE_JPEG_DECODER.setDefaultValue(False)
# CONFIG_USE_JPEG_DECODER.setDependencies(enableOpusDecoder, ["CONFIG_USE_JPEG_DECODER"])
############################################################################
#### Code Generation ####
############################################################################
configName = Variables.get("__CONFIGURATION_NAME") # e.g. "default"
Log.writeInfoMessage("Audio Decoders instantiated")
for fileSymbol, srcPath, file, destPath in wavTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Create unique file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
exec(symbol + ".setOutputName(\"" + file + "\")")
exec(symbol + ".setDestPath(\"" + destPath + "\")")
exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder/audio_decoders/wav\")")
exec(symbol + ".setType(\"" + type + "\")")
exec(symbol + ".setEnabled(CONFIG_USE_WAV_STREAMING.getValue() == True)")
for fileSymbol, srcPath, file, destPath in adpcmTable:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Create unique file symbol
symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
exec(symbol + ".setOutputName(\"" + file + "\")")
exec(symbol + ".setDestPath(\"" + destPath + "\")")
exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder/audio_decoders/adpcm\")")
exec(symbol + ".setType(\"" + type + "\")")
exec(symbol + ".setEnabled(CONFIG_USE_ADPCM_STREAMING.getValue() == True)")
for fileSymbol, srcPath, file, destPath in mp3Table:
# Set type
baseFileName = os.path.splitext(file)[0]
ext = os.path.splitext(file)[-1].lower()
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Create unique file symbol
modifiedSrcPath = srcPath.replace("../../../", "")
symbol = fileSymbol + modifiedSrcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
exec(symbol + ".setOutputName(\"" + file + "\")")
exec(symbol + ".setDestPath(\"" + destPath + "\")")
exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder/audio_decoders/mp3\")")
exec(symbol + ".setType(\"" + type + "\")")
exec(symbol + ".setEnabled(CONFIG_USE_MP3_DECODER.getValue() == True)")
# for fileSymbol, srcPath, file, destPath in flacTable:
# # Set type
# baseFileName = os.path.splitext(file)[0]
# ext = os.path.splitext(file)[-1].lower()
# if ext in src_ext:
# type = "SOURCE"
# elif ext in hdr_ext:
# type = "HEADER"
# else:
# type = "IMPORTANT"
# # Create unique file symbol
# symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
# exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
# exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
# exec(symbol + ".setOutputName(\"" + file + "\")")
# exec(symbol + ".setDestPath(\"" + destPath + "\")")
# exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder/audio_decoders/flac\")")
# exec(symbol + ".setType(\"" + type + "\")")
# exec(symbol + ".setEnabled(CONFIG_USE_FLAC_DECODER.getValue() == True)")
# for fileSymbol, srcPath, file, destPath in speexTable:
# # Set type
# baseFileName = os.path.splitext(file)[0]
# ext = os.path.splitext(file)[-1].lower()
# if ext in src_ext:
# type = "SOURCE"
# elif ext in hdr_ext:
# type = "HEADER"
# else:
# type = "IMPORTANT"
# # Create unique file symbol
# symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
# exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
# exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
# exec(symbol + ".setOutputName(\"" + file + "\")")
# exec(symbol + ".setDestPath(\"" + destPath + "\")")
# exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder/audio_decoders/speex\")")
# exec(symbol + ".setType(\"" + type + "\")")
# exec(symbol + ".setEnabled(CONFIG_USE_OGG_SPEEX_DECODER.getValue() == True)")
# for fileSymbol, srcPath, file, destPath in opusTable:
# # Set type
# baseFileName = os.path.splitext(file)[0]
# ext = os.path.splitext(file)[-1].lower()
# if ext in src_ext:
# type = "SOURCE"
# elif ext in hdr_ext:
# type = "HEADER"
# else:
# type = "IMPORTANT"
# # Create unique file symbol
# symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
# exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
# exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
# exec(symbol + ".setOutputName(\"" + file + "\")")
# exec(symbol + ".setDestPath(\"" + destPath + "\")")
# exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder/audio_decoders/opus\")")
# exec(symbol + ".setType(\"" + type + "\")")
# exec(symbol + ".setEnabled(CONFIG_USE_OGG_OPUS_DECODER.getValue() == True)")
# for fileSymbol, srcPath, file, destPath in oggTable:
# # Set type
# baseFileName = os.path.splitext(file)[0]
# ext = os.path.splitext(file)[-1].lower()
# if ext in src_ext:
# type = "SOURCE"
# elif ext in hdr_ext:
# type = "HEADER"
# else:
# type = "IMPORTANT"
# # Create unique file symbol
# symbol = fileSymbol + srcPath.replace("/", "_").upper() + baseFileName.upper() + "_" + type.upper()
# exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
# exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
# exec(symbol + ".setOutputName(\"" + file + "\")")
# exec(symbol + ".setDestPath(\"" + destPath + "\")")
# exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder/audio_decoders/ogg\")")
# exec(symbol + ".setType(\"" + type + "\")")
# exec(symbol + ".setEnabled(CONFIG_USE_OGG_OPUS_DECODER.getValue() == True)")
for fileSymbol, srcPath, file, destPath in ftlTable:
# Set type
baseFileName1 = os.path.splitext(file)[0] # Strip the .ftl extension
baseFileName = os.path.splitext(baseFileName1)[0]
ext = os.path.splitext(baseFileName1)[-1].lower()
#print("baseFileName1: " + baseFileName1 + ", baseFileName: " + baseFileName + ", ext: " + ext)
if ext in src_ext:
type = "SOURCE"
elif ext in hdr_ext:
type = "HEADER"
else:
type = "IMPORTANT"
# Create unique file symbol
symbol = fileSymbol + srcPath.replace("/", "_").replace(".", "").upper() + baseFileName.upper() + "_" + type.upper()
exec(symbol + " = audioDecoderComponent.createFileSymbol(\"" + symbol + "\", None)")
exec(symbol + ".setSourcePath(\"" + srcPath + file + "\")")
exec(symbol + ".setOutputName(\"" + baseFileName1 + "\")")
exec(symbol + ".setDestPath(\"" + destPath + "\")")
exec(symbol + ".setProjectPath(\"config/" + configName + "/audio/decoder\")")
exec(symbol + ".setType(\"" + type + "\")")
exec(symbol + ".setEnabled(True)")
exec(symbol + ".setMarkup(True)")
# if("PIC32" in Variables.get("__PROCESSOR")):
# CONFIG_USE_WMA_DECODER.setReadOnly(True)
# CONFIG_USE_AAC_DECODER.setReadOnly(True)
# CONFIG_USE_MP3_DECODER.setReadOnly(True)
# CONFIG_USE_WMA_DECODER.setVisible(False)
# CONFIG_USE_AAC_DECODER.setVisible(False)
# CONFIG_USE_MP3_DECODER.setVisible(False)
# else:
# CONFIG_USE_FLAC_DECODER.setVisible(False)
# CONFIG_USE_OGG_SPEEX_DECODER.setVisible(False)
# CONFIG_USE_OGG_OPUS_DECODER.setVisible(False)
| 78.799569
| 152
| 0.606638
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| 73,126
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| 0.097015
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0
| 8
|
e5b8c862c326152e0fbc87565d24a2352b5dd608
| 22,167
|
py
|
Python
|
src/state_setters.py
|
laurirasanen/goliath
|
7c888934406f8b3c778d3eefe6174c5d3e0be157
|
[
"MIT"
] | null | null | null |
src/state_setters.py
|
laurirasanen/goliath
|
7c888934406f8b3c778d3eefe6174c5d3e0be157
|
[
"MIT"
] | null | null | null |
src/state_setters.py
|
laurirasanen/goliath
|
7c888934406f8b3c778d3eefe6174c5d3e0be157
|
[
"MIT"
] | null | null | null |
import random
import numpy as np
from rlgym.utils.state_setters import StateSetter
from rlgym.utils.state_setters import StateWrapper
from rlgym.utils.common_values import (
BACK_WALL_Y,
BALL_RADIUS,
CEILING_Z,
SIDE_WALL_X,
CAR_MAX_SPEED,
CAR_MAX_ANG_VEL,
)
# Fix spawning inside the rounded corners of the arena.
# Prevent x and y both being less than 1024 units away from wall
# at the same time.
# TODO: use the actual curve instead of cutting a rect out of corners
def fix_spawn_position(x: float, y: float):
min_x = -SIDE_WALL_X + 1024
max_x = SIDE_WALL_X - 1024
min_y = -BACK_WALL_Y + 1024
max_y = BACK_WALL_Y - 1024
if x < min_x:
if y < min_y:
x_diff = x - min_x
y_diff = y - min_y
if x_diff < y_diff:
x = min_x
else:
y = min_y
return x, y
if y > max_y:
x_diff = x - min_x
y_diff = max_y - y
if x_diff < y_diff:
x = min_x
else:
y = max_y
return x, y
if x > max_x:
if y < min_y:
x_diff = max_x - x
y_diff = y - min_y
if x_diff < y_diff:
x = max_x
else:
y = min_y
return x, y
if y > max_y:
x_diff = max_x - x
y_diff = max_y - y
if x_diff < y_diff:
x = max_x
else:
y = max_y
return x, y
return x, y
class KickoffState(StateSetter):
SPAWN_BLUE_POS = [
[-2048, -2560, 17],
[2048, -2560, 17],
[-256, -3840, 17],
[256, -3840, 17],
[0, -4608, 17],
]
SPAWN_BLUE_YAW = [0.25 * np.pi, 0.75 * np.pi, 0.5 * np.pi, 0.5 * np.pi, 0.5 * np.pi]
SPAWN_ORANGE_POS = [
[2048, 2560, 17],
[-2048, 2560, 17],
[256, 3840, 17],
[-256, 3840, 17],
[0, 4608, 17],
]
SPAWN_ORANGE_YAW = [
-0.75 * np.pi,
-0.25 * np.pi,
-0.5 * np.pi,
-0.5 * np.pi,
-0.5 * np.pi,
]
def __init__(self):
super().__init__()
def reset(self, state_wrapper: StateWrapper):
"""
Modifies state_wrapper values to emulate a randomly selected default kickoff.
:param state_wrapper: StateWrapper object to be modified with desired state values.
"""
# possible kickoff indices are shuffled
# 0 = diag right
# 1 = diag left
# 2 = center right
# 3 = center left
# 4 = center
spawn_inds = [0, 1, 2, 3, 4]
# the bot sucks at starting on the right side,
# force it to train these positions more often.
# spawn_inds = [0, 0, 0, 0, 1, 2, 2, 3, 4]
random.shuffle(spawn_inds)
blue_count = 0
orange_count = 0
for car in state_wrapper.cars:
pos = [0.0, 0.0, 0.0]
yaw = 0.0
# team_num = 0 = blue team
if car.team_num == 0:
# select a unique spawn state from pre-determined values
pos = self.SPAWN_BLUE_POS[spawn_inds[blue_count]]
yaw = self.SPAWN_BLUE_YAW[spawn_inds[blue_count]]
blue_count += 1
# team_num = 1 = orange team
elif car.team_num == 1:
# select a unique spawn state from pre-determined values
pos = self.SPAWN_ORANGE_POS[spawn_inds[orange_count]]
yaw = self.SPAWN_ORANGE_YAW[spawn_inds[orange_count]]
orange_count += 1
# set car state values
pos[0], pos[1] = fix_spawn_position(pos[0], pos[1])
car.set_pos(*pos)
car.set_rot(yaw=yaw)
car.boost = 0.33
class AttackState(StateSetter):
FLOOR_SPAWN_CHANCE = 0.5
# Spawn blue near center field
SPAWN_BLUE_POS_X = [-SIDE_WALL_X + 256, SIDE_WALL_X - 256]
SPAWN_BLUE_POS_Y = [-1500, 500]
SPAWN_BLUE_POS_Z = [17, CEILING_Z - 150]
SPAWN_BLUE_YAW = [0.25 * np.pi, 0.75 * np.pi]
SPAWN_BLUE_PITCH = [-0.25 * np.pi, 0.25 * np.pi]
SPAWN_BLUE_ROLL = [-0.25 * np.pi, 0.25 * np.pi]
SPAWN_BLUE_VELOCITY_X = [-200, 200]
SPAWN_BLUE_VELOCITY_Y = [-100, 800]
SPAWN_BLUE_VELOCITY_Z = [-200, 500]
SPAWN_BLUE_ANG_VELOCITY = [-2.0, 2.0]
# Spawn orange near goal
SPAWN_ORANGE_POS_X = [-SIDE_WALL_X + 256, SIDE_WALL_X - 256]
SPAWN_ORANGE_POS_Y = [BACK_WALL_Y - 512, BACK_WALL_Y - 256]
SPAWN_ORANGE_POS_Z = [17, CEILING_Z - 150]
SPAWN_ORANGE_YAW = [-0.25 * np.pi, -0.75 * np.pi]
SPAWN_ORANGE_PITCH = [-0.25 * np.pi, 0.25 * np.pi]
SPAWN_ORANGE_ROLL = [-0.25 * np.pi, 0.25 * np.pi]
SPAWN_ORANGE_VELOCITY_X = [-200, 200]
SPAWN_ORANGE_VELOCITY_Y = [-800, 100]
SPAWN_ORANGE_VELOCITY_Z = [-200, 500]
SPAWN_ORANGE_ANG_VELOCITY = [-2.0, 2.0]
# Spawn goal somewhere in the middle
SPAWN_BALL_POS_X = [-SIDE_WALL_X + 256, SIDE_WALL_X - 256]
SPAWN_BALL_POS_Y = [1000, 1500]
SPAWN_BALL_POS_Z = [17, CEILING_Z - 128]
SPAWN_BALL_VELOCITY = [-800, 800]
def __init__(self):
super().__init__()
def reset(self, state_wrapper: StateWrapper):
"""
Modifies state_wrapper values to emulate a randomly selected attack scenario.
:param state_wrapper: StateWrapper object to be modified with desired state values.
"""
floor_spawn = random.random() < self.FLOOR_SPAWN_CHANCE
# set ball pos
state_wrapper.ball.position[0] = random.uniform(
self.SPAWN_BALL_POS_X[0], self.SPAWN_BALL_POS_X[1]
)
state_wrapper.ball.position[1] = random.uniform(
self.SPAWN_BALL_POS_Y[0], self.SPAWN_BALL_POS_Y[1]
)
if floor_spawn:
state_wrapper.ball.position[2] = 17 + BALL_RADIUS
else:
state_wrapper.ball.position[2] = random.uniform(
self.SPAWN_BALL_POS_Z[0], self.SPAWN_BALL_POS_Z[1]
)
(
state_wrapper.ball.position[0],
state_wrapper.ball.position[1],
) = fix_spawn_position(
state_wrapper.ball.position[0], state_wrapper.ball.position[1]
)
# set ball vel
state_wrapper.ball.linear_velocity[0] = random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
)
state_wrapper.ball.linear_velocity[1] = random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
)
state_wrapper.ball.linear_velocity[2] = random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
)
for car in state_wrapper.cars:
pos = [0.0, 0.0, 0.0]
yaw = 0.0
pitch = 0.0
roll = 0.0
vel = [0.0, 0.0, 0.0]
ang_vel = [0.0, 0.0, 0.0]
# team_num = 0 = blue team
if car.team_num == 0:
# select a random spawn
pos[0] = random.uniform(
self.SPAWN_BLUE_POS_X[0], self.SPAWN_BLUE_POS_X[1]
)
pos[1] = random.uniform(
self.SPAWN_BLUE_POS_Y[0], self.SPAWN_BLUE_POS_Y[1]
)
yaw = random.uniform(self.SPAWN_BLUE_YAW[0], self.SPAWN_BLUE_YAW[1])
vel = [
random.uniform(
self.SPAWN_BLUE_VELOCITY_X[0],
self.SPAWN_BLUE_VELOCITY_X[1],
),
random.uniform(
self.SPAWN_BLUE_VELOCITY_Y[0],
self.SPAWN_BLUE_VELOCITY_Y[1],
),
random.uniform(
self.SPAWN_BLUE_VELOCITY_Z[0],
self.SPAWN_BLUE_VELOCITY_Z[1],
),
]
if floor_spawn:
pos[2] = 17
vel[2] = 0
else:
pos[2] = random.uniform(
self.SPAWN_BLUE_POS_Z[0], self.SPAWN_BLUE_POS_Z[1]
)
pitch = random.uniform(
self.SPAWN_BLUE_PITCH[0], self.SPAWN_BLUE_PITCH[1]
)
roll = random.uniform(
self.SPAWN_BLUE_ROLL[0], self.SPAWN_BLUE_ROLL[1]
)
ang_vel = [
random.uniform(
self.SPAWN_BLUE_ANG_VELOCITY[0],
self.SPAWN_BLUE_ANG_VELOCITY[1],
),
random.uniform(
self.SPAWN_BLUE_ANG_VELOCITY[0],
self.SPAWN_BLUE_ANG_VELOCITY[1],
),
random.uniform(
self.SPAWN_BLUE_ANG_VELOCITY[0],
self.SPAWN_BLUE_ANG_VELOCITY[1],
),
]
# team_num = 1 = orange team
elif car.team_num == 1:
# select a random spawn
pos[0] = random.uniform(
self.SPAWN_ORANGE_POS_X[0], self.SPAWN_ORANGE_POS_X[1]
)
pos[1] = random.uniform(
self.SPAWN_ORANGE_POS_Y[0], self.SPAWN_ORANGE_POS_Y[1]
)
yaw = random.uniform(self.SPAWN_ORANGE_YAW[0], self.SPAWN_ORANGE_YAW[1])
vel = [
random.uniform(
self.SPAWN_ORANGE_VELOCITY_X[0],
self.SPAWN_ORANGE_VELOCITY_X[1],
),
random.uniform(
self.SPAWN_ORANGE_VELOCITY_Y[0],
self.SPAWN_ORANGE_VELOCITY_Y[1],
),
random.uniform(
self.SPAWN_ORANGE_VELOCITY_Z[0],
self.SPAWN_ORANGE_VELOCITY_Z[1],
),
]
if floor_spawn:
pos[2] = 17
vel[2] = 0
else:
pos[2] = random.uniform(
self.SPAWN_ORANGE_POS_Z[0], self.SPAWN_ORANGE_POS_Z[1]
)
pitch = random.uniform(
self.SPAWN_ORANGE_PITCH[0], self.SPAWN_ORANGE_PITCH[1]
)
roll = random.uniform(
self.SPAWN_ORANGE_ROLL[0], self.SPAWN_ORANGE_ROLL[1]
)
ang_vel = [
random.uniform(
self.SPAWN_ORANGE_ANG_VELOCITY[0],
self.SPAWN_ORANGE_ANG_VELOCITY[1],
),
random.uniform(
self.SPAWN_ORANGE_ANG_VELOCITY[0],
self.SPAWN_ORANGE_ANG_VELOCITY[1],
),
random.uniform(
self.SPAWN_ORANGE_ANG_VELOCITY[0],
self.SPAWN_ORANGE_ANG_VELOCITY[1],
),
]
# set car state values
pos[0], pos[1] = fix_spawn_position(pos[0], pos[1])
car.set_pos(*pos)
car.set_rot(yaw=yaw, pitch=pitch, roll=roll)
car.boost = random.uniform(0.2, 0.8)
car.set_lin_vel(*vel)
car.set_ang_vel(*ang_vel)
class ShootState(StateSetter):
# blue spawn
SPAWN_BLUE_POS_X = [-SIDE_WALL_X + 256, SIDE_WALL_X - 256]
SPAWN_BLUE_POS_Y = [-BACK_WALL_Y + 256, 500]
SPAWN_BLUE_POS_Z = [17, 17]
SPAWN_BLUE_YAW = [0.4 * np.pi, 0.6 * np.pi]
SPAWN_BLUE_VELOCITY_X = [-50, 50]
SPAWN_BLUE_VELOCITY_Y = [CAR_MAX_SPEED * 0.5, CAR_MAX_SPEED]
SPAWN_BLUE_VELOCITY_Z = [0, 0]
# Spawn orange near goal
SPAWN_ORANGE_POS_X = [-SIDE_WALL_X + 256, SIDE_WALL_X - 256]
SPAWN_ORANGE_POS_Y = [BACK_WALL_Y - 512, BACK_WALL_Y - 256]
SPAWN_ORANGE_POS_Z = [17, 17]
SPAWN_ORANGE_YAW = [-1 * np.pi, 1 * np.pi]
SPAWN_ORANGE_VELOCITY_X = [-50, 50]
SPAWN_ORANGE_VELOCITY_Y = [-50, 50]
SPAWN_ORANGE_VELOCITY_Z = [0, 0]
# Spawn ball relative to blue
SPAWN_BALL_POS_X = [-50, 50]
SPAWN_BALL_POS_Y = [800, 2000]
SPAWN_BALL_POS_Z = [17, 17]
SPAWN_BALL_VELOCITY = [-200, 200]
def __init__(self):
super().__init__()
def reset(self, state_wrapper: StateWrapper):
"""
Modifies state_wrapper values to emulate a randomly selected attack scenario.
:param state_wrapper: StateWrapper object to be modified with desired state values.
"""
for car in state_wrapper.cars:
pos = [0.0, 0.0, 0.0]
yaw = 0.0
vel = [0.0, 0.0, 0.0]
# team_num = 0 = blue team
if car.team_num == 0:
# select a random spawn
pos = [
random.uniform(self.SPAWN_BLUE_POS_X[0], self.SPAWN_BLUE_POS_X[1]),
random.uniform(self.SPAWN_BLUE_POS_Y[0], self.SPAWN_BLUE_POS_Y[1]),
random.uniform(self.SPAWN_BLUE_POS_Z[0], self.SPAWN_BLUE_POS_Z[1]),
]
yaw = random.uniform(self.SPAWN_BLUE_YAW[0], self.SPAWN_BLUE_YAW[1])
vel = [
random.uniform(
self.SPAWN_BLUE_VELOCITY_X[0],
self.SPAWN_BLUE_VELOCITY_X[1],
),
random.uniform(
self.SPAWN_BLUE_VELOCITY_Y[0],
self.SPAWN_BLUE_VELOCITY_Y[1],
),
random.uniform(
self.SPAWN_BLUE_VELOCITY_Z[0],
self.SPAWN_BLUE_VELOCITY_Z[1],
),
]
# set ball pos
state_wrapper.ball.position = [
pos[0]
+ random.uniform(
self.SPAWN_BALL_POS_X[0], self.SPAWN_BALL_POS_X[1]
),
pos[1]
+ random.uniform(
self.SPAWN_BALL_POS_Y[0], self.SPAWN_BALL_POS_Y[1]
),
pos[2]
+ random.uniform(
self.SPAWN_BALL_POS_Z[0], self.SPAWN_BALL_POS_Z[1]
),
]
(
state_wrapper.ball.position[0],
state_wrapper.ball.position[1],
) = fix_spawn_position(
state_wrapper.ball.position[0], state_wrapper.ball.position[1]
)
# team_num = 1 = orange team
elif car.team_num == 1:
# select a random spawn
pos = [
random.uniform(
self.SPAWN_ORANGE_POS_X[0], self.SPAWN_ORANGE_POS_X[1]
),
random.uniform(
self.SPAWN_ORANGE_POS_Y[0], self.SPAWN_ORANGE_POS_Y[1]
),
random.uniform(
self.SPAWN_ORANGE_POS_Z[0], self.SPAWN_ORANGE_POS_Z[1]
),
]
yaw = random.uniform(self.SPAWN_ORANGE_YAW[0], self.SPAWN_ORANGE_YAW[1])
vel = [
random.uniform(
self.SPAWN_ORANGE_VELOCITY_X[0],
self.SPAWN_ORANGE_VELOCITY_X[1],
),
random.uniform(
self.SPAWN_ORANGE_VELOCITY_Y[0],
self.SPAWN_ORANGE_VELOCITY_Y[1],
),
random.uniform(
self.SPAWN_ORANGE_VELOCITY_Z[0],
self.SPAWN_ORANGE_VELOCITY_Z[1],
),
]
# set ball vel
state_wrapper.ball.linear_velocity = [
random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
),
random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
),
random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
),
]
# set car state values
pos[0], pos[1] = fix_spawn_position(pos[0], pos[1])
car.set_pos(*pos)
car.set_rot(yaw=yaw)
car.boost = random.uniform(0.2, 0.8)
car.set_lin_vel(*vel)
class DribbleState(StateSetter):
# blue spawn
SPAWN_BLUE_POS_X = [-SIDE_WALL_X + 256, SIDE_WALL_X - 256]
SPAWN_BLUE_POS_Y = [-BACK_WALL_Y + 256, 500]
SPAWN_BLUE_POS_Z = [17, 17]
SPAWN_BLUE_YAW = [0.4 * np.pi, 0.6 * np.pi]
SPAWN_BLUE_VELOCITY_X = [-50, 50]
SPAWN_BLUE_VELOCITY_Y = [100, 0.5 * CAR_MAX_SPEED]
SPAWN_BLUE_VELOCITY_Z = [0, 0]
# Spawn orange near goal
SPAWN_ORANGE_POS_X = [-SIDE_WALL_X + 256, SIDE_WALL_X - 256]
SPAWN_ORANGE_POS_Y = [BACK_WALL_Y - 512, BACK_WALL_Y - 256]
SPAWN_ORANGE_POS_Z = [17, 17]
SPAWN_ORANGE_YAW = [-1 * np.pi, 1 * np.pi]
SPAWN_ORANGE_VELOCITY_X = [-50, 50]
SPAWN_ORANGE_VELOCITY_Y = [-50, 50]
SPAWN_ORANGE_VELOCITY_Z = [0, 0]
# Spawn ball relative to blue
SPAWN_BALL_POS_X = [-5, 5]
SPAWN_BALL_POS_Y = [-5, 5]
SPAWN_BALL_POS_Z = [1.5 * BALL_RADIUS, 3 * BALL_RADIUS]
SPAWN_BALL_VELOCITY = [-5, 5]
def __init__(self):
super().__init__()
def reset(self, state_wrapper: StateWrapper):
"""
Modifies state_wrapper values to emulate a randomly selected attack scenario.
:param state_wrapper: StateWrapper object to be modified with desired state values.
"""
for car in state_wrapper.cars:
pos = [0.0, 0.0, 0.0]
yaw = 0.0
vel = [0.0, 0.0, 0.0]
# team_num = 0 = blue team
if car.team_num == 0:
# select a random spawn
pos = [
random.uniform(self.SPAWN_BLUE_POS_X[0], self.SPAWN_BLUE_POS_X[1]),
random.uniform(self.SPAWN_BLUE_POS_Y[0], self.SPAWN_BLUE_POS_Y[1]),
random.uniform(self.SPAWN_BLUE_POS_Z[0], self.SPAWN_BLUE_POS_Z[1]),
]
yaw = random.uniform(self.SPAWN_BLUE_YAW[0], self.SPAWN_BLUE_YAW[1])
vel = [
random.uniform(
self.SPAWN_BLUE_VELOCITY_X[0],
self.SPAWN_BLUE_VELOCITY_X[1],
),
random.uniform(
self.SPAWN_BLUE_VELOCITY_Y[0],
self.SPAWN_BLUE_VELOCITY_Y[1],
),
random.uniform(
self.SPAWN_BLUE_VELOCITY_Z[0],
self.SPAWN_BLUE_VELOCITY_Z[1],
),
]
# set ball pos
state_wrapper.ball.position = [
pos[0]
+ random.uniform(
self.SPAWN_BALL_POS_X[0], self.SPAWN_BALL_POS_X[1]
),
pos[1]
+ random.uniform(
self.SPAWN_BALL_POS_Y[0], self.SPAWN_BALL_POS_Y[1]
),
pos[2]
+ random.uniform(
self.SPAWN_BALL_POS_Z[0], self.SPAWN_BALL_POS_Z[1]
),
]
(
state_wrapper.ball.position[0],
state_wrapper.ball.position[1],
) = fix_spawn_position(
state_wrapper.ball.position[0], state_wrapper.ball.position[1]
)
# set ball vel
state_wrapper.ball.linear_velocity = [
vel[0]
+ random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
),
vel[1]
+ random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
),
vel[2]
+ random.uniform(
self.SPAWN_BALL_VELOCITY[0], self.SPAWN_BALL_VELOCITY[1]
),
]
# team_num = 1 = orange team
elif car.team_num == 1:
# select a random spawn
pos = [
random.uniform(
self.SPAWN_ORANGE_POS_X[0], self.SPAWN_ORANGE_POS_X[1]
),
random.uniform(
self.SPAWN_ORANGE_POS_Y[0], self.SPAWN_ORANGE_POS_Y[1]
),
random.uniform(
self.SPAWN_ORANGE_POS_Z[0], self.SPAWN_ORANGE_POS_Z[1]
),
]
yaw = random.uniform(self.SPAWN_ORANGE_YAW[0], self.SPAWN_ORANGE_YAW[1])
vel = [
random.uniform(
self.SPAWN_ORANGE_VELOCITY_X[0],
self.SPAWN_ORANGE_VELOCITY_X[1],
),
random.uniform(
self.SPAWN_ORANGE_VELOCITY_Y[0],
self.SPAWN_ORANGE_VELOCITY_Y[1],
),
random.uniform(
self.SPAWN_ORANGE_VELOCITY_Z[0],
self.SPAWN_ORANGE_VELOCITY_Z[1],
),
]
# set car state values
pos[0], pos[1] = fix_spawn_position(pos[0], pos[1])
car.set_pos(*pos)
car.set_rot(yaw=yaw)
car.boost = random.uniform(0.2, 0.8)
car.set_lin_vel(*vel)
| 36.339344
| 91
| 0.486399
| 2,657
| 22,167
| 3.74332
| 0.065111
| 0.130304
| 0.119646
| 0.154836
| 0.862759
| 0.831591
| 0.821436
| 0.803439
| 0.790569
| 0.78514
| 0
| 0.057376
| 0.420535
| 22,167
| 610
| 92
| 36.339344
| 0.716933
| 0.08666
| 0
| 0.70082
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.001639
| 0
| 1
| 0.018443
| false
| 0
| 0.010246
| 0
| 0.180328
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| 0
| 1
| 0
| 0
| 0
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
e5ef5e548d750ef5fc59bd8e0eb06081ab225a65
| 15,445
|
py
|
Python
|
src/abaqus/Load/SurfaceTraction.py
|
Haiiliin/PyAbaqus
|
f20db6ebea19b73059fe875a53be370253381078
|
[
"MIT"
] | 7
|
2022-01-21T09:15:45.000Z
|
2022-02-15T09:31:58.000Z
|
src/abaqus/Load/SurfaceTraction.py
|
Haiiliin/PyAbaqus
|
f20db6ebea19b73059fe875a53be370253381078
|
[
"MIT"
] | null | null | null |
src/abaqus/Load/SurfaceTraction.py
|
Haiiliin/PyAbaqus
|
f20db6ebea19b73059fe875a53be370253381078
|
[
"MIT"
] | null | null | null |
import typing
from abaqusConstants import *
from .Load import Load
from ..Region.Region import Region
class SurfaceTraction(Load):
"""The SurfaceTraction object defines surface traction on a region.
The SurfaceTraction object is derived from the Load object.
Attributes
----------
name: str
A String specifying the load repository key.
angle: float
A Float specifying an additional rotation of **directionVector** about an axis. The
default value is 0.0.
axis: SymbolicConstant
A SymbolicConstant specifying the axis about which to apply an additional rotation of
**directionVector**. Possible values are AXIS_1, AXIS_2, and AXIS_3. The default value is
AXIS_1.
follower: Boolean
A Boolean specifying whether the direction of the force changes with rotation. The
default value is ON.This parameter may be modified only if **traction** is GENERAL. You
should provide the **follower** argument only if it is valid for the specified step.
resultant: Boolean
A Boolean specifying whether the to maintain a constant resultant force by defining
traction per unit undeformed area. If **resultant** is OFF, traction is defined per unit
deformed area. The default value is OFF.You should provide the **resultant** argument only
if it is valid for the specified step.
traction: SymbolicConstant
A SymbolicConstant specifying how to apply surface traction. Possible values are SHEAR
and GENERAL. The default value is SHEAR.
distributionType: SymbolicConstant
A SymbolicConstant specifying how the surface traction is distributed spatially.
Possible values are UNIFORM, USER_DEFINED, and FIELD. The default value is UNIFORM.
field: str
A String specifying the name of the :py:class:`~abaqus.Field.AnalyticalField.AnalyticalField` object associated with this load.
The **field** argument applies only when **distributionType=FIELD**. The default value is an
empty string.
userCsys: str
A String specifying a CSYS defined by a user-subroutine. If **userCsys=None**, the degrees
of freedom are defined in the global coordinate system or by the **localCsys** parameter
if defined. The default value is "None".
localCsys: int
None or a :py:class:`~abaqus.Datum.DatumCsys.DatumCsys` object specifying the local coordinate system of the load's degrees
of freedom. If **localCsys=None**, the degrees of freedom are defined in the global
coordinate system or by the **userCsys** parameter if defined. When this member is
queried, it returns an Int. The default value is None.
directionVector: tuple
A :py:class:`~abaqus.BasicGeometry.VertexArray.VertexArray` object of length 2 specifying the direction of the load. Instead of
through a ConstrainedSketchVertex, each point may be specified through a tuple of coordinates. If
**traction** is SHEAR, then **directionVector** will be projected onto the region surface.
This parameter is available only if **traction** is GENERAL or SHEAR.
region: Region
A :py:class:`~abaqus.Region.Region.Region` object specifying the region to which the load is applied.
Notes
-----
This object can be accessed by:
.. code-block:: python
import load
mdb.models[name].loads[name]
"""
# A String specifying the load repository key.
name: str = ''
# A Float specifying an additional rotation of *directionVector* about an axis. The
# default value is 0.0.
angle: float = 0
# A SymbolicConstant specifying the axis about which to apply an additional rotation of
# *directionVector*. Possible values are AXIS_1, AXIS_2, and AXIS_3. The default value is
# AXIS_1.
axis: SymbolicConstant = AXIS_1
# A Boolean specifying whether the direction of the force changes with rotation. The
# default value is ON.This parameter may be modified only if *traction* is GENERAL. You
# should provide the *follower* argument only if it is valid for the specified step.
follower: Boolean = ON
# A Boolean specifying whether the to maintain a constant resultant force by defining
# traction per unit undeformed area. If *resultant* is OFF, traction is defined per unit
# deformed area. The default value is OFF.You should provide the *resultant* argument only
# if it is valid for the specified step.
resultant: Boolean = OFF
# A SymbolicConstant specifying how to apply surface traction. Possible values are SHEAR
# and GENERAL. The default value is SHEAR.
traction: SymbolicConstant = SHEAR
# A SymbolicConstant specifying how the surface traction is distributed spatially.
# Possible values are UNIFORM, USER_DEFINED, and FIELD. The default value is UNIFORM.
distributionType: SymbolicConstant = UNIFORM
# A String specifying the name of the AnalyticalField object associated with this load.
# The *field* argument applies only when *distributionType*=FIELD. The default value is an
# empty string.
field: str = ''
# A String specifying a CSYS defined by a user-subroutine. If *userCsys*=None, the degrees
# of freedom are defined in the global coordinate system or by the *localCsys* parameter
# if defined. The default value is "None".
userCsys: str = ''
# None or a DatumCsys object specifying the local coordinate system of the load's degrees
# of freedom. If *localCsys*=None, the degrees of freedom are defined in the global
# coordinate system or by the *userCsys* parameter if defined. When this member is
# queried, it returns an Int. The default value is None.
localCsys: int = None
# A VertexArray object of length 2 specifying the direction of the load. Instead of
# through a ConstrainedSketchVertex, each point may be specified through a tuple of coordinates. If
# *traction* is SHEAR, then *directionVector* will be projected onto the region surface.
# This parameter is available only if *traction* is GENERAL or SHEAR.
directionVector: tuple = ()
# A Region object specifying the region to which the load is applied.
region: Region = Region()
def __init__(self, name: str, createStepName: str, region: Region, magnitude: float,
distributionType: SymbolicConstant = UNIFORM, field: str = '', amplitude: str = UNSET,
angle: float = 0, axis: SymbolicConstant = AXIS_1, localCsys: int = None,
userCsys: str = '', directionVector: tuple = (), follower: Boolean = ON,
resultant: Boolean = OFF, traction: SymbolicConstant = SHEAR):
"""This method creates a SurfaceTraction object.
Notes
-----
This function can be accessed by:
.. code-block:: python
mdb.models[name].SurfaceTraction
Parameters
----------
name
A String specifying the load repository key.
createStepName
A String specifying the name of the step in which the load is created.
region
A Region object specifying the region to which the load is applied.
magnitude
A Float or Complex specifying the load magnitude. *magnitude* is optional if
*distributionType*=USER_DEFINED.
distributionType
A SymbolicConstant specifying how the surface traction is distributed spatially.
Possible values are UNIFORM, USER_DEFINED, and FIELD. The default value is UNIFORM.
field
A String specifying the name of the AnalyticalField object associated with this load.
The *field* argument applies only when *distributionType*=FIELD. The default value is an
empty string.
amplitude
A String or the SymbolicConstant UNSET specifying the name of the amplitude reference.
UNSET should be used if the load has no amplitude reference. The default value is UNSET.
You should provide the *amplitude* argument only if it is valid for the specified step.
angle
A Float specifying an additional rotation of *directionVector* about an axis. The
default value is 0.0.
axis
A SymbolicConstant specifying the axis about which to apply an additional rotation of
*directionVector*. Possible values are AXIS_1, AXIS_2, and AXIS_3. The default value is
AXIS_1.
localCsys
None or a DatumCsys object specifying the local coordinate system of the load's degrees
of freedom. If *localCsys*=None, the degrees of freedom are defined in the global
coordinate system or by the *userCsys* parameter if defined. When this member is
queried, it returns an Int. The default value is None.
userCsys
A String specifying a CSYS defined by a user-subroutine. If *userCsys*=None, the degrees
of freedom are defined in the global coordinate system or by the *localCsys* parameter
if defined. The default value is "None".
directionVector
A VertexArray object of length 2 specifying the direction of the load. Instead of
through a ConstrainedSketchVertex, each point may be specified through a tuple of coordinates. If
*traction* is SHEAR, then *directionVector* will be projected onto the region surface.
This parameter is available only if *traction* is GENERAL or SHEAR.
follower
A Boolean specifying whether the direction of the force changes with rotation. The
default value is ON.This parameter may be modified only if *traction* is GENERAL. You
should provide the *follower* argument only if it is valid for the specified step.
resultant
A Boolean specifying whether the to maintain a constant resultant force by defining
traction per unit undeformed area. If *resultant* is OFF, traction is defined per unit
deformed area. The default value is OFF.You should provide the *resultant* argument only
if it is valid for the specified step.
traction
A SymbolicConstant specifying how to apply surface traction. Possible values are SHEAR
and GENERAL. The default value is SHEAR.
Returns
-------
A SurfaceTraction object.
"""
super().__init__()
pass
def setValues(self, distributionType: SymbolicConstant = UNIFORM, field: str = '', amplitude: str = UNSET,
angle: float = 0, axis: SymbolicConstant = AXIS_1, localCsys: int = None,
userCsys: str = '', directionVector: tuple = (), follower: Boolean = ON,
resultant: Boolean = OFF, traction: SymbolicConstant = SHEAR):
"""This method modifies the data for an existing SurfaceTraction object in the step where
it is created.
Parameters
----------
distributionType
A SymbolicConstant specifying how the surface traction is distributed spatially.
Possible values are UNIFORM, USER_DEFINED, and FIELD. The default value is UNIFORM.
field
A String specifying the name of the AnalyticalField object associated with this load.
The *field* argument applies only when *distributionType*=FIELD. The default value is an
empty string.
amplitude
A String or the SymbolicConstant UNSET specifying the name of the amplitude reference.
UNSET should be used if the load has no amplitude reference. The default value is UNSET.
You should provide the *amplitude* argument only if it is valid for the specified step.
angle
A Float specifying an additional rotation of *directionVector* about an axis. The
default value is 0.0.
axis
A SymbolicConstant specifying the axis about which to apply an additional rotation of
*directionVector*. Possible values are AXIS_1, AXIS_2, and AXIS_3. The default value is
AXIS_1.
localCsys
None or a DatumCsys object specifying the local coordinate system of the load's degrees
of freedom. If *localCsys*=None, the degrees of freedom are defined in the global
coordinate system or by the *userCsys* parameter if defined. When this member is
queried, it returns an Int. The default value is None.
userCsys
A String specifying a CSYS defined by a user-subroutine. If *userCsys*=None, the degrees
of freedom are defined in the global coordinate system or by the *localCsys* parameter
if defined. The default value is "None".
directionVector
A VertexArray object of length 2 specifying the direction of the load. Instead of
through a ConstrainedSketchVertex, each point may be specified through a tuple of coordinates. If
*traction* is SHEAR, then *directionVector* will be projected onto the region surface.
This parameter is available only if *traction* is GENERAL or SHEAR.
follower
A Boolean specifying whether the direction of the force changes with rotation. The
default value is ON.This parameter may be modified only if *traction* is GENERAL. You
should provide the *follower* argument only if it is valid for the specified step.
resultant
A Boolean specifying whether the to maintain a constant resultant force by defining
traction per unit undeformed area. If *resultant* is OFF, traction is defined per unit
deformed area. The default value is OFF.You should provide the *resultant* argument only
if it is valid for the specified step.
traction
A SymbolicConstant specifying how to apply surface traction. Possible values are SHEAR
and GENERAL. The default value is SHEAR.
"""
pass
def setValuesInStep(self, stepName: str,
magnitude: typing.Union[SymbolicConstant, float] = None,
amplitude: str = ''):
"""This method modifies the propagating data for an existing SurfaceTraction object in the
specified step.
Parameters
----------
stepName
A String specifying the name of the step in which the load is modified.
magnitude
A Float, a Complex, or the SymbolicConstant UNCHANGED specifying the load magnitude.
UNCHANGED should be used if the magnitude is propagated from the previous analysis step.
amplitude
A String or a SymbolicConstant specifying the name of the amplitude reference. Possible
values for the SymbolicConstant are UNCHANGED and FREED. UNCHANGED should be used if the
amplitude is propagated from the previous analysis step. FREED should be used if the
load has no amplitude reference. You should provide the *amplitude* argument only if it
is valid for the specified step.
"""
pass
| 54.964413
| 135
| 0.677307
| 1,975
| 15,445
| 5.280506
| 0.08962
| 0.036437
| 0.054655
| 0.061943
| 0.868252
| 0.864129
| 0.856266
| 0.83287
| 0.813693
| 0.813693
| 0
| 0.003036
| 0.274976
| 15,445
| 280
| 136
| 55.160714
| 0.928291
| 0.801036
| 0
| 0.272727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.090909
| false
| 0.090909
| 0.121212
| 0
| 0.606061
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 9
|
f921b6c43db5e798f0f87eca803db6c11c7fbe51
| 122
|
py
|
Python
|
common/TimeUtils.py
|
liujiage/DevOps
|
87e7343f1a574bb05fe7915ba05d8757e6913779
|
[
"Apache-2.0"
] | 3
|
2022-01-11T14:03:19.000Z
|
2022-01-11T14:25:24.000Z
|
common/TimeUtils.py
|
liujiage/DevOpsK8s
|
b722a855394887a91f42c46586b9724f3258f620
|
[
"Apache-2.0"
] | null | null | null |
common/TimeUtils.py
|
liujiage/DevOpsK8s
|
b722a855394887a91f42c46586b9724f3258f620
|
[
"Apache-2.0"
] | null | null | null |
from datetime import datetime, timezone
def getTimeStr():
return datetime.now(timezone.utc).strftime('%Y%m%d%H%M%S')
| 24.4
| 62
| 0.729508
| 19
| 122
| 4.684211
| 0.789474
| 0
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| 0
| 0.114754
| 122
| 5
| 62
| 24.4
| 0.824074
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| 0.333333
| true
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| 0.333333
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|
0
| 7
|
005eda53025e6c13fdd00cb7016859d016fe7961
| 13,890
|
py
|
Python
|
test/cmu_swapped_legs_tests.py
|
AndrewRLawrence/dp_gp_lvm
|
b0d4c776714f22e83de31127fbfbbd511f017dcd
|
[
"MIT"
] | 1
|
2021-01-17T11:44:36.000Z
|
2021-01-17T11:44:36.000Z
|
test/cmu_swapped_legs_tests.py
|
AndrewRLawrence/dp_gp_lvm
|
b0d4c776714f22e83de31127fbfbbd511f017dcd
|
[
"MIT"
] | 1
|
2020-07-19T20:47:02.000Z
|
2020-07-19T20:47:02.000Z
|
test/cmu_swapped_legs_tests.py
|
AndrewRLawrence/dp_gp_lvm
|
b0d4c776714f22e83de31127fbfbbd511f017dcd
|
[
"MIT"
] | 1
|
2020-07-21T07:13:13.000Z
|
2020-07-21T07:13:13.000Z
|
"""
This module tests the various models using the concatenated CMU walking data set with normal and swapped legs.
"""
from src.models.dp_gp_lvm import dp_gp_lvm
from src.models.gaussian_process import bayesian_gp_lvm as bgplvm, manifold_relevance_determination as mrd
from src.utils.constants import ResultKeys, RESULTS_FILE_NAME, DATA_PATH
from src.utils.types import NP_DTYPE
import src.visualisation.plotters as vis
import matplotlib.pyplot as plot
import numpy as np
from os.path import isfile
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import tensorflow as tf
from time import time
if __name__ == '__main__':
# Train model. Model/optimisation parameters. Using values from elros as larger ones use too much GPU memory.
num_inducing_points = 30
num_latent_dimensions = 20
truncation_level = 20
train_iter = 5000
learning_rate = 0.01
# Read data.
cmu_data = np.load(DATA_PATH + 'cmu_mocap/35_01.npz')
normal_swapped_motions = cmu_data['concatenated_motion_cols']
# TODO: Maybe subsample frames as N is rather large now.
# Normalise data to zero mean and unit variance.
scaler = StandardScaler()
y_train = scaler.fit_transform(normal_swapped_motions[::4]) # subsample frames.
num_samples, num_output_dimensions = y_train.shape
# Print info.
print('\nCMU Walking 35 with Normal and Swapped Legs Motion:')
print(' Total number of observations (N): {}'.format(num_samples))
print(' Total number of output dimensions (D): {}'.format(num_output_dimensions))
print(' Total number of inducing points (M): {}'.format(num_inducing_points))
print(' Total number of latent dimensions (Q): {}'.format(num_latent_dimensions))
# Define file path for results.
dataset_str = 'cmu_walking_normal_swapped'
bgplvm_results_file = RESULTS_FILE_NAME.format(model='bgplvm', dataset=dataset_str)
mrd_results_file = RESULTS_FILE_NAME.format(model='mrd', dataset=dataset_str) # 93 dims per view.
# 3 dims per view so keep 3d points together.
mrd_fully_independent_results_file = RESULTS_FILE_NAME.format(model='mrd_fully_independent', dataset=dataset_str)
gpdp_results_file = RESULTS_FILE_NAME.format(model='dp_gp_lvm', dataset=dataset_str) # Keep 3d points together.
gpdp_mask_results_file = RESULTS_FILE_NAME.format(model='dp_gp_lvm_mask_93', dataset=dataset_str)
# Define instance of necessary model.
if not isfile(gpdp_results_file):
# Reset default graph before building new model graph. This speeds up script.
tf.reset_default_graph()
np.random.seed(1) # Random seed.
# Define instance of DP-GP-LVM.
model = dp_gp_lvm(y_train=y_train,
num_inducing_points=num_inducing_points,
num_latent_dims=num_latent_dimensions,
truncation_level=truncation_level,
mask_size=3)
model_training_objective = model.objective
# Optimisation.
model_opt_train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
loss=model_training_objective)
with tf.Session() as s:
# Initialise variables.
s.run(tf.global_variables_initializer())
# Training optimisation loop.
start_time = time()
print('\nTraining DP-GP-LVM:')
for c in range(train_iter):
s.run(model_opt_train)
if (c % 100) == 0:
print(' DP-GP-LVM opt iter {:5}: {}'.format(c, s.run(model_training_objective)))
end_time = time()
train_opt_time = end_time - start_time
final_cost = s.run(model_training_objective)
print('Final iter {:5}:'.format(c))
print(' DP-GP-LVM: {}'.format(s.run(model_training_objective)))
print('Time to optimise: {} s'.format(train_opt_time))
# Get converged values as numpy arrays.
ard_weights, noise_precision, signal_variance, inducing_input, assignments = \
s.run((model.ard_weights, model.noise_precision, model.signal_variance, model.inducing_input,
model.assignments))
x_mean, x_covar = s.run(model.q_x)
w_1, w_2 = s.run(model.dp.q_alpha)
gamma_atoms, alpha_atoms, beta_atoms = s.run(model.dp_atoms)
# Save results.
print('\nSaving results to .npz file.')
np.savez(gpdp_results_file, original_data=normal_swapped_motions, y_train=y_train,
ard_weights=ard_weights, noise_precision=noise_precision, signal_variance=signal_variance,
x_u=inducing_input, assignments=assignments, x_mean=x_mean, x_covar=x_covar,
gamma_atoms=gamma_atoms, alpha_atoms=alpha_atoms, beta_atoms=beta_atoms,
q_alpha_w1=w_1, q_alpha_w2=w_2, train_opt_time=train_opt_time, final_cost=final_cost)
if not isfile(gpdp_mask_results_file):
# Reset default graph before building new model graph. This speeds up script.
tf.reset_default_graph()
np.random.seed(1) # Random seed.
# Define instance of DP-GP-LVM with DP mask of 93.
model = dp_gp_lvm(y_train=y_train,
num_inducing_points=num_inducing_points,
num_latent_dims=num_latent_dimensions,
truncation_level=truncation_level,
mask_size=93)
model_training_objective = model.objective
# Optimisation.
model_opt_train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
loss=model_training_objective)
with tf.Session() as s:
# Initialise variables.
s.run(tf.global_variables_initializer())
# Training optimisation loop.
start_time = time()
print('\nTraining DP-GP-LVM:')
for c in range(train_iter):
s.run(model_opt_train)
if (c % 100) == 0:
print(' DP-GP-LVM opt iter {:5}: {}'.format(c, s.run(model_training_objective)))
end_time = time()
train_opt_time = end_time - start_time
final_cost = s.run(model_training_objective)
print('Final iter {:5}:'.format(c))
print(' DP-GP-LVM: {}'.format(s.run(model_training_objective)))
print('Time to optimise: {} s'.format(train_opt_time))
# Get converged values as numpy arrays.
ard_weights, noise_precision, signal_variance, inducing_input, assignments = \
s.run((model.ard_weights, model.noise_precision, model.signal_variance, model.inducing_input,
model.assignments))
x_mean, x_covar = s.run(model.q_x)
w_1, w_2 = s.run(model.dp.q_alpha)
gamma_atoms, alpha_atoms, beta_atoms = s.run(model.dp_atoms)
# Save results.
print('\nSaving results to .npz file.')
np.savez(gpdp_mask_results_file, original_data=normal_swapped_motions, y_train=y_train,
ard_weights=ard_weights, noise_precision=noise_precision, signal_variance=signal_variance,
x_u=inducing_input, assignments=assignments, x_mean=x_mean, x_covar=x_covar,
gamma_atoms=gamma_atoms, alpha_atoms=alpha_atoms, beta_atoms=beta_atoms,
q_alpha_w1=w_1, q_alpha_w2=w_2, train_opt_time=train_opt_time, final_cost=final_cost)
if not isfile(bgplvm_results_file):
# Reset default graph before building new model graph. This speeds up script.
tf.reset_default_graph()
np.random.seed(1) # Random seed.
model = bgplvm(y_train=y_train,
num_inducing_points=num_inducing_points,
num_latent_dims=num_latent_dimensions)
model_training_objective = model.objective
# Optimisation.
model_opt_train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
loss=model_training_objective)
with tf.Session() as s:
# Initialise variables.
s.run(tf.global_variables_initializer())
# Training optimisation loop.
start_time = time()
print('\nTraining BGP-LVM:')
for c in range(train_iter):
s.run(model_opt_train)
if (c % 100) == 0:
print(' BGP-LVM opt iter {:5}: {}'.format(c, s.run(model_training_objective)))
end_time = time()
train_opt_time = end_time - start_time
final_cost = s.run(model_training_objective)
print('Final iter {:5}:'.format(c))
print(' BGP-LVM: {}'.format(s.run(model_training_objective)))
print('Time to optimise: {} s'.format(train_opt_time))
# Get converged values as numpy arrays.
ard_weights, noise_precision, signal_variance, inducing_input = \
s.run((model.ard_weights, model.noise_precision, model.signal_variance, model.inducing_input))
x_mean, x_covar = s.run(model.q_x)
# Save results.
print('\nSaving results to .npz file.')
np.savez(bgplvm_results_file, original_data=normal_swapped_motions, y_train=y_train,
ard_weights=ard_weights, noise_precision=noise_precision, signal_variance=signal_variance,
x_u=inducing_input, x_mean=x_mean, x_covar=x_covar, train_opt_time=train_opt_time,
final_cost=final_cost)
if not isfile(mrd_results_file):
# Reset default graph before building new model graph. This speeds up script.
tf.reset_default_graph()
np.random.seed(1) # Random seed.
# Define instance of MRD with known views.
model = mrd(views_train=[y_train[:, i:i + 93] for i in range(0, 186, 93)],
num_inducing_points=num_inducing_points,
num_latent_dims=num_latent_dimensions)
model_training_objective = model.objective
# Optimisation.
model_opt_train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
loss=model_training_objective)
with tf.Session() as s:
# Initialise variables.
s.run(tf.global_variables_initializer())
# Training optimisation loop.
start_time = time()
print('\nTraining MRD:')
for c in range(train_iter):
s.run(model_opt_train)
if (c % 100) == 0:
print(' MRD opt iter {:5}: {}'.format(c, s.run(model_training_objective)))
end_time = time()
train_opt_time = end_time - start_time
final_cost = s.run(model_training_objective)
print('Final iter {:5}:'.format(c))
print(' MRD: {}'.format(s.run(model_training_objective)))
print('Time to optimise: {} s'.format(train_opt_time))
# Get converged values as numpy arrays.
ard_weights, noise_precision, signal_variance, inducing_input = \
s.run((model.ard_weights, model.noise_precision, model.signal_variance, model.inducing_input))
x_mean, x_covar = s.run(model.q_x)
# Save results.
print('\nSaving results to .npz file.')
np.savez(mrd_results_file, original_data=normal_swapped_motions, y_train=y_train,
ard_weights=ard_weights, noise_precision=noise_precision, signal_variance=signal_variance,
x_u=inducing_input, x_mean=x_mean, x_covar=x_covar, train_opt_time=train_opt_time,
final_cost=final_cost)
if not isfile(mrd_fully_independent_results_file):
# Reset default graph before building new model graph. This speeds up script.
tf.reset_default_graph()
np.random.seed(1) # Random seed.
# Define instance of fully independent MRD.
model = mrd(views_train=[y_train[:, i:i + 3] for i in range(0, 186, 3)],
num_inducing_points=num_inducing_points,
num_latent_dims=num_latent_dimensions)
model_training_objective = model.objective
# Optimisation.
model_opt_train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
loss=model_training_objective)
with tf.Session() as s:
# Initialise variables.
s.run(tf.global_variables_initializer())
# Training optimisation loop.
start_time = time()
print('\nTraining F.I. MRD:')
for c in range(train_iter):
s.run(model_opt_train)
if (c % 100) == 0:
print(' F.I. MRD opt iter {:5}: {}'.format(c, s.run(model_training_objective)))
end_time = time()
train_opt_time = end_time - start_time
final_cost = s.run(model_training_objective)
print('Final iter {:5}:'.format(c))
print(' F.I. MRD: {}'.format(s.run(model_training_objective)))
print('Time to optimise: {} s'.format(train_opt_time))
# Get converged values as numpy arrays.
ard_weights, noise_precision, signal_variance, inducing_input = \
s.run((model.ard_weights, model.noise_precision, model.signal_variance, model.inducing_input))
x_mean, x_covar = s.run(model.q_x)
# Save results.
print('\nSaving results to .npz file.')
np.savez(mrd_fully_independent_results_file, original_data=normal_swapped_motions, y_train=y_train,
ard_weights=ard_weights, noise_precision=noise_precision, signal_variance=signal_variance,
x_u=inducing_input, x_mean=x_mean, x_covar=x_covar, train_opt_time=train_opt_time,
final_cost=final_cost)
| 48.566434
| 117
| 0.643988
| 1,788
| 13,890
| 4.70302
| 0.122483
| 0.018552
| 0.03639
| 0.030325
| 0.807349
| 0.795576
| 0.792009
| 0.787609
| 0.771911
| 0.771911
| 0
| 0.009114
| 0.265371
| 13,890
| 285
| 118
| 48.736842
| 0.814975
| 0.122534
| 0
| 0.715
| 0
| 0
| 0.081527
| 0.005853
| 0
| 0
| 0
| 0.003509
| 0
| 1
| 0
| false
| 0
| 0.055
| 0
| 0.055
| 0.175
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
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