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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4e3f9bc8d389771977ef4ceb2b288615ebd07656
| 20,074
|
py
|
Python
|
tests/test_parser_property.py
|
mvisat/kopyt
|
48e59ed4196cb80c5498cca62ecffbcb27e6599b
|
[
"MIT"
] | 2
|
2021-07-21T10:24:30.000Z
|
2022-01-11T11:25:25.000Z
|
tests/test_parser_property.py
|
mvisat/kopyt
|
48e59ed4196cb80c5498cca62ecffbcb27e6599b
|
[
"MIT"
] | null | null | null |
tests/test_parser_property.py
|
mvisat/kopyt
|
48e59ed4196cb80c5498cca62ecffbcb27e6599b
|
[
"MIT"
] | 1
|
2021-07-28T05:47:28.000Z
|
2021-07-28T05:47:28.000Z
|
import unittest
from kopyt import node
from . import TestParserBase
class TestParserProperty(TestParserBase):
def do_test(
self,
code: str,
test_str: bool = True,
top_level_declaration: bool = True) -> node.PropertyDeclaration:
return super().do_test(
"parse_declaration",
code,
node.PropertyDeclaration,
test_str=test_str,
top_level_declaration=top_level_declaration,
)
def do_test_exception(self,
code: str,
top_level_declaration: bool = True) -> None:
return super().do_test_exception(
"parse_property_declaration",
code,
top_level_declaration=top_level_declaration,
)
def test_parser_property(self):
code = "val simple: Int?"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("simple: Int?", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_modifiers(self):
code = "@Annotated private val annotated: Int?"
result = self.do_test(code)
self.assertEqual(2, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("annotated: Int?", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_inferred(self):
code = "var inferred = 1"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("inferred", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNotNone(result.value)
self.assertEqual("1", str(result.value))
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_destructure(self):
code = "val (x) = X(1)"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration,
node.MultiVariableDeclaration)
self.assertEqual("(x)", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNotNone(result.value)
self.assertEqual("X(1)", str(result.value))
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_destructure_multiple(self):
code = "val (foo, bar: Int) get() = Tuple(1, 2)"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration,
node.MultiVariableDeclaration)
self.assertEqual("(foo, bar: Int)", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_destructure_unexpected_type(self):
code = "val (foo): bar = Baz(1)"
self.do_test_exception(code)
def test_parser_property_destructure_unexpected_type_multiple(self):
code = "val (foo, bar: Int): Tuple get() = Tuple(1, 2)"
self.do_test_exception(code)
def test_parser_property_receiver(self):
code = "val Int.foo: Int get() = this + 1"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNotNone(result.receiver)
self.assertEqual("Int", str(result.receiver))
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("foo: Int", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_parenthesized_receiver(self):
code = "val (Int).foo: Int get() = this + 1"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNotNone(result.receiver)
self.assertEqual("(Int)", str(result.receiver))
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("foo: Int", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_nullable_receiver(self):
code = "val String?.foo get() = this + \"bar\""
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNotNone(result.receiver)
self.assertEqual("String?", str(result.receiver))
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("foo", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_receiver_unexpected_destructure(self):
code = "val Int.(foo): Int get() = this + 1"
self.do_test_exception(code)
def test_parser_property_receiver_unexpected_destructure_parenthesized(
self):
code = "val (Int).(foo): Int get() = this + 1"
self.do_test_exception(code)
def test_parser_property_receiver_unexpected_destructure_nullable(self):
code = "val Int?.(foo): Int get() = this + 1"
self.do_test_exception(code)
def test_parser_property_delegate(self):
code = "val delegate by lazy { DelegateForObject() }"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("delegate", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNotNone(result.delegate)
self.assertEqual("by lazy { DelegateForObject() }",
str(result.delegate))
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_delegate_ignore_after_lambda(self):
code = "val A = object : B by C() {}\noverride fun D() { }"
result = self.do_test(code, False)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("A", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNotNone(result.value)
self.assertEqual("object : B by C() {}", str(result.value))
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_getter(self):
code = "val isEmpty: Boolean get"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("isEmpty: Boolean", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.getter.body)
self.assertIsNone(result.setter)
def test_parser_property_getter_modifiers(self):
code = "val isEmpty: Boolean private get() = this.size == 0"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("isEmpty: Boolean", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertEqual(1, len(result.getter.modifiers))
self.assertIsNone(result.getter.type)
self.assertIsNotNone(result.getter.body)
self.assertIsNone(result.setter)
def test_parser_property_getter_type(self):
code = "val isEmpty get(): Boolean { }"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("isEmpty", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNotNone(result.getter.type)
self.assertIsNone(result.setter)
def test_parser_property_getter_expression(self):
code = "val isEmpty get() = true"
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("isEmpty", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.getter.type)
self.assertIsNone(result.setter)
def test_parser_property_setter(self):
code = """\
var setterVisibility: String = "abc"
private set"""
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("setterVisibility: String", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNotNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNotNone(result.setter)
self.assertEqual(1, len(result.setter.modifiers))
self.assertIsNone(result.setter.type)
self.assertIsNone(result.setter.body)
def test_parser_property_setter_type(self):
code = """\
var setterVisibility: String = "abc"
set(value): Unit { }"""
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("setterVisibility: String", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNotNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNotNone(result.setter)
self.assertEqual(0, len(result.setter.modifiers))
self.assertIsNotNone(result.setter.type)
self.assertIsNotNone(result.setter.body)
def test_parser_property_setter_parameter(self):
code = """\
var setterVisibility: String = "abc"
set(@Annotation value): Unit { }"""
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("setterVisibility: String", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNotNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNotNone(result.setter)
self.assertEqual(0, len(result.setter.modifiers))
self.assertIsNotNone(result.setter.parameter)
self.assertEqual("@Annotation value", str(result.setter.parameter))
self.assertIsNotNone(result.setter.type)
self.assertIsNotNone(result.setter.body)
def test_parser_property_setter_expression(self):
code = """\
var setterVisibility: String = "abc"
set(value) = 1"""
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("setterVisibility: String", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNotNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNotNone(result.setter)
self.assertEqual(0, len(result.setter.modifiers))
self.assertIsNone(result.setter.type)
self.assertIsNotNone(result.setter.body)
def test_parser_property_getter_setter(self):
code = """\
var stringRepresentation: String
get() = this.toString()
set(value): Unit {
setDataFromString(value)
}"""
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("stringRepresentation: String",
str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.getter.type)
self.assertIsNotNone(result.getter.body)
self.assertIsNotNone(result.setter)
self.assertIsNotNone(result.setter.type)
self.assertIsNotNone(result.setter.body)
def test_parser_property_getter_duplicate(self):
code = """\
val isEmpty: Boolean
get() = this.size == 0
get() = this.size == 0"""
self.do_test_exception(code)
def test_parser_property_setter_duplicate(self):
code = """\
val isEmpty: Boolean
set(value) = 1
set(value) = 2"""
self.do_test_exception(code)
def test_parser_property_getter_without_body(self):
code = """\
val isEmpty: Boolean
get()"""
self.do_test_exception(code)
def test_parser_property_setter_without_body(self):
code = """\
val isEmpty: Boolean
set(value)"""
self.do_test_exception(code)
def test_parser_property_constraints(self):
code = """\
val <T> List<T>.foo: T where T : CharSequence
get(): T = this[0]"""
result = self.do_test(code)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("val", result.mutability)
self.assertIsNotNone(result.generics)
self.assertEqual("<T>", str(result.generics))
self.assertIsNotNone(result.receiver)
self.assertEqual("List<T>", str(result.receiver))
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("foo: T", str(result.declaration))
self.assertIsNotNone(result.constraints)
self.assertEqual("where T : CharSequence", str(result.constraints))
self.assertIsNone(result.value)
self.assertIsNone(result.delegate)
self.assertIsNotNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_expecting_val_or_var(self):
code = "isEmpty: Boolean"
self.do_test_exception(code)
def test_parser_property_local_declaration_ignoring_getter(self):
code = """\
var x = 1
get()"""
result = self.do_test(code, False, False)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("x", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertEqual("1", str(result.value))
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
def test_parser_property_local_ignoring_setter(self):
code = """\
var x = 1
set(x, y)"""
result = self.do_test(code, False, False)
self.assertEqual(0, len(result.modifiers))
self.assertEqual("var", result.mutability)
self.assertEqual(0, len(result.generics))
self.assertIsNone(result.receiver)
self.assertIsInstance(result.declaration, node.VariableDeclaration)
self.assertEqual("x", str(result.declaration))
self.assertEqual(0, len(result.constraints))
self.assertEqual("1", str(result.value))
self.assertIsNone(result.delegate)
self.assertIsNone(result.getter)
self.assertIsNone(result.setter)
if __name__ == "__main__":
unittest.main()
| 42.619958
| 77
| 0.672312
| 2,140
| 20,074
| 6.208879
| 0.051402
| 0.142244
| 0.145706
| 0.094378
| 0.896064
| 0.875141
| 0.864906
| 0.837962
| 0.820727
| 0.812825
| 0
| 0.005948
| 0.212713
| 20,074
| 470
| 78
| 42.710638
| 0.834789
| 0
| 0
| 0.715278
| 0
| 0
| 0.090814
| 0.004583
| 0
| 0
| 0
| 0
| 0.638889
| 1
| 0.078704
| false
| 0
| 0.006944
| 0.00463
| 0.092593
| 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
|
9dadac36ca1f2dbcb86b10a4aa7f05f47aa8b8fc
| 2,923
|
py
|
Python
|
utils/metric.py
|
mrazekv/BLASYS
|
f5cfbbd26eaffd355c7510342634804f54aed49a
|
[
"BSD-3-Clause"
] | 11
|
2020-04-22T20:46:56.000Z
|
2022-02-21T07:38:16.000Z
|
utils/metric.py
|
mrazekv/BLASYS
|
f5cfbbd26eaffd355c7510342634804f54aed49a
|
[
"BSD-3-Clause"
] | 2
|
2021-03-05T03:38:42.000Z
|
2021-09-22T08:41:24.000Z
|
utils/metric.py
|
mrazekv/BLASYS
|
f5cfbbd26eaffd355c7510342634804f54aed49a
|
[
"BSD-3-Clause"
] | 10
|
2019-11-25T01:06:09.000Z
|
2022-03-14T16:32:34.000Z
|
import numpy as np
def HD(original_path, approximate_path):
with open(original_path, 'r') as fo:
org_line_list = fo.readlines()
with open(approximate_path, 'r') as fa:
app_line_list = fa.readlines()
org = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in org_line_list]
app = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in app_line_list]
if len(org_line_list) != len(app_line_list):
print('ERROR! sizes of input files are not equal! Aborting...')
return -1
org = np.array(org)
app = np.array(app)
total = org.size
HD = np.sum(org != app)
return HD/total
def MAE(original_path, approximate_path):
with open(original_path, 'r') as fo:
org_line_list = fo.readlines()
with open(approximate_path, 'r') as fa:
app_line_list = fa.readlines()
org = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in org_line_list]
app = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in app_line_list]
if len(org_line_list) != len(app_line_list):
print('ERROR! sizes of input files are not equal! Aborting...')
return -1
num_vec = len(org)
num_pos = len(org[0])
maxnum = 2 ** num_pos - 1
err = []
for i in range(num_vec):
orgnum = int(''.join(org[i]), 2)
appnum = int(''.join(app[i]), 2)
err.append( np.abs(orgnum - appnum) )
return np.mean(err) / maxnum
def ER(original_path, approximate_path):
with open(original_path, 'r') as fo:
org_line_list = fo.readlines()
with open(approximate_path, 'r') as fa:
app_line_list = fa.readlines()
# org = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in org_line_list]
# app = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in app_line_list]
if len(org_line_list) != len(app_line_list):
print('ERROR! sizes of input files are not equal! Aborting...')
return -1
num_vec = len(org_line_list)
compare = [i != j for i,j in zip(org_line_list, app_line_list)]
return sum(compare) / num_vec
def MRE(original_path, approximate_path):
with open(original_path, 'r') as fo:
org_line_list = fo.readlines()
with open(approximate_path, 'r') as fa:
app_line_list = fa.readlines()
org = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in org_line_list]
app = [list(filter(lambda a: a != ' ', list(i[:-1]))) for i in app_line_list]
if len(org_line_list) != len(app_line_list):
print('ERROR! sizes of input files are not equal! Aborting...')
return -1
num_vec = len(org)
num_pos = len(org[0])
maxnum = 2 ** num_pos - 1
err = []
for i in range(num_vec):
orgnum = int(''.join(org[i]), 2)
appnum = int(''.join(app[i]), 2)
err.append( np.abs(orgnum - appnum) / np.max((1, orgnum)) )
return np.mean(err)
| 30.134021
| 83
| 0.593226
| 463
| 2,923
| 3.572354
| 0.140389
| 0.130593
| 0.093108
| 0.082225
| 0.866989
| 0.866989
| 0.866989
| 0.866989
| 0.866989
| 0.866989
| 0
| 0.010493
| 0.250086
| 2,923
| 96
| 84
| 30.447917
| 0.744069
| 0.053028
| 0
| 0.738462
| 0
| 0
| 0.083183
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.061538
| false
| 0
| 0.015385
| 0
| 0.2
| 0.061538
| 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
|
9dfad06aafd6b2006e2e00df7a220fb54892404b
| 134
|
py
|
Python
|
causal_world/sim2real_tools/__init__.py
|
michaelfeil/CausalWorld
|
ff866159ef0ee9c407893ae204e93eb98dd68be2
|
[
"MIT"
] | 2
|
2021-09-22T08:20:12.000Z
|
2021-11-16T14:20:45.000Z
|
causal_world/sim2real_tools/__init__.py
|
michaelfeil/CausalWorld
|
ff866159ef0ee9c407893ae204e93eb98dd68be2
|
[
"MIT"
] | null | null | null |
causal_world/sim2real_tools/__init__.py
|
michaelfeil/CausalWorld
|
ff866159ef0ee9c407893ae204e93eb98dd68be2
|
[
"MIT"
] | null | null | null |
from causal_world.sim2real_tools.utils import RealisticRobotWrapper
from causal_world.sim2real_tools.transfer_real import TransferReal
| 67
| 67
| 0.91791
| 17
| 134
| 6.941176
| 0.647059
| 0.169492
| 0.254237
| 0.389831
| 0.474576
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015748
| 0.052239
| 134
| 2
| 68
| 67
| 0.913386
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
|
d17975a8a619aaf9a8ac5561bc1768f3adb91fb9
| 262
|
py
|
Python
|
tests/dataset/complex/nested_list.py
|
hugovk/reiz.io
|
26b93fc1e58097bcb97989e916f549a04eb14cae
|
[
"Apache-2.0"
] | 43
|
2020-09-20T09:37:06.000Z
|
2021-11-12T11:56:27.000Z
|
tests/dataset/complex/nested_list.py
|
hugovk/reiz.io
|
26b93fc1e58097bcb97989e916f549a04eb14cae
|
[
"Apache-2.0"
] | 37
|
2020-09-20T09:37:49.000Z
|
2021-06-25T11:08:38.000Z
|
tests/dataset/complex/nested_list.py
|
hugovk/reiz.io
|
26b93fc1e58097bcb97989e916f549a04eb14cae
|
[
"Apache-2.0"
] | 4
|
2020-10-04T13:47:06.000Z
|
2022-01-02T19:35:13.000Z
|
class T: # reiz: tp
@classmethod
def _():
...
class Z: # reiz: tp
@classmethod
def _():
...
class Q:
def _():
...
@classmethod
def __():
...
class Q:
@staticmethod
def _():
...
| 10.076923
| 20
| 0.389313
| 21
| 262
| 4.571429
| 0.428571
| 0.4375
| 0.59375
| 0.416667
| 0.520833
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.450382
| 262
| 25
| 21
| 10.48
| 0.666667
| 0.064886
| 0
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.277778
| true
| 0
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d1954a9d0c07c34dd790ed28f9d6253c86c07ecb
| 238
|
py
|
Python
|
main/controller/__init__.py
|
nguyentranhoan/uit-mobile
|
8546312b01373d94cf00c64f7eacb769e0f4ccce
|
[
"BSD-3-Clause"
] | null | null | null |
main/controller/__init__.py
|
nguyentranhoan/uit-mobile
|
8546312b01373d94cf00c64f7eacb769e0f4ccce
|
[
"BSD-3-Clause"
] | null | null | null |
main/controller/__init__.py
|
nguyentranhoan/uit-mobile
|
8546312b01373d94cf00c64f7eacb769e0f4ccce
|
[
"BSD-3-Clause"
] | null | null | null |
import controller.demo
import controller.register_controller
import controller.user_controller
import controller.login_controller
import controller.news_controller
import controller.test_controller
import controller.reset_pass_controller
| 29.75
| 39
| 0.911765
| 28
| 238
| 7.5
| 0.357143
| 0.533333
| 0.619048
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 238
| 7
| 40
| 34
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.142857
| 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
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
d1b309b375e0a17f2df844ab99324cfb2c284e84
| 13,891
|
py
|
Python
|
api/tests/test_student_profile_abilities.py
|
matchd-ch/matchd-backend
|
84be4aab1b4708cae50a8988301b15df877c8db0
|
[
"Apache-2.0"
] | 1
|
2022-03-03T09:55:57.000Z
|
2022-03-03T09:55:57.000Z
|
api/tests/test_student_profile_abilities.py
|
matchd-ch/matchd-backend
|
84be4aab1b4708cae50a8988301b15df877c8db0
|
[
"Apache-2.0"
] | 7
|
2022-02-09T10:44:53.000Z
|
2022-03-28T03:29:43.000Z
|
api/tests/test_student_profile_abilities.py
|
matchd-ch/matchd-backend
|
84be4aab1b4708cae50a8988301b15df877c8db0
|
[
"Apache-2.0"
] | null | null | null |
import pytest
from django.contrib.auth import get_user_model
from django.contrib.auth.models import AnonymousUser
from db.models import Skill, Language, LanguageLevel, Hobby, OnlineProject, UserLanguageRelation
# pylint: disable=R0913
@pytest.mark.django_db
def test_abilities(login, user_student, student_abilities, skill_objects, language_objects,
language_level_objects):
user_student.student.profile_step = 4
user_student.student.save()
login(user_student)
data, errors = student_abilities(
user_student,
skill_objects,
(
(language_objects[0], language_level_objects[0]),
(language_objects[1], language_level_objects[0]),
(language_objects[0], language_level_objects[1]) # duplicate language
),
[{
'name': 'hobby'
}, {
'name': 'hobby 2'
}],
[{
'url': 'www.google.com'
}, {
'url': 'www.google2.com'
}],
'distinction')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success')
user = get_user_model().objects.get(pk=user_student.id)
skills = user.student.skills.all()
for obj in skill_objects[:6]:
assert obj in skills
# test if only two languages was added (third language is duplicate)
languages = user.student.languages.all()
assert len(languages) == 2
hobbies = user.student.hobbies.all()
assert len(hobbies) == 2
online_projects = user.student.online_projects.all()
assert len(online_projects) == 2
assert user.student.distinction == 'distinction'
assert user_student.student.profile_step == 5
@pytest.mark.django_db
def test_abilities_without_login(user_student, student_abilities, skill_objects, language_objects,
language_level_objects):
data, errors = student_abilities(AnonymousUser(), skill_objects,
((language_objects[0], language_level_objects[0]), ), None,
None, '')
assert errors is not None
assert data is not None
assert data.get('studentProfileAbilities') is None
user = get_user_model().objects.get(pk=user_student.id)
assert len(user.student.soft_skills.all()) == 0
assert len(user.student.cultural_fits.all()) == 0
@pytest.mark.django_db
def test_abilities_as_company(login, user_employee, student_abilities, skill_objects,
language_objects, language_level_objects):
login(user_employee)
data, errors = student_abilities(user_employee, skill_objects,
((language_objects[0], language_level_objects[0]), ), None,
None, '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
errors = data.get('studentProfileAbilities').get('errors')
assert errors is not None
assert 'type' in errors
@pytest.mark.django_db
def test_abilities_invalid_step(login, user_student, student_abilities, skill_objects,
language_objects, language_level_objects):
user_student.student.profile_step = 0
user_student.student.save()
login(user_student)
data, errors = student_abilities(user_student, skill_objects,
((language_objects[0], language_level_objects[0]), ), None,
None, '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success') is False
errors = data.get('studentProfileAbilities').get('errors')
assert errors is not None
assert 'profileStep' in errors
user = get_user_model().objects.get(pk=user_student.id)
assert user.student.profile_step == 0
@pytest.mark.django_db
def test_abilities_with_invalid_data(login, user_student, student_abilities):
user_student.student.profile_step = 4
user_student.student.save()
login(user_student)
data, errors = student_abilities(
user_student,
[Skill(id=1337)],
(
(Language(id=1337, short_list=True),
LanguageLevel(id=1337)), # invalid languages are automatically ignored
),
[{
'name': ''
}, {
'name': 'hobby 2'
}],
[{
'url': ''
}, {
'url': 'www.google2.com'
}],
'a' * 1001)
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success') is False
errors = data.get('studentProfileAbilities').get('errors')
assert errors is not None
assert 'skills' in errors
assert 'name' in errors
assert 'url' in errors
assert 'distinction' in errors
user = get_user_model().objects.get(pk=user_student.id)
assert len(user.student.skills.all()) == 0
assert len(user.student.languages.all()) == 0
assert user_student.student.profile_step == 4
@pytest.mark.django_db
def test_abilities_update_delete_hobbies(login, user_student, student_abilities, skill_objects):
user_student.student.profile_step = 4
Hobby.objects.create(id=1, name='hobby 1', student=user_student.student)
Hobby.objects.create(id=2, name='hobby 2', student=user_student.student)
user_student.student.save()
assert len(user_student.student.hobbies.all()) == 2
login(user_student)
data, errors = student_abilities(user_student, skill_objects, [], [{
'id': 1,
'name': 'hobby edited'
}], [], '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success')
user = get_user_model().objects.get(pk=user_student.id)
hobbies = user.student.hobbies.all()
assert len(hobbies) == 1
assert hobbies[0].id == 1
assert hobbies[0].name == 'hobby edited'
assert user_student.student.profile_step == 5
@pytest.mark.django_db
def test_abilities_update_delete_online_projects(login, user_student, student_abilities,
skill_objects):
user_student.student.profile_step = 4
OnlineProject.objects.create(id=1, url='http://www.project1.lo', student=user_student.student)
OnlineProject.objects.create(id=2, url='http://www.project2.lo', student=user_student.student)
user_student.student.save()
assert len(user_student.student.online_projects.all()) == 2
login(user_student)
data, errors = student_abilities(user_student, skill_objects, [], [],
[{
'id': 1,
'url': 'http://www.project1-edited.lo'
}], '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success')
user = get_user_model().objects.get(pk=user_student.id)
online_projects = user.student.online_projects.all()
assert len(online_projects) == 1
assert online_projects[0].id == 1
assert online_projects[0].url == 'http://www.project1-edited.lo'
assert user_student.student.profile_step == 5
@pytest.mark.django_db
def test_abilities_update_delete_languages(login, user_student, student_abilities, skill_objects,
language_objects, language_level_objects):
user_student.student.profile_step = 4
UserLanguageRelation.objects.create(id=1,
student=user_student.student,
language=language_objects[0],
language_level=language_level_objects[0])
UserLanguageRelation.objects.create(id=2,
student=user_student.student,
language=language_objects[1],
language_level=language_level_objects[0])
user_student.student.save()
assert len(user_student.student.languages.all()) == 2
login(user_student)
data, errors = student_abilities(user_student, skill_objects,
((language_objects[0], language_level_objects[1]), ), [], [],
'')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success')
user = get_user_model().objects.get(pk=user_student.id)
languages = user.student.languages.all()
assert len(languages) == 1
assert languages[0].language.id == language_objects[0].id
assert languages[0].language_level.id == language_level_objects[1].id
assert user_student.student.profile_step == 5
@pytest.mark.django_db
def test_abilities_unique_hobbies_update(login, user_student, student_abilities, skill_objects):
user_student.student.profile_step = 4
Hobby.objects.create(id=1, name='hobby 1', student=user_student.student)
Hobby.objects.create(id=2, name='hobby 2', student=user_student.student)
user_student.student.save()
assert len(user_student.student.hobbies.all()) == 2
login(user_student)
data, errors = student_abilities(user_student, skill_objects, [], [{
'id': 1,
'name': 'hobby 1'
}, {
'id': 2,
'name': 'hobby 1'
}], [], '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success') is False
errors = data.get('studentProfileAbilities').get('errors')
assert errors is not None
assert 'nonFieldErrors' in errors
assert errors.get('nonFieldErrors')[0].get('code') == 'unique_together'
@pytest.mark.django_db
def test_abilities_unique_hobbies_create(login, user_student, student_abilities, skill_objects):
user_student.student.profile_step = 4
Hobby.objects.create(id=1, name='hobby 1', student=user_student.student)
user_student.student.save()
assert len(user_student.student.hobbies.all()) == 1
login(user_student)
# new hobby should be ignored
data, errors = student_abilities(user_student, skill_objects, [], [{
'id': 1,
'name': 'hobby 1'
}, {
'name': 'hobby 1'
}], [], '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success')
user = get_user_model().objects.get(pk=user_student.id)
hobbies = user.student.hobbies.all()
assert len(hobbies) == 1
assert hobbies[0].id == 1
assert hobbies[0].name == 'hobby 1'
assert user_student.student.profile_step == 5
@pytest.mark.django_db
def test_abilities_unique_online_projects_update(login, user_student, student_abilities,
skill_objects):
user_student.student.profile_step = 4
OnlineProject.objects.create(id=1, url='http://www.project1.lo', student=user_student.student)
OnlineProject.objects.create(id=2, url='http://www.project2.lo', student=user_student.student)
user_student.student.save()
assert len(user_student.student.online_projects.all()) == 2
login(user_student)
data, errors = student_abilities(user_student, skill_objects, [], [],
[{
'id': 1,
'url': 'http://www.project1.lo'
}, {
'id': 2,
'url': 'http://www.project1.lo'
}], '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success') is False
errors = data.get('studentProfileAbilities').get('errors')
assert errors is not None
assert 'nonFieldErrors' in errors
assert errors.get('nonFieldErrors')[0].get('code') == 'unique_together'
@pytest.mark.django_db
def test_abilities_unique_online_projects_create(login, user_student, student_abilities,
skill_objects):
user_student.student.profile_step = 4
OnlineProject.objects.create(id=1, url='http://www.project1.lo', student=user_student.student)
user_student.student.save()
assert len(user_student.student.online_projects.all()) == 1
login(user_student)
data, errors = student_abilities(user_student, skill_objects, [], [],
[{
'id': 1,
'url': 'http://www.project1.lo'
}, {
'url': 'http://www.project1.lo'
}], '')
assert errors is None
assert data is not None
assert data.get('studentProfileAbilities') is not None
assert data.get('studentProfileAbilities').get('success')
user = get_user_model().objects.get(pk=user_student.id)
online_projects = user.student.online_projects.all()
assert len(online_projects) == 1
assert online_projects[0].id == 1
assert online_projects[0].url == 'http://www.project1.lo'
assert user_student.student.profile_step == 5
| 40.0317
| 98
| 0.628464
| 1,592
| 13,891
| 5.295226
| 0.067839
| 0.131791
| 0.121708
| 0.049822
| 0.871768
| 0.86121
| 0.844247
| 0.811862
| 0.781732
| 0.761329
| 0
| 0.013661
| 0.262256
| 13,891
| 346
| 99
| 40.147399
| 0.808938
| 0.012886
| 0
| 0.752542
| 0
| 0
| 0.101488
| 0.045309
| 0
| 0
| 0
| 0
| 0.338983
| 1
| 0.040678
| false
| 0
| 0.013559
| 0
| 0.054237
| 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
|
ae45e9065e74fba14882e3ecbdb434dcbd340559
| 68
|
py
|
Python
|
wif_to_wvdd.py
|
yupyupp/weaving
|
9c2dca6e1cbff79f746054d3d2cc4574257131f3
|
[
"MIT"
] | null | null | null |
wif_to_wvdd.py
|
yupyupp/weaving
|
9c2dca6e1cbff79f746054d3d2cc4574257131f3
|
[
"MIT"
] | null | null | null |
wif_to_wvdd.py
|
yupyupp/weaving
|
9c2dca6e1cbff79f746054d3d2cc4574257131f3
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
import sys
print(sys.argv[1])
print(sys.argv[2])
| 11.333333
| 18
| 0.691176
| 13
| 68
| 3.615385
| 0.692308
| 0.340426
| 0.510638
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.032258
| 0.088235
| 68
| 5
| 19
| 13.6
| 0.725806
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0.666667
| 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
| 0
| 1
|
0
| 7
|
ae693a2553d1e919e79b3bd4c21f7b2ab129cb6f
| 62
|
py
|
Python
|
mlp_gpt_jax/__init__.py
|
lucidrains/mlp-gpt-jax
|
571ccf0bcc724f9b893db256be62e70c9f0b6bda
|
[
"MIT"
] | 51
|
2021-05-21T20:02:56.000Z
|
2021-12-23T22:24:19.000Z
|
mlp_gpt_jax/__init__.py
|
lucidrains/mlp-gpt-jax
|
571ccf0bcc724f9b893db256be62e70c9f0b6bda
|
[
"MIT"
] | 1
|
2021-05-28T12:00:14.000Z
|
2021-06-02T21:03:35.000Z
|
mlp_gpt_jax/__init__.py
|
lucidrains/mlp-gpt-jax
|
571ccf0bcc724f9b893db256be62e70c9f0b6bda
|
[
"MIT"
] | null | null | null |
from mlp_gpt_jax.mlp_gpt_jax import MLPGpt, TransformedMLPGpt
| 31
| 61
| 0.887097
| 10
| 62
| 5.1
| 0.7
| 0.235294
| 0.352941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.080645
| 62
| 1
| 62
| 62
| 0.894737
| 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
|
ae7a9e536f0769b196a5e6b1069b23f7fbadb772
| 190
|
py
|
Python
|
zad1_1.py
|
kamilhabrych/python-semestr5-lista1
|
65faeffe83bcc4706b2818e2e7802d986b19244b
|
[
"MIT"
] | null | null | null |
zad1_1.py
|
kamilhabrych/python-semestr5-lista1
|
65faeffe83bcc4706b2818e2e7802d986b19244b
|
[
"MIT"
] | null | null | null |
zad1_1.py
|
kamilhabrych/python-semestr5-lista1
|
65faeffe83bcc4706b2818e2e7802d986b19244b
|
[
"MIT"
] | null | null | null |
a = 2
b = 5
c = 5.0
d = 4.2
print(a)
print(b)
print(c)
print(d)
print()
print(a+b)
print(a+c)
print(a+d)
print()
print(a*b)
print(a*c)
print(a*d)
print()
print(a/b)
print(a/c)
print(a/d)
| 7.916667
| 10
| 0.589474
| 48
| 190
| 2.333333
| 0.1875
| 0.535714
| 0.294643
| 0.321429
| 0.705357
| 0.705357
| 0.705357
| 0.705357
| 0.705357
| 0.705357
| 0
| 0.037736
| 0.163158
| 190
| 24
| 11
| 7.916667
| 0.666667
| 0
| 0
| 0.15
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.8
| 0
| 0
| 0
| null | 1
| 1
| 1
| 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
| 0
| 1
|
0
| 9
|
88591a7d05818d4372d09d0c866c70f6d7c54209
| 69,502
|
py
|
Python
|
mode_choice/mc_util.py
|
johnpgliebe/camsys-cs_fm_tool
|
022274d0b7fcc59ef0438b1a97a5fe23bf1d27dd
|
[
"BSD-3-Clause"
] | null | null | null |
mode_choice/mc_util.py
|
johnpgliebe/camsys-cs_fm_tool
|
022274d0b7fcc59ef0438b1a97a5fe23bf1d27dd
|
[
"BSD-3-Clause"
] | null | null | null |
mode_choice/mc_util.py
|
johnpgliebe/camsys-cs_fm_tool
|
022274d0b7fcc59ef0438b1a97a5fe23bf1d27dd
|
[
"BSD-3-Clause"
] | null | null | null |
# coding: utf-8
# CS FutureMobility Tool
# See full license in LICENSE.txt.
import numpy as np
import pandas as pd
#import openmatrix as omx
from IPython.display import display
from openpyxl import load_workbook,Workbook
from time import strftime
import os.path
import mode_choice.model_defs as md
import mode_choice.matrix_utils as mtx
import config
''' Utilities to summarize the outputs of Mode Choice '''
def display_mode_share(mc_obj):
'''
This displays a mode share summary by market segment (with / without vehicle, peak / off-peak) on the IPython notebook.
:param mc_obj: mode choice module object as defined in the IPython notebook
'''
# display mode share tables
avg_trips_by_mode = pd.DataFrame(None)
for purpose in ['HBW','HBO', 'NHB', 'HBSc1', 'HBSc2', 'HBSc3']:
avg_trips_by_mode = avg_trips_by_mode.add(pd.DataFrame({pv:{mode:(mc_obj.table_container.get_table(purpose)[pv][mode].sum()) for mode in mc_obj.table_container.get_table(purpose)[pv]} for pv in ['0_PK','1_PK','0_OP','1_OP']}).T,
fill_value = 0)
avg_mode_share = avg_trips_by_mode.divide(avg_trips_by_mode.sum(1),axis = 0)
display(avg_mode_share.style.format("{:.2%}"))
def write_boston_neighbortown_mode_share_to_excel(mc_obj):
'''
Writes mode share summary by purpose and market segment to an Excel workbook.
Applies only to trips to/from Boston
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_excel_fn: output Excel filename, by default in the output path defined in config.py
'''
out_excel_fn = mc_obj.config.out_path + "mode_share_bosNB_{0}.xlsx".format(strftime("%Y%m%d"))
# check if file exists.
if os.path.isfile(out_excel_fn):
book = load_workbook(out_excel_fn)
else:
book = Workbook()
book.save(out_excel_fn)
writer = pd.ExcelWriter(out_excel_fn,engine = 'openpyxl')
writer.book = book
for purp in md.purposes:
mode_share = pd.DataFrame(columns = md.peak_veh)
trip_table = mc_obj.table_container.get_table(purp)
for pv in md.peak_veh:
for mode in trip_table[pv].keys():
#study area zones might not start at zone 0 and could have discontinous TAZ IDs
trip_table_o = mtx.OD_slice(trip_table[pv][mode], O_slice = md.taz['BOSTON'], D_slice = md.taz['BOS_AND_NEI'])
trip_table_d = mtx.OD_slice(trip_table[pv][mode], O_slice = md.taz['BOS_AND_NEI'], D_slice = md.taz['BOSTON'])
trip_table_b = mtx.OD_slice(trip_table[pv][mode], O_slice = md.taz['BOSTON'], D_slice = md.taz['BOSTON'])
trip_table_bos = trip_table_o + trip_table_d - trip_table_b
mode_share.loc[mode,pv] = trip_table_bos.sum()
mode_share['Total'] = mode_share.sum(1)
mode_share['Share'] = mode_share['Total'] / mode_share['Total'].sum()
if purp in book.sheetnames: # if sheetname exists, delete
book.remove(book[purp])
writer.save()
mode_share.to_excel(writer, sheet_name = purp)
writer.save()
def write_study_area_mode_share_to_excel(mc_obj, out_excel_fn = None):
'''
Writes mode share summary by purpose and market segment to an Excel workbook.
Applies only to trips to/from study area
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_excel_fn: output Excel filename, by default in the output path defined in config.py
'''
if out_excel_fn is None:
out_excel_fn = mc_obj.config.out_path + "mode_share_study_area_{0}.xlsx".format(strftime("%Y%m%d"))
# check if file exists.
if os.path.isfile(out_excel_fn):
book = load_workbook(out_excel_fn)
else:
book = Workbook()
book.save(out_excel_fn)
writer = pd.ExcelWriter(out_excel_fn,engine = 'openpyxl')
writer.book = book
for purp in md.purposes:
mode_share = pd.DataFrame(columns = md.peak_veh)
trip_table = mc_obj.table_container.get_table(purp)
for pv in md.peak_veh:
for mode in trip_table[pv].keys():
trip_table_o = mtx.OD_slice(trip_table[pv][mode], O_slice = md.study_area)
trip_table_d = mtx.OD_slice(trip_table[pv][mode], D_slice = md.study_area)
trip_table_ii = mtx.OD_slice(trip_table[pv][mode], O_slice = md.study_area, D_slice = md.study_area)
trip_table_sa = trip_table_o + trip_table_d - trip_table_ii
mode_share.loc[mode,pv] = trip_table_sa.sum()
mode_share['Total'] = mode_share.sum(1)
mode_share['Share'] = mode_share['Total'] / mode_share['Total'].sum()
if purp in book.sheetnames: # if sheetname exists, delete
book.remove(book[purp])
writer.save()
mode_share.to_excel(writer, sheet_name = purp)
writer.save()
def write_mode_share_to_excel(mc_obj,purpose, out_excel_fn = None):
'''
Writes mode share summary by purpose and market segment to an Excel workbook.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param purpose: can be a single purpose or 'all', in which case the Excel workbook has six sheets, one for each purpose.
:param out_excel_fn: output Excel filename, by default in the output path defined in config.py
'''
if out_excel_fn is None:
out_excel_fn = mc_obj.config.out_path + "MC_mode_share_{0}_{1}.xlsx".format(purpose, strftime("%Y%m%d"))
if purpose == 'all':
# check if file exists.
if os.path.isfile(out_excel_fn):
book = load_workbook(out_excel_fn)
else:
book = Workbook()
book.save(out_excel_fn)
writer = pd.ExcelWriter(out_excel_fn,engine = 'openpyxl')
writer.book = book
for purp in md.purposes:
trip_table = mc_obj.table_container.get_table(purp)
mode_share = pd.DataFrame(columns = md.peak_veh)
for pv in md.peak_veh:
for mode in trip_table[pv].keys():
mode_share.loc[mode,pv] = trip_table[pv][mode].sum()
mode_share['Total'] = mode_share.sum(1)
mode_share['Share'] = mode_share['Total'] / mode_share['Total'].sum()
if purp in book.sheetnames: # if sheetname exists, delete
book.remove(book[purp])
writer.save()
mode_share.to_excel(writer, sheet_name = purp)
writer.save()
elif purpose in md.purposes:
# check if file exists.
if os.path.isfile(out_excel_fn):
book = load_workbook(out_excel_fn)
else:
book = Workbook()
book.save(out_excel_fn)
writer = pd.ExcelWriter(out_excel_fn,engine = 'openpyxl')
writer.book = book
mode_share = pd.DataFrame(columns = md.peak_veh)
for pv in md.peak_veh:
for mode in mc_obj.trips_by_mode[pv].keys():
mode_share.loc[mode,pv] = mc_obj.trips_by_mode[pv][mode].sum()
mode_share['Total'] = mode_share.sum(1)
mode_share['Share'] = mode_share['Total'] / mode_share['Total'].sum()
if purpose in book.sheetnames: # if sheetname exists, delete
book.remove(book[purpose])
writer.save()
mode_share.to_excel(writer, sheet_name = purpose)
writer.save()
def __mt_prod_attr_nhood(mc_obj, trip_table, skim): # miles traveled. For VMT and PMT, by neighborhood
# sum prodct of trip_table - skims
mt_total = trip_table * skim['Length (Skim)']
# calculate marginals
prod = pd.DataFrame(np.sum(mt_total,axis = 1)/2, columns = ['Production'])
attr = pd.DataFrame(np.sum(mt_total,axis = 0) / 2, columns = ['Attraction'])
towns = mc_obj.taz.sort_values(md.taz_ID_field).iloc[0:md.max_zone]
mt_taz = pd.concat([towns[[md.taz_ID_field,'BOSTON_NB']],prod,attr],axis = 1,join = 'inner')
mt_taz.index.names=['Boston Neighborhood']
return mt_taz.groupby(['BOSTON_NB']).sum()[['Production','Attraction']].reset_index()
def __trip_prod_attr_nhood(mc_obj, trip_table):
mt_total = trip_table
# calculate marginals
prod = pd.DataFrame(np.sum(mt_total,axis = 1), columns = ['Production'])
attr = pd.DataFrame(np.sum(mt_total,axis = 0), columns = ['Attraction'])
towns = mc_obj.taz.sort_values(md.taz_ID_field).iloc[0:md.max_zone]
mt_taz = pd.concat([towns[[md.taz_ID_field,'BOSTON_NB']],prod,attr],axis = 1,join = 'inner')
mt_taz.index.names=['Boston Neighborhood']
return mt_taz.groupby(['BOSTON_NB']).sum()[['Production','Attraction']].reset_index()
def sm_vmt_by_neighborhood(mc_obj, out_fn = None, by = None, sm_mode = 'SM_RA'):
'''
Summarizes VMT production and attraction by the 26 Boston neighborhoods for Shared Mobility Modes.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary; if None specified, only aggregate production and attraction will be provided.
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + sm_mode + f'_vmt_by_neighborhood.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + sm_mode + f'_vmt_by_neighborhood_by_{by}.csv'
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
if by in ['peak','veh_own','purpose'] == False:
print('Only supports VMT by neighborhood, peak / vehicle ownership, purpose.')
return
else:
vmt_master_table = pd.DataFrame(columns = ['Production','Attraction','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
auto_trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][sm_mode] / md.AO_dict[sm_mode]
vmt_table = __mt_prod_attr_nhood(mc_obj,auto_trip_table,skim_dict[peak])
vmt_table['peak'] = peak
vmt_table['veh_own'] = veh_own
vmt_table['purpose'] = purpose
vmt_master_table = vmt_master_table.append(vmt_table, sort = True)
if by == None:
vmt_summary = vmt_master_table.groupby('BOSTON_NB').sum()
elif by == 'peak':
vmt_summary = pd.concat([
vmt_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak] for peak in ['PK','OP']], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
vmt_summary = pd.concat([
vmt_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own] for veh_own in ['0','1']], axis = 1, keys = ['No car', 'With car']
)
elif by == 'purpose':
vmt_summary = pd.concat([
vmt_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose] for purpose in vmt_master_table.purpose.unique()],axis = 1, keys= vmt_master_table.purpose.unique())
vmt_summary.to_csv(out_fn)
def vmt_by_neighborhood(mc_obj, out_fn = None, by = None):
'''
Summarizes VMT production and attraction by the 26 Boston neighborhoods.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary; if None specified, only aggregate production and attraction will be provided.
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + f'vmt_by_neighborhood.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + f'vmt_by_neighborhood_by_{by}.csv'
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
if by in ['peak','veh_own','purpose'] == False:
print('Only supports VMT by neighborhood, peak / vehicle ownership, purpose.')
return
else:
vmt_master_table = pd.DataFrame(columns = ['Production','Attraction','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
auto_trip_table = sum([
mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode] / md.AO_dict[mode]
for mode in ['DA','SR2','SR3+','SM_RA','SM_SH'] if mode in drive_modes])
vmt_table = __mt_prod_attr_nhood(mc_obj,auto_trip_table,skim_dict[peak])
vmt_table['peak'] = peak
vmt_table['veh_own'] = veh_own
vmt_table['purpose'] = purpose
vmt_master_table = vmt_master_table.append(vmt_table, sort = True)
if by == None:
vmt_summary = vmt_master_table.groupby('BOSTON_NB').sum()
elif by == 'peak':
vmt_summary = pd.concat([
vmt_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak] for peak in ['PK','OP']], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
vmt_summary = pd.concat([
vmt_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own] for veh_own in ['0','1']], axis = 1, keys = ['No car', 'With car']
)
elif by == 'purpose':
vmt_summary = pd.concat([
vmt_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose] for purpose in vmt_master_table.purpose.unique()],axis = 1, keys= vmt_master_table.purpose.unique())
vmt_summary.to_csv(out_fn)
def pmt_by_neighborhood(mc_obj, out_fn = None, by = None):
'''
Summarizes PMT production and attraction by the 26 Boston neighborhoods.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary; if None specified, only aggregate production and attraction will be provided.
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + f'pmt_by_neighborhood.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + f'pmt_by_neighborhood_by_{by}.csv'
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
if by in ['peak','veh_own','purpose'] == False:
print('Only supports PMT by neighborhood, peak / vehicle ownership, purpose.')
return
else:
pmt_master_table = pd.DataFrame(columns = ['Production','Attraction','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
person_trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode] for mode in md.modes if mode in drive_modes])
pmt_table = __mt_prod_attr_nhood(mc_obj,person_trip_table,skim_dict[peak])
pmt_table['peak'] = peak
pmt_table['veh_own'] = veh_own
pmt_table['purpose'] = purpose
pmt_master_table = pmt_master_table.append(pmt_table, sort = True)
if by == None:
pmt_summary = pmt_master_table.groupby('BOSTON_NB').sum()
elif by == 'peak':
pmt_summary = pd.concat([
pmt_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak] for peak in ['PK','OP']], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
pmt_summary = pd.concat([
pmt_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own] for veh_own in ['0','1']], axis = 1, keys = ['No car', 'With car']
)
elif by == 'purpose':
pmt_summary = pd.concat([
pmt_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose] for purpose in pmt_master_table.purpose.unique()],axis = 1, keys= pmt_master_table.purpose.unique())
pmt_summary.to_csv(out_fn)
def act_pmt_by_neighborhood(mc_obj, out_fn = None, by = None):
'''
Summarizes PMT production and attraction by the 26 Boston neighborhoods for active modes.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary; if None specified, only aggregate production and attraction will be provided.
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + f'act_pmt_by_neighborhood.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + f'act_pmt_by_neighborhood_by_{by}.csv'
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
if by in ['peak','veh_own','purpose'] == False:
print('Only supports PMT by neighborhood, peak / vehicle ownership, purpose.')
return
else:
pmt_master_table = pd.DataFrame(columns = ['Production','Attraction','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
person_trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode] for mode in ['Walk','Bike'] if mode in drive_modes])
pmt_table = __mt_prod_attr_nhood(mc_obj,person_trip_table,skim_dict[peak])
pmt_table['peak'] = peak
pmt_table['veh_own'] = veh_own
pmt_table['purpose'] = purpose
pmt_master_table = pmt_master_table.append(pmt_table, sort = True)
if by == None:
pmt_summary = pmt_master_table.groupby('BOSTON_NB').sum()
elif by == 'peak':
pmt_summary = pd.concat([
pmt_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak] for peak in ['PK','OP']], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
pmt_summary = pd.concat([
pmt_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own] for veh_own in ['0','1']], axis = 1, keys = ['No car', 'With car']
)
elif by == 'purpose':
pmt_summary = pd.concat([
pmt_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose] for purpose in pmt_master_table.purpose.unique()],axis = 1, keys= pmt_master_table.purpose.unique())
pmt_summary.to_csv(out_fn)
def sm_trips_by_neighborhood(mc_obj, out_fn = None, by = None, sm_mode = 'SM_RA'):
'''
Summarizes PMT production and attraction by the 26 Boston neighborhoods for Shared Mobility Modes.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary; if None specified, only aggregate production and attraction will be provided.
:param sm_mode: Smart Mobility Mode name
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + sm_mode + f'_trips_by_neighborhood.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + sm_mode + f'_trips_by_neighborhood_by_{by}.csv'
if by in ['peak','veh_own','purpose'] == False:
print('Only supports Trips by neighborhood, peak / vehicle ownership, purpose.')
return
else:
trp_master_table = pd.DataFrame(columns = ['Production','Attraction','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
person_trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][sm_mode]
trp_table = __trip_prod_attr_nhood(mc_obj,person_trip_table)
trp_table['peak'] = peak
trp_table['veh_own'] = veh_own
trp_table['purpose'] = purpose
trp_master_table = trp_master_table.append(trp_table, sort = True)
if by == None:
trp_summary = trp_master_table.groupby('BOSTON_NB').sum()
elif by == 'peak':
trp_summary = pd.concat([
trp_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak] for peak in ['PK','OP']], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
trp_summary = pd.concat([
trp_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own] for veh_own in ['0','1']], axis = 1, keys = ['No car', 'With car']
)
elif by == 'purpose':
trp_summary = pd.concat([
trp_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose] for purpose in trp_master_table.purpose.unique()],axis = 1, keys= trp_master_table.purpose.unique())
trp_summary.to_csv(out_fn)
def trips_by_neighborhood(mc_obj, out_fn = None, by = None):
'''
Summarizes PMT production and attraction by the 26 Boston neighborhoods.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary; if None specified, only aggregate production and attraction will be provided.
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + f'trips_by_neighborhood.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + f'trips_by_neighborhood_by_{by}.csv'
if by in ['peak','veh_own','purpose'] == False:
print('Only supports Trips by neighborhood, peak / vehicle ownership, purpose.')
return
else:
trp_master_table = pd.DataFrame(columns = ['Production','Attraction','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
person_trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode] for mode in md.modes if mode in drive_modes])
trp_table = __trip_prod_attr_nhood(mc_obj,person_trip_table)
trp_table['peak'] = peak
trp_table['veh_own'] = veh_own
trp_table['purpose'] = purpose
trp_master_table = trp_master_table.append(trp_table, sort = True)
if by == None:
trp_summary = trp_master_table.groupby('BOSTON_NB').sum()
elif by == 'peak':
trp_summary = pd.concat([
trp_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak] for peak in ['PK','OP']], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
trp_summary = pd.concat([
trp_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own] for veh_own in ['0','1']], axis = 1, keys = ['No car', 'With car']
)
elif by == 'purpose':
trp_summary = pd.concat([
trp_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose] for purpose in trp_master_table.purpose.unique()],axis = 1, keys= trp_master_table.purpose.unique())
trp_summary.to_csv(out_fn)
def mode_share_by_neighborhood(mc_obj, out_fn = None, by = None):
'''
Summarizes mode share as the average of trips to/from the 26 Boston neighborhoods, in three categories - drive, non-motorized and transit.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + f'mode_share_by_neighborhood.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + f'mode_share_by_neighborhood_by_{by}.csv'
if by in ['peak','veh_own','purpose'] == False:
print('Only supports mode share by neighborhood, peak / vehicle ownership, purpose.')
return
else:
share_master_table = pd.DataFrame(columns = ['drive','non-motorized','transit','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
share_table = pd.DataFrame(index = range(0,md.max_zone),columns = ['drive','non-motorized','transit','smart mobility']).fillna(0)
for mode in mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}']:
trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
category = md.mode_categories[mode]
share_table[category] += (trip_table.sum(axis = 1)+trip_table.sum(axis = 0))/2
towns = mc_obj.taz.sort_values(md.taz_ID_field).iloc[0:md.max_zone]
trips = pd.concat([towns[[md.taz_ID_field,'BOSTON_NB']],share_table],axis = 1,join = 'inner').groupby(['BOSTON_NB']).sum().drop([md.taz_ID_field],axis = 1)
trips['peak'] = peak
trips['veh_own'] = veh_own
trips['purpose'] = purpose
share_master_table = share_master_table.append(trips.reset_index(), sort = True)
if by == None:
trip_summary = share_master_table.groupby('BOSTON_NB').sum()
share_summary = trip_summary.divide(trip_summary.sum(axis = 1),axis = 0)
elif by == 'peak':
share_summary = pd.concat([
share_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak].divide(
share_master_table.groupby(['peak','BOSTON_NB']).sum().loc[peak].sum(axis=1),axis = 0)
for peak in ['PK','OP']
], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
share_summary = pd.concat([
share_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own].divide(
share_master_table.groupby(['veh_own','BOSTON_NB']).sum().loc[veh_own].sum(axis=1),axis = 0)
for veh_own in ['0','1']
], axis = 1, keys = ['No car', 'With car'])
elif by == 'purpose':
share_summary = pd.concat([
share_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose].divide(
share_master_table.groupby(['purpose','BOSTON_NB']).sum().loc[purpose].sum(axis=1),axis = 0)
for purpose in share_master_table.purpose.unique()
],axis = 1, keys= share_master_table.purpose.unique())
share_summary.to_csv(out_fn)
# Seaport method
def mode_share_by_subarea(mc_obj, out_fn = None, by = None):
'''
Summarizes mode share as the average of trips to/from the 7 Seaport sub-areas, in three categories - drive, non-motorized and transit.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param out_fn: output csv filename; if None specified, in the output path defined in config.py
:param by: grouping used for the summary
'''
if out_fn is None and by is None:
out_fn = mc_obj.config.out_path + f'mode_share_by_subarea.csv'
elif out_fn is None and by:
out_fn = mc_obj.config.out_path + f'mode_share_by_subarea_by_{by}.csv'
if by in ['peak','veh_own','purpose'] == False:
print('Only supports mode share by subarea, peak / vehicle ownership, purpose.')
return
else:
share_master_table = pd.DataFrame(columns = ['drive','non-motorized','transit','peak','veh_own','purpose'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
share_table = pd.DataFrame(index = range(0,md.max_zone),columns = ['drive','non-motorized','transit','smart mobility']).fillna(0)
for mode in mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}']:
trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
category = md.mode_categories[mode]
share_table[category] += (trip_table.sum(axis = 1)+trip_table.sum(axis = 0))/2
towns = mc_obj.taz.sort_values(md.taz_ID_field).iloc[0:md.max_zone]
towns['REPORT_AREA'] = towns['REPORT_AREA'][towns['REPORT_AREA'].isin(['South Station', 'Seaport Blvd', 'Design Center',
'Southeast Seaport', 'BCEC', 'Fort Point', 'Broadway'])]
trips = pd.concat([towns[[md.taz_ID_field,'REPORT_AREA']],share_table],axis = 1,join = 'inner').groupby(['REPORT_AREA']).sum().drop([md.taz_ID_field],axis = 1)
trips['peak'] = peak
trips['veh_own'] = veh_own
trips['purpose'] = purpose
share_master_table = share_master_table.append(trips.reset_index(), sort = True)
if by == None:
trip_summary = share_master_table.groupby('REPORT_AREA').sum()
share_summary = trip_summary.divide(trip_summary.sum(axis = 1),axis = 0)
elif by == 'peak':
share_summary = pd.concat([
share_master_table.groupby(['peak','REPORT_AREA']).sum().loc[peak].divide(
share_master_table.groupby(['peak','REPORT_AREA']).sum().loc[peak].sum(axis=1),axis = 0)
for peak in ['PK','OP']
], axis = 1, keys = ['PK','OP'])
elif by == 'veh_own':
share_summary = pd.concat([
share_master_table.groupby(['veh_own','REPORT_AREA']).sum().loc[veh_own].divide(
share_master_table.groupby(['veh_own','REPORT_AREA']).sum().loc[veh_own].sum(axis=1),axis = 0)
for veh_own in ['0','1']
], axis = 1, keys = ['No car', 'With car'])
elif by == 'purpose':
share_summary = pd.concat([
share_master_table.groupby(['purpose','REPORT_AREA']).sum().loc[purpose].divide(
share_master_table.groupby(['purpose','REPORT_AREA']).sum().loc[purpose].sum(axis=1),axis = 0)
for purpose in share_master_table.purpose.unique()
],axis = 1, keys= share_master_table.purpose.unique())
share_summary.to_csv(out_fn)
def __sm_compute_summary_by_subregion(mc_obj,metric = 'VMT',subregion = 'neighboring', sm_mode='SM_RA'):
''' Computing function used by write_summary_by_subregion(), does not produce outputs'''
if metric.lower() not in ('vmt','pmt','mode share','trip', 'pmt_act'):
print('Only supports trip, VMT, PMT and mode share calculations.')
return
if subregion.lower() not in ('boston','neighboring','i93','i495','region'):
print('Only supports within boston, "neighboring" for towns neighboring Boston, I93, I495 or Region.')
return
subregion_dict = {'boston':'BOSTON','neighboring':'BOS_AND_NEI','i93':'in_i95i93','i495':'in_i495'}
if metric.lower() == 'vmt':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
vmt_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][sm_mode] / md.AO_dict[sm_mode]
vmt_table += trip_table * skim_dict[peak]['Length (Skim)']
if subregion.lower() in subregion_dict:
field = subregion_dict[subregion.lower()]
boston_o_auto_vmt = mtx.OD_slice(vmt_table,O_slice = md.taz['BOSTON'], D_slice = md.taz[field]== True)
boston_d_auto_vmt = mtx.OD_slice(vmt_table,md.taz[field]== True,D_slice = md.taz['BOSTON'])
#boston_o_auto_vmt = vmt_table[md.taz['BOSTON'],:][:, md.taz[field]== True]
#boston_d_auto_vmt = vmt_table[md.taz[field]== True,:][:,md.taz['BOSTON']]
town_definition = md.taz[md.taz[field]== True]
elif subregion.lower() == 'region':
boston_o_auto_vmt = mtx.OD_slice(vmt_table,O_slice = md.taz['BOSTON'])
boston_d_auto_vmt = mtx.OD_slice(vmt_table,D_slice = md.taz['BOSTON'])
#boston_o_auto_vmt = vmt_table[md.taz['BOSTON'],:]
#boston_d_auto_vmt = vmt_table[:][:,md.taz['BOSTON']]
town_definition = md.taz
zone_vmt_daily_o = pd.DataFrame(np.sum(boston_o_auto_vmt,axis=1)/2 ,columns=["VMT"])
zone_vmt_daily_d = pd.DataFrame(np.sum(boston_d_auto_vmt,axis=0)/2 ,columns=["VMT"])
town_vmt_o=pd.concat([town_definition,zone_vmt_daily_o],axis=1,join='inner')
town_vmt_d=pd.concat([town_definition,zone_vmt_daily_d],axis=1,join='inner')
vmt_sum_o = town_vmt_o[town_vmt_o['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['VMT']
vmt_sum_d = town_vmt_d[town_vmt_d['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['VMT']
subregion_vmt = (vmt_sum_o + vmt_sum_d).values[0]
return subregion_vmt
elif metric.lower() == 'trip':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
tripsum_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][sm_mode]
tripsum_table += trip_table
if subregion.lower() in subregion_dict:
field = subregion_dict[subregion.lower()]
boston_o_trip = mtx.OD_slice(tripsum_table, O_slice = md.taz['BOSTON'],D_slice = md.taz[field]== True)
boston_d_trip = mtx.OD_slice(tripsum_table, O_slice = md.taz[field]== True, D_slice = md.taz['BOSTON'])
#boston_o_trip = tripsum_table[md.taz['BOSTON'],:][:, md.taz[field]== True]
#boston_d_trip = tripsum_table[md.taz[field]== True,:][:,md.taz['BOSTON']]
town_definition = md.taz[md.taz[field]== True]
elif subregion.lower() == 'region':
boston_o_trip = mtx.OD_slice(tripsum_table, O_slice = md.taz['BOSTON'])
boston_d_trip = mtx.OD_slice(tripsum_table, D_slice = md.taz['BOSTON'])
#boston_o_trip = tripsum_table[md.taz['BOSTON'],:]
#boston_d_trip = tripsum_table[:][:,md.taz['BOSTON']]
town_definition = md.taz
zone_daily_o = pd.DataFrame(np.sum(boston_o_trip,axis=1) ,columns=["trips"])
zone_daily_d = pd.DataFrame(np.sum(boston_d_trip,axis=0) ,columns=["trips"])
town_o=pd.concat([town_definition,zone_daily_o],axis=1,join='inner')
town_d=pd.concat([town_definition,zone_daily_d],axis=1,join='inner')
sum_o = town_o[town_o['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['trips']
sum_d = town_d[town_d['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['trips']
subregion_trip = (sum_o + sum_d).values[0]
return subregion_trip
def __compute_metric_by_zone(mc_obj,metric = 'VMT'):
''' Computing function used by write_summary_by_subregion(), does not produce outputs'''
if metric.lower() == 'vmt':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
vmt_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode] / md.AO_dict[mode] for mode in md.auto_modes if mode in drive_modes])
vmt_table += trip_table * skim_dict[peak]['Length (Skim)']
boston_o_auto_vmt = mtx.OD_slice(vmt_table, O_slice = md.taz['BOSTON'])
boston_d_auto_vmt = mtx.OD_slice(vmt_table,D_slice = md.taz['BOSTON'])
#boston_o_auto_vmt = vmt_table[md.taz['BOSTON'],:]
#boston_d_auto_vmt = vmt_table[:][:,md.taz['BOSTON']]
town_definition = md.taz
zone_vmt_daily_o = pd.DataFrame(np.sum(boston_o_auto_vmt,axis=0)/2 ,columns=["VMT"])
zone_vmt_daily_d = pd.DataFrame(np.sum(boston_d_auto_vmt,axis=1)/2 ,columns=["VMT"])
town_vmt_o=pd.concat([town_definition,zone_vmt_daily_o],axis=1,join='inner')
town_vmt_d=pd.concat([town_definition,zone_vmt_daily_d],axis=1,join='inner')
town_vmt = town_vmt_o.groupby(['TOWN']).sum()['VMT'] + town_vmt_d.groupby(['TOWN']).sum()['VMT']
return town_vmt
elif metric.lower() == 'pmt':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
pmt_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in md.modes if mode in drive_modes])
pmt_table += trip_table * skim_dict[peak]['Length (Skim)']
boston_o_auto_pmt = mtx.OD_slice(pmt_table, O_slice = md.taz['BOSTON'])
boston_d_auto_pmt = mtx.OD_slice(pmt_table, D_slice = md.taz['BOSTON'])
#boston_o_auto_pmt = pmt_table[md.taz['BOSTON'],:]
#boston_d_auto_pmt = pmt_table[:][:,md.taz['BOSTON']]
town_definition = md.taz
zone_pmt_daily_o = pd.DataFrame(np.sum(boston_o_auto_pmt,axis=0)/2 ,columns=["VMT"])
zone_pmt_daily_d = pd.DataFrame(np.sum(boston_d_auto_pmt,axis=1)/2 ,columns=["VMT"])
town_pmt_o=pd.concat([town_definition,zone_pmt_daily_o],axis=1,join='inner')
town_pmt_d=pd.concat([town_definition,zone_pmt_daily_d],axis=1,join='inner')
town_pmt = town_pmt_o.groupby(['TOWN']).sum()['VMT'] + town_pmt_d.groupby(['TOWN']).sum()['VMT']
return town_pmt
elif metric.lower() == 'pmt_act':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
pmt_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in ['Walk','Bike'] if mode in drive_modes])
pmt_table += trip_table * skim_dict[peak]['Length (Skim)']
boston_o_auto_pmt = mtx.OD_slice(pmt_table, O_slice = md.taz['BOSTON'])
boston_d_auto_pmt = mtx.OD_slice(pmt_table, D_slice = md.taz['BOSTON'])
#boston_o_auto_pmt = pmt_table[taz['BOSTON'],:]
#boston_d_auto_pmt = pmt_table[:][:,taz['BOSTON']]
town_definition = md.taz
zone_pmt_daily_o = pd.DataFrame(np.sum(boston_o_auto_pmt,axis=0)/2 ,columns=["VMT"])
zone_pmt_daily_d = pd.DataFrame(np.sum(boston_d_auto_pmt,axis=1)/2 ,columns=["VMT"])
town_pmt_o=pd.concat([town_definition,zone_pmt_daily_o],axis=1,join='inner')
town_pmt_d=pd.concat([town_definition,zone_pmt_daily_d],axis=1,join='inner')
town_pmt = town_pmt_o.groupby(['TOWN']).sum()['VMT'] + town_pmt_d.groupby(['TOWN']).sum()['VMT']
return town_pmt
def __compute_summary_by_subregion(mc_obj,metric = 'VMT',subregion = 'neighboring'):
''' Computing function used by write_summary_by_subregion(), does not produce outputs'''
if metric.lower() not in ('vmt','pmt','mode share','trip', 'pmt_act'):
print('Only supports trip, VMT, PMT and mode share calculations.')
return
if subregion.lower() not in ('boston','neighboring','i93','i495','region'):
print('Only supports within boston, "neighboring" for towns neighboring Boston, I93, I495 or Region.')
return
subregion_dict = {'boston':'BOSTON','neighboring':'BOS_AND_NEI','i93':'in_i95i93','i495':'in_i495'}
if metric.lower() == 'vmt':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
vmt_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode] / md.AO_dict[mode] for mode in md.auto_modes if mode in modes])
vmt_table += trip_table * skim_dict[peak]['Length (Skim)']
if subregion.lower() in subregion_dict:
field = subregion_dict[subregion.lower()]
#boston_o_auto_vmt = vmt_table[md.taz['BOSTON'],:][:, md.taz[field]== True]
#boston_d_auto_vmt = vmt_table[md.taz[field]== True,:][:,md.taz['BOSTON']]
boston_o_auto_vmt = mtx.OD_slice(vmt_table, O_slice = md.taz['BOSTON'], D_slice = md.taz[field]== True)
boston_d_auto_vmt = mtx.OD_slice(vmt_table, O_slice = md.taz[field]== True, D_slice = md.taz['BOSTON'])
town_definition = md.taz[md.taz[field]== True]
elif subregion.lower() == 'region':
# boston_o_auto_vmt = vmt_table[md.taz['BOSTON'],:]
# boston_d_auto_vmt = vmt_table[:][:,md.taz['BOSTON']]
boston_o_auto_vmt = mtx.OD_slice(vmt_table, O_slice = md.taz['BOSTON'])
boston_d_auto_vmt = mtx.OD_slice(vmt_table, D_slice = md.taz['BOSTON'])
town_definition = md.taz
zone_vmt_daily_o = pd.DataFrame(np.sum(boston_o_auto_vmt,axis=1)/2 ,columns=["VMT"])
zone_vmt_daily_d = pd.DataFrame(np.sum(boston_d_auto_vmt,axis=0)/2 ,columns=["VMT"])
town_vmt_o=pd.concat([town_definition,zone_vmt_daily_o],axis=1,join='inner')
town_vmt_d=pd.concat([town_definition,zone_vmt_daily_d],axis=1,join='inner')
vmt_sum_o = town_vmt_o[town_vmt_o['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['VMT']
vmt_sum_d = town_vmt_d[town_vmt_d['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['VMT']
subregion_vmt = (vmt_sum_o + vmt_sum_d).values[0]
return subregion_vmt
elif metric.lower() == 'pmt':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
pmt_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in md.modes if mode in drive_modes])
pmt_table += trip_table * skim_dict[peak]['Length (Skim)']
if subregion.lower() in subregion_dict:
field = subregion_dict[subregion.lower()]
boston_o_auto_pmt = mtx.OD_slice(pmt_table, O_slice = md.taz['BOSTON'],D_slice = md.taz[field]== True)
boston_d_auto_pmt = mtx.OD_slice(pmt_table ,O_slice = md.taz[field]== True, D_slice = md.taz['BOSTON'])
#boston_o_auto_pmt = pmt_table[md.taz['BOSTON'],:][:, md.taz[field]== True]
#boston_d_auto_pmt = pmt_table[md.taz[field]== True,:][:,md.taz['BOSTON']]
town_definition = md.taz[md.taz[field]== True]
elif subregion.lower() == 'region':
boston_o_auto_pmt = mtx.OD_slice(pmt_table, O_slice = md.taz['BOSTON'])
boston_d_auto_pmt = mtx.OD_slice(pmt_table, D_slice = md.taz['BOSTON'])
#boston_o_auto_pmt = pmt_table[md.taz['BOSTON'],:]
#boston_d_auto_pmt = pmt_table[:][:,md.taz['BOSTON']]
town_definition = md.taz
zone_pmt_daily_o = pd.DataFrame(np.sum(boston_o_auto_pmt,axis=1)/2 ,columns=["PMT"])
zone_pmt_daily_d = pd.DataFrame(np.sum(boston_d_auto_pmt,axis=0)/2 ,columns=["PMT"])
town_pmt_o=pd.concat([town_definition,zone_pmt_daily_o],axis=1,join='inner')
town_pmt_d=pd.concat([town_definition,zone_pmt_daily_d],axis=1,join='inner')
pmt_sum_o = town_pmt_o[town_pmt_o['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['PMT']
pmt_sum_d = town_pmt_d[town_pmt_d['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['PMT']
boston_portion_pmt = (pmt_sum_o + pmt_sum_d).values[0]
return boston_portion_pmt
elif metric.lower() == 'pmt_act':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
pmt_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in ['Walk','Bike'] if mode in drive_modes])
pmt_table += trip_table * skim_dict[peak]['Length (Skim)']
if subregion.lower() in subregion_dict:
field = subregion_dict[subregion.lower()]
boston_o_auto_pmt = mtx.OD_slice(pmt_table, O_slice = md.taz['BOSTON'],D_slice = md.taz[field]== True)
boston_d_auto_pmt = mtx.OD_slice(pmt_table, O_slice = md.taz[field]== True, D_slice = md.taz['BOSTON'])
#boston_o_auto_pmt = pmt_table[md.taz['BOSTON'],:][:, md.taz[field]== True]
#boston_d_auto_pmt = pmt_table[md.taz[field]== True,:][:,md.taz['BOSTON']]
town_definition = md.taz[md.taz[field]== True]
elif subregion.lower() == 'region':
boston_o_auto_pmt = mtx.OD_slice(pmt_table,O_slice = md.taz['BOSTON'])
boston_d_auto_pmt = mtx.OD_slice(pmt_table,D_slice = md.taz['BOSTON'])
#boston_o_auto_pmt = pmt_table[md.taz['BOSTON'],:]
#boston_d_auto_pmt = pmt_table[:][:,md.taz['BOSTON']]
town_definition = md.taz
zone_pmt_daily_o = pd.DataFrame(np.sum(boston_o_auto_pmt,axis=1)/2 ,columns=["PMT"])
zone_pmt_daily_d = pd.DataFrame(np.sum(boston_d_auto_pmt,axis=0)/2 ,columns=["PMT"])
town_pmt_o=pd.concat([town_definition,zone_pmt_daily_o],axis=1,join='inner')
town_pmt_d=pd.concat([town_definition,zone_pmt_daily_d],axis=1,join='inner')
pmt_sum_o = town_pmt_o[town_pmt_o['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['PMT']
pmt_sum_d = town_pmt_d[town_pmt_d['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['PMT']
boston_portion_pmt = (pmt_sum_o + pmt_sum_d).values[0]
return boston_portion_pmt
elif metric.lower() == 'mode share':
share_table = dict(zip(['drive','non-motorized','transit','smart mobility'],[0,0,0,0]))
if subregion.lower() in subregion_dict:
field = subregion_dict[subregion.lower()]
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
for mode in mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}']:
trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
boston_ii_trips = trip_table[md.taz['BOSTON'],:][:,md.taz['BOSTON']].sum()
trips = trip_table[md.taz['BOSTON'],:][:, md.taz[field]== True].sum() + trip_table[md.taz[field]== True,:][:,md.taz['BOSTON']].sum() - boston_ii_trips
category = md.mode_categories[mode]
share_table[category]+=trips
elif subregion.lower() == 'region':
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
for mode in mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}']:
trip_table = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
boston_ii_trips = trip_table[md.taz['BOSTON'],:][:,md.taz['BOSTON']].sum()
trips = trip_table[md.taz['BOSTON'],:][:].sum() + trip_table[:][:,md.taz['BOSTON']].sum() - boston_ii_trips
category = md.mode_categories[mode]
share_table[category]+=trips
# normalize
return (pd.DataFrame.from_dict(share_table,orient = 'index') / (pd.DataFrame.from_dict(share_table,orient = 'index').sum())).to_dict()[0]
elif metric.lower() == 'trip':
skim_dict = {'PK': mc_obj.drive_skim_PK,'OP':mc_obj.drive_skim_OP}
tripsum_table = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
drive_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trip_table = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in md.modes if mode in drive_modes])
tripsum_table += trip_table
if subregion.lower() in subregion_dict:
field = subregion_dict[subregion.lower()]
boston_o_trip = mtx.OD_slice(tripsum_table, O_slice = md.taz['BOSTON'],D_slice = md.taz[field]== True)
boston_d_trip = mtx.OD_slice(tripsum_table, O_slice = md.taz[field]== True,D_slice = md.taz['BOSTON'])
#boston_o_trip = tripsum_table[md.taz['BOSTON'],:][:, md.taz[field]== True]
#boston_d_trip = tripsum_table[md.taz[field]== True,:][:,md.taz['BOSTON']]
town_definition = md.taz[md.taz[field]== True]
elif subregion.lower() == 'region':
boston_o_trip = mtx.OD_slice(tripsum_table, O_slice = md.taz['BOSTON'])
boston_d_trip = mtx.OD_slice(tripsum_table, D_slice = md.taz['BOSTON'])
#boston_o_trip = tripsum_table[md.taz['BOSTON'],:]
#boston_d_trip = tripsum_table[:][:,md.taz['BOSTON']]
town_definition = md.taz
zone_daily_o = pd.DataFrame(np.sum(boston_o_trip,axis=1) ,columns=["trips"])
zone_daily_d = pd.DataFrame(np.sum(boston_d_trip,axis=0) ,columns=["trips"])
town_o=pd.concat([town_definition,zone_daily_o],axis=1,join='inner')
town_d=pd.concat([town_definition,zone_daily_d],axis=1,join='inner')
sum_o = town_o[town_o['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['trips']
sum_d = town_d[town_d['TOWN']=='BOSTON,MA'].groupby(['TOWN']).sum()['trips']
subregion_trip = (sum_o + sum_d).values[0]
return subregion_trip
def __trips_to_from_boston(taz, mode, tripsum_table):
boston_o_trip = mtx.OD_slice(tripsum_table, O_slice = taz['BOSTON'])
boston_d_trip = mtx.OD_slice(tripsum_table, D_slice = taz['BOSTON'])
#boston_o_trip = tripsum_table[taz['BOSTON'],:]
#boston_d_trip = tripsum_table[:][:,taz['BOSTON']]
zone_daily_o = pd.DataFrame(np.sum(boston_o_trip,axis=1) ,columns=[mode])
zone_daily_d = pd.DataFrame(np.sum(boston_d_trip,axis=0) ,columns=[mode])
town_o=pd.concat([taz,zone_daily_o],axis=1,join='inner')
town_d=pd.concat([taz,zone_daily_d],axis=1,join='inner')
zone_o = town_o[town_o['TOWN']=='BOSTON,MA'].groupby([md.taz_ID_field]).sum()[mode]
zone_d = town_d[town_d['TOWN']=='BOSTON,MA'].groupby([md.taz_ID_field]).sum()[mode]
return zone_o, zone_d
def trips_by_mode(mc_obj, mode='all'):
auto_trip = np.zeros((md.max_zone,md.max_zone))
transit_trip = np.zeros((md.max_zone,md.max_zone))
nm_trip = np.zeros((md.max_zone,md.max_zone))
sm_trip = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
avail_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.drive_modes])
auto_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.transit_modes])
transit_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.active_modes])
nm_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.smart_mobility_modes])
sm_trip += trips
auto_o, auto_d = __trips_to_from_boston(md.taz, "auto", auto_trip)
transit_o, transit_d = __trips_to_from_boston(md.taz, "transit", transit_trip)
nm_o, nm_d = __trips_to_from_boston(md.taz, "nm", nm_trip)
sm_o, sm_d = __trips_to_from_boston(md.taz, "sm", sm_trip)
trips_o = auto_o.to_frame().join(transit_o)
trips_o = trips_o.join(nm_o)
trips_o = trips_o.join(sm_o)
trips_d = auto_d.to_frame().join(transit_d)
trips_d = trips_d.join(nm_d)
trips_d = trips_d.join(sm_d)
trips_o.to_csv(mc_obj.config.out_path + 'trip_p_mode_zone.csv')
trips_d.to_csv(mc_obj.config.out_path + 'trip_a_mode_zone.csv')
def __trips_to_region(mask, taz, mode, tripsum_table):
boston_o_sums = np.sum(mtx.OD_slice(tripsum_table, O_slice = taz['BOSTON'],D_slice = mask==1),axis=1)
nonboston_o_sums = np.sum(mtx.OD_slice(tripsum_table,D_slice = ((mask * (taz['BOSTON']).values)==1)),axis=1)
boston_zone = taz.join(pd.DataFrame(boston_o_sums,columns=[mode + "boston"]))
nonboston_zone = taz.join(pd.DataFrame(nonboston_o_sums,columns=[mode + "nonboston"]))
nonboston_zone.loc[nonboston_zone['TOWN']=='BOSTON,MA',mode + 'nonboston']=0
zone_daily = pd.DataFrame(boston_zone[mode + "boston"]).join(nonboston_zone[mode + 'nonboston'])
return pd.DataFrame(zone_daily.sum(axis=1), columns=[mode])
def productions_by_region(mc_obj, region='all', cordon_area=[]):
auto_trip = np.zeros((md.max_zone,md.max_zone))
da_trip = np.zeros((md.max_zone,md.max_zone))
sr_trip = np.zeros((md.max_zone,md.max_zone))
wat_trip = np.zeros((md.max_zone,md.max_zone))
dat_trip = np.zeros((md.max_zone,md.max_zone))
nm_trip = np.zeros((md.max_zone,md.max_zone))
smra_trip = np.zeros((md.max_zone,md.max_zone))
smsh_trip = np.zeros((md.max_zone,md.max_zone))
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
if mc_obj.table_container.get_table(purpose):
avail_modes = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'].keys()
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.drive_modes])
auto_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.da_mode])
da_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.sr_mode])
sr_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.WAT_modes])
wat_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.DAT_modes])
dat_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.active_modes])
nm_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.sm_ride_alone])
smra_trip += trips
trips = sum([mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode]
for mode in avail_modes if mode in md.sm_shared_ride])
smsh_trip += trips
if region=='all':
mask = np.ones(md.max_zone)
outfile = 'trip_p_to_region.csv'
elif region=='Boston':
mask = (taz['BOSTON']).values * 1 #[1]*447 + [0]*(md.max_zone - 447)
outfile = 'trip_p_to_boston.csv'
elif region=='cordon':
mask = taz['BOSTON_NB'].isin(cordon_area).values * 1
outfile = 'trip_p_to_cordon.csv'
auto_d = __trips_to_region(mask, md.taz, "auto", auto_trip)
da_d = __trips_to_region(mask, md.taz, "da", da_trip)
sr_d = __trips_to_region(mask, md.taz, "sr", sr_trip)
wat_d = __trips_to_region(mask, md.taz, "wat", wat_trip)
dat_d = __trips_to_region(mask, md.taz, "dat", dat_trip)
nm_d = __trips_to_region(mask, md.taz, "nm", nm_trip)
smra_d = __trips_to_region(mask, md.taz, "smra", smra_trip)
smsh_d = __trips_to_region(mask, md.taz, "smsh", smsh_trip)
trips_d = auto_d.join(da_d)
trips_d = trips_d.join(sr_d)
trips_d = trips_d.join(wat_d)
trips_d = trips_d.join(dat_d)
trips_d = trips_d.join(nm_d)
trips_d = trips_d.join(smra_d)
trips_d = trips_d.join(smsh_d)
trips_d = taz.join(trips_d)
trips_d.to_csv(mc_obj.config.out_path + outfile)
def write_summary_by_subregion(mc_obj, by='all'):
'''
Summarizes VMT, PMT or mode share by subregions of Massachusetts surrounding Boston (neighboring towns of Boston / within I-93/95 / within I-495).
:param mc_obj: mode choice module object as defined in the IPython notebook
:param taz_fn: TAZ file that contains subregion definition
:param out_path: output path.
'''
subregion_dict = dict(zip(['boston','neighboring','i93','i495','region'],['Within Boston','Boston and Neighboring Towns', 'Within I-93/95', 'Within I-495', 'Entire Region']))
vmt_summary_df = pd.DataFrame(index = subregion_dict.values(), columns = ['VMT to/from Boston'])
pmt_summary_df = pd.DataFrame(index = subregion_dict.values(), columns = ['PMT to/from Boston'])
pmtact_summary_df = pd.DataFrame(index = subregion_dict.values(), columns = ['PMT Active Modes to/from Boston'])
mode_share_df = pd.DataFrame(index = subregion_dict.values(),columns = ['drive','non-motorized','transit','smart mobility'])
trip_summary_df = pd.DataFrame(index = subregion_dict.values(),columns = ['Trips to/from Boston'])
for subregion in subregion_dict:
vmt_summary_df.loc[subregion_dict[subregion]] = __compute_summary_by_subregion(mc_obj, metric = 'VMT',subregion = subregion)
pmt_summary_df.loc[subregion_dict[subregion]] = __compute_summary_by_subregion(mc_obj, metric = 'PMT',subregion = subregion)
pmtact_summary_df.loc[subregion_dict[subregion]] = __compute_summary_by_subregion(mc_obj, metric = 'pmt_act',subregion = subregion)
mode_share_df.loc[subregion_dict[subregion]] = __compute_summary_by_subregion(mc_obj, metric = 'mode share',subregion = subregion)
trip_summary_df.loc[subregion_dict[subregion]] = __compute_summary_by_subregion(mc_obj, metric = 'trip',subregion = subregion)
vmt_summary_df.to_csv(mc_obj.config.out_path + 'vmt_summary_subregions.csv')
pmt_summary_df.to_csv(mc_obj.config.out_path + 'pmt_summary_subregions.csv')
pmtact_summary_df.to_csv(mc_obj.config.out_path + 'act_pmt_summary_subregions.csv')
mode_share_df.to_csv(mc_obj.config.out_path + 'mode_share_summary_subregions.csv')
trip_summary_df.to_csv(mc_obj.config.out_path + 'trip_summary_subregions.csv')
def write_summary_by_subregion_sm(mc_obj, by='all'):
'''
Summarizes Smart Mobility VMT and trips by subregions of Massachusetts surrounding Boston (neighboring towns of Boston / within I-93/95 / within I-495).
:param mc_obj: mode choice module object as defined in the IPython notebook
:param taz_fn: TAZ file that contains subregion definition
:param out_path: output path.
'''
subregion_dict = dict(zip(['boston','neighboring','i93','i495','region'],['Within Boston','Boston and Neighboring Towns', 'Within I-93/95', 'Within I-495', 'Entire Region']))
smra_vmt_summary_df = pd.DataFrame(index = subregion_dict.values(), columns = ['VMT to/from Boston'])
smsh_vmt_summary_df = pd.DataFrame(index = subregion_dict.values(), columns = ['VMT to/from Boston'])
smra_trip_summary_df = pd.DataFrame(index = subregion_dict.values(),columns = ['Trips to/from Boston'])
smsh_trip_summary_df = pd.DataFrame(index = subregion_dict.values(),columns = ['Trips to/from Boston'])
for subregion in subregion_dict:
smra_vmt_summary_df.loc[subregion_dict[subregion]] = __sm_compute_summary_by_subregion(mc_obj, metric = 'VMT',subregion = subregion, sm_mode='SM_RA')
smsh_vmt_summary_df.loc[subregion_dict[subregion]] = __sm_compute_summary_by_subregion(mc_obj, metric = 'VMT',subregion = subregion, sm_mode='SM_SH')
smra_trip_summary_df.loc[subregion_dict[subregion]] = __sm_compute_summary_by_subregion(mc_obj, metric = 'trip',subregion = subregion, sm_mode='SM_RA')
smsh_trip_summary_df.loc[subregion_dict[subregion]] = __sm_compute_summary_by_subregion(mc_obj, metric = 'trip',subregion = subregion, sm_mode='SM_SH')
smra_vmt_summary_df.to_csv(mc_obj.config.out_path + 'sm_ra_vmt_summary_subregions.csv')
smsh_vmt_summary_df.to_csv(mc_obj.config.out_path + 'sm_sh_vmt_summary_subregions.csv')
smra_trip_summary_df.to_csv(mc_obj.config.out_path + 'sm_ra_trip_summary_subregions.csv')
smsh_trip_summary_df.to_csv(mc_obj.config.out_path + 'sm_sh_trip_summary_subregions.csv')
def transit_ridership(mc_obj, by='all'):
'''
Summarizes transit ridership by peak period in cities and towns with MBTA subway service.
:param mc_obj: mode choice module object as defined in the IPython notebook
:param mbta_fn: TAZ file that contains MBTA coverage definition
:param out_path: output path.
'''
MBTA_fn =mc_obj.config.data_path + "..\MBTA_coverage.csv"
MBTA_cvg = pd.read_csv(MBTA_fn)
taz_cvg = mc_obj.taz.merge(MBTA_cvg, how = 'left', on = 'TOWN')
taz_cvg = taz_cvg[['ID_FOR_CS','subway','TOWN']]
taz_cvg['covered'] = taz_cvg['subway']==1 # 870 TAZs included.
ridership_master = pd.DataFrame(columns=['region','subway'])
for purpose in md.purposes:
for peak in ['PK','OP']:
for veh_own in ['0','1']:
ridership = pd.DataFrame(index=range(0,2),columns=['region','subway']).fillna(0)
if mc_obj.table_container.get_table(purpose):
for mode in set(mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'])&set(md.transit_modes):
boston_ii = mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode][(taz_cvg['TOWN']=='BOSTON,MA'),:][:,(taz_cvg['TOWN']=='BOSTON,MA')].sum()
ridership['subway'] += (mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode][taz_cvg['covered'],:][:,(taz_cvg['TOWN']=='BOSTON,MA')].sum() +
mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode][(taz_cvg['TOWN']=='BOSTON,MA'),:][:,taz_cvg['covered']].sum() -
boston_ii)
ridership['region'] += (mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode][:][:,(taz_cvg['TOWN']=='BOSTON,MA')].sum() +
mc_obj.table_container.get_table(purpose)[f'{veh_own}_{peak}'][mode][(taz_cvg['TOWN']=='BOSTON,MA'),:].sum() - boston_ii)
ridership['peak'] = peak
ridership_master = ridership_master.append(ridership.reset_index(), sort = True)
#ridership_summary = ridership_master.groupby(['peak']).sum()
# calculate ridership
ridership_master.groupby('peak').sum().to_csv(mc_obj.config.out_path + 'transit_ridership_summary.csv')
| 53.339985
| 236
| 0.603292
| 9,677
| 69,502
| 4.043919
| 0.036582
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| 69,502
| 1,303
| 237
| 53.339985
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| 0
|
0
| 7
|
88702287606a32aefbfe0ea35407f6ec6cc98dcc
| 2,529
|
py
|
Python
|
test/data/example_request.py
|
minhoryang/clova-cek-sdk-python
|
0f001f5a0f6d6428640cfe31ad7fad91806ab1fd
|
[
"Apache-2.0"
] | null | null | null |
test/data/example_request.py
|
minhoryang/clova-cek-sdk-python
|
0f001f5a0f6d6428640cfe31ad7fad91806ab1fd
|
[
"Apache-2.0"
] | null | null | null |
test/data/example_request.py
|
minhoryang/clova-cek-sdk-python
|
0f001f5a0f6d6428640cfe31ad7fad91806ab1fd
|
[
"Apache-2.0"
] | null | null | null |
# coding: utf-8
# Copyright 2018 LINE Corporation
#
# LINE Corporation licenses this file to you 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:
#
# https://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.
REQUEST_BODY = b'{"version":"1.0","session":{"sessionId":"73ed88b7-5219-4ca0-9467-e44f852cafc1","user":{"userId":"as-CA80nSomiFI-LAg2u6w","accessToken":"5cc2949c-900c-48a0-a31b-c9544a042377"},"new":true},"context":{"System":{"user":{"userId":"as-CA80nSomiFI-LAg2u6w","accessToken":"5cc2949c-900c-48a0-a31b-c9544a042377"},"device":{"deviceId":"1c62230d-af17-47c5-941b-07ec252c22a4","display":{"size":"l100","orientation":"landscape","dpi":96,"contentLayer":{"width":640,"height":360}}}}},"request":{"type":"IntentRequest","intent":{"name":"Clova.GuideIntent","slots":null}}}'
REQUEST_SIGNATURE = 'rXQ9Bs4Ngj79ZjcjgcQRPc2YUOD+H+U5CV3NnFdKneCXfYLN8hy0PrAj+H0j38BSIeWU6wHJTQf+xEO0xBDuNXbG/hlnQsy2kOFg8U7D2wfopSJ2Tgn/65AmaRs1CSpxRDoLrDyd0kHsLzNfs6MVlb/t+qvOf6WdMo24Ad4f04wtQxd7sS/SWFMNIXdty8VolviYnAjENYPV+bUm4DesJYjBSMLRZcUrAAfNIq+frD25IGAR3Nry85F0DmCLJPk4UgWI/IeKTGsyrkJe+/oH7m6ymkNZRiVxDzEkQgtoD9Vtv2HAiL3B/G95BTWIz4CBZWw6CNsSkrqmjR2VxFMVrw=='
WRONG_REQUEST_BODY = b'{"version":"1.0","session":{"sessionId":"83ed88b7-5219-4ca0-9467-e44f852cafc1","user":{"userId":"as-CA80nSomiFI-LAg2u6w","accessToken":"5cc2949c-900c-48a0-a31b-c9544a042377"},"new":true},"context":{"System":{"user":{"userId":"as-CA80nSomiFI-LAg2u6w","accessToken":"5cc2949c-900c-48a0-a31b-c9544a042377"},"device":{"deviceId":"1c62230d-af17-47c5-941b-07ec252c22a4","display":{"size":"l100","orientation":"landscape","dpi":96,"contentLayer":{"width":640,"height":360}}}}},"request":{"type":"IntentRequest","intent":{"name":"Clova.GuideIntent","slots":null}}}'
WRONG_REQUEST_SIGNATURE = 'sXQ9Bs4Ngj79ZjcjgcQRPc2YUOD+H+U5CV3NnFdKneCXfYLN8hy0PrAj+H0j38BSIeWU6wHJTQf+xEO0xBDuNXbG/hlnQsy2kOFg8U7D2wfopSJ2Tgn/65AmaRs1CSpxRDoLrDyd0kHsLzNfs6MVlb/t+qvOf6WdMo24Ad4f04wtQxd7sS/SWFMNIXdty8VolviYnAjENYPV+bUm4DesJYjBSMLRZcUrAAfNIq+frD25IGAR3Nry85F0DmCLJPk4UgWI/IeKTGsyrkJe+/oH7m6ymkNZRiVxDzEkQgtoD9Vtv2HAiL3B/G95BTWIz4CBZWw6CNsSkrqmjR2VxFMVrw=='
| 114.954545
| 580
| 0.792013
| 269
| 2,529
| 7.423792
| 0.505576
| 0.030045
| 0.024036
| 0.046069
| 0.702053
| 0.702053
| 0.702053
| 0.702053
| 0.664998
| 0.664998
| 0
| 0.136628
| 0.047845
| 2,529
| 21
| 581
| 120.428571
| 0.692691
| 0.237643
| 0
| 0
| 0
| 1
| 0.94093
| 0.94093
| 0
| 0
| 0
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| 0
| 1
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| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
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| 0
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| 1
| 1
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| 1
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| 0
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|
0
| 8
|
8896e624cd117946167fa3dc17fa7a53a971b501
| 41,344
|
py
|
Python
|
venv/Lib/site-packages/nbdime/tests/test_merge_notebooks_inline.py
|
PeerHerholz/guideline_jupyter_book
|
ce445e4be0d53370b67708a22550565b90d71ac6
|
[
"BSD-3-Clause"
] | 2
|
2021-02-16T16:17:07.000Z
|
2021-11-08T20:27:13.000Z
|
venv/Lib/site-packages/nbdime/tests/test_merge_notebooks_inline.py
|
PeerHerholz/guideline_jupyter_book
|
ce445e4be0d53370b67708a22550565b90d71ac6
|
[
"BSD-3-Clause"
] | null | null | null |
venv/Lib/site-packages/nbdime/tests/test_merge_notebooks_inline.py
|
PeerHerholz/guideline_jupyter_book
|
ce445e4be0d53370b67708a22550565b90d71ac6
|
[
"BSD-3-Clause"
] | 4
|
2020-11-14T17:05:36.000Z
|
2020-11-16T18:44:54.000Z
|
# coding: utf-8
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
import pytest
import nbformat
from nbformat.v4 import new_notebook, new_code_cell
from collections import defaultdict
from nbdime import merge_notebooks, diff
from nbdime.diff_format import op_patch
from nbdime.utils import Strategies
from nbdime.merging.generic import decide_merge, decide_merge_with_diff
from nbdime.merging.decisions import apply_decisions
from nbdime.merging.strategies import _cell_marker_format
from .utils import outputs_to_notebook, sources_to_notebook
def test_decide_merge_strategy_fail(reset_log):
"""Check that "fail" strategy results in proper exception raised."""
# One level dict
base = {"foo": 1}
local = {"foo": 2}
remote = {"foo": 3}
strategies = Strategies({"/foo": "fail"})
with pytest.raises(RuntimeError):
# pylint: disable=unused-variable
conflicted_decisions = decide_merge(base, local, remote, strategies)
# Nested dicts
base = {"foo": {"bar": 1}}
local = {"foo": {"bar": 2}}
remote = {"foo": {"bar": 3}}
strategies = Strategies({"/foo/bar": "fail"})
with pytest.raises(RuntimeError):
# pylint: disable=unused-variable
decisions = decide_merge(base, local, remote, strategies)
# We don't need this for non-leaf nodes and it's currently not implemented
# strategies = Strategies({"/foo": "fail"})
# with pytest.raises(RuntimeError):
# decisions = decide_merge(base, local, remote, strategies)
def test_decide_merge_strategy_clear1():
"""Check strategy "clear" in various cases."""
# One level dict, clearing item value (think foo==execution_count)
base = {"foo": 1}
local = {"foo": 2}
remote = {"foo": 3}
strategies = Strategies({"/foo": "clear"})
decisions = decide_merge(base, local, remote, strategies)
assert apply_decisions(base, decisions) == {"foo": None}
assert not any([d.conflict for d in decisions])
def test_decide_merge_strategy_clear2():
base = {"foo": "1"}
local = {"foo": "2"}
remote = {"foo": "3"}
strategies = Strategies({"/foo": "clear"})
decisions = decide_merge(base, local, remote, strategies)
#assert decisions == []
assert apply_decisions(base, decisions) == {"foo": ""}
assert not any([d.conflict for d in decisions])
# We don't need this for non-leaf nodes and it's currently not implemented
# base = {"foo": [1]}
# local = {"foo": [2]}
# remote = {"foo": [3]}
# strategies = Strategies({"/foo": "clear"})
# decisions = decide_merge(base, local, remote, strategies)
# assert apply_decisions(base, decisions) == {"foo": []}
# assert not any([d.conflict for d in decisions])
def test_decide_merge_strategy_clear_all():
base = {"foo": [1, 2]}
local = {"foo": [1, 4, 2]}
remote = {"foo": [1, 3, 2]}
strategies = Strategies({"/foo": "clear-all"})
decisions = decide_merge(base, local, remote, strategies)
assert apply_decisions(base, decisions) == {"foo": []}
base = {"foo": [1, 2]}
local = {"foo": [1, 4, 2]}
remote = {"foo": [1, 2, 3]}
strategies = Strategies({"/foo": "clear-all"})
decisions = decide_merge(base, local, remote, strategies)
assert apply_decisions(base, decisions) == {"foo": [1, 4, 2, 3]}
def test_decide_merge_strategy_remove():
base = {"foo": [1, 2]}
local = {"foo": [1, 4, 2]}
remote = {"foo": [1, 3, 2]}
strategies = Strategies({"/foo": "remove"})
decisions = decide_merge(base, local, remote, strategies)
assert apply_decisions(base, decisions) == {"foo": [1, 2]}
assert decisions[0].local_diff != []
assert decisions[0].remote_diff != []
strategies = Strategies({})
decisions = decide_merge(base, local, remote, strategies)
assert apply_decisions(base, decisions) == {"foo": [1, 2]}
assert decisions[0].local_diff != []
assert decisions[0].remote_diff != []
def test_decide_merge_strategy_use_foo_on_dict_items():
base = {"foo": 1}
local = {"foo": 2}
remote = {"foo": 3}
strategies = Strategies({"/foo": "use-base"})
decisions = decide_merge(base, local, remote, strategies)
assert not any([d.conflict for d in decisions])
assert apply_decisions(base, decisions) == {"foo": 1}
strategies = Strategies({"/foo": "use-local"})
decisions = decide_merge(base, local, remote, strategies)
assert not any([d.conflict for d in decisions])
assert apply_decisions(base, decisions) == {"foo": 2}
strategies = Strategies({"/foo": "use-remote"})
decisions = decide_merge(base, local, remote, strategies)
assert not any([d.conflict for d in decisions])
assert apply_decisions(base, decisions) == {"foo": 3}
base = {"foo": {"bar": 1}}
local = {"foo": {"bar": 2}}
remote = {"foo": {"bar": 3}}
strategies = Strategies({"/foo/bar": "use-base"})
decisions = decide_merge(base, local, remote, strategies)
assert not any([d.conflict for d in decisions])
assert apply_decisions(base, decisions) == {"foo": {"bar": 1}}
strategies = Strategies({"/foo/bar": "use-local"})
decisions = decide_merge(base, local, remote, strategies)
assert not any([d.conflict for d in decisions])
assert apply_decisions(base, decisions) == {"foo": {"bar": 2}}
strategies = Strategies({"/foo/bar": "use-remote"})
decisions = decide_merge(base, local, remote, strategies)
assert not any([d.conflict for d in decisions])
assert apply_decisions(base, decisions) == {"foo": {"bar": 3}}
def test_decide_merge_simple_list_insert_conflict_resolution():
# local and remote adds an entry each
b = [1]
l = [1, 2]
r = [1, 3]
strategies = Strategies({"/*": "use-local"})
decisions = decide_merge(b, l, r, strategies)
assert apply_decisions(b, decisions) == l
assert not any(d.conflict for d in decisions)
strategies = Strategies({"/*": "use-remote"})
decisions = decide_merge(b, l, r, strategies)
assert apply_decisions(b, decisions) == r
assert not any(d.conflict for d in decisions)
strategies = Strategies({"/*": "use-base"})
decisions = decide_merge(b, l, r, strategies)
assert apply_decisions(b, decisions) == b
assert not any(d.conflict for d in decisions)
strategies = Strategies({"/": "clear-all"})
decisions = decide_merge(b, l, r, strategies)
assert apply_decisions(b, decisions) == []
assert not any(d.conflict for d in decisions)
@pytest.mark.skip
def test_decide_merge_simple_list_insert_conflict_resolution__union():
# local and remote adds an entry each
b = [1]
l = [1, 2]
r = [1, 3]
strategies = Strategies({"/": "union"})
decisions = decide_merge(b, l, r, strategies)
assert apply_decisions(b, decisions) == [1, 2, 3]
assert not any(d.conflict for d in decisions)
def test_decide_merge_list_conflicting_insertions_separate_chunks_v1():
# local and remote adds an equal entry plus a different entry each
# First, test when insertions DO NOT chunk together:
b = [1, 9]
l = [1, 2, 9, 11]
r = [1, 3, 9, 11]
# Check strategyless resolution
strategies = Strategies({})
resolved = decide_merge(b, l, r, strategies)
expected_partial = [1, 9, 11]
assert apply_decisions(b, resolved) == expected_partial
assert len(resolved) == 2
assert resolved[0].conflict
assert not resolved[1].conflict
strategies = Strategies({"/*": "use-local"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == l
assert not any(d.conflict for d in resolved)
strategies = Strategies({"/*": "use-remote"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == r
assert not any(d.conflict for d in resolved)
strategies = Strategies({"/*": "use-base"})
resolved = decide_merge(b, l, r, strategies)
# Strategy is only applied to conflicted decisions:
assert apply_decisions(b, resolved) == expected_partial
assert not any(d.conflict for d in resolved)
strategies = Strategies({"/": "clear-all"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == []
assert not any(d.conflict for d in resolved)
# from _merge_concurrent_inserts:
# FIXME: This function doesn't work out so well with new conflict handling,
# when an insert (e.g. [2,7] vs [3,7]) gets split into agreement on [7] and
# conflict on [2] vs [3], the ordering gets lost. I think this was always
# slightly ambiguous in the decision format, as the new inserts will get
# the same key and decisions are supposed to be possible to reorder (sort)
# without considering original ordering of decisions. To preserve the
# ordering, perhaps we can add relative local/remote indices to the decisions?
# We had this, where ordering made it work out correctly:
# "conflicting insert [2] vs [3] at 1 (base index);
# insert [7] at 1 (base index)"
# instead we now have this which messes up the ordering:
# "insert [7] at 1 (base index);
# conflicting insert [2] vs [3] at 1 (base index)"
# perhaps change to this:
# "insert [7] at key=1 (base index) lkey=1 rkey=1;
# conflicting insert [2] vs [3] at key=1 (base index) lkey=0 rkey=0"
# then decisions can be sorted on (key,lkey) or (key,rkey) depending on chosen side.
# This test covers the behaviour:
# py.test -k test_shallow_merge_lists_insert_conflicted -s -vv
#DEBUGGING = 1
#if DEBUGGING: import ipdb; ipdb.set_trace()
# Example:
# b l r
# 1 a x
# 2 b y
# 3 c 3
# 4 4 4
# Diffs:
# b/l: insert a, b, c; remove 1-3
# b/r: insert x, y; remove 1-2
# The current chunking splits the removes here:
# [insert a, b, c; remove 1-2]; [remove 3]
# [insert x, y; remove 1-2]
# That results in remove 3 not being conflicted.
def test_decide_merge_list_conflicting_insertions_separate_chunks_v2():
# local and remote adds an equal entry plus a different entry each
# First, test when insertions DO NOT chunk together:
b = [1, 9]
l = [1, 2, 9, 11]
r = [1, 3, 9, 11]
# Check strategyless resolution
strategies = Strategies({})
resolved = decide_merge(b, l, r, strategies)
expected_partial = [1, 9, 11]
assert apply_decisions(b, resolved) == expected_partial
assert len(resolved) == 2
assert resolved[0].conflict
assert not resolved[1].conflict
@pytest.mark.skip
def test_decide_merge_list_conflicting_insertions_separate_chunks__union():
# local and remote adds an equal entry plus a different entry each
# First, test when insertions DO NOT chunk together:
b = [1, 9]
l = [1, 2, 9, 11]
r = [1, 3, 9, 11]
strategies = Strategies({"/": "union"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == [1, 2, 3, 9, 11]
assert not any(d.conflict for d in resolved)
def test_decide_merge_list_conflicting_insertions_in_chunks():
# Next, test when insertions DO chunk together:
b = [1, 9]
l = [1, 2, 7, 9]
r = [1, 3, 7, 9]
# Check strategyless resolution
strategies = Strategies({})
resolved = decide_merge(b, l, r, strategies)
expected_partial = [1, 7, 9]
assert apply_decisions(b, resolved) == expected_partial
strategies = Strategies({"/*": "use-local"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == l
assert not any(d.conflict for d in resolved)
strategies = Strategies({"/*": "use-remote"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == r
assert not any(d.conflict for d in resolved)
strategies = Strategies({"/*": "use-base"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == expected_partial
assert not any(d.conflict for d in resolved)
strategies = Strategies({"/": "clear-all"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == []
assert not any(d.conflict for d in resolved)
@pytest.mark.skip
def test_decide_merge_list_conflicting_insertions_in_chunks__union():
# Next, test when insertions DO chunk together:
b = [1, 9]
l = [1, 2, 7, 9]
r = [1, 3, 7, 9]
strategies = Strategies({"/": "union"})
resolved = decide_merge(b, l, r, strategies)
assert apply_decisions(b, resolved) == [1, 2, 3, 7, 9]
assert not any(d.conflict for d in resolved)
def test_decide_merge_list_transients():
# For this test, we need to use a custom predicate to ensure alignment
common = {'id': 'This ensures alignment'}
predicates = defaultdict(lambda: [operator.__eq__], {
'/': [lambda a, b: a['id'] == b['id']],
})
# Setup transient difference in base and local, deletion in remote
b = [{'transient': 22}]
l = [{'transient': 242}]
b[0].update(common)
l[0].update(common)
r = []
# Make decisions based on diffs with predicates
ld = diff(b, l, path="", predicates=predicates)
rd = diff(b, r, path="", predicates=predicates)
# Assert that generic merge without strategies gives conflict:
strategies = Strategies()
decisions = decide_merge_with_diff(b, l, r, ld, rd, strategies)
assert len(decisions) == 1
assert decisions[0].conflict
assert apply_decisions(b, decisions) == b
# Supply transient list to autoresolve, and check that transient is ignored
strategies = Strategies(transients=[
'/*/transient'
])
decisions = decide_merge_with_diff(b, l, r, ld, rd, strategies)
assert apply_decisions(b, decisions) == r
assert not any(d.conflict for d in decisions)
def test_decide_merge_dict_transients():
# Setup transient difference in base and local, deletion in remote
b = {'a': {'transient': 22}}
l = {'a': {'transient': 242}}
r = {}
# Assert that generic merge gives conflict
strategies = Strategies()
decisions = decide_merge(b, l, r, strategies)
assert apply_decisions(b, decisions) == b
assert len(decisions) == 1
assert decisions[0].conflict
# Supply transient list to autoresolve, and check that transient is ignored
strategies = Strategies(transients=[
'/a/transient'
])
decisions = decide_merge(b, l, r, strategies)
assert apply_decisions(b, decisions) == r
assert not any(d.conflict for d in decisions)
def test_decide_merge_mixed_nested_transients():
# For this test, we need to use a custom predicate to ensure alignment
common = {'id': 'This ensures alignment'}
predicates = defaultdict(lambda: [operator.__eq__], {
'/': [lambda a, b: a['id'] == b['id']],
})
# Setup transient difference in base and local, deletion in remote
b = [{'a': {'transient': 22}}]
l = [{'a': {'transient': 242}}]
b[0].update(common)
l[0].update(common)
r = []
# Make decisions based on diffs with predicates
ld = diff(b, l, path="", predicates=predicates)
rd = diff(b, r, path="", predicates=predicates)
# Assert that generic merge gives conflict
strategies = Strategies()
decisions = decide_merge_with_diff(b, l, r, ld, rd, strategies)
assert apply_decisions(b, decisions) == b
assert len(decisions) == 1
assert decisions[0].conflict
# Supply transient list to autoresolve, and check that transient is ignored
strategies = Strategies(transients=[
'/*/a/transient'
])
decisions = decide_merge_with_diff(b, l, r, ld, rd, strategies)
assert apply_decisions(b, decisions) == r
assert not any(d.conflict for d in decisions)
def test_inline_merge_empty_notebooks():
"Missing fields all around passes through."
base = {}
local = {}
remote = {}
expected = {}
merged, decisions = merge_notebooks(base, local, remote)
assert expected == merged
def test_inline_merge_dummy_notebooks():
"Just the basic empty notebook passes through."
base = new_notebook()
local = new_notebook()
remote = new_notebook()
expected = new_notebook()
merged, decisions = merge_notebooks(base, local, remote)
assert expected == merged
def test_inline_merge_notebook_version():
"Minor version gets bumped to max."
base = new_notebook(nbformat=4, nbformat_minor=0)
local = new_notebook(nbformat=4, nbformat_minor=1)
remote = new_notebook(nbformat=4, nbformat_minor=2)
expected = new_notebook(nbformat=4, nbformat_minor=2)
merged, decisions = merge_notebooks(base, local, remote)
assert expected == merged
def test_inline_merge_notebook_metadata(reset_log):
"""Merging a wide range of different value types
and conflict types in the root /metadata dicts.
The goal is to exercise a decent part of the
generic diff and merge functionality.
"""
untouched = {
"string": "untouched string",
"integer": 123,
"float": 16.0,
"list": ["hello", "world"],
"dict": {"first": "Hello", "second": "World"},
}
md_in = {
1: {
"untouched": untouched,
"unconflicted": {
"int_deleteme": 7,
"string_deleteme": "deleteme",
"list_deleteme": [7, "deleteme"],
"dict_deleteme": {"deleteme": "now", "removeme": True},
"list_deleteitem": [7, "deleteme", 3, "notme", 5, "deletemetoo"],
"string": "string v1",
"integer": 456,
"float": 32.0,
"list": ["hello", "universe"],
"dict": {"first": "Hello", "second": "World", "third": "!"},
},
"conflicted": {
"int_delete_replace": 3,
"string_delete_replace": "string that will be deleted and modified",
"list_delete_replace": [1],
"dict_delete_replace": {"k":"v"},
# "string": "string v1",
# "integer": 456,
# "float": 32.0,
# "list": ["hello", "universe"],
# "dict": {"first": "Hello", "second": "World"},
}
},
2: {
"untouched": untouched,
"unconflicted": {
"dict_deleteme": {"deleteme": "now", "removeme": True},
"list_deleteitem": [7, 3, "notme", 5, "deletemetoo"],
"string": "string v1 equal addition",
"integer": 123, # equal change
"float": 16.0, # equal change
# Equal delete at beginning and insert of two values at end:
"list": ["universe", "new items", "same\non\nboth\nsides"],
# cases covered: twosided equal value change, onesided delete, onesided replace, onesided insert, twosided insert of same value
"dict": {"first": "changed", "second": "World", "third": "!", "newkey": "newvalue", "otherkey": "othervalue"},
},
"conflicted": {
"int_delete_replace": 5,
"list_delete_replace": [2],
# "string": "another text",
#"integer": 456,
# "float": 16.0,
# "list": ["hello", "world"],
# "dict": {"new": "value", "first": "Hello"}, #"second": "World"},
# "added_string": "another text",
# "added_integer": 9,
# "added_float": 16.0,
# "added_list": ["another", "multiverse"],
# "added_dict": {"1st": "hey", "2nd": "there"},
}
},
3: {
"untouched": untouched,
"unconflicted": {
"list_deleteme": [7, "deleteme"],
"list_deleteitem": [7, "deleteme", 3, "notme", 5],
"string": "string v1 equal addition",
"integer": 123, # equal change
"float": 16.0, # equal change
# Equal delete at beginning and insert of two values at end:
"list": ["universe", "new items", "same\non\nboth\nsides"],
"dict": {"first": "changed", "third": ".", "newkey": "newvalue"},
},
"conflicted": {
"string_delete_replace": "string that is modified here and deleted in the other version",
"dict_delete_replace": {"k":"x","q":"r"},
# "string": "different message",
# "integer": 456,
# #"float": 16.0,
# "list": ["hello", "again", "world"],
# "dict": {"new": "but different", "first": "Hello"}, #"second": "World"},
# "added_string": "but not the same string",
# #"added_integer": 9,
# "added_float": 64.0,
# "added_list": ["initial", "values", "another", "multiverse", "trailing", "values"],
# "added_dict": {"3rt": "mergeme", "2nd": "conflict"},
}
}
}
def join_dicts(dicta, dictb):
d = {}
d.update(dicta)
d.update(dictb)
return d
shared_unconflicted = {
"list_deleteitem": [7, 3, "notme", 5],
"string": "string v1 equal addition",
"integer": 123,
"float": 16.0,
"list": ["universe", "new items", "same\non\nboth\nsides"],
"dict": {"first": "changed", "third": ".", "newkey": "newvalue", "otherkey": "othervalue"},
}
shared_conflicted = {
"int_delete_replace": 3,
"string_delete_replace": "string that will be deleted and modified",
"list_delete_replace": [1],
"dict_delete_replace": {"k":"v"},
# #"string": "string v1",
# "string": "another textdifferent message",
# "float": 32.0,
# "list": ["hello", "universe"],
# "dict": {"first": "Hello", "second": "World"},
# # FIXME
}
md_out = {
(1,2,3): {
"untouched": untouched,
"unconflicted": join_dicts(shared_unconflicted, {
# ...
}),
"conflicted": join_dicts(shared_conflicted, {
# ...
}),
},
(1,3,2): {
"untouched": untouched,
"unconflicted": join_dicts(shared_unconflicted, {
# ...
}),
"conflicted": join_dicts(shared_conflicted, {
# ...
}),
},
}
# Fill in expected conflict records
for triplet in sorted(md_out.keys()):
i, j, k = triplet
local_diff = diff(md_in[i]["conflicted"], md_in[j]["conflicted"])
remote_diff = diff(md_in[i]["conflicted"], md_in[k]["conflicted"])
# This may not be a necessary test, just checking my expectations
assert local_diff == sorted(local_diff, key=lambda x: x.key)
assert remote_diff == sorted(remote_diff, key=lambda x: x.key)
c = {
# These are patches on the /metadata dict
"local_diff": [op_patch("conflicted", local_diff)],
"remote_diff": [op_patch("conflicted", remote_diff)],
}
md_out[triplet]["nbdime-conflicts"] = c
# Fill in the trivial merge results
for i in (1, 2, 3):
for j in (1, 2, 3):
for k in (i, j):
# For any combination i,j,i or i,j,j the
# result should be j with no conflicts
md_out[(i,j,k)] = md_in[j]
tested = set()
# Check the trivial merge results
for i in (1, 2, 3):
for j in (1, 2, 3):
for k in (i, j):
triplet = (i, j, k)
tested.add(triplet)
base = new_notebook(metadata=md_in[i])
local = new_notebook(metadata=md_in[j])
remote = new_notebook(metadata=md_in[k])
# For any combination i,j,i or i,j,j the result should be j
expected = new_notebook(metadata=md_in[j])
merged, decisions = merge_notebooks(base, local, remote)
assert "nbdime-conflicts" not in merged["metadata"]
assert not any([d.conflict for d in decisions])
assert expected == merged
# Check handcrafted merge results
for triplet in sorted(md_out.keys()):
i, j, k = triplet
tested.add(triplet)
base = new_notebook(metadata=md_in[i])
local = new_notebook(metadata=md_in[j])
remote = new_notebook(metadata=md_in[k])
expected = new_notebook(metadata=md_out[triplet])
merged, decisions = merge_notebooks(base, local, remote)
if "nbdime-conflicts" in merged["metadata"]:
assert any([d.conflict for d in decisions])
else:
assert not any([d.conflict for d in decisions])
assert expected == merged
# At least try to run merge without crashing for permutations
# of md_in that we haven't constructed expected results for
for i in (1, 2, 3):
for j in (1, 2, 3):
for k in (1, 2, 3):
triplet = (i, j, k)
if triplet not in tested:
base = new_notebook(metadata=md_in[i])
local = new_notebook(metadata=md_in[j])
remote = new_notebook(metadata=md_in[k])
merged, decisions = merge_notebooks(base, local, remote)
def test_inline_merge_notebook_metadata_reproduce_bug(reset_log):
md_in = {
1: {
"unconflicted": {
"list_deleteitem": [7, "deleteme", 3, "notme", 5, "deletemetoo"],
},
"conflicted": {
"dict_delete_replace": {"k":"v"},
}
},
2: {
"unconflicted": {
"list_deleteitem": [7, 3, "notme", 5, "deletemetoo"],
},
"conflicted": {
}
},
3: {
"unconflicted": {
"list_deleteitem": [7, "deleteme", 3, "notme", 5],
},
"conflicted": {
"dict_delete_replace": {"k":"x"},
}
}
}
shared_unconflicted = {
"list_deleteitem": [7, 3, "notme", 5],
}
shared_conflicted = {
"dict_delete_replace": {"k":"v"},
}
md_out = {
(1,2,3): {
"unconflicted": shared_unconflicted,
"conflicted": shared_conflicted
},
}
# Fill in expected conflict records
for triplet in sorted(md_out.keys()):
i, j, k = triplet
local_diff = diff(md_in[i]["conflicted"], md_in[j]["conflicted"])
remote_diff = diff(md_in[i]["conflicted"], md_in[k]["conflicted"])
# This may not be a necessary test, just checking my expectations
assert local_diff == sorted(local_diff, key=lambda x: x.key)
assert remote_diff == sorted(remote_diff, key=lambda x: x.key)
c = {
# These are patches on the /metadata dict
"local_diff": [op_patch("conflicted", local_diff)],
"remote_diff": [op_patch("conflicted", remote_diff)],
}
md_out[triplet]["nbdime-conflicts"] = c
# Check handcrafted merge results
triplet = (1,2,3)
i, j, k = triplet
base = new_notebook(metadata=md_in[i])
local = new_notebook(metadata=md_in[j])
remote = new_notebook(metadata=md_in[k])
expected = new_notebook(metadata=md_out[triplet])
merged, decisions = merge_notebooks(base, local, remote)
if "nbdime-conflicts" in merged["metadata"]:
assert any([d.conflict for d in decisions])
else:
assert not any([d.conflict for d in decisions])
assert expected == merged
def test_inline_merge_source_empty():
base = new_notebook()
local = new_notebook()
remote = new_notebook()
expected = new_notebook()
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
def code_nb(sources):
return new_notebook(cells=[new_code_cell(s) for s in sources])
def test_inline_merge_source_all_equal():
base = code_nb([
"first source",
"other text",
"yet more content",
])
local = base
remote = base
expected = base
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
def test_inline_merge_source_cell_deletions():
"Cell deletions on both sides, onesided and agreed."
base = code_nb([
"first source",
"other text",
"yet more content",
"and a final line",
])
local = code_nb([
#"first source",
"other text",
#"yet more content",
#"and a final line",
])
remote = code_nb([
"first source",
#"other text",
"yet more content",
#"and a final line",
])
empty = code_nb([])
for a in [base, local, remote, empty]:
for b in [base, local, remote, empty]:
merged, decisions = merge_notebooks(base, a, b)
if a is b:
assert merged == a
elif a is base:
assert merged == b
elif b is base:
assert merged == a
else:
# All other combinations will delete all cells
assert merged == empty
def test_inline_merge_source_onesided_only():
"A mix of changes on one side (delete, patch, remove)."
base = code_nb([
"first source",
"other text",
"yet more content",
])
changed = code_nb([
#"first source", # deleted
"other text v2",
"a different cell inserted",
"yet more content",
])
merged, decisions = merge_notebooks(base, changed, base)
assert merged == changed
merged, decisions = merge_notebooks(base, base, changed)
assert merged == changed
def test_inline_merge_source_replace_line():
"More elaborate test of cell deletions on both sides, onesided and agreed."
# Note: Merge rendering of conflicted sources here will depend on git/diff/builtin params and availability
base = code_nb([
"first source",
"other text",
"this cell will be deleted and patched",
"yet more content",
"and a final line",
])
local = code_nb([
"1st source", # onesided change
"other text",
#"this cell will be deleted and patched",
"some more content", # twosided equal change
"And a Final line", # twosided conflicted change
])
remote = code_nb([
"first source",
"other text?", # onesided change
"this cell will be deleted and modified",
"some more content", # equal
"and The final Line", # conflicted
])
expected = code_nb([
"1st source",
"other text?",
#'<<<<<<< local <CELL DELETED>\n\n=======\nthis cell will be deleted and modified\n>>>>>>> remote'
'<<<<<<< LOCAL CELL DELETED >>>>>>>\nthis cell will be deleted and modified',
"some more content", # equal
'<<<<<<< local\nAnd a Final line\n=======\nand The final Line\n>>>>>>> remote'
])
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
expected = code_nb([
"1st source",
"other text?",
#'<<<<<<< local\nthis cell will be deleted and modified\n=======\n>>>>>>> remote <CELL DELETED>'
'<<<<<<< REMOTE CELL DELETED >>>>>>>\nthis cell will be deleted and modified',
"some more content",
'<<<<<<< local\nand The final Line\n=======\nAnd a Final line\n>>>>>>> remote'
])
merged, decisions = merge_notebooks(base, remote, local)
assert merged == expected
def test_inline_merge_source_add_to_line():
"More elaborate test of cell deletions on both sides, onesided and agreed."
# Note: Merge rendering of conflicted sources here will depend on git/diff/builtin params and availability
base = code_nb([
"first source",
"other text",
"this cell will be deleted and patched\nhere we add",
"yet more content",
"and a final line",
])
local = code_nb([
"1st source", # onesided change
"other text",
#"this cell will be deleted and patched",
"some more content", # twosided equal change
"And a Final line", # twosided conflicted change
])
remote = code_nb([
"first source",
"other text?", # onesided change
"this cell will be deleted and patched\nhere we add text to a line",
"some more content", # equal
"and The final Line", # conflicted
])
expected = code_nb([
"1st source",
"other text?",
#'<<<<<<< local <CELL DELETED>\n\n=======\nthis cell will be deleted and modified\n>>>>>>> remote'
'<<<<<<< LOCAL CELL DELETED >>>>>>>\nthis cell will be deleted and patched\nhere we add text to a line',
"some more content", # equal
'<<<<<<< local\nAnd a Final line\n=======\nand The final Line\n>>>>>>> remote'
])
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
expected = code_nb([
"1st source",
"other text?",
#'<<<<<<< local\nthis cell will be deleted and modified\n=======\n>>>>>>> remote <CELL DELETED>'
'<<<<<<< REMOTE CELL DELETED >>>>>>>\nthis cell will be deleted and patched\nhere we add text to a line',
"some more content",
'<<<<<<< local\nand The final Line\n=======\nAnd a Final line\n>>>>>>> remote'
])
merged, decisions = merge_notebooks(base, remote, local)
assert merged == expected
def test_inline_merge_source_patches_both_ends():
"More elaborate test of cell deletions on both sides, onesided and agreed."
# Note: Merge rendering of conflicted sources here will depend on git/diff/builtin params and availability
base = code_nb([
"first source will be modified",
"other text",
"this cell will be untouched",
"yet more content",
"and final line will be changed",
])
local = code_nb([
"first source will be modified locally",
"other text",
"this cell will be untouched",
"yet more content",
"and final line will be changed locally",
])
remote = code_nb([
"first source will be modified remotely",
"other text",
"this cell will be untouched",
"yet more content",
"and final line will be changed remotely",
])
expected = code_nb([
'<<<<<<< local\nfirst source will be modified locally\n=======\nfirst source will be modified remotely\n>>>>>>> remote',
"other text",
"this cell will be untouched",
"yet more content",
'<<<<<<< local\nand final line will be changed locally\n=======\nand final line will be changed remotely\n>>>>>>> remote',
])
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
expected = code_nb([
'<<<<<<< local\nfirst source will be modified remotely\n=======\nfirst source will be modified locally\n>>>>>>> remote',
"other text",
"this cell will be untouched",
"yet more content",
'<<<<<<< local\nand final line will be changed remotely\n=======\nand final line will be changed locally\n>>>>>>> remote',
])
merged, decisions = merge_notebooks(base, remote, local)
assert merged == expected
def test_inline_merge_source_patch_delete_conflicts_both_ends():
"More elaborate test of cell deletions on both sides, onesided and agreed."
# Note: Merge rendering of conflicted sources here will depend on git/diff/builtin params and availability
base = code_nb([
"first source will be modified",
"other text",
"this cell will be untouched",
"yet more content",
"and final line will be changed",
])
local = code_nb([
"first source will be modified on one side",
"other text",
"this cell will be untouched",
"yet more content",
#"and final line will be deleted locally",
])
remote = code_nb([
#"first source will be deleted remotely",
"other text",
"this cell will be untouched",
"yet more content",
"and final line will be changed on one side",
])
expected = code_nb([
'<<<<<<< REMOTE CELL DELETED >>>>>>>\nfirst source will be modified on one side',
"other text",
"this cell will be untouched",
"yet more content",
'<<<<<<< LOCAL CELL DELETED >>>>>>>\nand final line will be changed on one side',
])
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
expected = code_nb([
'<<<<<<< LOCAL CELL DELETED >>>>>>>\nfirst source will be modified on one side',
"other text",
"this cell will be untouched",
"yet more content",
'<<<<<<< REMOTE CELL DELETED >>>>>>>\nand final line will be changed on one side',
])
merged, decisions = merge_notebooks(base, remote, local)
assert merged == expected
def test_inline_merge_attachments():
# FIXME: Use output creation utils Vidar wrote in another test file
base = new_notebook()
local = new_notebook()
remote = new_notebook()
expected = new_notebook()
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
def test_inline_merge_outputs():
# One cell with two outputs:
base = outputs_to_notebook([['unmodified', 'base']])
local = outputs_to_notebook([['unmodified', 'local']])
remote = outputs_to_notebook([['unmodified', 'remote']])
expected = outputs_to_notebook([[
'unmodified',
nbformat.v4.new_output(
output_type='stream', name='stderr',
text='<<<<<<< local <modified: text/plain>\n'),
'local',
nbformat.v4.new_output(
output_type='stream', name='stderr',
text='=======\n'),
'remote',
nbformat.v4.new_output(
output_type='stream', name='stderr',
text='>>>>>>> remote <modified: text/plain>\n'),
]])
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
def test_inline_merge_cells_insertion_similar():
base = sources_to_notebook([['unmodified']], cell_type='markdown')
local = sources_to_notebook([['unmodified'], ['local']], cell_type='markdown')
remote = sources_to_notebook([['unmodified'], ['remote']], cell_type='markdown')
expected = sources_to_notebook([
'unmodified',
[
("<"*7) + ' local\n',
'local\n',
("="*7) + '\n',
'remote\n',
(">"*7) + ' remote'
]
], cell_type='markdown')
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
def test_inline_merge_cells_insertion_unsimilar():
base = sources_to_notebook([['unmodified']], cell_type='markdown')
local = sources_to_notebook([['unmodified'], ['local\n', 'friendly faces\n', '3.14']], cell_type='markdown')
remote = sources_to_notebook([['unmodified'], ['remote\n', 'foo bar baz\n']], cell_type='markdown')
expected = sources_to_notebook([
['unmodified'],
[_cell_marker_format(("<"*7) + ' local')],
['local\n', 'friendly faces\n', '3.14'],
[_cell_marker_format("="*7)],
['remote\n', 'foo bar baz\n'],
[_cell_marker_format((">"*7) + ' remote')],
], cell_type='markdown')
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
def test_inline_merge_cells_replacement_similar():
base = sources_to_notebook([['unmodified'], ['base']], cell_type='markdown')
local = sources_to_notebook([['unmodified'], ['local']], cell_type='markdown')
remote = sources_to_notebook([['unmodified'], ['remote']], cell_type='markdown')
expected = sources_to_notebook([
['unmodified'],
[
("<"*7) + ' local\n',
'local\n',
("="*7) + '\n',
'remote\n',
(">"*7) + ' remote'
]
], cell_type='markdown')
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
def test_inline_merge_cells_replacement_unsimilar():
base = sources_to_notebook([['unmodified'], ['base']], cell_type='markdown')
local = sources_to_notebook([['unmodified'], ['local\n', 'friendly faces\n', '3.14']], cell_type='markdown')
remote = sources_to_notebook([['unmodified'], ['remote\n', 'foo bar baz\n']], cell_type='markdown')
expected = sources_to_notebook([
['unmodified'],
[_cell_marker_format(("<"*7) + ' local')],
['local\n', 'friendly faces\n', '3.14'],
[_cell_marker_format("="*7)],
['remote\n', 'foo bar baz\n'],
[_cell_marker_format((">"*7) + ' remote')],
], cell_type='markdown')
merged, decisions = merge_notebooks(base, local, remote)
assert merged == expected
| 36.848485
| 143
| 0.593315
| 5,014
| 41,344
| 4.767052
| 0.090148
| 0.026692
| 0.023847
| 0.020082
| 0.830098
| 0.802234
| 0.78073
| 0.755376
| 0.732449
| 0.705548
| 0
| 0.013833
| 0.269132
| 41,344
| 1,121
| 144
| 36.881356
| 0.777178
| 0.195119
| 0
| 0.729064
| 0
| 0.004926
| 0.209224
| 0.006431
| 0
| 0
| 0
| 0.001784
| 0.142857
| 1
| 0.045567
| false
| 0.002463
| 0.013547
| 0.001232
| 0.061576
| 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
|
ee54e5dda0a59bde2a46ec595739f77c73bdb7e4
| 3,864
|
py
|
Python
|
scData.py
|
kyeser/scTools
|
c4c7dee0c41c8afe1da6350243df5f9d9b929c7f
|
[
"MIT"
] | null | null | null |
scData.py
|
kyeser/scTools
|
c4c7dee0c41c8afe1da6350243df5f9d9b929c7f
|
[
"MIT"
] | null | null | null |
scData.py
|
kyeser/scTools
|
c4c7dee0c41c8afe1da6350243df5f9d9b929c7f
|
[
"MIT"
] | null | null | null |
sc1 = [[],
[0]]
sc2 = [[],
[0,1],
[0,2],
[0,3],
[0,4],
[0,5],
[0,6]]
sc3 = [[],
[0,1,2],
[0,1,3],
[0,1,4],
[0,1,5],
[0,1,6],
[0,2,4],
[0,2,5],
[0,2,6],
[0,2,7],
[0,3,6],
[0,3,7],
[0,4,8]]
sc4 = [[],
[0,1,2,3],
[0,1,2,4],
[0,1,3,4],
[0,1,2,5],
[0,1,2,6],
[0,1,2,7],
[0,1,4,5],
[0,1,5,6],
[0,1,6,7],
[0,2,3,5],
[0,1,3,5],
[0,2,3,6],
[0,1,3,6],
[0,2,3,7],
[0,1,4,6],
[0,1,5,7],
[0,3,4,7],
[0,1,4,7],
[0,1,4,8],
[0,1,5,8],
[0,2,4,6],
[0,2,4,7],
[0,2,5,7],
[0,2,4,8],
[0,2,6,8],
[0,3,5,8],
[0,2,5,8],
[0,3,6,9],
[0,1,3,7]]
sc5 = [[],
[0,1,2,3,4],
[0,1,2,3,5],
[0,1,2,4,5],
[0,1,2,3,6],
[0,1,2,3,7],
[0,1,2,5,6],
[0,1,2,6,7],
[0,2,3,4,6],
[0,1,2,4,6],
[0,1,3,4,6],
[0,2,3,4,7],
[0,1,3,5,6],
[0,1,2,4,8],
[0,1,2,5,7],
[0,1,2,6,8],
[0,1,3,4,7],
[0,1,3,4,8],
[0,1,4,5,7],
[0,1,3,6,7],
[0,1,5,6,8],
[0,1,4,5,8],
[0,1,4,7,8],
[0,2,3,5,7],
[0,1,3,5,7],
[0,2,3,5,8],
[0,2,4,5,8],
[0,1,3,5,8],
[0,2,3,6,8],
[0,1,3,6,8],
[0,1,4,6,8],
[0,1,3,6,9],
[0,1,4,6,9],
[0,2,4,6,8],
[0,2,4,6,9],
[0,2,4,7,9],
[0,1,2,4,7],
[0,3,4,5,8],
[0,1,2,5,8]]
sc6 = [[],
[0,1,2,3,4,5],
[0,1,2,3,4,6],
[0,1,2,3,5,6],
[0,1,2,4,5,6],
[0,1,2,3,6,7],
[0,1,2,5,6,7],
[0,1,2,6,7,8],
[0,2,3,4,5,7],
[0,1,2,3,5,7],
[0,1,3,4,5,7],
[0,1,2,4,5,7],
[0,1,2,4,6,7],
[0,1,3,4,6,7],
[0,1,3,4,5,8],
[0,1,2,4,5,8],
[0,1,4,5,6,8],
[0,1,2,4,7,8],
[0,1,2,5,7,8],
[0,1,3,4,7,8],
[0,1,4,5,8,9],
[0,2,3,4,6,8],
[0,1,2,4,6,8],
[0,2,3,5,6,8],
[0,1,3,4,6,8],
[0,1,3,5,6,8],
[0,1,3,5,7,8],
[0,1,3,4,6,9],
[0,1,3,5,6,9],
[0,2,3,6,7,9],
[0,1,3,6,7,9],
[0,1,4,5,7,9],
[0,2,4,5,7,9],
[0,2,3,5,7,9],
[0,1,3,5,7,9],
[0,2,4,6,8,10],
[0,1,2,3,4,7],
[0,1,2,3,4,8],
[0,1,2,3,7,8],
[0,2,3,4,5,8],
[0,1,2,3,5,8],
[0,1,2,3,6,8],
[0,1,2,3,6,9],
[0,1,2,5,6,8],
[0,1,2,5,6,9],
[0,2,3,4,6,9],
[0,1,2,4,6,9],
[0,1,2,4,7,9],
[0,1,2,5,7,9],
[0,1,3,4,7,9],
[0,1,4,6,7,9]]
sc7 = [[],
[0,1,2,3,4,5,6],
[0,1,2,3,4,5,7],
[0,1,2,3,4,5,8],
[0,1,2,3,4,6,7],
[0,1,2,3,5,6,7],
[0,1,2,3,4,7,8],
[0,1,2,3,6,7,8],
[0,2,3,4,5,6,8],
[0,1,2,3,4,6,8],
[0,1,2,3,4,6,9],
[0,1,3,4,5,6,8],
[0,1,2,3,4,7,9],
[0,1,2,4,5,6,8],
[0,1,2,3,5,7,8],
[0,1,2,4,6,7,8],
[0,1,2,3,5,6,9],
[0,1,2,4,5,6,9],
[0,1,4,5,6,7,9],
[0,1,2,3,6,7,9],
[0,1,2,5,6,7,9],
[0,1,2,4,5,8,9],
[0,1,2,5,6,8,9],
[0,2,3,4,5,7,9],
[0,1,2,3,5,7,9],
[0,2,3,4,6,7,9],
[0,1,3,4,5,7,9],
[0,1,2,4,5,7,9],
[0,1,3,5,6,7,9],
[0,1,2,4,6,7,9],
[0,1,2,4,6,8,9],
[0,1,3,4,6,7,9],
[0,1,3,4,6,8,9],
[0,1,2,4,6,8,10],
[0,1,3,4,6,8,10],
[0,1,3,5,6,8,10],
[0,1,2,3,5,6,8],
[0,1,3,4,5,7,8],
[0,1,2,4,5,7,8]]
sc8 = [[],
[0,1,2,3,4,5,6,7],
[0,1,2,3,4,5,6,8],
[0,1,2,3,4,5,6,9],
[0,1,2,3,4,5,7,8],
[0,1,2,3,4,6,7,8],
[0,1,2,3,5,6,7,8],
[0,1,2,3,4,5,8,9],
[0,1,2,3,4,7,8,9],
[0,1,2,3,6,7,8,9],
[0,2,3,4,5,6,7,9],
[0,1,2,3,4,5,7,9],
[0,1,3,4,5,6,7,9],
[0,1,2,3,4,6,7,9],
[0,1,2,4,5,6,7,9],
[0,1,2,3,4,6,8,9],
[0,1,2,3,5,7,8,9],
[0,1,3,4,5,6,8,9],
[0,1,2,3,5,6,8,9],
[0,1,2,4,5,6,8,9],
[0,1,2,4,5,7,8,9],
[0,1,2,3,4,6,8,10],
[0,1,2,3,5,6,8,10],
[0,1,2,3,5,7,8,10],
[0,1,2,4,5,6,8,10],
[0,1,2,4,6,7,8,10],
[0,1,3,4,5,7,8,10],
[0,1,2,4,5,7,8,10],
[0,1,3,4,6,7,9,10],
[0,1,2,3,5,6,7,9]]
sc9 = [[],
[0,1,2,3,4,5,6,7,8],
[0,1,2,3,4,5,6,7,9],
[0,1,2,3,4,5,6,8,9],
[0,1,2,3,4,5,7,8,9],
[0,1,2,3,4,6,7,8,9],
[0,1,2,3,4,5,6,8,10],
[0,1,2,3,4,5,7,8,10],
[0,1,2,3,4,6,7,8,10],
[0,1,2,3,5,6,7,8,10],
[0,1,2,3,4,6,7,9,10],
[0,1,2,3,5,6,7,9,10],
[0,1,2,4,5,6,8,9,10]]
sc10 = [[],
[0,1,2,3,4,5,6,7,8,9],
[0,1,2,3,4,5,6,7,8,10],
[0,1,2,3,4,5,6,7,9,10],
[0,1,2,3,4,5,6,8,9,10],
[0,1,2,3,4,5,7,8,9,10],
[0,1,2,3,4,6,7,8,9,10]]
sc11 = [[],
[0,1,2,3,4,5,6,7,8,9,10]]
sc12 = [[],
[0,1,2,3,4,5,6,7,8,9,10,11]]
def convert(n):
if n == 1: n = sc1
elif n == 2: n = sc2
elif n == 3: n = sc3
elif n == 4: n = sc4
elif n == 5: n = sc5
elif n == 6: n = sc6
elif n == 7: n = sc7
elif n == 8: n = sc8
elif n == 9: n = sc9
elif n == 10: n = sc10
elif n == 11: n = sc11
elif n == 12: n = sc12
return n
| 14.748092
| 28
| 0.401656
| 1,421
| 3,864
| 1.092189
| 0.021816
| 0.225515
| 0.220361
| 0.175258
| 0.804768
| 0.720361
| 0.597294
| 0.410438
| 0.298325
| 0.189433
| 0
| 0.410078
| 0.106366
| 3,864
| 261
| 29
| 14.804598
| 0.039386
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.004016
| false
| 0
| 0
| 0
| 0.008032
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 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
|
c98e7553a341da1fc4a5985d9d5d440c0322fd2e
| 2,394
|
py
|
Python
|
solutions/0130.Surrounded_Regions/python_solution.py
|
garyzccisme/leetcode
|
56be6aeb07253c9da2d354eb239bd016b7574b22
|
[
"MIT"
] | 2
|
2020-06-16T17:15:17.000Z
|
2021-07-26T12:17:54.000Z
|
solutions/0130.Surrounded_Regions/python_solution.py
|
garyzccisme/leetcode
|
56be6aeb07253c9da2d354eb239bd016b7574b22
|
[
"MIT"
] | null | null | null |
solutions/0130.Surrounded_Regions/python_solution.py
|
garyzccisme/leetcode
|
56be6aeb07253c9da2d354eb239bd016b7574b22
|
[
"MIT"
] | 1
|
2020-10-03T18:34:56.000Z
|
2020-10-03T18:34:56.000Z
|
# DFS
class Solution:
def solve(self, board: List[List[str]]) -> None:
"""
Do not return anything, modify board in-place instead.
"""
if not board or len(board) == 1:
return
self.W = len(board[0])
self.H = len(board)
# Check border to find static 'O'
for i in range(self.H):
for j in (0, self.W - 1):
if board[i][j] == 'O':
self.dfs(i, j, board)
for i in (0, self.H - 1):
for j in range(self.W):
if board[i][j] == 'O':
self.dfs(i, j, board)
# Start Change
for i in range(self.H):
for j in range(self.W):
if board[i][j] == 'O':
board[i][j] = 'X'
elif board[i][j] == 'V':
board[i][j] = 'O'
def dfs(self, i, j, board):
# Mark static 'O' as 'V'
board[i][j] = 'V'
for x, y in [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]:
if 0 <= x < self.H and 0 <= y < self.W and board[x][y] == 'O':
self.dfs(x, y, board)
# BFS
class Solution:
def solve(self, board: List[List[str]]) -> None:
"""
Do not return anything, modify board in-place instead.
"""
if not board or len(board) == 1:
return
self.W = len(board[0])
self.H = len(board)
static_list = []
# Check border to find static 'O'
for i in range(self.H):
for j in (0, self.W - 1):
if board[i][j] == 'O':
static_list.append((i, j))
for i in (0, self.H - 1):
for j in range(self.W):
if board[i][j] == 'O':
static_list.append((i, j))
# Mark static 'O' as 'V'
while static_list:
i, j = static_list.pop(0)
board[i][j] = 'V'
for x, y in [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]:
if 0 <= x < self.H and 0 <= y < self.W and board[x][y] == 'O':
static_list.append((x, y))
# Start Change
for i in range(self.H):
for j in range(self.W):
if board[i][j] == 'O':
board[i][j] = 'X'
elif board[i][j] == 'V':
board[i][j] = 'O'
| 29.925
| 78
| 0.401003
| 349
| 2,394
| 2.733524
| 0.137536
| 0.050314
| 0.102725
| 0.067086
| 0.899371
| 0.870021
| 0.870021
| 0.870021
| 0.870021
| 0.870021
| 0
| 0.018519
| 0.43609
| 2,394
| 80
| 79
| 29.925
| 0.688148
| 0.106099
| 0
| 0.884615
| 0
| 0
| 0.007674
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057692
| false
| 0
| 0
| 0
| 0.134615
| 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
|
4e72bd7b6fd11be94f5907094bcdb35d0ed1da2e
| 1,279
|
py
|
Python
|
tests/parser/min_sp_prim2.dl.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
tests/parser/min_sp_prim2.dl.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
tests/parser/min_sp_prim2.dl.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
input = """
% Computes the minimum spanning tree by a weighted graph by using Prim
% algorithm.
% Version with weakconstraints with weights as variables.
root(a).
node(a). node(b). node(c). node(d). node(e).
edge(a,b,4). edge(a,c,3). edge(c,b,2). edge(c,d,3). edge(b,e,4). edge(d,e,5).
redundantEdge(X,Y,C) :- edge(X,Y,C), edge(X,Y,C1), C>C1.
in_tree(X,Y,C) | out_tree(X,Y) :-
edge(X,Y,C),
not redundantEdge(X,Y,C),
reached(X).
:- root(X), in_tree(_,X,C).
:- in_tree(X,Y,_), in_tree(Z,Y,_), X != Z.
reached(X):- root(X).
reached(Y):- in_tree(X,Y,C).
:-node(X), not reached(X).
%:- in_tree(X,Y,C). [C:1]
"""
output = """
% Computes the minimum spanning tree by a weighted graph by using Prim
% algorithm.
% Version with weakconstraints with weights as variables.
root(a).
node(a). node(b). node(c). node(d). node(e).
edge(a,b,4). edge(a,c,3). edge(c,b,2). edge(c,d,3). edge(b,e,4). edge(d,e,5).
redundantEdge(X,Y,C) :- edge(X,Y,C), edge(X,Y,C1), C>C1.
in_tree(X,Y,C) | out_tree(X,Y) :-
edge(X,Y,C),
not redundantEdge(X,Y,C),
reached(X).
:- root(X), in_tree(_,X,C).
:- in_tree(X,Y,_), in_tree(Z,Y,_), X != Z.
reached(X):- root(X).
reached(Y):- in_tree(X,Y,C).
:-node(X), not reached(X).
%:- in_tree(X,Y,C). [C:1]
"""
| 25.078431
| 78
| 0.587177
| 260
| 1,279
| 2.811538
| 0.157692
| 0.05472
| 0.057456
| 0.087551
| 0.984952
| 0.984952
| 0.984952
| 0.984952
| 0.984952
| 0.984952
| 0
| 0.016775
| 0.161063
| 1,279
| 50
| 79
| 25.58
| 0.664492
| 0
| 0
| 0.947368
| 0
| 0.157895
| 0.974858
| 0.034063
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
4eda72dc6bcf20a6cabf64d08a7d83797862efa6
| 56
|
py
|
Python
|
BERT/__init__.py
|
vd1371/CBSA
|
f2b3f03c91ccd9ec02c2331f43573d7d6e72fd47
|
[
"MIT"
] | null | null | null |
BERT/__init__.py
|
vd1371/CBSA
|
f2b3f03c91ccd9ec02c2331f43573d7d6e72fd47
|
[
"MIT"
] | null | null | null |
BERT/__init__.py
|
vd1371/CBSA
|
f2b3f03c91ccd9ec02c2331f43573d7d6e72fd47
|
[
"MIT"
] | null | null | null |
from .train_bert_and_report import train_bert_and_report
| 56
| 56
| 0.928571
| 10
| 56
| 4.6
| 0.6
| 0.391304
| 0.521739
| 0.782609
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.053571
| 56
| 1
| 56
| 56
| 0.867925
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| true
| 0
| 1
| 0
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| 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
| 0
| 0
|
0
| 7
|
f503bcc7c8b15649b8e780aed8107b3e199e5684
| 102
|
py
|
Python
|
loafang/utils.py
|
Adwaith-Rajesh/loafang
|
2ccea64ddbc19b7a4ba5219ec2bb5185919146be
|
[
"MIT"
] | 3
|
2021-11-17T13:32:21.000Z
|
2021-11-27T04:20:48.000Z
|
loafang/utils.py
|
Adwaith-Rajesh/loafang
|
2ccea64ddbc19b7a4ba5219ec2bb5185919146be
|
[
"MIT"
] | null | null | null |
loafang/utils.py
|
Adwaith-Rajesh/loafang
|
2ccea64ddbc19b7a4ba5219ec2bb5185919146be
|
[
"MIT"
] | null | null | null |
from ._const import ERROR_CODES
def err_msg(code: int) -> str:
return ERROR_CODES.get(code, "")
| 17
| 36
| 0.696078
| 16
| 102
| 4.1875
| 0.8125
| 0.298507
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 102
| 5
| 37
| 20.4
| 0.797619
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 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
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
eef9cc2f0f89665f61331696719a9aba074b4da9
| 28,312
|
py
|
Python
|
OpenGLCffi/FFI/_gles1ffi.py
|
cydenix/OpenGLCffi
|
c78f51ae5e6b655eb2ea98f072771cf69e2197f3
|
[
"MIT"
] | null | null | null |
OpenGLCffi/FFI/_gles1ffi.py
|
cydenix/OpenGLCffi
|
c78f51ae5e6b655eb2ea98f072771cf69e2197f3
|
[
"MIT"
] | null | null | null |
OpenGLCffi/FFI/_gles1ffi.py
|
cydenix/OpenGLCffi
|
c78f51ae5e6b655eb2ea98f072771cf69e2197f3
|
[
"MIT"
] | null | null | null |
# auto-generated file
import _cffi_backend
ffi = _cffi_backend.FFI('FFI._gles1ffi',
_version = 0x2601,
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\x23glOrthox',0,b'\x00\x00\xC5\x23glOrthoxOES',0,b'\x00\x00\xAC\x23glPassThroughxOES',0,b'\x00\x01\x81\x23glPixelMapx',0,b'\x00\x01\x34\x23glPixelStorei',0,b'\x00\x01\xD2\x23glPixelStorex',0,b'\x00\x01\xD2\x23glPixelTransferxOES',0,b'\x00\x00\xAF\x23glPixelZoomxOES',0,b'\x00\x01\x18\x23glPointParameterf',0,b'\x00\x01\x14\x23glPointParameterfv',0,b'\x00\x01\xD2\x23glPointParameterx',0,b'\x00\x01\xD2\x23glPointParameterxOES',0,b'\x00\x01\xCE\x23glPointParameterxv',0,b'\x00\x01\xCE\x23glPointParameterxvOES',0,b'\x00\x00\x28\x23glPointSize',0,b'\x00\x01\xC5\x23glPointSizePointerOES',0,b'\x00\x00\xAC\x23glPointSizex',0,b'\x00\x00\xAC\x23glPointSizexOES',0,b'\x00\x00\x2B\x23glPolygonOffset',0,b'\x00\x00\xAF\x23glPolygonOffsetx',0,b'\x00\x00\xAF\x23glPolygonOffsetxOES',0,b'\x00\x02\xD0\x23glPopMatrix',0,b'\x00\x00\x95\x23glPrioritizeTexturesxOES',0,b'\x00\x02\xD0\x23glPushMatrix',0,b'\x00\x00\x0D\x23glQueryMatrixxOES',0,b'\x00\x00\xAF\x23glRasterPos2xOES',0,b'\x00\x00\xA5\x23glRasterPos2xvOES',0,b'\x00\x00\xB3\x23glRasterPos3xOES',0,b'\x00\x00\xA5\x23glRasterPos3xvOES',0,b'\x00\x00\xB8\x23glRasterPos4xOES',0,b'\x00\x00\xA5\x23glRasterPos4xvOES',0,b'\x00\x00\x6F\x23glReadPixels',0,b'\x00\x00\x65\x23glReadnPixelsEXT',0,b'\x00\x00\xB8\x23glRectxOES',0,b'\x00\x00\xA8\x23glRectxvOES',0,b'\x00\x01\x96\x23glRenderbufferStorageMultisampleAPPLE',0,b'\x00\x01\x96\x23glRenderbufferStorageMultisampleEXT',0,b'\x00\x01\x96\x23glRenderbufferStorageMultisampleIMG',0,b'\x00\x02\x57\x23glRenderbufferStorageOES',0,b'\x00\x02\xD0\x23glResolveMultisampleFramebufferAPPLE',0,b'\x00\x00\x34\x23glRotatef',0,b'\x00\x00\xB8\x23glRotatex',0,b'\x00\x00\xB8\x23glRotatexOES',0,b'\x00\x00\x49\x23glSampleCoverage',0,b'\x00\x00\xCD\x23glSampleCoveragex',0,b'\x00\x00\xCD\x23glSampleCoveragexOES',0,b'\x00\x00\x2F\x23glScalef',0,b'\x00\x00\xB3\x23glScalex',0,b'\x00\x00\xB3\x23glScalexOES',0,b'\x00\x00\x58\x23glScissor',0,b'\x00\x02\x2F\x23glSetFenceNV',0,b'\x00\x01\x0D\x23glShadeModel',0,b'\x00\x02\xBC\x23glStartTilingQCOM',0,b'\x00\x01\x8B\x23glStencilFunc',0,b'\x00\x01\x0D\x23glStencilMask',0,b'\x00\x02\x8B\x23glStencilOp',0,b'\x00\x00\x0A\x23glTestFenceNV',0,b'\x00\x00\xDE\x23glTexCoord1bOES',0,b'\x00\x00\xDB\x23glTexCoord1bvOES',0,b'\x00\x00\xAC\x23glTexCoord1xOES',0,b'\x00\x00\xA5\x23glTexCoord1xvOES',0,b'\x00\x00\xE1\x23glTexCoord2bOES',0,b'\x00\x00\xDB\x23glTexCoord2bvOES',0,b'\x00\x00\xAF\x23glTexCoord2xOES',0,b'\x00\x00\xA5\x23glTexCoord2xvOES',0,b'\x00\x00\xE5\x23glTexCoord3bOES',0,b'\x00\x00\xDB\x23glTexCoord3bvOES',0,b'\x00\x00\xB3\x23glTexCoord3xOES',0,b'\x00\x00\xA5\x23glTexCoord3xvOES',0,b'\x00\x00\xEA\x23glTexCoord4bOES',0,b'\x00\x00\xDB\x23glTexCoord4bvOES',0,b'\x00\x00\xB8\x23glTexCoord4xOES',0,b'\x00\x00\xA5\x23glTexCoord4xvOES',0,b'\x00\x00\x9A\x23glTexCoordPointer',0,b'\x00\x02\x43\x23glTexEnvf',0,b'\x00\x02\x3E\x23glTexEnvfv',0,b'\x00\x02\x52\x23glTexEnvi',0,b'\x00\x02\x4D\x23glTexEnviv',0,b'\x00\x02\x86\x23glTexEnvx',0,b'\x00\x02\x86\x23glTexEnvxOES',0,b'\x00\x02\x81\x23glTexEnvxv',0,b'\x00\x02\x81\x23glTexEnvxvOES',0,b'\x00\x02\x43\x23glTexGenfOES',0,b'\x00\x02\x3E\x23glTexGenfvOES',0,b'\x00\x02\x52\x23glTexGeniOES',0,b'\x00\x02\x4D\x23glTexGenivOES',0,b'\x00\x02\x86\x23glTexGenxOES',0,b'\x00\x02\x81\x23glTexGenxvOES',0,b'\x00\x01\x71\x23glTexImage2D',0,b'\x00\x02\x43\x23glTexParameterf',0,b'\x00\x02\x3E\x23glTexParameterfv',0,b'\x00\x02\x52\x23glTexParameteri',0,b'\x00\x02\x4D\x23glTexParameteriv',0,b'\x00\x02\x86\x23glTexParameterx',0,b'\x00\x02\x86\x23glTexParameterxOES',0,b'\x00\x02\x81\x23glTexParameterxv',0,b'\x00\x02\x81\x23glTexParameterxvOES',0,b'\x00\x01\x90\x23glTexStorage1DEXT',0,b'\x00\x01\x96\x23glTexStorage2DEXT',0,b'\x00\x01\x9D\x23glTexStorage3DEXT',0,b'\x00\x01\x71\x23glTexSubImage2D',0,b'\x00\x02\x64\x23glTextureStorage1DEXT',0,b'\x00\x02\x6B\x23glTextureStorage2DEXT',0,b'\x00\x02\x73\x23glTextureStorage3DEXT',0,b'\x00\x00\x2F\x23glTranslatef',0,b'\x00\x00\xB3\x23glTranslatex',0,b'\x00\x00\xB3\x23glTranslatexOES',0,b'\x00\x00\x0A\x23glUnmapBufferOES',0,b'\x00\x00\xE1\x23glVertex2bOES',0,b'\x00\x00\xDB\x23glVertex2bvOES',0,b'\x00\x00\xAC\x23glVertex2xOES',0,b'\x00\x00\xA5\x23glVertex2xvOES',0,b'\x00\x00\xE5\x23glVertex3bOES',0,b'\x00\x00\xDB\x23glVertex3bvOES',0,b'\x00\x00\xAF\x23glVertex3xOES',0,b'\x00\x00\xA5\x23glVertex3xvOES',0,b'\x00\x00\xEA\x23glVertex4bOES',0,b'\x00\x00\xDB\x23glVertex4bvOES',0,b'\x00\x00\xB3\x23glVertex4xOES',0,b'\x00\x00\xA5\x23glVertex4xvOES',0,b'\x00\x00\x9A\x23glVertexPointer',0,b'\x00\x00\x58\x23glViewport',0,b'\x00\x00\xFA\x23glWaitSyncAPPLE',0,b'\x00\x00\x9A\x23glWeightPointerOES',0),
_struct_unions = ((b'\x00\x00\x02\xD6\x00\x00\x00\x10__GLsync',),(b'\x00\x00\x02\xD7\x00\x00\x00\x10_cl_context',),(b'\x00\x00\x02\xD8\x00\x00\x00\x10_cl_event',)),
_typenames = (b'\x00\x00\x02\xABGLDEBUGPROC',b'\x00\x00\x02\x96GLDEBUGPROCAMD',b'\x00\x00\x02\xABGLDEBUGPROCARB',b'\x00\x00\x02\xABGLDEBUGPROCKHR',b'\x00\x00\x00\x01GLbitfield',b'\x00\x00\x00\x4BGLboolean',b'\x00\x00\x00\xDFGLbyte',b'\x00\x00\x02\xD2GLchar',b'\x00\x00\x02\xD2GLcharARB',b'\x00\x00\x02\xD3GLclampd',b'\x00\x00\x00\x29GLclampf',b'\x00\x00\x00\x7BGLclampx',b'\x00\x00\x02\xD3GLdouble',b'\x00\x00\x00\x6DGLeglImageOES',b'\x00\x00\x00\x01GLenum',b'\x00\x00\x00\x7BGLfixed',b'\x00\x00\x00\x29GLfloat',b'\x00\x00\x02\xDBGLhalf',b'\x00\x00\x02\xDBGLhalfARB',b'\x00\x00\x02\xDBGLhalfNV',b'\x00\x00\x00\x01GLhandleARB',b'\x00\x00\x00\x4FGLint',b'\x00\x00\x02\xD4GLint64',b'\x00\x00\x02\xD4GLint64EXT',b'\x00\x00\x00\x1DGLintptr',b'\x00\x00\x02\xD5GLintptrARB',b'\x00\x00\x00\xD5GLshort',b'\x00\x00\x00\x4FGLsizei',b'\x00\x00\x00\x1DGLsizeiptr',b'\x00\x00\x02\xD5GLsizeiptrARB',b'\x00\x00\x00\x08GLsync',b'\x00\x00\x00\x4BGLubyte',b'\x00\x00\x00\x01GLuint',b'\x00\x00\x00\x14GLuint64',b'\x00\x00\x00\x14GLuint64EXT',b'\x00\x00\x02\xDBGLushort',b'\x00\x00\x00\x1DGLvdpauSurfaceNV',b'\x00\x00\x02\xDCGLvoid',b'\x00\x00\x00\x29khronos_float_t',b'\x00\x00\x00\xD5khronos_int16_t',b'\x00\x00\x00\x7Bkhronos_int32_t',b'\x00\x00\x02\xD4khronos_int64_t',b'\x00\x00\x00\xDFkhronos_int8_t',b'\x00\x00\x00\x1Dkhronos_intptr_t',b'\x00\x00\x00\x1Dkhronos_ssize_t',b'\x00\x00\x02\xD4khronos_stime_nanoseconds_t',b'\x00\x00\x02\xDBkhronos_uint16_t',b'\x00\x00\x02\xD9khronos_uint32_t',b'\x00\x00\x00\x14khronos_uint64_t',b'\x00\x00\x00\x4Bkhronos_uint8_t',b'\x00\x00\x02\xDAkhronos_uintptr_t',b'\x00\x00\x02\xDAkhronos_usize_t',b'\x00\x00\x00\x14khronos_utime_nanoseconds_t'),
)
| 2,573.818182
| 14,614
| 0.771157
| 5,579
| 28,312
| 3.904642
| 0.107725
| 0.272953
| 0.159475
| 0.060228
| 0.562982
| 0.403645
| 0.394418
| 0.392628
| 0.392628
| 0.390562
| 0
| 0.316482
| 0.001589
| 28,312
| 10
| 14,615
| 2,831.2
| 0.454169
| 0.000671
| 0
| 0
| 1
| 0.125
| 0.905023
| 0.904563
| 0
| 1
| 0.000212
| 0
| 0
| 1
| 0
| false
| 0.125
| 0.125
| 0
| 0.125
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 10
|
6db6a243290eb56e490c3fda7e7425206069e9ec
| 49
|
py
|
Python
|
ChromProcess/Loading/analysis_info/__init__.py
|
thijsdejong10/ChromProcess
|
aba9c261824d0f29e0a92d7ca7c4a78e03249d62
|
[
"BSD-3-Clause"
] | null | null | null |
ChromProcess/Loading/analysis_info/__init__.py
|
thijsdejong10/ChromProcess
|
aba9c261824d0f29e0a92d7ca7c4a78e03249d62
|
[
"BSD-3-Clause"
] | null | null | null |
ChromProcess/Loading/analysis_info/__init__.py
|
thijsdejong10/ChromProcess
|
aba9c261824d0f29e0a92d7ca7c4a78e03249d62
|
[
"BSD-3-Clause"
] | null | null | null |
from .analysis_from_csv import analysis_from_csv
| 24.5
| 48
| 0.897959
| 8
| 49
| 5
| 0.5
| 0.6
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081633
| 49
| 1
| 49
| 49
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 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
| 8
|
09a11859324fb1bbda844d1926d288726fdc74f8
| 22,914
|
py
|
Python
|
tests/unit/states/test_vmc_networks.py
|
kdsalvy/salt-ext-modules-vmware-1
|
9fdc941692e4c526f575f33b2ce23c1470582934
|
[
"Apache-2.0"
] | 10
|
2021-11-02T20:24:44.000Z
|
2022-03-11T05:54:27.000Z
|
tests/unit/states/test_vmc_networks.py
|
waynew/salt-ext-modules-vmware
|
9f693382772061676c846c850df6ff508b7f3a91
|
[
"Apache-2.0"
] | 83
|
2021-10-01T15:13:02.000Z
|
2022-03-31T16:22:40.000Z
|
tests/unit/states/test_vmc_networks.py
|
waynew/salt-ext-modules-vmware
|
9f693382772061676c846c850df6ff508b7f3a91
|
[
"Apache-2.0"
] | 15
|
2021-09-30T23:17:27.000Z
|
2022-03-23T06:54:22.000Z
|
"""
Unit tests for vmc_networks state module
"""
from unittest.mock import create_autospec
from unittest.mock import patch
import pytest
import saltext.vmware.modules.vmc_networks as vmc_networks_exec
import saltext.vmware.states.vmc_networks as vmc_networks
@pytest.fixture
def configure_loader_modules():
return {vmc_networks: {}}
@pytest.fixture
def mocked_ok_response():
response = {
"type": "ROUTED",
"subnets": [
{
"gateway_address": "192.168.1.1/24",
"dhcp_ranges": ["192.168.1.2-192.168.1.254"],
"network": "192.168.1.0/24",
}
],
"connectivity_path": "/infra/tier-1s/cgw",
"admin_state": "UP",
"replication_mode": "MTEP",
"resource_type": "Segment",
"id": "sddc-cgw-network-1",
"display_name": "sddc-cgw-network-1",
"path": "/infra/tier-1s/cgw/segments/sddc-cgw-network-1",
"relative_path": "sddc-cgw-network-1",
"parent_path": "/infra/tier-1s/cgw",
"unique_id": "f21c4570-c771-4923-aeb7-126691d339e7",
"marked_for_delete": False,
"overridden": False,
"_create_time": 1618213319210,
"_create_user": "admin",
"_last_modified_time": 1618213319235,
"_last_modified_user": "admin",
"_system_owned": False,
"_system_owned": False,
"_protection": "NOT_PROTECTED",
"_revision": 0,
}
return response
@pytest.fixture
def mocked_error_response():
error_response = {
"error": "The credentials were incorrect or the account specified has been locked."
}
return error_response
def test_present_state_when_error_from_get_by_id(mocked_error_response):
mock_get_by_id = create_autospec(
vmc_networks_exec.get_by_id, return_value=mocked_error_response
)
with patch.dict(vmc_networks.__salt__, {"vmc_networks.get_by_id": mock_get_by_id}):
result = vmc_networks.present(
name="network_id",
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"] == {}
assert (
result["comment"]
== "The credentials were incorrect or the account specified has been locked."
)
assert not result["result"]
def test_present_state_when_error_from_create(mocked_error_response):
mock_get_by_id = create_autospec(vmc_networks_exec.get_by_id, return_value={})
mock_create = create_autospec(vmc_networks_exec.create, return_value=mocked_error_response)
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id,
"vmc_networks.create": mock_create,
},
):
result = vmc_networks.present(
name="network-id",
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"] == {}
assert (
result["comment"]
== "The credentials were incorrect or the account specified has been locked."
)
assert not result["result"]
def test_present_state_when_error_from_update(mocked_error_response, mocked_ok_response):
mock_get_by_id = create_autospec(vmc_networks_exec.get_by_id, return_value=mocked_ok_response)
mock_update = create_autospec(vmc_networks_exec.update, return_value=mocked_error_response)
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id,
"vmc_networks.update": mock_update,
},
):
result = vmc_networks.present(
name=mocked_ok_response["id"],
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
display_name="network-1",
)
assert result is not None
assert result["changes"] == {}
assert (
result["comment"]
== "The credentials were incorrect or the account specified has been locked."
)
assert not result["result"]
def test_present_state_during_update_to_add_a_new_field(mocked_ok_response):
mocked_updated_response = mocked_ok_response.copy()
mocked_ok_response.pop("display_name")
mock_get_by_id = create_autospec(
vmc_networks_exec.get_by_id, side_effect=[mocked_ok_response, mocked_updated_response]
)
mocked_updated_response["display_name"] = "network-1"
mock_update = create_autospec(vmc_networks_exec.update, return_value=mocked_updated_response)
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id,
"vmc_networks.update": mock_update,
},
):
result = vmc_networks.present(
name=mocked_ok_response["id"],
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
display_name="network-1",
)
assert result is not None
assert result["changes"]["old"] == mocked_ok_response
assert result["changes"]["new"] == mocked_updated_response
assert result["comment"] == "Updated network {}".format(mocked_ok_response["id"])
assert result["result"]
def test_present_to_create_when_module_returns_success_response(mocked_ok_response):
mock_get_by_id_response = create_autospec(vmc_networks_exec.get_by_id, return_value={})
mock_create_response = create_autospec(
vmc_networks_exec.create, return_value=mocked_ok_response
)
network_id = mocked_ok_response["id"]
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id_response,
"vmc_networks.create": mock_create_response,
},
):
result = vmc_networks.present(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"]["new"] == mocked_ok_response
assert result["changes"]["old"] is None
assert result["comment"] == "Created network {}".format(network_id)
assert result["result"]
def test_present_to_update_when_module_returns_success_response(mocked_ok_response):
mocked_updated_network = mocked_ok_response.copy()
mocked_updated_network["display_name"] = "network-1"
mock_get_by_id_response = create_autospec(
vmc_networks_exec.get_by_id, side_effect=[mocked_ok_response, mocked_updated_network]
)
mock_update_response = create_autospec(
vmc_networks_exec.update, return_value=mocked_updated_network
)
network_id = mocked_ok_response["id"]
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id_response,
"vmc_networks.update": mock_update_response,
},
):
result = vmc_networks.present(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
display_name="network-1",
)
assert result is not None
assert result["changes"]["new"] == mocked_updated_network
assert result["changes"]["old"] == mocked_ok_response
assert result["comment"] == "Updated network {}".format(network_id)
assert result["result"]
def test_present_to_update_when_get_by_id_after_update_returns_error(
mocked_ok_response, mocked_error_response
):
mocked_updated_network = mocked_ok_response.copy()
mocked_updated_network["display_name"] = "network-1"
mock_get_by_id_response = create_autospec(
vmc_networks_exec.get_by_id, side_effect=[mocked_ok_response, mocked_error_response]
)
mock_update_response = create_autospec(
vmc_networks_exec.update, return_value=mocked_updated_network
)
network_id = mocked_ok_response["id"]
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id_response,
"vmc_networks.update": mock_update_response,
},
):
result = vmc_networks.present(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
display_name="network-1",
)
assert result is not None
assert result["changes"] == {}
assert (
result["comment"]
== "The credentials were incorrect or the account specified has been locked."
)
assert not result["result"]
def test_present_to_update_when_user_input_and_existing_network_has_identical_fields(
mocked_ok_response,
):
mock_get_by_id_response = create_autospec(
vmc_networks_exec.get_by_id, return_value=mocked_ok_response
)
with patch.dict(
vmc_networks.__salt__,
{"vmc_networks.get_by_id": mock_get_by_id_response},
):
result = vmc_networks.present(
name=mocked_ok_response["id"],
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert len(result["changes"]) == 0
assert result["comment"] == "Network exists already, no action to perform"
assert result["result"]
def test_present_state_for_create_when_opts_test_is_true():
mock_get_by_id_response = create_autospec(vmc_networks_exec.get_by_id, return_value={})
network_id = "sddc-cgw-network-1"
with patch.dict(
vmc_networks.__salt__,
{"vmc_networks.get_by_id": mock_get_by_id_response},
):
with patch.dict(vmc_networks.__opts__, {"test": True}):
result = vmc_networks.present(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert len(result["changes"]) == 0
assert result["comment"] == "State present will create network {}".format(network_id)
assert result["result"] is None
def test_present_state_for_update_when_opts_test_is_true(mocked_ok_response):
mock_get_by_id_response = create_autospec(
vmc_networks_exec.get_by_id, return_value=mocked_ok_response
)
network_id = mocked_ok_response["id"]
with patch.dict(
vmc_networks.__salt__,
{"vmc_networks.get_by_id": mock_get_by_id_response},
):
with patch.dict(vmc_networks.__opts__, {"test": True}):
result = vmc_networks.present(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert len(result["changes"]) == 0
assert result["comment"] == "State present will update network {}".format(network_id)
assert result["result"] is None
def test_absent_state_to_delete_when_module_returns_success_response(mocked_ok_response):
mock_get_by_id_response = create_autospec(
vmc_networks_exec.get_by_id, return_value=mocked_ok_response
)
mock_delete_response = create_autospec(
vmc_networks_exec.delete, ok=True, return_value="Network Deleted Successfully"
)
network_id = mocked_ok_response["id"]
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id_response,
"vmc_networks.delete": mock_delete_response,
},
):
result = vmc_networks.absent(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"] == {"new": None, "old": mocked_ok_response}
assert result["comment"] == "Deleted network {}".format(network_id)
assert result["result"]
def test_absent_state_when_object_to_delete_does_not_exists():
mock_get_by_id_response = create_autospec(vmc_networks_exec.get_by_id, return_value={})
network_id = "sddc-cgw-network-1"
with patch.dict(
vmc_networks.__salt__,
{"vmc_networks.get_by_id": mock_get_by_id_response},
):
result = vmc_networks.absent(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"] == {}
assert result["comment"] == "No network found with ID {}".format(network_id)
assert result["result"]
def test_absent_state_to_delete_when_opts_test_mode_is_true(mocked_ok_response):
mock_get_by_id_response = create_autospec(
vmc_networks_exec.get_by_id, return_value={"results": [mocked_ok_response]}
)
network_id = mocked_ok_response["id"]
with patch.dict(
vmc_networks.__salt__,
{"vmc_networks.get_by_id": mock_get_by_id_response},
):
with patch.dict(vmc_networks.__opts__, {"test": True}):
result = vmc_networks.absent(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert len(result["changes"]) == 0
assert result["comment"] == "State absent will delete network with ID {}".format(network_id)
assert result["result"] is None
def test_absent_state_when_object_to_delete_doesn_not_exists_and_opts_test_mode_is_true():
mock_get_by_id_response = create_autospec(vmc_networks_exec.get_by_id, return_value={})
network_id = "sddc-cgw-network-1"
with patch.dict(
vmc_networks.__salt__,
{"vmc_networks.get_by_id": mock_get_by_id_response},
):
with patch.dict(vmc_networks.__opts__, {"test": True}):
result = vmc_networks.absent(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert len(result["changes"]) == 0
assert result[
"comment"
] == "State absent will do nothing as no network found with ID {}".format(network_id)
assert result["result"] is None
def test_absent_with_error_from_delete(mocked_ok_response, mocked_error_response):
mock_get_by_id = create_autospec(
vmc_networks_exec.get_by_id, return_value={"results": [mocked_ok_response]}
)
mock_delete = create_autospec(vmc_networks_exec.delete, return_value=mocked_error_response)
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id,
"vmc_networks.delete": mock_delete,
},
):
result = vmc_networks.absent(
name=mocked_ok_response["id"],
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"] == {}
assert (
result["comment"]
== "The credentials were incorrect or the account specified has been locked."
)
assert not result["result"]
def test_absent_state_when_error_from_get_by_id(mocked_error_response):
mock_get_by_id = create_autospec(
vmc_networks_exec.get_by_id, return_value=mocked_error_response
)
with patch.dict(vmc_networks.__salt__, {"vmc_networks.get_by_id": mock_get_by_id}):
result = vmc_networks.absent(
name="network-id",
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"] == {}
assert (
result["comment"]
== "The credentials were incorrect or the account specified has been locked."
)
assert not result["result"]
@pytest.mark.parametrize(
"actual_args",
[
# all actual args are None
({}),
# allow none have values
({"tags": [{"tag": "tag1", "scope": "scope1"}], "description": "network segment"}),
# all args have values
(
{
"subnets": [{"gateway_address": "40.1.1.1/16", "dhcp_ranges": ["40.1.2.0/24"]}],
"admin_state": "UP",
"description": "network segment",
"domain_name": "net.eng.vmware.com",
"tags": [{"tag": "tag1", "scope": "scope1"}],
"advanced_config": {"address_pool_paths": [], "connectivity": "ON"},
"l2_extension": None,
"dhcp_config_path": "/infra/dhcp-server-configs/default",
}
),
],
)
def test_present_state_during_create_should_correctly_pass_args(mocked_ok_response, actual_args):
mocked_updated_response = mocked_ok_response.copy()
mock_get_by_id_response = create_autospec(vmc_networks_exec.get_by_id, return_value={})
common_actual_args = {
"hostname": "hostname",
"refresh_key": "refresh_key",
"authorization_host": "authorization_host",
"org_id": "org_id",
"sddc_id": "sddc_id",
"verify_ssl": False,
}
mocked_updated_response.update(actual_args)
actual_args.update(common_actual_args)
mock_create = create_autospec(vmc_networks_exec.create, return_value=mocked_updated_response)
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id_response,
"vmc_networks.create": mock_create,
},
):
result = vmc_networks.present(name=mocked_ok_response["id"], **actual_args)
call_kwargs = mock_create.mock_calls[0][-1]
subset = {k: v for k, v in call_kwargs.items() if k in actual_args}
assert subset == actual_args
assert result is not None
assert result["changes"]["old"] is None
assert result["changes"]["new"] == mocked_updated_response
assert result["comment"] == "Created network {}".format(mocked_ok_response["id"])
assert result["result"]
@pytest.mark.parametrize(
"actual_args",
[
# all actual args are None
({"display_name": "updated_network"}),
# allow none have values
({"tags": [{"tag": "tag1", "scope": "scope1"}], "description": "network segment"}),
# all args have values
(
{
"display_name": "UPDATED_DISPLAY_NAME",
"subnets": [{"gateway_address": "40.1.1.1/16", "dhcp_ranges": ["40.1.2.0/24"]}],
"admin_state": "UP",
"description": "network segment",
"domain_name": "net.eng.vmware.com",
"tags": [{"tag": "tag1", "scope": "scope1"}],
"advanced_config": {"address_pool_paths": [], "connectivity": "ON"},
"l2_extension": None,
"dhcp_config_path": "/infra/dhcp-server-configs/default",
}
),
],
)
def test_present_state_during_update_should_correctly_pass_args(mocked_ok_response, actual_args):
mocked_updated_response = mocked_ok_response.copy()
mocked_ok_response.pop("display_name")
mock_get_by_id = create_autospec(
vmc_networks_exec.get_by_id, side_effect=[mocked_ok_response, mocked_updated_response]
)
common_actual_args = {
"hostname": "hostname",
"refresh_key": "refresh_key",
"authorization_host": "authorization_host",
"org_id": "org_id",
"sddc_id": "sddc_id",
"verify_ssl": False,
}
mocked_updated_response.update(actual_args)
actual_args.update(common_actual_args)
mock_update = create_autospec(vmc_networks_exec.update, return_value=mocked_updated_response)
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id,
"vmc_networks.update": mock_update,
},
):
result = vmc_networks.present(name=mocked_ok_response["id"], **actual_args)
call_kwargs = mock_update.mock_calls[0][-1]
subset = {k: v for k, v in call_kwargs.items() if k in actual_args}
assert subset == actual_args
assert result is not None
assert result["changes"]["old"] == mocked_ok_response
assert result["changes"]["new"] == mocked_updated_response
assert result["comment"] == "Updated network {}".format(mocked_ok_response["id"])
assert result["result"]
def test_present_when_get_by_id_returns_not_found_error(mocked_ok_response):
error_response = {"error": "network could not be found"}
mock_get_by_id_response = create_autospec(
vmc_networks_exec.get_by_id, return_value=error_response
)
mock_create_response = create_autospec(
vmc_networks_exec.create, return_value=mocked_ok_response
)
network_id = mocked_ok_response["id"]
with patch.dict(
vmc_networks.__salt__,
{
"vmc_networks.get_by_id": mock_get_by_id_response,
"vmc_networks.create": mock_create_response,
},
):
result = vmc_networks.present(
name=network_id,
hostname="hostname",
refresh_key="refresh_key",
authorization_host="authorization_host",
org_id="org_id",
sddc_id="sddc_id",
)
assert result is not None
assert result["changes"]["new"] == mocked_ok_response
assert result["changes"]["old"] is None
assert result["comment"] == "Created network {}".format(network_id)
assert result["result"]
| 33.451095
| 98
| 0.64022
| 2,742
| 22,914
| 4.924143
| 0.075857
| 0.087987
| 0.041475
| 0.030958
| 0.906829
| 0.883277
| 0.87039
| 0.85928
| 0.851281
| 0.844319
| 0
| 0.008922
| 0.251593
| 22,914
| 684
| 99
| 33.5
| 0.778413
| 0.007812
| 0
| 0.704028
| 0
| 0
| 0.202835
| 0.026103
| 0
| 0
| 0
| 0
| 0.14711
| 1
| 0.038529
| false
| 0.003503
| 0.008757
| 0.001751
| 0.052539
| 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
|
09f77e783c6a36effcafb59684f682cefb208376
| 503
|
py
|
Python
|
trading_algorithm_framework/algorithm.py
|
devonindustries/trading_algorithm_framework
|
b88dcac5aa4ad164e005d8426915dffcbfa75f5f
|
[
"MIT"
] | null | null | null |
trading_algorithm_framework/algorithm.py
|
devonindustries/trading_algorithm_framework
|
b88dcac5aa4ad164e005d8426915dffcbfa75f5f
|
[
"MIT"
] | null | null | null |
trading_algorithm_framework/algorithm.py
|
devonindustries/trading_algorithm_framework
|
b88dcac5aa4ad164e005d8426915dffcbfa75f5f
|
[
"MIT"
] | 1
|
2021-03-05T12:34:18.000Z
|
2021-03-05T12:34:18.000Z
|
# The file for storing trading algorithm procedures
from datetime import datetime
# Import all classes
from trading_algorithm_framework.equities import *
from trading_algorithm_framework.portfolio import *
from trading_algorithm_framework.stock import *
from trading_algorithm_framework.validation import *
#----------------
# Classes
#----------------
class Algorithm:
'''
A class for writing and testing trading algorithms.
CURRENTLY A PLACEHOLDER FOR A FUTURE UPDATE!
'''
pass
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0
| 7
|
1124e77882b80b7b106e86127df03456976d96e5
| 8,241
|
py
|
Python
|
docs/seaman/Y_function.py
|
martinlarsalbert/wPCC
|
16e0d4cc850d503247916c9f5bd9f0ddb07f8930
|
[
"MIT"
] | null | null | null |
docs/seaman/Y_function.py
|
martinlarsalbert/wPCC
|
16e0d4cc850d503247916c9f5bd9f0ddb07f8930
|
[
"MIT"
] | null | null | null |
docs/seaman/Y_function.py
|
martinlarsalbert/wPCC
|
16e0d4cc850d503247916c9f5bd9f0ddb07f8930
|
[
"MIT"
] | null | null | null |
from numpy import *
def Y_function(delta, u_w, v_w, r_w, s, T_prop, n_prop, Y_Tdelta, Y_uudelta, k_r, k_v, Y_uv, Y_uuv, Y_ur, Y_uur, C_d, t_a, t_f, disp, rho, L, g, xx_rud, l_cg, n_rud):
return (0.000328125*L**2*r_w*rho*t_a*C_d*abs(0.025*L*r_w - v_w) - 0.000296875*L**2*r_w*rho*t_a*C_d*abs(0.025*L*r_w + v_w) + 0.001078125*L**2*r_w*rho*t_a*C_d*abs(0.075*L*r_w - v_w) - 0.000796875*L**2*r_w*rho*t_a*C_d*abs(0.075*L*r_w + v_w) + 0.001953125*L**2*r_w*rho*t_a*C_d*abs(0.125*L*r_w - v_w) - 0.001171875*L**2*r_w*rho*t_a*C_d*abs(0.125*L*r_w + v_w) + 0.002953125*L**2*r_w*rho*t_a*C_d*abs(0.175*L*r_w - v_w) - 0.001421875*L**2*r_w*rho*t_a*C_d*abs(0.175*L*r_w + v_w) + 0.004078125*L**2*r_w*rho*t_a*C_d*abs(0.225*L*r_w - v_w) - 0.001546875*L**2*r_w*rho*t_a*C_d*abs(0.225*L*r_w + v_w) + 0.005328125*L**2*r_w*rho*t_a*C_d*abs(0.275*L*r_w - v_w) - 0.001546875*L**2*r_w*rho*t_a*C_d*abs(0.275*L*r_w + v_w) + 0.006703125*L**2*r_w*rho*t_a*C_d*abs(0.325*L*r_w - v_w) - 0.001421875*L**2*r_w*rho*t_a*C_d*abs(0.325*L*r_w + v_w) + 0.008203125*L**2*r_w*rho*t_a*C_d*abs(0.375*L*r_w - v_w) - 0.001171875*L**2*r_w*rho*t_a*C_d*abs(0.375*L*r_w + v_w) + 0.009828125*L**2*r_w*rho*t_a*C_d*abs(0.425*L*r_w - v_w) - 0.000796875*L**2*r_w*rho*t_a*C_d*abs(0.425*L*r_w + v_w) + 0.011578125*L**2*r_w*rho*t_a*C_d*abs(0.475*L*r_w - v_w) - 0.000296875*L**2*r_w*rho*t_a*C_d*abs(0.475*L*r_w + v_w) + 0.000296875*L**2*r_w*rho*t_f*C_d*abs(0.025*L*r_w - v_w) - 0.000328125*L**2*r_w*rho*t_f*C_d*abs(0.025*L*r_w + v_w) + 0.000796875*L**2*r_w*rho*t_f*C_d*abs(0.075*L*r_w - v_w) - 0.001078125*L**2*r_w*rho*t_f*C_d*abs(0.075*L*r_w + v_w) + 0.001171875*L**2*r_w*rho*t_f*C_d*abs(0.125*L*r_w - v_w) - 0.001953125*L**2*r_w*rho*t_f*C_d*abs(0.125*L*r_w + v_w) + 0.001421875*L**2*r_w*rho*t_f*C_d*abs(0.175*L*r_w - v_w) - 0.002953125*L**2*r_w*rho*t_f*C_d*abs(0.175*L*r_w + v_w) + 0.001546875*L**2*r_w*rho*t_f*C_d*abs(0.225*L*r_w - v_w) - 0.004078125*L**2*r_w*rho*t_f*C_d*abs(0.225*L*r_w + v_w) + 0.001546875*L**2*r_w*rho*t_f*C_d*abs(0.275*L*r_w - v_w) - 0.005328125*L**2*r_w*rho*t_f*C_d*abs(0.275*L*r_w + v_w) + 0.001421875*L**2*r_w*rho*t_f*C_d*abs(0.325*L*r_w - v_w) - 0.006703125*L**2*r_w*rho*t_f*C_d*abs(0.325*L*r_w + v_w) + 0.001171875*L**2*r_w*rho*t_f*C_d*abs(0.375*L*r_w - v_w) - 0.008203125*L**2*r_w*rho*t_f*C_d*abs(0.375*L*r_w + v_w) + 0.000796875*L**2*r_w*rho*t_f*C_d*abs(0.425*L*r_w - v_w) - 0.009828125*L**2*r_w*rho*t_f*C_d*abs(0.425*L*r_w + v_w) + 0.000296875*L**2*r_w*rho*t_f*C_d*abs(0.475*L*r_w - v_w) - 0.011578125*L**2*r_w*rho*t_f*C_d*abs(0.475*L*r_w + v_w) - 0.013125*L*rho*t_a*v_w*C_d*abs(0.025*L*r_w - v_w) - 0.011875*L*rho*t_a*v_w*C_d*abs(0.025*L*r_w + v_w) - 0.014375*L*rho*t_a*v_w*C_d*abs(0.075*L*r_w - v_w) - 0.010625*L*rho*t_a*v_w*C_d*abs(0.075*L*r_w + v_w) - 0.015625*L*rho*t_a*v_w*C_d*abs(0.125*L*r_w - v_w) - 0.009375*L*rho*t_a*v_w*C_d*abs(0.125*L*r_w + v_w) - 0.016875*L*rho*t_a*v_w*C_d*abs(0.175*L*r_w - v_w) - 0.008125*L*rho*t_a*v_w*C_d*abs(0.175*L*r_w + v_w) - 0.018125*L*rho*t_a*v_w*C_d*abs(0.225*L*r_w - v_w) - 0.006875*L*rho*t_a*v_w*C_d*abs(0.225*L*r_w + v_w) - 0.019375*L*rho*t_a*v_w*C_d*abs(0.275*L*r_w - v_w) - 0.005625*L*rho*t_a*v_w*C_d*abs(0.275*L*r_w + v_w) - 0.020625*L*rho*t_a*v_w*C_d*abs(0.325*L*r_w - v_w) - 0.004375*L*rho*t_a*v_w*C_d*abs(0.325*L*r_w + v_w) - 0.021875*L*rho*t_a*v_w*C_d*abs(0.375*L*r_w - v_w) - 0.003125*L*rho*t_a*v_w*C_d*abs(0.375*L*r_w + v_w) - 0.023125*L*rho*t_a*v_w*C_d*abs(0.425*L*r_w - v_w) - 0.001875*L*rho*t_a*v_w*C_d*abs(0.425*L*r_w + v_w) - 0.024375*L*rho*t_a*v_w*C_d*abs(0.475*L*r_w - v_w) - 0.000625*L*rho*t_a*v_w*C_d*abs(0.475*L*r_w + v_w) - 0.011875*L*rho*t_f*v_w*C_d*abs(0.025*L*r_w - v_w) - 0.013125*L*rho*t_f*v_w*C_d*abs(0.025*L*r_w + v_w) - 0.010625*L*rho*t_f*v_w*C_d*abs(0.075*L*r_w - v_w) - 0.014375*L*rho*t_f*v_w*C_d*abs(0.075*L*r_w + v_w) - 0.009375*L*rho*t_f*v_w*C_d*abs(0.125*L*r_w - v_w) - 0.015625*L*rho*t_f*v_w*C_d*abs(0.125*L*r_w + v_w) - 0.008125*L*rho*t_f*v_w*C_d*abs(0.175*L*r_w - v_w) - 0.016875*L*rho*t_f*v_w*C_d*abs(0.175*L*r_w + v_w) - 0.006875*L*rho*t_f*v_w*C_d*abs(0.225*L*r_w - v_w) - 0.018125*L*rho*t_f*v_w*C_d*abs(0.225*L*r_w + v_w) - 0.005625*L*rho*t_f*v_w*C_d*abs(0.275*L*r_w - v_w) - 0.019375*L*rho*t_f*v_w*C_d*abs(0.275*L*r_w + v_w) - 0.004375*L*rho*t_f*v_w*C_d*abs(0.325*L*r_w - v_w) - 0.020625*L*rho*t_f*v_w*C_d*abs(0.325*L*r_w + v_w) - 0.003125*L*rho*t_f*v_w*C_d*abs(0.375*L*r_w - v_w) - 0.021875*L*rho*t_f*v_w*C_d*abs(0.375*L*r_w + v_w) - 0.001875*L*rho*t_f*v_w*C_d*abs(0.425*L*r_w - v_w) - 0.023125*L*rho*t_f*v_w*C_d*abs(0.425*L*r_w + v_w) - 0.000625*L*rho*t_f*v_w*C_d*abs(0.475*L*r_w - v_w) - 0.024375*L*rho*t_f*v_w*C_d*abs(0.475*L*r_w + v_w) + 7.28*T_prop*delta**3*u_w*Y_Tdelta*n_rud*s*(L*g)**(7/2)/(L**4*g**4) + T_prop*delta**3*Y_Tdelta*n_rud*s + 14.56*T_prop*delta**2*l_cg*r_w*Y_Tdelta*k_r*n_rud*s*g**4/(L**5*(g/L)**(9/2)) + 2.0*T_prop*delta**2*l_cg*r_w*Y_Tdelta*k_r*n_rud*s*(L*g)**(9/2)/(u_w*L**9*(g/L)**(9/2)) - 14.56*T_prop*delta**2*r_w*xx_rud*Y_Tdelta*k_r*n_rud*s*g**4/(L**5*(g/L)**(9/2)) - 2.0*T_prop*delta**2*r_w*xx_rud*Y_Tdelta*k_r*n_rud*s*(L*g)**(9/2)/(u_w*L**9*(g/L)**(9/2)) + 14.56*T_prop*delta**2*v_w*Y_Tdelta*k_v*n_rud*s*(L*g)**(9/2)/(L**5*g**5) + 2.0*T_prop*delta**2*v_w*Y_Tdelta*k_v*n_rud*s/u_w + 7.28*T_prop*delta*l_cg**2*r_w**2*Y_Tdelta*k_r**2*n_rud*s*(L*g)**(9/2)/(u_w*L**5*g**5) + T_prop*delta*l_cg**2*r_w**2*Y_Tdelta*k_r**2*n_rud*s/u_w**2 - 14.56*T_prop*delta*l_cg*r_w**2*xx_rud*Y_Tdelta*k_r**2*n_rud*s*(L*g)**(9/2)/(u_w*L**5*g**5) - 2.0*T_prop*delta*l_cg*r_w**2*xx_rud*Y_Tdelta*k_r**2*n_rud*s/u_w**2 + 14.56*T_prop*delta*l_cg*r_w*v_w*Y_Tdelta*k_r*k_v*n_rud*s*g**4/(u_w*L**5*(g/L)**(9/2)) + 2.0*T_prop*delta*l_cg*r_w*v_w*Y_Tdelta*k_r*k_v*n_rud*s*(L*g)**(9/2)/(u_w**2*L**9*(g/L)**(9/2)) + 7.28*T_prop*delta*r_w**2*xx_rud**2*Y_Tdelta*k_r**2*n_rud*s*(L*g)**(9/2)/(u_w*L**5*g**5) + T_prop*delta*r_w**2*xx_rud**2*Y_Tdelta*k_r**2*n_rud*s/u_w**2 - 14.56*T_prop*delta*r_w*v_w*xx_rud*Y_Tdelta*k_r*k_v*n_rud*s*g**4/(u_w*L**5*(g/L)**(9/2)) - 2.0*T_prop*delta*r_w*v_w*xx_rud*Y_Tdelta*k_r*k_v*n_rud*s*(L*g)**(9/2)/(u_w**2*L**9*(g/L)**(9/2)) + 7.28*T_prop*delta*u_w*Y_Tdelta*n_rud*(L*g)**(7/2)/(L**4*g**4) + T_prop*delta*Y_Tdelta*n_rud + 7.28*T_prop*delta*v_w**2*Y_Tdelta*k_v**2*n_rud*s*(L*g)**(9/2)/(u_w*L**5*g**5) + T_prop*delta*v_w**2*Y_Tdelta*k_v**2*n_rud*s/u_w**2 + delta**3*u_w**2*Y_uudelta*n_rud*s*disp*rho/L + 3.0*delta**2*l_cg*r_w*u_w*Y_uudelta*k_r*n_rud*s*disp*g*rho*(L*g)**(7/2)/(L**9*(g/L)**(9/2)) - 3.0*delta**2*r_w*u_w*xx_rud*Y_uudelta*k_r*n_rud*s*disp*g*rho*(L*g)**(7/2)/(L**9*(g/L)**(9/2)) + 3.0*delta**2*u_w*v_w*Y_uudelta*k_v*n_rud*s*disp*rho/L + 3.0*delta*l_cg**2*r_w**2*Y_uudelta*k_r**2*n_rud*s*disp*rho/L - 6.0*delta*l_cg*r_w**2*xx_rud*Y_uudelta*k_r**2*n_rud*s*disp*rho/L + 6.0*delta*l_cg*r_w*v_w*Y_uudelta*k_r*k_v*n_rud*s*disp*rho*(L*g)**(9/2)/(L**10*(g/L)**(9/2)) + 3.0*delta*r_w**2*xx_rud**2*Y_uudelta*k_r**2*n_rud*s*disp*rho/L - 6.0*delta*r_w*v_w*xx_rud*Y_uudelta*k_r*k_v*n_rud*s*disp*rho*(L*g)**(9/2)/(L**10*(g/L)**(9/2)) + delta*u_w**2*Y_uudelta*n_rud*disp*rho/L + 3.0*delta*v_w**2*Y_uudelta*k_v**2*n_rud*s*disp*rho/L + l_cg**3*r_w**3*Y_uudelta*k_r**3*n_rud*s*disp*rho*(L*g)**(9/2)/(u_w*L**10*(g/L)**(9/2)) - 3.0*l_cg**2*r_w**3*xx_rud*Y_uudelta*k_r**3*n_rud*s*disp*rho*(L*g)**(9/2)/(u_w*L**10*(g/L)**(9/2)) + 3.0*l_cg**2*r_w**2*v_w*Y_uudelta*k_r**2*k_v*n_rud*s*disp*rho/(u_w*L) + 3.0*l_cg*r_w**3*xx_rud**2*Y_uudelta*k_r**3*n_rud*s*disp*rho*(L*g)**(9/2)/(u_w*L**10*(g/L)**(9/2)) - 6.0*l_cg*r_w**2*v_w*xx_rud*Y_uudelta*k_r**2*k_v*n_rud*s*disp*rho/(u_w*L) + l_cg*r_w*u_w*Y_uudelta*k_r*n_rud*disp*g*rho*(L*g)**(7/2)/(L**9*(g/L)**(9/2)) + 3.0*l_cg*r_w*v_w**2*Y_uudelta*k_r*k_v**2*n_rud*s*disp*rho*(L*g)**(9/2)/(u_w*L**10*(g/L)**(9/2)) - r_w**3*xx_rud**3*Y_uudelta*k_r**3*n_rud*s*disp*rho*(L*g)**(9/2)/(u_w*L**10*(g/L)**(9/2)) + 3.0*r_w**2*v_w*xx_rud**2*Y_uudelta*k_r**2*k_v*n_rud*s*disp*rho/(u_w*L) + r_w*u_w**2*Y_uur*disp*g**4*rho/(L**5*(g/L)**(9/2)) - r_w*u_w*xx_rud*Y_uudelta*k_r*n_rud*disp*g*rho*(L*g)**(7/2)/(L**9*(g/L)**(9/2)) + r_w*u_w*Y_ur*disp*g**4*rho*sqrt(L*g)/(L**5*(g/L)**(9/2)) - 3.0*r_w*v_w**2*xx_rud*Y_uudelta*k_r*k_v**2*n_rud*s*disp*rho*(L*g)**(9/2)/(u_w*L**10*(g/L)**(9/2)) + u_w**2*v_w*Y_uuv*disp*rho*sqrt(L*g)/(L**2*g) + u_w*v_w*Y_uudelta*k_v*n_rud*disp*rho/L + u_w*v_w*Y_uv*disp*rho/L + v_w**3*Y_uudelta*k_v**3*n_rud*s*disp*rho/(u_w*L))
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| null | 0
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|
0
| 15
|
feea793ae6b89d543a6e603680639ca83d10f81a
| 16,705
|
py
|
Python
|
projects/src/main/python/CodeJam/Y13R5P1/ZILIANG/generated_py_83628e5d4436476898fb025a23100ab2.py
|
DynamicCodeSearch/CodeSeer
|
ee985ece7691691585952eb88565f0e08bdc9113
|
[
"MIT"
] | 5
|
2020-04-05T18:04:13.000Z
|
2021-04-13T20:34:19.000Z
|
projects/src/main/python/CodeJam/Y13R5P1/ZILIANG/generated_py_83628e5d4436476898fb025a23100ab2.py
|
DynamicCodeSearch/CodeSeer
|
ee985ece7691691585952eb88565f0e08bdc9113
|
[
"MIT"
] | 1
|
2020-04-29T21:42:26.000Z
|
2020-05-01T23:45:45.000Z
|
projects/src/main/python/CodeJam/Y13R5P1/ZILIANG/generated_py_83628e5d4436476898fb025a23100ab2.py
|
DynamicCodeSearch/CodeSeer
|
ee985ece7691691585952eb88565f0e08bdc9113
|
[
"MIT"
] | 3
|
2020-01-27T16:02:14.000Z
|
2021-02-08T13:25:15.000Z
|
import sys
sys.path.append('/home/george2/Raise/ProgramRepair/CodeSeer/projects/src/main/python')
from CodeJam.Y13R5P1.ZILIANG.A3 import *
def func_97db3f493f37426ab43e24dffd76c6bc(convertor, infile):
ret = infile.readline().split()
if convertor:
ret = map(convertor, ret)
return ret
def func_031c5603ba074eb5a22bd261c3904765(convertor):
if convertor:
ret = map(convertor, ret)
return ret
def func_c2fd208e01d546eb84a5134bef56356e(convertor, infile):
ret = infile.readline().split()
if convertor:
ret = map(convertor, ret)
return ret
def func_e73135718184433c94fed71926967e44(r):
ans = r
used = 0
return used
def func_23287435a172415890a002e4dcace837(r):
ans = r
used = 0
return ans
def func_669bec975c7946f8a13737fba2882899(used):
exp -= used
return exp
def func_155db986f3d34c31a3196027a899945c(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
return ns
def func_a93e7ee972f64e72aafeb1bef47b8419(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
return B
def func_f306663052a94ff6ab250705e4fd4e9e(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
return N
def func_745058eab6954606869ccfe813b2fd20(infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
return ns
def func_f57ae80b50024a6a9073c04c45cbc117(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
return ns
def func_3607d725ca2346cc8ef1f8b17ed77306(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
return B
def func_fff1fbb367784a2c914150c551943462(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
return N
def func_253a18c32014417d94342ad39080e8f7(infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
return ns
def func_16c15dbd43d449e7bb5cb0165b6cfe80(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
return B
def func_55b5dc48324f472f9f9b5666b695a807(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
return ns
def func_abc2731045f740fbb611f0ee54445beb(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
return N
def func_1945b93d2d794446bd5205df46668fcc(infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
return ns
def func_68e298e1ddd743ddb3c138747b3f20c8(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
return B
def func_65bab3d1337344e293415586cd099b75(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
return N
def func_f733ae35e03b421689c349eb7657bf9b(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
return ns
def func_68dfd9abbb924b65b0c91a81a8679193(infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
return ns
def func_ece66dc6f4a64765bb9f6f4c87308456(infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
return ans
def func_3149e41783af44668d72a55fefff5f89(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
return N
def func_ebb836aecbda403eb790719db114324b(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
return ns
def func_86324b8bf1f34cc1aba21ad2850e49bb(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
return ans
def func_ec70e72f98004df7afb06189e7fbaf11(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
return B
def func_c5dcca77fd284a519ac6e2fc31599806(B, infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return ns
def func_451913f41b2a4157af3687615778bf57(B, infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return i
def func_5f291a8da0524c1199b6f5c9aa3b812f(B, infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return ans
def func_a0f57f05ba6d4d1a96e68a2038e236ce(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return B
def func_49d0e97257ea4c43adde1b7a56082668(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return i
def func_635c34729e39491e85da402f3c0b5c2c(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return N
def func_5bcf85e9e6c7442bab4b89dd7f162237(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return ans
def func_3d472b2a7aa34b5d943cedbeacf13a29(infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
return ns
def func_dcaea6bdac6841abaa92eafc0c4e5daf(_, B, infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return i
def func_1474f1769818467f8dd9e7b8037a6c2d(_, B, infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return ns
def func_1e44b9d2a15f4b27902df52c45350f0e(_, B, infile):
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return ans
def func_c7445fe126434158abf4d55674b61d17(_, infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return i
def func_ed913b1eb3444a1092fa5c4b743670e0(_, infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return ans
def func_fd59ed5d38f542ac854a415f81f2a9b9(_, infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return ns
def func_7b560a82705b4e97b99c6442319fc0f0(_, infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return N
def func_6c9cb8593a5a415aa9c9379ad321dcec(_, infile):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))('Case #%d: %.9f' % (_ + 1, ans))
return B
def func_b1a411a47255405595b9df641801a2a5():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
return T
def func_f6a7056899924c1d8dcdc7d9057ae4a8():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
return infile
def func_68be91515e144edf989db07f65765b66(infile):
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return ns
def func_e7ecbf7cf1db4508bcd4f1f0b0e480de(infile):
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return _
def func_45b2b016a6924e2c8c3c4b8f4e6e4477(infile):
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return B
def func_bcb991bbef5745fca021f58c4a768b7c(infile):
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return ans
def func_eeb5df1d96804aa89917ed5afeb252ab(infile):
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return T
def func_979b73f8f7b844189ea783f1c7c540c7(infile):
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return N
def func_6433056c41a5462ea0f287908da10ca4(infile):
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return i
def func_522ad381a33d4025b2aee0027e760a6f():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return i
def func_2fd12e490001494d9cb75c39d58852ff():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return ns
def func_d05061c022f0486f832f3523c9036ec7():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return ans
def func_c5abdb3fd9c54b25b6f9c88ff7669c07():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return B
def func_ae69639f966d40cc964099f57a70ef4b():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return _
def func_4486cf6282094534a027704ebbb82845():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return N
def func_94233b26bf2947a399e7480be868665b():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return T
def func_feccf82c690440f3b5fb7ab30ff3381b():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for _ in range(T):
B, N = read_array(infile, int)
ns = read_array(infile, int)
while len(ns) < 37:
ns += [0]
ns += [10 ** 100]
ns.sort()
ans = 0.0
for i in range(len(ns)):
ans = max(ans, cal(ns, i, B))
print 'Case #%d: %.9f' % (_ + 1, ans)
return infile
| 24.245283
| 86
| 0.544687
| 2,281
| 16,705
| 3.896537
| 0.053047
| 0.094172
| 0.156953
| 0.188344
| 0.753938
| 0.749212
| 0.745612
| 0.745612
| 0.745612
| 0.743812
| 0
| 0.153986
| 0.308411
| 16,705
| 688
| 87
| 24.280523
| 0.615338
| 0
| 0
| 0.884007
| 0
| 0
| 0.041844
| 0.022568
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.003515
| null | null | 0.026362
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 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
|
fef1466d9d63eb70670e2edc8338ddfc96f218a5
| 3,490
|
py
|
Python
|
dfspy/alt_lineups.py
|
jason-r-becker/dfspy
|
a3dd2d49dd6d1a3349eefc7f2515a562c798867f
|
[
"MIT"
] | 8
|
2019-03-14T19:51:41.000Z
|
2021-08-13T16:35:34.000Z
|
dfspy/alt_lineups.py
|
jason-r-becker/dfspy
|
a3dd2d49dd6d1a3349eefc7f2515a562c798867f
|
[
"MIT"
] | null | null | null |
dfspy/alt_lineups.py
|
jason-r-becker/dfspy
|
a3dd2d49dd6d1a3349eefc7f2515a562c798867f
|
[
"MIT"
] | 2
|
2019-04-11T12:33:48.000Z
|
2019-04-13T19:56:42.000Z
|
import os
import numpy as np
import cvxpy as cp
import pandas as pd
from scoring import *
# %%
def get_diverse_teams_lineup(df, budget, pt_lim, teams):
N = len(df)
W = cp.Variable((N, 1), boolean=True)
constrs = [cp.matmul(W.T, df['cost'].values.reshape(N, 1))<=budget,
cp.matmul(W.T, df['proj'].values.reshape(N, 1))<=pt_lim,
cp.sum(W)==9,
cp.matmul(W.T, df['QB'].values.reshape(N, 1))==1,
cp.matmul(W.T, df['RB'].values.reshape(N, 1))<=3,
cp.matmul(W.T, df['WR'].values.reshape(N, 1))<=3,
cp.matmul(W.T, df['TE'].values.reshape(N, 1))<=2,
cp.matmul(W.T, df['TE'].values.reshape(N, 1))>=1,
cp.matmul(W.T, df['K'].values.reshape(N, 1))==1,
cp.matmul(W.T, df['DST'].values.reshape(N, 1))==1,
cp.max(cp.matmul(W.T, df.iloc[:, 10:-1]))<=1]
obj = cp.Maximize(cp.matmul(W.T, df['proj'].values.reshape(N, 1)))
prob = cp.Problem(obj, constrs)
prob.solve()
W.value = W.value.round()
idx = []
for i, w in enumerate(W.value):
if w == 1:
idx.append(i)
proj_pts = df.iloc[idx]['proj'].sum()
lineup = df.iloc[idx]['player team pos proj cost'.split()]
pos_map = {'QB': 1, 'RB': 2, 'WR': 3, 'TE': 4, 'K': 5, 'DST': 6}
pos_num = [pos_map[pos] for pos in lineup['pos'].values]
lineup['pos_num'] = pos_num
lineup = lineup.sort_values('pos_num')
lineup.drop('pos_num', axis=1, inplace=True)
lineup = lineup.append(lineup.sum(numeric_only=True), ignore_index=True)
return lineup, proj_pts
def get_cust_team_stack(df, budget, pt_lim, teams, nums):
"""
allow for specification of which teams to stack
Parameters:
teams: list(str) ['NE', 'GB']
nums: list(int) [2, 2]
Example call:
get_cust_team_stack(df, 10000, 1000, ['NE', 'GB', 'NO'], [3, 2, 2])
"""
if np.sum(nums)>9:
raise ValueError('Too many players specified')
N = len(df)
W = cp.Variable((N, 1), boolean=True)
constrs = [cp.matmul(W.T, df['cost'].values.reshape(N, 1))<=budget,
cp.matmul(W.T, df['proj'].values.reshape(N, 1))<=pt_lim,
cp.sum(W)==9,
cp.matmul(W.T, df['QB'].values.reshape(N, 1))==1,
cp.matmul(W.T, df['RB'].values.reshape(N, 1))<=3,
cp.matmul(W.T, df['WR'].values.reshape(N, 1))<=3,
cp.matmul(W.T, df['TE'].values.reshape(N, 1))<=2,
cp.matmul(W.T, df['TE'].values.reshape(N, 1))>=1,
cp.matmul(W.T, df['K'].values.reshape(N, 1))==1,
cp.matmul(W.T, df['DST'].values.reshape(N, 1))==1]
for t, n in zip(teams, nums):
constrs.append(cp.matmul(W.T, df[t].values.reshape(N, 1))>=n)
obj = cp.Maximize(cp.matmul(W.T, df['proj'].values.reshape(N, 1)))
prob = cp.Problem(obj, constrs)
prob.solve()
W.value = W.value.round()
idx = []
for i, w in enumerate(W.value):
if w == 1:
idx.append(i)
proj_pts = df.iloc[idx]['proj'].sum()
lineup = df.iloc[idx]['player team pos proj cost'.split()]
pos_map = {'QB': 1, 'RB': 2, 'WR': 3, 'TE': 4, 'K': 5, 'DST': 6}
pos_num = [pos_map[pos] for pos in lineup['pos'].values]
lineup['pos_num'] = pos_num
lineup = lineup.sort_values('pos_num')
lineup.drop('pos_num', axis=1, inplace=True)
lineup = lineup.append(lineup.sum(numeric_only=True), ignore_index=True)
return lineup, proj_pts
| 40.581395
| 76
| 0.553295
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| 0.184669
| 0.024287
| 0.104541
| 0.116156
| 0.831045
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| 0.780359
| 0.780359
| 0.780359
| 0
| 0.027851
| 0.238682
| 3,490
| 85
| 77
| 41.058824
| 0.684983
| 0.05702
| 0
| 0.8
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| 0.063902
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.028571
| false
| 0
| 0.071429
| 0
| 0.128571
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
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| 0
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| 0
| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3a534f51522c79205b85718088d045144ea04d8c
| 31,120
|
py
|
Python
|
0_joan_stark/Beach_Scene.py
|
wang0618/ascii-art
|
7ce6f152541716034bf0a22d341a898b17e2865f
|
[
"MIT"
] | 1
|
2021-08-29T09:52:06.000Z
|
2021-08-29T09:52:06.000Z
|
0_joan_stark/Beach_Scene.py
|
wang0618/ascii-art
|
7ce6f152541716034bf0a22d341a898b17e2865f
|
[
"MIT"
] | null | null | null |
0_joan_stark/Beach_Scene.py
|
wang0618/ascii-art
|
7ce6f152541716034bf0a22d341a898b17e2865f
|
[
"MIT"
] | null | null | null |
# Beach Scene
# https://web.archive.org/web/20000306223234/http://geocities.com/SoHo/Gallery/6446/amntrop.htm
duration = 200
name = "Beach"
frames = [
" \n"+
" .-.\n"+
" ( _),\n"+
" _ .-. (__) ,_)\n"+
" ( ) _)\n"+
" (_(__,__)\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/.\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|\'' '\"\" \"\" \" (~~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" .-.\n"+
" ( _),\n"+
" _ .-. (__) ,_)\n"+
" ( ) _)\n"+
" (_(__,__)\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/.\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| .-\"-.\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" .-. ^^\n"+
" ( _),\n"+
" _ .-. (__) ,_)\n"+
" ( ) _)\n"+
" (_(__,__)\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/. |\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \\ ' /\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| .-\"-.\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" .-.\n"+
" ( _), ^^\n"+
" _ .-. (__) ,_)\n"+
" ( ) _)\n"+
" (_(__,__)\n"+
" .\\/. |\n"+
" .\\\\//o\\\\ ,\\/. \\ ' /\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ .=\"=.\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| - == ( ) == -\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" ._ .-.\n"+
" ) ) ( _),\n"+
" ,__) _ .-. (__) ,_) ^^\n"+
" ( ) _)\n"+
" (_(__,__) |\n"+
" .\\/. \\ ' /\n"+
" .\\\\//o\\\\ ,\\/. .-'-.\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ -- = ( ) = --\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| '-.-'\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" .-._ .-.\n"+
" ( ) ) ( _), ^^\n"+
" ,__) _ .-. (__) ,_)\n"+
" '-' ( ) _) |\n"+
" (_(__,__) \\ ' /\n"+
" .\\/. .-.\n"+
" .\\\\//o\\\\ ,\\/. - -= ( ) =- - \n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ '-'\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| / . \\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" .-._ .-.\n"+
" .-( ) ) ( _),\n"+
" (_, ,__) _ .-. (__) ,_) ^^ |\n"+
" '-' ( ) _) \\ ' /\n"+
" (_(__,__) .-.\n"+
" .\\/. -=- ( ) -=-\n"+
" .\\\\//o\\\\ ,\\/. '-'\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ / . \\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| |\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" .-._ .-.\n"+
" .-( ) ) ( _), |\n"+
" (_, ,__) _ .-. (__) ,_) \\ ' /\n"+
" '-' ( ) _) .-.\n"+
" (_(__,__) ^^ - -=( )=- -\n"+
" .\\/. '-'\n"+
" .\\\\//o\\\\ ,\\/. / . \\ \n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ |\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" .-._ .-. |\n"+
" .-( ) ) ( _),\\ ' /\n"+
" (_, ,__) _ .-. (__) ,_) .-.\n"+
" '-' ( ) _) - -==( )==- -\n"+
" ^^ (_(__,__) '-'\n"+
" .\\/. ^^ / . \\ \n"+
" .\\\\//o\\\\ ,\\/. | ,\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ |\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| /|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^~~^~~^~~^~~^~~/_|\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~~^~~^~~^~~^~~^===\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" .-._ .-. |\n"+
" ^^ .-( ) ) ( _),_ /\n"+
" ^^ (_, ,__) _ .-. (__) ,_)) ==-\n"+
" '-' ( ) _) / ~ \\ \n"+
" (_(__,__) | \n"+
" .\\\/.\n"+
" .\\\\//o\\\\ ,\\/. ^^ ,~\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ |\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| /| \\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~~^~~^~~/_|__\\~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~~^~~^~~^~~^======~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" ^^ .-._ .-.\n"+
" ^^ .-( ) ) ( _),/\n"+
" (_, ,__) _ .-. (__) ,_)=-\n"+
" '-' ( ) _) / ~ \\ \n"+
" (_(__,__) | \n"+
" .\\/. ^^\n"+
" .\\\\//o\\\\ ,\\/. ,~\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ |\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| /| \\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^~~^~~^~~/_|__\\~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~~^~~^~~^======~~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" .-._ |.-.\n"+
" ^^ .-( ) ) \\ ( _),\n"+
" ^^(_, ,__) _ .-.-= ((__) ,_)\n"+
" '-' ( ) _) / ~ \\ \n"+
" ^^ (_(__,__) | \n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/. ,~\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ |\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| /| \\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^~~/_|__\\~^~~^~~^~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~~^======~~~^~~^~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" ^^ .-._ | .-.\n"+
" ^^.-( ) ) \\ _ /( _),\n"+
" (_, ,__) _ .-.=(_)(__) ,_)\n"+
" '-' ( ) _) ~ \\ \n"+
" (_(__,__)| \n"+
" .\\/. ^^\n"+
" .\\\\//o\\\\ ,\\/. ,~\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ |\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| /| \\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^/_|__\\~^~~^~~^~~^~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~======~~~^~~^~~^~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" ^^ .-._ | .-.\n"+
" ^^ .-( ) ) \\ _ / ( _),\n"+
" (_, ,__) _-.-.) =-(__) ,_\n"+
" '-' ( ) _)\\ \n"+
" ^^ (_(__,__)\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/. ~,\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ /|\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| / |\\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^/__|_\\~^~~^~~^~~^~~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~======~~~^~~^~~^~~^\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" ^^ .-._ | .\n"+
" ^^.-( ) ) \\ _ / ( \n"+
" (_, ,__) -= ( .-. (__\n"+
" ^^ '-' ( ) _)\n"+
" (_(__,__)\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/. ~,\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ /|\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| / |\\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^/__|_\\~^~~^~~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~======~~~^~~^~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" .-._ |\n"+
" ^^ .-( ) ) \\ _ /\n"+
" ^^ ^^(_, ,__) -= (_) =.-.\n"+
" '-' / ( ) _)\n"+
" (_(__,__)\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/. ~,\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ /|\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| / |\\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~/__|_\\~^~~^~~^\n"+
" .|'' . | '''\"\"\'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~~^~~======~~~^~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" .-._ |\n"+
" .-( ) ) \\ _ /\n"+
" ^^ (_, ,__) -= (_) =- _.-.\n"+
" ^^ '-' / \\ ( ) _)\n"+
" | (_(__,__\n"+
" .\\\/.\n"+
" .\\\\//o\\\\ ,\\/. ~,\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ /|\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| / |\\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^~~^~~^~~/__|_\\~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~~^~~^~~^======~~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" .-._ |\n"+
" .-( ) ) \\ _ /\n"+
" (_, ,__) -= (_) =- _.-\n"+
" '-' / \\ ( ) \n"+
" ^^ | (_(__,\n"+
" .\\/. ^^\n"+
" .\\\\//o\\\\ ,\\/. ~,\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ /|\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| / |\\ \n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^~~^~~^~~^/__|_\\~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~~^~~^~~^~======~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" .-._ |\n"+
" .-( ) ) \\ _ /\n"+
" (_, ,__) -= (_) =- \n"+
" '-' / \\ ( \n"+
" ^^ | (_(\n"+
" .\\/. ^^\n"+
" .\\\\//o\\\\ ,\\/. ~,\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ /|\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\| / |\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~~^~~^~~^~~/__|\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~~^~~^~~^~~^~~^====\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" .-._ |\n"+
" .-( ) ) \\ _ /\n"+
" (_, ,__) -= (_) =-\n"+
" '-' / \\ \n"+
" |\n"+
" .\\/. ^^\n"+
" .\\\\//o\\\\ ,\\/. ^^\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" .-._ |\n"+
" .-( ) ) \\ _ /\n"+
" (_, ,__)-= (_) =-\n"+
" '-' / \\ \n"+
" |\n"+
" .\\/. ^^\n"+
" .\\\\//o\\\\ ,\\/. ^^\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" .-._ |\n"+
" .-( ) )\\ _ /\n"+
" (_, ,__)(_) =-\n"+
" '-' / \\ \n"+
" |\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/. ^^\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ ^^\n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~^~~",
" \n"+
" .-._|\n"+
" .-( ) ) /\n"+
" (_, ,__) =-\n"+
" '-'/ \\ \n"+
" |\n"+
" .\\/.\n"+
" .\\\\//o\\\\ ,\\/. ^^ \n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^~~^",
" \n"+
" .-._\n"+
" .-( ) )\n"+
" (_, ,__)\n"+
" '-'\n"+
" / \\ \n"+
" .\\/. |\n"+
" .\\\\//o\\\\ ,\\/.\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(^~~^~~^~~^~~^~~^~~^~~^~~\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (^~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" jgs^^ ^^^ ^ ^^^ ^^^^ ^^^ ^^ ^^ \"\" \"\"\"(~^~~^~~^~~^~~^~~^~~^~~^~~^~",
" \n"+
" .-._\n"+
" .-( ) )\n"+
" (_, ,__)\n"+
" -= (_)'-'\n"+
" / \\ \n"+
" .\\/. |\n"+
" .\\\\//o\\\\ ,\\/.\n"+
" //o\\\\|,\\/. ,.,., ,\\/. ,\\//o\\\\ \n"+
" | |//o\\ /###/#\\ //o\\ /o\\\\|\n"+
" ^^|^^|^~|^^^|' '|:|^^^|^^^^^|^^|^^^\"\"\"\"\"\"\"\"(~~^~~^~~^~~^~~^~~^~~^~~^\n"+
" .|'' . | '''\"\"'\"''. |`===`|'' '\"\" \"\" \" (~~^~~^~~^~~^~~^~~^~~^~~^~\n"+
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]
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0
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28d8eab68ee142cd81b0269038bab3ee1872cbc5
| 49
|
py
|
Python
|
client/main.py
|
cs460-group1/chat-client
|
92074bbd787073fd0f1d8fa8ea8aace07da03e03
|
[
"MIT"
] | null | null | null |
client/main.py
|
cs460-group1/chat-client
|
92074bbd787073fd0f1d8fa8ea8aace07da03e03
|
[
"MIT"
] | null | null | null |
client/main.py
|
cs460-group1/chat-client
|
92074bbd787073fd0f1d8fa8ea8aace07da03e03
|
[
"MIT"
] | null | null | null |
from client import ui
def main():
ui.run()
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0
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|
28e437a67ee222ae6d6819c8136c3bc071ca9d69
| 81
|
py
|
Python
|
waffles/gems/web/npm.py
|
IonTeLOS/wasf
|
2e77dd65afffbbf1545e9ced2296dcbd0ab3c8e4
|
[
"Zlib"
] | 507
|
2019-08-12T16:15:55.000Z
|
2022-03-28T15:49:39.000Z
|
waffles/gems/web/npm.py
|
IonTeLOS/wasf
|
2e77dd65afffbbf1545e9ced2296dcbd0ab3c8e4
|
[
"Zlib"
] | 176
|
2019-08-14T02:35:21.000Z
|
2022-03-31T21:43:56.000Z
|
waffles/gems/web/npm.py
|
IonTeLOS/wasf
|
2e77dd65afffbbf1545e9ced2296dcbd0ab3c8e4
|
[
"Zlib"
] | 57
|
2019-09-02T04:09:22.000Z
|
2022-03-21T21:37:16.000Z
|
import shutil
def is_available() -> bool:
return bool(shutil.which('npm'))
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0
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|
e91c0d81433c5c8777af7c03470d4bee6532b8d5
| 6,707
|
py
|
Python
|
openair/events/forms.py
|
kraeki/openair-jac
|
760b1b1be7efebde1146b31cf0a9326a7362a82c
|
[
"BSD-3-Clause"
] | null | null | null |
openair/events/forms.py
|
kraeki/openair-jac
|
760b1b1be7efebde1146b31cf0a9326a7362a82c
|
[
"BSD-3-Clause"
] | null | null | null |
openair/events/forms.py
|
kraeki/openair-jac
|
760b1b1be7efebde1146b31cf0a9326a7362a82c
|
[
"BSD-3-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
"""Participant forms."""
from flask_wtf import FlaskForm
from wtforms import FloatField, IntegerField, RadioField, SelectField, StringField, TextAreaField
from wtforms.validators import DataRequired, Length, NumberRange
class ParticipantDeleteForm(FlaskForm):
"""Delete Participant."""
def __init__(self, *args, **kwargs):
"""Create instance."""
super(ParticipantDeleteForm, self).__init__(*args, **kwargs)
def validate(self):
"""Validate the form."""
initial_validation = super(ParticipantDeleteForm, self).validate()
if not initial_validation:
return False
return True
class ParticipantFormJudoTurnier(FlaskForm):
"""Participant form for Judo Turnier."""
firstname = StringField('Vorname',
validators=[
DataRequired(message='Btte angeben'),
Length(min=3, max=25)])
lastname = StringField('Nachname',
validators=[
DataRequired(message='Bitte angeben'),
Length(min=3, max=25)])
sex = RadioField('Geschlecht', choices=[('m', 'Männlich'), ('w', 'Weiblich')])
birthday = IntegerField('Jahrgang',
validators=[NumberRange(min=1900, max=2015, message='Muss zwischen 1900 und 2015 sein.'),
DataRequired(message='Bitte angeben')])
level = SelectField('Kyu/Dan',
choices=[('6. Kyu', '6. Kyu'),
('5. Kyu', '5. Kyu'),
('4. Kyu', '4. Kyu'),
('3. Kyu', '3. Kyu'),
('2. Kyu', '2. Kyu'),
('1. Kyu', '1. Kyu'),
('1. Dan', '1. Dan'),
('2. Dan', '2. Dan'),
('3. Dan', '3. Dan'),
('4. Dan', '4. Dan'),
('5. Dan', '5. Dan'),
('6. Dan', '6. Dan')
])
weight = FloatField('Gewicht',
validators=[
DataRequired(message='Bitte angeben')])
remark = TextAreaField('Bemerkung', [])
def __init__(self, *args, **kwargs):
"""Create instance."""
super(ParticipantFormJudoTurnier, self).__init__(*args, **kwargs)
def validate(self):
"""Validate the form."""
initial_validation = super(ParticipantFormJudoTurnier, self).validate()
if not initial_validation:
return False
return True
class ParticipantFormJudoTraining(FlaskForm):
"""Participant form for Judo Training."""
firstname = StringField('Vorname',
validators=[
DataRequired(message='Btte angeben'),
Length(min=3, max=25)])
lastname = StringField('Nachname',
validators=[
DataRequired(message='Bitte angeben'),
Length(min=3, max=25)])
sex = RadioField('Geschlecht', choices=[('m', 'Männlich'), ('w', 'Weiblich')])
birthday = IntegerField('Jahrgang',
validators=[NumberRange(min=1900, max=2015, message='Muss zwischen 1900 und 2015 sein.'),
DataRequired(message='Bitte angeben')])
level = SelectField('Kyu/Dan',
choices=[('6. Kyu', '6. Kyu'),
('5. Kyu', '5. Kyu'),
('4. Kyu', '4. Kyu'),
('3. Kyu', '3. Kyu'),
('2. Kyu', '2. Kyu'),
('1. Kyu', '1. Kyu'),
('1. Dan', '1. Dan'),
('2. Dan', '2. Dan'),
('3. Dan', '3. Dan'),
('4. Dan', '4. Dan'),
('5. Dan', '5. Dan'),
('6. Dan', '6. Dan')
])
remark = TextAreaField('Bemerkung', [])
def __init__(self, *args, **kwargs):
"""Create instance."""
super(ParticipantFormJudoTraining, self).__init__(*args, **kwargs)
def validate(self):
"""Validate the form."""
initial_validation = super(ParticipantFormJudoTraining, self).validate()
if not initial_validation:
return False
return True
class ParticipantFormAikidoStage(FlaskForm):
"""Participant form for Judo Turnier."""
firstname = StringField('Vorname',
validators=[
DataRequired(message='Btte angeben'),
Length(min=3, max=25)])
lastname = StringField('Nachname',
validators=[
DataRequired(message='Bitte angeben'),
Length(min=3, max=25)])
sex = RadioField('Geschlecht', choices=[('m', 'Männlich'), ('w', 'Weiblich')])
birthday = IntegerField('Jahrgang',
validators=[NumberRange(min=1900, max=2015, message='Muss zwischen 1900 und 2015 sein.'),
DataRequired(message='Bitte angeben')])
level = SelectField('Kyu/Dan',
choices=[('6. Kyu', '6. Kyu'),
('5. Kyu', '5. Kyu'),
('4. Kyu', '4. Kyu'),
('3. Kyu', '3. Kyu'),
('2. Kyu', '2. Kyu'),
('1. Kyu', '1. Kyu'),
('1. Dan', '1. Dan'),
('2. Dan', '2. Dan'),
('3. Dan', '3. Dan'),
('4. Dan', '4. Dan'),
('5. Dan', '5. Dan'),
('6. Dan', '6. Dan')
])
remark = TextAreaField('Bemerkung', [])
def __init__(self, *args, **kwargs):
"""Create instance."""
super(ParticipantFormAikidoStage, self).__init__(*args, **kwargs)
def validate(self):
"""Validate the form."""
initial_validation = super(ParticipantFormAikidoStage, self).validate()
if not initial_validation:
return False
return True
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| 0.067227
| false
| 0
| 0.02521
| 0
| 0.352941
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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|
0
| 7
|
3a646b788ddbf768c97c605a641861eda5ea730b
| 4,937
|
py
|
Python
|
tests/test_order_SolLiq.py
|
phmalek/freud
|
cb0781f2009758638cd79a0bb6d44801e5473774
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_order_SolLiq.py
|
phmalek/freud
|
cb0781f2009758638cd79a0bb6d44801e5473774
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_order_SolLiq.py
|
phmalek/freud
|
cb0781f2009758638cd79a0bb6d44801e5473774
|
[
"BSD-3-Clause"
] | null | null | null |
import numpy as np
import numpy.testing as npt
import freud
import unittest
import util
class TestSolLiq(unittest.TestCase):
def test_shape(self):
N = 1000
L = 10
box, positions = util.make_box_and_random_points(L, N)
comp = freud.order.SolLiq(box, 2, .7, 6, 6)
comp.compute(positions)
npt.assert_equal(comp.clusters.shape[0], N)
self.assertEqual(box, comp.box)
box2 = freud.box.Box.cube(20)
comp.box = box2
self.assertNotEqual(box, comp.box)
self.assertEqual(box2, comp.box)
def test_identical_environments(self):
(box, positions) = util.make_fcc(4, 4, 4)
comp = freud.order.SolLiq(box, 2, .7, 6, 6)
comp.compute(positions)
self.assertTrue(np.allclose(comp.largest_cluster_size, len(positions)))
self.assertEqual(len(comp.cluster_sizes), 1)
comp.computeSolLiqNoNorm(positions)
self.assertTrue(np.allclose(comp.largest_cluster_size, len(positions)))
self.assertEqual(len(comp.cluster_sizes), 1)
comp.computeSolLiqVariant(positions)
self.assertEqual(comp.largest_cluster_size, 1)
def test_attribute_access(self):
(box, positions) = util.make_fcc(4, 4, 4)
func_names = ["compute", "computeSolLiqVariant", "computeSolLiqNoNorm"]
for f in func_names:
comp = freud.order.SolLiq(box, 2, .7, 6, 6)
with self.assertRaises(AttributeError):
comp.largest_cluster_size
with self.assertRaises(AttributeError):
comp.cluster_sizes
with self.assertRaises(AttributeError):
comp.Ql_mi
with self.assertRaises(AttributeError):
comp.clusters
with self.assertRaises(AttributeError):
comp.num_connections
with self.assertRaises(AttributeError):
comp.Ql_dot_ij
with self.assertRaises(AttributeError):
comp.num_particles
func = getattr(comp, f)
func(positions)
comp.largest_cluster_size
comp.cluster_sizes
comp.Ql_mi
comp.clusters
comp.num_connections
comp.Ql_dot_ij
comp.num_particles
def test_repr(self):
box = freud.box.Box.cube(10)
comp = freud.order.SolLiq(box, 2, .7, 6, 6)
self.assertEqual(str(comp), str(eval(repr(comp))))
class TestSolLiqNear(unittest.TestCase):
def test_shape(self):
N = 1000
L = 10
box, positions = util.make_box_and_random_points(L, N)
comp = freud.order.SolLiqNear(box, 2, .7, 6, 6, 12)
comp.compute(positions)
npt.assert_equal(comp.clusters.shape[0], N)
self.assertEqual(box, comp.box)
box2 = freud.box.Box.cube(20)
comp.box = box2
self.assertNotEqual(box, comp.box)
self.assertEqual(box2, comp.box)
def test_identical_environments(self):
(box, positions) = util.make_fcc(4, 4, 4)
comp = freud.order.SolLiqNear(box, 2, .7, 6, 6, 12)
comp.compute(positions)
self.assertTrue(np.allclose(comp.largest_cluster_size, len(positions)))
self.assertEqual(len(comp.cluster_sizes), 1)
comp.computeSolLiqNoNorm(positions)
self.assertTrue(np.allclose(comp.largest_cluster_size, len(positions)))
self.assertEqual(len(comp.cluster_sizes), 1)
comp.computeSolLiqVariant(positions)
self.assertEqual(comp.largest_cluster_size, 1)
def test_attribute_access(self):
(box, positions) = util.make_fcc(4, 4, 4)
func_names = ["compute", "computeSolLiqVariant", "computeSolLiqNoNorm"]
for f in func_names:
comp = freud.order.SolLiqNear(box, 2, .7, 6, 6, 12)
with self.assertRaises(AttributeError):
comp.largest_cluster_size
with self.assertRaises(AttributeError):
comp.cluster_sizes
with self.assertRaises(AttributeError):
comp.Ql_mi
with self.assertRaises(AttributeError):
comp.clusters
with self.assertRaises(AttributeError):
comp.num_connections
with self.assertRaises(AttributeError):
comp.Ql_dot_ij
with self.assertRaises(AttributeError):
comp.num_particles
func = getattr(comp, f)
func(positions)
comp.largest_cluster_size
comp.cluster_sizes
comp.Ql_mi
comp.clusters
comp.num_connections
comp.Ql_dot_ij
comp.num_particles
def test_repr(self):
box = freud.box.Box.cube(10)
comp = freud.order.SolLiqNear(box, 2, .7, 6, 6, 12)
self.assertEqual(str(comp), str(eval(repr(comp))))
if __name__ == '__main__':
unittest.main()
| 32.058442
| 79
| 0.609885
| 574
| 4,937
| 5.095819
| 0.144599
| 0.038291
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| 0.955897
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| 4,937
| 153
| 80
| 32.267974
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|
0
| 8
|
3a6d07601cb9d3eded5ed678a5aa346d3e9d2df9
| 149
|
py
|
Python
|
models/__init__.py
|
aroodooteam/insurance_broker_management
|
61dbfff5685ba06dd6f4a84386a8133b24012dad
|
[
"BSD-2-Clause"
] | null | null | null |
models/__init__.py
|
aroodooteam/insurance_broker_management
|
61dbfff5685ba06dd6f4a84386a8133b24012dad
|
[
"BSD-2-Clause"
] | null | null | null |
models/__init__.py
|
aroodooteam/insurance_broker_management
|
61dbfff5685ba06dd6f4a84386a8133b24012dad
|
[
"BSD-2-Clause"
] | null | null | null |
# -*- coding: utf-8 -*-
import analytic_broker_line
import account_analytic_account
# import analytic_history_broker_line
# import analytic_history
| 21.285714
| 37
| 0.818792
| 19
| 149
| 6
| 0.473684
| 0.368421
| 0.280702
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| 0.107383
| 149
| 6
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| 0
|
0
| 7
|
3ad8fa596d689b65eddddbf8727d0c6bb5f0974e
| 8,638
|
py
|
Python
|
bullet_safety_gym/__init__.py
|
liuzuxin/Bullet-Safety-Gym
|
6420fb2a5fabd3369ba613066559dd13b39de37f
|
[
"MIT"
] | 21
|
2021-05-10T05:05:18.000Z
|
2022-03-29T10:50:41.000Z
|
bullet_safety_gym/__init__.py
|
liuzuxin/Bullet-Safety-Gym
|
6420fb2a5fabd3369ba613066559dd13b39de37f
|
[
"MIT"
] | 2
|
2021-05-10T05:02:59.000Z
|
2021-09-15T12:21:11.000Z
|
bullet_safety_gym/__init__.py
|
liuzuxin/Bullet-Safety-Gym
|
6420fb2a5fabd3369ba613066559dd13b39de37f
|
[
"MIT"
] | 2
|
2021-05-14T09:21:24.000Z
|
2021-09-05T14:25:26.000Z
|
r"""Open-Safety Gym
Copyright (c) 2021 Sven Gronauer: Technical University Munich (TUM)
Distributed under the MIT License.
"""
import gym
from gym.envs.registration import register
# from bullet_safety_gym.envs.builder import EnvironmentBuilder
def get_bullet_safety_gym_env_list():
env_list = []
for env_spec in gym.envs.registry.all():
if 'Safety' in env_spec.id:
env_list.append(env_spec.id)
return env_list
"""Register environments at OpenAI's Gym."""
# ==============================================================================
# Reach Tasks
# ==============================================================================
# ===== Ball =====
register(
id='SafetyBallReach-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=250,
kwargs=dict(
agent='Ball',
task='ReachGoalTask',
obstacles={'Box': {'number': 1, 'fixed_base': False,
'movement': 'circular'},
'Puddle': {'number': 8, 'fixed_base': True,
'movement': 'static'},
},
world={'name': 'SmallRoom', 'factor': 1},
# debug=True
),
)
# ===== Car =====
register(
id='SafetyCarReach-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='RaceCar',
task='ReachGoalTask',
obstacles={'Box': {'number': 1, 'fixed_base': False,
'movement': 'circular'},
'Puddle': {'number': 8, 'fixed_base': True,
'movement': 'static'}
},
world={'name': 'SmallRoom'},
# debug=True
),
)
# ===== Ant =====
register(
id='SafetyAntReach-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=1000,
kwargs=dict(
agent='Ant',
task='ReachGoalTask',
obstacles={'Box': {'number': 1, 'fixed_base': False,
'movement': 'circular'},
'Puddle': {'number': 8, 'fixed_base': True,
'movement': 'static'}
},
world={'name': 'SmallRoom'},
),
)
# ===== Drone =====
register(
id='SafetyDroneReach-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='Drone',
task='ReachGoalTask',
obstacles={'Box': {'number': 1, 'fixed_base': False,
'movement': 'circular'},
'Pillar': {'number': 8, 'fixed_base': True,
'movement': 'static'}
},
world={'name': 'SmallRoom'},
),
)
# ==============================================================================
# Push Tasks
# ==============================================================================
# ===== Ball =====
register(
id='SafetyBallPush-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=250,
kwargs=dict(
agent='Ball',
task='PushTask',
obstacles={},
world={'name': 'SmallRoom', 'factor': 1},
# debug=True
),
)
# ===== Ball =====
register(
id='SafetyCarPush-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='RaceCar',
task='PushTask',
obstacles={},
world={'name': 'SmallRoom', 'factor': 1},
# debug=True
),
)
# ==============================================================================
# Circle Run Tasks
# ==============================================================================
register(
id='SafetyBallCircle-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=250,
kwargs=dict(
agent='Ball',
task='CircleTask',
obstacles={},
world={'name': 'Octagon'},
# debug=True
)
)
register(
id='SafetyCarCircle-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='RaceCar',
task='CircleTask',
obstacles={},
world={'name': 'Octagon'},
# debug=True
)
)
register(
id='SafetyAntCircle-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=1000,
kwargs=dict(
agent='Ant',
task='CircleTask',
obstacles={},
world={'name': 'Octagon'},
)
)
# ===== Drone =====
register(
id='SafetyDroneCircle-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='Drone',
task='CircleTask',
obstacles={},
world={'name': 'Octagon'},
)
)
# ==============================================================================
# Run Tasks
# ==============================================================================
register(
id='SafetyBallRun-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=250,
kwargs=dict(
agent='Ball',
task='RunTask',
obstacles={},
world={'name': 'Plane200', 'factor': 1},
# debug=True
),
)
register(
id='SafetyCarRun-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='RaceCar',
task='RunTask',
obstacles={},
world={'name': 'Plane200', 'factor': 1},
# debug=True
),
)
register(
id='SafetyAntRun-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=1000,
kwargs=dict(
agent='Ant',
task='RunTask',
obstacles={},
world={'name': 'Plane200', 'factor': 1},
# debug=True
),
)
# ===== Drone =====
register(
id='SafetyDroneRun-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='Drone',
task='RunTask',
obstacles={},
world={'name': 'Plane200', 'factor': 1},
),
)
# ==============================================================================
# Gather Tasks
# ==============================================================================
register(
id='SafetyBallGather-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=250,
kwargs=dict(
agent='Ball',
task='GatherTask',
obstacles={'Apple': {'number': 8, 'fixed_base': True,
'movement': 'static'},
'Bomb': {'number': 8, 'fixed_base': True,
'movement': 'static'}
},
world={'name': 'SmallRoom', 'factor': 1},
# debug=True
),
)
register(
id='SafetyCarGather-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='RaceCar',
task='GatherTask',
obstacles={'Apple': {'number': 8, 'fixed_base': True,
'movement': 'static'},
'Bomb': {'number': 8, 'fixed_base': True,
'movement': 'static'}
},
world={'name': 'SmallRoom', 'factor': 1},
# debug=True
),
)
register(
id='SafetyAntGather-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=1000,
kwargs=dict(
agent='Ant',
task='GatherTask',
obstacles={'Apple': {'number': 8, 'fixed_base': True,
'movement': 'static'},
'Bomb': {'number': 8, 'fixed_base': True,
'movement': 'static'}
},
world={'name': 'SmallRoom', 'factor': 1}
),
)
# ===== Drone =====
register(
id='SafetyDroneGather-v0',
entry_point='bullet_safety_gym.envs.builder:EnvironmentBuilder',
max_episode_steps=500,
kwargs=dict(
agent='Drone',
task='GatherTask',
obstacles={'Apple': {'number': 8, 'fixed_base': True,
'movement': 'static'},
'Bomb': {'number': 8, 'fixed_base': True,
'movement': 'static'}
},
world={'name': 'SmallRoom', 'factor': 1}
),
)
| 27.249211
| 80
| 0.488423
| 732
| 8,638
| 5.601093
| 0.154372
| 0.046098
| 0.073171
| 0.088049
| 0.802439
| 0.796098
| 0.779024
| 0.779024
| 0.769268
| 0.769268
| 0
| 0.019028
| 0.27599
| 8,638
| 316
| 81
| 27.335443
| 0.636553
| 0.15536
| 0
| 0.757322
| 0
| 0
| 0.321577
| 0.122466
| 0
| 0
| 0
| 0
| 0
| 1
| 0.004184
| false
| 0
| 0.008368
| 0
| 0.016736
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| 0
| null | 0
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|
0
| 7
|
aafd12e927ffe59a8ed9655afa4b2ad5d5868298
| 6,376
|
py
|
Python
|
tests/test_model_configs.py
|
r9y9/dnnsvs
|
b028f76fd4f081859ec99a2034e0e0dc8ce1a409
|
[
"MIT"
] | 72
|
2020-04-19T16:14:09.000Z
|
2020-05-02T04:02:05.000Z
|
tests/test_model_configs.py
|
r9y9/dnnsvs
|
b028f76fd4f081859ec99a2034e0e0dc8ce1a409
|
[
"MIT"
] | 1
|
2020-04-19T16:28:03.000Z
|
2020-05-02T13:49:13.000Z
|
tests/test_model_configs.py
|
r9y9/dnnsvs
|
b028f76fd4f081859ec99a2034e0e0dc8ce1a409
|
[
"MIT"
] | 3
|
2020-04-20T02:34:31.000Z
|
2020-04-26T01:04:35.000Z
|
from pathlib import Path
import hydra
import nnsvs.bin.train
import nnsvs.bin.train_postfilter
import nnsvs.bin.train_resf0
import pytest
import torch
from nnsvs.base import PredictionType
from nnsvs.util import init_seed
from omegaconf import OmegaConf
RECIPE_DIR = Path(__file__).parent.parent / "recipes"
def _test_model_impl(model, model_config):
B = 4
T = 100
init_seed(B * T)
x = torch.rand(B, T, model_config.netG.in_dim)
lengths = torch.Tensor([T] * B).long()
# warmup forward pass
with torch.no_grad():
y = model(x, lengths)
y_inf = model.inference(x, lengths)
# MDN case
if model.prediction_type() == PredictionType.PROBABILISTIC:
log_pi, log_sigma, mu = y
num_gaussian = log_pi.shape[2]
assert mu.shape == (B, T, num_gaussian, model_config.netG.out_dim)
assert log_sigma.shape == (B, T, num_gaussian, model_config.netG.out_dim)
# NOTE: infernece output shouldn't have num_gaussian axis
mu_inf, sigma_inf = y_inf
assert mu_inf.shape == (B, T, model_config.netG.out_dim)
assert sigma_inf.shape == (B, T, model_config.netG.out_dim)
else:
assert y.shape == (B, T, model_config.netG.out_dim)
assert y.shape == y_inf.shape
def _test_resf0_model_impl(model, model_config):
B = 4
T = 100
init_seed(B * T)
x = torch.rand(B, T, model_config.netG.in_dim)
lengths = torch.Tensor([T] * B).long()
# warmup forward pass
with torch.no_grad():
y, lf0_residual = model(x, lengths)
y_inf = model.inference(x, lengths)
# MDN case
if model.prediction_type() == PredictionType.PROBABILISTIC:
log_pi, log_sigma, mu = y
num_gaussian = log_pi.shape[2]
assert mu.shape == (B, T, num_gaussian, model_config.netG.out_dim)
assert log_sigma.shape == (B, T, num_gaussian, model_config.netG.out_dim)
assert lf0_residual.shape == (B, T, num_gaussian)
# NOTE: infernece output shouldn't have num_gaussian axis
mu_inf, sigma_inf = y_inf
assert mu_inf.shape == (B, T, model_config.netG.out_dim)
assert sigma_inf.shape == (B, T, model_config.netG.out_dim)
else:
assert lf0_residual.shape == (B, T, 1)
assert y.shape == (B, T, model_config.netG.out_dim)
assert y.shape == y_inf.shape
def _test_postfilter_impl(model, model_config):
B = 4
T = 100
init_seed(B * T)
in_dim = sum(model_config.stream_sizes)
x = torch.rand(B, T, in_dim)
lengths = torch.Tensor([T] * B).long()
# warmup forward pass
with torch.no_grad():
y = model(x, lengths)
y_inf = model.inference(x, lengths)
assert x.shape == y.shape
assert y_inf.shape == y.shape
@pytest.mark.parametrize(
"model_config",
(Path(nnsvs.bin.train.__file__).parent / "conf" / "train" / "model").glob("*.yaml"),
)
def test_model_config(model_config):
model_config = OmegaConf.load(model_config)
model = hydra.utils.instantiate(model_config.netG)
_test_model_impl(model, model_config)
@pytest.mark.parametrize(
"model_config",
(
Path(nnsvs.bin.train_resf0.__file__).parent / "conf" / "train_resf0" / "model"
).glob("*.yaml"),
)
def test_resf0_acoustic_model_config(model_config):
model_config = OmegaConf.load(model_config)
# Dummy
model_config.netG.in_lf0_idx = 10
model_config.netG.in_lf0_min = 5.3936276
model_config.netG.in_lf0_max = 6.491111
model_config.netG.out_lf0_idx = 180
model_config.netG.out_lf0_mean = 5.953093881972361
model_config.netG.out_lf0_scale = 0.23435173188961034
model = hydra.utils.instantiate(model_config.netG)
_test_resf0_model_impl(model, model_config)
@pytest.mark.parametrize(
"model_config",
(
Path(nnsvs.bin.train_postfilter.__file__).parent
/ "conf"
/ "train_postfilter"
/ "model"
).glob("*.yaml"),
)
def test_postfilter_model_config(model_config):
model_config = OmegaConf.load(model_config)
if "stream_sizes" in model_config.netG:
model_config.netG.stream_sizes = model_config.stream_sizes
# Post-filter config should have netD
hydra.utils.instantiate(model_config.netD)
model = hydra.utils.instantiate(model_config.netG)
_test_postfilter_impl(model, model_config)
@pytest.mark.parametrize(
"model_config", RECIPE_DIR.glob("**/conf/train/timelag/model/*.yaml")
)
def test_timelag_model_config_recipes(model_config):
model_config = OmegaConf.load(model_config)
model = hydra.utils.instantiate(model_config.netG)
_test_model_impl(model, model_config)
@pytest.mark.parametrize(
"model_config", RECIPE_DIR.glob("**/conf/train/duration/model/*.yaml")
)
def test_duration_model_config_recipes(model_config):
model_config = OmegaConf.load(model_config)
model = hydra.utils.instantiate(model_config.netG)
_test_model_impl(model, model_config)
@pytest.mark.parametrize(
"model_config", RECIPE_DIR.glob("**/conf/train/acoustic/model/*.yaml")
)
def test_acoustic_model_config_recipes(model_config):
model_config = OmegaConf.load(model_config)
model = hydra.utils.instantiate(model_config.netG)
_test_model_impl(model, model_config)
@pytest.mark.parametrize(
"model_config", RECIPE_DIR.glob("**/conf/train_resf0/acoustic/model/*.yaml")
)
def test_resf0_acoustic_model_config_recipes(model_config):
model_config = OmegaConf.load(model_config)
# Dummy
model_config.netG.in_lf0_idx = 10
model_config.netG.in_lf0_min = 5.3936276
model_config.netG.in_lf0_max = 6.491111
model_config.netG.out_lf0_idx = 180
model_config.netG.out_lf0_mean = 5.953093881972361
model_config.netG.out_lf0_scale = 0.23435173188961034
model = hydra.utils.instantiate(model_config.netG)
_test_resf0_model_impl(model, model_config)
@pytest.mark.parametrize(
"model_config", RECIPE_DIR.glob("**/conf/train_postfilter/model/*.yaml")
)
def test_postfilter_config_recipes(model_config):
model_config = OmegaConf.load(model_config)
if "stream_sizes" in model_config.netG:
model_config.netG.stream_sizes = model_config.stream_sizes
# Post-filter config should have netD
hydra.utils.instantiate(model_config.netD)
model = hydra.utils.instantiate(model_config.netG)
_test_postfilter_impl(model, model_config)
| 32.20202
| 88
| 0.705144
| 904
| 6,376
| 4.675885
| 0.119469
| 0.236811
| 0.12775
| 0.068133
| 0.873433
| 0.847646
| 0.835817
| 0.825172
| 0.825172
| 0.813579
| 0
| 0.028183
| 0.181932
| 6,376
| 197
| 89
| 32.365482
| 0.782209
| 0.042817
| 0
| 0.66443
| 0
| 0
| 0.063372
| 0.02988
| 0
| 0
| 0
| 0
| 0.107383
| 1
| 0.073826
| false
| 0
| 0.067114
| 0
| 0.14094
| 0
| 0
| 0
| 0
| null | 1
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c93f4b5381cc5288fabc85d184d371c6fb3e8226
| 154
|
py
|
Python
|
models/__init__.py
|
zihaomu/HID_2020_baseline
|
c2c3705707695a969d24aa52c225aa3f85c7a4f3
|
[
"Apache-2.0"
] | 34
|
2020-08-26T14:53:13.000Z
|
2021-09-26T12:41:55.000Z
|
models/__init__.py
|
zihaomu/HID_2020_baseline
|
c2c3705707695a969d24aa52c225aa3f85c7a4f3
|
[
"Apache-2.0"
] | 1
|
2020-10-10T14:29:25.000Z
|
2020-10-10T19:28:03.000Z
|
models/__init__.py
|
zihaomu/HID_2020_baseline
|
c2c3705707695a969d24aa52c225aa3f85c7a4f3
|
[
"Apache-2.0"
] | 7
|
2020-09-06T06:49:45.000Z
|
2022-03-11T11:13:39.000Z
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .model_factory import get_model
| 22
| 39
| 0.837662
| 20
| 154
| 5.65
| 0.5
| 0.265487
| 0.424779
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155844
| 154
| 6
| 40
| 25.666667
| 0.869231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0.25
| 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
|
c9408f01cf99f536d9f5ad6d26b4578f279aafff
| 2,288
|
py
|
Python
|
Python Basics/Pre-Exam Exercise/03. Cat Life.py
|
a-shiro/SoftUni-Courses
|
7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77
|
[
"MIT"
] | null | null | null |
Python Basics/Pre-Exam Exercise/03. Cat Life.py
|
a-shiro/SoftUni-Courses
|
7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77
|
[
"MIT"
] | null | null | null |
Python Basics/Pre-Exam Exercise/03. Cat Life.py
|
a-shiro/SoftUni-Courses
|
7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77
|
[
"MIT"
] | null | null | null |
import math
cat_breed = input()
cat_gender = input()
if cat_breed != "British Shorthair" and cat_breed != "Siamese" and cat_breed != "Persian" \
and cat_breed != "Ragdoll" and cat_breed != "American Shorthair" and cat_breed != "Siberian":
print(f"{cat_breed} is invalid cat!")
elif cat_breed == "British Shorthair":
if cat_gender == "m":
human_months = 13 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_gender == "f":
human_months = 14 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_breed == "Siamese":
if cat_gender == "m":
human_months = 15 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_gender == "f":
human_months = 16 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_breed == "Persian":
if cat_gender == "m":
human_months = 14 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_gender == "f":
human_months = 15 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_breed == "Ragdoll":
if cat_gender == "m":
human_months = 16 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_gender == "f":
human_months = 17 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_breed == "American Shorthair":
if cat_gender == "m":
human_months = 12 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_gender == "f":
human_months = 13 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_breed == "Siberian":
if cat_gender == "m":
human_months = 11 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
elif cat_gender == "f":
human_months = 12 * 12
cat_months = human_months / 6
print(f"{math.floor(cat_months)} cat months")
| 33.647059
| 97
| 0.604458
| 319
| 2,288
| 4.100313
| 0.100313
| 0.247706
| 0.100917
| 0.146789
| 0.808869
| 0.808869
| 0.763761
| 0.731651
| 0.731651
| 0.731651
| 0
| 0.035971
| 0.270979
| 2,288
| 67
| 98
| 34.149254
| 0.748201
| 0
| 0
| 0.766667
| 0
| 0
| 0.256556
| 0.125874
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.016667
| 0
| 0.016667
| 0.216667
| 0
| 0
| 0
| null | 1
| 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
| 8
|
c970a2c4dbd6200c81ea579536b95e34235525fb
| 160
|
py
|
Python
|
temas/tema1/codigo/t2e03a.py
|
GabJL/FP2021
|
9c2c80c3bd0b7e112f66475c48ecdcf20b611338
|
[
"MIT"
] | 1
|
2021-11-29T12:12:48.000Z
|
2021-11-29T12:12:48.000Z
|
temas/tema1/codigo/t2e03a.py
|
GabJL/FP2021
|
9c2c80c3bd0b7e112f66475c48ecdcf20b611338
|
[
"MIT"
] | null | null | null |
temas/tema1/codigo/t2e03a.py
|
GabJL/FP2021
|
9c2c80c3bd0b7e112f66475c48ecdcf20b611338
|
[
"MIT"
] | null | null | null |
from turtle import *
print("Dibujando un pentágono")
forward(80)
left(72)
forward(80)
left(72)
forward(80)
left(72)
forward(80)
left(72)
forward(80)
left(72)
| 10.666667
| 31
| 0.725
| 27
| 160
| 4.296296
| 0.407407
| 0.387931
| 0.560345
| 0.646552
| 0.646552
| 0.646552
| 0.646552
| 0.646552
| 0.646552
| 0.646552
| 0
| 0.141844
| 0.11875
| 160
| 14
| 32
| 11.428571
| 0.680851
| 0
| 0
| 0.833333
| 0
| 0
| 0.1375
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.083333
| 0
| 0.083333
| 0.083333
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
a30c5c272b7c2cc3935720a3213977a7f7d7fb6c
| 93
|
py
|
Python
|
bfgame/factories/recipes/__init__.py
|
ChrisLR/BasicDungeonRL
|
b293d40bd9a0d3b7aec41b5e1d58441165997ff1
|
[
"MIT"
] | 3
|
2017-10-28T11:28:38.000Z
|
2018-09-12T09:47:00.000Z
|
bfgame/factories/recipes/__init__.py
|
ChrisLR/BasicDungeonRL
|
b293d40bd9a0d3b7aec41b5e1d58441165997ff1
|
[
"MIT"
] | null | null | null |
bfgame/factories/recipes/__init__.py
|
ChrisLR/BasicDungeonRL
|
b293d40bd9a0d3b7aec41b5e1d58441165997ff1
|
[
"MIT"
] | null | null | null |
from bfgame.factories.recipes.items import *
from bfgame.factories.recipes.monsters import *
| 31
| 47
| 0.827957
| 12
| 93
| 6.416667
| 0.583333
| 0.25974
| 0.493506
| 0.675325
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086022
| 93
| 2
| 48
| 46.5
| 0.905882
| 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
| 0
| 0
|
0
| 7
|
a334bbd986f95c4c9568390d7337b4dd81d17cd9
| 4,490
|
py
|
Python
|
tests/test_ingester.py
|
CitrineInformatics/calphad-tdb-ingester
|
0aa77a833609248485e53df2f02483a8b9b615fc
|
[
"Apache-2.0"
] | null | null | null |
tests/test_ingester.py
|
CitrineInformatics/calphad-tdb-ingester
|
0aa77a833609248485e53df2f02483a8b9b615fc
|
[
"Apache-2.0"
] | null | null | null |
tests/test_ingester.py
|
CitrineInformatics/calphad-tdb-ingester
|
0aa77a833609248485e53df2f02483a8b9b615fc
|
[
"Apache-2.0"
] | null | null | null |
from calphad_tdb_ingester.converter import convert
def test_pbte():
"""
Tests that correct number of properties, their names, and values were parsed into the pifs created
"""
pif = convert(files=["./test_files/test_PbTe.TDB"], database_name="2017Bajaj")
assert pif.chemical_formula == "PbTe", "Incorrectly parsed formula of parent PIF"
assert pif.ids[0].value == "2017Bajaj", "Incorrectly parsed argument 'database_name'"
assert len(pif.sub_systems) > 0, "At least one sub-system must be present in a PIF"
assert len(pif.properties) > 0, "At least one property must be present in a PIF"
assert pif.properties[0].name == "Thermodynamic database", "Filename not added as property"
subsystem_tags = [sub_sys.tags[0] for sub_sys in pif.sub_systems]
# Check for tags in sub-systems
assert "Element" in subsystem_tags
assert "Specie" in subsystem_tags
assert "Phase" in subsystem_tags
# extract and test for all elements, species, and phases
elements = [sub_sys.chemical_formula for sub_sys in pif.sub_systems if sub_sys.tags[0] == "Element"]
assert elements == ["Pb", None, "Te"]
species = [sub_sys.names[0] for sub_sys in pif.sub_systems if sub_sys.tags[0] == "Specie"]
assert species == ["Pbte_L"]
phases = [sub_sys.names[0] for sub_sys in pif.sub_systems if sub_sys.tags[0] == "Phase"]
assert phases == ["RHOMBOHEDRAL_A7", "HEXAGONAL_A8", "LIQUID", "PbTe"]
for sub_sys in pif.sub_systems:
subsys_prop_names = [subsys_prop.name for subsys_prop in sub_sys.properties]
if sub_sys.tags[0] == "Element" and sub_sys.chemical_formula == "Pb":
assert "Enthalpy of reference state" in subsys_prop_names
for prop in sub_sys.properties:
if prop.name == "Enthalpy of reference state":
assert prop.scalars[0].value == 6870.0, "Incorrectly parsed element enthalpy"
elif sub_sys.tags[0] == "Specie" and sub_sys.names[0] == "Pbte_L":
assert sub_sys.chemical_formula == "PbTe", "Incorrectly parse specie property value"
elif sub_sys.tags[0] == "Phase" and sub_sys.names[0] == "HEXAGONAL_A8":
assert sub_sys.composition[0].element == "(Te)"
assert sub_sys.composition[0].ideal_atomic_percent == 100.0
def test_ausi():
"""
Tests that correct number of properties, their names, and values were parsed into the pifs created
"""
pif = convert(files=["./test_files/test_AuSi.TDB"], database_name="2018AuSi")
assert pif.chemical_formula == "SiAu", "Incorrectly parsed formula of parent PIF"
assert pif.ids[0].value == "2018AuSi", "Incorrectly parsed argument 'database_name'"
assert len(pif.sub_systems) > 0, "At least one sub-system must be present in a PIF"
assert len(pif.properties) > 0, "At least one property must be present in a PIF"
assert pif.properties[0].name == "Thermodynamic database", "Filename not added as property"
subsystem_tags = [sub_sys.tags[0] for sub_sys in pif.sub_systems]
# Check for tags in sub-systems
assert "Element" in subsystem_tags
assert "Specie" not in subsystem_tags
assert "Phase" in subsystem_tags
# extract and test for all elements, species, and phases
elements = [sub_sys.chemical_formula for sub_sys in pif.sub_systems if sub_sys.tags[0] == "Element"]
assert elements == [None, "Si", "Au"]
phases = [sub_sys.names[0] for sub_sys in pif.sub_systems if sub_sys.tags[0] == "Phase"]
assert phases == ['HCP_Zn', 'BCC_A2', 'LIQUID', 'HCP_A3', 'CUB_A13', 'FCC_A1', 'DIAMOND_A4', 'CBCC_A12']
for sub_sys in pif.sub_systems:
subsys_prop_names = [subsys_prop.name for subsys_prop in sub_sys.properties]
if sub_sys.tags[0] == "Element" and sub_sys.chemical_formula == "Si":
assert "Enthalpy of reference state" in subsys_prop_names
for prop in sub_sys.properties:
if prop.name == "Enthalpy of reference state":
assert prop.scalars[0].value == 3217.5, "Incorrectly parsed element enthalpy"
elif sub_sys.tags[0] == "Phase" and sub_sys.names[0] == "CUB_A13":
assert sub_sys.composition[0].element == "(Si)"
assert sub_sys.composition[0].ideal_atomic_percent == 50.0
assert sub_sys.composition[1].element == "(Va)"
assert sub_sys.composition[1].ideal_atomic_percent == 50.0
if __name__ == '__main__':
test_pbte()
test_ausi()
| 45.816327
| 108
| 0.676615
| 653
| 4,490
| 4.467075
| 0.182236
| 0.08639
| 0.041138
| 0.045252
| 0.840247
| 0.806308
| 0.785053
| 0.785053
| 0.756256
| 0.729174
| 0
| 0.02338
| 0.209354
| 4,490
| 97
| 109
| 46.28866
| 0.79831
| 0.08196
| 0
| 0.433333
| 0
| 0
| 0.250489
| 0.01272
| 0
| 0
| 0
| 0
| 0.533333
| 1
| 0.033333
| false
| 0
| 0.016667
| 0
| 0.05
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
a39c1785183868bba82f337f08c95847f7db80e2
| 15,558
|
py
|
Python
|
src/genie/libs/parser/iosxe/tests/ShowIpBgpAll/cli/equal/golden_output1_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 204
|
2018-06-27T00:55:27.000Z
|
2022-03-06T21:12:18.000Z
|
src/genie/libs/parser/iosxe/tests/ShowIpBgpAll/cli/equal/golden_output1_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 468
|
2018-06-19T00:33:18.000Z
|
2022-03-31T23:23:35.000Z
|
src/genie/libs/parser/iosxe/tests/ShowIpBgpAll/cli/equal/golden_output1_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 309
|
2019-01-16T20:21:07.000Z
|
2022-03-30T12:56:41.000Z
|
expected_output = {
"vrf": {
"default": {
"address_family": {
"l2vpn vpls RD 100:1051": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1051",
"route_identifier": "10.169.197.254",
"routes": {
"100:1051:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1052": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1052",
"route_identifier": "10.169.197.254",
"routes": {
"100:1052:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1053": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1053",
"route_identifier": "10.169.197.254",
"routes": {
"100:1053:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1054": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1054",
"route_identifier": "10.169.197.254",
"routes": {
"100:1054:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1055": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1055",
"route_identifier": "10.169.197.254",
"routes": {
"100:1055:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1056": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1056",
"route_identifier": "10.169.197.254",
"routes": {
"100:1056:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1057": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1057",
"route_identifier": "10.169.197.254",
"routes": {
"100:1057:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1058": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1058",
"route_identifier": "10.169.197.254",
"routes": {
"100:1058:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1059": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1059",
"route_identifier": "10.169.197.254",
"routes": {
"100:1059:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1060": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1060",
"route_identifier": "10.169.197.254",
"routes": {
"100:1060:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1061": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1061",
"route_identifier": "10.169.197.254",
"routes": {
"100:1061:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1062": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1062",
"route_identifier": "10.169.197.254",
"routes": {
"100:1062:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1063": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1063",
"route_identifier": "10.169.197.254",
"routes": {
"100:1063:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1064": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1064",
"route_identifier": "10.169.197.254",
"routes": {
"100:1064:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1065": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1065",
"route_identifier": "10.169.197.254",
"routes": {
"100:1065:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1066": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1066",
"route_identifier": "10.169.197.254",
"routes": {
"100:1066:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1067": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1067",
"route_identifier": "10.169.197.254",
"routes": {
"100:1067:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1068": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1068",
"route_identifier": "10.169.197.254",
"routes": {
"100:1068:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1069": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1069",
"route_identifier": "10.169.197.254",
"routes": {
"100:1069:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
"l2vpn vpls RD 100:1070": {
"bgp_table_version": 1841,
"default_vrf": "default",
"route_distinguisher": "100:1070",
"route_identifier": "10.169.197.254",
"routes": {
"100:1070:VEID-2:Blk-1/136": {
"index": {
1: {
"next_hop": "0.0.0.0",
"origin_codes": "?",
"status_codes": "*>",
"weight": 32768,
}
}
}
},
},
}
}
}
}
| 42.162602
| 58
| 0.244183
| 886
| 15,558
| 4.104966
| 0.063205
| 0.032994
| 0.032994
| 0.076987
| 0.919989
| 0.919989
| 0.919989
| 0.919989
| 0.727523
| 0.727523
| 0
| 0.187827
| 0.644106
| 15,558
| 368
| 59
| 42.277174
| 0.469027
| 0
| 0
| 0.543478
| 0
| 0
| 0.256074
| 0.032138
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 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
|
6e8bcd5fe0ebb720691889c3dc12602ada257289
| 4,911
|
py
|
Python
|
config_test.py
|
anhlt59/Cgnat
|
14fd02075b97e25bc71e95d7bde0f6d325745e78
|
[
"MIT"
] | null | null | null |
config_test.py
|
anhlt59/Cgnat
|
14fd02075b97e25bc71e95d7bde0f6d325745e78
|
[
"MIT"
] | null | null | null |
config_test.py
|
anhlt59/Cgnat
|
14fd02075b97e25bc71e95d7bde0f6d325745e78
|
[
"MIT"
] | null | null | null |
"""File config for test."""
# !/usr/bin/python
# -*- coding: utf-8 -*-
import asyncio
KIBANA = {"result": True, "data": [{'card': '11/0/0',
'device_ip': '118.70.0.137',
'device_name': 'HNI-MX960-LAB',
'fpc_slot': '11',
'log_message': 'SPD_CONN_OPEN_FAILURE',
'pic_slot': '0',
'time_stamp': '2018-12-26 16:50:00'}]}
# KIBANA_LOG_1 = {"result": True, "data": [{'card': '11/0/0',
# 'device_ip': '118.70.0.137',
# 'device_name': 'HNI-TEST',
# 'fpc_slot': '11',
# 'log_message': 'SPD_CONN_OPEN_FAILURE',
# 'pic_slot': '0',
# 'time_stamp': '2018-09-23 07:17:02'}]}
# KIBANA_LOG_2 = {"result": True, "data": [{'card': '11/0/0',
# 'device_ip': '118.70.0.137',
# 'device_name': 'HNI-TEST',
# 'fpc_slot': '11',
# 'log_message': 'Unexpected shutdown of connection' +
# 'to datapath-traced',
# 'pic_slot': '0',
# 'time_stamp': '2018-10-17 12:50:10'},
# {'card': '11/1/0',
# 'device_ip': '118.70.0.137',
# 'device_name': 'HNI-TEST',
# 'fpc_slot': '11',
# 'log_message': 'Unexpected shutdown of connection to' +
# 'datapath-traced',
# 'pic_slot': '1',
# 'time_stamp': '2018-10-16 13:50:27'},
# {'card': '11/2/0',
# 'device_ip': '118.70.0.137',
# 'device_name': 'HNI-TEST',
# 'fpc_slot': '11',
# 'log_message': 'Unexpected shutdown of connection to' +
# 'datapath-traced',
# 'pic_slot': '2',
# 'time_stamp': '2018-10-16 13:50:27'}]}
LOG_1 = [{'device_name': 'HNI-TEST',
'device_ip': '118.70.0.137',
'fpc_slot': '11',
'pic_slot': '0',
'card': '11/0/0',
'status': 'OK',
'msg_aopt': 'Shutdown by AOPT',
'reason': 'Da reboot/shutdown >= 2 lan trong 120 phut',
'time_stamp': '2018-09-23 07:17:02'}
]
LOG_2 = [{'device_name': 'HNI-TEST',
'device_ip': '118.70.0.137',
'fpc_slot': '11',
'pic_slot': '0',
'card': '11/0/0',
'status': 'OK',
'msg_aopt': 'Shutdown by AOPT',
'reason': 'Da reboot/shutdown >= 2 lan trong 120 phut',
'time_stamp': '2018-09-23 07:17:02'},
{'device_name': 'HNI-TEST',
'device_ip': '118.70.0.137',
'fpc_slot': '11',
'pic_slot': '1',
'card': '11/1/0',
'status': 'OK',
'msg_aopt': 'Shutdown by AOPT',
'reason': 'Da reboot/shutdown >= 2 lan trong 120 phut',
'time_stamp': '2018-09-23 07:17:02'},
{'device_name': 'HNI-TEST',
'device_ip': '118.70.0.137',
'fpc_slot': '11',
'pic_slot': '2',
'card': '11/2/0',
'status': 'OK',
'msg_aopt': 'Shutdown by AOPT',
'reason': 'Da reboot/shutdown >= 2 lan trong 120 phut',
'time_stamp': '2018-09-23 07:17:02'},
]
async def uptime_check(n):
"""Test."""
await asyncio.sleep(12)
return n
async def shutdown_pic(n):
"""Test."""
await asyncio.sleep(12)
return n
async def reboot_pic(n):
"""Test."""
await asyncio.sleep(12)
return n
async def picup_traffic_check(n):
"""Test."""
await asyncio.sleep(12)
return n
async def traffic_check_by_hour(n):
"""Test."""
await asyncio.sleep(12)
return n
syncio.sleep(12)
return n
syncio.sleep(12)
return n
n n
syncio.sleep(12)
return n
io.sleep(12)
return n
syncio.sleep(12)
return n
syncio.sleep(12)
return n
syncio.sleep(12)
return n
syncio.sleep(12)
return n
syncio.sleep(12)
return n
n n
syncio.sleep(12)
return n
n n
syncio.sleep(12)
return n
n n
synio.sleep(12)
return n
| 33.182432
| 99
| 0.395032
| 519
| 4,911
| 3.595376
| 0.169557
| 0.063773
| 0.118435
| 0.127546
| 0.879421
| 0.879421
| 0.871919
| 0.871919
| 0.832797
| 0.832797
| 0
| 0.121933
| 0.45225
| 4,911
| 147
| 100
| 33.408163
| 0.571747
| 0.446956
| 0
| 0.764045
| 0
| 0
| 0.329615
| 0.008077
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.011236
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
6ec9c4327d36dd5d76d0eb2172bf02878249570e
| 131
|
py
|
Python
|
agent/discrete/seperate/__init__.py
|
SunandBean/tensorflow_RL
|
a248cbfb99b2041f6f7cc008fcad53fb83ac486e
|
[
"MIT"
] | 60
|
2019-01-29T14:13:00.000Z
|
2020-11-24T09:08:05.000Z
|
agent/discrete/seperate/__init__.py
|
SunandBean/tensorflow_RL
|
a248cbfb99b2041f6f7cc008fcad53fb83ac486e
|
[
"MIT"
] | 2
|
2019-08-14T06:44:32.000Z
|
2020-11-12T12:57:55.000Z
|
agent/discrete/seperate/__init__.py
|
SunandBean/tensorflow_RL
|
a248cbfb99b2041f6f7cc008fcad53fb83ac486e
|
[
"MIT"
] | 37
|
2019-01-22T05:19:34.000Z
|
2021-04-12T02:27:50.000Z
|
from agent.discrete.seperate.a2c import A2C
from agent.discrete.seperate.ppo import PPO
from agent.discrete.seperate.vpg import VPG
| 43.666667
| 43
| 0.847328
| 21
| 131
| 5.285714
| 0.380952
| 0.243243
| 0.459459
| 0.675676
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.016667
| 0.083969
| 131
| 3
| 44
| 43.666667
| 0.908333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
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| 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
|
42ca182cf775fb6b0e46f1f819f2066fb6e1c0c2
| 15,230
|
py
|
Python
|
src/unicon/plugins/tests/test_plugin_iosxe_cat9k.py
|
ykoehler/unicon.plugins
|
a38e887683552d82dac8dea79093882ccc54c3d9
|
[
"Apache-2.0"
] | null | null | null |
src/unicon/plugins/tests/test_plugin_iosxe_cat9k.py
|
ykoehler/unicon.plugins
|
a38e887683552d82dac8dea79093882ccc54c3d9
|
[
"Apache-2.0"
] | null | null | null |
src/unicon/plugins/tests/test_plugin_iosxe_cat9k.py
|
ykoehler/unicon.plugins
|
a38e887683552d82dac8dea79093882ccc54c3d9
|
[
"Apache-2.0"
] | null | null | null |
"""
Unittests for iosxe/cat9k plugin
"""
import unittest
import unicon
from unicon import Connection
from unicon.eal.dialogs import Statement, Dialog
from unicon.plugins.tests.mock.mock_device_iosxe import MockDeviceTcpWrapperIOSXE
from unicon.plugins.tests.mock.mock_device_iosxe_cat9k import MockDeviceTcpWrapperIOSXECat9k
unicon.settings.Settings.POST_DISCONNECT_WAIT_SEC = 0
unicon.settings.Settings.GRACEFUL_DISCONNECT_WAIT_SEC = 0.2
class TestIosXeCat9kPlugin(unittest.TestCase):
def test_connect(self):
d = Connection(hostname='Router',
start=['mock_device_cli --os iosxe --state c9k_login'],
os='iosxe',
platform='cat9k',
credentials=dict(default=dict(username='admin', password='cisco')),
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
log_buffer=True
)
d.connect()
d.disconnect()
def test_connect_learn_hostname(self):
d = Connection(hostname='Router',
start=['mock_device_cli --os iosxe --state c9k_login --hostname WLC'],
os='iosxe',
platform='cat9k',
credentials=dict(default=dict(username='admin', password='cisco')),
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
learn_hostname=True,
log_buffer=True
)
try:
d.connect()
self.assertEqual(d.hostname, 'WLC')
finally:
d.disconnect()
def test_connect_learn_hostname_config_mode(self):
d = Connection(hostname='Router',
start=['mock_device_cli --os iosxe --state c9k_config --hostname c9300-55'],
os='iosxe',
platform='cat9k',
credentials=dict(default=dict(username='admin', password='cisco')),
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
learn_hostname=True,
log_buffer=True,
connection_timeout=3
)
try:
d.connect()
self.assertEqual(d.hostname, 'c9300-55')
finally:
d.disconnect()
def test_boot_from_rommon(self):
md = MockDeviceTcpWrapperIOSXE(port=0, state='cat9k_rommon')
md.start()
c = Connection(
hostname='switch',
start=['telnet 127.0.0.1 {}'.format(md.ports[0])],
os='iosxe',
platform='cat9k',
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab'))
)
try:
c.connect()
self.assertEqual(c.state_machine.current_state, 'enable')
finally:
c.disconnect()
md.stop()
def test_reload_image_from_rommon(self):
md = MockDeviceTcpWrapperIOSXE(port=0, state='cat9k_rommon')
md.start()
c = Connection(
hostname='switch',
start=['telnet 127.0.0.1 {}'.format(md.ports[0])],
os='iosxe',
platform='cat9k',
mit=True,
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab'))
)
try:
c.connect()
self.assertEqual(c.state_machine.current_state, 'rommon')
c.execute('unlock flash:')
c.settings.POST_RELOAD_WAIT = 1
c.reload(image_to_boot='tftp://1.1.1.1/latest.bin')
self.assertEqual(c.state_machine.current_state, 'enable')
finally:
c.disconnect()
md.stop()
def test_connect_cat9k_rommon_init(self):
md = MockDeviceTcpWrapperIOSXECat9k(port=0, state='cat9k_rommon', hostname='R1')
md.start()
con = Connection(
hostname='R1',
start=[
'telnet 127.0.0.1 {}'.format(md.ports[0]),
],
os='iosxe',
platform='cat9k',
connection_timeout=10,
settings={'FIND_BOOT_IMAGE': False},
credentials=dict(default=dict(password='cisco')),
log_buffer=True,
image_to_boot='tftp://1.1.1.1/cat9k_iosxe.SSA.bin',
)
try:
con.connect()
except Exception:
raise
finally:
con.disconnect()
md.stop()
def test_connect_cat9k_rommon_init_commands(self):
md = MockDeviceTcpWrapperIOSXECat9k(port=0, state='cat9k_rommon', hostname='R1')
md.start()
con = Connection(
hostname='R1',
start=[
'telnet 127.0.0.1 {}'.format(md.ports[0]),
],
os='iosxe',
platform='cat9k',
connection_timeout=10,
settings={
'FIND_BOOT_IMAGE': False,
'ROMMON_INIT_COMMANDS': [
'set',
'ping 1.1.1.1'
]
},
credentials=dict(default=dict(password='cisco')),
log_buffer=True,
image_to_boot='tftp://1.1.1.1/cat9k_iosxe.SSA.bin',
)
try:
con.connect()
except Exception:
raise
finally:
con.disconnect()
md.stop()
def test_connect_cat9k_ha_rommon_init_commands(self):
md = MockDeviceTcpWrapperIOSXECat9k(port=0, state='cat9k_ha_active_rommon,cat9k_ha_standby_rommon')
md.start()
c = Connection(
hostname='switch',
start=[
'telnet 127.0.0.1 {}'.format(md.ports[0]),
'telnet 127.0.0.1 {}'.format(md.ports[1]),
],
os='iosxe',
platform='cat9k',
log_buffer=True,
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
settings={
'FIND_BOOT_IMAGE': False,
'ROMMON_INIT_COMMANDS': [
'set',
'ping 1.1.1.1'
]
}
)
try:
c.connect()
self.assertEqual(c.state_machine.current_state, 'enable')
self.assertEqual(c.hostname, 'switch')
finally:
c.disconnect()
md.stop()
def test_connect_cat9k_ha_rommon_init_commands_learn_hostname(self):
md = MockDeviceTcpWrapperIOSXECat9k(port=0, state='cat9k_ha_active_rommon,cat9k_ha_standby_rommon')
md.start()
c = Connection(
hostname='switch',
start=[
'telnet 127.0.0.1 {}'.format(md.ports[0]),
'telnet 127.0.0.1 {}'.format(md.ports[1]),
],
os='iosxe',
platform='cat9k',
log_buffer=True,
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
settings={
'FIND_BOOT_IMAGE': False,
'ROMMON_INIT_COMMANDS': [
'set',
'ping 1.1.1.1'
]
},
learn_hostname=True
)
try:
c.connect()
self.assertEqual(c.state_machine.current_state, 'enable')
self.assertEqual(c.hostname, 'Router')
finally:
c.disconnect()
md.stop()
def test_connect_cat9k_ha_learn_hostname(self):
md = MockDeviceTcpWrapperIOSXECat9k(hostname='R1', port=0, state='cat9k_ha_active_enable,cat9k_ha_standby_enable')
md.start()
c = Connection(
hostname='switch',
start=[
'telnet 127.0.0.1 {}'.format(md.ports[0]),
'telnet 127.0.0.1 {}'.format(md.ports[1]),
],
os='iosxe',
platform='cat9k',
log_buffer=True,
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
learn_hostname=True
)
try:
c.connect()
self.assertEqual(c.state_machine.current_state, 'enable')
self.assertEqual(c.hostname, 'R1')
finally:
c.disconnect()
md.stop()
class TestIosXECat9kPluginReload(unittest.TestCase):
def test_reload(self):
md = MockDeviceTcpWrapperIOSXE(port=0, state='c9k_login4')
md.start()
c = Connection(
hostname='switch',
start=['telnet 127.0.0.1 {}'.format(md.ports[0])],
os='iosxe',
platform='cat9k',
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
mit=True
)
try:
c.connect()
c.settings.POST_RELOAD_WAIT = 1
c.reload()
self.assertEqual(c.state_machine.current_state, 'enable')
finally:
c.disconnect()
md.stop()
def test_rommon(self):
c = Connection(hostname='switch',
start=['mock_device_cli --os iosxe --state cat9k_enable_reload_to_rommon'],
os='iosxe',
platform='cat9k',
mit=True,
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
log_buffer=True)
c.connect()
c.rommon()
self.assertEqual(c.state_machine.current_state, 'rommon')
c.disconnect()
def test_rommon_enable_break(self):
c = Connection(hostname='switch',
start=['mock_device_cli --os iosxe --state cat9k_enable_reload_to_rommon_break'],
os='iosxe',
platform='cat9k',
mit=True,
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
log_buffer=True)
c.connect()
c.rommon()
self.assertEqual(c.state_machine.current_state, 'rommon')
c.disconnect()
def test_reload_with_image(self):
c = Connection(hostname='switch',
start=['mock_device_cli --os iosxe --state cat9k_enable_reload_to_rommon'],
os='iosxe',
platform='cat9k',
mit=True,
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
log_buffer=True)
c.connect()
c.settings.POST_RELOAD_WAIT = 1
c.reload(image_to_boot='tftp://1.1.1.1/latest.bin')
self.assertEqual(c.state_machine.current_state, 'enable')
c.disconnect()
def test_reload_ha(self):
md = MockDeviceTcpWrapperIOSXECat9k(port=0, state='cat9k_ha_active_escape,cat9k_ha_standby_escape')
md.start()
c = Connection(
hostname='switch',
start=[
'telnet 127.0.0.1 {}'.format(md.ports[0]),
'telnet 127.0.0.1 {}'.format(md.ports[1]),
],
os='iosxe',
platform='cat9k',
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
# debug=True
)
try:
c.connect()
c.settings.POST_RELOAD_WAIT = 1
c.reload()
self.assertEqual(c.state_machine.current_state, 'enable')
finally:
c.disconnect()
md.stop()
def test_reload_ha_adding_dialog(self):
md = MockDeviceTcpWrapperIOSXECat9k(port=0, state='cat9k_ha_active_escape,cat9k_ha_standby_escape')
md.start()
c = Connection(
hostname='switch',
start=[
'telnet 127.0.0.1 {}'.format(md.ports[0]),
'telnet 127.0.0.1 {}'.format(md.ports[1]),
],
os='iosxe',
platform='cat9k',
settings=dict(POST_DISCONNECT_WAIT_SEC=0, GRACEFUL_DISCONNECT_WAIT_SEC=0.2),
credentials=dict(default=dict(username='cisco', password='cisco'),
alt=dict(username='admin', password='lab')),
)
install_add_one_shot_dialog = Dialog([
Statement(pattern=r".*reload of the system\. "
r"Do you want to proceed\? \[y\/n\]",
action='sendline(y)',
loop_continue=True,
continue_timer=False),
])
try:
c.connect()
c.settings.POST_RELOAD_WAIT = 1
c.reload('install add file activate commit',
reply=install_add_one_shot_dialog,)
self.assertEqual(c.state_machine.current_state, 'enable')
finally:
c.disconnect()
md.stop()
class TestIosXeCat9kPluginContainer(unittest.TestCase):
def test_container_exit(self):
c = Connection(hostname='switch',
start=['mock_device_cli --os iosxe --state meraki_container_shell'],
os='iosxe',
platform='cat9k',
log_buffer=True,
init_config_commands=[])
c.connect()
c.disconnect()
def test_container_ssh(self):
c = Connection(hostname='switch',
start=['mock_device_cli --os iosxe --state meraki_container_ssh'],
os='iosxe',
platform='cat9k',
log_buffer=True,
mit=True,
init_config_commands=[])
c.connect()
c.disconnect()
if __name__ == '__main__':
unittest.main()
| 36.876513
| 122
| 0.521799
| 1,532
| 15,230
| 4.984987
| 0.097258
| 0.023831
| 0.053424
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| 0.891057
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| 0
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0
| 8
|
6e194059be07a63f808d864703410e4a38b9cd62
| 43
|
py
|
Python
|
__init__.py
|
osufx/national-gallery
|
0e429853c9d6c86bb0e9f1e4431a1aceb177824d
|
[
"Unlicense"
] | 1
|
2020-05-13T01:46:14.000Z
|
2020-05-13T01:46:14.000Z
|
__init__.py
|
osufx/national-gallery
|
0e429853c9d6c86bb0e9f1e4431a1aceb177824d
|
[
"Unlicense"
] | null | null | null |
__init__.py
|
osufx/national-gallery
|
0e429853c9d6c86bb0e9f1e4431a1aceb177824d
|
[
"Unlicense"
] | 5
|
2018-11-26T20:35:20.000Z
|
2021-04-29T02:55:15.000Z
|
from . import utils
from . import handlers
| 14.333333
| 22
| 0.767442
| 6
| 43
| 5.5
| 0.666667
| 0.606061
| 0
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| 43
| 2
| 23
| 21.5
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| 1
| 0
| 1
| 0
|
0
| 7
|
284dd419bdf1a99e088b089789790e6c3198887d
| 1,572
|
py
|
Python
|
test/statements/if1.py
|
Setonas/MagicSetonas
|
ef76da5f27a0506b194c58072b81424e3ce985d7
|
[
"MIT"
] | 5
|
2017-02-22T10:17:39.000Z
|
2021-04-06T16:36:13.000Z
|
test/statements/if1.py
|
Setonas/MagicSetonas
|
ef76da5f27a0506b194c58072b81424e3ce985d7
|
[
"MIT"
] | null | null | null |
test/statements/if1.py
|
Setonas/MagicSetonas
|
ef76da5f27a0506b194c58072b81424e3ce985d7
|
[
"MIT"
] | 1
|
2020-08-29T02:30:52.000Z
|
2020-08-29T02:30:52.000Z
|
jei (a jei b kitas c):
1
kijei b arba c ir d:
2
kitas:
3
jei : keyword.control.flow.python, source.python
: source.python
( : punctuation.parenthesis.begin.python, source.python
a : source.python
: source.python
jei : keyword.control.flow.python, source.python
: source.python
b : source.python
: source.python
kitas : keyword.control.flow.python, source.python
: source.python
c : source.python
) : punctuation.parenthesis.end.python, source.python
: : punctuation.separator.colon.python, source.python
: source.python
1 : constant.numeric.dec.python, source.python
kijei : keyword.control.flow.python, source.python
: source.python
b : source.python
: source.python
arba : keyword.operator.logical.python, source.python
: source.python
c : source.python
: source.python
ir : keyword.operator.logical.python, source.python
: source.python
d : source.python
: : punctuation.separator.colon.python, source.python
: source.python
2 : constant.numeric.dec.python, source.python
kitas : keyword.control.flow.python, source.python
: : punctuation.separator.colon.python, source.python
: source.python
3 : constant.numeric.dec.python, source.python
| 35.727273
| 67
| 0.562977
| 160
| 1,572
| 5.53125
| 0.175
| 0.461017
| 0.569492
| 0.352542
| 0.821469
| 0.821469
| 0.719774
| 0.719774
| 0.559322
| 0.498305
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| 0.349237
| 1,572
| 43
| 68
| 36.55814
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0
| 8
|
28711f53c440adb28e34267e91127c4433faee27
| 178
|
py
|
Python
|
additional/__init__.py
|
Vladimir37/Sanelotto
|
94dfa1dfc74776cc6a954d26b6ce5d38f2cf6bf1
|
[
"MIT"
] | 8
|
2016-03-21T17:09:02.000Z
|
2019-01-11T20:22:31.000Z
|
additional/__init__.py
|
Vladimir37/Sanelotto
|
94dfa1dfc74776cc6a954d26b6ce5d38f2cf6bf1
|
[
"MIT"
] | null | null | null |
additional/__init__.py
|
Vladimir37/Sanelotto
|
94dfa1dfc74776cc6a954d26b6ce5d38f2cf6bf1
|
[
"MIT"
] | null | null | null |
from additional import signals
from additional import creating
from additional import start_local
from additional import start_server
from additional.sshpass import ssh_exec_pass
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| 25
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0
| 8
|
2895e678aeff5441e19a1cb1cdffd8acbb9d95aa
| 129
|
py
|
Python
|
regym/rl_loops/multiagent_loops/__init__.py
|
KnwSondess/Regym
|
825c7dacf955a3e2f6c658c0ecb879a0ca036c1a
|
[
"MIT"
] | 2
|
2020-09-13T15:53:20.000Z
|
2020-12-08T15:57:05.000Z
|
regym/rl_loops/multiagent_loops/__init__.py
|
KnwSondess/Regym
|
825c7dacf955a3e2f6c658c0ecb879a0ca036c1a
|
[
"MIT"
] | null | null | null |
regym/rl_loops/multiagent_loops/__init__.py
|
KnwSondess/Regym
|
825c7dacf955a3e2f6c658c0ecb879a0ca036c1a
|
[
"MIT"
] | 1
|
2021-09-20T13:48:30.000Z
|
2021-09-20T13:48:30.000Z
|
from . import simultaneous_action_rl_loop
from . import sequential_action_rl_loop
from .self_play_loop import self_play_training
| 32.25
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|
0
| 7
|
954a4ddcd9804daa85bae2208da544d241aea992
| 9,351
|
py
|
Python
|
MY-Syok-Bot/bot/draw.py
|
josephkokchin/MY-Syok-Bot
|
dad0ad4ea0f89717e19e0901990bada8b29927cc
|
[
"MIT"
] | null | null | null |
MY-Syok-Bot/bot/draw.py
|
josephkokchin/MY-Syok-Bot
|
dad0ad4ea0f89717e19e0901990bada8b29927cc
|
[
"MIT"
] | null | null | null |
MY-Syok-Bot/bot/draw.py
|
josephkokchin/MY-Syok-Bot
|
dad0ad4ea0f89717e19e0901990bada8b29927cc
|
[
"MIT"
] | null | null | null |
##
# @author Joseph Goh
# @email [joseph.kokchin.goh@outlook.com]
# @create date 2019-07-20 15:09:24
# @modify date 2019-07-20 15:09:24
# @desc [The following code will extract the 4D Results]
#/
""" Lucky Draw Methods """
from requests import get
from parsel import Selector as sel
def Magnum4D():
"""Magnum4D Methods!"""
# Connect to Source
url='https://www.gidapp.com/lottery/malaysia/4d'
data=get(url)
# Find latest Result
latest_result_date=sel(text=data.text).xpath('.//article/div/div[1]/header/div/h5[1]/time/text()').get()
# TOP PRIZES
first=sel(text=data.text).xpath('.//article/div/div[1]/table/tbody/tr[2]/td[1]/span/text()').get()
second=sel(text=data.text).xpath('.//article/div/div[1]/table/tbody/tr[2]/td[2]/span/text()').get()
third=sel(text=data.text).xpath('.//article/div/div[1]/table/tbody/tr[2]/td[3]/span/text()').get()
top_text = "1st - " + first + "\n2nd - " + second + "\n3rd - " + third + "\n\n"
# SPECIAL PRIZE
special_prize_ls=sel(text=data.text).xpath('.//article/div/div[1]/table/tbody/tr[4]/td//text()').getall()
special_prize_ls=list(filter(lambda a: a !=' ', special_prize_ls))
special_prize = "Special/Starter Prizes\n\n"
for i in special_prize_ls:
special_prize += i + " "
# CONSOLATION PRIZE
consol_prize_ls=sel(text=data.text).xpath('.//article/div/div[1]/table/tbody/tr[6]/td//text()').getall()
consol_prize_ls=list(filter(lambda a: a !=' ', consol_prize_ls))
consol_prize = "\n\nConsolation Prizes\n\n"
for i in consol_prize_ls:
consol_prize += i + " "
# Create Reply
chat_reply = "<b>Latest Magnum 4D Draw Results on " + latest_result_date + "</b> \n\n"
chat_reply += top_text
chat_reply += special_prize
chat_reply += consol_prize
chat_reply += "\n\n No need check la sure never win!"
return chat_reply
def TOTO4D():
"""TOTO4D Methods!"""
# Connect to Source
url='https://www.gidapp.com/lottery/malaysia/4d'
data=get(url)
# Find latest Result
latest_result_date=sel(text=data.text).xpath('.//article/div/div[2]/header/div/h5[1]/time/text()').get()
# TOP PRIZES
first=sel(text=data.text).xpath('.//article/div/div[2]/table/tbody/tr[2]/td[1]/span/text()').get()
second=sel(text=data.text).xpath('.//article/div/div[2]/table/tbody/tr[2]/td[2]/span/text()').get()
third=sel(text=data.text).xpath('.//article/div/div[2]/table/tbody/tr[2]/td[3]/span/text()').get()
top_text = "1st - " + first + "\n2nd - " + second + "\n3rd - " + third + "\n\n"
# SPECIAL PRIZE
special_prize_ls=sel(text=data.text).xpath('.//article/div/div[2]/table/tbody/tr[4]/td//text()').getall()
special_prize_ls=list(filter(lambda a: a !=' ', special_prize_ls))
special_prize = "Special/Starter Prizes\n\n"
for i in special_prize_ls:
special_prize += i + " "
# CONSOLATION PRIZE
consol_prize_ls=sel(text=data.text).xpath('.//article/div/div[2]/table/tbody/tr[6]/td//text()').getall()
consol_prize_ls=list(filter(lambda a: a !=' ', consol_prize_ls))
consol_prize = "\n\nConsolation Prizes\n\n"
for i in consol_prize_ls:
consol_prize += i + " "
# Create Reply
chat_reply = "<b>Latest Sports TOTO 4D Draw Results on " + latest_result_date + "</b> \n\n"
chat_reply += top_text
chat_reply += special_prize
chat_reply += consol_prize
chat_reply += "\n\n No need check la sure never win!"
return chat_reply
def DaMaCai4D():
"""DaMaCai4D Methods!"""
# Connect to Source
url='https://www.gidapp.com/lottery/malaysia/4d'
data=get(url)
# Find latest Result
latest_result_date=sel(text=data.text).xpath('.//article/div/div[3]/header/div/h5[1]/time/text()').get()
# TOP PRIZES
first=sel(text=data.text).xpath('.//article/div/div[3]/table/tbody/tr[2]/td[1]/span/text()').get()
second=sel(text=data.text).xpath('.//article/div/div[3]/table/tbody/tr[2]/td[2]/span/text()').get()
third=sel(text=data.text).xpath('.//article/div/div[3]/table/tbody/tr[2]/td[3]/span/text()').get()
top_text = "1st - " + first + "\n2nd - " + second + "\n3rd - " + third + "\n\n"
# SPECIAL PRIZE
special_prize_ls=sel(text=data.text).xpath('.//article/div/div[3]/table/tbody/tr[4]/td//text()').getall()
special_prize_ls=list(filter(lambda a: a !=' ', special_prize_ls))
special_prize = "Special/Starter Prizes\n\n"
for i in special_prize_ls:
special_prize += i + " "
# CONSOLATION PRIZE
consol_prize_ls=sel(text=data.text).xpath('.//article/div/div[3]/table/tbody/tr[6]/td//text()').getall()
consol_prize_ls=list(filter(lambda a: a !=' ', consol_prize_ls))
consol_prize = "\n\nConsolation Prizes\n\n"
for i in consol_prize_ls:
consol_prize += i + " "
# Create Reply
chat_reply = "<b>Latest DaMaCai 4D Draw Results on " + latest_result_date + "</b> \n\n"
chat_reply += top_text
chat_reply += special_prize
chat_reply += consol_prize
chat_reply += "\n\n No need check la sure never win!"
return chat_reply
def DaMaCai3D():
"""DaMaCai3D Methods!"""
# Connect to Source
url='https://www.gidapp.com/lottery/malaysia/damacai'
data=get(url)
# Find latest Result
latest_result_date=sel(text=data.text).xpath('.//*[@id="result-prizes3"]/div/div/h5[1]/b/time/text()').get()
# TOP PRIZES
first=sel(text=data.text).xpath('.//*[@id="result-prizes3"]/table/tbody/tr/td[1]/p/span/text()').get()
second=sel(text=data.text).xpath('.//*[@id="result-prizes3"]/table/tbody/tr/td[1]/p/span/text()').get()
third=sel(text=data.text).xpath('.//*[@id="result-prizes3"]/table/tbody/tr/td[1]/p/span/text()').get()
top_text = "1st - " + first + "\n2nd - " + second + "\n3rd - " + third
# Create Reply
chat_reply = "<b>Latest DaMaCai 3D Draw Results on " + latest_result_date + "</b> \n\n"
chat_reply += top_text
chat_reply += "\n\n No need check la sure never win!"
return chat_reply
def DaMaCai3DJackPot():
"""DaMaCai3DJackPot Methods!"""
# Connect to Source
url='https://www.gidapp.com/lottery/malaysia/damacai'
data=get(url)
# Find latest Result
latest_result_date=sel(text=data.text).xpath('.//*[@id="result-jackpot3"]/div/div/h5[1]/b/time/text()').get()
# Winning numbers
winning_numbers= "Winning Numbers\n\n"
for i in range(1,(len(sel(text=data.text).xpath('//*[@id="result-jackpot3"]/table/tbody/tr/td/p/span')))+1):
result=sel(text=data.text).xpath('.//*[@id="result-jackpot3"]/table/tbody/tr/td/p/span'+"["+str(i)+"]"+'//text()').getall()
result=''.join(result)
winning_numbers+=result + " "
# Create Reply
chat_reply = "<b>Latest DaMaCai 3D JackPot Draw Results on " + latest_result_date + "</b> \n\n"
chat_reply += winning_numbers
chat_reply += "\n\n No need check la sure never win!"
return chat_reply
def DaMaCai4DJackPot():
"""DaMaCai4DJackPot Methods!"""
# Connect to Source
url='https://www.gidapp.com/lottery/malaysia/damacai'
data=get(url)
# Find latest Result
latest_result_date=sel(text=data.text).xpath('.//*[@id="result-jackpot3"]/div/div/h5[1]/b/time/text()').get()
# Winning numbers
winning_numbers= "Winning Numbers\n\n"
for i in range(1,(len(sel(text=data.text).xpath('//*[@id="result-jackpot3"]/table/tbody/tr/td/p/span')))+1):
result=sel(text=data.text).xpath('.//*[@id="result-jackpot3"]/table/tbody/tr/td/p/span'+"["+str(i)+"]"+'//text()').getall()
result=''.join(result)
winning_numbers+=result + " "
# Create Reply
chat_reply = "<b>Latest DaMaCai 3D JackPot Draw Results on " + latest_result_date + "</b> \n\n"
chat_reply += winning_numbers
chat_reply += "\n\n No need check la sure never win!"
return chat_reply
def DaMaCaiDMCJackPot():
"""DaMaCaiDMCJackPot Methods!"""
# Connect to Source
url='https://www.gidapp.com/lottery/malaysia/damacai'
data=get(url)
# Find latest Result
latest_result_date=sel(text=data.text).xpath('.//*[@id="result-jackpotdmc"]/div/div/h5[1]/b/time/text()').get()
# Winning numbers 1
jp1_winning_numbers= "Jackpot1 Winning Numbers\n\n"
for i in range(1,(len(sel(text=data.text).xpath('//*[@id="result-jackpotdmc"]/table[1]/tbody/tr/td/p[1]/span')))+1):
result=sel(text=data.text).xpath('.//*[@id="result-jackpotdmc"]/table[1]/tbody/tr/td/p[1]/span'+"["+str(i)+"]"+'//text()').getall()
result=''.join(result)
jp1_winning_numbers+=result + " "
# Winning numbers 2
jp2_winning_numbers= "\n\nJackpot2 Winning Numbers\n\n"
for i in range(1,(len(sel(text=data.text).xpath('//*[@id="result-jackpotdmc"]/table[2]/tbody/tr/td/p[1]/span')))+1):
result=sel(text=data.text).xpath('.//*[@id="result-jackpotdmc"]/table[2]/tbody/tr/td/p[1]/span'+"["+str(i)+"]"+'//text()').getall()
result=''.join(result)
jp2_winning_numbers+=result + " "
# Create Reply
chat_reply = "<b>Latest DaMaCai DMC JackPot Draw Results on " + latest_result_date + "</b> \n\n"
chat_reply += jp1_winning_numbers
chat_reply += jp2_winning_numbers
chat_reply += "\n\n No need check la sure never win!"
return chat_reply
if __name__ == "__main__":
DaMaCai3DJackPot()
| 39.125523
| 139
| 0.636509
| 1,402
| 9,351
| 4.129101
| 0.095578
| 0.054414
| 0.062705
| 0.085507
| 0.913629
| 0.913629
| 0.913629
| 0.906201
| 0.895319
| 0.8929
| 0
| 0.020249
| 0.165544
| 9,351
| 238
| 140
| 39.289916
| 0.721646
| 0.097316
| 0
| 0.62406
| 0
| 0.218045
| 0.378365
| 0.216705
| 0.030075
| 0
| 0
| 0
| 0
| 1
| 0.052632
| false
| 0
| 0.015038
| 0
| 0.120301
| 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
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9565d2c5f39a696f3936ae6b6de954a264972b45
| 1,640
|
py
|
Python
|
Numbers/calculator.py
|
Kchakz/Basic-Python-Projects
|
a84e7e75488f1b5343e0997b75e2d0d6233ed13b
|
[
"Apache-2.0"
] | null | null | null |
Numbers/calculator.py
|
Kchakz/Basic-Python-Projects
|
a84e7e75488f1b5343e0997b75e2d0d6233ed13b
|
[
"Apache-2.0"
] | null | null | null |
Numbers/calculator.py
|
Kchakz/Basic-Python-Projects
|
a84e7e75488f1b5343e0997b75e2d0d6233ed13b
|
[
"Apache-2.0"
] | null | null | null |
def add(x, y):
return x + y
def subtract(x, y):
return x - y
def multiply(x, y):
return x * y
def divide(x, y):
return x / y
a = "add"
b = "subtract"
c = "multiply"
d = "divide"
g = "yes"
h = "no"
print("Select Operation")
print("a.", a)
print("b.", b)
print("c.", c)
print("d.", d)
e = input()
if e in ('a', 'b', 'c', 'd'):
num1 = float(input("Enter first number:"))
num2 = float(input("Enter second number:"))
if e == 'a':
print(num1, "+", num2, "=", add(num1, num2))
elif e == 'b':
print(num1, "-", num2, "=", subtract(num1, num2))
elif e == 'c':
print(num1, "*", num2, "=", multiply(num1, num2))
elif e == 'd':
print(num1, "/", num2, "=", divide(num1, num2))
print("Do you want to continue?")
f = input()
if f == g:
while True:
print("Select Operation")
print("a.", a)
print("b.", b)
print("c.", c)
print("d.", d)
e = input()
if e in ('a', 'b', 'c', 'd'):
num1 = float(input("Enter first number:"))
num2 = float(input("Enter second number:"))
if e == 'a':
print(num1, "+", num2, "=", add(num1, num2))
elif e == 'b':
print(num1, "-", num2, "=", subtract(num1, num2))
elif e == 'c':
print(num1, "*", num2, "=", multiply(num1, num2))
elif e == 'd':
print(num1, "/", num2, "=", divide(num1, num2))
print("Do you want to continue?")
f = input()
if f == g:
pass
else:
quit()
else:
quit()
| 18.850575
| 65
| 0.439024
| 213
| 1,640
| 3.380282
| 0.197183
| 0.177778
| 0.144444
| 0.108333
| 0.870833
| 0.856944
| 0.802778
| 0.802778
| 0.802778
| 0.802778
| 0
| 0.033676
| 0.348171
| 1,640
| 86
| 66
| 19.069767
| 0.63985
| 0
| 0
| 0.733333
| 0
| 0
| 0.143902
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.066667
| false
| 0.016667
| 0
| 0.066667
| 0.133333
| 0.333333
| 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
|
9580fda5c6a05c2cf3556b26217a12eb359a40ef
| 126
|
py
|
Python
|
map.py
|
Mantis-Maniac/intro2python
|
111dec4def3f08ea6a16719e34d204a075a2d73f
|
[
"MIT"
] | null | null | null |
map.py
|
Mantis-Maniac/intro2python
|
111dec4def3f08ea6a16719e34d204a075a2d73f
|
[
"MIT"
] | null | null | null |
map.py
|
Mantis-Maniac/intro2python
|
111dec4def3f08ea6a16719e34d204a075a2d73f
|
[
"MIT"
] | null | null | null |
def f(x):
return x*x
#print f(100)
print map(f, [1, 2, 3, 4, 5, 6, 7, 8, 9])
print map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9])
| 18
| 43
| 0.47619
| 33
| 126
| 1.818182
| 0.515152
| 0.266667
| 0.1
| 0.133333
| 0.3
| 0.3
| 0.3
| 0.3
| 0.3
| 0
| 0
| 0.225806
| 0.261905
| 126
| 6
| 44
| 21
| 0.419355
| 0.095238
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.5
| 0
| 0
| 1
| null | 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
95a958d6466fb71ce62e06c949210bcc1d5060ac
| 124
|
py
|
Python
|
hlhi/time_difference.py
|
safuya/hlhi
|
a12d3e6b2245cd2dc89f2c1548d91672286d0b1f
|
[
"MIT"
] | null | null | null |
hlhi/time_difference.py
|
safuya/hlhi
|
a12d3e6b2245cd2dc89f2c1548d91672286d0b1f
|
[
"MIT"
] | null | null | null |
hlhi/time_difference.py
|
safuya/hlhi
|
a12d3e6b2245cd2dc89f2c1548d91672286d0b1f
|
[
"MIT"
] | null | null | null |
from datetime import datetime, timedelta
def run(bought: datetime, sold: datetime) -> timedelta:
return sold - bought
| 20.666667
| 55
| 0.741935
| 15
| 124
| 6.133333
| 0.6
| 0.369565
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177419
| 124
| 5
| 56
| 24.8
| 0.901961
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 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
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
251c11816189814275a2228aeb01d4f175d19099
| 36,766
|
py
|
Python
|
tests/test_sdc_resource_properties.py
|
krasm/python-onapsdk
|
87cd3017fc542a8afd3be51fbd89934ed87ed3a7
|
[
"Apache-2.0"
] | 4
|
2020-06-13T04:51:27.000Z
|
2021-01-06T15:00:51.000Z
|
tests/test_sdc_resource_properties.py
|
krasm/python-onapsdk
|
87cd3017fc542a8afd3be51fbd89934ed87ed3a7
|
[
"Apache-2.0"
] | 5
|
2019-11-26T16:15:15.000Z
|
2021-04-08T08:03:18.000Z
|
tests/test_sdc_resource_properties.py
|
krasm/python-onapsdk
|
87cd3017fc542a8afd3be51fbd89934ed87ed3a7
|
[
"Apache-2.0"
] | 8
|
2020-08-28T10:56:02.000Z
|
2022-02-11T17:06:03.000Z
|
from unittest import mock
import pytest
from onapsdk.exceptions import ParameterError
from onapsdk.sdc.properties import Input, Property
from onapsdk.sdc.sdc_resource import SdcResource
from onapsdk.sdc.service import Service
from onapsdk.sdc.vf import Vf
from onapsdk.sdc.vl import Vl
INPUTS = {
'inputs': [
{
'uniqueId': '9ee5fb23-4c4a-46bd-8682-68698559ee9c.skip_post_instantiation_configuration',
'type': 'boolean',
'required': False,
'definition': False,
'defaultValue': 'true',
'description': None,
'schema': None,
'password': False,
'name': 'skip_post_instantiation_configuration',
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': '9ee5fb23-4c4a-46bd-8682-68698559ee9c',
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'inputs': None,
'properties': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': '9ee5fb23-4c4a-46bd-8682-68698559ee9c',
'empty': False
},
{
'uniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079.test',
'type': 'string',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': {
'derivedFrom': None,
'constraints': None,
'properties': None,
'property': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'version': None,
'ownerId': None,
'empty': False,
'type': None
},
'password': False,
'name': 'test',
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'propertyId': '4a84415b-4580-4a78-aa33-501f0cd3d079.sraka',
'parentPropertyType': 'string',
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': 'cs0008',
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'inputs': None,
'properties': None,
'schemaType': '',
'schemaProperty': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'getInputProperty': False,
'version': None,
'ownerId': 'cs0008',
'empty': False
},
{
'uniqueId': '9ee5fb23-4c4a-46bd-8682-68698559ee9c.controller_actor',
'type': 'string',
'required': False,
'definition': False,
'defaultValue': 'SO-REF-DATA',
'description': None,
'schema': None,
'password': False,
'name': 'controller_actor',
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': '9ee5fb23-4c4a-46bd-8682-68698559ee9c',
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'inputs': None,
'properties': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': '9ee5fb23-4c4a-46bd-8682-68698559ee9c',
'empty': False
},
{
'uniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079.lililili',
'type': 'list',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': {
'derivedFrom': None,
'constraints': None,
'properties': None,
'property': {
'uniqueId': None,
'type': 'abc',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'version': None,
'ownerId': None,
'empty': False,
'type': None
},
'password': False,
'name': 'lililili',
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': True,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'inputs': None,
'properties': None,
'schemaType': 'abc',
'schemaProperty': {
'uniqueId': None,
'type': 'abc',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
}
]
}
PROPERTIES = {
"properties": [{
'uniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079.llllll',
'type': 'integer',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': {
'derivedFrom': None,
'constraints': None,
'properties': None,
'property': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'version': None,
'ownerId': None,
'empty': False,
'type': None
},
'password': False,
'name': 'llllll',
'value': '{"get_input":["lililili","INDEX","llllll"]}',
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'getInputValues': [
{
'propName': None,
'inputName': 'lililili',
'inputId': '4a84415b-4580-4a78-aa33-501f0cd3d079.lililili',
'indexValue': None,
'getInputIndex': None,
'list': False,
'version': None,
'ownerId': None,
'empty': False,
'type': None
}
],
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'schemaType': '',
'schemaProperty': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'getInputProperty': True,
'version': None,
'ownerId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'empty': False
},
{
'uniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079.test',
'type': 'string',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': {
'derivedFrom': None,
'constraints': None,
'properties': None,
'property': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'version': None,
'ownerId': None,
'empty': False,
'type': None
},
'password': False,
'name': 'test',
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'getInputValues': [],
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'schemaType': '',
'schemaProperty': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'getInputProperty': True,
'version': None,
'ownerId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'empty': False
},
{
'uniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079.yyy',
'type': 'string',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': {
'derivedFrom': None,
'constraints': None,
'properties': None,
'property': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'version': None,
'ownerId': None,
'empty': False,
'type': None
},
'password': False,
'name': 'yyy',
'value': 'lalala',
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'schemaType': '',
'schemaProperty': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'getInputProperty': False,
'version': None,
'ownerId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'empty': False
},
{
'uniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079.test2',
'type': 'boolean',
'required': False,
'definition': False,
'defaultValue': None,
'description': 'test2',
'schema': {
'derivedFrom': None,
'constraints': None,
'properties': None,
'property': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'version': None,
'ownerId': None,
'empty': False,
'type': None
},
'password': False,
'name': 'test2',
'value': '{"get_input":"test2"}',
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'getInputValues': [
{
'propName': None,
'inputName': 'test2',
'inputId': '4a84415b-4580-4a78-aa33-501f0cd3d079.test2',
'indexValue': None,
'getInputIndex': None,
'list': False,
'version': None,
'ownerId': None,
'empty': False,
'type': None
}
],
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'schemaType': '',
'schemaProperty': {
'uniqueId': None,
'type': '',
'required': False,
'definition': False,
'defaultValue': None,
'description': None,
'schema': None,
'password': False,
'name': None,
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': None,
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': None,
'empty': False
},
'getInputProperty': True,
'version': None,
'ownerId': '4a84415b-4580-4a78-aa33-501f0cd3d079',
'empty': False
}]
}
VL_PROPERTIES = {
"properties": [{
'uniqueId': 'd37cd65e-9842-4490-9343-a1a874e6b52a.network_role',
'type': 'string',
'required': False,
'definition': False,
'defaultValue': None,
'description': 'Unique label that defines the role that this network performs. example: vce oam network, vnat sr-iov1 network\n',
'schema': None,
'password': False,
'name': 'network_role',
'value': None,
'label': None,
'hidden': False,
'immutable': False,
'inputPath': None,
'status': None,
'inputId': None,
'instanceUniqueId': None,
'propertyId': None,
'parentPropertyType': None,
'subPropertyInputPath': None,
'annotations': None,
'parentUniqueId': '1af9771b-0f79-4e98-8747-30fd06da85cb',
'getInputValues': None,
'isDeclaredListInput': False,
'getPolicyValues': None,
'propertyConstraints': None,
'constraints': None,
'schemaType': None,
'schemaProperty': None,
'getInputProperty': False,
'version': None,
'ownerId': '1af9771b-0f79-4e98-8747-30fd06da85cb',
'empty': False
}]
}
@mock.patch.object(Service, "send_message_json")
@mock.patch.object(Service, "send_message")
def test_service_properties(mock_send, mock_send_json):
service = Service(name="test")
service.unique_identifier = "toto"
mock_send_json.return_value = {}
assert len(list(service.properties)) == 0
mock_send_json.return_value = PROPERTIES
properties_list = list(service.properties)
assert len(properties_list) == 4
prop1, prop2, prop3, prop4 = properties_list
mock_send_json.return_value = INPUTS
assert prop1.sdc_resource == service
assert prop1.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.llllll"
assert prop1.name == "llllll"
assert prop1.property_type == "integer"
assert prop1.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop1.value == '{"get_input":["lililili","INDEX","llllll"]}'
assert prop1.description is None
assert prop1.get_input_values
prop1_input = prop1.input
assert prop1_input.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.lililili"
assert prop1_input.input_type == "list"
assert prop1_input.name == "lililili"
assert prop1_input.default_value is None
assert prop2.sdc_resource == service
assert prop2.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.test"
assert prop2.name == "test"
assert prop2.property_type == "string"
assert prop2.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop2.value is None
assert prop2.description is None
assert prop2.get_input_values == []
assert prop2.input is None
assert prop3.sdc_resource == service
assert prop3.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.yyy"
assert prop3.name == "yyy"
assert prop3.property_type == "string"
assert prop3.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop3.value == "lalala"
assert prop3.description is None
assert prop3.get_input_values is None
assert prop3.input is None
assert prop4.sdc_resource == service
assert prop4.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.test2"
assert prop4.name == "test2"
assert prop4.property_type == "boolean"
assert prop4.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop4.value == '{"get_input":"test2"}'
assert prop4.description == "test2"
assert prop4.get_input_values
with pytest.raises(ParameterError):
prop4.input
@mock.patch.object(Service, "send_message_json")
def test_service_inputs(mock_send_json):
service = Service(name="test")
service.unique_identifier = "toto"
mock_send_json.return_value = {}
assert len(list(service.inputs)) == 0
mock_send_json.return_value = INPUTS
inputs_list = list(service.inputs)
assert len(inputs_list) == 4
input1, input2, input3, input4 = inputs_list
assert input1.unique_id == "9ee5fb23-4c4a-46bd-8682-68698559ee9c.skip_post_instantiation_configuration"
assert input1.input_type == "boolean"
assert input1.name == "skip_post_instantiation_configuration"
assert input1.default_value == "true"
assert input2.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.test"
assert input2.input_type == "string"
assert input2.name == "test"
assert input2.default_value is None
assert input3.unique_id == "9ee5fb23-4c4a-46bd-8682-68698559ee9c.controller_actor"
assert input3.input_type == "string"
assert input3.name == "controller_actor"
assert input3.default_value == "SO-REF-DATA"
assert input4.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.lililili"
assert input4.input_type == "list"
assert input4.name == "lililili"
assert input4.default_value is None
@mock.patch.object(Vf, "send_message_json")
def test_vf_properties(mock_send_json):
vf = Vf(name="test")
vf.unique_identifier = "toto"
mock_send_json.return_value = {}
assert len(list(vf.properties)) == 0
mock_send_json.return_value = PROPERTIES
properties_list = list(vf.properties)
assert len(properties_list) == 4
prop1, prop2, prop3, prop4 = properties_list
mock_send_json.return_value = INPUTS
assert prop1.sdc_resource == vf
assert prop1.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.llllll"
assert prop1.name == "llllll"
assert prop1.property_type == "integer"
assert prop1.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop1.value == '{"get_input":["lililili","INDEX","llllll"]}'
assert prop1.description is None
assert prop1.get_input_values
prop1_input = prop1.input
assert prop1_input.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.lililili"
assert prop1_input.input_type == "list"
assert prop1_input.name == "lililili"
assert prop1_input.default_value is None
assert prop2.sdc_resource == vf
assert prop2.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.test"
assert prop2.name == "test"
assert prop2.property_type == "string"
assert prop2.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop2.value is None
assert prop2.description is None
assert prop2.get_input_values == []
assert prop2.input is None
assert prop3.sdc_resource == vf
assert prop3.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.yyy"
assert prop3.name == "yyy"
assert prop3.property_type == "string"
assert prop3.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop3.value == "lalala"
assert prop3.description is None
assert prop3.get_input_values is None
assert prop3.input is None
assert prop4.sdc_resource == vf
assert prop4.unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079.test2"
assert prop4.name == "test2"
assert prop4.property_type == "boolean"
assert prop4.parent_unique_id == "4a84415b-4580-4a78-aa33-501f0cd3d079"
assert prop4.value == '{"get_input":"test2"}'
assert prop4.description == "test2"
assert prop4.get_input_values
with pytest.raises(ParameterError):
prop4.input
@mock.patch.object(Vl, "send_message_json")
@mock.patch.object(Vl, "exists")
def test_vl_properties(mock_exists, mock_send_json):
mock_exists.return_value = True
vl = Vl(name="test")
vl.unique_identifier = "toto"
mock_send_json.return_value = {}
assert len(list(vl.properties)) == 0
mock_send_json.return_value = VL_PROPERTIES
properties_list = list(vl.properties)
assert len(properties_list) == 1
prop = properties_list[0]
assert prop.sdc_resource == vl
assert prop.unique_id == "d37cd65e-9842-4490-9343-a1a874e6b52a.network_role"
assert prop.name == "network_role"
assert prop.property_type == "string"
assert prop.parent_unique_id == "1af9771b-0f79-4e98-8747-30fd06da85cb"
assert prop.value is None
assert prop.description == "Unique label that defines the role that this network performs. example: vce oam network, vnat sr-iov1 network\n"
assert prop.get_input_values is None
assert prop.input is None
@mock.patch.object(SdcResource, "send_message_json")
def test_sdc_resource_is_own_property(mock_send_json):
sdc_resource = SdcResource(name="test")
sdc_resource.unique_identifier = "toto"
mock_send_json.return_value = PROPERTIES
prop1 = Property(
name="llllll",
property_type="integer"
)
prop2 = Property(
name="test2",
property_type="string"
)
assert sdc_resource.is_own_property(prop1)
assert not sdc_resource.is_own_property(prop2)
@mock.patch.object(SdcResource, "properties", new_callable=mock.PropertyMock)
@mock.patch.object(SdcResource, "send_message_json")
def test_sdc_resource_set_property_value(mock_send_message_json, mock_sdc_resource_properties):
sdc_resource = SdcResource(name="test")
sdc_resource.unique_identifier = "toto"
mock_sdc_resource_properties.return_value = [
Property(name="test",
property_type="string",
sdc_resource=sdc_resource)
]
with pytest.raises(ParameterError):
sdc_resource.set_property_value(Property(name="test2",
property_type="integer",
sdc_resource=sdc_resource),
value="lalala")
prop = sdc_resource.get_property(property_name="test")
assert prop.name == "test"
assert prop.property_type == "string"
assert not prop.value
prop.value = "test"
mock_send_message_json.assert_called_once()
assert prop.value == "test"
@mock.patch.object(SdcResource, "inputs", new_callable=mock.PropertyMock)
@mock.patch.object(SdcResource, "send_message_json")
def test_sdc_resource_input_default_value(mock_send_message_json, mock_inputs):
sdc_resource = SdcResource(name="test")
sdc_resource.unique_identifier = "toto"
mock_inputs.return_value = [
Input(unique_id="123",
input_type="integer",
name="test",
sdc_resource=sdc_resource)
]
assert sdc_resource.get_input("test")
input_obj = sdc_resource.get_input("test")
assert not input_obj.default_value
input_obj.default_value = "123"
mock_send_message_json.assert_called_once()
assert input_obj.default_value == "123"
| 34.425094
| 144
| 0.511995
| 2,888
| 36,766
| 6.416205
| 0.055055
| 0.025256
| 0.033675
| 0.042094
| 0.898435
| 0.865623
| 0.837992
| 0.808851
| 0.802644
| 0.786994
| 0
| 0.057342
| 0.368193
| 36,766
| 1,067
| 145
| 34.457357
| 0.740368
| 0
| 0
| 0.819785
| 0
| 0.001959
| 0.300413
| 0.065468
| 0
| 0
| 0
| 0
| 0.117532
| 1
| 0.006856
| false
| 0.020568
| 0.007835
| 0
| 0.014691
| 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
|
256c195efeb0676ae71cb92d4543c541c9ccd953
| 99
|
py
|
Python
|
kaldi/fstext/_log_ops.py
|
mxmpl/pykaldi
|
0570307138c5391cc47b019450d08bcb9686dd98
|
[
"Apache-2.0"
] | 916
|
2017-11-22T19:33:36.000Z
|
2022-03-31T11:51:58.000Z
|
kaldi/fstext/_log_ops.py
|
mxmpl/pykaldi
|
0570307138c5391cc47b019450d08bcb9686dd98
|
[
"Apache-2.0"
] | 268
|
2018-01-16T22:06:45.000Z
|
2022-03-29T03:24:41.000Z
|
kaldi/fstext/_log_ops.py
|
mxmpl/pykaldi
|
0570307138c5391cc47b019450d08bcb9686dd98
|
[
"Apache-2.0"
] | 260
|
2018-01-23T18:39:40.000Z
|
2022-03-24T08:17:39.000Z
|
from _log_inplace_ops import *
from _log_construct1_ops import *
from _log_construct2_ops import *
| 24.75
| 33
| 0.848485
| 15
| 99
| 5
| 0.466667
| 0.28
| 0.346667
| 0.426667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022989
| 0.121212
| 99
| 3
| 34
| 33
| 0.83908
| 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
|
c275e29861a815fb229a5bccf9af86b7d2857da2
| 7,510
|
py
|
Python
|
tests/minter/test_mint.py
|
overlay-market/planckcats
|
4daa8b0f7c2a1bcc891675e99446534d406832d3
|
[
"MIT"
] | 2
|
2022-01-27T20:02:35.000Z
|
2022-02-17T13:01:14.000Z
|
tests/minter/test_mint.py
|
overlay-market/planckcats
|
4daa8b0f7c2a1bcc891675e99446534d406832d3
|
[
"MIT"
] | 1
|
2022-02-09T15:55:03.000Z
|
2022-02-09T15:55:03.000Z
|
tests/minter/test_mint.py
|
overlay-market/planckcats
|
4daa8b0f7c2a1bcc891675e99446534d406832d3
|
[
"MIT"
] | null | null | null |
import pytest
from brownie import reverts
# NOTE: Have fixture so current id from PFP token creation
# starts back at 0 for each test
@pytest.fixture(autouse=True)
def isolation(fn_isolation):
pass
def test_mint_batch(minter, cat, alice, bob, rando, gov):
tos = [alice, bob, rando]
current_id = 0
expect_balance = cat.balanceOf(minter)
# mint pcds to this contract
tx = minter.mintBatch(current_id, tos, {"from": gov})
# check that mint events emitted
assert "Mint" in tx.events
assert len(tx.events["Mint"]) == len(tos)
# loop through each receiver to check state after mint
for i, to in enumerate(tos):
# check claimable bool has flipped
assert minter.claimable(i, to) is True
# check minter is current owner of minted planck cat (escrowed)
assert cat.ownerOf(i) == minter
# check escrowed for each to in tos has added associated id
assert minter.escrowed(to, 0) == i
# NOTE: canClaim() tests in test_views.py
assert minter.canClaim(to) == [i]
# check count for number available to claim increased
assert minter.count(to) == 1
# check mint event for the individual mint
tx.events["Mint"][i]["to"] == to
tx.events["Mint"][i]["id"] == i
# check pcds escrowed in minter
expect_balance += len(tos)
actual_balance = cat.balanceOf(minter)
assert actual_balance == expect_balance
def test_mint_batch_many_to_one(minter, cat, alice, bob, rando, gov):
tos = [alice, alice, alice]
current_id = 0
expect_balance = cat.balanceOf(minter)
# mint pcds to this contract
tx = minter.mintBatch(current_id, tos, {"from": gov})
# check that mint events emitted
assert "Mint" in tx.events
assert len(tx.events["Mint"]) == len(tos)
# check canClaim has ids for all minted to alice
expect_ids = [i for i in range(len(tos))]
expect_count = len(expect_ids)
# check ids added to alice's escrowed
# NOTE: canClaim() tests in test_views.py
assert minter.canClaim(alice) == expect_ids
# check count increased by number minted for alice
assert minter.count(alice) == expect_count
# check per id based properties ..
for i, id in enumerate(expect_ids):
assert minter.claimable(id, alice) is True
assert cat.ownerOf(id) == minter
assert minter.escrowed(alice, i) == id
assert tx.events["Mint"][i]["to"] == alice
assert tx.events["Mint"][i]["id"] == id
# check pcds escrowed in minter
expect_balance += len(tos)
actual_balance = cat.balanceOf(minter)
assert actual_balance == expect_balance
def test_mint_batch_reverts_when_not_minter_role(minter, rando):
current_id = 0
with reverts("!minter"):
_ = minter.mintBatch(current_id, [rando], {"from": rando})
def test_mint_batch_reverts_when_not_current_id(minter, gov, alice, bob,
rando):
tos = [alice, bob, rando]
current_id = 100
with reverts("!currentId"):
_ = minter.mintBatch(current_id, tos, {"from": gov})
def test_mint_custom_batch(minter, cat, alice, bob, rando, gov):
tos = [alice, bob, rando]
uris = ["https://alice.lol/", "https://bob.lol/", "https://rando.lol/"]
current_id = 0
expect_balance = cat.balanceOf(minter)
# mint pcds to this contract
tx = minter.mintCustomBatch(current_id, tos, uris, {"from": gov})
# check that mint events emitted
assert "Mint" in tx.events
assert len(tx.events["Mint"]) == len(tos)
# loop through each receiver to check state after mint
for i, to in enumerate(tos):
# check claimable bool has flipped
assert minter.claimable(i, to) is True
# check minter is current owner of minted planck cat (escrowed)
assert cat.ownerOf(i) == minter
# check escrowed for each to in tos has added associated id
assert minter.escrowed(to, 0) == i
# NOTE: canClaim() tests in test_views.py
assert minter.canClaim(to) == [i]
# check count for number available to claim increased
assert minter.count(to) == 1
# check mint event for the individual mint
tx.events["Mint"][i]["to"] == to
tx.events["Mint"][i]["id"] == i
# check pcds escrowed in minter
expect_balance += len(tos)
actual_balance = cat.balanceOf(minter)
assert actual_balance == expect_balance
def test_mint_custom_batch_many_to_one(minter, cat, alice, bob, rando, gov):
tos = [alice, alice, alice]
uris = ["https://alice.lol/", "https://bob.lol/", "https://rando.lol/"]
current_id = 0
expect_balance = cat.balanceOf(minter)
# mint pcds to this contract
tx = minter.mintCustomBatch(current_id, tos, uris, {"from": gov})
# check that mint events emitted
assert "Mint" in tx.events
assert len(tx.events["Mint"]) == len(tos)
# check canClaim has ids for all minted to alice
expect_ids = [i for i in range(len(tos))]
expect_count = len(expect_ids)
# check ids added to alice's escrowed
# NOTE: canClaim() tests in test_views.py
assert minter.canClaim(alice) == expect_ids
# check count increased by number minted for alice
assert minter.count(alice) == expect_count
# check per id based properties ..
for i, id in enumerate(expect_ids):
assert minter.claimable(id, alice) is True
assert cat.ownerOf(id) == minter
assert minter.escrowed(alice, i) == id
assert tx.events["Mint"][i]["to"] == alice
assert tx.events["Mint"][i]["id"] == id
# check pcds escrowed in minter
expect_balance += len(tos)
actual_balance = cat.balanceOf(minter)
assert actual_balance == expect_balance
def test_mint_custom_batch_reverts_when_not_minter_role(minter, rando):
current_id = 0
with reverts("!minter"):
_ = minter.mintCustomBatch(current_id, [rando], ["https://rando.lol"],
{"from": rando})
def test_mint_custom_batch_reverts_when_arrays_not_same_length(minter, gov,
alice, bob):
tos = [alice, bob]
uris = ["https://alice.lol/", "https://bob.lol/", "https://rando.lol/"]
current_id = 0
with reverts("tos != uris"):
_ = minter.mintCustomBatch(current_id, tos, uris, {"from": gov})
def test_mint_custom_batch_reverts_when_not_current_id(minter, gov, alice, bob,
rando):
tos = [alice, bob, rando]
uris = ["https://alice.lol/", "https://bob.lol/", "https://rando.lol/"]
current_id = 100
with reverts("!currentId"):
_ = minter.mintCustomBatch(current_id, tos, uris, {"from": gov})
def test_external_call_by_mint_batch_with_minter_role(cat, gov, minter,
attacc_minter, alice):
# governance grants minter role to attacking contract
cat.grantRole(cat.MINTER_ROLE(), attacc_minter, {"from": gov})
# attacker tries a re-entrancy attack
attacc_minter.reenter({"from": alice})
# attacking contract hopes to have 4 NFTs:
# Two through reenter() + two through onERC721Received()
# but actually has 0, because all NFTs are minted to the minter itself
assert cat.balanceOf(attacc_minter) == 0
# Minter holds 2 NFTs only (not 4), cuz re-entry is not working/impossible
assert cat.balanceOf(minter) == 2
| 33.526786
| 79
| 0.639547
| 1,017
| 7,510
| 4.596853
| 0.138643
| 0.040428
| 0.030802
| 0.042781
| 0.84107
| 0.834866
| 0.834866
| 0.802781
| 0.800642
| 0.800642
| 0
| 0.004785
| 0.248602
| 7,510
| 223
| 80
| 33.67713
| 0.823675
| 0.246738
| 0
| 0.771186
| 0
| 0
| 0.070207
| 0
| 0
| 0
| 0
| 0
| 0.322034
| 1
| 0.09322
| false
| 0.008475
| 0.016949
| 0
| 0.110169
| 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
|
c2806172f319d705e194b3e17aa37b1aa5603a24
| 120
|
py
|
Python
|
data/queries.py
|
brzozasr/codecool_series
|
6d33f686bd7eb17460abe51e6edd22708fbf4f8a
|
[
"Apache-2.0"
] | null | null | null |
data/queries.py
|
brzozasr/codecool_series
|
6d33f686bd7eb17460abe51e6edd22708fbf4f8a
|
[
"Apache-2.0"
] | null | null | null |
data/queries.py
|
brzozasr/codecool_series
|
6d33f686bd7eb17460abe51e6edd22708fbf4f8a
|
[
"Apache-2.0"
] | null | null | null |
from data import data_manager
def get_shows():
return data_manager.execute_select('SELECT id, title FROM shows;')
| 20
| 70
| 0.766667
| 18
| 120
| 4.888889
| 0.666667
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 120
| 5
| 71
| 24
| 0.862745
| 0
| 0
| 0
| 0
| 0
| 0.233333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 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
| 1
| 1
| 0
|
0
| 8
|
6655bbcba8e0073e0c1cc957715765396e0b3f63
| 6,700
|
py
|
Python
|
powercmd/test/test_command_line.py
|
dextero/powercmd
|
6d3652e9d1a60d7227e95ce943a9d3a6ec6a25bf
|
[
"MIT"
] | null | null | null |
powercmd/test/test_command_line.py
|
dextero/powercmd
|
6d3652e9d1a60d7227e95ce943a9d3a6ec6a25bf
|
[
"MIT"
] | 8
|
2017-06-13T15:27:09.000Z
|
2020-08-19T19:11:08.000Z
|
powercmd/test/test_command_line.py
|
dextero/powercmd
|
6d3652e9d1a60d7227e95ce943a9d3a6ec6a25bf
|
[
"MIT"
] | 4
|
2017-06-13T15:01:10.000Z
|
2020-08-05T10:00:20.000Z
|
import unittest
from powercmd.command import Command, Parameter
from powercmd.command_line import CommandLine, NamedArg, PositionalArg, IncompleteArg
from powercmd.commands_dict import CommandsDict
class TestCommandLine(unittest.TestCase):
def test_empty(self):
cmdline = CommandLine('')
self.assertEqual(cmdline.command, '')
self.assertEqual(cmdline.args, [])
self.assertEqual(cmdline.named_args, {})
self.assertEqual(cmdline.free_args, [])
self.assertEqual(cmdline.has_trailing_whitespace, False)
def test_split_on_whitespace(self):
cmdline = CommandLine('foo bar')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [PositionalArg('bar')])
self.assertEqual(cmdline.named_args, {})
self.assertEqual(cmdline.free_args, ['bar'])
cmdline = CommandLine('foo\tbar')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [PositionalArg('bar')])
self.assertEqual(cmdline.named_args, {})
self.assertEqual(cmdline.free_args, ['bar'])
cmdline = CommandLine('foo \tbar')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [PositionalArg('bar')])
self.assertEqual(cmdline.named_args, {})
self.assertEqual(cmdline.free_args, ['bar'])
def test_positional_args(self):
cmdline = CommandLine('foo bar baz')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [PositionalArg('bar'), PositionalArg('baz')])
self.assertEqual(cmdline.named_args, {})
self.assertEqual(cmdline.free_args, ['bar', 'baz'])
def test_named_args(self):
cmdline = CommandLine('foo bar=baz qux=quux')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [NamedArg('bar', 'baz'), NamedArg('qux', 'quux')])
self.assertEqual(cmdline.named_args, {'bar': 'baz', 'qux': 'quux'})
self.assertEqual(cmdline.free_args, [])
def test_mixed_named_positional(self):
cmdline = CommandLine('foo bar baz=qux')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [PositionalArg('bar'), NamedArg('baz', 'qux')])
self.assertEqual(cmdline.named_args, {'baz': 'qux'})
self.assertEqual(cmdline.free_args, ['bar'])
cmdline = CommandLine('foo bar=baz qux')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [NamedArg('bar', 'baz'), PositionalArg('qux')])
self.assertEqual(cmdline.named_args, {'bar': 'baz'})
self.assertEqual(cmdline.free_args, ['qux'])
def test_quoted_args(self):
cmdline = CommandLine('foo "bar" \'baz\'')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [PositionalArg('bar'), PositionalArg('baz')])
self.assertEqual(cmdline.named_args, {})
self.assertEqual(cmdline.free_args, ['bar', 'baz'])
cmdline = CommandLine('foo \'bar=baz\' "qux=quux"')
self.assertEqual(cmdline.command, 'foo')
self.assertEqual(cmdline.args, [NamedArg('bar', 'baz'), NamedArg('qux', 'quux')])
self.assertEqual(cmdline.named_args, {'bar': 'baz', 'qux': 'quux'})
self.assertEqual(cmdline.free_args, [])
def test_command(self):
self.assertEqual(CommandLine('foo').command, 'foo')
self.assertEqual(CommandLine(' foo').command, 'foo')
self.assertEqual(CommandLine('foo ').command, 'foo')
self.assertEqual(CommandLine('"foo"').command, 'foo')
self.assertEqual(CommandLine(' "foo"').command, 'foo')
self.assertEqual(CommandLine('"foo" ').command, 'foo')
self.assertEqual(CommandLine('" foo').command, '" foo')
self.assertEqual(CommandLine('"foo ').command, '"foo ')
self.assertEqual(CommandLine('" foo"').command, ' foo')
self.assertEqual(CommandLine('"foo " ').command, 'foo ')
def test_has_trailing_whitespace(self):
self.assertEqual(CommandLine('foo ').has_trailing_whitespace, True)
self.assertEqual(CommandLine('foo\t').has_trailing_whitespace, True)
self.assertEqual(CommandLine('foo bar ').has_trailing_whitespace, True)
self.assertEqual(CommandLine('foo bar\t').has_trailing_whitespace, True)
self.assertEqual(CommandLine('foo bar=baz ').has_trailing_whitespace, True)
self.assertEqual(CommandLine('foo bar=baz\t').has_trailing_whitespace, True)
self.assertEqual(CommandLine('foo').has_trailing_whitespace, False)
self.assertEqual(CommandLine('"foo"').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo bar').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo "bar"').has_trailing_whitespace, False)
self.assertEqual(CommandLine('"foo ').has_trailing_whitespace, False)
self.assertEqual(CommandLine('\'foo ').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo "').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo \'').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo " ').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo \' ').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo "bar ').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo \'bar ').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo "bar=baz ').has_trailing_whitespace, False)
self.assertEqual(CommandLine('foo \'bar=baz ').has_trailing_whitespace, False)
def test_get_current_arg(self):
def do_foo(self,
bar: str = '',
baz: str = ''):
pass
cmd = Command('foo', do_foo)
self.assertEqual(CommandLine('').get_current_arg(cmd), None)
self.assertEqual(CommandLine('foo').get_current_arg(cmd), None)
self.assertEqual(CommandLine('foo ').get_current_arg(cmd),
IncompleteArg(Parameter('bar', str, ''), ''))
self.assertEqual(CommandLine('foo arg').get_current_arg(cmd),
IncompleteArg(Parameter('bar', str, ''), 'arg'))
self.assertEqual(CommandLine('foo arg ').get_current_arg(cmd),
IncompleteArg(Parameter('baz', str, ''), ''))
self.assertEqual(CommandLine('foo arg arg').get_current_arg(cmd),
IncompleteArg(Parameter('baz', str, ''), 'arg'))
self.assertEqual(CommandLine('foo baz=arg bar=').get_current_arg(cmd),
IncompleteArg(Parameter('bar', str, ''), ''))
| 49.264706
| 89
| 0.654925
| 714
| 6,700
| 6.005602
| 0.071429
| 0.272854
| 0.210354
| 0.24347
| 0.870802
| 0.853312
| 0.819729
| 0.801539
| 0.770756
| 0.731343
| 0
| 0
| 0.192537
| 6,700
| 135
| 90
| 49.62963
| 0.792606
| 0
| 0
| 0.473214
| 0
| 0
| 0.08403
| 0
| 0
| 0
| 0
| 0
| 0.696429
| 1
| 0.089286
| false
| 0.008929
| 0.035714
| 0
| 0.133929
| 0
| 0
| 0
| 0
| null | 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
6661cf1f28299eeb01fb6105e79d088ab527332f
| 166
|
py
|
Python
|
mankey/__init__.py
|
dBlueG/mankey_stats
|
657ace43828126daf8cebf2a7fa155cf8abcb82d
|
[
"MIT"
] | null | null | null |
mankey/__init__.py
|
dBlueG/mankey_stats
|
657ace43828126daf8cebf2a7fa155cf8abcb82d
|
[
"MIT"
] | null | null | null |
mankey/__init__.py
|
dBlueG/mankey_stats
|
657ace43828126daf8cebf2a7fa155cf8abcb82d
|
[
"MIT"
] | null | null | null |
from . import mankey_dataframe, charting_helper, custom_helpers, stats_helpers
__all__ = ["mankey_dataframe", "charting_helper", "custom_helpers", "stats_helpers"]
| 55.333333
| 85
| 0.801205
| 19
| 166
| 6.368421
| 0.526316
| 0.247934
| 0.380165
| 0.479339
| 0.892562
| 0.892562
| 0.892562
| 0.892562
| 0
| 0
| 0
| 0
| 0.090361
| 166
| 2
| 86
| 83
| 0.801325
| 0
| 0
| 0
| 0
| 0
| 0.353659
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 10
|
dd29b2d87c6902b451ef35d77d9ef03811b92a37
| 219
|
py
|
Python
|
kt/image/__init__.py
|
tkianai/tk-cv
|
b8b264b59e119396440071c3aa6cf9978c2fddad
|
[
"MIT"
] | 2
|
2019-09-25T12:18:04.000Z
|
2020-04-25T05:30:56.000Z
|
kt/image/__init__.py
|
tkianai/tk-cv
|
b8b264b59e119396440071c3aa6cf9978c2fddad
|
[
"MIT"
] | null | null | null |
kt/image/__init__.py
|
tkianai/tk-cv
|
b8b264b59e119396440071c3aa6cf9978c2fddad
|
[
"MIT"
] | null | null | null |
from .utils import image_format_check
from .preprocess import is_blurry_by_gradient
from .preprocess import get_image_hashcode
from .preprocess import get_md5_code
from .io import imread
from .merge import make_overlay
| 31.285714
| 45
| 0.863014
| 34
| 219
| 5.264706
| 0.588235
| 0.234637
| 0.335196
| 0.256983
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005128
| 0.109589
| 219
| 7
| 46
| 31.285714
| 0.912821
| 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
|
dd7d7563c17f324f5a32ea362a76747610eb8939
| 11,737
|
py
|
Python
|
tests/test_splunk_logging.py
|
NHSDigital/shared-flow-testing
|
d253444a8c857444f9b6ec9cecdbed97fdc38992
|
[
"MIT"
] | null | null | null |
tests/test_splunk_logging.py
|
NHSDigital/shared-flow-testing
|
d253444a8c857444f9b6ec9cecdbed97fdc38992
|
[
"MIT"
] | 41
|
2021-04-23T10:52:20.000Z
|
2022-02-26T02:11:16.000Z
|
tests/test_splunk_logging.py
|
NHSDigital/shared-flow-testing
|
d253444a8c857444f9b6ec9cecdbed97fdc38992
|
[
"MIT"
] | null | null | null |
import base64
import hashlib
import hmac
import json
import pytest
import requests
from jsonschema import validate
from .configuration.config import SERVICE_BASE_PATH, ENVIRONMENT, ACCESS_TOKEN_HASH_SECRET, APP_CLIENT_ID
class TestSplunkLogging:
oauth_protected_url = f"https://{ENVIRONMENT}.api.service.nhs.uk/{SERVICE_BASE_PATH}/splunk-test"
apikey_protected_url = f"https://{ENVIRONMENT}.api.service.nhs.uk/{SERVICE_BASE_PATH}/apikey-protected"
open_access_url = f"https://{ENVIRONMENT}.api.service.nhs.uk/{SERVICE_BASE_PATH}/open-access"
ping_url = f"https://{ENVIRONMENT}.api.service.nhs.uk/{SERVICE_BASE_PATH}/_ping"
@staticmethod
async def _get_payload_from_splunk(debug):
splunk_content_json = await debug.get_apigee_variable_from_trace(name='splunkCalloutRequest.content')
return json.loads(splunk_content_json)
@staticmethod
def _calculate_hmac_sha512(content: str) -> str:
binary_content = bytes(content, "utf-8")
hmac_key = bytes(ACCESS_TOKEN_HASH_SECRET, "utf-8")
signature = hmac.new(hmac_key, binary_content, hashlib.sha512)
return base64.b64encode(signature.digest()).decode("utf-8")
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_client_credentials(self, get_token_client_credentials, debug):
# Given
token = get_token_client_credentials["access_token"]
expected_hashed_token = self._calculate_hmac_sha512(token)
# When
await debug.start_trace()
requests.get(
url=self.oauth_protected_url,
headers={"Authorization": f"Bearer {token}"},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == expected_hashed_token
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "app"
assert auth_meta["grant_type"] == "client_credentials"
assert auth_meta["level"] == "level3"
assert auth_meta["provider"] == "apim"
auth_user = auth["user"]
assert auth_user["user_id"] == ""
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_authorization_code(self, get_token, debug):
# Given
token = get_token["access_token"]
expected_hashed_token = self._calculate_hmac_sha512(token)
# When
await debug.start_trace()
requests.get(
url=self.oauth_protected_url,
headers={"Authorization": f"Bearer {token}"},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == expected_hashed_token
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "user"
assert auth_meta["grant_type"] == "authorization_code"
assert auth_meta["level"] == "aal3"
assert auth_meta["provider"] == "nhs-cis2"
auth_user = auth["user"]
assert auth_user["user_id"] == "787807429511"
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_cis2_token_exchange(self, get_token_cis2_token_exchange, debug):
# Given
token = get_token_cis2_token_exchange["access_token"]
expected_hashed_token = self._calculate_hmac_sha512(token)
# When
await debug.start_trace()
requests.get(
url=self.oauth_protected_url,
headers={"Authorization": f"Bearer {token}"},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == expected_hashed_token
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "user"
assert auth_meta["grant_type"] == "token_exchange"
assert auth_meta["level"] == "aal3"
assert auth_meta["provider"] == "nhs-cis2"
auth_user = auth["user"]
assert auth_user["user_id"] == "lala"
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_nhs_login_token_exchange(self, get_token_nhs_login_token_exchange, debug):
# Given
token = get_token_nhs_login_token_exchange["access_token"]
expected_hashed_token = self._calculate_hmac_sha512(token)
# When
await debug.start_trace()
requests.get(
url=self.oauth_protected_url,
headers={"Authorization": f"Bearer {token}"},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == expected_hashed_token
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "user"
assert auth_meta["grant_type"] == "token_exchange"
assert auth_meta["level"] == "p9"
assert auth_meta["provider"] == "apim-mock-nhs-login"
auth_user = auth["user"]
assert auth_user["user_id"] == "900000000001"
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_invalid_token(self, debug):
# Given
token = "invalid token"
expected_hashed_token = "empty"
# When
await debug.start_trace()
requests.get(
url=self.oauth_protected_url,
headers={"Authorization": f"Bearer {token}"},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == expected_hashed_token
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "unknown"
assert auth_meta["grant_type"] == ""
assert auth_meta["level"] == "-"
assert auth_meta["provider"] == "apim"
auth_user = auth["user"]
assert auth_user["user_id"] == ""
meta = payload["meta"]
assert meta["client_id"] == "empty"
assert meta["application"] == "unknown"
assert meta["product"] == ""
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_expired_token(self, debug):
# Given
token = "zRygtc34R2pwxbiUktLsMJWX0iJW"
expected_hashed_token = "empty"
# When
await debug.start_trace()
requests.get(
url=self.oauth_protected_url,
headers={"Authorization": f"Bearer {token}"},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == expected_hashed_token
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "unknown"
assert auth_meta["grant_type"] == ""
assert auth_meta["level"] == "-"
assert auth_meta["provider"] == "apim"
auth_user = auth["user"]
assert auth_user["user_id"] == ""
meta = payload["meta"]
assert meta["client_id"] == "empty"
assert meta["application"] == "unknown"
assert meta["product"] == ""
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_apikey(self, debug):
# Given
apikey = APP_CLIENT_ID
# When
await debug.start_trace()
requests.get(
url=self.apikey_protected_url,
headers={"apikey": apikey},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == ""
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "app"
assert auth_meta["grant_type"] == ""
assert auth_meta["level"] == "-"
assert auth_meta["provider"] == "apim"
auth_user = auth["user"]
assert auth_user["user_id"] == ""
meta = payload["meta"]
assert meta["client_id"] == apikey
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_with_invalid_apikey(self, debug):
# Given
apikey = "invalid api key"
# When
await debug.start_trace()
requests.get(
url=self.apikey_protected_url,
headers={"apikey": apikey},
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == ""
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "app"
assert auth_meta["grant_type"] == ""
assert auth_meta["level"] == "-"
assert auth_meta["provider"] == "apim"
auth_user = auth["user"]
assert auth_user["user_id"] == ""
meta = payload["meta"]
assert meta["client_id"] == ""
assert meta["application"] == "unknown"
assert meta["product"] == ""
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_open_access(self, debug):
# When
await debug.start_trace()
requests.get(
url=self.open_access_url,
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == ""
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "app"
assert auth_meta["grant_type"] == ""
assert auth_meta["level"] == "open"
assert auth_meta["provider"] == "apim"
auth_user = auth["user"]
assert auth_user["user_id"] == ""
meta = payload["meta"]
assert meta["client_id"] == "empty"
assert meta["application"] == "unknown"
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_auth_open_access_ping(self, debug):
# There is nothing especial about /_ping itself. It's an endpoint that doesn't have a target backend
# When
await debug.start_trace()
requests.get(
url=self.ping_url,
)
payload = await self._get_payload_from_splunk(debug)
# Then
auth = payload["auth"]
assert auth["access_token_hash"] == ""
auth_meta = auth["meta"]
assert auth_meta["auth_type"] == "app"
assert auth_meta["grant_type"] == ""
assert auth_meta["level"] == "open"
assert auth_meta["provider"] == "apim"
auth_user = auth["user"]
assert auth_user["user_id"] == ""
meta = payload["meta"]
assert meta["client_id"] == "empty"
assert meta["application"] == "unknown"
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_payload_schema(self, get_token, debug):
# Given
token = get_token["access_token"]
# When
await debug.start_trace()
requests.get(
url=self.oauth_protected_url,
headers={"Authorization": f"Bearer {token}"},
)
payload = await self._get_payload_from_splunk(debug)
with open('splunk_logging_schema.json') as f:
schema = json.load(f)
# If no exception is raised by validate(), the instance is valid.
validate(instance=payload, schema=schema)
@pytest.mark.splunk
@pytest.mark.asyncio
async def test_splunk_payload_schema_open_access(self, debug):
# When hitting an open-access endpoint
await debug.start_trace()
requests.get(url=self.open_access_url)
payload = await self._get_payload_from_splunk(debug)
with open('splunk_logging_schema.json') as f:
schema = json.load(f)
# Then
# If no exception is raised by validate(), the instance is valid.
validate(instance=payload, schema=schema)
| 32.512465
| 110
| 0.616597
| 1,360
| 11,737
| 5.032353
| 0.100735
| 0.087668
| 0.081824
| 0.037989
| 0.852133
| 0.814728
| 0.811222
| 0.800701
| 0.800701
| 0.78872
| 0
| 0.007409
| 0.264037
| 11,737
| 360
| 111
| 32.602778
| 0.784904
| 0.036381
| 0
| 0.750973
| 0
| 0
| 0.161611
| 0.00958
| 0
| 0
| 0
| 0
| 0.287938
| 1
| 0.003891
| false
| 0
| 0.031128
| 0
| 0.062257
| 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
|
dd9918a225cc2fdd7cbe3cb9354aed9bbc85a2fc
| 33
|
py
|
Python
|
splitcli/splitio_selectors/metric_selectors.py
|
stephencsnow/splitcli
|
f0b9a451215bb052c91e4802bd6d0dcca0407dab
|
[
"Apache-2.0"
] | 36
|
2021-03-14T19:46:24.000Z
|
2021-05-20T22:57:00.000Z
|
splitcli/splitio_selectors/metric_selectors.py
|
stephencsnow/splitcli
|
f0b9a451215bb052c91e4802bd6d0dcca0407dab
|
[
"Apache-2.0"
] | 2
|
2021-04-02T22:04:23.000Z
|
2021-04-06T20:45:39.000Z
|
splitcli/splitio_selectors/metric_selectors.py
|
stephencsnow/splitcli
|
f0b9a451215bb052c91e4802bd6d0dcca0407dab
|
[
"Apache-2.0"
] | 2
|
2021-03-27T16:16:50.000Z
|
2021-06-18T21:00:18.000Z
|
def manage_metrics():
return
| 11
| 21
| 0.69697
| 4
| 33
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.212121
| 33
| 3
| 22
| 11
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0.5
| 1
| 0
| 1
| 1
| 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
| 0
| 1
| 1
| 0
|
0
| 7
|
06f2d45ae62716916f1d134fb13dfac11e9e1bb9
| 98,266
|
py
|
Python
|
msgraph/cli/command_modules/identitydirmgt/azext_identitydirmgt/generated/_params.py
|
microsoftgraph/msgraph-cli-archived
|
489f70bf4ede1ce67b84bfb31e66da3e4db76062
|
[
"MIT"
] | null | null | null |
msgraph/cli/command_modules/identitydirmgt/azext_identitydirmgt/generated/_params.py
|
microsoftgraph/msgraph-cli-archived
|
489f70bf4ede1ce67b84bfb31e66da3e4db76062
|
[
"MIT"
] | 22
|
2022-03-29T22:54:37.000Z
|
2022-03-29T22:55:27.000Z
|
msgraph/cli/command_modules/identitydirmgt/azext_identitydirmgt/generated/_params.py
|
microsoftgraph/msgraph-cli-archived
|
489f70bf4ede1ce67b84bfb31e66da3e4db76062
|
[
"MIT"
] | null | null | null |
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
# pylint: disable=line-too-long
# pylint: disable=too-many-lines
# pylint: disable=too-many-statements
from azure.cli.core.commands.parameters import (
get_three_state_flag,
get_enum_type
)
from azure.cli.core.commands.validators import validate_file_or_dict
from azext_identitydirmgt.action import (
AddAddresses,
AddOnPremisesProvisioningErrors,
AddPhones,
AddDirectReports,
AddManager,
AddContactsOrgcontactMemberOf,
AddContactsOrgcontactTransitiveMemberOf,
AddAlternativeSecurityIds,
AddDevicesDeviceMemberOf,
AddRegisteredOwners,
AddRegisteredUsers,
AddDevicesDeviceTransitiveMemberOf,
AddDevicesDeviceExtensions,
AddDeletedItems,
AddDirectoryMembers,
AddDirectoryExtensions,
AddRoleMemberInfo,
AddDirectoryrolesDirectoryroleMembers,
AddState,
AddDomainNameReferences,
AddServiceConfigurationRecords,
AddVerificationDnsRecords,
AddAssignedPlans,
AddPrivacyProfile,
AddProvisionedPlans,
AddVerifiedDomains,
AddExtensions,
AddPrepaidUnits,
AddServicePlans
)
def load_arguments(self, _):
with self.argument_context('identitydirmgt contact-org-contact create-org-contact') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('addresses', action=AddAddresses, nargs='+', help='')
c.argument('company_name', type=str, help='')
c.argument('department', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('given_name', type=str, help='')
c.argument('job_title', type=str, help='')
c.argument('mail', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_premises_last_sync_date_time', help='')
c.argument('on_premises_provisioning_errors', action=AddOnPremisesProvisioningErrors, nargs='+', help='')
c.argument('on_premises_sync_enabled', arg_type=get_three_state_flag(), help='')
c.argument('phones', action=AddPhones, nargs='+', help='')
c.argument('proxy_addresses', nargs='+', help='')
c.argument('surname', type=str, help='')
c.argument('direct_reports', action=AddDirectReports, nargs='+', help='')
c.argument('manager', action=AddManager, nargs='+', help='Represents an Azure Active Directory object. The '
'directoryObject type is the base type for many other directory entity types.')
c.argument('member_of', action=AddContactsOrgcontactMemberOf, nargs='+', help='')
c.argument('transitive_member_of', action=AddContactsOrgcontactTransitiveMemberOf, nargs='+', help='')
with self.argument_context('identitydirmgt contact-org-contact delete-org-contact') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt contact-org-contact list-org-contact') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contact-org-contact show-org-contact') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contact-org-contact update-org-contact') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('addresses', action=AddAddresses, nargs='+', help='')
c.argument('company_name', type=str, help='')
c.argument('department', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('given_name', type=str, help='')
c.argument('job_title', type=str, help='')
c.argument('mail', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_premises_last_sync_date_time', help='')
c.argument('on_premises_provisioning_errors', action=AddOnPremisesProvisioningErrors, nargs='+', help='')
c.argument('on_premises_sync_enabled', arg_type=get_three_state_flag(), help='')
c.argument('phones', action=AddPhones, nargs='+', help='')
c.argument('proxy_addresses', nargs='+', help='')
c.argument('surname', type=str, help='')
c.argument('direct_reports', action=AddDirectReports, nargs='+', help='')
c.argument('manager', action=AddManager, nargs='+', help='Represents an Azure Active Directory object. The '
'directoryObject type is the base type for many other directory entity types.')
c.argument('member_of', action=AddContactsOrgcontactMemberOf, nargs='+', help='')
c.argument('transitive_member_of', action=AddContactsOrgcontactTransitiveMemberOf, nargs='+', help='')
with self.argument_context('identitydirmgt contact check-member-group') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('group_ids', nargs='+', help='')
with self.argument_context('identitydirmgt contact check-member-object') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('ids', nargs='+', help='')
with self.argument_context('identitydirmgt contact create-ref-direct-report') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt contact create-ref-member-of') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt contact create-ref-transitive-member-of') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt contact delete-ref-manager') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt contact get-available-extension-property') as c:
c.argument('is_synced_from_on_premises', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt contact get-by-id') as c:
c.argument('ids', nargs='+', help='')
c.argument('types', nargs='+', help='')
with self.argument_context('identitydirmgt contact get-member-group') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt contact get-member-object') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt contact list-direct-report') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contact list-member-of') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contact list-ref-direct-report') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt contact list-ref-member-of') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt contact list-ref-transitive-member-of') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt contact list-transitive-member-of') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contact restore') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
with self.argument_context('identitydirmgt contact set-ref-manager') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref values Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt contact show-manager') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contact show-ref-manager') as c:
c.argument('org_contact_id', type=str, help='key: id of orgContact')
with self.argument_context('identitydirmgt contact validate-property') as c:
c.argument('entity_type', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_behalf_of_user_id', help='')
with self.argument_context('identitydirmgt contract-contract create-contract') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('contract_type', type=str, help='Type of contract.Possible values are: SyndicationPartner - Partner '
'that exclusively resells and manages O365 and Intune for this customer. They resell and support '
'their customers. BreadthPartner - Partner has the ability to provide administrative support for '
'this customer. However, the partner is not allowed to resell to the customer.ResellerPartner - '
'Partner that is similar to a syndication partner, except that the partner doesn’t have exclusive '
'access to a tenant. In the syndication case, the customer cannot buy additional direct '
'subscriptions from Microsoft or from other partners.')
c.argument('customer_id', help='The unique identifier for the customer tenant referenced by this partnership. '
'Corresponds to the id property of the customer tenant\'s organization resource.')
c.argument('default_domain_name', type=str, help='A copy of the customer tenant\'s default domain name. The '
'copy is made when the partnership with the customer is established. It is not automatically '
'updated if the customer tenant\'s default domain name changes.')
c.argument('display_name', type=str, help='A copy of the customer tenant\'s display name. The copy is made '
'when the partnership with the customer is established. It is not automatically updated if the '
'customer tenant\'s display name changes.')
with self.argument_context('identitydirmgt contract-contract delete-contract') as c:
c.argument('contract_id', type=str, help='key: id of contract')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt contract-contract list-contract') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contract-contract show-contract') as c:
c.argument('contract_id', type=str, help='key: id of contract')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt contract-contract update-contract') as c:
c.argument('contract_id', type=str, help='key: id of contract')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('contract_type', type=str, help='Type of contract.Possible values are: SyndicationPartner - Partner '
'that exclusively resells and manages O365 and Intune for this customer. They resell and support '
'their customers. BreadthPartner - Partner has the ability to provide administrative support for '
'this customer. However, the partner is not allowed to resell to the customer.ResellerPartner - '
'Partner that is similar to a syndication partner, except that the partner doesn’t have exclusive '
'access to a tenant. In the syndication case, the customer cannot buy additional direct '
'subscriptions from Microsoft or from other partners.')
c.argument('customer_id', help='The unique identifier for the customer tenant referenced by this partnership. '
'Corresponds to the id property of the customer tenant\'s organization resource.')
c.argument('default_domain_name', type=str, help='A copy of the customer tenant\'s default domain name. The '
'copy is made when the partnership with the customer is established. It is not automatically '
'updated if the customer tenant\'s default domain name changes.')
c.argument('display_name', type=str, help='A copy of the customer tenant\'s display name. The copy is made '
'when the partnership with the customer is established. It is not automatically updated if the '
'customer tenant\'s display name changes.')
with self.argument_context('identitydirmgt contract check-member-group') as c:
c.argument('contract_id', type=str, help='key: id of contract')
c.argument('group_ids', nargs='+', help='')
with self.argument_context('identitydirmgt contract check-member-object') as c:
c.argument('contract_id', type=str, help='key: id of contract')
c.argument('ids', nargs='+', help='')
with self.argument_context('identitydirmgt contract get-available-extension-property') as c:
c.argument('is_synced_from_on_premises', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt contract get-by-id') as c:
c.argument('ids', nargs='+', help='')
c.argument('types', nargs='+', help='')
with self.argument_context('identitydirmgt contract get-member-group') as c:
c.argument('contract_id', type=str, help='key: id of contract')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt contract get-member-object') as c:
c.argument('contract_id', type=str, help='key: id of contract')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt contract restore') as c:
c.argument('contract_id', type=str, help='key: id of contract')
with self.argument_context('identitydirmgt contract validate-property') as c:
c.argument('entity_type', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_behalf_of_user_id', help='')
with self.argument_context('identitydirmgt device-device create-device') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('account_enabled', arg_type=get_three_state_flag(), help='true if the account is enabled; '
'otherwise, false. Required.')
c.argument('alternative_security_ids', action=AddAlternativeSecurityIds, nargs='+', help='For internal use '
'only. Not nullable.')
c.argument('approximate_last_sign_in_date_time', help='The timestamp type represents date and time information '
'using ISO 8601 format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 would '
'look like this: \'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('compliance_expiration_date_time', help='The timestamp when the device is no longer deemed '
'compliant. The timestamp type represents date and time information using ISO 8601 format and is '
'always in UTC time. For example, midnight UTC on Jan 1, 2014 would look like this: '
'\'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('device_id', type=str, help='Unique identifier set by Azure Device Registration Service at the time '
'of registration.')
c.argument('device_metadata', type=str, help='For interal use only. Set to null.')
c.argument('device_version', type=int, help='For interal use only.')
c.argument('display_name', type=str, help='The display name for the device. Required.')
c.argument('is_compliant', arg_type=get_three_state_flag(), help='true if the device complies with Mobile '
'Device Management (MDM) policies; otherwise, false. Read-only. This can only be updated by Intune '
'for any device OS type or by an approved MDM app for Windows OS devices.')
c.argument('is_managed', arg_type=get_three_state_flag(), help='true if the device is managed by a Mobile '
'Device Management (MDM) app; otherwise, false. This can only be updated by Intune for any device '
'OS type or by an approved MDM app for Windows OS devices.')
c.argument('mdm_app_id', type=str, help='Application identifier used to register device into MDM. Read-only. '
'Supports $filter.')
c.argument('on_premises_last_sync_date_time', help='The last time at which the object was synced with the '
'on-premises directory.The Timestamp type represents date and time information using ISO 8601 '
'format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 would look like this: '
'\'2014-01-01T00:00:00Z\' Read-only.')
c.argument('on_premises_sync_enabled', arg_type=get_three_state_flag(), help='true if this object is synced '
'from an on-premises directory; false if this object was originally synced from an on-premises '
'directory but is no longer synced; null if this object has never been synced from an on-premises '
'directory (default). Read-only.')
c.argument('operating_system', type=str, help='The type of operating system on the device. Required.')
c.argument('operating_system_version', type=str, help='The version of the operating system on the device. '
'Required.')
c.argument('physical_ids', nargs='+', help='For interal use only. Not nullable.')
c.argument('profile_type', type=str, help='The profile type of the device. Possible values:RegisteredDevice '
'(default)SecureVMPrinterSharedIoT')
c.argument('system_labels', nargs='+', help='List of labels applied to the device by the system.')
c.argument('trust_type', type=str, help='Type of trust for the joined device. Read-only. Possible values: '
'Workplace - indicates bring your own personal devicesAzureAd - Cloud only joined devicesServerAd - '
'on-premises domain joined devices joined to Azure AD. For more details, see Introduction to device '
'management in Azure Active Directory')
c.argument('member_of', action=AddDevicesDeviceMemberOf, nargs='+', help='Groups that this group is a member '
'of. HTTP Methods: GET (supported for all groups). Read-only. Nullable.')
c.argument('registered_owners', action=AddRegisteredOwners, nargs='+', help='The user that cloud joined the '
'device or registered their personal device. The registered owner is set at the time of '
'registration. Currently, there can be only one owner. Read-only. Nullable.')
c.argument('registered_users', action=AddRegisteredUsers, nargs='+', help='Collection of registered users of '
'the device. For cloud joined devices and registered personal devices, registered users are set to '
'the same value as registered owners at the time of registration. Read-only. Nullable.')
c.argument('transitive_member_of', action=AddDevicesDeviceTransitiveMemberOf, nargs='+', help='')
c.argument('extensions', action=AddDevicesDeviceExtensions, nargs='+', help='The collection of open extensions '
'defined for the device. Read-only. Nullable.')
with self.argument_context('identitydirmgt device-device delete-device') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt device-device list-device') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device-device show-device') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device-device update-device') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('account_enabled', arg_type=get_three_state_flag(), help='true if the account is enabled; '
'otherwise, false. Required.')
c.argument('alternative_security_ids', action=AddAlternativeSecurityIds, nargs='+', help='For internal use '
'only. Not nullable.')
c.argument('approximate_last_sign_in_date_time', help='The timestamp type represents date and time information '
'using ISO 8601 format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 would '
'look like this: \'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('compliance_expiration_date_time', help='The timestamp when the device is no longer deemed '
'compliant. The timestamp type represents date and time information using ISO 8601 format and is '
'always in UTC time. For example, midnight UTC on Jan 1, 2014 would look like this: '
'\'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('microsoft_graph_device_id', type=str, help='Unique identifier set by Azure Device Registration '
'Service at the time of registration.')
c.argument('device_metadata', type=str, help='For interal use only. Set to null.')
c.argument('device_version', type=int, help='For interal use only.')
c.argument('display_name', type=str, help='The display name for the device. Required.')
c.argument('is_compliant', arg_type=get_three_state_flag(), help='true if the device complies with Mobile '
'Device Management (MDM) policies; otherwise, false. Read-only. This can only be updated by Intune '
'for any device OS type or by an approved MDM app for Windows OS devices.')
c.argument('is_managed', arg_type=get_three_state_flag(), help='true if the device is managed by a Mobile '
'Device Management (MDM) app; otherwise, false. This can only be updated by Intune for any device '
'OS type or by an approved MDM app for Windows OS devices.')
c.argument('mdm_app_id', type=str, help='Application identifier used to register device into MDM. Read-only. '
'Supports $filter.')
c.argument('on_premises_last_sync_date_time', help='The last time at which the object was synced with the '
'on-premises directory.The Timestamp type represents date and time information using ISO 8601 '
'format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 would look like this: '
'\'2014-01-01T00:00:00Z\' Read-only.')
c.argument('on_premises_sync_enabled', arg_type=get_three_state_flag(), help='true if this object is synced '
'from an on-premises directory; false if this object was originally synced from an on-premises '
'directory but is no longer synced; null if this object has never been synced from an on-premises '
'directory (default). Read-only.')
c.argument('operating_system', type=str, help='The type of operating system on the device. Required.')
c.argument('operating_system_version', type=str, help='The version of the operating system on the device. '
'Required.')
c.argument('physical_ids', nargs='+', help='For interal use only. Not nullable.')
c.argument('profile_type', type=str, help='The profile type of the device. Possible values:RegisteredDevice '
'(default)SecureVMPrinterSharedIoT')
c.argument('system_labels', nargs='+', help='List of labels applied to the device by the system.')
c.argument('trust_type', type=str, help='Type of trust for the joined device. Read-only. Possible values: '
'Workplace - indicates bring your own personal devicesAzureAd - Cloud only joined devicesServerAd - '
'on-premises domain joined devices joined to Azure AD. For more details, see Introduction to device '
'management in Azure Active Directory')
c.argument('member_of', action=AddDevicesDeviceMemberOf, nargs='+', help='Groups that this group is a member '
'of. HTTP Methods: GET (supported for all groups). Read-only. Nullable.')
c.argument('registered_owners', action=AddRegisteredOwners, nargs='+', help='The user that cloud joined the '
'device or registered their personal device. The registered owner is set at the time of '
'registration. Currently, there can be only one owner. Read-only. Nullable.')
c.argument('registered_users', action=AddRegisteredUsers, nargs='+', help='Collection of registered users of '
'the device. For cloud joined devices and registered personal devices, registered users are set to '
'the same value as registered owners at the time of registration. Read-only. Nullable.')
c.argument('transitive_member_of', action=AddDevicesDeviceTransitiveMemberOf, nargs='+', help='')
c.argument('extensions', action=AddDevicesDeviceExtensions, nargs='+', help='The collection of open extensions '
'defined for the device. Read-only. Nullable.')
with self.argument_context('identitydirmgt device check-member-group') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('group_ids', nargs='+', help='')
with self.argument_context('identitydirmgt device check-member-object') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('ids', nargs='+', help='')
with self.argument_context('identitydirmgt device create-extension') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
with self.argument_context('identitydirmgt device create-ref-member-of') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt device create-ref-registered-owner') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt device create-ref-registered-user') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt device create-ref-transitive-member-of') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt device delete-extension') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt device get-available-extension-property') as c:
c.argument('is_synced_from_on_premises', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt device get-by-id') as c:
c.argument('ids', nargs='+', help='')
c.argument('types', nargs='+', help='')
with self.argument_context('identitydirmgt device get-member-group') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt device get-member-object') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt device list-extension') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device list-member-of') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device list-ref-member-of') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt device list-ref-registered-owner') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt device list-ref-registered-user') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt device list-ref-transitive-member-of') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt device list-registered-owner') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device list-registered-user') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device list-transitive-member-of') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device restore') as c:
c.argument('device_id', type=str, help='key: id of device')
with self.argument_context('identitydirmgt device show-extension') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt device update-extension') as c:
c.argument('device_id', type=str, help='key: id of device')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
with self.argument_context('identitydirmgt device validate-property') as c:
c.argument('entity_type', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_behalf_of_user_id', help='')
with self.argument_context('identitydirmgt directory-directory show-directory') as c:
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-directory update-directory') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('administrative_units', type=validate_file_or_dict,
help=' Expected value: json-string/@json-file.')
c.argument('deleted_items', action=AddDeletedItems, nargs='+', help='Recently deleted items. Read-only. '
'Nullable.')
with self.argument_context('identitydirmgt directory create-administrative-unit') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('description', type=str, help='An optional description for the administrative unit.')
c.argument('display_name', type=str, help='Display name for the administrative unit.')
c.argument('visibility', type=str, help='Controls whether the adminstrative unit and its members are hidden or '
'public. Can be set to HiddenMembership or Public. If not set, default behavior is Public. When set '
'to HiddenMembership, only members of the administrative unit can list other members of the '
'adminstrative unit.')
c.argument('members', action=AddDirectoryMembers, nargs='+', help='Users and groups that are members of this '
'Adminsitrative Unit. HTTP Methods: GET (list members), POST (add members), DELETE (remove '
'members).')
c.argument('scoped_role_members', type=validate_file_or_dict, help='Scoped-role members of this Administrative '
'Unit. HTTP Methods: GET (list scopedRoleMemberships), POST (add scopedRoleMembership), DELETE '
'(remove scopedRoleMembership). Expected value: json-string/@json-file.')
c.argument('extensions', action=AddDirectoryExtensions, nargs='+', help='')
with self.argument_context('identitydirmgt directory create-deleted-item') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
with self.argument_context('identitydirmgt directory delete-administrative-unit') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt directory delete-deleted-item') as c:
c.argument('directory_object_id', type=str, help='key: id of directoryObject')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt directory list-administrative-unit') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory list-deleted-item') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory show-administrative-unit') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory show-deleted-item') as c:
c.argument('directory_object_id', type=str, help='key: id of directoryObject')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory update-administrative-unit') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('description', type=str, help='An optional description for the administrative unit.')
c.argument('display_name', type=str, help='Display name for the administrative unit.')
c.argument('visibility', type=str, help='Controls whether the adminstrative unit and its members are hidden or '
'public. Can be set to HiddenMembership or Public. If not set, default behavior is Public. When set '
'to HiddenMembership, only members of the administrative unit can list other members of the '
'adminstrative unit.')
c.argument('members', action=AddDirectoryMembers, nargs='+', help='Users and groups that are members of this '
'Adminsitrative Unit. HTTP Methods: GET (list members), POST (add members), DELETE (remove '
'members).')
c.argument('scoped_role_members', type=validate_file_or_dict, help='Scoped-role members of this Administrative '
'Unit. HTTP Methods: GET (list scopedRoleMemberships), POST (add scopedRoleMembership), DELETE '
'(remove scopedRoleMembership). Expected value: json-string/@json-file.')
c.argument('extensions', action=AddDirectoryExtensions, nargs='+', help='')
with self.argument_context('identitydirmgt directory update-deleted-item') as c:
c.argument('directory_object_id', type=str, help='key: id of directoryObject')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
with self.argument_context('identitydirmgt directory-administrative-unit create-extension') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
with self.argument_context('identitydirmgt directory-administrative-unit create-ref-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt directory-administrative-unit create-scoped-role-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('microsoft_graph_scoped_role_membership_administrative_unit_id_administrative_unit_id', type=str,
help='Unique identifier for the administrative unit that the directory role is scoped to')
c.argument('role_id', type=str, help='Unique identifier for the directory role that the member is in.')
c.argument('role_member_info', action=AddRoleMemberInfo, nargs='+', help='identity')
with self.argument_context('identitydirmgt directory-administrative-unit delete-extension') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt directory-administrative-unit delete-scoped-role-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt directory-administrative-unit list-extension') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-administrative-unit list-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-administrative-unit list-ref-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt directory-administrative-unit list-scoped-role-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-administrative-unit show-extension') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-administrative-unit show-scoped-role-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-administrative-unit update-extension') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
with self.argument_context('identitydirmgt directory-administrative-unit update-scoped-role-member') as c:
c.argument('administrative_unit_id', type=str, help='key: id of administrativeUnit')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('microsoft_graph_scoped_role_membership_administrative_unit_id_administrative_unit_id', type=str,
help='Unique identifier for the administrative unit that the directory role is scoped to')
c.argument('role_id', type=str, help='Unique identifier for the directory role that the member is in.')
c.argument('role_member_info', action=AddRoleMemberInfo, nargs='+', help='identity')
with self.argument_context('identitydirmgt directory-role-directory-role create-directory-role') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('description', type=str, help='The description for the directory role. Read-only.')
c.argument('display_name', type=str, help='The display name for the directory role. Read-only.')
c.argument('role_template_id', type=str, help='The id of the directoryRoleTemplate that this role is based on. '
'The property must be specified when activating a directory role in a tenant with a POST operation. '
'After the directory role has been activated, the property is read only.')
c.argument('members', action=AddDirectoryrolesDirectoryroleMembers, nargs='+', help='Users that are members of '
'this directory role. HTTP Methods: GET, POST, DELETE. Read-only. Nullable.')
c.argument('scoped_members', type=validate_file_or_dict, help=' Expected value: json-string/@json-file.')
with self.argument_context('identitydirmgt directory-role-directory-role delete-directory-role') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt directory-role-directory-role list-directory-role') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-role-directory-role show-directory-role') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-role-directory-role update-directory-role') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('description', type=str, help='The description for the directory role. Read-only.')
c.argument('display_name', type=str, help='The display name for the directory role. Read-only.')
c.argument('role_template_id', type=str, help='The id of the directoryRoleTemplate that this role is based on. '
'The property must be specified when activating a directory role in a tenant with a POST operation. '
'After the directory role has been activated, the property is read only.')
c.argument('members', action=AddDirectoryrolesDirectoryroleMembers, nargs='+', help='Users that are members of '
'this directory role. HTTP Methods: GET, POST, DELETE. Read-only. Nullable.')
c.argument('scoped_members', type=validate_file_or_dict, help=' Expected value: json-string/@json-file.')
with self.argument_context('identitydirmgt directory-role check-member-group') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('group_ids', nargs='+', help='')
with self.argument_context('identitydirmgt directory-role check-member-object') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('ids', nargs='+', help='')
with self.argument_context('identitydirmgt directory-role create-ref-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt directory-role create-scoped-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('administrative_unit_id', type=str, help='Unique identifier for the administrative unit that the '
'directory role is scoped to')
c.argument('role_id', type=str, help='Unique identifier for the directory role that the member is in.')
c.argument('role_member_info', action=AddRoleMemberInfo, nargs='+', help='identity')
with self.argument_context('identitydirmgt directory-role delete-scoped-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt directory-role get-available-extension-property') as c:
c.argument('is_synced_from_on_premises', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt directory-role get-by-id') as c:
c.argument('ids', nargs='+', help='')
c.argument('types', nargs='+', help='')
with self.argument_context('identitydirmgt directory-role get-member-group') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt directory-role get-member-object') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt directory-role list-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-role list-ref-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt directory-role list-scoped-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-role restore') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
with self.argument_context('identitydirmgt directory-role show-scoped-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-role update-scoped-member') as c:
c.argument('directory_role_id', type=str, help='key: id of directoryRole')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('administrative_unit_id', type=str, help='Unique identifier for the administrative unit that the '
'directory role is scoped to')
c.argument('role_id', type=str, help='Unique identifier for the directory role that the member is in.')
c.argument('role_member_info', action=AddRoleMemberInfo, nargs='+', help='identity')
with self.argument_context('identitydirmgt directory-role validate-property') as c:
c.argument('entity_type', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_behalf_of_user_id', help='')
with self.argument_context('identitydirmgt directory-role-template-directory-role-template create-directory-role-template') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('description', type=str, help='The description to set for the directory role. Read-only.')
c.argument('display_name', type=str, help='The display name to set for the directory role. Read-only.')
with self.argument_context('identitydirmgt directory-role-template-directory-role-template delete-directory-role-template') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt directory-role-template-directory-role-template list-directory-role-template') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-role-template-directory-role-template show-directory-role-template') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt directory-role-template-directory-role-template update-directory-role-template') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('description', type=str, help='The description to set for the directory role. Read-only.')
c.argument('display_name', type=str, help='The display name to set for the directory role. Read-only.')
with self.argument_context('identitydirmgt directory-role-template check-member-group') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
c.argument('group_ids', nargs='+', help='')
with self.argument_context('identitydirmgt directory-role-template check-member-object') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
c.argument('ids', nargs='+', help='')
with self.argument_context('identitydirmgt directory-role-template get-available-extension-property') as c:
c.argument('is_synced_from_on_premises', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt directory-role-template get-by-id') as c:
c.argument('ids', nargs='+', help='')
c.argument('types', nargs='+', help='')
with self.argument_context('identitydirmgt directory-role-template get-member-group') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt directory-role-template get-member-object') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt directory-role-template restore') as c:
c.argument('directory_role_template_id', type=str, help='key: id of directoryRoleTemplate')
with self.argument_context('identitydirmgt directory-role-template validate-property') as c:
c.argument('entity_type', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_behalf_of_user_id', help='')
with self.argument_context('identitydirmgt domain-domain create-domain') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('authentication_type', type=str, help='Indicates the configured authentication type for the domain. '
'The value is either Managed or Federated. Managed indicates a cloud managed domain where Azure AD '
'performs user authentication.Federated indicates authentication is federated with an identity '
'provider such as the tenant\'s on-premises Active Directory via Active Directory Federation '
'Services. This property is read-only and is not nullable.')
c.argument('availability_status', type=str, help='This property is always null except when the verify action '
'is used. When the verify action is used, a domain entity is returned in the response. The '
'availabilityStatus property of the domain entity in the response is either AvailableImmediately or '
'EmailVerifiedDomainTakeoverScheduled.')
c.argument('is_admin_managed', arg_type=get_three_state_flag(), help='The value of the property is false if '
'the DNS record management of the domain has been delegated to Microsoft 365. Otherwise, the value '
'is true. Not nullable')
c.argument('is_default', arg_type=get_three_state_flag(), help='True if this is the default domain that is '
'used for user creation. There is only one default domain per company. Not nullable')
c.argument('is_initial', arg_type=get_three_state_flag(), help='True if this is the initial domain created by '
'Microsoft Online Services (companyname.onmicrosoft.com). There is only one initial domain per '
'company. Not nullable')
c.argument('is_root', arg_type=get_three_state_flag(), help='True if the domain is a verified root domain. '
'Otherwise, false if the domain is a subdomain or unverified. Not nullable')
c.argument('is_verified', arg_type=get_three_state_flag(), help='True if the domain has completed domain '
'ownership verification. Not nullable')
c.argument('manufacturer', type=str, help='')
c.argument('model', type=str, help='')
c.argument('password_notification_window_in_days', type=int, help='Specifies the number of days before a user '
'receives notification that their password will expire. If the property is not set, a default value '
'of 14 days will be used.')
c.argument('password_validity_period_in_days', type=int, help='Specifies the length of time that a password is '
'valid before it must be changed. If the property is not set, a default value of 90 days will be '
'used.')
c.argument('state', action=AddState, nargs='+', help='domainState')
c.argument('supported_services', nargs='+', help='The capabilities assigned to the domain.Can include 0, 1 or '
'more of following values: Email, Sharepoint, EmailInternalRelayOnly, OfficeCommunicationsOnline, '
'SharePointDefaultDomain, FullRedelegation, SharePointPublic, OrgIdAuthentication, Yammer, Intune '
'The values which you can add/remove using Graph API include: Email, OfficeCommunicationsOnline, '
'YammerNot nullable')
c.argument('domain_name_references', action=AddDomainNameReferences, nargs='+', help='Read-only, Nullable')
c.argument('service_configuration_records', action=AddServiceConfigurationRecords, nargs='+', help='DNS '
'records the customer adds to the DNS zone file of the domain before the domain can be used by '
'Microsoft Online services.Read-only, Nullable')
c.argument('verification_dns_records', action=AddVerificationDnsRecords, nargs='+', help='DNS records that the '
'customer adds to the DNS zone file of the domain before the customer can complete domain ownership '
'verification with Azure AD.Read-only, Nullable')
with self.argument_context('identitydirmgt domain-domain delete-domain') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt domain-domain list-domain') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt domain-domain show-domain') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt domain-domain update-domain') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('authentication_type', type=str, help='Indicates the configured authentication type for the domain. '
'The value is either Managed or Federated. Managed indicates a cloud managed domain where Azure AD '
'performs user authentication.Federated indicates authentication is federated with an identity '
'provider such as the tenant\'s on-premises Active Directory via Active Directory Federation '
'Services. This property is read-only and is not nullable.')
c.argument('availability_status', type=str, help='This property is always null except when the verify action '
'is used. When the verify action is used, a domain entity is returned in the response. The '
'availabilityStatus property of the domain entity in the response is either AvailableImmediately or '
'EmailVerifiedDomainTakeoverScheduled.')
c.argument('is_admin_managed', arg_type=get_three_state_flag(), help='The value of the property is false if '
'the DNS record management of the domain has been delegated to Microsoft 365. Otherwise, the value '
'is true. Not nullable')
c.argument('is_default', arg_type=get_three_state_flag(), help='True if this is the default domain that is '
'used for user creation. There is only one default domain per company. Not nullable')
c.argument('is_initial', arg_type=get_three_state_flag(), help='True if this is the initial domain created by '
'Microsoft Online Services (companyname.onmicrosoft.com). There is only one initial domain per '
'company. Not nullable')
c.argument('is_root', arg_type=get_three_state_flag(), help='True if the domain is a verified root domain. '
'Otherwise, false if the domain is a subdomain or unverified. Not nullable')
c.argument('is_verified', arg_type=get_three_state_flag(), help='True if the domain has completed domain '
'ownership verification. Not nullable')
c.argument('manufacturer', type=str, help='')
c.argument('model', type=str, help='')
c.argument('password_notification_window_in_days', type=int, help='Specifies the number of days before a user '
'receives notification that their password will expire. If the property is not set, a default value '
'of 14 days will be used.')
c.argument('password_validity_period_in_days', type=int, help='Specifies the length of time that a password is '
'valid before it must be changed. If the property is not set, a default value of 90 days will be '
'used.')
c.argument('state', action=AddState, nargs='+', help='domainState')
c.argument('supported_services', nargs='+', help='The capabilities assigned to the domain.Can include 0, 1 or '
'more of following values: Email, Sharepoint, EmailInternalRelayOnly, OfficeCommunicationsOnline, '
'SharePointDefaultDomain, FullRedelegation, SharePointPublic, OrgIdAuthentication, Yammer, Intune '
'The values which you can add/remove using Graph API include: Email, OfficeCommunicationsOnline, '
'YammerNot nullable')
c.argument('domain_name_references', action=AddDomainNameReferences, nargs='+', help='Read-only, Nullable')
c.argument('service_configuration_records', action=AddServiceConfigurationRecords, nargs='+', help='DNS '
'records the customer adds to the DNS zone file of the domain before the domain can be used by '
'Microsoft Online services.Read-only, Nullable')
c.argument('verification_dns_records', action=AddVerificationDnsRecords, nargs='+', help='DNS records that the '
'customer adds to the DNS zone file of the domain before the customer can complete domain ownership '
'verification with Azure AD.Read-only, Nullable')
with self.argument_context('identitydirmgt domain create-ref-domain-name-reference') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('body', type=validate_file_or_dict, help='New navigation property ref value Expected value: '
'json-string/@json-file.')
with self.argument_context('identitydirmgt domain create-service-configuration-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('is_optional', arg_type=get_three_state_flag(), help='If false, this record must be configured by '
'the customer at the DNS host for Microsoft Online Services to operate correctly with the domain.')
c.argument('label', type=str, help='Value used when configuring the name of the DNS record at the DNS host.')
c.argument('record_type', type=str, help='Indicates what type of DNS record this entity represents.The value '
'can be one of the following: CName, Mx, Srv, TxtKey')
c.argument('supported_service', type=str, help='Microsoft Online Service or feature that has a dependency on '
'this DNS record.Can be one of the following values: null, Email, Sharepoint, '
'EmailInternalRelayOnly, OfficeCommunicationsOnline, SharePointDefaultDomain, FullRedelegation, '
'SharePointPublic, OrgIdAuthentication, Yammer, Intune')
c.argument('ttl', type=int, help='Value to use when configuring the time-to-live (ttl) property of the DNS '
'record at the DNS host. Not nullable')
with self.argument_context('identitydirmgt domain create-verification-dns-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('is_optional', arg_type=get_three_state_flag(), help='If false, this record must be configured by '
'the customer at the DNS host for Microsoft Online Services to operate correctly with the domain.')
c.argument('label', type=str, help='Value used when configuring the name of the DNS record at the DNS host.')
c.argument('record_type', type=str, help='Indicates what type of DNS record this entity represents.The value '
'can be one of the following: CName, Mx, Srv, TxtKey')
c.argument('supported_service', type=str, help='Microsoft Online Service or feature that has a dependency on '
'this DNS record.Can be one of the following values: null, Email, Sharepoint, '
'EmailInternalRelayOnly, OfficeCommunicationsOnline, SharePointDefaultDomain, FullRedelegation, '
'SharePointPublic, OrgIdAuthentication, Yammer, Intune')
c.argument('ttl', type=int, help='Value to use when configuring the time-to-live (ttl) property of the DNS '
'record at the DNS host. Not nullable')
with self.argument_context('identitydirmgt domain delete-service-configuration-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('domain_dns_record_id', type=str, help='key: id of domainDnsRecord')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt domain delete-verification-dns-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('domain_dns_record_id', type=str, help='key: id of domainDnsRecord')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt domain force-delete') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('disable_user_accounts', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt domain list-domain-name-reference') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt domain list-ref-domain-name-reference') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('orderby', nargs='+', help='Order items by property values')
with self.argument_context('identitydirmgt domain list-service-configuration-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt domain list-verification-dns-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt domain show-service-configuration-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('domain_dns_record_id', type=str, help='key: id of domainDnsRecord')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt domain show-verification-dns-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('domain_dns_record_id', type=str, help='key: id of domainDnsRecord')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt domain update-service-configuration-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('domain_dns_record_id', type=str, help='key: id of domainDnsRecord')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('is_optional', arg_type=get_three_state_flag(), help='If false, this record must be configured by '
'the customer at the DNS host for Microsoft Online Services to operate correctly with the domain.')
c.argument('label', type=str, help='Value used when configuring the name of the DNS record at the DNS host.')
c.argument('record_type', type=str, help='Indicates what type of DNS record this entity represents.The value '
'can be one of the following: CName, Mx, Srv, TxtKey')
c.argument('supported_service', type=str, help='Microsoft Online Service or feature that has a dependency on '
'this DNS record.Can be one of the following values: null, Email, Sharepoint, '
'EmailInternalRelayOnly, OfficeCommunicationsOnline, SharePointDefaultDomain, FullRedelegation, '
'SharePointPublic, OrgIdAuthentication, Yammer, Intune')
c.argument('ttl', type=int, help='Value to use when configuring the time-to-live (ttl) property of the DNS '
'record at the DNS host. Not nullable')
with self.argument_context('identitydirmgt domain update-verification-dns-record') as c:
c.argument('domain_id', type=str, help='key: id of domain')
c.argument('domain_dns_record_id', type=str, help='key: id of domainDnsRecord')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('is_optional', arg_type=get_three_state_flag(), help='If false, this record must be configured by '
'the customer at the DNS host for Microsoft Online Services to operate correctly with the domain.')
c.argument('label', type=str, help='Value used when configuring the name of the DNS record at the DNS host.')
c.argument('record_type', type=str, help='Indicates what type of DNS record this entity represents.The value '
'can be one of the following: CName, Mx, Srv, TxtKey')
c.argument('supported_service', type=str, help='Microsoft Online Service or feature that has a dependency on '
'this DNS record.Can be one of the following values: null, Email, Sharepoint, '
'EmailInternalRelayOnly, OfficeCommunicationsOnline, SharePointDefaultDomain, FullRedelegation, '
'SharePointPublic, OrgIdAuthentication, Yammer, Intune')
c.argument('ttl', type=int, help='Value to use when configuring the time-to-live (ttl) property of the DNS '
'record at the DNS host. Not nullable')
with self.argument_context('identitydirmgt domain verify') as c:
c.argument('domain_id', type=str, help='key: id of domain')
with self.argument_context('identitydirmgt organization-organization create-organization') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('assigned_plans', action=AddAssignedPlans, nargs='+', help='The collection of service plans '
'associated with the tenant. Not nullable.')
c.argument('business_phones', nargs='+', help='Telephone number for the organization. NOTE: Although this is a '
'string collection, only one number can be set for this property.')
c.argument('city', type=str, help='City name of the address for the organization.')
c.argument('country', type=str, help='Country/region name of the address for the organization.')
c.argument('country_letter_code', type=str, help='Country/region abbreviation for the organization.')
c.argument('created_date_time', help='Timestamp of when the organization was created. The value cannot be '
'modified and is automatically populated when the organization is created. The Timestamp type '
'represents date and time information using ISO 8601 format and is always in UTC time. For example, '
'midnight UTC on Jan 1, 2014 would look like this: \'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('display_name', type=str, help='The display name for the tenant.')
c.argument('marketing_notification_emails', nargs='+', help='Not nullable.')
c.argument('on_premises_last_sync_date_time', help='The time and date at which the tenant was last synced with '
'the on-premise directory. The Timestamp type represents date and time information using ISO 8601 '
'format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 would look like this: '
'\'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('on_premises_sync_enabled', arg_type=get_three_state_flag(), help='true if this object is synced '
'from an on-premises directory; false if this object was originally synced from an on-premises '
'directory but is no longer synced; null if this object has never been synced from an on-premises '
'directory (default).')
c.argument('postal_code', type=str, help='Postal code of the address for the organization.')
c.argument('preferred_language', type=str, help='The preferred language for the organization. Should follow '
'ISO 639-1 Code; for example \'en\'.')
c.argument('privacy_profile', action=AddPrivacyProfile, nargs='+', help='privacyProfile')
c.argument('provisioned_plans', action=AddProvisionedPlans, nargs='+', help='Not nullable.')
c.argument('security_compliance_notification_mails', nargs='+', help='')
c.argument('security_compliance_notification_phones', nargs='+', help='')
c.argument('state', type=str, help='State name of the address for the organization.')
c.argument('street', type=str, help='Street name of the address for organization.')
c.argument('technical_notification_mails', nargs='+', help='Not nullable.')
c.argument('tenant_type', type=str, help='')
c.argument('verified_domains', action=AddVerifiedDomains, nargs='+', help='The collection of domains '
'associated with this tenant. Not nullable.')
c.argument('mobile_device_management_authority', arg_type=get_enum_type(['unknown', 'intune', 'sccm',
'office365']), help='')
c.argument('certificate_based_auth_configuration', type=validate_file_or_dict, help='Navigation property to '
'manage certificate-based authentication configuration. Only a single instance of '
'certificateBasedAuthConfiguration can be created in the collection. Expected value: '
'json-string/@json-file.')
c.argument('extensions', action=AddExtensions, nargs='+', help='The collection of open extensions defined for '
'the organization. Read-only. Nullable.')
with self.argument_context('identitydirmgt organization-organization delete-organization') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt organization-organization list-organization') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt organization-organization show-organization') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt organization-organization update-organization') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('deleted_date_time', help='')
c.argument('assigned_plans', action=AddAssignedPlans, nargs='+', help='The collection of service plans '
'associated with the tenant. Not nullable.')
c.argument('business_phones', nargs='+', help='Telephone number for the organization. NOTE: Although this is a '
'string collection, only one number can be set for this property.')
c.argument('city', type=str, help='City name of the address for the organization.')
c.argument('country', type=str, help='Country/region name of the address for the organization.')
c.argument('country_letter_code', type=str, help='Country/region abbreviation for the organization.')
c.argument('created_date_time', help='Timestamp of when the organization was created. The value cannot be '
'modified and is automatically populated when the organization is created. The Timestamp type '
'represents date and time information using ISO 8601 format and is always in UTC time. For example, '
'midnight UTC on Jan 1, 2014 would look like this: \'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('display_name', type=str, help='The display name for the tenant.')
c.argument('marketing_notification_emails', nargs='+', help='Not nullable.')
c.argument('on_premises_last_sync_date_time', help='The time and date at which the tenant was last synced with '
'the on-premise directory. The Timestamp type represents date and time information using ISO 8601 '
'format and is always in UTC time. For example, midnight UTC on Jan 1, 2014 would look like this: '
'\'2014-01-01T00:00:00Z\'. Read-only.')
c.argument('on_premises_sync_enabled', arg_type=get_three_state_flag(), help='true if this object is synced '
'from an on-premises directory; false if this object was originally synced from an on-premises '
'directory but is no longer synced; null if this object has never been synced from an on-premises '
'directory (default).')
c.argument('postal_code', type=str, help='Postal code of the address for the organization.')
c.argument('preferred_language', type=str, help='The preferred language for the organization. Should follow '
'ISO 639-1 Code; for example \'en\'.')
c.argument('privacy_profile', action=AddPrivacyProfile, nargs='+', help='privacyProfile')
c.argument('provisioned_plans', action=AddProvisionedPlans, nargs='+', help='Not nullable.')
c.argument('security_compliance_notification_mails', nargs='+', help='')
c.argument('security_compliance_notification_phones', nargs='+', help='')
c.argument('state', type=str, help='State name of the address for the organization.')
c.argument('street', type=str, help='Street name of the address for organization.')
c.argument('technical_notification_mails', nargs='+', help='Not nullable.')
c.argument('tenant_type', type=str, help='')
c.argument('verified_domains', action=AddVerifiedDomains, nargs='+', help='The collection of domains '
'associated with this tenant. Not nullable.')
c.argument('mobile_device_management_authority', arg_type=get_enum_type(['unknown', 'intune', 'sccm',
'office365']), help='')
c.argument('certificate_based_auth_configuration', type=validate_file_or_dict, help='Navigation property to '
'manage certificate-based authentication configuration. Only a single instance of '
'certificateBasedAuthConfiguration can be created in the collection. Expected value: '
'json-string/@json-file.')
c.argument('extensions', action=AddExtensions, nargs='+', help='The collection of open extensions defined for '
'the organization. Read-only. Nullable.')
with self.argument_context('identitydirmgt organization check-member-group') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('group_ids', nargs='+', help='')
with self.argument_context('identitydirmgt organization check-member-object') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('ids', nargs='+', help='')
with self.argument_context('identitydirmgt organization create-extension') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
with self.argument_context('identitydirmgt organization delete-extension') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt organization get-available-extension-property') as c:
c.argument('is_synced_from_on_premises', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt organization get-by-id') as c:
c.argument('ids', nargs='+', help='')
c.argument('types', nargs='+', help='')
with self.argument_context('identitydirmgt organization get-member-group') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt organization get-member-object') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('security_enabled_only', arg_type=get_three_state_flag(), help='')
with self.argument_context('identitydirmgt organization list-extension') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt organization restore') as c:
c.argument('organization_id', type=str, help='key: id of organization')
with self.argument_context('identitydirmgt organization set-mobile-device-management-authority') as c:
c.argument('organization_id', type=str, help='key: id of organization')
with self.argument_context('identitydirmgt organization show-extension') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt organization update-extension') as c:
c.argument('organization_id', type=str, help='key: id of organization')
c.argument('extension_id', type=str, help='key: id of extension')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
with self.argument_context('identitydirmgt organization validate-property') as c:
c.argument('entity_type', type=str, help='')
c.argument('display_name', type=str, help='')
c.argument('mail_nickname', type=str, help='')
c.argument('on_behalf_of_user_id', help='')
with self.argument_context('identitydirmgt subscribed-sku-subscribed-sku create-subscribed-sku') as c:
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('applies_to', type=str, help='For example, \'User\' or \'Company\'.')
c.argument('capability_status', type=str, help='Possible values are: Enabled, Warning, Suspended, Deleted, '
'LockedOut.')
c.argument('consumed_units', type=int, help='The number of licenses that have been assigned.')
c.argument('prepaid_units', action=AddPrepaidUnits, nargs='+', help='licenseUnitsDetail')
c.argument('service_plans', action=AddServicePlans, nargs='+', help='Information about the service plans that '
'are available with the SKU. Not nullable')
c.argument('sku_id', help='The unique identifier (GUID) for the service SKU.')
c.argument('sku_part_number', type=str, help='The SKU part number; for example: \'AAD_PREMIUM\' or '
'\'RMSBASIC\'. To get a list of commercial subscriptions that an organization has acquired, see '
'List subscribedSkus.')
with self.argument_context('identitydirmgt subscribed-sku-subscribed-sku delete-subscribed-sku') as c:
c.argument('subscribed_sku_id', type=str, help='key: id of subscribedSku')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt subscribed-sku-subscribed-sku list-subscribed-sku') as c:
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt subscribed-sku-subscribed-sku show-subscribed-sku') as c:
c.argument('subscribed_sku_id', type=str, help='key: id of subscribedSku')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt subscribed-sku-subscribed-sku update-subscribed-sku') as c:
c.argument('subscribed_sku_id', type=str, help='key: id of subscribedSku')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('applies_to', type=str, help='For example, \'User\' or \'Company\'.')
c.argument('capability_status', type=str, help='Possible values are: Enabled, Warning, Suspended, Deleted, '
'LockedOut.')
c.argument('consumed_units', type=int, help='The number of licenses that have been assigned.')
c.argument('prepaid_units', action=AddPrepaidUnits, nargs='+', help='licenseUnitsDetail')
c.argument('service_plans', action=AddServicePlans, nargs='+', help='Information about the service plans that '
'are available with the SKU. Not nullable')
c.argument('sku_id', help='The unique identifier (GUID) for the service SKU.')
c.argument('sku_part_number', type=str, help='The SKU part number; for example: \'AAD_PREMIUM\' or '
'\'RMSBASIC\'. To get a list of commercial subscriptions that an organization has acquired, see '
'List subscribedSkus.')
with self.argument_context('identitydirmgt user create-scoped-role-member-of') as c:
c.argument('user_id', type=str, help='key: id of user')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('administrative_unit_id', type=str, help='Unique identifier for the administrative unit that the '
'directory role is scoped to')
c.argument('role_id', type=str, help='Unique identifier for the directory role that the member is in.')
c.argument('role_member_info', action=AddRoleMemberInfo, nargs='+', help='identity')
with self.argument_context('identitydirmgt user delete-scoped-role-member-of') as c:
c.argument('user_id', type=str, help='key: id of user')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('if_match', type=str, help='ETag')
with self.argument_context('identitydirmgt user list-scoped-role-member-of') as c:
c.argument('user_id', type=str, help='key: id of user')
c.argument('orderby', nargs='+', help='Order items by property values')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt user show-scoped-role-member-of') as c:
c.argument('user_id', type=str, help='key: id of user')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('select', nargs='+', help='Select properties to be returned')
c.argument('expand', nargs='+', help='Expand related entities')
with self.argument_context('identitydirmgt user update-scoped-role-member-of') as c:
c.argument('user_id', type=str, help='key: id of user')
c.argument('scoped_role_membership_id', type=str, help='key: id of scopedRoleMembership')
c.argument('id_', options_list=['--id'], type=str, help='Read-only.')
c.argument('administrative_unit_id', type=str, help='Unique identifier for the administrative unit that the '
'directory role is scoped to')
c.argument('role_id', type=str, help='Unique identifier for the directory role that the member is in.')
c.argument('role_member_info', action=AddRoleMemberInfo, nargs='+', help='identity')
| 72.951745
| 133
| 0.676948
| 12,637
| 98,266
| 5.164042
| 0.044235
| 0.096126
| 0.058154
| 0.043029
| 0.977612
| 0.975145
| 0.970762
| 0.964204
| 0.959974
| 0.954565
| 0
| 0.003398
| 0.197403
| 98,266
| 1,346
| 134
| 73.005944
| 0.824035
| 0.005455
| 0
| 0.805893
| 0
| 0.011265
| 0.514158
| 0.074818
| 0
| 0
| 0
| 0
| 0
| 1
| 0.000867
| false
| 0.005199
| 0.0026
| 0
| 0.003466
| 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
|
b08484c51e0aa9071fe4883e1a0bbaa5781b78df
| 139
|
py
|
Python
|
arep/Validators/__init__.py
|
aalireza/arep
|
95f0ec6282c4f5d12462d2a64e82d6777f51bf06
|
[
"BSD-3-Clause"
] | 1
|
2022-01-14T00:15:26.000Z
|
2022-01-14T00:15:26.000Z
|
arep/Validators/__init__.py
|
aalireza/arep
|
95f0ec6282c4f5d12462d2a64e82d6777f51bf06
|
[
"BSD-3-Clause"
] | null | null | null |
arep/Validators/__init__.py
|
aalireza/arep
|
95f0ec6282c4f5d12462d2a64e82d6777f51bf06
|
[
"BSD-3-Clause"
] | null | null | null |
from arep.Validators import action as Action
from arep.Validators import kind as Kind
from arep.Validators import properties as Properties
| 34.75
| 52
| 0.848921
| 21
| 139
| 5.619048
| 0.380952
| 0.20339
| 0.457627
| 0.610169
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129496
| 139
| 3
| 53
| 46.333333
| 0.975207
| 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
| 0
| 0
|
0
| 7
|
b02d2400f3a46bc798034728f5690e94ace389e3
| 154
|
py
|
Python
|
some_data.py
|
tsaklidis/car-service-monitor
|
2cdd495cc49bc0154bced221cfcf6fe12d4a739d
|
[
"MIT"
] | null | null | null |
some_data.py
|
tsaklidis/car-service-monitor
|
2cdd495cc49bc0154bced221cfcf6fe12d4a739d
|
[
"MIT"
] | null | null | null |
some_data.py
|
tsaklidis/car-service-monitor
|
2cdd495cc49bc0154bced221cfcf6fe12d4a739d
|
[
"MIT"
] | null | null | null |
import os
os.system("echo '[info] Lazy data dump started...'")
os.system("python manage.py create_owners 7")
os.system("python manage.py create_cars 3")
| 25.666667
| 52
| 0.733766
| 26
| 154
| 4.269231
| 0.653846
| 0.216216
| 0.252252
| 0.36036
| 0.504505
| 0.504505
| 0
| 0
| 0
| 0
| 0
| 0.014599
| 0.11039
| 154
| 5
| 53
| 30.8
| 0.79562
| 0
| 0
| 0
| 0
| 0
| 0.655844
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.25
| 0
| 0.25
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c674784dd3468d0015f97869511a814ffa63a0f7
| 4,783
|
py
|
Python
|
tests/test_data_transfer_rate_kibibits_per_second.py
|
putridparrot/PyUnits
|
4f1095c6fc0bee6ba936921c391913dbefd9307c
|
[
"MIT"
] | null | null | null |
tests/test_data_transfer_rate_kibibits_per_second.py
|
putridparrot/PyUnits
|
4f1095c6fc0bee6ba936921c391913dbefd9307c
|
[
"MIT"
] | null | null | null |
tests/test_data_transfer_rate_kibibits_per_second.py
|
putridparrot/PyUnits
|
4f1095c6fc0bee6ba936921c391913dbefd9307c
|
[
"MIT"
] | null | null | null |
# <auto-generated>
# This code was generated by the UnitCodeGenerator tool
#
# Changes to this file will be lost if the code is regenerated
# </auto-generated>
import unittest
import units.data_transfer_rate.kibibits_per_second
class TestKibibitsPerSecondMethods(unittest.TestCase):
def test_convert_known_kibibits_per_second_to_bits_per_second(self):
self.assertAlmostEqual(2048.0, units.data_transfer_rate.kibibits_per_second.to_bits_per_second(2.0), places=1)
self.assertAlmostEqual(9216.0, units.data_transfer_rate.kibibits_per_second.to_bits_per_second(9.0), places=1)
self.assertAlmostEqual(18227.2, units.data_transfer_rate.kibibits_per_second.to_bits_per_second(17.8), places=1)
def test_convert_known_kibibits_per_second_to_kilo_bits_per_second(self):
self.assertAlmostEqual(6.3488, units.data_transfer_rate.kibibits_per_second.to_kilo_bits_per_second(6.2), places=1)
self.assertAlmostEqual(0.9216, units.data_transfer_rate.kibibits_per_second.to_kilo_bits_per_second(0.9), places=1)
self.assertAlmostEqual(89.088, units.data_transfer_rate.kibibits_per_second.to_kilo_bits_per_second(87.0), places=1)
def test_convert_known_kibibits_per_second_to_mega_bits_per_second(self):
self.assertAlmostEqual(0.089088, units.data_transfer_rate.kibibits_per_second.to_mega_bits_per_second(87.0), places=1)
self.assertAlmostEqual(0.01263616, units.data_transfer_rate.kibibits_per_second.to_mega_bits_per_second(12.34), places=1)
self.assertAlmostEqual(126.418879, units.data_transfer_rate.kibibits_per_second.to_mega_bits_per_second(123456.0), places=1)
def test_convert_known_kibibits_per_second_to_giga_bits_per_second(self):
self.assertAlmostEqual(0.126418944, units.data_transfer_rate.kibibits_per_second.to_giga_bits_per_second(123456.0), places=1)
self.assertAlmostEqual(8.192, units.data_transfer_rate.kibibits_per_second.to_giga_bits_per_second(8000000.0), places=1)
self.assertAlmostEqual(1.307521024, units.data_transfer_rate.kibibits_per_second.to_giga_bits_per_second(1276876.0), places=1)
def test_convert_known_kibibits_per_second_to_tera_bits_per_second(self):
self.assertAlmostEqual(0.8192, units.data_transfer_rate.kibibits_per_second.to_tera_bits_per_second(800000000.0), places=1)
self.assertAlmostEqual(1536.0, units.data_transfer_rate.kibibits_per_second.to_tera_bits_per_second(1.5e12), places=1)
self.assertAlmostEqual(0.01023999898, units.data_transfer_rate.kibibits_per_second.to_tera_bits_per_second(9999999.0), places=1)
def test_convert_known_kibibits_per_second_to_kilo_bytes_per_second(self):
self.assertAlmostEqual(117.632, units.data_transfer_rate.kibibits_per_second.to_kilo_bytes_per_second(919.0), places=1)
self.assertAlmostEqual(9.9072, units.data_transfer_rate.kibibits_per_second.to_kilo_bytes_per_second(77.4), places=1)
self.assertAlmostEqual(13.965952, units.data_transfer_rate.kibibits_per_second.to_kilo_bytes_per_second(109.109), places=1)
def test_convert_known_kibibits_per_second_to_mega_bytes_per_second(self):
self.assertAlmostEqual(0.128, units.data_transfer_rate.kibibits_per_second.to_mega_bytes_per_second(1000.0), places=1)
self.assertAlmostEqual(0.102415744, units.data_transfer_rate.kibibits_per_second.to_mega_bytes_per_second(800.123), places=1)
self.assertAlmostEqual(15.802368, units.data_transfer_rate.kibibits_per_second.to_mega_bytes_per_second(123456.0), places=1)
def test_convert_known_kibibits_per_second_to_giga_bytes_per_second(self):
self.assertAlmostEqual(1.580347926, units.data_transfer_rate.kibibits_per_second.to_giga_bytes_per_second(12345678.0), places=1)
self.assertAlmostEqual(1024000.00, units.data_transfer_rate.kibibits_per_second.to_giga_bytes_per_second(8e12), places=1)
self.assertAlmostEqual(0.01536, units.data_transfer_rate.kibibits_per_second.to_giga_bytes_per_second(1.2e5), places=1)
def test_convert_known_kibibits_per_second_to_tera_bytes_per_second(self):
self.assertAlmostEqual(0.01536, units.data_transfer_rate.kibibits_per_second.to_tera_bytes_per_second(120000000.0), places=1)
self.assertAlmostEqual(11264.0, units.data_transfer_rate.kibibits_per_second.to_tera_bytes_per_second(88e12), places=1)
self.assertAlmostEqual(0.009216, units.data_transfer_rate.kibibits_per_second.to_tera_bytes_per_second(9000000.0), places=1)
def test_convert_known_kibibits_per_second_to_mebibits_per_second(self):
self.assertAlmostEqual(0.5859375, units.data_transfer_rate.kibibits_per_second.to_mebibits_per_second(600.0), places=1)
self.assertAlmostEqual(12.055664, units.data_transfer_rate.kibibits_per_second.to_mebibits_per_second(12345.0), places=1)
self.assertAlmostEqual(0.0986328, units.data_transfer_rate.kibibits_per_second.to_mebibits_per_second(101.0), places=1)
if __name__ == '__main__':
unittest.main()
| 74.734375
| 130
| 0.858039
| 763
| 4,783
| 4.927916
| 0.146789
| 0.193883
| 0.185372
| 0.202128
| 0.831383
| 0.739096
| 0.683777
| 0.630053
| 0.591223
| 0.591223
| 0
| 0.087071
| 0.049132
| 4,783
| 63
| 131
| 75.920635
| 0.739666
| 0.031152
| 0
| 0
| 1
| 0
| 0.001729
| 0
| 0
| 0
| 0
| 0
| 0.666667
| 1
| 0.222222
| false
| 0
| 0.044444
| 0
| 0.288889
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c678d7912c0397424c201ae98371f4f8a69aba67
| 813,439
|
py
|
Python
|
rivendell/volatility/RHELServer59.py
|
ezaspy/elrond
|
3e358f20112be403b895d873a7e3892ce4181d8b
|
[
"MIT"
] | 1
|
2021-03-29T08:05:31.000Z
|
2021-03-29T08:05:31.000Z
|
rivendell/volatility/RHELServer59.py
|
ezaspy/elrond
|
3e358f20112be403b895d873a7e3892ce4181d8b
|
[
"MIT"
] | 17
|
2020-11-24T11:00:38.000Z
|
2021-05-18T18:20:21.000Z
|
rivendell/volatility/RHELServer59.py
|
ezaspy/elrond
|
3e358f20112be403b895d873a7e3892ce4181d8b
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3 -tt
def RHELServer59():
ziphexdump = 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return ziphexdump
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0
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c694d337411602da14d4ccd6217a892e917c7e1a
| 136
|
py
|
Python
|
pybabblesdk/rpc/__init__.py
|
mosaicnetworks/pybabblesdk
|
6fe09cbe02ed8dc674aa849723bad5336a9b9017
|
[
"MIT"
] | 3
|
2019-04-24T19:42:37.000Z
|
2020-06-09T03:36:04.000Z
|
pybabblesdk/rpc/__init__.py
|
mosaicnetworks/pybabblesdk
|
6fe09cbe02ed8dc674aa849723bad5336a9b9017
|
[
"MIT"
] | null | null | null |
pybabblesdk/rpc/__init__.py
|
mosaicnetworks/pybabblesdk
|
6fe09cbe02ed8dc674aa849723bad5336a9b9017
|
[
"MIT"
] | null | null | null |
from pybabblesdk.rpc.jsonrpctcpclient import JSONRPCTCPClient
from pybabblesdk.rpc.jsonrpctcpserver import JSONRPCTCPServer, Dispatcher
| 45.333333
| 73
| 0.897059
| 13
| 136
| 9.384615
| 0.538462
| 0.245902
| 0.295082
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.066176
| 136
| 2
| 74
| 68
| 0.96063
| 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
|
c6c803771fcc80144b1be4313f0e36bb80c41a2d
| 205
|
py
|
Python
|
integrations.py
|
felixterpstra/classy
|
90a439d11f664b825bef0a67de16d447778d9895
|
[
"MIT"
] | null | null | null |
integrations.py
|
felixterpstra/classy
|
90a439d11f664b825bef0a67de16d447778d9895
|
[
"MIT"
] | null | null | null |
integrations.py
|
felixterpstra/classy
|
90a439d11f664b825bef0a67de16d447778d9895
|
[
"MIT"
] | null | null | null |
class S3Sync():
def __init__(self, s3_key, s3_secret):
self.s3_key = s3_key
self.s3_secret = s3_secret
def keys(self):
return '{} - {}'.format(self.s3_key, self.s3_secret)
| 25.625
| 60
| 0.609756
| 30
| 205
| 3.766667
| 0.366667
| 0.265487
| 0.238938
| 0.19469
| 0.300885
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0.253659
| 205
| 7
| 61
| 29.285714
| 0.679739
| 0
| 0
| 0
| 0
| 0
| 0.034146
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.166667
| 0.666667
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
c6e605978defc7a1b47f3fe3944b14a7b984e71e
| 87
|
py
|
Python
|
gooch_maf_tools/util/__init__.py
|
kotoroshinoto/TCGA_MAF_Analysis
|
48e9293015d47ee0f97ea9707896798b84f14feb
|
[
"Unlicense"
] | null | null | null |
gooch_maf_tools/util/__init__.py
|
kotoroshinoto/TCGA_MAF_Analysis
|
48e9293015d47ee0f97ea9707896798b84f14feb
|
[
"Unlicense"
] | 2
|
2017-03-15T17:55:43.000Z
|
2017-03-15T17:57:50.000Z
|
gooch_maf_tools/util/__init__.py
|
kotoroshinoto/TCGA_MAF_Analysis
|
48e9293015d47ee0f97ea9707896798b84f14feb
|
[
"Unlicense"
] | null | null | null |
import gooch_maf_tools.util.MAFcounters
import gooch_maf_tools.util.MAFSampleCountsList
| 43.5
| 47
| 0.91954
| 12
| 87
| 6.333333
| 0.583333
| 0.289474
| 0.368421
| 0.5
| 0.605263
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034483
| 87
| 2
| 47
| 43.5
| 0.904762
| 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
| 0
| 0
|
0
| 7
|
05aa6a804fdefd310417c8417ee3f5855e445f5c
| 52,023
|
py
|
Python
|
tests/test_defaults_list.py
|
romesco/hydra
|
a3e1859da4135093cd4762094ff648a789322227
|
[
"MIT"
] | 1
|
2021-09-06T09:27:28.000Z
|
2021-09-06T09:27:28.000Z
|
tests/test_defaults_list.py
|
paantya/hydra
|
599205ffa771045429a6a32ef69a464602d31e15
|
[
"MIT"
] | 7
|
2021-06-28T20:41:38.000Z
|
2022-02-27T11:23:34.000Z
|
tests/test_defaults_list.py
|
paantya/hydra
|
599205ffa771045429a6a32ef69a464602d31e15
|
[
"MIT"
] | null | null | null |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import re
from textwrap import dedent
from typing import Any, List
import pytest
from hydra._internal.config_repository import ConfigRepository
from hydra._internal.config_search_path_impl import ConfigSearchPathImpl
from hydra._internal.defaults_list import (
compute_element_defaults_list,
convert_overrides_to_defaults,
expand_defaults_list,
)
from hydra.core import DefaultElement
from hydra.core.override_parser.overrides_parser import OverridesParser
from hydra.core.plugins import Plugins
from hydra.errors import ConfigCompositionException, OverrideParseException
from hydra.test_utils.test_utils import chdir_hydra_root
chdir_hydra_root()
# registers config source plugins
Plugins.instance()
@pytest.mark.parametrize( # type: ignore
"element,expected",
[
pytest.param(
DefaultElement(config_name="no_defaults", parent="this_test"),
[
DefaultElement(config_name="no_defaults", parent="this_test"),
],
id="no_defaults",
),
pytest.param(
DefaultElement(config_name="duplicate_self", parent="this_test"),
pytest.raises(
ConfigCompositionException,
match="Duplicate _self_ defined in duplicate_self",
),
id="duplicate_self",
),
pytest.param(
DefaultElement(config_name="trailing_self", parent="this_test"),
[
DefaultElement(config_name="no_defaults", parent="trailing_self"),
DefaultElement(config_name="trailing_self", parent="this_test"),
],
id="trailing_self",
),
pytest.param(
DefaultElement(config_name="implicit_leading_self", parent="this_test"),
[
DefaultElement(config_name="implicit_leading_self", parent="this_test"),
DefaultElement(
config_name="no_defaults",
parent="implicit_leading_self",
),
],
id="implicit_leading_self",
),
pytest.param(
DefaultElement(
config_name="explicit_leading_self",
parent="this_test",
),
[
DefaultElement(
config_name="explicit_leading_self",
parent="this_test",
),
DefaultElement(
config_name="no_defaults",
parent="explicit_leading_self",
),
],
id="explicit_leading_self",
),
pytest.param(
DefaultElement(config_name="a/a1"),
[
DefaultElement(config_name="a/a1"),
],
id="primary_in_config_group_no_defaults",
),
pytest.param(
DefaultElement(config_group="a", config_name="a1"),
[
DefaultElement(config_group="a", config_name="a1"),
],
id="primary_in_config_group_no_defaults",
),
pytest.param(
DefaultElement(config_name="a/global"),
[
DefaultElement(config_name="a/global"),
],
id="a/global",
),
pytest.param(
DefaultElement(config_name="b/b1"),
[
DefaultElement(config_name="b/b1"),
],
id="b/b1",
),
pytest.param(
DefaultElement(config_group="b", config_name="b1"),
[
DefaultElement(config_group="b", config_name="b1"),
],
id="b/b1",
),
pytest.param(
DefaultElement(config_group="a", config_name="a2", parent="this_test"),
[
DefaultElement(config_group="a", config_name="a2", parent="this_test"),
DefaultElement(config_group="b", config_name="b1", parent="a/a2"),
],
id="a/a2",
),
pytest.param(
DefaultElement(
config_name="recursive_item_explicit_self", parent="this_test"
),
[
DefaultElement(
config_name="recursive_item_explicit_self", parent="this_test"
),
DefaultElement(
config_group="a",
config_name="a2",
parent="recursive_item_explicit_self",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="a/a2",
),
],
id="recursive_item_explicit_self",
),
pytest.param(
DefaultElement(
config_name="recursive_item_explicit_self", parent="this_test"
),
[
DefaultElement(
config_name="recursive_item_explicit_self", parent="this_test"
),
DefaultElement(
config_group="a",
config_name="a2",
parent="recursive_item_explicit_self",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="a/a2",
),
],
id="recursive_item_implicit_self",
),
pytest.param(
DefaultElement(config_group="a", config_name="a3", parent="this_test"),
[
DefaultElement(config_group="a", config_name="a3", parent="this_test"),
DefaultElement(config_group="c", config_name="c2", parent="a/a3"),
DefaultElement(config_group="b", config_name="b2", parent="a/a3"),
],
id="multiple_item_definitions",
),
pytest.param(
DefaultElement(config_group="a", config_name="a4", parent="this_test"),
[
DefaultElement(config_group="a", config_name="a4", parent="this_test"),
DefaultElement(
config_group="b",
config_name="b1",
package="file_pkg",
parent="a/a4",
),
],
id="a/a4_pkg_override_in_config",
),
pytest.param(
DefaultElement(config_group="b", config_name="b3", parent="this_test"),
[
DefaultElement(config_group="b", config_name="b3", parent="this_test"),
],
id="b/b3",
),
pytest.param(
DefaultElement(config_group="a", config_name="a5", parent="this_test"),
[
DefaultElement(config_group="a", config_name="a5", parent="this_test"),
DefaultElement(config_group="b", config_name="b3", parent="a/a5"),
DefaultElement(
config_group="b",
config_name="b3",
package="file_pkg",
parent="a/a5",
),
],
id="a/a5",
),
pytest.param(
DefaultElement(
config_group="b", config_name="base_from_a", parent="this_test"
),
[
DefaultElement(config_name="a/a1", parent="b/base_from_a"),
DefaultElement(
config_group="b",
config_name="base_from_a",
parent="this_test",
),
],
id="b/base_from_a",
),
pytest.param(
DefaultElement(
config_group="b", config_name="base_from_b", parent="this_test"
),
[
DefaultElement(config_name="b/b1", parent="b/base_from_b"),
DefaultElement(
config_group="b", config_name="base_from_b", parent="this_test"
),
],
id="b/base_from_b",
),
# rename
pytest.param(
DefaultElement(config_group="rename", config_name="r1", parent="this_test"),
[
DefaultElement(
config_group="rename", config_name="r1", parent="this_test"
),
DefaultElement(
config_group="b",
package="pkg",
config_name="b1",
parent="rename/r1",
),
],
id="rename_package_from_none",
),
pytest.param(
DefaultElement(config_group="rename", config_name="r2", parent="this_test"),
[
DefaultElement(
config_group="rename", config_name="r2", parent="this_test"
),
DefaultElement(
config_group="b",
package="pkg2",
config_name="b1",
parent="rename/r2",
),
],
id="rename_package_from_something",
),
pytest.param(
DefaultElement(config_group="rename", config_name="r3", parent="this_test"),
[
DefaultElement(
config_group="rename", config_name="r3", parent="this_test"
),
DefaultElement(
config_group="b",
package="pkg",
config_name="b4",
parent="rename/r3",
),
],
id="rename_package_from_none_and_change_option:r3",
),
pytest.param(
DefaultElement(config_group="rename", config_name="r4", parent="this_test"),
[
DefaultElement(
config_group="rename",
config_name="r4",
parent="this_test",
),
DefaultElement(
config_group="b",
package="pkg2",
config_name="b4",
parent="rename/r4",
),
],
id="rename_package_and_change_option:r4",
),
pytest.param(
DefaultElement(config_group="rename", config_name="r5", parent="this_test"),
[
DefaultElement(
config_group="rename",
config_name="r5",
parent="this_test",
),
DefaultElement(
config_name="rename/r4",
parent="rename/r5",
),
DefaultElement(
config_group="b",
package="pkg2",
config_name="b4",
parent="rename/r4",
),
DefaultElement(
config_group="a",
config_name="a1",
parent="rename/r5",
),
],
id="rename_package_and_change_option:r5",
),
# delete
pytest.param(
DefaultElement(config_group="delete", config_name="d1", parent="this_test"),
[
DefaultElement(
config_group="delete", config_name="d1", parent="this_test"
),
DefaultElement(
config_group="b",
config_name="b1",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="delete/d1",
),
],
id="delete_with_null",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d2", parent="this_test"),
[
DefaultElement(
config_group="delete", config_name="d2", parent="this_test"
),
DefaultElement(
config_group="b",
config_name="b1",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="delete/d2",
),
],
id="delete_with_tilda",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d3", parent="this_test"),
[
DefaultElement(
config_group="delete", config_name="d3", parent="this_test"
),
DefaultElement(
config_group="b",
config_name="b1",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="delete/d3",
),
],
id="delete_with_tilda_k=v",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d4", parent="this_test"),
[
DefaultElement(
config_group="delete",
config_name="d4",
parent="this_test",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="delete/d4",
),
],
id="file_delete_not_mandatory",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d5", parent="this_test"),
[
DefaultElement(
config_group="delete", config_name="d5", parent="this_test"
),
DefaultElement(config_group="b", config_name="b1", parent="delete/d5"),
],
id="file_delete_not_mandatory",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d7", parent="this_test"),
[
DefaultElement(
config_group="delete", config_name="d7", parent="this_test"
),
DefaultElement(config_group="b", config_name="b1", parent="delete/d7"),
],
id="file_delete_not_mandatory",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d6", parent="this_test"),
[
DefaultElement(
config_group="delete",
config_name="d6",
parent="this_test",
),
DefaultElement(
config_group="b",
config_name="b1",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="delete/d6",
),
],
id="specific_delete",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d8", parent="this_test"),
[
DefaultElement(
config_group="delete", config_name="d8", parent="this_test"
),
DefaultElement(config_group="b", config_name="b2", parent="delete/d8"),
DefaultElement(
config_group="c",
config_name="c2",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="b/b2",
),
],
id="delete_from_included",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d9"),
[
DefaultElement(config_group="delete", config_name="d9"),
],
id="file_delete_not_mandatory",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d11"),
[
DefaultElement(config_group="delete", config_name="d11"),
DefaultElement(
config_group="b",
config_name="b1",
parent="delete/d11",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
DefaultElement(
config_group="b",
package="pkg1",
config_name="b1",
parent="delete/d11",
),
],
id="delete_is_specific",
),
pytest.param(
DefaultElement(config_group="delete", config_name="d12"),
[
DefaultElement(config_group="delete", config_name="d12"),
DefaultElement(
config_group="b",
config_name="b1",
parent="delete/d12",
),
DefaultElement(
config_group="b",
package="pkg1",
config_name="b1",
parent="delete/d12",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
],
id="delete_is_specific",
),
# interpolation
pytest.param(
DefaultElement(
config_group="interpolation",
config_name="i1",
parent="this_test",
),
[
DefaultElement(
config_group="interpolation",
config_name="i1",
parent="this_test",
),
DefaultElement(
config_group="a",
config_name="a1",
parent="interpolation/i1",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="interpolation/i1",
),
DefaultElement(
config_group="a_b",
config_name="a1_b1",
parent="interpolation/i1",
),
],
id="interpolation",
),
pytest.param(
DefaultElement(
config_group="interpolation",
config_name="i2_legacy_with_self",
parent="this_test",
),
[
DefaultElement(
config_group="interpolation",
config_name="i2_legacy_with_self",
parent="this_test",
),
DefaultElement(
config_group="a",
config_name="a1",
parent="interpolation/i2_legacy_with_self",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="interpolation/i2_legacy_with_self",
),
DefaultElement(
config_group="a_b",
config_name="a1_b1",
parent="interpolation/i2_legacy_with_self",
),
],
id="interpolation_legacy",
),
pytest.param(
DefaultElement(
config_group="interpolation",
config_name="i3_legacy_without_self",
parent="this_test",
),
[
DefaultElement(
config_group="interpolation",
config_name="i3_legacy_without_self",
parent="this_test",
),
DefaultElement(
config_group="a",
config_name="a1",
parent="interpolation/i3_legacy_without_self",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="interpolation/i3_legacy_without_self",
),
DefaultElement(
config_group="a_b",
config_name="a1_b1",
parent="interpolation/i3_legacy_without_self",
),
],
id="interpolation_legacy",
),
pytest.param(
DefaultElement(
config_group="interpolation",
config_name="i4_forward",
parent="this_test",
),
[
DefaultElement(
config_group="interpolation",
config_name="i4_forward",
parent="this_test",
),
DefaultElement(
config_group="a_b",
config_name="a1_b1",
parent="interpolation/i4_forward",
),
DefaultElement(
config_group="a",
config_name="a1",
parent="interpolation/i4_forward",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="interpolation/i4_forward",
),
],
id="forward_interpolation",
),
# optional
pytest.param(
DefaultElement(config_name="with_optional", parent="this_test"),
[
DefaultElement(config_name="with_optional", parent="this_test"),
DefaultElement(
config_group="a",
config_name="a1",
optional=True,
parent="with_optional",
),
DefaultElement(
config_group="foo",
config_name="bar",
optional=True,
skip_load=True,
skip_load_reason="missing_optional_config",
parent="with_optional",
),
],
id="optional",
),
# missing
pytest.param(
DefaultElement(config_name="with_missing"),
pytest.raises(
ConfigCompositionException,
match=dedent(
"""\
You must specify 'a', e.g, a=<OPTION>
Available options:
\ta1
\ta2
\ta3
\ta4
\ta5
\ta6
\tglobal"""
),
),
id="missing",
),
# delete renamed
pytest.param(
DefaultElement(config_group="delete_rename", config_name="dr1"),
[
DefaultElement(config_group="delete_rename", config_name="dr1"),
DefaultElement(
config_group="b",
config_name="b1",
parent="delete_rename/dr1",
package="pkg",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
],
id="delete_src_after_rename_in_file",
),
pytest.param(
DefaultElement(config_group="delete_rename", config_name="dr2"),
[
DefaultElement(config_group="delete_rename", config_name="dr2"),
DefaultElement(
config_group="b",
config_name="b1",
parent="delete_rename/dr2",
package="pkg",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
],
id="delete_dst_after_rename_in_file",
),
# delete renamed
pytest.param(
DefaultElement(config_group="delete_rename", config_name="rd1"),
[
DefaultElement(config_group="delete_rename", config_name="rd1"),
DefaultElement(
config_group="b",
config_name="b1",
parent="delete_rename/rd1",
package="pkg",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
],
id="rename_delete",
),
pytest.param(
DefaultElement(config_group="delete_rename", config_name="rd2"),
[
DefaultElement(config_group="delete_rename", config_name="rd2"),
DefaultElement(
config_group="b",
config_name="b1",
parent="delete_rename/rd2",
package="pkg",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
],
id="rename_delete",
),
],
)
def test_compute_element_defaults_list(
hydra_restore_singletons: Any,
element: DefaultElement,
expected: Any,
recwarn: Any,
) -> None:
csp = ConfigSearchPathImpl()
csp.append(provider="test", path="file://tests/test_data/new_defaults_lists")
repo = ConfigRepository(config_search_path=csp)
if isinstance(expected, list):
ret = compute_element_defaults_list(
element=element, skip_missing=False, repo=repo
)
assert ret == expected
else:
with expected:
compute_element_defaults_list(
element=element, skip_missing=False, repo=repo
)
@pytest.mark.parametrize( # type: ignore
"input_defaults,expected",
[
pytest.param(
[
DefaultElement(config_group="a", config_name="a1", parent="foo"),
DefaultElement(config_group="a", config_name="a6", parent="bar"),
],
[
DefaultElement(config_group="a", config_name="a6", parent="foo"),
],
id="simple",
),
pytest.param(
[
DefaultElement(config_group="a", config_name="a2", parent="foo"),
DefaultElement(config_group="a", config_name="a6", parent="bar"),
],
[
DefaultElement(config_group="a", config_name="a6", parent="foo"),
],
id="simple",
),
pytest.param(
[
DefaultElement(config_group="a", config_name="a5", parent="foo"),
DefaultElement(config_group="b", config_name="b1", parent="bar"),
DefaultElement(
config_group="b",
package="file_pkg",
config_name="b1",
parent="zoo",
),
],
[
DefaultElement(config_group="a", config_name="a5", parent="foo"),
DefaultElement(config_group="b", config_name="b1", parent="a/a5"),
DefaultElement(
config_group="b",
config_name="b1",
package="file_pkg",
parent="a/a5",
),
],
id="a/a5",
),
],
)
def test_expand_defaults_list(
hydra_restore_singletons: Any,
input_defaults: List[DefaultElement],
expected: List[DefaultElement],
) -> None:
csp = ConfigSearchPathImpl()
csp.append(provider="test", path="file://tests/test_data/new_defaults_lists")
repo = ConfigRepository(config_search_path=csp)
ret = expand_defaults_list(defaults=input_defaults, skip_missing=False, repo=repo)
assert ret == expected
@pytest.mark.parametrize( # type: ignore
"config_with_defaults,overrides,expected",
[
# change item
pytest.param(
"test_overrides",
["a=a6"],
[
DefaultElement(config_name="test_overrides", parent="this_test"),
DefaultElement(
config_group="a",
config_name="a6",
parent="test_overrides",
),
DefaultElement(
config_group="a",
package="pkg",
config_name="a1",
parent="test_overrides",
),
DefaultElement(
config_group="c", config_name="c1", parent="test_overrides"
),
],
id="change_option",
),
pytest.param(
"test_overrides",
["a@:pkg2=a6"],
[
DefaultElement(config_name="test_overrides", parent="this_test"),
DefaultElement(
config_group="a",
package="pkg2",
config_name="a6",
parent="test_overrides",
),
DefaultElement(
config_group="a",
package="pkg",
config_name="a1",
parent="test_overrides",
),
DefaultElement(
config_group="c", config_name="c1", parent="test_overrides"
),
],
id="change_both",
),
pytest.param(
"test_overrides",
["a@pkg:pkg2=a6"],
[
DefaultElement(config_name="test_overrides", parent="this_test"),
DefaultElement(
config_group="a", config_name="a1", parent="test_overrides"
),
DefaultElement(
config_group="a",
package="pkg2",
config_name="a6",
parent="test_overrides",
),
DefaultElement(
config_group="c", config_name="c1", parent="test_overrides"
),
],
id="change_both",
),
pytest.param(
"test_overrides",
["a@XXX:dest=a6"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not rename package. No match for 'a@XXX' in the defaults list"
),
),
id="change_both_invalid_package",
),
# adding item
pytest.param(
"no_defaults",
["+b=b1"],
[
DefaultElement(config_name="no_defaults", parent="this_test"),
DefaultElement(
config_group="b",
config_name="b1",
is_add=True,
parent="overrides",
),
],
id="adding_item",
),
pytest.param(
"no_defaults",
["+b=b2"],
[
DefaultElement(config_name="no_defaults", parent="this_test"),
DefaultElement(config_group="b", config_name="b2", parent="overrides"),
DefaultElement(config_group="c", config_name="c2", parent="b/b2"),
],
id="adding_item_recursive",
),
pytest.param(
"test_overrides",
["+b@pkg=b1"],
[
DefaultElement(config_name="test_overrides", parent="this_test"),
DefaultElement(
config_group="a",
config_name="a1",
parent="test_overrides",
),
DefaultElement(
config_group="a",
package="pkg",
config_name="a1",
parent="test_overrides",
),
DefaultElement(
config_group="c", config_name="c1", parent="test_overrides"
),
DefaultElement(
config_group="b",
package="pkg",
config_name="b1",
is_add=True,
parent="overrides",
),
],
id="adding_item_at_package",
),
pytest.param(
"one_missing_item",
["+a=a1"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not add 'a=a1'. 'a' is already in the defaults list."
),
),
id="adding_duplicate_item",
),
pytest.param(
"test_overrides",
["+a=a2"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not add 'a=a2'. 'a' is already in the defaults list."
),
),
id="adding_duplicate_item",
),
pytest.param(
"test_overrides",
["+a=a6", "+c=c2"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not add 'c=c2'. 'c' is already in the defaults list."
),
),
id="adding_duplicate_item_recursive",
),
pytest.param(
"test_overrides",
["+a@pkg:pkg2=a1"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Add syntax does not support package rename, remove + prefix"
),
),
id="add_rename_error",
),
pytest.param(
"test_overrides",
["+a@pkg=a2"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not add 'a@pkg=a2'. 'a@pkg' is already in the defaults list."
),
),
id="adding_duplicate_item@pkg",
),
pytest.param(
"no_defaults",
["c=c1"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not override 'c'. No match in the defaults list."
"\nTo append to your default list use +c=c1"
),
),
id="adding_without_plus",
),
# deleting item
pytest.param(
"no_defaults",
["~db=mysql"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not delete. No match for 'db=mysql' in the defaults list."
),
),
id="delete_no_match",
),
pytest.param(
"no_defaults",
["~db"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not delete. No match for 'db' in the defaults list."
),
),
id="delete_no_match",
),
pytest.param(
"no_defaults",
["~db=foo"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not delete. No match for 'db=foo' in the defaults list."
),
),
id="delete_no_match",
),
pytest.param(
"test_overrides",
["~a"],
[
DefaultElement(config_name="test_overrides", parent="this_test"),
DefaultElement(
config_group="a",
config_name="a1",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="test_overrides",
),
DefaultElement(
config_group="a",
package="pkg",
config_name="a1",
parent="test_overrides",
),
DefaultElement(
config_group="c", config_name="c1", parent="test_overrides"
),
],
id="delete ~a",
),
pytest.param(
"test_overrides",
["~a=a1"],
[
DefaultElement(config_name="test_overrides", parent="this_test"),
DefaultElement(
config_group="a",
config_name="a1",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="test_overrides",
),
DefaultElement(
config_group="a",
package="pkg",
config_name="a1",
parent="test_overrides",
),
DefaultElement(
config_group="c", config_name="c1", parent="test_overrides"
),
],
id="delete ~a=a1",
),
pytest.param(
"no_defaults",
["~a=zzz"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not delete. No match for 'a=zzz' in the defaults list."
),
),
id="delete ~a=zzz",
),
pytest.param(
"test_overrides",
["~a=zzz"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Could not delete. No match for 'a=zzz' in the defaults list."
),
),
id="delete ~a=zzz",
),
pytest.param(
"test_overrides",
["~a@pkg"],
[
DefaultElement(config_name="test_overrides", parent="this_test"),
DefaultElement(
config_group="a", config_name="a1", parent="test_overrides"
),
DefaultElement(
config_group="a",
package="pkg",
config_name="a1",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="test_overrides",
),
DefaultElement(
config_group="c", config_name="c1", parent="test_overrides"
),
],
id="delete ~a@pkg",
),
pytest.param(
"no_defaults",
["a=foo", "~a"],
[
DefaultElement(config_name="no_defaults", parent="this_test"),
DefaultElement(
config_group="a",
config_name="foo",
from_override=True,
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
parent="overrides",
),
],
id="delete_after_set_from_overrides",
),
pytest.param(
"a/a2",
["b=b2"],
[
DefaultElement(config_name="a/a2", parent="this_test"),
DefaultElement(config_group="b", config_name="b2", parent="a/a2"),
DefaultElement(config_group="c", config_name="c2", parent="b/b2"),
],
id="delete_after_set_from_overrides:baseline",
),
pytest.param(
"a/a2",
["b=b2", "~b"],
[
DefaultElement(config_name="a/a2", parent="this_test"),
DefaultElement(
config_group="b",
config_name="b1",
parent="a/a2",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
],
id="delete_after_set_from_overrides",
),
pytest.param(
"a/a2",
["b=b2", "~c"],
[
DefaultElement(config_name="a/a2", parent="this_test"),
DefaultElement(
config_group="b",
config_name="b2",
parent="a/a2",
),
DefaultElement(
config_group="c",
config_name="c2",
parent="b/b2",
is_deleted=True,
skip_load=True,
skip_load_reason="deleted_from_list",
),
],
id="delete_after_set_from_overrides",
),
pytest.param(
"delete/d10",
["b=b1"],
[
DefaultElement(config_name="delete/d10", parent="this_test"),
DefaultElement(config_group="b", config_name="b1", parent="delete/d10"),
],
id="override_deletion",
),
pytest.param(
"delete/d10",
["b=b1"],
[
DefaultElement(config_name="delete/d10", parent="this_test"),
DefaultElement(config_group="b", config_name="b1", parent="delete/d10"),
],
id="delete_overriden_2",
),
# syntax error
pytest.param(
"test_overrides",
["db"],
pytest.raises(
OverrideParseException,
match=re.escape(
"Error parsing override 'db'\nmissing EQUAL at '<EOF>'"
),
),
id="syntax_error",
),
pytest.param(
"test_overrides",
["db=[a,b,c]"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Defaults list supported delete syntax is in the form "
"~group and ~group=value, where value is a group name (string)"
),
),
id="syntax_error",
),
pytest.param(
"test_overrides",
["db={a:1,b:2}"],
pytest.raises(
ConfigCompositionException,
match=re.escape(
"Defaults list supported delete syntax is in the form "
"~group and ~group=value, where value is a group name (string)"
),
),
id="syntax_error",
),
# interpolation
pytest.param(
"interpolation/i1",
[],
[
DefaultElement(config_name="interpolation/i1", parent="this_test"),
DefaultElement(
config_group="a", config_name="a1", parent="interpolation/i1"
),
DefaultElement(
config_group="b", config_name="b1", parent="interpolation/i1"
),
DefaultElement(
config_group="a_b", config_name="a1_b1", parent="interpolation/i1"
),
],
id="interpolation",
),
pytest.param(
"interpolation/i1",
["a=a6"],
[
DefaultElement(config_name="interpolation/i1", parent="this_test"),
DefaultElement(
config_group="a", config_name="a6", parent="interpolation/i1"
),
DefaultElement(
config_group="b", config_name="b1", parent="interpolation/i1"
),
DefaultElement(
config_group="a_b", config_name="a6_b1", parent="interpolation/i1"
),
],
id="interpolation",
),
pytest.param(
"interpolation/i2_legacy_with_self",
["a=a6"],
[
DefaultElement(
config_name="interpolation/i2_legacy_with_self", parent="this_test"
),
DefaultElement(
config_group="a",
config_name="a6",
parent="interpolation/i2_legacy_with_self",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="interpolation/i2_legacy_with_self",
),
DefaultElement(
config_group="a_b",
config_name="a6_b1",
parent="interpolation/i2_legacy_with_self",
),
],
id="interpolation_legacy",
),
pytest.param(
"interpolation/i3_legacy_without_self",
["a=a6"],
[
DefaultElement(
config_name="interpolation/i3_legacy_without_self",
parent="this_test",
),
DefaultElement(
config_group="a",
config_name="a6",
parent="interpolation/i3_legacy_without_self",
),
DefaultElement(
config_group="b",
config_name="b1",
parent="interpolation/i3_legacy_without_self",
),
DefaultElement(
config_group="a_b",
config_name="a6_b1",
parent="interpolation/i3_legacy_without_self",
),
],
id="interpolation_legacy",
),
# overriding groups with schema
pytest.param(
"config_with_schema",
[],
[
DefaultElement(config_name="config_with_schema", parent="this_test"),
DefaultElement(config_name="schema/c/c1", parent="c/c1_with_schema"),
DefaultElement(
config_group="c",
config_name="c1_with_schema",
parent="config_with_schema",
),
],
id="schema::no_override",
),
pytest.param(
"config_with_schema",
# c1_with_schema is already the choice for c, should be no-op:
["c=c1_with_schema"],
[
DefaultElement(config_name="config_with_schema", parent="this_test"),
DefaultElement(config_name="schema/c/c1", parent="c/c1_with_schema"),
DefaultElement(
config_group="c",
config_name="c1_with_schema",
parent="config_with_schema",
),
],
id="schema:override_to_same",
),
pytest.param(
"config_with_schema",
["c=c2_with_schema"],
[
DefaultElement(config_name="config_with_schema", parent="this_test"),
DefaultElement(config_name="schema/c/c2", parent="c/c2_with_schema"),
DefaultElement(
config_group="c",
config_name="c2_with_schema",
parent="config_with_schema",
),
],
id="schema:override_to_c2_with_schema",
),
],
)
def test_apply_overrides_to_defaults(
config_with_defaults: str,
overrides: List[str],
expected: Any,
recwarn: Any, # this tests some deprecated functionality
) -> None:
assert isinstance(config_with_defaults, str)
csp = ConfigSearchPathImpl()
csp.append(provider="test", path="file://tests/test_data/new_defaults_lists")
repo = ConfigRepository(config_search_path=csp)
def create_defaults() -> Any:
parser = OverridesParser.create()
parsed_overrides = parser.parse_overrides(overrides=overrides)
overrides_as_defaults = convert_overrides_to_defaults(parsed_overrides)
ret = [
DefaultElement(config_name=config_with_defaults, parent="this_test"),
]
ret.extend(overrides_as_defaults)
return ret
if isinstance(expected, list):
defaults = create_defaults()
ret = expand_defaults_list(defaults=defaults, skip_missing=False, repo=repo)
assert ret == expected
else:
with expected:
defaults = create_defaults()
expand_defaults_list(defaults=defaults, skip_missing=False, repo=repo)
@pytest.mark.parametrize( # type: ignore
"element,expected",
[
pytest.param(
DefaultElement(config_name="with_missing", parent="this_test"),
[
DefaultElement(config_name="with_missing", parent="this_test"),
DefaultElement(
config_group="a",
config_name="???",
skip_load=True,
skip_load_reason="missing_skipped",
parent="with_missing",
),
],
id="with_missing",
),
],
)
def test_missing_with_skip_missing(
hydra_restore_singletons: Any,
element: DefaultElement,
expected: Any,
) -> None:
csp = ConfigSearchPathImpl()
csp.append(provider="test", path="file://tests/test_data/new_defaults_lists")
repo = ConfigRepository(config_search_path=csp)
ret = compute_element_defaults_list(element=element, skip_missing=True, repo=repo)
assert ret == expected
@pytest.mark.parametrize( # type: ignore
"element",
[
pytest.param(
DefaultElement(
config_group="interpolation", config_name="i2_legacy_with_self"
),
),
],
)
def test_legacy_interpolation_are_deprecated(
hydra_restore_singletons: Any,
element: DefaultElement,
) -> None:
csp = ConfigSearchPathImpl()
csp.append(provider="test", path="file://tests/test_data/new_defaults_lists")
repo = ConfigRepository(config_search_path=csp)
msg = dedent(
"""
Defaults list element 'a_b=${defaults.1.a}_${defaults.2.b}' is using a deprecated interpolation form.
See http://hydra.cc/docs/next/upgrades/1.0_to_1.1/defaults_list_interpolation for migration information.
"""
)
with pytest.warns(UserWarning, match=re.escape(msg)):
compute_element_defaults_list(element=element, skip_missing=True, repo=repo)
@pytest.mark.parametrize( # type: ignore
"element",
[
pytest.param(
DefaultElement(config_group="a", config_name="invalid_defaults_list"),
),
],
)
def test_load_invalid_defaults(
hydra_restore_singletons: Any,
element: DefaultElement,
) -> None:
csp = ConfigSearchPathImpl()
csp.append(provider="test", path="file://tests/test_data/new_defaults_lists")
repo = ConfigRepository(config_search_path=csp)
msg = dedent(
f"""\
Invalid defaults list in '{element.config_path()}', defaults must be a list.
Example of a valid defaults:
defaults:
- dataset: imagenet
- model: alexnet
optional: true
- optimizer: nesterov
"""
)
with pytest.raises(ValueError, match=re.escape(msg)):
compute_element_defaults_list(element=element, skip_missing=True, repo=repo)
| 34.384005
| 112
| 0.45897
| 4,207
| 52,023
| 5.397433
| 0.066556
| 0.204342
| 0.193773
| 0.099881
| 0.857929
| 0.826353
| 0.810807
| 0.778086
| 0.750165
| 0.715903
| 0
| 0.012074
| 0.434808
| 52,023
| 1,512
| 113
| 34.406746
| 0.760195
| 0.00865
| 0
| 0.779835
| 0
| 0.000686
| 0.172705
| 0.044826
| 0
| 0
| 0
| 0
| 0.003429
| 1
| 0.004801
| false
| 0
| 0.00823
| 0
| 0.013717
| 0
| 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
| 0
|
0
| 9
|
af2e3d30548a4110dbb9942a721a20d05b58444b
| 1,434
|
py
|
Python
|
opt/resource/payloads.py
|
cosee-concourse/serverless-resource
|
3a2e83b29de1e7e4b87a4c4c5fa961bded5ab9bd
|
[
"MIT"
] | null | null | null |
opt/resource/payloads.py
|
cosee-concourse/serverless-resource
|
3a2e83b29de1e7e4b87a4c4c5fa961bded5ab9bd
|
[
"MIT"
] | null | null | null |
opt/resource/payloads.py
|
cosee-concourse/serverless-resource
|
3a2e83b29de1e7e4b87a4c4c5fa961bded5ab9bd
|
[
"MIT"
] | 4
|
2017-02-23T14:54:04.000Z
|
2020-03-15T13:55:12.000Z
|
check_payload = ('{"source":{'
'"access_key_id":"apiKey123",'
'"secret_access_key":"secretKey321",'
'"region_name":"eu-west-1"'
'},'
'"version":{"stage":"release"}}')
in_payload = ('{"source":{'
'"access_key_id":"apiKey123",'
'"secret_access_key":"secretKey321",'
'"region_name":"eu-west-1"'
'},'
'"version":{"stage":"release"}}')
out_deploy_payload = ('{"params":{'
'"stage":"version-v1-dev",'
'"deploy": true,'
'"artifact_folder": "artifact/lambda",'
'"serverless_file": "source/ci'
'"},'
'"source":{'
'"access_key_id":"apiKey123",'
'"secret_access_key":"secretKey321",'
'"region_name":"eu-west-1'
'"},'
'"version":{"stage":"release"}}')
out_remove_payload = ('{"params":{'
'"stage":"version-v1-dev",'
'"remove": true,'
'"artifact_folder": "artifact/lambda",'
'"serverless_file": "source/ci'
'"},'
'"source":{'
'"access_key_id":"apiKey123",'
'"secret_access_key":"secretKey321",'
'"region_name":"eu-west-1'
'"},'
'"version":{"stage":"release"}}')
| 38.756757
| 54
| 0.421897
| 108
| 1,434
| 5.324074
| 0.287037
| 0.125217
| 0.104348
| 0.118261
| 0.946087
| 0.946087
| 0.841739
| 0.841739
| 0.841739
| 0.841739
| 0
| 0.03337
| 0.373082
| 1,434
| 36
| 55
| 39.833333
| 0.606229
| 0
| 0
| 0.833333
| 0
| 0
| 0.531381
| 0.362622
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 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
|
bb6a4f2880a312620febc5d1899947e795abdfdf
| 73
|
py
|
Python
|
Week1/test1/test4.py
|
johndolotko/pynet_course
|
55372a0977994fd26ef59885f6068d831ccdeac4
|
[
"Apache-2.0"
] | null | null | null |
Week1/test1/test4.py
|
johndolotko/pynet_course
|
55372a0977994fd26ef59885f6068d831ccdeac4
|
[
"Apache-2.0"
] | 6
|
2020-02-26T20:21:27.000Z
|
2021-12-13T19:59:14.000Z
|
Week1/test1/test4.py
|
johndolotko/pynet_course
|
55372a0977994fd26ef59885f6068d831ccdeac4
|
[
"Apache-2.0"
] | null | null | null |
print("hello everyone")
print("hello everyone")
print("hello everyone")
| 14.6
| 23
| 0.739726
| 9
| 73
| 6
| 0.333333
| 0.555556
| 1
| 0.851852
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09589
| 73
| 4
| 24
| 18.25
| 0.818182
| 0
| 0
| 1
| 0
| 0
| 0.583333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 10
|
bb6e40348ae4028bc2dd68ed23c4bea505551a99
| 4,883
|
py
|
Python
|
tests/excerptexport/templates/email/test_all_exports_of_extraction_order_done_subject.py
|
tyrasd/osmaxx
|
da4454083d17b2ef8b0623cad62e39992b6bd52a
|
[
"MIT"
] | 27
|
2015-03-30T14:17:26.000Z
|
2022-02-19T17:30:44.000Z
|
tests/excerptexport/templates/email/test_all_exports_of_extraction_order_done_subject.py
|
tyrasd/osmaxx
|
da4454083d17b2ef8b0623cad62e39992b6bd52a
|
[
"MIT"
] | 483
|
2015-03-09T16:58:03.000Z
|
2022-03-14T09:29:06.000Z
|
tests/excerptexport/templates/email/test_all_exports_of_extraction_order_done_subject.py
|
tyrasd/osmaxx
|
da4454083d17b2ef8b0623cad62e39992b6bd52a
|
[
"MIT"
] | 6
|
2015-04-07T07:38:30.000Z
|
2020-04-01T12:45:53.000Z
|
from django.template.loader import render_to_string
def test_some_success_some_failed(rf, extraction_order, exports):
successful_exports = exports[::2]
failed_exports = exports[1::2]
view_context = dict(
extraction_order=extraction_order,
successful_exports=successful_exports,
failed_exports=failed_exports,
request=rf.get('/foo/bar'),
)
email_body = render_to_string(
'excerptexport/email/all_exports_of_extraction_order_done_body.txt',
context=view_context,
).strip()
expected_body = '\n'.join(
[
'This is an automated email from testserver',
'',
'The extraction order #{order_id} "Neverland" has been processed and is available for download:',
'- Esri File Geodatabase',
'- GeoPackage',
'- Garmin navigation & map data',
'',
'Unfortunately, the following exports have failed:',
'- Esri Shapefile',
'- SpatiaLite',
'- OSM Protocolbuffer Binary Format',
'',
'Please order them anew if you need them. '
'If there are repeated failures, '
'please report them on https://github.com/geometalab/osmaxx/issues '
'unless the problem is already known there.',
'',
'View the complete order at http://testserver/exports/ (login required)',
'',
'Thank you for using OSMaxx.',
'The team at Geometa Lab HSR',
'geometalab@hsr.ch',
]
).format(
order_id=extraction_order.id,
)
assert email_body == expected_body
def test_some_success_1_failed(rf, extraction_order, exports):
successful_exports = exports[:-1]
failed_exports = exports[-1:]
view_context = dict(
extraction_order=extraction_order,
successful_exports=successful_exports,
failed_exports=failed_exports,
request=rf.get('/foo/bar'),
)
email_body = render_to_string(
'excerptexport/email/all_exports_of_extraction_order_done_body.txt',
context=view_context,
).strip()
expected_body = '\n'.join(
[
'This is an automated email from testserver',
'',
'The extraction order #{order_id} "Neverland" has been processed and is available for download:',
'- Esri File Geodatabase',
'- Esri Shapefile',
'- GeoPackage',
'- SpatiaLite',
'- Garmin navigation & map data',
'',
'Unfortunately, the following export has failed:',
'- OSM Protocolbuffer Binary Format',
'',
'Please order it anew if you need it. '
'If there are repeated failures, '
'please report them on https://github.com/geometalab/osmaxx/issues '
'unless the problem is already known there.',
'',
'View the complete order at http://testserver/exports/ (login required)',
'',
'Thank you for using OSMaxx.',
'The team at Geometa Lab HSR',
'geometalab@hsr.ch',
]
).format(
order_id=extraction_order.id,
)
assert email_body == expected_body
def test_no_success_1_failed(rf, extraction_order, exports):
successful_exports = tuple()
failed_exports = exports
view_context = dict(
extraction_order=extraction_order,
successful_exports=successful_exports,
failed_exports=failed_exports,
request=rf.get('/foo/bar'),
)
email_body = render_to_string(
'excerptexport/email/all_exports_of_extraction_order_done_body.txt',
context=view_context,
).strip()
expected_body = '\n'.join(
[
'This is an automated email from testserver',
'',
'The extraction order #{order_id} "Neverland" has been processed.',
'',
'Unfortunately, the following exports have failed:',
'- Esri File Geodatabase',
'- Esri Shapefile',
'- GeoPackage',
'- SpatiaLite',
'- Garmin navigation & map data',
'- OSM Protocolbuffer Binary Format',
'',
'Please order them anew if you need them. '
'If there are repeated failures, '
'please report them on https://github.com/geometalab/osmaxx/issues '
'unless the problem is already known there.',
'',
'View the complete order at http://testserver/exports/ (login required)',
'',
'Thank you for using OSMaxx.',
'The team at Geometa Lab HSR',
'geometalab@hsr.ch',
]
).format(
order_id=extraction_order.id,
)
assert email_body == expected_body
| 35.642336
| 109
| 0.583862
| 509
| 4,883
| 5.420432
| 0.21611
| 0.097862
| 0.052193
| 0.025009
| 0.926423
| 0.926423
| 0.911925
| 0.88909
| 0.839435
| 0.799565
| 0
| 0.002098
| 0.316813
| 4,883
| 136
| 110
| 35.904412
| 0.82494
| 0
| 0
| 0.80315
| 0
| 0
| 0.430678
| 0.039934
| 0
| 0
| 0
| 0
| 0.023622
| 1
| 0.023622
| false
| 0
| 0.007874
| 0
| 0.031496
| 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
|
a52718f116044eb1ae764ae333d31017d0456a31
| 270
|
py
|
Python
|
data/imagenet/move.py
|
wannieman98/RandWireNN
|
a025d0318c77f42f49437de8e65b39432d681932
|
[
"Apache-1.1"
] | null | null | null |
data/imagenet/move.py
|
wannieman98/RandWireNN
|
a025d0318c77f42f49437de8e65b39432d681932
|
[
"Apache-1.1"
] | 2
|
2021-09-26T18:53:42.000Z
|
2021-09-26T20:36:14.000Z
|
data/imagenet/move.py
|
wannieman98/RandlyWiredNN
|
a025d0318c77f42f49437de8e65b39432d681932
|
[
"Apache-1.1"
] | null | null | null |
import os
import shutil
def move_file():
shutil.copyfile("imagenet-object-localization-challenge19.tar.gz", "/content/imagenet-object-localization-challenge19.tar.gz")
cmd = "tar -xf '/content/imagenet-object-localization-challenge19.tar.gz'"
os.system(cmd)
| 38.571429
| 130
| 0.759259
| 35
| 270
| 5.828571
| 0.485714
| 0.205882
| 0.382353
| 0.544118
| 0.686275
| 0.686275
| 0.480392
| 0
| 0
| 0
| 0
| 0.02459
| 0.096296
| 270
| 7
| 131
| 38.571429
| 0.811475
| 0
| 0
| 0
| 0
| 0
| 0.623616
| 0.594096
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.5
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
a56b377fbab0cb1c2fe4527957cd0fbd2f6e3f1c
| 22,230
|
py
|
Python
|
tests/test_dynamics.py
|
lvayssac/bioptim
|
526abff72a8a1b2cb84ccc40c6067b7a18f537e3
|
[
"MIT"
] | null | null | null |
tests/test_dynamics.py
|
lvayssac/bioptim
|
526abff72a8a1b2cb84ccc40c6067b7a18f537e3
|
[
"MIT"
] | null | null | null |
tests/test_dynamics.py
|
lvayssac/bioptim
|
526abff72a8a1b2cb84ccc40c6067b7a18f537e3
|
[
"MIT"
] | null | null | null |
import pytest
import numpy as np
from casadi import MX, SX
import biorbd_casadi as biorbd
from bioptim.dynamics.configure_problem import ConfigureProblem
from bioptim.dynamics.dynamics_functions import DynamicsFunctions
from bioptim.interfaces.biorbd_interface import BiorbdInterface
from bioptim.misc.enums import ControlType
from bioptim.optimization.non_linear_program import NonLinearProgram
from bioptim.optimization.optimization_vector import OptimizationVector
from bioptim.dynamics.configure_problem import DynamicsFcn, Dynamics
from .utils import TestUtils
class OptimalControlProgram:
def __init__(self, nlp):
self.n_phases = 1
self.nlp = [nlp]
self.v = OptimizationVector(self)
@pytest.mark.parametrize("cx", [MX, SX])
@pytest.mark.parametrize("with_external_force", [False, True])
@pytest.mark.parametrize("with_contact", [False, True])
def test_torque_driven(with_contact, with_external_force, cx):
# Prepare the program
nlp = NonLinearProgram()
nlp.model = biorbd.Model(TestUtils.bioptim_folder() + "/examples/getting_started/2segments_4dof_2contacts.bioMod")
nlp.ns = 5
nlp.cx = cx
nlp.x_bounds = np.zeros((nlp.model.nbQ() * 3, 1))
nlp.u_bounds = np.zeros((nlp.model.nbQ(), 1))
ocp = OptimalControlProgram(nlp)
nlp.control_type = ControlType.CONSTANT
NonLinearProgram.add(ocp, "dynamics_type", Dynamics(DynamicsFcn.TORQUE_DRIVEN, with_contact=with_contact), False)
np.random.seed(42)
if with_external_force:
external_forces = [np.random.rand(6, nlp.model.nbSegment(), nlp.ns)]
nlp.external_forces = BiorbdInterface.convert_array_to_external_forces(external_forces)[0]
# Prepare the dynamics
ConfigureProblem.initialize(ocp, nlp)
# Test the results
states = np.random.rand(nlp.states.shape, nlp.ns)
controls = np.random.rand(nlp.controls.shape, nlp.ns)
params = np.random.rand(nlp.parameters.shape, nlp.ns)
x_out = np.array(nlp.dynamics_func(states, controls, params))
if with_contact:
contact_out = np.array(nlp.contact_forces_func(states, controls, params))
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.8631034, 0.3251833, 0.1195942, 0.4937956, -7.7700092, -7.5782306, 21.7073786, -16.3059315],
)
np.testing.assert_almost_equal(contact_out[:, 0], [-47.8131136, 111.1726516, -24.4449121])
else:
np.testing.assert_almost_equal(
x_out[:, 0], [0.6118529, 0.785176, 0.6075449, 0.8083973, -0.3214905, -0.1912131, 0.6507164, -0.2359716]
)
np.testing.assert_almost_equal(contact_out[:, 0], [-2.444071, 128.8816865, 2.7245124])
else:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.86310343, 0.32518332, 0.11959425, 0.4937956, 0.30731739, -9.97912778, 1.15263778, 36.02430956],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.61185289, 0.78517596, 0.60754485, 0.80839735, -0.30241366, -10.38503791, 1.60445173, 35.80238642],
)
@pytest.mark.parametrize("cx", [MX, SX])
@pytest.mark.parametrize("with_external_force", [False, True])
@pytest.mark.parametrize("with_contact", [False, True])
def test_torque_derivative_driven(with_contact, with_external_force, cx):
# Prepare the program
nlp = NonLinearProgram()
nlp.model = biorbd.Model(TestUtils.bioptim_folder() + "/examples/getting_started/2segments_4dof_2contacts.bioMod")
nlp.ns = 5
nlp.cx = cx
nlp.x_bounds = np.zeros((nlp.model.nbQ() * 3, 1))
nlp.u_bounds = np.zeros((nlp.model.nbQ(), 1))
ocp = OptimalControlProgram(nlp)
nlp.control_type = ControlType.CONSTANT
NonLinearProgram.add(
ocp, "dynamics_type", Dynamics(DynamicsFcn.TORQUE_DERIVATIVE_DRIVEN, with_contact=with_contact), False
)
np.random.seed(42)
if with_external_force:
external_forces = [np.random.rand(6, nlp.model.nbSegment(), nlp.ns)]
nlp.external_forces = BiorbdInterface.convert_array_to_external_forces(external_forces)[0]
# Prepare the dynamics
ConfigureProblem.initialize(ocp, nlp)
# Test the results
states = np.random.rand(nlp.states.shape, nlp.ns)
controls = np.random.rand(nlp.controls.shape, nlp.ns)
params = np.random.rand(nlp.parameters.shape, nlp.ns)
x_out = np.array(nlp.dynamics_func(states, controls, params))
if with_contact:
contact_out = np.array(nlp.contact_forces_func(states, controls, params))
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[
0.8631034,
0.3251833,
0.1195942,
0.4937956,
-7.7700092,
-7.5782306,
21.7073786,
-16.3059315,
0.8074402,
0.4271078,
0.417411,
0.3232029,
],
)
np.testing.assert_almost_equal(contact_out[:, 0], [-47.8131136, 111.1726516, -24.4449121])
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[
0.61185289,
0.78517596,
0.60754485,
0.80839735,
-0.32149054,
-0.19121314,
0.65071636,
-0.23597164,
0.38867729,
0.54269608,
0.77224477,
0.72900717,
],
)
np.testing.assert_almost_equal(contact_out[:, 0], [-2.444071, 128.8816865, 2.7245124])
else:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[
0.86310343,
0.32518332,
0.11959425,
0.4937956,
0.30731739,
-9.97912778,
1.15263778,
36.02430956,
0.80744016,
0.42710779,
0.417411,
0.32320293,
],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[
0.61185289,
0.78517596,
0.60754485,
0.80839735,
-0.30241366,
-10.38503791,
1.60445173,
35.80238642,
0.38867729,
0.54269608,
0.77224477,
0.72900717,
],
)
@pytest.mark.parametrize("cx", [MX, SX])
@pytest.mark.parametrize("with_external_force", [False, True])
@pytest.mark.parametrize("with_contact", [False, True])
def test_torque_activation_driven(with_contact, with_external_force, cx):
# Prepare the program
nlp = NonLinearProgram()
nlp.model = biorbd.Model(TestUtils.bioptim_folder() + "/examples/getting_started/2segments_4dof_2contacts.bioMod")
nlp.ns = 5
nlp.cx = cx
nlp.x_bounds = np.zeros((nlp.model.nbQ() * 2, 1))
nlp.u_bounds = np.zeros((nlp.model.nbQ(), 1))
ocp = OptimalControlProgram(nlp)
nlp.control_type = ControlType.CONSTANT
NonLinearProgram.add(
ocp, "dynamics_type", Dynamics(DynamicsFcn.TORQUE_ACTIVATIONS_DRIVEN, with_contact=with_contact), False
)
np.random.seed(42)
if with_external_force:
external_forces = [np.random.rand(6, nlp.model.nbSegment(), nlp.ns)]
nlp.external_forces = BiorbdInterface.convert_array_to_external_forces(external_forces)[0]
# Prepare the dynamics
ConfigureProblem.initialize(ocp, nlp)
# Test the results
states = np.random.rand(nlp.states.shape, nlp.ns)
controls = np.random.rand(nlp.controls.shape, nlp.ns)
params = np.random.rand(nlp.parameters.shape, nlp.ns)
x_out = np.array(nlp.dynamics_func(states, controls, params))
if with_contact:
contact_out = np.array(nlp.contact_forces_func(states, controls, params))
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.8631, 0.32518, 0.11959, 0.4938, 19.01887, 18.51503, -53.08574, 58.48719],
decimal=5,
)
np.testing.assert_almost_equal(contact_out[:, 0], [109.8086936, 3790.3932439, -3571.7858574])
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.61185289, 0.78517596, 0.60754485, 0.80839735, 0.78455384, -0.16844256, -1.56184114, 1.97658587],
decimal=5,
)
np.testing.assert_almost_equal(contact_out[:, 0], [-7.88958997, 329.70828173, -263.55516549])
else:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[
8.63103426e-01,
3.25183322e-01,
1.19594246e-01,
4.93795596e-01,
1.73558072e01,
-4.69891264e01,
1.81396922e02,
3.61170139e03,
],
decimal=5,
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[
6.11852895e-01,
7.85175961e-01,
6.07544852e-01,
8.08397348e-01,
-2.38262975e01,
-5.82033454e01,
1.27439020e02,
3.66531163e03,
],
decimal=5,
)
@pytest.mark.parametrize("cx", [MX, SX])
@pytest.mark.parametrize("with_external_force", [False, True])
@pytest.mark.parametrize("with_contact", [False, True])
@pytest.mark.parametrize("with_residual_torque", [False, True])
@pytest.mark.parametrize("with_excitations", [False, True])
def test_muscle_driven(with_excitations, with_contact, with_residual_torque, with_external_force, cx):
# Prepare the program
nlp = NonLinearProgram()
nlp.model = biorbd.Model(TestUtils.bioptim_folder() + "/examples/muscle_driven_ocp/arm26_with_contact.bioMod")
nlp.ns = 5
nlp.cx = cx
nlp.x_bounds = np.zeros((nlp.model.nbQ() * 2 + nlp.model.nbMuscles(), 1))
nlp.u_bounds = np.zeros((nlp.model.nbMuscles(), 1))
ocp = OptimalControlProgram(nlp)
nlp.control_type = ControlType.CONSTANT
NonLinearProgram.add(
ocp,
"dynamics_type",
Dynamics(
DynamicsFcn.MUSCLE_DRIVEN,
with_residual_torque=with_residual_torque,
with_excitations=with_excitations,
with_contact=with_contact,
),
False,
)
np.random.seed(42)
if with_external_force:
external_forces = [np.random.rand(6, nlp.model.nbSegment(), nlp.ns)]
nlp.external_forces = BiorbdInterface.convert_array_to_external_forces(external_forces)[0]
# Prepare the dynamics
ConfigureProblem.initialize(ocp, nlp)
# Test the results
states = np.random.rand(nlp.states.shape, nlp.ns)
controls = np.random.rand(nlp.controls.shape, nlp.ns)
params = np.random.rand(nlp.parameters.shape, nlp.ns)
x_out = np.array(nlp.dynamics_func(states, controls, params))
if with_contact: # Warning this test is a bit bogus, there since the model does not have contacts
if with_residual_torque:
if with_excitations:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[
0.6158501,
0.50313626,
0.64241928,
0.3264777,
-1.57134516,
0.87073117,
46.87928022,
-1.80189035,
53.3914525,
48.30056919,
63.69373374,
-28.15700995,
],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[
1.83404510e-01,
6.11852895e-01,
7.85175961e-01,
3.92710810e-02,
2.24914101e00,
-9.32712397e00,
8.60630831e00,
3.19433638e00,
2.97405608e01,
-2.02754226e01,
-2.32467778e01,
-4.19135012e01,
],
)
else:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.6158501, 0.50313626, 0.64241928, 0.02002169, 2.81525506, -9.39083155],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.18340451, 0.61185289, 0.78517596, 0.16825028, -0.08046333, -3.94434684],
)
else:
if with_excitations:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[
6.15850098e-01,
5.03136259e-01,
6.42419278e-01,
3.91853634e-02,
-1.76074913e00,
1.02811024e00,
5.56555782e01,
5.04705269e01,
3.60255887e-01,
5.89237749e01,
2.97009419e01,
-1.51353494e01,
],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[
1.83404510e-01,
6.11852895e-01,
7.85175961e-01,
-7.74768714e-02,
2.30892158e00,
-9.64013318e00,
-7.72228930e00,
-1.13759732e01,
9.51906209e01,
4.45077128e00,
-5.20261014e00,
-2.80864106e01,
],
)
else:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.6158501, 0.50313626, 0.64241928, 0.03918536, -1.76074913, 1.02811024],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.18340451, 0.61185289, 0.78517596, -0.07747687, 2.30892158, -9.64013318],
)
else:
if with_residual_torque:
if with_excitations:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[
0.6158501,
0.50313626,
0.64241928,
0.3264777,
-1.57134516,
0.87073117,
46.87928022,
-1.80189035,
53.3914525,
48.30056919,
63.69373374,
-28.15700995,
],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[
1.83404510e-01,
6.11852895e-01,
7.85175961e-01,
3.92710810e-02,
2.24914101e00,
-9.32712397e00,
8.60630831e00,
3.19433638e00,
2.97405608e01,
-2.02754226e01,
-2.32467778e01,
-4.19135012e01,
],
)
else:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.6158501, 0.50313626, 0.64241928, 0.02002169, 2.81525506, -9.39083155],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.18340451, 0.61185289, 0.78517596, 0.16825028, -0.08046333, -3.94434684],
)
else:
if with_excitations:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[
6.15850098e-01,
5.03136259e-01,
6.42419278e-01,
3.91853634e-02,
-1.76074913e00,
1.02811024e00,
5.56555782e01,
5.04705269e01,
3.60255887e-01,
5.89237749e01,
2.97009419e01,
-1.51353494e01,
],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[
1.83404510e-01,
6.11852895e-01,
7.85175961e-01,
-7.74768714e-02,
2.30892158e00,
-9.64013318e00,
-7.72228930e00,
-1.13759732e01,
9.51906209e01,
4.45077128e00,
-5.20261014e00,
-2.80864106e01,
],
)
else:
if with_external_force:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.6158501, 0.50313626, 0.64241928, 0.03918536, -1.76074913, 1.02811024],
)
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.18340451, 0.61185289, 0.78517596, -0.07747687, 2.30892158, -9.64013318],
)
@pytest.mark.parametrize("with_contact", [False, True])
def test_custom_dynamics(with_contact):
def custom_dynamic(states, controls, parameters, nlp, with_contact=False) -> tuple:
DynamicsFunctions.apply_parameters(parameters, nlp)
q = DynamicsFunctions.get(nlp.states["q"], states)
qdot = DynamicsFunctions.get(nlp.states["qdot"], states)
tau = DynamicsFunctions.get(nlp.controls["tau"], controls)
dq = DynamicsFunctions.compute_qdot(nlp, q, qdot)
ddq = DynamicsFunctions.forward_dynamics(nlp, q, qdot, tau, with_contact)
return dq, ddq
def configure(ocp, nlp, with_contact=None):
ConfigureProblem.configure_q(nlp, True, False)
ConfigureProblem.configure_qdot(nlp, True, False)
ConfigureProblem.configure_tau(nlp, False, True)
ConfigureProblem.configure_dynamics_function(ocp, nlp, custom_dynamic, with_contact=with_contact)
if with_contact:
ConfigureProblem.configure_contact_function(ocp, nlp, DynamicsFunctions.forces_from_torque_driven)
# Prepare the program
nlp = NonLinearProgram()
nlp.model = biorbd.Model(TestUtils.bioptim_folder() + "/examples/getting_started/2segments_4dof_2contacts.bioMod")
nlp.ns = 5
nlp.cx = MX
nlp.x_bounds = np.zeros((nlp.model.nbQ() * 3, 1))
nlp.u_bounds = np.zeros((nlp.model.nbQ(), 1))
ocp = OptimalControlProgram(nlp)
nlp.control_type = ControlType.CONSTANT
NonLinearProgram.add(
ocp, "dynamics_type", Dynamics(configure, dynamic_function=custom_dynamic, with_contact=with_contact), False
)
np.random.seed(42)
# Prepare the dynamics
ConfigureProblem.initialize(ocp, nlp)
# Test the results
states = np.random.rand(nlp.states.shape, nlp.ns)
controls = np.random.rand(nlp.controls.shape, nlp.ns)
params = np.random.rand(nlp.parameters.shape, nlp.ns)
x_out = np.array(nlp.dynamics_func(states, controls, params))
if with_contact:
contact_out = np.array(nlp.contact_forces_func(states, controls, params))
np.testing.assert_almost_equal(
x_out[:, 0], [0.6118529, 0.785176, 0.6075449, 0.8083973, -0.3214905, -0.1912131, 0.6507164, -0.2359716]
)
np.testing.assert_almost_equal(contact_out[:, 0], [-2.444071, 128.8816865, 2.7245124])
else:
np.testing.assert_almost_equal(
x_out[:, 0],
[0.61185289, 0.78517596, 0.60754485, 0.80839735, -0.30241366, -10.38503791, 1.60445173, 35.80238642],
)
| 38.460208
| 119
| 0.496941
| 2,164
| 22,230
| 4.941774
| 0.144177
| 0.031139
| 0.051898
| 0.072658
| 0.829811
| 0.818683
| 0.796989
| 0.796989
| 0.787825
| 0.776136
| 0
| 0.215986
| 0.404543
| 22,230
| 577
| 120
| 38.526863
| 0.591901
| 0.016554
| 0
| 0.703125
| 0
| 0
| 0.024445
| 0.012863
| 0
| 0
| 0
| 0
| 0.072266
| 1
| 0.015625
| false
| 0
| 0.023438
| 0
| 0.042969
| 0
| 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
|
a5ab743425b8d557c20fde232db5df13ec774765
| 4,184
|
py
|
Python
|
vn_re/formats/pna.py
|
Forlos/vn_re
|
cce0798ad2771034cee74d1ea92d70efd2a4d27d
|
[
"MIT"
] | 3
|
2020-12-14T08:12:36.000Z
|
2021-09-02T12:38:13.000Z
|
vn_re/formats/pna.py
|
Forlos/vn_re
|
cce0798ad2771034cee74d1ea92d70efd2a4d27d
|
[
"MIT"
] | null | null | null |
vn_re/formats/pna.py
|
Forlos/vn_re
|
cce0798ad2771034cee74d1ea92d70efd2a4d27d
|
[
"MIT"
] | null | null | null |
# This is a generated file! Please edit source .ksy file and use kaitai-struct-compiler to rebuild
from pkg_resources import parse_version
from kaitaistruct import __version__ as ks_version, KaitaiStruct, KaitaiStream, BytesIO
if parse_version(ks_version) < parse_version('0.7'):
raise Exception("Incompatible Kaitai Struct Python API: 0.7 or later is required, but you have %s" % (ks_version))
class Pna(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = _root if _root else self
self._read()
def _read(self):
self.magic = self._io.read_bytes(4)
_on = self.magic
if _on == b"\x50\x4E\x41\x50":
self.data = self._root.Pnap(self._io, self, self._root)
elif _on == b"\x57\x50\x41\x50":
self.data = self._root.Wpap(self._io, self, self._root)
self.image_data = self._io.read_bytes_full()
class WpapEntry(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = _root if _root else self
self._read()
def _read(self):
self.type = self._io.read_u4le()
self.id = self._io.read_u4le()
self.left_offset = self._io.read_u4le()
self.top_offset = self._io.read_u4le()
self.width = self._io.read_u4le()
self.height = self._io.read_u4le()
self.unk0 = self._io.read_bytes(12)
self.size = self._io.read_u4le()
class Wpap(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = _root if _root else self
self._read()
def _read(self):
self.header = self._root.WpapHeader(self._io, self, self._root)
self.entries = [None] * (self.header.some_count)
for i in range(self.header.some_count):
self.entries[i] = self._root.WpapEntry(self._io, self, self._root)
class PnapEntry(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = _root if _root else self
self._read()
def _read(self):
self.type = self._io.read_u4le()
self.id = self._io.read_u4le()
self.left_offset = self._io.read_u4le()
self.top_offset = self._io.read_u4le()
self.width = self._io.read_u4le()
self.height = self._io.read_u4le()
self.unk0 = self._io.read_bytes(12)
self.size = self._io.read_u4le()
class PnapHeader(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = _root if _root else self
self._read()
def _read(self):
self.unk0 = self._io.read_u4le()
self.unk1 = self._io.read_u4le()
self.unk2 = self._io.read_u4le()
self.some_count = self._io.read_u4le()
class WpapHeader(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = _root if _root else self
self._read()
def _read(self):
self.unk0 = self._io.read_u4le()
self.unk1 = self._io.read_u4le()
self.unk2 = self._io.read_u4le()
self.some_count = self._io.read_u4le()
class Pnap(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = _root if _root else self
self._read()
def _read(self):
self.header = self._root.PnapHeader(self._io, self, self._root)
self.entries = [None] * (self.header.some_count)
for i in range(self.header.some_count):
self.entries[i] = self._root.PnapEntry(self._io, self, self._root)
| 34.578512
| 118
| 0.58413
| 540
| 4,184
| 4.140741
| 0.172222
| 0.123435
| 0.116279
| 0.137746
| 0.776386
| 0.752236
| 0.722719
| 0.722719
| 0.722719
| 0.722719
| 0
| 0.018614
| 0.306644
| 4,184
| 120
| 119
| 34.866667
| 0.752154
| 0.022945
| 0
| 0.76087
| 1
| 0
| 0.028172
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.152174
| false
| 0
| 0.021739
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 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
| 7
|
a5b8929f1d750ac8d7efdd077fca8716c218c878
| 71,778
|
py
|
Python
|
ast_graph.py
|
MayankJasoria/Compiler-Project
|
bde8d15984e256d0ac1d12c6541e18fb90a60eab
|
[
"MIT"
] | null | null | null |
ast_graph.py
|
MayankJasoria/Compiler-Project
|
bde8d15984e256d0ac1d12c6541e18fb90a60eab
|
[
"MIT"
] | null | null | null |
ast_graph.py
|
MayankJasoria/Compiler-Project
|
bde8d15984e256d0ac1d12c6541e18fb90a60eab
|
[
"MIT"
] | null | null | null |
from pyvis.network import Network
net = Network(height="70%", width="100%", directed=True, layout=True)
net.add_node("h1", hidden=True, physics=False)
net.add_node("h2", hidden=True, physics=False)
net.add_node("AST_NODE_PROGRAM_0x843e990", title=r"AST_NODE_PROGRAM_0x843e990 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_PROGRAM_0x843e990", "h1", hidden=True, physics=False)
net.add_edge("AST_NODE_PROGRAM_0x843e990", "h2", hidden=True, physics=False)
net.add_node("AST_NODE_MODULEDECLARATION_0x843e9d0", title=r"AST_NODE_MODULEDECLARATION_0x843e9d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_PROGRAM_0x843e990", "AST_NODE_MODULEDECLARATION_0x843e9d0")
net.add_node("AST_NODE_MODULELIST_0x843ead0", title=r"AST_NODE_MODULELIST_0x843ead0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_PROGRAM_0x843e990", "AST_NODE_MODULELIST_0x843ead0")
net.add_node("AST_NODE_MODULELIST_0x84400d0", title=r"AST_NODE_MODULELIST_0x84400d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_PROGRAM_0x843e990", "AST_NODE_MODULELIST_0x84400d0")
net.add_node("AST_NODE_MODULELIST_0x8445d70", title=r"AST_NODE_MODULELIST_0x8445d70 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_PROGRAM_0x843e990", "AST_NODE_MODULELIST_0x8445d70")
net.add_node("AST_NODE_MODULEDECLARATION_0x843e9d0", title=r"AST_NODE_MODULEDECLARATION_0x843e9d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843ea10", title=r"AST_NODE_LEAF_0x843ea10 {<br \>  type: AST_LEAF_ID<br \> lex: d--<br \>}")
net.add_edge("AST_NODE_MODULEDECLARATION_0x843e9d0", "AST_NODE_LEAF_0x843ea10")
net.add_node("AST_NODE_MODULEDECLARATION_0x843ea50", title=r"AST_NODE_MODULEDECLARATION_0x843ea50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULEDECLARATION_0x843e9d0", "AST_NODE_MODULEDECLARATION_0x843ea50")
net.add_node("AST_NODE_MODULELIST_0x843ead0", title=r"AST_NODE_MODULELIST_0x843ead0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_MODULE_0x843eb10", title=r"AST_NODE_MODULE_0x843eb10 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULELIST_0x843ead0", "AST_NODE_MODULE_0x843eb10")
net.add_node("AST_NODE_MODULELIST_0x84400d0", title=r"AST_NODE_MODULELIST_0x84400d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8442ef0", title=r"AST_NODE_STATEMENT_0x8442ef0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULELIST_0x84400d0", "AST_NODE_STATEMENT_0x8442ef0")
net.add_node("AST_NODE_MODULELIST_0x8445d70", title=r"AST_NODE_MODULELIST_0x8445d70 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_MODULE_0x8445dd0", title=r"AST_NODE_MODULE_0x8445dd0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULELIST_0x8445d70", "AST_NODE_MODULE_0x8445dd0")
net.add_node("AST_NODE_LEAF_0x843ea10", title=r"AST_NODE_LEAF_0x843ea10 {<br \>  type: AST_LEAF_ID<br \> lex: d--<br \>}")
net.add_node("AST_NODE_MODULEDECLARATION_0x843ea50", title=r"AST_NODE_MODULEDECLARATION_0x843ea50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843ea90", title=r"AST_NODE_LEAF_0x843ea90 {<br \>  type: AST_LEAF_ID<br \> lex: c--<br \>}")
net.add_edge("AST_NODE_MODULEDECLARATION_0x843ea50", "AST_NODE_LEAF_0x843ea90")
net.add_node("AST_NODE_MODULE_0x843eb10", title=r"AST_NODE_MODULE_0x843eb10 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843eb50", title=r"AST_NODE_LEAF_0x843eb50 {<br \>  type: AST_LEAF_ID<br \> lex: c--<br \>}")
net.add_edge("AST_NODE_MODULE_0x843eb10", "AST_NODE_LEAF_0x843eb50")
net.add_node("AST_NODE_INPUTLIST_0x843eb90", title=r"AST_NODE_INPUTLIST_0x843eb90 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULE_0x843eb10", "AST_NODE_INPUTLIST_0x843eb90")
net.add_node("AST_NODE_STATEMENT_0x843f6d0", title=r"AST_NODE_STATEMENT_0x843f6d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULE_0x843eb10", "AST_NODE_STATEMENT_0x843f6d0")
net.add_node("AST_NODE_STATEMENT_0x8442ef0", title=r"AST_NODE_STATEMENT_0x8442ef0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8442f50", title=r"AST_NODE_LEAF_0x8442f50 {<br \>  type: AST_LEAF_ID<br \> lex: START<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8442ef0", "AST_NODE_LEAF_0x8442f50")
net.add_node("AST_NODE_STATEMENT_0x8442fb0", title=r"AST_NODE_STATEMENT_0x8442fb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8442ef0", "AST_NODE_STATEMENT_0x8442fb0")
net.add_node("AST_NODE_MODULE_0x8445dd0", title=r"AST_NODE_MODULE_0x8445dd0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445e30", title=r"AST_NODE_LEAF_0x8445e30 {<br \>  type: AST_LEAF_ID<br \> lex: d--<br \>}")
net.add_edge("AST_NODE_MODULE_0x8445dd0", "AST_NODE_LEAF_0x8445e30")
net.add_node("AST_NODE_INPUTLIST_0x8445e90", title=r"AST_NODE_INPUTLIST_0x8445e90 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULE_0x8445dd0", "AST_NODE_INPUTLIST_0x8445e90")
net.add_node("AST_NODE_STATEMENT_0x8446f50", title=r"AST_NODE_STATEMENT_0x8446f50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_MODULE_0x8445dd0", "AST_NODE_STATEMENT_0x8446f50")
net.add_node("AST_NODE_LEAF_0x843ea90", title=r"AST_NODE_LEAF_0x843ea90 {<br \>  type: AST_LEAF_ID<br \> lex: c--<br \>}")
net.add_node("AST_NODE_LEAF_0x843eb50", title=r"AST_NODE_LEAF_0x843eb50 {<br \>  type: AST_LEAF_ID<br \> lex: c--<br \>}")
net.add_node("AST_NODE_INPUTLIST_0x843eb90", title=r"AST_NODE_INPUTLIST_0x843eb90 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843ebd0", title=r"AST_NODE_LEAF_0x843ebd0 {<br \>  type: AST_LEAF_ID<br \> lex: list<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x843eb90", "AST_NODE_LEAF_0x843ebd0")
net.add_node("AST_NODE_ARRAY_0x843ec10", title=r"AST_NODE_ARRAY_0x843ec10 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x843eb90", "AST_NODE_ARRAY_0x843ec10")
net.add_node("AST_NODE_INPUTLIST_0x843ed50", title=r"AST_NODE_INPUTLIST_0x843ed50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x843eb90", "AST_NODE_INPUTLIST_0x843ed50")
net.add_node("AST_NODE_STATEMENT_0x843f6d0", title=r"AST_NODE_STATEMENT_0x843f6d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843f730", title=r"AST_NODE_LEAF_0x843f730 {<br \>  type: AST_LEAF_ID<br \> lex: START<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843f6d0", "AST_NODE_LEAF_0x843f730")
net.add_node("AST_NODE_STATEMENT_0x843f790", title=r"AST_NODE_STATEMENT_0x843f790 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843f6d0", "AST_NODE_STATEMENT_0x843f790")
net.add_node("AST_NODE_LEAF_0x8442f50", title=r"AST_NODE_LEAF_0x8442f50 {<br \>  type: AST_LEAF_ID<br \> lex: START<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8442fb0", title=r"AST_NODE_STATEMENT_0x8442fb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8443010", title=r"AST_NODE_DECLARESTMT_0x8443010 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8442fb0", "AST_NODE_DECLARESTMT_0x8443010")
net.add_node("AST_NODE_STATEMENT_0x8443190", title=r"AST_NODE_STATEMENT_0x8443190 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8442fb0", "AST_NODE_STATEMENT_0x8443190")
net.add_node("AST_NODE_LEAF_0x8445e30", title=r"AST_NODE_LEAF_0x8445e30 {<br \>  type: AST_LEAF_ID<br \> lex: d--<br \>}")
net.add_node("AST_NODE_INPUTLIST_0x8445e90", title=r"AST_NODE_INPUTLIST_0x8445e90 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445ef0", title=r"AST_NODE_LEAF_0x8445ef0 {<br \>  type: AST_LEAF_ID<br \> lex: list<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x8445e90", "AST_NODE_LEAF_0x8445ef0")
net.add_node("AST_NODE_ARRAY_0x8445f50", title=r"AST_NODE_ARRAY_0x8445f50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x8445e90", "AST_NODE_ARRAY_0x8445f50")
net.add_node("AST_NODE_INPUTLIST_0x8446130", title=r"AST_NODE_INPUTLIST_0x8446130 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x8445e90", "AST_NODE_INPUTLIST_0x8446130")
net.add_node("AST_NODE_STATEMENT_0x8446f50", title=r"AST_NODE_STATEMENT_0x8446f50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8446fb0", title=r"AST_NODE_LEAF_0x8446fb0 {<br \>  type: AST_LEAF_ID<br \> lex: START<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8446f50", "AST_NODE_LEAF_0x8446fb0")
net.add_node("AST_NODE_STATEMENT_0x8447010", title=r"AST_NODE_STATEMENT_0x8447010 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8446f50", "AST_NODE_STATEMENT_0x8447010")
net.add_node("AST_NODE_LEAF_0x843ebd0", title=r"AST_NODE_LEAF_0x843ebd0 {<br \>  type: AST_LEAF_ID<br \> lex: list<br \>}")
net.add_node("AST_NODE_ARRAY_0x843ec10", title=r"AST_NODE_ARRAY_0x843ec10 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_RANGEARRAYS_0x843ec50", title=r"AST_NODE_RANGEARRAYS_0x843ec50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_ARRAY_0x843ec10", "AST_NODE_RANGEARRAYS_0x843ec50")
net.add_node("AST_NODE_LEAF_0x843ed10", title=r"AST_NODE_LEAF_0x843ed10 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_edge("AST_NODE_ARRAY_0x843ec10", "AST_NODE_LEAF_0x843ed10")
net.add_node("AST_NODE_INPUTLIST_0x843ed50", title=r"AST_NODE_INPUTLIST_0x843ed50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843ed90", title=r"AST_NODE_LEAF_0x843ed90 {<br \>  type: AST_LEAF_ID<br \> lex: n--<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x843ed50", "AST_NODE_LEAF_0x843ed90")
net.add_node("AST_NODE_LEAF_0x843edd0", title=r"AST_NODE_LEAF_0x843edd0 {<br \>  type: AST_LEAF_INT<br \> lex: INTEGER<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x843ed50", "AST_NODE_LEAF_0x843edd0")
net.add_node("AST_NODE_LEAF_0x843f730", title=r"AST_NODE_LEAF_0x843f730 {<br \>  type: AST_LEAF_ID<br \> lex: START<br \>}")
net.add_node("AST_NODE_STATEMENT_0x843f790", title=r"AST_NODE_STATEMENT_0x843f790 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_SIMPLESTMT_0x843f7f0", title=r"AST_NODE_SIMPLESTMT_0x843f7f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843f790", "AST_NODE_SIMPLESTMT_0x843f7f0")
net.add_node("AST_NODE_STATEMENT_0x843f970", title=r"AST_NODE_STATEMENT_0x843f970 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843f790", "AST_NODE_STATEMENT_0x843f970")
net.add_node("AST_NODE_DECLARESTMT_0x8443010", title=r"AST_NODE_DECLARESTMT_0x8443010 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443070", title=r"AST_NODE_IDLIST_0x8443070 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8443010", "AST_NODE_IDLIST_0x8443070")
net.add_node("AST_NODE_LEAF_0x8443130", title=r"AST_NODE_LEAF_0x8443130 {<br \>  type: AST_LEAF_INT<br \> lex: INTEGER<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8443010", "AST_NODE_LEAF_0x8443130")
net.add_node("AST_NODE_STATEMENT_0x8443190", title=r"AST_NODE_STATEMENT_0x8443190 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x84431f0", title=r"AST_NODE_DECLARESTMT_0x84431f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443190", "AST_NODE_DECLARESTMT_0x84431f0")
net.add_node("AST_NODE_STATEMENT_0x8443370", title=r"AST_NODE_STATEMENT_0x8443370 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443190", "AST_NODE_STATEMENT_0x8443370")
net.add_node("AST_NODE_LEAF_0x8445ef0", title=r"AST_NODE_LEAF_0x8445ef0 {<br \>  type: AST_LEAF_ID<br \> lex: list<br \>}")
net.add_node("AST_NODE_ARRAY_0x8445f50", title=r"AST_NODE_ARRAY_0x8445f50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_RANGEARRAYS_0x8445fb0", title=r"AST_NODE_RANGEARRAYS_0x8445fb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_ARRAY_0x8445f50", "AST_NODE_RANGEARRAYS_0x8445fb0")
net.add_node("AST_NODE_LEAF_0x84460d0", title=r"AST_NODE_LEAF_0x84460d0 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_edge("AST_NODE_ARRAY_0x8445f50", "AST_NODE_LEAF_0x84460d0")
net.add_node("AST_NODE_INPUTLIST_0x8446130", title=r"AST_NODE_INPUTLIST_0x8446130 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8446190", title=r"AST_NODE_LEAF_0x8446190 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x8446130", "AST_NODE_LEAF_0x8446190")
net.add_node("AST_NODE_LEAF_0x84461f0", title=r"AST_NODE_LEAF_0x84461f0 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_edge("AST_NODE_INPUTLIST_0x8446130", "AST_NODE_LEAF_0x84461f0")
net.add_node("AST_NODE_LEAF_0x8446fb0", title=r"AST_NODE_LEAF_0x8446fb0 {<br \>  type: AST_LEAF_ID<br \> lex: START<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8447010", title=r"AST_NODE_STATEMENT_0x8447010 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8447070", title=r"AST_NODE_DECLARESTMT_0x8447070 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8447010", "AST_NODE_DECLARESTMT_0x8447070")
net.add_node("AST_NODE_STATEMENT_0x84471f0", title=r"AST_NODE_STATEMENT_0x84471f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8447010", "AST_NODE_STATEMENT_0x84471f0")
net.add_node("AST_NODE_RANGEARRAYS_0x843ec50", title=r"AST_NODE_RANGEARRAYS_0x843ec50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843ec90", title=r"AST_NODE_LEAF_0x843ec90 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_RANGEARRAYS_0x843ec50", "AST_NODE_LEAF_0x843ec90")
net.add_node("AST_NODE_LEAF_0x843ecd0", title=r"AST_NODE_LEAF_0x843ecd0 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_RANGEARRAYS_0x843ec50", "AST_NODE_LEAF_0x843ecd0")
net.add_node("AST_NODE_LEAF_0x843ed10", title=r"AST_NODE_LEAF_0x843ed10 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_node("AST_NODE_LEAF_0x843ed90", title=r"AST_NODE_LEAF_0x843ed90 {<br \>  type: AST_LEAF_ID<br \> lex: n--<br \>}")
net.add_node("AST_NODE_LEAF_0x843edd0", title=r"AST_NODE_LEAF_0x843edd0 {<br \>  type: AST_LEAF_INT<br \> lex: INTEGER<br \>}")
net.add_node("AST_NODE_SIMPLESTMT_0x843f7f0", title=r"AST_NODE_SIMPLESTMT_0x843f7f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_ASSIGN_0x843f850", title=r"AST_NODE_ASSIGN_0x843f850 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_SIMPLESTMT_0x843f7f0", "AST_NODE_ASSIGN_0x843f850")
net.add_node("AST_NODE_STATEMENT_0x843f970", title=r"AST_NODE_STATEMENT_0x843f970 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x843f9d0", title=r"AST_NODE_DECLARESTMT_0x843f9d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843f970", "AST_NODE_DECLARESTMT_0x843f9d0")
net.add_node("AST_NODE_STATEMENT_0x843fb50", title=r"AST_NODE_STATEMENT_0x843fb50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843f970", "AST_NODE_STATEMENT_0x843fb50")
net.add_node("AST_NODE_IDLIST_0x8443070", title=r"AST_NODE_IDLIST_0x8443070 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84430d0", title=r"AST_NODE_LEAF_0x84430d0 {<br \>  type: AST_LEAF_ID<br \> lex: index<br \>}")
net.add_edge("AST_NODE_IDLIST_0x8443070", "AST_NODE_LEAF_0x84430d0")
net.add_node("AST_NODE_LEAF_0x8443130", title=r"AST_NODE_LEAF_0x8443130 {<br \>  type: AST_LEAF_INT<br \> lex: INTEGER<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x84431f0", title=r"AST_NODE_DECLARESTMT_0x84431f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443250", title=r"AST_NODE_IDLIST_0x8443250 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x84431f0", "AST_NODE_IDLIST_0x8443250")
net.add_node("AST_NODE_LEAF_0x8443310", title=r"AST_NODE_LEAF_0x8443310 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x84431f0", "AST_NODE_LEAF_0x8443310")
net.add_node("AST_NODE_STATEMENT_0x8443370", title=r"AST_NODE_STATEMENT_0x8443370 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x84433d0", title=r"AST_NODE_DECLARESTMT_0x84433d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443370", "AST_NODE_DECLARESTMT_0x84433d0")
net.add_node("AST_NODE_STATEMENT_0x8443550", title=r"AST_NODE_STATEMENT_0x8443550 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443370", "AST_NODE_STATEMENT_0x8443550")
net.add_node("AST_NODE_RANGEARRAYS_0x8445fb0", title=r"AST_NODE_RANGEARRAYS_0x8445fb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8446010", title=r"AST_NODE_LEAF_0x8446010 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_RANGEARRAYS_0x8445fb0", "AST_NODE_LEAF_0x8446010")
net.add_node("AST_NODE_LEAF_0x8446070", title=r"AST_NODE_LEAF_0x8446070 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_RANGEARRAYS_0x8445fb0", "AST_NODE_LEAF_0x8446070")
net.add_node("AST_NODE_LEAF_0x84460d0", title=r"AST_NODE_LEAF_0x84460d0 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_node("AST_NODE_LEAF_0x8446190", title=r"AST_NODE_LEAF_0x8446190 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_node("AST_NODE_LEAF_0x84461f0", title=r"AST_NODE_LEAF_0x84461f0 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8447070", title=r"AST_NODE_DECLARESTMT_0x8447070 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x84470d0", title=r"AST_NODE_IDLIST_0x84470d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8447070", "AST_NODE_IDLIST_0x84470d0")
net.add_node("AST_NODE_LEAF_0x8447190", title=r"AST_NODE_LEAF_0x8447190 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8447070", "AST_NODE_LEAF_0x8447190")
net.add_node("AST_NODE_STATEMENT_0x84471f0", title=r"AST_NODE_STATEMENT_0x84471f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8447250", title=r"AST_NODE_DECLARESTMT_0x8447250 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x84471f0", "AST_NODE_DECLARESTMT_0x8447250")
net.add_node("AST_NODE_STATEMENT_0x84473d0", title=r"AST_NODE_STATEMENT_0x84473d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x84471f0", "AST_NODE_STATEMENT_0x84473d0")
net.add_node("AST_NODE_LEAF_0x843ec90", title=r"AST_NODE_LEAF_0x843ec90 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_LEAF_0x843ecd0", title=r"AST_NODE_LEAF_0x843ecd0 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_ASSIGN_0x843f850", title=r"AST_NODE_ASSIGN_0x843f850 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843f8b0", title=r"AST_NODE_LEAF_0x843f8b0 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_edge("AST_NODE_ASSIGN_0x843f850", "AST_NODE_LEAF_0x843f8b0")
net.add_node("AST_NODE_LEAF_0x843f910", title=r"AST_NODE_LEAF_0x843f910 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_ASSIGN_0x843f850", "AST_NODE_LEAF_0x843f910")
net.add_node("AST_NODE_DECLARESTMT_0x843f9d0", title=r"AST_NODE_DECLARESTMT_0x843f9d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x843fa30", title=r"AST_NODE_IDLIST_0x843fa30 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x843f9d0", "AST_NODE_IDLIST_0x843fa30")
net.add_node("AST_NODE_LEAF_0x843faf0", title=r"AST_NODE_LEAF_0x843faf0 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x843f9d0", "AST_NODE_LEAF_0x843faf0")
net.add_node("AST_NODE_STATEMENT_0x843fb50", title=r"AST_NODE_STATEMENT_0x843fb50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x843fbb0", title=r"AST_NODE_CONDSTMT_0x843fbb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843fb50", "AST_NODE_CONDSTMT_0x843fbb0")
net.add_node("AST_NODE_LEAF_0x84430d0", title=r"AST_NODE_LEAF_0x84430d0 {<br \>  type: AST_LEAF_ID<br \> lex: index<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443250", title=r"AST_NODE_IDLIST_0x8443250 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84432b0", title=r"AST_NODE_LEAF_0x84432b0 {<br \>  type: AST_LEAF_ID<br \> lex: t--<br \>}")
net.add_edge("AST_NODE_IDLIST_0x8443250", "AST_NODE_LEAF_0x84432b0")
net.add_node("AST_NODE_LEAF_0x8443310", title=r"AST_NODE_LEAF_0x8443310 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x84433d0", title=r"AST_NODE_DECLARESTMT_0x84433d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443430", title=r"AST_NODE_IDLIST_0x8443430 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x84433d0", "AST_NODE_IDLIST_0x8443430")
net.add_node("AST_NODE_LEAF_0x84434f0", title=r"AST_NODE_LEAF_0x84434f0 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x84433d0", "AST_NODE_LEAF_0x84434f0")
net.add_node("AST_NODE_STATEMENT_0x8443550", title=r"AST_NODE_STATEMENT_0x8443550 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_ITERSTMT_0x84435b0", title=r"AST_NODE_ITERSTMT_0x84435b0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443550", "AST_NODE_ITERSTMT_0x84435b0")
net.add_node("AST_NODE_LEAF_0x8446010", title=r"AST_NODE_LEAF_0x8446010 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_LEAF_0x8446070", title=r"AST_NODE_LEAF_0x8446070 {<br \>  type: AST_LEAF_IDXNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_IDLIST_0x84470d0", title=r"AST_NODE_IDLIST_0x84470d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447130", title=r"AST_NODE_LEAF_0x8447130 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_edge("AST_NODE_IDLIST_0x84470d0", "AST_NODE_LEAF_0x8447130")
net.add_node("AST_NODE_LEAF_0x8447190", title=r"AST_NODE_LEAF_0x8447190 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8447250", title=r"AST_NODE_DECLARESTMT_0x8447250 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x84472b0", title=r"AST_NODE_IDLIST_0x84472b0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8447250", "AST_NODE_IDLIST_0x84472b0")
net.add_node("AST_NODE_LEAF_0x8447370", title=r"AST_NODE_LEAF_0x8447370 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8447250", "AST_NODE_LEAF_0x8447370")
net.add_node("AST_NODE_STATEMENT_0x84473d0", title=r"AST_NODE_STATEMENT_0x84473d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_SIMPLESTMT_0x8447430", title=r"AST_NODE_SIMPLESTMT_0x8447430 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x84473d0", "AST_NODE_SIMPLESTMT_0x8447430")
net.add_node("AST_NODE_STATEMENT_0x8447790", title=r"AST_NODE_STATEMENT_0x8447790 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x84473d0", "AST_NODE_STATEMENT_0x8447790")
net.add_node("AST_NODE_LEAF_0x843f8b0", title=r"AST_NODE_LEAF_0x843f8b0 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_node("AST_NODE_LEAF_0x843f910", title=r"AST_NODE_LEAF_0x843f910 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_IDLIST_0x843fa30", title=r"AST_NODE_IDLIST_0x843fa30 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843fa90", title=r"AST_NODE_LEAF_0x843fa90 {<br \>  type: AST_LEAF_ID<br \> lex: q--<br \>}")
net.add_edge("AST_NODE_IDLIST_0x843fa30", "AST_NODE_LEAF_0x843fa90")
net.add_node("AST_NODE_LEAF_0x843faf0", title=r"AST_NODE_LEAF_0x843faf0 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x843fbb0", title=r"AST_NODE_CONDSTMT_0x843fbb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843fc10", title=r"AST_NODE_LEAF_0x843fc10 {<br \>  type: AST_LEAF_ID<br \> lex: q--<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x843fbb0", "AST_NODE_LEAF_0x843fc10")
net.add_node("AST_NODE_CASESTMT_0x843fc90", title=r"AST_NODE_CASESTMT_0x843fc90 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x843fbb0", "AST_NODE_CASESTMT_0x843fc90")
net.add_node("AST_NODE_LEAF_0x84432b0", title=r"AST_NODE_LEAF_0x84432b0 {<br \>  type: AST_LEAF_ID<br \> lex: t--<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443430", title=r"AST_NODE_IDLIST_0x8443430 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8443490", title=r"AST_NODE_LEAF_0x8443490 {<br \>  type: AST_LEAF_ID<br \> lex: bee<br \>}")
net.add_edge("AST_NODE_IDLIST_0x8443430", "AST_NODE_LEAF_0x8443490")
net.add_node("AST_NODE_LEAF_0x84434f0", title=r"AST_NODE_LEAF_0x84434f0 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_node("AST_NODE_ITERSTMT_0x84435b0", title=r"AST_NODE_ITERSTMT_0x84435b0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8443610", title=r"AST_NODE_LEAF_0x8443610 {<br \>  type: AST_LEAF_ID<br \> lex: index<br \>}")
net.add_edge("AST_NODE_ITERSTMT_0x84435b0", "AST_NODE_LEAF_0x8443610")
net.add_node("AST_NODE_RANGEARRAYS_0x8443670", title=r"AST_NODE_RANGEARRAYS_0x8443670 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_ITERSTMT_0x84435b0", "AST_NODE_RANGEARRAYS_0x8443670")
net.add_node("AST_NODE_STATEMENT_0x8443790", title=r"AST_NODE_STATEMENT_0x8443790 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_ITERSTMT_0x84435b0", "AST_NODE_STATEMENT_0x8443790")
net.add_node("AST_NODE_LEAF_0x8447130", title=r"AST_NODE_LEAF_0x8447130 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_node("AST_NODE_IDLIST_0x84472b0", title=r"AST_NODE_IDLIST_0x84472b0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447310", title=r"AST_NODE_LEAF_0x8447310 {<br \>  type: AST_LEAF_ID<br \> lex: oo<br \>}")
net.add_edge("AST_NODE_IDLIST_0x84472b0", "AST_NODE_LEAF_0x8447310")
net.add_node("AST_NODE_LEAF_0x8447370", title=r"AST_NODE_LEAF_0x8447370 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_node("AST_NODE_SIMPLESTMT_0x8447430", title=r"AST_NODE_SIMPLESTMT_0x8447430 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_ASSIGN_0x8447490", title=r"AST_NODE_ASSIGN_0x8447490 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_SIMPLESTMT_0x8447430", "AST_NODE_ASSIGN_0x8447490")
net.add_node("AST_NODE_STATEMENT_0x8447790", title=r"AST_NODE_STATEMENT_0x8447790 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x84477f0", title=r"AST_NODE_CONDSTMT_0x84477f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8447790", "AST_NODE_CONDSTMT_0x84477f0")
net.add_node("AST_NODE_LEAF_0x843fa90", title=r"AST_NODE_LEAF_0x843fa90 {<br \>  type: AST_LEAF_ID<br \> lex: q--<br \>}")
net.add_node("AST_NODE_LEAF_0x843fc10", title=r"AST_NODE_LEAF_0x843fc10 {<br \>  type: AST_LEAF_ID<br \> lex: q--<br \>}")
net.add_node("AST_NODE_CASESTMT_0x843fc90", title=r"AST_NODE_CASESTMT_0x843fc90 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843fcf0", title=r"AST_NODE_LEAF_0x843fcf0 {<br \>  type: AST_LEAF_VALTRUE<br \> lex: TRUE<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x843fc90", "AST_NODE_LEAF_0x843fcf0")
net.add_node("AST_NODE_STATEMENT_0x843fd50", title=r"AST_NODE_STATEMENT_0x843fd50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x843fc90", "AST_NODE_STATEMENT_0x843fd50")
net.add_node("AST_NODE_CASESTMT_0x843fef0", title=r"AST_NODE_CASESTMT_0x843fef0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x843fc90", "AST_NODE_CASESTMT_0x843fef0")
net.add_node("AST_NODE_LEAF_0x8443490", title=r"AST_NODE_LEAF_0x8443490 {<br \>  type: AST_LEAF_ID<br \> lex: bee<br \>}")
net.add_node("AST_NODE_LEAF_0x8443610", title=r"AST_NODE_LEAF_0x8443610 {<br \>  type: AST_LEAF_ID<br \> lex: index<br \>}")
net.add_node("AST_NODE_RANGEARRAYS_0x8443670", title=r"AST_NODE_RANGEARRAYS_0x8443670 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8443730", title=r"AST_NODE_LEAF_0x8443730 {<br \>  type: AST_LEAF_NUM<br \> lex: RANGEOP<br \>}")
net.add_edge("AST_NODE_RANGEARRAYS_0x8443670", "AST_NODE_LEAF_0x8443730")
net.add_node("AST_NODE_LEAF_0x84436d0", title=r"AST_NODE_LEAF_0x84436d0 {<br \>  type: AST_LEAF_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_RANGEARRAYS_0x8443670", "AST_NODE_LEAF_0x84436d0")
net.add_node("AST_NODE_STATEMENT_0x8443790", title=r"AST_NODE_STATEMENT_0x8443790 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_SIMPLESTMT_0x84437f0", title=r"AST_NODE_SIMPLESTMT_0x84437f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443790", "AST_NODE_SIMPLESTMT_0x84437f0")
net.add_node("AST_NODE_STATEMENT_0x8443af0", title=r"AST_NODE_STATEMENT_0x8443af0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443790", "AST_NODE_STATEMENT_0x8443af0")
net.add_node("AST_NODE_LEAF_0x8447310", title=r"AST_NODE_LEAF_0x8447310 {<br \>  type: AST_LEAF_ID<br \> lex: oo<br \>}")
net.add_node("AST_NODE_ASSIGN_0x8447490", title=r"AST_NODE_ASSIGN_0x8447490 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84474f0", title=r"AST_NODE_LEAF_0x84474f0 {<br \>  type: AST_LEAF_ID<br \> lex: oo<br \>}")
net.add_edge("AST_NODE_ASSIGN_0x8447490", "AST_NODE_LEAF_0x84474f0")
net.add_node("AST_NODE_AOBEXPR_0x8447610", title=r"AST_NODE_AOBEXPR_0x8447610 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_ASSIGN_0x8447490", "AST_NODE_AOBEXPR_0x8447610")
net.add_node("AST_NODE_CONDSTMT_0x84477f0", title=r"AST_NODE_CONDSTMT_0x84477f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447850", title=r"AST_NODE_LEAF_0x8447850 {<br \>  type: AST_LEAF_ID<br \> lex: c--<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x84477f0", "AST_NODE_LEAF_0x8447850")
net.add_node("AST_NODE_CASESTMT_0x84478d0", title=r"AST_NODE_CASESTMT_0x84478d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x84477f0", "AST_NODE_CASESTMT_0x84478d0")
net.add_node("AST_NODE_LEAF_0x843fcf0", title=r"AST_NODE_LEAF_0x843fcf0 {<br \>  type: AST_LEAF_VALTRUE<br \> lex: TRUE<br \>}")
net.add_node("AST_NODE_STATEMENT_0x843fd50", title=r"AST_NODE_STATEMENT_0x843fd50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x843fdb0", title=r"AST_NODE_IO_0x843fdb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843fd50", "AST_NODE_IO_0x843fdb0")
net.add_node("AST_NODE_CASESTMT_0x843fef0", title=r"AST_NODE_CASESTMT_0x843fef0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843ff50", title=r"AST_NODE_LEAF_0x843ff50 {<br \>  type: AST_LEAF_VALFALSE<br \> lex: FALSE<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x843fef0", "AST_NODE_LEAF_0x843ff50")
net.add_node("AST_NODE_STATEMENT_0x843ffb0", title=r"AST_NODE_STATEMENT_0x843ffb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x843fef0", "AST_NODE_STATEMENT_0x843ffb0")
net.add_node("AST_NODE_LEAF_0x8443730", title=r"AST_NODE_LEAF_0x8443730 {<br \>  type: AST_LEAF_NUM<br \> lex: RANGEOP<br \>}")
net.add_node("AST_NODE_LEAF_0x84436d0", title=r"AST_NODE_LEAF_0x84436d0 {<br \>  type: AST_LEAF_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_SIMPLESTMT_0x84437f0", title=r"AST_NODE_SIMPLESTMT_0x84437f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_ASSIGN_0x8443850", title=r"AST_NODE_ASSIGN_0x8443850 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_SIMPLESTMT_0x84437f0", "AST_NODE_ASSIGN_0x8443850")
net.add_node("AST_NODE_STATEMENT_0x8443af0", title=r"AST_NODE_STATEMENT_0x8443af0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8443b50", title=r"AST_NODE_DECLARESTMT_0x8443b50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443af0", "AST_NODE_DECLARESTMT_0x8443b50")
net.add_node("AST_NODE_STATEMENT_0x8443cd0", title=r"AST_NODE_STATEMENT_0x8443cd0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443af0", "AST_NODE_STATEMENT_0x8443cd0")
net.add_node("AST_NODE_LEAF_0x84474f0", title=r"AST_NODE_LEAF_0x84474f0 {<br \>  type: AST_LEAF_ID<br \> lex: oo<br \>}")
net.add_node("AST_NODE_AOBEXPR_0x8447610", title=r"AST_NODE_AOBEXPR_0x8447610 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x8447550", title=r"AST_NODE_VARIDNUM_0x8447550 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_AOBEXPR_0x8447610", "AST_NODE_VARIDNUM_0x8447550")
net.add_node("AST_NODE_LEAF_0x8447670", title=r"AST_NODE_LEAF_0x8447670 {<br \>  type: AST_LEAF_EQ<br \> lex: EQ-<br \>}")
net.add_edge("AST_NODE_AOBEXPR_0x8447610", "AST_NODE_LEAF_0x8447670")
net.add_node("AST_NODE_VARIDNUM_0x84476d0", title=r"AST_NODE_VARIDNUM_0x84476d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_AOBEXPR_0x8447610", "AST_NODE_VARIDNUM_0x84476d0")
net.add_node("AST_NODE_LEAF_0x8447850", title=r"AST_NODE_LEAF_0x8447850 {<br \>  type: AST_LEAF_ID<br \> lex: c--<br \>}")
net.add_node("AST_NODE_CASESTMT_0x84478d0", title=r"AST_NODE_CASESTMT_0x84478d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447930", title=r"AST_NODE_LEAF_0x8447930 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84478d0", "AST_NODE_LEAF_0x8447930")
net.add_node("AST_NODE_STATEMENT_0x8447990", title=r"AST_NODE_STATEMENT_0x8447990 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84478d0", "AST_NODE_STATEMENT_0x8447990")
net.add_node("AST_NODE_CASESTMT_0x8447b30", title=r"AST_NODE_CASESTMT_0x8447b30 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84478d0", "AST_NODE_CASESTMT_0x8447b30")
net.add_node("AST_NODE_IO_0x843fdb0", title=r"AST_NODE_IO_0x843fdb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x843fe10", title=r"AST_NODE_VARIDNUM_0x843fe10 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_IO_0x843fdb0", "AST_NODE_VARIDNUM_0x843fe10")
net.add_node("AST_NODE_LEAF_0x843ff50", title=r"AST_NODE_LEAF_0x843ff50 {<br \>  type: AST_LEAF_VALFALSE<br \> lex: FALSE<br \>}")
net.add_node("AST_NODE_STATEMENT_0x843ffb0", title=r"AST_NODE_STATEMENT_0x843ffb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8440010", title=r"AST_NODE_IO_0x8440010 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x843ffb0", "AST_NODE_IO_0x8440010")
net.add_node("AST_NODE_ASSIGN_0x8443850", title=r"AST_NODE_ASSIGN_0x8443850 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84438b0", title=r"AST_NODE_LEAF_0x84438b0 {<br \>  type: AST_LEAF_ID<br \> lex: t--<br \>}")
net.add_edge("AST_NODE_ASSIGN_0x8443850", "AST_NODE_LEAF_0x84438b0")
net.add_node("AST_NODE_AOBEXPR_0x84439d0", title=r"AST_NODE_AOBEXPR_0x84439d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_ASSIGN_0x8443850", "AST_NODE_AOBEXPR_0x84439d0")
net.add_node("AST_NODE_DECLARESTMT_0x8443b50", title=r"AST_NODE_DECLARESTMT_0x8443b50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443bb0", title=r"AST_NODE_IDLIST_0x8443bb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8443b50", "AST_NODE_IDLIST_0x8443bb0")
net.add_node("AST_NODE_LEAF_0x8443c70", title=r"AST_NODE_LEAF_0x8443c70 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8443b50", "AST_NODE_LEAF_0x8443c70")
net.add_node("AST_NODE_STATEMENT_0x8443cd0", title=r"AST_NODE_STATEMENT_0x8443cd0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8443d30", title=r"AST_NODE_DECLARESTMT_0x8443d30 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443cd0", "AST_NODE_DECLARESTMT_0x8443d30")
net.add_node("AST_NODE_STATEMENT_0x8443eb0", title=r"AST_NODE_STATEMENT_0x8443eb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443cd0", "AST_NODE_STATEMENT_0x8443eb0")
net.add_node("AST_NODE_VARIDNUM_0x8447550", title=r"AST_NODE_VARIDNUM_0x8447550 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84475b0", title=r"AST_NODE_LEAF_0x84475b0 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x8447550", "AST_NODE_LEAF_0x84475b0")
net.add_node("AST_NODE_LEAF_0x8447670", title=r"AST_NODE_LEAF_0x8447670 {<br \>  type: AST_LEAF_EQ<br \> lex: EQ-<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x84476d0", title=r"AST_NODE_VARIDNUM_0x84476d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447730", title=r"AST_NODE_LEAF_0x8447730 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x84476d0", "AST_NODE_LEAF_0x8447730")
net.add_node("AST_NODE_LEAF_0x8447930", title=r"AST_NODE_LEAF_0x8447930 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8447990", title=r"AST_NODE_STATEMENT_0x8447990 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x84479f0", title=r"AST_NODE_IO_0x84479f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8447990", "AST_NODE_IO_0x84479f0")
net.add_node("AST_NODE_CASESTMT_0x8447b30", title=r"AST_NODE_CASESTMT_0x8447b30 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447b90", title=r"AST_NODE_LEAF_0x8447b90 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8447b30", "AST_NODE_LEAF_0x8447b90")
net.add_node("AST_NODE_STATEMENT_0x8447bf0", title=r"AST_NODE_STATEMENT_0x8447bf0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8447b30", "AST_NODE_STATEMENT_0x8447bf0")
net.add_node("AST_NODE_VARIDNUM_0x843fe10", title=r"AST_NODE_VARIDNUM_0x843fe10 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x843fe70", title=r"AST_NODE_LEAF_0x843fe70 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x843fe10", "AST_NODE_LEAF_0x843fe70")
net.add_node("AST_NODE_IO_0x8440010", title=r"AST_NODE_IO_0x8440010 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8440070", title=r"AST_NODE_LEAF_0x8440070 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_IO_0x8440010", "AST_NODE_LEAF_0x8440070")
net.add_node("AST_NODE_LEAF_0x84438b0", title=r"AST_NODE_LEAF_0x84438b0 {<br \>  type: AST_LEAF_ID<br \> lex: t--<br \>}")
net.add_node("AST_NODE_AOBEXPR_0x84439d0", title=r"AST_NODE_AOBEXPR_0x84439d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x8443910", title=r"AST_NODE_VARIDNUM_0x8443910 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_AOBEXPR_0x84439d0", "AST_NODE_VARIDNUM_0x8443910")
net.add_node("AST_NODE_LEAF_0x8443a30", title=r"AST_NODE_LEAF_0x8443a30 {<br \>  type: AST_LEAF_LE<br \> lex: LE-<br \>}")
net.add_edge("AST_NODE_AOBEXPR_0x84439d0", "AST_NODE_LEAF_0x8443a30")
net.add_node("AST_NODE_LEAF_0x8443a90", title=r"AST_NODE_LEAF_0x8443a90 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_AOBEXPR_0x84439d0", "AST_NODE_LEAF_0x8443a90")
net.add_node("AST_NODE_IDLIST_0x8443bb0", title=r"AST_NODE_IDLIST_0x8443bb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8443c10", title=r"AST_NODE_LEAF_0x8443c10 {<br \>  type: AST_LEAF_ID<br \> lex: akki<br \>}")
net.add_edge("AST_NODE_IDLIST_0x8443bb0", "AST_NODE_LEAF_0x8443c10")
net.add_node("AST_NODE_LEAF_0x8443c70", title=r"AST_NODE_LEAF_0x8443c70 {<br \>  type: AST_LEAF_RNUM<br \> lex: REAL<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8443d30", title=r"AST_NODE_DECLARESTMT_0x8443d30 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443d90", title=r"AST_NODE_IDLIST_0x8443d90 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8443d30", "AST_NODE_IDLIST_0x8443d90")
net.add_node("AST_NODE_LEAF_0x8443e50", title=r"AST_NODE_LEAF_0x8443e50 {<br \>  type: AST_LEAF_INT<br \> lex: INTEGER<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8443d30", "AST_NODE_LEAF_0x8443e50")
net.add_node("AST_NODE_STATEMENT_0x8443eb0", title=r"AST_NODE_STATEMENT_0x8443eb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x8443f10", title=r"AST_NODE_CONDSTMT_0x8443f10 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443eb0", "AST_NODE_CONDSTMT_0x8443f10")
net.add_node("AST_NODE_STATEMENT_0x8444430", title=r"AST_NODE_STATEMENT_0x8444430 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8443eb0", "AST_NODE_STATEMENT_0x8444430")
net.add_node("AST_NODE_LEAF_0x84475b0", title=r"AST_NODE_LEAF_0x84475b0 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_node("AST_NODE_LEAF_0x8447730", title=r"AST_NODE_LEAF_0x8447730 {<br \>  type: AST_LEAF_ID<br \> lex: a--<br \>}")
net.add_node("AST_NODE_IO_0x84479f0", title=r"AST_NODE_IO_0x84479f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x8447a50", title=r"AST_NODE_VARIDNUM_0x8447a50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_IO_0x84479f0", "AST_NODE_VARIDNUM_0x8447a50")
net.add_node("AST_NODE_LEAF_0x8447b90", title=r"AST_NODE_LEAF_0x8447b90 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8447bf0", title=r"AST_NODE_STATEMENT_0x8447bf0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8447c50", title=r"AST_NODE_IO_0x8447c50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8447bf0", "AST_NODE_IO_0x8447c50")
net.add_node("AST_NODE_LEAF_0x843fe70", title=r"AST_NODE_LEAF_0x843fe70 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_LEAF_0x8440070", title=r"AST_NODE_LEAF_0x8440070 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x8443910", title=r"AST_NODE_VARIDNUM_0x8443910 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8443970", title=r"AST_NODE_LEAF_0x8443970 {<br \>  type: AST_LEAF_ID<br \> lex: index<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x8443910", "AST_NODE_LEAF_0x8443970")
net.add_node("AST_NODE_LEAF_0x8443a30", title=r"AST_NODE_LEAF_0x8443a30 {<br \>  type: AST_LEAF_LE<br \> lex: LE-<br \>}")
net.add_node("AST_NODE_LEAF_0x8443a90", title=r"AST_NODE_LEAF_0x8443a90 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_LEAF_0x8443c10", title=r"AST_NODE_LEAF_0x8443c10 {<br \>  type: AST_LEAF_ID<br \> lex: akki<br \>}")
net.add_node("AST_NODE_IDLIST_0x8443d90", title=r"AST_NODE_IDLIST_0x8443d90 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8443df0", title=r"AST_NODE_LEAF_0x8443df0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_IDLIST_0x8443d90", "AST_NODE_LEAF_0x8443df0")
net.add_node("AST_NODE_LEAF_0x8443e50", title=r"AST_NODE_LEAF_0x8443e50 {<br \>  type: AST_LEAF_INT<br \> lex: INTEGER<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x8443f10", title=r"AST_NODE_CONDSTMT_0x8443f10 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8443f70", title=r"AST_NODE_LEAF_0x8443f70 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8443f10", "AST_NODE_LEAF_0x8443f70")
net.add_node("AST_NODE_CASESTMT_0x8443ff0", title=r"AST_NODE_CASESTMT_0x8443ff0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8443f10", "AST_NODE_CASESTMT_0x8443ff0")
net.add_node("AST_NODE_STATEMENT_0x8444430", title=r"AST_NODE_STATEMENT_0x8444430 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x8444490", title=r"AST_NODE_CONDSTMT_0x8444490 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444430", "AST_NODE_CONDSTMT_0x8444490")
net.add_node("AST_NODE_STATEMENT_0x84449b0", title=r"AST_NODE_STATEMENT_0x84449b0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444430", "AST_NODE_STATEMENT_0x84449b0")
net.add_node("AST_NODE_VARIDNUM_0x8447a50", title=r"AST_NODE_VARIDNUM_0x8447a50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447ab0", title=r"AST_NODE_LEAF_0x8447ab0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x8447a50", "AST_NODE_LEAF_0x8447ab0")
net.add_node("AST_NODE_IO_0x8447c50", title=r"AST_NODE_IO_0x8447c50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8447cb0", title=r"AST_NODE_LEAF_0x8447cb0 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_IO_0x8447c50", "AST_NODE_LEAF_0x8447cb0")
net.add_node("AST_NODE_LEAF_0x8443970", title=r"AST_NODE_LEAF_0x8443970 {<br \>  type: AST_LEAF_ID<br \> lex: index<br \>}")
net.add_node("AST_NODE_LEAF_0x8443df0", title=r"AST_NODE_LEAF_0x8443df0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_LEAF_0x8443f70", title=r"AST_NODE_LEAF_0x8443f70 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_CASESTMT_0x8443ff0", title=r"AST_NODE_CASESTMT_0x8443ff0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444050", title=r"AST_NODE_LEAF_0x8444050 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8443ff0", "AST_NODE_LEAF_0x8444050")
net.add_node("AST_NODE_STATEMENT_0x84440b0", title=r"AST_NODE_STATEMENT_0x84440b0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8443ff0", "AST_NODE_STATEMENT_0x84440b0")
net.add_node("AST_NODE_CASESTMT_0x8444250", title=r"AST_NODE_CASESTMT_0x8444250 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8443ff0", "AST_NODE_CASESTMT_0x8444250")
net.add_node("AST_NODE_CONDSTMT_0x8444490", title=r"AST_NODE_CONDSTMT_0x8444490 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84444f0", title=r"AST_NODE_LEAF_0x84444f0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8444490", "AST_NODE_LEAF_0x84444f0")
net.add_node("AST_NODE_CASESTMT_0x8444570", title=r"AST_NODE_CASESTMT_0x8444570 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8444490", "AST_NODE_CASESTMT_0x8444570")
net.add_node("AST_NODE_STATEMENT_0x84449b0", title=r"AST_NODE_STATEMENT_0x84449b0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x8444a10", title=r"AST_NODE_CONDSTMT_0x8444a10 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x84449b0", "AST_NODE_CONDSTMT_0x8444a10")
net.add_node("AST_NODE_STATEMENT_0x8444f30", title=r"AST_NODE_STATEMENT_0x8444f30 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x84449b0", "AST_NODE_STATEMENT_0x8444f30")
net.add_node("AST_NODE_LEAF_0x8447ab0", title=r"AST_NODE_LEAF_0x8447ab0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_LEAF_0x8447cb0", title=r"AST_NODE_LEAF_0x8447cb0 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_LEAF_0x8444050", title=r"AST_NODE_LEAF_0x8444050 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x84440b0", title=r"AST_NODE_STATEMENT_0x84440b0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8444110", title=r"AST_NODE_IO_0x8444110 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x84440b0", "AST_NODE_IO_0x8444110")
net.add_node("AST_NODE_CASESTMT_0x8444250", title=r"AST_NODE_CASESTMT_0x8444250 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84442b0", title=r"AST_NODE_LEAF_0x84442b0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444250", "AST_NODE_LEAF_0x84442b0")
net.add_node("AST_NODE_STATEMENT_0x8444310", title=r"AST_NODE_STATEMENT_0x8444310 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444250", "AST_NODE_STATEMENT_0x8444310")
net.add_node("AST_NODE_LEAF_0x84444f0", title=r"AST_NODE_LEAF_0x84444f0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_CASESTMT_0x8444570", title=r"AST_NODE_CASESTMT_0x8444570 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84445d0", title=r"AST_NODE_LEAF_0x84445d0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444570", "AST_NODE_LEAF_0x84445d0")
net.add_node("AST_NODE_STATEMENT_0x8444630", title=r"AST_NODE_STATEMENT_0x8444630 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444570", "AST_NODE_STATEMENT_0x8444630")
net.add_node("AST_NODE_CASESTMT_0x84447d0", title=r"AST_NODE_CASESTMT_0x84447d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444570", "AST_NODE_CASESTMT_0x84447d0")
net.add_node("AST_NODE_CONDSTMT_0x8444a10", title=r"AST_NODE_CONDSTMT_0x8444a10 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444a70", title=r"AST_NODE_LEAF_0x8444a70 {<br \>  type: AST_LEAF_ID<br \> lex: akki<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8444a10", "AST_NODE_LEAF_0x8444a70")
net.add_node("AST_NODE_CASESTMT_0x8444af0", title=r"AST_NODE_CASESTMT_0x8444af0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8444a10", "AST_NODE_CASESTMT_0x8444af0")
net.add_node("AST_NODE_STATEMENT_0x8444f30", title=r"AST_NODE_STATEMENT_0x8444f30 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_DECLARESTMT_0x8444f90", title=r"AST_NODE_DECLARESTMT_0x8444f90 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444f30", "AST_NODE_DECLARESTMT_0x8444f90")
net.add_node("AST_NODE_STATEMENT_0x8445110", title=r"AST_NODE_STATEMENT_0x8445110 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444f30", "AST_NODE_STATEMENT_0x8445110")
net.add_node("AST_NODE_IO_0x8444110", title=r"AST_NODE_IO_0x8444110 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x8444170", title=r"AST_NODE_VARIDNUM_0x8444170 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_IO_0x8444110", "AST_NODE_VARIDNUM_0x8444170")
net.add_node("AST_NODE_LEAF_0x84442b0", title=r"AST_NODE_LEAF_0x84442b0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8444310", title=r"AST_NODE_STATEMENT_0x8444310 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8444370", title=r"AST_NODE_IO_0x8444370 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444310", "AST_NODE_IO_0x8444370")
net.add_node("AST_NODE_LEAF_0x84445d0", title=r"AST_NODE_LEAF_0x84445d0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8444630", title=r"AST_NODE_STATEMENT_0x8444630 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8444690", title=r"AST_NODE_IO_0x8444690 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444630", "AST_NODE_IO_0x8444690")
net.add_node("AST_NODE_CASESTMT_0x84447d0", title=r"AST_NODE_CASESTMT_0x84447d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444830", title=r"AST_NODE_LEAF_0x8444830 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84447d0", "AST_NODE_LEAF_0x8444830")
net.add_node("AST_NODE_STATEMENT_0x8444890", title=r"AST_NODE_STATEMENT_0x8444890 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84447d0", "AST_NODE_STATEMENT_0x8444890")
net.add_node("AST_NODE_LEAF_0x8444a70", title=r"AST_NODE_LEAF_0x8444a70 {<br \>  type: AST_LEAF_ID<br \> lex: akki<br \>}")
net.add_node("AST_NODE_CASESTMT_0x8444af0", title=r"AST_NODE_CASESTMT_0x8444af0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444b50", title=r"AST_NODE_LEAF_0x8444b50 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444af0", "AST_NODE_LEAF_0x8444b50")
net.add_node("AST_NODE_STATEMENT_0x8444bb0", title=r"AST_NODE_STATEMENT_0x8444bb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444af0", "AST_NODE_STATEMENT_0x8444bb0")
net.add_node("AST_NODE_CASESTMT_0x8444d50", title=r"AST_NODE_CASESTMT_0x8444d50 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444af0", "AST_NODE_CASESTMT_0x8444d50")
net.add_node("AST_NODE_DECLARESTMT_0x8444f90", title=r"AST_NODE_DECLARESTMT_0x8444f90 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IDLIST_0x8444ff0", title=r"AST_NODE_IDLIST_0x8444ff0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8444f90", "AST_NODE_IDLIST_0x8444ff0")
net.add_node("AST_NODE_LEAF_0x84450b0", title=r"AST_NODE_LEAF_0x84450b0 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_edge("AST_NODE_DECLARESTMT_0x8444f90", "AST_NODE_LEAF_0x84450b0")
net.add_node("AST_NODE_STATEMENT_0x8445110", title=r"AST_NODE_STATEMENT_0x8445110 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x8445170", title=r"AST_NODE_CONDSTMT_0x8445170 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8445110", "AST_NODE_CONDSTMT_0x8445170")
net.add_node("AST_NODE_STATEMENT_0x8445770", title=r"AST_NODE_STATEMENT_0x8445770 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8445110", "AST_NODE_STATEMENT_0x8445770")
net.add_node("AST_NODE_VARIDNUM_0x8444170", title=r"AST_NODE_VARIDNUM_0x8444170 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84441d0", title=r"AST_NODE_LEAF_0x84441d0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x8444170", "AST_NODE_LEAF_0x84441d0")
net.add_node("AST_NODE_IO_0x8444370", title=r"AST_NODE_IO_0x8444370 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84443d0", title=r"AST_NODE_LEAF_0x84443d0 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_IO_0x8444370", "AST_NODE_LEAF_0x84443d0")
net.add_node("AST_NODE_IO_0x8444690", title=r"AST_NODE_IO_0x8444690 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x84446f0", title=r"AST_NODE_VARIDNUM_0x84446f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_IO_0x8444690", "AST_NODE_VARIDNUM_0x84446f0")
net.add_node("AST_NODE_LEAF_0x8444830", title=r"AST_NODE_LEAF_0x8444830 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8444890", title=r"AST_NODE_STATEMENT_0x8444890 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x84448f0", title=r"AST_NODE_IO_0x84448f0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444890", "AST_NODE_IO_0x84448f0")
net.add_node("AST_NODE_LEAF_0x8444b50", title=r"AST_NODE_LEAF_0x8444b50 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8444bb0", title=r"AST_NODE_STATEMENT_0x8444bb0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8444c10", title=r"AST_NODE_IO_0x8444c10 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444bb0", "AST_NODE_IO_0x8444c10")
net.add_node("AST_NODE_CASESTMT_0x8444d50", title=r"AST_NODE_CASESTMT_0x8444d50 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444db0", title=r"AST_NODE_LEAF_0x8444db0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444d50", "AST_NODE_LEAF_0x8444db0")
net.add_node("AST_NODE_STATEMENT_0x8444e10", title=r"AST_NODE_STATEMENT_0x8444e10 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8444d50", "AST_NODE_STATEMENT_0x8444e10")
net.add_node("AST_NODE_IDLIST_0x8444ff0", title=r"AST_NODE_IDLIST_0x8444ff0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445050", title=r"AST_NODE_LEAF_0x8445050 {<br \>  type: AST_LEAF_ID<br \> lex: bool<br \>}")
net.add_edge("AST_NODE_IDLIST_0x8444ff0", "AST_NODE_LEAF_0x8445050")
net.add_node("AST_NODE_LEAF_0x84450b0", title=r"AST_NODE_LEAF_0x84450b0 {<br \>  type: AST_LEAF_BOOL<br \> lex: BOOLEAN<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x8445170", title=r"AST_NODE_CONDSTMT_0x8445170 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84451d0", title=r"AST_NODE_LEAF_0x84451d0 {<br \>  type: AST_LEAF_ID<br \> lex: bool<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8445170", "AST_NODE_LEAF_0x84451d0")
net.add_node("AST_NODE_CASESTMT_0x8445250", title=r"AST_NODE_CASESTMT_0x8445250 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x8445170", "AST_NODE_CASESTMT_0x8445250")
net.add_node("AST_NODE_STATEMENT_0x8445770", title=r"AST_NODE_STATEMENT_0x8445770 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_CONDSTMT_0x84457d0", title=r"AST_NODE_CONDSTMT_0x84457d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8445770", "AST_NODE_CONDSTMT_0x84457d0")
net.add_node("AST_NODE_LEAF_0x84441d0", title=r"AST_NODE_LEAF_0x84441d0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_LEAF_0x84443d0", title=r"AST_NODE_LEAF_0x84443d0 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x84446f0", title=r"AST_NODE_VARIDNUM_0x84446f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444750", title=r"AST_NODE_LEAF_0x8444750 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x84446f0", "AST_NODE_LEAF_0x8444750")
net.add_node("AST_NODE_IO_0x84448f0", title=r"AST_NODE_IO_0x84448f0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444950", title=r"AST_NODE_LEAF_0x8444950 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_IO_0x84448f0", "AST_NODE_LEAF_0x8444950")
net.add_node("AST_NODE_IO_0x8444c10", title=r"AST_NODE_IO_0x8444c10 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x8444c70", title=r"AST_NODE_VARIDNUM_0x8444c70 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_IO_0x8444c10", "AST_NODE_VARIDNUM_0x8444c70")
net.add_node("AST_NODE_LEAF_0x8444db0", title=r"AST_NODE_LEAF_0x8444db0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8444e10", title=r"AST_NODE_STATEMENT_0x8444e10 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8444e70", title=r"AST_NODE_IO_0x8444e70 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8444e10", "AST_NODE_IO_0x8444e70")
net.add_node("AST_NODE_LEAF_0x8445050", title=r"AST_NODE_LEAF_0x8445050 {<br \>  type: AST_LEAF_ID<br \> lex: bool<br \>}")
net.add_node("AST_NODE_LEAF_0x84451d0", title=r"AST_NODE_LEAF_0x84451d0 {<br \>  type: AST_LEAF_ID<br \> lex: bool<br \>}")
net.add_node("AST_NODE_CASESTMT_0x8445250", title=r"AST_NODE_CASESTMT_0x8445250 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x84452b0", title=r"AST_NODE_LEAF_0x84452b0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8445250", "AST_NODE_LEAF_0x84452b0")
net.add_node("AST_NODE_STATEMENT_0x8445310", title=r"AST_NODE_STATEMENT_0x8445310 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8445250", "AST_NODE_STATEMENT_0x8445310")
net.add_node("AST_NODE_CASESTMT_0x84454b0", title=r"AST_NODE_CASESTMT_0x84454b0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8445250", "AST_NODE_CASESTMT_0x84454b0")
net.add_node("AST_NODE_CONDSTMT_0x84457d0", title=r"AST_NODE_CONDSTMT_0x84457d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445830", title=r"AST_NODE_LEAF_0x8445830 {<br \>  type: AST_LEAF_ID<br \> lex: bool<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x84457d0", "AST_NODE_LEAF_0x8445830")
net.add_node("AST_NODE_CASESTMT_0x84458b0", title=r"AST_NODE_CASESTMT_0x84458b0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CONDSTMT_0x84457d0", "AST_NODE_CASESTMT_0x84458b0")
net.add_node("AST_NODE_LEAF_0x8444750", title=r"AST_NODE_LEAF_0x8444750 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_LEAF_0x8444950", title=r"AST_NODE_LEAF_0x8444950 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x8444c70", title=r"AST_NODE_VARIDNUM_0x8444c70 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444cd0", title=r"AST_NODE_LEAF_0x8444cd0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x8444c70", "AST_NODE_LEAF_0x8444cd0")
net.add_node("AST_NODE_IO_0x8444e70", title=r"AST_NODE_IO_0x8444e70 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8444ed0", title=r"AST_NODE_LEAF_0x8444ed0 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_IO_0x8444e70", "AST_NODE_LEAF_0x8444ed0")
net.add_node("AST_NODE_LEAF_0x84452b0", title=r"AST_NODE_LEAF_0x84452b0 {<br \>  type: AST_LEAF_VALNUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8445310", title=r"AST_NODE_STATEMENT_0x8445310 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x8445370", title=r"AST_NODE_IO_0x8445370 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8445310", "AST_NODE_IO_0x8445370")
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net.add_node("AST_NODE_LEAF_0x8445510", title=r"AST_NODE_LEAF_0x8445510 {<br \>  type: AST_LEAF_VALFALSE<br \> lex: FALSE<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84454b0", "AST_NODE_LEAF_0x8445510")
net.add_node("AST_NODE_STATEMENT_0x8445570", title=r"AST_NODE_STATEMENT_0x8445570 {<br \>  No information here!<br \>}")
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net.add_node("AST_NODE_LEAF_0x8445830", title=r"AST_NODE_LEAF_0x8445830 {<br \>  type: AST_LEAF_ID<br \> lex: bool<br \>}")
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net.add_node("AST_NODE_LEAF_0x8445910", title=r"AST_NODE_LEAF_0x8445910 {<br \>  type: AST_LEAF_VALTRUE<br \> lex: TRUE<br \>}")
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net.add_node("AST_NODE_STATEMENT_0x8445970", title=r"AST_NODE_STATEMENT_0x8445970 {<br \>  No information here!<br \>}")
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net.add_node("AST_NODE_CASESTMT_0x8445ab0", title=r"AST_NODE_CASESTMT_0x8445ab0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84458b0", "AST_NODE_CASESTMT_0x8445ab0")
net.add_node("AST_NODE_LEAF_0x8444cd0", title=r"AST_NODE_LEAF_0x8444cd0 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_node("AST_NODE_LEAF_0x8444ed0", title=r"AST_NODE_LEAF_0x8444ed0 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_node("AST_NODE_IO_0x8445370", title=r"AST_NODE_IO_0x8445370 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_VARIDNUM_0x84453d0", title=r"AST_NODE_VARIDNUM_0x84453d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_IO_0x8445370", "AST_NODE_VARIDNUM_0x84453d0")
net.add_node("AST_NODE_LEAF_0x8445510", title=r"AST_NODE_LEAF_0x8445510 {<br \>  type: AST_LEAF_VALFALSE<br \> lex: FALSE<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8445570", title=r"AST_NODE_STATEMENT_0x8445570 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x84455d0", title=r"AST_NODE_IO_0x84455d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8445570", "AST_NODE_IO_0x84455d0")
net.add_node("AST_NODE_CASESTMT_0x84456b0", title=r"AST_NODE_CASESTMT_0x84456b0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445710", title=r"AST_NODE_LEAF_0x8445710 {<br \>  type: AST_LEAF_VALTRUE<br \> lex: TRUE<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x84456b0", "AST_NODE_LEAF_0x8445710")
net.add_node("AST_NODE_LEAF_0x8445910", title=r"AST_NODE_LEAF_0x8445910 {<br \>  type: AST_LEAF_VALTRUE<br \> lex: TRUE<br \>}")
net.add_node("AST_NODE_STATEMENT_0x8445970", title=r"AST_NODE_STATEMENT_0x8445970 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_IO_0x84459d0", title=r"AST_NODE_IO_0x84459d0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_STATEMENT_0x8445970", "AST_NODE_IO_0x84459d0")
net.add_node("AST_NODE_CASESTMT_0x8445ab0", title=r"AST_NODE_CASESTMT_0x8445ab0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445b10", title=r"AST_NODE_LEAF_0x8445b10 {<br \>  type: AST_LEAF_VALFALSE<br \> lex: FALSE<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8445ab0", "AST_NODE_LEAF_0x8445b10")
net.add_node("AST_NODE_STATEMENT_0x8445b70", title=r"AST_NODE_STATEMENT_0x8445b70 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8445ab0", "AST_NODE_STATEMENT_0x8445b70")
net.add_node("AST_NODE_CASESTMT_0x8445cb0", title=r"AST_NODE_CASESTMT_0x8445cb0 {<br \>  No information here!<br \>}")
net.add_edge("AST_NODE_CASESTMT_0x8445ab0", "AST_NODE_CASESTMT_0x8445cb0")
net.add_node("AST_NODE_VARIDNUM_0x84453d0", title=r"AST_NODE_VARIDNUM_0x84453d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445430", title=r"AST_NODE_LEAF_0x8445430 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_VARIDNUM_0x84453d0", "AST_NODE_LEAF_0x8445430")
net.add_node("AST_NODE_IO_0x84455d0", title=r"AST_NODE_IO_0x84455d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445630", title=r"AST_NODE_LEAF_0x8445630 {<br \>  type: AST_LEAF_VARIDNUM_NUM<br \> lex: ----<br \>}")
net.add_edge("AST_NODE_IO_0x84455d0", "AST_NODE_LEAF_0x8445630")
net.add_node("AST_NODE_LEAF_0x8445710", title=r"AST_NODE_LEAF_0x8445710 {<br \>  type: AST_LEAF_VALTRUE<br \> lex: TRUE<br \>}")
net.add_node("AST_NODE_IO_0x84459d0", title=r"AST_NODE_IO_0x84459d0 {<br \>  No information here!<br \>}")
net.add_node("AST_NODE_LEAF_0x8445a30", title=r"AST_NODE_LEAF_0x8445a30 {<br \>  type: AST_LEAF_ID<br \> lex: temp<br \>}")
net.add_edge("AST_NODE_IO_0x84459d0", "AST_NODE_LEAF_0x8445a30")
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net.show_buttons(filter_=['physics'])
net.show("ast.html")
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0
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3c07f6599c2663eec7094d01757ca47fc2d2ecdc
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|
py
|
Python
|
codes/src/DMRG/DMRG_simulation.py
|
mert-kurttutan/qh_fm_01
|
b5a56b2a671b3198b5517f0f50b9bb8f9a043df3
|
[
"MIT"
] | null | null | null |
codes/src/DMRG/DMRG_simulation.py
|
mert-kurttutan/qh_fm_01
|
b5a56b2a671b3198b5517f0f50b9bb8f9a043df3
|
[
"MIT"
] | null | null | null |
codes/src/DMRG/DMRG_simulation.py
|
mert-kurttutan/qh_fm_01
|
b5a56b2a671b3198b5517f0f50b9bb8f9a043df3
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
## Example Python script calling DMRG.
import pyten as ptn
from .DMRG_lat import FHH_Ham_SU2, FHH_Ham_U1
from ..helpers import mps_nm, mps_load, n_arr_save, cur_arr_save
import numpy as np
import sys, time, csv, os
def run_dmrg_FHH_SU2(par, tar_folder):
Lx = par.Lx; Ly = par.Ly #Number of sites along x and y directions
Nphi = par.Nphi
U = par.U
N = par.N
S = par.S
pbc = par.pbc
g = par.g
#where files stored, e.g. tar_folder="/project/th-scratch/m/Mert.Kurttutan/QH_FM_02/Lx"+str(Lx)+"_Ly"+str(Ly) + "/"
chis = par.chis #bond dimensions for each stage
sweep = par.sweep
Q_nums = str(N) + " " + str(S)
##################
###### MAIN ######
##################
## path prefix
pref = "log-files"
pref = tar_folder + pref
try:
os.system("mkdir "+pref)
except:
pass
print("Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_PBC"+str(pbc))
print("Generating lattice…")
## the lattice to be used
lat = par.lat
#lat=FHH_Ham_SU2(Ly, Lx, Nphi, 1.0, pbc) #tperp=1.0
print("Generating random state…")
## our initial random state, here generated with keyword arguments
rnd = ptn.mp.generateCompleteState(lat, Q_nums)
## define Hamiltonians
H = lat.get("Hj") + U*lat.get("Hu")
lat.add("H", "full Hamiltonian", H)
## dmrg config object
dmrgconf = ptn.dmrg.DMRGConfig()
pre_str = "Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_PBC"+str(pbc)
pre_str = tar_folder + "log-files/" + pre_str
## prefix to be used for log files
dmrgconf.prefix = pre_str
for chi in chis:
## (m 100 x sweep[0])
dmrgconf.stages += [ptn.dmrg.DMRGStage("(m "+str(chi)+" x "+ str(sweep[0]) +")")]
dmrgconf.stages += [ptn.dmrg.DMRGStage("(m "+str(chi)+" x "+ str(sweep[1]) +" l 2 eb 0)")]
## set multi-threading
ptn.threading.setTensorNum(4)
## set log-output
ptn.setLogGLvl(0)
ptn.setLogTLvl(0)
## PDMRG management object. Initialised with our random state, a list
# of the desired Hamiltonians, the config object and a list of the
# to-be-orthogonal states
pdmrg = ptn.mp.dmrg.PDMRG(rnd, [lat.get("H")], dmrgconf)
out_variance = "Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_variance_FHH_SU2.dat"
#mps_file = "Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_PBC"+str(pbc)
#mps_file = tar_folder + mps_file
out_variance = tar_folder + out_variance #location if submitted via job
e_new = 0
for i in range(len(chis)):
e_old = e_new
par.ind = i; par.bond = chis[i]
starttime = time.time()
mps_0 = pdmrg.run()
mps_tmp = pdmrg.run()
if i > 6:
mps_tmp.save(tar_folder + mps_nm(par))
endtime = time.time()
timediff = endtime - starttime
e_new = ptn.mp.expectation(mps_tmp, lat.get("H"))
esq = ptn.mp.expectation(mps_tmp, lat.get("H")*lat.get("H"))
var = abs(esq - e_new**2)
print("E = ", e_new)
print("Δ = ", e_new - e_old)
print("Var = ", var)
f = open(out_variance, 'a')
writer = csv.writer(f, delimiter=',')
writer.writerow([Lx, Ly, Nphi, U, N, S, str(pbc), chis[i], var, np.real(e_new), np.real(e_new - e_old), timediff])
f.close()
def run_dmrg_FHH_SU2_conv(par, tar_folder1, tar_folder2):
Lx = par.Lx; Ly = par.Ly #Number of sites along x and y directions
Nphi = par.Nphi
U = par.U
N = par.N
S = par.S
pbc = par.pbc
g = par.g
#where files stored, e.g. tar_folder="/project/th-scratch/m/Mert.Kurttutan/QH_FM_02/Lx"+str(Lx)+"_Ly"+str(Ly) + "/"
chis = par.chis #bond dimensions for each stage
sweep = par.sweep
Q_nums = str(N) + " " + str(S)
##################
###### MAIN ######
##################
## path prefix
pref = "log-files"
pref = tar_folder1 + pref
try:
os.system("mkdir "+pref)
except:
pass
print("Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_PBC"+str(pbc))
print("Generating lattice…")
## the lattice to be used
lat = par.lat
#lat=FHH_Ham_SU2(Ly, Lx, Nphi, 1.0, pbc) #tperp=1.0
print("Generating random state…")
## our initial random state, here generated with keyword arguments
rnd = ptn.mp.generateCompleteState(lat, Q_nums)
## define Hamiltonians
H = lat.get("Hj") + U*lat.get("Hu")
if par.pin:
H = H + lat.get("H_pin")
lat.add("H", "full Hamiltonian", H)
## dmrg config object
dmrgconf = ptn.dmrg.DMRGConfig()
pre_str = "Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_PBC"+str(pbc)
pre_str = tar_folder1 + "log-files/" + pre_str
## prefix to be used for log files
dmrgconf.prefix = pre_str
for chi in chis:
## (m 100 x sweep[0])
dmrgconf.stages += [ptn.dmrg.DMRGStage("(m "+str(chi)+" x "+ str(sweep[0]) +")")]
dmrgconf.stages += [ptn.dmrg.DMRGStage("(m "+str(chi)+" x "+ str(sweep[1]) +" l 2 eb 0)")]
## set multi-threading
ptn.threading.setTensorNum(4)
## set log-output
ptn.setLogGLvl(0)
ptn.setLogTLvl(0)
## PDMRG management object. Initialised with our random state, a list
# of the desired Hamiltonians, the config object and a list of the
# to-be-orthogonal states
pdmrg = ptn.mp.dmrg.PDMRG(rnd, [lat.get("H")], dmrgconf)
out_variance = "Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_variance_FHH_SU2.dat"
if par.pin:
out_variance="Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_g"+ str(g)+ "_variance_FHH_SU2.dat"
out_variance = tar_folder1 + out_variance #location if submitted via job
e_new = 0
for i in range(len(chis)):
e_old = e_new
par.ind = i; par.bond = chis[i]
starttime = time.time()
mps_0 = pdmrg.run()
mps_tmp = pdmrg.run()
if i > 6:
file_nm=n_arr_save(mps_tmp, tar_folder2, par)
file_nm=cur_arr_save(mps_tmp, tar_folder2, par)
mps_tmp.save(tar_folder1 + mps_nm(par)) #save the most recent state
if i > 7:
par.ind += -1; par.bond = chis[i-1]
os.remove(tar_folder1 + mps_nm(par)) #delete the previously produced state
endtime = time.time()
timediff = endtime - starttime
e_new = ptn.mp.expectation(mps_tmp, lat.get("H"))
esq = ptn.mp.expectation(mps_tmp, lat.get("H")*lat.get("H"))
var = abs(esq - e_new**2)
print("E = ", e_new)
print("Δ = ", e_new - e_old)
print("Var = ", var)
f = open(out_variance, 'a')
writer = csv.writer(f, delimiter=',')
writer.writerow([Lx, Ly, Nphi, U, g, N, S, str(pbc), chis[i], var, np.real(e_new), np.real(e_new - e_old), timediff])
f.close()
def conv_FHH_SU2_n(par, tar_loc, src_folder):
'''
Calculates the particle density and current density for states of parameter object par,
Used for ensuring covergence
'''
source = src_folder + "Lx" + str(par.Lx) + "_Ly" + str(par.Ly) + "/"
for i in range(7,len(par.chis)):
par.bond=par.chis[i]; par.ind=i
par.source = source
try:
mps_obj = mps_load(par)
file_nm=n_arr_save(mps_obj, tar_loc, par) #save it in the local dir
except:
print("State with " + "m_B=" + str(par.bond) + " is not produced")
def conv_FHH_SU2_cur(par, tar_loc, src_folder):
'''
Calculates the particle density and current density for states of parameter object par,
Used for ensuring covergence
'''
source = src_folder + "Lx" + str(par.Lx) + "_Ly" + str(par.Ly) + "/"
par.source = source
for i in range(7,len(par.chis)):
par.bond=par.chis[i]; par.ind=i
try:
mps_obj = mps_load(par)
file_nm=cur_arr_save(mps_obj, tar_loc, par) #save it in the local dir
except:
print("State with " + "m_B=" + str(par.bond) + " is not produced")
def run_dmrg_FHH_SU2_conv2(par, tar_folder1, tar_folder2, contn=False):
Lx = par.Lx; Ly = par.Ly #Number of sites along x and y directions
Nphi = par.Nphi
U = par.U
N = par.N
S = par.S
pbc = par.pbc
g = par.g
#where files stored, e.g. tar_folder="/project/th-scratch/m/Mert.Kurttutan/QH_FM_02/Lx"+str(Lx)+"_Ly"+str(Ly) + "/"
chis = par.chis #bond dimensions for each stage
sweep = par.sweep
Q_nums = str(N) + " " + str(S)
##################
###### MAIN ######
##################
## path prefix
pref = "log-files"
pref = tar_folder1 + pref
try:
os.system("mkdir "+pref)
except:
pass
print("Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_PBC"+str(pbc))
print("Generating lattice…")
## the lattice to be used
lat = par.lat
#lat=FHH_Ham_SU2(Ly, Lx, Nphi, 1.0, pbc) #tperp=1.0
print("Generating random state…")
if contn:
idx=len(chis)-1
flag=True
while flag and idx > -1:
try:
par.ind = idx; par.bond = chis[idx]
init_stt=ptn.mp.MPS(tar_folder1 + mps_nm(par))
flag = False
#print("found: " +mps_file+str(chis[k])+ "_"+str(k) +".mps")
except:
#print("nothing")
idx += -1
start=idx+1
else:
## our initial random state, here generated with keyword arguments
start=0
if start==0:
init_stt = ptn.mp.generateCompleteState(lat, Q_nums)
## define Hamiltonians
H = lat.get("Hj") + U*lat.get("Hu")
if par.pin:
H = H + lat.get("H_pin")
lat.add("H", "full Hamiltonian", H)
## dmrg config object
dmrgconf = ptn.dmrg.DMRGConfig()
pre_str = "Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_PBC"+str(pbc)
pre_str = tar_folder1 + "log-files/" + pre_str
## prefix to be used for log files
dmrgconf.prefix = pre_str
for c_idx in range(start, len(chis)):
## (m 100 x sweep[0])
dmrgconf.stages += [ptn.dmrg.DMRGStage("(m "+str(chis[c_idx])+" x "+ str(sweep[0]) +")")]
dmrgconf.stages += [ptn.dmrg.DMRGStage("(m "+str(chis[c_idx])+" x "+ str(sweep[1]) +" l 2 eb 0)")]
## set multi-threading
ptn.threading.setTensorNum(4)
## set log-output
ptn.setLogGLvl(0)
ptn.setLogTLvl(0)
## PDMRG management object. Initialised with our random state, a list
# of the desired Hamiltonians, the config object and a list of the
# to-be-orthogonal states
pdmrg = ptn.mp.dmrg.PDMRG(init_stt, [lat.get("H")], dmrgconf)
out_variance = "Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_variance_FHH_SU2.dat"
if par.pin:
out_variance="Lx"+str(Lx)+"_Ly"+str(Ly)+"_Nphi"+str(Nphi)+"_U"+str(U)+"_N"+str(N)+"_S"+str(S)+"_g"+ str(g)+ "_variance_FHH_SU2.dat"
out_variance = tar_folder1 + out_variance #location if submitted via job
e_new = 0
for i in range(start, len(chis)):
e_old = e_new
par.ind = i; par.bond = chis[i]
starttime = time.time()
mps_0 = pdmrg.run()
mps_tmp = pdmrg.run()
file_nm=n_arr_save(mps_tmp, tar_folder2, par)
file_nm=cur_arr_save(mps_tmp, tar_folder2, par)
print(mps_nm(par))
mps_tmp.save(tar_folder1 + mps_nm(par)) #save the most recent state
if i > 0:
par.ind += -1; par.bond = chis[i-1]
os.remove(tar_folder1 + mps_nm(par)) #delete the previously produced state
endtime = time.time()
timediff = endtime - starttime
e_new = ptn.mp.expectation(mps_tmp, lat.get("H"))
esq = ptn.mp.expectation(mps_tmp, lat.get("H")*lat.get("H"))
var = abs(esq - e_new**2)
print("E = ", e_new)
print("Δ = ", e_new - e_old)
print("Var = ", var)
f = open(out_variance, 'a')
writer = csv.writer(f, delimiter=',')
writer.writerow([Lx, Ly, Nphi, U, g, N, S, str(pbc), chis[i], var, np.real(e_new), np.real(e_new - e_old), timediff])
f.close()
| 32.71867
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0
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b1b8dac8fbd4bee1742ccb76702f1c937947e136
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py
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Python
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common/appenginepatch/appenginepatcher/serializers/yaml.py
|
certik/chess
|
dc806fccc0fb9acc57c40db56e620f2c55157425
|
[
"MIT"
] | 1
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2016-05-09T00:40:16.000Z
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2016-05-09T00:40:16.000Z
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common/appenginepatch/appenginepatcher/serializers/yaml.py
|
certik/chess
|
dc806fccc0fb9acc57c40db56e620f2c55157425
|
[
"MIT"
] | null | null | null |
common/appenginepatch/appenginepatcher/serializers/yaml.py
|
certik/chess
|
dc806fccc0fb9acc57c40db56e620f2c55157425
|
[
"MIT"
] | null | null | null |
from django.core.serializers import pyyaml
from python import Deserializer
pyyaml.PythonDeserializer = Deserializer
from django.core.serializers.pyyaml import *
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b1f6617f80e89dd536e33837a9b843d855930730
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py
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Python
|
appendix/torch_nsolt/test_nsoltIntermediateRotation2dLayer.py
|
msiplab/SaivDr
|
7015dea02e955c71337db6e7b29bb8c35294fa0e
|
[
"BSD-2-Clause"
] | 7
|
2018-11-26T08:49:24.000Z
|
2021-09-15T08:46:35.000Z
|
appendix/torch_nsolt/test_nsoltIntermediateRotation2dLayer.py
|
msiplab/SaivDr
|
7015dea02e955c71337db6e7b29bb8c35294fa0e
|
[
"BSD-2-Clause"
] | 11
|
2019-12-02T11:20:18.000Z
|
2022-02-14T05:17:11.000Z
|
appendix/torch_nsolt/test_nsoltIntermediateRotation2dLayer.py
|
msiplab/SaivDr
|
7015dea02e955c71337db6e7b29bb8c35294fa0e
|
[
"BSD-2-Clause"
] | 7
|
2019-06-05T07:45:20.000Z
|
2021-09-15T08:46:36.000Z
|
import itertools
import unittest
from parameterized import parameterized
import math
import torch
import torch.nn as nn
from nsoltIntermediateRotation2dLayer import NsoltIntermediateRotation2dLayer
from nsoltUtility import Direction, OrthonormalMatrixGenerationSystem
from nsoltLayerExceptions import InvalidMode, InvalidMus
nchs = [ [2, 2], [3, 3], [4, 4] ]
mus = [ -1, 1 ]
datatype = [ torch.float, torch.double ]
nrows = [ 4, 8, 16 ]
ncols = [ 4, 8, 16 ]
isdevicetest = True
class NsoltIntermediateRotation2dLayerTestCase(unittest.TestCase):
"""
NSOLTINTERMEDIATEROTATION2DLAYERTESTCASE
コンポーネント別に入力(nComponents):
nSamples x nRows x nCols x nChs
コンポーネント別に出力(nComponents):
nSamples x nRows x nCols x nChs
Requirements: Python 3.7.x, PyTorch 1.7.x
Copyright (c) 2021, Shogo MURAMATSU
All rights reserved.
Contact address: Shogo MURAMATSU,
Faculty of Engineering, Niigata University,
8050 2-no-cho Ikarashi, Nishi-ku,
Niigata, 950-2181, JAPAN
http://msiplab.eng.niigata-u.ac.jp/
"""
@parameterized.expand(
list(itertools.product(nchs))
)
def testConstructor(self,
nchs):
# Expected values
expctdName = 'Vn~'
expctdMode = 'Synthesis'
expctdDescription = "Synthesis NSOLT intermediate rotation " \
+ "(ps,pa) = (" \
+ str(nchs[0]) + "," + str(nchs[1]) + ")"
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
name=expctdName
)
# Actual values
actualName = layer.name
actualMode = layer.mode
actualDescription = layer.description
# Evaluation
self.assertTrue(isinstance(layer, nn.Module))
self.assertEqual(actualName,expctdName)
self.assertEqual(actualMode,expctdMode)
self.assertEqual(actualDescription,expctdDescription)
@parameterized.expand(
list(itertools.product(nchs,nrows,ncols,mus,datatype))
)
def testPredictGrayscale(self,
nchs, nrows, ncols, mus, datatype):
rtol,atol=1e-5,1e-8
if isdevicetest:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
# Parameters
nSamples = 8
nChsTotal = sum(nchs)
# nSamples x nRows x nCols x nChsTotal
X = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype,device=device,requires_grad=True)
# Expected values
# nSamples x nRows x nCols x nChsTotal
ps,pa = nchs
UnT = mus*torch.eye(pa,dtype=datatype).to(device)
expctdZ = X.clone()
Ya = X[:,:,:,ps:].view(-1,pa).T
Za = UnT @ Ya
expctdZ[:,:,:,ps:] = Za.T.view(nSamples,nrows,ncols,pa)
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
name='Vn~')
layer.orthTransUn.mus = mus
layer = layer.to(device)
# Actual values
with torch.no_grad():
actualZ = layer.forward(X)
# Evaluation
self.assertEqual(actualZ.dtype,datatype)
self.assertTrue(torch.allclose(actualZ,expctdZ,rtol=rtol,atol=atol))
self.assertFalse(actualZ.requires_grad)
@parameterized.expand(
list(itertools.product(nchs,nrows,ncols,mus,datatype))
)
def testPredictGrayscaleWithRandomAngles(self,
nchs, nrows, ncols, mus, datatype):
rtol,atol=1e-3,1e-6
if isdevicetest:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
gen = OrthonormalMatrixGenerationSystem(dtype=datatype)
# Parameters
nSamples = 8
nChsTotal = sum(nchs)
# nSamples x nRows x nCols x nChsTotal
X = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype,device=device,requires_grad=True)
angles = torch.randn(int((nChsTotal-2)*nChsTotal/8),dtype=datatype)
# Expected values
# nSamples x nRows x nCols x nChsTotal
ps,pa = nchs
UnT = gen(angles,mus).T.to(device)
expctdZ = X.clone()
Ya = X[:,:,:,ps:].view(-1,pa).T
Za = UnT @ Ya
expctdZ[:,:,:,ps:] = Za.T.view(nSamples,nrows,ncols,pa)
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
name='Vn~')
layer.orthTransUn.angles.data = angles
layer.orthTransUn.mus = mus
layer = layer.to(device)
# Actual values
with torch.no_grad():
actualZ = layer.forward(X)
# Evaluation
self.assertEqual(actualZ.dtype,datatype)
self.assertTrue(torch.allclose(actualZ,expctdZ,rtol=rtol,atol=atol))
self.assertFalse(actualZ.requires_grad)
@parameterized.expand(
list(itertools.product(nchs,nrows,ncols,mus,datatype))
)
def testPredictGrayscaleAnalysisMode(self,
nchs, nrows, ncols, mus, datatype):
rtol,atol=1e-3,1e-6
if isdevicetest:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
gen = OrthonormalMatrixGenerationSystem(dtype=datatype)
# Parameters
nSamples = 8
nChsTotal = sum(nchs)
# nSamples x nRows x nCols x nChsTotal
X = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype,device=device,requires_grad=True)
angles = torch.randn(int((nChsTotal-2)*nChsTotal/8),dtype=datatype)
# Expected values
# nSamples x nRows x nCols x nChsTotal
ps,pa = nchs
Un = gen(angles,mus).to(device)
expctdZ = X.clone()
Ya = X[:,:,:,ps:].view(-1,pa).T
Za = Un @ Ya
expctdZ[:,:,:,ps:] = Za.T.view(nSamples,nrows,ncols,pa)
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
name='Vn',
mode='Analysis')
layer.orthTransUn.angles.data = angles
layer.orthTransUn.mus = mus
layer = layer.to(device)
# Actual values
with torch.no_grad():
actualZ = layer.forward(X)
# Evaluation
self.assertEqual(actualZ.dtype,datatype)
self.assertTrue(torch.allclose(actualZ,expctdZ,rtol=rtol,atol=atol))
self.assertFalse(actualZ.requires_grad)
@parameterized.expand(
list(itertools.product(datatype,nchs,nrows,ncols,mus))
)
def testBackwardGrayscale(self,
datatype, nchs, nrows, ncols, mus):
rtol,atol=1e-3,1e-6
if isdevicetest:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
omgs = OrthonormalMatrixGenerationSystem(dtype=datatype,partial_difference=False)
# Parameters
nSamples = 8
nChsTotal = sum(nchs)
nAngles = int((nChsTotal-2)*nChsTotal/8)
angles = torch.zeros(nAngles,dtype=datatype)
# nSamples x nRows x nCols x nChsTotal
X = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype,device=device,requires_grad=True)
dLdZ = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype)
dLdZ = dLdZ.to(device)
# Expected values
ps,pa = nchs
Un = omgs(angles,mus).to(device)
# dLdX = dZdX x dLdZ
expctddLdX = dLdZ.clone()
Ya = dLdZ[:,:,:,ps:].view(nSamples*nrows*ncols,pa).T # pa * n
Za = Un @ Ya
expctddLdX[:,:,:,ps:] = Za.T.view(nSamples,nrows,ncols,pa)
# dLdWi = <dLdZ,(dVdWi)X>
expctddLdW_U = torch.zeros(nAngles,dtype=datatype).to(device)
omgs.partial_difference = True
for iAngle in range(nAngles):
dUn_T = omgs(angles,mus,index_pd_angle=iAngle).T.to(device)
Xa = X[:,:,:,ps:].view(-1,pa).T
Za = dUn_T @ Xa # pa x n
expctddLdW_U[iAngle] = torch.sum(Ya * Za)
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
name='Vn~')
layer.orthTransUn.angles.data = angles
layer.orthTransUn.mus = mus
layer = layer.to(device)
# Actual values
torch.autograd.set_detect_anomaly(True)
Z = layer.forward(X)
layer.zero_grad()
Z.backward(dLdZ)
actualdLdX = X.grad
actualdLdW_U = layer.orthTransUn.angles.grad
# Evaluation
self.assertEqual(actualdLdX.dtype,datatype)
self.assertEqual(actualdLdW_U.dtype,datatype)
self.assertTrue(torch.allclose(actualdLdX,expctddLdX,rtol=rtol,atol=atol))
self.assertTrue(torch.allclose(actualdLdW_U,expctddLdW_U,rtol=rtol,atol=atol))
self.assertTrue(Z.requires_grad)
@parameterized.expand(
list(itertools.product(datatype,nchs,nrows,ncols,mus))
)
def testBackwardGrayscaleWithRandomAngles(self,
datatype, nchs, nrows, ncols, mus):
rtol,atol=1e-3,1e-6
if isdevicetest:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
omgs = OrthonormalMatrixGenerationSystem(dtype=datatype,partial_difference=False)
# Parameters
nSamples = 8
nChsTotal = sum(nchs)
nAngles = int((nChsTotal-2)*nChsTotal/8)
angles = torch.randn(nAngles,dtype=datatype)
# nSamples x nRows x nCols x nChsTotal
X = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype,device=device,requires_grad=True)
dLdZ = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype)
dLdZ = dLdZ.to(device)
# Expected values
ps,pa = nchs
Un = omgs(angles,mus).to(device)
# dLdX = dZdX x dLdZ
expctddLdX = dLdZ.clone()
Ya = dLdZ[:,:,:,ps:].view(nSamples*nrows*ncols,pa).T # pa * n
Za = Un @ Ya
expctddLdX[:,:,:,ps:] = Za.T.view(nSamples,nrows,ncols,pa)
# dLdWi = <dLdZ,(dVdWi)X>
expctddLdW_U = torch.zeros(nAngles,dtype=datatype).to(device)
omgs.partial_difference = True
for iAngle in range(nAngles):
dUn_T = omgs(angles,mus,index_pd_angle=iAngle).T.to(device)
Xa = X[:,:,:,ps:].view(-1,pa).T
Za = dUn_T @ Xa # pa x n
expctddLdW_U[iAngle] = torch.sum(Ya * Za)
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
name='Vn~')
layer.orthTransUn.angles.data = angles
layer.orthTransUn.mus = mus
layer = layer.to(device)
# Actual values
torch.autograd.set_detect_anomaly(True)
Z = layer.forward(X)
layer.zero_grad()
Z.backward(dLdZ)
actualdLdX = X.grad
actualdLdW_U = layer.orthTransUn.angles.grad
# Evaluation
self.assertEqual(actualdLdX.dtype,datatype)
self.assertEqual(actualdLdW_U.dtype,datatype)
self.assertTrue(torch.allclose(actualdLdX,expctddLdX,rtol=rtol,atol=atol))
self.assertTrue(torch.allclose(actualdLdW_U,expctddLdW_U,rtol=rtol,atol=atol))
self.assertTrue(Z.requires_grad)
@parameterized.expand(
list(itertools.product(datatype,nchs,nrows,ncols,mus))
)
def testBackwardGrayscaleAnalysisMode(self,
datatype, nchs, nrows, ncols, mus):
rtol,atol=1e-3,1e-6
if isdevicetest:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
omgs = OrthonormalMatrixGenerationSystem(dtype=datatype,partial_difference=False)
# Parameters
nSamples = 8
nChsTotal = sum(nchs)
nAngles = int((nChsTotal-2)*nChsTotal/8)
angles = torch.randn(nAngles,dtype=datatype)
# nSamples x nRows x nCols x nChsTotal
X = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype,device=device,requires_grad=True)
dLdZ = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype)
dLdZ = dLdZ.to(device)
# Expected values
ps,pa = nchs
UnT = omgs(angles,mus).T.to(device)
# dLdX = dZdX x dLdZ
expctddLdX = dLdZ.clone()
Ya = dLdZ[:,:,:,ps:].view(nSamples*nrows*ncols,pa).T # pa * n
Za = UnT @ Ya
expctddLdX[:,:,:,ps:] = Za.T.view(nSamples,nrows,ncols,pa)
# dLdWi = <dLdZ,(dVdWi)X>
expctddLdW_U = torch.zeros(nAngles,dtype=datatype).to(device)
omgs.partial_difference = True
for iAngle in range(nAngles):
dUn = omgs(angles,mus,index_pd_angle=iAngle).to(device)
Xa = X[:,:,:,ps:].view(-1,pa).T
Za = dUn @ Xa # pa x n
expctddLdW_U[iAngle] = torch.sum(Ya * Za)
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
mode='Analysis',
name='Vn')
layer.orthTransUn.angles.data = angles
layer.orthTransUn.mus = mus
layer = layer.to(device)
# Actual values
torch.autograd.set_detect_anomaly(True)
Z = layer.forward(X)
layer.zero_grad()
Z.backward(dLdZ)
actualdLdX = X.grad
actualdLdW_U = layer.orthTransUn.angles.grad
# Evaluation
self.assertEqual(actualdLdX.dtype,datatype)
self.assertEqual(actualdLdW_U.dtype,datatype)
self.assertTrue(torch.allclose(actualdLdX,expctddLdX,rtol=rtol,atol=atol))
self.assertTrue(torch.allclose(actualdLdW_U,expctddLdW_U,rtol=rtol,atol=atol))
self.assertTrue(Z.requires_grad)
def testInvalidModeException(self):
nchs = [2,2]
with self.assertRaises(InvalidMode):
NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
mode='Dummy')
def testInvalidMusException(self):
nchs = [2,2]
with self.assertRaises(InvalidMus):
NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
mus=2)
@parameterized.expand(
list(itertools.product(nchs,nrows,ncols,mus,datatype))
)
def testConstructionWithMus(self,
nchs, nrows, ncols, mus, datatype):
rtol,atol=1e-5,1e-8
if isdevicetest:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
else:
device = torch.device("cpu")
# Parameters
nSamples = 8
nChsTotal = sum(nchs)
# nSamples x nRows x nCols x nChsTotal
X = torch.randn(nSamples,nrows,ncols,nChsTotal,dtype=datatype,device=device,requires_grad=True)
# Expected values
# nSamples x nRows x nCols x nChsTotal
ps,pa = nchs
UnT = mus*torch.eye(pa,dtype=datatype).to(device)
expctdZ = X.clone()
Ya = X[:,:,:,ps:].view(-1,pa).T
Za = UnT @ Ya
expctdZ[:,:,:,ps:] = Za.T.view(nSamples,nrows,ncols,pa)
# Instantiation of target class
layer = NsoltIntermediateRotation2dLayer(
number_of_channels=nchs,
name='Vn~',
mus = mus)
layer = layer.to(device)
# Actual values
with torch.no_grad():
actualZ = layer.forward(X)
# Evaluation
self.assertEqual(actualZ.dtype,datatype)
self.assertTrue(torch.allclose(actualZ,expctdZ,rtol=rtol,atol=atol))
self.assertFalse(actualZ.requires_grad)
if __name__ == '__main__':
unittest.main()
| 35.788889
| 103
| 0.605526
| 1,789
| 16,105
| 5.397429
| 0.10844
| 0.047121
| 0.037283
| 0.024648
| 0.848695
| 0.840721
| 0.830054
| 0.82063
| 0.813173
| 0.813173
| 0
| 0.010053
| 0.283514
| 16,105
| 449
| 104
| 35.868597
| 0.826761
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| 0
| 0.786834
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| 0.013679
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| 0
| 0
| 0
| 0
| 0.103448
| 1
| 0.031348
| false
| 0
| 0.028213
| 0
| 0.062696
| 0
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| 0
| null | 0
| 0
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| 1
| 1
| 1
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| 1
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| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
59028b8b305b1224d78e84d7b7e51bbdde78b4a2
| 405
|
py
|
Python
|
cauldron/cli/server/__init__.py
|
JohnnyPeng18/cauldron
|
09120c2a4cef65df46f8c0c94f5d79395b3298cd
|
[
"MIT"
] | 90
|
2016-09-02T15:11:10.000Z
|
2022-01-02T11:37:57.000Z
|
cauldron/cli/server/__init__.py
|
JohnnyPeng18/cauldron
|
09120c2a4cef65df46f8c0c94f5d79395b3298cd
|
[
"MIT"
] | 86
|
2016-09-23T16:52:22.000Z
|
2022-03-31T21:39:56.000Z
|
cauldron/cli/server/__init__.py
|
JohnnyPeng18/cauldron
|
09120c2a4cef65df46f8c0c94f5d79395b3298cd
|
[
"MIT"
] | 261
|
2016-12-22T05:36:48.000Z
|
2021-11-26T12:40:42.000Z
|
from cauldron.cli.server.routes import display # noqa
from cauldron.cli.server.routes import status # noqa
from cauldron.cli.server.routes import execution # noqa
from cauldron.cli.server.routes import synchronize # noqa
from cauldron.cli.server.routes import ui_statuses # noqa
from cauldron.cli.server import run as server_run # noqa
from cauldron.cli.server.run import create_application # noqa
| 50.625
| 62
| 0.807407
| 60
| 405
| 5.4
| 0.283333
| 0.259259
| 0.324074
| 0.453704
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| 0.558642
| 0.45679
| 0
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| 0
| 0.125926
| 405
| 7
| 63
| 57.857143
| 0.915254
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| true
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| null | 1
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| 0
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| 0
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| null | 0
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| 0
| 0
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
a707066e18ba32b6a6f2e81d60c9ba699f507d08
| 10,863
|
py
|
Python
|
modules/Bak.July2013/Later/SelPul.py
|
TrentFranks/ssNMR-Topspin-Python
|
95f99dc66bc665493d81d075088486f55ccae964
|
[
"MIT"
] | 3
|
2016-08-24T12:01:15.000Z
|
2021-12-02T21:45:34.000Z
|
modules/Bak.July2013/Later/SelPul.py
|
TrentFranks/ssNMR-Topspin-Python
|
95f99dc66bc665493d81d075088486f55ccae964
|
[
"MIT"
] | null | null | null |
modules/Bak.July2013/Later/SelPul.py
|
TrentFranks/ssNMR-Topspin-Python
|
95f99dc66bc665493d81d075088486f55ccae964
|
[
"MIT"
] | null | null | null |
"""
Modules to Set default parameters:
W.T. Franks FMP Berlin
"""
import de.bruker.nmr.mfw.root as root
import de.bruker.nmr.prsc.toplib as top
#import os
import sys
from sys import argv
import TopCmds
import math
import IntShape
import PWR as pwr
import CPDtools
import FREQ as fq
import GetNUCs as NUC
WAIT_TILL_DONE = 1;
#these are Carbon pulses, so we need to know which channel is the Carbon Channel
Nucs=NUC.list()
if Nucs[0]=='13C':
CFrq=fq.O1()
elif Nucs[1]=='13C':
CFrq=fq.O2()
elif Nucs[2]=='13C':
CFrq=fq.O3()
def S6purge():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MAS =float(TopCmds.GETPAR("CNST 31"))
MaxB1 = 1000000./4./p90C
p90sC=float(TopCmds.GETPAR("P 6"))
SPname=(TopCmds.GETPAR2("SPNAM 6"))
if p90sC == 0: p90sC = 1500000./MAS
SP=SPname
#Check for existence and default
if SP == "gauss" or SP == "None" :
#TopCmds.MSG("Please set spnam6")
TopCmds.XCMD("spnam6")
SP=(TopCmds.GETPAR2("SPNAM 6"))
offs = float(TopCmds.GETPAR("SPOFFS 6"))
ppm=CFrq.offs2ppm(offs)
if ppm > 140.0 : ppm=55.0
if ppm < -10.0 : ppm=55.0
index = TopCmds.INPUT_DIALOG("CA 90 purge", "S6 soft 90", \
["Duration","Offset","Pulse Name (3pi/2 Sinc)"],\
[str('%3.2f' %p90sC),str('%3.2f' %ppm),SP],\
["us","ppm",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p90sC=float(index[0])
ppm=float(index[1])
SP=index[2]
offs=CFrq.ppm2offs(ppm)
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(p90C/p90sC/AvgAmp)
Power=ampC-adjust
PowerW=pwr.dBtoW(Power)
confirm = TopCmds.SELECT("Adjusting the S6 purge pulse:",\
"This will set\n \
13C amp (pl26) to :" + str('%3.2f' %PowerW)+ " W\n \
Pulse offset to :" + str('%8.0f' %offs) + " Hz\n \
Equivalent to :" + str('%3.1f' %ppm ) + " ppm\n \
p6 to :" + str('%6.1f' %p90sC)+ " us\n "\
,["Update", "Keep Previous"])
if confirm != 1:
TopCmds.PUTPAR("PLdB 26",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM 6",SP)
TopCmds.PUTPAR("SPOFFS 6",str('%8.2f' %offs))
TopCmds.PUTPAR("P 6",str('%3.2f' %p90sC))
def S7purge():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MAS =float(TopCmds.GETPAR("CNST 31"))
MaxB1 = 1000000./4./p90C
p90sC=float(TopCmds.GETPAR("P 7"))
SPname=(TopCmds.GETPAR2("SPNAM 7"))
if p90sC == 0: p90sC = 1500000./MAS
SP=SPname
#Check for existence and default
if SP == "gauss" or SP == "None" :
#TopCmds.MSG("Please set spnam7")
TopCmds.XCMD("spnam7")
SP=(TopCmds.GETPAR("SPNAM 7"))
offs = float(TopCmds.GETPAR2("SPOFFS 7"))
ppm=CFrq.offs2ppm(offs)
if ppm < 140.0 : ppm=175.0
if ppm > 220.0 : ppm=175.0
index = TopCmds.INPUT_DIALOG("CO 90 purge", "S7 soft 90", \
["Duration","Offset","Pulse Name (3pi/2 Sinc)"],\
[str('%3.2f' %p90sC),str('%3.2f' %ppm),SP],\
["us","ppm",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p90sC=float(index[0])
ppm=float(index[1])
SP=index[2]
offs=CFrq.ppm2offs(ppm)
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(p90C/p90sC/AvgAmp)
Power=ampC-adjust
PowerW=pwr.dBtoW(Power)
confirm = TopCmds.SELECT("Adjusting the S7 purge pulse:",\
"This will set\n \
13C amp (pl27) to :" + str('%3.2f' %PowerW)+ " W\n \
Pulse offset to :" + str('%8.0f' %offs) + " Hz\n \
Equivalent to :" + str('%3.1f' %ppm ) + " ppm\n \
p7 to :" + str('%6.1f' %p90sC)+ " us\n "\
,["Update", "Keep Previous"])
if confirm != 1:
TopCmds.PUTPAR("PLdB 27",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM 7",SP)
TopCmds.PUTPAR("SPOFFS 7",str('%8.2f' %offs))
TopCmds.PUTPAR("P 7",str('%3.2f' %p90sC))
def S8refocus():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MAS =float(TopCmds.GETPAR("CNST 31"))
MaxB1 = 1000000./4./p90C
p180sC=float(TopCmds.GETPAR("P 8"))
SPname=(TopCmds.GETPAR2("SPNAM 8"))
if p180sC == 0: p180sC = 1500000./MAS
SP=SPname
#Check for existence and default
if SP == "gauss" or SP == "None" :
#TopCmds.MSG("Please set spnam8")
TopCmds.XCMD("spnam8")
SP=(TopCmds.GETPAR2("SPNAM 8"))
offs = float(TopCmds.GETPAR("SPOFFS 8"))
ppm=CFrq.offs2ppm(offs)
if ppm > 140.0 : ppm=55.0
if ppm < -10.0 : ppm=55.0
index = TopCmds.INPUT_DIALOG("CA 180 refocus", "S8 soft 180", \
["Duration","Offset","Pulse Name (RSnob)"],\
[str('%3.2f' %p180sC),str('%3.2f' %ppm),SP],\
["us","ppm",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p180sC=float(index[0])
ppm=float(index[1])
SP=index[2]
offs=CFrq.ppm2offs(ppm)
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(2*p90C/p180sC/AvgAmp)
Power=ampC-adjust
PowerW=pwr.dBtoW(Power)
confirm = TopCmds.SELECT("Adjusting the S8 refocus pulse:",\
"This will set\n \
13C amp (pl28) to :" + str('%3.2f' %PowerW)+ " W\n \
Pulse offset to :" + str('%8.0f' %offs) + " Hz\n \
Equivalent to :" + str('%3.1f' %ppm ) + " ppm\n \
p8 to :" + str('%6.1f' %p180sC)+ " us\n "\
,["Update", "Keep Previous"])
if confirm != 1:
TopCmds.PUTPAR("PLdB 28",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM 8",SP)
TopCmds.PUTPAR("SPOFFS 8",str('%8.2f' %offs))
TopCmds.PUTPAR("P 8",str('%3.2f' %p180sC))
def S9refocus():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MAS =float(TopCmds.GETPAR("CNST 31"))
MaxB1 = 1000000./4./p90C
p180sC=float(TopCmds.GETPAR("P 9"))
SPname=(TopCmds.GETPAR2("SPNAM 9"))
if p180sC == 0: p180sC = 1500000./MAS
SP=SPname
#Check for existence and default
if SP == "gauss" or SP == "None" :
#TopCmds.MSG("Please set spnam9")
TopCmds.XCMD("spnam9")
SP=(TopCmds.GETPAR2("SPNAM 9"))
offs = float(TopCmds.GETPAR("SPOFFS 9"))
ppm=CFrq.offs2ppm(offs)
if ppm < 140.0 : ppm=175.0
if ppm > 220.0 : ppm=175.0
index = TopCmds.INPUT_DIALOG("CO 180 refocus", "S9 soft 180", \
["Duration","Offset","Pulse Name (RSnob)"],\
[str('%3.2f' %p180sC),str('%3.2f' %ppm),SP],\
["us","ppm",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p180sC=float(index[0])
ppm=float(index[1])
SP=index[2]
offs=CFrq.ppm2offs(ppm)
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(2*p90C/p180sC/AvgAmp)
Power=ampC-adjust
PowerW=pwr.dBtoW(Power)
confirm = TopCmds.SELECT("Adjusting the S9 refocus pulse:",\
"This will set\n \
13C amp (pl29) to :" + str('%3.2f' %PowerW)+ " W\n \
Pulse offset to :" + str('%8.0f' %offs) + " Hz\n \
Equivalent to :" + str('%3.1f' %ppm ) + " ppm\n \
p9 to :" + str('%6.1f' %p180sC)+ " us\n "\
,["Update", "Keep Previous"])
if confirm != 1:
TopCmds.PUTPAR("PLdB 29",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM 9",SP)
TopCmds.PUTPAR("SPOFFS 9",str('%8.2f' %offs))
TopCmds.PUTPAR("P 9",str('%3.2f' %p180sC))
def CalS6purge():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MaxB1 = 1000000./4./p90C
p90sC=float(TopCmds.GETPAR("P 6"))
SPname=(TopCmds.GETPAR("SPNAM6"))
if p90sC == 0: p90sC = 1500000./MAS
SP=SPname
if SP == "gauss" : SP = "3pi2SINC.wtf"
offs = float(TopCmds.GETPAR("SPOFFS 6"))
index = TopCmds.INPUT_DIALOG("OFF-resonance 90 purge", "S6 soft 90", \
["Duration","Offset","Pulse Name (3pi/2 Sinc)"],\
[str(p90sC),str(offs),SP],\
["us","Hz",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p90sC=float(index[0])
offs=float(index[1])
SP=index[2]
#TopCmds.MSG(str(p90sC)+' p90sC')
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(p90C/p90sC/AvgAmp)
TopCmds.MSG(str(adjust)+'adjust')
Power=ampC-adjust
#TopCmds.MSG(str(Power))
TopCmds.PUTPAR("PLdB 26",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM6",SP)
TopCmds.PUTPAR("SPOFFS 6",str('%8.2f' %offs))
TopCmds.PUTPAR("P 6",str('%3.2f' %p90sC))
def CalS7purge():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MaxB1 = 1000000./4./p90C
p90sC=float(TopCmds.GETPAR("P 7"))
SPname=(TopCmds.GETPAR("SPNAM7"))
if p90sC == 0: p90sC = 1500000./MAS
SP=SPname
if SP == "gauss" : SP = "3pi2SINC.wtf"
offs = float(TopCmds.GETPAR("SPOFFS 7"))
index = TopCmds.INPUT_DIALOG("ON-resonance 90 purge", "S7 soft 90", \
["Duration","Offset","Pulse Name (3pi/2 Sinc)"],\
[str(p90sC),str(offs),SP],\
["us","Hz",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p90sC=float(index[0])
offs=float(index[1])
SP=index[2]
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(p90C/p90sC/AvgAmp)
Power=ampC-adjust
TopCmds.PUTPAR("PLdB 27",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM7",SP)
TopCmds.PUTPAR("SPOFFS 7",str('%8.2f' %offs))
TopCmds.PUTPAR("P 7",str('%3.2f' %p90sC))
def CalS8refocus():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MaxB1 = 1000000./4./p90C
p180sC=float(TopCmds.GETPAR("P 8"))
SPname=(TopCmds.GETPAR("SPNAM8"))
if p180sC == 0: p180sC = 1500000./MAS
SP=SPname
if SP == "gauss" : SP = "RSnob"
offs = float(TopCmds.GETPAR("SPOFFS 8"))
index = TopCmds.INPUT_DIALOG("ON-resonance 180 Refocussing", "S8 soft 180", \
["Duration","Offset","Pulse Name (rSNOB)"],\
[str(p180sC),str(offs),SP],\
["us","Hz",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p180sC=float(index[0])
offs=float(index[1])
SP=index[2]
#TopCmds.MSG(str(p90sC)+' p90sC')
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(2*p90C/p180sC/AvgAmp)
#TopCmds.MSG(str(adjust)+'adjust')
Power=ampC-adjust
#opCmds.MSG(str(Power))
TopCmds.PUTPAR("PLdB 28",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM8",SP)
TopCmds.PUTPAR("SPOFFS 8",str('%8.2f' %offs))
TopCmds.PUTPAR("P 8",str('%3.2f' %p180sC))
def CalS9refocus():
p90C=float(TopCmds.GETPAR("P 1"))
ampC=float(TopCmds.GETPAR("PLdB 1"))
MaxB1 = 1000000./4./p90C
p180sC=float(TopCmds.GETPAR("P 9"))
SPname=(TopCmds.GETPAR("SPNAM9"))
if p180sC == 0: p90sC = 1500000./MAS
SP=SPname
if SP == "gauss" : SP = "RSnob"
offs = float(TopCmds.GETPAR("SPOFFS 9"))
index = TopCmds.INPUT_DIALOG("OFF-resonance 180 Refocussing", "S9 soft 180", \
["Duration","Offset","Pulse Name (RSnob)"],\
[str(p180sC),str(offs),SP],\
["us","Hz",""],\
["1","1","1"],\
["Accept","Close"], ['a','c'], 10)
p180sC=float(index[0])
offs=float(index[1])
SP=index[2]
#TopCmds.MSG(str(p90sC)+' p90sC')
AvgAmp=IntShape.Integrate(SP)/100.
adjust=20*math.log10(2*p90C/p180sC/AvgAmp)
#TopCmds.MSG(str(adjust)+'adjust')
Power=ampC-adjust
#TopCmds.MSG(str(Power))
TopCmds.PUTPAR("PLdB 29",str('%3.2f' %Power))
TopCmds.PUTPAR("SPNAM9",SP)
TopCmds.PUTPAR("SPOFFS 9",str('%8.2f' %offs))
TopCmds.PUTPAR("P 9",str('%3.2f' %p180sC))
| 27.36272
| 80
| 0.603609
| 1,645
| 10,863
| 3.979939
| 0.114286
| 0.079426
| 0.096227
| 0.046433
| 0.863143
| 0.862685
| 0.82618
| 0.823125
| 0.803116
| 0.801894
| 0
| 0.094611
| 0.174906
| 10,863
| 396
| 81
| 27.431818
| 0.635836
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0
| 7
|
5959db2121317bf6c4d2dd1245acd2f320a698dc
| 122
|
py
|
Python
|
src/vivarium_conic_vitamin_a_supp_gbd2019/__init__.py
|
ihmeuw/vivarium_conic_vitamin_a_supp_gbd2019
|
5cd99c9fad9d93b69801e82835dfb1f843e7782a
|
[
"BSD-3-Clause"
] | null | null | null |
src/vivarium_conic_vitamin_a_supp_gbd2019/__init__.py
|
ihmeuw/vivarium_conic_vitamin_a_supp_gbd2019
|
5cd99c9fad9d93b69801e82835dfb1f843e7782a
|
[
"BSD-3-Clause"
] | null | null | null |
src/vivarium_conic_vitamin_a_supp_gbd2019/__init__.py
|
ihmeuw/vivarium_conic_vitamin_a_supp_gbd2019
|
5cd99c9fad9d93b69801e82835dfb1f843e7782a
|
[
"BSD-3-Clause"
] | null | null | null |
"""vivarium_conic_vitamin_a_supp_gbd2019
Research repository for the vivarium_conic_vitamin_a_supp_gbd2019 project.
"""
| 20.333333
| 74
| 0.860656
| 17
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| 0
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| 122
| 5
| 75
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0
| 7
|
59731579073ab89b5bc9a6324d0c1794bffd92ca
| 8,294
|
py
|
Python
|
z2/part2/interactive/jm/random_normal_1/741696380.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 1
|
2020-04-16T12:13:47.000Z
|
2020-04-16T12:13:47.000Z
|
z2/part2/interactive/jm/random_normal_1/741696380.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 18
|
2020-03-06T17:50:15.000Z
|
2020-05-19T14:58:30.000Z
|
z2/part2/interactive/jm/random_normal_1/741696380.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 18
|
2020-03-06T17:45:13.000Z
|
2020-06-09T19:18:31.000Z
|
from part1 import (
gamma_board,
gamma_busy_fields,
gamma_delete,
gamma_free_fields,
gamma_golden_move,
gamma_golden_possible,
gamma_move,
gamma_new,
)
"""
scenario: test_random_actions
uuid: 741696380
"""
"""
random actions, total chaos
"""
board = gamma_new(8, 6, 3, 11)
assert board is not None
assert gamma_move(board, 1, 0, 2) == 1
assert gamma_move(board, 1, 3, 4) == 1
assert gamma_move(board, 2, 0, 0) == 1
assert gamma_move(board, 3, 0, 7) == 0
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 1, 2, 5) == 1
assert gamma_move(board, 2, 1, 0) == 1
assert gamma_move(board, 3, 0, 0) == 0
assert gamma_move(board, 3, 6, 3) == 1
assert gamma_move(board, 1, 1, 5) == 1
assert gamma_move(board, 2, 4, 0) == 1
assert gamma_golden_possible(board, 2) == 1
assert gamma_move(board, 1, 0, 0) == 0
assert gamma_move(board, 1, 0, 2) == 0
assert gamma_move(board, 2, 5, 6) == 0
assert gamma_move(board, 2, 5, 5) == 1
assert gamma_move(board, 3, 4, 1) == 1
assert gamma_busy_fields(board, 3) == 2
board432372920 = gamma_board(board)
assert board432372920 is not None
assert board432372920 == (".11..2..\n"
"...1....\n"
"......3.\n"
"1.......\n"
"....3...\n"
"22..2...\n")
del board432372920
board432372920 = None
assert gamma_move(board, 1, 6, 3) == 0
assert gamma_move(board, 1, 0, 0) == 0
assert gamma_move(board, 2, 6, 4) == 1
assert gamma_golden_possible(board, 2) == 1
assert gamma_golden_move(board, 2, 5, 2) == 0
assert gamma_move(board, 3, 3, 5) == 1
assert gamma_move(board, 1, 3, 1) == 1
assert gamma_move(board, 1, 0, 4) == 1
assert gamma_move(board, 2, 5, 4) == 1
assert gamma_move(board, 2, 5, 5) == 0
assert gamma_move(board, 3, 5, 1) == 1
assert gamma_move(board, 1, 3, 1) == 0
assert gamma_move(board, 2, 5, 6) == 0
assert gamma_move(board, 2, 5, 0) == 1
assert gamma_move(board, 1, 4, 1) == 0
assert gamma_move(board, 1, 5, 2) == 1
assert gamma_move(board, 2, 5, 4) == 0
assert gamma_move(board, 2, 6, 2) == 1
assert gamma_golden_possible(board, 2) == 1
assert gamma_move(board, 3, 4, 2) == 1
assert gamma_free_fields(board, 3) == 28
assert gamma_move(board, 1, 7, 4) == 1
assert gamma_move(board, 1, 5, 5) == 0
assert gamma_move(board, 2, 4, 1) == 0
assert gamma_move(board, 2, 6, 1) == 1
assert gamma_golden_possible(board, 2) == 1
assert gamma_move(board, 3, 3, 0) == 1
assert gamma_move(board, 1, 0, 7) == 0
assert gamma_golden_possible(board, 1) == 1
assert gamma_move(board, 2, 3, 1) == 0
assert gamma_move(board, 2, 3, 2) == 1
assert gamma_move(board, 3, 1, 0) == 0
assert gamma_move(board, 3, 1, 3) == 1
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 1, 3, 0) == 0
assert gamma_golden_possible(board, 1) == 1
assert gamma_move(board, 2, 4, 2) == 0
assert gamma_move(board, 2, 3, 2) == 0
assert gamma_move(board, 3, 0, 3) == 1
assert gamma_move(board, 3, 0, 4) == 0
assert gamma_move(board, 1, 3, 7) == 0
assert gamma_move(board, 1, 2, 3) == 1
assert gamma_move(board, 2, 1, 2) == 1
assert gamma_move(board, 2, 5, 0) == 0
assert gamma_move(board, 3, 5, 5) == 0
assert gamma_move(board, 1, 3, 4) == 0
assert gamma_move(board, 1, 5, 2) == 0
assert gamma_move(board, 3, 7, 0) == 1
assert gamma_move(board, 3, 5, 1) == 0
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 1, 4, 2) == 0
assert gamma_move(board, 1, 2, 3) == 0
assert gamma_move(board, 2, 3, 5) == 0
assert gamma_move(board, 3, 1, 7) == 0
assert gamma_move(board, 1, 2, 7) == 0
assert gamma_move(board, 1, 6, 1) == 0
assert gamma_move(board, 2, 3, 5) == 0
assert gamma_move(board, 2, 1, 5) == 0
assert gamma_move(board, 3, 2, 2) == 1
assert gamma_move(board, 1, 0, 6) == 0
assert gamma_move(board, 1, 0, 2) == 0
assert gamma_move(board, 2, 3, 5) == 0
assert gamma_move(board, 3, 1, 5) == 0
assert gamma_move(board, 3, 4, 2) == 0
assert gamma_move(board, 1, 4, 2) == 0
assert gamma_move(board, 2, 3, 1) == 0
assert gamma_move(board, 3, 0, 2) == 0
assert gamma_move(board, 1, 5, 1) == 0
assert gamma_golden_possible(board, 1) == 1
assert gamma_move(board, 2, 1, 7) == 0
assert gamma_move(board, 2, 0, 2) == 0
assert gamma_busy_fields(board, 2) == 11
assert gamma_free_fields(board, 2) == 18
assert gamma_move(board, 3, 1, 1) == 1
assert gamma_move(board, 1, 3, 1) == 0
assert gamma_move(board, 1, 6, 1) == 0
assert gamma_golden_move(board, 1, 0, 1) == 0
assert gamma_move(board, 2, 0, 1) == 1
assert gamma_free_fields(board, 2) == 16
assert gamma_golden_possible(board, 2) == 1
board900606393 = gamma_board(board)
assert board900606393 is not None
assert board900606393 == (".113.2..\n"
"1..1.221\n"
"331...3.\n"
"1232312.\n"
"23.1332.\n"
"22.322.3\n")
del board900606393
board900606393 = None
assert gamma_move(board, 3, 5, 1) == 0
assert gamma_move(board, 1, 5, 6) == 0
assert gamma_move(board, 1, 7, 2) == 1
assert gamma_move(board, 2, 3, 3) == 1
assert gamma_move(board, 2, 7, 5) == 1
assert gamma_move(board, 3, 0, 2) == 0
assert gamma_move(board, 2, 1, 7) == 0
board477407951 = gamma_board(board)
assert board477407951 is not None
assert board477407951 == (".113.2.2\n"
"1..1.221\n"
"3312..3.\n"
"12323121\n"
"23.1332.\n"
"22.322.3\n")
del board477407951
board477407951 = None
assert gamma_move(board, 3, 4, 1) == 0
assert gamma_move(board, 1, 1, 5) == 0
assert gamma_move(board, 2, 0, 2) == 0
assert gamma_move(board, 2, 2, 3) == 0
assert gamma_move(board, 3, 4, 4) == 1
assert gamma_move(board, 1, 3, 4) == 0
assert gamma_move(board, 1, 7, 2) == 0
assert gamma_move(board, 2, 1, 2) == 0
assert gamma_move(board, 2, 6, 2) == 0
assert gamma_busy_fields(board, 2) == 14
assert gamma_move(board, 3, 1, 2) == 0
assert gamma_move(board, 3, 6, 0) == 1
assert gamma_move(board, 1, 3, 5) == 0
assert gamma_move(board, 1, 7, 3) == 1
assert gamma_move(board, 2, 5, 0) == 0
assert gamma_move(board, 2, 5, 0) == 0
assert gamma_golden_move(board, 2, 2, 2) == 1
assert gamma_move(board, 3, 5, 0) == 0
board587665326 = gamma_board(board)
assert board587665326 is not None
assert board587665326 == (".113.2.2\n"
"1..13221\n"
"3312..31\n"
"12223121\n"
"23.1332.\n"
"22.32233\n")
del board587665326
board587665326 = None
assert gamma_move(board, 1, 3, 4) == 0
assert gamma_move(board, 2, 1, 7) == 0
assert gamma_move(board, 2, 4, 5) == 1
assert gamma_busy_fields(board, 2) == 16
assert gamma_move(board, 3, 7, 5) == 0
assert gamma_free_fields(board, 3) == 9
assert gamma_move(board, 1, 1, 7) == 0
assert gamma_move(board, 1, 3, 3) == 0
assert gamma_move(board, 2, 2, 0) == 1
assert gamma_move(board, 2, 5, 1) == 0
assert gamma_golden_move(board, 2, 1, 1) == 0
assert gamma_move(board, 3, 1, 2) == 0
assert gamma_move(board, 3, 6, 4) == 0
assert gamma_free_fields(board, 3) == 8
assert gamma_move(board, 1, 0, 0) == 0
assert gamma_golden_move(board, 1, 3, 6) == 0
assert gamma_move(board, 2, 3, 5) == 0
assert gamma_move(board, 2, 1, 0) == 0
board594084359 = gamma_board(board)
assert board594084359 is not None
assert board594084359 == (".11322.2\n"
"1..13221\n"
"3312..31\n"
"12223121\n"
"23.1332.\n"
"22232233\n")
del board594084359
board594084359 = None
assert gamma_move(board, 3, 3, 5) == 0
assert gamma_move(board, 3, 7, 1) == 1
assert gamma_move(board, 1, 3, 4) == 0
assert gamma_move(board, 1, 6, 5) == 1
assert gamma_golden_possible(board, 2) == 0
assert gamma_golden_move(board, 2, 4, 3) == 0
assert gamma_move(board, 3, 3, 4) == 0
assert gamma_move(board, 1, 4, 1) == 0
assert gamma_move(board, 1, 2, 4) == 1
assert gamma_busy_fields(board, 1) == 13
assert gamma_move(board, 2, 5, 0) == 0
assert gamma_move(board, 2, 0, 3) == 0
assert gamma_busy_fields(board, 2) == 17
assert gamma_move(board, 3, 5, 0) == 0
assert gamma_move(board, 1, 5, 0) == 0
assert gamma_free_fields(board, 1) == 5
assert gamma_move(board, 2, 3, 5) == 0
assert gamma_move(board, 3, 1, 2) == 0
assert gamma_golden_move(board, 3, 4, 7) == 0
assert gamma_move(board, 1, 1, 0) == 0
assert gamma_move(board, 2, 2, 0) == 0
assert gamma_move(board, 2, 5, 2) == 0
assert gamma_move(board, 3, 0, 2) == 0
assert gamma_move(board, 1, 5, 0) == 0
assert gamma_move(board, 1, 6, 0) == 0
assert gamma_move(board, 2, 4, 1) == 0
assert gamma_move(board, 3, 3, 4) == 0
gamma_delete(board)
| 32.653543
| 46
| 0.654449
| 1,537
| 8,294
| 3.388419
| 0.04229
| 0.354839
| 0.394585
| 0.526114
| 0.834677
| 0.830837
| 0.764593
| 0.587174
| 0.451997
| 0.433948
| 0
| 0.150941
| 0.179648
| 8,294
| 253
| 47
| 32.782609
| 0.614491
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| 0
| 0.329004
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| 0.036559
| 0
| 0
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| 0
| 0.774892
| 1
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| 0
| 0.004329
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| 0.004329
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| null | 1
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| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
59978c06752fe254a4e92fe6eed707e6ddc48781
| 4,924
|
py
|
Python
|
tests/test_boundary.py
|
scottprahl/iadpython
|
df04f6446c73b5c5c1aabed072e986877f81104b
|
[
"MIT"
] | 4
|
2017-09-13T14:01:32.000Z
|
2021-11-09T04:48:17.000Z
|
tests/test_boundary.py
|
scottprahl/iadpython
|
df04f6446c73b5c5c1aabed072e986877f81104b
|
[
"MIT"
] | null | null | null |
tests/test_boundary.py
|
scottprahl/iadpython
|
df04f6446c73b5c5c1aabed072e986877f81104b
|
[
"MIT"
] | 1
|
2020-06-16T21:09:44.000Z
|
2020-06-16T21:09:44.000Z
|
# pylint: disable=invalid-name
# pylint: disable=bad-whitespace
# pylint: disable=no-self-use
# pylint: disable=too-many-statements
# pylint: disable=protected-access
"""Tests for Boundary reflection."""
import unittest
import numpy as np
import iadpython
class boundary(unittest.TestCase):
"""Boundary layer calculations."""
def test_01_boundary(self):
"""Matrices for light entering slab."""
n_glass = 1.5
n_slab = 1.3
s = iadpython.Sample(n=n_slab, n_above=n_glass, n_below=n_glass)
r, t = iadpython.start._boundary(s, 1.0, n_glass, n_slab, 0)
rr = np.array([0.08628, 0.32200, 0.03502, 0.00807])
tt = np.array([0.00000, 0.00000, 0.91484, 0.95530])
np.testing.assert_allclose(r, rr, atol=1e-5)
np.testing.assert_allclose(t, tt, atol=1e-5)
def test_02_boundary(self):
"""Matrices for light exiting slab."""
n_glass = 1.5
n_slab = 1.3
s = iadpython.Sample(n=n_slab, n_above=n_glass, n_below=n_glass)
r, t = iadpython.start._boundary(s, n_slab, n_glass, 1.0, 0)
rr = np.array([0.08628, 0.32200, 0.03502, 0.00807])
tt = np.array([0.00000, 0.00000, 0.91484, 0.95530])
np.testing.assert_allclose(r, rr, atol=1e-5)
np.testing.assert_allclose(t, tt, atol=1e-5)
def test_03_boundary(self):
"""Initialization of boundary matrix without glass slides."""
s = iadpython.Sample(n=1.5, n_above=1.0, n_below=1.0)
rr = np.array([0.11740, 0.43815, 0.02393, 0.00509])
tt = np.array([0.00000, 0.00000, 0.92455, 0.96000])
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=True)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
np.testing.assert_allclose(r01, rr, atol=1e-5)
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=False)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
np.testing.assert_allclose(r01, rr, atol=1e-5)
def test_04_boundary(self):
"""Initialization of boundary matrix without glass slides."""
s = iadpython.Sample(n=1.5, n_above=1.5, n_below=1.5)
rr = np.array([0.11740, 0.43815, 0.02393, 0.00509])
tt = np.array([0.00000, 0.00000, 0.92455, 0.96000])
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=True)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
np.testing.assert_allclose(r01, rr, atol=1e-5)
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=False)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
np.testing.assert_allclose(r01, rr, atol=1e-5)
def test_05_boundary(self):
"""Initialization of boundary matrices with glass slides."""
s = iadpython.Sample(n=1.3, n_above=1.5, n_below=1.5)
rr = np.array([0.08628, 0.32200, 0.03502, 0.00807])
tt = np.array([0.00000, 0.00000, 0.91484, 0.95530])
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=True)
np.testing.assert_allclose(r01, rr, atol=1e-5)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=False)
np.testing.assert_allclose(r01, rr, atol=1e-5)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
def test_06_boundary(self):
"""Initialization of boundary matrices with glass slides."""
s = iadpython.Sample(n=1.3, n_above=1.5, n_below=1.6)
rr = np.array([0.08628, 0.32200, 0.03502, 0.00807])
tt = np.array([0.00000, 0.00000, 0.91484, 0.95530])
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=True)
np.testing.assert_allclose(r01, rr, atol=1e-5)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
r01, r10, t01, t10 = iadpython.boundary_layer(s, top=False)
rr = np.array([0.08628, 0.32200, 0.04371, 0.01135])
tt = np.array([0.00000, 0.00000, 0.89370, 0.93715])
np.testing.assert_allclose(r01, rr, atol=1e-5)
np.testing.assert_allclose(t01, tt, atol=1e-5)
np.testing.assert_allclose(r10, rr, atol=1e-5)
np.testing.assert_allclose(t10, tt, atol=1e-5)
if __name__ == '__main__':
unittest.main()
| 45.592593
| 72
| 0.636271
| 798
| 4,924
| 3.810777
| 0.119048
| 0.106544
| 0.177573
| 0.272279
| 0.879316
| 0.860572
| 0.85827
| 0.85827
| 0.843801
| 0.843801
| 0
| 0.15107
| 0.212226
| 4,924
| 107
| 73
| 46.018692
| 0.632895
| 0.102762
| 0
| 0.756098
| 0
| 0
| 0.001829
| 0
| 0
| 0
| 0
| 0
| 0.439024
| 1
| 0.073171
| false
| 0
| 0.036585
| 0
| 0.121951
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
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| 0
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|
0
| 9
|
abd2b0d6a767f117d40cc51f2032963f8bddb95d
| 122,882
|
py
|
Python
|
pandas/lib/dataAnalysis.py
|
philip-shen/note_python
|
db0ad84af25464a22ac52e348960107c81e74a56
|
[
"MIT"
] | null | null | null |
pandas/lib/dataAnalysis.py
|
philip-shen/note_python
|
db0ad84af25464a22ac52e348960107c81e74a56
|
[
"MIT"
] | 11
|
2021-02-08T20:45:23.000Z
|
2022-03-12T01:00:11.000Z
|
pandas/lib/dataAnalysis.py
|
philip-shen/note_python
|
db0ad84af25464a22ac52e348960107c81e74a56
|
[
"MIT"
] | null | null | null |
# 2018/09/01 Add class PandasDataAnalysis from test_TALib.py
# 2018/09/06 Add def file1_updownrate_LastMonthYear()
# def get_tradedays_dfinfo()
# def file2_updownrate_QuarterYear()
# def file3_updownrate_threeYearoneYear()
# from test_TALib.py
# 2018/09/07 Add def plotMAchart(), def plotMA05MA20MA30() and
# def plotMACrossOver()
# 2018/09/10 Add class PandasDA_Excel
# 2018/09/12 Add def MACrossOverDate_Interval_lastdate()
# 2018/09/15 Add def file1_main(), file1_call(), file1_put()
# def file2_main(), file2_call(), file2_put()
# def file3_main(), file3_call(), file3_put()
# def percent2float()
# 2018/0/917 Add def SeymourExcel01_call(),def SeymourExcel01_put()
# def SeymourExcel02_call(),def SeymourExcel02_put() in class PandasDA_Excel
# 2018/09/20 Add def plotCandlestickandMA() in class PandasDataAnalysis
# Add def file_plotCandlestickMA
# 2018/09/21 Add def SeymourExcel03_call(), def SeymourExcel03_put()
# add def compare_twoarrarys() in class PandasDA_Excel
# 2018/09/24 Add def file4_updownrate_YearQuarterMonth() in class PandasDataAnalysis
# add file4_main(), file4_call() and file4_put()
# Solve issue:TypeError: unsupported operand type(s) for -: 'str' and 'str'
# 2018/09/28 For uploading Google drive purpose: to creat candlestick_weeklyfolder in def plotCandlestickandMA()
# 2018/10/06 Add def buildup_output_csv
# 2018/10/27 Add class PandasSqliteAnalysis
# 2018/10/31 Add def purgelocalfiles() in def plotCandlestickandMA()
########################################################
import talib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# from matplotlib.finance import candlestick_ohlc
# finance module is no longer part of matplotlib
# see: https://github.com/matplotlib/mpl_finance
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates
from matplotlib.dates import num2date, DateFormatter, WeekdayLocator,\
DayLocator, MONDAY
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pylab as mpl
from datetime import datetime, timedelta
import time
import os,sys, datetime
import re
import excelRW as excelrw
import googleDrive as google_drive
import sqlite3
from sqlite3 import Error
class PandasDataAnalysis:
#2018/11/17 config font type to show TChinese
#mpl.rcParams['font.sans-serif'] = ['SimHei'] #將預設字體改用SimHei字體for中文
def __init__(self,stkidx,dirnamelog,dirdatafolder,str_first_year_month_day,opt_verbose='OFF'):
FOLDER = dirdatafolder
csv_datafolder = '{}/{}.csv'.format(FOLDER,stkidx)
self.stkidx = stkidx
self.dirnamelog = dirnamelog
self.str_first_year_month_day = str_first_year_month_day
self.opt_verbose = opt_verbose
# get date, open, high, low, close price and volume from csv file
################## remark index_col = [0] ###############
## then 'date' become a column name \
# date volume open high low close CmpName
#0 2018-05-02 4715058 17.20 18.10 17.00 17.05 台航
#1 2018-05-03 956738 16.85 16.95 16.65 16.85 台航
#2 2018-05-04 612524 17.00 17.30 16.90 16.95 台航
#3 2018-05-07 776401 17.15 17.25 16.70 16.75 台航
# get date and close from csv file
csv_stockfile = pd.read_csv(csv_datafolder, header = None, encoding = 'cp950',
usecols = [0,3,4,5,6,9,10], #index_col = [0],
names = ['date', 'open', 'high', 'low', 'close', 'Stkidx','CmpName'],
parse_dates = [0],
date_parser = lambda x:pd.datetime.strptime(x,'%Y/%m/%d'))
df = csv_stockfile.copy()
self.df = df
#self.df.sort_index(ascending=1,inplace=True)
# get row count after sort index
#print("original row counts: {}".format(len(self.df.index)))
def MACrossOver(self):
# Get present time
local_time = time.localtime(time.time())
df_delduplicates = self.df.drop_duplicates()
# get row count after delet duplicated row
print("row counts after drop duplicated rows: {}".format(len(df_delduplicates.index)) )
#print(df.duplicated().to_string())
# sort pandas dataframe from one column
df_delduplicates_sortasc = df_delduplicates.sort_values('date',ascending=1)
# filter rows of pandas dataframe by timestamp column backward 90 days.
df_delduplicates_back90D = df_delduplicates_sortasc.iloc[-90:,0:6]
#print(df_delduplicates_back90D)
# to add the calculated Moving Average as a new column to the right after 'Value'
# to get 2 digitals after point by using np
df_delduplicates_back90D['SMA_05'] = np.round(df_delduplicates_back90D['close'].rolling(window=5).mean(),2 )
df_delduplicates_back90D['SMA_20'] = np.round(df_delduplicates_back90D['close'].rolling(window=20).mean(),2 )
df_delduplicates_back90D['SMA_30'] = np.round(df_delduplicates_back90D['close'].rolling(window=30).mean(),2 )
#print(df_delduplicates_back90D)
# calculate SMA_05 Moving Average Crossover SMA_20
previous_05 = df_delduplicates_back90D['SMA_05'].shift(1)
previous_20 = df_delduplicates_back90D['SMA_20'].shift(1)
crossing = (((df_delduplicates_back90D['SMA_05'] <= df_delduplicates_back90D['SMA_20']) & (previous_05 >= previous_20))
| ((df_delduplicates_back90D['SMA_05'] >= df_delduplicates_back90D['SMA_20']) & (previous_05 <= previous_20)))
golden_crossing = ((df_delduplicates_back90D['SMA_05'] >= df_delduplicates_back90D['SMA_20'])
& (previous_05 <= previous_20))
dead_crossing = ((df_delduplicates_back90D['SMA_05'] <= df_delduplicates_back90D['SMA_20'])
& (previous_05 >= previous_20))
#crossing_dates = df_delduplicates_back90D.loc[crossing, 'date']
#print(crossing_dates)
crossing = df_delduplicates_back90D.loc[crossing]
golden_crossing = df_delduplicates_back90D.loc[golden_crossing]
dead_crossing = df_delduplicates_back90D.loc[dead_crossing]
#print(crossing)
print('MA Godlen CrossOver')
print(golden_crossing)
print('\n')
print('MA Deaded CrossOver')
print(dead_crossing)
# Output CSV file including path
filename_csv_macross=str(local_time.tm_mon)+str(local_time.tm_mday)+'_'+self.stkidx+'_'+"MACrossOver"+".csv"
dirlog_csv_macross=os.path.join(self.dirnamelog,filename_csv_macross)
golden_crossing.to_csv(dirlog_csv_macross, mode = 'w',sep=' ', header='Golden Crossing',encoding='cp950')
dead_crossing.to_csv(dirlog_csv_macross, mode = 'a',sep=' ', header='Dead Crossing',encoding='cp950')
# check both golden and dead MACrossOver is below 20 days
timedelta_golden_crossing = self.MACrossOverDate_Interval_lastdate(golden_crossing)
timedelta_dead_crossing = self.MACrossOverDate_Interval_lastdate(dead_crossing)
if (timedelta_golden_crossing <= timedelta(days=20)).bool() | (timedelta_dead_crossing<= timedelta(days=16)).bool():
#print('\n{} or {} <= 16 days.'.format(timedelta_golden_crossing,timedelta_dead_crossing))
return 1
else:
#print('\n{} or {} > 16 days.'.format(timedelta_golden_crossing,timedelta_dead_crossing))
return 0
# to calcualte interval days
def MACrossOverDate_Interval_lastdate(self,df_macrossover):
last_date = self.str_first_year_month_day.split(',')
dt_last_date = datetime.datetime(int(last_date[0]), int(last_date[1]), int(last_date[2]))
# get date of last row to calculate delta
interval = dt_last_date - df_macrossover['date'].iloc[-1:]
#if interval <= timedelta(15):
# print('{} from {} is {} day(s). '.format(dt_last_date.date(),i.date(),interval))
#print('{} from {} is {} day(s). '.format(df_macrossover['date'].iloc[-1:],
# dt_last_date.date(),interval))
return interval
def file1_updownrate_LastMonthYear(self,valuerate):#"循環投資追蹤股"
df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
# filter Pandas Dataframe rolling max min backward Month,Quarter,Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).max()
#df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
#df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['low'].astype(float))/
df_delduplicates_sortasc_tradeday['low'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['high'].astype(float))/
df_delduplicates_sortasc_tradeday['high'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
# 2018/08/31,0,0,---,---,---,---,---,0,4747,強生
# 2018/09/03,0,0,---,---,---,---,---,0,4747,強生
# 2018/09/04 last trade day maybe no trade happening, so forget assign date to index
# Assigning an index column (and drop index column) to pandas dataframe to filter specific row
#df_delduplicates_sortasc_tradeday_dateidx = df_delduplicates_sortasc_tradeday.set_index("date", drop = True)
#print(df_delduplicates_sortasc_tradeday_dateidx)
#df_delduplicates_sortasc_tradeday_dateidx_lastday = df_delduplicates_sortasc_tradeday_dateidx.loc[str_lastday,:]
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
# flatten the lists then get its value like [['27.70']]-->27.7
#list_temp = df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0]
#print( list_temp)
#list_rows_bothprices=[]
#head_rows=["代碼","公司","市價","1Y下跌率","1M下跌率","Lastday下跌率",
# "1Y上昇率","1M上昇率","Lastday上昇率",
# "價格比","last trade day"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_30D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_01D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_30D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_01D']].values.flatten()[0] *100),
valuerate,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
# 2018/11/5 class GoogleSS def update_GSpreadworksheet_datafolderCSV() need
# nonetradeday dfinof
def get_tradedaysANDnonetradeday_dfinfo(self):
df_delduplicates = self.df.drop_duplicates()
return df_delduplicates
# delete dataframe of both duplicates and nonetradeday
def get_tradedays_dfinfo(self):
df_delduplicates = self.df.drop_duplicates()
if self.opt_verbose.lower == 'on':
# get row count after delet duplicated row
print("row counts after drop duplicated rows: {}".format(len(df_delduplicates.index)) )
# sort pandas dataframe from column 'date'
df_delduplicates_sortasc = df_delduplicates.sort_values('date',ascending=1)
# check clsoe price if includes '---' or '--' or not, but
# 2018/09/04 dtype of close price icluding '---' and '--' is object except float64
# convert value to string if value does have digitals
if self.df['close'].dtype == np.object:
# DataFrame filter close column by regex
df_delduplicates_sortasc_nonetradeday = df_delduplicates_sortasc.loc[
df_delduplicates_sortasc['close'].str.contains(r'^-+-$')]
if self.opt_verbose.lower == 'on':
#print(df_delduplicates_sortasc_nonetradeday)
print("row counts with none trade: {}".format(len(df_delduplicates_sortasc_nonetradeday)) )
# df_delduplicates_sortasc['close'] exclude (r'^-+-$')
df_delduplicates_sortasc_tradeday = df_delduplicates_sortasc[~df_delduplicates_sortasc['close'].str.contains(r'^-+-$')]
elif self.df['close'].dtype == np.float64:
df_delduplicates_sortasc_tradeday = df_delduplicates_sortasc
if self.opt_verbose.lower == 'on':
print("row counts with trade: {}".format(len(df_delduplicates_sortasc_tradeday)) )
return df_delduplicates_sortasc_tradeday
def file2_updownrate_QuarterYear(self,valuerate):#"波段投機追蹤股"
df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
# filter Pandas Dataframe rolling max min backward Month,Quarter,Year
#df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).min()
#df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['low'].astype(float))/
df_delduplicates_sortasc_tradeday['low'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['high'].astype(float))/
df_delduplicates_sortasc_tradeday['high'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#list_rows_bothprices=[]
#head_rows=["代碼","公司","市價","1Q上昇率","1Y下跌率","Lastday上昇率",
# "1Q下跌率","1Y上昇率","Lastday下跌率",
# "價格比","last trade day"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_01D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_01D']].values.flatten()[0] *100),
valuerate,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
def file3_updownrate_threeYearoneYear(self,pbr):#"景氣循環追蹤股"
df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
# filter Pandas Dataframe rolling max min backward Quarter,Year, 3Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_730D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=730).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_730D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=730).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_730D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_730D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_730D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_730D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_730D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_730D'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#list_rows_bothprices=[]
#head_rows=["代碼","公司","市價","3Y下跌率","1Y下跌率","1Q下跌率",
# "3Y上昇率","1Y上昇率","1Q上昇率",
# "PBR","last trade day"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_730D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_730D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_60D']].values.flatten()[0] *100),
pbr,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
def file4_updownrate_YearQuarterMonth(self,valuerate):#"公用事業追蹤股"
df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
# filter Pandas Dataframe rolling max min backward Month,Quarter,Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#head_rows=["代碼","公司","市價","1Y下跌率(%)","1Q下跌率(%)","1M下跌率(%)",
# "1Y上昇率(%)","1Q上昇率(%)","1M上昇率(%)","價值比"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_30D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_30D']].values.flatten()[0] *100),
valuerate,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
def plotMAchart(self,list_ptr_df,list_label,str_title):
plt.figure(figsize=(10,5))
plt.plot(list_ptr_df[0], color='#DE5B49', label=list_label[0], alpha=0.8, linewidth=3)
plt.plot(list_ptr_df[1], color='#324D5C', label=list_label[1], alpha=0.8, linewidth=3)
plt.plot(list_ptr_df[2], color='#46B29D', label=list_label[2], alpha=0.8, linewidth=3)
plt.legend(loc='upper left')
plt.xlabel('date', color='c')
plt.ylabel('value', color='c')
plt.grid(True)
plt.title(str_title)
plt.tick_params(labelcolor='tab:orange')
#plt.show()
#可以存PNG、JPG、EPS、SVG、PGF、PDF
#也可以選擇輸出的DPI
plt.savefig('{}/{}.jpg'.format(self.dirnamelog,str_title), dpi=300)
def plotMA05MA20MA30(self,data_frame,str_title):
#在talib中，輸入輸出都需要用array，參數二則是你要選擇的n天，第三參數選擇均線的類型
SMA_05 = talib.MA(np.array(data_frame.close), timeperiod=5, matype=0)
SMA_20 = talib.MA(np.array(data_frame.close), timeperiod=20, matype=0)
SMA_30 = talib.MA(np.array(data_frame.close), timeperiod=30, matype=0)
#使用matplotlib繪圖之前先將array轉成DataFrame
df_SMA_05 = pd.DataFrame(SMA_05, index = data_frame.index, columns = ['SMA05'])
df_SMA_20 = pd.DataFrame(SMA_20, index = data_frame.index, columns = ['SMA20'])
df_SMA_30 = pd.DataFrame(SMA_30, index = data_frame.index, columns = ['SMA30'])
list_ptr_df = [df_SMA_05,df_SMA_20,df_SMA_30]
list_label = ['SMA_05','SMA_20','SMA_30']
self.plotMAchart(list_ptr_df,list_label,str_title)
def plotMACrossOver(self):
# have to sort column 'date'
df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
#print(df_delduplicates.iloc[0,5])
list_str = [df_delduplicates_sortasc_tradeday.iloc[0,5].astype(str) , df_delduplicates_sortasc_tradeday.iloc[0,6]]
title = ''.join(list_str)#stkidx+CmpName
# get last day value
ts_endday = df_delduplicates_sortasc_tradeday[-1:].index.tolist()[0]
# Pandas: Convert Timestamp to datetime.date
dt_endday = pd.Timestamp(ts_endday).date()
#print(dt_endday)
#subtract 90 days
dt_startdate = dt_endday - timedelta(days=90)
print("Start Date:{} End Date:{}".format(dt_startdate,dt_endday))
# Assigning an index column (and drop index column) to pandas dataframe to filter specific row
# for matplotlib draw purpose
df_delduplicates_sortasc_tradeday_dateidx = df_delduplicates_sortasc_tradeday.set_index("date", drop = True)
#print(df_delduplicates_dateidx)
#chose start position from startpos_idx
startpos_idx = -90
#print(df_delduplicates_sortasc_tradeday_dateidx.iloc[startpos_idx:])
self.plotMA05MA20MA30(df_delduplicates_sortasc_tradeday_dateidx.iloc[startpos_idx:], title)
# plot Candlestick overlaps MA
def plotCandlestickandMA(self,list_color_ma,str_candlestick_weeklysubfolder,str_buysell_opt = 'call'):
# to get stock index
#for stkidx in df_file_stock_call[['代碼']].values.flatten():
#print(stkidx)
df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
#print(df_delduplicates_sortasc_tradeday)
##############################################################
# Issue:
#File "C:\ProgramData\Anaconda3\lib\site-packages\mpl_finance.py", line 288, in _candlestick
#height = close - open
#TypeError: unsupported operand type(s) for -: 'str' and 'str'
###############################################################
# Solution: cast data to float
df_delduplicates_sortasc_tradeday['open'] = df_delduplicates_sortasc_tradeday['open'].astype(float)
df_delduplicates_sortasc_tradeday['high'] = df_delduplicates_sortasc_tradeday['high'].astype(float)
df_delduplicates_sortasc_tradeday['low'] = df_delduplicates_sortasc_tradeday['low'].astype(float)
df_delduplicates_sortasc_tradeday['close'] = df_delduplicates_sortasc_tradeday['close'].astype(float)
# Converting date to pandas datetime format
df_delduplicates_sortasc_tradeday['date'] = pd.to_datetime(df_delduplicates_sortasc_tradeday['date'])
df_delduplicates_sortasc_tradeday['date'] = df_delduplicates_sortasc_tradeday['date'].apply(mdates.date2num)
#print(df_delduplicates_sortasc_tradeday['date'])
# Creating required data in new DataFrame OHLC
df_ohlc= df_delduplicates_sortasc_tradeday[['date', 'open', 'high', 'low','close']].copy()
# to add the calculated Moving Average as a new column to the right after 'Value'
# to get 2 digitals after point by using np
df_ohlc['SMA_05'] = np.round(df_ohlc['close'].rolling(window=5).mean(),2 )
df_ohlc['SMA_20'] = np.round(df_ohlc['close'].rolling(window=20).mean(),2 )
df_ohlc['SMA_30'] = np.round(df_ohlc['close'].rolling(window=30).mean(),2 )
list_str = [df_delduplicates_sortasc_tradeday.iloc[-1,-2].astype(str) ,
df_delduplicates_sortasc_tradeday.iloc[-1,-1]]
str_title = '_'.join(list_str)
f1, ax = plt.subplots(figsize = (12,6))
# In case you want to check for shorter timespan
if len(df_ohlc) >= 180:
df_ohlc =df_ohlc.tail(170)
else:
df_ohlc =df_ohlc.tail(len(df_ohlc))
if self.opt_verbose.lower == 'on':
print('Len of dataframe ohlc:{} '.format(len(df_ohlc)))
# plot the candlesticks
candlestick_ohlc(ax, df_ohlc.values, width=.6, colorup='red', colordown='green')
#ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) # e.g., 2018-09-12
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%Y-%m-%d') # e.g., 2018-09-12; Jan 12
#dayFormatter = DateFormatter('%d') # e.g., 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)
#plot_day_summary(ax, quotes, ticksize=3)
# Plotting SMA columns
ax.plot(df_ohlc['date'], df_ohlc['SMA_05'], color = list_color_ma[0], label = 'SMA05')
ax.plot(df_ohlc['date'], df_ohlc['SMA_20'], color = list_color_ma[1], label = 'SMA20')
ax.plot(df_ohlc['date'], df_ohlc['SMA_30'], color = list_color_ma[2], label = 'SMA30')
#plt.grid(True)
plt.title(str_title)
ax.yaxis.grid(True)
plt.legend(loc='best')
ax.xaxis_date()
ax.autoscale_view()
# format the x-ticks with a human-readable date.
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
# In case you dont want to save image but just displya it
#plt.show()
# Check image sudfloder is existing or not
candlestick_weeklyfolder = os.path.join(self.dirnamelog,str_candlestick_weeklysubfolder)
if not os.path.isdir(candlestick_weeklyfolder):
os.makedirs(candlestick_weeklyfolder)
# build filename of saving image
str_stock_buysell = '_'.join([str_buysell_opt,str_title])
#Delete prvious candle stick jpg files if exist.
localgoogle_drive = google_drive.GoogleCloudDrive(candlestick_weeklyfolder)
re_exp = r'{}.jpg$'.format(str_stock_buysell)
localgoogle_drive.purgelocalfiles(re_exp)
# Saving image
print('{}/{}.jpg would be saved.'.format(candlestick_weeklyfolder,str_stock_buysell))
plt.savefig('{}/{}.jpg'.format(candlestick_weeklyfolder,str_stock_buysell), dpi=400)
class PandasSqliteAnalysis:
def __init__(self,stkidx,dirnamelog,path_db,str_first_year_month_day,opt_verbose='OFF'):
self.stkidx = stkidx
self.dirnamelog = dirnamelog
self.path_db = path_db
self.str_first_year_month_day = str_first_year_month_day
self.opt_verbose = opt_verbose
# to filter clsoe price if includes '---' or '--' or not in WHERE
# 2019/1/2 cause below case so can't filter clsoe price that includes '---' or '--'
#"2019/01/02" "0" "0" "---" "---" "---" " ---" "--- " "0" "5209" "新鼎"
# 2019/1/3 line 696, in file1_updownrate_LastMonthYear
# df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).min()
# ValueError: could not convert string to float: ' ---'
# must filter clsoe price if includes '---' or '--' or not in WHERE
sql_query_TseOtcDaily_table = """ SELECT DISTINCT
trade_date AS date,
open_price AS open,
high_price AS high,
low_price AS low,
close_price AS close,
stkidx,
cmp_name AS CmpName
FROM TseOtcDaily
WHERE (
stkidx LIKE {} AND
close_price NOT LIKE '%-'
)
ORDER BY trade_date ASC; """.format(self.stkidx)
sql_query_nonetrade_TseOtcDaily_table = """ SELECT DISTINCT
trade_date AS date,
open_price AS open,
high_price AS high,
low_price AS low,
close_price AS close,
stkidx,
cmp_name AS CmpName
FROM TseOtcDaily
WHERE (
stkidx LIKE {}
)
ORDER BY trade_date ASC; """.format(self.stkidx)
# get date, open, high, low, close price and volume from TWTSEOTCDaily.db
# date open high low close stkidx cmp_name
#235 2018/10/22 70.40 72.80 70.20 72.10 9951 皇田
#236 2018/10/23 72.20 72.70 71.60 71.60 9951 皇田
#237 2018/10/24 71.80 71.80 70.90 71.70 9951 皇田
#238 2018/10/25 70.30 70.40 69.30 69.80 9951 皇田
#239 2018/10/26 70.00 70.60 69.70 70.00 9951 皇田
# create a database connection
conn = sqlite3.connect(self.path_db)
if conn is not None:
# get date and close from TWTSEOTCDaily.db
df_sql_stockfile = pd.read_sql_query(sql_query_TseOtcDaily_table, conn,
parse_dates = ['date'])
df = df_sql_stockfile.copy()
#2019/1/3 add
df_sql_nonetrade_stockfile = pd.read_sql_query(sql_query_nonetrade_TseOtcDaily_table, conn,
parse_dates = ['date'])
df_nonetrade = df_sql_nonetrade_stockfile.copy()
else:
print("Error! cannot create t he database connection.")
self.df = df
#2019/1/3 add
self.df_nonetrade = df_nonetrade
# close a database connection
conn.close()
#print(self.df)
#print(self.opt_verbose.lower())
# get row count
if self.opt_verbose.lower() == 'on':
#print(self.df)
print(self.df['date'],self.df['close'],self.df['stkidx'],self.df['CmpName'])
print("original row counts: {}".format(len(self.df.index)))
# 2018/11/5 class GoogleSS def update_GSpreadworksheet_datafolderCSV() need
# nonetradeday dfinof
def get_tradedaysANDnonetradeday_dfinfo(self):
#2019/1/3
df_nonetrade_delduplicates = self.df_nonetrade.drop_duplicates()
return df_nonetrade_delduplicates
# delete dataframe of both duplicates and nonetradeday
# 2018/10/29 cause get_tradedays_dfinfo() can't get rid of nonetradeday
def get_tradedays_dfinfo(self):
df_delduplicates = self.df.drop_duplicates()
if self.opt_verbose.lower == 'on':
# get row count after delet duplicated row
print("row counts after drop duplicated rows: {}".format(len(df_delduplicates.index)) )
# sort pandas dataframe from column 'date'
df_delduplicates_sortasc = df_delduplicates.sort_values('date',ascending=1)
# check clsoe price if includes '---' or '--' or not, but
# 2018/09/04 dtype of close price icluding '---' and '--' is object except float64
# convert value to string if value does have digitals
if self.df['close'].dtype == np.object:
# DataFrame filter close column by regex
df_delduplicates_sortasc_nonetradeday = df_delduplicates_sortasc.loc[
df_delduplicates_sortasc['close'].astype(str).str.contains(r'^-+-$')]
print(df_delduplicates_sortasc_nonetradeday)
if self.opt_verbose.lower == 'on':
#print(df_delduplicates_sortasc_nonetradeday)
print("row counts with none trade: {}".format(len(df_delduplicates_sortasc_nonetradeday)) )
# df_delduplicates_sortasc['close'] exclude (r'^-+-$')
df_delduplicates_sortasc_tradeday = df_delduplicates_sortasc[~df_delduplicates_sortasc['close'].str.contains(r'^-+-$')]
elif self.df['close'].dtype == np.float64:
df_delduplicates_sortasc_tradeday = df_delduplicates_sortasc
if self.opt_verbose.lower == 'on':
print("row counts with trade: {}".format(len(df_delduplicates_sortasc_tradeday)) )
return df_delduplicates_sortasc_tradeday
def file1_updownrate_LastMonthYear(self,valuerate):#"循環投資追蹤股"
# get dataframe that is rid of both duplicates and nonetradeday
# 2018/10/29 cause get_tradedays_dfinfo() can't get rid of nonetradeday
# update sql_query_TseOtcDaily_table in _init_() anatomy of where
#df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
df_delduplicates_sortasc_tradeday = self.df
# filter Pandas Dataframe rolling max min backward Month,Quarter,Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).max()
#df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
#df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['low'].astype(float))/
df_delduplicates_sortasc_tradeday['low'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['high'].astype(float))/
df_delduplicates_sortasc_tradeday['high'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#head_rows=["代碼","公司","市價","1Y下跌率","1M下跌率","Lastday下跌率",
# "1Y上昇率","1M上昇率","Lastday上昇率",
# "價格比","last trade day"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_30D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_01D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_30D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_01D']].values.flatten()[0] *100),
valuerate,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
if self.opt_verbose.lower == 'on':
for row_value_finalprice in list_row_value_finalprice:
print(row_value_finalprice)
return list_row_value_finalprice
def file2_updownrate_QuarterYear(self,valuerate):#"波段投機追蹤股"
# 2018/10/29 cause get_tradedays_dfinfo() can't get rid of nonetradeday
# update sql_query_TseOtcDaily_table in _init_() anatomy of where
df_delduplicates_sortasc_tradeday = self.df
# filter Pandas Dataframe rolling max min backward Month,Quarter,Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['low'].astype(float))/
df_delduplicates_sortasc_tradeday['low'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_01D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['high'].astype(float))/
df_delduplicates_sortasc_tradeday['high'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#list_rows_bothprices=[]
#head_rows=["代碼","公司","市價","1Q上昇率","1Y下跌率","Lastday上昇率",
# "1Q下跌率","1Y上昇率","Lastday下跌率",
# "價格比","last trade day"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_01D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_01D']].values.flatten()[0] *100),
valuerate,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
def file3_updownrate_threeYearoneYear(self,pbr):#"景氣循環追蹤股"
df_delduplicates_sortasc_tradeday = self.df
# filter Pandas Dataframe rolling max min backward Quarter,Year, 3Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_730D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=730).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_730D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=730).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_730D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_730D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_730D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_730D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_730D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_730D'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#list_rows_bothprices=[]
#head_rows=["代碼","公司","市價","3Y下跌率","1Y下跌率","1Q下跌率",
# "3Y上昇率","1Y上昇率","1Q上昇率",
# "PBR","last trade day"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_730D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_730D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_60D']].values.flatten()[0] *100),
pbr,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
def file4_updownrate_YearQuarterMonth(self,valuerate):#"公用事業追蹤股"
# 2018/10/29 cause get_tradedays_dfinfo() can't get rid of nonetradeday
# update sql_query_TseOtcDaily_table in _init_() anatomy of where
df_delduplicates_sortasc_tradeday = self.df
# filter Pandas Dataframe rolling max min backward Month,Quarter,Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#head_rows=["代碼","公司","市價","1Y下跌率(%)","1Q下跌率(%)","1M下跌率(%)",
# "1Y上昇率(%)","1Q上昇率(%)","1M上昇率(%)","價值比"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_30D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_30D']].values.flatten()[0] *100),
valuerate,
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
def file4_01_updownrate_YearQuarterMonth(self,valuerate,dividend):#"低波固收追蹤股"
df_delduplicates_sortasc_tradeday = self.df
# filter Pandas Dataframe rolling max min backward Month,Quarter,Year
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_30D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=30).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_60D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=60).max()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_min_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).min()
df_delduplicates_sortasc_tradeday.loc[:,'rolling_max_250D'] = df_delduplicates_sortasc_tradeday['close'].astype(float).rolling(window=250).max()
#calcuate raiserate_decreaserate
df_delduplicates_sortasc_tradeday.loc[:,'uprate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_30D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_30D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_60D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_60D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'uprate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_min_250D'].astype(float) )
df_delduplicates_sortasc_tradeday.loc[:,'downrate_250D'] = ( (df_delduplicates_sortasc_tradeday['close'].astype(float)-
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float))/
df_delduplicates_sortasc_tradeday['rolling_max_250D'].astype(float) )
#2019/02/19 add dividend_yield
df_delduplicates_sortasc_tradeday.loc[:,'dividend_yield'] = ( dividend/df_delduplicates_sortasc_tradeday['close'].astype(float) )
df_delduplicates_sortasc_tradeday_lastday = df_delduplicates_sortasc_tradeday.iloc[-1:,:]
#head_rows=["代碼","公司","市價","1Y下跌率(%)","1Q下跌率(%)","1M下跌率(%)",
# "1Y上昇率(%)","1Q上昇率(%)","1M上昇率(%)","價值比","現金殖利率"]
list_row_value_finalprice = [self.stkidx,
df_delduplicates_sortasc_tradeday_lastday[['CmpName']].values.flatten()[0],
df_delduplicates_sortasc_tradeday_lastday[['close']].values.flatten()[0],
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['downrate_30D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_250D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_60D']].values.flatten()[0] *100),
"%.3f%%" %(df_delduplicates_sortasc_tradeday_lastday[['uprate_30D']].values.flatten()[0] *100),
valuerate,
dividend,
"%.3f" %(df_delduplicates_sortasc_tradeday_lastday[['dividend_yield']].values.flatten()[0] *100),
df_delduplicates_sortasc_tradeday_lastday[['date']].values.flatten()[0]
]
return list_row_value_finalprice
# plot Candlestick overlaps MA
def plotCandlestickandMA(self,list_color_ma,str_candlestick_weeklysubfolder,str_buysell_opt = 'call'):
# 2018/10/29 cause get_tradedays_dfinfo() can't get rid of nonetradeday
# update sql_query_TseOtcDaily_table in _init_() anatomy of where
#df_delduplicates_sortasc_tradeday = self.get_tradedays_dfinfo()
df_delduplicates_sortasc_tradeday = self.df
##############################################################
# Issue:
#File "C:\ProgramData\Anaconda3\lib\site-packages\mpl_finance.py", line 288, in _candlestick
#height = close - open
#TypeError: unsupported operand type(s) for -: 'str' and 'str'
###############################################################
# Solution: cast data to float
df_delduplicates_sortasc_tradeday['open'] = df_delduplicates_sortasc_tradeday['open'].astype(float)
df_delduplicates_sortasc_tradeday['high'] = df_delduplicates_sortasc_tradeday['high'].astype(float)
df_delduplicates_sortasc_tradeday['low'] = df_delduplicates_sortasc_tradeday['low'].astype(float)
df_delduplicates_sortasc_tradeday['close'] = df_delduplicates_sortasc_tradeday['close'].astype(float)
# Converting date to pandas datetime format
df_delduplicates_sortasc_tradeday['date'] = pd.to_datetime(df_delduplicates_sortasc_tradeday['date'])
df_delduplicates_sortasc_tradeday['date'] = df_delduplicates_sortasc_tradeday['date'].apply(mdates.date2num)
#print(df_delduplicates_sortasc_tradeday['date'])
# Creating required data in new DataFrame OHLC
df_ohlc= df_delduplicates_sortasc_tradeday[['date', 'open', 'high', 'low','close']].copy()
# to add the calculated Moving Average as a new column to the right after 'Value'
# to get 2 digitals after point by using np
df_ohlc['SMA_05'] = np.round(df_ohlc['close'].rolling(window=5).mean(),2 )
df_ohlc['SMA_20'] = np.round(df_ohlc['close'].rolling(window=20).mean(),2 )
df_ohlc['SMA_30'] = np.round(df_ohlc['close'].rolling(window=30).mean(),2 )
# 2018/10/30 Error msg: line 752, in plotCandlestickandMA
# "list_str = [df_delduplicates_sortasc_tradeday.iloc[-1,-2].astype(str) ,
# AttributeError: 'str' object has no attribute 'astype'"
# then udate below
#list_str = [df_delduplicates_sortasc_tradeday.iloc[-1,-2].astype(str) ,
list_str = [df_delduplicates_sortasc_tradeday.iloc[-1,-2],
df_delduplicates_sortasc_tradeday.iloc[-1,-1]]
str_title = '_'.join(list_str)
f1, ax = plt.subplots(figsize = (12,6))
# In case you want to check for shorter timespan
if len(df_ohlc) >= 180:
df_ohlc =df_ohlc.tail(170)
else:
df_ohlc =df_ohlc.tail(len(df_ohlc))
if self.opt_verbose.lower == 'on':
print('Len of dataframe ohlc:{} '.format(len(df_ohlc)))
# plot the candlesticks
candlestick_ohlc(ax, df_ohlc.values, width=.6, colorup='red', colordown='green')
#ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) # e.g., 2018-09-12
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%Y-%m-%d') # e.g., 2018-09-12; Jan 12
#dayFormatter = DateFormatter('%d') # e.g., 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#plot_day_summary(ax, quotes, ticksize=3)
# Plotting SMA columns
ax.plot(df_ohlc['date'], df_ohlc['SMA_05'], color = list_color_ma[0], label = 'SMA05')
ax.plot(df_ohlc['date'], df_ohlc['SMA_20'], color = list_color_ma[1], label = 'SMA20')
ax.plot(df_ohlc['date'], df_ohlc['SMA_30'], color = list_color_ma[2], label = 'SMA30')
#plt.grid(True)
plt.title(str_title)
ax.yaxis.grid(True)
plt.legend(loc='best')
ax.xaxis_date()
ax.autoscale_view()
# format the x-ticks with a human-readable date.
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
# In case you dont want to save image but just displya it
#plt.show()
# Check image sudfloder is existing or not
candlestick_weeklyfolder = os.path.join(self.dirnamelog,str_candlestick_weeklysubfolder)
if not os.path.isdir(candlestick_weeklyfolder):
os.makedirs(candlestick_weeklyfolder)
# build filename of saving image
str_stock_buysell = '_'.join([str_buysell_opt,str_title])
#Delete prvious candle stick jpg files if exist.
localgoogle_drive = google_drive.GoogleCloudDrive(candlestick_weeklyfolder)
re_exp = r'{}.jpg$'.format(str_stock_buysell)
localgoogle_drive.purgelocalfiles(re_exp)
# Saving image
print('{}/{}.jpg would be saved.'.format(candlestick_weeklyfolder,str_stock_buysell))
plt.savefig('{}/{}.jpg'.format(candlestick_weeklyfolder,str_stock_buysell), dpi=400)
class PandasDA_Excel:
def __init__(self,dirnamelog,list_xlsfile):
self.list_xlsfile = list_xlsfile
self.dirnamelog = dirnamelog
def diff_twodataframes(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
xls02_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[1])
# get stkidx and CmpName from excel file
xls01_Seymour = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2])
df01 = xls01_Seymour.copy()
# 2018/12/30 add exception handle
try:
xls02_Seymour = pd.read_excel(xls02_logfolder, encoding = 'cp950',
usecols = [1,2])
except FileNotFoundError as fnf_error:
print(fnf_error)
return
df02 = xls02_Seymour.copy()
#print(df)
# get row count after sort index
print("Lastest file row counts of {}: {}".format(self.list_xlsfile[0],len(df01.index)))
print("Previous file row counts of {}: {}".format(self.list_xlsfile[1],len(df02.index)))
pd_diff = pd.concat([df01,df02]).drop_duplicates(keep=False)
print(pd_diff)
def SeymourExce_filterbystockidx(self,list_stkidx,df_forfilter):
# header of dataframe "代碼 名稱 價值比 一年回跌率 季漲升率 一個月漲升率"
#Select rows whose column value is in a list:
df_filterbystockidx = df_forfilter.loc[df_forfilter['代碼'].isin(list_stkidx)]
print(df_filterbystockidx)
return df_filterbystockidx
def SeymourExcel01_call(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 價值比 一年回跌率 季回跌率 一個月回跌率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,10,19,20,21])
#print(df_xls)
return df_xls
def SeymourExcel01_put(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 價值比 一年漲升率 季漲升率 一個月漲升率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,10,23,24,25])
#print(df_xls)
return df_xls
def SeymourExcel02_call(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 價值比 一年回跌率 季漲升率 一個月漲升率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,10,19,24,25])
#print(df_xls)
return df_xls
def SeymourExcel02_put(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 價值比 季回跌率 一年漲升率 一個月漲升率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,10,20,23,25])
#print(df_xls)
return df_xls
def SeymourExcel03_call(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 PBR 三年回跌率 一年回跌率 季回跌率 一個月回跌率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,4,18,19,20,21])
#print(df_xls)
return df_xls
def SeymourExcel03_put(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 PBR 三年漲升率 一年漲升率 季漲升率 一個月漲升率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,4,22,23,24,25])
#print(df_xls)
return df_xls
def SeymourExcel04_call(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 價值比 一年回跌率 季回跌率 一個月回跌率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,10,19,20,21])
#print(df_xls)
return df_xls
def SeymourExcel04_put(self):
xls01_logfolder = '{}/{}'.format(self.dirnamelog,self.list_xlsfile[0])
# get "代碼 名稱 價值比 一年漲升率 季漲升率 一個月漲升率" from excel file
df_xls = pd.read_excel(xls01_logfolder, encoding = 'cp950',
usecols = [1,2,10,23,24,25])
#print(df_xls)
return df_xls
def compare_twoarrarys(self,df_base,df_comp):
#cause different column indexs so flatten dataframe
arr_stkidx_df_base = df_base[['名稱']].values#.flatten()
arr_stkidx_df_comp = df_comp[['公司']].values#.flatten()
#comparsion between two arrarys
arr_diff = np.setdiff1d(arr_stkidx_df_base,arr_stkidx_df_comp)
#print(arr_diff)
#to get stock index by diff df_base btw df_file03_stock_call
df_base_diff = df_base.loc[df_base['名稱'].isin(arr_diff)]
#print(df_base_diff)
return df_base_diff
# filter orinigal Seymour's Excel '波段投機追蹤股 - 20180928.xls'
#####################################################################
def buildup_output_csv(excel_Seymour,str_addition="bothprices"):
filename_csv_bothprices = ''.join([datetime.date.today().strftime('%m%d'),\
excel_Seymour.split(' ')[0],str_addition,".csv"])
#print(filename_csv_bothprices)
return filename_csv_bothprices
def file1_main(list_excel_Seymour,dirnamelog,dirdatafolder,str_first_year_month_day):#"循環投資追蹤股"
# Get present time
#local_time = time.localtime(time.time())
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
#filename_csv_bothprices=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"bothprices"+".csv"
#filename_csv_belowprice=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"belowprice"+".csv"
filename_csv_bothprices=buildup_output_csv(excel_Seymour,"bothprices")
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
#dirlog_csv_belowprice=os.path.join(dirnamelog,filename_csv_belowprice)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","1Y下跌率(%)","1M下跌率(%)","Lastday下跌率(%)",
"1Y上昇率(%)","1M上昇率(%)","Lastday上昇率(%)","價值比"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
for list_row_value in list_row_value_price:
# get key=idx value=價值比 to store in dict
dict_rows[list_row_value[0]] = list_row_value[2]
list_temp2 =[]#to store return list
# by key=idx value=價值比
for key,value in dict_rows.items():
print("\nStkIdx:{}, 價值比:{}".format(key,value))
local_pdDA = PandasDataAnalysis(key,dirnamelog,dirdatafolder,str_first_year_month_day)
list_temp = local_pdDA.file1_updownrate_LastMonthYear(value)
list_temp2.append(list_temp)
#print(list_temp2)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
def file1_main_fromsqlite(list_excel_Seymour,dirnamelog,path_db,str_first_year_month_day,opt_verbose='OFF'):#"循環投資追蹤股"
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
filename_csv_bothprices=buildup_output_csv(excel_Seymour,"bothprices")
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","1Y下跌率(%)","1M下跌率(%)","Lastday下跌率(%)",
"1Y上昇率(%)","1M上昇率(%)","Lastday上昇率(%)","價值比"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
# 20190721 cause StkIdx:1210.0, 價值比:38.16
# str-->float-->int-->str; '1210.0'-->1210.0-->1210-->'1210'
# str(int(float(list_row_value[0])))
for list_row_value in list_row_value_price:
# get key=idx value=價值比 to store in dict
# 20190721 cause StkIdx:1210.0, 價值比:38.16
#dict_rows[list_row_value[0]] = list_row_value[2]
dict_rows[str(int(float(list_row_value[0])))] = list_row_value[2]
list_temp2 =[]#to store return list
# by key=idx value=價值比
for key,value in dict_rows.items():
print("\nStkIdx:{}, 價值比:{}".format(key,value))
# get daily trade inof rom sqilte DB
local_pdSqlA = PandasSqliteAnalysis(key,dirnamelog,path_db,str_first_year_month_day,opt_verbose)
list_temp = local_pdSqlA.file1_updownrate_LastMonthYear(value)
list_temp2.append(list_temp)
#print(list_temp2)
list_rows_bothprices.extend(list_temp2)
if opt_verbose.lower == 'on':
print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
# custom function taken from https://stackoverflow.com/questions/12432663/what-is-a-clean-way-to-convert-a-string-percent-to-a-float
def percent2float(x):
return float(x.strip('%'))/100
# sorting stock to buy
def file1_call(str_dirlogcsv):#"循環投資追蹤股"
# read daily csv file of 循環投資追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,3,4,5,9,10],
converters={'1Y下跌率(%)':percent2float,
'1M下跌率(%)':percent2float,
'Lastday下跌率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_call = df_csv.sort_values(['1Y下跌率(%)','1M下跌率(%)','Lastday下跌率(%)','價值比'], ascending=[True, True, False, True])
#df_csv_call = df_csv.sort_values(['1Y下跌率','1M下跌率','Lastday下跌率'], ascending=[False, False, False])
#print(df_csv_call)
# convert float to percentage
df_csv_call['1Y下跌率(%)'] = df_csv_call[['1Y下跌率(%)']].values *100
df_csv_call['1M下跌率(%)'] = df_csv_call[['1M下跌率(%)']].values *100
df_csv_call['Lastday下跌率(%)'] = df_csv_call[['Lastday下跌率(%)']].values *100
#print("%.3f%%" %(df_csv_call[['1Y下跌率']].values*100))
#print(df_csv_call)
#1. 技術滿足
# 1. 一年回跌率 < -25%
# 2. 一個月回跌率 < -10%
# 3. 當日跌幅超過 2%
# 4. 大盤季線下彎
# 5. 價值比大於 60
#2018/09/17 base from Seymour's Email adjust:
# 1. 一年回跌率 < -30%
# 2. 一個月回跌率 < -15%
# 3. 當日跌幅超過 3%
df_csv_call_stock=df_csv_call.loc[(df_csv_call['1Y下跌率(%)'] < -30) &
(df_csv_call['1M下跌率(%)'] < -15) ]#&
# (df_csv_call['Lastday下跌率(%)'] < -3)]
print('Stock to buy by {}'.format(str_dirlogcsv))
print(df_csv_call_stock)
# output *_buy.csv
str_dirlogcsv_buy = re.sub(r"bothprices", "_buyranking", str_dirlogcsv)
#str_dirlogcsv_buy = re.search(r"both+?", str_dirlogcsv)
df_csv_call.to_csv(str_dirlogcsv_buy, encoding = 'cp950')
str_dirlogcsv_buy_stock = re.sub(r"bothprices", "_buystock", str_dirlogcsv)
df_csv_call_stock.to_csv(str_dirlogcsv_buy_stock, encoding = 'cp950')
return df_csv_call_stock
# sorting stock to to sell
def file1_put(str_dirlogcsv):#"循環投資追蹤股"
# read daily csv file of 循環投資追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,6,7,8,9,10],
converters={'1Y上昇率(%)':percent2float,
'1M上昇率(%)':percent2float,
'Lastday上昇率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_put = df_csv.sort_values(['1Y上昇率(%)','1M上昇率(%)','Lastday上昇率(%)'], ascending=[False, False, False])
# convert float to percentage
df_csv_put['1Y上昇率(%)'] = df_csv_put[['1Y上昇率(%)']].values *100
df_csv_put['1M上昇率(%)'] = df_csv_put[['1M上昇率(%)']].values *100
df_csv_put['Lastday上昇率(%)'] = df_csv_put[['Lastday上昇率(%)']].values *100
#1. 技術滿足
# 1.1. 一年漲升率 > 35%
# 1.2. 一個月漲升率 > 10%
# 1.3. 當日漲幅超過 2%
# 1.4. 大盤季線上彎
df_csv_put_stock=df_csv_put.loc[(df_csv_put['1Y上昇率(%)'] > 35) &
(df_csv_put['1M上昇率(%)'] > 10) &
(df_csv_put['Lastday上昇率(%)'] > 2)]
print('\nStock to sell by {}'.format(str_dirlogcsv))
print(df_csv_put_stock)
# output *_buy.csv
str_dirlogcsv_sell = re.sub(r"bothprices", "_sellranking", str_dirlogcsv)
#str_dirlogcsv_buy = re.search(r"both+?", str_dirlogcsv)
df_csv_put.to_csv(str_dirlogcsv_sell, encoding = 'cp950')
str_dirlogcsv_sell_stock = re.sub(r"bothprices", "_sellstock", str_dirlogcsv)
df_csv_put_stock.to_csv(str_dirlogcsv_sell_stock, encoding = 'cp950')
return df_csv_put_stock
def file2_main(list_excel_Seymour,dirnamelog,dirdatafolder,str_first_year_month_day):#"波段投機追蹤股"
# Get present time
#local_time = time.localtime(time.time())
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
#filename_csv_bothprices=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"bothprices"+".csv"
#filename_csv_belowprice=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"belowprice"+".csv"
filename_csv_bothprices=buildup_output_csv(excel_Seymour,'bothprices')
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
#dirlog_csv_belowprice=os.path.join(dirnamelog,filename_csv_belowprice)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","1Q上昇率(%)","1Y下跌率(%)","Lastday上昇率(%)",
"1Q下跌率(%)","1Y上昇率(%)","Lastday下跌率(%)","價值比"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
for list_row_value in list_row_value_price:
# get key=idx value=價值比 to store in dict
dict_rows[list_row_value[0]] = list_row_value[2]
list_temp2 =[]#to store return list
# by key=idx value=價值比
for key,value in dict_rows.items():
print("\nStkIdx:{}, 價值比:{}".format(key,value))
local_pdDA = PandasDataAnalysis(key,dirnamelog,dirdatafolder,str_first_year_month_day)
list_temp = local_pdDA.file2_updownrate_QuarterYear(value)
list_temp2.append(list_temp)
#print(list_temp2)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
def file2_main_fromsqlite(list_excel_Seymour,dirnamelog,path_db,str_first_year_month_day,opt_verbose='OFF'):#"波段投機追蹤股"
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
filename_csv_bothprices=buildup_output_csv(excel_Seymour,'bothprices')
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","1Q上昇率(%)","1Y下跌率(%)","Lastday上昇率(%)",
"1Q下跌率(%)","1Y上昇率(%)","Lastday下跌率(%)","價值比"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
# 20190721 cause StkIdx:1210.0, 價值比:38.16
# str-->float-->int-->str; '1210.0'-->1210.0-->1210-->'1210'
# str(int(float(list_row_value[0])))
for list_row_value in list_row_value_price:
# get key=idx value=價值比 to store in dict
# 20190721 cause StkIdx:1210.0, 價值比:38.16
#dict_rows[list_row_value[0]] = list_row_value[2]
dict_rows[str(int(float(list_row_value[0])))] = list_row_value[2]
list_temp2 =[]#to store return list
# by key=idx value=價值比
for key,value in dict_rows.items():
print("\nStkIdx:{}, 價值比:{}".format(key,value))
# get daily trade inof rom sqilte DB
local_pdSqlA = PandasSqliteAnalysis(key,dirnamelog,path_db,str_first_year_month_day,opt_verbose)
list_temp = local_pdSqlA.file2_updownrate_QuarterYear(value)
list_temp2.append(list_temp)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
# sorting stock to buy
def file2_call(str_dirlogcsv):#"波段投機追蹤股"
# read daily csv file of 波段投機追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,3,4,5,9,10],
converters={'1Q上昇率(%)':percent2float,
'1Y下跌率(%)':percent2float,
'Lastday上昇率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_call = df_csv.sort_values(['1Q上昇率(%)','1Y下跌率(%)','Lastday上昇率(%)','價值比'], ascending=[False, True, False, True])
# convert float to percentage
df_csv_call['1Q上昇率(%)'] = df_csv_call[['1Q上昇率(%)']].values *100
df_csv_call['1Y下跌率(%)'] = df_csv_call[['1Y下跌率(%)']].values *100
df_csv_call['Lastday上昇率(%)'] = df_csv_call[['Lastday上昇率(%)']].values *100
#進場訊號: 成長股回檔反轉向上
# 1. 價值比 > 60
# 2. 一年回跌率 < -25%
# 3. 季漲升率突破 10%
#2018/09/17 base from Seymour's Email adjust:
# 1. 價值比 > 60
# 2. 一年回跌率 < -30%
# 3. 季漲升率突破 2%
df_csv_call_stock=df_csv_call.loc[(df_csv_call['1Y下跌率(%)'] < -30) &
(df_csv_call['1Q上昇率(%)'] > 2) &
(df_csv_call['價值比'] >= 60)]
print('\nStock to buy by {}'.format(str_dirlogcsv))
print(df_csv_call_stock)
# output *_buy.csv
str_dirlogcsv_buy = re.sub(r"bothprices", "_buyranking", str_dirlogcsv)
df_csv_call.to_csv(str_dirlogcsv_buy, encoding = 'cp950')
str_dirlogcsv_buy_stock = re.sub(r"bothprices", "_buystock", str_dirlogcsv)
df_csv_call_stock.to_csv(str_dirlogcsv_buy_stock, encoding = 'cp950')
return df_csv_call_stock
# sorting stock to sell
def file2_put(str_dirlogcsv):#"波段投機追蹤股"
# read daily csv file of 波段投機追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,6,7,8,9,10],
converters={'1Q下跌率(%)':percent2float,
'1Y上昇率(%)':percent2float,
'Lastday下跌率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_put = df_csv.sort_values(['1Y上昇率(%)','1Q下跌率(%)','Lastday下跌率(%)'], ascending=[False, False, False])
# convert float to percentage
df_csv_put['1Y上昇率(%)'] = df_csv_put[['1Y上昇率(%)']].values *100
df_csv_put['1Q下跌率(%)'] = df_csv_put[['1Q下跌率(%)']].values *100
df_csv_put['Lastday下跌率(%)'] = df_csv_put[['Lastday下跌率(%)']].values *100
#出場訊號:
#1. 技術滿足: 高檔反轉向下
# 1.1. 一年漲升率 > 35%
# 1.2. 季回跌率破 -10%
#2018/09/17 base from Seymour's Email adjust:
# 1.1. 一年漲升率 > 40%
# 1.2. 季回跌率破 -6%
df_csv_put_stock=df_csv_put.loc[(df_csv_put['1Y上昇率(%)'] > 40) &
(df_csv_put['1Q下跌率(%)'] > -10)]
print('\nStock to sell by {}'.format(str_dirlogcsv))
print(df_csv_put_stock)
# output *_buy.csv
str_dirlogcsv_sell = re.sub(r"bothprices", "_sellranking", str_dirlogcsv)
df_csv_put.to_csv(str_dirlogcsv_sell, encoding = 'cp950')
str_dirlogcsv_sell_stock = re.sub(r"bothprices", "_sellstock", str_dirlogcsv)
df_csv_put_stock.to_csv(str_dirlogcsv_sell_stock, encoding = 'cp950')
return df_csv_put_stock
def file3_main(list_excel_Seymour,dirnamelog,dirdatafolder,str_first_year_month_day):#"景氣循環追蹤股"
# Get present time
#local_time = time.localtime(time.time())
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
#filename_csv_bothprices=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"bothprices"+".csv"
#filename_csv_belowprice=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"belowprice"+".csv"
filename_csv_bothprices=buildup_output_csv(excel_Seymour,'bothprices')
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
#dirlog_csv_belowprice=os.path.join(dirnamelog,filename_csv_belowprice)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","3Y下跌率(%)","1Y下跌率(%)","1Q下跌率(%)",
"3Y上昇率(%)","1Y上昇率(%)","1Q上昇率(%)","PBR"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
for list_row_value in list_row_value_price:
# get key=idx value=PBR to store in dict
dict_rows[list_row_value[0]] = list_row_value[3]
list_temp2 =[]#to store return list
# by key=idx value=PBR
for key,value in dict_rows.items():
print("\nStkIdx:{}, PBR:{}".format(key,value))
local_pdDA = PandasDataAnalysis(key,dirnamelog,dirdatafolder,str_first_year_month_day)
list_temp = local_pdDA.file3_updownrate_threeYearoneYear(value)
list_temp2.append(list_temp)
#print(list_temp2)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
def file3_main_fromsqlite(list_excel_Seymour,dirnamelog,path_db,str_first_year_month_day,opt_verbose='OFF'):#"景氣循環追蹤股"
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
filename_csv_bothprices=buildup_output_csv(excel_Seymour,'bothprices')
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","3Y下跌率(%)","1Y下跌率(%)","1Q下跌率(%)",
"3Y上昇率(%)","1Y上昇率(%)","1Q上昇率(%)","PBR"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
# 20190721 cause StkIdx:1210.0, 價值比:38.16
# str-->float-->int-->str; '1210.0'-->1210.0-->1210-->'1210'
# str(int(float(list_row_value[0])))
for list_row_value in list_row_value_price:
# get key=idx value=PBR to store in dict
# 20190721 cause StkIdx:1210.0, 價值比:38.16
#dict_rows[list_row_value[0]] = list_row_value[3]
dict_rows[str(int(float(list_row_value[0])))] = list_row_value[3]
list_temp2 =[]#to store return list
# by key=idx value=PBR
for key,value in dict_rows.items():
print("\nStkIdx:{}, PBR:{}".format(key,value))
local_pdsql = PandasSqliteAnalysis(key,dirnamelog,path_db,str_first_year_month_day)
list_temp = local_pdsql.file3_updownrate_threeYearoneYear(value)
list_temp2.append(list_temp)
#print(list_temp2)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
# sorting stock to buy
def file3_call(str_dirlogcsv):#"景氣循環追蹤股"
# read daily csv file of 景氣循環追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,3,4,5,9,10],
converters={'3Y下跌率(%)':percent2float,
'1Y下跌率(%)':percent2float,
'1Q下跌率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_call = df_csv.sort_values(['3Y下跌率(%)','1Y下跌率(%)','1Q下跌率(%)','PBR'], ascending=[False, False, False, False])
# convert float to percentage
df_csv_call['3Y下跌率(%)'] = df_csv_call[['3Y下跌率(%)']].values *100
df_csv_call['1Y下跌率(%)'] = df_csv_call[['1Y下跌率(%)']].values *100
df_csv_call['1Q下跌率(%)'] = df_csv_call[['1Q下跌率(%)']].values *100
#進場訊號: 景氣循環低點
# 1. PBR < 1
# 2. 三年回跌率 < -40%
# 3. 一年回跌率 < -20%
# 4. 5,20 日均線黃金交叉
df_csv_call_stock=df_csv_call.loc[(df_csv_call['3Y下跌率(%)'] > -40) &
(df_csv_call['1Y下跌率(%)'] > -20) &
(df_csv_call['PBR'] <= 1)]
print('\nStock to buy by {}'.format(str_dirlogcsv))
print(df_csv_call_stock)
# output *_buy.csv
str_dirlogcsv_buy = re.sub(r"bothprices", "_buyranking", str_dirlogcsv)
df_csv_call.to_csv(str_dirlogcsv_buy, encoding = 'cp950')
str_dirlogcsv_buy_stock = re.sub(r"bothprices", "_buystock", str_dirlogcsv)
df_csv_call_stock.to_csv(str_dirlogcsv_buy_stock, encoding = 'cp950')
return df_csv_call_stock
# sorting stock to sell
def file3_put(str_dirlogcsv):#"景氣循環追蹤股"
# read daily csv file of 景氣循環追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,6,7,8,9,10],
converters={'3Y上昇率(%)':percent2float,
'1Y上昇率(%)':percent2float,
'1Q上昇率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_put = df_csv.sort_values(['3Y上昇率(%)','1Y上昇率(%)','1Q上昇率(%)'], ascending=[False, False, False])
# convert float to percentage
df_csv_put['3Y上昇率(%)'] = df_csv_put[['3Y上昇率(%)']].values *100
df_csv_put['1Y上昇率(%)'] = df_csv_put[['1Y上昇率(%)']].values *100
df_csv_put['1Q上昇率(%)'] = df_csv_put[['1Q上昇率(%)']].values *100
#出場訊號:
#1. 技術滿足: 高檔反轉向下
# 1.1. 三年漲升率 > 65%
# 1.2. 一年漲升率 > 25%
# 1.3. 5,20 日均線死亡交叉
df_csv_put_stock=df_csv_put.loc[(df_csv_put['3Y上昇率(%)'] > 65) &
(df_csv_put['1Y上昇率(%)'] > 25)]
print('\nStock to sell by {}'.format(str_dirlogcsv))
print(df_csv_put_stock)
# output *_buy.csv
str_dirlogcsv_sell = re.sub(r"bothprices", "_sellranking", str_dirlogcsv)
df_csv_put.to_csv(str_dirlogcsv_sell, encoding = 'cp950')
str_dirlogcsv_sell_stock = re.sub(r"bothprices", "_sellstock", str_dirlogcsv)
df_csv_put_stock.to_csv(str_dirlogcsv_sell_stock, encoding = 'cp950')
return df_csv_put_stock
def file4_main(list_excel_Seymour,dirnamelog,dirdatafolder,str_first_year_month_day):#"公用事業追蹤股"
# Get present time
#local_time = time.localtime(time.time())
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
#filename_csv_bothprices=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"bothprices"+".csv"
#filename_csv_belowprice=str(local_time.tm_mon)+str(local_time.tm_mday)+excel_Seymour[8:14]+"belowprice"+".csv"
filename_csv_bothprices=buildup_output_csv(excel_Seymour,'bothprices')
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
#dirlog_csv_belowprice=os.path.join(dirnamelog,filename_csv_belowprice)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","1Y下跌率(%)","1Q下跌率(%)","1M下跌率(%)",
"1Y上昇率(%)","1Q上昇率(%)","1M上昇率(%)","價值比"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
for list_row_value in list_row_value_price:
# get key=idx value=價值比 to store in dict
dict_rows[list_row_value[0]] = list_row_value[2]
list_temp2 =[]#to store return list
# by key=idx value=PBR
for key,value in dict_rows.items():
print("\nStkIdx:{}, 價值比:{}".format(key,value))
local_pdDA = PandasDataAnalysis(key,dirnamelog,dirdatafolder,str_first_year_month_day)
list_temp = local_pdDA.file4_updownrate_YearQuarterMonth(value)
list_temp2.append(list_temp)
#print(list_temp2)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
def file4_main_fromsqlite(list_excel_Seymour,dirnamelog,path_db,str_first_year_month_day,opt_verbose='OFF'):#"公用事業追蹤股"
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
filename_csv_bothprices=buildup_output_csv(excel_Seymour,'bothprices')
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
# Declare output contents
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","1Y下跌率(%)","1Q下跌率(%)","1M下跌率(%)",
"1Y上昇率(%)","1Q上昇率(%)","1M上昇率(%)","價值比"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
dict_rows = {}
# get all CSV files name under data folder
# 20190721 cause StkIdx:1210.0, 價值比:38.16
# str-->float-->int-->str; '1210.0'-->1210.0-->1210-->'1210'
# str(int(float(list_row_value[0])))
for list_row_value in list_row_value_price:
# get key=idx value=價值比 to store in dict
# 20190721 cause StkIdx:1210.0, 價值比:38.16
#dict_rows[list_row_value[0]] = list_row_value[2]
dict_rows[str(int(float(list_row_value[0])))] = list_row_value[2]
list_temp2 =[]#to store return list
# by key=idx value=PBR
for key,value in dict_rows.items():
print("\nStkIdx:{}, 價值比:{}".format(key,value))
local_pdsql = PandasSqliteAnalysis(key,dirnamelog,path_db,str_first_year_month_day)
list_temp = local_pdsql.file4_updownrate_YearQuarterMonth(value)
list_temp2.append(list_temp)
#print(list_temp2)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
def file4_01_main_fromsqlite(list_excel_Seymour,dirnamelog,path_db,str_first_year_month_day,opt_verbose='OFF'):#"低波固收追蹤股"
localexcelrw = excelrw.ExcelRW()
for excel_Seymour in list_excel_Seymour:
print('將讀取Excel file:', excel_Seymour, '的資料')
# Excel file including path
dirlog_ExcelFile=os.path.join(dirnamelog,excel_Seymour)
# Read values of each row
# 2019/02/19 add column '現金殖利率'
list_row_value_price=localexcelrw.readExcel(dirlog_ExcelFile)
# Output CSV file including path
filename_csv_bothprices=buildup_output_csv(excel_Seymour,'bothprices')
dirlog_csv_bothprices=os.path.join(dirnamelog,filename_csv_bothprices)
# Declare output contents
# 2019/02/19 add column '現金股利'(Dividend) '現金殖利率'(Dividend yield)
list_rows_bothprices=[]
list_rows_belowprice=[]
head_rows=["代碼","公司","市價","1Y下跌率(%)","1Q下跌率(%)","1M下跌率(%)",
"1Y上昇率(%)","1Q上昇率(%)","1M上昇率(%)","價值比", "現金股利", "現金殖利率(%)"]
list_rows_bothprices.append(head_rows)
list_rows_belowprice.append(head_rows)
list_temp2 =[]#to store return list
# sort idx, value_ratio and dividend
for list_row_value in list_row_value_price:
# 20190721 cause StkIdx:1210.0, 價值比:38.16
# str-->float-->int-->str; '1210.0'-->1210.0-->1210-->'1210'
# str(int(float(list_row_value[0])))
#idx = list_row_value[0]# idx
idx = str(int(float(list_row_value[0])))
value_ratio = list_row_value[2]# value_ratio
dividend = list_row_value[4]# dividend
print("\nStkIdx:{}, 價值比:{}, 現金股利:{}".format(idx,value_ratio,dividend))
local_pdsql = PandasSqliteAnalysis(idx,dirnamelog,path_db,str_first_year_month_day)
list_temp = local_pdsql.file4_01_updownrate_YearQuarterMonth(value_ratio,dividend)
list_temp2.append(list_temp)
list_rows_bothprices.extend(list_temp2)
#print(list_rows_bothprices)
print("Output file(s): {}".format(dirlog_csv_bothprices))
# Output results to CSV files
localexcelrw.writeCSVbyTable(dirlog_csv_bothprices,list_rows_bothprices)
return dirlog_csv_bothprices
# sorting stock to buy
def file4_call(str_dirlogcsv):#"公用事業追蹤股"
# read daily csv file of 公用事業追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,3,4,5,9,10],
converters={'1Y下跌率(%)':percent2float,
'1Q下跌率(%)':percent2float,
'1M下跌率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_call = df_csv.sort_values(['1Y下跌率(%)','1Q下跌率(%)','1M下跌率(%)','價值比'], ascending=[False, False, False, False])
# convert float to percentage
df_csv_call['1Y下跌率(%)'] = df_csv_call[['1Y下跌率(%)']].values *100
df_csv_call['1Q下跌率(%)'] = df_csv_call[['1Q下跌率(%)']].values *100
df_csv_call['1M下跌率(%)'] = df_csv_call[['1M下跌率(%)']].values *100
#進場訊號: 成長股回檔反轉向上
# 1. 價值比 > 80
# 2. 5,20 日均線黃金交叉(圖形判斷)
df_csv_call_stock=df_csv_call.loc[(df_csv_call['價值比'] >= 70)]
print('\nStock to buy by {}'.format(str_dirlogcsv))
print(df_csv_call_stock)
# output *_buy.csv
str_dirlogcsv_buy = re.sub(r"bothprices", "_buyranking", str_dirlogcsv)
df_csv_call.to_csv(str_dirlogcsv_buy, encoding = 'cp950')
str_dirlogcsv_buy_stock = re.sub(r"bothprices", "_buystock", str_dirlogcsv)
df_csv_call_stock.to_csv(str_dirlogcsv_buy_stock, encoding = 'cp950')
return df_csv_call_stock
# sorting stock to buy
def file4_01_call(str_dirlogcsv):#"低波固收追蹤股"
# read daily csv file of 低波固收追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,3,4,5,9,10,11],
converters={'1Y下跌率(%)':percent2float,
'1Q下跌率(%)':percent2float,
'1M下跌率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_call = df_csv.sort_values(['1Y下跌率(%)','1Q下跌率(%)','1M下跌率(%)','價值比','現金殖利率(%)'],
ascending=[False, False, False, False, False])
# convert float to percentage
df_csv_call['1Y下跌率(%)'] = df_csv_call[['1Y下跌率(%)']].values *100
df_csv_call['1Q下跌率(%)'] = df_csv_call[['1Q下跌率(%)']].values *100
df_csv_call['1M下跌率(%)'] = df_csv_call[['1M下跌率(%)']].values *100
'''
進場訊號:
1.建立基本持股: 存股
1.1 價值比 > 60
or
1.2 殖利率 > 4%
or
2. 逢低加碼: 回檔買進. 每檔最多加碼二次. 每次加碼需間隔一個月以上. 規則是除了建立基本持股的兩項條件之一外,
再加上以下幾項,.
2.1 . 一年回跌率 < -15%
2.2 一個月回跌率 < -6%
2.3. 當日跌幅超過 1%
'''
#df_csv_call_stock=df_csv_call.loc[(df_csv_call['價值比'] >= 60)]
df_csv_call_stock=df_csv_call.loc[(df_csv_call['現金殖利率(%)'] >= 4)]
print('\nStock to buy by {}'.format(str_dirlogcsv))
print(df_csv_call_stock)
# output *_buy.csv
str_dirlogcsv_buy = re.sub(r"bothprices", "_buyranking", str_dirlogcsv)
df_csv_call.to_csv(str_dirlogcsv_buy, encoding = 'cp950')
str_dirlogcsv_buy_stock = re.sub(r"bothprices", "_buystock", str_dirlogcsv)
df_csv_call_stock.to_csv(str_dirlogcsv_buy_stock, encoding = 'cp950')
return df_csv_call_stock
# sorting stock to sell
def file4_put(str_dirlogcsv):#"公用事業追蹤股"
# read daily csv file of 公用事業追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,6,7,8,9,10],
converters={'1Y上昇率(%)':percent2float,
'1Q上昇率(%)':percent2float,
'1M上昇率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_put = df_csv.sort_values(['1Y上昇率(%)','1Q上昇率(%)','1M上昇率(%)','價值比'], ascending=[True, True, True, True])
# convert float to percentage
df_csv_put['1Y上昇率(%)'] = df_csv_put[['1Y上昇率(%)']].values *100
df_csv_put['1Q上昇率(%)'] = df_csv_put[['1Q上昇率(%)']].values *100
df_csv_put['1M上昇率(%)'] = df_csv_put[['1M上昇率(%)']].values *100
#出場訊號:
#1. 技術滿足: 高檔反轉向下
# 1.1. 價值比 < 20
# 1.2. 5,20 日均線死亡交叉(圖形判斷)
df_csv_put_stock=df_csv_put.loc[df_csv_put['價值比'] <= 20]
print('\nStock to sell by {}'.format(str_dirlogcsv))
print(df_csv_put_stock)
# output *_buy.csv
str_dirlogcsv_sell = re.sub(r"bothprices", "_sellranking", str_dirlogcsv)
df_csv_put.to_csv(str_dirlogcsv_sell, encoding = 'cp950')
str_dirlogcsv_sell_stock = re.sub(r"bothprices", "_sellstock", str_dirlogcsv)
df_csv_put_stock.to_csv(str_dirlogcsv_sell_stock, encoding = 'cp950')
return df_csv_put_stock
# sorting stock to sell
def file4_01_put(str_dirlogcsv):#"低波固收追蹤股"
# read daily csv file of 低波固收追蹤股
# # pass to convertes param as a dict
df_csv = pd.read_csv(str_dirlogcsv, encoding = 'cp950', engine='python',
#header = 0,
index_col = False,
usecols = [0,1,2,6,7,8,9,10,11],
converters={'1Y上昇率(%)':percent2float,
'1Q上昇率(%)':percent2float,
'1M上昇率(%)':percent2float} )#sep=',',
# sort by below citeria
df_csv_put = df_csv.sort_values(['1Y上昇率(%)','1Q上昇率(%)','1M上昇率(%)','價值比','現金殖利率(%)'],
ascending=[True, True, True, True, True])
# convert float to percentage
df_csv_put['1Y上昇率(%)'] = df_csv_put[['1Y上昇率(%)']].values *100
df_csv_put['1Q上昇率(%)'] = df_csv_put[['1Q上昇率(%)']].values *100
df_csv_put['1M上昇率(%)'] = df_csv_put[['1M上昇率(%)']].values *100
'''
出場訊號:
1.價格遠高於價值: 沒有存股的價值
1.1. 價值比 < 20
1.2. 殖利率 < 3%
or
2. 停利
2.1. 獲利超過 50%
'''
#df_csv_put_stock=df_csv_put.loc[df_csv_put['價值比'] < 20]
df_csv_put_stock=df_csv_put.loc[df_csv_put['現金殖利率(%)'] <= 3]
print('\nStock to sell by {}'.format(str_dirlogcsv))
print(df_csv_put_stock)
# output *_buy.csv
str_dirlogcsv_sell = re.sub(r"bothprices", "_sellranking", str_dirlogcsv)
df_csv_put.to_csv(str_dirlogcsv_sell, encoding = 'cp950')
str_dirlogcsv_sell_stock = re.sub(r"bothprices", "_sellstock", str_dirlogcsv)
df_csv_put_stock.to_csv(str_dirlogcsv_sell_stock, encoding = 'cp950')
return df_csv_put_stock
# plot file01~04 candle stick and MA curve by each stock index
def file_plotCandlestickMA(df_file_stock_call,dirnamelog,dirdatafolder,str_first_year_month_day,
list_color_ma, str_candlestick_weeklysubfolder,str_buysell_opt):
# to get stock index then plot Candlestick and MA cruve
for stkidx in df_file_stock_call[['代碼']].values.flatten():
localdata_analysis = PandasDataAnalysis(stkidx,dirnamelog,dirdatafolder,str_first_year_month_day)
localdata_analysis.plotCandlestickandMA(list_color_ma,str_candlestick_weeklysubfolder,str_buysell_opt)
def file_plotCandlestickMA_fromsqlite(df_file_stock_call,dirnamelog,path_db,str_first_year_month_day,
list_color_ma, str_candlestick_weeklysubfolder,str_buysell_opt):
# to get stock index then plot Candlestick and MA cruve
for stkidx in df_file_stock_call[['代碼']].values.flatten():
localsql_analysis = PandasSqliteAnalysis(stkidx,dirnamelog,path_db,str_first_year_month_day)
localsql_analysis.plotCandlestickandMA(list_color_ma,str_candlestick_weeklysubfolder,str_buysell_opt)
| 57.207635
| 152
| 0.61022
| 13,749
| 122,882
| 5.1315
| 0.055204
| 0.117571
| 0.161212
| 0.210055
| 0.877992
| 0.861678
| 0.852565
| 0.838986
| 0.827222
| 0.819398
| 0
| 0.038713
| 0.269934
| 122,882
| 2,148
| 153
| 57.207635
| 0.747726
| 0.192746
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| 0.108337
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| 1
| 0.048264
| false
| 0
| 0.015241
| 0.000847
| 0.106689
| 0.063506
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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0
| 8
|
2801e5d9583e2047915c956d2f1e3a86f691e0aa
| 8,805
|
py
|
Python
|
pirates/leveleditor/worldData/port_royal_building_int_14.py
|
itsyaboyrocket/pirates
|
6ca1e7d571c670b0d976f65e608235707b5737e3
|
[
"BSD-3-Clause"
] | 3
|
2021-02-25T06:38:13.000Z
|
2022-03-22T07:00:15.000Z
|
pirates/leveleditor/worldData/port_royal_building_int_14.py
|
itsyaboyrocket/pirates
|
6ca1e7d571c670b0d976f65e608235707b5737e3
|
[
"BSD-3-Clause"
] | null | null | null |
pirates/leveleditor/worldData/port_royal_building_int_14.py
|
itsyaboyrocket/pirates
|
6ca1e7d571c670b0d976f65e608235707b5737e3
|
[
"BSD-3-Clause"
] | 1
|
2021-02-25T06:38:17.000Z
|
2021-02-25T06:38:17.000Z
|
# uncompyle6 version 3.2.0
# Python bytecode 2.4 (62061)
# Decompiled from: Python 2.7.14 (v2.7.14:84471935ed, Sep 16 2017, 20:19:30) [MSC v.1500 32 bit (Intel)]
# Embedded file name: pirates.leveleditor.worldData.port_royal_building_int_14
from pandac.PandaModules import Point3, VBase3, Vec4, Vec3
objectStruct = {'AmbientColors': {}, 'DirectionalColors': {}, 'FogColors': {}, 'FogRanges': {}, 'Objects': {'1155767402.81fxlara0': {'Type': 'Building Interior', 'Name': '', 'AdditionalData': ['interior_spanish_store_tattoo'], 'Instanced': True, 'Objects': {'1175635584.0dxschafe': {'Type': 'Townsperson', 'Category': 'Commoner', 'AnimSet': 'tatoo', 'CustomModel': 'None', 'GhostColor': 'None', 'GhostFX': 0, 'Greeting Animation': '', 'Hpr': VBase3(136.268, 0.0, 0.0), 'Instanced World': 'None', 'Level': '37', 'Notice Animation 1': '', 'Notice Animation 2': '', 'Patrol Radius': '12.0000', 'Pos': Point3(9.164, -0.04, -0.137), 'PoseAnim': '', 'PoseFrame': '', 'Private Status': 'All', 'PropLeft': 'None', 'PropRight': 'None', 'Requires Quest Interest': False, 'Respawns': True, 'Scale': VBase3(1.0, 1.0, 1.0), 'ShopID': 'PORT_ROYAL_DEFAULTS', 'Start State': 'Idle', 'StartFrame': '0', 'Team': 'Villager', 'TrailFX': 'None', 'TrailLeft': 'None', 'TrailRight': 'None'}, '1175635840.0dxschafe': {'Type': 'Furniture - Fancy', 'DisableCollision': False, 'Hpr': VBase3(92.139, 0.0, 0.0), 'Pos': Point3(-22.854, -3.677, 0.0), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/props/bookshelf_fancy'}}, '1175635840.0dxschafe1': {'Type': 'Furniture - Fancy', 'DisableCollision': False, 'Hpr': VBase3(-88.772, 0.0, 0.0), 'Pos': Point3(21.472, 3.55, 0.0), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/props/chair_fancy'}}, '1175636096.0dxschafe2': {'Type': 'Furniture - Fancy', 'DisableCollision': False, 'Hpr': VBase3(-86.618, 0.0, 0.0), 'Pos': Point3(21.64, -1.181, 0.0), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/props/chair_fancy'}}, '1175636096.0dxschafe3': {'Type': 'Furniture - Fancy', 'DisableCollision': False, 'Hpr': VBase3(-84.678, 0.0, 0.0), 'Pos': Point3(21.489, -6.129, 0.0), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/props/chair_fancy'}}, '1178654336.0dchiappe': {'Type': 'Interactive Prop', 'Hpr': VBase3(36.206, 0.0, 0.0), 'Objects': {}, 'Pos': Point3(9.091, -0.238, 0.0), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/props/chair_bar'}, 'interactAble': 'npc', 'interactType': 'sit'}, '1178654464.0dchiappe': {'Type': 'Interactive Prop', 'Hpr': VBase3(-57.685, 0.0, 0.0), 'Pos': Point3(12.073, -2.827, 0.0), 'Scale': VBase3(1.0, 1.0, 1.0), 'VisSize': '', 'Visual': {'Model': 'models/props/chair_bar'}, 'interactAble': 'npc', 'interactType': 'sit'}, '1178654720.0dchiappe': {'Type': 'Townsperson', 'Category': 'Commoner', 'Aggro Radius': '12.0000', 'AnimSet': 'tatoo_receive', 'CustomModel': 'None', 'GhostColor': 'None', 'GhostFX': 0, 'Greeting Animation': '', 'HelpID': 'NONE', 'Holiday': '', 'Hpr': VBase3(124.323, 0.784, 0.0), 'Instanced World': 'None', 'Level': '37', 'Notice Animation 1': '', 'Notice Animation 2': '', 'Patrol Radius': '12.0000', 'Pos': Point3(12.283, -2.401, -0.098), 'PoseAnim': '', 'PoseFrame': '', 'Private Status': 'All', 'PropLeft': 'None', 'PropRight': 'None', 'Requires Quest Interest': False, 'Respawns': True, 'Scale': VBase3(1.0, 1.0, 1.0), 'ShopID': 'PORT_ROYAL_DEFAULTS', 'Start State': 'Idle', 'StartFrame': '0', 'Team': 'Villager', 'TrailFX': 'None', 'TrailLeft': 'None', 'TrailRight': 'None', 'VisSize': ''}, '1201027200.0dxschafe': {'Type': 'Door Locator Node', 'Name': 'door_locator', 'Hpr': VBase3(-90.0, 0.0, 0.0), 'Pos': Point3(12.363, 6.985, 0.805), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1201028352.0dxschafe': {'Type': 'Townsperson', 'Category': 'Tattoo', 'AnimSet': 'default', 'CustomModel': 'None', 'GhostColor': 'None', 'GhostFX': 0, 'Greeting Animation': '', 'Hpr': VBase3(-153.843, 0.0, 0.0), 'Instanced World': 'None', 'Level': '37', 'Notice Animation 1': '', 'Notice Animation 2': '', 'Patrol Radius': '12.0000', 'Pos': Point3(-0.627, 7.127, 0.0), 'PoseAnim': '', 'PoseFrame': '', 'Private Status': 'All', 'PropLeft': 'None', 'PropRight': 'None', 'Requires Quest Interest': False, 'Respawns': True, 'Scale': VBase3(1.0, 1.0, 1.0), 'ShopID': 'PORT_ROYAL_DEFAULTS', 'Start State': 'Idle', 'StartFrame': '0', 'Team': 'Villager', 'TrailFX': 'None', 'TrailLeft': 'None', 'TrailRight': 'None'}, '1201112671.28dxschafe': {'Type': 'Light - Dynamic', 'Attenuation': '0.005', 'ConeAngle': '60.0000', 'DropOff': '31.9880', 'FlickRate': '0.5000', 'Flickering': False, 'Hpr': VBase3(-147.293, -18.16, -2.614), 'Intensity': '1.3012', 'LightType': 'SPOT', 'Pos': Point3(-6.215, 16.25, 10.428), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (1, 1, 1, 1), 'Model': 'models/props/light_tool_bulb'}}, '1201113663.94dxschafe': {'Type': 'Light - Dynamic', 'Attenuation': '0.005', 'ConeAngle': '48.5241', 'DropOff': '52.0482', 'FlickRate': '0.5000', 'Flickering': False, 'Hpr': VBase3(158.542, -11.41, 4.535), 'Intensity': '1.2651', 'LightType': 'SPOT', 'Pos': Point3(6.098, 15.663, 8.9), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (1, 1, 1, 1), 'Model': 'models/props/light_tool_bulb'}}, '1201114356.14dxschafe': {'Type': 'Light - Dynamic', 'Attenuation': '0.005', 'ConeAngle': '60.0000', 'DropOff': '66.1446', 'FlickRate': '0.5000', 'Flickering': False, 'Hpr': VBase3(-21.055, 42.215, 3.401), 'Intensity': '0.5904', 'LightType': 'SPOT', 'Pos': Point3(8.168, -6.146, -0.624), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (1, 1, 1, 1), 'Model': 'models/props/light_tool_bulb'}}}, 'Visual': {'Model': 'models/buildings/interior_spanish_npc'}}}, 'Node Links': [], 'Layers': {'Collisions': ['1184008208.59kmuller', '1184016064.62kmuller', '1184013852.84kmuller', '1185822696.06kmuller', '1184006140.32kmuller', '1184002350.98kmuller', '1184007573.29kmuller', '1184021176.59kmuller', '1184005963.59kmuller', '1188324241.31akelts', '1184006537.34kmuller', '1184006605.81kmuller', '1187139568.33kmuller', '1188324186.98akelts', '1184006730.66kmuller', '1184007538.51kmuller', '1184006188.41kmuller', '1184021084.27kmuller', '1185824396.94kmuller', '1185824250.16kmuller', '1185823630.52kmuller', '1185823760.23kmuller', '1185824497.83kmuller', '1185824751.45kmuller', '1187739103.34akelts', '1188323993.34akelts', '1184016538.29kmuller', '1185822200.97kmuller', '1184016225.99kmuller', '1195241421.34akelts', '1195242796.08akelts', '1184020642.13kmuller', '1195237994.63akelts', '1184020756.88kmuller', '1184020833.4kmuller', '1185820992.97kmuller', '1185821053.83kmuller', '1184015068.54kmuller', '1184014935.82kmuller', '1185821432.88kmuller', '1185821701.86kmuller', '1195240137.55akelts', '1195241539.38akelts', '1195238422.3akelts', '1195238473.22akelts', '1185821453.17kmuller', '1184021269.96kmuller', '1185821310.89kmuller', '1185821165.59kmuller', '1185821199.36kmuller', '1185822035.98kmuller', '1184015806.59kmuller', '1185822059.48kmuller', '1185920461.76kmuller', '1194984449.66akelts', '1185824206.22kmuller', '1184003446.23kmuller', '1184003254.85kmuller', '1184003218.74kmuller', '1184002700.44kmuller', '1186705073.11kmuller', '1187658531.86akelts', '1186705214.3kmuller', '1185824927.28kmuller', '1184014204.54kmuller', '1184014152.84kmuller']}, 'ObjectIds': {'1155767402.81fxlara0': '["Objects"]["1155767402.81fxlara0"]', '1175635584.0dxschafe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1175635584.0dxschafe"]', '1175635840.0dxschafe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1175635840.0dxschafe"]', '1175635840.0dxschafe1': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1175635840.0dxschafe1"]', '1175636096.0dxschafe2': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1175636096.0dxschafe2"]', '1175636096.0dxschafe3': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1175636096.0dxschafe3"]', '1178654336.0dchiappe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1178654336.0dchiappe"]', '1178654464.0dchiappe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1178654464.0dchiappe"]', '1178654720.0dchiappe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1178654720.0dchiappe"]', '1201027200.0dxschafe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1201027200.0dxschafe"]', '1201028352.0dxschafe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1201028352.0dxschafe"]', '1201112671.28dxschafe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1201112671.28dxschafe"]', '1201113663.94dxschafe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1201113663.94dxschafe"]', '1201114356.14dxschafe': '["Objects"]["1155767402.81fxlara0"]["Objects"]["1201114356.14dxschafe"]'}}
extraInfo = {'camPos': Point3(576.782, -179.895, 58.8649), 'camHpr': VBase3(111.591, -16.2399, 0), 'focalLength': 1.39999997616, 'skyState': 2, 'fog': 0}
| 1,257.857143
| 8,351
| 0.67314
| 1,058
| 8,805
| 5.574669
| 0.362949
| 0.013225
| 0.013225
| 0.017633
| 0.464564
| 0.38335
| 0.3686
| 0.297219
| 0.285181
| 0.285181
| 0
| 0.255235
| 0.078024
| 8,805
| 7
| 8,352
| 1,257.857143
| 0.471298
| 0.026349
| 0
| 0
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| 0.628078
| 0.1747
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| 0
| 0
| 0
| 1
| 0
| false
| 0
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| 0
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| 0
| null | 0
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| 1
| 1
| 1
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| null | 0
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| 0
| 0
|
0
| 7
|
e6563d352053217704f4580596656fb6eaf866aa
| 2,012
|
py
|
Python
|
tests/test_matcher_header_re.py
|
sanjioh/django-header-filter
|
d348449619c71bdd6a2c957ee47c1c67a57bdec2
|
[
"MIT"
] | 11
|
2016-12-03T21:45:30.000Z
|
2022-01-11T08:57:55.000Z
|
tests/test_matcher_header_re.py
|
sanjioh/django-header-filter
|
d348449619c71bdd6a2c957ee47c1c67a57bdec2
|
[
"MIT"
] | 17
|
2019-07-12T20:36:40.000Z
|
2020-01-09T15:03:40.000Z
|
tests/test_matcher_header_re.py
|
sanjioh/django-header-filter
|
d348449619c71bdd6a2c957ee47c1c67a57bdec2
|
[
"MIT"
] | null | null | null |
import re
from header_filter.matchers import HeaderRegexp
def test_header_name_and_value_match_re_pattern(rf):
matcher = HeaderRegexp(r'^HTTP_X_A.*$', r'^val_.$')
request = rf.get('/', **{'HTTP_X_A_XYZ': 'val_x'})
assert matcher.match(request) is True
def test_header_name_and_value_match_re_object(rf):
matcher = HeaderRegexp(re.compile(r'^HTTP_X_A.*$'), re.compile(r'^val_.$'))
request = rf.get('/', **{'HTTP_X_A_XYZ': 'val_x'})
assert matcher.match(request) is True
def test_header_name_doesnt_match_re_pattern(rf):
matcher = HeaderRegexp(r'^HTTP_X_A.*$', r'^val_.$')
request = rf.get('/', **{'HTTP_X_B_XYZ': 'val_x'})
assert matcher.match(request) is False
def test_header_name_doesnt_match_re_object(rf):
matcher = HeaderRegexp(re.compile(r'^HTTP_X_A.*$'), re.compile(r'^val_.$'))
request = rf.get('/', **{'HTTP_X_B_XYZ': 'val_x'})
assert matcher.match(request) is False
def test_header_value_doesnt_match_re_pattern(rf):
matcher = HeaderRegexp(r'^HTTP_X_A.*$', r'^val_.$')
request = rf.get('/', **{'HTTP_X_A_XYZ': 'val_'})
assert matcher.match(request) is False
def test_header_value_doesnt_match_re_object(rf):
matcher = HeaderRegexp(re.compile(r'^HTTP_X_A.*$'), re.compile(r'^val_.$'))
request = rf.get('/', **{'HTTP_X_A_XYZ': 'val_'})
assert matcher.match(request) is False
def test_header_name_and_value_dont_match_re_pattern(rf):
matcher = HeaderRegexp(r'^HTTP_X_A.*$', r'^val_.$')
request = rf.get('/', **{'HTTP_X_B_XYZ': 'val_'})
assert matcher.match(request) is False
def test_header_name_and_value_dont_match_re_object(rf):
matcher = HeaderRegexp(re.compile(r'^HTTP_X_A.*$'), re.compile(r'^val_.$'))
request = rf.get('/', **{'HTTP_X_B_XYZ': 'val_'})
assert matcher.match(request) is False
def test_repr():
assert (
repr(HeaderRegexp(re.compile(r'^HTTP_X_A.*$'), re.compile(r'^val_.$')))
== "HeaderRegexp(re.compile('^HTTP_X_A.*$'), re.compile('^val_.$'))"
)
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| 79
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| 2,012
| 58
| 80
| 34.689655
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| 0.185885
| 0.030815
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| 0
| 0
| 0.230769
| 1
| 0.230769
| false
| 0
| 0.051282
| 0
| 0.282051
| 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
| 0
| 0
| 0
|
0
| 7
|
0521727cca937a5672cfd77efc13e41db0438393
| 41,969
|
py
|
Python
|
main.py
|
shadsbot/AutoSaver
|
3e9cede8f91c880be45637efbf28492cae2bf2f5
|
[
"MIT"
] | null | null | null |
main.py
|
shadsbot/AutoSaver
|
3e9cede8f91c880be45637efbf28492cae2bf2f5
|
[
"MIT"
] | null | null | null |
main.py
|
shadsbot/AutoSaver
|
3e9cede8f91c880be45637efbf28492cae2bf2f5
|
[
"MIT"
] | null | null | null |
#pip install win10toast
from win32gui import GetWindowText, GetForegroundWindow
from win10toast import ToastNotifier
import time
import win32com.client
import pystray
import PIL.Image
from ConfigParser import SafeConfigParser
import tempfile
import os
import sys
import threading
from Tkinter import *
from tkFileDialog import askopenfilename
from io import BytesIO
import base64
defaulticon = 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'''
im = PIL.Image.open(BytesIO(base64.b64decode(defaulticon)))
# Create that toast!
toaster = ToastNotifier()
# Import settings, if none available, make them
parser = SafeConfigParser()
configfile = tempfile.gettempdir() + "\\autosave_prog.ini"
if not os.path.isfile(configfile):
with open(configfile, 'w+') as f:
f.write("[config]\n")
f.write("interval = 300\n") # Time between saves in seconds
f.write("deftitle = CLIP STUDIO PAINT\n") # default title
f.write("trayicon = asdf.png\n") # default icon
f.write("toasticon = asdf.ico\n") # default toast icon
f.write("duration = 10") # how long the toast stays up for
parser.read(configfile)
# Globals are bad, mmkay
global INTERVAL, TRAYICON, TOASTICON, PROGRAM, DURATION, shell, icon, cont, configfile, state
INTERVAL = float(parser.get('config','interval'))
TRAYICON = parser.get('config','trayicon')
TOASTICON = parser.get('config','toasticon')
PROGRAM = parser.get('config', 'deftitle')
DURATION = float(parser.get('config','duration'))
cont = True
state = False
print "Debug: %s %s %s %s %s" % (INTERVAL,TRAYICON,TOASTICON,PROGRAM,DURATION)
shell = win32com.client.Dispatch("WScript.Shell")
def askopenfileico(ti):
file = askopenfilename(filetypes=(("ICO files","*.ico"),("All files","*.*")))
print file
ti.set(file)
return file
def askopenfileimg(ti):
file = askopenfilename(filetypes=(("PNG files","*.png"),("JPEG files","*.jpg"),("ICO files","*.ico")))
ti.set(file)
return file
def callback(_interval,_toasticon,_toastlength,_trayicon,_progtitle, main):
print "We have a winner!"
INTERVAL = _interval
TOASTICON = _toasticon
DURATION = _toastlength
TRAYICON = _trayicon
PROGRAM = _progtitle
os.remove(configfile) # Honestly I can probably do better than this
with open(configfile, 'w+') as f:
f.write("[config]\n")
f.write("interval = %s\n" % INTERVAL) # Time between saves in seconds
f.write("deftitle = %s\n" % PROGRAM) # default title
f.write("trayicon = %s\n" % TRAYICON) # default icon
f.write("toasticon = %s\n" % TOASTICON) # default toast icon
f.write("duration = %s" % DURATION) # how long the toast stays up for
main.destroy()
# Settings window
def settingswindow():
main = Tk()
main.title("Settings")
label = []
entry = []
label.append(Label(main,text="Interval (seconds)"))
label.append(Label(main,text="Toast Icon"))
label.append(Label(main,text="Toast Length"))
label.append(Label(main,text="Tray Icon"))
label.append(Label(main,text="Program Title"))
_intrvl = StringVar()
_toasticon = StringVar()
_toastlen = StringVar()
_trayicon = StringVar()
_progtitle = StringVar()
_intrvl.set(INTERVAL)
_toasticon.set(TOASTICON)
_toastlen.set(DURATION)
_trayicon.set(TRAYICON)
_progtitle.set(PROGRAM)
entry.append(Entry(main, textvariable=_intrvl))
entry.append(Entry(main, textvariable=_toasticon))
entry.append(Entry(main, textvariable=_toastlen))
entry.append(Entry(main, textvariable=_trayicon))
entry.append(Entry(main, textvariable=_progtitle))
b = 2
for a in label:
a.grid(column=0,row=b)
b = b+1
b = 2
for a in entry:
a.grid(column=1,row=b)
b = b+1
Button(main,text="Browse",command=lambda: askopenfileico(_toasticon)).grid(column=3,row=3)
Button(main,text="Browse",command=lambda: askopenfileimg(_trayicon)).grid(column=3,row=5)
Button(main,text="Save Configuration", command=lambda: callback(_intrvl.get(),_toasticon.get(),_toastlen.get(),_trayicon.get(),_progtitle.get(), main)).grid(column=0,row=b)
main.mainloop()
settingswindow()
def actual_prog(self):
while self.running:
while state:
time.sleep(INTERVAL)
current_window = GetWindowText(GetForegroundWindow())
print current_window
if PROGRAM in current_window:
toaster.show_toast(
"Autosaving in 10s",
"%s" % current_window, icon_path=TOASTICON, duration=DURATION)
keystrokes(current_window)
time.sleep(5) # There's gonna be a five second delay after saving config settings and it actually taking effect but whatever
def show_settings():
return True
class prog_thread (threading.Thread):
def __init__(self, threadID):
threading.Thread.__init__(self)
self.threadID = threadID
self.running = True
def run(self):
print "Starting thread %s" % self.threadID
actual_prog(self)
def stop():
self.running = False
class settings_thread (threading.Thread):
def __init__(self, threadID):
threading.Thread.__init__(self)
self.threadID = threadID
def run(self):
print "Starting thread %s" % self.threadID
show_settings()
print "Starting thread"
thread1 = prog_thread(1)
threadSettings = prog_thread(2)
thread1.setDaemon(True)
thread1.start()
print "Thread started"
# System tray
state = True
def keystrokes(current_window):
shell.AppActivate(current_window)
shell.SendKeys("^s")
def on_clicked(icon, item):
global state
state = not item.checked
def open_settings():
print "testing!"
main.deiconify()
def exit_prog():
icon.stop()
state = False
thread1.stop()
thread1.join()
sys.exit(0)
try:
imageIcon = PIL.Image.open(TRAYICON)
except:
imageIcon = im
icon = pystray.Icon("AutoSave", imageIcon, "AutoSave", menu=pystray.Menu(
pystray.MenuItem("Enable", on_clicked,checked=lambda item: state),
#pystray.MenuItem("Settings", open_settings),
pystray.MenuItem("Exit", exit_prog)
))
icon.run()
sys.exit(0)
| 217.455959
| 35,625
| 0.890467
| 2,964
| 41,969
| 12.584345
| 0.384278
| 0.123861
| 0.138499
| 0.166649
| 0.19008
| 0.172895
| 0.148686
| 0.137212
| 0.128391
| 0.10504
| 0
| 0.113634
| 0.033358
| 41,969
| 192
| 35,626
| 218.588542
| 0.805788
| 0.013129
| 0
| 0.157576
| 0
| 0.006061
| 0.879317
| 0.864028
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0.090909
| null | null | 0.054545
| 0
| 0
| 1
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
055272743f9ec8915690d3b3e14ee5af5054e0a4
| 16,695
|
py
|
Python
|
snakepit/test/test_connect.py
|
tv42/snakepit
|
be70505d8e838c6e9ba68828d84df280d714aedb
|
[
"MIT"
] | null | null | null |
snakepit/test/test_connect.py
|
tv42/snakepit
|
be70505d8e838c6e9ba68828d84df280d714aedb
|
[
"MIT"
] | null | null | null |
snakepit/test/test_connect.py
|
tv42/snakepit
|
be70505d8e838c6e9ba68828d84df280d714aedb
|
[
"MIT"
] | 1
|
2021-09-02T13:53:48.000Z
|
2021-09-02T13:53:48.000Z
|
import nose
from nose.tools import eq_
import os
import sqlalchemy as sq
from snakepit import create, connect
from snakepit.test.util import maketemp, assert_raises
class Get_Hive_Test(object):
def test_simple(self):
tmp = maketemp()
hive_uri = 'sqlite:///%s' % os.path.join(tmp, 'hive.db')
hive_metadata = create.create_hive(
hive_uri=hive_uri,
)
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=hive_uri,
db_type='INTEGER',
)
hive_metadata.bind.dispose()
hive_metadata = connect.get_hive(
hive_uri=hive_uri,
)
t = hive_metadata.tables['partition_dimension_metadata']
got = t.select().execute().fetchall()
got = [dict(row) for row in got]
#TODO
for row in got: del row['db_type']
eq_(
got,
[
dict(
id=dimension_id,
name='frob',
index_uri=hive_uri,
),
],
)
hive_metadata.bind.dispose()
class Get_Engine_Test(object):
def test_simple(self):
tmp = maketemp()
p42_metadata = sq.MetaData()
p42_metadata.bind = sq.create_engine(
'sqlite:///%s' % os.path.join(tmp, 'p42.db'),
strategy='threadlocal',
)
t_frob = sq.Table(
'frob',
p42_metadata,
sq.Column('id', sq.Integer, primary_key=True),
sq.Column('xyzzy', sq.Integer, nullable=False),
)
p42_metadata.create_all()
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' \
% os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
directory_metadata.bind.dispose()
node_id = create.create_node(
hive_metadata=hive_metadata,
dimension_id=dimension_id,
node_name='node42',
node_uri=str(p42_metadata.bind.url),
)
node_engine = connect.assign_node(
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=1,
)
node_engine.dispose()
got = connect.get_engine(
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=1,
)
assert isinstance(got, sq.engine.Engine)
eq_(str(got.url), str(p42_metadata.bind.url))
got.dispose()
hive_metadata.bind.dispose()
p42_metadata.bind.dispose()
def test_bad_dimension(self):
tmp = maketemp()
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='these-are-nt-the-droids',
directory_uri='fake',
db_type='INTEGER',
)
e = assert_raises(
connect.NoSuchDimensionError,
connect.get_engine,
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=123,
)
eq_(
str(e),
'No such dimension: %r' % 'frob',
)
hive_metadata.bind.dispose()
def test_bad_id(self):
tmp = maketemp()
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' \
% os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
create.create_node(
hive_metadata=hive_metadata,
dimension_id=dimension_id,
node_name='node42',
node_uri='sqlite://',
)
dimension_value = 1
node_engine = connect.assign_node(
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=dimension_value,
)
node_engine.dispose()
directory_metadata.bind.dispose()
e = assert_raises(
connect.NoSuchIdError,
connect.get_engine,
hive_metadata=hive_metadata,
dimension_name='frob',
# make it wrong to trigger the error
dimension_value=dimension_value+1,
)
eq_(
str(e),
'No such id: dimension %r, dimension_value %r' \
% ('frob', dimension_value+1),
)
hive_metadata.bind.dispose()
def test_bad_node(self):
tmp = maketemp()
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' % os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
directory_metadata.bind.dispose()
node_id = create.create_node(
hive_metadata=hive_metadata,
dimension_id=dimension_id,
node_name='node34',
node_uri='sqlite://',
)
node_engine = connect.assign_node(
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=1,
)
node_engine.dispose()
hive_metadata.tables['node_metadata'].delete().execute()
hive_metadata.bind.dispose()
e = assert_raises(
connect.NoSuchNodeError,
connect.get_engine,
hive_metadata,
'frob',
1,
)
eq_(
str(e),
'No such node: dimension %r, node_id %d' \
% ('frob', node_id)
)
class AssignNode_Test(object):
def test_simple(self):
tmp = maketemp()
p42_metadata = sq.MetaData()
p42_metadata.bind = sq.create_engine(
'sqlite:///%s' % os.path.join(tmp, 'p42.db'),
strategy='threadlocal',
)
t_frob = sq.Table(
'frob',
p42_metadata,
sq.Column('id', sq.Integer, primary_key=True),
sq.Column('xyzzy', sq.Integer, nullable=False),
)
p42_metadata.create_all()
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' % os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
directory_metadata.bind.dispose()
create.create_node(
hive_metadata=hive_metadata,
dimension_id=dimension_id,
node_name='node42',
node_uri=str(p42_metadata.bind.url),
)
node_engine = connect.assign_node(hive_metadata, 'frob', 1)
assert isinstance(node_engine, sq.engine.Engine)
eq_(str(node_engine.url), str(p42_metadata.bind.url))
node_engine.dispose()
def test_repeat(self):
# assign_node is idempotent and shouldn't even be racy against
# itself (latter not really unit testable)
tmp = maketemp()
p42_metadata = sq.MetaData()
p42_metadata.bind = sq.create_engine(
'sqlite:///%s' % os.path.join(tmp, 'p42.db'),
strategy='threadlocal',
)
t_frob = sq.Table(
'frob',
p42_metadata,
sq.Column('id', sq.Integer, primary_key=True),
sq.Column('xyzzy', sq.Integer, nullable=False),
)
p42_metadata.create_all()
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' % os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
create.create_node(
hive_metadata=hive_metadata,
dimension_id=dimension_id,
node_name='node42',
node_uri=str(p42_metadata.bind.url),
)
node_engine = connect.assign_node(hive_metadata, 'frob', 1)
assert isinstance(node_engine, sq.engine.Engine)
eq_(str(node_engine.url), str(p42_metadata.bind.url))
node_engine.dispose()
node_engine = connect.assign_node(hive_metadata, 'frob', 1)
assert isinstance(node_engine, sq.engine.Engine)
eq_(str(node_engine.url), str(p42_metadata.bind.url))
node_engine.dispose()
t = directory_metadata.tables['hive_primary_frob']
q = sq.select(
[sq.func.count('*').label('count')],
from_obj=[t],
)
r = q.execute().fetchone()
got = r['count']
eq_(got, 1)
directory_metadata.bind.dispose()
def test_bad_no_node(self):
tmp = maketemp()
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' \
% os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
node_id = create.create_node(
hive_metadata=hive_metadata,
# make it wrong to trigger the error
dimension_id=dimension_id+1,
node_name='node42',
node_uri='fake',
)
e = assert_raises(
connect.NoNodesForDimensionError,
connect.assign_node,
hive_metadata,
'frob',
1,
)
eq_(
str(e),
'No nodes found for dimension: %r' % 'frob',
)
class UnassignNode_Test(object):
def test_simple(self):
tmp = maketemp()
p42_metadata = sq.MetaData()
p42_metadata.bind = sq.create_engine(
'sqlite:///%s' % os.path.join(tmp, 'p42.db'),
strategy='threadlocal',
)
t_frob = sq.Table(
'frob',
p42_metadata,
sq.Column('id', sq.Integer, primary_key=True),
sq.Column('xyzzy', sq.Integer, nullable=False),
)
p42_metadata.create_all()
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' % os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
directory_metadata.bind.dispose()
create.create_node(
hive_metadata=hive_metadata,
dimension_id=dimension_id,
node_name='node42',
node_uri=str(p42_metadata.bind.url),
)
node_engine = connect.assign_node(hive_metadata, 'frob', 1)
assert isinstance(node_engine, sq.engine.Engine)
eq_(str(node_engine.url), str(p42_metadata.bind.url))
node_engine.dispose()
got = connect.unassign_node(
hive_metadata=hive_metadata,
dimension_name= 'frob',
dimension_value=1,
node_name='node42',
)
eq_(got, None)
e = assert_raises(
connect.NoSuchIdError,
connect.get_engine,
hive_metadata,
'frob',
1,
)
eq_(
str(e),
'No such id: dimension %r, dimension_value %r'
% ('frob', 1),
)
def test_bad_no_dimension(self):
tmp = maketemp()
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
e = assert_raises(
connect.NoSuchDimensionError,
connect.unassign_node,
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=1,
node_name='fake',
)
eq_(
str(e),
'No such dimension: %r' % 'frob',
)
def test_bad_no_node(self):
tmp = maketemp()
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' \
% os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
node_id = create.create_node(
hive_metadata=hive_metadata,
# make it wrong to trigger the error
dimension_id=dimension_id+1,
node_name='node42',
node_uri='fake',
)
e = assert_raises(
connect.NoNodesForDimensionError,
connect.unassign_node,
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=1,
node_name='not-exist',
)
eq_(
str(e),
'No nodes found for dimension: %r' % 'frob',
)
def test_bad_no_assignment(self):
tmp = maketemp()
directory_metadata = create.create_primary_index(
directory_uri='sqlite:///%s' \
% os.path.join(tmp, 'directory.db'),
dimension_name='frob',
db_type='INTEGER',
)
hive_metadata = create.create_hive(
'sqlite:///%s' % os.path.join(tmp, 'hive.db'))
dimension_id = create.create_dimension(
hive_metadata=hive_metadata,
dimension_name='frob',
directory_uri=str(directory_metadata.bind.url),
db_type='INTEGER',
)
node_id = create.create_node(
hive_metadata=hive_metadata,
dimension_id=dimension_id,
node_name='node42',
node_uri='fake',
)
e = assert_raises(
connect.NoSuchNodeForDimensionValueError,
connect.unassign_node,
hive_metadata=hive_metadata,
dimension_name='frob',
dimension_value=1,
node_name='node42',
)
eq_(
str(e),
'Node not found for dimension value:'
+' dimension %r value %r, node name %r'
% ('frob', 1, 'node42'),
)
| 31.8
| 77
| 0.538365
| 1,723
| 16,695
| 4.949507
| 0.07603
| 0.123827
| 0.056285
| 0.084428
| 0.871951
| 0.857294
| 0.83947
| 0.821998
| 0.802064
| 0.795263
| 0
| 0.010061
| 0.351063
| 16,695
| 524
| 78
| 31.860687
| 0.777091
| 0.012639
| 0
| 0.719222
| 0
| 0
| 0.087693
| 0.003095
| 0
| 0
| 0
| 0.001908
| 0.030238
| 1
| 0.025918
| false
| 0
| 0.012959
| 0
| 0.047516
| 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
|
5530e831952a57565ad66c675259522cc29d5741
| 34,241
|
py
|
Python
|
switch_network_LQUBO/switch_networks/network_instance_data.py
|
seangholson/lqubo
|
05bf1dd03cf76349b981a543e751217beb4a1b0b
|
[
"Apache-2.0"
] | 1
|
2020-03-05T18:32:04.000Z
|
2020-03-05T18:32:04.000Z
|
switch_network_LQUBO/switch_networks/network_instance_data.py
|
seangholson/lqubo
|
05bf1dd03cf76349b981a543e751217beb4a1b0b
|
[
"Apache-2.0"
] | 22
|
2020-05-04T19:01:25.000Z
|
2021-01-01T22:02:59.000Z
|
switch_network_LQUBO/switch_networks/network_instance_data.py
|
seangholson/QAP-Quantum-Computing
|
05bf1dd03cf76349b981a543e751217beb4a1b0b
|
[
"Apache-2.0"
] | 1
|
2021-11-12T04:06:34.000Z
|
2021-11-12T04:06:34.000Z
|
permutation_network_data = {
4: {
'switch_stages': [[[0, 1], [2, 3]],
[[0, 1], [2, 3]],
[[2, 3]]],
'swap_stages': [[[1, 2]],
[[1, 2]],
[[0, 0]]]
},
8: {
'switch_stages': [[[0, 1], [2, 3], [4, 5], [6, 7]],
[[0, 1], [2, 3], [4, 5], [6, 7]],
[[0, 1], [2, 3], [4, 5], [6, 7]],
[[2, 3], [6, 7]],
[[2, 3], [4, 5], [6, 7]]],
'swap_stages': [[[1, 4], [1, 2], [3, 5], [3, 6]],
[[1, 2], [5, 6]],
[[1, 2], [5, 6]],
[[3, 6], [3, 5], [1, 2], [1, 4]],
[[0, 0]]]
},
16: {
'switch_stages': [[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15]],
[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15]],
[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15]],
[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15]],
[[2, 3], [6, 7], [10, 11], [14, 15]],
[[2, 3], [4, 5], [6, 7], [10, 11], [12, 13], [14, 15]],
[[2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15]]],
'swap_stages': [[[1, 8], [1, 4], [1, 2], [3, 9], [3, 12], [3, 6], [5, 10], [7, 11], [7, 13], [7, 14]],
[[1, 4], [1, 2], [3, 5], [3, 6], [9, 12], [9, 10], [11, 13], [11, 14]],
[[1, 2], [5, 6], [9, 10], [13, 14]],
[[1, 2], [5, 6], [9, 10], [13, 14]],
[[3, 6], [3, 5], [1, 2], [1, 4], [11, 14], [11, 13], [9, 10], [9, 12]],
[[7, 14], [7, 13], [7, 11], [5, 10], [3, 6], [3, 12], [3, 9], [1, 2], [1, 4], [1, 8]],
[[0, 0]]]
},
32: {
'switch_stages': [[[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]],
[[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]],
[[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]],
[[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]],
[[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]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23], [26, 27], [30, 31]],
[[2, 3], [4, 5], [6, 7], [10, 11], [12, 13], [14, 15], [18, 19], [20, 21], [22, 23], [26, 27], [28, 29],
[30, 31]],
[[2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15], [18, 19], [20, 21], [22, 23], [24, 25],
[26, 27], [28, 29], [30, 31]],
[[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]]],
'swap_stages': [[[1, 16], [1, 8], [1, 4], [1, 2], [3, 17], [3, 24], [3, 12], [3, 6], [5, 18], [5, 9], [5, 20], [5, 10],
[7, 19], [7, 25], [7, 28], [7, 14], [11, 21], [11, 26], [11, 13], [11, 22], [15, 23], [15, 27], [15, 29],
[15, 30]],
[[1, 8], [1, 4], [1, 2], [3, 9], [3, 12], [3, 6], [5, 10], [7, 11], [7, 13], [7, 14], [17, 24], [17, 20],
[17, 18], [19, 25], [19, 28], [19, 22], [21, 26], [23, 27], [23, 29], [23, 30]],
[[1, 4], [1, 2], [3, 5], [3, 6], [9, 12], [9, 10], [11, 13], [11, 14], [17, 20], [17, 18], [19, 21], [19, 22],
[25, 28], [25, 26], [27, 29], [27, 30]],
[[1, 2], [5, 6], [9, 10], [13, 14], [17, 18], [21, 22], [25, 26], [29, 30]],
[[1, 2], [5, 6], [9, 10], [13, 14], [17, 18], [21, 22], [25, 26], [29, 30]],
[[3, 6], [3, 5], [1, 2], [1, 4], [11, 14], [11, 13], [9, 10], [9, 12], [19, 22], [19, 21], [17, 18], [17, 20],
[27, 30], [27, 29], [25, 26], [25, 28]],
[[7, 14], [7, 13], [7, 11], [5, 10], [3, 6], [3, 12], [3, 9], [1, 2], [1, 4], [1, 8], [23, 30], [23, 29],
[23, 27], [21, 26], [19, 22], [19, 28], [19, 25], [17, 18], [17, 20], [17, 24]],
[[15, 30], [15, 29], [15, 27], [15, 23], [11, 22], [11, 13], [11, 26], [11, 21], [7, 14], [7, 28], [7, 25],
[7, 19], [5, 10], [5, 20], [5, 9], [5, 18], [3, 6], [3, 12], [3, 24], [3, 17], [1, 2], [1, 4], [1, 8],
[1, 16]],
[[0, 0]]]
},
64: {
'switch_stages': [[[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, 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]],
[[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, 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]],
[[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, 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]],
[[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, 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]],
[[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, 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]],
[[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, 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]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23], [26, 27], [30, 31], [34, 35], [38, 39], [42, 43],
[46, 47], [50, 51], [54, 55], [58, 59], [62, 63]],
[[2, 3], [4, 5], [6, 7], [10, 11], [12, 13], [14, 15], [18, 19], [20, 21], [22, 23], [26, 27], [28, 29],
[30, 31], [34, 35], [36, 37], [38, 39], [42, 43], [44, 45], [46, 47], [50, 51], [52, 53], [54, 55], [58, 59],
[60, 61], [62, 63]],
[[2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15], [18, 19], [20, 21], [22, 23], [24, 25],
[26, 27], [28, 29], [30, 31], [34, 35], [36, 37], [38, 39], [40, 41], [42, 43], [44, 45], [46, 47], [50, 51],
[52, 53], [54, 55], [56, 57], [58, 59], [60, 61], [62, 63]],
[[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], [34, 35], [36, 37], [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]],
[[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, 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]]],
'swap_stages': [[[1, 32], [1, 16], [1, 8], [1, 4], [1, 2], [3, 33], [3, 48], [3, 24], [3, 12], [3, 6], [5, 34], [5, 17],
[5, 40], [5, 20], [5, 10], [7, 35], [7, 49], [7, 56], [7, 28], [7, 14], [9, 36], [9, 18], [11, 37], [11, 50],
[11, 25], [11, 44], [11, 22], [13, 38], [13, 19], [13, 41], [13, 52], [13, 26], [15, 39], [15, 51], [15, 57],
[15, 60], [15, 30], [21, 42], [23, 43], [23, 53], [23, 58], [23, 29], [23, 46], [27, 45], [27, 54], [31, 47],
[31, 55], [31, 59], [31, 61], [31, 62]],
[[1, 16], [1, 8], [1, 4], [1, 2], [3, 17], [3, 24], [3, 12], [3, 6], [5, 18], [5, 9], [5, 20], [5, 10],
[7, 19], [7, 25], [7, 28], [7, 14], [11, 21], [11, 26], [11, 13], [11, 22], [15, 23], [15, 27], [15, 29],
[15, 30], [33, 48], [33, 40], [33, 36], [33, 34], [35, 49], [35, 56], [35, 44], [35, 38], [37, 50], [37, 41],
[37, 52], [37, 42], [39, 51], [39, 57], [39, 60], [39, 46], [43, 53], [43, 58], [43, 45], [43, 54], [47, 55],
[47, 59], [47, 61], [47, 62]],
[[1, 8], [1, 4], [1, 2], [3, 9], [3, 12], [3, 6], [5, 10], [7, 11], [7, 13], [7, 14], [17, 24], [17, 20],
[17, 18], [19, 25], [19, 28], [19, 22], [21, 26], [23, 27], [23, 29], [23, 30], [33, 40], [33, 36], [33, 34],
[35, 41], [35, 44], [35, 38], [37, 42], [39, 43], [39, 45], [39, 46], [49, 56], [49, 52], [49, 50], [51, 57],
[51, 60], [51, 54], [53, 58], [55, 59], [55, 61], [55, 62]],
[[1, 4], [1, 2], [3, 5], [3, 6], [9, 12], [9, 10], [11, 13], [11, 14], [17, 20], [17, 18], [19, 21], [19, 22],
[25, 28], [25, 26], [27, 29], [27, 30], [33, 36], [33, 34], [35, 37], [35, 38], [41, 44], [41, 42], [43, 45],
[43, 46], [49, 52], [49, 50], [51, 53], [51, 54], [57, 60], [57, 58], [59, 61], [59, 62]],
[[1, 2], [5, 6], [9, 10], [13, 14], [17, 18], [21, 22], [25, 26], [29, 30], [33, 34], [37, 38], [41, 42],
[45, 46], [49, 50], [53, 54], [57, 58], [61, 62]],
[[1, 2], [5, 6], [9, 10], [13, 14], [17, 18], [21, 22], [25, 26], [29, 30], [33, 34], [37, 38], [41, 42],
[45, 46], [49, 50], [53, 54], [57, 58], [61, 62]],
[[3, 6], [3, 5], [1, 2], [1, 4], [11, 14], [11, 13], [9, 10], [9, 12], [19, 22], [19, 21], [17, 18], [17, 20],
[27, 30], [27, 29], [25, 26], [25, 28], [35, 38], [35, 37], [33, 34], [33, 36], [43, 46], [43, 45], [41, 42],
[41, 44], [51, 54], [51, 53], [49, 50], [49, 52], [59, 62], [59, 61], [57, 58], [57, 60]],
[[7, 14], [7, 13], [7, 11], [5, 10], [3, 6], [3, 12], [3, 9], [1, 2], [1, 4], [1, 8], [23, 30], [23, 29],
[23, 27], [21, 26], [19, 22], [19, 28], [19, 25], [17, 18], [17, 20], [17, 24], [39, 46], [39, 45], [39, 43],
[37, 42], [35, 38], [35, 44], [35, 41], [33, 34], [33, 36], [33, 40], [55, 62], [55, 61], [55, 59], [53, 58],
[51, 54], [51, 60], [51, 57], [49, 50], [49, 52], [49, 56]],
[[15, 30], [15, 29], [15, 27], [15, 23], [11, 22], [11, 13], [11, 26], [11, 21], [7, 14], [7, 28], [7, 25],
[7, 19], [5, 10], [5, 20], [5, 9], [5, 18], [3, 6], [3, 12], [3, 24], [3, 17], [1, 2], [1, 4], [1, 8],
[1, 16], [47, 62], [47, 61], [47, 59], [47, 55], [43, 54], [43, 45], [43, 58], [43, 53], [39, 46], [39, 60],
[39, 57], [39, 51], [37, 42], [37, 52], [37, 41], [37, 50], [35, 38], [35, 44], [35, 56], [35, 49], [33, 34],
[33, 36], [33, 40], [33, 48]],
[[31, 62], [31, 61], [31, 59], [31, 55], [31, 47], [27, 54], [27, 45], [23, 46], [23, 29], [23, 58], [23, 53],
[23, 43], [21, 42], [15, 30], [15, 60], [15, 57], [15, 51], [15, 39], [13, 26], [13, 52], [13, 41], [13, 19],
[13, 38], [11, 22], [11, 44], [11, 25], [11, 50], [11, 37], [9, 18], [9, 36], [7, 14], [7, 28], [7, 56],
[7, 49], [7, 35], [5, 10], [5, 20], [5, 40], [5, 17], [5, 34], [3, 6], [3, 12], [3, 24], [3, 48], [3, 33],
[1, 2], [1, 4], [1, 8], [1, 16], [1, 32]],
[[0, 0]]]
},
128: {
'switch_stages': [[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23], [26, 27], [30, 31], [34, 35], [38, 39], [42, 43],
[46, 47], [50, 51], [54, 55], [58, 59], [62, 63], [66, 67], [70, 71], [74, 75], [78, 79], [82, 83], [86, 87],
[90, 91], [94, 95], [98, 99], [102, 103], [106, 107], [110, 111], [114, 115], [118, 119], [122, 123],
[126, 127]],
[[2, 3], [4, 5], [6, 7], [10, 11], [12, 13], [14, 15], [18, 19], [20, 21], [22, 23], [26, 27], [28, 29],
[30, 31], [34, 35], [36, 37], [38, 39], [42, 43], [44, 45], [46, 47], [50, 51], [52, 53], [54, 55], [58, 59],
[60, 61], [62, 63], [66, 67], [68, 69], [70, 71], [74, 75], [76, 77], [78, 79], [82, 83], [84, 85], [86, 87],
[90, 91], [92, 93], [94, 95], [98, 99], [100, 101], [102, 103], [106, 107], [108, 109], [110, 111],
[114, 115], [116, 117], [118, 119], [122, 123], [124, 125], [126, 127]],
[[2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15], [18, 19], [20, 21], [22, 23], [24, 25],
[26, 27], [28, 29], [30, 31], [34, 35], [36, 37], [38, 39], [40, 41], [42, 43], [44, 45], [46, 47], [50, 51],
[52, 53], [54, 55], [56, 57], [58, 59], [60, 61], [62, 63], [66, 67], [68, 69], [70, 71], [72, 73], [74, 75],
[76, 77], [78, 79], [82, 83], [84, 85], [86, 87], [88, 89], [90, 91], [92, 93], [94, 95], [98, 99],
[100, 101], [102, 103], [104, 105], [106, 107], [108, 109], [110, 111], [114, 115], [116, 117], [118, 119],
[120, 121], [122, 123], [124, 125], [126, 127]],
[[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], [34, 35], [36, 37], [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], [66, 67], [68, 69], [70, 71],
[72, 73], [74, 75], [76, 77], [78, 79], [80, 81], [82, 83], [84, 85], [86, 87], [88, 89], [90, 91], [92, 93],
[94, 95], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109], [110, 111], [112, 113],
[114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [66, 67], [68, 69],
[70, 71], [72, 73], [74, 75], [76, 77], [78, 79], [80, 81], [82, 83], [84, 85], [86, 87], [88, 89], [90, 91],
[92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]],
[[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, 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], [88, 89],
[90, 91], [92, 93], [94, 95], [96, 97], [98, 99], [100, 101], [102, 103], [104, 105], [106, 107], [108, 109],
[110, 111], [112, 113], [114, 115], [116, 117], [118, 119], [120, 121], [122, 123], [124, 125], [126, 127]]],
'swap_stages': [[[1, 64], [1, 32], [1, 16], [1, 8], [1, 4], [1, 2], [3, 65], [3, 96], [3, 48], [3, 24], [3, 12], [3, 6],
[5, 66], [5, 33], [5, 80], [5, 40], [5, 20], [5, 10], [7, 67], [7, 97], [7, 112], [7, 56], [7, 28], [7, 14],
[9, 68], [9, 34], [9, 17], [9, 72], [9, 36], [9, 18], [11, 69], [11, 98], [11, 49], [11, 88], [11, 44],
[11, 22], [13, 70], [13, 35], [13, 81], [13, 104], [13, 52], [13, 26], [15, 71], [15, 99], [15, 113],
[15, 120], [15, 60], [15, 30], [19, 73], [19, 100], [19, 50], [19, 25], [19, 76], [19, 38], [21, 74],
[21, 37], [21, 82], [21, 41], [21, 84], [21, 42], [23, 75], [23, 101], [23, 114], [23, 57], [23, 92],
[23, 46], [27, 77], [27, 102], [27, 51], [27, 89], [27, 108], [27, 54], [29, 78], [29, 39], [29, 83],
[29, 105], [29, 116], [29, 58], [31, 79], [31, 103], [31, 115], [31, 121], [31, 124], [31, 62], [43, 85],
[43, 106], [43, 53], [43, 90], [43, 45], [43, 86], [47, 87], [47, 107], [47, 117], [47, 122], [47, 61],
[47, 94], [55, 91], [55, 109], [55, 118], [55, 59], [55, 93], [55, 110], [63, 95], [63, 111], [63, 119],
[63, 123], [63, 125], [63, 126]],
[[1, 32], [1, 16], [1, 8], [1, 4], [1, 2], [3, 33], [3, 48], [3, 24], [3, 12], [3, 6], [5, 34], [5, 17],
[5, 40], [5, 20], [5, 10], [7, 35], [7, 49], [7, 56], [7, 28], [7, 14], [9, 36], [9, 18], [11, 37], [11, 50],
[11, 25], [11, 44], [11, 22], [13, 38], [13, 19], [13, 41], [13, 52], [13, 26], [15, 39], [15, 51], [15, 57],
[15, 60], [15, 30], [21, 42], [23, 43], [23, 53], [23, 58], [23, 29], [23, 46], [27, 45], [27, 54], [31, 47],
[31, 55], [31, 59], [31, 61], [31, 62], [65, 96], [65, 80], [65, 72], [65, 68], [65, 66], [67, 97],
[67, 112], [67, 88], [67, 76], [67, 70], [69, 98], [69, 81], [69, 104], [69, 84], [69, 74], [71, 99],
[71, 113], [71, 120], [71, 92], [71, 78], [73, 100], [73, 82], [75, 101], [75, 114], [75, 89], [75, 108],
[75, 86], [77, 102], [77, 83], [77, 105], [77, 116], [77, 90], [79, 103], [79, 115], [79, 121], [79, 124],
[79, 94], [85, 106], [87, 107], [87, 117], [87, 122], [87, 93], [87, 110], [91, 109], [91, 118], [95, 111],
[95, 119], [95, 123], [95, 125], [95, 126]],
[[1, 16], [1, 8], [1, 4], [1, 2], [3, 17], [3, 24], [3, 12], [3, 6], [5, 18], [5, 9], [5, 20], [5, 10],
[7, 19], [7, 25], [7, 28], [7, 14], [11, 21], [11, 26], [11, 13], [11, 22], [15, 23], [15, 27], [15, 29],
[15, 30], [33, 48], [33, 40], [33, 36], [33, 34], [35, 49], [35, 56], [35, 44], [35, 38], [37, 50], [37, 41],
[37, 52], [37, 42], [39, 51], [39, 57], [39, 60], [39, 46], [43, 53], [43, 58], [43, 45], [43, 54], [47, 55],
[47, 59], [47, 61], [47, 62], [65, 80], [65, 72], [65, 68], [65, 66], [67, 81], [67, 88], [67, 76], [67, 70],
[69, 82], [69, 73], [69, 84], [69, 74], [71, 83], [71, 89], [71, 92], [71, 78], [75, 85], [75, 90], [75, 77],
[75, 86], [79, 87], [79, 91], [79, 93], [79, 94], [97, 112], [97, 104], [97, 100], [97, 98], [99, 113],
[99, 120], [99, 108], [99, 102], [101, 114], [101, 105], [101, 116], [101, 106], [103, 115], [103, 121],
[103, 124], [103, 110], [107, 117], [107, 122], [107, 109], [107, 118], [111, 119], [111, 123], [111, 125],
[111, 126]],
[[1, 8], [1, 4], [1, 2], [3, 9], [3, 12], [3, 6], [5, 10], [7, 11], [7, 13], [7, 14], [17, 24], [17, 20],
[17, 18], [19, 25], [19, 28], [19, 22], [21, 26], [23, 27], [23, 29], [23, 30], [33, 40], [33, 36], [33, 34],
[35, 41], [35, 44], [35, 38], [37, 42], [39, 43], [39, 45], [39, 46], [49, 56], [49, 52], [49, 50], [51, 57],
[51, 60], [51, 54], [53, 58], [55, 59], [55, 61], [55, 62], [65, 72], [65, 68], [65, 66], [67, 73], [67, 76],
[67, 70], [69, 74], [71, 75], [71, 77], [71, 78], [81, 88], [81, 84], [81, 82], [83, 89], [83, 92], [83, 86],
[85, 90], [87, 91], [87, 93], [87, 94], [97, 104], [97, 100], [97, 98], [99, 105], [99, 108], [99, 102],
[101, 106], [103, 107], [103, 109], [103, 110], [113, 120], [113, 116], [113, 114], [115, 121], [115, 124],
[115, 118], [117, 122], [119, 123], [119, 125], [119, 126]],
[[1, 4], [1, 2], [3, 5], [3, 6], [9, 12], [9, 10], [11, 13], [11, 14], [17, 20], [17, 18], [19, 21], [19, 22],
[25, 28], [25, 26], [27, 29], [27, 30], [33, 36], [33, 34], [35, 37], [35, 38], [41, 44], [41, 42], [43, 45],
[43, 46], [49, 52], [49, 50], [51, 53], [51, 54], [57, 60], [57, 58], [59, 61], [59, 62], [65, 68], [65, 66],
[67, 69], [67, 70], [73, 76], [73, 74], [75, 77], [75, 78], [81, 84], [81, 82], [83, 85], [83, 86], [89, 92],
[89, 90], [91, 93], [91, 94], [97, 100], [97, 98], [99, 101], [99, 102], [105, 108], [105, 106], [107, 109],
[107, 110], [113, 116], [113, 114], [115, 117], [115, 118], [121, 124], [121, 122], [123, 125], [123, 126]],
[[1, 2], [5, 6], [9, 10], [13, 14], [17, 18], [21, 22], [25, 26], [29, 30], [33, 34], [37, 38], [41, 42],
[45, 46], [49, 50], [53, 54], [57, 58], [61, 62], [65, 66], [69, 70], [73, 74], [77, 78], [81, 82], [85, 86],
[89, 90], [93, 94], [97, 98], [101, 102], [105, 106], [109, 110], [113, 114], [117, 118], [121, 122],
[125, 126]],
[[1, 2], [5, 6], [9, 10], [13, 14], [17, 18], [21, 22], [25, 26], [29, 30], [33, 34], [37, 38], [41, 42],
[45, 46], [49, 50], [53, 54], [57, 58], [61, 62], [65, 66], [69, 70], [73, 74], [77, 78], [81, 82], [85, 86],
[89, 90], [93, 94], [97, 98], [101, 102], [105, 106], [109, 110], [113, 114], [117, 118], [121, 122],
[125, 126]],
[[3, 6], [3, 5], [1, 2], [1, 4], [11, 14], [11, 13], [9, 10], [9, 12], [19, 22], [19, 21], [17, 18], [17, 20],
[27, 30], [27, 29], [25, 26], [25, 28], [35, 38], [35, 37], [33, 34], [33, 36], [43, 46], [43, 45], [41, 42],
[41, 44], [51, 54], [51, 53], [49, 50], [49, 52], [59, 62], [59, 61], [57, 58], [57, 60], [67, 70], [67, 69],
[65, 66], [65, 68], [75, 78], [75, 77], [73, 74], [73, 76], [83, 86], [83, 85], [81, 82], [81, 84], [91, 94],
[91, 93], [89, 90], [89, 92], [99, 102], [99, 101], [97, 98], [97, 100], [107, 110], [107, 109], [105, 106],
[105, 108], [115, 118], [115, 117], [113, 114], [113, 116], [123, 126], [123, 125], [121, 122], [121, 124]],
[[7, 14], [7, 13], [7, 11], [5, 10], [3, 6], [3, 12], [3, 9], [1, 2], [1, 4], [1, 8], [23, 30], [23, 29],
[23, 27], [21, 26], [19, 22], [19, 28], [19, 25], [17, 18], [17, 20], [17, 24], [39, 46], [39, 45], [39, 43],
[37, 42], [35, 38], [35, 44], [35, 41], [33, 34], [33, 36], [33, 40], [55, 62], [55, 61], [55, 59], [53, 58],
[51, 54], [51, 60], [51, 57], [49, 50], [49, 52], [49, 56], [71, 78], [71, 77], [71, 75], [69, 74], [67, 70],
[67, 76], [67, 73], [65, 66], [65, 68], [65, 72], [87, 94], [87, 93], [87, 91], [85, 90], [83, 86], [83, 92],
[83, 89], [81, 82], [81, 84], [81, 88], [103, 110], [103, 109], [103, 107], [101, 106], [99, 102], [99, 108],
[99, 105], [97, 98], [97, 100], [97, 104], [119, 126], [119, 125], [119, 123], [117, 122], [115, 118],
[115, 124], [115, 121], [113, 114], [113, 116], [113, 120]],
[[15, 30], [15, 29], [15, 27], [15, 23], [11, 22], [11, 13], [11, 26], [11, 21], [7, 14], [7, 28], [7, 25],
[7, 19], [5, 10], [5, 20], [5, 9], [5, 18], [3, 6], [3, 12], [3, 24], [3, 17], [1, 2], [1, 4], [1, 8],
[1, 16], [47, 62], [47, 61], [47, 59], [47, 55], [43, 54], [43, 45], [43, 58], [43, 53], [39, 46], [39, 60],
[39, 57], [39, 51], [37, 42], [37, 52], [37, 41], [37, 50], [35, 38], [35, 44], [35, 56], [35, 49], [33, 34],
[33, 36], [33, 40], [33, 48], [79, 94], [79, 93], [79, 91], [79, 87], [75, 86], [75, 77], [75, 90], [75, 85],
[71, 78], [71, 92], [71, 89], [71, 83], [69, 74], [69, 84], [69, 73], [69, 82], [67, 70], [67, 76], [67, 88],
[67, 81], [65, 66], [65, 68], [65, 72], [65, 80], [111, 126], [111, 125], [111, 123], [111, 119], [107, 118],
[107, 109], [107, 122], [107, 117], [103, 110], [103, 124], [103, 121], [103, 115], [101, 106], [101, 116],
[101, 105], [101, 114], [99, 102], [99, 108], [99, 120], [99, 113], [97, 98], [97, 100], [97, 104],
[97, 112]],
[[31, 62], [31, 61], [31, 59], [31, 55], [31, 47], [27, 54], [27, 45], [23, 46], [23, 29], [23, 58], [23, 53],
[23, 43], [21, 42], [15, 30], [15, 60], [15, 57], [15, 51], [15, 39], [13, 26], [13, 52], [13, 41], [13, 19],
[13, 38], [11, 22], [11, 44], [11, 25], [11, 50], [11, 37], [9, 18], [9, 36], [7, 14], [7, 28], [7, 56],
[7, 49], [7, 35], [5, 10], [5, 20], [5, 40], [5, 17], [5, 34], [3, 6], [3, 12], [3, 24], [3, 48], [3, 33],
[1, 2], [1, 4], [1, 8], [1, 16], [1, 32], [95, 126], [95, 125], [95, 123], [95, 119], [95, 111], [91, 118],
[91, 109], [87, 110], [87, 93], [87, 122], [87, 117], [87, 107], [85, 106], [79, 94], [79, 124], [79, 121],
[79, 115], [79, 103], [77, 90], [77, 116], [77, 105], [77, 83], [77, 102], [75, 86], [75, 108], [75, 89],
[75, 114], [75, 101], [73, 82], [73, 100], [71, 78], [71, 92], [71, 120], [71, 113], [71, 99], [69, 74],
[69, 84], [69, 104], [69, 81], [69, 98], [67, 70], [67, 76], [67, 88], [67, 112], [67, 97], [65, 66],
[65, 68], [65, 72], [65, 80], [65, 96]],
[[63, 126], [63, 125], [63, 123], [63, 119], [63, 111], [63, 95], [55, 110], [55, 93], [55, 59], [55, 118],
[55, 109], [55, 91], [47, 94], [47, 61], [47, 122], [47, 117], [47, 107], [47, 87], [43, 86], [43, 45],
[43, 90], [43, 53], [43, 106], [43, 85], [31, 62], [31, 124], [31, 121], [31, 115], [31, 103], [31, 79],
[29, 58], [29, 116], [29, 105], [29, 83], [29, 39], [29, 78], [27, 54], [27, 108], [27, 89], [27, 51],
[27, 102], [27, 77], [23, 46], [23, 92], [23, 57], [23, 114], [23, 101], [23, 75], [21, 42], [21, 84],
[21, 41], [21, 82], [21, 37], [21, 74], [19, 38], [19, 76], [19, 25], [19, 50], [19, 100], [19, 73],
[15, 30], [15, 60], [15, 120], [15, 113], [15, 99], [15, 71], [13, 26], [13, 52], [13, 104], [13, 81],
[13, 35], [13, 70], [11, 22], [11, 44], [11, 88], [11, 49], [11, 98], [11, 69], [9, 18], [9, 36], [9, 72],
[9, 17], [9, 34], [9, 68], [7, 14], [7, 28], [7, 56], [7, 112], [7, 97], [7, 67], [5, 10], [5, 20], [5, 40],
[5, 80], [5, 33], [5, 66], [3, 6], [3, 12], [3, 24], [3, 48], [3, 96], [3, 65], [1, 2], [1, 4], [1, 8],
[1, 16], [1, 32], [1, 64]],
[[0, 0]]]
}
}
sorting_network_data = {
3: [[1, 2], [0, 2], [0, 1]],
4: [[0, 1], [2, 3], [0, 2], [1, 3], [1, 2]],
5: [[0, 1], [3, 4], [2, 4], [2, 3], [1, 4], [0, 3], [0, 2], [1, 3], [1, 2]],
6: [[1, 2], [4, 5], [0, 2], [3, 5], [0, 1], [3, 4], [2, 5], [0, 3], [1, 4], [2, 4], [1, 3], [2, 3]],
7: [[1, 2], [3, 4], [5, 6], [0, 2], [3, 5], [4, 6], [0, 1], [4, 5], [2, 6], [0, 4], [1, 5], [0, 3], [2, 5], [1, 3],
[2, 4], [2, 3]],
8: [[0, 1], [2, 3], [4, 5], [6, 7], [0, 2], [1, 3], [4, 6], [5, 7], [1, 2], [5, 6], [0, 4], [3, 7], [1, 5], [2, 6],
[1, 4], [3, 6], [2, 4], [3, 5], [3, 4]],
9: [[0, 1], [3, 4], [6, 7], [1, 2], [4, 5], [7, 8], [0, 1], [3, 4], [6, 7], [2, 5], [0, 3], [1, 4], [5, 8], [3, 6],
[4, 7], [2, 5], [0, 3], [1, 4], [5, 7], [2, 6], [1, 3], [4, 6], [2, 4], [5, 6], [2, 3]],
10: [[4, 9], [3, 8], [2, 7], [1, 6], [0, 5], [1, 4], [6, 9], [0, 3], [5, 8], [0, 2], [3, 6], [7, 9], [0, 1], [2, 4],
[5, 7], [8, 9], [1, 2], [4, 6], [7, 8], [3, 5], [2, 5], [6, 8], [1, 3], [4, 7], [2, 3], [6, 7], [3, 4], [5, 6],
[4, 5]],
11: [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [1, 3], [5, 7], [0, 2], [4, 6], [8, 10], [1, 2], [5, 6], [9, 10],
[0, 4], [3, 7], [1, 5], [6, 10], [4, 8], [5, 9], [2, 6], [0, 4], [3, 8], [1, 5], [6, 10], [2, 3], [8, 9],
[1, 4], [7, 10], [3, 5], [6, 8], [2, 4], [7, 9], [5, 6], [3, 4], [7, 8]],
12: [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [1, 3], [5, 7], [9, 11], [0, 2], [4, 6], [8, 10], [1, 2],
[5, 6], [9, 10], [0, 4], [7, 11], [1, 5], [6, 10], [3, 7], [4, 8], [5, 9], [2, 6], [0, 4], [7, 11], [3, 8],
[1, 5], [6, 10], [2, 3], [8, 9], [1, 4], [7, 10], [3, 5], [6, 8], [2, 4], [7, 9], [5, 6], [3, 4], [7, 8]],
13: [[1, 7], [9, 11], [3, 4], [5, 8], [0, 12], [2, 6], [0, 1], [2, 3], [4, 6], [8, 11], [7, 12], [5, 9], [0, 2],
[3, 7], [10, 11], [1, 4], [6, 12], [7, 8], [11, 12], [4, 9], [6, 10], [3, 4], [5, 6], [8, 9], [10, 11], [1, 7],
[2, 6], [9, 11], [1, 3], [4, 7], [8, 10], [0, 5], [2, 5], [6, 8], [9, 10], [1, 2], [3, 5], [7, 8], [4, 6],
[2, 3], [4, 5], [6, 7], [8, 9], [3, 4], [5, 6]],
14: [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [0, 2], [4, 6], [8, 10], [1, 3], [5, 7], [9, 11],
[0, 4], [8, 12], [1, 5], [9, 13], [2, 6], [3, 7], [0, 8], [1, 9], [2, 10], [3, 11], [4, 12], [5, 13], [5, 10],
[6, 9], [3, 12], [7, 11], [1, 2], [4, 8], [1, 4], [7, 13], [2, 8], [5, 6], [9, 10], [2, 4], [11, 13], [3, 8],
[7, 12], [6, 8], [10, 12], [3, 5], [7, 9], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [6, 7], [8, 9]],
}
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0
| 8
|
55886041c8e2340f9b789e3d26ae7a9182b1e226
| 57,965
|
py
|
Python
|
Old model/Old v2/fbr_maincode.py
|
zenmood/IndoorFarmWiz
|
0f5075007cbd1d15c83ed3aef820ec3d72048a90
|
[
"MIT"
] | 11
|
2020-06-28T04:30:26.000Z
|
2022-03-26T08:40:47.000Z
|
Old model/Old v2/fbr_maincode.py
|
zenmood/IndoorFarmWiz
|
0f5075007cbd1d15c83ed3aef820ec3d72048a90
|
[
"MIT"
] | 4
|
2020-07-27T19:45:27.000Z
|
2020-07-28T13:58:41.000Z
|
Old model/Old v2/fbr_maincode.py
|
zenmood/IndoorFarmWiz
|
0f5075007cbd1d15c83ed3aef820ec3d72048a90
|
[
"MIT"
] | null | null | null |
"""
FBR Code for VF Wiz
Created on 25 Aug 2019
Author: Francis Baumont De Oliveira
Contact: sgfbaumo@liv.ac.uk
"""
# ==================================== IMPORT LIBRARIES ========================================== #
import json
import math
from random import gauss
from Economic_model.Old.vf_inputs import Scenario
# ==================================== CONSTANTS ========================================== #
PSYCHOMETRIC_CONSTANT = 65.0 # Pa/K
# ==================================== INPUT SCENARIO ========================================== #
def get_scenario(input_file):
with open(input_file) as f:
inputs = json.load(f)
scenario = Scenario()
scenario.currency = inputs['currency']
scenario.country = inputs['country']
scenario.capex = inputs["start_loan"]
scenario.repayment = inputs["loan_repayment"]
scenario.interest = inputs['loan_interest']
scenario.lights = inputs['lights']
scenario.crop = inputs['crop']
scenario.area = inputs['grow_area']
scenario.surface = inputs['surface_area']
scenario.volume = inputs['farm_volume']
scenario.building = inputs['building_type']
scenario.rent = inputs['rental_costs']
scenario.system = inputs['grow_system']
scenario.co2 = inputs['co2_enrichment']
scenario.energy = inputs['energy_price']
scenario.energy_standing = inputs['energy_standing_charge']
scenario.water = inputs['water_price']
scenario.water_standing = inputs['water_standing_charge']
scenario.renewable = inputs['ratio_of_renewable_energy_created_to_sourced']
scenario.toutdoors = inputs['average_outdoor_temperature']
scenario.crop_price = inputs['crop_price_per_kilo']
scenario.farm_staff = inputs['number_of_farm_staff']
scenario.salaries = inputs['annual_salaries_of_employees']
scenario.standard_wage = inputs['standard_wage']
scenario.insurance = inputs['insurance_premium']
scenario.coverage = inputs['insurance_coverage']
scenario.days = inputs['days_for_simulation']
return scenario
# ============================================== SYSTEM AND EXPECTED YIELDS #==================================#
def calc_no_of_racks(grow_system, grow_area):
if grow_system == 'ziprack_8':
no_of_racks = math.floor(grow_area/4.62963) # 54 Zipracks per 250 sq-m (including aisles, work bench & plumbing kit)
else:
raise RuntimeError("Unknown grow_system: {}".format(grow_system))
return no_of_racks
# ------------------------------------ HARVEST WEIGHT ---------------------------------------------- #
def calc_harvest_weight(crop):
if crop == "lettuce":
harvest_weight = 0.5 # kg
else:
raise RuntimeError("Unknown crop {}".format(crop))
return harvest_weight
def get_gross_yield(crop):
if crop == 'lettuce':
ys = 78.5 # kg / m2 / year
else:
raise RuntimeError("Unknown crop {}".format(crop))
return ys
# ------------------------------------ PLANT CAPACITY ---------------------------------------------- #
def calc_plant_capacity(crop, grow_system, no_of_racks): # Excluding propagation and only within the VFS
if crop == "lettuce" and grow_system == 'ziprack_8':
no_of_towers = no_of_racks*30 # Tight spacing with lettuce (30 towers per rack)
yield_capacity = no_of_towers*3.3 # 3.3kg of greens per tower
harvest_weight = calc_harvest_weight(crop)
farm_plant_capacity = yield_capacity / harvest_weight # Potential yield divided by harvest weight of product
else:
raise RuntimeError("Unknown crop {}".format(crop))
return farm_plant_capacity, yield_capacity
# ------------------------------------ LIGHTING SOLUTION ---------------------------------------------- #
def get_spec(lights):
if lights == "intraspectra_spectrablade_8":
light_wattage = 75
light_efficiency = 0.4
print("The {} light is {} watts with an efficiency of: {}".format(lights, light_wattage, light_efficiency))
else:
raise RuntimeError("Unknown lights {}".format(lights))
return light_wattage, light_efficiency
def calc_no_of_lights(grow_system, no_of_racks):
if grow_system == 'ziprack_8':
no_of_lights = no_of_racks*24 # Assumption that 24 lighting units are require to cover crop area of 1 Ziprack (30 towers)
else:
raise RuntimeError("Unknown grow system {}".format(grow_system))
return no_of_lights
# ------------------------------------ TEMPERATURE CROP REQUIREMENTS ---------------------------------------------- #
def get_temp_crop_reqs(crop):
if crop == 'lettuce':
Tin = 23.9 # Temperature optimal for lettuce growth
else:
Tin = 22 # 'general temperature'
return Tin
# ====================================== FACTORS AND CROP YIELD =============================================#
def calc_crop_ppfd_reqs(crop):
if crop == 'lettuce':
crop_ppfd_reqs = 295
else:
raise RuntimeError("Unknown crop: {}".format(crop))
return crop_ppfd_reqs
def calc_PAR_factor(ppfd_lights, crop_ppfd_reqs):
parf = ppfd_lights/crop_ppfd_reqs
return parf
def calc_co2_factor(co2_enrichment):
if co2_enrichment:
co2f = 1
else:
co2f = 0.9
return co2f
def calc_failure_rate():
fr = gauss(0.05, 0.02)
return fr
def calc_standard_yield(crop): # Standard yield per year
""" Taken from table from Shao Economic Estimation Tool (2017)"""
if crop == 'lettuce':
return 78.5 # kg/m2/year
else:
raise RuntimeError("Unknown crop: {}".format(crop))
def calc_plant_area(grow_area, grow_system, no_of_racks):
""" Plant area calculated using space taken by Racks - formula from Refarmers spreadsheet 2018"""
if grow_system == 'ziprack_8':
pa = (no_of_racks*4.300986) + 3.0612
else:
pa = grow_area
return pa
def calc_temperature_factor(hvac_control):
"""
Temperature Factor Equation
Notes:
--------
The reduction in yield caused by over heating or freezing of the grow area, especially if the farm is uncontrolled by hvac or other systems
If no hvac control, preliminary value set to 0.85. This should be assessed depending on climate, crop reqs and level of hvac control
High:
Med:
Low:
"""
if hvac_control == "high": # If advanced hvac control then temperature factor is 1
tf = 1
else:
tf = 0.85
return tf
def calc_system_multiplier(grow_system):
"""
System Multiplier
Notes
-----
Standard yield isn't 100% accurate and doesn't consider high density vertical farming systems. The estimated yield
from ZipGrow Ziprack_8 is 66,825 kg/year for 235m2 plant area. Adjusted yield without multiplier is 18447.5 kg/year
66,825/18447.5 kg = 3.622442065320504
"""
if grow_system == 'ziprack_8':
system_multiplier = 3.622442065320504
else:
raise RuntimeError("Unknown grow system {}".format(grow_system))
return system_multiplier
# ---------------------------------------------- ADJUSTED YIELD ---------------------------------------------------- #
def calc_adjusted_yield(ys, pa, parf, co2f, tf, fr, system_multiplier):
"""
Adjusted Plant Yield Equation
Notes
-----
Ya = Ys x PA x parf x co2f x Tf x (1 - Fr)
Adjusted Plant Yield = Standard Yield x Plant Area x PAR factor
parf = ratio of actual PAR delivered to plant canopy compared to theoretical plant requirements. In artificial lighting
VF the value was 1 as controlled at optimal level. Sun-fed plant level from EcoTect simulation.) x
co2f = Increment by co2 enrichment
Tf = Temperature factor (reflects reduction of yield caused by overheating or freezing of the growing area
if indoor temperature is uncontrolled by hvac or other systems, value can be set for 0.9 for preliminary estimation)
Fr = Failure rate, by default set 30% year 1, 20% year 2, 10% year 3 and 5% onwards
Sm = System multiplier (best case scenario x system multiplier)
"""
ya = ys * pa * parf * co2f * tf * (1 - fr) * system_multiplier
return ya
# ============================================== SALES ==================================================#
def calc_sales(ya, crop_price, sale_cycle):
crop_sales = (ya*crop_price)/ sale_cycle
return crop_sales # per sales or delivery cycle
# ============================================== COST OF GOODS SOLD ==================================================#
# ---------------------------------------------- COGS: SEEDS COSTS ----------------------------------------------------#
def calc_seeds_cost(crop, ya, harvest_weight):
"""
Seeds Calculator
Notes
------
:param crop: The crop that is selected to be grown on the farm
:param ya: The expected yield for that crop
:param harvest_weight: The harvest weight selected for that crop
The seeds required are 40% more than the plants harvested. This is to account for error, unsuccessful
propagation or thinning.
:return: The cost per seed, and the number of seeds required to calculate the overall seed cost per year
"""
if crop == 'lettuce':
cost_per_seed = 0.10
else:
raise RuntimeError("Unknown crop: {}".format(crop))
seeds_required = (ya/harvest_weight)*1.4
seeds_cost = seeds_required * cost_per_seed # costs of seeds
return seeds_cost
# ------------------------------------------------------ COGS: NUTRIENTS COSTS ------------------------------------- #
def calc_nutrients_cost(ya):
nutrients_cost = ya*0.20 # £0.20 worth of nutrients per kg of crop produced
return nutrients_cost
# --------------------------------- COGS: MEDIA COSTS --------------------------------------------------------#
def calc_media_cost(ya):
media_cost = ya*0.75 # £0.30 worth of media per kg of crop produced
return media_cost
# ------------------------------------------------- COGS: co2 ENRICHMENT --------------------------------------------- #
def calc_co2_cost(co2_enrichment):
if co2_enrichment:
co2_cost = ya*0.1
else:
co2_cost = 0
return co2_cost
# ----------------------------------------------------- COGS: LABOUR COSTS ----------------------------------#
def calc_labour_cost(farm_staff, standard_wage):
"""
Labour Costs Formaula
Notes
------
Direct farm labour cost = Number of staff working full-time x wages x 30 hours
Generalisation if statement on farm labour required if unknown
"""
labour_cost = farm_staff * standard_wage * 35
return labour_cost
# ------------------------------------------ COGS: PACKAGING COSTS -------------------------------------------------- #
def calc_packaging_cost(ya):
packaging_cost = 0.5*ya # 0.5 is cost per kilo of produce (User specified)
return packaging_cost
# ---------------------------------------------- COGS: OVERALL COGS ------------------------------------------------- #
def calc_cogs(seeds_cost, nutrients_cost, media_cost, co2_cost, labour_cost, packaging_cost):
cogs_annual = seeds_cost + nutrients_cost + co2_cost + (labour_cost * 50) + packaging_cost + media_cost # Annual cost of goods sold
cogs_quarterly = cogs_annual / 4
cogs_monthly = cogs_annual / 12
cogs_weekly = cogs_annual / 50
cogs_daily = cogs_annual / 365
return cogs_annual, cogs_quarterly, cogs_monthly, cogs_weekly, cogs_daily
def calc_cogs_time_series(days, cogs_quarterly):
"""
Cost of Goods Sold Formaula
Notes
-----
Can adjust for days/weekly/monthly/annually in the future - ASSUMED: CONSUMABLES PURCHASED QUARTERLY
"""
cogs_time_series = []
for i in range(days):
if i % 365/4 == 0:
cogs_time_series.append(cogs_quarterly)
else:
cogs_time_series.append(0.0)
return cogs_time_series
# ==================================== OPERATIONAL EXPENDITURE ============================== #
# ---------------------------------------------- OPEX: SALARIES ------------------------------------------------------#
def calc_salary_payments(salaries):
monthly_salary_payments = salaries/12
return monthly_salary_payments
# -------------------------------------------- OPEX: WATER CALCULATIONS ---------------------------------------------- #
def calc_water_consumption(grow_system, no_of_racks, grow_area):
if grow_system == "ziprack_8":
water_consumption_per_month = no_of_racks * 0.95 * 30.42 # Litres of water per tower per day (0.25 gallons) multiplied by month
water_buffer = 1900 # Litres of water for buffer per month (500 gallons)
water_consumption_per_month += water_buffer # Water consumption could be used here.
water_consumption_per_day = water_consumption_per_month/30.42
water_consumption_per_year = water_consumption_per_month*12
water_consumption_per_week = (water_consumption_per_month*12)/52
return water_consumption_per_day, water_consumption_per_week, water_consumption_per_month, water_consumption_per_year
else:
water_consumption_per_year = grow_area * 200 # Average from Agrilyst survey - 4 Gallons per sq ft per year
water_consumption_per_month = water_consumption_per_year / 12 # consumption per month
water_consumption_per_week = water_consumption_per_year/52
water_consumption_per_day = water_consumption_per_year/365
return water_consumption_per_day, water_consumption_per_week, water_consumption_per_month, water_consumption_per_year
def calc_water_cost(water_consumption_per_day, water_consumption_per_week, water_consumption_per_month, water_consumption_per_year, water_price, water_standing_charge): # need to include standing charges
water_cost_per_day = (water_consumption_per_day/1000) * water_price
water_cost_per_week = (water_consumption_per_week/1000) * water_price
water_cost_per_month = (water_consumption_per_month/1000) * water_price + water_standing_charge
water_cost_per_year = (water_consumption_per_year/1000) * water_price
return water_cost_per_day, water_cost_per_week, water_cost_per_month, water_cost_per_year
# ------------------------------------- OPEX: LIGHT ENERGY CALCULATIONS --------------------------------------------#
def calc_lights_energy(lights, no_of_lights):
lights_watts, efficiency = get_spec(lights)
lighting_kw_usage = lights_watts*no_of_lights/1000
lights_kwh_per_day = lighting_kw_usage*12 # Assuming 12 hours of light for plants
lights_kwh_per_month = lights_kwh_per_day * 30.417 # 365 days/12 months
lights_kwh_per_week = lights_kwh_per_day*7
lights_kwh_per_year = lights_kwh_per_day * 365
return lights_kwh_per_day, lights_kwh_per_week, lights_kwh_per_month, lights_kwh_per_year
# ---------------------------------- OPEX: hvac ENERGY CALCULATIONS --------------------------------------------------#
def calc_hvac_energy(surface_area, building_type, Tin, Tout):
"""
Heat Transfer Equation
Notes
-----
Q = U x SA x (Tin - Tout)
Q - Heat lost or gained due to outside temperature (kJ·h−1)
U - Overall heat transfer coefficient (kJ·h−1·m−2·°C−1)
SA - Surface Area of the space
Tin - Inside air set point temperature (°C)
Tout - Outside air temperature (°C)
"""
if building_type == 'basement':
U = 0.5
else:
U = 24 # generic heat transfer coefficient
Q = U*surface_area*(Tin-Tout)
hvac_kwh = Q*0.00666667*24 # Conversion factor of kJ/h to kWh x 24 hours
# Rudimentary hvac calculations - general
hvac_kwh_per_day = hvac_kwh*1
hvac_kwh_per_month = hvac_kwh_per_day * 30.417 # 365 days/12 months
hvac_kwh_per_week = hvac_kwh_per_day * 7
hvac_kwh_per_year = hvac_kwh_per_day * 365
return hvac_kwh_per_day, hvac_kwh_per_week, hvac_kwh_per_month, hvac_kwh_per_year
# ------------------------------------- OPEX: MISC. ENERGY CALCULATIONS ---------------------------------------------- #
def calc_pump_energy(grow_system, no_of_racks):
if grow_system == 'ziprack_8':
no_of_plumbing_kits = math.ceil(no_of_racks/45) # spec for plumbing kit provided by Refarmers - 45 racks
plumbing_kit_wattage = 1800 # spec for plumbing kit provided by Refarmers
pumps_kw_usage = no_of_plumbing_kits * plumbing_kit_wattage / 1000
pumps_kwh_per_day = pumps_kw_usage * 24 # 24 hours on
pumps_kwh_per_month = pumps_kwh_per_day * 30.417 # 365 days/12 months
pumps_kwh_per_week = pumps_kwh_per_day * 7
pumps_kwh_per_year = pumps_kwh_per_day * 365
return pumps_kwh_per_day, pumps_kwh_per_week, pumps_kwh_per_month, pumps_kwh_per_year
else:
raise RuntimeError("Unknown grow_system: {}".format(grow_system))
def calc_misc_energy(pumps_kwh_per_day):
"""
Misc Energy Consumption
Notes
------
Energy consumption for miscellaneous elements such as: Filtration, Sensors, Internet, Office Lighting,
Automation, Computers, etc.
"""
misc_kwh_per_day = pumps_kwh_per_day
return misc_kwh_per_day
# ----------------------------------- OPEX: OVERALL ENERGY CALC (LIGHTS+hvac+MISC) -----------------------#
def calc_energy_consumption(hvac_kwh_per_day, lights_kwh_per_day, misc_kwh_per_day):
"""
Energy Consumption
Notes
------
Energy consumption for different time periods
"""
farm_kwh_per_day = hvac_kwh_per_day + lights_kwh_per_day + misc_kwh_per_day
farm_kwh_per_week = farm_kwh_per_day * 7 # 7 days in a week
farm_kwh_per_month = farm_kwh_per_day * 30.417 # 365 days/12 months
farm_kwh_per_year = farm_kwh_per_day * 365
return farm_kwh_per_day, farm_kwh_per_week, farm_kwh_per_month, farm_kwh_per_year
def calc_energy_cost(farm_kwh_per_day, farm_kwh_per_week, farm_kwh_per_month, farm_kwh_per_year, energy_price):
"""
Energy Costs
Notes
------
Energy cost for different time periods
"""
energy_cost_per_day = farm_kwh_per_day * energy_price
energy_cost_per_week = farm_kwh_per_week * energy_price # 365 days/12 months
energy_cost_per_month = farm_kwh_per_month * energy_price # 365 days/12 months
energy_cost_per_year = farm_kwh_per_year * energy_price
return energy_cost_per_day, energy_cost_per_week, energy_cost_per_month, energy_cost_per_year
# -------------------------------- OPEX: MAINTENANCE COST ----------------------------------------------------------#
def calc_maintenance_cost(grow_system, no_of_racks):
if grow_system == 'ziprack_8':
maintenance_cost_per_month = no_of_racks*2.50 # £2.50 worth of labour per month to maintain
return maintenance_cost_per_month
else:
raise RuntimeError("Unknown grow_system: {}".format(grow_system))
# ---------------------------------------- OPEX: DISTRIBUTION COST ----------------------------------------------------#
def calc_distribution_cost(sales, sale_cycle): # Distribution cost per delivery
distribution_cost_per_sale_cycle = sales*0.15
distribution_cost_per_month = distribution_cost_per_sale_cycle*(30.417/sale_cycle) # The number of delivery (sale) cycles in a month
return distribution_cost_per_month
# ---------------------------------- OPEX: RENEWABLE ENERGY REDUCTION ------------------------------------------------#
def calc_renewable_energy_reduction(renewable, energy_cost_per_day): # Distribution cost per delivery
renewable_energy_reduction_per_day = energy_cost_per_day*renewable
renewable_energy_reduction_per_week = energy_cost_per_day*7*renewable
renewable_energy_reduction_per_month = energy_cost_per_day*30.417*renewable
renewable_energy_reduction_per_year = energy_cost_per_day*365*renewable
return renewable_energy_reduction_per_day, renewable_energy_reduction_per_week, renewable_energy_reduction_per_month, renewable_energy_reduction_per_year
# ---------------------------------------------- OPEX: OVERALL OPEX --------------------------------------------------#
def calc_opex_time_series(days, monthly_salary_payments, energy_cost_per_month, water_cost_per_month,
rent, maintenance_cost_per_month, distribution_cost_per_month, renewable_energy_reduction_per_month):
"""
Can adjust for days/weekly/monthly/annually in the future - ASSUMED: CONSUMABLES PURCHASED QUARTERLY
Operations = Bill Growth Lights + Bill Environmental Control + Bill Misc Energy + Water Bill + Salary Cost + Maintenance Cost +
Distribution cost - Reduction from Renewable Energy
"""
opex_time_series = []
for i in range(days):
if i % 30 == 0:
opex_time_series_bill += monthly_salary_payments # Fixed costs
opex_time_series_bill += energy_cost_per_month # Lights and hvac energy costs
opex_time_series_bill += water_cost_per_month
opex_time_series_bill += misc_energy_cost_per_month
opex_time_series_bill += maintenance_cost_per_month
opex_time_series_bill += rent
opex_time_series_bill += distribution_cost_per_month
opex_time_series_bill -= renewable_energy_reduction_per_month
opex_time_series.append(opex_time_series_bill)
elif i % 365 == 0:
opex_time_series_annual += 0 # Standing charge
opex_time_series_annual += insurance_premium # Insurance premium annual charge
opex_time_series.append(opex_time_series_annual)
else:
opex_time_series.append(0.0)
return opex_time_series
# ========================================== REVENUE TIME SERIES ============================================= #
def calc_revenue_time_series(sales, sale_cycle):
"""
Revenue Time Series
Notes:
-----
Currently people pay per harvest cycle - consistent customers per delivery
:param sales: The revenue generated from a sale
:param sale_cycle: How often sales are made (days)
:return: A time series for revenue generated for the number of days
"""
revenue_time_series = []
for i in range(days):
if i % sale_cycle == 0:
revenue_time_series.append(sales)
else:
revenue_time_series.append(0.0)
return revenue_time_series
# ============================================== PROFIT AND MARGINS ===============================================#
# ---------------------------------------------- PROFIT ----------------------------------------------------------#
def calc_profit(revenue_time_series, opex_time_series, cogs_time_series):
"""
Profit Formula
Notes
------------
Profit = revenue from sales - running costs (OpEx and COGS)
"""
profit_time_series = revenue_time_series - opex_time_series - cogs_time_series
return profit_time_series
# ----------------------------------- GROSS PROFIT MARGIN ----------------------------------------------------------#
def calc_gross_profit_margin(revenue_time_series, cogs_time_series): # Profit and Cost of Goods Sold - i.e. cost of materials and director labour costs
"""
Gross Profit Margins Formula
Notes
------------
Profit Margins = Total revenue - Cost of goods sold (COGS) / revenue
= Profit / Revenue = Cost of Materials and Direct Labour Costs
"""
gross_profit_margin = (revenue_time_series - cogs_time_series) / revenue_time_series
return gross_profit_margin
# -------------------------------------- LOAN & REPAYMENT INTEREST --------------------------------------------------#
def calc_loan_balance(capex, interest, days, repayment):
"""
Loan Balance Equation
Notes
----
The formula for the remaining balance on a loan can be used to calculate the remaining balance at a given time(time n),
whether at a future date or at present. The remaining balance on a loan formula shown is only used for a loan that is amortized,
meaning that the portion of interest and principal applied to each payment is predetermined.
FV / loan_balance = Future value - remaining balance
PV = Present value - original balance
P = Payment
r = rate per payment
n = number of payments
"""
loan_balance: int(capex)
monthly_interest = interest/12
loan_balance_time_series = []
for i in range(days):
if i % 30 == 0:
loan_balance = loan_balance * (1 + monthly_interest)**(i/30) - repayment * (((1+monthly_interest)**(i/30) - 1) / monthly_interest)
loan_balance_time_series.append(loan_balance)
else:
loan_balance_time_series.append(0.0)
return loan_balance_time_series
# ------------------------------------- TAX TIME SERIES ------------------------------------------------------------#
def calc_tax_rate(country):
if country == uk:
tax_rate = 0.2
tax_deadline = "6th April"
return tax_rate, tax_deadline
else:
raise RuntimeError("Unknown country: {}".format(country))
def calc_tax_time_series(tax_rate, days, profit_time_series):
tax_time_series = []
for i in range(days):
if i % 365 == 0:
tax_time_series.append((profit_time_series[i]-profit_time_series[i-365])*tax_rate)
else:
tax_time_series.append(0.0)
return tax_time_series
# ---------------------------------------- RETURN ON INVESTMENT ---------------------------------------#
def calc_roi(revenue_time_series, opex_time_series, cogs_time_series, interest, tax_time_series, capex):
"""
Return on Investment Equation
Notes
-----
Calculates ROI by calculating profit divided by total investment, and then multiplying by 100 for a percentage.
The profit is calculated as the revenue computed from Eqn. 5, subtracting OpEx (Eqn. 1), COGS (Eqn. 2), the interest from the loan or investment,
and taxes associated with the specified operation. The user has two options, to calculate ROI for a tax-year with annual revenue, or to calculate
by using the computed monthly revenue with risk and uncertainty analysis applied on yield and sales. The ROI is calculated per month,
which is then used for risk assessment
""""""
FAST BAD WRONG Code for VF Wiz
Created on 25 Aug 2019
Author: Francis Baumont De Oliveira
Contact: sgfbaumo@liv.ac.uk
"""
# ==================================== IMPORT LIBRARIES ========================================== #
import json
import math
from random import gauss
from Economic_model.Old.vf_inputs import Scenario
# ==================================== CONSTANTS ========================================== #
PSYCHOMETRIC_CONSTANT = 65.0 # Pa/K
# ==================================== INPUT SCENARIO ========================================== #
def get_scenario(input_file):
with open(input_file) as f:
inputs = json.load(f)
scenario = Scenario()
scenario.currency = inputs['currency']
scenario.country = inputs['country']
scenario.capex = inputs["start_loan"]
scenario.repayment = inputs["loan_repayment"]
scenario.interest = inputs['loan_interest']
scenario.lights = inputs['lights']
scenario.crop = inputs['crop']
scenario.area = inputs['grow_area']
scenario.surface = inputs['surface_area']
scenario.volume = inputs['farm_volume']
scenario.building = inputs['building_type']
scenario.rent = inputs['rental_costs']
scenario.system = inputs['grow_system']
scenario.co2 = inputs['co2_enrichment']
scenario.energy = inputs['energy_price']
scenario.energy_standing = inputs['energy_standing_charge']
scenario.water = inputs['water_price']
scenario.water_standing = inputs['water_standing_charge']
scenario.renewable = inputs['ratio_of_renewable_energy_created_to_sourced']
scenario.toutdoors = inputs['average_outdoor_temperature']
scenario.crop_price = inputs['crop_price_per_kilo']
scenario.farm_staff = inputs['number_of_farm_staff']
scenario.salaries = inputs['annual_salaries_of_employees']
scenario.standard_wage = inputs['standard_wage']
scenario.insurance = inputs['insurance_premium']
scenario.coverage = inputs['insurance_coverage']
scenario.days = inputs['days_for_simulation']
return scenario
# ============================================== SYSTEM AND EXPECTED YIELDS #==================================#
def calc_no_of_racks(grow_system, grow_area):
if grow_system == 'ziprack_8':
no_of_racks = math.floor(grow_area/4.62963) # 54 Zipracks per 250 sq-m (including aisles, work bench & plumbing kit)
else:
raise RuntimeError("Unknown grow_system: {}".format(grow_system))
return no_of_racks
# ------------------------------------ HARVEST WEIGHT ---------------------------------------------- #
def calc_harvest_weight(crop):
if crop == "lettuce":
harvest_weight = 0.5 # kg
else:
raise RuntimeError("Unknown crop {}".format(crop))
return harvest_weight
def get_gross_yield(crop):
if crop == 'lettuce':
ys = 78.5 # kg / m2 / year
else:
raise RuntimeError("Unknown crop {}".format(crop))
return ys
# ------------------------------------ PLANT CAPACITY ---------------------------------------------- #
def calc_plant_capacity(crop, grow_system, no_of_racks): # Excluding propagation and only within the VFS
if crop == "lettuce" and grow_system == 'ziprack_8':
no_of_towers = no_of_racks*30 # Tight spacing with lettuce (30 towers per rack)
yield_capacity = no_of_towers*3.3 # 3.3kg of greens per tower
harvest_weight = calc_harvest_weight(crop)
farm_plant_capacity = yield_capacity / harvest_weight # Potential yield divided by harvest weight of product
else:
raise RuntimeError("Unknown crop {}".format(crop))
return farm_plant_capacity, yield_capacity
# ------------------------------------ LIGHTING SOLUTION ---------------------------------------------- #
def get_spec(lights):
if lights == "intraspectra_spectrablade_8":
light_wattage = 75
light_efficiency = 0.4
print("The {} light is {} watts with an efficiency of: {}".format(lights, light_wattage, light_efficiency))
else:
raise RuntimeError("Unknown lights {}".format(lights))
return light_wattage, light_efficiency
def calc_no_of_lights(grow_system, no_of_racks):
if grow_system == 'ziprack_8':
no_of_lights = no_of_racks*24 # Assumption that 24 lighting units are require to cover crop area of 1 Ziprack (30 towers)
else:
raise RuntimeError("Unknown grow system {}".format(grow_system))
return no_of_lights
# ------------------------------------ TEMPERATURE CROP REQUIREMENTS ---------------------------------------------- #
def get_temp_crop_reqs(crop):
if crop == 'lettuce':
Tin = 23.9 # Temperature optimal for lettuce growth
else:
Tin = 22 # 'general temperature'
return Tin
# ====================================== FACTORS AND CROP YIELD =============================================#
def calc_crop_ppfd_reqs(crop):
if crop == 'lettuce':
crop_ppfd_reqs = 295
else:
raise RuntimeError("Unknown crop: {}".format(crop))
return crop_ppfd_reqs
def calc_PAR_factor(ppfd_lights, crop_ppfd_reqs):
parf = ppfd_lights/crop_ppfd_reqs
return parf
def calc_co2_factor(co2_enrichment):
if co2_enrichment:
co2f = 1
else:
co2f = 0.9
return co2f
def calc_failure_rate():
fr = gauss(0.05, 0.02)
return fr
def calc_standard_yield(crop): # Standard yield per year
""" Taken from table from Shao Economic Estimation Tool (2017)"""
if crop == 'lettuce':
return 78.5 # kg/m2/year
else:
raise RuntimeError("Unknown crop: {}".format(crop))
def calc_plant_area(grow_area, grow_system, no_of_racks):
""" Plant area calculated using space taken by Racks - formula from Refarmers spreadsheet 2018"""
if grow_system == 'ziprack_8':
pa = (no_of_racks*4.300986) + 3.0612
else:
pa = grow_area
return pa
def calc_temperature_factor(hvac_control):
"""
Temperature Factor Equation
Notes:
--------
The reduction in yield caused by over heating or freezing of the grow area, especially if the farm is uncontrolled by hvac or other systems
If no hvac control, preliminary value set to 0.85. This should be assessed depending on climate, crop reqs and level of hvac control
High:
Med:
Low:
"""
if hvac_control == "high": # If advanced hvac control then temperature factor is 1
tf = 1
else:
tf = 0.85
return tf
def calc_system_multiplier(grow_system):
"""
System Multiplier
Notes
-----
Standard yield isn't 100% accurate and doesn't consider high density vertical farming systems. The estimated yield
from ZipGrow Ziprack_8 is 66,825 kg/year for 235m2 plant area. Adjusted yield without multiplier is 18447.5 kg/year
66,825/18447.5 kg = 3.622442065320504
"""
if grow_system == 'ziprack_8':
system_multiplier = 3.622442065320504
else:
raise RuntimeError("Unknown grow system {}".format(grow_system))
return system_multiplier
# ---------------------------------------------- ADJUSTED YIELD ---------------------------------------------------- #
def calc_adjusted_yield(ys, pa, parf, co2f, tf, fr, system_multiplier):
"""
Adjusted Plant Yield Equation
Notes
-----
Ya = Ys x PA x parf x co2f x Tf x (1 - Fr)
Adjusted Plant Yield = Standard Yield x Plant Area x PAR factor
parf = ratio of actual PAR delivered to plant canopy compared to theoretical plant requirements. In artificial lighting
VF the value was 1 as controlled at optimal level. Sun-fed plant level from EcoTect simulation.) x
co2f = Increment by co2 enrichment
Tf = Temperature factor (reflects reduction of yield caused by overheating or freezing of the growing area
if indoor temperature is uncontrolled by hvac or other systems, value can be set for 0.9 for preliminary estimation)
Fr = Failure rate, by default set 30% year 1, 20% year 2, 10% year 3 and 5% onwards
Sm = System multiplier (best case scenario x system multiplier)
"""
ya = ys * pa * parf * co2f * tf * (1 - fr) * system_multiplier
return ya
# ============================================== SALES ==================================================#
def calc_sales(ya, crop_price, sale_cycle):
crop_sales = (ya*crop_price)/ sale_cycle
return crop_sales # per sales or delivery cycle
# ============================================== COST OF GOODS SOLD ==================================================#
# ---------------------------------------------- COGS: SEEDS COSTS ----------------------------------------------------#
def calc_seeds_cost(crop, ya, harvest_weight):
"""
Seeds Calculator
Notes
------
:param crop: The crop that is selected to be grown on the farm
:param ya: The expected yield for that crop
:param harvest_weight: The harvest weight selected for that crop
The seeds required are 40% more than the plants harvested. This is to account for error, unsuccessful
propagation or thinning.
:return: The cost per seed, and the number of seeds required to calculate the overall seed cost per year
"""
if crop == 'lettuce':
cost_per_seed = 0.10
else:
raise RuntimeError("Unknown crop: {}".format(crop))
seeds_required = (ya/harvest_weight)*1.4
seeds_cost = seeds_required * cost_per_seed # costs of seeds
return seeds_cost
# ------------------------------------------------------ COGS: NUTRIENTS COSTS ------------------------------------- #
def calc_nutrients_cost(ya):
nutrients_cost = ya*0.20 # £0.20 worth of nutrients per kg of crop produced
return nutrients_cost
# --------------------------------- COGS: MEDIA COSTS --------------------------------------------------------#
def calc_media_cost(ya):
media_cost = ya*0.75 # £0.30 worth of media per kg of crop produced
return media_cost
# ------------------------------------------------- COGS: co2 ENRICHMENT --------------------------------------------- #
def calc_co2_cost(co2_enrichment):
if co2_enrichment:
co2_cost = ya*0.1
else:
co2_cost = 0
return co2_cost
# ----------------------------------------------------- COGS: LABOUR COSTS ----------------------------------#
def calc_labour_cost(farm_staff, standard_wage):
"""
Labour Costs Formaula
Notes
------
Direct farm labour cost = Number of staff working full-time x wages x 30 hours
Generalisation if statement on farm labour required if unknown
"""
labour_cost = farm_staff * standard_wage * 35
return labour_cost
# ------------------------------------------ COGS: PACKAGING COSTS -------------------------------------------------- #
def calc_packaging_cost(ya):
packaging_cost = 0.5*ya # 0.5 is cost per kilo of produce (User specified)
return packaging_cost
# ---------------------------------------------- COGS: OVERALL COGS ------------------------------------------------- #
def calc_cogs(seeds_cost, nutrients_cost, media_cost, co2_cost, labour_cost, packaging_cost):
cogs_annual = seeds_cost + nutrients_cost + co2_cost + (labour_cost * 50) + packaging_cost + media_cost # Annual cost of goods sold
cogs_quarterly = cogs_annual / 4
cogs_monthly = cogs_annual / 12
cogs_weekly = cogs_annual / 50
cogs_daily = cogs_annual / 365
return cogs_annual, cogs_quarterly, cogs_monthly, cogs_weekly, cogs_daily
def calc_cogs_time_series(days, cogs_quarterly):
"""
Cost of Goods Sold Formaula
Notes
-----
Can adjust for days/weekly/monthly/annually in the future - ASSUMED: CONSUMABLES PURCHASED QUARTERLY
"""
cogs_time_series = []
for i in range(days):
if i % 365/4 == 0:
cogs_time_series.append(cogs_quarterly)
else:
cogs_time_series.append(0.0)
return cogs_time_series
# ==================================== OPERATIONAL EXPENDITURE ============================== #
# ---------------------------------------------- OPEX: SALARIES ------------------------------------------------------#
def calc_salary_payments(salaries):
monthly_salary_payments = salaries/12
return monthly_salary_payments
# -------------------------------------------- OPEX: WATER CALCULATIONS ---------------------------------------------- #
def calc_water_consumption(grow_system, no_of_racks, grow_area):
if grow_system == "ziprack_8":
water_consumption_per_month = no_of_racks * 0.95 * 30.42 # Litres of water per tower per day (0.25 gallons) multiplied by month
water_buffer = 1900 # Litres of water for buffer per month (500 gallons)
water_consumption_per_month += water_buffer # Water consumption could be used here.
water_consumption_per_day = water_consumption_per_month/30.42
water_consumption_per_year = water_consumption_per_month*12
water_consumption_per_week = (water_consumption_per_month*12)/52
return water_consumption_per_day, water_consumption_per_week, water_consumption_per_month, water_consumption_per_year
else:
water_consumption_per_year = grow_area * 200 # Average from Agrilyst survey - 4 Gallons per sq ft per year
water_consumption_per_month = water_consumption_per_year / 12 # consumption per month
water_consumption_per_week = water_consumption_per_year/52
water_consumption_per_day = water_consumption_per_year/365
return water_consumption_per_day, water_consumption_per_week, water_consumption_per_month, water_consumption_per_year
def calc_water_cost(water_consumption_per_day, water_consumption_per_week, water_consumption_per_month, water_consumption_per_year, water_price, water_standing_charge): # need to include standing charges
water_cost_per_day = (water_consumption_per_day/1000) * water_price
water_cost_per_week = (water_consumption_per_week/1000) * water_price
water_cost_per_month = (water_consumption_per_month/1000) * water_price + water_standing_charge
water_cost_per_year = (water_consumption_per_year/1000) * water_price
return water_cost_per_day, water_cost_per_week, water_cost_per_month, water_cost_per_year
# ------------------------------------- OPEX: LIGHT ENERGY CALCULATIONS --------------------------------------------#
def calc_lights_energy(lights, no_of_lights):
lights_watts, efficiency = get_spec(lights)
lighting_kw_usage = lights_watts*no_of_lights/1000
lights_kwh_per_day = lighting_kw_usage*12 # Assuming 12 hours of light for plants
lights_kwh_per_month = lights_kwh_per_day * 30.417 # 365 days/12 months
lights_kwh_per_week = lights_kwh_per_day*7
lights_kwh_per_year = lights_kwh_per_day * 365
return lights_kwh_per_day, lights_kwh_per_week, lights_kwh_per_month, lights_kwh_per_year
# ---------------------------------- OPEX: hvac ENERGY CALCULATIONS --------------------------------------------------#
def calc_hvac_energy(surface_area, building_type, Tin, Tout):
"""
Heat Transfer Equation
Notes
-----
Q = U x SA x (Tin - Tout)
Q - Heat lost or gained due to outside temperature (kJ·h−1)
U - Overall heat transfer coefficient (kJ·h−1·m−2·°C−1)
SA - Surface Area of the space
Tin - Inside air set point temperature (°C)
Tout - Outside air temperature (°C)
"""
if building_type == 'basement':
U = 0.5
else:
U = 24 # generic heat transfer coefficient
Q = U*surface_area*(Tin-Tout)
hvac_kwh = Q*0.00666667*24 # Conversion factor of kJ/h to kWh x 24 hours
# Rudimentary hvac calculations - general
hvac_kwh_per_day = hvac_kwh*1
hvac_kwh_per_month = hvac_kwh_per_day * 30.417 # 365 days/12 months
hvac_kwh_per_week = hvac_kwh_per_day * 7
hvac_kwh_per_year = hvac_kwh_per_day * 365
return hvac_kwh_per_day, hvac_kwh_per_week, hvac_kwh_per_month, hvac_kwh_per_year
# ------------------------------------- OPEX: MISC. ENERGY CALCULATIONS ---------------------------------------------- #
def calc_pump_energy(grow_system, no_of_racks):
if grow_system == 'ziprack_8':
no_of_plumbing_kits = math.ceil(no_of_racks/45) # spec for plumbing kit provided by Refarmers - 45 racks
plumbing_kit_wattage = 1800 # spec for plumbing kit provided by Refarmers
pumps_kw_usage = no_of_plumbing_kits * plumbing_kit_wattage / 1000
pumps_kwh_per_day = pumps_kw_usage * 24 # 24 hours on
pumps_kwh_per_month = pumps_kwh_per_day * 30.417 # 365 days/12 months
pumps_kwh_per_week = pumps_kwh_per_day * 7
pumps_kwh_per_year = pumps_kwh_per_day * 365
return pumps_kwh_per_day, pumps_kwh_per_week, pumps_kwh_per_month, pumps_kwh_per_year
else:
raise RuntimeError("Unknown grow_system: {}".format(grow_system))
def calc_misc_energy(pumps_kwh_per_day):
"""
Misc Energy Consumption
Notes
------
Energy consumption for miscellaneous elements such as: Filtration, Sensors, Internet, Office Lighting,
Automation, Computers, etc.
"""
misc_kwh_per_day = pumps_kwh_per_day
return misc_kwh_per_day
# ----------------------------------- OPEX: OVERALL ENERGY CALC (LIGHTS+hvac+MISC) -----------------------#
def calc_energy_consumption(hvac_kwh_per_day, lights_kwh_per_day, misc_kwh_per_day):
"""
Energy Consumption
Notes
------
Energy consumption for different time periods
"""
farm_kwh_per_day = hvac_kwh_per_day + lights_kwh_per_day + misc_kwh_per_day
farm_kwh_per_week = farm_kwh_per_day * 7 # 7 days in a week
farm_kwh_per_month = farm_kwh_per_day * 30.417 # 365 days/12 months
farm_kwh_per_year = farm_kwh_per_day * 365
return farm_kwh_per_day, farm_kwh_per_week, farm_kwh_per_month, farm_kwh_per_year
def calc_energy_cost(farm_kwh_per_day, farm_kwh_per_week, farm_kwh_per_month, farm_kwh_per_year, energy_price):
"""
Energy Costs
Notes
------
Energy cost for different time periods
"""
energy_cost_per_day = farm_kwh_per_day * energy_price
energy_cost_per_week = farm_kwh_per_week * energy_price # 365 days/12 months
energy_cost_per_month = farm_kwh_per_month * energy_price # 365 days/12 months
energy_cost_per_year = farm_kwh_per_year * energy_price
return energy_cost_per_day, energy_cost_per_week, energy_cost_per_month, energy_cost_per_year
# -------------------------------- OPEX: MAINTENANCE COST ----------------------------------------------------------#
def calc_maintenance_cost(grow_system, no_of_racks):
if grow_system == 'ziprack_8':
maintenance_cost_per_month = no_of_racks*2.50 # £2.50 worth of labour per month to maintain
return maintenance_cost_per_month
else:
raise RuntimeError("Unknown grow_system: {}".format(grow_system))
# ---------------------------------------- OPEX: DISTRIBUTION COST ----------------------------------------------------#
def calc_distribution_cost(sales, sale_cycle): # Distribution cost per delivery
distribution_cost_per_sale_cycle = sales*0.15
distribution_cost_per_month = distribution_cost_per_sale_cycle*(30.417/sale_cycle) # The number of delivery (sale) cycles in a month
return distribution_cost_per_month
# ---------------------------------- OPEX: RENEWABLE ENERGY REDUCTION ------------------------------------------------#
def calc_renewable_energy_reduction(renewable, energy_cost_per_day): # Distribution cost per delivery
renewable_energy_reduction_per_day = energy_cost_per_day*renewable
renewable_energy_reduction_per_week = energy_cost_per_day*7*renewable
renewable_energy_reduction_per_month = energy_cost_per_day*30.417*renewable
renewable_energy_reduction_per_year = energy_cost_per_day*365*renewable
return renewable_energy_reduction_per_day, renewable_energy_reduction_per_week, renewable_energy_reduction_per_month, renewable_energy_reduction_per_year
# ---------------------------------------------- OPEX: OVERALL OPEX --------------------------------------------------#
def calc_opex_time_series(days, monthly_salary_payments, energy_cost_per_month, water_cost_per_month,
rent, maintenance_cost_per_month, distribution_cost_per_month, renewable_energy_reduction_per_month):
"""
Can adjust for days/weekly/monthly/annually in the future - ASSUMED: CONSUMABLES PURCHASED QUARTERLY
Operations = Bill Growth Lights + Bill Environmental Control + Bill Misc Energy + Water Bill + Salary Cost + Maintenance Cost +
Distribution cost - Reduction from Renewable Energy
"""
opex_time_series = []
for i in range(days):
if i % 30 == 0:
opex_time_series_bill += monthly_salary_payments # Fixed costs
opex_time_series_bill += energy_cost_per_month # Lights and hvac energy costs
opex_time_series_bill += water_cost_per_month
opex_time_series_bill += misc_energy_cost_per_month
opex_time_series_bill += maintenance_cost_per_month
opex_time_series_bill += rent
opex_time_series_bill += distribution_cost_per_month
opex_time_series_bill -= renewable_energy_reduction_per_month
opex_time_series.append(opex_time_series_bill)
elif i % 365 == 0:
opex_time_series_annual += 0 # Standing charge
opex_time_series_annual += insurance_premium # Insurance premium annual charge
opex_time_series.append(opex_time_series_annual)
else:
opex_time_series.append(0.0)
return opex_time_series
# ========================================== REVENUE TIME SERIES ============================================= #
def calc_revenue_time_series(sales, sale_cycle):
"""
Revenue Time Series
Notes:
-----
Currently people pay per harvest cycle - consistent customers per delivery
:param sales: The revenue generated from a sale
:param sale_cycle: How often sales are made (days)
:return: A time series for revenue generated for the number of days
"""
revenue_time_series = []
for i in range(days):
if i % sale_cycle == 0:
revenue_time_series.append(sales)
else:
revenue_time_series.append(0.0)
return revenue_time_series
# ============================================== PROFIT AND MARGINS ===============================================#
# ---------------------------------------------- PROFIT ----------------------------------------------------------#
def calc_profit(revenue_time_series, opex_time_series, cogs_time_series):
"""
Profit Formula
Notes
------------
Profit = revenue from sales - running costs (OpEx and COGS)
"""
profit_time_series = revenue_time_series - opex_time_series - cogs_time_series
return profit_time_series
# ----------------------------------- GROSS PROFIT MARGIN ----------------------------------------------------------#
def calc_gross_profit_margin(revenue_time_series, cogs_time_series): # Profit and Cost of Goods Sold - i.e. cost of materials and director labour costs
"""
Gross Profit Margins Formula
Notes
------------
Profit Margins = Total revenue - Cost of goods sold (COGS) / revenue
= Profit / Revenue = Cost of Materials and Direct Labour Costs
"""
gross_profit_margin = (revenue_time_series - cogs_time_series) / revenue_time_series
return gross_profit_margin
# -------------------------------------- LOAN & REPAYMENT INTEREST --------------------------------------------------#
def calc_loan_balance(capex, interest, days, repayment):
"""
Loan Balance Equation
Notes
----
The formula for the remaining balance on a loan can be used to calculate the remaining balance at a given time(time n),
whether at a future date or at present. The remaining balance on a loan formula shown is only used for a loan that is amortized,
meaning that the portion of interest and principal applied to each payment is predetermined.
FV / loan_balance = Future value - remaining balance
PV = Present value - original balance
P = Payment
r = rate per payment
n = number of payments
"""
loan_balance: int(capex)
monthly_interest = interest/12
loan_balance_time_series = []
for i in range(days):
if i % 30 == 0:
loan_balance = loan_balance * (1 + monthly_interest)**(i/30) - repayment * (((1+monthly_interest)**(i/30) - 1) / monthly_interest)
loan_balance_time_series.append(loan_balance)
else:
loan_balance_time_series.append(0.0)
return loan_balance_time_series
# ------------------------------------- TAX TIME SERIES ------------------------------------------------------------#
def calc_tax_rate(country):
if country == uk:
tax_rate = 0.2
tax_deadline = "6th April"
return tax_rate, tax_deadline
else:
raise RuntimeError("Unknown country: {}".format(country))
def calc_tax_time_series(tax_rate, days, profit_time_series):
tax_time_series = []
for i in range(days):
if i % 365 == 0:
tax_time_series.append((profit_time_series[i]-profit_time_series[i-365])*tax_rate)
else:
tax_time_series.append(0.0)
return tax_time_series
# ---------------------------------------- RETURN ON INVESTMENT ---------------------------------------#
def calc_roi(revenue_time_series, opex_time_series, cogs_time_series, interest, tax_time_series, capex):
"""
Return on Investment Equation
Notes
-----
Calculates ROI by calculating profit divided by total investment, and then multiplying by 100 for a percentage.
The profit is calculated as the revenue computed from Eqn. 5, subtracting OpEx (Eqn. 1), COGS (Eqn. 2), the interest from the loan or investment,
and taxes associated with the specified operation. The user has two options, to calculate ROI for a tax-year with annual revenue, or to calculate
by using the computed monthly revenue with risk and uncertainty analysis applied on yield and sales. The ROI is calculated per month,
which is then used for risk assessment
"""
r = revenue_time_series - opex_time_series - cogs_time_series - interest - tax_time_series
roi_array = (r / capex) * 100
return roi_array
# ====================================================================================================================#
# ============================================== SCRIPT ==============================================================#
# ====================================================================================================================#
# OPEX
# opex_time_series: int = 0
# days = 366
# opex_array = []
# sales: int = 0
# sales_array = []
#
# print("days",days-1)
# input_file = 'input_file.json'
# scenario = get_scenario(input_file)
#
# no_of_racks = calc_no_of_racks(scenario.system, scenario.area)
# no_of_lights = calc_no_of_lights(scenario.system, no_of_racks)
# lights_daily_energy = calc_lights_energy(scenario.lights, no_of_lights)
#
# hvac_daily_energy = calc_hvac_energy(surface_area=scenario.surface, building_type=scenario.building,
# Tin=get_temp_crop_reqs(scenario.crop), Tout=scenario.toutdoors)
# daily_energy_consumption_farm, monthly_energy_consumption_farm = calc_energy_consumption(hvac_daily_energy, lights_daily_energy)
# farm_plant_capacity, standard_yield = calc_plant_capacity(scenario.crop, scenario.system, no_of_racks)
# ys = standard_yield
# crop_ppfd_reqs = calc_crop_ppfd_reqs(scenario.crop)
# ppfd_lights = 295 # placeholder
#
# tf = 1
# opex_array.append(opex_time_series)
# # ARRAY conversion
# sales_array.append(sales)
# sales_array = np.asarray(sales_array) # Sales as an array
# opex_array = np.asarray(opex_array) # OpEx as an array
# profit_array = profit(sales_array, opex_array)
# gross_profit_margin(sales_array, cogs)
# print("Profit £:", profit_array[-1])
# plt.plot(profit_array)
# plt.xlabel('Days')
# plt.ylabel('Gross Profit')
# plt.show()
# plt.figure()
# plt.plot(gross_profit_margin)
# plt.xlabel('Days')
# plt.ylabel('Gross Profit Margin')
# plt.show()
#
# print("Gross Profit Margin:",gross_profit_margin[-1])
# print("GOT costs ", costs)
r = revenue_time_series - opex_time_series - cogs_time_series - interest - tax_time_series
roi_array = (r / capex) * 100
return roi_array
# ====================================================================================================================#
# ============================================== SCRIPT ==============================================================#
# ====================================================================================================================#
# OPEX
# opex_time_series: int = 0
# days = 366
# opex_array = []
# sales: int = 0
# sales_array = []
#
# print("days",days-1)
# input_file = 'input_file.json'
# scenario = get_scenario(input_file)
#
# no_of_racks = calc_no_of_racks(scenario.system, scenario.area)
# no_of_lights = calc_no_of_lights(scenario.system, no_of_racks)
# lights_daily_energy = calc_lights_energy(scenario.lights, no_of_lights)
#
# hvac_daily_energy = calc_hvac_energy(surface_area=scenario.surface, building_type=scenario.building,
# Tin=get_temp_crop_reqs(scenario.crop), Tout=scenario.toutdoors)
# daily_energy_consumption_farm, monthly_energy_consumption_farm = calc_energy_consumption(hvac_daily_energy, lights_daily_energy)
# farm_plant_capacity, standard_yield = calc_plant_capacity(scenario.crop, scenario.system, no_of_racks)
# ys = standard_yield
# crop_ppfd_reqs = calc_crop_ppfd_reqs(scenario.crop)
# ppfd_lights = 295 # placeholder
#
# tf = 1
# opex_array.append(opex_time_series)
# # ARRAY conversion
# sales_array.append(sales)
# sales_array = np.asarray(sales_array) # Sales as an array
# opex_array = np.asarray(opex_array) # OpEx as an array
# profit_array = profit(sales_array, opex_array)
# gross_profit_margin(sales_array, cogs)
# print("Profit £:", profit_array[-1])
# plt.plot(profit_array)
# plt.xlabel('Days')
# plt.ylabel('Gross Profit')
# plt.show()
# plt.figure()
# plt.plot(gross_profit_margin)
# plt.xlabel('Days')
# plt.ylabel('Gross Profit Margin')
# plt.show()
#
# print("Gross Profit Margin:",gross_profit_margin[-1])
# print("GOT costs ", costs)
| 41.080794
| 204
| 0.613387
| 7,078
| 57,965
| 4.733682
| 0.075445
| 0.039994
| 0.017191
| 0.021728
| 0.999552
| 0.999552
| 0.999552
| 0.999552
| 0.999552
| 0.999552
| 0
| 0.021548
| 0.197775
| 57,965
| 1,411
| 205
| 41.080794
| 0.6982
| 0.445976
| 0
| 0.993884
| 0
| 0
| 0.063075
| 0.011298
| 0
| 0
| 0
| 0
| 0
| 1
| 0.140673
| false
| 0
| 0.012232
| 0
| 0.296636
| 0.003058
| 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
|
55995e8adcca0956e10aa4cea237b4dfd7f918d9
| 7,001
|
py
|
Python
|
test/test_update.py
|
evi0s/pyqudie
|
5d088482dd2b56e9aaf0991ea182fb11d6a1fc14
|
[
"MIT"
] | null | null | null |
test/test_update.py
|
evi0s/pyqudie
|
5d088482dd2b56e9aaf0991ea182fb11d6a1fc14
|
[
"MIT"
] | null | null | null |
test/test_update.py
|
evi0s/pyqudie
|
5d088482dd2b56e9aaf0991ea182fb11d6a1fc14
|
[
"MIT"
] | null | null | null |
"""
Unit Tests
function: Update
"""
import unittest
import sys
import pymongo
import testconfig as config
sys.path.append("..")
from pyqudie import Mongo
from pyqudie.MongoExceptions import *
class TestUpdate(unittest.TestCase):
def setUp(self):
Client = pymongo.MongoClient("mongodb://{}:{}@{}:{}/".format(config.database_username, config.database_password,
config.database_host, config.database_port))
Database = Client['test']
Collection = Database['test']
Collection.delete_many({})
print "Data in database has been cleared."
Collection.insert_many([
{"test1": "asdf", "test2": "asdasd"},
{"test1": "qwe", "test2": "qaq"},
{"test1": "asdf", "test2": "qwert"}
])
def test_update1(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "test"
updateQuery = {"test1": "asdf"}
updateDict = {"$set": {"test1": "12345"}}
result = test.update(collection, updateQuery, updateDict)
self.assertEquals(result, 1)
datas = self.getData()
self.assertEquals(len(datas), 3)
self.assertEquals(datas[0]['test1'], "12345")
self.assertEquals(datas[1]['test1'], "qwe")
self.assertEquals(datas[1]['test2'], "qaq")
self.assertEquals(datas[2]['test1'], "asdf")
self.assertEquals(datas[2]['test2'], "qwert")
def test_update2(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "test"
updateQuery = {"test1": "asdf"}
updateDict = {"$set": {"test1": "12345"}}
result = test.update(collection, updateQuery, updateDict, updateMany = True)
self.assertEquals(result, 2)
datas = self.getData()
self.assertEquals(len(datas), 3)
self.assertEquals(datas[0]['test1'], "12345")
self.assertEquals(datas[1]['test1'], "qwe")
self.assertEquals(datas[1]['test2'], "qaq")
self.assertEquals(datas[2]['test1'], "12345")
def test_update3(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "testqwer"
updateQuery = {"test1": "asdf"}
updateDict = {"$set": {"test1": "12345"}}
try:
test.update(collection, updateQuery, updateDict)
except InvalidCollectionException as err:
self.assertEquals(err.message, "Invalid Collection!")
else:
raise AssertionError
def test_update4(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = 1234
updateQuery = {"test1": "asdf"}
updateDict = {"$set": {"test1": "12345"}}
try:
test.update(collection, updateQuery, updateDict)
except InvalidCollectionException as err:
self.assertEquals(err.message, "Invalid Collection!")
else:
raise AssertionError
def test_update5(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "test"
updateQuery = "asdasd"
updateDict = {"$set": {"test1": "12345"}}
try:
test.update(collection, updateQuery, updateDict)
except InvalidUpdateQueryException as err:
self.assertEquals(err.message, "Invalid Update Query!")
else:
raise AssertionError
def test_update6(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "test"
updateQuery = {"test1": "asdf"}
updateDict = "123123"
try:
test.update(collection, updateQuery, updateDict)
except InvalidUpdateDictException as err:
self.assertEquals(err.message, "Invalid Update Dict!")
else:
raise AssertionError
def test_update7(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "test"
updateQuery = {"test1": "asdf"}
updateDict = {"$set": {"test1": "12345"}}
try:
test.update(collection, updateQuery, updateDict, updateMany = 123)
except InvalidUpdateOptionException as err:
self.assertEquals(err.message, "Invalid Update Option!")
else:
raise AssertionError
def test_update8(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "test"
updateQuery = {"test1": "asdf"}
updateDict = {"$st": {"test1": "12345"}}
try:
test.update(collection, updateQuery, updateDict)
except OperationFailedException as err:
self.assertEquals(err.message, "Operation Failed!")
else:
raise AssertionError
def test_update9(self):
test = Mongo.Mongo(config.database_host, config.database_port, True,
config.database_username, config.database_password)
collection = "test"
updateQuery = {"test1": "asdf"}
updateDict = {"$st": {"test1": "12345"}}
try:
test.update(collection, updateQuery, updateDict, updateMany = True)
except OperationFailedException as err:
self.assertEquals(err.message, "Operation Failed!")
else:
raise AssertionError
def getData(self):
Client = pymongo.MongoClient("mongodb://{}:{}@{}:{}/".format(config.database_username, config.database_password,
config.database_host, config.database_port))
Database = Client['test']
Collection = Database['test']
Cursor = Collection.find({})
datas = []
for data in Cursor:
datas.append(data)
return datas
def tearDown(self):
Client = pymongo.MongoClient("mongodb://{}:{}@{}:{}/".format(config.database_username, config.database_password,
config.database_host, config.database_port))
Database = Client['test']
Collection = Database['test']
Collection.delete_many({})
print "Test data set in database has been cleared."
| 34.830846
| 120
| 0.589202
| 647
| 7,001
| 6.282844
| 0.157651
| 0.165314
| 0.064945
| 0.082657
| 0.8369
| 0.801968
| 0.801968
| 0.78524
| 0.752768
| 0.747847
| 0
| 0.02439
| 0.291387
| 7,001
| 200
| 121
| 35.005
| 0.795001
| 0
| 0
| 0.66
| 0
| 0
| 0.096181
| 0.009475
| 0
| 0
| 0
| 0
| 0.18
| 0
| null | null | 0.08
| 0.04
| null | null | 0.013333
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
e94745b9b4c9cb17a2dab86bd7629e9ac6a770b3
| 159
|
py
|
Python
|
notebooks/platform/xarray/lib/stats/ld_matrix/__init__.py
|
tomwhite/gwas-analysis
|
5b219607b8311722f16f7df8a8aad09ba69dc448
|
[
"Apache-2.0"
] | 19
|
2020-03-18T01:06:58.000Z
|
2022-02-06T19:59:30.000Z
|
notebooks/platform/xarray/lib/stats/ld_matrix/__init__.py
|
tomwhite/gwas-analysis
|
5b219607b8311722f16f7df8a8aad09ba69dc448
|
[
"Apache-2.0"
] | 39
|
2020-01-20T19:50:19.000Z
|
2021-01-07T19:01:48.000Z
|
notebooks/platform/xarray/lib/stats/ld_matrix/__init__.py
|
tomwhite/gwas-analysis
|
5b219607b8311722f16f7df8a8aad09ba69dc448
|
[
"Apache-2.0"
] | 5
|
2020-03-13T20:47:24.000Z
|
2022-01-13T09:43:35.000Z
|
from . import numba_backend
try:
from . import dask_backend
except ImportError:
pass
try:
from . import cuda_backend
except ImportError:
pass
| 14.454545
| 30
| 0.72327
| 20
| 159
| 5.6
| 0.5
| 0.267857
| 0.232143
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.232704
| 159
| 11
| 31
| 14.454545
| 0.918033
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.222222
| 0.555556
| 0
| 0.555556
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
e9673651e86945de7513f53873542ad1f021798d
| 127
|
py
|
Python
|
tests/test_simple.py
|
dmitriyVasilievich1986/git_actions_test
|
40f980d761ce8d7295300ad543786c337089f1a3
|
[
"MIT"
] | null | null | null |
tests/test_simple.py
|
dmitriyVasilievich1986/git_actions_test
|
40f980d761ce8d7295300ad543786c337089f1a3
|
[
"MIT"
] | null | null | null |
tests/test_simple.py
|
dmitriyVasilievich1986/git_actions_test
|
40f980d761ce8d7295300ad543786c337089f1a3
|
[
"MIT"
] | null | null | null |
from simple.simple_function import simple_function
def test_simple():
assert simple_function("some text") == "some text"
| 21.166667
| 54
| 0.76378
| 17
| 127
| 5.470588
| 0.529412
| 0.451613
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141732
| 127
| 5
| 55
| 25.4
| 0.853211
| 0
| 0
| 0
| 0
| 0
| 0.141732
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 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
|
e9c2af299a52a49dac6b9147f1ba37ab6f40e91e
| 32
|
py
|
Python
|
tests/integration/testdata/sync/nested/before/root_layer/root_layer.py
|
praneetap/aws-sam-cli
|
2a713566c8de72a68eb8954584674a61a2d807ac
|
[
"Apache-2.0"
] | 2,285
|
2017-08-11T16:57:31.000Z
|
2018-05-08T20:38:25.000Z
|
tests/integration/testdata/sync/nested/before/root_layer/root_layer.py
|
praneetap/aws-sam-cli
|
2a713566c8de72a68eb8954584674a61a2d807ac
|
[
"Apache-2.0"
] | 314
|
2017-08-11T17:29:27.000Z
|
2018-05-08T20:51:47.000Z
|
tests/integration/testdata/sync/nested/before/root_layer/root_layer.py
|
praneetap/aws-sam-cli
|
2a713566c8de72a68eb8954584674a61a2d807ac
|
[
"Apache-2.0"
] | 284
|
2017-08-11T17:35:48.000Z
|
2018-05-08T20:15:59.000Z
|
def layer_method():
return 5
| 16
| 19
| 0.6875
| 5
| 32
| 4.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04
| 0.21875
| 32
| 2
| 20
| 16
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0.5
| 1
| 0
| 1
| 1
| 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
| 0
| 1
| 1
| 0
|
0
| 7
|
e9e20e3258fa3c1a53b2e9c8bc3c0939400b78d1
| 126,459
|
py
|
Python
|
main_dilated_filters_for_edge_detection_algorithms.py
|
CipiOrhei/eecvf
|
759fb2127c8d65a570ba2df536ff8429ccf5bdf2
|
[
"MIT"
] | 1
|
2021-04-02T15:33:12.000Z
|
2021-04-02T15:33:12.000Z
|
main_dilated_filters_for_edge_detection_algorithms.py
|
CipiOrhei/eecvf
|
759fb2127c8d65a570ba2df536ff8429ccf5bdf2
|
[
"MIT"
] | null | null | null |
main_dilated_filters_for_edge_detection_algorithms.py
|
CipiOrhei/eecvf
|
759fb2127c8d65a570ba2df536ff8429ccf5bdf2
|
[
"MIT"
] | 1
|
2021-08-14T09:07:22.000Z
|
2021-08-14T09:07:22.000Z
|
# noinspection PyUnresolvedReferences
import Application
# noinspection PyUnresolvedReferences
import Benchmarking
# noinspection PyUnresolvedReferences
import MachineLearning
# noinspection PyUnresolvedReferences
import config_main as CONFIG
# noinspection PyUnresolvedReferences
import Utils
"""
Code for paper:
title = {Dilated Filters for Edge-Detection Algorithms},
author = {Orhei, Ciprian and Bogdan, Victor and Bonchis, Cosmin and Vasiu, Radu},
journal = {Applied Sciences},
volume = {11},
year = {2021},
number = {22},
publisher={Multidisciplinary Digital Publishing Institute}
pages = {10716},
url = {https://www.mdpi.com/2076-3417/11/22/10716},
issn = {2076-3417},
doi = {10.3390/app112210716}
"""
def main_find_param_first_order_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
edges = [
CONFIG.FILTERS.SOBEL_3x3,
CONFIG.FILTERS.SOBEL_5x5,
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.SOBEL_DILATED_5x5,
CONFIG.FILTERS.SOBEL_DILATED_7x7
]
for edge in edges:
# find best threshold for first level
for thr in range(30, 160, 10):
for sigma in range(25, 300, 25):
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s, port_output_name='BLURED_S_' + str(s).replace('.', '_'))
edge_result = Application.do_first_order_derivative_operators(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=False)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='first_order_thr_sigma_param_finder',
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_L0',
list_of_data=list_to_save, number_of_series=40,
replace_list=[('_SOBEL', ''),('_DILATED', ' Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('THR_', ' TG='), ('_BLURED_S_', ' S='), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=False,
save_plot=True, show_plot=False)
Utils.close_files()
def main_first_order_edge_detection(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
first_order_edge = [
CONFIG.FILTERS.PIXEL_DIFF_3x3, CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_3x3
, CONFIG.FILTERS.SOBEL_3x3
, CONFIG.FILTERS.PREWITT_3x3
, CONFIG.FILTERS.KIRSCH_3x3
, CONFIG.FILTERS.KITCHEN_MALIN_3x3
, CONFIG.FILTERS.KAYYALI_3x3
, CONFIG.FILTERS.SCHARR_3x3
, CONFIG.FILTERS.KROON_3x3
, CONFIG.FILTERS.ORHEI_3x3
]
threshold = 50
sigma = 2.75
for edge in first_order_edge:
########################################################################################################################
# First order edge detection magnitude
########################################################################################################################
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=sigma)
edge_result = Application.do_first_order_derivative_operators(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
first_order_edge = [
CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_5x5
, CONFIG.FILTERS.PIXEL_DIFF_5x5
, CONFIG.FILTERS.SOBEL_5x5
, CONFIG.FILTERS.PREWITT_5x5
, CONFIG.FILTERS.KIRSCH_5x5
, CONFIG.FILTERS.SCHARR_5x5
, CONFIG.FILTERS.ORHEI_B_5x5
]
threshold = 50
sigma = 2.5
for edge in first_order_edge:
########################################################################################################################
# First order edge detection magnitude
########################################################################################################################
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=sigma)
edge_result = Application.do_first_order_derivative_operators(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
first_order_edge = [
CONFIG.FILTERS.SOBEL_7x7
, CONFIG.FILTERS.PREWITT_7x7
]
threshold = 30
sigma = 2.75
for edge in first_order_edge:
########################################################################################################################
# First order edge detection magnitude
########################################################################################################################
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=sigma)
edge_result = Application.do_first_order_derivative_operators(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
first_order_edge = [
CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_5x5
, CONFIG.FILTERS.PIXEL_DIFF_5x5
, CONFIG.FILTERS.SOBEL_DILATED_5x5
, CONFIG.FILTERS.PREWITT_DILATED_5x5
, CONFIG.FILTERS.KIRSCH_DILATED_5x5
, CONFIG.FILTERS.KITCHEN_MALIN_DILATED_5x5
, CONFIG.FILTERS.KAYYALI_DILATED_5x5
, CONFIG.FILTERS.SCHARR_DILATED_5x5
, CONFIG.FILTERS.KROON_DILATED_5x5
, CONFIG.FILTERS.ORHEI_DILATED_5x5
]
threshold = 50
sigma = 2.25
for edge in first_order_edge:
########################################################################################################################
# First order edge detection magnitude
########################################################################################################################
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=sigma)
edge_result = Application.do_first_order_derivative_operators(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
first_order_edge = [
CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_7x7
, CONFIG.FILTERS.PIXEL_DIFF_7x7
, CONFIG.FILTERS.SOBEL_DILATED_7x7
, CONFIG.FILTERS.PREWITT_DILATED_7x7
, CONFIG.FILTERS.KIRSCH_DILATED_7x7
, CONFIG.FILTERS.KITCHEN_MALIN_DILATED_7x7
, CONFIG.FILTERS.KAYYALI_DILATED_7x7
, CONFIG.FILTERS.SCHARR_DILATED_7x7
, CONFIG.FILTERS.KROON_DILATED_7x7
, CONFIG.FILTERS.ORHEI_DILATED_7x7
]
threshold = 50
sigma = 2.00
for edge in first_order_edge:
########################################################################################################################
# First order edge detection magnitude
########################################################################################################################
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=sigma)
edge_result = Application.do_first_order_derivative_operators(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='first_order_results',
list_of_data=list_to_save, number_of_series=50,
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend=None,
replace_list=[('SEPARATED_PIXEL_DIFFERENCE_', 'Sep Px Dif '),
('PIXEL_DIFFERENCE_', 'Px Dif '),
('PREWITT_', 'Prewitt '), ('KIRSCH_', 'Kirsch '), ('SOBEL_', 'Sobel '),
('SCHARR_', 'Scharr '), ('KROON_', 'Kroon '), ('ORHEI_V1_', 'Orhei '), ('ORHEI_', 'Orhei '),
('KITCHEN_', 'Kitchen '), ('KAYYALI_', 'Kayyali '),
('DILATED_', 'dilated '),
('_GAUSS_BLUR_K_0_S_2_25_GREY_L0', ''),
('_GAUSS_BLUR_K_0_S_2_5_GREY_L0', ''),
('_GAUSS_BLUR_K_0_S_2_75_GREY_L0', ''),
('_GAUSS_BLUR_K_0_S_2_0_GREY_L0', ''),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=True,
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='first_order_latex_table_results', print_to_console=True,
header_list=['Variant', '', '3x3', '5x5', 'Dilated 5x5', '7x7', 'Dilated 7x7'],
prefix_data_name='FINAL', suffix_data_name='BLURED', level_data_name='L0',
version_data_name=['3x3', '5x5', 'DILATED_5x5', '7x7', 'DILATED_7x7'],
data_per_variant=['R', 'P', 'F1'], version_separation='DILATED')
Utils.close_files()
def main_find_sigma_compass_first_order_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
threshold = 65
# find best threshold for first level
for sigma in range(25, 500, 25):
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_SIGMA_' + str(s).replace('.', '_'))
edge_result = Application.do_compass_edge_job(port_input_name=blured_img, operator=CONFIG.FILTERS.ROBINSON_CROSS_3x3,
)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by=None, name='compass_first_order_sigma_results_finder',
list_of_data=list_to_save, number_of_series=30,
inputs=[''], self_contained_list=True,
replace_list=[('ROBINSON_CROSS_3x3_BLURED_SIGMA_', 'S='), ('_', '.')],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_L0',
save_plot=True, show_plot=False)
Utils.close_files()
def main_find_thr_sig_compass_first_order_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
edges = [
CONFIG.FILTERS.ROBINSON_CROSS_3x3,
CONFIG.FILTERS.ROBINSON_CROSS_DILATED_5x5,
CONFIG.FILTERS.ROBINSON_CROSS_DILATED_7x7
]
# find best threshold for first level
for edge in edges:
for thr in range(30, 160, 10):
for sigma in range(25, 350, 25):
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_SIGMA_' + str(s).replace('.', '_'))
edge_result = Application.do_compass_edge_job(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='compass_first_order_thr_sigma_results_finder',
list_of_data=list_to_save, number_of_series=40, set_all_to_legend=False,
inputs=[''], self_contained_list=True, set_legend_left=True,
replace_list=[
('_ROBINSON_CROSS_', ' ')
, ('DILATED', 'Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('THR_', 'TG='), ('_BLURED_SIGMA_', ' S='), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_L0',
save_plot=True, show_plot=False)
Utils.close_files()
def main_first_order_compass_edge_detection(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
threshold = 50
sigma = 2.00
compass_filters = [
CONFIG.FILTERS.ROBINSON_CROSS_DILATED_7x7
, CONFIG.FILTERS.ROBINSON_MODIFIED_CROSS_7x7
, CONFIG.FILTERS.KIRSCH_DILATED_7x7
, CONFIG.FILTERS.PREWITT_CROSS_DILATED_7x7
]
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=sigma, port_output_name='BLURED')
for edge in compass_filters:
edge_results = Application.do_compass_edge_job(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_results, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_results)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_results)
list_to_save.append(thin_thr_edge_result + '_L0')
threshold = 50
sigma = 2.5
compass_filters = [
CONFIG.FILTERS.ROBINSON_CROSS_3x3
, CONFIG.FILTERS.ROBINSON_CROSS_DILATED_5x5
, CONFIG.FILTERS.ROBINSON_MODIFIED_CROSS_3x3
, CONFIG.FILTERS.ROBINSON_MODIFIED_CROSS_5x5
, CONFIG.FILTERS.KIRSCH_CROSS_3x3
, CONFIG.FILTERS.KIRSCH_DILATED_5x5
, CONFIG.FILTERS.PREWITT_CROSS_3x3
, CONFIG.FILTERS.PREWITT_CROSS_DILATED_5x5
]
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=sigma, port_output_name='BLURED')
for edge in compass_filters:
edge_results = Application.do_compass_edge_job(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_results, input_value=threshold,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_results)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_results)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=False)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='compass_first_order_results',
list_of_data=list_to_save, number_of_series=50,
inputs=[''], self_contained_list=True,
replace_list=[('ROBINSON_CROSS_', 'Robinson Cross '), ('KIRSCH_', 'Kirsch Cross '),
('ROBINSON_MODIFIED_CROSS_', 'Robinson Mod Cross '),
('PREWITT_COMPASS_', 'Prewitt Compass '),
('DILATED_', 'Dilated '),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_BLURED_L0', set_legend_left=True, set_all_to_legend=True,
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='compass_first_order_latex_table_results', print_to_console=True,
header_list=['Variant', '', '3x3', 'Dilated 5x5', 'Dilated 7x7'],
prefix_data_name='FINAL', suffix_data_name='BLURED', level_data_name='L0',
version_data_name=['3x3', 'DILATED_5x5', 'DILATED_7x7'], version_separation='DILATED',
data_per_variant=['R', 'P', 'F1']
)
Utils.close_files()
def main_find_thr_sigma_frei_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
# find best threshold for first level
for dilatation in [0, 1, 2]:
for thr in range(10, 150, 10):
for sigma in range(25, 300, 25):
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_SIGMA_' + str(s).replace('.', '_'))
edge_frei, line_frei = Application.do_frei_chen_edge_job(port_input_name=blured_img, dilated_kernel=dilatation)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_frei, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_frei)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='frei_edge_sigma_thr_finder',
list_of_data=list_to_save, number_of_series=40,
replace_list=[
('_FREI_CHEN_EDGE_', ' ')
, ('DILATED', 'Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('THR_', 'Thr='), ('_BLURED_SIGMA_', ' S='), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_L0',
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=False,
save_plot=True, show_plot=False)
Utils.close_files()
def main_frei_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
thr = 50
s = 2.5
# find best threshold for first level
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_SIGMA_' + str(s).replace('.', '_'))
for dilatation in range(3):
edge_frei, line_frei = Application.do_frei_chen_edge_job(port_input_name=blured_img, dilated_kernel=dilatation)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_frei, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + edge_frei)
thr_line_result = Application.do_image_threshold_job(port_input_name=line_frei, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + line_frei)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + edge_frei)
thin_thr_line_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_line_result,
port_output_name='FINAL_' + line_frei)
list_to_save.append(thin_thr_edge_result + '_L0')
list_to_save.append(thin_thr_line_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='frei_edge_results',
list_of_data=list_to_save, number_of_series=30,
inputs=[''], self_contained_list=True,
replace_list=[('FREI_CHEN_EDGE_', 'Frei-Chen Edge '), ('FREI_CHEN_LINE_', 'Frei-Chen Line '),
('_BLURED_SIGMA_2_5_L0', ''),
('DILATED_', 'Dilated '),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_BLURED_SIGMA_0_075_L0',
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='frei_edge_latex_table_results', print_to_console=True,
list_of_series=['FREI_CHEN_EDGE', 'FREI_CHEN_LINE'],
header_list=['Variant', '', '3x3', 'Dilated 5x5', 'Dilated 7x7'],
prefix_data_name='FINAL', suffix_data_name='BLURED', level_data_name='L0',
version_data_name=['3x3', 'DILATED_5x5', 'DILATED_7x7'],
data_per_variant=['R', 'P', 'F1']
)
Utils.close_files()
def main_find_thr_laplace_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
]
# find best threshold for first level
for edge in laplace_edges:
for thr in range(25, 265, 10):
edge_result = Application.do_laplace_job(port_input_name='GREY', kernel=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
# Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=False)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='laplace_thr_results_finder',
list_of_data=list_to_save, number_of_series=40,
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=False,
replace_list=[
('_LAPLACE_V1_', ' '), ('DILATED_', ' Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('THR_', 'TG='),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_GREY_L0',
save_plot=True, show_plot=False)
Utils.close_files()
def main_laplace_edges(dataset):
# Application.delete_folder_appl_out()
# Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_2, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_3, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_3
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_4, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_4
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_5, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_5
]
thr = 95
for edge in laplace_edges:
edge_result = Application.do_laplace_job(port_input_name='GREY', kernel=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_3
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_4
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5
]
thr = 75
for edge in laplace_edges:
edge_result = Application.do_laplace_job(port_input_name='GREY', kernel=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
# Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='laplace_edge_results',
list_of_data=list_to_save, number_of_series=30,
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=True,
replace_list=[('THR_75_LAPLACE_', ''), ('THR_95_LAPLACE_', ''),
('_DILATED_', ' Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_GREY_L0',
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='laplace_latex_table_results', print_to_console=True,
header_list=['Variant', '', '3x3', '5x5', 'Dilated 5x5', 'Dilated 7x7'],
list_of_series=['LAPLACE_V1', 'LAPLACE_V2', 'LAPLACE_V3', 'LAPLACE_V4', 'LAPLACE_V5'],
prefix_data_name='FINAL', suffix_data_name='GREY', level_data_name='L0',
version_data_name=['3x3', '5x5', 'DILATED_5x5', 'DILATED_7x7'], version_separation='DILATED',
data_per_variant=['R', 'P', 'F1']
)
Utils.close_files()
def main_find_sigma_log_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
]
# find best threshold for first level
for edge in laplace_edges:
for sigma in range(20, 200, 20):
s = sigma / 100
for thr in range(5, 100, 5):
edge_result = Application.do_log_job(port_input_name='GREY', gaussian_sigma=s,
laplacian_kernel=edge,
port_output_name='LOG_' + edge +'_S_' + str(s).replace('.', '_') + '_GREY')
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='log_thr_results_finder',
list_of_data=list_to_save, number_of_series=40, set_all_to_legend=False,
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_GREY_L0',
replace_list=[
('THR_', 'TG='),
('_LOG_LAPLACE_V1', ''), ('_DILATED_', ' Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('_S_', ' S='), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
inputs=[''], self_contained_list=True, set_legend_left=True,
save_plot=True, show_plot=False)
Utils.close_files()
def main_log_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_2,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_3,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_4,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5
]
thr = 5
s = 1.80
for edge in laplace_edges:
edge_result = Application.do_log_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge,
port_output_name='LOG_' + edge + '_S_' + str(s).replace('.', '_') + '_GREY')
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_2
]
thr = 5
s = 1.40
for edge in laplace_edges:
edge_result = Application.do_log_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge,
port_output_name='LOG_' + edge + '_S_' + str(s).replace('.', '_') + '_GREY')
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_2,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_3,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_4,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_5
]
thr = 15
s = 1.80
for edge in laplace_edges:
edge_result = Application.do_log_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge,
port_output_name='LOG_' + edge + '_S_' + str(s).replace('.', '_') + '_GREY')
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_2,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_3,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_4,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_5
]
thr = 30
s = 1.80
for edge in laplace_edges:
edge_result = Application.do_log_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge,
port_output_name='LOG_' + edge + '_S_' + str(s).replace('.', '_') + '_GREY')
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
# Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='log_edge_results',
list_of_data=list_to_save, number_of_series=30, set_legend_left=True,
inputs=[''], self_contained_list=True, set_all_to_legend=True,
replace_list=[('THR_5_LOG_LAPLACE_', ''), ('THR_15_LOG_LAPLACE_', ''),('THR_30_LOG_LAPLACE_', ''),
('_DILATED_', ' Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'),
('_S_1_8_GREY_L0', ''), ('_S_1_4_GREY_L0', ''),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend=None,
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='log_latex_table_results', print_to_console=True,
header_list=['Variant', '', '3x3', '5x5', 'Dilated 5x5', 'Dilated 7x7'],
list_of_series=['LAPLACE_V1', 'LAPLACE_V2', 'LAPLACE_V3', 'LAPLACE_V4', 'LAPLACE_V5'],
prefix_data_name='FINAL', suffix_data_name='GREY', level_data_name='L0',
version_data_name=['3x3', '5x5', 'DILATED_5x5', 'DILATED_7x7'], version_separation='DILATED',
data_per_variant=['R', 'P', 'F1']
)
Utils.close_files()
def main_find_sigma_marr_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
]
for edge in laplace_edges:
for sigma in range(20, 300, 20):
s = sigma / 100
for thr in range(20, 100, 10):
t = thr / 100
edge_result = Application.do_marr_hildreth_job(port_input_name='GREY', gaussian_sigma=s,
laplacian_kernel=edge,
threshold=t)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='mar_sigma_results_finder',
list_of_data=list_to_save, number_of_series=40, set_all_to_legend=False,
prefix_to_cut_legend='FINAL_MARR_HILDRETH_LAPLACE_V1_', suffix_to_cut_legend='_GREY_L0',
replace_list=[
('DILATED_', 'Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('_S_', ' S='), ('_THR_', ' TG='), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
inputs=[''], self_contained_list=True, set_legend_left=True,
save_plot=True, show_plot=False)
Utils.close_files()
def main_marr_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_3
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_4
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5
]
s = 1.8
t = 0.3
for edge in laplace_edges:
edge_result = Application.do_marr_hildreth_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge, threshold=t)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_2
]
s = 1.6
t = 0.2
for edge in laplace_edges:
edge_result = Application.do_marr_hildreth_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge, threshold=t)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_3
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_4
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_5
]
s = 2.0
t = 0.3
for edge in laplace_edges:
edge_result = Application.do_marr_hildreth_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge, threshold=t)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_3
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_4
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_5
]
s = 2.0
t = 0.2
for edge in laplace_edges:
edge_result = Application.do_marr_hildreth_job(port_input_name='GREY', gaussian_sigma=s, laplacian_kernel=edge, threshold=t)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='marr_edge_results',
list_of_data=list_to_save, number_of_series=30,
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=True,
prefix_to_cut_legend='FINAL_MARR_HILDRETH_LAPLACE_',
replace_list=[('_DILATED_', ' Dilated '), ('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_GREY_L0', ''),
('_S_2_0', ''), ('_S_1_8', ''), ('_S_1_6', ''),
('_THR_0_2', ''), ('_THR_0_3', ''),
('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='marr_latex_table_results', print_to_console=True,
header_list=['Variant', '', '3x3', '5x5', 'Dilated 5x5', 'Dilated 7x7'],
list_of_series=['LAPLACE_V1', 'LAPLACE_V2', 'LAPLACE_V3', 'LAPLACE_V4', 'LAPLACE_V5'],
prefix_data_name='FINAL', suffix_data_name='GREY', level_data_name='L0',
version_data_name=['3x3', '5x5', 'DILATED_5x5', 'DILATED_7x7'], version_separation='DILATED',
data_per_variant=['R', 'P', 'F1']
)
Utils.close_files()
def main_sigma_finder_canny_2(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
edges = [
CONFIG.FILTERS.SOBEL_3x3,
CONFIG.FILTERS.SOBEL_5x5,
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.SOBEL_DILATED_5x5,
CONFIG.FILTERS.SOBEL_DILATED_7x7
]
# find best threshold for first level
for edge in edges:
for sigma in range(100, 175, 25):
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_S_' + str(s).replace('.', '_'))
for low in range(70, 150, 10):
for high in range(90, 200, 10):
# for high in [90]:
if low < high:
canny_result = Application.do_canny_config_job(port_input_name=blured_img, edge_detector=edge, canny_config=CONFIG.CANNY_VARIANTS.MANUAL_THRESHOLD,
low_manual_threshold = low, high_manual_threshold=high, canny_config_value=None,
port_output_name='CANNY_' + edge + '_S_' + str(s).replace('.', '_') + '_L_' + str(low) + '_H_' + str(high),
do_blur=False)
list_to_save.append(canny_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='canny_sigma_results_finder',
suffix_to_cut_legend='_L0', set_all_to_legend=False,
list_of_data=list_to_save, number_of_series=50, set_legend_left=True,
replace_list=[('CANNY_SOBEL', ''),('_DILATED', ' Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('_S_', ' S='), ('_L_', ' TL='), ('_H_', ' TH='), ('_L0', ''), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
inputs=[''], self_contained_list=True,
save_plot=True, show_plot=False)
Utils.close_files()
def main_canny_2(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
first_order_edge_3x3 = [
CONFIG.FILTERS.PIXEL_DIFF_3x3, CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_3x3
, CONFIG.FILTERS.SOBEL_3x3
, CONFIG.FILTERS.PREWITT_3x3
, CONFIG.FILTERS.KIRSCH_3x3
, CONFIG.FILTERS.KITCHEN_MALIN_3x3
, CONFIG.FILTERS.KAYYALI_3x3
, CONFIG.FILTERS.SCHARR_3x3
, CONFIG.FILTERS.KROON_3x3
, CONFIG.FILTERS.ORHEI_3x3
]
s = 1.5
# find best threshold for first level
for edge in first_order_edge_3x3:
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_S_' + str(s).replace('.', '_'))
low = 80
high = 90
canny_result = Application.do_canny_config_job(port_input_name=blured_img, edge_detector=edge, canny_config=CONFIG.CANNY_VARIANTS.MANUAL_THRESHOLD,
low_manual_threshold=low, high_manual_threshold=high, canny_config_value=None,
port_output_name='CANNY_' + edge + '_S_' + str(s).replace('.', '_') + '_L_' + str(low) + '_H_' + str(high),
do_blur=False)
list_to_save.append(canny_result + '_L0')
first_order_edge_7x7 = [
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.PREWITT_7x7,
]
# find best threshold for first level
for edge in first_order_edge_7x7:
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_S_' + str(s).replace('.', '_'))
low = 70
high = 90
canny_result = Application.do_canny_config_job(port_input_name=blured_img, edge_detector=edge, canny_config=CONFIG.CANNY_VARIANTS.MANUAL_THRESHOLD,
low_manual_threshold=low, high_manual_threshold=high, canny_config_value=None,
port_output_name='CANNY_' + edge + '_S_' + str(s).replace('.', '_') + '_L_' + str(low) + '_H_' + str(high),
do_blur=False)
list_to_save.append(canny_result + '_L0')
first_order_edge = [
CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_5x5, CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_7x7
, CONFIG.FILTERS.PIXEL_DIFF_5x5, CONFIG.FILTERS.PIXEL_DIFF_7x7
, CONFIG.FILTERS.SOBEL_5x5
, CONFIG.FILTERS.SOBEL_DILATED_5x5, CONFIG.FILTERS.SOBEL_DILATED_7x7
, CONFIG.FILTERS.PREWITT_5x5
, CONFIG.FILTERS.PREWITT_DILATED_5x5, CONFIG.FILTERS.PREWITT_DILATED_7x7
, CONFIG.FILTERS.KIRSCH_5x5
, CONFIG.FILTERS.KIRSCH_DILATED_5x5, CONFIG.FILTERS.KIRSCH_DILATED_7x7
, CONFIG.FILTERS.KITCHEN_MALIN_DILATED_5x5, CONFIG.FILTERS.KITCHEN_MALIN_DILATED_7x7
, CONFIG.FILTERS.KAYYALI_DILATED_5x5, CONFIG.FILTERS.KAYYALI_DILATED_7x7
, CONFIG.FILTERS.SCHARR_5x5
, CONFIG.FILTERS.SCHARR_DILATED_5x5, CONFIG.FILTERS.SCHARR_DILATED_7x7
, CONFIG.FILTERS.KROON_DILATED_5x5, CONFIG.FILTERS.KROON_DILATED_7x7
, CONFIG.FILTERS.ORHEI_B_5x5
, CONFIG.FILTERS.ORHEI_DILATED_5x5, CONFIG.FILTERS.ORHEI_DILATED_7x7
]
s = 1.5
# find best threshold for first level
for edge in first_order_edge:
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_S_' + str(s).replace('.', '_'))
low = 90
high = 130
canny_result = Application.do_canny_config_job(port_input_name=blured_img, edge_detector=edge, canny_config=CONFIG.CANNY_VARIANTS.MANUAL_THRESHOLD,
low_manual_threshold = low, high_manual_threshold=high, canny_config_value=None,
port_output_name='CANNY_' + edge + '_S_' + str(s).replace('.', '_') + '_L_' + str(low) + '_H_' + str(high),
do_blur=False)
list_to_save.append(canny_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='canny_results',
suffix_to_cut_legend=None, prefix_to_cut_legend='CANNY_',
list_of_data=list_to_save, number_of_series=40,
replace_list=[('SEPARATED_PIXEL_DIFFERENCE_', 'Sep Px Dif '),
('PIXEL_DIFFERENCE_', 'Px Dif '),
('PREWITT_', 'Prewitt '), ('KIRSCH_', 'Kirsch '), ('SOBEL_', 'Sobel '),
('SCHARR_', 'Scharr '), ('KROON_', 'Kroon '), ('ORHEI_V1_', 'Orhei '),
('ORHEI_', 'Orhei '),
('KITCHEN_', 'Kitchen '), ('KAYYALI_', 'Kayyali '),
('DILATED_', 'dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('_S_1_5', ''),
('_L_90', ''), ('_L_80', ''), ('_L_70', ''),
('_H_130', ''), ('_H_90', ''),
('_L0', ''), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=True,
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='canny_latex_table_results', print_to_console=True,
header_list=['Variant', '', '3x3', '5x5', 'Dilated 5x5', '7x7', 'Dilated 7x7'],
prefix_data_name='CA', suffix_data_name='BLURED', level_data_name='L0',
version_data_name=['3x3', '5x5', 'DILATED_5x5', '7x7', 'DILATED_7x7'],
data_per_variant=['R', 'P', 'F1'], version_separation='DILATED')
Utils.close_files()
def main_param_shen_finder(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1,
]
for edge in laplace_edges:
for s in [0.5, 0.9]:
for w in [5, 7, 11]:
for r in [0.5, 0.9]:
for th in [0, 0.5, 0.9]:
for thr in [4]:
edge_result = Application.do_shen_castan_job(port_input_name='GREY',
laplacian_kernel=edge,
laplacian_threhold=thr, smoothing_factor=s, zc_window_size=w,
thinning_factor=th, ratio=r,
port_output_name='SHEN_CASTAN_' + edge + '_THR_' + str(thr).replace('.', '_')
+ '_S_' + str(s).replace('.', '_') + '_W_' + str(w) +
'_R_' + str(r).replace('.', '_') + '_TH_' + str(
th).replace('.', '_'))
list_to_save.append(edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='', level='L0', order_by='f1', name='shen_tunning',
list_of_data=list_to_save, number_of_series=40,
suffix_to_cut_legend='_L0', set_all_to_legend=False,
replace_list=[('SHEN_CASTAN_', ''), ('LAPLACE_V1_', ''),
('DILATED_', 'Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('_THR_', ' TG='), ('_S_', ' SF='), ('_W_', ' W='),
('_R_', ' R='), ('_TH_', ' TH='), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
inputs=[''], self_contained_list=True, set_legend_left=True,
save_plot=True, show_plot=False)
Utils.close_files()
def main_shen_edges(dataset):
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_2, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_2, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_2
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_3
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_3, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_3
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_4
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_4, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_4
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_5, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_5
]
thr = 4
s = 0.5
w = 11
th = 0.0
r = 0.5
for edge in laplace_edges:
if 'DILATED_7x7' in edge:
s = 0.9
edge_result = Application.do_shen_castan_job(port_input_name='GREY', laplacian_kernel=edge,
laplacian_threhold=thr, smoothing_factor=s, zc_window_size=w,
thinning_factor=th, ratio=r,
port_output_name='SHEN_CASTAN_' + edge)
list_to_save.append(edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_save, do_thinning=False)
Utils.plot_first_cpm_results(prefix='FINAL', level='L0', order_by='f1', name='shen_edge_results',
list_of_data=list_to_save, number_of_series=30,
inputs=[''], self_contained_list=True, set_legend_left=True, set_all_to_legend=True,
replace_list=[('SHEN_CASTAN_LAPLACE_', ''), ('_DILATED_', ' Dilated '), ('_3x3', ' 3x3'), ('_5x5', ' 5x5'),
('_L0', ''),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
prefix_to_cut_legend='FINAL_', suffix_to_cut_legend='_GREY_L0',
save_plot=True, show_plot=False)
Utils.create_latex_cpm_table(list_of_data=list_to_save, name_of_table='shen_latex_table_results', print_to_console=True,
header_list=['Variant', '', '3x3', '5x5', 'Dilated 5x5', 'Dilated 7x7'],
list_of_series=['LAPLACE_V1', 'LAPLACE_V2', 'LAPLACE_V3', 'LAPLACE_V4', 'LAPLACE_V5'],
prefix_data_name='FINAL', suffix_data_name='GREY', level_data_name='L0',
version_data_name=['3x3', '5x5', 'DILATED_5x5', 'DILATED_7x7'], version_separation='DILATED',
data_per_variant=['R', 'P', 'F1']
)
Utils.close_files()
def main_ed_parsing(dataset):
"""
Main function of framework Please look in example_main for all functions
you can use
"""
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GRAY_RAW')
list = []
first_order_edge = [
CONFIG.FILTERS.SOBEL_3x3,
CONFIG.FILTERS.SOBEL_5x5,
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.SOBEL_DILATED_5x5,
CONFIG.FILTERS.SOBEL_DILATED_7x7
]
for edge in first_order_edge:
for kernel_gaus in [3, 5, 7, 9]:
for grad_thr in [10, 30, 40, 50, 60, 70, 90, 110, 130, 150]:
for anc_thr in [5, 10, 20, 30, 40, 60]:
for sc_int in [1, 3, 5]:
blur = Application.do_gaussian_blur_image_job(port_input_name='GRAY_RAW', kernel_size=kernel_gaus, sigma=0)
e3, e4 = Application.do_edge_drawing_mod_job(port_input_name=blur, operator=edge,
gradient_thr=grad_thr, anchor_thr=anc_thr, scan_interval=sc_int,
max_edges=100, max_points_edge=100)
list.append(e3 + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list)
# Application.configure_show_pictures(ports_to_show=list, time_to_show=0)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/test',
raw_image='TestData/BSR/BSDS500/data/images/test',
jobs_set=list, do_thinning=False)
Utils.plot_first_cpm_results(prefix='EDGE_DRAWING_MOD_', level='L0', order_by='f1', name='ed_finder_thr',
list_of_data=list, number_of_series=40,
inputs=[''], self_contained_list=True, set_legend_left=True,
suffix_to_cut_legend='_S_0_GRAY_RAW_L0',
replace_list=[('_SOBEL', ''),('_DILATED', ' Dilated '),
('_3x3', ' 3x3'), ('_5x5', ' 5x5'), ('_7x7', ' 7x7'),
('EDGE_DRAWING_MOD_THR_', 'TG='), ('_ANC_THR_', ' TA='), ('_SCAN_', ' SI='),
('_GAUSS_BLUR_K_', ' GK='), ('_', '.'),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
save_plot=True, show_plot=False, set_all_to_legend=False)
Utils.close_files()
def main_ededge(dataset):
"""
Main function of framework Please look in example_main for all functions
you can use
"""
Application.set_input_image_folder('TestData/BSR/BSDS500/data/images/' + dataset)
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GRAY_RAW')
blur = Application.do_gaussian_blur_image_job(port_input_name='GRAY_RAW', sigma=0, kernel_size=9)
list_to_eval_edge = []
first_order_edge = [
CONFIG.FILTERS.PIXEL_DIFF_3x3, CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_3x3
, CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_5x5, CONFIG.FILTERS.PIXEL_DIFF_SEPARATED_7x7
, CONFIG.FILTERS.PIXEL_DIFF_5x5, CONFIG.FILTERS.PIXEL_DIFF_7x7
, CONFIG.FILTERS.SOBEL_3x3, CONFIG.FILTERS.SOBEL_5x5, CONFIG.FILTERS.SOBEL_7x7
, CONFIG.FILTERS.SOBEL_DILATED_5x5, CONFIG.FILTERS.SOBEL_DILATED_7x7
, CONFIG.FILTERS.PREWITT_3x3, CONFIG.FILTERS.PREWITT_5x5, CONFIG.FILTERS.PREWITT_7x7
, CONFIG.FILTERS.PREWITT_DILATED_5x5, CONFIG.FILTERS.PREWITT_DILATED_7x7
, CONFIG.FILTERS.KIRSCH_3x3, CONFIG.FILTERS.KIRSCH_5x5
, CONFIG.FILTERS.KIRSCH_DILATED_5x5, CONFIG.FILTERS.KIRSCH_DILATED_7x7
, CONFIG.FILTERS.KITCHEN_MALIN_3x3
, CONFIG.FILTERS.KITCHEN_MALIN_DILATED_5x5, CONFIG.FILTERS.KITCHEN_MALIN_DILATED_7x7
, CONFIG.FILTERS.KAYYALI_3x3
, CONFIG.FILTERS.KAYYALI_DILATED_5x5, CONFIG.FILTERS.KAYYALI_DILATED_7x7
, CONFIG.FILTERS.SCHARR_3x3, CONFIG.FILTERS.SCHARR_5x5
, CONFIG.FILTERS.SCHARR_DILATED_5x5, CONFIG.FILTERS.SCHARR_DILATED_7x7
, CONFIG.FILTERS.KROON_3x3
, CONFIG.FILTERS.KROON_DILATED_5x5, CONFIG.FILTERS.KROON_DILATED_7x7
, CONFIG.FILTERS.ORHEI_3x3, CONFIG.FILTERS.ORHEI_B_5x5
, CONFIG.FILTERS.ORHEI_DILATED_5x5, CONFIG.FILTERS.ORHEI_DILATED_7x7
]
for edge in first_order_edge:
for gr_thr in [50]:
for anc_thr in [5]:
e1, e2, = Application.do_edge_drawing_mod_job(port_input_name=blur, operator=edge,
gradient_thr=gr_thr, anchor_thr=anc_thr, scan_interval=1,
max_edges=100, max_points_edge=100)
list_to_eval_edge.append(e1 + '_L0')
Application.create_config_file(verbose=False)
Application.configure_save_pictures(job_name_in_port=True, ports_to_save='ALL')
# Application.configure_show_pictures(ports_to_show=list_to_save, time_to_show=200)
Application.run_application()
# Do bsds benchmarking
# Be ware not to activate job_name_in_port in Application.configure_save_pictures
Benchmarking.run_bsds500_boundary_benchmark(input_location='Logs/application_results',
gt_location='TestData/BSR/BSDS500/data/groundTruth/' + dataset,
raw_image='TestData/BSR/BSDS500/data/images/' + dataset,
jobs_set=list_to_eval_edge, do_thinning=False)
Utils.plot_first_cpm_results(prefix='EDGE_DRAWING_MOD_', level='L0', order_by='f1', name='ed_results',
list_of_data=list_to_eval_edge, number_of_series=50,
inputs=[''], self_contained_list=True, set_legend_left=True,
suffix_to_cut_legend='_S_0_GRAY_RAW_L0',
replace_list=[('EDGE_DRAWING_MOD_THR_50_ANC_THR_5_SCAN_1_', ''),
('SEPARATED_PIXEL_DIFFERENCE_', 'Sep Px Dif '),
('PIXEL_DIFFERENCE_', 'Px Dif '),
('PREWITT_', 'Prewitt '), ('KIRSCH_', 'Kirsch '), ('SOBEL_', 'Sobel '),
('SCHARR_', 'Scharr '), ('KROON_', 'Kroon '), ('ORHEI_V1_', 'Orhei '),
('ORHEI_', 'Orhei '),
('KITCHEN_', 'Kitchen '), ('KAYYALI_', 'Kayyali '),
('DILATED_', 'dilated '),
('_GAUSS_BLUR_K_9', ''),
('dilated 7x7', '7x7(D)'),
('dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
('Dilated 7x7', '7x7(D)'),
('Dilated 5x5', '5x5(D)'),
],
save_plot=True, show_plot=False, set_all_to_legend=True)
Utils.close_files()
def main_find_param_first_order_edges_SFOM():
"""
Main function of framework Please look in example_main for all functions
you can use
"""
Application.set_input_image_folder('TestData/dilation_test/test')
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GRAY_RAW')
list_to_save = list()
edges = [
CONFIG.FILTERS.SOBEL_3x3,
CONFIG.FILTERS.SOBEL_5x5,
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.SOBEL_DILATED_5x5,
CONFIG.FILTERS.SOBEL_DILATED_7x7
]
for edge in edges:
# find best threshold for first level
# for thr in range(30, 160, 10):
for thr in [30]:
# for sigma in range(25, 300, 25):
for sigma in [275]:
s = sigma / 100
# print('thr=', thr)
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GRAY_RAW', sigma=s,
port_output_name='BLURED_S_' + str(s).replace('.', '_'))
edge_result = Application.do_first_order_derivative_operators(port_input_name=blured_img,
operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file(verbose=False)
Application.configure_save_pictures(job_name_in_port=True, ports_to_save=list_to_save)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save,)
Utils.plot_box_benchmark_values(name_to_save='SFOM_first_tunning', number_decimal=3,
data='SFOM', data_subsets=edges)
def main_ed_parsing_SFOM():
"""
Main function of framework Please look in example_main for all functions
you can use
"""
Application.set_input_image_folder('TestData/dilation_test/test')
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GRAY_RAW')
list = []
first_order_edge = [
CONFIG.FILTERS.SOBEL_3x3,
CONFIG.FILTERS.SOBEL_5x5,
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.SOBEL_DILATED_5x5,
CONFIG.FILTERS.SOBEL_DILATED_7x7
]
for edge in first_order_edge:
for kernel_gaus in [3, 5, 7]:
for grad_thr in [10, 20, 30, 40, 50, 60, 70, 90, 110, 130, 150]:
for anc_thr in [5, 10, 20]:
for sc_int in [1]:
blur = Application.do_gaussian_blur_image_job(port_input_name='GRAY_RAW', kernel_size=kernel_gaus, sigma=0)
e3, e4 = Application.do_edge_drawing_mod_job(port_input_name=blur, operator=edge,
gradient_thr=grad_thr, anchor_thr=anc_thr, scan_interval=sc_int,
max_edges=100, max_points_edge=100)
list.append(e3 + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list, job_name_in_port=True)
# Application.configure_show_pictures(ports_to_show=list, time_to_show=0)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list,)
Utils.plot_box_benchmark_values(name_to_save='SFOM_ED_tunning', number_decimal=3,
data='SFOM', data_subsets=first_order_edge)
Utils.close_files()
def main_find_thr_sig_compass_first_order_edges_SFOM():
Application.set_input_image_folder('TestData/dilation_test/test')
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
edges = [
CONFIG.FILTERS.ROBINSON_CROSS_3x3
, CONFIG.FILTERS.ROBINSON_CROSS_DILATED_5x5
, CONFIG.FILTERS.ROBINSON_CROSS_DILATED_7x7
]
# find best threshold for first level
for edge in edges:
for thr in range(30, 160, 10):
for sigma in range(25, 350, 25):
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_SIGMA_' + str(s).replace('.', '_'))
edge_result = Application.do_compass_edge_job(port_input_name=blured_img, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save, )
edge_data = ['ROBINSON_CROSS_3x3', 'ROBINSON_CROSS_DILATED_5x5', 'ROBINSON_CROSS_DILATED_7x7']
Utils.plot_box_benchmark_values(name_to_save='SFOM_compass_tunning', number_decimal=3,
data='SFOM', data_subsets=edge_data)
Utils.close_files()
def main_param_shen_finder_SFOM():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test')
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1,
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
]
for edge in laplace_edges:
for s in [0.5, 0.9]:
for w in [5, 11]:
for r in [0.5, 0.9]:
for th in [0, 0.5]:
for thr in [4]:
edge_result = Application.do_shen_castan_job(port_input_name='GREY',
laplacian_kernel=edge,
laplacian_threhold=thr, smoothing_factor=s, zc_window_size=w,
thinning_factor=th, ratio=r,
port_output_name='SHEN_CASTAN_' + edge + '_THR_' + str(thr).replace('.', '_')
+ '_S_' + str(s).replace('.', '_') + '_W_' + str(w) +
'_R_' + str(r).replace('.', '_') + '_TH_' + str(
th).replace('.', '_'))
list_to_save.append(edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save, )
Utils.plot_box_benchmark_values(name_to_save='SFOM_shen_tunning', number_decimal=3,
data='SFOM', data_subsets=laplace_edges)
Utils.close_files()
def main_sigma_finder_canny_SFOM():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test')
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
edges = [
CONFIG.FILTERS.SOBEL_3x3,
CONFIG.FILTERS.SOBEL_5x5,
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.SOBEL_DILATED_5x5,
CONFIG.FILTERS.SOBEL_DILATED_7x7
]
# find best threshold for first level
for edge in edges:
# for sigma in range(25, 300, 25):
for sigma in [150,200,225]:
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_S_' + str(s).replace('.', '_'))
for low in range(70, 150, 10):
for high in range(90, 200, 10):
if low < high:
canny_result = Application.do_canny_config_job(port_input_name=blured_img, edge_detector=edge, canny_config=CONFIG.CANNY_VARIANTS.MANUAL_THRESHOLD,
low_manual_threshold = low, high_manual_threshold=high, canny_config_value=None,
port_output_name='CANNY_' + edge + '_S_' + str(s).replace('.', '_') + '_L_' + str(low) + '_H_' + str(high),
do_blur=False)
list_to_save.append(canny_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=True)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save, )
Utils.plot_box_benchmark_values(name_to_save='SFOM_canny_tunning', number_decimal=3,
data='SFOM', data_subsets=edges)
Utils.close_files()
def main_find_sigma_marr_edges_SFOM():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test')
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1]
for edge in laplace_edges:
for sigma in range(160, 220, 20):
s = sigma / 100
for thr in range(20, 50, 10):
t = thr / 100
edge_result = Application.do_marr_hildreth_job(port_input_name='GREY', gaussian_sigma=s,
laplacian_kernel=edge,
threshold=t)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=edge_result,
port_output_name='FINAL_' + edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save, )
Utils.plot_box_benchmark_values(name_to_save='SFOM_marr_tunning', number_decimal=3,
data='SFOM', data_subsets=laplace_edges)
Utils.close_files()
def main_find_sigma_log_edges_SFOM():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test')
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1]
# find best threshold for first level
for edge in laplace_edges:
for sigma in range(100, 200, 20):
s = sigma / 100
for thr in range(5, 40, 5):
edge_result = Application.do_log_job(port_input_name='GREY', gaussian_sigma=s,
laplacian_kernel=edge,
port_output_name='LOG_' + edge +'_S_' + str(s).replace('.', '_') + '_GREY')
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save, )
Utils.plot_box_benchmark_values(name_to_save='SFOM_log_tunning', number_decimal=3,
data='SFOM', data_subsets=laplace_edges)
Utils.close_files()
def main_find_thr_laplace_edges_SFOM():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test')
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1]
# find best threshold for first level
for edge in laplace_edges:
for thr in range(15, 245, 10):
edge_result = Application.do_laplace_job(port_input_name='GREY', kernel=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
# Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=False)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save, )
Utils.plot_box_benchmark_values(name_to_save='SFOM_laplace_tunning', number_decimal=3,
data='SFOM', data_subsets=laplace_edges)
Utils.close_files()
def main_find_thr_sigma_frei_edges_SFOM():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test')
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
# find best threshold for first level
for dilatation in range(3):
for thr in range(30, 160, 10):
for sigma in range(25, 320, 25):
s = sigma / 100
blured_img = Application.do_gaussian_blur_image_job(port_input_name='GREY', sigma=s,
port_output_name='BLURED_SIGMA_' + str(s).replace('.', '_'))
edge_frei, line_frei = Application.do_frei_chen_edge_job(port_input_name=blured_img, dilated_kernel=dilatation)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_frei, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_frei)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
Application.create_config_file()
Application.configure_save_pictures(ports_to_save=list_to_save, job_name_in_port=False)
# Application.configure_save_pictures(ports_to_save='ALL', job_name_in_port=True)
Application.run_application()
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate',
raw_image='TestData/dilation_test/test',
jobs_set=list_to_save, )
edges = ['FREI_CHEN_EDGE_3x3', 'FREI_CHEN_EDGE_DILATED_5x5', 'FREI_CHEN_EDGE_DILATED_7x7']
Utils.plot_box_benchmark_values(name_to_save='SFOM_frei_tunning', number_decimal=3,
data='SFOM', data_subsets=edges, eval=list_to_save)
Utils.close_files()
def main_signal_to_noise():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test_')
list_input = []
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_10dB', mean_value=0, variance=0.2)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_12dB', mean_value=0, variance=0.09)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_14dB', mean_value=0, variance=0.06)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_16dB', mean_value=0, variance=0.04)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
edges = [
CONFIG.FILTERS.SOBEL_3x3,
CONFIG.FILTERS.SOBEL_5x5,
CONFIG.FILTERS.SOBEL_7x7,
CONFIG.FILTERS.SOBEL_DILATED_5x5,
CONFIG.FILTERS.SOBEL_DILATED_7x7
]
list_to_eval = list()
for input in list_input:
list_to_eval_tmp = list()
for edge in edges:
# find best threshold for first level
for thr in range(30, 160, 10):
# for thr in [10]:
for sigma in range(25, 300, 25):
# for sigma in [200]:
s = sigma / 100
# print('thr=', thr)
# blured_img = Application.do_gaussian_blur_image_job(port_input_name=input, sigma=s,
# port_output_name='BLURED_SIGMA_' + str(s).replace('.', '_') + '_' + input)
edge_result = Application.do_first_order_derivative_operators(port_input_name=input, operator=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
list_to_eval_tmp.append(thin_thr_edge_result + '_L0')
list_to_eval.append(list_to_eval_tmp)
Application.create_config_file()
Application.configure_save_pictures(ports_to_save='ALL')
# Application.configure_show_pictures(ports_to_show=list, time_to_show=0)
Application.run_application()
# Benchmarking.run_PSNR_benchmark(input_location='Logs/application_results',
# gt_location='TestData/dilation_test/test_',
# raw_image='TestData/dilation_test/test_',
# jobs_set=list_to_save, db_calc=False)
idx = 10
for eval in list_to_eval:
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate_',
raw_image='TestData/dilation_test/test_',
jobs_set=eval,)
Utils.plot_box_benchmark_values(name_to_save='SFOM_first_noise_' + idx.__str__(), number_decimal=3,
data='SFOM', data_subsets=edges, eval=eval)
idx += 2
Utils.close_files()
def laplace_signal_to_noise():
Application.delete_folder_appl_out()
Benchmarking.delete_folder_benchmark_out()
Application.set_input_image_folder('TestData/dilation_test/test_')
list_input = []
list_to_save = []
Application.do_get_image_job(port_output_name='RAW')
Application.do_grayscale_transform_job(port_input_name='RAW', port_output_name='GREY')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_10dB', mean_value=0, variance=0.2)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_12dB', mean_value=0, variance=0.09)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_14dB', mean_value=0, variance=0.06)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
noise_image = Application.do_add_gaussian_blur_noise_job(port_input_name='GREY', port_output_name='GREY_16dB', mean_value=0, variance=0.04)
list_input.append(noise_image)
list_to_save.append(noise_image + '_L0')
laplace_edges = [
CONFIG.FILTERS_SECOND_ORDER.LAPLACE_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_5x5_1
, CONFIG.FILTERS_SECOND_ORDER.LAPLACE_DILATED_7x7_1
]
list_to_eval = list()
for input in list_input:
list_to_eval_tmp = list()
for edge in laplace_edges:
for thr in range(15, 245, 10):
edge_result = Application.do_laplace_job(port_input_name=input, kernel=edge)
thr_edge_result = Application.do_image_threshold_job(port_input_name=edge_result, input_value=thr,
input_threshold_type='cv2.THRESH_BINARY',
port_output_name='THR_' + str(thr) + '_' + edge_result)
thin_thr_edge_result = Application.do_thinning_guo_hall_image_job(port_input_name=thr_edge_result,
port_output_name='FINAL_' + thr_edge_result)
list_to_save.append(thin_thr_edge_result + '_L0')
list_to_eval_tmp.append(thin_thr_edge_result + '_L0')
list_to_eval.append(list_to_eval_tmp)
Application.create_config_file()
Application.configure_save_pictures(ports_to_save='ALL')
# Application.configure_show_pictures(ports_to_show=list, time_to_show=0)
Application.run_application()
# Benchmarking.run_PSNR_benchmark(input_location='Logs/application_results',
# gt_location='TestData/dilation_test/test_',
# raw_image='TestData/dilation_test/test_',
# jobs_set=list_to_save, db_calc=False)
idx = 10
for eval in list_to_eval:
Benchmarking.run_SFOM_benchmark(input_location='Logs/application_results',
gt_location='TestData/dilation_test/validate_',
raw_image='TestData/dilation_test/test_',
jobs_set=eval,)
Utils.plot_box_benchmark_values(name_to_save='SFOM_laplace_noise_' + idx.__str__(), number_decimal=3,
data='SFOM', data_subsets=laplace_edges, eval=eval)
idx += 2
Utils.close_files()
if __name__ == "__main__":
dataset = 'test'
# dataset = 'small'
main_find_param_first_order_edges(dataset)
Utils.reopen_files()
main_first_order_edge_detection(dataset)
Utils.reopen_files()
main_find_thr_sig_compass_first_order_edges(dataset)
Utils.reopen_files()
main_first_order_compass_edge_detection(dataset)
Utils.reopen_files()
main_find_thr_sigma_frei_edges(dataset)
Utils.reopen_files()
main_frei_edges(dataset)
Utils.reopen_files()
main_find_thr_laplace_edges(dataset)
Utils.reopen_files()
main_laplace_edges(dataset)
Utils.reopen_files()
main_find_sigma_log_edges(dataset)
Utils.reopen_files()
main_log_edges(dataset)
Utils.reopen_files()
main_find_sigma_marr_edges(dataset)
Utils.reopen_files()
main_marr_edges(dataset)
Utils.reopen_files()
main_sigma_finder_canny_2(dataset)
Utils.reopen_files()
main_canny_2(dataset)
Utils.reopen_files()
main_param_shen_finder(dataset)
Utils.reopen_files()
main_shen_edges(dataset)
Utils.reopen_files()
main_ed_parsing(dataset)
Utils.reopen_files()
main_ededge(dataset)
main_find_param_first_order_edges_SFOM()
Utils.reopen_files()
main_ed_parsing_SFOM()
Utils.reopen_files()
main_find_thr_sig_compass_first_order_edges_SFOM()
Utils.reopen_files()
main_param_shen_finder_SFOM()
Utils.reopen_files()
main_sigma_finder_canny_SFOM()
Utils.reopen_files()
main_find_sigma_marr_edges_SFOM()
Utils.reopen_files()
main_find_sigma_log_edges_SFOM()
Utils.reopen_files()
main_find_thr_laplace_edges_SFOM()
Utils.reopen_files()
main_find_thr_sigma_frei_edges_SFOM()
Utils.reopen_files()
main_signal_to_noise()
Utils.reopen_files()
laplace_signal_to_noise()
| 53.675297
| 178
| 0.565638
| 13,596
| 126,459
| 4.790894
| 0.025522
| 0.054086
| 0.033729
| 0.039547
| 0.97103
| 0.96145
| 0.954327
| 0.94071
| 0.923362
| 0.911279
| 0
| 0.029642
| 0.33414
| 126,459
| 2,355
| 179
| 53.698089
| 0.74392
| 0.052222
| 0
| 0.788773
| 0
| 0
| 0.10211
| 0.039292
| 0
| 0
| 0
| 0
| 0
| 1
| 0.017361
| false
| 0.012731
| 0.002894
| 0
| 0.020255
| 0.00463
| 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
|
756c602e7bca60c560cf1f670114088f02f509fa
| 174
|
py
|
Python
|
website/rentals/models/__init__.py
|
JobDoesburg/landolfio
|
4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e
|
[
"MIT"
] | 1
|
2021-02-24T14:33:09.000Z
|
2021-02-24T14:33:09.000Z
|
website/rentals/models/__init__.py
|
JobDoesburg/landolfio
|
4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e
|
[
"MIT"
] | 2
|
2022-01-13T04:03:38.000Z
|
2022-03-12T01:03:10.000Z
|
website/rentals/models/__init__.py
|
JobDoesburg/landolfio
|
4cbf31c2e6f93745f5aa0d20893bf20f3acecc6e
|
[
"MIT"
] | null | null | null |
from rentals.models.issuance_unprocessed import *
from rentals.models.issuance_loan import *
from rentals.models.issuance_rent import *
from rentals.models.returnal import *
| 34.8
| 49
| 0.83908
| 23
| 174
| 6.217391
| 0.391304
| 0.307692
| 0.475524
| 0.524476
| 0.433566
| 0
| 0
| 0
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| 0
| 0
| 0.091954
| 174
| 4
| 50
| 43.5
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|
0
| 8
|
75f0e4f2d615051643d279339eeb51e2ee4656a4
| 16,739
|
py
|
Python
|
plugins/bank.py
|
Redjon1/Bot
|
0174fc92811799a5feca4ee8721df4f60813772b
|
[
"MIT"
] | 9
|
2020-07-13T10:50:10.000Z
|
2022-03-30T03:55:27.000Z
|
plugins/bank.py
|
Redjon1/Bot
|
0174fc92811799a5feca4ee8721df4f60813772b
|
[
"MIT"
] | null | null | null |
plugins/bank.py
|
Redjon1/Bot
|
0174fc92811799a5feca4ee8721df4f60813772b
|
[
"MIT"
] | 4
|
2020-05-14T23:05:59.000Z
|
2022-03-30T04:26:44.000Z
|
import json
import os
import time
users_dir = os.path.join(r"users/")
def loadjson(filepath):
with open(filepath, 'r', encoding='utf-8') as jsonfile:
return json.load(jsonfile, encoding='utf-8')
def dumpjson(data, filepath):
with open(filepath, 'w', encoding='utf-8') as jsonfile:
return json.dump(data, jsonfile, ensure_ascii=False)
def bankSys(sourceText, id):
bankHelp = '\n\n❓ Помощь:\n⠀⠀📈 Банк курс\n⠀⠀💱 Банк обмен\n⠀⠀💸 Банк снять [сумма/все]\n⠀⠀💶 Банк пополнить [сумма/все]'
procHelp = '\n\n✅ Автоматический вклад под 1.2% каждый день!'
NoprocHelp = '\n\n🔔 Авто-вклад работает, когда на карте меньше 10.000.000€!'
if sourceText != '':
if 'банк' == sourceText.split()[0].lower():
get_data = loadjson(users_dir + str(id) + ".json")
if int(get_data['own_smart']) >= 1:
if len(sourceText.split()) > 1:
if sourceText.split()[1].lower() == 'обмен':
get_data = loadjson("curs.json")
price_coin = int(get_data['coin'])
get_data = loadjson(users_dir + str(id) + ".json")
bank_cr_balance = int(get_data['bank_cr_balance'])
if bank_cr_balance >= 1:
get_data = loadjson(users_dir + str(id) + ".json")
user_balance = int(get_data['balance'])
algo_obmen_euro = price_coin * bank_cr_balance
algo_update_balance = user_balance + algo_obmen_euro
algo_obmen_btc = bank_cr_balance - bank_cr_balance
get_data.update({"balance": '{}'.format(algo_update_balance)})
get_data.update({"bank_cr_balance": '{}'.format(algo_obmen_btc)})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', вы обменяли: ' + str(bank_cr_balance) + '฿ на ' + str(algo_obmen_euro) + '€! 🤑\n💰 В кошельке: ' + str(algo_update_balance) + '€'
else:
return ', на счёте в банке - у вас меньше 1 биткоина! 🙁'
elif sourceText.split()[1].lower() == 'пополнить':
if len(sourceText.split()) > 2:
get_data = loadjson(users_dir + str(id) + ".json")
summa_up = sourceText.split()[2].lower()
user_balance = get_data['balance']
if summa_up.isdigit():
if int(summa_up) == 0: return ', сумма должна быть больше 0! 😕'
if int(user_balance) >= int(summa_up):
get_data = loadjson(users_dir + str(id) + ".json")
bank_balance = int(get_data['bank_balance'])
user_balance = int(get_data['balance'])
algo_popoln_bank_balance = int(bank_balance) + int(summa_up)
algo_snyat_user_balance = int(user_balance) - int(summa_up)
get_data.update({"bank_balance": '{}'.format(int(algo_popoln_bank_balance))})
get_data.update({"balance": '{}'.format(int(algo_snyat_user_balance))})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', вы пополнили карту на: ' + str(summa_up) + '€ 😀\n💳 В банке: ' + str(algo_popoln_bank_balance) + '€\n💰 В кошельке: ' + str(algo_snyat_user_balance) + '€'
else:
get_data = loadjson(users_dir + str(id) + ".json")
balanсe_out = get_data['balance']
return ', у вас недостаточно средств в кошельке, для пополнение карты! 😔\n💰 У вас в кошельке: ' + str(balanсe_out) + '€'
elif sourceText.split()[2].lower() == 'все':
if int(user_balance) >= int(1):
get_data = loadjson(users_dir + str(id) + ".json")
bank_balance = int(get_data['bank_balance'])
user_balance = int(get_data['balance'])
algo_popoln_bank_balance = int(bank_balance) + int(user_balance)
algo_snyat_user_balance = int(user_balance) - int(user_balance)
get_data.update({"bank_balance": '{}'.format(int(algo_popoln_bank_balance))})
get_data.update({"balance": '{}'.format(int(algo_snyat_user_balance))})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', вы пополнили карту на: ' + str(user_balance) + '€ 😀\n💳 В банке: ' + str(algo_popoln_bank_balance) + '€\n💰 В кошельке: ' + str(algo_snyat_user_balance) + '€'
else:
get_data = loadjson(users_dir + str(id) + ".json")
balanсe_out = get_data['balance']
return ', у вас недостаточно средств в кошельке, для пополнение карты! 😔\n💰 В кошельке: ' + str(balanсe_out) + '€'
elif sourceText.split()[2].lower() == 'всё':
if int(user_balance) >= int(1):
get_data = loadjson(users_dir + str(id) + ".json")
bank_balance = int(get_data['bank_balance'])
user_balance = int(get_data['balance'])
algo_popoln_bank_balance = int(bank_balance) + int(user_balance)
algo_snyat_user_balance = int(user_balance) - int(user_balance)
get_data.update({"bank_balance": '{}'.format(int(algo_popoln_bank_balance))})
get_data.update({"balance": '{}'.format(int(algo_snyat_user_balance))})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', вы пополнили карту на: ' + str(user_balance) + '€ 😀\n💳 В банке: ' + str(algo_popoln_bank_balance) + '€\n💰 В кошельке: ' + str(algo_snyat_user_balance) + '€'
else:
get_data = loadjson(users_dir + str(id) + ".json")
balanсe_out = get_data['balance']
return ', у вас недостаточно средств в кошельке, для пополнение карты! 😔\n💰 У вас в кошельке: ' + str(balanсe_out) + '€'
else:
return ', для пополнения карты, используйте для суммы - цифры! 😉'
else:
return ', использование: 💶 Банк пополнить [сумма/все]'
elif sourceText.split()[1].lower() == 'курс':
get_data = loadjson("curs.json")
price_coin = int(get_data['coin'])
return ', курс игровой валюты!\n\n⠀📈 По информации Банка на сегодня, цена за каждую единицу валюты составляет:\n\n⠀⠀🏮 Биткоин: ' + str(price_coin) + '€ за 1฿.'
elif sourceText.split()[1].lower() == 'снять':
if len(sourceText.split()) > 2:
get_data = loadjson(users_dir + str(id) + ".json")
summa_up = sourceText.split()[2].lower()
bank_balance = get_data['bank_balance']
if summa_up.isdigit():
if int(summa_up) == 0: return ', сумма должна быть больше 0! 😕'
if int(bank_balance) >= int(summa_up):
get_data = loadjson(users_dir + str(id) + ".json")
bank_balance = int(get_data['bank_balance'])
user_balance = int(get_data['balance'])
algo_popoln_bank_balance = int(bank_balance) - int(summa_up)
algo_snyat_user_balance = int(user_balance) + int(summa_up)
get_data.update({"bank_balance": '{}'.format(int(algo_popoln_bank_balance))})
get_data.update({"balance": '{}'.format(int(algo_snyat_user_balance))})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', вы сняли: ' + str(summa_up) + '€ с карты! 😀\n💳 В банке: ' + str(algo_popoln_bank_balance) + '€\n💰 В кошельке: ' + str(algo_snyat_user_balance) + '€'
else:
get_data = loadjson(users_dir + str(id) + ".json")
user_balance = get_data['balance']
bank_balance = get_data['bank_balance']
return ', у вас недостаточно средств на карте, для получение наличных! 😔\n💳 В банке: ' + str(bank_balance) + '€\n💰 В кошельке: ' + str(user_balance) + '€'
elif sourceText.split()[2].lower() == 'все':
if int(bank_balance) >= int(1):
get_data = loadjson(users_dir + str(id) + ".json")
bank_balance = int(get_data['bank_balance'])
user_balance = int(get_data['balance'])
algo_snyat_user_balance = int(user_balance) + int(bank_balance)
algo_popoln_bank_balance = int(bank_balance) - int(bank_balance)
get_data.update({"bank_balance": '{}'.format(int(algo_popoln_bank_balance))})
get_data.update({"balance": '{}'.format(int(algo_snyat_user_balance))})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', вы сняли ' + str(bank_balance) + '€ с карты! 😀\n💳 В банке: ' + str(algo_popoln_bank_balance) + '€\n💰 В кошельке: ' + str(algo_snyat_user_balance) + '€'
else:
get_data = loadjson(users_dir + str(id) + ".json")
user_balance = get_data['balance']
bank_balance = get_data['bank_balance']
return ', у вас недостаточно средств на карте, для получение наличных! 😔\n💳 В банке: ' + str(bank_balance) + '€\n💰 В кошельке: ' + str(user_balance) + '€'
elif sourceText.split()[2].lower() == 'всё':
if int(bank_balance) >= int(1):
get_data = loadjson(users_dir + str(id) + ".json")
bank_balance = int(get_data['bank_balance'])
user_balance = int(get_data['balance'])
algo_snyat_user_balance = int(user_balance) + int(bank_balance)
algo_popoln_bank_balance = int(bank_balance) - int(bank_balance)
get_data.update({"bank_balance": '{}'.format(int(algo_popoln_bank_balance))})
get_data.update({"balance": '{}'.format(int(algo_snyat_user_balance))})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', вы сняли ' + str(bank_balance) + '€ с карты! 😀\n💳 В банке: ' + str(algo_popoln_bank_balance) + '€\n💰 В кошельке: ' + str(algo_snyat_user_balance) + '€'
else:
get_data = loadjson(users_dir + str(id) + ".json")
user_balance = get_data['balance']
bank_balance = get_data['bank_balance']
return ', у вас недостаточно средств на карте, для получение наличных! 😔\n💳 В банке: ' + str(bank_balance) + '€\n💰 В кошельке: ' + str(user_balance) + '€'
else:
return ', для снятие денег с банковского счёта, используйте для суммы - цифры! 😉'
else:
return ', использование: 💸 Банк снять [сумма/все]'
else:
get_data = loadjson(users_dir + str(id) + ".json")
if int(get_data['bank_balance']) <= 20000000:
bank_proc_raznica_time = float(time.time()) - float(get_data['bank_vd_time'])
bank_hours = int(bank_proc_raznica_time) / 3600
bank_balance = int(get_data['bank_balance'])
if bank_hours >= 24:
bank_proc_profit = int(1.2 * bank_balance)
get_data = loadjson(users_dir + str(id) + ".json")
get_data.update({"bank_balance": '{}'.format(int(bank_proc_profit))})
get_data.update({"bank_vd_time": '{}'.format(time.time())})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', помощь по банку:\n\n📋 Счёт в банке:\n⠀⠀💳 На карте: ' + str(bank_proc_profit) + '€\n⠀⠀🏮 Биткоинов: ' + str(get_data['bank_cr_balance']) + '฿' + bankHelp + procHelp
else:
get_data = loadjson(users_dir + str(id) + ".json")
return ', помощь по банку:\n\n📋 Счёт в банке:\n⠀⠀💳 На карте: ' + str(get_data['bank_balance']) + '€\n⠀⠀🏮 Биткоинов: ' + str(get_data['bank_cr_balance']) + '฿' + bankHelp + procHelp
else:
get_data = loadjson(users_dir + str(id) + ".json")
return ', помощь по банку:\n\n📋 Счёт в банке:\n⠀⠀💳 На карте: ' + str(get_data['bank_balance']) + '€\n⠀⠀🏮 Биткоинов: ' + str(get_data['bank_cr_balance']) + '฿' + bankHelp + NoprocHelp
else:
get_data = loadjson(users_dir + str(id) + ".json")
if int(get_data['bank_balance']) <= 20000000:
bank_proc_raznica_time = float(time.time()) - float(get_data['bank_vd_time'])
bank_hours = int(bank_proc_raznica_time) / 3600
bank_balance = int(get_data['bank_balance'])
if bank_hours >= 24:
bank_proc_profit = int(1.2 * bank_balance)
get_data = loadjson(users_dir + str(id) + ".json")
get_data.update({"bank_balance": '{}'.format(int(bank_proc_profit))})
get_data.update({"bank_vd_time": '{}'.format(time.time())})
dumpjson(get_data, users_dir + str(id) + ".json")
return ', помощь по банку:\n\n📋 Счёт в банке:\n⠀⠀💳 На карте: ' + str(bank_proc_profit) + '€\n⠀⠀🏮 Биткоинов: ' + str(get_data['bank_cr_balance']) + '฿' + bankHelp + procHelp
else:
get_data = loadjson(users_dir + str(id) + ".json")
return ', помощь по банку:\n\n📋 Счёт в банке:\n⠀⠀💳 На карте: ' + str(
get_data['bank_balance']) + '€\n⠀⠀🏮 Биткоинов: ' + str(get_data['bank_cr_balance']) + '฿' + bankHelp + procHelp
else:
get_data = loadjson(users_dir + str(id) + ".json")
return ', помощь по банку:\n\n📋 Счёт в банке:\n⠀⠀💳 На карте: ' + str(get_data['bank_balance']) + '€\n⠀⠀🏮 Биткоинов: ' + str(get_data['bank_cr_balance']) + '฿' + bankHelp + NoprocHelp
else:
return ', для использования банка, преобретите телефон! 😐\n📱 Посмотреть телефоны: Магазин телефон'
else:
return None
pass
| 82.053922
| 213
| 0.463349
| 1,786
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| 82.053922
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|
0
| 7
|
ddb52111a646b6fa9181d015c3987f1a8775f0ab
| 64
|
py
|
Python
|
src/canvacord/__init__.py
|
TrendingTechnology/canvacord
|
fb82d8dda486af7e485da2fe2abab633ed10de0a
|
[
"MIT"
] | 1
|
2021-08-07T11:11:58.000Z
|
2021-08-07T11:11:58.000Z
|
src/canvacord/__init__.py
|
TrendingTechnology/canvacord
|
fb82d8dda486af7e485da2fe2abab633ed10de0a
|
[
"MIT"
] | null | null | null |
src/canvacord/__init__.py
|
TrendingTechnology/canvacord
|
fb82d8dda486af7e485da2fe2abab633ed10de0a
|
[
"MIT"
] | null | null | null |
from .generators import rankcard
from .generators import trigger
| 32
| 32
| 0.859375
| 8
| 64
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| 0.727273
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0
| 7
|
ddbcf54245287224fe7c3c1592fa73775b802fbb
| 21,834
|
py
|
Python
|
sdk/python/pulumi_snowflake/schema.py
|
Hacker0x01/pulumi-snowflake
|
f6ebcf2c3f73b103a7c2001fae231998ce1323b2
|
[
"ECL-2.0",
"Apache-2.0"
] | 3
|
2021-07-01T17:03:33.000Z
|
2022-03-01T19:29:04.000Z
|
sdk/python/pulumi_snowflake/schema.py
|
Hacker0x01/pulumi-snowflake
|
f6ebcf2c3f73b103a7c2001fae231998ce1323b2
|
[
"ECL-2.0",
"Apache-2.0"
] | 102
|
2021-07-14T13:12:58.000Z
|
2022-03-31T18:34:04.000Z
|
sdk/python/pulumi_snowflake/schema.py
|
Hacker0x01/pulumi-snowflake
|
f6ebcf2c3f73b103a7c2001fae231998ce1323b2
|
[
"ECL-2.0",
"Apache-2.0"
] | 1
|
2022-03-25T07:24:45.000Z
|
2022-03-25T07:24:45.000Z
|
# 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
from . import outputs
from ._inputs import *
__all__ = ['SchemaArgs', 'Schema']
@pulumi.input_type
class SchemaArgs:
def __init__(__self__, *,
database: pulumi.Input[str],
comment: Optional[pulumi.Input[str]] = None,
data_retention_days: Optional[pulumi.Input[int]] = None,
is_managed: Optional[pulumi.Input[bool]] = None,
is_transient: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]]] = None):
"""
The set of arguments for constructing a Schema resource.
:param pulumi.Input[str] database: The database in which to create the schema.
:param pulumi.Input[str] comment: Specifies a comment for the schema.
:param pulumi.Input[int] data_retention_days: Specifies the number of days for which Time Travel actions (CLONE and UNDROP) can be performed on the schema, as well as specifying the default Time Travel retention time for all tables created in the schema.
:param pulumi.Input[bool] is_managed: Specifies a managed schema. Managed access schemas centralize privilege management with the schema owner.
:param pulumi.Input[bool] is_transient: Specifies a schema as transient. Transient schemas do not have a Fail-safe period so they do not incur additional storage costs once they leave Time Travel; however, this means they are also not protected by Fail-safe in the event of a data loss.
:param pulumi.Input[str] name: Specifies the identifier for the schema; must be unique for the database in which the schema is created.
:param pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]] tags: Definitions of a tag to associate with the resource.
"""
pulumi.set(__self__, "database", database)
if comment is not None:
pulumi.set(__self__, "comment", comment)
if data_retention_days is not None:
pulumi.set(__self__, "data_retention_days", data_retention_days)
if is_managed is not None:
pulumi.set(__self__, "is_managed", is_managed)
if is_transient is not None:
pulumi.set(__self__, "is_transient", is_transient)
if name is not None:
pulumi.set(__self__, "name", name)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter
def database(self) -> pulumi.Input[str]:
"""
The database in which to create the schema.
"""
return pulumi.get(self, "database")
@database.setter
def database(self, value: pulumi.Input[str]):
pulumi.set(self, "database", value)
@property
@pulumi.getter
def comment(self) -> Optional[pulumi.Input[str]]:
"""
Specifies a comment for the schema.
"""
return pulumi.get(self, "comment")
@comment.setter
def comment(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "comment", value)
@property
@pulumi.getter(name="dataRetentionDays")
def data_retention_days(self) -> Optional[pulumi.Input[int]]:
"""
Specifies the number of days for which Time Travel actions (CLONE and UNDROP) can be performed on the schema, as well as specifying the default Time Travel retention time for all tables created in the schema.
"""
return pulumi.get(self, "data_retention_days")
@data_retention_days.setter
def data_retention_days(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "data_retention_days", value)
@property
@pulumi.getter(name="isManaged")
def is_managed(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies a managed schema. Managed access schemas centralize privilege management with the schema owner.
"""
return pulumi.get(self, "is_managed")
@is_managed.setter
def is_managed(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "is_managed", value)
@property
@pulumi.getter(name="isTransient")
def is_transient(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies a schema as transient. Transient schemas do not have a Fail-safe period so they do not incur additional storage costs once they leave Time Travel; however, this means they are also not protected by Fail-safe in the event of a data loss.
"""
return pulumi.get(self, "is_transient")
@is_transient.setter
def is_transient(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "is_transient", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the identifier for the schema; must be unique for the database in which the schema is created.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]]]:
"""
Definitions of a tag to associate with the resource.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]]]):
pulumi.set(self, "tags", value)
@pulumi.input_type
class _SchemaState:
def __init__(__self__, *,
comment: Optional[pulumi.Input[str]] = None,
data_retention_days: Optional[pulumi.Input[int]] = None,
database: Optional[pulumi.Input[str]] = None,
is_managed: Optional[pulumi.Input[bool]] = None,
is_transient: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]]] = None):
"""
Input properties used for looking up and filtering Schema resources.
:param pulumi.Input[str] comment: Specifies a comment for the schema.
:param pulumi.Input[int] data_retention_days: Specifies the number of days for which Time Travel actions (CLONE and UNDROP) can be performed on the schema, as well as specifying the default Time Travel retention time for all tables created in the schema.
:param pulumi.Input[str] database: The database in which to create the schema.
:param pulumi.Input[bool] is_managed: Specifies a managed schema. Managed access schemas centralize privilege management with the schema owner.
:param pulumi.Input[bool] is_transient: Specifies a schema as transient. Transient schemas do not have a Fail-safe period so they do not incur additional storage costs once they leave Time Travel; however, this means they are also not protected by Fail-safe in the event of a data loss.
:param pulumi.Input[str] name: Specifies the identifier for the schema; must be unique for the database in which the schema is created.
:param pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]] tags: Definitions of a tag to associate with the resource.
"""
if comment is not None:
pulumi.set(__self__, "comment", comment)
if data_retention_days is not None:
pulumi.set(__self__, "data_retention_days", data_retention_days)
if database is not None:
pulumi.set(__self__, "database", database)
if is_managed is not None:
pulumi.set(__self__, "is_managed", is_managed)
if is_transient is not None:
pulumi.set(__self__, "is_transient", is_transient)
if name is not None:
pulumi.set(__self__, "name", name)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter
def comment(self) -> Optional[pulumi.Input[str]]:
"""
Specifies a comment for the schema.
"""
return pulumi.get(self, "comment")
@comment.setter
def comment(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "comment", value)
@property
@pulumi.getter(name="dataRetentionDays")
def data_retention_days(self) -> Optional[pulumi.Input[int]]:
"""
Specifies the number of days for which Time Travel actions (CLONE and UNDROP) can be performed on the schema, as well as specifying the default Time Travel retention time for all tables created in the schema.
"""
return pulumi.get(self, "data_retention_days")
@data_retention_days.setter
def data_retention_days(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "data_retention_days", value)
@property
@pulumi.getter
def database(self) -> Optional[pulumi.Input[str]]:
"""
The database in which to create the schema.
"""
return pulumi.get(self, "database")
@database.setter
def database(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "database", value)
@property
@pulumi.getter(name="isManaged")
def is_managed(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies a managed schema. Managed access schemas centralize privilege management with the schema owner.
"""
return pulumi.get(self, "is_managed")
@is_managed.setter
def is_managed(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "is_managed", value)
@property
@pulumi.getter(name="isTransient")
def is_transient(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies a schema as transient. Transient schemas do not have a Fail-safe period so they do not incur additional storage costs once they leave Time Travel; however, this means they are also not protected by Fail-safe in the event of a data loss.
"""
return pulumi.get(self, "is_transient")
@is_transient.setter
def is_transient(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "is_transient", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the identifier for the schema; must be unique for the database in which the schema is created.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]]]:
"""
Definitions of a tag to associate with the resource.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['SchemaTagArgs']]]]):
pulumi.set(self, "tags", value)
class Schema(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
comment: Optional[pulumi.Input[str]] = None,
data_retention_days: Optional[pulumi.Input[int]] = None,
database: Optional[pulumi.Input[str]] = None,
is_managed: Optional[pulumi.Input[bool]] = None,
is_transient: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SchemaTagArgs']]]]] = None,
__props__=None):
"""
## Example Usage
```python
import pulumi
import pulumi_snowflake as snowflake
schema = snowflake.Schema("schema",
comment="A schema.",
data_retention_days=1,
database="db",
is_managed=False,
is_transient=False)
```
## Import
# format is dbName | schemaName
```sh
$ pulumi import snowflake:index/schema:Schema example 'dbName|schemaName'
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] comment: Specifies a comment for the schema.
:param pulumi.Input[int] data_retention_days: Specifies the number of days for which Time Travel actions (CLONE and UNDROP) can be performed on the schema, as well as specifying the default Time Travel retention time for all tables created in the schema.
:param pulumi.Input[str] database: The database in which to create the schema.
:param pulumi.Input[bool] is_managed: Specifies a managed schema. Managed access schemas centralize privilege management with the schema owner.
:param pulumi.Input[bool] is_transient: Specifies a schema as transient. Transient schemas do not have a Fail-safe period so they do not incur additional storage costs once they leave Time Travel; however, this means they are also not protected by Fail-safe in the event of a data loss.
:param pulumi.Input[str] name: Specifies the identifier for the schema; must be unique for the database in which the schema is created.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SchemaTagArgs']]]] tags: Definitions of a tag to associate with the resource.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: SchemaArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
## Example Usage
```python
import pulumi
import pulumi_snowflake as snowflake
schema = snowflake.Schema("schema",
comment="A schema.",
data_retention_days=1,
database="db",
is_managed=False,
is_transient=False)
```
## Import
# format is dbName | schemaName
```sh
$ pulumi import snowflake:index/schema:Schema example 'dbName|schemaName'
```
:param str resource_name: The name of the resource.
:param SchemaArgs 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(SchemaArgs, 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,
comment: Optional[pulumi.Input[str]] = None,
data_retention_days: Optional[pulumi.Input[int]] = None,
database: Optional[pulumi.Input[str]] = None,
is_managed: Optional[pulumi.Input[bool]] = None,
is_transient: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SchemaTagArgs']]]]] = 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__ = SchemaArgs.__new__(SchemaArgs)
__props__.__dict__["comment"] = comment
__props__.__dict__["data_retention_days"] = data_retention_days
if database is None and not opts.urn:
raise TypeError("Missing required property 'database'")
__props__.__dict__["database"] = database
__props__.__dict__["is_managed"] = is_managed
__props__.__dict__["is_transient"] = is_transient
__props__.__dict__["name"] = name
__props__.__dict__["tags"] = tags
super(Schema, __self__).__init__(
'snowflake:index/schema:Schema',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
comment: Optional[pulumi.Input[str]] = None,
data_retention_days: Optional[pulumi.Input[int]] = None,
database: Optional[pulumi.Input[str]] = None,
is_managed: Optional[pulumi.Input[bool]] = None,
is_transient: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SchemaTagArgs']]]]] = None) -> 'Schema':
"""
Get an existing Schema 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] comment: Specifies a comment for the schema.
:param pulumi.Input[int] data_retention_days: Specifies the number of days for which Time Travel actions (CLONE and UNDROP) can be performed on the schema, as well as specifying the default Time Travel retention time for all tables created in the schema.
:param pulumi.Input[str] database: The database in which to create the schema.
:param pulumi.Input[bool] is_managed: Specifies a managed schema. Managed access schemas centralize privilege management with the schema owner.
:param pulumi.Input[bool] is_transient: Specifies a schema as transient. Transient schemas do not have a Fail-safe period so they do not incur additional storage costs once they leave Time Travel; however, this means they are also not protected by Fail-safe in the event of a data loss.
:param pulumi.Input[str] name: Specifies the identifier for the schema; must be unique for the database in which the schema is created.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SchemaTagArgs']]]] tags: Definitions of a tag to associate with the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _SchemaState.__new__(_SchemaState)
__props__.__dict__["comment"] = comment
__props__.__dict__["data_retention_days"] = data_retention_days
__props__.__dict__["database"] = database
__props__.__dict__["is_managed"] = is_managed
__props__.__dict__["is_transient"] = is_transient
__props__.__dict__["name"] = name
__props__.__dict__["tags"] = tags
return Schema(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def comment(self) -> pulumi.Output[Optional[str]]:
"""
Specifies a comment for the schema.
"""
return pulumi.get(self, "comment")
@property
@pulumi.getter(name="dataRetentionDays")
def data_retention_days(self) -> pulumi.Output[Optional[int]]:
"""
Specifies the number of days for which Time Travel actions (CLONE and UNDROP) can be performed on the schema, as well as specifying the default Time Travel retention time for all tables created in the schema.
"""
return pulumi.get(self, "data_retention_days")
@property
@pulumi.getter
def database(self) -> pulumi.Output[str]:
"""
The database in which to create the schema.
"""
return pulumi.get(self, "database")
@property
@pulumi.getter(name="isManaged")
def is_managed(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies a managed schema. Managed access schemas centralize privilege management with the schema owner.
"""
return pulumi.get(self, "is_managed")
@property
@pulumi.getter(name="isTransient")
def is_transient(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies a schema as transient. Transient schemas do not have a Fail-safe period so they do not incur additional storage costs once they leave Time Travel; however, this means they are also not protected by Fail-safe in the event of a data loss.
"""
return pulumi.get(self, "is_transient")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Specifies the identifier for the schema; must be unique for the database in which the schema is created.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Sequence['outputs.SchemaTag']]]:
"""
Definitions of a tag to associate with the resource.
"""
return pulumi.get(self, "tags")
| 46.160677
| 294
| 0.655491
| 2,707
| 21,834
| 5.121537
| 0.073513
| 0.08569
| 0.082227
| 0.038084
| 0.878606
| 0.867426
| 0.854732
| 0.842325
| 0.838647
| 0.829126
| 0
| 0.000183
| 0.249611
| 21,834
| 472
| 295
| 46.258475
| 0.846008
| 0.388706
| 0
| 0.78327
| 1
| 0
| 0.087231
| 0.002375
| 0
| 0
| 0
| 0
| 0
| 1
| 0.159696
| false
| 0.003802
| 0.026616
| 0
| 0.281369
| 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
|
5519ed1359f9f680f1c97255c8f5cb037d636e7d
| 2,906
|
py
|
Python
|
covid19/cvd2019/models.py
|
jeonghaknam/cvd2019
|
045f2a6f63c97e176cd757d1cd5a86358f424a0a
|
[
"MIT"
] | null | null | null |
covid19/cvd2019/models.py
|
jeonghaknam/cvd2019
|
045f2a6f63c97e176cd757d1cd5a86358f424a0a
|
[
"MIT"
] | null | null | null |
covid19/cvd2019/models.py
|
jeonghaknam/cvd2019
|
045f2a6f63c97e176cd757d1cd5a86358f424a0a
|
[
"MIT"
] | null | null | null |
from django.db import models
from db.base_model import BaseModel
# Create your models here.
class WorldTotal(BaseModel):
'''total세계현황'''
death = models.IntegerField(default=0, verbose_name='사망자')
cure = models.IntegerField(default=0, verbose_name='격리해제')
quarantine = models.IntegerField(default=0, verbose_name='격리중')
cumulative = models.IntegerField(default=0, verbose_name='누적확진')
class Meta:
db_table = 'df_world_wide_total'
verbose_name = 'total세계현황'
verbose_name_plural = verbose_name
class World(BaseModel):
'''세계 국가별현황'''
area_name = models.CharField(max_length=30, verbose_name='지역이름')
cumulative = models.IntegerField(default=0, verbose_name='누적확진')
quarantine = models.IntegerField(default=0, verbose_name='격리중')
cure = models.IntegerField(default=0, verbose_name='격리해제')
death = models.IntegerField(default=0, verbose_name='사망자')
class Meta:
db_table = 'df_world_wide'
verbose_name = '세계 국가별현황'
verbose_name_plural = verbose_name
class DomesticTotal(BaseModel):
'''total국내현황'''
death = models.IntegerField(default=0, verbose_name='사망자')
cure = models.IntegerField(default=0, verbose_name='격리해제')
overseas = models.IntegerField(default=0, verbose_name='해외유입')
quarantine = models.IntegerField(default=0, verbose_name='격리중')
cumulative = models.IntegerField(default=0, verbose_name='누적확진')
class Meta:
db_table = 'df_domestic_total'
verbose_name = 'total국내현황'
verbose_name_plural = verbose_name
class Domestic(BaseModel):
'''국내 지역현황'''
area_name = models.CharField(max_length=30, verbose_name='지역이름')
cumulative = models.IntegerField(default=0, verbose_name='누적확진')
quarantine = models.IntegerField(default=0, verbose_name='격리중')
cure = models.IntegerField(default=0, verbose_name='격리해제')
death = models.IntegerField(default=0, verbose_name='사망자')
class Meta:
db_table = 'df_domestic'
verbose_name = '국내지역현황'
verbose_name_plural = verbose_name
class WorldName(models.Model):
'''나라이름'''
enname = models.CharField(max_length=30, unique=True, verbose_name='영문명칭')
krname = models.CharField(max_length=30, unique=True, verbose_name='한글명칭')
cnname = models.CharField(max_length=30, unique=True, verbose_name='한문명칭')
class Meta:
db_table = 'df_worldname'
verbose_name = '나라이름'
verbose_name_plural = verbose_name
class DomesticName(models.Model):
'''국내 지역이름'''
enname = models.CharField(max_length=30, unique=True, verbose_name='영문명칭')
krname = models.CharField(max_length=30, unique=True, verbose_name='한글명칭')
cnname = models.CharField(max_length=30, unique=True, verbose_name='한문명칭')
class Meta:
db_table = 'df_domesticname'
verbose_name = '국내 지역이름'
verbose_name_plural = verbose_name
| 32.651685
| 78
| 0.700964
| 356
| 2,906
| 5.508427
| 0.171348
| 0.241203
| 0.216726
| 0.225395
| 0.803672
| 0.789393
| 0.678225
| 0.669046
| 0.669046
| 0.669046
| 0
| 0.013854
| 0.180317
| 2,906
| 89
| 79
| 32.651685
| 0.809404
| 0.025809
| 0
| 0.631579
| 0
| 0
| 0.079257
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.035088
| 0
| 0.684211
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 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
|
5519fcc480d0df898dd028cf44d0fe326ffec313
| 51,883
|
py
|
Python
|
keras_retinanet/models/vgg.py
|
hu64/RN-VID
|
3a9038778ca96b0697d13ed7fd9d281847bb6f4d
|
[
"MIT"
] | 3
|
2021-03-18T17:15:56.000Z
|
2021-12-16T09:12:56.000Z
|
keras_retinanet/models/vgg.py
|
hu64/RN-VID
|
3a9038778ca96b0697d13ed7fd9d281847bb6f4d
|
[
"MIT"
] | null | null | null |
keras_retinanet/models/vgg.py
|
hu64/RN-VID
|
3a9038778ca96b0697d13ed7fd9d281847bb6f4d
|
[
"MIT"
] | 1
|
2021-08-25T08:53:39.000Z
|
2021-08-25T08:53:39.000Z
|
"""
Copyright 2017-2018 cgratie (https://github.com/cgratie/)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import keras
from keras.utils import get_file
from . import retinanet
from . import Backbone
from ..utils.image import preprocess_image
from keras import backend
from keras import engine
from keras import layers
from keras import models
import numpy as np
import tensorflow as tf
from keras import backend as K
from .. import layers as custom_layers
class VGGBackbone(Backbone):
""" Describes backbone information and provides utility functions.
"""
def retinanet(self, *args, **kwargs):
""" Returns a retinanet model using the correct backbone.
"""
return vgg_retinanet(*args, backbone=self.backbone, **kwargs)
def download_imagenet(self):
""" Downloads ImageNet weights and returns path to weights file.
Weights can be downloaded at https://github.com/fizyr/keras-models/releases .
"""
if self.backbone == 'vgg16' \
or self.backbone == 'vgg16_flow_s' \
or self.backbone == 'vgg16_flow_y' \
or self.backbone == 'vgg16_flow_3d' \
or self.backbone == 'vgg16_flow_c' \
or self.backbone == 'vgg16_sf'\
or self.backbone == 'vgg16_sf_flow' \
or self.backbone == 'vgg16_5f' \
or self.backbone == 'vgg16_3f':
resource = ('https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.1/'
'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5')
checksum = '6d6bbae143d832006294945121d1f1fc'
elif self.backbone == 'vgg19':
resource = ('https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.1/'
'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5')
checksum = '253f8cb515780f3b799900260a226db6'
else:
raise ValueError("Backbone '{}' not recognized.".format(self.backbone))
return get_file(
'{}_weights_tf_dim_ordering_tf_kernels_notop.h5'.format(self.backbone),
resource,
cache_subdir='models',
file_hash=checksum
)
def validate(self):
""" Checks whether the backbone string is correct.
"""
allowed_backbones = ['vgg16', 'vgg19', 'vgg16_flow_s','vgg16_sf', 'vgg16_sf_flow', 'vgg16_flow_y', 'vgg16_flow_3d', 'vgg16_flow_c', 'vgg16_5f', 'vgg16_3f']
if self.backbone not in allowed_backbones:
raise ValueError('Backbone (\'{}\') not in allowed backbones ({}).'.format(self.backbone, allowed_backbones))
def preprocess_image(self, inputs):
""" Takes as input an image and prepares it for being passed through the network.
"""
return preprocess_image(inputs, mode='caffe')
def VGG16_flow_s(include_top=True,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the VGG16 architecture.
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor
(i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)`
(with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 input channels,
and width and height should be no smaller than 48.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
# Determine proper input shape
input_shape = (None, None, 6)
#average or max pooling
average_pooling = False
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(img_input)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
x = layers.Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = engine.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = models.Model(inputs, x, name='vgg16_flow_s')
return model
def VGG16_flow_y(include_top=True,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the VGG16 architecture.
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor
(i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)`
(with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 input channels,
and width and height should be no smaller than 48.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
# Determine proper input shape
input_shape = None, None, 3, 2
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# split block
# split = layers.Lambda(lambda x: tf.split(x, 2, axis=4), name='split')(img_input)
# img_input1 = layers.Lambda(lambda x: keras.backend.squeeze(x, axis=4), name='squeeze1')(split[0])
# img_input2 = layers.Lambda(lambda x: keras.backend.squeeze(x, axis=4), name='squeeze2')(split[1])
def split_f(img_input, num_or_size_splits=2, axis=3):
import keras.backend as K
import tensorflow as tf
return tf.split(img_input, num_or_size_splits, axis)
split = layers.Lambda(split_f, name='split2d', arguments={'num_or_size_splits': 2, 'axis': 3})(img_input)
img_input1 = split[0]
img_input2 = split[1]
# Block 1-1
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1-1')(img_input1)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2-1')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1-1_pool')(x)
# Block 2-1
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1-1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2-1')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2-1_pool')(x)
# Block 1-2
y = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1-2')(img_input2)
y = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2-2')(y)
y = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1-2_pool')(y)
# Block 2-2
y = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1-2')(y)
y = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2-2')(y)
y = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2-2_pool')(y)
# x = layers.Concatenate(axis=2)([x, y])
x = layers.Concatenate()([x, y])
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
x = layers.Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = engine.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = models.Model(inputs, x, name='vgg16_flow_y')
return model
def VGG16_flow_3d(include_top=True,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
"""Instantiates the VGG16 architecture.
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
include_top: whether to include the 3 fully-connected
layers at the top of the network.
weights: one of `None` (random initialization),
'imagenet' (pre-training on ImageNet),
or the path to the weights file to be loaded.
input_tensor: optional Keras tensor
(i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)`
(with `channels_last` data format)
or `(3, 224, 224)` (with `channels_first` data format).
It should have exactly 3 input channels,
and width and height should be no smaller than 48.
E.g. `(200, 200, 3)` would be one valid value.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
# Determine proper input shape
input_shape = (2, None, None, 3)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# Block 1
x = layers.ConvLSTM2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(img_input)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x) #print(x.shape)
# Block 2
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
x = layers.Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = engine.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = models.Model(inputs, x, name='vgg16_flow_3d')
return model
def VGG16_sf(include_top=True,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
# Determine proper input shape
input_shape = (None, None, 15)
#average or max pooling
average_pooling = False
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(shape=input_shape)
else:
img_input = input_tensor
x1 = custom_layers.Split1(name='split1')(img_input)
x2 = custom_layers.Split2(name='split2')(img_input)
x3 = custom_layers.Split3(name='split3')(img_input)
x4 = custom_layers.Split4(name='split4')(img_input)
x5 = custom_layers.Split5(name='split5')(img_input)
# Block 1
b1c1 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1', trainable=False)
b1c2 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2', trainable=False)
if average_pooling:
b1p = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block1_pool')
else:
b1p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')
x1 = b1c1(x1)
x1 = b1c2(x1)
x1 = b1p(x1)
x2 = b1c1(x2)
x2 = b1c2(x2)
x2 = b1p(x2)
x3 = b1c1(x3)
x3 = b1c2(x3)
x3 = b1p(x3)
x4 = b1c1(x4)
x4 = b1c2(x4)
x4 = b1p(x4)
x5 = b1c1(x5)
x5 = b1c2(x5)
x5 = b1p(x5)
"""
three_way_merge = True
one_by_one_per_channel = True
if one_by_one_per_channel:
merge = custom_layers.OneByOneMergeConv3D()
x11 = merge([x1, x2, x3])
if three_way_merge:
x12 = merge([x2, x3, x4])
x13 = merge([x3, x4, x5])
else:
x11 = layers.Maximum()([x1, x2, x3])
if three_way_merge:
x12 = layers.Maximum()([x2, x3, x4])
x13 = layers.Maximum()([x3, x4, x5])
"""
# Block 2
b2c1 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1', trainable=False)
b2c2 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2', trainable=False)
if average_pooling:
b2p = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block2_pool')
else:
b2p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')
x1 = b2c1(x1)
x1 = b2c2(x1)
x1 = b2p(x1)
x2 = b2c1(x2)
x2 = b2c2(x2)
x2 = b2p(x2)
x3 = b2c1(x3)
x3 = b2c2(x3)
x3 = b2p(x3)
x4 = b2c1(x4)
x4 = b2c2(x4)
x4 = b2p(x4)
x5 = b2c1(x5)
x5 = b2c2(x5)
x5 = b2p(x5)
x21 = custom_layers.OneByOneMerge()([x1, x2, x3])
x22 = custom_layers.OneByOneMerge()([x2, x3, x4])
x23 = custom_layers.OneByOneMerge()([x3, x4, x5])
# Block 3
b3c1 = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1', trainable=False)
b3c2 = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2', trainable=False)
b3c3 = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3', trainable=False)
if average_pooling:
b3p = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block3_pool_0')
else:
b3p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_0')
x21 = b3c1(x21)
x21 = b3c2(x21)
x21 = b3c3(x21)
x21 = b3p(x21)
x22 = b3c1(x22)
x22 = b3c2(x22)
x22 = b3c3(x22)
x22 = b3p(x22)
x23 = b3c1(x23)
x23 = b3c2(x23)
x23 = b3c3(x23)
x23 = b3p(x23)
# x = custom_layers.OneByOneMergeConv3D(name='block3_pool')([x21, x22, x23])
x = custom_layers.OneByOneMerge(name='block3_pool')([x21, x22, x23])
# x = layers.Maximum(name='block3_pool')([x21, x22, x23])
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1', trainable=False)(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2', trainable=False)(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3', trainable=False)(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1', trainable=False)(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2', trainable=False)(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3', trainable=False)(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
x = layers.Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = engine.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
# model = models.Model(inputs, x, name='vgg16_sf')
model = models.Model(img_input, x, name='vgg16_sf')
return model
def VGG16_sf_flow(include_top=True,
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000):
# Determine proper input shape
input_shape = (None, None, 11)
#average or max pooling
average_pooling = False
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(shape=input_shape)
else:
img_input = input_tensor
x2 = custom_layers.Split1(name='split2')(img_input)
x3 = custom_layers.Split2(name='split3')(img_input)
x4 = custom_layers.Split3(name='split4')(img_input)
f1 = custom_layers.SplitFlow1(name='flow1')(img_input)
f2 = custom_layers.SplitFlow2(name='flow2')(img_input)
# Block 1
b1c1 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')
b1c2 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')
if average_pooling:
b1p = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block1_pool')
else:
b1p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')
x2 = b1c1(x2)
x2 = b1c2(x2)
x2 = b1p(x2)
x3 = b1c1(x3)
x3 = b1c2(x3)
x3 = b1p(x3)
x4 = b1c1(x4)
x4 = b1c2(x4)
x4 = b1p(x4)
f1 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_flow_pool')(f1)
f2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_flow_pool')(f2)
x11 = layers.Maximum()([x2, x3])
x11 = layers.Multiply()([x11, f1])
x12 = layers.Maximum()([x3, x4])
x12 = layers.Multiply()([x12, f2])
# Block 2
b2c1 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')
b2c2 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')
if average_pooling:
b2p = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block2_pool')
else:
b2p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')
x11 = b2c1(x11)
x11 = b2c2(x11)
x11 = b2p(x11)
x12 = b2c1(x12)
x12 = b2c2(x12)
x12 = b2p(x12)
x = layers.Maximum()([x11, x12])
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
if average_pooling:
x = layers.AveragePooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
else:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = layers.Flatten(name='flatten')(x)
x = layers.Dense(4096, activation='relu', name='fc1')(x)
x = layers.Dense(4096, activation='relu', name='fc2')(x)
x = layers.Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D()(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = engine.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
# model = models.Model(inputs, x, name='vgg16_sf')
model = models.Model(img_input, x, name='vgg16_sf_flow')
return model
def vgg_retinanet(num_classes, backbone='vgg16', inputs=None, modifier=None, **kwargs):
""" Constructs a retinanet model using a vgg backbone.
Args
num_classes: Number of classes to predict.
backbone: Which backbone to use (one of ('vgg16', 'vgg19')).
inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)).
modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example).
Returns
RetinaNet model with a VGG backbone.
"""
if backbone == 'vgg16_5f':
return vgg16_retinanet_5f(num_classes=num_classes, inputs=inputs, modifier=modifier, **kwargs)
if backbone == 'vgg16_3f':
return vgg16_retinanet_3f(num_classes=num_classes, inputs=inputs, modifier=modifier, **kwargs)
# choose default input
if inputs is None and '_sf' not in backbone:
inputs = keras.layers.Input(shape=(None, None, 3))
# create the vgg backbone
if backbone == 'vgg16':
vgg = keras.applications.VGG16(input_tensor=inputs, include_top=False)
# weights = '/store/datasets/UAV/models/vgg16-rn-w-s-1-on/snapshots/vgg16_csv_14.h5'
# weights = '/store/datasets/UA-Detrac/models2/vgg16-1-on/snapshots/vgg16_csv_07.h5'
# vgg.load_weights(weights, by_name=True)
# for layer in vgg.layers[:-4]:
# layer.trainable = False
elif backbone == 'vgg19':
vgg = keras.applications.VGG19(input_tensor=inputs, include_top=False)
elif backbone == 'vgg16_flow_s':
inputs = keras.layers.Input(shape=(None, None, 6))
vgg = VGG16_flow_s(input_tensor=inputs, include_top=False)
elif backbone == 'vgg16_flow_y':
inputs = keras.layers.Input(shape=(None, None, 6))
vgg = VGG16_flow_y(input_tensor=inputs, include_top=False)
elif backbone == 'vgg16_flow_3d':
inputs = keras.layers.Input(shape=(2, None, None, 3))
vgg = VGG16_flow_3d(input_tensor=inputs, include_top=False)
elif backbone == 'vgg16_sf':
if inputs is None:
inputs = keras.layers.Input(shape=(None, None, 15))
vgg = VGG16_sf(input_tensor=inputs, include_top=False)
elif backbone == 'vgg16_sf_flow':
if inputs is None:
inputs = keras.layers.Input(shape=(None, None, 11))
vgg = VGG16_sf_flow(input_tensor=inputs, include_top=False)
# elif backbone == 'vgg16_flow_c':
# inputs = keras.layers.Input(shape=(None, None, 6))
# vgg = VGG16_flow_c(input_tensor=inputs, include_top=False)
else:
raise ValueError("Backbone '{}' not recognized.".format(backbone))
if modifier:
vgg = modifier(vgg)
# create the full model
layer_names = ["block3_pool", "block4_pool", "block5_pool"]
layer_outputs = [vgg.get_layer(name).output for name in layer_names]
model = retinanet.retinanet(inputs=inputs, num_classes=num_classes, backbone_layers=layer_outputs, **kwargs)
# model.save('/store/datasets/ILSVRC2015/models/rn-vgg16-sm256/model.h5')
# exit(0)
return model
def vgg16_retinanet_5f(num_classes, inputs=None, modifier=None, **kwargs):
inputs = keras.layers.Input(shape=(None, None, 15))
x1 = custom_layers.Split1(name='split1')(inputs)
x2 = custom_layers.Split2(name='split2')(inputs)
x3 = custom_layers.Split3(name='split3')(inputs)
x4 = custom_layers.Split4(name='split4')(inputs)
x5 = custom_layers.Split5(name='split5')(inputs)
#
"""
vgg1 = keras.applications.VGG16(input_tensor=x1, include_top=False, weights='imagenet')
vgg1.load_weights(weights, by_name=True)
vgg2 = keras.applications.VGG16(input_tensor=x2, include_top=False, weights='imagenet')
vgg2.load_weights(weights, by_name=True)
vgg3 = keras.applications.VGG16(input_tensor=x3, include_top=False, weights='imagenet')
vgg3.load_weights(weights, by_name=True)
vgg4 = keras.applications.VGG16(input_tensor=x4, include_top=False, weights='imagenet')
vgg4.load_weights(weights, by_name=True)
vgg5 = keras.applications.VGG16(input_tensor=x5, include_top=False, weights='imagenet')
vgg5.load_weights(weights, by_name=True)
"""
layer_outputs1 = []
layer_outputs2 = []
layer_outputs3 = []
layer_outputs4 = []
layer_outputs5 = []
vgg1 = keras.applications.VGG16(input_tensor=x1, include_top=False, weights='imagenet')
# vgg1.load_weights(weights, by_name=True)
for layer in vgg1.layers[:-4]:
layer.trainable = False
for layer in vgg1.layers:
if 'block3_pool' in layer.name:
layer_outputs1.append(layer.output)
layer2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_1')
x2 = layer2(x2)
layer_outputs2.append(layer2.output)
layer3 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_2')
x3 = layer3(x3)
layer_outputs3.append(layer3.output)
layer4 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_3')
x4 = layer4(x4)
layer_outputs4.append(layer4.output)
layer5 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_4')
x5 = layer5(x5)
layer_outputs5.append(layer5.output)
elif 'block4_pool' in layer.name:
layer_outputs1.append(layer.output)
layer2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool_1')
x2 = layer2(x2)
layer_outputs2.append(layer2.output)
layer3 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool_2')
x3 = layer3(x3)
layer_outputs3.append(layer3.output)
layer4 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool_3')
x4 = layer4(x4)
layer_outputs4.append(layer4.output)
layer5 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool_4')
x5 = layer5(x5)
layer_outputs5.append(layer5.output)
elif 'block5_pool' in layer.name:
layer_outputs1.append(layer.output)
layer2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool_1')
x2 = layer2(x2)
layer_outputs2.append(layer2.output)
layer3 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool_2')
x3 = layer3(x3)
layer_outputs3.append(layer3.output)
layer4 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool_3')
x4 = layer4(x4)
layer_outputs4.append(layer4.output)
layer5 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool_4')
x5 = layer5(x5)
layer_outputs5.append(layer5.output)
elif 'block' in layer.name:
x2 = layer(x2)
x3 = layer(x3)
x4 = layer(x4)
x5 = layer(x5)
"""
networks = [vgg1, vgg2, vgg3, vgg4, vgg5]
for j, network in enumerate(networks):
for layer in network.layers:
layer.name += '_' + str(j)
# for layer in network.layers[:-4]:
for layer in network.layers:
layer.trainable = False
"""
if modifier:
vgg1 = modifier(vgg1)
# vgg2 = modifier(vgg2)
# vgg3 = modifier(vgg3)
# vgg4 = modifier(vgg4)
# vgg5 = modifier(vgg5)
# create the full model
layer_names = ["block3_pool", "block4_pool", "block5_pool"]
# layer_outputs1 = [vgg1.get_layer(name + '_0').output for name in layer_names]
# layer_outputs2 = [vgg2.get_layer(name + '_1').output for name in layer_names]
# layer_outputs3 = [vgg3.get_layer(name + '_2').output for name in layer_names]
# layer_outputs4 = [vgg4.get_layer(name + '_3').output for name in layer_names]
# layer_outputs5 = [vgg5.get_layer(name + '_4').output for name in layer_names]
# layer_outputs1 = [vgg1.get_layer(name).output for name in layer_names]
# layer_outputs2 = [vgg2.get_layer(name).output for name in layer_names]
# layer_outputs3 = [vgg3.get_layer(name).output for name in layer_names]
# layer_outputs4 = [vgg4.get_layer(name).output for name in layer_names]
# layer_outputs5 = [vgg5.get_layer(name).output for name in layer_names]
model = retinanet.retinanet_5f(inputs=inputs, num_classes=num_classes, backbone_layers=[layer_outputs1,
layer_outputs2,
layer_outputs3,
layer_outputs4,
layer_outputs5,], **kwargs)
# model.save('/store/datasets/ILSVRC2015/models/5f_b/model.h5')
# exit()
weights = '/store/datasets/ILSVRC2015/models/5f/snapshots2/vgg16_5f_csv_07.h5'
model.load_weights(weights, by_name=True)
for layer in model.layers:
print(layer.name)
#exit()
return model
def vgg16_retinanet_3f(num_classes, inputs=None, modifier=None, **kwargs):
inputs = keras.layers.Input(shape=(None, None, 9))
x1 = custom_layers.Split1(name='split1')(inputs)
x2 = custom_layers.Split2(name='split2')(inputs)
x3 = custom_layers.Split3(name='split3')(inputs)
layer_outputs1 = []
layer_outputs2 = []
layer_outputs3 = []
weights = '/store/datasets/UAV/models/vgg16-fbeb5/snapshots-pt/vgg16_csv_20.h5'
vgg1 = keras.applications.VGG16(input_tensor=x1, include_top=False, weights='imagenet')
vgg1.load_weights(weights, by_name=True)
for layer in vgg1.layers[:-4]:
layer.trainable = False
for layer in vgg1.layers:
if 'block3_pool' in layer.name:
layer_outputs1.append(layer.output)
layer2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_1')
x2 = layer2(x2)
layer_outputs2.append(layer2.output)
layer3 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_2')
x3 = layer3(x3)
layer_outputs3.append(layer3.output)
elif 'block4_pool' in layer.name:
layer_outputs1.append(layer.output)
layer2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool_1')
x2 = layer2(x2)
layer_outputs2.append(layer2.output)
layer3 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool_2')
x3 = layer3(x3)
layer_outputs3.append(layer3.output)
elif 'block5_pool' in layer.name:
layer_outputs1.append(layer.output)
layer2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool_1')
x2 = layer2(x2)
layer_outputs2.append(layer2.output)
layer3 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool_2')
x3 = layer3(x3)
layer_outputs3.append(layer3.output)
elif 'block' in layer.name:
x2 = layer(x2)
x3 = layer(x3)
if modifier:
vgg1 = modifier(vgg1)
# create the full model
layer_names = ["block3_pool", "block4_pool", "block5_pool"]
model = retinanet.retinanet_3f(inputs=inputs, num_classes=num_classes, backbone_layers=[layer_outputs1,
layer_outputs2,
layer_outputs3], **kwargs)
model.load_weights('/store/datasets/UAV/models/vgg16-fbeb5/snapshots-pt/vgg16_csv_20.h5', by_name=True)
# model.save('/store/datasets/UAV/models/vgg16-3f-2D/model.h5')
# exit()
return model
def vgg16_retinanet_5f_0(num_classes, inputs=None, modifier=None, **kwargs):
inputs = keras.layers.Input(shape=(None, None, 15))
x1 = custom_layers.Split1(name='split1')(inputs)
x2 = custom_layers.Split2(name='split2')(inputs)
x3 = custom_layers.Split3(name='split3')(inputs)
x4 = custom_layers.Split4(name='split4')(inputs)
x5 = custom_layers.Split5(name='split5')(inputs)
# layers:
b1c1 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')
b1c2 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')
b1p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')
b2c1 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')
b2c2 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')
b2p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')
b3c1= layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')
b3c2 = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')
b3c3 = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')
b4c1 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')
b4c2 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')
b4c3 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')
b5c1 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')
b5c2 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')
b5c3 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')
layer_outputs = []
for i, frame in enumerate([x1, x2, x3, x4, x5]):
layer_output = []
x = b1c1(frame)
x = b1c2(x)
x = b1p(x)
x = b2c1(x)
x = b2c2(x)
x = b2p(x)
x = b3c1(x)
x = b3c2(x)
x = b3c3(x)
b3p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool_' + str(i))
x = b3p(x)
layer_output.append(b3p.output)
x = b4c1(x)
x = b4c2(x)
x = b4c3(x)
b4p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool_' + str(i))
x = b4p(x)
layer_output.append(b4p.output)
x = b5c1(x)
x = b5c2(x)
x = b5c3(x)
b5p = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool_' + str(i))
x = b5p(x)
layer_output.append(b5p.output)
layer_outputs.append(layer_output)
model = retinanet.retinanet_5f(inputs=inputs, num_classes=num_classes, backbone_layers=[layer_outputs[0],
layer_outputs[1],
layer_outputs[2],
layer_outputs[3],
layer_outputs[4],], **kwargs)
if modifier:
model = modifier(model)
weights_path = keras.utils.get_file(
'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
('https://github.com/fchollet/deep-learning-models/'
'releases/download/v0.1/'
'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'),
cache_subdir='models',
file_hash='6d6bbae143d832006294945121d1f1fc')
model.load_weights(weights_path, by_name=True)
for layer in model.layers:
print(layer.name)
return model
| 36.614679
| 163
| 0.5486
| 6,132
| 51,883
| 4.5212
| 0.068656
| 0.009739
| 0.035493
| 0.047324
| 0.835125
| 0.798658
| 0.775177
| 0.758476
| 0.755374
| 0.742786
| 0
| 0.06844
| 0.329183
| 51,883
| 1,416
| 164
| 36.640537
| 0.728135
| 0.197965
| 0
| 0.709677
| 0
| 0
| 0.107952
| 0.015382
| 0
| 0
| 0
| 0
| 0
| 1
| 0.015573
| false
| 0
| 0.016685
| 0
| 0.050056
| 0.002225
| 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
|
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