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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
affff27bd256439ed6dbbae54854f6c5514e1763
| 305
|
py
|
Python
|
lib/exceptions.py
|
CatalogueOfLife/frontend-tests
|
81ce0b2999487c60a7e2c7c6673e5da1a8ff028d
|
[
"Apache-2.0"
] | null | null | null |
lib/exceptions.py
|
CatalogueOfLife/frontend-tests
|
81ce0b2999487c60a7e2c7c6673e5da1a8ff028d
|
[
"Apache-2.0"
] | 2
|
2019-07-08T21:24:55.000Z
|
2019-08-15T14:21:18.000Z
|
lib/exceptions.py
|
Sp2000/colplus-frontend-tests
|
81ce0b2999487c60a7e2c7c6673e5da1a8ff028d
|
[
"Apache-2.0"
] | null | null | null |
class ErrorMessageDisplayed(BaseException):
def __init__(self, m):
self.message = m
def __str__(self):
return self.message
class WarningMessageDisplayed(BaseException):
def __init__(self, m):
self.message = m
def __str__(self):
return self.message
| 15.25
| 45
| 0.64918
| 32
| 305
| 5.6875
| 0.34375
| 0.241758
| 0.21978
| 0.263736
| 0.703297
| 0.703297
| 0.703297
| 0.703297
| 0.703297
| 0.703297
| 0
| 0
| 0.265574
| 305
| 19
| 46
| 16.052632
| 0.8125
| 0
| 0
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0
| 0.2
| 0.8
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 12
|
b3754bdeec47667331df90972e3c5f0578e4054d
| 1,459
|
py
|
Python
|
app/jwt_tokens.py
|
SiwyKrzysiek/bibliography-cloud
|
a4003708904e500ffa44ef1a412a4abff9e417a8
|
[
"MIT"
] | null | null | null |
app/jwt_tokens.py
|
SiwyKrzysiek/bibliography-cloud
|
a4003708904e500ffa44ef1a412a4abff9e417a8
|
[
"MIT"
] | 5
|
2019-10-23T08:52:20.000Z
|
2022-02-26T22:04:33.000Z
|
app/jwt_tokens.py
|
SiwyKrzysiek/bibliography-cloud
|
a4003708904e500ffa44ef1a412a4abff9e417a8
|
[
"MIT"
] | 1
|
2020-01-05T23:57:31.000Z
|
2020-01-05T23:57:31.000Z
|
"""Generate JWT tokens"""
import jwt
import datetime
from config import Config
def create_delete_token(user_name: str):
exp = datetime.datetime.utcnow() + datetime.timedelta(seconds=Config.JWT_SESSION_TIME)
payload = {
"iss": "bibiograpy-cloud.pl",
"exp": exp,
"user": user_name,
"delete": True
}
return jwt.encode(payload, Config.JWT_SECRET, 'HS256').decode()
def create_upload_token(user_name: str):
exp = datetime.datetime.utcnow() + datetime.timedelta(seconds=Config.JWT_SESSION_TIME)
payload = {
"iss": "bibiograpy-cloud.pl",
"exp": exp,
"user": user_name,
"upload": True
}
return jwt.encode(payload, Config.JWT_SECRET, 'HS256').decode()
def create_download_token(user_name: str, file_name: str):
exp = datetime.datetime.utcnow() + datetime.timedelta(seconds=Config.JWT_SESSION_TIME)
payload = {
"iss": "bibiograpy-cloud.pl",
"exp": exp,
"user": user_name,
"download": True,
"fileName": file_name
}
return jwt.encode(payload, Config.JWT_SECRET, 'HS256').decode()
def create_list_token(user_name: str):
exp = datetime.datetime.utcnow() + datetime.timedelta(seconds=Config.JWT_SESSION_TIME)
payload = {
"iss": "bibiograpy-cloud.pl",
"exp": exp,
"user": user_name,
"list": True
}
return jwt.encode(payload, Config.JWT_SECRET, 'HS256').decode()
| 26.527273
| 90
| 0.638108
| 173
| 1,459
| 5.208092
| 0.208092
| 0.071032
| 0.057714
| 0.071032
| 0.823529
| 0.823529
| 0.823529
| 0.823529
| 0.823529
| 0.823529
| 0
| 0.010573
| 0.22207
| 1,459
| 54
| 91
| 27.018519
| 0.78326
| 0.013023
| 0
| 0.6
| 1
| 0
| 0.117155
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0.075
| 0
| 0.275
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2fb8f81d727123361ab98bc2377d88c8624dcb45
| 102
|
py
|
Python
|
templates/python-bare/task.py
|
mircodezorzi/secretary
|
e71e076427fc34f6bdb5f0abe9fc68a95d0869cc
|
[
"MIT"
] | null | null | null |
templates/python-bare/task.py
|
mircodezorzi/secretary
|
e71e076427fc34f6bdb5f0abe9fc68a95d0869cc
|
[
"MIT"
] | null | null | null |
templates/python-bare/task.py
|
mircodezorzi/secretary
|
e71e076427fc34f6bdb5f0abe9fc68a95d0869cc
|
[
"MIT"
] | null | null | null |
from secretary import replace
def setup(options):
replace('<NAME>', options.name, options.name)
| 17
| 49
| 0.72549
| 13
| 102
| 5.692308
| 0.615385
| 0.297297
| 0.405405
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147059
| 102
| 5
| 50
| 20.4
| 0.850575
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 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
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ff32b0345263db30a27b9684f00db1ce88cf0716
| 12,608
|
py
|
Python
|
tests/test_response.py
|
SpainTrain/hc-api-python
|
2bb9e2208b34e8617575de45934357ee33b8531c
|
[
"MIT"
] | 18
|
2017-04-12T20:34:37.000Z
|
2021-09-12T01:21:19.000Z
|
tests/test_response.py
|
SpainTrain/hc-api-python
|
2bb9e2208b34e8617575de45934357ee33b8531c
|
[
"MIT"
] | 13
|
2016-12-02T01:38:52.000Z
|
2021-06-07T15:10:51.000Z
|
tests/test_response.py
|
SpainTrain/hc-api-python
|
2bb9e2208b34e8617575de45934357ee33b8531c
|
[
"MIT"
] | 8
|
2017-07-06T15:36:23.000Z
|
2021-10-10T17:31:29.000Z
|
# pylint: disable=missing-docstring
import unittest
import requests_mock
from housecanary.apiclient import ApiClient
from housecanary.object import Property
from housecanary.object import Block
from housecanary.object import ZipCode
from housecanary.object import Msa
@requests_mock.Mocker()
class PropertyResponseTestCase(unittest.TestCase):
def setUp(self):
self.client = ApiClient()
self.test_data = [{"address": "43 Valmonte Plaza", "zipcode": "90274"}]
self.headers = {'content-type': 'application/json', 'X-RateLimit-Limit': '5000', 'X-RateLimit-Reset': '1491920221', 'X-RateLimit-Period': '60', 'X-RateLimit-Remaining': '4999'}
self.response = [{'property/value': {'api_code_description': 'ok', 'result': {'value': {'price_upr': 1780318, 'fsd': 0.0836871, 'price_mean': 1642834, 'price_lwr': 1505350}}, 'api_code': 0}, 'address_info': {'state': 'CA', 'address_full': '43 Valmonte Plz Palos Verdes Estates CA 90274', 'block_id': '060376703241005', 'slug': '43-Valmonte-Plz-Palos-Verdes-Estates-CA-90274', 'msa': '31080', 'zipcode': '90274', 'county_fips': '06037', 'city': 'Palos Verdes Estates', 'geo_precision': 'rooftop', 'unit': None, 'address': '43 Valmonte Plz', 'lat': 33.79814, 'zipcode_plus4': '1444', 'metrodiv': '31084', 'lng': -118.36455}}]
self.multi_property_response = [{'property/value': {'api_code_description': 'ok', 'result': {'value': {'price_upr': 1780318, 'fsd': 0.0836871, 'price_mean': 1642834, 'price_lwr': 1505350}}, 'api_code': 0}, 'address_info': {'state': 'CA', 'address_full': '43 Valmonte Plz Palos Verdes Estates CA 90274', 'block_id': '060376703241005', 'slug': '43-Valmonte-Plz-Palos-Verdes-Estates-CA-90274', 'msa': '31080', 'zipcode': '90274', 'county_fips': '06037', 'city': 'Palos Verdes Estates', 'geo_precision': 'rooftop', 'unit': None, 'address': '43 Valmonte Plz', 'lat': 33.79814, 'zipcode_plus4': '1444', 'metrodiv': '31084', 'lng': -118.36455}}, {'property/value': {'api_code_description': 'no content', 'result': None, 'api_code': 204}, 'address_info': {'state': None, 'address_full': None, 'block_id': None, 'slug': None, 'msa': None, 'zipcode': None, 'county_fips': None, 'city': None, 'geo_precision': 'unknown', 'unit': None, 'address': None, 'lat': None, 'zipcode_plus4': None, 'metrodiv': None, 'lng': None}}]
self.error_response = [{'property/value': {'api_code_description': 'no content', 'result': None, 'api_code': 204}, 'address_info': {'state': None, 'address_full': None, 'block_id': None, 'slug': None, 'msa': None, 'zipcode': None, 'county_fips': None, 'city': None, 'geo_precision': 'unknown', 'unit': None, 'address': None, 'lat': None, 'zipcode_plus4': None, 'metrodiv': None, 'lng': None}}]
def test_endpoint_name(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.response)
response = self.client.fetch("property/value", self.test_data)
self.assertEqual(response.endpoint_name, "property/value")
def test_json(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.response)
response = self.client.fetch("property/value", self.test_data)
self.assertTrue(isinstance(response.json(), list))
def test_response(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.response)
response = self.client.fetch("property/value", self.test_data)
self.assertIsNotNone(response.response)
def test_get_object_errors(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.response)
response = self.client.fetch("property/value", self.test_data)
self.assertEqual(response.get_object_errors(), [])
def test_get_object_errors_with_error(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.error_response)
self.test_data[0]["zipcode"] = "00000"
response = self.client.fetch("property/value", self.test_data)
self.assertEqual(len(response.get_object_errors()), 1)
def test_has_object_error(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.response)
response = self.client.fetch("property/value", self.test_data)
self.assertFalse(response.has_object_error())
def test_has_object_error_with_error(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.error_response)
self.test_data[0]["zipcode"] = "00000"
response = self.client.fetch("property/value", self.test_data)
self.assertTrue(response.has_object_error())
def test_properties(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.response)
response = self.client.fetch("property/value", self.test_data)
self.assertTrue(len(response.properties()), 1)
self.assertTrue(isinstance(response.properties()[0], Property))
def test_properties_with_multiple(self, mock):
mock.post("/v2/property/value", headers=self.headers, json=self.multi_property_response)
self.test_data.append({"address": "85 Clay St", "zipcode": "02140"})
response = self.client.fetch("property/value", self.test_data)
self.assertTrue(len(response.properties()), 2)
self.assertTrue(isinstance(response.properties()[0], Property))
self.assertTrue(isinstance(response.properties()[1], Property))
def test_response_rate_limits(self, mock):
mock.get("/v2/property/value", headers=self.headers, json=self.response)
response = self.client.fetch("property/value", self.test_data)
self.assertTrue(isinstance(response.rate_limits, list))
self.assertTrue(isinstance(response.rate_limits[0]['period'], str))
@requests_mock.Mocker()
class BlockResponseTestCase(unittest.TestCase):
def setUp(self):
self.client = ApiClient()
self.test_data = [{"block_id": "060376703241005"}]
self.headers = {'content-type': 'application/json', 'X-RateLimit-Limit': '5000', 'X-RateLimit-Reset': '1491920221', 'X-RateLimit-Period': '60', 'X-RateLimit-Remaining': '4999'}
self.response = [{'block_info': {'block_id': '060376703241005'}, 'block/value_ts': {'api_code_description': 'ok', 'result': {'time_series': [{'value_sqft_median': 296.89, 'month': '1994-02-01', 'value_median': 513529}], 'property_type': 'SFD'}, 'api_code': 0}}]
self.response_multi = [{'block_info': {'block_id': '060376703241005'}, 'block/value_ts': {'api_code_description': 'ok', 'result': {'time_series': [{'value_sqft_median': 296.89, 'month': '1994-02-01', 'value_median': 513529}], 'property_type': 'SFD'}, 'api_code': 0}}, {'block_info': {'block_id': '160376703241005'}, 'block/value_ts': {'api_code_description': 'ok', 'result': {'time_series': [{'value_sqft_median': 296.89, 'month': '1994-02-01', 'value_median': 513529}], 'property_type': 'SFD'}, 'api_code': 0}}]
def test_blocks(self, mock):
mock.get("/v2/block/value_ts", headers=self.headers, json=self.response)
response = self.client.fetch("block/value_ts", self.test_data)
self.assertTrue(len(response.blocks()), 1)
self.assertTrue(isinstance(response.blocks()[0], Block))
def test_blocks_with_multiple(self, mock):
mock.post("/v2/block/value_ts", headers=self.headers, json=self.response_multi)
self.test_data.append({"block_id": "160376703241005"})
response = self.client.fetch("block/value_ts", self.test_data)
self.assertTrue(len(response.blocks()), 2)
self.assertTrue(isinstance(response.blocks()[0], Block))
self.assertTrue(isinstance(response.blocks()[1], Block))
@requests_mock.Mocker()
class ZipCodeResponseTestCase(unittest.TestCase):
def setUp(self):
self.client = ApiClient()
self.test_data = [{"zipcode": "90274"}]
self.headers = {'content-type': 'application/json', 'X-RateLimit-Limit': '5000', 'X-RateLimit-Reset': '1491920221', 'X-RateLimit-Period': '60', 'X-RateLimit-Remaining': '4999'}
self.response = [{'zip/details': {'api_code_description': 'ok', 'result': {'single_family': {'inventory_total': 78.538, 'price_median': 2748899.085, 'estimated_sales_total': None, 'days_on_market_median': 116.132, 'months_of_inventory_median': None, 'market_action_median': 62.34}, 'multi_family': {'inventory_total': None, 'price_median': None, 'estimated_sales_total': None, 'days_on_market_median': None, 'months_of_inventory_median': None, 'market_action_median': None}}, 'api_code': 0}, 'zipcode_info': {'zipcode': '90274'}}]
self.response_multi = [{'zip/details': {'api_code_description': 'ok', 'result': {'single_family': {'inventory_total': 78.538, 'price_median': 2748899.085, 'estimated_sales_total': None, 'days_on_market_median': 116.132, 'months_of_inventory_median': None, 'market_action_median': 62.34}, 'multi_family': {'inventory_total': None, 'price_median': None, 'estimated_sales_total': None, 'days_on_market_median': None, 'months_of_inventory_median': None, 'market_action_median': None}}, 'api_code': 0}, 'zipcode_info': {'zipcode': '90274'}}, {'zip/details': {'api_code_description': 'ok', 'result': {'single_family': {'inventory_total': 28.462, 'price_median': 453311.462, 'estimated_sales_total': 24.908, 'days_on_market_median': 46.308, 'months_of_inventory_median': 1.143, 'market_action_median': 79.47}, 'multi_family': {'inventory_total': 8.923, 'price_median': 236076.923, 'estimated_sales_total': 6.73, 'days_on_market_median': 67.038, 'months_of_inventory_median': 1.326, 'market_action_median': 81.33}}, 'api_code': 0}, 'zipcode_info': {'zipcode': '01960'}}]
def test_zipcodes(self, mock):
mock.get("/v2/zip/details", headers=self.headers, json=self.response)
response = self.client.fetch("zip/details", self.test_data)
self.assertTrue(len(response.zipcodes()), 1)
self.assertTrue(isinstance(response.zipcodes()[0], ZipCode))
def test_zipcodes_with_multiple(self, mock):
mock.post("/v2/zip/details", headers=self.headers, json=self.response_multi)
self.test_data.append({"zipcode": "01960"})
response = self.client.fetch("zip/details", self.test_data)
self.assertTrue(len(response.zipcodes()), 2)
self.assertTrue(isinstance(response.zipcodes()[0], ZipCode))
self.assertTrue(isinstance(response.zipcodes()[1], ZipCode))
@requests_mock.Mocker()
class MsaResponseTestCase(unittest.TestCase):
def setUp(self):
self.client = ApiClient()
self.test_data = [{"msa": "41860"}]
self.headers = {'content-type': 'application/json', 'X-RateLimit-Limit': '5000', 'X-RateLimit-Reset': '1491920221', 'X-RateLimit-Period': '60', 'X-RateLimit-Remaining': '4999'}
self.response = [{'msa_info': {'msa': '41860', 'msa_name': 'San Francisco-Oakland-Hayward, CA'}, 'msa/details': {'api_code_description': 'ok', 'result': {'returns_5': 0.8724, 'cagr_1': 0.0673, 'cagr_5': 0.1336, 'max_12mo_loss': -0.210943, 'cagr_10': 0.0183, 'returns_1': 0.0673, 'risk_12mo_loss': 0.080268, 'cagr_20': 0.0658, 'returns_10': 0.1985}, 'api_code': 0}}]
self.response_multi = [{'msa_info': {'msa': '41860', 'msa_name': 'San Francisco-Oakland-Hayward, CA'}, 'msa/details': {'api_code_description': 'ok', 'result': {'returns_5': 0.8724, 'cagr_1': 0.0673, 'cagr_5': 0.1336, 'max_12mo_loss': -0.210943, 'cagr_10': 0.0183, 'returns_1': 0.0673, 'risk_12mo_loss': 0.080268, 'cagr_20': 0.0658, 'returns_10': 0.1985}, 'api_code': 0}}, {'msa_info': {'msa': '41861', 'msa_name': 'San Francisco-Oakland-Hayward, CA'}, 'msa/details': {'api_code_description': 'ok', 'result': {'returns_5': 0.8724, 'cagr_1': 0.0673, 'cagr_5': 0.1336, 'max_12mo_loss': -0.210943, 'cagr_10': 0.0183, 'returns_1': 0.0673, 'risk_12mo_loss': 0.080268, 'cagr_20': 0.0658, 'returns_10': 0.1985}, 'api_code': 0}}]
def test_msas(self, mock):
mock.get("/v2/msa/details", headers=self.headers, json=self.response)
response = self.client.fetch("msa/details", self.test_data)
self.assertTrue(len(response.msas()), 1)
self.assertTrue(isinstance(response.msas()[0], Msa))
def test_msas_with_multiple(self, mock):
mock.post("/v2/msa/details", headers=self.headers, json=self.response_multi)
self.test_data.append({"msa": "41861"})
response = self.client.fetch("msa/details", self.test_data)
self.assertTrue(len(response.msas()), 2)
self.assertTrue(isinstance(response.msas()[0], Msa))
self.assertTrue(isinstance(response.msas()[1], Msa))
if __name__ == "__main__":
unittest.main()
| 81.87013
| 1,070
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| 1,646
| 12,608
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0
| 7
|
ff555a30d5ff0f39e15c683a503e58a3e1c4db0a
| 47
|
py
|
Python
|
channels/__init__.py
|
MLRG-CEFET-RJ/parsl-ml-workflow
|
db727c1258c606359964fcfa681b08294d16de36
|
[
"MIT"
] | 1
|
2019-11-21T20:00:20.000Z
|
2019-11-21T20:00:20.000Z
|
channels/__init__.py
|
MLRG-CEFET-RJ/parsl-ml-workflow
|
db727c1258c606359964fcfa681b08294d16de36
|
[
"MIT"
] | null | null | null |
channels/__init__.py
|
MLRG-CEFET-RJ/parsl-ml-workflow
|
db727c1258c606359964fcfa681b08294d16de36
|
[
"MIT"
] | null | null | null |
from .SSHChannelCustom import SSHChannelCustom
| 23.5
| 46
| 0.893617
| 4
| 47
| 10.5
| 0.75
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0
| 7
|
ff605843656b4b009681bfc58a82a817370a9d07
| 96
|
py
|
Python
|
tests/cli/test_help.py
|
shahzebsiddiqui/buildtest-1
|
6c47424b82ec1e92ce0930f99be4ba10da62515c
|
[
"MIT"
] | null | null | null |
tests/cli/test_help.py
|
shahzebsiddiqui/buildtest-1
|
6c47424b82ec1e92ce0930f99be4ba10da62515c
|
[
"MIT"
] | 42
|
2021-04-20T11:11:06.000Z
|
2022-03-30T16:54:24.000Z
|
tests/cli/test_help.py
|
shahzebsiddiqui/buildtest-1
|
6c47424b82ec1e92ce0930f99be4ba10da62515c
|
[
"MIT"
] | null | null | null |
from buildtest.cli.help import buildtest_help
def test_buildtest_help():
buildtest_help()
| 16
| 45
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0
| 7
|
ff94c4e8a4c9c12bb27bc9c6110cbd579183d481
| 27,794
|
py
|
Python
|
cvnets/layers/conv_layer.py
|
apple/ml-cvnets
|
84d992f413e52c0468f86d23196efd9dad885e6f
|
[
"AML"
] | 209
|
2021-10-30T08:32:10.000Z
|
2022-03-31T16:18:03.000Z
|
cvnets/layers/conv_layer.py
|
apple/ml-cvnets
|
84d992f413e52c0468f86d23196efd9dad885e6f
|
[
"AML"
] | 12
|
2021-12-04T10:47:11.000Z
|
2022-03-31T15:39:40.000Z
|
cvnets/layers/conv_layer.py
|
apple/ml-cvnets
|
84d992f413e52c0468f86d23196efd9dad885e6f
|
[
"AML"
] | 50
|
2021-11-01T08:15:02.000Z
|
2022-03-29T08:17:34.000Z
|
#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
import torch
from torch import nn, Tensor
from typing import Optional, Union, Tuple
import argparse
from utils import logger
from .base_layer import BaseLayer
from .normalization_layers import get_normalization_layer
from .non_linear_layers import get_activation_fn
class Conv2d(nn.Conv2d):
"""
Applies a 2D convolution over an input
Args:
in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H_{in}, W_{in})`
out_channels (int): :math:`C_{out}` from an expected output of size :math:`(N, C_{out}, H_{out}, W_{out})`
kernel_size (Union[int, Tuple[int, int]]): Kernel size for convolution.
stride (Union[int, Tuple[int, int]]): Stride for convolution. Defaults to 1
padding (Union[int, Tuple[int, int]]): Padding for convolution. Defaults to 0
dilation (Union[int, Tuple[int, int]]): Dilation rate for convolution. Default: 1
groups (Optional[int]): Number of groups in convolution. Default: 1
bias (bool): Use bias. Default: ``False``
padding_mode (Optional[str]): Padding mode. Default: ``zeros``
use_norm (Optional[bool]): Use normalization layer after convolution. Default: ``True``
use_act (Optional[bool]): Use activation layer after convolution (or convolution and normalization).
Default: ``True``
act_name (Optional[str]): Use specific activation function. Overrides the one specified in command line args.
Shape:
- Input: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})`
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
stride: Optional[Union[int, Tuple[int, int]]] = 1,
padding: Optional[Union[int, Tuple[int, int]]] = 0,
dilation: Optional[Union[int, Tuple[int, int]]] = 1,
groups: Optional[int] = 1,
bias: Optional[bool] = False,
padding_mode: Optional[str] = "zeros",
*args,
**kwargs
) -> None:
super().__init__(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode,
)
class ConvLayer(BaseLayer):
"""
Applies a 2D convolution over an input
Args:
opts: command line arguments
in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H_{in}, W_{in})`
out_channels (int): :math:`C_{out}` from an expected output of size :math:`(N, C_{out}, H_{out}, W_{out})`
kernel_size (Union[int, Tuple[int, int]]): Kernel size for convolution.
stride (Union[int, Tuple[int, int]]): Stride for convolution. Default: 1
dilation (Union[int, Tuple[int, int]]): Dilation rate for convolution. Default: 1
groups (Optional[int]): Number of groups in convolution. Default: 1
bias (Optional[bool]): Use bias. Default: ``False``
padding_mode (Optional[str]): Padding mode. Default: ``zeros``
use_norm (Optional[bool]): Use normalization layer after convolution. Default: ``True``
use_act (Optional[bool]): Use activation layer after convolution (or convolution and normalization).
Default: ``True``
act_name (Optional[str]): Use specific activation function. Overrides the one specified in command line args.
Shape:
- Input: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})`
.. note::
For depth-wise convolution, `groups=C_{in}=C_{out}`.
"""
def __init__(
self,
opts,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
stride: Optional[Union[int, Tuple[int, int]]] = 1,
dilation: Optional[Union[int, Tuple[int, int]]] = 1,
groups: Optional[int] = 1,
bias: Optional[bool] = False,
padding_mode: Optional[str] = "zeros",
use_norm: Optional[bool] = True,
use_act: Optional[bool] = True,
act_name: Optional[str] = None,
*args,
**kwargs
) -> None:
super().__init__()
if use_norm:
norm_type = getattr(opts, "model.normalization.name", "batch_norm")
if norm_type is not None and norm_type.find("batch") > -1:
assert not bias, "Do not use bias when using normalization layers."
elif norm_type is not None and norm_type.find("layer") > -1:
bias = True
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
if isinstance(stride, int):
stride = (stride, stride)
if isinstance(dilation, int):
dilation = (dilation, dilation)
assert isinstance(kernel_size, Tuple)
assert isinstance(stride, Tuple)
assert isinstance(dilation, Tuple)
padding = (
int((kernel_size[0] - 1) / 2) * dilation[0],
int((kernel_size[1] - 1) / 2) * dilation[1],
)
if in_channels % groups != 0:
logger.error(
"Input channels are not divisible by groups. {}%{} != 0 ".format(
in_channels, groups
)
)
if out_channels % groups != 0:
logger.error(
"Output channels are not divisible by groups. {}%{} != 0 ".format(
out_channels, groups
)
)
block = nn.Sequential()
conv_layer = Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode,
)
block.add_module(name="conv", module=conv_layer)
self.norm_name = None
if use_norm:
norm_layer = get_normalization_layer(opts=opts, num_features=out_channels)
block.add_module(name="norm", module=norm_layer)
self.norm_name = norm_layer.__class__.__name__
self.act_name = None
act_type = (
getattr(opts, "model.activation.name", "prelu")
if act_name is None
else act_name
)
if act_type is not None and use_act:
neg_slope = getattr(opts, "model.activation.neg_slope", 0.1)
inplace = getattr(opts, "model.activation.inplace", False)
act_layer = get_activation_fn(
act_type=act_type,
inplace=inplace,
negative_slope=neg_slope,
num_parameters=out_channels,
)
block.add_module(name="act", module=act_layer)
self.act_name = act_layer.__class__.__name__
self.block = block
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
self.groups = groups
self.kernel_size = conv_layer.kernel_size
self.bias = bias
self.dilation = dilation
@classmethod
def add_arguments(cls, parser: argparse.ArgumentParser):
cls_name = "{} arguments".format(cls.__name__)
group = parser.add_argument_group(title=cls_name, description=cls_name)
group.add_argument(
"--model.layer.conv-init",
type=str,
default="kaiming_normal",
help="Init type for conv layers",
)
parser.add_argument(
"--model.layer.conv-init-std-dev",
type=float,
default=None,
help="Std deviation for conv layers",
)
return parser
def forward(self, x: Tensor) -> Tensor:
return self.block(x)
def __repr__(self):
repr_str = self.block[0].__repr__()
repr_str = repr_str[:-1]
if self.norm_name is not None:
repr_str += ", normalization={}".format(self.norm_name)
if self.act_name is not None:
repr_str += ", activation={}".format(self.act_name)
repr_str += ", bias={}".format(self.bias)
repr_str += ")"
return repr_str
def profile_module(self, input: Tensor) -> (Tensor, float, float):
if input.dim() != 4:
logger.error(
"Conv2d requires 4-dimensional input (BxCxHxW). Provided input has shape: {}".format(
input.size()
)
)
b, in_c, in_h, in_w = input.size()
assert in_c == self.in_channels, "{}!={}".format(in_c, self.in_channels)
stride_h, stride_w = self.stride
groups = self.groups
out_h = in_h // stride_h
out_w = in_w // stride_w
k_h, k_w = self.kernel_size
# compute MACS
macs = (k_h * k_w) * (in_c * self.out_channels) * (out_h * out_w) * 1.0
macs /= groups
if self.bias:
macs += self.out_channels * out_h * out_w
# compute parameters
params = sum([p.numel() for p in self.parameters()])
output = torch.zeros(
size=(b, self.out_channels, out_h, out_w),
dtype=input.dtype,
device=input.device,
)
# print(macs)
return output, params, macs
class TransposeConvLayer(BaseLayer):
"""
Applies a 2D Transpose convolution (aka as Deconvolution) over an input
Args:
opts: command line arguments
in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H_{in}, W_{in})`
out_channels (int): :math:`C_{out}` from an expected output of size :math:`(N, C_{out}, H_{out}, W_{out})`
kernel_size (Union[int, Tuple[int, int]]): Kernel size for convolution.
stride (Union[int, Tuple[int, int]]): Stride for convolution. Default: 1
dilation (Union[int, Tuple[int, int]]): Dilation rate for convolution. Default: 1
groups (Optional[int]): Number of groups in convolution. Default: 1
bias (Optional[bool]): Use bias. Default: ``False``
padding_mode (Optional[str]): Padding mode. Default: ``zeros``
use_norm (Optional[bool]): Use normalization layer after convolution. Default: ``True``
use_act (Optional[bool]): Use activation layer after convolution (or convolution and normalization).
Default: ``True``
padding (Optional[Union[int, Tuple]]): Padding will be done on both sides of each dimension in the input
output_padding (Optional[Union[int, Tuple]]): Additional padding on the output tensor
auto_padding (Optional[bool]): Compute padding automatically. Default: ``True``
Shape:
- Input: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})`
"""
def __init__(
self,
opts,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple],
stride: Optional[Union[int, Tuple]] = 1,
dilation: Optional[Union[int, Tuple]] = 1,
groups: Optional[int] = 1,
bias: Optional[bool] = False,
padding_mode: Optional[str] = "zeros",
use_norm: Optional[bool] = True,
use_act: Optional[bool] = True,
padding: Optional[Union[int, Tuple]] = (0, 0),
output_padding: Optional[Union[int, Tuple]] = None,
auto_padding: Optional[bool] = True,
*args,
**kwargs
):
super().__init__()
if use_norm:
assert not bias, "Do not use bias when using normalization layers."
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
if isinstance(stride, int):
stride = (stride, stride)
if isinstance(dilation, int):
dilation = (dilation, dilation)
if output_padding is None:
output_padding = (stride[0] - 1, stride[1] - 1)
assert isinstance(kernel_size, (tuple, list))
assert isinstance(stride, (tuple, list))
assert isinstance(dilation, (tuple, list))
if auto_padding:
padding = (
int((kernel_size[0] - 1) / 2) * dilation[0],
int((kernel_size[1] - 1) / 2) * dilation[1],
)
if in_channels % groups != 0:
logger.error(
"Input channels are not divisible by groups. {}%{} != 0 ".format(
in_channels, groups
)
)
if out_channels % groups != 0:
logger.error(
"Output channels are not divisible by groups. {}%{} != 0 ".format(
out_channels, groups
)
)
block = nn.Sequential()
conv_layer = nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode,
output_padding=output_padding,
)
block.add_module(name="conv", module=conv_layer)
self.norm_name = None
if use_norm:
norm_layer = get_normalization_layer(opts=opts, num_features=out_channels)
block.add_module(name="norm", module=norm_layer)
self.norm_name = norm_layer.__class__.__name__
self.act_name = None
act_type = getattr(opts, "model.activation.name", "relu")
if act_type is not None and use_act:
neg_slope = getattr(opts, "model.activation.neg_slope", 0.1)
inplace = getattr(opts, "model.activation.inplace", False)
act_layer = get_activation_fn(
act_type=act_type,
inplace=inplace,
negative_slope=neg_slope,
num_parameters=out_channels,
)
block.add_module(name="act", module=act_layer)
self.act_name = act_layer.__class__.__name__
self.block = block
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
self.groups = groups
self.kernel_size = conv_layer.kernel_size
self.bias = bias
def forward(self, x: Tensor) -> Tensor:
return self.block(x)
def __repr__(self):
repr_str = self.block[0].__repr__()
repr_str = repr_str[:-1]
if self.norm_name is not None:
repr_str += ", normalization={}".format(self.norm_name)
if self.act_name is not None:
repr_str += ", activation={}".format(self.act_name)
repr_str += ")"
return repr_str
def profile_module(self, input: Tensor) -> Tuple[Tensor, float, float]:
if input.dim() != 4:
logger.error(
"Conv2d requires 4-dimensional input (BxCxHxW). Provided input has shape: {}".format(
input.size()
)
)
b, in_c, in_h, in_w = input.size()
assert in_c == self.in_channels, "{}!={}".format(in_c, self.in_channels)
stride_h, stride_w = self.stride
groups = self.groups
out_h = in_h * stride_h
out_w = in_w * stride_w
k_h, k_w = self.kernel_size
# compute MACS
macs = (k_h * k_w) * (in_c * self.out_channels) * (out_h * out_w) * 1.0
macs /= groups
if self.bias:
macs += self.out_channels * out_h * out_w
# compute parameters
params = sum([p.numel() for p in self.parameters()])
output = torch.zeros(
size=(b, self.out_channels, out_h, out_w),
dtype=input.dtype,
device=input.device,
)
# print(macs)
return output, params, macs
class NormActLayer(BaseLayer):
"""
Applies a normalization layer followed by an activation layer
Args:
opts: command-line arguments
num_features: :math:`C` from an expected input of size :math:`(N, C, H, W)`
Shape:
- Input: :math:`(N, C, H, W)`
- Output: :math:`(N, C, H, W)`
"""
def __init__(self, opts, num_features, *args, **kwargs):
super().__init__()
block = nn.Sequential()
self.norm_name = None
norm_layer = get_normalization_layer(opts=opts, num_features=num_features)
block.add_module(name="norm", module=norm_layer)
self.norm_name = norm_layer.__class__.__name__
self.act_name = None
act_type = getattr(opts, "model.activation.name", "prelu")
neg_slope = getattr(opts, "model.activation.neg_slope", 0.1)
inplace = getattr(opts, "model.activation.inplace", False)
act_layer = get_activation_fn(
act_type=act_type,
inplace=inplace,
negative_slope=neg_slope,
num_parameters=num_features,
)
block.add_module(name="act", module=act_layer)
self.act_name = act_layer.__class__.__name__
self.block = block
def forward(self, x: Tensor) -> Tensor:
return self.block(x)
def profile_module(self, input: Tensor) -> Tuple[Tensor, float, float]:
# compute parameters
params = sum([p.numel() for p in self.parameters()])
macs = 0.0
return input, params, macs
def __repr__(self):
repr_str = "{}(normalization={}, activation={})".format(
self.__class__.__name__, self.norm_type, self.act_type
)
return repr_str
class ConvLayer3d(BaseLayer):
"""
Applies a 3D convolution over an input
Args:
opts: command line arguments
in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
out_channels (int): :math:`C_{out}` from an expected output of size :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
kernel_size (Union[int, Tuple[int, int]]): Kernel size for convolution.
stride (Union[int, Tuple[int, int]]): Stride for convolution. Default: 1
dilation (Union[int, Tuple[int, int]]): Dilation rate for convolution. Default: 1
groups (Optional[int]): Number of groups in convolution. Default: 1
bias (Optional[bool]): Use bias. Default: ``False``
padding_mode (Optional[str]): Padding mode. Default: ``zeros``
use_norm (Optional[bool]): Use normalization layer after convolution. Default: ``True``
use_act (Optional[bool]): Use activation layer after convolution (or convolution and normalization).
Default: ``True``
Shape:
- Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
.. note::
For depth-wise convolution, `groups=C_{in}=C_{out}`.
"""
def __init__(
self,
opts,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple],
stride: Optional[Union[int, Tuple]] = 1,
dilation: Optional[Union[int, Tuple]] = 1,
groups: Optional[int] = 1,
bias: Optional[bool] = False,
padding_mode: Optional[str] = "zeros",
use_norm: Optional[bool] = True,
use_act: Optional[bool] = True,
*args,
**kwargs
) -> None:
super().__init__()
if use_norm:
assert not bias, "Do not use bias when using normalization layers."
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size, kernel_size)
if isinstance(stride, int):
stride = (stride, stride, stride)
if isinstance(dilation, int):
dilation = (dilation, dilation, dilation)
assert isinstance(kernel_size, (tuple, list))
assert isinstance(stride, (tuple, list))
assert isinstance(dilation, (tuple, list))
padding = tuple([int((kernel_size[i] - 1) / 2) * dilation[i] for i in range(3)])
if in_channels % groups != 0:
logger.error(
"Input channels are not divisible by groups. {}%{} != 0 ".format(
in_channels, groups
)
)
if out_channels % groups != 0:
logger.error(
"Output channels are not divisible by groups. {}%{} != 0 ".format(
out_channels, groups
)
)
block = nn.Sequential()
conv_layer = nn.Conv3d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
padding_mode=padding_mode,
)
block.add_module(name="conv", module=conv_layer)
self.norm_name = None
norm_type = getattr(opts, "model.normalization.name", "batch_norm")
if use_norm and norm_type is not None:
if norm_type.find("batch") > -1:
norm_type = "batch_norm_3d"
norm_layer = get_normalization_layer(
opts=opts, num_features=out_channels, norm_type=norm_type
)
block.add_module(name="norm", module=norm_layer)
self.norm_name = norm_layer.__class__.__name__
self.act_name = None
act_type = getattr(opts, "model.activation.name", "prelu")
if act_type is not None and use_act:
neg_slope = getattr(opts, "model.activation.neg_slope", 0.1)
inplace = getattr(opts, "model.activation.inplace", False)
act_layer = get_activation_fn(
act_type=act_type,
inplace=inplace,
negative_slope=neg_slope,
num_parameters=out_channels,
)
block.add_module(name="act", module=act_layer)
self.act_name = act_layer.__class__.__name__
self.block = block
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
self.groups = groups
self.kernel_size = conv_layer.kernel_size
self.bias = bias
self.dilation = dilation
def forward(self, x: Tensor) -> Tensor:
return self.block(x)
def __repr__(self):
repr_str = self.block[0].__repr__()
repr_str = repr_str[:-1]
if self.norm_name is not None:
repr_str += ", normalization={}".format(self.norm_name)
if self.act_name is not None:
repr_str += ", activation={}".format(self.act_name)
repr_str += ")"
return repr_str
def profile_module(self, input: Tensor) -> Tuple[Tensor, float, float]:
if input.dim() != 4:
logger.error(
"Conv2d requires 4-dimensional input (BxCxHxW). Provided input has shape: {}".format(
input.size()
)
)
b, in_c, in_d, in_h, in_w = input.size()
assert in_c == self.in_channels, "{}!={}".format(in_c, self.in_channels)
stride_d, stride_h, stride_w = self.stride
groups = self.groups
out_h = in_h // stride_h
out_w = in_w // stride_w
out_d = in_d // stride_d
k_d, k_h, k_w = self.kernel_size
# compute MACS
macs = (
(k_d * k_h * k_w)
* (in_c * self.out_channels)
* (out_h * out_w * out_d)
* 1.0
)
macs /= groups
if self.bias:
macs += self.out_channels * out_h * out_w * out_d
# compute parameters
params = sum([p.numel() for p in self.parameters()])
output = torch.zeros(
size=(b, self.out_channels, out_d, out_h, out_w),
dtype=input.dtype,
device=input.device,
)
return output, params, macs
class SeparableConv(BaseLayer):
"""
Applies a `2D depth-wise separable convolution <https://arxiv.org/abs/1610.02357>`_ over a 4D input tensor
Args:
opts: command line arguments
in_channels (int): :math:`C_{in}` from an expected input of size :math:`(N, C_{in}, H_{in}, W_{in})`
out_channels (int): :math:`C_{out}` from an expected output of size :math:`(N, C_{out}, H_{out}, W_{out})`
kernel_size (Union[int, Tuple[int, int]]): Kernel size for convolution.
stride (Union[int, Tuple[int, int]]): Stride for convolution. Default: 1
dilation (Union[int, Tuple[int, int]]): Dilation rate for convolution. Default: 1
use_norm (Optional[bool]): Use normalization layer after convolution. Default: ``True``
use_act (Optional[bool]): Use activation layer after convolution (or convolution and normalization). Default: ``True``
bias (Optional[bool]): Use bias. Default: ``False``
padding_mode (Optional[str]): Padding mode. Default: ``zeros``
Shape:
- Input: :math:`(N, C_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C_{out}, H_{out}, W_{out})`
.. note::
For depth-wise convolution, `groups=C_{in}=C_{out}`.
"""
def __init__(
self,
opts,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple],
stride: Optional[Union[int, Tuple]] = 1,
dilation: Optional[Union[int, Tuple]] = 1,
use_norm: Optional[bool] = True,
use_act: Optional[bool] = True,
bias: Optional[bool] = False,
padding_mode: Optional[str] = "zeros",
*args,
**kwargs
) -> None:
super().__init__()
self.dw_conv = ConvLayer(
opts=opts,
in_channels=in_channels,
out_channels=in_channels,
kernel_size=kernel_size,
stride=stride,
dilation=dilation,
groups=in_channels,
bias=False,
padding_mode=padding_mode,
use_norm=True,
use_act=False,
)
self.pw_conv = ConvLayer(
opts=opts,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
dilation=1,
groups=1,
bias=bias,
padding_mode=padding_mode,
use_norm=use_norm,
use_act=use_act,
)
self.in_channels = in_channels
self.out_channels = out_channels
self.stride = stride
self.kernel_size = kernel_size
self.dilation = dilation
def __repr__(self):
repr_str = "{}(in_channels={}, out_channels={}, kernel_size={}, stride={}, dilation={})".format(
self.__class__.__name__,
self.in_channels,
self.out_channels,
self.kernel_size,
self.stride,
self.dilation,
)
return repr_str
def forward(self, x: Tensor) -> Tensor:
x = self.dw_conv(x)
x = self.pw_conv(x)
return x
def profile_module(self, input: Tensor) -> Tuple[Tensor, float, float]:
params, macs = 0.0, 0.0
input, p, m = self.dw_conv.profile_module(input)
params += p
macs += m
input, p, m = self.pw_conv.profile_module(input)
params += p
macs += m
return input, params, macs
| 34.829574
| 126
| 0.57113
| 3,346
| 27,794
| 4.519127
| 0.064854
| 0.03968
| 0.03095
| 0.024337
| 0.872429
| 0.849547
| 0.827855
| 0.825144
| 0.810595
| 0.800542
| 0
| 0.007067
| 0.312693
| 27,794
| 797
| 127
| 34.873275
| 0.784484
| 0.240951
| 0
| 0.719331
| 0
| 0
| 0.074658
| 0.018725
| 0
| 0
| 0
| 0
| 0.027881
| 1
| 0.040892
| false
| 0
| 0.01487
| 0.007435
| 0.096654
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ff968a45ad452c6d8900dab6d2a62cffae72f9d0
| 12,901
|
py
|
Python
|
yandex/cloud/iot/devices/v1/device_service_pb2_grpc.py
|
kbespalov/python-sdk
|
e86563ee850e46a35b4c84053ecd4affdf66a963
|
[
"MIT"
] | null | null | null |
yandex/cloud/iot/devices/v1/device_service_pb2_grpc.py
|
kbespalov/python-sdk
|
e86563ee850e46a35b4c84053ecd4affdf66a963
|
[
"MIT"
] | null | null | null |
yandex/cloud/iot/devices/v1/device_service_pb2_grpc.py
|
kbespalov/python-sdk
|
e86563ee850e46a35b4c84053ecd4affdf66a963
|
[
"MIT"
] | null | null | null |
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
import grpc
from yandex.cloud.iot.devices.v1 import device_pb2 as yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__pb2
from yandex.cloud.iot.devices.v1 import device_service_pb2 as yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2
from yandex.cloud.operation import operation_pb2 as yandex_dot_cloud_dot_operation_dot_operation__pb2
class DeviceServiceStub(object):
"""A set of methods for managing devices.
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.Get = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/Get',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.GetDeviceRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__pb2.Device.FromString,
)
self.List = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/List',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicesRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicesResponse.FromString,
)
self.Create = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/Create',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.CreateDeviceRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString,
)
self.Update = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/Update',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.UpdateDeviceRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString,
)
self.Delete = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/Delete',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.DeleteDeviceRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString,
)
self.ListCertificates = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/ListCertificates',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceCertificatesRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceCertificatesResponse.FromString,
)
self.AddCertificate = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/AddCertificate',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.AddDeviceCertificateRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString,
)
self.DeleteCertificate = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/DeleteCertificate',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.DeleteDeviceCertificateRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString,
)
self.ListPasswords = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/ListPasswords',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicePasswordsRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicePasswordsResponse.FromString,
)
self.AddPassword = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/AddPassword',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.AddDevicePasswordRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString,
)
self.DeletePassword = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/DeletePassword',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.DeleteDevicePasswordRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.FromString,
)
self.ListOperations = channel.unary_unary(
'/yandex.cloud.iot.devices.v1.DeviceService/ListOperations',
request_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceOperationsRequest.SerializeToString,
response_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceOperationsResponse.FromString,
)
class DeviceServiceServicer(object):
"""A set of methods for managing devices.
"""
def Get(self, request, context):
"""Returns the specified device.
To get the list of available devices, make a [List] request.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def List(self, request, context):
"""Retrieves the list of devices in the specified registry.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Create(self, request, context):
"""Creates a device in the specified registry.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Update(self, request, context):
"""Updates the specified device.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Delete(self, request, context):
"""Deletes the specified device.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def ListCertificates(self, request, context):
"""Retrieves the list of device certificates for the specified device.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def AddCertificate(self, request, context):
"""Adds a certificate.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def DeleteCertificate(self, request, context):
"""Deletes the specified device certificate.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def ListPasswords(self, request, context):
"""Retrieves the list of passwords for the specified device.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def AddPassword(self, request, context):
"""Adds password for the specified device.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def DeletePassword(self, request, context):
"""Deletes the specified password.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def ListOperations(self, request, context):
"""Lists operations for the specified device.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_DeviceServiceServicer_to_server(servicer, server):
rpc_method_handlers = {
'Get': grpc.unary_unary_rpc_method_handler(
servicer.Get,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.GetDeviceRequest.FromString,
response_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__pb2.Device.SerializeToString,
),
'List': grpc.unary_unary_rpc_method_handler(
servicer.List,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicesRequest.FromString,
response_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicesResponse.SerializeToString,
),
'Create': grpc.unary_unary_rpc_method_handler(
servicer.Create,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.CreateDeviceRequest.FromString,
response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString,
),
'Update': grpc.unary_unary_rpc_method_handler(
servicer.Update,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.UpdateDeviceRequest.FromString,
response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString,
),
'Delete': grpc.unary_unary_rpc_method_handler(
servicer.Delete,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.DeleteDeviceRequest.FromString,
response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString,
),
'ListCertificates': grpc.unary_unary_rpc_method_handler(
servicer.ListCertificates,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceCertificatesRequest.FromString,
response_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceCertificatesResponse.SerializeToString,
),
'AddCertificate': grpc.unary_unary_rpc_method_handler(
servicer.AddCertificate,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.AddDeviceCertificateRequest.FromString,
response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString,
),
'DeleteCertificate': grpc.unary_unary_rpc_method_handler(
servicer.DeleteCertificate,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.DeleteDeviceCertificateRequest.FromString,
response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString,
),
'ListPasswords': grpc.unary_unary_rpc_method_handler(
servicer.ListPasswords,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicePasswordsRequest.FromString,
response_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDevicePasswordsResponse.SerializeToString,
),
'AddPassword': grpc.unary_unary_rpc_method_handler(
servicer.AddPassword,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.AddDevicePasswordRequest.FromString,
response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString,
),
'DeletePassword': grpc.unary_unary_rpc_method_handler(
servicer.DeletePassword,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.DeleteDevicePasswordRequest.FromString,
response_serializer=yandex_dot_cloud_dot_operation_dot_operation__pb2.Operation.SerializeToString,
),
'ListOperations': grpc.unary_unary_rpc_method_handler(
servicer.ListOperations,
request_deserializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceOperationsRequest.FromString,
response_serializer=yandex_dot_cloud_dot_iot_dot_devices_dot_v1_dot_device__service__pb2.ListDeviceOperationsResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'yandex.cloud.iot.devices.v1.DeviceService', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
| 54.205882
| 148
| 0.796217
| 1,480
| 12,901
| 6.418243
| 0.074324
| 0.048321
| 0.075166
| 0.091273
| 0.850089
| 0.850089
| 0.841878
| 0.775345
| 0.762817
| 0.687441
| 0
| 0.009404
| 0.134563
| 12,901
| 237
| 149
| 54.434599
| 0.841379
| 0.062786
| 0
| 0.342541
| 1
| 0
| 0.113149
| 0.056783
| 0
| 0
| 0
| 0
| 0
| 1
| 0.077348
| false
| 0.127072
| 0.022099
| 0
| 0.110497
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
92205dc85cc17e2876c9bd3f2bdb2d42fdf2d305
| 1,452
|
py
|
Python
|
rev/goper/solver.py
|
vidner/codepwnda-ctf
|
7e086044b753fe555b44395b79827d2f5b89da1d
|
[
"Unlicense"
] | 6
|
2021-02-18T15:07:55.000Z
|
2022-02-04T01:38:10.000Z
|
rev/goper/solver.py
|
vidner/codepwnda-ctf
|
7e086044b753fe555b44395b79827d2f5b89da1d
|
[
"Unlicense"
] | null | null | null |
rev/goper/solver.py
|
vidner/codepwnda-ctf
|
7e086044b753fe555b44395b79827d2f5b89da1d
|
[
"Unlicense"
] | null | null | null |
encoded = "472817216138472861341721172161414728472861401721172161214728472861261721613517214728472861411721472817216136172147286114472861311721472861241721472861221721612517214728472817216086172147286136608617214728172147286113613447281721172147286123472817216086172161264728612847281721172147286125472861291721472860861721472861331721472817216128172147286133472860861721472861391721172161284728612517214728608617214728614047281721472861351721613817214728172161344728172147286141172147286128472861351721472817216142472860861721172161224728613617214728472817216141472861331721172161124728608647281721472861421721472861341721172147286086613917214728172161284728613547281721172160864728612217214728172147286125472817216123172147286128472861211721612117214728614017214728472817216141608617214728613217214728613647281721472861301721614017214728608617214728472861281721472817216125472860861721472861261721172147286134172161234728612247281721472861401721172160864728172161344728172161234728608647281721172147286128172147286122472860861721472817216128172161254728611617214728"
lll=map(''.join, zip(*[iter(encoded)]*4))
print len(lll)//3
# print lll
a = {}
b=[]
for i in lll:
a[i]=0
for i in lll:
a[i]+=1
for i in a:
b.append(a[i])
wl=[]
c=max(b)
for i in a:
if a[i]!=c:
wl.append(i)
xf=[]
for i in lll:
for j in wl:
if i==j:
xf.append(j)
print xf[0]
key=int(xf[0])^99
flag=''
for i in xf:
flag += chr((int(i)^key)%256)
print flag
# print len(flag)
| 50.068966
| 1,068
| 0.880854
| 93
| 1,452
| 13.752688
| 0.333333
| 0.018765
| 0.028147
| 0.02111
| 0.017201
| 0.017201
| 0
| 0
| 0
| 0
| 0
| 0.776565
| 0.053719
| 1,452
| 29
| 1,069
| 50.068966
| 0.154294
| 0.017218
| 0
| 0.185185
| 0
| 0
| 0.741053
| 0.741053
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.111111
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
9251a374d459bf048ab623cebb4facfd949458ef
| 2,340
|
py
|
Python
|
pycrunch_trace/server/tests/test_chunks_ordering.py
|
yswtrue/pycrunch-trace
|
67395a8ee578387c878cd85365cec186ab811984
|
[
"MIT"
] | 90
|
2020-06-02T09:43:34.000Z
|
2022-03-18T12:04:11.000Z
|
pycrunch_trace/server/tests/test_chunks_ordering.py
|
yswtrue/pycrunch-trace
|
67395a8ee578387c878cd85365cec186ab811984
|
[
"MIT"
] | 8
|
2020-06-05T19:20:07.000Z
|
2022-03-17T10:14:30.000Z
|
pycrunch_trace/server/tests/test_chunks_ordering.py
|
yswtrue/pycrunch-trace
|
67395a8ee578387c878cd85365cec186ab811984
|
[
"MIT"
] | 8
|
2020-06-06T08:08:53.000Z
|
2022-03-19T16:38:18.000Z
|
import pytest
from pycrunch_trace.server.chunks_ordering import ChunksOrdering, PyCrunchTraceServerException
def test_add_chunk_works():
sut = ChunksOrdering()
sut.session_will_start(session_id='s1')
sut.did_receive_chunk(session_id='s1', chunk_number=1)
assert 1 in sut.order_by_session['s1'].received_chunks
def test_duplicate_chunk_raises():
with pytest.raises(PyCrunchTraceServerException):
sut = ChunksOrdering()
sut.session_will_start(session_id='s1')
sut.did_receive_chunk(session_id='s1', chunk_number=1)
sut.did_receive_chunk(session_id='s1', chunk_number=2)
sut.did_receive_chunk(session_id='s1', chunk_number=2)
def test_lost_chunk_raises():
with pytest.raises(PyCrunchTraceServerException):
sut = ChunksOrdering()
sut.session_will_start(session_id='s1')
sut.did_receive_chunk(session_id='s1', chunk_number=1)
sut.did_receive_chunk(session_id='s1', chunk_number=2)
# lost 6 frames
sut.did_receive_chunk(session_id='s1', chunk_number=8)
def test_first_chunk_lost():
with pytest.raises(PyCrunchTraceServerException):
sut = ChunksOrdering()
sut.session_will_start(session_id='s1')
sut.did_receive_chunk(session_id='s1', chunk_number=2)
def test_happy_case_no_errors():
sut = ChunksOrdering()
sut.session_will_start(session_id='s1')
sut.did_receive_chunk(session_id='s1', chunk_number=1)
sut.did_receive_chunk(session_id='s1', chunk_number=2)
sut.did_receive_chunk(session_id='s1', chunk_number=3)
def test_multiple_sessions():
sut = ChunksOrdering()
sut.session_will_start(session_id='s1')
sut.did_receive_chunk(session_id='s1', chunk_number=1)
sut.session_will_start(session_id='s2')
sut.did_receive_chunk(session_id='s2', chunk_number=1)
sut.did_receive_chunk(session_id='s2', chunk_number=2)
sut.session_will_start(session_id='s3')
sut.did_receive_chunk(session_id='s3', chunk_number=1)
sut.did_receive_chunk(session_id='s3', chunk_number=2)
def test_chunk_after_session_finished():
with pytest.raises(PyCrunchTraceServerException):
sut = ChunksOrdering()
sut.session_will_start('s1')
sut.did_receive_chunk('s1', 1)
sut.session_will_finish('s1')
sut.did_receive_chunk('s1', 1)
| 37.142857
| 94
| 0.734188
| 328
| 2,340
| 4.862805
| 0.155488
| 0.135423
| 0.124138
| 0.203135
| 0.810031
| 0.805016
| 0.771787
| 0.742947
| 0.742947
| 0.616301
| 0
| 0.024698
| 0.152137
| 2,340
| 62
| 95
| 37.741935
| 0.779234
| 0.005556
| 0
| 0.591837
| 0
| 0
| 0.024946
| 0
| 0
| 0
| 0
| 0
| 0.020408
| 1
| 0.142857
| false
| 0
| 0.040816
| 0
| 0.183673
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9290f70838238d7c9ac1f03be6b9d0ff7645be28
| 3,701
|
py
|
Python
|
tests/api_resources/identity/test_verification_session.py
|
tony/stripe-python
|
ac1b4cabf65adcf62de9a4c3409d66d1881c5574
|
[
"MIT"
] | 1,078
|
2015-01-06T03:35:05.000Z
|
2022-03-25T13:25:48.000Z
|
tests/api_resources/identity/test_verification_session.py
|
tony/stripe-python
|
ac1b4cabf65adcf62de9a4c3409d66d1881c5574
|
[
"MIT"
] | 558
|
2015-01-07T19:05:02.000Z
|
2022-03-28T22:19:24.000Z
|
tests/api_resources/identity/test_verification_session.py
|
tony/stripe-python
|
ac1b4cabf65adcf62de9a4c3409d66d1881c5574
|
[
"MIT"
] | 382
|
2015-01-04T14:06:09.000Z
|
2022-03-16T04:52:04.000Z
|
from __future__ import absolute_import, division, print_function
import stripe
TEST_RESOURCE_ID = "vs_123"
class TestVerificationSession(object):
def test_is_creatable(self, request_mock):
resource = stripe.identity.VerificationSession.create(type="id_number")
request_mock.assert_requested(
"post", "/v1/identity/verification_sessions"
)
assert isinstance(resource, stripe.identity.VerificationSession)
def test_is_listable(self, request_mock):
resources = stripe.identity.VerificationSession.list()
request_mock.assert_requested(
"get", "/v1/identity/verification_sessions"
)
assert isinstance(resources.data, list)
assert isinstance(
resources.data[0], stripe.identity.VerificationSession
)
def test_is_modifiable(self, request_mock):
resource = stripe.identity.VerificationSession.modify(
TEST_RESOURCE_ID, metadata={"key": "value"}
)
request_mock.assert_requested(
"post", "/v1/identity/verification_sessions/%s" % TEST_RESOURCE_ID
)
assert isinstance(resource, stripe.identity.VerificationSession)
def test_is_retrievable(self, request_mock):
resource = stripe.identity.VerificationSession.retrieve(
TEST_RESOURCE_ID
)
request_mock.assert_requested(
"get", "/v1/identity/verification_sessions/%s" % TEST_RESOURCE_ID
)
assert isinstance(resource, stripe.identity.VerificationSession)
def test_is_saveable(self, request_mock):
resource = stripe.identity.VerificationSession.retrieve(
TEST_RESOURCE_ID
)
resource.metadata["key"] = "value"
verification_session = resource.save()
request_mock.assert_requested(
"post", "/v1/identity/verification_sessions/%s" % TEST_RESOURCE_ID
)
assert isinstance(resource, stripe.identity.VerificationSession)
assert resource is verification_session
def test_can_cancel(self, request_mock):
resource = stripe.identity.VerificationSession.retrieve(
TEST_RESOURCE_ID
)
verification_session = resource.cancel()
request_mock.assert_requested(
"post",
"/v1/identity/verification_sessions/%s/cancel" % TEST_RESOURCE_ID,
)
assert isinstance(resource, stripe.identity.VerificationSession)
assert resource is verification_session
def test_can_cancel_classmethod(self, request_mock):
resource = stripe.identity.VerificationSession.cancel(TEST_RESOURCE_ID)
request_mock.assert_requested(
"post",
"/v1/identity/verification_sessions/%s/cancel" % TEST_RESOURCE_ID,
)
assert isinstance(resource, stripe.identity.VerificationSession)
def test_can_redact(self, request_mock):
resource = stripe.identity.VerificationSession.retrieve(
TEST_RESOURCE_ID
)
verification_session = resource.redact()
request_mock.assert_requested(
"post",
"/v1/identity/verification_sessions/%s/redact" % TEST_RESOURCE_ID,
)
assert isinstance(resource, stripe.identity.VerificationSession)
assert resource is verification_session
def test_can_redact_classmethod(self, request_mock):
resource = stripe.identity.VerificationSession.redact(TEST_RESOURCE_ID)
request_mock.assert_requested(
"post",
"/v1/identity/verification_sessions/%s/redact" % TEST_RESOURCE_ID,
)
assert isinstance(resource, stripe.identity.VerificationSession)
| 38.552083
| 79
| 0.683329
| 360
| 3,701
| 6.747222
| 0.152778
| 0.081515
| 0.244545
| 0.27007
| 0.824207
| 0.824207
| 0.800329
| 0.75422
| 0.699053
| 0.610951
| 0
| 0.004579
| 0.23291
| 3,701
| 95
| 80
| 38.957895
| 0.851004
| 0
| 0
| 0.463415
| 0
| 0
| 0.113483
| 0.09592
| 0
| 0
| 0
| 0
| 0.268293
| 1
| 0.109756
| false
| 0
| 0.02439
| 0
| 0.146341
| 0.012195
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 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
| 7
|
92a0fadcde8483b4ac0eebebd14c6f4e5ec805c9
| 28,719
|
py
|
Python
|
tests/loan_constants.py
|
wanderindev/financial-calculator-backend
|
ad7e736c858298c240eb9af52fbadcb02c693968
|
[
"MIT"
] | 2
|
2021-01-08T04:26:54.000Z
|
2022-02-04T22:22:27.000Z
|
tests/loan_constants.py
|
wanderindev/financial-calculator-backend
|
ad7e736c858298c240eb9af52fbadcb02c693968
|
[
"MIT"
] | null | null | null |
tests/loan_constants.py
|
wanderindev/financial-calculator-backend
|
ad7e736c858298c240eb9af52fbadcb02c693968
|
[
"MIT"
] | 2
|
2019-06-06T19:36:17.000Z
|
2020-05-20T12:37:08.000Z
|
PRESTAMOS_JSON_0 = {
"loan": 1000,
"num_of_years": 1,
"rate": 9,
"freq": 12,
"extra_pmt": 150,
"extra_pmt_start": 6,
"extra_pmt_f": 1,
"pmt_when": 0,
}
PRESTAMOS_JSON_1 = {
"loan": 1000,
"num_of_years": 1,
"rate": 9,
"freq": 12,
"extra_pmt": 150,
"extra_pmt_start": 6,
"extra_pmt_f": 1,
"pmt_when": 1,
}
# noinspection DuplicatedCode
PRESTAMOS_RESULT_0 = {
"a_interests": [
7.5,
14.4004,
20.6966,
26.3842,
31.4585,
35.915,
38.624,
40.6975,
42.1306,
42.9186,
43.0566,
],
"a_payments": [
87.4515,
174.903,
262.3545,
349.806,
437.2575,
674.709,
762.1605,
849.612,
937.0635,
1024.515,
1043.0566,
],
"balances": [
920.0485,
839.4974,
758.3421000000001,
676.5781999999999,
594.201,
361.20599999999996,
276.46349999999995,
191.0854999999999,
105.06709999999993,
18.403599999999905,
0,
],
"cash_saved": 6.361399999999776,
"nper": 12.0,
"nper_t": 11,
"num_of_years": 1.0,
"num_of_years_t": 0.9166666666666666,
"periods": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
"reg_pmt": 87.4515,
"table": [
{
"b": "920.05",
"p": "1",
"pc": "79.95",
"pe": "0.00",
"pi": "7.50",
"pr": "87.45",
},
{
"b": "839.50",
"p": "2",
"pc": "80.55",
"pe": "0.00",
"pi": "6.90",
"pr": "87.45",
},
{
"b": "758.34",
"p": "3",
"pc": "81.16",
"pe": "0.00",
"pi": "6.30",
"pr": "87.45",
},
{
"b": "676.58",
"p": "4",
"pc": "81.76",
"pe": "0.00",
"pi": "5.69",
"pr": "87.45",
},
{
"b": "594.20",
"p": "5",
"pc": "82.38",
"pe": "0.00",
"pi": "5.07",
"pr": "87.45",
},
{
"b": "361.21",
"p": "6",
"pc": "233.00",
"pe": "150.00",
"pi": "4.46",
"pr": "87.45",
},
{
"b": "276.46",
"p": "7",
"pc": "84.74",
"pe": "0.00",
"pi": "2.71",
"pr": "87.45",
},
{
"b": "191.09",
"p": "8",
"pc": "85.38",
"pe": "0.00",
"pi": "2.07",
"pr": "87.45",
},
{
"b": "105.07",
"p": "9",
"pc": "86.02",
"pe": "0.00",
"pi": "1.43",
"pr": "87.45",
},
{
"b": "18.40",
"p": "10",
"pc": "86.66",
"pe": "0.00",
"pi": "0.79",
"pr": "87.45",
},
{
"b": "0.00",
"p": "11",
"pc": "18.40",
"pe": "0.00",
"pi": "0.14",
"pr": "18.54",
},
],
"table_a": [],
"table_m": [
{
"b": "920.05",
"p": "1",
"pc": "79.95",
"pe": "0.00",
"pi": "7.50",
"pr": "87.45",
},
{
"b": "839.50",
"p": "2",
"pc": "80.55",
"pe": "0.00",
"pi": "6.90",
"pr": "87.45",
},
{
"b": "758.34",
"p": "3",
"pc": "81.16",
"pe": "0.00",
"pi": "6.30",
"pr": "87.45",
},
{
"b": "676.58",
"p": "4",
"pc": "81.76",
"pe": "0.00",
"pi": "5.69",
"pr": "87.45",
},
{
"b": "594.20",
"p": "5",
"pc": "82.38",
"pe": "0.00",
"pi": "5.07",
"pr": "87.45",
},
{
"b": "361.21",
"p": "6",
"pc": "233.00",
"pe": "150.00",
"pi": "4.46",
"pr": "87.45",
},
{
"b": "276.46",
"p": "7",
"pc": "84.74",
"pe": "0.00",
"pi": "2.71",
"pr": "87.45",
},
{
"b": "191.09",
"p": "8",
"pc": "85.38",
"pe": "0.00",
"pi": "2.07",
"pr": "87.45",
},
{
"b": "105.07",
"p": "9",
"pc": "86.02",
"pe": "0.00",
"pi": "1.43",
"pr": "87.45",
},
{
"b": "18.40",
"p": "10",
"pc": "86.66",
"pe": "0.00",
"pi": "0.79",
"pr": "87.45",
},
{
"b": "0.00",
"p": "11",
"pc": "18.40",
"pe": "0.00",
"pi": "0.14",
"pr": "18.54",
},
],
"time_scale": "Mes",
"total_int": 49.41799999999989,
"total_int_t": 43.0566,
"total_pmt": 1049.418,
"total_pmt_t": 1043.0566000000001,
"total_prin": 1000.0,
"total_prin_t": 1000.0,
"years_saved": 0.08333333333333337,
}
# noinspection DuplicatedCode
PRESTAMOS_RESULT_1 = {
"a_interests": [
6.849,
13.0984,
18.7436,
23.7802,
28.2035,
30.884,
32.9336,
34.3476,
35.1212,
35.2496,
35.2496,
],
"a_payments": [
86.8005,
173.601,
260.4015,
347.202,
434.0025,
670.803,
757.6035,
844.404,
931.2045,
1018.005,
1035.2496,
],
"balances": [
920.0485,
839.4974,
758.3421000000001,
676.5782,
594.2009999999999,
360.081,
275.3301000000001,
189.9436,
103.91669999999995,
17.244599999999892,
0,
],
"cash_saved": 6.3564000000001215,
"nper": 12.0,
"nper_t": 11,
"num_of_years": 1.0,
"num_of_years_t": 0.9166666666666666,
"periods": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
"reg_pmt": 86.8005,
"table": [
{
"b": "920.05",
"p": "1",
"pc": "79.95",
"pe": "0.00",
"pi": "6.85",
"pr": "86.80",
},
{
"b": "839.50",
"p": "2",
"pc": "80.55",
"pe": "0.00",
"pi": "6.25",
"pr": "86.80",
},
{
"b": "758.34",
"p": "3",
"pc": "81.16",
"pe": "0.00",
"pi": "5.65",
"pr": "86.80",
},
{
"b": "676.58",
"p": "4",
"pc": "81.76",
"pe": "0.00",
"pi": "5.04",
"pr": "86.80",
},
{
"b": "594.20",
"p": "5",
"pc": "82.38",
"pe": "0.00",
"pi": "4.42",
"pr": "86.80",
},
{
"b": "360.08",
"p": "6",
"pc": "234.12",
"pe": "150.00",
"pi": "2.68",
"pr": "86.80",
},
{
"b": "275.33",
"p": "7",
"pc": "84.75",
"pe": "0.00",
"pi": "2.05",
"pr": "86.80",
},
{
"b": "189.94",
"p": "8",
"pc": "85.39",
"pe": "0.00",
"pi": "1.41",
"pr": "86.80",
},
{
"b": "103.92",
"p": "9",
"pc": "86.03",
"pe": "0.00",
"pi": "0.77",
"pr": "86.80",
},
{
"b": "17.24",
"p": "10",
"pc": "86.67",
"pe": "0.00",
"pi": "0.13",
"pr": "86.80",
},
{
"b": "0.00",
"p": "11",
"pc": "17.24",
"pe": "0.00",
"pi": "0.00",
"pr": "17.24",
},
],
"table_a": [],
"table_m": [
{
"b": "920.05",
"p": "1",
"pc": "79.95",
"pe": "0.00",
"pi": "6.85",
"pr": "86.80",
},
{
"b": "839.50",
"p": "2",
"pc": "80.55",
"pe": "0.00",
"pi": "6.25",
"pr": "86.80",
},
{
"b": "758.34",
"p": "3",
"pc": "81.16",
"pe": "0.00",
"pi": "5.65",
"pr": "86.80",
},
{
"b": "676.58",
"p": "4",
"pc": "81.76",
"pe": "0.00",
"pi": "5.04",
"pr": "86.80",
},
{
"b": "594.20",
"p": "5",
"pc": "82.38",
"pe": "0.00",
"pi": "4.42",
"pr": "86.80",
},
{
"b": "360.08",
"p": "6",
"pc": "234.12",
"pe": "150.00",
"pi": "2.68",
"pr": "86.80",
},
{
"b": "275.33",
"p": "7",
"pc": "84.75",
"pe": "0.00",
"pi": "2.05",
"pr": "86.80",
},
{
"b": "189.94",
"p": "8",
"pc": "85.39",
"pe": "0.00",
"pi": "1.41",
"pr": "86.80",
},
{
"b": "103.92",
"p": "9",
"pc": "86.03",
"pe": "0.00",
"pi": "0.77",
"pr": "86.80",
},
{
"b": "17.24",
"p": "10",
"pc": "86.67",
"pe": "0.00",
"pi": "0.13",
"pr": "86.80",
},
{
"b": "0.00",
"p": "11",
"pc": "17.24",
"pe": "0.00",
"pi": "0.00",
"pr": "17.24",
},
],
"time_scale": "Mes",
"total_int": 41.605999999999995,
"total_int_t": 35.2496,
"total_pmt": 1041.606,
"total_pmt_t": 1035.2495999999999,
"total_prin": 1000.0,
"total_prin_t": 1000.0,
"years_saved": 0.08333333333333337,
}
INTERES_JSON_0 = {
"loan": 25000,
"num_of_years": 8,
"reg_pmt": 400,
"freq": 12,
"pmt_when": 0,
}
INTERES_JSON_1 = {
"loan": 25000,
"num_of_years": 8,
"reg_pmt": 400,
"freq": 12,
"pmt_when": 1,
}
INTERES_RESULT_0 = {"rate": 11.537873104401955}
INTERES_RESULT_1 = {"rate": 11.826542516996529}
TIEMPO_PAGAR_JSON_0 = {
"loan": 5000,
"rate": 7,
"reg_pmt": 301,
"freq": 12,
"pmt_when": 0,
}
TIEMPO_PAGAR_JSON_1 = {
"loan": 5000,
"rate": 7,
"reg_pmt": 301,
"freq": 12,
"pmt_when": 1,
}
# noinspection DuplicatedCode
TIEMPO_PAGAR_RESULT_0 = {
"a_interests": [
29.1667,
56.7477,
82.7337,
107.1155,
129.8837,
151.0289,
170.5416,
188.4123,
204.6314,
219.1892,
232.0761,
243.2824,
252.7982,
260.6137,
266.7189,
271.1039,
273.7587,
274.6731,
],
"a_payments": [
301.0,
602.0,
903.0,
1204.0,
1505.0,
1806.0,
2107.0,
2408.0,
2709.0,
3010.0,
3311.0,
3612.0,
3913.0,
4214.0,
4515.0,
4816.0,
5117.0,
5274.6731,
],
"balances": [
4728.1667,
4454.7477,
4179.7337,
3903.1155,
3624.8837,
3345.0289,
3063.5416,
2780.4123,
2495.6314,
2209.1892,
1921.0761,
1631.2824,
1339.7982,
1046.6136999999999,
751.7189000000001,
455.1039,
156.75870000000003,
0,
],
"nper": 18,
"num_of_years": 1.5,
"periods": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
"table": [
{
"b": "4,728.17",
"p": "1",
"pc": "271.83",
"pe": "0.00",
"pi": "29.17",
"pr": "301.00",
},
{
"b": "4,454.75",
"p": "2",
"pc": "273.42",
"pe": "0.00",
"pi": "27.58",
"pr": "301.00",
},
{
"b": "4,179.73",
"p": "3",
"pc": "275.01",
"pe": "0.00",
"pi": "25.99",
"pr": "301.00",
},
{
"b": "3,903.12",
"p": "4",
"pc": "276.62",
"pe": "0.00",
"pi": "24.38",
"pr": "301.00",
},
{
"b": "3,624.88",
"p": "5",
"pc": "278.23",
"pe": "0.00",
"pi": "22.77",
"pr": "301.00",
},
{
"b": "3,345.03",
"p": "6",
"pc": "279.85",
"pe": "0.00",
"pi": "21.15",
"pr": "301.00",
},
{
"b": "3,063.54",
"p": "7",
"pc": "281.49",
"pe": "0.00",
"pi": "19.51",
"pr": "301.00",
},
{
"b": "2,780.41",
"p": "8",
"pc": "283.13",
"pe": "0.00",
"pi": "17.87",
"pr": "301.00",
},
{
"b": "2,495.63",
"p": "9",
"pc": "284.78",
"pe": "0.00",
"pi": "16.22",
"pr": "301.00",
},
{
"b": "2,209.19",
"p": "10",
"pc": "286.44",
"pe": "0.00",
"pi": "14.56",
"pr": "301.00",
},
{
"b": "1,921.08",
"p": "11",
"pc": "288.11",
"pe": "0.00",
"pi": "12.89",
"pr": "301.00",
},
{
"b": "1,631.28",
"p": "12",
"pc": "289.79",
"pe": "0.00",
"pi": "11.21",
"pr": "301.00",
},
{
"b": "1,339.80",
"p": "13",
"pc": "291.48",
"pe": "0.00",
"pi": "9.52",
"pr": "301.00",
},
{
"b": "1,046.61",
"p": "14",
"pc": "293.18",
"pe": "0.00",
"pi": "7.82",
"pr": "301.00",
},
{
"b": "751.72",
"p": "15",
"pc": "294.89",
"pe": "0.00",
"pi": "6.11",
"pr": "301.00",
},
{
"b": "455.10",
"p": "16",
"pc": "296.62",
"pe": "0.00",
"pi": "4.38",
"pr": "301.00",
},
{
"b": "156.76",
"p": "17",
"pc": "298.35",
"pe": "0.00",
"pi": "2.65",
"pr": "301.00",
},
{
"b": "0.00",
"p": "18",
"pc": "156.76",
"pe": "0.00",
"pi": "0.91",
"pr": "157.67",
},
],
"table_a": [
{
"b": "1,631.28",
"p": "1",
"pc": "3,368.72",
"pe": "0.00",
"pi": "243.28",
"pr": "3,612.00",
}
],
"table_m": [
{
"b": "4,728.17",
"p": "1",
"pc": "271.83",
"pe": "0.00",
"pi": "29.17",
"pr": "301.00",
},
{
"b": "4,454.75",
"p": "2",
"pc": "273.42",
"pe": "0.00",
"pi": "27.58",
"pr": "301.00",
},
{
"b": "4,179.73",
"p": "3",
"pc": "275.01",
"pe": "0.00",
"pi": "25.99",
"pr": "301.00",
},
{
"b": "3,903.12",
"p": "4",
"pc": "276.62",
"pe": "0.00",
"pi": "24.38",
"pr": "301.00",
},
{
"b": "3,624.88",
"p": "5",
"pc": "278.23",
"pe": "0.00",
"pi": "22.77",
"pr": "301.00",
},
{
"b": "3,345.03",
"p": "6",
"pc": "279.85",
"pe": "0.00",
"pi": "21.15",
"pr": "301.00",
},
{
"b": "3,063.54",
"p": "7",
"pc": "281.49",
"pe": "0.00",
"pi": "19.51",
"pr": "301.00",
},
{
"b": "2,780.41",
"p": "8",
"pc": "283.13",
"pe": "0.00",
"pi": "17.87",
"pr": "301.00",
},
{
"b": "2,495.63",
"p": "9",
"pc": "284.78",
"pe": "0.00",
"pi": "16.22",
"pr": "301.00",
},
{
"b": "2,209.19",
"p": "10",
"pc": "286.44",
"pe": "0.00",
"pi": "14.56",
"pr": "301.00",
},
{
"b": "1,921.08",
"p": "11",
"pc": "288.11",
"pe": "0.00",
"pi": "12.89",
"pr": "301.00",
},
{
"b": "1,631.28",
"p": "12",
"pc": "289.79",
"pe": "0.00",
"pi": "11.21",
"pr": "301.00",
},
{
"b": "1,339.80",
"p": "13",
"pc": "291.48",
"pe": "0.00",
"pi": "9.52",
"pr": "301.00",
},
{
"b": "1,046.61",
"p": "14",
"pc": "293.18",
"pe": "0.00",
"pi": "7.82",
"pr": "301.00",
},
{
"b": "751.72",
"p": "15",
"pc": "294.89",
"pe": "0.00",
"pi": "6.11",
"pr": "301.00",
},
{
"b": "455.10",
"p": "16",
"pc": "296.62",
"pe": "0.00",
"pi": "4.38",
"pr": "301.00",
},
{
"b": "156.76",
"p": "17",
"pc": "298.35",
"pe": "0.00",
"pi": "2.65",
"pr": "301.00",
},
{
"b": "0.00",
"p": "18",
"pc": "156.76",
"pe": "0.00",
"pi": "0.91",
"pr": "157.67",
},
],
"time_scale": "Mes",
"total_int": 274.67310000000003,
"total_pmt": 5274.6731,
"total_prin": 5000.0,
}
# noinspection DuplicatedCode
TIEMPO_PAGAR_RESULT_1 = {
"a_interests": [
27.4108,
53.2257,
77.4353,
100.0303,
121.0013,
140.3388,
158.0333,
174.0752,
188.4548,
201.1625,
212.1884,
221.5228,
229.1558,
235.0775,
239.278,
241.7471,
242.4748,
242.4748,
],
"a_payments": [
301.0,
602.0,
903.0,
1204.0,
1505.0,
1806.0,
2107.0,
2408.0,
2709.0,
3010.0,
3311.0,
3612.0,
3913.0,
4214.0,
4515.0,
4816.0,
5117.0,
5242.4748,
],
"balances": [
4726.4108,
4451.2257,
4174.4353,
3896.0303,
3616.0013,
3334.3388,
3051.0333,
2766.0752,
2479.4548,
2191.1625,
1901.1884,
1609.5228,
1316.1558,
1021.0775,
724.278,
425.7471,
125.47479999999999,
0,
],
"nper": 18,
"num_of_years": 1.5,
"periods": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
"table": [
{
"b": "4,726.41",
"p": "1",
"pc": "273.59",
"pe": "0.00",
"pi": "27.41",
"pr": "301.00",
},
{
"b": "4,451.23",
"p": "2",
"pc": "275.19",
"pe": "0.00",
"pi": "25.81",
"pr": "301.00",
},
{
"b": "4,174.44",
"p": "3",
"pc": "276.79",
"pe": "0.00",
"pi": "24.21",
"pr": "301.00",
},
{
"b": "3,896.03",
"p": "4",
"pc": "278.40",
"pe": "0.00",
"pi": "22.59",
"pr": "301.00",
},
{
"b": "3,616.00",
"p": "5",
"pc": "280.03",
"pe": "0.00",
"pi": "20.97",
"pr": "301.00",
},
{
"b": "3,334.34",
"p": "6",
"pc": "281.66",
"pe": "0.00",
"pi": "19.34",
"pr": "301.00",
},
{
"b": "3,051.03",
"p": "7",
"pc": "283.31",
"pe": "0.00",
"pi": "17.69",
"pr": "301.00",
},
{
"b": "2,766.08",
"p": "8",
"pc": "284.96",
"pe": "0.00",
"pi": "16.04",
"pr": "301.00",
},
{
"b": "2,479.45",
"p": "9",
"pc": "286.62",
"pe": "0.00",
"pi": "14.38",
"pr": "301.00",
},
{
"b": "2,191.16",
"p": "10",
"pc": "288.29",
"pe": "0.00",
"pi": "12.71",
"pr": "301.00",
},
{
"b": "1,901.19",
"p": "11",
"pc": "289.97",
"pe": "0.00",
"pi": "11.03",
"pr": "301.00",
},
{
"b": "1,609.52",
"p": "12",
"pc": "291.67",
"pe": "0.00",
"pi": "9.33",
"pr": "301.00",
},
{
"b": "1,316.16",
"p": "13",
"pc": "293.37",
"pe": "0.00",
"pi": "7.63",
"pr": "301.00",
},
{
"b": "1,021.08",
"p": "14",
"pc": "295.08",
"pe": "0.00",
"pi": "5.92",
"pr": "301.00",
},
{
"b": "724.28",
"p": "15",
"pc": "296.80",
"pe": "0.00",
"pi": "4.20",
"pr": "301.00",
},
{
"b": "425.75",
"p": "16",
"pc": "298.53",
"pe": "0.00",
"pi": "2.47",
"pr": "301.00",
},
{
"b": "125.47",
"p": "17",
"pc": "300.27",
"pe": "0.00",
"pi": "0.73",
"pr": "301.00",
},
{
"b": "0.00",
"p": "18",
"pc": "125.47",
"pe": "0.00",
"pi": "0.00",
"pr": "125.47",
},
],
"table_a": [
{
"b": "1,609.52",
"p": "1",
"pc": "3,390.48",
"pe": "0.00",
"pi": "221.52",
"pr": "3,612.00",
}
],
"table_m": [
{
"b": "4,726.41",
"p": "1",
"pc": "273.59",
"pe": "0.00",
"pi": "27.41",
"pr": "301.00",
},
{
"b": "4,451.23",
"p": "2",
"pc": "275.19",
"pe": "0.00",
"pi": "25.81",
"pr": "301.00",
},
{
"b": "4,174.44",
"p": "3",
"pc": "276.79",
"pe": "0.00",
"pi": "24.21",
"pr": "301.00",
},
{
"b": "3,896.03",
"p": "4",
"pc": "278.40",
"pe": "0.00",
"pi": "22.59",
"pr": "301.00",
},
{
"b": "3,616.00",
"p": "5",
"pc": "280.03",
"pe": "0.00",
"pi": "20.97",
"pr": "301.00",
},
{
"b": "3,334.34",
"p": "6",
"pc": "281.66",
"pe": "0.00",
"pi": "19.34",
"pr": "301.00",
},
{
"b": "3,051.03",
"p": "7",
"pc": "283.31",
"pe": "0.00",
"pi": "17.69",
"pr": "301.00",
},
{
"b": "2,766.08",
"p": "8",
"pc": "284.96",
"pe": "0.00",
"pi": "16.04",
"pr": "301.00",
},
{
"b": "2,479.45",
"p": "9",
"pc": "286.62",
"pe": "0.00",
"pi": "14.38",
"pr": "301.00",
},
{
"b": "2,191.16",
"p": "10",
"pc": "288.29",
"pe": "0.00",
"pi": "12.71",
"pr": "301.00",
},
{
"b": "1,901.19",
"p": "11",
"pc": "289.97",
"pe": "0.00",
"pi": "11.03",
"pr": "301.00",
},
{
"b": "1,609.52",
"p": "12",
"pc": "291.67",
"pe": "0.00",
"pi": "9.33",
"pr": "301.00",
},
{
"b": "1,316.16",
"p": "13",
"pc": "293.37",
"pe": "0.00",
"pi": "7.63",
"pr": "301.00",
},
{
"b": "1,021.08",
"p": "14",
"pc": "295.08",
"pe": "0.00",
"pi": "5.92",
"pr": "301.00",
},
{
"b": "724.28",
"p": "15",
"pc": "296.80",
"pe": "0.00",
"pi": "4.20",
"pr": "301.00",
},
{
"b": "425.75",
"p": "16",
"pc": "298.53",
"pe": "0.00",
"pi": "2.47",
"pr": "301.00",
},
{
"b": "125.47",
"p": "17",
"pc": "300.27",
"pe": "0.00",
"pi": "0.73",
"pr": "301.00",
},
{
"b": "0.00",
"p": "18",
"pc": "125.47",
"pe": "0.00",
"pi": "0.00",
"pr": "125.47",
},
],
"time_scale": "Mes",
"total_int": 242.4748,
"total_pmt": 5242.4748,
"total_prin": 5000.0,
}
| 22.401716
| 80
| 0.235628
| 2,830
| 28,719
| 2.348057
| 0.145936
| 0.056885
| 0.085779
| 0.12009
| 0.752144
| 0.718736
| 0.718736
| 0.718736
| 0.702182
| 0.702182
| 0
| 0.339755
| 0.548139
| 28,719
| 1,281
| 81
| 22.419204
| 0.172305
| 0.003865
| 0
| 0.680885
| 0
| 0
| 0.196399
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a632dc96ca4f6c074347b0a68560f3cc62ba0100
| 72
|
py
|
Python
|
lib/base.py
|
lakshmi-kannan/lib_test
|
7d736d6ee165c552d33a964e01cbd3578aab736b
|
[
"Apache-2.0"
] | null | null | null |
lib/base.py
|
lakshmi-kannan/lib_test
|
7d736d6ee165c552d33a964e01cbd3578aab736b
|
[
"Apache-2.0"
] | null | null | null |
lib/base.py
|
lakshmi-kannan/lib_test
|
7d736d6ee165c552d33a964e01cbd3578aab736b
|
[
"Apache-2.0"
] | null | null | null |
import os
def get_environ(env_key):
return os.environ.get(env_key)
| 14.4
| 34
| 0.75
| 13
| 72
| 3.923077
| 0.615385
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.152778
| 72
| 4
| 35
| 18
| 0.836066
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
a658adaf703535f038cbe0d93bfc3af8e381e49f
| 17,051
|
py
|
Python
|
SBaaS_resequencing/stage01_resequencing_count_execute.py
|
dmccloskey/SBaaS_resequencing
|
760049eec734a3f02a1172af68b20198713b785e
|
[
"MIT"
] | null | null | null |
SBaaS_resequencing/stage01_resequencing_count_execute.py
|
dmccloskey/SBaaS_resequencing
|
760049eec734a3f02a1172af68b20198713b785e
|
[
"MIT"
] | null | null | null |
SBaaS_resequencing/stage01_resequencing_count_execute.py
|
dmccloskey/SBaaS_resequencing
|
760049eec734a3f02a1172af68b20198713b785e
|
[
"MIT"
] | null | null | null |
#sbaas
from .stage01_resequencing_count_io import stage01_resequencing_count_io
from .stage01_resequencing_count_dependencies import stage01_resequencing_count_dependencies
from .stage01_resequencing_mutations_query import stage01_resequencing_mutations_query
from .stage01_resequencing_analysis_query import stage01_resequencing_analysis_query
from .stage01_resequencing_mutations_execute import stage01_resequencing_mutations_execute
#sbaas models
from .stage01_resequencing_count_postgresql_models import *
#resources
from sequencing_analysis.genome_annotations import genome_annotations
from python_statistics.calculate_count import calculate_count
class stage01_resequencing_count_execute(
stage01_resequencing_count_io,
stage01_resequencing_count_dependencies,
):
def execute_countElementsInFeatures(self,analysis_id_I,features_I=[]):
'''count unique features of discrete or categorical data
INPUT:
analysis_id_I = string,
features_I = [] of string, e.g. ['mutation_class','mutation_position','mutation_type','mutation_genes','mutation_locations','mutation_id']
OUTPUT:
'''
data_O = [];
calculatecount = calculate_count();
resequencing_analysis_query = stage01_resequencing_analysis_query(self.session,self.engine,self.settings);
mut01 = stage01_resequencing_mutations_execute(self.session,self.engine,self.settings)
#get the analysis information
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = resequencing_analysis_query.get_experimentIDAndSampleName_analysisID_dataStage01ResequencingAnalysis(analysis_id_I);
#bin each feature
for features_cnt,features in enumerate(features_I):
if features == 'mutation_class':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_allMutationClasses_experimentIDAndSampleName_dataStage01ResequencingMutationsSeqChanges(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_position':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_AllMutationPositions_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_chromosome':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_AllMutationChromosomes_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_chromosomeAndPosition':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_AllMutationChromosomesAndPositions_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_type':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_AllMutationTypes_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_genes':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_AllMutationGenes_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_locations':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_AllMutationLocations_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_id':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = mut01.get_AllMutationIDs_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
data_count.extend(data_tmp);
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_count);
data_O.extend(self.record_count(analysis_id_I,features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'parent_classes':
data_count = [];
experiment_ids_str = ','.join(list(set(experiment_ids)))
sample_names_str = ','.join(list(set(sample_names)))
data_count = mut01.calculate_distributionOfMutationsInBioCycParentClasses(
experiment_ids_I=experiment_ids_str,
sample_names_I = sample_names_str,
parent_classes_I=[],
database_I='ECOLI',
names_I=[],
unique_I=True)
for d in data_count: d['analysis_id']=analysis_id_I;
data_O.extend(data_count);
else:
print('feature not recongnized');
#add the data to the database
self.add_dataStage01ResequencingCount(data_O);
def execute_countElementsInFeaturesPerSample(self,analysis_id_I,features_I=[]):
'''count unique features of discrete or categorical data per sample
INPUT:
analysis_id_I = string,
features_I = [] of string, e.g. ['mutation_class','mutation_position','mutation_type','mutation_genes','mutation_locations','mutation_id']
OUTPUT:
'''
data_O = [];
calculatecount = calculate_count();
resequencing_mutations_query = stage01_resequencing_mutations_query(self.session,self.engine,self.settings)
resequencing_analysis_query = stage01_resequencing_analysis_query(self.session,self.engine,self.settings);
# get the analysis information
experiment_ids = []
lineage_names = []
sample_names = []
time_points = []
experiment_ids,lineage_names,sample_names,time_points = resequencing_analysis_query.get_experimentIDAndLineageNameAndSampleNameAndTimePoint_analysisID_dataStage01ResequencingAnalysis(analysis_id_I);
#order by time_points and convert to intermediates
intermediates,time_points,experiment_ids,sample_names,lineage_names = self.convert_timePoints2Intermediates(time_points,experiment_ids,sample_names,lineage_names)
#bin each feature
for features_cnt,features in enumerate(features_I):
if features == 'mutation_class':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_allMutationClasses_experimentIDAndSampleName_dataStage01ResequencingMutationsSeqChanges(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_position':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_AllMutationPositions_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_chromosome':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_AllMutationChromosomes_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_chromosomeAndPosition':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_AllMutationChromosomesAndPositions_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_type':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_AllMutationTypes_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_genes':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_AllMutationGenes_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_locations':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_AllMutationLocations_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
elif features == 'mutation_id':
data_count = [];
#get all the data for the analysis
for sn_cnt,sn in enumerate(sample_names):
data_tmp = [];
data_tmp = resequencing_mutations_query.get_AllMutationIDs_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sn_cnt],sn);
if data_tmp:
#count the elements of each feature
elements_unqiue,elements_count,elements_count_fraction = calculatecount.count_elements(data_tmp);
data_O.extend(self.record_countPerSample(analysis_id_I,experiment_ids[sn_cnt],lineage_names[sn_cnt],sn,time_points[sn_cnt],features,'',elements_unqiue,elements_count,elements_count_fraction));
else:
print('feature not recongnized');
#add the data to the database
self.add_dataStage01ResequencingCountPerSample(data_O);
| 69.032389
| 218
| 0.663187
| 1,763
| 17,051
| 6.036869
| 0.0692
| 0.042093
| 0.026308
| 0.08118
| 0.860472
| 0.8507
| 0.8507
| 0.84713
| 0.831063
| 0.831063
| 0
| 0.007759
| 0.266847
| 17,051
| 247
| 219
| 69.032389
| 0.843533
| 0.075773
| 0
| 0.71123
| 0
| 0
| 0.023104
| 0.003961
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.042781
| null | null | 0.010695
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a6dc7f0cedf6e3249d3aa45b8c9eb9883d74be01
| 160
|
py
|
Python
|
flash/image/classification/__init__.py
|
Site-Command/lightning-flash
|
bfff08ded9cf193cce1cd16e7034d8005de172ae
|
[
"Apache-2.0"
] | 1
|
2021-06-01T09:59:03.000Z
|
2021-06-01T09:59:03.000Z
|
flash/image/classification/__init__.py
|
Site-Command/lightning-flash
|
bfff08ded9cf193cce1cd16e7034d8005de172ae
|
[
"Apache-2.0"
] | null | null | null |
flash/image/classification/__init__.py
|
Site-Command/lightning-flash
|
bfff08ded9cf193cce1cd16e7034d8005de172ae
|
[
"Apache-2.0"
] | null | null | null |
from flash.image.classification.data import ImageClassificationData, ImageClassificationPreprocess
from flash.image.classification.model import ImageClassifier
| 53.333333
| 98
| 0.9
| 15
| 160
| 9.6
| 0.666667
| 0.125
| 0.194444
| 0.388889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.05625
| 160
| 2
| 99
| 80
| 0.953642
| 0
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| null | 0
| 1
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| 0
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| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
472a89a5f63ac5eb2c911304b276a40705c590b0
| 200
|
py
|
Python
|
flitton_fib_py/fib_calcs/fib_numbers.py
|
harupy/flitton-fib-py
|
07c9d3f558833211c6fc625faff522f4f9d30601
|
[
"MIT"
] | null | null | null |
flitton_fib_py/fib_calcs/fib_numbers.py
|
harupy/flitton-fib-py
|
07c9d3f558833211c6fc625faff522f4f9d30601
|
[
"MIT"
] | null | null | null |
flitton_fib_py/fib_calcs/fib_numbers.py
|
harupy/flitton-fib-py
|
07c9d3f558833211c6fc625faff522f4f9d30601
|
[
"MIT"
] | null | null | null |
from typing import List
from .fib_number import recurring_fibonacci_number
def calculate_numbers(numbers: List[int]) -> List[int]:
return [recurring_fibonacci_number(number=i) for i in numbers]
| 28.571429
| 66
| 0.795
| 29
| 200
| 5.275862
| 0.551724
| 0.235294
| 0.313725
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 200
| 6
| 67
| 33.333333
| 0.874286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0.25
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
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| 0
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| 0
| 0
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| 0
| 0
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| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 8
|
5b8482bc5621f50ffc3564feadfcd5a403d27153
| 1,318
|
py
|
Python
|
src/audio_management.py
|
Irsutoro/Pytler
|
87db346769a3f2fb54200d9ff2a18b7c99855a6c
|
[
"MIT"
] | null | null | null |
src/audio_management.py
|
Irsutoro/Pytler
|
87db346769a3f2fb54200d9ff2a18b7c99855a6c
|
[
"MIT"
] | null | null | null |
src/audio_management.py
|
Irsutoro/Pytler
|
87db346769a3f2fb54200d9ff2a18b7c99855a6c
|
[
"MIT"
] | null | null | null |
import pyaudio
class AudioRecorder:
def __init__(self, channels_=2, format_=pyaudio.paInt16, rate_=44100, chunk_=256):
self.audio = pyaudio.PyAudio()
self.stream = self.audio.open(format=format_, channels=channels_,
rate=rate_, input=True, frames_per_buffer=chunk_)
self.channels = channels_
self.format = format_
self.rate = rate_
self.chunk = chunk_
def record_chunk(self):
return self.stream.read(self.chunk)
def __enter__(self):
return self
def __exit__(self, *arg):
self.stream.stop_stream()
self.stream.close()
self.audio.terminate()
class AudioPlayer:
def __init__(self, channels_=2, format_=pyaudio.paInt16, rate_=44100, chunk_=256):
self.audio = pyaudio.PyAudio()
self.stream = self.audio.open(format=format_, channels=channels_,
rate=rate_, output=True)
self.channels = channels_
self.format = format_
self.rate = rate_
self.chunk = chunk_
def play_chunk(self, chunk):
self.stream.write(chunk)
def __enter__(self):
return self
def __exit__(self, *arg):
self.stream.stop_stream()
self.stream.close()
self.audio.terminate()
| 29.288889
| 86
| 0.612291
| 149
| 1,318
| 5.060403
| 0.241611
| 0.106101
| 0.055703
| 0.050398
| 0.790451
| 0.790451
| 0.790451
| 0.790451
| 0.790451
| 0.790451
| 0
| 0.02333
| 0.284522
| 1,318
| 45
| 87
| 29.288889
| 0.776246
| 0
| 0
| 0.742857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.228571
| false
| 0
| 0.028571
| 0.085714
| 0.4
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
5bca36065599cb5dd725c29b7d591299439533fc
| 237
|
py
|
Python
|
dashboard/admin.py
|
devqueue/assaydashboard
|
550a5392abb366d7b0cefbe9ce4141538a8ff801
|
[
"MIT"
] | null | null | null |
dashboard/admin.py
|
devqueue/assaydashboard
|
550a5392abb366d7b0cefbe9ce4141538a8ff801
|
[
"MIT"
] | null | null | null |
dashboard/admin.py
|
devqueue/assaydashboard
|
550a5392abb366d7b0cefbe9ce4141538a8ff801
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Samples, Revenue, Utilization, stats, monthlystats, MissedRevenue
# Register your models here.
admin.site.register([Samples, Revenue, Utilization, stats, monthlystats, MissedRevenue])
| 47.4
| 88
| 0.814346
| 27
| 237
| 7.148148
| 0.592593
| 0.145078
| 0.259067
| 0.310881
| 0.569948
| 0.569948
| 0
| 0
| 0
| 0
| 0
| 0
| 0.101266
| 237
| 5
| 88
| 47.4
| 0.906103
| 0.109705
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
5bca92cc6c98025aada248ca42b117cbb9853282
| 3,172
|
py
|
Python
|
tests/test_aberth.py
|
luk036/bairstow
|
706cd2592df1a4336f52764f2db4e7b706e3c9e6
|
[
"MIT"
] | null | null | null |
tests/test_aberth.py
|
luk036/bairstow
|
706cd2592df1a4336f52764f2db4e7b706e3c9e6
|
[
"MIT"
] | null | null | null |
tests/test_aberth.py
|
luk036/bairstow
|
706cd2592df1a4336f52764f2db4e7b706e3c9e6
|
[
"MIT"
] | null | null | null |
from bairstow.aberth import aberth, initial_aberth
from bairstow.aberth import aberth_autocorr, initial_aberth_autocorr
from bairstow.rootfinding import Options
def test_aberth1():
h = [5.0, 2.0, 9.0, 6.0, 2.0]
z0s = initial_aberth(h)
_, niter, found = aberth(h, z0s)
print([niter, found])
assert niter <= 8
def test_aberth2():
h = [10.0, 34.0, 75.0, 94.0, 150.0, 94.0, 75.0, 34.0, 10.0]
z0s = initial_aberth(h)
zs, niter, found = aberth(h, z0s)
print([niter, found])
print([z for z in zs])
assert niter <= 8
r = [
-0.00196191,
-0.00094597,
-0.00023823,
0.00134667,
0.00380494,
0.00681596,
0.0097864,
0.01186197,
0.0121238,
0.00985211,
0.00474894,
-0.00281751,
-0.01173923,
-0.0201885,
-0.02590168,
-0.02658216,
-0.02035729,
-0.00628271,
0.01534627,
0.04279982,
0.0732094,
0.10275561,
0.12753013,
0.14399228,
0.15265722,
0.14399228,
0.12753013,
0.10275561,
0.0732094,
0.04279982,
0.01534627,
-0.00628271,
-0.02035729,
-0.02658216,
-0.02590168,
-0.0201885,
-0.01173923,
-0.00281751,
0.00474894,
0.00985211,
0.0121238,
0.01186197,
0.0097864,
0.00681596,
0.00380494,
0.00134667,
-0.00023823,
-0.00094597,
-0.00196191,
]
def test_aberth_fir():
z0s = initial_aberth(r)
opt = Options()
opt.tol = 1e-8
zs, niter, found = aberth(r, z0s, opt)
print([niter, found])
for z in zs:
print(z)
assert niter <= 11
def test_aberth_autocorr_fir():
z0s = initial_aberth_autocorr(r)
opt = Options()
opt.tol = 1e-14
zs, niter, found = aberth_autocorr(r, z0s, opt)
print([niter, found])
for z in zs:
print(z)
assert niter <= 9
# def test_aberth_fir_lds():
# r = [
# -0.00196191,
# -0.00094597,
# -0.00023823,
# 0.00134667,
# 0.00380494,
# 0.00681596,
# 0.0097864,
# 0.01186197,
# 0.0121238,
# 0.00985211,
# 0.00474894,
# -0.00281751,
# -0.01173923,
# -0.0201885,
# -0.02590168,
# -0.02658216,
# -0.02035729,
# -0.00628271,
# 0.01534627,
# 0.04279982,
# 0.0732094,
# 0.10275561,
# 0.12753013,
# 0.14399228,
# 0.15265722,
# 0.14399228,
# 0.12753013,
# 0.10275561,
# 0.0732094,
# 0.04279982,
# 0.01534627,
# -0.00628271,
# -0.02035729,
# -0.02658216,
# -0.02590168,
# -0.0201885,
# -0.01173923,
# -0.00281751,
# 0.00474894,
# 0.00985211,
# 0.0121238,
# 0.01186197,
# 0.0097864,
# 0.00681596,
# 0.00380494,
# 0.00134667,
# -0.00023823,
# -0.00094597,
# -0.00196191,
# ]
# z0s = initial_aberth_lds(r)
# opt = Options()
# opt.tol = 1e-8
# zs, niter, found = aberth(r, z0s, opt)
# print([niter, found])
# print([z for z in zs])
# assert niter <= 13
| 20.203822
| 68
| 0.513241
| 396
| 3,172
| 4.05303
| 0.169192
| 0.062305
| 0.049844
| 0.04486
| 0.806854
| 0.747041
| 0.735202
| 0.735202
| 0.700935
| 0.700935
| 0
| 0.443702
| 0.339218
| 3,172
| 156
| 69
| 20.333333
| 0.322042
| 0.385876
| 0
| 0.729412
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.047059
| 1
| 0.047059
| false
| 0
| 0.035294
| 0
| 0.082353
| 0.082353
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
5bee35c92deb5a13d6e2299551ef2b818ed9a354
| 22,796
|
py
|
Python
|
python/seldon_deploy_sdk/api/monitor_api.py
|
SachinVarghese/seldon-deploy-sdk
|
2c70e249c084f113a998ab876c29843ae5f6a99a
|
[
"Apache-2.0"
] | 6
|
2021-02-18T14:37:54.000Z
|
2022-01-13T13:27:43.000Z
|
python/seldon_deploy_sdk/api/monitor_api.py
|
SachinVarghese/seldon-deploy-sdk
|
2c70e249c084f113a998ab876c29843ae5f6a99a
|
[
"Apache-2.0"
] | 14
|
2021-01-04T16:32:03.000Z
|
2021-12-13T17:53:59.000Z
|
python/seldon_deploy_sdk/api/monitor_api.py
|
SachinVarghese/seldon-deploy-sdk
|
2c70e249c084f113a998ab876c29843ae5f6a99a
|
[
"Apache-2.0"
] | 7
|
2021-03-17T09:05:55.000Z
|
2022-01-05T10:39:56.000Z
|
# coding: utf-8
"""
Seldon Deploy API
API to interact and manage the lifecycle of your machine learning models deployed through Seldon Deploy. # noqa: E501
OpenAPI spec version: v1alpha1
Contact: hello@seldon.io
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from seldon_deploy_sdk.api_client import ApiClient
class MonitorApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def inference_service_feature_distributions(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""inference_service_feature_distributions # noqa: E501
Get the specified Inference Service predictions feature distribution # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inference_service_feature_distributions(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureDistributionResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.inference_service_feature_distributions_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
else:
(data) = self.inference_service_feature_distributions_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
return data
def inference_service_feature_distributions_with_http_info(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""inference_service_feature_distributions # noqa: E501
Get the specified Inference Service predictions feature distribution # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inference_service_feature_distributions_with_http_info(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureDistributionResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['name', 'namespace', 'feature_data'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method inference_service_feature_distributions" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'name' is set
if ('name' not in params or
params['name'] is None):
raise ValueError("Missing the required parameter `name` when calling `inference_service_feature_distributions`") # noqa: E501
# verify the required parameter 'namespace' is set
if ('namespace' not in params or
params['namespace'] is None):
raise ValueError("Missing the required parameter `namespace` when calling `inference_service_feature_distributions`") # noqa: E501
# verify the required parameter 'feature_data' is set
if ('feature_data' not in params or
params['feature_data'] is None):
raise ValueError("Missing the required parameter `feature_data` when calling `inference_service_feature_distributions`") # noqa: E501
collection_formats = {}
path_params = {}
if 'name' in params:
path_params['name'] = params['name'] # noqa: E501
if 'namespace' in params:
path_params['namespace'] = params['namespace'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'feature_data' in params:
body_params = params['feature_data']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['OAuth2'] # noqa: E501
return self.api_client.call_api(
'/namespaces/{namespace}/inferenceservices/{name}/monitor/featuredistributions', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='FeatureDistributionResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def inference_service_feature_statistics(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""inference_service_feature_statistics # noqa: E501
Get the specified Inference Service predictions feature statistics # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inference_service_feature_statistics(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureStatisticsResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.inference_service_feature_statistics_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
else:
(data) = self.inference_service_feature_statistics_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
return data
def inference_service_feature_statistics_with_http_info(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""inference_service_feature_statistics # noqa: E501
Get the specified Inference Service predictions feature statistics # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inference_service_feature_statistics_with_http_info(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureStatisticsResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['name', 'namespace', 'feature_data'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method inference_service_feature_statistics" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'name' is set
if ('name' not in params or
params['name'] is None):
raise ValueError("Missing the required parameter `name` when calling `inference_service_feature_statistics`") # noqa: E501
# verify the required parameter 'namespace' is set
if ('namespace' not in params or
params['namespace'] is None):
raise ValueError("Missing the required parameter `namespace` when calling `inference_service_feature_statistics`") # noqa: E501
# verify the required parameter 'feature_data' is set
if ('feature_data' not in params or
params['feature_data'] is None):
raise ValueError("Missing the required parameter `feature_data` when calling `inference_service_feature_statistics`") # noqa: E501
collection_formats = {}
path_params = {}
if 'name' in params:
path_params['name'] = params['name'] # noqa: E501
if 'namespace' in params:
path_params['namespace'] = params['namespace'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'feature_data' in params:
body_params = params['feature_data']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['OAuth2'] # noqa: E501
return self.api_client.call_api(
'/namespaces/{namespace}/inferenceservices/{name}/monitor/featurestatistics', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='FeatureStatisticsResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def seldon_deployment_feature_distributions(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""seldon_deployment_feature_distributions # noqa: E501
Get the specified Seldon Deployment predictions feature distribution # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.seldon_deployment_feature_distributions(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureDistributionResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.seldon_deployment_feature_distributions_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
else:
(data) = self.seldon_deployment_feature_distributions_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
return data
def seldon_deployment_feature_distributions_with_http_info(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""seldon_deployment_feature_distributions # noqa: E501
Get the specified Seldon Deployment predictions feature distribution # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.seldon_deployment_feature_distributions_with_http_info(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureDistributionResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['name', 'namespace', 'feature_data'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method seldon_deployment_feature_distributions" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'name' is set
if ('name' not in params or
params['name'] is None):
raise ValueError("Missing the required parameter `name` when calling `seldon_deployment_feature_distributions`") # noqa: E501
# verify the required parameter 'namespace' is set
if ('namespace' not in params or
params['namespace'] is None):
raise ValueError("Missing the required parameter `namespace` when calling `seldon_deployment_feature_distributions`") # noqa: E501
# verify the required parameter 'feature_data' is set
if ('feature_data' not in params or
params['feature_data'] is None):
raise ValueError("Missing the required parameter `feature_data` when calling `seldon_deployment_feature_distributions`") # noqa: E501
collection_formats = {}
path_params = {}
if 'name' in params:
path_params['name'] = params['name'] # noqa: E501
if 'namespace' in params:
path_params['namespace'] = params['namespace'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'feature_data' in params:
body_params = params['feature_data']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['OAuth2'] # noqa: E501
return self.api_client.call_api(
'/namespaces/{namespace}/seldondeployments/{name}/monitor/featuredistributions', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='FeatureDistributionResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def seldon_deployment_feature_statistics(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""seldon_deployment_feature_statistics # noqa: E501
Get the specified Seldon Deployment predictions feature statistics # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.seldon_deployment_feature_statistics(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureStatisticsResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.seldon_deployment_feature_statistics_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
else:
(data) = self.seldon_deployment_feature_statistics_with_http_info(name, namespace, feature_data, **kwargs) # noqa: E501
return data
def seldon_deployment_feature_statistics_with_http_info(self, name, namespace, feature_data, **kwargs): # noqa: E501
"""seldon_deployment_feature_statistics # noqa: E501
Get the specified Seldon Deployment predictions feature statistics # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.seldon_deployment_feature_statistics_with_http_info(name, namespace, feature_data, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str name: Name identifies a resource (required)
:param str namespace: Namespace provides a logical grouping of resources (required)
:param DeploymentFeatureData feature_data: Deployment Feature Data (required)
:return: FeatureStatisticsResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['name', 'namespace', 'feature_data'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method seldon_deployment_feature_statistics" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'name' is set
if ('name' not in params or
params['name'] is None):
raise ValueError("Missing the required parameter `name` when calling `seldon_deployment_feature_statistics`") # noqa: E501
# verify the required parameter 'namespace' is set
if ('namespace' not in params or
params['namespace'] is None):
raise ValueError("Missing the required parameter `namespace` when calling `seldon_deployment_feature_statistics`") # noqa: E501
# verify the required parameter 'feature_data' is set
if ('feature_data' not in params or
params['feature_data'] is None):
raise ValueError("Missing the required parameter `feature_data` when calling `seldon_deployment_feature_statistics`") # noqa: E501
collection_formats = {}
path_params = {}
if 'name' in params:
path_params['name'] = params['name'] # noqa: E501
if 'namespace' in params:
path_params['namespace'] = params['namespace'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'feature_data' in params:
body_params = params['feature_data']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['OAuth2'] # noqa: E501
return self.api_client.call_api(
'/namespaces/{namespace}/seldondeployments/{name}/monitor/featurestatistics', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='FeatureStatisticsResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 46.052525
| 146
| 0.651913
| 2,512
| 22,796
| 5.681529
| 0.071656
| 0.043161
| 0.039238
| 0.047085
| 0.964546
| 0.962094
| 0.962094
| 0.956769
| 0.956769
| 0.956769
| 0
| 0.014517
| 0.265705
| 22,796
| 494
| 147
| 46.145749
| 0.838103
| 0.353395
| 0
| 0.815094
| 0
| 0
| 0.244901
| 0.09426
| 0
| 0
| 0
| 0
| 0
| 1
| 0.033962
| false
| 0
| 0.015094
| 0
| 0.098113
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
5bfebf82f4501519821c70f880e664f78cc42997
| 7,546
|
py
|
Python
|
tests/test_regionprops.py
|
elsandal/pyclesperanto_prototype
|
7bda828813b86b44b63d73d5e8f466d9769cded1
|
[
"BSD-3-Clause"
] | 64
|
2020-03-18T12:11:22.000Z
|
2022-03-31T08:19:18.000Z
|
tests/test_regionprops.py
|
elsandal/pyclesperanto_prototype
|
7bda828813b86b44b63d73d5e8f466d9769cded1
|
[
"BSD-3-Clause"
] | 148
|
2020-05-14T06:14:11.000Z
|
2022-03-26T15:02:31.000Z
|
tests/test_regionprops.py
|
elsandal/pyclesperanto_prototype
|
7bda828813b86b44b63d73d5e8f466d9769cded1
|
[
"BSD-3-Clause"
] | 16
|
2020-05-31T00:53:44.000Z
|
2022-03-23T13:20:57.000Z
|
import pyclesperanto_prototype as cle
import numpy as np
def test_regionprops():
intensity = cle.push(np.asarray([
[0, 1, 2],
[1, 2, 3],
[2, 3, 4]
]))
labels = cle.push(np.asarray([
[1, 1, 2],
[1, 2, 2],
[3, 3, 3]
]))
reference = cle.push(np.asarray([
#IDENTIFIER(0),
[1., 2., 3.],
# BOUNDING_BOX_X(1),
[0., 1., 0.],
# BOUNDING_BOX_Y(2),
[0., 0., 2.],
# BOUNDING_BOX_Z(3),
[0., 0., 0.],
# BOUNDING_BOX_END_X(4),
[1., 2., 2.],
# BOUNDING_BOX_END_Y(5),
[1., 1., 2.],
# BOUNDING_BOX_END_Z(6),
[0., 0., 0.],
# BOUNDING_BOX_WIDTH(7),
[2., 2., 3.],
# BOUNDING_BOX_HEIGHT(8),
[2., 2., 1.],
# BOUNDING_BOX_DEPTH(9),
[1., 1., 1.],
# MINIMUM_INTENSITY(10),
[0., 2., 2.],
# MAXIMUM_INTENSITY(11),
[1., 3., 4.],
# MEAN_INTENSITY(12)
[0.6666667, 2.3333333, 3.],
# SUM_INTENSITY(13),
[2., 7., 9.],
# STANDARD_DEVIATION_INTENSITY(14),
[0.47140452, 0.4714045, 0.8164966],
# PIXEL_COUNT(15),
[3., 3., 3.],
# SUM_INTENSITY_TIMES_X(16),
[0., 0., 0.],# Todo; not supported yet
# SUM_INTENSITY_TIMES_Y(17),
[0., 0., 0.],# Todo; not supported yet
# SUM_INTENSITY_TIMES_Z(18),
[0., 0., 0.],# Todo; not supported yet
# MASS_CENTER_X(19),
[0.5, 1.7142857, 1.2222222],
# MASS_CENTER_Y(20),
[0.5, 0.71428573, 2. ],
# MASS_CENTER_Z(21),
[0., 0., 0.],
# SUM_X(22),
[0., 0., 0.],# Todo; not supported yet
# SUM_Y(23),
[0., 0., 0.],# Todo; not supported yet
# SUM_Z(24),
[0., 0., 0.],# Todo; not supported yet
# CENTROID_X(25),
[0.33333334, 1.6666666, 1.],
# CENTROID_Y(26),
[0.33333334, 0.6666667, 2.],
# CENTROID_Z(27),
[0., 0., 0.],
# SUM_DISTANCE_TO_MASS_CENTER(28),
[0., 0., 0.],# Todo; not supported yet
# MEAN_DISTANCE_TO_MASS_CENTER(29),
[0., 0., 0.],# Todo; not supported yet
# MAX_DISTANCE_TO_MASS_CENTER(30),
[0., 0., 0.],# Todo; not supported yet
# MAX_MEAN_DISTANCE_TO_MASS_CENTER_RATIO(31),
[0., 0., 0.],# Todo; not supported yet
# SUM_DISTANCE_TO_CENTROID(32),
[0, 0, 0],# Todo; not supported yet
# MEAN_DISTANCE_TO_CENTROID(33),
[0, 0, 0],# Todo; not supported yet
# MAX_DISTANCE_TO_CENTROID(34),
[0, 0, 0],# Todo; not supported yet
# MAX_MEAN_DISTANCE_TO_CENTROID_RATIO(35);
[0, 0, 0]
]
))
result = cle.regionprops(labels, intensity)
result_image = cle.push_regionprops(result, first_row_index=1)
a = cle.pull(result_image)
b = cle.pull(reference)
print(a)
print(b)
assert (np.allclose(a, b, 0.0001))
def test_statistics_of_labelled_pixels_3d():
intensity = cle.push(np.asarray([
[
[0, 1, 2],
],[
[1, 2, 3],
], [
[2, 3, 4]
]
]))
labels = cle.push(np.asarray([
[
[1, 1, 2],
], [
[1, 2, 2],
], [
[3, 3, 3]
]
]))
reference = cle.push(np.asarray([
#IDENTIFIER(0),
[1., 2., 3.],
# BOUNDING_BOX_X(1),
[0., 1., 0.],
# BOUNDING_BOX_Y(2),
[0., 0., 0.],
# BOUNDING_BOX_Z(3),
[0., 0., 2.],
# BOUNDING_BOX_END_X(4),
[1., 2., 2.],
# BOUNDING_BOX_END_Y(5),
[0., 0., 0.],
# BOUNDING_BOX_END_Z(6),
[1., 1., 2.],
# BOUNDING_BOX_WIDTH(7),
[2., 2., 3.],
# BOUNDING_BOX_HEIGHT(8),
[1., 1., 1.],
# BOUNDING_BOX_DEPTH(9),
[2., 2., 1.],
# MINIMUM_INTENSITY(10),
[0., 2., 2.],
# MAXIMUM_INTENSITY(11),
[1., 3., 4.],
# MEAN_INTENSITY(12)
[0.6666667, 2.3333333, 3.],
# SUM_INTENSITY(13),
[2., 7., 9.],
# STANDARD_DEVIATION_INTENSITY(14),
[0.47140452, 0.4714045, 0.8164966],
# PIXEL_COUNT(15),
[3., 3., 3.],
# SUM_INTENSITY_TIMES_X(16),
[0., 0., 0.],# Todo; not supported yet
# SUM_INTENSITY_TIMES_Y(17),
[0., 0., 0.],# Todo; not supported yet
# SUM_INTENSITY_TIMES_Z(18),
[0., 0., 0.],# Todo; not supported yet
# MASS_CENTER_X(19),
[0.5, 1.7142857, 1.2222222],
# MASS_CENTER_Y(20),
[0., 0., 0.],
# MASS_CENTER_Z(21),
[0.5, 0.71428573, 2.],
# SUM_X(22),
[0., 0., 0.],# Todo; not supported yet
# SUM_Y(23),
[0., 0., 0.],# Todo; not supported yet
# SUM_Z(24),
[0., 0., 0.],# Todo; not supported yet
# CENTROID_X(25),
[0.33333334, 1.6666666, 1.],
# CENTROID_Y(26),
[0., 0., 0.],
# CENTROID_Z(27),
[0.33333334, 0.6666667, 2.],
# SUM_DISTANCE_TO_MASS_CENTER(28),
[0., 0., 0.],# Todo; not supported yet
# MEAN_DISTANCE_TO_MASS_CENTER(29),
[0., 0., 0.],# Todo; not supported yet
# MAX_DISTANCE_TO_MASS_CENTER(30),
[0., 0., 0.],# Todo; not supported yet
# MAX_MEAN_DISTANCE_TO_MASS_CENTER_RATIO(31),
[0., 0., 0.],# Todo; not supported yet
# SUM_DISTANCE_TO_CENTROID(32),
[0, 0, 0], # Todo; not supported yet
# MEAN_DISTANCE_TO_CENTROID(33),
[0, 0, 0], # Todo; not supported yet
# MAX_DISTANCE_TO_CENTROID(34),
[0, 0, 0], # Todo; not supported yet
# MAX_MEAN_DISTANCE_TO_CENTROID_RATIO(35);
[0 ,0 ,0]# Todo; not supported yet
]
))
result = cle.regionprops(labels, intensity)
result_image = cle.push_regionprops(result, first_row_index=1)
a = cle.pull(result_image)
b = cle.pull(reference)
print(a)
print(b)
assert (np.allclose(a, b, 0.0001))
def test_standard_deviation():
import numpy as np
image = np.asarray([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
])
labels = np.asarray([
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]
])
print("numpy standard deviation", np.std(image))
print("numpy variance", np.var(image))
stats = cle.regionprops(labels, image)
print("std", stats[0].standard_deviation_intensity)
from numpy import testing
testing.assert_almost_equal(stats[0].standard_deviation_intensity, 2.58, decimal=1)
| 30.8
| 87
| 0.428174
| 887
| 7,546
| 3.431793
| 0.133033
| 0.04862
| 0.03548
| 0.062089
| 0.880749
| 0.810118
| 0.79435
| 0.788108
| 0.785151
| 0.785151
| 0
| 0.134865
| 0.417307
| 7,546
| 244
| 88
| 30.92623
| 0.557426
| 0.344553
| 0
| 0.852113
| 0
| 0
| 0.008461
| 0
| 0
| 0
| 0
| 0.004098
| 0.021127
| 1
| 0.021127
| false
| 0
| 0.028169
| 0
| 0.049296
| 0.049296
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
75019ac6f379c04f7654e8ead3bcb75eb78e2447
| 98
|
py
|
Python
|
hello.py
|
zhuheec/git-net2008
|
8300305ef2bbcb0536d1be3392785c41feb274f3
|
[
"MIT"
] | null | null | null |
hello.py
|
zhuheec/git-net2008
|
8300305ef2bbcb0536d1be3392785c41feb274f3
|
[
"MIT"
] | null | null | null |
hello.py
|
zhuheec/git-net2008
|
8300305ef2bbcb0536d1be3392785c41feb274f3
|
[
"MIT"
] | null | null | null |
print("Hello NET2008, 2020")
print("Hello NET2008, 2019")
print("I need to print a second line")
| 19.6
| 38
| 0.714286
| 16
| 98
| 4.375
| 0.6875
| 0.285714
| 0.485714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 0.142857
| 98
| 4
| 39
| 24.5
| 0.642857
| 0
| 0
| 0
| 0
| 0
| 0.683673
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
750dd98ad0d8cdcfefb594f8a6e3da59850f338c
| 127
|
py
|
Python
|
pleiades/configurations/__init__.py
|
jcwright77/pleiades
|
e3e208e94feee299589a094f361b301131d1bd15
|
[
"MIT"
] | 3
|
2020-03-27T19:27:01.000Z
|
2021-07-15T16:28:54.000Z
|
pleiades/configurations/__init__.py
|
jcwright77/pleiades
|
e3e208e94feee299589a094f361b301131d1bd15
|
[
"MIT"
] | 6
|
2020-03-30T17:12:42.000Z
|
2020-07-14T03:07:02.000Z
|
pleiades/configurations/__init__.py
|
jcwright77/pleiades
|
e3e208e94feee299589a094f361b301131d1bd15
|
[
"MIT"
] | 6
|
2020-03-30T17:05:58.000Z
|
2021-08-18T19:21:00.000Z
|
from pleiades.configurations.brb import *
from pleiades.configurations.pcx import *
from pleiades.configurations.wham import *
| 31.75
| 42
| 0.834646
| 15
| 127
| 7.066667
| 0.466667
| 0.339623
| 0.735849
| 0.603774
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094488
| 127
| 3
| 43
| 42.333333
| 0.921739
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
7530ad1d06bf967d90362570639c63df075fc8d9
| 95
|
py
|
Python
|
hw3-cloudmesh/oliveral_cloudmesh_ex2.py
|
futuresystems/465-oliverlewis
|
c1a093e9de012e752a2277c4fb533cbd974a5a8d
|
[
"Apache-2.0"
] | null | null | null |
hw3-cloudmesh/oliveral_cloudmesh_ex2.py
|
futuresystems/465-oliverlewis
|
c1a093e9de012e752a2277c4fb533cbd974a5a8d
|
[
"Apache-2.0"
] | null | null | null |
hw3-cloudmesh/oliveral_cloudmesh_ex2.py
|
futuresystems/465-oliverlewis
|
c1a093e9de012e752a2277c4fb533cbd974a5a8d
|
[
"Apache-2.0"
] | null | null | null |
import cloudmesh
print cloudmesh.shell("cloud list")
print cloudmesh.shell("cloud on india")
| 15.833333
| 39
| 0.778947
| 13
| 95
| 5.692308
| 0.615385
| 0.378378
| 0.513514
| 0.648649
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115789
| 95
| 5
| 40
| 19
| 0.880952
| 0
| 0
| 0
| 0
| 0
| 0.255319
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.333333
| null | null | 0.666667
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
0
| 8
|
f32ad935729ef7bcbc8ef35de9640ea56eacc0c8
| 83
|
py
|
Python
|
lane-finding/davg/lanefinding/__init__.py
|
Sashulik/Detroit-Autonomous-Vehicle-Group
|
eba7d22dfbf4cd48977e952a58d043eb1e598449
|
[
"MIT"
] | 24
|
2017-06-03T20:14:27.000Z
|
2021-06-10T02:18:41.000Z
|
lane-finding/davg/lanefinding/__init__.py
|
Sashulik/Detroit-Autonomous-Vehicle-Group
|
eba7d22dfbf4cd48977e952a58d043eb1e598449
|
[
"MIT"
] | 1
|
2018-04-12T04:37:31.000Z
|
2018-04-12T04:37:31.000Z
|
lane-finding/davg/lanefinding/__init__.py
|
Sashulik/Detroit-Autonomous-Vehicle-Group
|
eba7d22dfbf4cd48977e952a58d043eb1e598449
|
[
"MIT"
] | 26
|
2017-05-07T14:38:08.000Z
|
2020-08-12T04:39:25.000Z
|
from davg.lanefinding import BirdsEyeTransform
from davg.lanefinding import ImgMgr
| 27.666667
| 46
| 0.879518
| 10
| 83
| 7.3
| 0.6
| 0.219178
| 0.520548
| 0.684932
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096386
| 83
| 2
| 47
| 41.5
| 0.973333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
f3304c6327ed39b3f86273e97b1b702c7b0fd20d
| 238
|
py
|
Python
|
sklift/datasets/__init__.py
|
bwbelljr/scikit-uplift
|
b2fbbb04bd10fc082f25aac2e04dc94cfc8bd64a
|
[
"MIT"
] | 1
|
2020-06-30T11:04:46.000Z
|
2020-06-30T11:04:46.000Z
|
sklift/datasets/__init__.py
|
bwbelljr/scikit-uplift
|
b2fbbb04bd10fc082f25aac2e04dc94cfc8bd64a
|
[
"MIT"
] | 2
|
2021-07-04T11:58:24.000Z
|
2021-07-04T12:36:10.000Z
|
sklift/datasets/__init__.py
|
tankudo/scikit-uplift
|
412b4904397628d1e1101b7c6c4a561cc2ef38f5
|
[
"MIT"
] | null | null | null |
from .datasets import (
get_data_dir,
clear_data_dir,
fetch_x5, fetch_lenta,
fetch_criteo, fetch_hillstrom
)
__all__ = [
get_data_dir,
clear_data_dir,
fetch_x5, fetch_lenta,
fetch_criteo, fetch_hillstrom
]
| 18.307692
| 33
| 0.710084
| 32
| 238
| 4.65625
| 0.40625
| 0.187919
| 0.134228
| 0.201342
| 0.85906
| 0.85906
| 0.85906
| 0.85906
| 0.85906
| 0.85906
| 0
| 0.010811
| 0.222689
| 238
| 13
| 34
| 18.307692
| 0.794595
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.083333
| 0
| 0.083333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
f39f3a642dd56f2afa2703b75feff9d2d5e3e6dd
| 361
|
py
|
Python
|
tests/internal/instance_type/test_instance_type_g4ad_auto.py
|
frolovv/aws.ec2.compare
|
582805823492f833d65c0441c4a14dce697c12aa
|
[
"Apache-2.0"
] | null | null | null |
tests/internal/instance_type/test_instance_type_g4ad_auto.py
|
frolovv/aws.ec2.compare
|
582805823492f833d65c0441c4a14dce697c12aa
|
[
"Apache-2.0"
] | null | null | null |
tests/internal/instance_type/test_instance_type_g4ad_auto.py
|
frolovv/aws.ec2.compare
|
582805823492f833d65c0441c4a14dce697c12aa
|
[
"Apache-2.0"
] | null | null | null |
# Testing module instance_type.g4ad
import pytest
import ec2_compare.internal.instance_type.g4ad
def test_get_internal_data_instance_type_g4ad_get_instances_list():
assert len(ec2_compare.internal.instance_type.g4ad.get_instances_list()) > 0
def test_get_internal_data_instance_type_g4ad_get():
assert len(ec2_compare.internal.instance_type.g4ad.get) > 0
| 36.1
| 78
| 0.853186
| 56
| 361
| 5.053571
| 0.339286
| 0.254417
| 0.339223
| 0.268551
| 0.826855
| 0.826855
| 0.614841
| 0.614841
| 0.614841
| 0
| 0
| 0.032836
| 0.072022
| 361
| 9
| 79
| 40.111111
| 0.81194
| 0.091413
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 9
|
f3bb2af15d3c67d7cb1bf6e4d3fb0b2c4168cc2a
| 1,063
|
py
|
Python
|
use_cases/MarzV3/backend/marzv2/models.py
|
einshoe/ssv-examples
|
5bdf430c21ba3048e0dee7bfc0b6790143d6bec5
|
[
"MIT"
] | null | null | null |
use_cases/MarzV3/backend/marzv2/models.py
|
einshoe/ssv-examples
|
5bdf430c21ba3048e0dee7bfc0b6790143d6bec5
|
[
"MIT"
] | null | null | null |
use_cases/MarzV3/backend/marzv2/models.py
|
einshoe/ssv-examples
|
5bdf430c21ba3048e0dee7bfc0b6790143d6bec5
|
[
"MIT"
] | null | null | null |
import os
from django.db import models
# Create your models here.
class FitsFiles(models.Model):
filepath = models.CharField(max_length=256)
published_date = models.DateTimeField('date published')
config = models.TextField(null=True)
survey = models.CharField(max_length=200)
redshift = models.FloatField()
def __str__(self):
return os.path.basename(self.filepath)
class JsonFiles(models.Model):
filepath = models.CharField(max_length=256)
published_date = models.DateTimeField('date published')
survey = models.CharField(max_length=200)
redshift = models.FloatField()
def __str__(self):
return os.path.basename(self.filepath)
class TemplateSpectra(models.Model):
name = models.CharField(max_length=200)
redshift = models.FloatField()
def __str__(self):
return self.name
class EmissionSpectra(models.Model):
name = models.CharField(max_length=200)
wavelength = models.FloatField()
redshift = models.FloatField()
def __str__(self):
return self.name
| 25.926829
| 59
| 0.714017
| 125
| 1,063
| 5.88
| 0.312
| 0.122449
| 0.146939
| 0.195918
| 0.77551
| 0.77551
| 0.77551
| 0.77551
| 0.697959
| 0.621769
| 0
| 0.020713
| 0.182502
| 1,063
| 40
| 60
| 26.575
| 0.825086
| 0.022578
| 0
| 0.714286
| 0
| 0
| 0.027027
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0
| 0.071429
| 0.142857
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 7
|
45f7700b0bae2014b121ae332c80fc4f447dcb12
| 223
|
py
|
Python
|
nanokontrolstudio_echolevel/__init__.py
|
echolevel/nanoKontrolStudio-Ableton-script
|
28fcca4ab38b1a1e82a27d4c223a059fde27b686
|
[
"MIT"
] | 2
|
2020-12-01T15:35:04.000Z
|
2020-12-01T15:38:34.000Z
|
nanokontrolstudio_echolevel/__init__.py
|
echolevel/nanoKontrolStudio-Ableton-script
|
28fcca4ab38b1a1e82a27d4c223a059fde27b686
|
[
"MIT"
] | null | null | null |
nanokontrolstudio_echolevel/__init__.py
|
echolevel/nanoKontrolStudio-Ableton-script
|
28fcca4ab38b1a1e82a27d4c223a059fde27b686
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import, print_function, unicode_literals
from nanokontrolstudio_echolevel import nanokontrolstudio_echolevel
def create_instance(c_instance):
return nanokontrolstudio_echolevel(c_instance)
| 37.166667
| 72
| 0.896861
| 25
| 223
| 7.48
| 0.6
| 0.417112
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071749
| 223
| 5
| 73
| 44.6
| 0.903382
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0.25
| 1
| 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
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
342631e36ea14f263f3359d61776d5174afce085
| 4,091
|
py
|
Python
|
src/azure-cli/azure/cli/command_modules/cdn/custom/__init__.py
|
xaliciayang/azure-cli
|
38c80c875e8a79d08d06a2f42ec82fd54934343e
|
[
"MIT"
] | 1
|
2020-12-23T03:23:33.000Z
|
2020-12-23T03:23:33.000Z
|
src/azure-cli/azure/cli/command_modules/cdn/custom/__init__.py
|
xaliciayang/azure-cli
|
38c80c875e8a79d08d06a2f42ec82fd54934343e
|
[
"MIT"
] | 1
|
2021-02-23T20:57:31.000Z
|
2021-02-23T20:57:31.000Z
|
src/azure-cli/azure/cli/command_modules/cdn/custom/__init__.py
|
xaliciayang/azure-cli
|
38c80c875e8a79d08d06a2f42ec82fd54934343e
|
[
"MIT"
] | 1
|
2020-04-24T13:27:18.000Z
|
2020-04-24T13:27:18.000Z
|
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from .custom import default_content_types # pylint: disable=unused-import
from .custom import list_profiles # pylint: disable=unused-import
from .custom import update_endpoint # pylint: disable=unused-import
from .custom import create_condition # pylint: disable=unused-import
from .custom import create_action # pylint: disable=unused-import
from .custom import add_rule # pylint: disable=unused-import
from .custom import add_condition # pylint: disable=unused-import
from .custom import add_action # pylint: disable=unused-import
from .custom import remove_rule # pylint: disable=unused-import
from .custom import remove_condition # pylint: disable=unused-import
from .custom import remove_action # pylint: disable=unused-import
from .custom import create_endpoint # pylint: disable=unused-import
from .custom import create_custom_domain # pylint: disable=unused-import
from .custom import enable_custom_https # pylint: disable=unused-import
from .custom import update_origin # pylint: disable=unused-import
from .custom import create_origin # pylint: disable=unused-import
from .custom import update_profile # pylint: disable=unused-import
from .custom import create_profile # pylint: disable=unused-import
from .custom import create_origin_group # pylint: disable=unused-import
from .custom import update_origin_group # pylint: disable=unused-import
from .custom_waf import show_endpoint_waf_policy_link # pylint: disable=unused-import
from .custom_waf import set_endpoint_waf_policy_link # pylint: disable=unused-import
from .custom_waf import remove_endpoint_waf_policy_link # pylint: disable=unused-import
from .custom_waf import list_waf_managed_rule_set # pylint: disable=unused-import
from .custom_waf import list_waf_managed_rule_groups # pylint: disable=unused-import
from .custom_waf import set_waf_policy # pylint: disable=unused-import
from .custom_waf import add_waf_policy_managed_rule_set # pylint: disable=unused-import
from .custom_waf import remove_waf_policy_managed_rule_set # pylint: disable=unused-import
from .custom_waf import list_waf_policy_managed_rule_sets # pylint: disable=unused-import
from .custom_waf import show_waf_policy_managed_rule_set # pylint: disable=unused-import
from .custom_waf import set_waf_managed_rule_group_override # pylint: disable=unused-import
from .custom_waf import delete_waf_managed_rule_group_override # pylint: disable=unused-import
from .custom_waf import list_waf_policy_managed_rule_group_overrides # pylint: disable=unused-import
from .custom_waf import show_waf_managed_rule_group_override # pylint: disable=unused-import
from .custom_waf import set_waf_custom_rule # pylint: disable=unused-import
from .custom_waf import delete_waf_custom_rule # pylint: disable=unused-import
from .custom_waf import show_waf_custom_rule # pylint: disable=unused-import
from .custom_waf import list_waf_custom_rules # pylint: disable=unused-import
from .custom_waf import set_waf_rate_limit_rule # pylint: disable=unused-import
from .custom_waf import delete_waf_rate_limit_rule # pylint: disable=unused-import
from .custom_waf import show_waf_rate_limit_rule # pylint: disable=unused-import
from .custom_waf import list_waf_rate_limit_rules # pylint: disable=unused-import
| 83.489796
| 101
| 0.695673
| 496
| 4,091
| 5.459677
| 0.116935
| 0.155096
| 0.294682
| 0.38774
| 0.908789
| 0.895864
| 0.895864
| 0.865583
| 0.655835
| 0.491137
| 0
| 0
| 0.205329
| 4,091
| 48
| 102
| 85.229167
| 0.832974
| 0.390125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 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
| 9
|
344eb32d63d60d30a002d576f3bac2fe645a0c34
| 407
|
py
|
Python
|
tfreplknet/__init__.py
|
shkarupa-alex/tfreplknet
|
1772ae27d32c0509eea921d4bf7db34cde09ee13
|
[
"MIT"
] | 9
|
2022-03-21T04:22:15.000Z
|
2022-03-30T06:58:36.000Z
|
tfreplknet/__init__.py
|
shkarupa-alex/tfreplknet
|
1772ae27d32c0509eea921d4bf7db34cde09ee13
|
[
"MIT"
] | null | null | null |
tfreplknet/__init__.py
|
shkarupa-alex/tfreplknet
|
1772ae27d32c0509eea921d4bf7db34cde09ee13
|
[
"MIT"
] | null | null | null |
from tfreplknet.model import RepLKNet, RepLKNetB, RepLKNetL, RepLKNetXL
from tfreplknet.model import RepLKNetB224In1k, RepLKNetB224In21k
from tfreplknet.model import RepLKNetB384In1k # , RepLKNetB384In21k
from tfreplknet.model import RepLKNetL384In1k, RepLKNetL384In21k
from tfreplknet.model import RepLKNetXL320In1k, RepLKNetXL320In21k
from tfreplknet.prep import preprocess_input_bl, preprocess_input_xl
| 58.142857
| 71
| 0.87715
| 42
| 407
| 8.404762
| 0.5
| 0.23796
| 0.269122
| 0.354108
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096774
| 0.085995
| 407
| 6
| 72
| 67.833333
| 0.852151
| 0.046683
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
cae9a5ba23147134f2c908863fe332cebb68814d
| 32
|
py
|
Python
|
function_79216003.py
|
jianhong1009/ASMART-14
|
4cf761776764cce2c6c277542874d792358b1ed1
|
[
"MIT"
] | null | null | null |
function_79216003.py
|
jianhong1009/ASMART-14
|
4cf761776764cce2c6c277542874d792358b1ed1
|
[
"MIT"
] | null | null | null |
function_79216003.py
|
jianhong1009/ASMART-14
|
4cf761776764cce2c6c277542874d792358b1ed1
|
[
"MIT"
] | null | null | null |
print('My student_id:79216003')
| 16
| 31
| 0.78125
| 5
| 32
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.266667
| 0.0625
| 32
| 1
| 32
| 32
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 0.6875
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
cafece1f2b1f0031918f64921b0be0ebfd0b6a17
| 594
|
py
|
Python
|
src/error.py
|
olivercoad/ChatPlaysRTS
|
38b031e70eaedc23d908b392c57ff3aa88b6ec0e
|
[
"MIT"
] | null | null | null |
src/error.py
|
olivercoad/ChatPlaysRTS
|
38b031e70eaedc23d908b392c57ff3aa88b6ec0e
|
[
"MIT"
] | 8
|
2021-07-15T20:20:58.000Z
|
2021-07-31T19:13:07.000Z
|
src/error.py
|
olivercoad/ChatPlaysRTS
|
38b031e70eaedc23d908b392c57ff3aa88b6ec0e
|
[
"MIT"
] | 2
|
2021-08-17T20:30:35.000Z
|
2022-02-03T15:12:50.000Z
|
class RegexError(Exception):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class TomlError(Exception):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class AuthenticationError(Exception):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class ConnectionFailedError(Exception):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class ClientDisconnectError(Exception):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
| 29.7
| 41
| 0.656566
| 60
| 594
| 5.833333
| 0.216667
| 0.285714
| 0.228571
| 0.285714
| 0.757143
| 0.757143
| 0.757143
| 0.757143
| 0.757143
| 0.757143
| 0
| 0
| 0.175084
| 594
| 19
| 42
| 31.263158
| 0.714286
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
1b05e0a9669b1e4c345bbfacae57c6a015a96bf0
| 10,413
|
py
|
Python
|
f5_openstack_agent/lbaasv2/drivers/bigip/test/test_ssl_profile.py
|
minw9545/f5-openstack-agent
|
90ab94bedf0b83ae077b145dfb888f2e9dbe5f79
|
[
"Apache-2.0"
] | 15
|
2016-08-03T18:32:43.000Z
|
2021-05-31T13:44:39.000Z
|
f5_openstack_agent/lbaasv2/drivers/bigip/test/test_ssl_profile.py
|
minw9545/f5-openstack-agent
|
90ab94bedf0b83ae077b145dfb888f2e9dbe5f79
|
[
"Apache-2.0"
] | 828
|
2016-01-21T16:54:52.000Z
|
2021-12-02T03:14:13.000Z
|
f5_openstack_agent/lbaasv2/drivers/bigip/test/test_ssl_profile.py
|
minw9545/f5-openstack-agent
|
90ab94bedf0b83ae077b145dfb888f2e9dbe5f79
|
[
"Apache-2.0"
] | 48
|
2016-02-01T17:56:47.000Z
|
2020-11-10T06:39:26.000Z
|
# coding=utf-8
# Copyright (c) 2014-2018, F5 Networks, 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.
#
from f5_openstack_agent.lbaasv2.drivers.bigip.ssl_profile import \
SSLProfileError
from f5_openstack_agent.lbaasv2.drivers.bigip.ssl_profile import \
SSLProfileHelper
import mock
import pytest
class TestSSLProfileHelper(object):
def test_get_ssl_profile_count(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.get_collection =\
mock.MagicMock(return_value=[1, 2, 3, 4])
assert(SSLProfileHelper.get_client_ssl_profile_count(bigip) == 4)
def test_create_client_ssl_profile_exists(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl =\
mock.MagicMock(return_value=True)
rv = SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey')
assert(rv is None)
def test_create_client_ssl_profile_parent_none(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.return_value = False
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey', parent_profile=None)
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': None,
'key': '/Common/testprofile.key',
'passphrase': None}
],
sniDefault=False,
defaultsFrom=None,
)
def exists_parent(self, *args, **kwargs):
name = kwargs.get('name', '')
if name.startswith('parent'):
return True
else:
return False
def test_create_client_ssl_profile_parent_not_exist(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.return_value = False
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
parent_profile="testparentprofile"
)
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': None,
'key': '/Common/testprofile.key',
'passphrase': None}
],
sniDefault=False,
defaultsFrom=None,
)
def test_create_client_ssl_profile_parent(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.side_effect =\
self.exists_parent
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
parent_profile="parentprofile"
)
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': None,
'key': '/Common/testprofile.key',
'passphrase': None}
],
sniDefault=False,
defaultsFrom='parentprofile',
)
def test_create_client_ssl_profile_raises(self):
err = Exception()
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.return_value = False
bigip.tm.ltm.profile.client_ssls.client_ssl.create =\
mock.MagicMock(side_effect=err)
with pytest.raises(SSLProfileError):
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
intermediates=None, key_passphrase=None,
parent_profile="parentprofile"
)
def test_create_client_ssl_intermediates_none(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.return_value = False
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
key_passphrase="password", intermediates=None)
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': None,
'key': '/Common/testprofile.key',
'passphrase': "password"}
],
sniDefault=False,
defaultsFrom=None,
)
def test_create_client_ssl_intermediates(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.return_value = False
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey', intermediates='inter')
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': '/Common/testprofile_inter.crt',
'key': '/Common/testprofile.key',
'passphrase': None}
],
sniDefault=False,
defaultsFrom=None,
)
def test_create_client_ssl_intermediates_parent_profile(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.side_effect =\
self.exists_parent
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
parent_profile="testparentprofile",
intermediates='inter'
)
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': '/Common/testprofile_inter.crt',
'key': '/Common/testprofile.key',
'passphrase': None}
],
sniDefault=False,
defaultsFrom=None,
)
def test_create_client_ssl_passphrase_none(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.return_value = False
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
key_passphrase=None, intermediates="inter")
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': '/Common/testprofile_inter.crt',
'key': '/Common/testprofile.key',
'passphrase': None}
],
sniDefault=False,
defaultsFrom=None,
)
def test_create_client_ssl_passphrase(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.return_value = False
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
key_passphrase='password', intermediates=None)
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': None,
'key': '/Common/testprofile.key',
'passphrase': "password"}
],
sniDefault=False,
defaultsFrom=None,
)
def test_create_client_ssl_inter_passphrase_profile(self):
bigip = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl = mock.MagicMock()
bigip.tm.ltm.profile.client_ssls.client_ssl.exists.side_effect =\
self.exists_parent
SSLProfileHelper.create_client_ssl_profile(
bigip, 'testprofile', 'testcert', 'testkey',
key_passphrase='password',
parent_profile="testparentprofile",
intermediates='inter'
)
bigip.tm.ltm.profile.client_ssls.client_ssl.create.assert_called_with(
name='testprofile',
partition='Common',
certKeyChain=[
{'name': 'testprofile',
'cert': '/Common/testprofile.crt',
'chain': '/Common/testprofile_inter.crt',
'key': '/Common/testprofile.key',
'passphrase': 'password'}
],
sniDefault=False,
defaultsFrom=None,
)
| 40.204633
| 79
| 0.602228
| 1,040
| 10,413
| 5.819231
| 0.134615
| 0.080304
| 0.052875
| 0.089888
| 0.844514
| 0.844514
| 0.824851
| 0.819068
| 0.810476
| 0.792135
| 0
| 0.003103
| 0.288101
| 10,413
| 258
| 80
| 40.360465
| 0.813301
| 0.055315
| 0
| 0.738938
| 0
| 0
| 0.149679
| 0.053966
| 0
| 0
| 0
| 0
| 0.048673
| 1
| 0.057522
| false
| 0.075221
| 0.017699
| 0
| 0.088496
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
1b239cd5e867d9739d01f50f36a18a65662b1a1d
| 27,946
|
py
|
Python
|
pytorch_segmentation/enet_pytorch_cityscapes.py
|
drat/Drat-Computer-Vision-AI
|
aa9b2f99a9e0d945e6a8ce87e4950dfcd7b4c10b
|
[
"Apache-2.0"
] | 3
|
2018-04-17T17:29:06.000Z
|
2019-06-04T16:04:51.000Z
|
pytorch_segmentation/enet_pytorch_cityscapes.py
|
drat/Drat-Computer-Vision-AI
|
aa9b2f99a9e0d945e6a8ce87e4950dfcd7b4c10b
|
[
"Apache-2.0"
] | null | null | null |
pytorch_segmentation/enet_pytorch_cityscapes.py
|
drat/Drat-Computer-Vision-AI
|
aa9b2f99a9e0d945e6a8ce87e4950dfcd7b4c10b
|
[
"Apache-2.0"
] | 2
|
2018-05-27T19:53:35.000Z
|
2019-01-17T20:40:22.000Z
|
import torch
import torch.nn as nn
from functools import reduce
from torch.autograd import Variable
import torch.nn.functional as F
class LambdaBase(nn.Sequential):
def __init__(self, fn, *args):
super(LambdaBase, self).__init__(*args)
self.lambda_func = fn
def forward_prepare(self, input):
output = []
for module in self._modules.values():
output.append(module(input))
return output if output else input
class Lambda(LambdaBase):
def forward(self, input):
return self.lambda_func(self.forward_prepare(input))
class LambdaMap(LambdaBase):
def forward(self, input):
# result is Variables list [Variable1, Variable2, ...]
return list(map(self.lambda_func, self.forward_prepare(input)))
class LambdaReduce(LambdaBase):
def forward(self, input):
# result is a Variable
return reduce(self.lambda_func, self.forward_prepare(input))
class Padding(nn.Module):
# pad puts in [pad] amount of [value] over dimension [dim], starting at
# index [index] in that dimension. If pad<0, index counts from the left.
# If pad>0 index counts from the right.
# When nInputDim is provided, inputs larger than that value will be considered batches
# where the actual dim to be padded will be dimension dim + 1.
def __init__(self, dim, pad, value, index, nInputDim):
super(Padding, self).__init__()
self.value = value
# self.index = index
self.dim = dim
self.pad = pad
self.nInputDim = nInputDim
if index != 0:
raise NotImplementedError("Padding: index != 0 not implemented")
def forward(self, input):
dim = self.dim
if self.nInputDim != 0:
dim += input.dim() - self.nInputDim
pad_size = list(input.size())
pad_size[dim] = self.pad
padder = Variable(input.data.new(*pad_size).fill_(self.value))
if self.pad < 0:
padded = torch.cat((padder, input), dim)
else:
padded = torch.cat((input, padder), dim)
return padded
class Dropout(nn.Dropout):
"""
Cancel out PyTorch rescaling by 1/(1-p)
"""
def forward(self, input):
input = input * (1 - self.p)
return super(Dropout, self).forward(input)
class Dropout2d(nn.Dropout2d):
"""
Cancel out PyTorch rescaling by 1/(1-p)
"""
def forward(self, input):
input = input * (1 - self.p)
return super(Dropout2d, self).forward(input)
class StatefulMaxPool2d(nn.MaxPool2d): # object keeps indices and input sizes
def __init__(self, *args, **kwargs):
super(StatefulMaxPool2d, self).__init__(*args, **kwargs)
self.indices = None
self.input_size = None
def forward(self, x):
return_indices, self.return_indices = self.return_indices, True
output, indices = super(StatefulMaxPool2d, self).forward(x)
self.return_indices = return_indices
self.indices = indices
self.input_size = x.size()
if return_indices:
return output, indices
return output
class StatefulMaxUnpool2d(nn.Module):
def __init__(self, pooling):
super(StatefulMaxUnpool2d, self).__init__()
self.pooling = pooling
self.unpooling = nn.MaxUnpool2d(pooling.kernel_size, pooling.stride, pooling.padding)
def forward(self, x):
return self.unpooling.forward(x, self.pooling.indices, self.pooling.input_size)
pooling_0 = StatefulMaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=False)
pooling_1 = StatefulMaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=False)
pooling_2 = StatefulMaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=False)
cityscapes_enet_pytorch = nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Conv2d(3, 13, (3, 3), (2, 2), (1, 1), (1, 1), 1),
pooling_0,
),
LambdaReduce(lambda x, y: torch.cat((x, y), 1)),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16, 16, (2, 2), (2, 2), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 16, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
Dropout2d(0.01),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
pooling_1,
Padding(0, 48, 0, 0, 3),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 16, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
Dropout2d(0.01),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 16, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
Dropout2d(0.01),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 16, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
Dropout2d(0.01),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 16, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
Dropout2d(0.01),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 32, (2, 2), (2, 2), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
pooling_2,
Padding(0, 64, 0, 0, 3),
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (2, 2), (2, 2), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (1, 5), (1, 1), (0, 2), (1, 1), 1, bias=False),
nn.Conv2d(32, 32, (5, 1), (1, 1), (2, 0), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (4, 4), (4, 4), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (8, 8), (8, 8), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (1, 5), (1, 1), (0, 2), (1, 1), 1, bias=False),
nn.Conv2d(32, 32, (5, 1), (1, 1), (2, 0), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (16, 16), (16, 16), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (2, 2), (2, 2), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (1, 5), (1, 1), (0, 2), (1, 1), 1, bias=False),
nn.Conv2d(32, 32, (5, 1), (1, 1), (2, 0), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (4, 4), (4, 4), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (8, 8), (8, 8), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (1, 5), (1, 1), (0, 2), (1, 1), 1, bias=False),
nn.Conv2d(32, 32, (5, 1), (1, 1), (2, 0), (1, 1), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 32, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 32, (3, 3), (1, 1), (16, 16), (16, 16), 1),
nn.BatchNorm2d(32, 0.001, 0.1, True),
nn.PReLU(32),
nn.Conv2d(32, 128, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(128, 0.001, 0.1, True),
Dropout2d(0.1),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(128),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(128, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.ConvTranspose2d(16, 16, (3, 3), (2, 2), (1, 1), (1, 1)),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
nn.Conv2d(128, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
StatefulMaxUnpool2d(pooling_2), #SpatialMaxUnpooling,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 16, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 16, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(16, 0.001, 0.1, True),
nn.PReLU(16),
nn.Conv2d(16, 64, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(64, 0.001, 0.1, True),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(64),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(64, 4, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(4, 0.001, 0.1, True),
nn.PReLU(4),
nn.ConvTranspose2d(4, 4, (3, 3), (2, 2), (1, 1), (1, 1)),
nn.BatchNorm2d(4, 0.001, 0.1, True),
nn.PReLU(4),
nn.Conv2d(4, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
nn.Conv2d(64, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
StatefulMaxUnpool2d(pooling_1), #SpatialMaxUnpooling,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(16),
),
nn.Sequential( # Sequential,
LambdaMap(lambda x: x, # ConcatTable,
nn.Sequential( # Sequential,
nn.Conv2d(16, 4, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(4, 0.001, 0.1, True),
nn.PReLU(4),
nn.Conv2d(4, 4, (3, 3), (1, 1), (1, 1), (1, 1), 1),
nn.BatchNorm2d(4, 0.001, 0.1, True),
nn.PReLU(4),
nn.Conv2d(4, 16, (1, 1), (1, 1), (0, 0), (1, 1), 1, bias=False),
nn.BatchNorm2d(16, 0.001, 0.1, True),
),
nn.Sequential( # Sequential,
Lambda(lambda x: x), # Identity,
),
),
LambdaReduce(lambda x,y: x+y), # CAddTable,
nn.PReLU(16),
),
nn.ConvTranspose2d(16, 20, (2, 2), (2, 2), (0, 0), (0, 0)),
)
# Quick n dirty hack to get this converted model to working
class ENet_CS(nn.Module):
def __init__(self, num_classes, encoder=None):
super().__init__()
self.model = cityscapes_enet_pytorch
def load(self, weights_path):
self.model.load_state_dict(torch.load(weights_path))
def forward(self, input):
return self.model(input)
| 40.501449
| 93
| 0.445502
| 3,533
| 27,946
| 3.500142
| 0.045287
| 0.064694
| 0.060165
| 0.035581
| 0.838994
| 0.82832
| 0.820718
| 0.806647
| 0.795811
| 0.790474
| 0
| 0.136954
| 0.382595
| 27,946
| 689
| 94
| 40.560232
| 0.57975
| 0.097116
| 0
| 0.85381
| 0
| 0
| 0.001399
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.024883
| false
| 0
| 0.007776
| 0.007776
| 0.065319
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1b5b0b852b47426637b98a062495b55e85ec1816
| 23,866
|
py
|
Python
|
functions/light_functions.py
|
mtasa-typescript/mtasa-wiki-dump
|
edea1746850fb6c99d6155d1d7891e2cceb33a5c
|
[
"MIT"
] | null | null | null |
functions/light_functions.py
|
mtasa-typescript/mtasa-wiki-dump
|
edea1746850fb6c99d6155d1d7891e2cceb33a5c
|
[
"MIT"
] | 1
|
2021-02-24T21:50:18.000Z
|
2021-02-24T21:50:18.000Z
|
functions/light_functions.py
|
mtasa-typescript/mtasa-wiki-dump
|
edea1746850fb6c99d6155d1d7891e2cceb33a5c
|
[
"MIT"
] | null | null | null |
# Autogenerated file. ANY CHANGES WILL BE OVERWRITTEN
from to_python.core.types import FunctionType, \
FunctionArgument, \
FunctionArgumentValues, \
FunctionReturnTypes, \
FunctionSignature, \
FunctionDoc, \
FunctionData, \
CompoundFunctionData
DUMP_PARTIAL = [
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='createLight',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['light'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='lightType',
argument_type=FunctionType(
names=['int'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='posX',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='posY',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='posZ',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='radius',
argument_type=FunctionType(
names=['float'],
is_optional=True,
),
default_value='3',
)
],
[
FunctionArgument(
name='r',
argument_type=FunctionType(
names=['int'],
is_optional=True,
),
default_value='255',
)
],
[
FunctionArgument(
name='g',
argument_type=FunctionType(
names=['int'],
is_optional=True,
),
default_value='0',
)
],
[
FunctionArgument(
name='b',
argument_type=FunctionType(
names=['int'],
is_optional=True,
),
default_value='0',
)
],
[
FunctionArgument(
name='dirX',
argument_type=FunctionType(
names=['float'],
is_optional=True,
),
default_value='0',
)
],
[
FunctionArgument(
name='dirY',
argument_type=FunctionType(
names=['float'],
is_optional=True,
),
default_value='0',
)
],
[
FunctionArgument(
name='dirZ',
argument_type=FunctionType(
names=['float'],
is_optional=True,
),
default_value='0',
)
],
[
FunctionArgument(
name='createsShadow',
argument_type=FunctionType(
names=['bool'],
is_optional=True,
),
default_value='false',
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='* The direction of the light only has any effect if the light type is spot light.\n* One light will only apply illumination effects to Element/Ped|peds, Element/Player|players, wheels and number plates (like a emergency vehicle siren light does).\n* Two or more lights will apply illumination effects to everything (excluding objects) that is in range of, at least, two of them.' ,
arguments={
"lightType": """An integer representing the type of light to create. """,
"posX": """A floating point number representing the X coordinate on the map. """,
"posY": """A floating point number representing the Y coordinate on the map. """,
"posZ": """A floating point number representing the Z coordinate on the map. """,
"radius": """A floating point number representing the radius of the light. """,
"r": """An integer number representing the amount of red to use in the colouring of the light (0 - 255). """,
"g": """An integer number representing the amount of green to use in the colouring of the light (0 - 255). """,
"b": """An integer number representing the amount of blue to use in the colouring of the light (0 - 255). """,
"dirX": """A floating point number representing the light directions X coordinate on the map. """,
"dirY": """A floating point number representing the light directions Y coordinate on the map. """,
"dirZ": """A floating point number representing the light directions Z coordinate on the map. """,
"createsShadow": """A boolean representing whether or not does the light cast shadows. """
},
result='returns the element/light|light element if creation was successful, false otherwise.' ,
),
url='createLight',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='getLightColor',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['int'],
is_optional=False,
),
FunctionType(
names=['int'],
is_optional=False,
),
FunctionType(
names=['int'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='theLight',
argument_type=FunctionType(
names=['light'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function returns the color for a Element/Light|light element.' ,
arguments={
"theLight": """The Element/Light|light that you wish to retrieve the color of. """
},
result='returns three ints corresponding to the amount of red, green and blue (respectively) of the light, false if invalid arguments were passed.' ,
),
url='getLightColor',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='getLightDirection',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['float'],
is_optional=False,
),
FunctionType(
names=['float'],
is_optional=False,
),
FunctionType(
names=['float'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='theLight',
argument_type=FunctionType(
names=['light'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function returns the direction for a Element/Light|light element.' ,
arguments={
"theLight": """The Element/Light|light that you wish to retrieve the direction of. """
},
result='returns three ints corresponding to the x, y and z coordinates (respectively) of the light direction, false if invalid arguments were passed.' ,
),
url='getLightDirection',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='getLightRadius',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['float'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='theLight',
argument_type=FunctionType(
names=['light'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function returns the radius for a Element/Light|light element.' ,
arguments={
"theLight": """The Element/Light|light that you wish to retrieve the radius of. """
},
result='returns a float containing the radius of the specified light, false if invalid arguments were passed.' ,
),
url='getLightRadius',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='getLightType',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['int'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='theLight',
argument_type=FunctionType(
names=['light'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function returns the type for a Element/Light|light element.' ,
arguments={
"theLight": """The Element/Light|light that you wish to retrieve the type of. """
},
result='returns an int containing the type of the specified light, false if invalid arguments were passed.' ,
),
url='getLightType',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='setLightColor',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='theLight',
argument_type=FunctionType(
names=['light'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='r',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='g',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='b',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function sets the color for a Element/Light|light element.' ,
arguments={
"theLight": """The Element/Light|light that you wish to set the color of. """
},
result='returns true if the function was successful, false otherwise.' ,
),
url='setLightColor',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='setLightDirection',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='theLight',
argument_type=FunctionType(
names=['light'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='x',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='y',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='z',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function sets the direction for a Element/Light|light element.' ,
arguments={
"theLight": """The Element/Light|light that you wish to set the direction of. """
},
result='returns true if the function was successful, false otherwise.' ,
),
url='setLightDirection',
)
],
),
CompoundFunctionData(
server=[
],
client=[
FunctionData(
signature=FunctionSignature(
name='setLightRadius',
return_types=FunctionReturnTypes(
return_types=[
FunctionType(
names=['bool'],
is_optional=False,
)
],
variable_length=False,
),
arguments=FunctionArgumentValues(
arguments=[
[
FunctionArgument(
name='theLight',
argument_type=FunctionType(
names=['light'],
is_optional=False,
),
default_value=None,
)
],
[
FunctionArgument(
name='radius',
argument_type=FunctionType(
names=['float'],
is_optional=False,
),
default_value=None,
)
]
],
variable_length=False,
),
generic_types=[
],
),
docs=FunctionDoc(
description='This function sets the radius for a Element/Light|light element.' ,
arguments={
"theLight": """The Element/Light|light that you wish to set the radius of. """
},
result='returns true if the function was successful, false otherwise.' ,
),
url='setLightRadius',
)
],
)
]
| 40.043624
| 410
| 0.326531
| 1,223
| 23,866
| 6.264105
| 0.133279
| 0.084323
| 0.058739
| 0.098421
| 0.843362
| 0.811252
| 0.792325
| 0.677718
| 0.627725
| 0.627725
| 0
| 0.002246
| 0.608145
| 23,866
| 595
| 411
| 40.110924
| 0.816938
| 0.002137
| 0
| 0.727903
| 1
| 0.010399
| 0.150758
| 0.000966
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.006932
| 0.001733
| 0
| 0.001733
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9420e37b030db2f014b904bd14b8c767f67f7f70
| 376
|
py
|
Python
|
books/master/math/calculus.py
|
Bingwen-Hu/hackaway
|
69727d76fd652390d9660e9ea4354ba5cc76dd5c
|
[
"BSD-2-Clause"
] | null | null | null |
books/master/math/calculus.py
|
Bingwen-Hu/hackaway
|
69727d76fd652390d9660e9ea4354ba5cc76dd5c
|
[
"BSD-2-Clause"
] | null | null | null |
books/master/math/calculus.py
|
Bingwen-Hu/hackaway
|
69727d76fd652390d9660e9ea4354ba5cc76dd5c
|
[
"BSD-2-Clause"
] | null | null | null |
"""
x
y = ln tan --- - cos x * ln tan x
2
(tan x/2)' (tan x)'
y' = ---------- - [ sin x * ln tan x + cos x * --------- ]
tan x/2 tan x
其中
1
(tan x)' = -----------
(cos x)^2
1
(tan x/2)' = --- (cos x/2)^2
2
"""
| 20.888889
| 58
| 0.202128
| 44
| 376
| 1.727273
| 0.204545
| 0.421053
| 0.263158
| 0.315789
| 0.302632
| 0
| 0
| 0
| 0
| 0
| 0
| 0.065359
| 0.593085
| 376
| 18
| 59
| 20.888889
| 0.431373
| 0.941489
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 1
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
94402d3d4d7002a4210f9b79a67c520eb6dd90e1
| 32
|
py
|
Python
|
rainbowprint/__init__.py
|
Techno-Hwizrdry/rainbowprint
|
f48b984b92f383213ef3dae48a9aa186d3e10aaa
|
[
"MIT"
] | null | null | null |
rainbowprint/__init__.py
|
Techno-Hwizrdry/rainbowprint
|
f48b984b92f383213ef3dae48a9aa186d3e10aaa
|
[
"MIT"
] | null | null | null |
rainbowprint/__init__.py
|
Techno-Hwizrdry/rainbowprint
|
f48b984b92f383213ef3dae48a9aa186d3e10aaa
|
[
"MIT"
] | null | null | null |
from .rainbowprint import rprint
| 32
| 32
| 0.875
| 4
| 32
| 7
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09375
| 32
| 1
| 32
| 32
| 0.965517
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 1
| 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
| 1
| 0
| 1
| 1
|
0
| 7
|
8472f2c11e49517ca19c0a2949400f83472a3cb1
| 135
|
py
|
Python
|
tests/test_attacks.py
|
jdai8/artificial-adversary
|
66763b7fa35c00d11596e3b60bb7b177b03a18db
|
[
"MIT"
] | 363
|
2018-08-09T16:34:23.000Z
|
2022-02-16T21:46:21.000Z
|
tests/test_attacks.py
|
afsconan/artificial-adversary
|
fe577e529becf90b4e8725e795f064c9523c16c3
|
[
"MIT"
] | 9
|
2018-08-09T04:05:21.000Z
|
2020-09-21T18:22:49.000Z
|
tests/test_attacks.py
|
afsconan/artificial-adversary
|
fe577e529becf90b4e8725e795f064c9523c16c3
|
[
"MIT"
] | 54
|
2018-08-09T17:35:07.000Z
|
2021-10-03T07:31:02.000Z
|
from Adversary.attacks import *
def test_num_to_word():
assert(num_to_word('1') == 'one')
assert(num_to_word('dog') == 'dog')
| 22.5
| 39
| 0.659259
| 21
| 135
| 3.904762
| 0.619048
| 0.182927
| 0.329268
| 0.365854
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008772
| 0.155556
| 135
| 5
| 40
| 27
| 0.710526
| 0
| 0
| 0
| 0
| 0
| 0.074074
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.25
| true
| 0
| 0.25
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
84fd2ed86cca801b8107ba577ef9d60be427cde0
| 289
|
py
|
Python
|
ex2.py
|
diogocezar/dctb-python-course-utfpr
|
2d56f6566ecba3b3755f13aa9e408e130877c353
|
[
"Apache-2.0"
] | null | null | null |
ex2.py
|
diogocezar/dctb-python-course-utfpr
|
2d56f6566ecba3b3755f13aa9e408e130877c353
|
[
"Apache-2.0"
] | null | null | null |
ex2.py
|
diogocezar/dctb-python-course-utfpr
|
2d56f6566ecba3b3755f13aa9e408e130877c353
|
[
"Apache-2.0"
] | null | null | null |
a = 10
b = 3.6
print("Imprimindo quatro operações entre duas variáveis:")
print("{:.2f} + {:.2f} = {:.2f}".format(a, b, (a+b)))
print("{:.2f} - {:.2f} = {:.2f}".format(a, b, (a-b)))
print("{:.2f} / {:.2f} = {:.2f}".format(a, b, (a/b)))
print("{:.2f} * {:.2f} = {:.2f}".format(a, b, (a*b)))
| 41.285714
| 58
| 0.477509
| 48
| 289
| 2.875
| 0.291667
| 0.231884
| 0.26087
| 0.318841
| 0.608696
| 0.608696
| 0.608696
| 0.608696
| 0.608696
| 0.608696
| 0
| 0.064
| 0.134948
| 289
| 7
| 59
| 41.285714
| 0.488
| 0
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.714286
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 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
| 1
|
0
| 7
|
ca02d56d921b1346f65e931aaf19615fd3018522
| 167
|
py
|
Python
|
Windows/WaitingRoom/model.py
|
SuperSecretPryncyMafia/PKN
|
3de43c3cd2f63d29098adb92b563bbc4bd79bbf8
|
[
"MIT"
] | null | null | null |
Windows/WaitingRoom/model.py
|
SuperSecretPryncyMafia/PKN
|
3de43c3cd2f63d29098adb92b563bbc4bd79bbf8
|
[
"MIT"
] | null | null | null |
Windows/WaitingRoom/model.py
|
SuperSecretPryncyMafia/PKN
|
3de43c3cd2f63d29098adb92b563bbc4bd79bbf8
|
[
"MIT"
] | null | null | null |
class Model:
@staticmethod
def get_waiting_time_secs() -> int:
return 5
@staticmethod
def get_waiting_time() -> list:
return [1, 30]
| 16.7
| 39
| 0.598802
| 20
| 167
| 4.75
| 0.7
| 0.315789
| 0.378947
| 0.526316
| 0.610526
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034483
| 0.305389
| 167
| 10
| 40
| 16.7
| 0.784483
| 0
| 0
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| true
| 0
| 0
| 0.285714
| 0.714286
| 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
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
8e178524a84030f57718dc3e960bdf9b6e04ccd9
| 10,194
|
py
|
Python
|
tests/integration/flag_test.py
|
candy-kingdom/mints
|
e68a2351cf3ff6823e978bc6a4b740bd2a974ca3
|
[
"MIT"
] | 4
|
2020-05-09T11:01:32.000Z
|
2020-06-03T14:44:06.000Z
|
tests/integration/flag_test.py
|
candy-kingdom/cli
|
e68a2351cf3ff6823e978bc6a4b740bd2a974ca3
|
[
"MIT"
] | 43
|
2020-01-27T21:14:16.000Z
|
2020-06-18T17:57:20.000Z
|
tests/integration/flag_test.py
|
candy-kingdom/mints
|
e68a2351cf3ff6823e978bc6a4b740bd2a974ca3
|
[
"MIT"
] | null | null | null |
"""Various tests for `mints.args.flag.Flag`."""
from typing import Any
from argparse import ArgumentError
import pytest
from mints.cli import cli, CLI
from mints.args.flag import Flag
from mints.args.arg import Arg
from tests.execution import execute, redirect_stderr
@pytest.fixture(autouse=True)
def reset():
# Reset `cli` before each test
# as if we have just imported it.
globals()['cli'] = CLI()
def test_one_flag():
# Arrange.
@cli
def main(x: Flag):
return x
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx is True
def test_one_flag_with_description():
# Arrange.
@cli
def main(x: Flag('Description.')):
return x
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx is True
def test_one_flag_with_default_false():
# Arrange.
@cli
def main(x: Flag = False):
return x
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx is True
def test_one_flag_with_default_true():
# Arrange.
@cli
def main(x: Flag = True):
return x
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx is True
def test_one_flag_with_default_int():
# Arrange.
@cli
def main(x: Flag = 5):
return x
# Act.
ex = execute(cli, '--x')
# Assert.
assert isinstance(ex, ValueError)
assert 'Expected a `bool`' in str(ex)
def test_one_flag_not_specified():
# Arrange.
@cli
def main(x: Flag):
return x
# Act.
cx = execute(cli, '')
# Assert.
assert cx is False
def test_one_flag_not_specified_with_default_false():
# Arrange.
@cli
def main(x: Flag = False):
return x
# Act.
cx = execute(cli, '')
# Assert.
assert cx is False
def test_one_flag_not_specified_with_default_true():
# Arrange.
@cli
def main(x: Flag = True):
return x
# Act.
cx = execute(cli, '')
# Assert.
assert cx is True
def test_two_flags():
# Arrange.
@cli
def main(x: Flag, y: Flag):
return x, y
# Act.
cx = execute(cli, '--x --y')
# Assert.
assert cx == (True, True)
def test_two_flags_in_different_order():
# Arrange.
@cli
def main(x: Flag, y: Flag):
return x, y
# Act.
cx = execute(cli, '--y --x')
# Assert.
assert cx == (True, True)
def test_two_flags_one_specified():
# Arrange.
@cli
def main(x: Flag, y: Flag):
return x, y
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx == (True, False)
def test_two_flags_not_specified():
# Arrange.
@cli
def main(x: Flag, y: Flag):
return x, y
# Act.
cx = execute(cli, '')
# Assert.
assert cx == (False, False)
def test_one_flag_after_arg():
# Arrange.
@cli
def main(x: Arg, y: Flag):
return x, y
# Act.
cx = execute(cli, '1 --y')
# Assert.
assert cx == ('1', True)
def test_one_flag_before_arg():
# Arrange.
@cli
def main(x: Arg, y: Flag):
return x, y
# Act.
cx = execute(cli, '--y 1')
# Assert.
assert cx == ('1', True)
def test_one_arg_between_two_flags():
# Arrange.
@cli
def main(x: Arg, y: Flag, z: Flag):
return x, y, z
# Act.
cx = execute(cli, '--y 1 --z')
# Assert.
assert cx == ('1', True, True)
def test_one_arg_with_flag_not_specified():
# Arrange.
@cli
def main(x: Arg, y: Flag):
return x, y
# Act.
cx = execute(cli, '1')
# Assert.
assert cx == ('1', False)
def test_one_flag_without_short():
# Arrange.
@cli
def main(xyz: Flag):
return xyz
# Act.
ex, err = execute(cli, '-x', redirect_stderr)
# Assert.
assert isinstance(ex, SystemExit)
assert 'unrecognized arguments: -x' in err
def test_one_flag_with_explicit_short():
# Arrange.
@cli
def main(xyz: Flag(short='a')):
return xyz
# Act.
cx = execute(cli, '-a')
# Assert.
assert cx is True
def test_one_flag_with_explicit_short_and_description():
# Arrange.
@cli
def main(xyz: Flag('Description.', short='a')):
return xyz
# Act.
cx = execute(cli, '-a')
# Assert.
assert cx is True
def test_two_flags_with_same_implicit_shorts():
# Arrange.
@cli
def main(xy: Flag, xz: Flag):
return xy, xz
# Act.
cx = execute(cli, '--xy --xz')
# Assert.
assert cx == (True, True)
def test_two_flags_with_same_explicit_shorts():
# Arrange.
@cli
def main(x: Flag(short='a'), y: Flag(short='a')):
return x, y
# Act.
ex = execute(cli, '--x --y')
# Assert.
assert isinstance(ex, ArgumentError)
assert 'conflicting option string: -a' in str(ex)
def test_flag_with_empty_short():
# Arrange.
@cli
def main(x: Flag(short='')):
return x
# Act.
ex = execute(cli, '--x')
# Assert.
assert isinstance(ex, ValueError)
assert "'x' has an invalid short name '': it is an empty" in str(ex)
def test_flag_with_too_long_short():
# Arrange.
@cli
def main(x: Flag(short='ab')):
return x
# Act.
ex = execute(cli, '--x')
# Assert.
assert isinstance(ex, ValueError)
assert "'x' has an invalid short name 'ab': it consists of more than one" in str(ex)
def test_flag_with_digit_short():
# Arrange.
@cli
def main(x: Flag(short='1')):
return x
# Act.
ex = execute(cli, '--x')
# Assert.
assert isinstance(ex, ValueError)
assert "'x' has an invalid short name '1': it is not an alphabet" in str(ex)
def test_flag_with_sign_short():
# Arrange.
@cli
def main(x: Flag(short='-')):
return x
# Act.
ex = execute(cli, '--x')
# Assert.
assert isinstance(ex, ValueError)
assert "'x' has an invalid short name '-': it is not an alphabet" in str(ex)
def test_flag_specified_twice():
# Arrange.
@cli
def main(x: Flag):
return x
# Act.
cx = execute(cli, '--x --x')
# Assert.
assert cx is True
def test_flag_with_custom_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='+')):
return x
# Act.
cx = execute(cli, '++x')
# Assert.
assert cx is True
def test_flag_with_custom_prefix_and_short():
# Arrange.
@cli
def main(xyz: Flag(short='x', prefix='+')):
return xyz
# Act.
cx = execute(cli, '+x')
# Assert.
assert cx is True
def test_flag_with_custom_but_default_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='-')):
return x
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx is True
def test_flag_with_letter_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='a')):
return x
# Act.
cx = execute(cli, 'aax')
# Assert.
assert cx is True
def test_flag_with_empty_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='')):
return x
# Act.
ex = execute(cli, 'x')
# Assert.
assert isinstance(ex, ValueError)
assert "'x' has an invalid prefix '': it is an empty" in str(ex)
def test_flag_with_none_prefix():
# Arrange.
@cli
def main(x: Flag(prefix=None)):
return x
# Act.
ex = execute(cli, 'x')
# Assert.
assert isinstance(ex, ValueError)
assert "'x' has an invalid prefix 'None': it is None" in str(ex)
def test_flag_with_long_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='--')):
return x
# Act.
ex = execute(cli, '----x')
# Assert.
assert isinstance(ex, ValueError)
assert "'x' has an invalid prefix '--': it consists of more than one" in str(ex)
def test_flag_specified_with_wrong_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='+')):
return x
# Act.
ex, err = execute(cli, '--x', redirect_stderr)
# Assert.
assert isinstance(ex, SystemExit)
assert 'unrecognized arguments: --x' in err
def test_flag_with_non_string_prefix():
# Arrange.
@cli
def main(x: Flag(prefix=[1])):
return x
# Act.
ex = execute(cli, '--x')
# Assert.
assert isinstance(ex, TypeError)
assert "unhashable type: 'list'" in str(ex)
def test_two_flags_and_one_with_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='+'), y: Flag):
return x, y
# Act.
cx = execute(cli, '++x --y')
# Assert.
assert cx == (True, True)
def test_two_flags_with_same_prefix():
# Arrange.
@cli
def main(x: Flag(prefix='+'), y: Flag(prefix='+')):
return x, y
# Act.
cx = execute(cli, '++x ++y')
# Assert.
assert cx == (True, True)
def test_flag_of_subcommand_with_custom_prefix():
# Arrange.
@cli
def main():
...
@main.command
def sub(x: Flag(prefix='+')):
return x
# Act.
cx = execute(cli, 'sub ++x')
# Assert.
assert cx is True
def test_flag_with_trailing_underscore():
# Arrange.
@cli
def main(x_: Flag):
return x_
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx is True
def test_flag_with_two_trailing_underscores():
# Arrange.
@cli
def main(x__: Flag):
return x__
# Act.
cx = execute(cli, '--x')
# Assert.
assert cx is True
def test_flag_with_trailing_underscore_called_with_original_name():
# Arrange.
@cli
def main(x_: Flag):
return x_
# Act.
cx = execute(cli, '--x_')
# Assert.
assert cx is True
def test_two_flags_with_trailing_underscores_but_same_name():
# Arrange.
@cli
def main(x_: Flag, x__: Flag):
return x_, x__
# Act.
ex, err = execute(cli, '--x', redirect_stderr)
# Assert.
assert '--x could match --x_, --x__' in err
def test_two_flags_with_trailing_underscores_but_same_name_called_with_original_names():
# Arrange.
@cli
def main(x_: Flag, x__: Flag):
return x_, x__
# Act.
cx = execute(cli, '--x_ --x__')
# Assert.
assert cx == (True, True)
| 17.132773
| 88
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| 1
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0
| 7
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8e4adbe9382d3e015c0174a2335c07634b0b2d25
| 66,603
|
py
|
Python
|
tests/test_peso.py
|
fscm/multicurrency
|
5eabdcbfbf427dcafe08d4d05cfce8c9348aeb91
|
[
"MIT"
] | 2
|
2021-03-26T18:19:57.000Z
|
2021-07-27T01:15:50.000Z
|
tests/test_peso.py
|
fscm/multicurrency
|
5eabdcbfbf427dcafe08d4d05cfce8c9348aeb91
|
[
"MIT"
] | null | null | null |
tests/test_peso.py
|
fscm/multicurrency
|
5eabdcbfbf427dcafe08d4d05cfce8c9348aeb91
|
[
"MIT"
] | null | null | null |
# -*- coding: UTF-8 -*-
#
# copyright: 2020-2022, Frederico Martins
# author: Frederico Martins <http://github.com/fscm>
# license: SPDX-License-Identifier: MIT
"""Tests for the Peso currency representation(s)."""
from decimal import Context
from pytest import raises
from multicurrency import Currency
from multicurrency import (
CurrencyMismatchException,
CurrencyTypeException)
CONTEXT = Context(prec=28, rounding='ROUND_HALF_EVEN').copy()
"""Tests for the Argentine Peso representation."""
from multicurrency import ArgentinePeso
class TestArgentinePeso:
"""ArgentinePeso currency tests."""
def test_argentine_peso(self):
"""test_argentine_peso."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
argentine_peso = ArgentinePeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert argentine_peso.amount == decimal
assert argentine_peso.numeric_code == '032'
assert argentine_peso.alpha_code == 'ARS'
assert argentine_peso.decimal_places == 2
assert argentine_peso.decimal_sign == ','
assert argentine_peso.grouping_places == 3
assert argentine_peso.grouping_sign == '.'
assert not argentine_peso.international
assert argentine_peso.symbol == '$'
assert argentine_peso.symbol_ahead
assert argentine_peso.symbol_separator == '\u00A0'
assert argentine_peso.localized_symbol == 'AR$'
assert argentine_peso.convertion == ''
assert argentine_peso.__hash__() == hash(
(argentine_peso.__class__, decimal, 'ARS', '032'))
assert argentine_peso.__repr__() == (
'ArgentinePeso(amount: 0.1428571428571428571428571429, '
'alpha_code: "ARS", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "\u00A0", '
'localized_symbol: "AR$", '
'numeric_code: "032", '
'decimal_places: "2", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert argentine_peso.__str__() == '$ 0,14'
def test_argentine_peso_negative(self):
"""test_argentine_peso_negative."""
amount = -100
argentine_peso = ArgentinePeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert argentine_peso.numeric_code == '032'
assert argentine_peso.alpha_code == 'ARS'
assert argentine_peso.decimal_places == 2
assert argentine_peso.decimal_sign == ','
assert argentine_peso.grouping_places == 3
assert argentine_peso.grouping_sign == '.'
assert not argentine_peso.international
assert argentine_peso.symbol == '$'
assert argentine_peso.symbol_ahead
assert argentine_peso.symbol_separator == '\u00A0'
assert argentine_peso.localized_symbol == 'AR$'
assert argentine_peso.convertion == ''
assert argentine_peso.__hash__() == hash(
(argentine_peso.__class__, decimal, 'ARS', '032'))
assert argentine_peso.__repr__() == (
'ArgentinePeso(amount: -100, '
'alpha_code: "ARS", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "\u00A0", '
'localized_symbol: "AR$", '
'numeric_code: "032", '
'decimal_places: "2", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert argentine_peso.__str__() == '$ -100,00'
def test_argentine_peso_custom(self):
"""test_argentine_peso_custom."""
amount = 1000
argentine_peso = ArgentinePeso(
amount=amount,
decimal_places=5,
decimal_sign='.',
grouping_places=2,
grouping_sign=',',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert argentine_peso.amount == decimal
assert argentine_peso.numeric_code == '032'
assert argentine_peso.alpha_code == 'ARS'
assert argentine_peso.decimal_places == 5
assert argentine_peso.decimal_sign == '.'
assert argentine_peso.grouping_places == 2
assert argentine_peso.grouping_sign == ','
assert argentine_peso.international
assert argentine_peso.symbol == '$'
assert not argentine_peso.symbol_ahead
assert argentine_peso.symbol_separator == '_'
assert argentine_peso.localized_symbol == 'AR$'
assert argentine_peso.convertion == ''
assert argentine_peso.__hash__() == hash(
(argentine_peso.__class__, decimal, 'ARS', '032'))
assert argentine_peso.__repr__() == (
'ArgentinePeso(amount: 1000, '
'alpha_code: "ARS", '
'symbol: "$", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "AR$", '
'numeric_code: "032", '
'decimal_places: "5", '
'decimal_sign: ".", '
'grouping_places: "2", '
'grouping_sign: ",", '
'convertion: "", '
'international: True)')
assert argentine_peso.__str__() == 'ARS 10,00.00000'
def test_argentine_peso_changed(self):
"""test_cargentine_peso_changed."""
argentine_peso = ArgentinePeso(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
argentine_peso.international = True
def test_argentine_peso_math_add(self):
"""test_argentine_peso_math_add."""
argentine_peso_one = ArgentinePeso(amount=1)
argentine_peso_two = ArgentinePeso(amount=2)
argentine_peso_three = ArgentinePeso(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency ARS and OTHER.'):
_ = argentine_peso_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.ArgentinePeso\'> '
'and <class \'str\'>.')):
_ = argentine_peso_one.__add__('1.00')
assert (
argentine_peso_one +
argentine_peso_two) == argentine_peso_three
def test_argentine_peso_slots(self):
"""test_argentine_peso_slots."""
argentine_peso = ArgentinePeso(amount=1000)
with raises(
AttributeError,
match=(
'\'ArgentinePeso\' '
'object has no attribute \'new_variable\'')):
argentine_peso.new_variable = 'fail' # pylint: disable=assigning-non-slot
"""Tests for the Chilean Peso representation."""
from multicurrency import ChileanPeso
class TestChileanPeso:
"""ChileanPeso currency tests."""
def test_chilean_peso(self):
"""test_chilean_peso."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
chilean_peso = ChileanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert chilean_peso.amount == decimal
assert chilean_peso.numeric_code == '152'
assert chilean_peso.alpha_code == 'CLP'
assert chilean_peso.decimal_places == 0
assert chilean_peso.decimal_sign == ','
assert chilean_peso.grouping_places == 3
assert chilean_peso.grouping_sign == '.'
assert not chilean_peso.international
assert chilean_peso.symbol == '$'
assert chilean_peso.symbol_ahead
assert chilean_peso.symbol_separator == ''
assert chilean_peso.localized_symbol == 'CL$'
assert chilean_peso.convertion == ''
assert chilean_peso.__hash__() == hash(
(chilean_peso.__class__, decimal, 'CLP', '152'))
assert chilean_peso.__repr__() == (
'ChileanPeso(amount: 0.1428571428571428571428571429, '
'alpha_code: "CLP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "CL$", '
'numeric_code: "152", '
'decimal_places: "0", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert chilean_peso.__str__() == '$0'
def test_chilean_peso_negative(self):
"""test_chilean_peso_negative."""
amount = -100
chilean_peso = ChileanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert chilean_peso.numeric_code == '152'
assert chilean_peso.alpha_code == 'CLP'
assert chilean_peso.decimal_places == 0
assert chilean_peso.decimal_sign == ','
assert chilean_peso.grouping_places == 3
assert chilean_peso.grouping_sign == '.'
assert not chilean_peso.international
assert chilean_peso.symbol == '$'
assert chilean_peso.symbol_ahead
assert chilean_peso.symbol_separator == ''
assert chilean_peso.localized_symbol == 'CL$'
assert chilean_peso.convertion == ''
assert chilean_peso.__hash__() == hash(
(chilean_peso.__class__, decimal, 'CLP', '152'))
assert chilean_peso.__repr__() == (
'ChileanPeso(amount: -100, '
'alpha_code: "CLP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "CL$", '
'numeric_code: "152", '
'decimal_places: "0", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert chilean_peso.__str__() == '$-100'
def test_chilean_peso_custom(self):
"""test_chilean_peso_custom."""
amount = 1000
chilean_peso = ChileanPeso(
amount=amount,
decimal_places=5,
decimal_sign='.',
grouping_places=2,
grouping_sign=',',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert chilean_peso.amount == decimal
assert chilean_peso.numeric_code == '152'
assert chilean_peso.alpha_code == 'CLP'
assert chilean_peso.decimal_places == 5
assert chilean_peso.decimal_sign == '.'
assert chilean_peso.grouping_places == 2
assert chilean_peso.grouping_sign == ','
assert chilean_peso.international
assert chilean_peso.symbol == '$'
assert not chilean_peso.symbol_ahead
assert chilean_peso.symbol_separator == '_'
assert chilean_peso.localized_symbol == 'CL$'
assert chilean_peso.convertion == ''
assert chilean_peso.__hash__() == hash(
(chilean_peso.__class__, decimal, 'CLP', '152'))
assert chilean_peso.__repr__() == (
'ChileanPeso(amount: 1000, '
'alpha_code: "CLP", '
'symbol: "$", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "CL$", '
'numeric_code: "152", '
'decimal_places: "5", '
'decimal_sign: ".", '
'grouping_places: "2", '
'grouping_sign: ",", '
'convertion: "", '
'international: True)')
assert chilean_peso.__str__() == 'CLP 10,00.00000'
def test_chilean_peso_changed(self):
"""test_cchilean_peso_changed."""
chilean_peso = ChileanPeso(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
chilean_peso.international = True
def test_chilean_peso_math_add(self):
"""test_chilean_peso_math_add."""
chilean_peso_one = ChileanPeso(amount=1)
chilean_peso_two = ChileanPeso(amount=2)
chilean_peso_three = ChileanPeso(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency CLP and OTHER.'):
_ = chilean_peso_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.ChileanPeso\'> '
'and <class \'str\'>.')):
_ = chilean_peso_one.__add__('1.00')
assert (
chilean_peso_one +
chilean_peso_two) == chilean_peso_three
def test_chilean_peso_slots(self):
"""test_chilean_peso_slots."""
chilean_peso = ChileanPeso(amount=1000)
with raises(
AttributeError,
match=(
'\'ChileanPeso\' '
'object has no attribute \'new_variable\'')):
chilean_peso.new_variable = 'fail' # pylint: disable=assigning-non-slot
"""Tests for the Colombian Peso representation."""
from multicurrency import ColombianPeso
class TestColombianPeso:
"""ColombianPeso currency tests."""
def test_colombian_peso(self):
"""test_colombian_peso."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
colombian_peso = ColombianPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert colombian_peso.amount == decimal
assert colombian_peso.numeric_code == '170'
assert colombian_peso.alpha_code == 'COP'
assert colombian_peso.decimal_places == 2
assert colombian_peso.decimal_sign == ','
assert colombian_peso.grouping_places == 3
assert colombian_peso.grouping_sign == '.'
assert not colombian_peso.international
assert colombian_peso.symbol == '$'
assert colombian_peso.symbol_ahead
assert colombian_peso.symbol_separator == '\u00A0'
assert colombian_peso.localized_symbol == 'CO$'
assert colombian_peso.convertion == ''
assert colombian_peso.__hash__() == hash(
(colombian_peso.__class__, decimal, 'COP', '170'))
assert colombian_peso.__repr__() == (
'ColombianPeso(amount: 0.1428571428571428571428571429, '
'alpha_code: "COP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "\u00A0", '
'localized_symbol: "CO$", '
'numeric_code: "170", '
'decimal_places: "2", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert colombian_peso.__str__() == '$ 0,14'
def test_colombian_peso_negative(self):
"""test_colombian_peso_negative."""
amount = -100
colombian_peso = ColombianPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert colombian_peso.numeric_code == '170'
assert colombian_peso.alpha_code == 'COP'
assert colombian_peso.decimal_places == 2
assert colombian_peso.decimal_sign == ','
assert colombian_peso.grouping_places == 3
assert colombian_peso.grouping_sign == '.'
assert not colombian_peso.international
assert colombian_peso.symbol == '$'
assert colombian_peso.symbol_ahead
assert colombian_peso.symbol_separator == '\u00A0'
assert colombian_peso.localized_symbol == 'CO$'
assert colombian_peso.convertion == ''
assert colombian_peso.__hash__() == hash(
(colombian_peso.__class__, decimal, 'COP', '170'))
assert colombian_peso.__repr__() == (
'ColombianPeso(amount: -100, '
'alpha_code: "COP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "\u00A0", '
'localized_symbol: "CO$", '
'numeric_code: "170", '
'decimal_places: "2", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert colombian_peso.__str__() == '$ -100,00'
def test_colombian_peso_custom(self):
"""test_colombian_peso_custom."""
amount = 1000
colombian_peso = ColombianPeso(
amount=amount,
decimal_places=5,
decimal_sign='.',
grouping_places=2,
grouping_sign=',',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert colombian_peso.amount == decimal
assert colombian_peso.numeric_code == '170'
assert colombian_peso.alpha_code == 'COP'
assert colombian_peso.decimal_places == 5
assert colombian_peso.decimal_sign == '.'
assert colombian_peso.grouping_places == 2
assert colombian_peso.grouping_sign == ','
assert colombian_peso.international
assert colombian_peso.symbol == '$'
assert not colombian_peso.symbol_ahead
assert colombian_peso.symbol_separator == '_'
assert colombian_peso.localized_symbol == 'CO$'
assert colombian_peso.convertion == ''
assert colombian_peso.__hash__() == hash(
(colombian_peso.__class__, decimal, 'COP', '170'))
assert colombian_peso.__repr__() == (
'ColombianPeso(amount: 1000, '
'alpha_code: "COP", '
'symbol: "$", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "CO$", '
'numeric_code: "170", '
'decimal_places: "5", '
'decimal_sign: ".", '
'grouping_places: "2", '
'grouping_sign: ",", '
'convertion: "", '
'international: True)')
assert colombian_peso.__str__() == 'COP 10,00.00000'
def test_colombian_peso_changed(self):
"""test_ccolombian_peso_changed."""
colombian_peso = ColombianPeso(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
colombian_peso.international = True
def test_colombian_peso_math_add(self):
"""test_colombian_peso_math_add."""
colombian_peso_one = ColombianPeso(amount=1)
colombian_peso_two = ColombianPeso(amount=2)
colombian_peso_three = ColombianPeso(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency COP and OTHER.'):
_ = colombian_peso_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.ColombianPeso\'> '
'and <class \'str\'>.')):
_ = colombian_peso_one.__add__('1.00')
assert (
colombian_peso_one +
colombian_peso_two) == colombian_peso_three
def test_colombian_peso_slots(self):
"""test_colombian_peso_slots."""
colombian_peso = ColombianPeso(amount=1000)
with raises(
AttributeError,
match=(
'\'ColombianPeso\' '
'object has no attribute \'new_variable\'')):
colombian_peso.new_variable = 'fail' # pylint: disable=assigning-non-slot
"""Tests for the Cuban Peso representation."""
from multicurrency import CubanPeso
class TestCubanPeso:
"""CubanPeso currency tests."""
def test_cuban_peso(self):
"""test_cuban_peso."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
cuban_peso = CubanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert cuban_peso.amount == decimal
assert cuban_peso.numeric_code == '192'
assert cuban_peso.alpha_code == 'CUP'
assert cuban_peso.decimal_places == 2
assert cuban_peso.decimal_sign == '.'
assert cuban_peso.grouping_places == 3
assert cuban_peso.grouping_sign == ','
assert not cuban_peso.international
assert cuban_peso.symbol == '$'
assert cuban_peso.symbol_ahead
assert cuban_peso.symbol_separator == ''
assert cuban_peso.localized_symbol == 'CU$'
assert cuban_peso.convertion == ''
assert cuban_peso.__hash__() == hash(
(cuban_peso.__class__, decimal, 'CUP', '192'))
assert cuban_peso.__repr__() == (
'CubanPeso(amount: 0.1428571428571428571428571429, '
'alpha_code: "CUP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "CU$", '
'numeric_code: "192", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert cuban_peso.__str__() == '$0.14'
def test_cuban_peso_negative(self):
"""test_cuban_peso_negative."""
amount = -100
cuban_peso = CubanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert cuban_peso.numeric_code == '192'
assert cuban_peso.alpha_code == 'CUP'
assert cuban_peso.decimal_places == 2
assert cuban_peso.decimal_sign == '.'
assert cuban_peso.grouping_places == 3
assert cuban_peso.grouping_sign == ','
assert not cuban_peso.international
assert cuban_peso.symbol == '$'
assert cuban_peso.symbol_ahead
assert cuban_peso.symbol_separator == ''
assert cuban_peso.localized_symbol == 'CU$'
assert cuban_peso.convertion == ''
assert cuban_peso.__hash__() == hash(
(cuban_peso.__class__, decimal, 'CUP', '192'))
assert cuban_peso.__repr__() == (
'CubanPeso(amount: -100, '
'alpha_code: "CUP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "CU$", '
'numeric_code: "192", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert cuban_peso.__str__() == '$-100.00'
def test_cuban_peso_custom(self):
"""test_cuban_peso_custom."""
amount = 1000
cuban_peso = CubanPeso(
amount=amount,
decimal_places=5,
decimal_sign=',',
grouping_places=2,
grouping_sign='.',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert cuban_peso.amount == decimal
assert cuban_peso.numeric_code == '192'
assert cuban_peso.alpha_code == 'CUP'
assert cuban_peso.decimal_places == 5
assert cuban_peso.decimal_sign == ','
assert cuban_peso.grouping_places == 2
assert cuban_peso.grouping_sign == '.'
assert cuban_peso.international
assert cuban_peso.symbol == '$'
assert not cuban_peso.symbol_ahead
assert cuban_peso.symbol_separator == '_'
assert cuban_peso.localized_symbol == 'CU$'
assert cuban_peso.convertion == ''
assert cuban_peso.__hash__() == hash(
(cuban_peso.__class__, decimal, 'CUP', '192'))
assert cuban_peso.__repr__() == (
'CubanPeso(amount: 1000, '
'alpha_code: "CUP", '
'symbol: "$", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "CU$", '
'numeric_code: "192", '
'decimal_places: "5", '
'decimal_sign: ",", '
'grouping_places: "2", '
'grouping_sign: ".", '
'convertion: "", '
'international: True)')
assert cuban_peso.__str__() == 'CUP 10,00.00000'
def test_cuban_peso_changed(self):
"""test_ccuban_peso_changed."""
cuban_peso = CubanPeso(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
cuban_peso.international = True
def test_cuban_peso_math_add(self):
"""test_cuban_peso_math_add."""
cuban_peso_one = CubanPeso(amount=1)
cuban_peso_two = CubanPeso(amount=2)
cuban_peso_three = CubanPeso(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency CUP and OTHER.'):
_ = cuban_peso_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.CubanPeso\'> '
'and <class \'str\'>.')):
_ = cuban_peso_one.__add__('1.00')
assert (
cuban_peso_one +
cuban_peso_two) == cuban_peso_three
def test_cuban_peso_slots(self):
"""test_cuban_peso_slots."""
cuban_peso = CubanPeso(amount=1000)
with raises(
AttributeError,
match=(
'\'CubanPeso\' '
'object has no attribute \'new_variable\'')):
cuban_peso.new_variable = 'fail' # pylint: disable=assigning-non-slot
"""Tests for the Dominican Peso representation."""
from multicurrency import DominicanPeso
class TestDominicanPeso:
"""DominicanPeso currency tests."""
def test_dominican_peso(self):
"""test_dominican_peso."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
dominican_peso = DominicanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert dominican_peso.amount == decimal
assert dominican_peso.numeric_code == '214'
assert dominican_peso.alpha_code == 'DOP'
assert dominican_peso.decimal_places == 2
assert dominican_peso.decimal_sign == '.'
assert dominican_peso.grouping_places == 3
assert dominican_peso.grouping_sign == ','
assert not dominican_peso.international
assert dominican_peso.symbol == '$'
assert dominican_peso.symbol_ahead
assert dominican_peso.symbol_separator == ''
assert dominican_peso.localized_symbol == 'DO$'
assert dominican_peso.convertion == ''
assert dominican_peso.__hash__() == hash(
(dominican_peso.__class__, decimal, 'DOP', '214'))
assert dominican_peso.__repr__() == (
'DominicanPeso(amount: 0.1428571428571428571428571429, '
'alpha_code: "DOP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "DO$", '
'numeric_code: "214", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert dominican_peso.__str__() == '$0.14'
def test_dominican_peso_negative(self):
"""test_dominican_peso_negative."""
amount = -100
dominican_peso = DominicanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert dominican_peso.numeric_code == '214'
assert dominican_peso.alpha_code == 'DOP'
assert dominican_peso.decimal_places == 2
assert dominican_peso.decimal_sign == '.'
assert dominican_peso.grouping_places == 3
assert dominican_peso.grouping_sign == ','
assert not dominican_peso.international
assert dominican_peso.symbol == '$'
assert dominican_peso.symbol_ahead
assert dominican_peso.symbol_separator == ''
assert dominican_peso.localized_symbol == 'DO$'
assert dominican_peso.convertion == ''
assert dominican_peso.__hash__() == hash(
(dominican_peso.__class__, decimal, 'DOP', '214'))
assert dominican_peso.__repr__() == (
'DominicanPeso(amount: -100, '
'alpha_code: "DOP", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "DO$", '
'numeric_code: "214", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert dominican_peso.__str__() == '$-100.00'
def test_dominican_peso_custom(self):
"""test_dominican_peso_custom."""
amount = 1000
dominican_peso = DominicanPeso(
amount=amount,
decimal_places=5,
decimal_sign=',',
grouping_places=2,
grouping_sign='.',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert dominican_peso.amount == decimal
assert dominican_peso.numeric_code == '214'
assert dominican_peso.alpha_code == 'DOP'
assert dominican_peso.decimal_places == 5
assert dominican_peso.decimal_sign == ','
assert dominican_peso.grouping_places == 2
assert dominican_peso.grouping_sign == '.'
assert dominican_peso.international
assert dominican_peso.symbol == '$'
assert not dominican_peso.symbol_ahead
assert dominican_peso.symbol_separator == '_'
assert dominican_peso.localized_symbol == 'DO$'
assert dominican_peso.convertion == ''
assert dominican_peso.__hash__() == hash(
(dominican_peso.__class__, decimal, 'DOP', '214'))
assert dominican_peso.__repr__() == (
'DominicanPeso(amount: 1000, '
'alpha_code: "DOP", '
'symbol: "$", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "DO$", '
'numeric_code: "214", '
'decimal_places: "5", '
'decimal_sign: ",", '
'grouping_places: "2", '
'grouping_sign: ".", '
'convertion: "", '
'international: True)')
assert dominican_peso.__str__() == 'DOP 10,00.00000'
def test_dominican_peso_changed(self):
"""test_cdominican_peso_changed."""
dominican_peso = DominicanPeso(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
dominican_peso.international = True
def test_dominican_peso_math_add(self):
"""test_dominican_peso_math_add."""
dominican_peso_one = DominicanPeso(amount=1)
dominican_peso_two = DominicanPeso(amount=2)
dominican_peso_three = DominicanPeso(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency DOP and OTHER.'):
_ = dominican_peso_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.DominicanPeso\'> '
'and <class \'str\'>.')):
_ = dominican_peso_one.__add__('1.00')
assert (
dominican_peso_one +
dominican_peso_two) == dominican_peso_three
def test_dominican_peso_slots(self):
"""test_dominican_peso_slots."""
dominican_peso = DominicanPeso(amount=1000)
with raises(
AttributeError,
match=(
'\'DominicanPeso\' '
'object has no attribute \'new_variable\'')):
dominican_peso.new_variable = 'fail' # pylint: disable=assigning-non-slot
"""Tests for the Mexican Peso representation."""
from multicurrency import MexicanPeso
class TestMexicanPeso:
"""MexicanPeso currency tests."""
def test_mexican_peso(self):
"""test_mexican_peso."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
mexican_peso = MexicanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert mexican_peso.amount == decimal
assert mexican_peso.numeric_code == '484'
assert mexican_peso.alpha_code == 'MXN'
assert mexican_peso.decimal_places == 2
assert mexican_peso.decimal_sign == '.'
assert mexican_peso.grouping_places == 3
assert mexican_peso.grouping_sign == ','
assert not mexican_peso.international
assert mexican_peso.symbol == '$'
assert mexican_peso.symbol_ahead
assert mexican_peso.symbol_separator == ''
assert mexican_peso.localized_symbol == 'MX$'
assert mexican_peso.convertion == ''
assert mexican_peso.__hash__() == hash(
(mexican_peso.__class__, decimal, 'MXN', '484'))
assert mexican_peso.__repr__() == (
'MexicanPeso(amount: 0.1428571428571428571428571429, '
'alpha_code: "MXN", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "MX$", '
'numeric_code: "484", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert mexican_peso.__str__() == '$0.14'
def test_mexican_peso_negative(self):
"""test_mexican_peso_negative."""
amount = -100
mexican_peso = MexicanPeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert mexican_peso.numeric_code == '484'
assert mexican_peso.alpha_code == 'MXN'
assert mexican_peso.decimal_places == 2
assert mexican_peso.decimal_sign == '.'
assert mexican_peso.grouping_places == 3
assert mexican_peso.grouping_sign == ','
assert not mexican_peso.international
assert mexican_peso.symbol == '$'
assert mexican_peso.symbol_ahead
assert mexican_peso.symbol_separator == ''
assert mexican_peso.localized_symbol == 'MX$'
assert mexican_peso.convertion == ''
assert mexican_peso.__hash__() == hash(
(mexican_peso.__class__, decimal, 'MXN', '484'))
assert mexican_peso.__repr__() == (
'MexicanPeso(amount: -100, '
'alpha_code: "MXN", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "MX$", '
'numeric_code: "484", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert mexican_peso.__str__() == '$-100.00'
def test_mexican_peso_custom(self):
"""test_mexican_peso_custom."""
amount = 1000
mexican_peso = MexicanPeso(
amount=amount,
decimal_places=5,
decimal_sign=',',
grouping_places=2,
grouping_sign='.',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert mexican_peso.amount == decimal
assert mexican_peso.numeric_code == '484'
assert mexican_peso.alpha_code == 'MXN'
assert mexican_peso.decimal_places == 5
assert mexican_peso.decimal_sign == ','
assert mexican_peso.grouping_places == 2
assert mexican_peso.grouping_sign == '.'
assert mexican_peso.international
assert mexican_peso.symbol == '$'
assert not mexican_peso.symbol_ahead
assert mexican_peso.symbol_separator == '_'
assert mexican_peso.localized_symbol == 'MX$'
assert mexican_peso.convertion == ''
assert mexican_peso.__hash__() == hash(
(mexican_peso.__class__, decimal, 'MXN', '484'))
assert mexican_peso.__repr__() == (
'MexicanPeso(amount: 1000, '
'alpha_code: "MXN", '
'symbol: "$", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "MX$", '
'numeric_code: "484", '
'decimal_places: "5", '
'decimal_sign: ",", '
'grouping_places: "2", '
'grouping_sign: ".", '
'convertion: "", '
'international: True)')
assert mexican_peso.__str__() == 'MXN 10,00.00000'
def test_mexican_peso_changed(self):
"""test_cmexican_peso_changed."""
mexican_peso = MexicanPeso(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
mexican_peso.international = True
def test_mexican_peso_math_add(self):
"""test_mexican_peso_math_add."""
mexican_peso_one = MexicanPeso(amount=1)
mexican_peso_two = MexicanPeso(amount=2)
mexican_peso_three = MexicanPeso(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency MXN and OTHER.'):
_ = mexican_peso_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.MexicanPeso\'> '
'and <class \'str\'>.')):
_ = mexican_peso_one.__add__('1.00')
assert (
mexican_peso_one +
mexican_peso_two) == mexican_peso_three
def test_mexican_peso_slots(self):
"""test_mexican_peso_slots."""
mexican_peso = MexicanPeso(amount=1000)
with raises(
AttributeError,
match=(
'\'MexicanPeso\' '
'object has no attribute \'new_variable\'')):
mexican_peso.new_variable = 'fail' # pylint: disable=assigning-non-slot
"""Tests for the Philippine Peso representation."""
from multicurrency import PhilippinePeso
class TestPhilippinePeso:
"""PhilippinePeso currency tests."""
def test_philippine_peso(self):
"""test_philippine_peso."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
philippine_peso = PhilippinePeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert philippine_peso.amount == decimal
assert philippine_peso.numeric_code == '608'
assert philippine_peso.alpha_code == 'PHP'
assert philippine_peso.decimal_places == 2
assert philippine_peso.decimal_sign == '.'
assert philippine_peso.grouping_places == 3
assert philippine_peso.grouping_sign == ','
assert not philippine_peso.international
assert philippine_peso.symbol == '₱'
assert philippine_peso.symbol_ahead
assert philippine_peso.symbol_separator == ''
assert philippine_peso.localized_symbol == '₱'
assert philippine_peso.convertion == ''
assert philippine_peso.__hash__() == hash(
(philippine_peso.__class__, decimal, 'PHP', '608'))
assert philippine_peso.__repr__() == (
'PhilippinePeso(amount: 0.1428571428571428571428571429, '
'alpha_code: "PHP", '
'symbol: "₱", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "₱", '
'numeric_code: "608", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert philippine_peso.__str__() == '₱0.14'
def test_philippine_peso_negative(self):
"""test_philippine_peso_negative."""
amount = -100
philippine_peso = PhilippinePeso(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert philippine_peso.numeric_code == '608'
assert philippine_peso.alpha_code == 'PHP'
assert philippine_peso.decimal_places == 2
assert philippine_peso.decimal_sign == '.'
assert philippine_peso.grouping_places == 3
assert philippine_peso.grouping_sign == ','
assert not philippine_peso.international
assert philippine_peso.symbol == '₱'
assert philippine_peso.symbol_ahead
assert philippine_peso.symbol_separator == ''
assert philippine_peso.localized_symbol == '₱'
assert philippine_peso.convertion == ''
assert philippine_peso.__hash__() == hash(
(philippine_peso.__class__, decimal, 'PHP', '608'))
assert philippine_peso.__repr__() == (
'PhilippinePeso(amount: -100, '
'alpha_code: "PHP", '
'symbol: "₱", '
'symbol_ahead: True, '
'symbol_separator: "", '
'localized_symbol: "₱", '
'numeric_code: "608", '
'decimal_places: "2", '
'decimal_sign: ".", '
'grouping_places: "3", '
'grouping_sign: ",", '
'convertion: "", '
'international: False)')
assert philippine_peso.__str__() == '₱-100.00'
def test_philippine_peso_custom(self):
"""test_philippine_peso_custom."""
amount = 1000
philippine_peso = PhilippinePeso(
amount=amount,
decimal_places=5,
decimal_sign=',',
grouping_places=2,
grouping_sign='.',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert philippine_peso.amount == decimal
assert philippine_peso.numeric_code == '608'
assert philippine_peso.alpha_code == 'PHP'
assert philippine_peso.decimal_places == 5
assert philippine_peso.decimal_sign == ','
assert philippine_peso.grouping_places == 2
assert philippine_peso.grouping_sign == '.'
assert philippine_peso.international
assert philippine_peso.symbol == '₱'
assert not philippine_peso.symbol_ahead
assert philippine_peso.symbol_separator == '_'
assert philippine_peso.localized_symbol == '₱'
assert philippine_peso.convertion == ''
assert philippine_peso.__hash__() == hash(
(philippine_peso.__class__, decimal, 'PHP', '608'))
assert philippine_peso.__repr__() == (
'PhilippinePeso(amount: 1000, '
'alpha_code: "PHP", '
'symbol: "₱", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "₱", '
'numeric_code: "608", '
'decimal_places: "5", '
'decimal_sign: ",", '
'grouping_places: "2", '
'grouping_sign: ".", '
'convertion: "", '
'international: True)')
assert philippine_peso.__str__() == 'PHP 10,00.00000'
def test_philippine_peso_changed(self):
"""test_cphilippine_peso_changed."""
philippine_peso = PhilippinePeso(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
philippine_peso.international = True
def test_philippine_peso_math_add(self):
"""test_philippine_peso_math_add."""
philippine_peso_one = PhilippinePeso(amount=1)
philippine_peso_two = PhilippinePeso(amount=2)
philippine_peso_three = PhilippinePeso(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency PHP and OTHER.'):
_ = philippine_peso_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.PhilippinePeso\'> '
'and <class \'str\'>.')):
_ = philippine_peso_one.__add__('1.00')
assert (
philippine_peso_one +
philippine_peso_two) == philippine_peso_three
def test_philippine_peso_slots(self):
"""test_philippine_peso_slots."""
philippine_peso = PhilippinePeso(amount=1000)
with raises(
AttributeError,
match=(
'\'PhilippinePeso\' '
'object has no attribute \'new_variable\'')):
philippine_peso.new_variable = 'fail' # pylint: disable=assigning-non-slot
"""Tests for the Peso Uruguayo representation."""
from multicurrency import PesoUruguayo
class TestPesoUruguayo:
"""PesoUruguayo currency tests."""
def test_peso_uruguayo(self):
"""test_peso_uruguayo."""
amount = CONTEXT.create_decimal(1) / CONTEXT.create_decimal(7)
peso_uruguayo = PesoUruguayo(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert peso_uruguayo.amount == decimal
assert peso_uruguayo.numeric_code == '858'
assert peso_uruguayo.alpha_code == 'UYU'
assert peso_uruguayo.decimal_places == 2
assert peso_uruguayo.decimal_sign == ','
assert peso_uruguayo.grouping_places == 3
assert peso_uruguayo.grouping_sign == '.'
assert not peso_uruguayo.international
assert peso_uruguayo.symbol == '$'
assert peso_uruguayo.symbol_ahead
assert peso_uruguayo.symbol_separator == '\u00A0'
assert peso_uruguayo.localized_symbol == 'UY$'
assert peso_uruguayo.convertion == ''
assert peso_uruguayo.__hash__() == hash(
(peso_uruguayo.__class__, decimal, 'UYU', '858'))
assert peso_uruguayo.__repr__() == (
'PesoUruguayo(amount: 0.1428571428571428571428571429, '
'alpha_code: "UYU", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "\u00A0", '
'localized_symbol: "UY$", '
'numeric_code: "858", '
'decimal_places: "2", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert peso_uruguayo.__str__() == '$ 0,14'
def test_peso_uruguayo_negative(self):
"""test_peso_uruguayo_negative."""
amount = -100
peso_uruguayo = PesoUruguayo(amount=amount)
decimal = CONTEXT.create_decimal(amount)
assert peso_uruguayo.numeric_code == '858'
assert peso_uruguayo.alpha_code == 'UYU'
assert peso_uruguayo.decimal_places == 2
assert peso_uruguayo.decimal_sign == ','
assert peso_uruguayo.grouping_places == 3
assert peso_uruguayo.grouping_sign == '.'
assert not peso_uruguayo.international
assert peso_uruguayo.symbol == '$'
assert peso_uruguayo.symbol_ahead
assert peso_uruguayo.symbol_separator == '\u00A0'
assert peso_uruguayo.localized_symbol == 'UY$'
assert peso_uruguayo.convertion == ''
assert peso_uruguayo.__hash__() == hash(
(peso_uruguayo.__class__, decimal, 'UYU', '858'))
assert peso_uruguayo.__repr__() == (
'PesoUruguayo(amount: -100, '
'alpha_code: "UYU", '
'symbol: "$", '
'symbol_ahead: True, '
'symbol_separator: "\u00A0", '
'localized_symbol: "UY$", '
'numeric_code: "858", '
'decimal_places: "2", '
'decimal_sign: ",", '
'grouping_places: "3", '
'grouping_sign: ".", '
'convertion: "", '
'international: False)')
assert peso_uruguayo.__str__() == '$ -100,00'
def test_peso_uruguayo_custom(self):
"""test_peso_uruguayo_custom."""
amount = 1000
peso_uruguayo = PesoUruguayo(
amount=amount,
decimal_places=5,
decimal_sign='.',
grouping_places=2,
grouping_sign=',',
international=True,
symbol_ahead=False,
symbol_separator='_')
decimal = CONTEXT.create_decimal(amount)
assert peso_uruguayo.amount == decimal
assert peso_uruguayo.numeric_code == '858'
assert peso_uruguayo.alpha_code == 'UYU'
assert peso_uruguayo.decimal_places == 5
assert peso_uruguayo.decimal_sign == '.'
assert peso_uruguayo.grouping_places == 2
assert peso_uruguayo.grouping_sign == ','
assert peso_uruguayo.international
assert peso_uruguayo.symbol == '$'
assert not peso_uruguayo.symbol_ahead
assert peso_uruguayo.symbol_separator == '_'
assert peso_uruguayo.localized_symbol == 'UY$'
assert peso_uruguayo.convertion == ''
assert peso_uruguayo.__hash__() == hash(
(peso_uruguayo.__class__, decimal, 'UYU', '858'))
assert peso_uruguayo.__repr__() == (
'PesoUruguayo(amount: 1000, '
'alpha_code: "UYU", '
'symbol: "$", '
'symbol_ahead: False, '
'symbol_separator: "_", '
'localized_symbol: "UY$", '
'numeric_code: "858", '
'decimal_places: "5", '
'decimal_sign: ".", '
'grouping_places: "2", '
'grouping_sign: ",", '
'convertion: "", '
'international: True)')
assert peso_uruguayo.__str__() == 'UYU 10,00.00000'
def test_peso_uruguayo_changed(self):
"""test_cpeso_uruguayo_changed."""
peso_uruguayo = PesoUruguayo(amount=1000)
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.amount = 999
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.alpha_code = 'EUR'
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.convertion = '0123456789,.'
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.symbol_ahead = False
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.symbol_separator = '_'
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.localized_symbol = '€'
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.numeric_code = '978'
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.decimal_places = 3
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.decimal_sign = ','
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.grouping_places = 4
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.grouping_sign = '.'
with raises(
AttributeError,
match='can\'t set attribute'):
peso_uruguayo.international = True
def test_peso_uruguayo_math_add(self):
"""test_peso_uruguayo_math_add."""
peso_uruguayo_one = PesoUruguayo(amount=1)
peso_uruguayo_two = PesoUruguayo(amount=2)
peso_uruguayo_three = PesoUruguayo(amount=3)
currency = Currency(amount=1, alpha_code='OTHER')
with raises(
CurrencyMismatchException,
match='unsupported operation between currency UYU and OTHER.'):
_ = peso_uruguayo_one + currency
with raises(
CurrencyTypeException,
match=(
'unsupported operation between <class \'multicurrency.'
'peso.PesoUruguayo\'> '
'and <class \'str\'>.')):
_ = peso_uruguayo_one.__add__('1.00')
assert (
peso_uruguayo_one +
peso_uruguayo_two) == peso_uruguayo_three
def test_peso_uruguayo_slots(self):
"""test_peso_uruguayo_slots."""
peso_uruguayo = PesoUruguayo(amount=1000)
with raises(
AttributeError,
match=(
'\'PesoUruguayo\' '
'object has no attribute \'new_variable\'')):
peso_uruguayo.new_variable = 'fail' # pylint: disable=assigning-non-slot
| 39.040445
| 87
| 0.563999
| 6,119
| 66,603
| 5.808792
| 0.028273
| 0.036012
| 0.075625
| 0.09138
| 0.862846
| 0.8281
| 0.798419
| 0.797434
| 0.777262
| 0.749803
| 0
| 0.02524
| 0.327223
| 66,603
| 1,705
| 88
| 39.063343
| 0.767324
| 0.029879
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| 0.757615
| 0
| 0
| 0.144971
| 0.007869
| 0
| 0
| 0
| 0
| 0.248866
| 1
| 0.031108
| false
| 0
| 0.007777
| 0
| 0.04407
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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0
| 7
|
6d01d5a19e01905f9338ef782108a2ea8c8da840
| 21,497
|
py
|
Python
|
tensorforce/core/layers/convolution.py
|
YannBerthelot/tensorforce
|
ff97c3d1fabd4f00fb38eb0f3e14ac1af62bbdc3
|
[
"Apache-2.0"
] | null | null | null |
tensorforce/core/layers/convolution.py
|
YannBerthelot/tensorforce
|
ff97c3d1fabd4f00fb38eb0f3e14ac1af62bbdc3
|
[
"Apache-2.0"
] | null | null | null |
tensorforce/core/layers/convolution.py
|
YannBerthelot/tensorforce
|
ff97c3d1fabd4f00fb38eb0f3e14ac1af62bbdc3
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2020 Tensorforce Team. All Rights Reserved.
#
# 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 tensorflow as tf
from tensorflow.python.keras.utils.conv_utils import conv_output_length, deconv_output_length
from tensorforce import TensorforceError, util
from tensorforce.core import TensorSpec, tf_function, tf_util
from tensorforce.core.layers import TransformationBase
class Conv1d(TransformationBase):
"""
1-dimensional convolutional layer (specification key: `conv1d`).
Args:
size (int >= 0): Layer output size, 0 implies additionally removing the axis
(<span style="color:#C00000"><b>required</b></span>).
window (int > 0): Window size
(<span style="color:#00C000"><b>default</b></span>: 3).
stride (int > 0): Stride size
(<span style="color:#00C000"><b>default</b></span>: 1).
padding ('same' | 'valid'): Padding type, see
`TensorFlow docs <https://www.tensorflow.org/api_docs/python/tf/nn/convolution>`__
(<span style="color:#00C000"><b>default</b></span>: 'same').
dilation (int > 0 | (int > 0, int > 0)): Dilation value
(<span style="color:#00C000"><b>default</b></span>: 1).
bias (bool): Whether to add a trainable bias variable
(<span style="color:#00C000"><b>default</b></span>: true).
activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' |
'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity
(<span style="color:#00C000"><b>default</b></span>: relu).
dropout (parameter, 0.0 <= float < 1.0): Dropout rate
(<span style="color:#00C000"><b>default</b></span>: 0.0).
initialization_scale (float > 0.0): Initialization scale
(<span style="color:#00C000"><b>default</b></span>: 1.0).
vars_trainable (bool): Whether layer variables are trainable
(<span style="color:#00C000"><b>default</b></span>: true).
l2_regularization (float >= 0.0): Scalar controlling L2 regularization
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
name (string): Layer name
(<span style="color:#00C000"><b>default</b></span>: internally chosen).
input_spec (specification): <span style="color:#00C000"><b>internal use</b></span>.
"""
def __init__(
self, *, size, window=3, stride=1, padding='same', dilation=1, bias=True, activation='relu',
dropout=0.0, initialization_scale=1.0, vars_trainable=True, l2_regularization=None,
name=None, input_spec=None
):
self.window = window
self.stride = stride
self.padding = padding
self.dilation = dilation
super().__init__(
size=size, bias=bias, activation=activation, dropout=dropout,
vars_trainable=vars_trainable, l2_regularization=l2_regularization, name=name,
input_spec=input_spec
)
self.initialization_scale = initialization_scale
def default_input_spec(self):
return TensorSpec(type='float', shape=(0, 0))
def output_spec(self):
output_spec = super().output_spec()
length = conv_output_length(
input_length=output_spec.shape[0], filter_size=self.window, padding=self.padding,
stride=self.stride, dilation=self.dilation
)
if self.squeeze:
output_spec.shape = (length,)
else:
output_spec.shape = (length, self.size)
output_spec.min_value = None
output_spec.max_value = None
return output_spec
def initialize(self):
super().initialize()
in_size = self.input_spec.shape[1]
initializer = 'orthogonal'
if self.activation is not None and self.activation.nonlinearity == 'relu':
initializer += '-relu'
self.weights = self.variable(
name='weights', spec=TensorSpec(type='float', shape=(self.window, in_size, self.size)),
initializer=initializer, initialization_scale=self.initialization_scale,
is_trainable=self.vars_trainable, is_saved=True
)
@tf_function(num_args=1)
def apply(self, *, x):
x = tf.nn.conv1d(
input=x, filters=self.weights, stride=self.stride, padding=self.padding.upper(),
dilations=self.dilation
)
return super().apply(x=x)
class Conv2d(TransformationBase):
"""
2-dimensional convolutional layer (specification key: `conv2d`).
Args:
size (int >= 0): Layer output size, 0 implies additionally removing the axis
(<span style="color:#C00000"><b>required</b></span>).
window (int > 0 | (int > 0, int > 0)): Window size
(<span style="color:#00C000"><b>default</b></span>: 3).
stride (int > 0 | (int > 0, int > 0)): Stride size
(<span style="color:#00C000"><b>default</b></span>: 1).
padding ('same' | 'valid'): Padding type, see
`TensorFlow docs <https://www.tensorflow.org/api_docs/python/tf/nn/convolution>`__
(<span style="color:#00C000"><b>default</b></span>: 'same').
dilation (int > 0 | (int > 0, int > 0)): Dilation value
(<span style="color:#00C000"><b>default</b></span>: 1).
bias (bool): Whether to add a trainable bias variable
(<span style="color:#00C000"><b>default</b></span>: true).
activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' |
'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity
(<span style="color:#00C000"><b>default</b></span>: "relu").
dropout (parameter, 0.0 <= float < 1.0): Dropout rate
(<span style="color:#00C000"><b>default</b></span>: 0.0).
initialization_scale (float > 0.0): Initialization scale
(<span style="color:#00C000"><b>default</b></span>: 1.0).
vars_trainable (bool): Whether layer variables are trainable
(<span style="color:#00C000"><b>default</b></span>: true).
l2_regularization (float >= 0.0): Scalar controlling L2 regularization
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
name (string): Layer name
(<span style="color:#00C000"><b>default</b></span>: internally chosen).
input_spec (specification): <span style="color:#00C000"><b>internal use</b></span>.
"""
def __init__(
self, *, size, window=3, stride=1, padding='same', dilation=1, bias=True, activation='relu',
dropout=0.0, initialization_scale=1.0, vars_trainable=True, l2_regularization=None,
name=None, input_spec=None
):
if isinstance(window, int):
self.window = (window, window)
elif util.is_iterable(x=window) and len(window) == 2:
self.window = tuple(window)
else:
raise TensorforceError.type(name='Conv2d', argument='window', dtype=type(window))
if isinstance(stride, int):
self.stride = (1, stride, stride, 1)
elif util.is_iterable(x=stride) and len(stride) == 2:
self.stride = (1, stride[0], stride[1], 1)
else:
raise TensorforceError.type(name='Conv2d', argument='stride', dtype=type(stride))
self.padding = padding
if isinstance(dilation, int):
self.dilation = (1, dilation, dilation, 1)
elif util.is_iterable(x=dilation) and len(dilation) == 2:
self.dilation = (1, dilation[0], dilation[1], 1)
else:
raise TensorforceError.type(name='Conv2d', argument='dilation', dtype=type(dilation))
super().__init__(
name=name, size=size, bias=bias, activation=activation, dropout=dropout,
vars_trainable=vars_trainable, input_spec=input_spec,
l2_regularization=l2_regularization
)
self.initialization_scale = initialization_scale
def default_input_spec(self):
return TensorSpec(type='float', shape=(0, 0, 0))
def output_spec(self):
output_spec = super().output_spec()
height = conv_output_length(
input_length=output_spec.shape[0], filter_size=self.window[0], padding=self.padding,
stride=self.stride[1], dilation=self.dilation[1]
)
width = conv_output_length(
input_length=output_spec.shape[1], filter_size=self.window[1], padding=self.padding,
stride=self.stride[2], dilation=self.dilation[2]
)
if self.squeeze:
output_spec.shape = (height, width)
else:
output_spec.shape = (height, width, self.size)
output_spec.min_value = None
output_spec.max_value = None
return output_spec
def initialize(self):
super().initialize()
in_size = self.input_spec.shape[2]
initializer = 'orthogonal'
if self.activation is not None and self.activation.nonlinearity == 'relu':
initializer += '-relu'
self.weights = self.variable(
name='weights',
spec=TensorSpec(type='float', shape=(self.window + (in_size, self.size))),
initializer=initializer, initialization_scale=self.initialization_scale,
is_trainable=self.vars_trainable, is_saved=True
)
@tf_function(num_args=1)
def apply(self, *, x):
x = tf.nn.conv2d(
input=x, filters=self.weights, strides=self.stride, padding=self.padding.upper(),
dilations=self.dilation
)
return super().apply(x=x)
class Conv1dTranspose(TransformationBase):
"""
1-dimensional transposed convolutional layer, also known as deconvolution layer
(specification key: `deconv1d`).
Args:
size (int >= 0): Layer output size, 0 implies additionally removing the axis
(<span style="color:#C00000"><b>required</b></span>).
window (int > 0): Window size
(<span style="color:#00C000"><b>default</b></span>: 3).
output_width (int > 0): Output width
(<span style="color:#00C000"><b>default</b></span>: same as input).
stride (int > 0): Stride size
(<span style="color:#00C000"><b>default</b></span>: 1).
padding ('same' | 'valid'): Padding type, see
`TensorFlow docs <https://www.tensorflow.org/api_docs/python/tf/nn/convolution>`__
(<span style="color:#00C000"><b>default</b></span>: 'same').
dilation (int > 0 | (int > 0, int > 0)): Dilation value
(<span style="color:#00C000"><b>default</b></span>: 1).
bias (bool): Whether to add a trainable bias variable
(<span style="color:#00C000"><b>default</b></span>: true).
activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' |
'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity
(<span style="color:#00C000"><b>default</b></span>: "relu").
dropout (parameter, 0.0 <= float < 1.0): Dropout rate
(<span style="color:#00C000"><b>default</b></span>: 0.0).
initialization_scale (float > 0.0): Initialization scale
(<span style="color:#00C000"><b>default</b></span>: 1.0).
vars_trainable (bool): Whether layer variables are trainable
(<span style="color:#00C000"><b>default</b></span>: true).
l2_regularization (float >= 0.0): Scalar controlling L2 regularization
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
name (string): Layer name
(<span style="color:#00C000"><b>default</b></span>: internally chosen).
input_spec (specification): <span style="color:#00C000"><b>internal use</b></span>.
"""
def __init__(
self, *, size, window=3, output_width=None, stride=1, padding='same', dilation=1, bias=True,
activation='relu', dropout=0.0, initialization_scale=1.0, vars_trainable=True,
l2_regularization=None, name=None, input_spec=None
):
self.window = window
if output_width is None:
self.output_shape = None
elif self.squeeze:
self.output_shape = (output_width, max(1, size))
self.stride = stride
self.padding = padding
self.dilation = dilation
super().__init__(
name=name, size=size, bias=bias, activation=activation, dropout=dropout,
vars_trainable=vars_trainable, input_spec=input_spec,
l2_regularization=l2_regularization
)
self.initialization_scale = initialization_scale
def default_input_spec(self):
return TensorSpec(type='float', shape=(0, 0))
def output_spec(self):
output_spec = super().output_spec()
width = deconv_output_length(
input_length=output_spec.shape[0], filter_size=self.window, padding=self.padding,
stride=self.stride, dilation=self.dilation
)
if self.output_shape is None:
self.output_shape = (width, self.size)
if self.squeeze:
output_spec.shape = self.output_shape[:1]
else:
output_spec.shape = self.output_shape
output_spec.min_value = None
output_spec.max_value = None
return output_spec
def initialize(self):
super().initialize()
in_size = self.input_spec.shape[1]
initializer = 'orthogonal'
if self.activation is not None and self.activation.nonlinearity == 'relu':
initializer += '-relu'
self.weights = self.variable(
name='weights', spec=TensorSpec(type='float', shape=(self.window, in_size, self.size)),
initializer=initializer, initialization_scale=self.initialization_scale,
is_trainable=self.vars_trainable, is_saved=True
)
@tf_function(num_args=1)
def apply(self, *, x):
output_shape = tf.concat(values=[
tf_util.cast(x=tf.shape(input=x)[:1], dtype='int'),
tf_util.constant(value=self.output_shape, dtype='int')
], axis=0)
x = tf.nn.conv1d_transpose(
input=x, filters=self.weights, output_shape=tf_util.int32(x=output_shape),
strides=self.stride, padding=self.padding.upper(), dilations=self.dilation
)
return super().apply(x=x)
class Conv2dTranspose(TransformationBase):
"""
2-dimensional transposed convolutional layer, also known as deconvolution layer
(specification key: `deconv2d`).
Args:
size (int >= 0): Layer output size, 0 implies additionally removing the axis
(<span style="color:#C00000"><b>required</b></span>).
window (int > 0 | (int > 0, int > 0)): Window size
(<span style="color:#00C000"><b>default</b></span>: 3).
output_shape (int > 0 | (int > 0, int > 0)): Output shape
(<span style="color:#00C000"><b>default</b></span>: same as input).
stride (int > 0 | (int > 0, int > 0)): Stride size
(<span style="color:#00C000"><b>default</b></span>: 1).
padding ('same' | 'valid'): Padding type, see
`TensorFlow docs <https://www.tensorflow.org/api_docs/python/tf/nn/convolution>`__
(<span style="color:#00C000"><b>default</b></span>: 'same').
dilation (int > 0 | (int > 0, int > 0)): Dilation value
(<span style="color:#00C000"><b>default</b></span>: 1).
bias (bool): Whether to add a trainable bias variable
(<span style="color:#00C000"><b>default</b></span>: true).
activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' |
'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity
(<span style="color:#00C000"><b>default</b></span>: "relu").
dropout (parameter, 0.0 <= float < 1.0): Dropout rate
(<span style="color:#00C000"><b>default</b></span>: 0.0).
initialization_scale (float > 0.0): Initialization scale
(<span style="color:#00C000"><b>default</b></span>: 1.0).
vars_trainable (bool): Whether layer variables are trainable
(<span style="color:#00C000"><b>default</b></span>: true).
l2_regularization (float >= 0.0): Scalar controlling L2 regularization
(<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
name (string): Layer name
(<span style="color:#00C000"><b>default</b></span>: internally chosen).
input_spec (specification): <span style="color:#00C000"><b>internal use</b></span>.
"""
def __init__(
self, *, size, window=3, output_shape=None, stride=1, padding='same', dilation=1, bias=True,
activation='relu', dropout=0.0, initialization_scale=1.0, vars_trainable=True,
l2_regularization=None, name=None, input_spec=None
):
if isinstance(window, int):
self.window = (window, window)
elif util.is_iterable(x=window) and len(window) == 2:
self.window = tuple(window)
else:
raise TensorforceError.type(
name='Conv2dTranspose', argument='window', dtype=type(window)
)
if output_shape is None:
self.output_shape = None
elif isinstance(output_shape, int):
self.output_shape = (output_shape, output_shape, max(1, size))
elif util.is_iterable(x=window) and len(output_shape) == 2:
self.output_shape = (output_shape[0], output_shape[1], max(1, size))
else:
raise TensorforceError.type(
name='Conv2dTranspose', argument='window', dtype=type(output_shape)
)
if isinstance(stride, int):
self.stride = (1, stride, stride, 1)
elif util.is_iterable(x=stride) and len(stride) == 2:
self.stride = (1, stride[0], stride[1], 1)
else:
raise TensorforceError.type(
name='Conv2dTranspose', argument='stride', dtype=type(stride)
)
self.padding = padding
if isinstance(dilation, int):
self.dilation = (1, dilation, dilation, 1)
elif len(dilation) == 2:
self.dilation = (1, dilation[0], dilation[1], 1)
else:
raise TensorforceError.type(
name='Conv2dTranspose', argument='dilation', dtype=type(dilation)
)
super().__init__(
name=name, size=size, bias=bias, activation=activation, dropout=dropout,
vars_trainable=vars_trainable, input_spec=input_spec,
l2_regularization=l2_regularization
)
self.initialization_scale = initialization_scale
def default_input_spec(self):
return TensorSpec(type='float', shape=(0, 0, 0))
def output_spec(self):
output_spec = super().output_spec()
height = deconv_output_length(
input_length=output_spec.shape[0], filter_size=self.window[0], padding=self.padding,
stride=self.stride[1], dilation=self.dilation[1]
)
width = deconv_output_length(
input_length=output_spec.shape[1], filter_size=self.window[1], padding=self.padding,
stride=self.stride[2], dilation=self.dilation[2]
)
if self.output_shape is None:
self.output_shape = (height, width, self.size)
if self.squeeze:
output_spec.shape = self.output_shape[: 2]
else:
output_spec.shape = self.output_shape
output_spec.min_value = None
output_spec.max_value = None
return output_spec
def initialize(self):
super().initialize()
in_size = self.input_spec.shape[2]
initializer = 'orthogonal'
if self.activation is not None and self.activation.nonlinearity == 'relu':
initializer += '-relu'
self.weights = self.variable(
name='weights',
spec=TensorSpec(type='float', shape=(self.window + (in_size, self.size))),
initializer=initializer, initialization_scale=self.initialization_scale,
is_trainable=self.vars_trainable, is_saved=True
)
@tf_function(num_args=1)
def apply(self, *, x):
output_shape = tf.concat(values=[
tf_util.cast(x=tf.shape(input=x)[:1], dtype='int'),
tf_util.constant(value=self.output_shape, dtype='int')
], axis=0)
x = tf.nn.conv2d_transpose(
input=x, filters=self.weights, output_shape=tf_util.int32(x=output_shape),
strides=self.stride, padding=self.padding.upper(), dilations=self.dilation
)
return super().apply(x=x)
| 42.908184
| 100
| 0.606875
| 2,589
| 21,497
| 4.930089
| 0.082271
| 0.038076
| 0.059229
| 0.078345
| 0.907161
| 0.888906
| 0.881072
| 0.874569
| 0.871827
| 0.862661
| 0
| 0.032865
| 0.248407
| 21,497
| 500
| 101
| 42.994
| 0.757133
| 0.397637
| 0
| 0.739927
| 0
| 0
| 0.025155
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.07326
| false
| 0
| 0.018315
| 0.014652
| 0.150183
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 1
| 0
| 0
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| null | 0
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|
0
| 7
|
6d17632551751868e8863e1dc716ac6e9f2fe9cf
| 33,128
|
py
|
Python
|
sdk/python/pulumi_aws_native/cloudtrail/trail.py
|
AaronFriel/pulumi-aws-native
|
5621690373ac44accdbd20b11bae3be1baf022d1
|
[
"Apache-2.0"
] | 29
|
2021-09-30T19:32:07.000Z
|
2022-03-22T21:06:08.000Z
|
sdk/python/pulumi_aws_native/cloudtrail/trail.py
|
AaronFriel/pulumi-aws-native
|
5621690373ac44accdbd20b11bae3be1baf022d1
|
[
"Apache-2.0"
] | 232
|
2021-09-30T19:26:26.000Z
|
2022-03-31T23:22:06.000Z
|
sdk/python/pulumi_aws_native/cloudtrail/trail.py
|
AaronFriel/pulumi-aws-native
|
5621690373ac44accdbd20b11bae3be1baf022d1
|
[
"Apache-2.0"
] | 4
|
2021-11-10T19:42:01.000Z
|
2022-02-05T10:15:49.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['TrailArgs', 'Trail']
@pulumi.input_type
class TrailArgs:
def __init__(__self__, *,
is_logging: pulumi.Input[bool],
s3_bucket_name: pulumi.Input[str],
cloud_watch_logs_log_group_arn: Optional[pulumi.Input[str]] = None,
cloud_watch_logs_role_arn: Optional[pulumi.Input[str]] = None,
enable_log_file_validation: Optional[pulumi.Input[bool]] = None,
event_selectors: Optional[pulumi.Input[Sequence[pulumi.Input['TrailEventSelectorArgs']]]] = None,
include_global_service_events: Optional[pulumi.Input[bool]] = None,
insight_selectors: Optional[pulumi.Input[Sequence[pulumi.Input['TrailInsightSelectorArgs']]]] = None,
is_multi_region_trail: Optional[pulumi.Input[bool]] = None,
is_organization_trail: Optional[pulumi.Input[bool]] = None,
k_ms_key_id: Optional[pulumi.Input[str]] = None,
s3_key_prefix: Optional[pulumi.Input[str]] = None,
sns_topic_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input['TrailTagArgs']]]] = None,
trail_name: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a Trail resource.
:param pulumi.Input[bool] is_logging: Whether the CloudTrail is currently logging AWS API calls.
:param pulumi.Input[str] s3_bucket_name: Specifies the name of the Amazon S3 bucket designated for publishing log files. See Amazon S3 Bucket Naming Requirements.
:param pulumi.Input[str] cloud_watch_logs_log_group_arn: Specifies a log group name using an Amazon Resource Name (ARN), a unique identifier that represents the log group to which CloudTrail logs will be delivered. Not required unless you specify CloudWatchLogsRoleArn.
:param pulumi.Input[str] cloud_watch_logs_role_arn: Specifies the role for the CloudWatch Logs endpoint to assume to write to a user's log group.
:param pulumi.Input[bool] enable_log_file_validation: Specifies whether log file validation is enabled. The default is false.
:param pulumi.Input[Sequence[pulumi.Input['TrailEventSelectorArgs']]] event_selectors: Use event selectors to further specify the management and data event settings for your trail. By default, trails created without specific event selectors will be configured to log all read and write management events, and no data events. When an event occurs in your account, CloudTrail evaluates the event selector for all trails. For each trail, if the event matches any event selector, the trail processes and logs the event. If the event doesn't match any event selector, the trail doesn't log the event. You can configure up to five event selectors for a trail.
:param pulumi.Input[bool] include_global_service_events: Specifies whether the trail is publishing events from global services such as IAM to the log files.
:param pulumi.Input[Sequence[pulumi.Input['TrailInsightSelectorArgs']]] insight_selectors: Lets you enable Insights event logging by specifying the Insights selectors that you want to enable on an existing trail.
:param pulumi.Input[bool] is_multi_region_trail: Specifies whether the trail applies only to the current region or to all regions. The default is false. If the trail exists only in the current region and this value is set to true, shadow trails (replications of the trail) will be created in the other regions. If the trail exists in all regions and this value is set to false, the trail will remain in the region where it was created, and its shadow trails in other regions will be deleted. As a best practice, consider using trails that log events in all regions.
:param pulumi.Input[bool] is_organization_trail: Specifies whether the trail is created for all accounts in an organization in AWS Organizations, or only for the current AWS account. The default is false, and cannot be true unless the call is made on behalf of an AWS account that is the master account for an organization in AWS Organizations.
:param pulumi.Input[str] k_ms_key_id: Specifies the KMS key ID to use to encrypt the logs delivered by CloudTrail. The value can be an alias name prefixed by 'alias/', a fully specified ARN to an alias, a fully specified ARN to a key, or a globally unique identifier.
:param pulumi.Input[str] s3_key_prefix: Specifies the Amazon S3 key prefix that comes after the name of the bucket you have designated for log file delivery. For more information, see Finding Your CloudTrail Log Files. The maximum length is 200 characters.
:param pulumi.Input[str] sns_topic_name: Specifies the name of the Amazon SNS topic defined for notification of log file delivery. The maximum length is 256 characters.
"""
pulumi.set(__self__, "is_logging", is_logging)
pulumi.set(__self__, "s3_bucket_name", s3_bucket_name)
if cloud_watch_logs_log_group_arn is not None:
pulumi.set(__self__, "cloud_watch_logs_log_group_arn", cloud_watch_logs_log_group_arn)
if cloud_watch_logs_role_arn is not None:
pulumi.set(__self__, "cloud_watch_logs_role_arn", cloud_watch_logs_role_arn)
if enable_log_file_validation is not None:
pulumi.set(__self__, "enable_log_file_validation", enable_log_file_validation)
if event_selectors is not None:
pulumi.set(__self__, "event_selectors", event_selectors)
if include_global_service_events is not None:
pulumi.set(__self__, "include_global_service_events", include_global_service_events)
if insight_selectors is not None:
pulumi.set(__self__, "insight_selectors", insight_selectors)
if is_multi_region_trail is not None:
pulumi.set(__self__, "is_multi_region_trail", is_multi_region_trail)
if is_organization_trail is not None:
pulumi.set(__self__, "is_organization_trail", is_organization_trail)
if k_ms_key_id is not None:
pulumi.set(__self__, "k_ms_key_id", k_ms_key_id)
if s3_key_prefix is not None:
pulumi.set(__self__, "s3_key_prefix", s3_key_prefix)
if sns_topic_name is not None:
pulumi.set(__self__, "sns_topic_name", sns_topic_name)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if trail_name is not None:
pulumi.set(__self__, "trail_name", trail_name)
@property
@pulumi.getter(name="isLogging")
def is_logging(self) -> pulumi.Input[bool]:
"""
Whether the CloudTrail is currently logging AWS API calls.
"""
return pulumi.get(self, "is_logging")
@is_logging.setter
def is_logging(self, value: pulumi.Input[bool]):
pulumi.set(self, "is_logging", value)
@property
@pulumi.getter(name="s3BucketName")
def s3_bucket_name(self) -> pulumi.Input[str]:
"""
Specifies the name of the Amazon S3 bucket designated for publishing log files. See Amazon S3 Bucket Naming Requirements.
"""
return pulumi.get(self, "s3_bucket_name")
@s3_bucket_name.setter
def s3_bucket_name(self, value: pulumi.Input[str]):
pulumi.set(self, "s3_bucket_name", value)
@property
@pulumi.getter(name="cloudWatchLogsLogGroupArn")
def cloud_watch_logs_log_group_arn(self) -> Optional[pulumi.Input[str]]:
"""
Specifies a log group name using an Amazon Resource Name (ARN), a unique identifier that represents the log group to which CloudTrail logs will be delivered. Not required unless you specify CloudWatchLogsRoleArn.
"""
return pulumi.get(self, "cloud_watch_logs_log_group_arn")
@cloud_watch_logs_log_group_arn.setter
def cloud_watch_logs_log_group_arn(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cloud_watch_logs_log_group_arn", value)
@property
@pulumi.getter(name="cloudWatchLogsRoleArn")
def cloud_watch_logs_role_arn(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the role for the CloudWatch Logs endpoint to assume to write to a user's log group.
"""
return pulumi.get(self, "cloud_watch_logs_role_arn")
@cloud_watch_logs_role_arn.setter
def cloud_watch_logs_role_arn(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cloud_watch_logs_role_arn", value)
@property
@pulumi.getter(name="enableLogFileValidation")
def enable_log_file_validation(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether log file validation is enabled. The default is false.
"""
return pulumi.get(self, "enable_log_file_validation")
@enable_log_file_validation.setter
def enable_log_file_validation(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "enable_log_file_validation", value)
@property
@pulumi.getter(name="eventSelectors")
def event_selectors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['TrailEventSelectorArgs']]]]:
"""
Use event selectors to further specify the management and data event settings for your trail. By default, trails created without specific event selectors will be configured to log all read and write management events, and no data events. When an event occurs in your account, CloudTrail evaluates the event selector for all trails. For each trail, if the event matches any event selector, the trail processes and logs the event. If the event doesn't match any event selector, the trail doesn't log the event. You can configure up to five event selectors for a trail.
"""
return pulumi.get(self, "event_selectors")
@event_selectors.setter
def event_selectors(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['TrailEventSelectorArgs']]]]):
pulumi.set(self, "event_selectors", value)
@property
@pulumi.getter(name="includeGlobalServiceEvents")
def include_global_service_events(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether the trail is publishing events from global services such as IAM to the log files.
"""
return pulumi.get(self, "include_global_service_events")
@include_global_service_events.setter
def include_global_service_events(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "include_global_service_events", value)
@property
@pulumi.getter(name="insightSelectors")
def insight_selectors(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['TrailInsightSelectorArgs']]]]:
"""
Lets you enable Insights event logging by specifying the Insights selectors that you want to enable on an existing trail.
"""
return pulumi.get(self, "insight_selectors")
@insight_selectors.setter
def insight_selectors(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['TrailInsightSelectorArgs']]]]):
pulumi.set(self, "insight_selectors", value)
@property
@pulumi.getter(name="isMultiRegionTrail")
def is_multi_region_trail(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether the trail applies only to the current region or to all regions. The default is false. If the trail exists only in the current region and this value is set to true, shadow trails (replications of the trail) will be created in the other regions. If the trail exists in all regions and this value is set to false, the trail will remain in the region where it was created, and its shadow trails in other regions will be deleted. As a best practice, consider using trails that log events in all regions.
"""
return pulumi.get(self, "is_multi_region_trail")
@is_multi_region_trail.setter
def is_multi_region_trail(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "is_multi_region_trail", value)
@property
@pulumi.getter(name="isOrganizationTrail")
def is_organization_trail(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether the trail is created for all accounts in an organization in AWS Organizations, or only for the current AWS account. The default is false, and cannot be true unless the call is made on behalf of an AWS account that is the master account for an organization in AWS Organizations.
"""
return pulumi.get(self, "is_organization_trail")
@is_organization_trail.setter
def is_organization_trail(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "is_organization_trail", value)
@property
@pulumi.getter(name="kMSKeyId")
def k_ms_key_id(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the KMS key ID to use to encrypt the logs delivered by CloudTrail. The value can be an alias name prefixed by 'alias/', a fully specified ARN to an alias, a fully specified ARN to a key, or a globally unique identifier.
"""
return pulumi.get(self, "k_ms_key_id")
@k_ms_key_id.setter
def k_ms_key_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "k_ms_key_id", value)
@property
@pulumi.getter(name="s3KeyPrefix")
def s3_key_prefix(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the Amazon S3 key prefix that comes after the name of the bucket you have designated for log file delivery. For more information, see Finding Your CloudTrail Log Files. The maximum length is 200 characters.
"""
return pulumi.get(self, "s3_key_prefix")
@s3_key_prefix.setter
def s3_key_prefix(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "s3_key_prefix", value)
@property
@pulumi.getter(name="snsTopicName")
def sns_topic_name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of the Amazon SNS topic defined for notification of log file delivery. The maximum length is 256 characters.
"""
return pulumi.get(self, "sns_topic_name")
@sns_topic_name.setter
def sns_topic_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "sns_topic_name", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['TrailTagArgs']]]]:
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['TrailTagArgs']]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="trailName")
def trail_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "trail_name")
@trail_name.setter
def trail_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "trail_name", value)
class Trail(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
cloud_watch_logs_log_group_arn: Optional[pulumi.Input[str]] = None,
cloud_watch_logs_role_arn: Optional[pulumi.Input[str]] = None,
enable_log_file_validation: Optional[pulumi.Input[bool]] = None,
event_selectors: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailEventSelectorArgs']]]]] = None,
include_global_service_events: Optional[pulumi.Input[bool]] = None,
insight_selectors: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailInsightSelectorArgs']]]]] = None,
is_logging: Optional[pulumi.Input[bool]] = None,
is_multi_region_trail: Optional[pulumi.Input[bool]] = None,
is_organization_trail: Optional[pulumi.Input[bool]] = None,
k_ms_key_id: Optional[pulumi.Input[str]] = None,
s3_bucket_name: Optional[pulumi.Input[str]] = None,
s3_key_prefix: Optional[pulumi.Input[str]] = None,
sns_topic_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailTagArgs']]]]] = None,
trail_name: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Creates a trail that specifies the settings for delivery of log data to an Amazon S3 bucket. A maximum of five trails can exist in a region, irrespective of the region in which they were created.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] cloud_watch_logs_log_group_arn: Specifies a log group name using an Amazon Resource Name (ARN), a unique identifier that represents the log group to which CloudTrail logs will be delivered. Not required unless you specify CloudWatchLogsRoleArn.
:param pulumi.Input[str] cloud_watch_logs_role_arn: Specifies the role for the CloudWatch Logs endpoint to assume to write to a user's log group.
:param pulumi.Input[bool] enable_log_file_validation: Specifies whether log file validation is enabled. The default is false.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailEventSelectorArgs']]]] event_selectors: Use event selectors to further specify the management and data event settings for your trail. By default, trails created without specific event selectors will be configured to log all read and write management events, and no data events. When an event occurs in your account, CloudTrail evaluates the event selector for all trails. For each trail, if the event matches any event selector, the trail processes and logs the event. If the event doesn't match any event selector, the trail doesn't log the event. You can configure up to five event selectors for a trail.
:param pulumi.Input[bool] include_global_service_events: Specifies whether the trail is publishing events from global services such as IAM to the log files.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailInsightSelectorArgs']]]] insight_selectors: Lets you enable Insights event logging by specifying the Insights selectors that you want to enable on an existing trail.
:param pulumi.Input[bool] is_logging: Whether the CloudTrail is currently logging AWS API calls.
:param pulumi.Input[bool] is_multi_region_trail: Specifies whether the trail applies only to the current region or to all regions. The default is false. If the trail exists only in the current region and this value is set to true, shadow trails (replications of the trail) will be created in the other regions. If the trail exists in all regions and this value is set to false, the trail will remain in the region where it was created, and its shadow trails in other regions will be deleted. As a best practice, consider using trails that log events in all regions.
:param pulumi.Input[bool] is_organization_trail: Specifies whether the trail is created for all accounts in an organization in AWS Organizations, or only for the current AWS account. The default is false, and cannot be true unless the call is made on behalf of an AWS account that is the master account for an organization in AWS Organizations.
:param pulumi.Input[str] k_ms_key_id: Specifies the KMS key ID to use to encrypt the logs delivered by CloudTrail. The value can be an alias name prefixed by 'alias/', a fully specified ARN to an alias, a fully specified ARN to a key, or a globally unique identifier.
:param pulumi.Input[str] s3_bucket_name: Specifies the name of the Amazon S3 bucket designated for publishing log files. See Amazon S3 Bucket Naming Requirements.
:param pulumi.Input[str] s3_key_prefix: Specifies the Amazon S3 key prefix that comes after the name of the bucket you have designated for log file delivery. For more information, see Finding Your CloudTrail Log Files. The maximum length is 200 characters.
:param pulumi.Input[str] sns_topic_name: Specifies the name of the Amazon SNS topic defined for notification of log file delivery. The maximum length is 256 characters.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: TrailArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Creates a trail that specifies the settings for delivery of log data to an Amazon S3 bucket. A maximum of five trails can exist in a region, irrespective of the region in which they were created.
:param str resource_name: The name of the resource.
:param TrailArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(TrailArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
cloud_watch_logs_log_group_arn: Optional[pulumi.Input[str]] = None,
cloud_watch_logs_role_arn: Optional[pulumi.Input[str]] = None,
enable_log_file_validation: Optional[pulumi.Input[bool]] = None,
event_selectors: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailEventSelectorArgs']]]]] = None,
include_global_service_events: Optional[pulumi.Input[bool]] = None,
insight_selectors: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailInsightSelectorArgs']]]]] = None,
is_logging: Optional[pulumi.Input[bool]] = None,
is_multi_region_trail: Optional[pulumi.Input[bool]] = None,
is_organization_trail: Optional[pulumi.Input[bool]] = None,
k_ms_key_id: Optional[pulumi.Input[str]] = None,
s3_bucket_name: Optional[pulumi.Input[str]] = None,
s3_key_prefix: Optional[pulumi.Input[str]] = None,
sns_topic_name: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['TrailTagArgs']]]]] = None,
trail_name: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = TrailArgs.__new__(TrailArgs)
__props__.__dict__["cloud_watch_logs_log_group_arn"] = cloud_watch_logs_log_group_arn
__props__.__dict__["cloud_watch_logs_role_arn"] = cloud_watch_logs_role_arn
__props__.__dict__["enable_log_file_validation"] = enable_log_file_validation
__props__.__dict__["event_selectors"] = event_selectors
__props__.__dict__["include_global_service_events"] = include_global_service_events
__props__.__dict__["insight_selectors"] = insight_selectors
if is_logging is None and not opts.urn:
raise TypeError("Missing required property 'is_logging'")
__props__.__dict__["is_logging"] = is_logging
__props__.__dict__["is_multi_region_trail"] = is_multi_region_trail
__props__.__dict__["is_organization_trail"] = is_organization_trail
__props__.__dict__["k_ms_key_id"] = k_ms_key_id
if s3_bucket_name is None and not opts.urn:
raise TypeError("Missing required property 's3_bucket_name'")
__props__.__dict__["s3_bucket_name"] = s3_bucket_name
__props__.__dict__["s3_key_prefix"] = s3_key_prefix
__props__.__dict__["sns_topic_name"] = sns_topic_name
__props__.__dict__["tags"] = tags
__props__.__dict__["trail_name"] = trail_name
__props__.__dict__["arn"] = None
__props__.__dict__["sns_topic_arn"] = None
super(Trail, __self__).__init__(
'aws-native:cloudtrail:Trail',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'Trail':
"""
Get an existing Trail resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = TrailArgs.__new__(TrailArgs)
__props__.__dict__["arn"] = None
__props__.__dict__["cloud_watch_logs_log_group_arn"] = None
__props__.__dict__["cloud_watch_logs_role_arn"] = None
__props__.__dict__["enable_log_file_validation"] = None
__props__.__dict__["event_selectors"] = None
__props__.__dict__["include_global_service_events"] = None
__props__.__dict__["insight_selectors"] = None
__props__.__dict__["is_logging"] = None
__props__.__dict__["is_multi_region_trail"] = None
__props__.__dict__["is_organization_trail"] = None
__props__.__dict__["k_ms_key_id"] = None
__props__.__dict__["s3_bucket_name"] = None
__props__.__dict__["s3_key_prefix"] = None
__props__.__dict__["sns_topic_arn"] = None
__props__.__dict__["sns_topic_name"] = None
__props__.__dict__["tags"] = None
__props__.__dict__["trail_name"] = None
return Trail(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def arn(self) -> pulumi.Output[str]:
return pulumi.get(self, "arn")
@property
@pulumi.getter(name="cloudWatchLogsLogGroupArn")
def cloud_watch_logs_log_group_arn(self) -> pulumi.Output[Optional[str]]:
"""
Specifies a log group name using an Amazon Resource Name (ARN), a unique identifier that represents the log group to which CloudTrail logs will be delivered. Not required unless you specify CloudWatchLogsRoleArn.
"""
return pulumi.get(self, "cloud_watch_logs_log_group_arn")
@property
@pulumi.getter(name="cloudWatchLogsRoleArn")
def cloud_watch_logs_role_arn(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the role for the CloudWatch Logs endpoint to assume to write to a user's log group.
"""
return pulumi.get(self, "cloud_watch_logs_role_arn")
@property
@pulumi.getter(name="enableLogFileValidation")
def enable_log_file_validation(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies whether log file validation is enabled. The default is false.
"""
return pulumi.get(self, "enable_log_file_validation")
@property
@pulumi.getter(name="eventSelectors")
def event_selectors(self) -> pulumi.Output[Optional[Sequence['outputs.TrailEventSelector']]]:
"""
Use event selectors to further specify the management and data event settings for your trail. By default, trails created without specific event selectors will be configured to log all read and write management events, and no data events. When an event occurs in your account, CloudTrail evaluates the event selector for all trails. For each trail, if the event matches any event selector, the trail processes and logs the event. If the event doesn't match any event selector, the trail doesn't log the event. You can configure up to five event selectors for a trail.
"""
return pulumi.get(self, "event_selectors")
@property
@pulumi.getter(name="includeGlobalServiceEvents")
def include_global_service_events(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies whether the trail is publishing events from global services such as IAM to the log files.
"""
return pulumi.get(self, "include_global_service_events")
@property
@pulumi.getter(name="insightSelectors")
def insight_selectors(self) -> pulumi.Output[Optional[Sequence['outputs.TrailInsightSelector']]]:
"""
Lets you enable Insights event logging by specifying the Insights selectors that you want to enable on an existing trail.
"""
return pulumi.get(self, "insight_selectors")
@property
@pulumi.getter(name="isLogging")
def is_logging(self) -> pulumi.Output[bool]:
"""
Whether the CloudTrail is currently logging AWS API calls.
"""
return pulumi.get(self, "is_logging")
@property
@pulumi.getter(name="isMultiRegionTrail")
def is_multi_region_trail(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies whether the trail applies only to the current region or to all regions. The default is false. If the trail exists only in the current region and this value is set to true, shadow trails (replications of the trail) will be created in the other regions. If the trail exists in all regions and this value is set to false, the trail will remain in the region where it was created, and its shadow trails in other regions will be deleted. As a best practice, consider using trails that log events in all regions.
"""
return pulumi.get(self, "is_multi_region_trail")
@property
@pulumi.getter(name="isOrganizationTrail")
def is_organization_trail(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies whether the trail is created for all accounts in an organization in AWS Organizations, or only for the current AWS account. The default is false, and cannot be true unless the call is made on behalf of an AWS account that is the master account for an organization in AWS Organizations.
"""
return pulumi.get(self, "is_organization_trail")
@property
@pulumi.getter(name="kMSKeyId")
def k_ms_key_id(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the KMS key ID to use to encrypt the logs delivered by CloudTrail. The value can be an alias name prefixed by 'alias/', a fully specified ARN to an alias, a fully specified ARN to a key, or a globally unique identifier.
"""
return pulumi.get(self, "k_ms_key_id")
@property
@pulumi.getter(name="s3BucketName")
def s3_bucket_name(self) -> pulumi.Output[str]:
"""
Specifies the name of the Amazon S3 bucket designated for publishing log files. See Amazon S3 Bucket Naming Requirements.
"""
return pulumi.get(self, "s3_bucket_name")
@property
@pulumi.getter(name="s3KeyPrefix")
def s3_key_prefix(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the Amazon S3 key prefix that comes after the name of the bucket you have designated for log file delivery. For more information, see Finding Your CloudTrail Log Files. The maximum length is 200 characters.
"""
return pulumi.get(self, "s3_key_prefix")
@property
@pulumi.getter(name="snsTopicArn")
def sns_topic_arn(self) -> pulumi.Output[str]:
return pulumi.get(self, "sns_topic_arn")
@property
@pulumi.getter(name="snsTopicName")
def sns_topic_name(self) -> pulumi.Output[Optional[str]]:
"""
Specifies the name of the Amazon SNS topic defined for notification of log file delivery. The maximum length is 256 characters.
"""
return pulumi.get(self, "sns_topic_name")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Sequence['outputs.TrailTag']]]:
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="trailName")
def trail_name(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "trail_name")
| 62.387947
| 679
| 0.705596
| 4,472
| 33,128
| 4.987701
| 0.063953
| 0.060659
| 0.058776
| 0.031562
| 0.907241
| 0.879444
| 0.838422
| 0.78525
| 0.760054
| 0.713831
| 0
| 0.003069
| 0.213083
| 33,128
| 530
| 680
| 62.50566
| 0.852545
| 0.411404
| 0
| 0.442815
| 1
| 0
| 0.146181
| 0.076275
| 0
| 0
| 0
| 0
| 0
| 1
| 0.155425
| false
| 0.002933
| 0.02346
| 0.017595
| 0.281525
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6103d75ab7ad78635128fe5abc3d80a3daa76b41
| 156
|
py
|
Python
|
starfish/purchase/__init__.py
|
datacraft-dsc/starfish-py
|
95ff24410f056e8e2d313c3af97439fe003e294a
|
[
"Apache-2.0"
] | 4
|
2019-02-08T03:47:36.000Z
|
2019-10-17T21:45:23.000Z
|
starfish/purchase/__init__.py
|
datacraft-dsc/starfish-py
|
95ff24410f056e8e2d313c3af97439fe003e294a
|
[
"Apache-2.0"
] | 81
|
2019-02-09T01:01:51.000Z
|
2020-07-01T08:35:07.000Z
|
starfish/purchase/__init__.py
|
oceanprotocol/ocean-py
|
318ad0de2519e61d0a301c040a48d1839cd82425
|
[
"Apache-2.0"
] | 1
|
2021-01-28T12:14:03.000Z
|
2021-01-28T12:14:03.000Z
|
from starfish.purchase.purchase import Purchase # noqa: F401
from starfish.purchase.purchase_base import PurchaseBase # noqa: F401
| 31.2
| 76
| 0.698718
| 17
| 156
| 6.352941
| 0.470588
| 0.222222
| 0.37037
| 0.518519
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.051282
| 0.25
| 156
| 4
| 77
| 39
| 0.871795
| 0.134615
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
fcf686bf539355eb79f10c01f77474d1da7b440a
| 54
|
py
|
Python
|
dash_wrapper/dash_table/__init__.py
|
gioxc88/dash-api
|
b9baed4ac404ee04a612334a9b88c15175d970bd
|
[
"MIT"
] | 1
|
2020-10-14T08:22:23.000Z
|
2020-10-14T08:22:23.000Z
|
dash_wrapper/dash_table/__init__.py
|
gioxc88/dash-wrapper
|
b9baed4ac404ee04a612334a9b88c15175d970bd
|
[
"MIT"
] | null | null | null |
dash_wrapper/dash_table/__init__.py
|
gioxc88/dash-wrapper
|
b9baed4ac404ee04a612334a9b88c15175d970bd
|
[
"MIT"
] | null | null | null |
from ._wrapper import *
from ._wrapper import __all__
| 18
| 29
| 0.796296
| 7
| 54
| 5.285714
| 0.571429
| 0.594595
| 0.918919
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 54
| 2
| 30
| 27
| 0.804348
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
1e4078cea54f58cc875212f6723cce2252c31290
| 6,844
|
py
|
Python
|
Jun9.py
|
TianshuangQiu/AODGraph
|
49274bcbd1e9e5664e176d8a3a0341d83a26ef24
|
[
"MIT"
] | null | null | null |
Jun9.py
|
TianshuangQiu/AODGraph
|
49274bcbd1e9e5664e176d8a3a0341d83a26ef24
|
[
"MIT"
] | null | null | null |
Jun9.py
|
TianshuangQiu/AODGraph
|
49274bcbd1e9e5664e176d8a3a0341d83a26ef24
|
[
"MIT"
] | null | null | null |
# read Monthly data from the Excel file and plot time series
import pandas as pd
import datetime as dt
from datetime import date
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
def plot_aod_separate(aot_excel_file, cityname, sheetname_raw, sheetname_clim, sheetname_anomaly):
## read AOD raw data
df = pd.read_excel(aot_excel_file, header=None, sheet_name=sheetname_raw)
x1 = df[0]
y1 = df[1]
## read AOD anomaly data
df = pd.read_excel(aot_excel_file, header=None, sheet_name=sheetname_anomaly)
x2 = df[0]
y2 = df[1]
## read AOD climatology data
df = pd.read_excel(aot_excel_file, header=None, sheet_name=sheetname_clim)
xc = df[0]
yc = df[1]
## calculate anomay
fontsize_title = 20
fontsize_label = 20
fontsize_tick = 20
fontsize_legend = 18
matplotlib.rc('xtick', labelsize=fontsize_tick)
matplotlib.rc('ytick', labelsize=fontsize_tick)
##--------------------------------------------------------------------------------------------
## plot raw data
fig, ax = plt.subplots()
fig.set_figheight(5)
fig.set_figwidth(20)
ax.plot(x1, y1, '#d95f0e', linewidth=2, label=cityname)
years = mdates.YearLocator(5) # every year
years_fmt = mdates.DateFormatter('%Y')
years_minor = mdates.YearLocator(1) # every year
xmin = np.min(x1)
xmax = np.max(x1)
ax.set_xlim([xmin, xmax])
ax.set_ylim([0, 0.8])
ax.legend(loc='upper left', fontsize=fontsize_legend, frameon=None)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)
ax.xaxis.set_minor_locator(years_minor)
ax.grid(which='major', linestyle='-', linewidth='0.5', color='black')
plt.title('Monthly AOD (MERRA 2 data)', fontsize=fontsize_title)
plt.show()
##--------------------------------------------------------------------------------------------
## plot climatology data
fig, ax = plt.subplots()
fig.set_figheight(4)
fig.set_figwidth(10)
ax.plot(xc, yc, 'g-*', linewidth=2, markersize=10, label=cityname)
ax.set_ylim([0, 0.5])
ax.grid(which='major', linestyle='-', linewidth='0.5', color='black')
ax.legend(lolc='upper left', fontsize=fontsize_legend, frameon=None)
plt.title('Monthly AOD Climatology (MERRA 2 data)', fontsize=fontsize_title)
plt.show()
##--------------------------------------------------------------------------------------------
## plot anomaly data
fig, ax = plt.subplots()
fig.set_figheight(5)
fig.set_figwidth(20)
ax.plot(x2, y2, '#2c7fb8', linewidth=2, label=cityname)
years = mdates.YearLocator(5) # every year
years_fmt = mdates.DateFormatter('%Y')
years_minor = mdates.YearLocator(1) # every year
xmin = np.min(x2)
xmax = np.max(x2)
ax.set_xlim([xmin, xmax])
ax.set_ylim([-0.5, 0.5])
ax.legend(loc='upper left', fontsize=fontsize_legend, frameon=None)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)
ax.xaxis.set_minor_locator(years_minor)
ax.grid(which='major', linestyle='-', linewidth='0.5', color='black')
plt.title('Monthly AOD Anomaly(MERRA 2 data)', fontsize=fontsize_title)
plt.show()
def plot_aod_stacked(aot_excel_file, cityname, sheetname_raw, sheetname_clim, sheetname_anomaly):
## read AOD raw data
df = pd.read_excel(aot_excel_file, header=None, sheet_name=sheetname_raw)
x1 = df[0]+df[3]
y1 = df[1]
## read AOD anomaly data
df = pd.read_excel(aot_excel_file, header=None, sheet_name=sheetname_anomaly)
x2 = df[0]
y2 = df[1]
## read AOD climatology data
df = pd.read_excel(aot_excel_file, header=None, sheet_name=sheetname_clim)
xc = df[0]
yc = df[1]
## calculate anomay
fontsize_title = 20
fontsize_label = 20
fontsize_tick = 20
fontsize_legend = 18
matplotlib.rc('xtick', labelsize=fontsize_tick)
matplotlib.rc('ytick', labelsize=fontsize_tick)
##--------------------------------------------------------------------------------------------
## plot raw data
fig, ax = plt.subplots()
fig.set_figheight(5)
fig.set_figwidth(20)
ax.plot(x1, y1, '#d95f0e', linewidth=2, label=cityname)
years = mdates.YearLocator(5) # every year
years_fmt = mdates.DateFormatter('%Y')
years_minor = mdates.YearLocator(1) # every year
xmin = np.min(x1)
xmax = np.max(x1)
ax.set_xlim([xmin, xmax])
ax.set_ylim([0, 0.8])
ax.legend(loc='upper left', fontsize=fontsize_legend, frameon=None)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)
ax.xaxis.set_minor_locator(years_minor)
ax.grid(which='major', linestyle='-', linewidth='0.5', color='black')
plt.title('Monthly AOD (MERRA 2 data)', fontsize=fontsize_title)
plt.show()
##--------------------------------------------------------------------------------------------
## plot climatology data
fig, ax = plt.subplots()
fig.set_figheight(4)
fig.set_figwidth(10)
ax.plot(xc, yc, 'g-*', linewidth=2, markersize=10, label=cityname)
ax.set_ylim([0, 0.5])
ax.grid(which='major', linestyle='-', linewidth='0.5', color='black')
ax.legend(loc='upper left', fontsize=fontsize_legend, frameon=None)
plt.title('Monthly AOD Climatology (MERRA 2 data)', fontsize=fontsize_title)
plt.show()
##--------------------------------------------------------------------------------------------
## plot anomaly data
fig, ax = plt.subplots()
fig.set_figheight(5)
fig.set_figwidth(20)
ax.plot(x2, y2, '#2c7fb8', linewidth=2, label=cityname)
years = mdates.YearLocator(5) # every year
years_fmt = mdates.DateFormatter('%Y')
years_minor = mdates.YearLocator(1) # every year
xmin = np.min(x2)
xmax = np.max(x2)
ax.set_xlim([xmin, xmax])
ax.set_ylim([-0.5, 0.5])
ax.legend(loc='upper left', fontsize=fontsize_legend, frameon=None)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(years_fmt)
ax.xaxis.set_minor_locator(years_minor)
ax.grid(which='major', linestyle='-', linewidth='0.5', color='black')
plt.title('Monthly AOD Anomaly(MERRA 2 data)', fontsize=fontsize_title)
plt.show()
def main():
aot_file = r"D:\GitRepo\AODAnalysis\Output\AODJun14.xlsx"
print(aot_file.splitlines())
plot_aod_separate(aot_file, 'Addis Ababa', 'Addis_Ababa_RAW', 'AA_MONTH', 'AA_ANOMALY')
plot_aod_separate(aot_file, 'Cassablanca', 'Cassablanca_RAW', 'C_MONTH', 'C_ANOMALY')
plot_aod_separate(aot_file, 'Gaborone', 'Gaborone_RAW', 'G_MONTH', 'G_ANOMALY')
plot_aod_separate(aot_file, 'Nairobi', 'Nairobi_RAW', 'N_MONTH', 'N_ANOMALY')
if __name__=="__main__":
main()
| 33.714286
| 98
| 0.621712
| 919
| 6,844
| 4.45049
| 0.141458
| 0.017604
| 0.02934
| 0.02934
| 0.886064
| 0.880685
| 0.859413
| 0.859413
| 0.859413
| 0.859413
| 0
| 0.024815
| 0.169784
| 6,844
| 202
| 99
| 33.881188
| 0.695002
| 0.142022
| 0
| 0.841727
| 0
| 0
| 0.10426
| 0.007386
| 0
| 0
| 0
| 0
| 0
| 1
| 0.021583
| false
| 0
| 0.057554
| 0
| 0.079137
| 0.007194
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1e8281f1a5eff5b10ad4eb5f6534db264bc61b41
| 4,113
|
py
|
Python
|
src/anchor/system.py
|
luojiahai/anchor
|
0c95dcf7789f7ff005451a430cfc1b13fac153de
|
[
"MIT"
] | 3
|
2021-12-04T07:27:17.000Z
|
2021-12-17T03:15:05.000Z
|
src/anchor/system.py
|
luojiahai/anchor
|
0c95dcf7789f7ff005451a430cfc1b13fac153de
|
[
"MIT"
] | null | null | null |
src/anchor/system.py
|
luojiahai/anchor
|
0c95dcf7789f7ff005451a430cfc1b13fac153de
|
[
"MIT"
] | null | null | null |
import sys
import typing
import logging
__all__: typing.List[str] = list(['GLOBAL',])
class Global(object):
class __Global(object):
def __init__(self) -> None:
self.__debug: bool = False
self.__debuglex: bool = False
self.__debugyacc: bool = False
self.__inputstream: typing.TextIO = sys.stdin
self.__outputstream: typing.TextIO = sys.stdout
self.__errorstream: typing.TextIO = sys.stderr
self.__logstream: typing.TextIO = sys.stderr
self.__logger: logging.Logger = None
@property
def debug(self) -> bool:
return self.__debug
@debug.setter
def debug(self, other: bool):
self.__debug = other
@property
def debuglex(self) -> bool:
return self.__debuglex
@debuglex.setter
def debuglex(self, other: bool):
self.__debuglex = other
@property
def debugyacc(self) -> bool:
return self.__debugyacc
@debugyacc.setter
def debugyacc(self, other: bool):
self.__debugyacc = other
@property
def inputstream(self) -> typing.TextIO:
return self.__inputstream
@inputstream.setter
def inputstream(self, other: typing.TextIO):
self.__inputstream = other
@property
def outputstream(self) -> typing.TextIO:
return self.__outputstream
@outputstream.setter
def outputstream(self, other: typing.TextIO):
self.__outputstream = other
@property
def errorstream(self) -> typing.TextIO:
return self.__errorstream
@errorstream.setter
def errorstream(self, other: typing.TextIO):
self.__errorstream = other
@property
def logstream(self) -> typing.TextIO:
return self.__logstream
@logstream.setter
def logstream(self, other: typing.TextIO):
self.__logstream = other
@property
def logger(self) -> logging.Logger:
return self.__logger
@logger.setter
def logger(self, other: logging.Logger):
self.__logger = other
__instance: __Global = None
def __new__(cls) -> __Global:
if (not Global.__instance):
Global.__instance = Global.__Global()
return Global.__instance
@property
def debug(self) -> bool:
return self.__instance.debug
@debug.setter
def debug(self, other: bool):
self.__instance.debug = other
@property
def debuglex(self) -> bool:
return self.__instance.debuglex
@debuglex.setter
def debuglex(self, other: bool):
self.__instance.debuglex = other
@property
def debugyacc(self) -> bool:
return self.__instance.debugyacc
@debugyacc.setter
def debugyacc(self, other: bool):
self.__instance.debugyacc = other
@property
def inputstream(self) -> typing.TextIO:
return self.__instance.inputstream
@inputstream.setter
def inputstream(self, other: typing.TextIO):
self.__instance.inputstream = other
@property
def outputstream(self) -> typing.TextIO:
return self.__instance.outputstream
@outputstream.setter
def outputstream(self, other: typing.TextIO):
self.__instance.outputstream = other
@property
def errorstream(self) -> typing.TextIO:
return self.__instance.errorstream
@errorstream.setter
def errorstream(self, other: typing.TextIO):
self.__instance.errorstream = other
@property
def logstream(self) -> typing.TextIO:
return self.__instance.logstream
@logstream.setter
def logstream(self, other: typing.TextIO):
self.__instance.logstream = other
@property
def logger(self) -> logging.Logger:
return self.__instance.logger
@logger.setter
def logger(self, other: logging.Logger):
self.__instance.logger = other
GLOBAL: Global = Global()
| 25.867925
| 59
| 0.613907
| 408
| 4,113
| 5.92402
| 0.090686
| 0.099297
| 0.092677
| 0.072818
| 0.816715
| 0.776169
| 0.74969
| 0.721556
| 0.721556
| 0.521307
| 0
| 0
| 0.296377
| 4,113
| 158
| 60
| 26.031646
| 0.835176
| 0
| 0
| 0.547009
| 0
| 0
| 0.001459
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.290598
| false
| 0
| 0.025641
| 0.136752
| 0.487179
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 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
| 7
|
1e877ca08a8d60de694f222f487521f97c4996c7
| 478
|
py
|
Python
|
src/node/ext/ldap/ugm/__init__.py
|
enfold/node.ext.ldap
|
28127057be6ba3092389f3c920575292d43d9f94
|
[
"BSD-2-Clause"
] | 3
|
2016-04-22T00:37:17.000Z
|
2020-04-03T07:14:54.000Z
|
src/node/ext/ldap/ugm/__init__.py
|
enfold/node.ext.ldap
|
28127057be6ba3092389f3c920575292d43d9f94
|
[
"BSD-2-Clause"
] | 51
|
2015-02-10T11:14:01.000Z
|
2021-05-05T11:06:59.000Z
|
src/node/ext/ldap/ugm/__init__.py
|
enfold/node.ext.ldap
|
28127057be6ba3092389f3c920575292d43d9f94
|
[
"BSD-2-Clause"
] | 12
|
2016-08-09T09:39:35.000Z
|
2020-04-18T14:53:56.000Z
|
from node.ext.ldap.ugm._api import Group
from node.ext.ldap.ugm._api import Groups
from node.ext.ldap.ugm._api import GroupsConfig
from node.ext.ldap.ugm._api import PrincipalsConfig
from node.ext.ldap.ugm._api import Role
from node.ext.ldap.ugm._api import Roles
from node.ext.ldap.ugm._api import RolesConfig
from node.ext.ldap.ugm._api import Ugm
from node.ext.ldap.ugm._api import User
from node.ext.ldap.ugm._api import Users
from node.ext.ldap.ugm._api import UsersConfig
| 39.833333
| 51
| 0.8159
| 88
| 478
| 4.306818
| 0.193182
| 0.23219
| 0.319261
| 0.435356
| 0.783641
| 0.783641
| 0.783641
| 0
| 0
| 0
| 0
| 0
| 0.09205
| 478
| 11
| 52
| 43.454545
| 0.873272
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 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
| 0
| 0
|
0
| 7
|
1ec87396f3fe1318749c9220a68d5d3305e246bf
| 45
|
py
|
Python
|
fager/blueprints/page/__init__.py
|
jaideep2/fager
|
f6951b8e4679a1a8a6fcdcee6b620619001c9d2e
|
[
"Apache-2.0"
] | null | null | null |
fager/blueprints/page/__init__.py
|
jaideep2/fager
|
f6951b8e4679a1a8a6fcdcee6b620619001c9d2e
|
[
"Apache-2.0"
] | null | null | null |
fager/blueprints/page/__init__.py
|
jaideep2/fager
|
f6951b8e4679a1a8a6fcdcee6b620619001c9d2e
|
[
"Apache-2.0"
] | null | null | null |
from fager.blueprints.page.views import page
| 22.5
| 44
| 0.844444
| 7
| 45
| 5.428571
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088889
| 45
| 1
| 45
| 45
| 0.926829
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 1
| 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
| 1
| 0
| 1
| 1
|
0
| 7
|
1eca72bb370069f5d2b0ae73871708c73cceba98
| 23
|
py
|
Python
|
login.py
|
SpringKeven/test15
|
febf70c99542de0a00b95b44c8fb29a40295ea98
|
[
"MIT"
] | null | null | null |
login.py
|
SpringKeven/test15
|
febf70c99542de0a00b95b44c8fb29a40295ea98
|
[
"MIT"
] | null | null | null |
login.py
|
SpringKeven/test15
|
febf70c99542de0a00b95b44c8fb29a40295ea98
|
[
"MIT"
] | null | null | null |
a = 1
c = 3
b = 10
| 2.875
| 6
| 0.304348
| 6
| 23
| 1.166667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.4
| 0.565217
| 23
| 7
| 7
| 3.285714
| 0.3
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
94c0aeda55438250b08c48b2e660815d3b989609
| 28,175
|
py
|
Python
|
tests/milvus_python_test/test_search_by_id.py
|
youny626/milvus
|
9e55802c5d515ceecc4cadab9f2fd1cb477d75d5
|
[
"Apache-2.0"
] | null | null | null |
tests/milvus_python_test/test_search_by_id.py
|
youny626/milvus
|
9e55802c5d515ceecc4cadab9f2fd1cb477d75d5
|
[
"Apache-2.0"
] | null | null | null |
tests/milvus_python_test/test_search_by_id.py
|
youny626/milvus
|
9e55802c5d515ceecc4cadab9f2fd1cb477d75d5
|
[
"Apache-2.0"
] | null | null | null |
import pdb
import copy
import struct
import pytest
import threading
import datetime
import logging
from time import sleep
from multiprocessing import Process
import numpy
from milvus import Milvus, IndexType, MetricType
from utils import *
dim = 128
table_id = "test_search"
add_interval_time = 2
vectors = gen_vectors(6000, dim)
# vectors /= numpy.linalg.norm(vectors)
# vectors = vectors.tolist()
nprobe = 1
epsilon = 0.001
tag = "overallpaper"
top_k = 5
nprobe = 1
non_exist_id = 9527
small_size = 6000
raw_vectors, binary_vectors = gen_binary_vectors(6000, dim)
class TestSearchBase:
@pytest.fixture(scope="function", autouse=True)
def skip_check(self, connect):
if str(connect._cmd("mode")[1]) == "GPU":
reason = "GPU mode not support"
logging.getLogger().info(reason)
pytest.skip(reason)
def init_data(self, connect, table, nb=6000):
'''
Generate vectors and add it in table, before search vectors
'''
global vectors
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
status, ids = connect.add_vectors(table, add_vectors)
sleep(add_interval_time)
return add_vectors, ids
def init_data_binary(self, connect, table, nb=6000):
'''
Generate vectors and add it in table, before search vectors
'''
global binary_vectors
if nb == 6000:
add_vectors = binary_vectors
else:
add_vectors = gen_binary_vectors(nb, dim)
status, ids = connect.add_vectors(table, add_vectors)
sleep(add_interval_time)
return add_vectors, ids
def init_data_no_flush(self, connect, table, nb=6000):
global vectors
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
status, ids = connect.add_vectors(table, add_vectors)
# sleep(add_interval_time)
return add_vectors, ids
def init_data_no_flush_ids(self, connect, table, nb=6000):
global vectors
my_ids = [i for i in range(nb)]
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
status, ids = connect.add_vectors(table, add_vectors, my_ids)
# sleep(add_interval_time)
return add_vectors, ids
def init_data_ids(self, connect, table, nb=6000):
global vectors
my_ids = [i for i in range(nb)]
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
status, ids = connect.add_vectors(table, add_vectors, my_ids)
sleep(add_interval_time)
return add_vectors, ids
def add_data(self, connect, table, vectors):
'''
Add specified vectors to table
'''
status, ids = connect.add_vectors(table, vectors)
# sleep(add_interval_time)
sleep(10)
return vectors, ids
def add_data_ids(self, connect, table, vectors):
my_ids = [i for i in range(len(vectors))]
status, ids = connect.add_vectors(table, vectors, my_ids)
sleep(add_interval_time)
return vectors, ids
def add_data_and_flush(self, connect, table, vectors):
status, ids = connect.add_vectors(table, vectors)
connect.flush([table])
return vectors, ids
def add_data_and_flush_ids(self, connect, table, vectors):
my_ids = [i for i in range(len(vectors))]
status, ids = connect.add_vectors(table, vectors, my_ids)
connect.flush([table])
return vectors, ids
def add_data_no_flush(self, connect, table, vectors):
'''
Add specified vectors to table
'''
status, ids = connect.add_vectors(table, vectors)
return vectors, ids
def add_data_no_flush_ids(self, connect, table, vectors):
my_ids = [i for i in range(len(vectors))]
status, ids = connect.add_vectors(table, vectors, my_ids)
return vectors, ids
# delete data and auto flush - timeout due to the flush interval in config file
def delete_data(self, connect, table, ids):
'''
delete vectors by id
'''
status = connect.delete_by_id(table, ids)
sleep(add_interval_time)
return status
# delete data and auto flush - timeout due to the flush interval in config file
def delete_data_no_flush(self, connect, table, ids):
'''
delete vectors by id
'''
status = connect.delete_by_id(table, ids)
return status
# delete data and manual flush
def delete_data_and_flush(self, connect, table, ids):
'''
delete vectors by id
'''
status = connect.delete_by_id(table, ids)
connect.flush([table])
return status
def check_no_result(self, results):
if len(results) == 0:
return True
flag = True
for r in results:
flag = flag and (r.id == -1)
if not flag:
return False
return flag
def init_data_partition(self, connect, table, partition_tag, nb=6000):
'''
Generate vectors and add it in table, before search vectors
'''
global vectors
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
# add_vectors /= numpy.linalg.norm(add_vectors)
# add_vectors = add_vectors.tolist()
status, ids = connect.add_vectors(table, add_vectors, partition_tag=partition_tag)
sleep(add_interval_time)
return add_vectors, ids
def init_data_and_flush(self, connect, table, nb=6000):
'''
Generate vectors and add it in table, before search vectors
'''
global vectors
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
# add_vectors /= numpy.linalg.norm(add_vectors)
# add_vectors = add_vectors.tolist()
status, ids = connect.add_vectors(table, add_vectors)
connect.flush([table])
return add_vectors, ids
def init_data_and_flush_ids(self, connect, table, nb=6000):
global vectors
my_ids = [i for i in range(nb)]
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
status, ids = connect.add_vectors(table, add_vectors, my_ids)
connect.flush([table])
return add_vectors, ids
def init_data_partition_and_flush(self, connect, table, partition_tag, nb=6000):
'''
Generate vectors and add it in table, before search vectors
'''
global vectors
if nb == 6000:
add_vectors = vectors
else:
add_vectors = gen_vectors(nb, dim)
# add_vectors /= numpy.linalg.norm(add_vectors)
# add_vectors = add_vectors.tolist()
status, ids = connect.add_vectors(table, add_vectors, partition_tag=partition_tag)
connect.flush([table])
return add_vectors, ids
@pytest.fixture(
scope="function",
params=gen_simple_index_params()
)
def get_simple_index_params(self, request, connect):
if request.param["index_type"] not in [IndexType.FLAT, IndexType.IVF_FLAT, IndexType.IVF_SQ8]:
pytest.skip("Skip PQ Temporary")
return request.param
@pytest.fixture(
scope="function",
params=gen_simple_index_params()
)
def get_jaccard_index_params(self, request, connect):
logging.getLogger().info(request.param)
if request.param["index_type"] == IndexType.IVFLAT or request.param["index_type"] == IndexType.FLAT:
return request.param
else:
pytest.skip("Skip index Temporary")
@pytest.fixture(
scope="function",
params=gen_simple_index_params()
)
def get_hamming_index_params(self, request, connect):
logging.getLogger().info(request.param)
if request.param["index_type"] == IndexType.IVFLAT or request.param["index_type"] == IndexType.FLAT:
return request.param
else:
pytest.skip("Skip index Temporary")
"""
generate top-k params
"""
@pytest.fixture(
scope="function",
params=[1, 99, 1024, 2048]
)
def get_top_k(self, request):
yield request.param
# auto flush
def test_search_flat_normal_topk(self, connect, table, get_top_k):
'''
target: test basic search fuction, all the search params is corrent, change top-k value
method: search with the given vector id, check the result
expected: search status ok, and the length of the result is top_k
'''
top_k = get_top_k
vectors, ids = self.init_data(connect, table, nb=small_size)
query_id = ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert result[0][0].distance <= epsilon
assert check_result(result[0], ids[0])
def test_search_flat_max_topk(self, connect, table):
'''
target: test basic search fuction, all the search params is corrent, change top-k value
method: search with the given vector id, check the result
expected: search status ok, and the length of the result is top_k
'''
top_k = 2049
vectors, ids = self.init_data(connect, table, nb=small_size)
query_id = ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert not status.OK()
def test_search_id_not_existed(self, connect, table):
'''
target: test basic search fuction, all the search params is corrent, change top-k value
method: search with the given vector id, check the result
expected: search status ok, and the length of the result is top_k
'''
vectors, ids = self.init_data_and_flush(connect, table, nb=small_size)
query_id = non_exist_id
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
# auto flush
def test_search_ids(self, connect, table):
vectors, ids = self.init_data_ids(connect, table, nb=small_size)
query_id = ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert result[0][0].distance <= epsilon
assert check_result(result[0], ids[0])
# manual flush
def test_search_ids_flush(self, connect, table):
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
query_id = non_exist_id
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert self.check_no_result(result[0])
# ------------------------------------------------------------- l2, add manual flush, delete, search ------------------------------------------------------------- #
# ids, manual flush, search table, exist
def test_search_index_l2(self, connect, table, get_simple_index_params):
'''
target: test basic search fuction, all the search params is corrent, test all index params, and build
method: search with the given vectors, check the result
expected: search status ok, and the length of the result is top_k
'''
index_params = get_simple_index_params
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert result[0][0].distance <= epsilon
assert check_result(result[0], ids[0])
# ids, manual flush, search table, non exist
def test_search_index_l2_id_not_existed(self, connect, table, get_simple_index_params):
'''
target: test basic search fuction, all the search params is corrent, test all index params, and build
method: search with the given vectors, check the result
expected: search status ok, and the length of the result is top_k
'''
index_params = get_simple_index_params
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = non_exist_id
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
# ids, manual flush, delete, manual flush, search table, exist
def test_search_index_delete(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status = self.delete_data_and_flush(connect, table, [query_id])
assert status.OK()
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert self.check_no_result(result[0])
# ids, manual flush, delete, manual flush, search table, non exist
def test_search_index_delete_id_not_existed(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status = self.delete_data_and_flush(connect, table, [query_id])
assert status.OK()
query_id = non_exist_id
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert self.check_no_result(result[0])
def test_search_index_delete_no_flush(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status = self.delete_data_no_flush(connect, table, [query_id])
assert status.OK()
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert check_result(result[0], query_id)
# ids, manual flush, delete, no flush, search table, non exist
def test_search_index_delete_no_flush_id_not_existed(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status = self.delete_data_no_flush(connect, table, [query_id])
assert status.OK()
query_id = non_exist_id
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert self.check_no_result(result[0])
def test_search_index_delete_add(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
vectors, ids = self.init_data_and_flush_ids(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status = self.delete_data_no_flush(connect, table, [query_id])
assert status.OK()
vectors, new_ids = self.add_data_and_flush_ids(connect, table, vectors)
status = connect.create_index(table, index_params)
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert result[0][0].distance <= epsilon
assert check_result(result[0], query_id)
status = self.delete_data_no_flush(connect, table, [query_id])
assert status.OK()
# add to table, auto flush, search table, search partition exist
def test_search_l2_index_partition(self, connect, table, get_simple_index_params):
'''
target: test basic search fuction, all the search params is corrent, test all index params, and build
method: add vectors into table, search with the given vectors, check the result
expected: search status ok, and the length of the result is top_k, search table with partition tag return empty
'''
index_params = get_simple_index_params
status = connect.create_partition(table, tag)
vectors, ids = self.init_data(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert check_result(result[0], ids[0])
assert result[0][0].distance <= epsilon
status, result = connect.search_by_id(table, top_k, nprobe, query_id, partition_tag_array=[tag])
assert status.OK()
assert len(result) == 0
# add to partition, auto flush, search partition exist
def test_search_l2_index_params_partition_2(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
status = connect.create_partition(table, tag)
vectors, ids = self.init_data_partition(connect, table, tag, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id, partition_tag_array=[tag])
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert check_result(result[0], query_id)
def test_search_l2_index_partition_id_not_existed(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
status = connect.create_partition(table, tag)
vectors, ids = self.init_data(connect, table, nb=small_size)
status = connect.create_index(table, index_params)
query_id = non_exist_id
status, result = connect.search_by_id(table, top_k, nprobe, query_id, partition_tag_array=[tag])
assert status.OK()
assert len(result) == 0
# add to table, manual flush, search non-existing partition non exist
def test_search_l2_index_partition_tag_not_existed(self, connect, table, get_simple_index_params):
index_params = get_simple_index_params
status = connect.create_partition(table, tag)
vectors, ids = self.init_data_partition_and_flush(connect, table, tag, nb=small_size)
status = connect.create_index(table, index_params)
query_id = non_exist_id
status, result = connect.search_by_id(table, top_k, nprobe, query_id, partition_tag_array=['non_existing_tag'])
assert status.OK()
assert len(result) == 0
def test_search_l2_index_partitions(self, connect, table, get_simple_index_params):
new_tag = "new_tag"
index_params = get_simple_index_params
status = connect.create_partition(table, tag)
status = connect.create_partition(table, new_tag)
vectors, ids = self.init_data_partition_and_flush(connect, table, tag, nb=small_size)
vectors, new_ids = self.init_data_partition_and_flush(connect, table, new_tag, nb=small_size)
status = connect.create_index(table, index_params)
query_id = ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id, partition_tag_array=[tag, new_tag])
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert check_result(result[0], ids[0])
assert result[0][0].distance <= epsilon
query_id = new_ids[0]
status, result = connect.search_by_id(table, top_k, nprobe, query_id, partition_tag_array=[tag, new_tag])
assert status.OK()
assert len(result[0]) == min(len(vectors), top_k)
assert check_result(result[0], new_ids[0])
assert result[0][0].distance <= epsilon
@pytest.mark.level(2)
def test_search_by_id_without_connect(self, dis_connect, table):
'''
target: test search vectors without connection
method: use dis connected instance, call search method and check if search successfully
expected: raise exception
'''
query_idtors = 123
with pytest.raises(Exception) as e:
status, ids = dis_connect.search_by_id(table, top_k, nprobe, query_idtors)
def test_search_table_name_not_existed(self, connect, table):
'''
target: search table not existed
method: search with the random table_name, which is not in db
expected: status not ok
'''
table_name = gen_unique_str("not_existed_table")
query_id = non_exist_id
status, result = connect.search_by_id(table_name, top_k, nprobe, query_id)
assert not status.OK()
def test_search_table_name_None(self, connect, table):
'''
target: search table that table name is None
method: search with the table_name: None
expected: status not ok
'''
table_name = None
query_ids = non_exist_id
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(table_name, top_k, nprobe, query_id)
def test_search_jac(self, connect, jac_table, get_jaccard_index_params):
index_params = get_jaccard_index_params
vectors, ids = self.init_data_binary(connect, jac_table)
status = connect.create_index(jac_table, index_params)
assert status.OK()
query_id = ids[0]
status, result = connect.search_by_id(jac_table, top_k, nprobe, query_id)
logging.getLogger().info(status)
logging.getLogger().info(result)
assert status.OK()
assert check_result(result[0], ids[0])
assert result[0][0].distance <= epsilon
def test_search_ham(self, connect, ham_table, get_hamming_index_params):
index_params = get_hamming_index_params
vectors, ids = self.init_data_binary(connect, ham_table)
status = connect.create_index(ham_table, index_params)
assert status.OK()
query_id = ids[0]
status, result = connect.search_by_id(ham_table, top_k, nprobe, query_id)
logging.getLogger().info(status)
logging.getLogger().info(result)
assert status.OK()
assert check_result(result[0], ids[0])
assert result[0][0].distance <= epsilon
"""
******************************************************************
# The following cases are used to test `search_by_id` function
# with invalid table_name top-k / nprobe / query_range
******************************************************************
"""
class TestSearchParamsInvalid(object):
nlist = 16384
index_param = {"index_type": IndexType.IVF_SQ8, "nlist": nlist}
"""
Test search table with invalid table names
"""
@pytest.fixture(
scope="function",
params=gen_invalid_table_names()
)
def get_table_name(self, request):
yield request.param
@pytest.mark.level(2)
def test_search_with_invalid_tablename(self, connect, get_table_name):
table_name = get_table_name
query_id = non_exist_id
status, result = connect.search_by_id(table_name, top_k, nprobe, query_id)
assert not status.OK()
@pytest.mark.level(1)
def test_search_with_invalid_tag_format(self, connect, table):
query_id = non_exist_id
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(table_name, top_k, nprobe, query_id, partition_tag_array="tag")
"""
Test search table with invalid top-k
"""
@pytest.fixture(
scope="function",
params=gen_invalid_top_ks()
)
def get_top_k(self, request):
yield request.param
@pytest.mark.level(1)
def test_search_with_invalid_top_k(self, connect, table, get_top_k):
top_k = get_top_k
query_id = non_exist_id
if isinstance(top_k, int):
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert not status.OK()
else:
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
@pytest.mark.level(2)
def test_search_with_invalid_top_k_ip(self, connect, ip_table, get_top_k):
top_k = get_top_k
query_id = non_exist_id
if isinstance(top_k, int):
status, result = connect.search_by_id(ip_table, top_k, nprobe, query_id)
assert not status.OK()
else:
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(ip_table, top_k, nprobe, query_id)
"""
Test search table with invalid nprobe
"""
@pytest.fixture(
scope="function",
params=gen_invalid_nprobes()
)
def get_nprobes(self, request):
yield request.param
@pytest.mark.level(1)
def test_search_with_invalid_nprobe(self, connect, table, get_nprobes):
nprobe = get_nprobes
logging.getLogger().info(nprobe)
query_id = non_exist_id
if isinstance(nprobe, int):
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
assert not status.OK()
else:
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
@pytest.mark.level(2)
def test_search_with_invalid_nprobe_ip(self, connect, ip_table, get_nprobes):
'''
target: test search fuction, with the wrong top_k
method: search with top_k
expected: raise an error, and the connection is normal
'''
nprobe = get_nprobes
logging.getLogger().info(nprobe)
query_id = non_exist_id
if isinstance(nprobe, int):
status, result = connect.search_by_id(ip_table, top_k, nprobe, query_id)
assert not status.OK()
else:
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(ip_table, top_k, nprobe, query_id)
"""
Test search table with invalid ids
"""
@pytest.fixture(
scope="function",
params=gen_invalid_vector_ids()
)
def get_vector_ids(self, request):
yield request.param
@pytest.mark.level(1)
def test_search_flat_with_invalid_vector_id(self, connect, table, get_vector_ids):
'''
target: test search fuction, with the wrong query_range
method: search with query_range
expected: raise an error, and the connection is normal
'''
query_id = get_vector_ids
logging.getLogger().info(query_id)
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(table, top_k, nprobe, query_id)
@pytest.mark.level(2)
def test_search_flat_with_invalid_vector_id_ip(self, connect, ip_table, get_vector_ids):
query_id = get_vector_ids
logging.getLogger().info(query_id)
with pytest.raises(Exception) as e:
status, result = connect.search_by_id(ip_table, top_k, nprobe, query_id)
def check_result(result, id):
if len(result) >= 5:
return id in [x.id for x in result[:5]]
else:
return id in (i.id for i in result)
| 40.135328
| 168
| 0.645963
| 3,776
| 28,175
| 4.566208
| 0.055879
| 0.018095
| 0.038047
| 0.032189
| 0.866431
| 0.834822
| 0.807795
| 0.773866
| 0.746491
| 0.711924
| 0
| 0.010431
| 0.2548
| 28,175
| 701
| 169
| 40.192582
| 0.810773
| 0.13079
| 0
| 0.713147
| 0
| 0
| 0.012322
| 0
| 0
| 0
| 0
| 0
| 0.145418
| 1
| 0.11753
| false
| 0
| 0.023904
| 0
| 0.201195
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
bfbe0a10cb662242715c3f1a7eaf02cff78d335e
| 47
|
py
|
Python
|
HUNKAR/ilk.py
|
hunkargenchcttp/temelbilgiler
|
be1b9c96ec297a2a0f1801a807dcd818b5ea7d3e
|
[
"MIT"
] | null | null | null |
HUNKAR/ilk.py
|
hunkargenchcttp/temelbilgiler
|
be1b9c96ec297a2a0f1801a807dcd818b5ea7d3e
|
[
"MIT"
] | null | null | null |
HUNKAR/ilk.py
|
hunkargenchcttp/temelbilgiler
|
be1b9c96ec297a2a0f1801a807dcd818b5ea7d3e
|
[
"MIT"
] | null | null | null |
print("Hünkar")
print("Hünkar")
print("Hünkar")
| 15.666667
| 15
| 0.702128
| 6
| 47
| 5.5
| 0.333333
| 1
| 0.969697
| 1.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.042553
| 47
| 3
| 16
| 15.666667
| 0.733333
| 0
| 0
| 1
| 0
| 0
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 9
|
bfc214e21eee656219ab22f8a1bfac66d0b90de9
| 14,382
|
py
|
Python
|
brightcove/PlayerManagement.py
|
asha-bc/BrightcovePY
|
52e220e8e95221745b4de52ba94689509d27c072
|
[
"MIT"
] | 3
|
2020-12-14T23:08:09.000Z
|
2021-08-05T05:44:19.000Z
|
brightcove/PlayerManagement.py
|
asha-bc/BrightcovePY
|
52e220e8e95221745b4de52ba94689509d27c072
|
[
"MIT"
] | null | null | null |
brightcove/PlayerManagement.py
|
asha-bc/BrightcovePY
|
52e220e8e95221745b4de52ba94689509d27c072
|
[
"MIT"
] | 2
|
2021-10-19T15:24:28.000Z
|
2022-03-08T08:17:30.000Z
|
"""
Implements wrapper class and methods to work with Brightcove's Player Management API.
See: https://apis.support.brightcove.com/player-management/
"""
from typing import Union
from requests.models import Response
from .Base import Base
from .OAuth import OAuth
class PlayerManagement(Base):
"""
Class to wrap the Brightcove Player Management API calls. Inherits from Base.
Attributes:
-----------
base_url (str)
Base URL for API calls.
Methods:
--------
GetListOfPlayers(self, account_id: str='') -> Response
Get a list of players in an account.
GetSinglePlayer(self, player_id: str, account_id: str='') -> Response
Get a player by ID from an account.
CreatePlayer(self, json_body: Union[str, dict], account_id: str='') -> Response
Create a player.
DeletePlayer(self, player_id: str, account_id: str='') -> Response
Delete a player and all embeds associated with it.
PublishPlayer(self, player_id: str, account_id: str='') -> Response
Publish a player for optimization and production use.
UpdatePlayer(self, player_id: str, json_body: Union[str, dict], account_id: str='') -> Response
Update a single player.
GetPlayerConfiguration(self, player_id: str, branch: str, account_id: str='') -> Response
Get a preview or published player configuration.
UpdatePlayerConfiguration(self, player_id: str, json_body: Union[str, dict], account_id: str='') -> Response
Update a player configuration.
GetAllPlugins(self, template_version: str='') -> Response
Get all plugins from plugin registry.
GetSinglePlugin(self, plugin_id: str) -> Response
Get a single plugin from plugin registry.
GetAllEmbeds(self, player_id: str, account_id: str='') -> Response
Get all the embeds (child players) for a player.
GetEmbed(self, player_id: str, embed_id: str, account_id: str='') -> Response
Get a specific embed (child player) for a player.
CreateEmbed(self, player_id: str, json_body: Union[str, dict], account_id: str='') -> Response
Create an embed (child player) for a player.
DeleteEmbed(self, player_id: str, embed_id: str, account_id: str='') -> Response
Delete a particular embed (child player) for a player.
GetPlayerEmbedConfiguration(self, player_id: str, embed_id: str, branch: str, account_id: str='') -> Response
Get the configuration for an embed. You must specify the branch, either "master" or "preview".
UpdateEmbedConfiguration(self, player_id: str, embed_id: str, json_body: Union[str, dict], account_id: str='', use_put: bool=False) -> Response
Update the configuration for an embed.
GetConfigurationCombinations(self, player_id: str, embed_id: str, query: str, account_id: str='') -> Response
Retrieve the configuration for a parent/child combination of master and preview branches.
"""
# base URL for all API calls
base_url = 'https://players.api.brightcove.com/v2/accounts/{account_id}'
def __init__(self, oauth: OAuth) -> None:
"""
Args:
oauth (OAuth): OAuth instance to use for the API calls.
"""
super().__init__(oauth=oauth)
def GetListOfPlayers(self, account_id: str='') -> Response:
"""
Get a list of players in an account.
Args:
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players'.format(account_id=account_id or self.oauth.account_id)
return self.session.get(url, headers=self.oauth.headers)
def GetSinglePlayer(self, player_id: str, account_id: str='') -> Response:
"""
Get a player by ID from an account.
Args:
player_id (str): Player ID to retrieve.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}'.format(account_id=account_id or self.oauth.account_id)
return self.session.get(url, headers=self.oauth.headers)
def CreatePlayer(self, json_body: Union[str, dict], account_id: str='') -> Response:
"""
Create a player. The POST method creates a player by submitting a player configuration. The properties
of Brightcove Player you can manipulate with player management are detailed in the API documentation.
To create a player, a publisher must decide what properties the final player will have. If no properties
are given at creation, a blank player will be created with only the base player skin applied to the
player. A user may then use an HTTP PATCH method to update properties after the player has been created.
Args:
json_body (Union[str, dict]): JSON data with the player configuration.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players'.format(account_id=account_id or self.oauth.account_id)
return self.session.post(url, headers=self.oauth.headers, data=self._json_to_string(json_body))
def DeletePlayer(self, player_id: str, account_id: str='') -> Response:
"""
Delete a player and all embeds associated with it.
Args:
player_id (str): Player ID to delete.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}'.format(account_id=account_id or self.oauth.account_id)
return self.session.delete(url, headers=self.oauth.headers)
def PublishPlayer(self, player_id: str, account_id: str='') -> Response:
"""
Publish a player for optimization and production use.
Args:
player_id (str): Player ID to publish.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/publish'.format(account_id=account_id or self.oauth.account_id)
return self.session.post(url, headers=self.oauth.headers)
def UpdatePlayer(self, player_id: str, json_body: Union[str, dict], account_id: str='') -> Response:
"""
Update a single player. The PATCH method can be used on a single player to do a VERY limited update.
The only fields you can update in this manner are the name and description properties. All other
player configuration must be done via the PLAYER CONFIGURATIONS APIs, detailed in the API docs.
Args:
player_id (str): Player ID to update.
json_body (Union[str, dict]): JSON data with the updated player configuration.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}'.format(account_id=account_id or self.oauth.account_id)
return self.session.patch(url, headers=self.oauth.headers, data=self._json_to_string(json_body))
def GetPlayerConfiguration(self, player_id: str, branch: str, account_id: str='') -> Response:
"""
Get a preview or published player configuration.
Args:
player_id (str): Player ID to get configuration from.
branch (str): ID of branch to get. Can be "master" or "preview".
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/configuration/{branch}'.format(account_id=account_id or self.oauth.account_id)
return self.session.get(url, headers=self.oauth.headers)
def UpdatePlayerConfiguration(self, player_id: str, json_body: Union[str, dict], account_id: str='') -> Response:
"""
Update a player configuration.
Args:
player_id (str): Player ID to update.
json_body (Union[str, dict]): JSON data with the updated player configuration.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/configuration'.format(account_id=account_id or self.oauth.account_id)
return self.session.patch(url, headers=self.oauth.headers, data=self._json_to_string(json_body))
def GetAllPlugins(self, template_version: str='') -> Response:
"""
Get all plugins from plugin registry.
Args:
template_version (str, optional): Limit results to plugins compatible with a
specific player template version. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
if template_version:
template_version = f'?template_version={template_version}'
url = f'https://players.api.brightcove.com/v2/plugins{template_version}'
return self.session.get(url, headers=self.oauth.headers)
def GetSinglePlugin(self, plugin_id: str) -> Response:
"""
Get a single plugin from plugin registry.
Args:
plugin_id (str): The plugin ID in the plugin registry.
Returns:
Response: API response as requests Response object.
"""
if plugin_id:
plugin_id = plugin_id.replace('@', '%40')
plugin_id = plugin_id.replace('/', '%2F')
url = f'https://players.api.brightcove.com/v2/plugins/{plugin_id}'
return self.session.get(url, headers=self.oauth.headers)
def GetAllEmbeds(self, player_id: str, account_id: str='') -> Response:
"""
Get all the embeds (child players) for a player.
Args:
player_id (str): Player ID to get the embeds from.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/embeds'.format(account_id=account_id or self.oauth.account_id)
return self.session.get(url, headers=self.oauth.headers)
def GetEmbed(self, player_id: str, embed_id: str, account_id: str='') -> Response:
"""
Get a specific embed (child player) for a player.
Args:
player_id (str): Player ID to get the embed from.
embed_id (str): Embed ID to get.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/embeds/{embed_id}'.format(account_id=account_id or self.oauth.account_id)
return self.session.get(url, headers=self.oauth.headers)
def CreateEmbed(self, player_id: str, json_body: Union[str, dict], account_id: str='') -> Response:
"""
Create an embed (child player) for a player.
Args:
player_id (str): Player ID to create the embed for.
json_body (Union[str, dict]): JSON data with the embed configuration.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/embeds'.format(account_id=account_id or self.oauth.account_id)
return self.session.post(url, headers=self.oauth.headers, data=self._json_to_string(json_body))
def DeleteEmbed(self, player_id: str, embed_id: str, account_id: str='') -> Response:
"""
Delete a particular embed (child player) for a player.
Args:
player_id (str): Player ID to delete the embed from.
embed_id (str): Embed ID to delete.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/embeds/{embed_id}'.format(account_id=account_id or self.oauth.account_id)
return self.session.delete(url, headers=self.oauth.headers)
def GetPlayerEmbedConfiguration(self, player_id: str, embed_id: str, branch: str, account_id: str='') -> Response:
"""
Get the configuration for an embed. You must specify the branch, either "master" or "preview".
Args:
player_id (str): Player ID to get the embed's config from.
embed_id (str): Embed ID to get configuration for.
branch (str): ID of branch to get embed config for. Can be "master" or "preview".
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/embeds/{embed_id}/configuration/{branch}'.format(account_id=account_id or self.oauth.account_id)
return self.session.get(url, headers=self.oauth.headers)
def UpdateEmbedConfiguration(self, player_id: str, embed_id: str, json_body: Union[str, dict], account_id: str='', use_put: bool=False) -> Response:
"""
Update the configuration for an embed.
Note that you will need to publish the altered embed for optimization and production use.
Args:
player_id (str): Player ID to update the embed's config in.
embed_id (str): Embed ID to update.
json_body (Union[str, dict]): JSON data with the embed configuration.
account_id (str, optional): Video Cloud account ID. Defaults to ''.
use_put (bool, optional): Use PUT instead of PATCH for API call. See docs for more info.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/embeds/{embed_id}/configuration/'.format(account_id=account_id or self.oauth.account_id)
if use_put:
return self.session.put(url, headers=self.oauth.headers, data=self._json_to_string(json_body))
return self.session.patch(url, headers=self.oauth.headers, data=self._json_to_string(json_body))
def GetConfigurationCombinations(self, player_id: str, embed_id: str, query: str, account_id: str='') -> Response:
"""
Retrieve the configuration for a parent/child combination of master and preview branches.
Using this endpoint provides a way to view what the resulting configuration would be when combining
different combinations of parent and child (also called embed) versions of players.
Using this endpoint does not change any configurations, it is only useful for seeing results of
merging changes to configurations.
Args:
player_id (str): Player ID to get configuration combinations from.
embed_id (str): Embed ID to get configuration combinations from.
query (str): Query parameters: "playerBranch" and "embedBranch", set to "master" or "preview".
account_id (str, optional): Video Cloud account ID. Defaults to ''.
Returns:
Response: API response as requests Response object.
"""
url = f'{self.base_url}/players/{player_id}/embeds/{embed_id}/configuration/merged?{query}'.format(account_id=account_id or self.oauth.account_id)
return self.session.get(url, headers=self.oauth.headers)
| 40.974359
| 149
| 0.733486
| 2,109
| 14,382
| 4.884305
| 0.105263
| 0.092612
| 0.052422
| 0.054364
| 0.814775
| 0.81031
| 0.802932
| 0.795845
| 0.79235
| 0.755364
| 0
| 0.000494
| 0.156098
| 14,382
| 350
| 150
| 41.091429
| 0.848233
| 0.640314
| 0
| 0.378788
| 0
| 0
| 0.178165
| 0.142722
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| false
| 0
| 0.060606
| 0
| 0.636364
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
bfd05e7addfffc8cbb0ba04209ffe9d538dc28f9
| 205
|
py
|
Python
|
web/admin.py
|
Hedwika/Czechitas-Data-Games
|
7b816830eba130a9f522637eb9b443dd7fc0ff3d
|
[
"Apache-2.0"
] | 1
|
2022-02-04T19:05:42.000Z
|
2022-02-04T19:05:42.000Z
|
web/admin.py
|
Hedwika/Czechitas-Data-Games
|
7b816830eba130a9f522637eb9b443dd7fc0ff3d
|
[
"Apache-2.0"
] | 30
|
2021-05-22T12:40:29.000Z
|
2022-01-28T11:04:03.000Z
|
web/admin.py
|
Hedwika/Czechitas-Data-Games
|
7b816830eba130a9f522637eb9b443dd7fc0ff3d
|
[
"Apache-2.0"
] | 1
|
2021-08-25T16:07:32.000Z
|
2021-08-25T16:07:32.000Z
|
from django.contrib import admin
from . import models
admin.site.register(models.Event)
admin.site.register(models.Assignment)
admin.site.register(models.NewUser)
admin.site.register(models.UserProgress)
| 25.625
| 40
| 0.829268
| 28
| 205
| 6.071429
| 0.428571
| 0.211765
| 0.4
| 0.541176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.063415
| 205
| 7
| 41
| 29.285714
| 0.885417
| 0
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| 0
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| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
44e7ff8ad06c31aee81dc6caf081d5265a8b24ad
| 4,316
|
py
|
Python
|
airports_boards/dashboard/views.py
|
xm4dn355x/yanao_airports
|
b4ae78600fe90e304981a5fde496c4cdd0e30c24
|
[
"MIT"
] | null | null | null |
airports_boards/dashboard/views.py
|
xm4dn355x/yanao_airports
|
b4ae78600fe90e304981a5fde496c4cdd0e30c24
|
[
"MIT"
] | 27
|
2020-03-01T16:14:48.000Z
|
2022-03-29T03:17:49.000Z
|
airports_boards/dashboard/views.py
|
xm4dn355x/yanao_airports
|
b4ae78600fe90e304981a5fde496c4cdd0e30c24
|
[
"MIT"
] | null | null | null |
"""
Airports dashboard views.py
"""
from django.db.models import Q
from django.http import HttpResponse
from django.template import loader
from django.shortcuts import render
from .models import Flights
# Create your views here.
def index(request):
problem_flights = Flights.objects.filter(Q(status='ОТМЕНЕН') | Q(status='ЗАДЕРЖАН') | Q(status='НЕИЗВЕСТЕН'))\
.order_by('orig_airport', 'flight_type', 'plan_time')
template = loader.get_template('dashboard/index.html')
context = {
'problems': problem_flights,
}
return HttpResponse(template.render(context, request))
def show_sly(request):
airport = 'Салехард'
flights = Flights.objects.filter(orig_airport=airport)
problem_flights = Flights.objects.filter(orig_airport=airport)\
.filter(Q(status='ОТМЕНЕН') | Q(status='ЗАДЕРЖАН')).order_by('orig_airport', 'flight_type', 'plan_time')
arrivals = flights.filter(flight_type='ПРИЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
departures = flights.filter(flight_type='ВЫЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
template = loader.get_template('dashboard/index.html')
context = {
'problems': problem_flights,
'arrivals': arrivals,
'departures': departures
}
return HttpResponse(template.render(context, request))
def show_noj(request):
airport = 'Ноябрьск'
flights = Flights.objects.filter(orig_airport=airport)
problem_flights = Flights.objects.filter(orig_airport=airport) \
.filter(Q(status='ОТМЕНЕН') | Q(status='ЗАДЕРЖАН')).order_by('orig_airport', 'flight_type', 'plan_time')
arrivals = flights.filter(flight_type='ПРИЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
departures = flights.filter(flight_type='ВЫЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
template = loader.get_template('dashboard/index.html')
context = {
'problems': problem_flights,
'arrivals': arrivals,
'departures': departures
}
return HttpResponse(template.render(context, request))
def show_nux(request):
airport = 'Новый Уренгой'
flights = Flights.objects.filter(orig_airport=airport)
problem_flights = Flights.objects.filter(orig_airport=airport) \
.filter(Q(status='ОТМЕНЕН') | Q(status='ЗАДЕРЖАН')).order_by('orig_airport', 'flight_type', 'plan_time')
arrivals = flights.filter(flight_type='ПРИЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
departures = flights.filter(flight_type='ВЫЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
template = loader.get_template('dashboard/index.html')
context = {
'problems': problem_flights,
'arrivals': arrivals,
'departures': departures
}
return HttpResponse(template.render(context, request))
def show_nym(request):
airport = 'Надым'
flights = Flights.objects.filter(orig_airport=airport)
problem_flights = Flights.objects.filter(orig_airport=airport) \
.filter(Q(status='ОТМЕНЕН') | Q(status='ЗАДЕРЖАН')).order_by('orig_airport', 'flight_type', 'plan_time')
arrivals = flights.filter(flight_type='ПРИЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
departures = flights.filter(flight_type='ВЫЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
template = loader.get_template('dashboard/index.html')
context = {
'problems': problem_flights,
'arrivals': arrivals,
'departures': departures
}
return HttpResponse(template.render(context, request))
def show_sbt(request):
airport = 'Сабетта'
flights = Flights.objects.filter(orig_airport=airport)
problem_flights = Flights.objects.filter(orig_airport=airport) \
.filter(Q(status='ОТМЕНЕН') | Q(status='ЗАДЕРЖАН')).order_by('orig_airport', 'flight_type', 'plan_time')
arrivals = flights.filter(flight_type='ПРИЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
departures = flights.filter(flight_type='ВЫЛЕТ').exclude(status='ОТМЕНЕН').exclude(status='ЗАДЕРЖАН')
template = loader.get_template('dashboard/index.html')
context = {
'problems': problem_flights,
'arrivals': arrivals,
'departures': departures
}
return HttpResponse(template.render(context, request))
| 42.732673
| 114
| 0.707831
| 481
| 4,316
| 6.212058
| 0.12474
| 0.087015
| 0.077309
| 0.099398
| 0.885877
| 0.876506
| 0.876506
| 0.864793
| 0.835007
| 0.835007
| 0
| 0
| 0.147822
| 4,316
| 101
| 115
| 42.732673
| 0.812398
| 0.012048
| 0
| 0.710843
| 0
| 0
| 0.186986
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.072289
| false
| 0
| 0.060241
| 0
| 0.204819
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
44fdafa3bff1fab2561d9f8bdb4796922edde926
| 8,364
|
py
|
Python
|
simulation/client_state_func.py
|
Kylin824/federated-learning
|
479a114712908fee5a9e06da3afb23a8469e44b1
|
[
"MIT"
] | null | null | null |
simulation/client_state_func.py
|
Kylin824/federated-learning
|
479a114712908fee5a9e06da3afb23a8469e44b1
|
[
"MIT"
] | null | null | null |
simulation/client_state_func.py
|
Kylin824/federated-learning
|
479a114712908fee5a9e06da3afb23a8469e44b1
|
[
"MIT"
] | null | null | null |
import numpy as np
def create_client_state():
np.random.seed(2)
client_state_list = np.zeros((100, 10))
client_dataset = np.load("dataset_noniid_200_1000.npy", allow_pickle=True)
client_dataset = client_dataset.item()
poi_cu_prob = [0.45, 0.5, 0.4, 0.45, 0.4, 0.4, 0.4, 0.4]
poi_cq_prob = [0.45, 0.4, 0.6, 0.45, 0.5, 0.5, 0.6, 0.6]
# 8个poi, 0和3分别20个, 其他各10个
# 0-20 : 0
for i in range(100):
if i < 20:
poi = 0
elif i >= 20 and i < 30:
poi = 1
elif i >= 30 and i < 40:
poi = 2
elif i >= 40 and i < 60:
poi = 3
elif i >= 60 and i < 70:
poi = 4
elif i >= 70 and i < 80:
poi = 5
elif i >= 80 and i < 90:
poi = 6
elif i >= 90 and i < 100:
poi = 7
c = client_state_list[i]
c[1] = poi # current_poi
c[5] = np.random.normal(loc=poi_cu_prob[int(c[1])], scale=0.1, size=1) # current_computation
c[6] = np.random.normal(loc=poi_cq_prob[int(c[1])], scale=0.1, size=1) # current_communication
# real_poi
if np.random.rand() <= 0.8: # 移动概率 (50%概率不动，50%概率移动）
c[0] = c[1]
else:
c[0] = np.random.randint(low=0, high=8)
c[3] = np.random.normal(loc=poi_cu_prob[int(c[0])], scale=0.1, size=1)
c[4] = np.random.normal(loc=poi_cq_prob[int(c[0])], scale=0.1, size=1)
# pred_poi
if np.random.rand() <= 0.9: # 预测准确度 (90%概率准确预测，10%概率错误预测)
c[2] = c[0]
else:
c[2] = np.random.randint(low=0, high=8)
c[7] = np.random.normal(loc=poi_cu_prob[int(c[2])], scale=0.1, size=1)
c[8] = np.random.normal(loc=poi_cq_prob[int(c[2])], scale=0.1, size=1)
c[9] = len(client_dataset[i]) / 1000
# print(client_state_list)
total_valid_num = 0
for idx in range(100):
beta = 0.5
client_state = client_state_list[idx]
cur_cq = client_state[5]
cur_nq = client_state[6]
client_datasize = client_state[9]
next_cq = client_state[3]
next_nq = client_state[4]
reward = 0
if cur_cq + cur_nq + next_cq + next_nq >= 2:
reward = cur_cq + cur_nq + next_cq + next_nq - 2 + client_datasize * beta
print('valid: ', idx)
total_valid_num += 1
else:
reward = -0.05
print('fail: ', idx)
print('total valid num: ', total_valid_num)
np.save('simulative_client_state.npy', client_state_list)
def create_change_client_state():
np.random.seed(2)
client_state_list = np.zeros((100, 10))
client_dataset = np.load("dataset_noniid_200_1000.npy", allow_pickle=True)
client_dataset = client_dataset.item()
poi_cu_prob = [0.6, 0.4, 0.6, 0.6, 0.5, 0.5, 0.6, 0.6]
poi_cq_prob = [0.6, 0.5, 0.4, 0.6, 0.4, 0.4, 0.4, 0.4]
# 8个poi, 0和3分别20个, 其他各10个
# 0-20 : 0
for i in range(100):
if i < 20:
poi = 0
elif i >= 20 and i < 30:
poi = 1
elif i >= 30 and i < 40:
poi = 2
elif i >= 40 and i < 60:
poi = 3
elif i >= 60 and i < 70:
poi = 4
elif i >= 70 and i < 80:
poi = 5
elif i >= 80 and i < 90:
poi = 6
elif i >= 90 and i < 100:
poi = 7
c = client_state_list[i]
c[1] = poi # current_poi
c[5] = np.random.normal(loc=poi_cu_prob[int(c[1])], scale=0.1, size=1) # current_computation
c[6] = np.random.normal(loc=poi_cq_prob[int(c[1])], scale=0.1, size=1) # current_communication
# real_poi
if np.random.rand() <= 0.8: # 移动概率 (50%概率不动，50%概率移动）
c[0] = c[1]
else:
c[0] = np.random.randint(low=0, high=8)
c[3] = np.random.normal(loc=poi_cu_prob[int(c[0])], scale=0.1, size=1)
c[4] = np.random.normal(loc=poi_cq_prob[int(c[0])], scale=0.1, size=1)
# pred_poi
if np.random.rand() <= 0.9: # 预测准确度 (90%概率准确预测，10%概率错误预测)
c[2] = c[0]
else:
c[2] = np.random.randint(low=0, high=8)
c[7] = np.random.normal(loc=poi_cu_prob[int(c[2])], scale=0.1, size=1) # pred_computation
c[8] = np.random.normal(loc=poi_cq_prob[int(c[2])], scale=0.1, size=1) # pred_communication
c[9] = len(client_dataset[i]) / 1000
# print(client_state_list)
total_valid_num = 0
for idx in range(100):
# beta = 0.5
client_state = client_state_list[idx]
cur_cq = client_state[5]
cur_nq = client_state[6]
client_datasize = client_state[9]
next_cq = client_state[3]
next_nq = client_state[4]
reward = 0
if cur_cq + cur_nq + next_cq + next_nq >= 2:
# reward = cur_cq + cur_nq + next_cq + next_nq - 2 + client_datasize * beta
print('valid: ', idx)
total_valid_num += 1
else:
# reward = -0.05
print('fail: ', idx)
print('total valid num: ', total_valid_num)
np.save('simulative__change_client_state.npy', client_state_list)
def create_change_client_state_plus():
np.random.seed(2)
client_state_list = np.load('simulative_client_state.npy')
print(client_state_list[0])
for idx in range(100):
# beta = 0.5
client_state = client_state_list[idx]
cur_cq = client_state[5]
cur_nq = client_state[6]
client_datasize = client_state[9]
next_cq = client_state[3]
next_nq = client_state[4]
reward = 0
if cur_cq + cur_nq + next_cq + next_nq >= 2:
# reward = cur_cq + cur_nq + next_cq + next_nq - 2 + client_datasize * beta
# client_state[6] = 2 - (cur_cq + next_cq + next_nq) - np.random.normal(loc=0.1, scale=0.1, size=1)
# client_state[4] = 2 - (cur_cq + cur_nq + next_cq) - np.random.normal(loc=0.1, scale=0.1, size=1)
# client_state[6] = 2 - (cur_cq + next_cq + next_nq) - np.random.normal(loc=0.1, scale=0.1, size=1)
client_state[6] = 1 - client_state[6]
client_state[4] = 1 - client_state[4]
# client_state[8] = 1 - client_state[8] - 0.075
print('valid: ', idx)
else:
# reward = -0.05
# client_state[6] = 2 - (cur_cq + next_cq + next_nq) + np.random.normal(loc=0.1, scale=0.1, size=1)
# client_state[4] = 2 - (cur_cq + cur_nq + next_cq) + np.random.normal(loc=0.1, scale=0.1, size=1)
client_state[6] = 1 - client_state[6]
client_state[4] = 1 - client_state[4]
# client_state[8] = 1 - client_state[8] + 0.075
print('fail: ', idx)
np.save('simulative__change_client_state.npy', client_state_list)
def create_real_client_state():
real_poi = np.loadtxt('../realenv/real_poi_pred.txt')
# print(real_poi[0])
last = 3
total_change = 0
for i in real_poi[0]:
if i != last:
total_change += 1
last = i
print(len(real_poi[0]))
print(total_change)
def read_client_state():
client_state = np.load('simulative_client_state.npy')
i = 0
valid = 0
for s in client_state:
print("idx: %2d, poi:[ next %d, cur %d, pred %d ], next: %.3f, %.3f, cur: %.3f, %.3f, pred: %.3f, %.3f, size: %d" % (
i, s[0], s[1], s[2], s[3], s[4], s[5], s[6], s[7], s[8], s[9] * 1000))
i += 1
cur_cq = s[5]
cur_nq = s[6]
next_cq = s[3]
next_nq = s[4]
if cur_cq + cur_nq + next_cq + next_nq >= 2:
valid += 1
print('total valid num: ', valid)
def print_valid_client_each_round():
# valid_list = np.loadtxt('valid_list_fedcs.txt')
# valid_list = np.loadtxt('valid_list_random.txt')
valid_list = np.loadtxt('valid_list_ucb.txt')
# valid_list = np.loadtxt('valid_list_linucb.txt')
for round in range(len(valid_list)):
total_this_round = valid_list[round]
valid_this_round = total_this_round[np.where(total_this_round != -1)]
print(valid_this_round)
if __name__ == "__main__":
# print_valid_client_each_round()
# read_client_state()
# create_client_state()
# create_real_client_state()
# create_change_client_state()
create_change_client_state_plus()
| 32.418605
| 126
| 0.551411
| 1,349
| 8,364
| 3.201631
| 0.08599
| 0.168094
| 0.055105
| 0.066914
| 0.846492
| 0.825191
| 0.792313
| 0.771475
| 0.75712
| 0.75712
| 0
| 0.080385
| 0.303922
| 8,364
| 258
| 127
| 32.418605
| 0.661457
| 0.176231
| 0
| 0.732558
| 0
| 0.005814
| 0.066462
| 0.034034
| 0
| 0
| 0
| 0
| 0
| 1
| 0.034884
| false
| 0
| 0.005814
| 0
| 0.040698
| 0.087209
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
78491080912e7d7c9f992c9e7c7bc4c24a9c2c62
| 8,555
|
py
|
Python
|
net/connector.py
|
aaiijmrtt/net
|
92594b0bb65fc721eabfedcfccfc797ea5a475c7
|
[
"MIT"
] | null | null | null |
net/connector.py
|
aaiijmrtt/net
|
92594b0bb65fc721eabfedcfccfc797ea5a475c7
|
[
"MIT"
] | null | null | null |
net/connector.py
|
aaiijmrtt/net
|
92594b0bb65fc721eabfedcfccfc797ea5a475c7
|
[
"MIT"
] | null | null | null |
'''
Module containing Connector Layers.
Classes embody Non Parametric Layers,
used to connect combinations of Layers.
'''
import numpy
from . import base
class Split(base.Net):
'''
Split Connector Function
Mathematically, f(x) = [x, x ... x]
'''
def __init__(self, inputs, parameter):
'''
Constructor
: param inputs : dimension of input feature space
: param parameter : determines dimension of output feature space,
as multiplicity of input feature space
'''
if not hasattr(self, 'dimensions'):
self.dimensions = dict()
self.dimensions['inputs'] = inputs
self.dimensions['parameter'] = parameter
self.dimensions['outputs'] = self.dimensions['inputs'] * self.dimensions['parameter']
if not hasattr(self, 'history'):
self.history = dict()
self.history['input'] = list()
self.history['output'] = list()
def feedforward(self, inputvector):
'''
Method to feedforward a vector through the layer
: param inputvector : vector in input feature space
: returns : fedforward vector mapped to output feature space
'''
if inputvector.shape != (self.dimensions['inputs'], 1):
self.dimensionsError(self.__class__.__name__)
self.history['input'].append(inputvector)
self.history['output'].append(numpy.concatenate([self.history['input'][-1]] * self.dimensions['parameter']))
return self.history['output'][-1]
def backpropagate(self, outputvector):
'''
Method to backpropagate derivatives through the layer
: param outputvector : derivative vector in output feature space
: returns : backpropagated vector mapped to input feature space
'''
self.history['output'].pop()
self.history['input'].pop()
deltas = numpy.zeros((self.dimensions['inputs'], 1), dtype = float)
for i in range(self.dimensions['outputs']):
deltas[i % self.dimensions['inputs']][0] += outputvector[i][0]
return deltas
class MergeSum(base.Net):
'''
Merge (Sum) Connector Function
Mathematically, f([x1, x2 .. xn])(i) = sum_over_j(xj(i))
'''
def __init__(self, outputs, parameter):
'''
Constructor
: param outputs : dimension of output feature space
: param parameter : determines dimension of input feature space,
as multiplicity of output feature space
'''
if not hasattr(self, 'dimensions'):
self.dimensions = dict()
self.dimensions['outputs'] = outputs
self.dimensions['parameter'] = parameter
self.dimensions['inputs'] = self.dimensions['outputs'] * self.dimensions['parameter']
if not hasattr(self, 'history'):
self.history = dict()
self.history['input'] = list()
self.history['output'] = list()
def feedforward(self, inputvector):
'''
Method to feedforward a vector through the layer
: param inputvector : vector in input feature space
: returns : fedforward vector mapped to output feature space
'''
if inputvector.shape != (self.dimensions['inputs'], 1):
self.dimensionsError(self.__class__.__name__)
self.history['input'].append(inputvector)
self.history['output'].append(numpy.zeros((self.dimensions['outputs'], 1), dtype = float))
for i in range(self.dimensions['inputs']):
self.history['output'][-1][i % self.dimensions['outputs']][0] += self.history['input'][-1][i][0]
return self.history['output'][-1]
def backpropagate(self, outputvector):
'''
Method to backpropagate derivatives through the layer
: param outputvector : derivative vector in output feature space
: returns : backpropagated vector mapped to input feature space
'''
self.history['output'].pop()
self.history['input'].pop()
return numpy.concatenate([outputvector] * self.dimensions['parameter'])
class MergeProduct(base.Net):
'''
Merge (Product) Connector Function
Mathematically, f([x1, x2 .. xn])(i) = product_over_j(xj(i))
'''
def __init__(self, outputs, parameter):
'''
Constructor
: param outputs : dimension of output feature space
: param parameter : determines dimension of input feature space,
as multiplicity of output feature space
'''
if not hasattr(self, 'dimensions'):
self.dimensions = dict()
self.dimensions['outputs'] = outputs
self.dimensions['parameter'] = parameter
self.dimensions['inputs'] = self.dimensions['outputs'] * self.dimensions['parameter']
if not hasattr(self, 'history'):
self.history = dict()
self.history['input'] = list()
self.history['output'] = list()
def feedforward(self, inputvector):
'''
Method to feedforward a vector through the layer
: param inputvector : vector in input feature space
: returns : fedforward vector mapped to output feature space
'''
if inputvector.shape != (self.dimensions['inputs'], 1):
self.dimensionsError(self.__class__.__name__)
self.history['input'].append(inputvector)
self.history['output'].append(numpy.ones((self.dimensions['outputs'], 1), dtype = float))
for i in range(self.dimensions['inputs']):
self.history['output'][-1][i % self.dimensions['outputs']][0] *= self.history['input'][-1][i][0]
return self.history['output'][-1]
def backpropagate(self, outputvector):
'''
Method to backpropagate derivatives through the layer
: param outputvector : derivative vector in output feature space
: returns : backpropagated vector mapped to input feature space
'''
deltas = numpy.concatenate([outputvector] * self.dimensions['parameter'])
for i in range(self.dimensions['inputs']):
deltas[i][0] *= self.history['output'][-1][i % self.dimensions['outputs']][0] / self.history['input'][-1][i][0]
self.history['output'].pop()
self.history['input'].pop()
return deltas
class Step(base.Net):
'''
Step Connector Function
Mathematically, f(x) = x
'''
def __init__(self, inputs):
'''
Constructor
: param inputs : dimension of input (and output) feature space
'''
if not hasattr(self, 'dimensions'):
self.dimensions = dict()
self.dimensions['inputs'] = inputs
self.dimensions['outputs'] = self.dimensions['inputs']
if not hasattr(self, 'history'):
self.history = dict()
self.history['input'] = list()
self.history['output'] = list()
def feedforward(self, inputvector):
'''
Method to feedforward a vector through the layer
: param inputvector : vector in input feature space
: returns : fedforward vector mapped to output feature space
'''
if inputvector.shape != (self.dimensions['inputs'], 1):
self.dimensionsError(self.__class__.__name__)
self.history['input'].append(inputvector)
self.history['output'].append(self.history['input'][-1])
return self.history['output'][-1]
def backpropagate(self, outputvector):
'''
Method to backpropagate derivatives through the layer
: param outputvector : derivative vector in output feature space
: returns : backpropagated vector mapped to input feature space
'''
self.history['input'].pop()
self.history['output'].pop()
return outputvector
class Constant(base.Net):
'''
Identity Connector Function
Mathematically, f(x) = p
'''
def __init__(self, inputs, outputs, parameter = None):
'''
Constructor
: param inputs : dimension of input (and output) feature space
: param outputs : dimension of output feature space
: param parameter : p, as given in its mathematical equation
'''
if not hasattr(self, 'dimensions'):
self.dimensions = dict()
self.dimensions['inputs'] = inputs
self.dimensions['outputs'] = outputs
if not hasattr(self, 'history'):
self.history = dict()
self.history['input'] = list()
self.history['output'] = list()
self.parameter = parameter if parameter is not None else 1.0
def feedforward(self, inputvector):
'''
Method to feedforward a vector through the layer
: param inputvector : vector in input feature space
: returns : fedforward vector mapped to output feature space
'''
if inputvector.shape != (self.dimensions['inputs'], 1):
self.dimensionsError(self.__class__.__name__)
self.history['input'].append(inputvector)
self.history['output'].append(numpy.multiply(self.parameter, numpy.ones((self.dimensions['outputs'], 1), dtype = float)))
return self.history['output'][-1]
def backpropagate(self, outputvector): # invisible during backpropagation
'''
Method to backpropagate derivatives through the layer
: param outputvector : derivative vector in output feature space
: returns : backpropagated vector mapped to input feature space
'''
if outputvector.shape != (self.dimensions['outputs'], 1):
self.dimensionsError(self.__class__.__name__)
self.history['input'].pop()
self.history['output'].pop()
return outputvector
| 36.097046
| 123
| 0.702864
| 1,033
| 8,555
| 5.75121
| 0.10455
| 0.098132
| 0.065814
| 0.026931
| 0.884868
| 0.873254
| 0.818886
| 0.805757
| 0.785053
| 0.746171
| 0
| 0.005272
| 0.157452
| 8,555
| 236
| 124
| 36.25
| 0.818951
| 0.351958
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| null | 0
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0
| 7
|
785897f2f5b8ec67c9da97dbbc8105809aeee030
| 14,831
|
py
|
Python
|
unet.py
|
liliangzhi110/BuildingChangeDetector
|
088f0b948c05a645cab43d00df41be94f34d1630
|
[
"MIT"
] | 191
|
2017-12-08T07:01:55.000Z
|
2022-03-23T01:32:57.000Z
|
unet.py
|
mingzhaochina/BuildingChangeDetector
|
088f0b948c05a645cab43d00df41be94f34d1630
|
[
"MIT"
] | 7
|
2017-12-13T10:52:45.000Z
|
2020-05-22T00:31:07.000Z
|
unet.py
|
mingzhaochina/BuildingChangeDetector
|
088f0b948c05a645cab43d00df41be94f34d1630
|
[
"MIT"
] | 89
|
2017-12-06T02:34:55.000Z
|
2021-11-29T09:29:21.000Z
|
# -*- coding: utf-8 -*-
"""
搭建U-Net网络。参考了开源代码。
"""
from keras.models import Model
from keras.layers import (
Input, concatenate, Conv2D, MaxPooling2D,
BatchNormalization, Activation, UpSampling2D)
def get_unet_128(input_shape=(128, 128, 3), num_classes=1):
#filter的数量由少变多，再变少。后半过程会重复前半过程提的feature，也就是在filter
# 维度上拼接
inputs = Input(shape=input_shape)
# 128
# padding='same'意思是添加padding使得输出的长宽和输入的一致
# 输出(samples, 128, 128, 64)
down1 = Conv2D(64, (3, 3), padding='same')(inputs)
# Normalize the activations of the previous layer at each batch,
#i.e. applies a transformation that maintains the mean activation
#close to 0 and the activation standard deviation close to 1
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
# 输出(samples, 128, 128, 64)
down1 = Conv2D(64, (3, 3), padding='same')(down1)
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
# 输出(samples, 64, 64, 64)
down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
# 64
# 输出(samples, 64, 64, 128)
down2 = Conv2D(128, (3, 3), padding='same')(down1_pool)
down2 = BatchNormalization()(down2)
down2 = Activation('relu')(down2)
# 输出(samples, 64, 64, 128)
down2 = Conv2D(128, (3, 3), padding='same')(down2)
down2 = BatchNormalization()(down2)
down2 = Activation('relu')(down2)
# 输出(samples, 32, 32, 128)
down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
# 32
# 输出(samples, 32, 32, 256)
down3 = Conv2D(256, (3, 3), padding='same')(down2_pool)
down3 = BatchNormalization()(down3)
down3 = Activation('relu')(down3)
down3 = Conv2D(256, (3, 3), padding='same')(down3)
down3 = BatchNormalization()(down3)
down3 = Activation('relu')(down3)
# 输出(samples, 16, 16, 256)
down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
# 16
# 输出(samples, 16, 16, 512)
down4 = Conv2D(512, (3, 3), padding='same')(down3_pool)
down4 = BatchNormalization()(down4)
down4 = Activation('relu')(down4)
down4 = Conv2D(512, (3, 3), padding='same')(down4)
down4 = BatchNormalization()(down4)
down4 = Activation('relu')(down4)
# 输出(samples, 8, 8, 512)
down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4)
# 8
# 输出(samples, 8, 8, 1024)
center = Conv2D(1024, (3, 3), padding='same')(down4_pool)
center = BatchNormalization()(center)
center = Activation('relu')(center)
center = Conv2D(1024, (3, 3), padding='same')(center)
center = BatchNormalization()(center)
center = Activation('relu')(center)
# center
# 输出(samples, 16, 16, 1024)
up4 = UpSampling2D((2, 2))(center)
# 输出(samples, 16, 16, 1024+512)
up4 = concatenate([down4, up4], axis=3)
# 输出(samples, 16, 16, 512)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
# 16
# 输出(samples, 32, 32, 256)
up3 = UpSampling2D((2, 2))(up4)
up3 = concatenate([down3, up3], axis=3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
# 32
# 输出(samples, 64, 64, 128)
up2 = UpSampling2D((2, 2))(up3)
up2 = concatenate([down2, up2], axis=3)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
# 64
# 输出(samples, 128, 128, 64)
up1 = UpSampling2D((2, 2))(up2)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
# 128
# 输出(samples, 128, 128, num_classes)，本来输出就是图片
classify = Conv2D(num_classes, (1, 1), activation='softmax')(up1)
model = Model(inputs=inputs, outputs=classify)
return model
def get_unet_256(input_shape=(256, 256, 3), num_classes=1):
inputs = Input(shape=input_shape)
# 256
down0 = Conv2D(32, (3, 3), padding='same')(inputs)
down0 = BatchNormalization()(down0)
down0 = Activation('relu')(down0)
down0 = Conv2D(32, (3, 3), padding='same')(down0)
down0 = BatchNormalization()(down0)
down0 = Activation('relu')(down0)
down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
# 128
down1 = Conv2D(64, (3, 3), padding='same')(down0_pool)
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
down1 = Conv2D(64, (3, 3), padding='same')(down1)
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
# 64
down2 = Conv2D(128, (3, 3), padding='same')(down1_pool)
down2 = BatchNormalization()(down2)
down2 = Activation('relu')(down2)
down2 = Conv2D(128, (3, 3), padding='same')(down2)
down2 = BatchNormalization()(down2)
down2 = Activation('relu')(down2)
down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
# 32
down3 = Conv2D(256, (3, 3), padding='same')(down2_pool)
down3 = BatchNormalization()(down3)
down3 = Activation('relu')(down3)
down3 = Conv2D(256, (3, 3), padding='same')(down3)
down3 = BatchNormalization()(down3)
down3 = Activation('relu')(down3)
down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
# 16
down4 = Conv2D(512, (3, 3), padding='same')(down3_pool)
down4 = BatchNormalization()(down4)
down4 = Activation('relu')(down4)
down4 = Conv2D(512, (3, 3), padding='same')(down4)
down4 = BatchNormalization()(down4)
down4 = Activation('relu')(down4)
down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4)
# 8
center = Conv2D(1024, (3, 3), padding='same')(down4_pool)
center = BatchNormalization()(center)
center = Activation('relu')(center)
center = Conv2D(1024, (3, 3), padding='same')(center)
center = BatchNormalization()(center)
center = Activation('relu')(center)
# center
up4 = UpSampling2D((2, 2))(center)
up4 = concatenate([down4, up4], axis=3)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
# 16
up3 = UpSampling2D((2, 2))(up4)
up3 = concatenate([down3, up3], axis=3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
# 32
up2 = UpSampling2D((2, 2))(up3)
up2 = concatenate([down2, up2], axis=3)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
# 64
up1 = UpSampling2D((2, 2))(up2)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
# 128
up0 = UpSampling2D((2, 2))(up1)
up0 = concatenate([down0, up0], axis=3)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
# 256
classify = Conv2D(num_classes, (1, 1), activation='softmax')(up0)
model = Model(inputs=inputs, outputs=classify)
return model
def get_unet_512(input_shape=(512, 512, 3), num_classes=1):
inputs = Input(shape=input_shape)
# 512
down0a = Conv2D(16, (3, 3), padding='same')(inputs)
down0a = BatchNormalization()(down0a)
down0a = Activation('relu')(down0a)
down0a = Conv2D(16, (3, 3), padding='same')(down0a)
down0a = BatchNormalization()(down0a)
down0a = Activation('relu')(down0a)
down0a_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0a)
# 256
down0 = Conv2D(32, (3, 3), padding='same')(down0a_pool)
down0 = BatchNormalization()(down0)
down0 = Activation('relu')(down0)
down0 = Conv2D(32, (3, 3), padding='same')(down0)
down0 = BatchNormalization()(down0)
down0 = Activation('relu')(down0)
down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
# 128
down1 = Conv2D(64, (3, 3), padding='same')(down0_pool)
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
down1 = Conv2D(64, (3, 3), padding='same')(down1)
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
# 64
down2 = Conv2D(128, (3, 3), padding='same')(down1_pool)
down2 = BatchNormalization()(down2)
down2 = Activation('relu')(down2)
down2 = Conv2D(128, (3, 3), padding='same')(down2)
down2 = BatchNormalization()(down2)
down2 = Activation('relu')(down2)
down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
# 32
down3 = Conv2D(256, (3, 3), padding='same')(down2_pool)
down3 = BatchNormalization()(down3)
down3 = Activation('relu')(down3)
down3 = Conv2D(256, (3, 3), padding='same')(down3)
down3 = BatchNormalization()(down3)
down3 = Activation('relu')(down3)
down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
# 16
down4 = Conv2D(512, (3, 3), padding='same')(down3_pool)
down4 = BatchNormalization()(down4)
down4 = Activation('relu')(down4)
down4 = Conv2D(512, (3, 3), padding='same')(down4)
down4 = BatchNormalization()(down4)
down4 = Activation('relu')(down4)
down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4)
# 8
center = Conv2D(1024, (3, 3), padding='same')(down4_pool)
center = BatchNormalization()(center)
center = Activation('relu')(center)
center = Conv2D(1024, (3, 3), padding='same')(center)
center = BatchNormalization()(center)
center = Activation('relu')(center)
# center
up4 = UpSampling2D((2, 2))(center)
up4 = concatenate([down4, up4], axis=3)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
up4 = Conv2D(512, (3, 3), padding='same')(up4)
up4 = BatchNormalization()(up4)
up4 = Activation('relu')(up4)
# 16
up3 = UpSampling2D((2, 2))(up4)
up3 = concatenate([down3, up3], axis=3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
up3 = Conv2D(256, (3, 3), padding='same')(up3)
up3 = BatchNormalization()(up3)
up3 = Activation('relu')(up3)
# 32
up2 = UpSampling2D((2, 2))(up3)
up2 = concatenate([down2, up2], axis=3)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
up2 = Conv2D(128, (3, 3), padding='same')(up2)
up2 = BatchNormalization()(up2)
up2 = Activation('relu')(up2)
# 64
up1 = UpSampling2D((2, 2))(up2)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
# 128
up0 = UpSampling2D((2, 2))(up1)
up0 = concatenate([down0, up0], axis=3)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
# 256
up0a = UpSampling2D((2, 2))(up0)
up0a = concatenate([down0a, up0a], axis=3)
up0a = Conv2D(16, (3, 3), padding='same')(up0a)
up0a = BatchNormalization()(up0a)
up0a = Activation('relu')(up0a)
up0a = Conv2D(16, (3, 3), padding='same')(up0a)
up0a = BatchNormalization()(up0a)
up0a = Activation('relu')(up0a)
up0a = Conv2D(16, (3, 3), padding='same')(up0a)
up0a = BatchNormalization()(up0a)
up0a = Activation('relu')(up0a)
# 512
classify = Conv2D(num_classes, (1, 1), activation='softmax')(up0a)
model = Model(inputs=inputs, outputs=classify)
return model
if __name__ == '__main__':
model = get_unet_128(input_shape=(128,128,8), num_classes=2)
model.summary()
| 34.896471
| 70
| 0.609602
| 1,839
| 14,831
| 4.883089
| 0.051659
| 0.100445
| 0.08118
| 0.117261
| 0.927171
| 0.908463
| 0.901002
| 0.88686
| 0.846882
| 0.830624
| 0
| 0.119844
| 0.203897
| 14,831
| 424
| 71
| 34.978774
| 0.640722
| 0.062504
| 0
| 0.932258
| 0
| 0
| 0.04893
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.009677
| false
| 0
| 0.006452
| 0
| 0.025806
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
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| null | 0
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|
0
| 7
|
788476a01cd94df5833875062f67e2ba67e1c033
| 11,129
|
py
|
Python
|
tests/circuit_breaker/test_async.py
|
luizalabs/lasier
|
edb0e850cb630fb55ce83c255bbb1e7fca08b21f
|
[
"MIT"
] | 61
|
2019-12-13T20:08:30.000Z
|
2022-03-22T11:51:04.000Z
|
tests/circuit_breaker/test_async.py
|
jairhenrique/lasier
|
29bf96cb888493d369a22400bec6acffe345d168
|
[
"MIT"
] | 25
|
2019-12-13T17:14:46.000Z
|
2022-03-17T18:49:34.000Z
|
tests/circuit_breaker/test_async.py
|
jairhenrique/lasier
|
29bf96cb888493d369a22400bec6acffe345d168
|
[
"MIT"
] | 6
|
2020-04-02T21:10:08.000Z
|
2022-03-17T15:31:15.000Z
|
import asyncio
from unittest import mock
import pytest
from lasier.circuit_breaker.asyncio import CircuitBreaker, circuit_breaker
from .exceptions import MyException
async def success_function():
return True
async def fail_function():
raise ValueError()
@pytest.mark.asyncio
class TestCircuitBreaker:
@pytest.fixture
def failure_cache_key(self):
return 'fail'
@pytest.fixture
def request_cache_key(self):
return 'request'
async def test_should_exec_func_with_success(
self,
async_cache,
should_not_open_rule
):
async with CircuitBreaker(
rule=should_not_open_rule,
cache=async_cache,
failure_exception=ValueError,
catch_exceptions=[]
):
await success_function()
async def test_should_exec_func_with_success_ussing_decorator(
self,
async_cache,
should_not_open_rule
):
@circuit_breaker(
rule=should_not_open_rule,
cache=async_cache,
failure_exception=ValueError,
catch_exceptions=[],
)
async def inner_func():
await success_function()
await inner_func()
async def test_should_raise_error(self, async_cache, should_open_rule):
with pytest.raises(MyException):
async with CircuitBreaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
async def test_should_raise_error_with_decorator(
self,
async_cache,
should_open_rule
):
@circuit_breaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
)
async def inner_func():
await fail_function()
with pytest.raises(MyException):
await inner_func()
async def test_should_increase_fail_cache_count(
self,
async_cache,
failure_cache_key,
should_not_open_rule
):
await async_cache.set(failure_cache_key, 1)
with pytest.raises(ValueError):
async with CircuitBreaker(
rule=should_not_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
assert await async_cache.get(failure_cache_key) == 2
async def test_should_increase_request_cache_count(
self,
async_cache,
request_cache_key,
should_not_open_rule
):
await async_cache.set(request_cache_key, 0)
async with CircuitBreaker(
rule=should_not_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await success_function()
with pytest.raises(ValueError):
async with CircuitBreaker(
rule=should_not_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
assert await async_cache.get(request_cache_key) == 2
async def test_should_open_circuit_when_failures_exceeds(
self,
async_cache,
should_open_rule,
failure_cache_key,
):
await async_cache.set(failure_cache_key, 3)
with pytest.raises(MyException):
async with CircuitBreaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
) as cb:
await fail_function()
assert await cb.is_circuit_open()
async def test_should_raise_exception_when_circuit_is_open(
self,
async_cache,
should_open_rule,
failure_cache_key
):
circuit_cache_key = f'circuit_{failure_cache_key}'
await async_cache.set(circuit_cache_key, 1)
with pytest.raises(MyException):
async with CircuitBreaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
) as cb:
await success_function()
assert await cb.is_circuit_open()
async def test_should_not_call_exit_when_circuit_is_open(
self,
async_cache,
should_open_rule,
failure_cache_key
):
circuit_cache_key = f'circuit_{failure_cache_key}'
await async_cache.set(circuit_cache_key, 1)
with pytest.raises(MyException):
with mock.patch(
'lasier.circuit_breaker.asyncio.CircuitBreaker.__aexit__'
) as exit_method:
async with CircuitBreaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await success_function()
assert not exit_method.called
async def test_should_not_increment_fail_when_circuit_is_open(
self,
async_cache,
should_open_rule,
failure_cache_key
):
"""
It should not increment fail count over the max failures limit, when
circuit breaker is open after a successful enter.
"""
await async_cache.set(failure_cache_key, 3)
with pytest.raises(MyException):
async with CircuitBreaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
assert (await async_cache.get(failure_cache_key)) is None
async def test_should_not_increment_request_when_circuit_is_open(
self,
async_cache,
should_open_rule,
failure_cache_key,
request_cache_key
):
"""
It should not increment request count over the max failures limit, when
circuit breaker is open after a successful enter.
"""
await asyncio.gather(
async_cache.set(failure_cache_key, 2),
async_cache.set(request_cache_key, 5)
)
with pytest.raises(MyException):
async with CircuitBreaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
assert (await async_cache.get(request_cache_key)) is None
async def test_should_not_increment_request_when_rule_is_false(
self,
async_cache,
should_not_increase_request_rule,
request_cache_key
):
await async_cache.set(request_cache_key, 5)
with pytest.raises(ValueError):
async with CircuitBreaker(
rule=should_not_increase_request_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
assert await async_cache.get(request_cache_key) == 5
async def test_should_not_increment_failure_when_rule_is_false(
self,
async_cache,
should_not_increase_failure_rule,
failure_cache_key
):
await async_cache.set(failure_cache_key, 5)
with pytest.raises(ValueError):
async with CircuitBreaker(
rule=should_not_increase_failure_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
assert await async_cache.get(failure_cache_key) == 5
async def test_should_delete_count_key_when_circuit_is_open(
self,
async_cache,
should_open_rule,
failure_cache_key,
request_cache_key
):
await asyncio.gather(
async_cache.set(failure_cache_key, 2),
async_cache.set(request_cache_key, 5)
)
with pytest.raises(MyException):
async with CircuitBreaker(
rule=should_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
) as cb:
await fail_function()
assert await cb.is_circuit_open()
assert await async_cache.get(failure_cache_key) is None
assert await async_cache.get(request_cache_key) is None
async def test_should_create_failure_cache_when_increase_request_count(
self,
async_cache,
should_not_open_rule,
failure_cache_key,
request_cache_key,
):
assert await async_cache.get(failure_cache_key) is None
async with CircuitBreaker(
rule=should_not_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await success_function()
assert await async_cache.get(failure_cache_key) == 0
async def test_should_create_failure_cache_when_no_request_cache_key(
self,
async_cache,
should_not_open_rule_without_request_cache_key,
failure_cache_key,
request_cache_key,
):
assert await async_cache.get(failure_cache_key) is None
with pytest.raises(ValueError):
async with CircuitBreaker(
rule=should_not_open_rule_without_request_cache_key,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
assert await async_cache.get(failure_cache_key) == 1
async def test_should_call_expire_if_incr_returns_one(
self,
async_cache,
should_not_open_rule
):
future = asyncio.Future()
future.set_result(None)
with mock.patch.object(
async_cache, 'expire', return_value=future, autospec=True
) as mock_expire:
for _ in range(5):
with pytest.raises(ValueError):
async with CircuitBreaker(
rule=should_not_open_rule,
cache=async_cache,
failure_exception=MyException,
catch_exceptions=(ValueError,),
):
await fail_function()
# for request and fail count
assert mock_expire.call_count == 2
| 30.159892
| 79
| 0.601042
| 1,157
| 11,129
| 5.389801
| 0.08643
| 0.096216
| 0.064945
| 0.063502
| 0.858724
| 0.829699
| 0.795542
| 0.746632
| 0.713759
| 0.704618
| 0
| 0.00272
| 0.339204
| 11,129
| 368
| 80
| 30.241848
| 0.845254
| 0.002336
| 0
| 0.768212
| 0
| 0
| 0.011669
| 0.010094
| 0
| 0
| 0
| 0
| 0.056291
| 1
| 0.006623
| false
| 0
| 0.016556
| 0.006623
| 0.036424
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1521ca837c8a7c1cce3f04ee813d92fd416de7f3
| 29,907
|
py
|
Python
|
gs_quant/target/backtests.py
|
webclinic017/gs-quant
|
ebb8ee5e1d954ab362aa567293906ce51818cfa8
|
[
"Apache-2.0"
] | null | null | null |
gs_quant/target/backtests.py
|
webclinic017/gs-quant
|
ebb8ee5e1d954ab362aa567293906ce51818cfa8
|
[
"Apache-2.0"
] | null | null | null |
gs_quant/target/backtests.py
|
webclinic017/gs-quant
|
ebb8ee5e1d954ab362aa567293906ce51818cfa8
|
[
"Apache-2.0"
] | null | null | null |
"""
Copyright 2019 Goldman Sachs.
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.
"""
from gs_quant.base import *
from gs_quant.common import *
import datetime
from typing import Dict, Optional, Tuple, Union
from dataclasses import dataclass, field
from dataclasses_json import LetterCase, config, dataclass_json
from enum import Enum
class BacktestRiskMeasureType(EnumBase, Enum):
"""The type of measure to perform risk on. e.g. Greeks"""
Price = 'Price'
Delta = 'Delta'
Gamma = 'Gamma'
Vega = 'Vega'
Forward = 'Forward'
Implied_Volatility = 'Implied Volatility'
Fair_Variance = 'Fair Variance'
Strike_Level = 'Strike Level'
class BacktestTradingQuantityType(EnumBase, Enum):
"""The trading quantity unit of a backtest strategy"""
notional = 'notional'
quantity = 'quantity'
vega = 'vega'
gamma = 'gamma'
NAV = 'NAV'
class BacktestType(EnumBase, Enum):
"""Backtest type differentiates the backtest type."""
Basket = 'Basket'
Volatility = 'Volatility'
Volatility_Flow = 'Volatility Flow'
Enhanced_Beta = 'Enhanced Beta'
ISelect = 'ISelect'
class EquityMarketModel(EnumBase, Enum):
"""Market model for pricing"""
SFK = 'SFK'
SD = 'SD'
class FlowVolBacktestMeasure(EnumBase, Enum):
"""Metric which can be calculated using Flow Vol. Backtester"""
ALL_MEASURES = 'ALL MEASURES'
PNL_spot = 'PNL_spot'
PNL_vol = 'PNL_vol'
PNL_carry = 'PNL_carry'
PNL_delta = 'PNL_delta'
PNL_gamma = 'PNL_gamma'
PNL_higher_order_spot = 'PNL_higher_order_spot'
PNL_higher_order_vol = 'PNL_higher_order_vol'
PNL_theta = 'PNL_theta'
Total = 'Total'
transaction_costs = 'transaction_costs'
PNL_unexplained = 'PNL_unexplained'
PNL_vega = 'PNL_vega'
PNL = 'PNL'
delta = 'delta'
gamma = 'gamma'
vega = 'vega'
portfolio = 'portfolio'
NAV = 'NAV'
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestComparison(Base):
id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))
correlation: Optional[float] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestRebalanceParameters(Base):
frequency_period: Optional[str] = field(default=None, metadata=field_metadata)
frequency: Optional[int] = field(default=None, metadata=field_metadata)
day_of_week: Optional[str] = field(default=None, metadata=field_metadata)
day_of_month: Optional[float] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestSignalSeriesItem(Base):
date: Optional[datetime.date] = field(default=None, metadata=field_metadata)
value: Optional[bool] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BaseIndexRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BuySellRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class DeltaHedgeParameters(Base):
frequency: str = field(default=None, metadata=field_metadata)
delta_type: Optional[str] = field(default='BlackScholes', metadata=field_metadata)
fixing_time: Optional[str] = field(default=None, metadata=field_metadata)
notional: Optional[float] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class DeltaHedgingParameters(Base):
enabled: bool = field(default=None, metadata=field_metadata)
frequency: str = field(default=None, metadata=field_metadata)
fixing_time: str = field(default=None, metadata=field_metadata)
notional_percentage: float = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class EnhancedBetaUnderlier(Base):
asset_id: str = field(default=None, metadata=field_metadata)
month_add: Optional[float] = field(default=None, metadata=field_metadata)
valid_months: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
is_included: Optional[bool] = field(default=None, metadata=field_metadata)
weight_scale: Optional[float] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class EnhancedBetaUnderlierRefData(Base):
asset_id: Optional[str] = field(default=None, metadata=field_metadata)
valid_months: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
current: Optional[bool] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class EntityCorrelation(Base):
primary_id: Optional[str] = field(default=None, metadata=field_metadata)
secondary_id: Optional[str] = field(default=None, metadata=field_metadata)
correlation: Optional[float] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class ExpirationRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class FixingTimeRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class FrequencyRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class HistoricalUnderlier(Base):
asset_id: str = field(default=None, metadata=field_metadata)
weight: Optional[float] = field(default=None, metadata=field_metadata)
date: Optional[str] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class LookBackPeriodRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class NotionalPercentageRefData(Base):
default: Optional[float] = field(default=None, metadata=field_metadata)
min_: Optional[float] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))
max_: Optional[float] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class OptionStrikeTypeRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class OptionTypeRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class ScalingMethodRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class StrikeRefData(Base):
default: Optional[float] = field(default=None, metadata=field_metadata)
min_: Optional[float] = field(default=None, metadata=config(field_name='min', exclude=exclude_none))
max_: Optional[float] = field(default=None, metadata=config(field_name='max', exclude=exclude_none))
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class TradeInMethodRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class TradeInTimeRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class VolatilityWeightedWeightingModifier(Base):
name: Optional[str] = field(default='Volatility Weighted', metadata=field_metadata)
em_aalpha: Optional[float] = field(default=None, metadata=config(field_name='EMAalpha', exclude=exclude_none))
look_back_period: Optional[str] = field(default=None, metadata=field_metadata)
use_log_return: Optional[bool] = field(default=False, metadata=field_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class VolatilityWeightedWeightingModifierRefData(Base):
em_aalpha: Optional[DictBase] = field(default=None, metadata=config(field_name='EMAalpha', exclude=exclude_none))
look_back_period: Optional[DictBase] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestStrategyUnderlierHedge(Base):
risk_details: Optional[DeltaHedgeParameters] = field(default=None, metadata=field_metadata)
quantity_percentage: Optional[float] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestTradingParameters(Base):
quantity_type: Optional[BacktestTradingQuantityType] = field(default=None, metadata=field_metadata)
quantity: Optional[float] = field(default=None, metadata=field_metadata)
trade_in_method: Optional[str] = field(default=None, metadata=field_metadata)
roll_frequency: Optional[str] = field(default=None, metadata=field_metadata)
roll_date_mode: Optional[str] = field(default=None, metadata=field_metadata)
scaling_method: Optional[str] = field(default=None, metadata=field_metadata)
trade_in_signals: Optional[Tuple[BacktestSignalSeriesItem, ...]] = field(default=None, metadata=field_metadata)
trade_out_signals: Optional[Tuple[BacktestSignalSeriesItem, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BasketBacktestParameters(Base):
underliers: Tuple[Union[float, str], ...] = field(default=None, metadata=field_metadata)
rebalance_parameters: Optional[BacktestRebalanceParameters] = field(default=None, metadata=field_metadata)
weighting_modifiers: Optional[Tuple[VolatilityWeightedWeightingModifier, ...]] = field(default=None, metadata=field_metadata)
weighting_strategy: Optional[str] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class CurrencyRefData(Base):
default: Optional[Currency] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[Currency, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class DeltaHedgingRefData(Base):
fixing_time: Optional[FixingTimeRefData] = field(default=None, metadata=field_metadata)
frequency: Optional[FrequencyRefData] = field(default=None, metadata=field_metadata)
notional_percentage: Optional[NotionalPercentageRefData] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class EnhancedBetaBacktestParameters(Base):
underliers: Tuple[EnhancedBetaUnderlier, ...] = field(default=None, metadata=field_metadata)
roll_start: float = field(default=None, metadata=field_metadata)
roll_end: float = field(default=None, metadata=field_metadata)
base_index: str = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class ISelectBacktestParameters(Base):
max_leverage: float = field(default=None, metadata=field_metadata)
underliers: Tuple[HistoricalUnderlier, ...] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class OptionBacktestUnderlier(Base):
buy_sell: str = field(default=None, metadata=field_metadata)
expiration: str = field(default=None, metadata=field_metadata)
option_type: str = field(default=None, metadata=field_metadata)
option_strike_type: str = field(default=None, metadata=field_metadata)
strike: float = field(default=None, metadata=field_metadata)
underlying_asset_id: str = field(default=None, metadata=field_metadata)
notional_percentage: Optional[float] = field(default=None, metadata=field_metadata)
delta_hedging: Optional[DeltaHedgingParameters] = field(default=None, metadata=field_metadata)
trade_in_time: Optional[str] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class PerformanceRange(Base):
horizon: Optional[str] = field(default=None, metadata=field_metadata)
stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class UnderlyingAssetIdDataRefData(Base):
asset_id: Optional[str] = field(default=None, metadata=field_metadata)
fixing_time: Optional[FixingTimeRefData] = field(default=None, metadata=field_metadata)
frequency: Optional[FrequencyRefData] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestPerformanceDecomposition(Base):
name: Optional[str] = field(default=None, metadata=field_metadata)
performance: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)
stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestRisk(Base):
name: Optional[str] = field(default=None, metadata=field_metadata)
timeseries: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestRiskPosition(Base):
instrument: DictBase = field(default=None, metadata=field_metadata)
quantity: Optional[float] = field(default=None, metadata=field_metadata)
market_model: Optional[EquityMarketModel] = field(default=None, metadata=field_metadata)
expiry_date_mode: Optional[str] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestStrategyUnderlier(Base):
instrument: DictBase = field(default=None, metadata=field_metadata)
market_model: str = field(default=None, metadata=field_metadata)
notional_percentage: Optional[float] = field(default=None, metadata=field_metadata)
expiry_date_mode: Optional[str] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=field_metadata)
hedge: Optional[BacktestStrategyUnderlierHedge] = field(default=None, metadata=field_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BasketBacktestRefData(Base):
currency: Optional[CurrencyRefData] = field(default=None, metadata=field_metadata)
look_back_period: Optional[LookBackPeriodRefData] = field(default=None, metadata=field_metadata)
weighting_strategy: Optional[DictBase] = field(default=None, metadata=field_metadata)
weighting_modifiers: Optional[DictBase] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class ComparisonBacktestResult(Base):
stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)
performance: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)
id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class EnhancedBetaRefData(Base):
look_back_period: Optional[LookBackPeriodRefData] = field(default=None, metadata=field_metadata)
currency: Optional[CurrencyRefData] = field(default=None, metadata=field_metadata)
base_index: Optional[BaseIndexRefData] = field(default=None, metadata=field_metadata)
MASJ8W49Y02X9CGS: Optional[DictBase] = field(default=None, metadata=field_metadata)
MAAHST8JED9B607H: Optional[DictBase] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class UnderlyingAssetIdRefData(Base):
default: Optional[str] = field(default=None, metadata=field_metadata)
enum: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
data: Optional[Tuple[UnderlyingAssetIdDataRefData, ...]] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class VolatilityBacktestParameters(Base):
underliers: Tuple[OptionBacktestUnderlier, ...] = field(default=None, metadata=field_metadata)
trade_in_method: Optional[str] = field(default=None, metadata=field_metadata)
scaling_method: Optional[str] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestResult(Base):
backtest_id: Optional[str] = field(default=None, metadata=field_metadata)
performance: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)
portfolio: Optional[Tuple[FieldValueMap, ...]] = field(default=None, metadata=field_metadata)
stats: Optional[PerformanceStats] = field(default=None, metadata=field_metadata)
performance_decompositions: Optional[Tuple[BacktestPerformanceDecomposition, ...]] = field(default=None, metadata=field_metadata)
risks: Optional[Tuple[BacktestRisk, ...]] = field(default=None, metadata=field_metadata)
history: Optional[Tuple[PerformanceRange, ...]] = field(default=None, metadata=field_metadata)
underlier_correlation: Optional[Tuple[EntityCorrelation, ...]] = field(default=None, metadata=field_metadata)
comparisons: Optional[Tuple[BacktestComparison, ...]] = field(default=None, metadata=field_metadata)
backtest_version: Optional[float] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestRiskRequest(Base):
positions: Tuple[BacktestRiskPosition, ...] = field(default=None, metadata=field_metadata)
measures: Tuple[BacktestRiskMeasureType, ...] = field(default=None, metadata=field_metadata)
start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)
end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class VolBacktestRefData(Base):
buy_sell: Optional[BuySellRefData] = field(default=None, metadata=field_metadata)
currency: Optional[CurrencyRefData] = field(default=None, metadata=field_metadata)
delta_hedging: Optional[DeltaHedgingRefData] = field(default=None, metadata=field_metadata)
delta_strike: Optional[StrikeRefData] = field(default=None, metadata=field_metadata)
notional_percentage: Optional[NotionalPercentageRefData] = field(default=None, metadata=field_metadata)
expiration: Optional[ExpirationRefData] = field(default=None, metadata=field_metadata)
look_back_period: Optional[LookBackPeriodRefData] = field(default=None, metadata=field_metadata)
option_type: Optional[OptionTypeRefData] = field(default=None, metadata=field_metadata)
option_strike_type: Optional[OptionStrikeTypeRefData] = field(default=None, metadata=field_metadata)
relative_strike: Optional[StrikeRefData] = field(default=None, metadata=field_metadata)
strike: Optional[StrikeRefData] = field(default=None, metadata=field_metadata)
scaling_method: Optional[ScalingMethodRefData] = field(default=None, metadata=field_metadata)
underlying_asset_id: Optional[UnderlyingAssetIdRefData] = field(default=None, metadata=field_metadata)
trade_in_method: Optional[TradeInMethodRefData] = field(default=None, metadata=field_metadata)
trade_in_time: Optional[TradeInTimeRefData] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class VolatilityFlowBacktestParameters(Base):
trading_parameters: BacktestTradingParameters = field(default=None, metadata=field_metadata)
index_initial_value: float = field(default=None, metadata=field_metadata)
underliers: Optional[Tuple[BacktestStrategyUnderlier, ...]] = field(default=None, metadata=field_metadata)
measures: Optional[Tuple[FlowVolBacktestMeasure, ...]] = field(default=('ALL MEASURES',), metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class Backtest(Base):
name: str = field(default=None, metadata=field_metadata)
type_: BacktestType = field(default=None, metadata=config(field_name='type', exclude=exclude_none))
asset_class: AssetClass = field(default=None, metadata=field_metadata)
cost_netting: Optional[bool] = field(default=False, metadata=field_metadata)
created_by_id: Optional[str] = field(default=None, metadata=field_metadata)
created_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)
currency: Optional[Currency] = field(default=None, metadata=field_metadata)
entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)
entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)
id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))
last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)
last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)
mq_symbol: Optional[str] = field(default=None, metadata=field_metadata)
owner_id: Optional[str] = field(default=None, metadata=field_metadata)
report_ids: Optional[Tuple[str, ...]] = field(default=None, metadata=field_metadata)
parameters: Optional[DictBase] = field(default=None, metadata=field_metadata)
start_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)
end_date: Optional[datetime.date] = field(default=None, metadata=field_metadata)
version: Optional[float] = field(default=None, metadata=field_metadata)
@handle_camel_case_args
@dataclass_json(letter_case=LetterCase.CAMEL)
@dataclass(unsafe_hash=True, repr=False)
class BacktestRefData(Base):
id_: Optional[str] = field(default=None, metadata=config(field_name='id', exclude=exclude_none))
volatility: Optional[DictBase] = field(default=None, metadata=field_metadata)
enhanced_beta: Optional[EnhancedBetaRefData] = field(default=None, metadata=config(field_name='enhanced_beta', exclude=exclude_none))
basket: Optional[BasketBacktestRefData] = field(default=None, metadata=field_metadata)
owner_id: Optional[str] = field(default=None, metadata=field_metadata)
entitlements: Optional[Entitlements] = field(default=None, metadata=field_metadata)
entitlement_exclusions: Optional[EntitlementExclusions] = field(default=None, metadata=field_metadata)
last_updated_by_id: Optional[str] = field(default=None, metadata=field_metadata)
last_updated_time: Optional[datetime.datetime] = field(default=None, metadata=field_metadata)
name: Optional[str] = field(default=None, metadata=name_metadata)
| 47.622611
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0
| 7
|
ec8d0d96e0eea1056d8e7023133e3f3781b02af0
| 62,617
|
py
|
Python
|
octopus_deploy_swagger_client/octopus_deploy_client/tag_sets_api.py
|
cvent/octopus-deploy-api-client
|
0e03e842e1beb29b132776aee077df570b88366a
|
[
"Apache-2.0"
] | null | null | null |
octopus_deploy_swagger_client/octopus_deploy_client/tag_sets_api.py
|
cvent/octopus-deploy-api-client
|
0e03e842e1beb29b132776aee077df570b88366a
|
[
"Apache-2.0"
] | null | null | null |
octopus_deploy_swagger_client/octopus_deploy_client/tag_sets_api.py
|
cvent/octopus-deploy-api-client
|
0e03e842e1beb29b132776aee077df570b88366a
|
[
"Apache-2.0"
] | null | null | null |
# coding: utf-8
"""
Octopus Server API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: 2019.6.7+Branch.tags-2019.6.7.Sha.aa18dc6809953218c66f57eff7d26481d9b23d6a
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from octopus_deploy_swagger_client.api_client import ApiClient
class TagSetsApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def create_response_descriptor_tag_sets_tag_set_tag_set_resource(self, **kwargs): # noqa: E501
"""Create a TagSetResource # noqa: E501
Creates a new tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_response_descriptor_tag_sets_tag_set_tag_set_resource(async_req=True)
>>> result = thread.get()
:param async_req bool
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.create_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(**kwargs) # noqa: E501
return data
def create_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(self, **kwargs): # noqa: E501
"""Create a TagSetResource # noqa: E501
Creates a new tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['tag_set_resource'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_response_descriptor_tag_sets_tag_set_tag_set_resource" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'tag_set_resource' in params:
body_params = params['tag_set_resource']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/tagsets', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(self, base_space_id, **kwargs): # noqa: E501
"""Create a TagSetResource # noqa: E501
Creates a new tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
else:
(data) = self.create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
return data
def create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501
"""Create a TagSetResource # noqa: E501
Creates a new tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['base_space_id', 'tag_set_resource'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'base_space_id' is set
if ('base_space_id' not in params or
params['base_space_id'] is None):
raise ValueError("Missing the required parameter `base_space_id` when calling `create_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
collection_formats = {}
path_params = {}
if 'base_space_id' in params:
path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'tag_set_resource' in params:
body_params = params['tag_set_resource']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/{baseSpaceId}/tagsets', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder(self, **kwargs): # noqa: E501
"""custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder # noqa: E501
Takes an array of tag set IDs as the request body, uses the order of items in the array to sort the tag sets on the server. The ID of every tag set must be specified. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_with_http_info(**kwargs) # noqa: E501
return data
def custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_with_http_info(self, **kwargs): # noqa: E501
"""custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder # noqa: E501
Takes an array of tag set IDs as the request body, uses the order of items in the array to sort the tag sets on the server. The ID of every tag set must be specified. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/tagsets/sortorder', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces(self, base_space_id, **kwargs): # noqa: E501
"""custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces # noqa: E501
Takes an array of tag set IDs as the request body, uses the order of items in the array to sort the tag sets on the server. The ID of every tag set must be specified. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
else:
(data) = self.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
return data
def custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501
"""custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces # noqa: E501
Takes an array of tag set IDs as the request body, uses the order of items in the array to sort the tag sets on the server. The ID of every tag set must be specified. NOTE: This definition is not complete. We will be adding more detail in future releases of Octopus. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces_with_http_info(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['base_space_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'base_space_id' is set
if ('base_space_id' not in params or
params['base_space_id'] is None):
raise ValueError("Missing the required parameter `base_space_id` when calling `custom_action_response_descriptor_octopus_server_web_api_actions_sort_tag_sets_responder_spaces`") # noqa: E501
collection_formats = {}
path_params = {}
if 'base_space_id' in params:
path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/{baseSpaceId}/tagsets/sortorder', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource(self, id, **kwargs): # noqa: E501
"""Delete a TagSetResource by ID # noqa: E501
Deletes an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: ID of the TagSetResource to delete (required)
:return: TaskResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, **kwargs) # noqa: E501
return data
def delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(self, id, **kwargs): # noqa: E501
"""Delete a TagSetResource by ID # noqa: E501
Deletes an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: ID of the TagSetResource to delete (required)
:return: TaskResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/tagsets/{id}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TaskResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(self, base_space_id, id, **kwargs): # noqa: E501
"""Delete a TagSetResource by ID # noqa: E501
Deletes an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(base_space_id, id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param str id: ID of the TagSetResource to delete (required)
:return: TaskResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501
else:
(data) = self.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501
return data
def delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501
"""Delete a TagSetResource by ID # noqa: E501
Deletes an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param str id: ID of the TagSetResource to delete (required)
:return: TaskResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['base_space_id', 'id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'base_space_id' is set
if ('base_space_id' not in params or
params['base_space_id'] is None):
raise ValueError("Missing the required parameter `base_space_id` when calling `delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `delete_on_background_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
collection_formats = {}
path_params = {}
if 'base_space_id' in params:
path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/{baseSpaceId}/tagsets/{id}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TaskResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def index_response_descriptor_tag_sets_tag_set_tag_set_resource(self, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted alphabetically by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.index_response_descriptor_tag_sets_tag_set_tag_set_resource(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int skip: Number of items to skip
:param int take: Number of items to take
:return: ResourceCollectionTagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.index_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.index_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(**kwargs) # noqa: E501
return data
def index_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(self, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted alphabetically by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.index_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int skip: Number of items to skip
:param int take: Number of items to take
:return: ResourceCollectionTagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['skip', 'take'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method index_response_descriptor_tag_sets_tag_set_tag_set_resource" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'skip' in params:
query_params.append(('skip', params['skip'])) # noqa: E501
if 'take' in params:
query_params.append(('take', params['take'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/tagsets', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResourceCollectionTagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(self, base_space_id, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted alphabetically by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param int skip: Number of items to skip
:param int take: Number of items to take
:return: ResourceCollectionTagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
else:
(data) = self.index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
return data
def index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted alphabetically by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param int skip: Number of items to skip
:param int take: Number of items to take
:return: ResourceCollectionTagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['base_space_id', 'skip', 'take'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'base_space_id' is set
if ('base_space_id' not in params or
params['base_space_id'] is None):
raise ValueError("Missing the required parameter `base_space_id` when calling `index_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
collection_formats = {}
path_params = {}
if 'base_space_id' in params:
path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501
query_params = []
if 'skip' in params:
query_params.append(('skip', params['skip'])) # noqa: E501
if 'take' in params:
query_params.append(('take', params['take'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/{baseSpaceId}/tagsets', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ResourceCollectionTagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def list_all_response_descriptor_tag_sets_tag_set_tag_set_resource(self, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists the details of all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: list[TagSetResource]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(**kwargs) # noqa: E501
return data
def list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(self, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists the details of all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: list[TagSetResource]
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method list_all_response_descriptor_tag_sets_tag_set_tag_set_resource" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/tagsets/all', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[TagSetResource]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(self, base_space_id, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists the details of all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:return: list[TagSetResource]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
else:
(data) = self.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, **kwargs) # noqa: E501
return data
def list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(self, base_space_id, **kwargs): # noqa: E501
"""Get a list of TagSetResources # noqa: E501
Lists the details of all of the tag sets in the supplied Octopus Deploy Space. The results will be sorted by the `SortOrder` field on each tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:return: list[TagSetResource]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['base_space_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'base_space_id' is set
if ('base_space_id' not in params or
params['base_space_id'] is None):
raise ValueError("Missing the required parameter `base_space_id` when calling `list_all_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
collection_formats = {}
path_params = {}
if 'base_space_id' in params:
path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/{baseSpaceId}/tagsets/all', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[TagSetResource]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def load_response_descriptor_tag_sets_tag_set_tag_set_resource(self, id, **kwargs): # noqa: E501
"""Get a TagSetResource by ID # noqa: E501
Gets a tag set by ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.load_response_descriptor_tag_sets_tag_set_tag_set_resource(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: ID of the TagSetResource to load (required)
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.load_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.load_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, **kwargs) # noqa: E501
return data
def load_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(self, id, **kwargs): # noqa: E501
"""Get a TagSetResource by ID # noqa: E501
Gets a tag set by ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.load_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: ID of the TagSetResource to load (required)
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method load_response_descriptor_tag_sets_tag_set_tag_set_resource" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `load_response_descriptor_tag_sets_tag_set_tag_set_resource`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/tagsets/{id}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(self, base_space_id, id, **kwargs): # noqa: E501
"""Get a TagSetResource by ID # noqa: E501
Gets a tag set by ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(base_space_id, id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param str id: ID of the TagSetResource to load (required)
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501
else:
(data) = self.load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501
return data
def load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501
"""Get a TagSetResource by ID # noqa: E501
Gets a tag set by ID. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param str id: ID of the TagSetResource to load (required)
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['base_space_id', 'id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'base_space_id' is set
if ('base_space_id' not in params or
params['base_space_id'] is None):
raise ValueError("Missing the required parameter `base_space_id` when calling `load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `load_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
collection_formats = {}
path_params = {}
if 'base_space_id' in params:
path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/{baseSpaceId}/tagsets/{id}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def modify_response_descriptor_tag_sets_tag_set_tag_set_resource(self, id, **kwargs): # noqa: E501
"""Modify a TagSetResource by ID # noqa: E501
Modifies an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.modify_response_descriptor_tag_sets_tag_set_tag_set_resource(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: ID of the TagSetResource to modify (required)
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.modify_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.modify_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, **kwargs) # noqa: E501
return data
def modify_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(self, id, **kwargs): # noqa: E501
"""Modify a TagSetResource by ID # noqa: E501
Modifies an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.modify_response_descriptor_tag_sets_tag_set_tag_set_resource_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: ID of the TagSetResource to modify (required)
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'tag_set_resource'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method modify_response_descriptor_tag_sets_tag_set_tag_set_resource" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `modify_response_descriptor_tag_sets_tag_set_tag_set_resource`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'tag_set_resource' in params:
body_params = params['tag_set_resource']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/tagsets/{id}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(self, base_space_id, id, **kwargs): # noqa: E501
"""Modify a TagSetResource by ID # noqa: E501
Modifies an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces(base_space_id, id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param str id: ID of the TagSetResource to modify (required)
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501
else:
(data) = self.modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, **kwargs) # noqa: E501
return data
def modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(self, base_space_id, id, **kwargs): # noqa: E501
"""Modify a TagSetResource by ID # noqa: E501
Modifies an existing tag set. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces_with_http_info(base_space_id, id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str base_space_id: ID of the space (required)
:param str id: ID of the TagSetResource to modify (required)
:param TagSetResource tag_set_resource: The TagSetResource resource to create
:return: TagSetResource
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['base_space_id', 'id', 'tag_set_resource'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'base_space_id' is set
if ('base_space_id' not in params or
params['base_space_id'] is None):
raise ValueError("Missing the required parameter `base_space_id` when calling `modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `modify_response_descriptor_tag_sets_tag_set_tag_set_resource_spaces`") # noqa: E501
collection_formats = {}
path_params = {}
if 'base_space_id' in params:
path_params['baseSpaceId'] = params['base_space_id'] # noqa: E501
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'tag_set_resource' in params:
body_params = params['tag_set_resource']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyHeader', 'APIKeyQuery', 'NugetApiKeyHeader'] # noqa: E501
return self.api_client.call_api(
'/api/{baseSpaceId}/tagsets/{id}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TagSetResource', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 45.080634
| 289
| 0.652187
| 7,720
| 62,617
| 4.94443
| 0.028497
| 0.038354
| 0.042545
| 0.062875
| 0.98656
| 0.98656
| 0.98656
| 0.983574
| 0.983574
| 0.983574
| 0
| 0.014011
| 0.269384
| 62,617
| 1,388
| 290
| 45.113112
| 0.820346
| 0.344363
| 0
| 0.831551
| 1
| 0
| 0.210247
| 0.082276
| 0
| 0
| 0
| 0
| 0
| 1
| 0.03877
| false
| 0
| 0.005348
| 0
| 0.101604
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
ecc0a09265ec6a918cbd8a0b001b668851ca4212
| 33
|
py
|
Python
|
xkcd_api/__init__.py
|
gregmundy/xkcd-api
|
9214375e0b82b1b08cc0a2a80c5df539617a198c
|
[
"MIT"
] | null | null | null |
xkcd_api/__init__.py
|
gregmundy/xkcd-api
|
9214375e0b82b1b08cc0a2a80c5df539617a198c
|
[
"MIT"
] | 1
|
2019-12-19T03:21:51.000Z
|
2019-12-19T03:21:51.000Z
|
xkcd_api/__init__.py
|
agileronin/xkcd-proxy-api
|
c064526aeab19ca1e20e6e55c60d72974da6fd4b
|
[
"MIT"
] | null | null | null |
from xkcd_api.api.api import api
| 16.5
| 32
| 0.818182
| 7
| 33
| 3.714286
| 0.571429
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.896552
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 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
| 7
|
01a230e4bdba0a905f58f2762c3e06e39e3ce5e1
| 101
|
py
|
Python
|
tests/misc/some_rec_module.py
|
brendan0powers/memestra
|
d68d7f416907fab067a0b7fc3975052b6ec9f182
|
[
"BSD-3-Clause"
] | null | null | null |
tests/misc/some_rec_module.py
|
brendan0powers/memestra
|
d68d7f416907fab067a0b7fc3975052b6ec9f182
|
[
"BSD-3-Clause"
] | null | null | null |
tests/misc/some_rec_module.py
|
brendan0powers/memestra
|
d68d7f416907fab067a0b7fc3975052b6ec9f182
|
[
"BSD-3-Clause"
] | null | null | null |
import some_module
def foo():
return some_module.foo()
def bar():
return some_module.bar()
| 12.625
| 28
| 0.683168
| 15
| 101
| 4.4
| 0.466667
| 0.454545
| 0.484848
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.19802
| 101
| 7
| 29
| 14.428571
| 0.814815
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| true
| 0
| 0.2
| 0.4
| 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
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
173704bc568a90aef39c7134fb544a2f285f8986
| 3,940
|
py
|
Python
|
NSEPyProject/PE_Earning_Analysis.py
|
m4ni5h/PythonScripts
|
7adffd478cf5ab3863eb69af1c2a04b3655a872f
|
[
"MIT"
] | null | null | null |
NSEPyProject/PE_Earning_Analysis.py
|
m4ni5h/PythonScripts
|
7adffd478cf5ab3863eb69af1c2a04b3655a872f
|
[
"MIT"
] | null | null | null |
NSEPyProject/PE_Earning_Analysis.py
|
m4ni5h/PythonScripts
|
7adffd478cf5ab3863eb69af1c2a04b3655a872f
|
[
"MIT"
] | null | null | null |
from nsepy import get_history
from nsepy import get_index_pe_history
from datetime import date
import pandas as pd
from pandas import Series, DataFrame
def indexhistory(indexsymbol):
index_history = get_history(symbol=indexsymbol, start=date(2009, 3, 31), end=date(2009, 3, 31), index=True)
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2010, 3, 31), end=date(2010, 3, 31), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2011, 3, 31), end=date(2011, 3, 31), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2012, 3, 30), end=date(2012, 3, 30), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2013, 3, 28), end=date(2013, 3, 28), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2014, 3, 31), end=date(2014, 3, 31), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2015, 3, 31), end=date(2015, 3, 31), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2016, 3, 31), end=date(2016, 3, 31), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2017, 3, 31), end=date(2017, 3, 31), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2018, 3, 28), end=date(2018, 3, 28), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2019, 3, 29), end=date(2019, 3, 29), index=True))
index_history = index_history.append(
get_history(symbol=indexsymbol, start=date(2020, 3, 31), end=date(2020, 3, 31), index=True))
print(index_history)
return index_history
def PEhistory(indexsymbol):
pe_history = get_index_pe_history(symbol=indexsymbol, start=date(2009, 3, 31), end=date(2009, 3, 31))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2010, 3, 31), end=date(2010, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2011, 3, 31), end=date(2011, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2012, 3, 30), end=date(2012, 3, 30)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2013, 3, 28), end=date(2013, 3, 28)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2014, 3, 31), end=date(2014, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2015, 3, 31), end=date(2015, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2016, 3, 31), end=date(2016, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2017, 3, 31), end=date(2017, 3, 31)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2018, 3, 28), end=date(2018, 3, 28)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2019, 3, 29), end=date(2019, 3, 29)))
pe_history = pe_history.append(
get_index_pe_history(symbol=indexsymbol, start=date(2020, 3, 31), end=date(2020, 3, 31)))
print(pe_history)
return pe_history
pe_history = PEhistory("NIFTY ENERGY")
index_history = indexhistory("NIFTY ENERGY")
pe_analysis = pd.merge(pe_history, index_history, on='Date')
earnings = (pe_analysis['Close'] / pe_analysis['P/E']).rename("Earnings")
earnings = pd.DataFrame(earnings)
pe_analysis = pd.merge(pe_analysis, earnings, on='Date')
pe_analysis.to_csv("NIFTY ENERGY_peanalysis.csv")
| 52.533333
| 111
| 0.708629
| 592
| 3,940
| 4.523649
| 0.092905
| 0.134429
| 0.215086
| 0.259895
| 0.815161
| 0.800971
| 0.799851
| 0.799851
| 0.796117
| 0.796117
| 0
| 0.10081
| 0.154061
| 3,940
| 74
| 112
| 53.243243
| 0.70267
| 0
| 0
| 0.34375
| 0
| 0
| 0.019036
| 0.00533
| 0
| 0
| 0
| 0
| 0
| 1
| 0.03125
| false
| 0
| 0.078125
| 0
| 0.140625
| 0.03125
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
17989edcf4b9dced36b0c4438c5a4b5dcc5eda42
| 83
|
py
|
Python
|
build/lib/accessible_output2/platform_utils/__init__.py
|
zywek123/accessible_output2
|
825688d5ee6b4680ca9b685d914f9e289f8bde7e
|
[
"MIT"
] | null | null | null |
build/lib/accessible_output2/platform_utils/__init__.py
|
zywek123/accessible_output2
|
825688d5ee6b4680ca9b685d914f9e289f8bde7e
|
[
"MIT"
] | 2
|
2019-11-26T16:56:09.000Z
|
2020-11-04T15:08:14.000Z
|
build/lib/accessible_output2/platform_utils/__init__.py
|
zywek123/accessible_output2
|
825688d5ee6b4680ca9b685d914f9e289f8bde7e
|
[
"MIT"
] | 2
|
2019-10-11T09:09:58.000Z
|
2021-09-15T07:13:53.000Z
|
import accessible_output2.libloader
import accessible_output2.platform_utils.paths
| 27.666667
| 46
| 0.915663
| 10
| 83
| 7.3
| 0.7
| 0.438356
| 0.630137
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025316
| 0.048193
| 83
| 2
| 47
| 41.5
| 0.898734
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
17a41fc4375f2f4edb9a85d87a31e886d7c3da53
| 19,524
|
py
|
Python
|
fnat_testset/testcase/lrg2/arp_ping.py
|
lizhouw-netscout/fnat
|
684958773379a9205857f1932de443ed0c4334a0
|
[
"Apache-2.0"
] | null | null | null |
fnat_testset/testcase/lrg2/arp_ping.py
|
lizhouw-netscout/fnat
|
684958773379a9205857f1932de443ed0c4334a0
|
[
"Apache-2.0"
] | null | null | null |
fnat_testset/testcase/lrg2/arp_ping.py
|
lizhouw-netscout/fnat
|
684958773379a9205857f1932de443ed0c4334a0
|
[
"Apache-2.0"
] | null | null | null |
from fnat_dev import FnatDevice
from switch import Switch
import time
import sys
import os
from gateway import Gateway
from dhcp import DHCP
from dns import DNS
from nistnet import Nistnet
from demo import UITest
import serial
import pymongo
from pymongo import MongoClient
from selenium import webdriver
from wifi import Cell,Scheme
from hp2910 import HP2910
from uiautomator import device as d
def setUp():
#print "Method setUp in class testclass_5"
print('These are ARP&PING function test testset')
def tearDown():
#print "Method tearDown in class testclass_5"
print('These are ARP&PING function test testset')
def testmethod_1():
'''<case_id>77</case_id>'''
print "Method testmethod_1 in class ARP&PING function test suite"
print "Method The target is within the same subnet: ARP response not received in ARP&PING function test suite"
try:
d(description='Apps').click()
d(text='LinkRunner G2').click()
time.sleep(5)
d.click(0,28)
time.sleep(2)
#d.drag(6,594,6,138)
d(resourceId='com.flukenetworks.yilian.prototype:id/set_text').click()
time.sleep(2)
d.drag(6,720,6,197)
d(resourceId='com.flukenetworks.yilian.prototype:id/spinner_ip_config').click()
time.sleep(0.5)
d(text='Static').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/ip_address_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.63")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/subnet_mask_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("255.255.255.0")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/gateway_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns1_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns2_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d.drag(6,724,6,90)
d(resourceId='com.flukenetworks.yilian.prototype:id/add_target').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/target_address').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.80")
d(resourceId='android:id/button1').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/test_type_switch').click()###disable tcp###
d.press.back()
time.sleep(1)
d.press.back()
time.sleep(50)
static_results = d(resourceId='com.flukenetworks.yilian.prototype:id/static_image').description
print(static_results)
string_static = str(static_results)
assert string_static == 'green'
static_value = d(resourceId='com.flukenetworks.yilian.prototype:id/static_text').text
static_str = str(static_value)
assert static_str == "192.168.10.63"
target_results = d(resourceId='com.flukenetworks.yilian.prototype:id/target_image').description
print(target_results)
string_target = str(target_results)
assert string_target == 'red'
d(resourceId='com.flukenetworks.yilian.prototype:id/target_text').click()
target_address = d(resourceId='com.flukenetworks.yilian.prototype:id/target_address_desc').text
print(target_address)
string_target_address = str(target_address)
assert string_target_address == '192.168.10.80'
target_type = d(resourceId='com.flukenetworks.yilian.prototype:id/target_type_desc').text
print(target_type)
string_target_type = str(target_type)
assert string_target_type == 'PING'
tcp_ping_time = d(resourceId='com.flukenetworks.yilian.prototype:id/targets_fail_reason').text
print(tcp_ping_time)
string_tcp_ping_time = str(tcp_ping_time)
assert string_tcp_ping_time == 'Can not find the target (ARP Failed)'
cloud_status = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_image').description
print(cloud_status)
string_cloud_status = str(cloud_status)
assert string_cloud_status == 'green'
cloud_value = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_text').text
cloud_str = str(cloud_value)
assert cloud_str == "Link-Live.com"
d.press.back()
time.sleep(1)
d.press.back()
except Exception as e:
print(e)
#return False
a = os.popen('adb shell ps | grep com.flukenetworks.yilian.prototype').readlines()[0]
lis = a.split()
lis1 = int(lis[1])
os.system('adb shell kill -9 %d' % lis1)
d.press.back()
time.sleep(6)
#return False
assert False
def testmethod_2():
'''<case_id>79</case_id>'''
print "Method testmethod_2 in class ARP&PING function test suite"
print "Method The target is within the same subnet: icmp received in ARP&PING function test suite"
try:
d(description='Apps').click()
d(text='LinkRunner G2').click()
time.sleep(5)
d.click(0,28)
time.sleep(2)
#d.drag(6,594,6,138)
d(resourceId='com.flukenetworks.yilian.prototype:id/set_text').click()
time.sleep(2)
d.drag(6,720,6,197)
d(resourceId='com.flukenetworks.yilian.prototype:id/spinner_ip_config').click()
time.sleep(0.5)
d(text='Static').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/ip_address_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.63")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/subnet_mask_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("255.255.255.0")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/gateway_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns1_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns2_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d.drag(6,724,6,90)
d(resourceId='com.flukenetworks.yilian.prototype:id/add_target').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/target_address').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/test_type_switch').click()###disable tcp###
d.press.back()
time.sleep(1)
d.press.back()
time.sleep(50)
static_results = d(resourceId='com.flukenetworks.yilian.prototype:id/static_image').description
print(static_results)
string_static = str(static_results)
assert string_static == 'green'
static_value = d(resourceId='com.flukenetworks.yilian.prototype:id/static_text').text
static_str = str(static_value)
assert static_str == "192.168.10.63"
target_results = d(resourceId='com.flukenetworks.yilian.prototype:id/target_image').description
print(target_results)
string_target = str(target_results)
assert string_target == 'green'
d(resourceId='com.flukenetworks.yilian.prototype:id/target_text').click()
target_address = d(resourceId='com.flukenetworks.yilian.prototype:id/target_address_desc').text
print(target_address)
string_target_address = str(target_address)
assert string_target_address == '192.168.10.1'
target_type = d(resourceId='com.flukenetworks.yilian.prototype:id/target_type_desc').text
print(target_type)
string_target_type = str(target_type)
assert string_target_type == 'PING'
tcp_ping_time = d(resourceId='com.flukenetworks.yilian.prototype:id/ping_time').text
print(tcp_ping_time)
string_tcp_ping_time = str(tcp_ping_time)
assert string_tcp_ping_time != ' --, --, --'
cloud_status = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_image').description
print(cloud_status)
string_cloud_status = str(cloud_status)
assert string_cloud_status == 'green'
cloud_value = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_text').text
cloud_str = str(cloud_value)
assert cloud_str == "Link-Live.com"
d.press.back()
time.sleep(1)
d.press.back()
except Exception as e:
print(e)
#return False
a = os.popen('adb shell ps | grep com.flukenetworks.yilian.prototype').readlines()[0]
lis = a.split()
lis1 = int(lis[1])
os.system('adb shell kill -9 %d' % lis1)
d.press.back()
time.sleep(6)
#return False
assert False
def testmethod_3():
'''<case_id>80</case_id>'''
print "Method testmethod_3 in class ARP&PING function test suite"
print "Method The target is out of the same subnet: ARP response not received in ARP&PING function test suite"
try:
d(description='Apps').click()
d(text='LinkRunner G2').click()
time.sleep(5)
d.click(0,28)
time.sleep(2)
#d.drag(6,594,6,138)
d(resourceId='com.flukenetworks.yilian.prototype:id/set_text').click()
time.sleep(2)
d.drag(6,720,6,197)
d(resourceId='com.flukenetworks.yilian.prototype:id/spinner_ip_config').click()
time.sleep(0.5)
d(text='Static').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/ip_address_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.63")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/subnet_mask_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("255.255.255.0")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/gateway_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns1_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns2_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d.drag(6,724,6,90)
d(resourceId='com.flukenetworks.yilian.prototype:id/add_target').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/target_address').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.20.10")
d(resourceId='android:id/button1').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/test_type_switch').click()###disable tcp###
d.press.back()
time.sleep(1)
d.press.back()
time.sleep(50)
static_results = d(resourceId='com.flukenetworks.yilian.prototype:id/static_image').description
print(static_results)
string_static = str(static_results)
assert string_static == 'green'
static_value = d(resourceId='com.flukenetworks.yilian.prototype:id/static_text').text
static_str = str(static_value)
assert static_str == "192.168.10.63"
target_results = d(resourceId='com.flukenetworks.yilian.prototype:id/target_image').description
print(target_results)
string_target = str(target_results)
assert string_target == 'red'
d(resourceId='com.flukenetworks.yilian.prototype:id/target_text').click()
target_address = d(resourceId='com.flukenetworks.yilian.prototype:id/target_address_desc').text
print(target_address)
string_target_address = str(target_address)
assert string_target_address == '192.168.20.10'
target_type = d(resourceId='com.flukenetworks.yilian.prototype:id/target_type_desc').text
print(target_type)
string_target_type = str(target_type)
assert string_target_type == 'PING'
tcp_ping_time = d(resourceId='com.flukenetworks.yilian.prototype:id/ping_time').text
print(tcp_ping_time)
string_tcp_ping_time = str(tcp_ping_time)
assert string_tcp_ping_time == ' --, --, --'
cloud_status = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_image').description
print(cloud_status)
string_cloud_status = str(cloud_status)
assert string_cloud_status == 'green'
cloud_value = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_text').text
cloud_str = str(cloud_value)
assert cloud_str == "Link-Live.com"
d.press.back()
time.sleep(1)
d.press.back()
except Exception as e:
print(e)
#return False
a = os.popen('adb shell ps | grep com.flukenetworks.yilian.prototype').readlines()[0]
lis = a.split()
lis1 = int(lis[1])
os.system('adb shell kill -9 %d' % lis1)
d.press.back()
time.sleep(6)
#return False
assert False
def testmethod_4():
'''<case_id>82</case_id>'''
print "Method testmethod_3 in class ARP&PING function test suite"
print "Method The target is out of the same subnet: ARP response not received in ARP&PING function test suite"
try:
d(description='Apps').click()
d(text='LinkRunner G2').click()
time.sleep(5)
d.click(0,28)
time.sleep(2)
#d.drag(6,594,6,138)
d(resourceId='com.flukenetworks.yilian.prototype:id/set_text').click()
time.sleep(2)
d.drag(6,720,6,197)
d(resourceId='com.flukenetworks.yilian.prototype:id/spinner_ip_config').click()
time.sleep(0.5)
d(text='Static').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/ip_address_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.63")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/subnet_mask_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("255.255.255.0")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/gateway_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns1_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d(resourceId='com.flukenetworks.yilian.prototype:id/dns2_edit').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.10.1")
d(resourceId='android:id/button1').click()
time.sleep(1)
d.drag(6,724,6,90)
d(resourceId='com.flukenetworks.yilian.prototype:id/add_target').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/target_address').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/edit').set_text("192.168.20.1")
d(resourceId='android:id/button1').click()
d(resourceId='com.flukenetworks.yilian.prototype:id/test_type_switch').click()###disable tcp###
d.press.back()
time.sleep(1)
d.press.back()
time.sleep(50)
static_results = d(resourceId='com.flukenetworks.yilian.prototype:id/static_image').description
print(static_results)
string_static = str(static_results)
assert string_static == 'green'
static_value = d(resourceId='com.flukenetworks.yilian.prototype:id/static_text').text
static_str = str(static_value)
assert static_str == "192.168.10.63"
target_results = d(resourceId='com.flukenetworks.yilian.prototype:id/target_image').description
print(target_results)
string_target = str(target_results)
assert string_target != 'red'
d(resourceId='com.flukenetworks.yilian.prototype:id/target_text').click()
target_address = d(resourceId='com.flukenetworks.yilian.prototype:id/target_address_desc').text
print(target_address)
string_target_address = str(target_address)
assert string_target_address == '192.168.20.1'
target_type = d(resourceId='com.flukenetworks.yilian.prototype:id/target_type_desc').text
print(target_type)
string_target_type = str(target_type)
assert string_target_type == 'PING'
tcp_ping_time = d(resourceId='com.flukenetworks.yilian.prototype:id/ping_time').text
print(tcp_ping_time)
string_tcp_ping_time = str(tcp_ping_time)
assert string_tcp_ping_time != ' --, --, --'
cloud_status = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_image').description
print(cloud_status)
string_cloud_status = str(cloud_status)
assert string_cloud_status == 'green'
cloud_value = d(resourceId='com.flukenetworks.yilian.prototype:id/cloud_text').text
cloud_str = str(cloud_value)
assert cloud_str == "Link-Live.com"
d.press.back()
time.sleep(1)
d.press.back()
except Exception as e:
print(e)
#return False
a = os.popen('adb shell ps | grep com.flukenetworks.yilian.prototype').readlines()[0]
lis = a.split()
lis1 = int(lis[1])
os.system('adb shell kill -9 %d' % lis1)
d.press.back()
time.sleep(6)
#return False
assert False
| 45.090069
| 114
| 0.658625
| 2,556
| 19,524
| 4.894757
| 0.06025
| 0.109024
| 0.182879
| 0.257693
| 0.960435
| 0.956119
| 0.956119
| 0.956119
| 0.956119
| 0.956119
| 0
| 0.03542
| 0.204671
| 19,524
| 432
| 115
| 45.194444
| 0.770286
| 0.015417
| 0
| 0.895442
| 0
| 0.005362
| 0.365309
| 0.261572
| 0
| 0
| 0
| 0
| 0.096515
| 0
| null | null | 0
| 0.045576
| null | null | 0.101877
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
bd665ee767bbc430987b2ad04707675ff7b7a79b
| 148
|
py
|
Python
|
strategy/__init__.py
|
yangqiufei/financial_data_pools
|
7642d6f8719f373a0b4c442256bce6a3a7fe22bb
|
[
"Apache-2.0"
] | 1
|
2021-12-08T15:21:19.000Z
|
2021-12-08T15:21:19.000Z
|
strategy/__init__.py
|
yangqiufei/financial_data_pools
|
7642d6f8719f373a0b4c442256bce6a3a7fe22bb
|
[
"Apache-2.0"
] | null | null | null |
strategy/__init__.py
|
yangqiufei/financial_data_pools
|
7642d6f8719f373a0b4c442256bce6a3a7fe22bb
|
[
"Apache-2.0"
] | 1
|
2022-02-12T08:56:38.000Z
|
2022-02-12T08:56:38.000Z
|
from .st_moment_beauty.main import update_sell_info as update_mb_sell_info
from .st_moment_beauty.main import update_buy_info as update_mb_buy_info
| 49.333333
| 74
| 0.891892
| 28
| 148
| 4.214286
| 0.428571
| 0.101695
| 0.20339
| 0.305085
| 0.576271
| 0.576271
| 0.576271
| 0
| 0
| 0
| 0
| 0
| 0.081081
| 148
| 2
| 75
| 74
| 0.867647
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
bd7c6714252432ebd081361777961bc8ca4e5f6d
| 753,213
|
py
|
Python
|
pybitcoin/passphrases/english_words.py
|
sea212/pybitcoin
|
d34a1179ee23c5fe72bc3b555d7b86e533fcfd31
|
[
"MIT"
] | 220
|
2015-06-26T08:04:53.000Z
|
2021-12-31T02:03:03.000Z
|
pybitcoin/passphrases/english_words.py
|
sea212/pybitcoin
|
d34a1179ee23c5fe72bc3b555d7b86e533fcfd31
|
[
"MIT"
] | 33
|
2015-06-25T22:15:51.000Z
|
2021-03-12T21:17:19.000Z
|
pybitcoin/passphrases/english_words.py
|
sea212/pybitcoin
|
d34a1179ee23c5fe72bc3b555d7b86e533fcfd31
|
[
"MIT"
] | 113
|
2015-07-24T13:04:56.000Z
|
2021-12-27T23:56:59.000Z
|
# -*- coding: utf-8 -*-
"""
pybitcoin
~~~~~
:copyright: (c) 2014 by Halfmoon Labs
:license: MIT, see LICENSE for more details.
"""
# 2048 words
english_words_bip39 = 'abandon,ability,able,about,above,absent,absorb,abstract,absurd,abuse,access,accident,account,accuse,achieve,acid,acoustic,acquire,across,act,action,actor,actress,actual,adapt,add,addict,address,adjust,admit,adult,advance,advice,aerobic,affair,afford,afraid,again,age,agent,agree,ahead,aim,air,airport,aisle,alarm,album,alcohol,alert,alien,all,alley,allow,almost,alone,alpha,already,also,alter,always,amateur,amazing,among,amount,amused,analyst,anchor,ancient,anger,angle,angry,animal,ankle,announce,annual,another,answer,antenna,antique,anxiety,any,apart,apology,appear,apple,approve,april,arch,arctic,area,arena,argue,arm,armed,armor,army,around,arrange,arrest,arrive,arrow,art,artefact,artist,artwork,ask,aspect,assault,asset,assist,assume,asthma,athlete,atom,attack,attend,attitude,attract,auction,audit,august,aunt,author,auto,autumn,average,avocado,avoid,awake,aware,away,awesome,awful,awkward,axis,baby,bachelor,bacon,badge,bag,balance,balcony,ball,bamboo,banana,banner,bar,barely,bargain,barrel,base,basic,basket,battle,beach,bean,beauty,because,become,beef,before,begin,behave,behind,believe,below,belt,bench,benefit,best,betray,better,between,beyond,bicycle,bid,bike,bind,biology,bird,birth,bitter,black,blade,blame,blanket,blast,bleak,bless,blind,blood,blossom,blouse,blue,blur,blush,board,boat,body,boil,bomb,bone,bonus,book,boost,border,boring,borrow,boss,bottom,bounce,box,boy,bracket,brain,brand,brass,brave,bread,breeze,brick,bridge,brief,bright,bring,brisk,broccoli,broken,bronze,broom,brother,brown,brush,bubble,buddy,budget,buffalo,build,bulb,bulk,bullet,bundle,bunker,burden,burger,burst,bus,business,busy,butter,buyer,buzz,cabbage,cabin,cable,cactus,cage,cake,call,calm,camera,camp,can,canal,cancel,candy,cannon,canoe,canvas,canyon,capable,capital,captain,car,carbon,card,cargo,carpet,carry,cart,case,cash,casino,castle,casual,cat,catalog,catch,category,cattle,caught,cause,caution,cave,ceiling,celery,cement,census,century,cereal,certain,chair,chalk,champion,change,chaos,chapter,charge,chase,chat,cheap,check,cheese,chef,cherry,chest,chicken,chief,child,chimney,choice,choose,chronic,chuckle,chunk,churn,cigar,cinnamon,circle,citizen,city,civil,claim,clap,clarify,claw,clay,clean,clerk,clever,click,client,cliff,climb,clinic,clip,clock,clog,close,cloth,cloud,clown,club,clump,cluster,clutch,coach,coast,coconut,code,coffee,coil,coin,collect,color,column,combine,come,comfort,comic,common,company,concert,conduct,confirm,congress,connect,consider,control,convince,cook,cool,copper,copy,coral,core,corn,correct,cost,cotton,couch,country,couple,course,cousin,cover,coyote,crack,cradle,craft,cram,crane,crash,crater,crawl,crazy,cream,credit,creek,crew,cricket,crime,crisp,critic,crop,cross,crouch,crowd,crucial,cruel,cruise,crumble,crunch,crush,cry,crystal,cube,culture,cup,cupboard,curious,current,curtain,curve,cushion,custom,cute,cycle,dad,damage,damp,dance,danger,daring,dash,daughter,dawn,day,deal,debate,debris,decade,december,decide,decline,decorate,decrease,deer,defense,define,defy,degree,delay,deliver,demand,demise,denial,dentist,deny,depart,depend,deposit,depth,deputy,derive,describe,desert,design,desk,despair,destroy,detail,detect,develop,device,devote,diagram,dial,diamond,diary,dice,diesel,diet,differ,digital,dignity,dilemma,dinner,dinosaur,direct,dirt,disagree,discover,disease,dish,dismiss,disorder,display,distance,divert,divide,divorce,dizzy,doctor,document,dog,doll,dolphin,domain,donate,donkey,donor,door,dose,double,dove,draft,dragon,drama,drastic,draw,dream,dress,drift,drill,drink,drip,drive,drop,drum,dry,duck,dumb,dune,during,dust,dutch,duty,dwarf,dynamic,eager,eagle,early,earn,earth,easily,east,easy,echo,ecology,economy,edge,edit,educate,effort,egg,eight,either,elbow,elder,electric,elegant,element,elephant,elevator,elite,else,embark,embody,embrace,emerge,emotion,employ,empower,empty,enable,enact,end,endless,endorse,enemy,energy,enforce,engage,engine,enhance,enjoy,enlist,enough,enrich,enroll,ensure,enter,entire,entry,envelope,episode,equal,equip,era,erase,erode,erosion,error,erupt,escape,essay,essence,estate,eternal,ethics,evidence,evil,evoke,evolve,exact,example,excess,exchange,excite,exclude,excuse,execute,exercise,exhaust,exhibit,exile,exist,exit,exotic,expand,expect,expire,explain,expose,express,extend,extra,eye,eyebrow,fabric,face,faculty,fade,faint,faith,fall,false,fame,family,famous,fan,fancy,fantasy,farm,fashion,fat,fatal,father,fatigue,fault,favorite,feature,february,federal,fee,feed,feel,female,fence,festival,fetch,fever,few,fiber,fiction,field,figure,file,film,filter,final,find,fine,finger,finish,fire,firm,first,fiscal,fish,fit,fitness,fix,flag,flame,flash,flat,flavor,flee,flight,flip,float,flock,floor,flower,fluid,flush,fly,foam,focus,fog,foil,fold,follow,food,foot,force,forest,forget,fork,fortune,forum,forward,fossil,foster,found,fox,fragile,frame,frequent,fresh,friend,fringe,frog,front,frost,frown,frozen,fruit,fuel,fun,funny,furnace,fury,future,gadget,gain,galaxy,gallery,game,gap,garage,garbage,garden,garlic,garment,gas,gasp,gate,gather,gauge,gaze,general,genius,genre,gentle,genuine,gesture,ghost,giant,gift,giggle,ginger,giraffe,girl,give,glad,glance,glare,glass,glide,glimpse,globe,gloom,glory,glove,glow,glue,goat,goddess,gold,good,goose,gorilla,gospel,gossip,govern,gown,grab,grace,grain,grant,grape,grass,gravity,great,green,grid,grief,grit,grocery,group,grow,grunt,guard,guess,guide,guilt,guitar,gun,gym,habit,hair,half,hammer,hamster,hand,happy,harbor,hard,harsh,harvest,hat,have,hawk,hazard,head,health,heart,heavy,hedgehog,height,hello,helmet,help,hen,hero,hidden,high,hill,hint,hip,hire,history,hobby,hockey,hold,hole,holiday,hollow,home,honey,hood,hope,horn,horror,horse,hospital,host,hotel,hour,hover,hub,huge,human,humble,humor,hundred,hungry,hunt,hurdle,hurry,hurt,husband,hybrid,ice,icon,idea,identify,idle,ignore,ill,illegal,illness,image,imitate,immense,immune,impact,impose,improve,impulse,inch,include,income,increase,index,indicate,indoor,industry,infant,inflict,inform,inhale,inherit,initial,inject,injury,inmate,inner,innocent,input,inquiry,insane,insect,inside,inspire,install,intact,interest,into,invest,invite,involve,iron,island,isolate,issue,item,ivory,jacket,jaguar,jar,jazz,jealous,jeans,jelly,jewel,job,join,joke,journey,joy,judge,juice,jump,jungle,junior,junk,just,kangaroo,keen,keep,ketchup,key,kick,kid,kidney,kind,kingdom,kiss,kit,kitchen,kite,kitten,kiwi,knee,knife,knock,know,lab,label,labor,ladder,lady,lake,lamp,language,laptop,large,later,latin,laugh,laundry,lava,law,lawn,lawsuit,layer,lazy,leader,leaf,learn,leave,lecture,left,leg,legal,legend,leisure,lemon,lend,length,lens,leopard,lesson,letter,level,liar,liberty,library,license,life,lift,light,like,limb,limit,link,lion,liquid,list,little,live,lizard,load,loan,lobster,local,lock,logic,lonely,long,loop,lottery,loud,lounge,love,loyal,lucky,luggage,lumber,lunar,lunch,luxury,lyrics,machine,mad,magic,magnet,maid,mail,main,major,make,mammal,man,manage,mandate,mango,mansion,manual,maple,marble,march,margin,marine,market,marriage,mask,mass,master,match,material,math,matrix,matter,maximum,maze,meadow,mean,measure,meat,mechanic,medal,media,melody,melt,member,memory,mention,menu,mercy,merge,merit,merry,mesh,message,metal,method,middle,midnight,milk,million,mimic,mind,minimum,minor,minute,miracle,mirror,misery,miss,mistake,mix,mixed,mixture,mobile,model,modify,mom,moment,monitor,monkey,monster,month,moon,moral,more,morning,mosquito,mother,motion,motor,mountain,mouse,move,movie,much,muffin,mule,multiply,muscle,museum,mushroom,music,must,mutual,myself,mystery,myth,naive,name,napkin,narrow,nasty,nation,nature,near,neck,need,negative,neglect,neither,nephew,nerve,nest,net,network,neutral,never,news,next,nice,night,noble,noise,nominee,noodle,normal,north,nose,notable,note,nothing,notice,novel,now,nuclear,number,nurse,nut,oak,obey,object,oblige,obscure,observe,obtain,obvious,occur,ocean,october,odor,off,offer,office,often,oil,okay,old,olive,olympic,omit,once,one,onion,online,only,open,opera,opinion,oppose,option,orange,orbit,orchard,order,ordinary,organ,orient,original,orphan,ostrich,other,outdoor,outer,output,outside,oval,oven,over,own,owner,oxygen,oyster,ozone,pact,paddle,page,pair,palace,palm,panda,panel,panic,panther,paper,parade,parent,park,parrot,party,pass,patch,path,patient,patrol,pattern,pause,pave,payment,peace,peanut,pear,peasant,pelican,pen,penalty,pencil,people,pepper,perfect,permit,person,pet,phone,photo,phrase,physical,piano,picnic,picture,piece,pig,pigeon,pill,pilot,pink,pioneer,pipe,pistol,pitch,pizza,place,planet,plastic,plate,play,please,pledge,pluck,plug,plunge,poem,poet,point,polar,pole,police,pond,pony,pool,popular,portion,position,possible,post,potato,pottery,poverty,powder,power,practice,praise,predict,prefer,prepare,present,pretty,prevent,price,pride,primary,print,priority,prison,private,prize,problem,process,produce,profit,program,project,promote,proof,property,prosper,protect,proud,provide,public,pudding,pull,pulp,pulse,pumpkin,punch,pupil,puppy,purchase,purity,purpose,purse,push,put,puzzle,pyramid,quality,quantum,quarter,question,quick,quit,quiz,quote,rabbit,raccoon,race,rack,radar,radio,rail,rain,raise,rally,ramp,ranch,random,range,rapid,rare,rate,rather,raven,raw,razor,ready,real,reason,rebel,rebuild,recall,receive,recipe,record,recycle,reduce,reflect,reform,refuse,region,regret,regular,reject,relax,release,relief,rely,remain,remember,remind,remove,render,renew,rent,reopen,repair,repeat,replace,report,require,rescue,resemble,resist,resource,response,result,retire,retreat,return,reunion,reveal,review,reward,rhythm,rib,ribbon,rice,rich,ride,ridge,rifle,right,rigid,ring,riot,ripple,risk,ritual,rival,river,road,roast,robot,robust,rocket,romance,roof,rookie,room,rose,rotate,rough,round,route,royal,rubber,rude,rug,rule,run,runway,rural,sad,saddle,sadness,safe,sail,salad,salmon,salon,salt,salute,same,sample,sand,satisfy,satoshi,sauce,sausage,save,say,scale,scan,scare,scatter,scene,scheme,school,science,scissors,scorpion,scout,scrap,screen,script,scrub,sea,search,season,seat,second,secret,section,security,seed,seek,segment,select,sell,seminar,senior,sense,sentence,series,service,session,settle,setup,seven,shadow,shaft,shallow,share,shed,shell,sheriff,shield,shift,shine,ship,shiver,shock,shoe,shoot,shop,short,shoulder,shove,shrimp,shrug,shuffle,shy,sibling,sick,side,siege,sight,sign,silent,silk,silly,silver,similar,simple,since,sing,siren,sister,situate,six,size,skate,sketch,ski,skill,skin,skirt,skull,slab,slam,sleep,slender,slice,slide,slight,slim,slogan,slot,slow,slush,small,smart,smile,smoke,smooth,snack,snake,snap,sniff,snow,soap,soccer,social,sock,soda,soft,solar,soldier,solid,solution,solve,someone,song,soon,sorry,sort,soul,sound,soup,source,south,space,spare,spatial,spawn,speak,special,speed,spell,spend,sphere,spice,spider,spike,spin,spirit,split,spoil,sponsor,spoon,sport,spot,spray,spread,spring,spy,square,squeeze,squirrel,stable,stadium,staff,stage,stairs,stamp,stand,start,state,stay,steak,steel,stem,step,stereo,stick,still,sting,stock,stomach,stone,stool,story,stove,strategy,street,strike,strong,struggle,student,stuff,stumble,style,subject,submit,subway,success,such,sudden,suffer,sugar,suggest,suit,summer,sun,sunny,sunset,super,supply,supreme,sure,surface,surge,surprise,surround,survey,suspect,sustain,swallow,swamp,swap,swarm,swear,sweet,swift,swim,swing,switch,sword,symbol,symptom,syrup,system,table,tackle,tag,tail,talent,talk,tank,tape,target,task,taste,tattoo,taxi,teach,team,tell,ten,tenant,tennis,tent,term,test,text,thank,that,theme,then,theory,there,they,thing,this,thought,three,thrive,throw,thumb,thunder,ticket,tide,tiger,tilt,timber,time,tiny,tip,tired,tissue,title,toast,tobacco,today,toddler,toe,together,toilet,token,tomato,tomorrow,tone,tongue,tonight,tool,tooth,top,topic,topple,torch,tornado,tortoise,toss,total,tourist,toward,tower,town,toy,track,trade,traffic,tragic,train,transfer,trap,trash,travel,tray,treat,tree,trend,trial,tribe,trick,trigger,trim,trip,trophy,trouble,truck,true,truly,trumpet,trust,truth,try,tube,tuition,tumble,tuna,tunnel,turkey,turn,turtle,twelve,twenty,twice,twin,twist,two,type,typical,ugly,umbrella,unable,unaware,uncle,uncover,under,undo,unfair,unfold,unhappy,uniform,unique,unit,universe,unknown,unlock,until,unusual,unveil,update,upgrade,uphold,upon,upper,upset,urban,urge,usage,use,used,useful,useless,usual,utility,vacant,vacuum,vague,valid,valley,valve,van,vanish,vapor,various,vast,vault,vehicle,velvet,vendor,venture,venue,verb,verify,version,very,vessel,veteran,viable,vibrant,vicious,victory,video,view,village,vintage,violin,virtual,virus,visa,visit,visual,vital,vivid,vocal,voice,void,volcano,volume,vote,voyage,wage,wagon,wait,walk,wall,walnut,want,warfare,warm,warrior,wash,wasp,waste,water,wave,way,wealth,weapon,wear,weasel,weather,web,wedding,weekend,weird,welcome,west,wet,whale,what,wheat,wheel,when,where,whip,whisper,wide,width,wife,wild,will,win,window,wine,wing,wink,winner,winter,wire,wisdom,wise,wish,witness,wolf,woman,wonder,wood,wool,word,work,world,worry,worth,wrap,wreck,wrestle,wrist,write,wrong,yard,year,yellow,you,young,youth,zebra,zero,zone,zoo'
# 25202 words
english_words_wiktionary = 'aardvark,aargh,aback,abacus,abandon,abandoned,abandoning,abandonment,abandons,abba,abbey,abbot,abbots,abdomen,abdominal,abduct,abducted,abducting,abduction,abductions,aberration,abetted,abide,abiding,abigail,abilities,ability,ablaze,able,abnormal,abnormalities,abnormality,abnormally,aboard,abode,abolish,abominable,abomination,aboriginal,abort,abortion,abortions,abound,about,above,aboveboard,abrasive,abreast,abroad,abrupt,abruptly,abs,absconded,absence,absences,absent,absentee,absolute,absolutely,absolutes,absolution,absolved,absorb,absorbed,absorbent,absorbing,absorption,abstain,abstinence,abstract,absurd,absurdity,absurdly,abundance,abundant,abundantly,abuse,abused,abuser,abusing,abusive,abut,abysmal,academia,academic,academics,academy,accelerant,accelerate,accelerated,accelerating,acceleration,accent,accents,accept,acceptable,acceptance,accepted,accepting,accepts,access,accessible,accessing,accessories,accessory,accident,accidental,accidentally,accidently,accidents,acclaim,acclimate,acclimated,accolades,accommodate,accommodating,accommodation,accommodations,accompanied,accompany,accompanying,accomplice,accomplices,accomplish,accomplished,accomplishing,accomplishment,accomplishments,accord,accordance,accorded,according,accordingly,account,accountability,accountable,accountant,accountants,accounted,accounting,accounts,accoutrements,accumulate,accumulated,accumulation,accuracy,accurate,accurately,accursed,accusation,accusations,accuse,accused,accuser,accusers,accuses,accusing,accustomed,ace,aced,aces,acetate,ache,ached,aches,achieve,achieved,achievement,achieving,aching,achingly,acid,acids,acing,acknowledge,acknowledged,acknowledgement,acknowledges,acme,acne,acorn,acoustic,acoustics,acquaint,acquaintance,acquaintances,acquainted,acquire,acquired,acquisition,acquisitions,acquitted,acre,acres,acrobat,across,act,acted,actin,acting,action,actionable,actions,activate,activated,activating,activation,activators,active,actively,activists,activities,activity,actor,actors,actress,actresses,acts,actual,actuality,actualization,actually,actuarial,acupuncture,acupuncturist,acute,ad,adage,adamant,adapt,adaptable,adapted,adapting,add,added,addendum,addict,addicted,addiction,addictive,addicts,adding,addition,additional,additions,additives,addled,address,addressed,addresses,addressing,adds,adenoids,adept,adequate,adequately,adhere,adherence,adhesive,adjacent,adjective,adjectives,adjourn,adjourned,adjust,adjustable,adjusted,adjusting,adjustment,adjustments,adjutant,administer,administered,administering,administration,administrative,administrator,administrators,admirable,admirably,admiral,admiration,admire,admired,admirer,admirers,admires,admiring,admission,admissions,admit,admits,admittance,admitted,admittedly,admitting,admonish,admonished,admonition,ado,adobe,adolescence,adolescent,adolescents,adopt,adopted,adopting,adoption,adoptive,adorable,adoration,adore,adored,adores,adoring,adrenaline,adrenals,ads,adult,adults,advance,advanced,advancement,advancements,advances,advancing,advantage,advantageous,advantages,advent,adventure,adventurer,adventures,adventurous,adversaries,adversary,adverse,adversely,adversity,advert,advertise,advertised,advertisement,advertisers,advertises,advertising,advice,advisable,advise,advised,adviser,advises,advising,advisor,advocacy,advocate,advocating,aerial,aerobic,aerobics,aerodynamics,aeroplane,aerosol,aerospace,aesthetic,aesthetics,afar,affair,affairs,affect,affected,affecting,affection,affectionate,affectionately,affections,affects,affidavit,affidavits,affiliated,affiliates,affiliation,affinity,affirm,affirmative,affirming,afflicted,afford,afforded,affront,afloat,afoot,aforementioned,aforethought,afraid,aft,after,afterglow,afterlife,aftermath,afternoon,afternoons,afterthought,afterward,afterwards,ag,aga,again,against,age,aged,ageing,ageless,agencies,agency,agenda,agendas,agent,agents,ages,aggravate,aggravated,aggravating,aggravation,aggression,aggressive,aggressively,aggressor,agile,agility,agin,aging,agitate,agitated,agitation,agitators,ago,agonized,agonizing,agony,agree,agreeable,agreed,agreeing,agreement,agreements,agrees,agricultural,aground,ah,aha,ahead,ahem,ahold,ahoy,ai,aid,aide,aided,aides,aiding,aids,ail,ailing,ailment,ailments,ails,aim,aimed,aiming,aimless,aimlessly,aims,ain,air,airborne,aircraft,airfield,airhead,airing,airlift,airlifted,airline,airlines,airman,airmen,airplane,airplanes,airport,airports,airs,airspace,airstrip,airtight,airwaves,airway,airways,aisle,aisles,ajar,al,ala,alabaster,alameda,alamo,alan,alarm,alarmed,alarming,alarmist,alarms,alas,alaska,alastor,albacore,albatross,albino,album,albums,alcazar,alchemist,alchemy,alcohol,alcoholic,alcoholics,alcoholism,alcove,alderman,ale,alec,alert,alerted,alerting,alerts,ales,alexander,alfalfa,algae,algebra,algorithms,alias,aliases,alibi,alibis,alien,alienate,alienated,alienating,alienation,aliens,alight,aligning,alike,alimony,alive,all,allee,allegation,allegations,alleged,allegedly,allegiance,allegiances,alleging,alleluia,allergi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ely,cruel,cruelly,cruelty,cruise,cruised,cruiser,cruisers,cruises,cruising,crumb,crumble,crumbled,crumbles,crumbling,crumbs,crummy,crumpets,crumpled,crunch,crunched,crunches,crunchy,crusade,crusader,crusades,crush,crushed,crusher,crushes,crushing,crust,crusts,crusty,crutch,crutches,crux,cry,crying,cryogenic,crypt,cryptic,crypto,crypts,crystal,crystals,cub,cubbies,cubby,cube,cubed,cubes,cubic,cubicle,cubs,cuckoo,cucumber,cud,cuddle,cuddled,cuddles,cuddly,cuddy,cue,cued,cuff,cuffed,cuffing,cuffs,cuisine,culinary,culminating,culmination,culottes,culpa,culpability,culpable,cult,cultivate,cultivated,cultivating,cultural,culturally,culture,cultured,cultures,cumin,cummerbund,cumulative,cuneiform,cunning,cup,cupboard,cupboards,cupcake,cupcakes,cupid,cuppa,cups,cur,curacao,curate,curator,curb,curd,curdle,cure,cured,cures,curfew,curfews,curie,curing,curiosity,curious,curiously,curl,curled,curlers,curling,curly,curmudgeon,curran,currency,current,currently,currents,curriculum,curry,curse,cursed,curses,cursing,cursive,cursory,curt,curtain,curtains,curtsy,curvaceous,curve,curveball,curves,cushion,cushions,cushy,cusp,cuss,cussing,custard,custodial,custodian,custody,custom,customarily,customary,customer,customers,customs,cut,cutaway,cutbacks,cute,cuteness,cuter,cutest,cutesy,cuticle,cuticles,cutie,cutlass,cutler,cutlery,cutoff,cutoffs,cutout,cuts,cutter,cutters,cutthroat,cutting,cyanide,cybernetic,cyberspace,cyborg,cycle,cycles,cyclone,cyclops,cyclotron,cylinder,cylinders,cymbal,cynic,cynical,cynicism,cynics,cypher,cypress,cyprus,cyst,cystic,dab,dabble,dabbled,dabbling,dad,daddies,daddy,dads,daffodils,daffy,daft,dag,dagger,dago,dah,dahl,dahlia,dailies,daily,dainty,daiquiri,dairy,dais,daisies,daisy,dale,dalliance,dally,dallying,dalton,dam,damage,damaged,damages,damaging,dame,damn,damnable,damned,damnedest,damning,damp,dampened,dampener,damper,damsel,damsels,dance,danced,dancer,dancers,dances,dancing,dandelion,dandelions,dandruff,dandy,dang,danger,dangerous,dangerously,dangers,dangle,dangled,dangling,danish,daphne,dapper,dare,dared,daredevil,dares,daring,dark,darken,darkened,darker,darkest,darkness,darkroom,darks,darling,darlings,darn,darndest,darned,dart,darts,dash,dashboard,dashed,dasher,dashing,data,database,databases,date,dated,dateless,dateline,dater,dates,dating,dato,daughter,daughters,daunting,dauphin,davenport,davy,dawdling,dawn,dawned,dawning,day,daybreak,daydream,daydreaming,daylight,daylights,days,daytime,daze,dazzle,dazzled,dazzling,de,deacon,dead,deadbeat,deadbeats,deader,deadlier,deadliest,deadline,deadlines,deadlock,deadly,deaf,deafening,deal,dealer,dealers,dealership,dealing,dealings,deals,dealt,dean,dear,dearest,dearie,dearly,dears,death,deathbed,deathly,deaths,deb,debacle,debatable,debate,debated,debates,debating,debilitating,debit,debonair,debrief,debriefed,debriefing,debris,debt,debts,debut,debutante,debutantes,decade,decadence,decadent,decades,decaf,decanter,decapitate,decapitated,decapitation,decay,decaying,deceased,deceit,deceitful,deceive,deceived,deceiving,decency,decent,deception,deceptions,deceptive,deceptively,decibel,decibels,decide,decided,decides,deciding,decipher,deciphering,decision,decisions,decisive,deck,decked,decker,decks,declaration,declare,declared,declaring,decline,declined,declining,deco,decoded,decoder,decompose,decomposed,decomposing,decompress,deconstruction,decontamination,decor,decorate,decorated,decorating,decoration,decorations,decorative,decorator,decorators,decorum,decoupage,decoy,decoys,decreased,decree,decrypted,decryption,dedicate,dedicated,dedicating,dedication,deduce,deduct,deducted,deductible,deduction,deductions,dee,deed,deeded,deeds,deejay,deem,deemed,deep,deeper,deepest,deeply,deer,deets,defaced,defacing,default,defeat,defeated,defeats,defect,defected,defective,defector,defects,defence,defend,defendant,defendants,defended,defender,defending,defense,defenseless,defenses,defensive,deferred,defiance,defiant,defiantly,defibrillator,deficiencies,deficiency,deficient,deficit,defied,defies,define,defined,defining,definite,definitely,definition,definitions,definitive,definitively,deflate,deflated,deflecting,deflection,deflower,deformity,defraud,defrost,deft,deftly,defuse,defused,defy,defying,degenerate,degeneration,degenerative,degradation,degrade,degraded,degrading,degree,degrees,dehydrated,dehydration,deigned,deities,deity,deke,del,delay,delayed,delaying,delays,delectable,delegate,delegates,delegation,delete,deleted,deli,deliberate,deliberately,deliberation,deliberations,delicacy,delicate,delicately,delicates,delicious,delight,delighted,delightful,delightfully,delights,delinquency,delinquent,delirious,delirium,deliver,delivered,deliveries,delivering,delivers,delivery,dell,delly,delta,deltas,delude,deluded,deluding,deluge,delusion,delusional,delusions,deluxe,delve,delving,demand,demanded,demanding,demands,demean,demeaning,demented,dementia,demerits,demise,demo,democracy,democrat,democratic,democrats,demographic,demographics,demolish,demolition,demolitions,demon,demonic,demo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otels,moth,mothballs,mother,motherhood,mothering,motherless,mothers,moths,motif,motion,motions,motivate,motivated,motivates,motivation,motivational,motivations,motive,motives,motley,motor,motorbike,motorcade,motorcycle,motorcycles,motorized,motors,motto,mould,moulin,mound,mounds,mount,mountain,mountaineer,mountains,mountainside,mountaintop,mounted,mounting,mourn,mourned,mourners,mourning,mouse,mousey,mousse,moustache,mousy,mouth,mouthed,mouthful,mouthing,mouthpiece,mouths,mouthwash,mouthy,move,moved,movement,movements,movers,moves,movie,movies,moving,mow,mowed,mower,mowers,mowing,moxie,mu,much,muchacho,muchachos,muck,muckraker,mucous,mucus,mud,muddle,muddy,muff,muffin,muffins,muffled,muffler,mug,mugged,mugger,muggers,mugging,muggings,muggy,mugs,mulberry,mulch,mule,mules,muley,mulled,muller,mullet,mulligan,mulling,multimedia,multimillion,multinational,multiple,multiples,multiplex,multiplication,multiplied,multiply,multitude,mum,mumble,mumbled,mumbles,mumbling,mummies,mummy,mumps,mums,munch,munching,munchkin,munchkins,mundane,munitions,mural,murals,murder,murdered,murderer,murderers,murderess,murdering,murderous,murders,murky,murmur,murphy,muscle,muscled,muscles,muscular,muse,muses,museum,museums,mush,mushroom,mushrooms,mushy,music,musical,musicals,musician,musicians,musing,musket,musketeer,musketeers,muskie,muskrat,muss,mussels,must,mustache,mustang,mustangs,mustard,muster,mutant,mutants,mutated,mutating,mute,mutilate,mutilated,mutilation,mutiny,mutt,mutton,mutual,mutually,muumuu,muzzle,my,myriad,myrtle,myself,mysteries,mysterious,mysteriously,mystery,mystic,mystical,mysticism,mystified,mystifying,myth,mythic,mythical,mythological,mythology,myths,na,nacho,nachos,nada,nag,nagged,nagging,nah,nail,nailed,nailing,nails,naive,naivete,naked,nam,name,named,nameless,namely,names,namesake,nametag,naming,nan,nana,nance,nancy,nannies,nanny,nanosecond,nanotechnology,nap,napalm,naphthalene,napkin,napkins,napoleon,napping,nappy,naps,narc,narcissism,narcissist,narcissistic,narcolepsy,narcotic,narcotics,narrative,narrator,narrow,narrowed,narrowing,narrowly,narrows,narwhal,nary,nasal,nastiest,nasty,nation,national,nationally,nationals,nations,native,natives,nativity,natty,natural,naturally,nature,natured,naught,naughty,nausea,nauseated,nauseating,nauseous,nautical,nautilus,naval,navigate,navigating,navigation,navigational,navigator,navy,naw,nay,nazi,nazis,ne,near,nearby,nearest,nearly,neat,neatly,neatness,nebula,necessarily,necessary,necessity,neck,necking,necklace,necklaces,neckline,necks,necromancer,necrosis,nectar,nee,need,needed,neediness,needing,needle,needlepoint,needles,needless,needlessly,needs,needy,nefarious,negate,negative,negatively,negatives,negativity,neglect,neglected,neglectful,neglecting,negligee,negligence,negligent,negligible,negotiable,negotiate,negotiated,negotiating,negotiation,negotiations,negotiator,neighbor,neighborhood,neighborhoods,neighboring,neighborly,neighbors,neighbour,neighbours,neither,nellie,nelly,nelson,neon,neonatal,nephew,nephews,nepotism,nerd,nerds,nerdy,nerve,nerves,nervous,nervously,nervousness,ness,nest,net,nether,network,networking,networks,neural,neurological,neurologist,neurology,neuroses,neurosis,neurosurgeon,neurosurgery,neurotic,neutered,neutral,neutralize,neutralized,neutron,neve,never,nevermore,nevertheless,new,newborn,newborns,newcomer,newcomers,newest,newfound,newly,newlywed,newlyweds,news,newscast,newsletter,newsman,newspaper,newspapers,newsroom,newsstand,newsstands,newt,newton,next,nexus,nibble,nibbling,nice,nicely,nicer,nicest,niceties,niche,nick,nicked,nickel,nickels,nickname,nicknamed,nicknames,nicks,nicotine,niece,nieces,nifty,nigh,night,nightcap,nightclub,nightfall,nightgown,nighthawk,nightingale,nightlife,nightly,nightmare,nightmares,nightmarish,nights,nightstand,nightstick,nighttime,nighty,nihilist,nil,nimrod,nine,nines,nineteen,nineteenth,nineties,ninety,ninja,ninny,ninth,nip,nipped,nipper,nipping,nipple,nipples,nippy,nirvana,nite,nitrogen,nitroglycerin,nitrous,nitty,nitwit,nix,nixed,no,nobility,noble,nobleman,nobodies,nobody,nocturnal,nod,nodded,nodding,node,nodes,nods,nodules,noel,noggin,noh,noir,noise,noises,noisy,nomad,nomadic,nomads,nome,nominal,nominate,nominated,nominating,nomination,nominations,nominee,nominees,nonchalant,none,nonetheless,nonexistent,nonissue,nonnegotiable,nonsense,nonsensical,nonstop,nonviolent,noo,noodle,noodles,noon,noose,nope,nor,nordic,norland,norm,normal,normalcy,normally,north,northeast,northeastern,norther,northern,northwest,northwestern,nos,nose,nosebleeds,nosed,nosedive,noses,nosey,nosh,nosing,nostalgia,nostalgic,nostril,nostrils,nosy,not,notable,notably,notary,notation,notch,notches,note,notebook,noted,notepad,notes,nothing,nothings,notice,noticeable,noticeably,noticed,notices,noticing,notification,notified,notify,noting,notion,notions,notorious,notoriously,notwithstanding,nougat,noun,nourish,nourishing,nourishment,nous,nouveau,nova,novel,novelist,novels,novelty,novocaine,now,nowadays,nowhere,noxious,nozzle,nu,nuance,nuances,nub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# 65536 words
english_words_google = 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0
| 10
|
bd819e07b0664a91fa2f17848fa30db334c81006
| 81,362
|
py
|
Python
|
dymos/trajectory/test/test_trajectory.py
|
kaushikponnapalli/dymos
|
3fba91d0fc2c0e8460717b1bec80774676287739
|
[
"Apache-2.0"
] | 104
|
2018-09-08T16:52:27.000Z
|
2022-03-10T23:35:30.000Z
|
dymos/trajectory/test/test_trajectory.py
|
kaushikponnapalli/dymos
|
3fba91d0fc2c0e8460717b1bec80774676287739
|
[
"Apache-2.0"
] | 628
|
2018-06-27T20:32:59.000Z
|
2022-03-31T19:24:32.000Z
|
dymos/trajectory/test/test_trajectory.py
|
kaushikponnapalli/dymos
|
3fba91d0fc2c0e8460717b1bec80774676287739
|
[
"Apache-2.0"
] | 46
|
2018-06-27T20:54:07.000Z
|
2021-12-19T07:23:32.000Z
|
import os
import unittest
import numpy as np
import openmdao.api as om
from openmdao.utils.assert_utils import assert_near_equal
from openmdao.utils.testing_utils import use_tempdirs
import dymos as dm
from dymos.examples.finite_burn_orbit_raise.finite_burn_eom import FiniteBurnODE
@use_tempdirs
class TestTrajectory(unittest.TestCase):
def tearDown(self):
for filename in ['test_trajectory_rec.db', 'total_coloring.pkl']:
if os.path.exists(filename):
os.remove(filename)
def setUp(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
def test_linked_phases(self):
burn1_accel = self.p.get_val('burn1.states:accel')
burn2_accel = self.p.get_val('burn2.states:accel')
accel_link_error = self.p.get_val('linkages.burn1:accel_final|burn2:accel_initial')
assert_near_equal(accel_link_error, burn1_accel[-1]-burn2_accel[0])
@use_tempdirs
class TestLinkages(unittest.TestCase):
def tearDown(self):
for filename in ['test_trajectory_rec.db', 'total_coloring.pkl']:
if os.path.exists(filename):
os.remove(filename)
def test_linked_controls(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['u1'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
burn1_u1_final = p.get_val('burn1.timeseries.controls:u1')[-1, ...]
burn2_u1_initial = p.get_val('burn2.timeseries.controls:u1')[0, ...]
u1_linkage_error = p.get_val('linkages.burn1:u1_final|burn2:u1_initial')
assert_near_equal(u1_linkage_error, burn1_u1_final - burn2_u1_initial)
def test_linked_parameters(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['c'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
burn1_c_final = p.get_val('burn1.timeseries.parameters:c')[-1, ...]
burn2_c_initial = p.get_val('burn2.timeseries.parameters:c')[0, ...]
c_linkage_error = p.get_val('linkages.burn1:c_final|burn2:c_initial')
assert_near_equal(c_linkage_error, burn1_c_final - burn2_c_initial)
def test_linked_control_to_polynomial_control(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_polynomial_control('u1', order=2, units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['u1'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.polynomial_controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
burn1_u1_final = p.get_val('burn1.timeseries.controls:u1')[-1, ...]
burn2_u1_initial = p.get_val('burn2.timeseries.polynomial_controls:u1')[0, ...]
u1_linkage_error = p.get_val('linkages.burn1:u1_final|burn2:u1_initial')
assert_near_equal(u1_linkage_error, burn1_u1_final - burn2_u1_initial)
def test_linked_control_rate(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['u1_rate'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
burn1_u1_final = p.get_val('burn1.timeseries.control_rates:u1_rate')[-1, ...]
burn2_u1_initial = p.get_val('burn2.timeseries.control_rates:u1_rate')[0, ...]
u1_linkage_error = p.get_val('linkages.burn1:u1_rate_final|burn2:u1_rate_initial')
assert_near_equal(u1_linkage_error, burn1_u1_final - burn2_u1_initial)
def test_linked_control_rate2(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=5))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=5))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['u1_rate2'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
burn1_u1_final = p.get_val('burn1.timeseries.control_rates:u1_rate2')[-1, ...]
burn2_u1_initial = p.get_val('burn2.timeseries.control_rates:u1_rate2')[0, ...]
u1_linkage_error = p.get_val('linkages.burn1:u1_rate2_final|burn2:u1_rate2_initial')
assert_near_equal(u1_linkage_error, burn1_u1_final - burn2_u1_initial)
def test_linked_polynomial_control_rate(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=5))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=5))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_polynomial_control('u1', units='deg', order=2, scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['u1_rate'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.polynomial_controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
burn1_u1_final = p.get_val('burn1.timeseries.control_rates:u1_rate')[-1, ...]
burn2_u1_initial = p.get_val('burn2.timeseries.polynomial_control_rates:u1_rate')[0, ...]
u1_linkage_error = p.get_val('linkages.burn1:u1_rate_final|burn2:u1_rate_initial')
assert_near_equal(u1_linkage_error, burn1_u1_final - burn2_u1_initial)
def test_linked_polynomial_control_rate2(self):
self.traj = dm.Trajectory()
p = self.p = om.Problem(model=self.traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=5))
self.traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
self.traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=5))
self.traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_polynomial_control('u1', units='deg', order=2, scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
self.traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['accel'])
self.traj.link_phases(phases=['burn1', 'burn2'], vars=['u1_rate2'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
p.setup(check=True)
# Set Initial Guesses
p.set_val('burn1.t_initial', value=0.0)
p.set_val('burn1.t_duration', value=2.25)
p.set_val('burn1.states:r', value=burn1.interp('r', [1, 1.5]))
p.set_val('burn1.states:theta', value=burn1.interp('theta', [0, 1.7]))
p.set_val('burn1.states:vr', value=burn1.interp('vr', [0, 0]))
p.set_val('burn1.states:vt', value=burn1.interp('vt', [1, 1]))
p.set_val('burn1.states:accel', value=burn1.interp('accel', [0.1, 0]))
p.set_val('burn1.states:deltav', value=burn1.interp('deltav', [0, 0.1]))
p.set_val('burn1.controls:u1', value=burn1.interp('u1', [-3.5, 13.0]))
p.set_val('burn1.parameters:c', value=1.5)
p.set_val('coast.t_initial', value=2.25)
p.set_val('coast.t_duration', value=3.0)
p.set_val('coast.states:r', value=coast.interp('r', [1.3, 1.5]))
p.set_val('coast.states:theta', value=coast.interp('theta', [2.1767, 1.7]))
p.set_val('coast.states:vr', value=coast.interp('vr', [0.3285, 0]))
p.set_val('coast.states:vt', value=coast.interp('vt', [0.97, 1]))
p.set_val('coast.states:accel', value=coast.interp('accel', [0, 0]))
p.set_val('coast.controls:u1', value=coast.interp('u1', [0, 0]))
p.set_val('coast.parameters:c', value=1.5)
p.set_val('burn2.t_initial', value=5.25)
p.set_val('burn2.t_duration', value=1.75)
p.set_val('burn2.states:r', value=burn2.interp('r', [1, 3]))
p.set_val('burn2.states:theta', value=burn2.interp('theta', [0, 4.0]))
p.set_val('burn2.states:vr', value=burn2.interp('vr', [0, 0]))
p.set_val('burn2.states:vt', value=burn2.interp('vt', [1, np.sqrt(1 / 3)]))
p.set_val('burn2.states:accel', value=burn2.interp('accel', [0.1, 0]))
p.set_val('burn2.states:deltav', value=burn2.interp('deltav', [0.1, 0.2]))
p.set_val('burn2.polynomial_controls:u1', value=burn2.interp('u1', [1, 1]))
p.set_val('burn2.parameters:c', value=1.5)
p.run_model()
burn1_u1_final = p.get_val('burn1.timeseries.control_rates:u1_rate2')[-1, ...]
burn2_u1_initial = p.get_val('burn2.timeseries.polynomial_control_rates:u1_rate2')[0, ...]
u1_linkage_error = p.get_val('linkages.burn1:u1_rate2_final|burn2:u1_rate2_initial')
assert_near_equal(u1_linkage_error, burn1_u1_final - burn2_u1_initial)
@use_tempdirs
class TestInvalidLinkages(unittest.TestCase):
def test_invalid_linkage_variable(self):
traj = dm.Trajectory()
p = om.Problem(model=traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
traj.link_phases(phases=['burn1', 'burn2'], vars=['u1', 'bar'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
with self.assertRaises(ValueError) as e:
p.setup(check=True)
self.assertEqual(str(e.exception), 'Error in linking bar from burn1 to bar in burn2: '
'Unable to find variable \'bar\' in phase '
'\'phases.burn1\' or its ODE.')
def test_invalid_linkage_phase(self):
p = om.Problem(model=om.Group())
traj = dm.Trajectory()
p.model.add_subsystem('traj', subsys=traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
traj.link_phases(phases=['burn1', 'foo'], vars=['u1', 'u1'])
# Finish Problem Setup
p.model.linear_solver = om.DirectSolver()
with self.assertRaises(ValueError) as e:
p.setup(check=True)
self.assertEqual(str(e.exception), 'Invalid linkage. Phase \'foo\' does not exist in '
'trajectory \'traj\'.')
def test_invalid_linkage_args(self):
p = om.Problem(model=om.Group())
traj = dm.Trajectory()
p.model.add_subsystem('traj', subsys=traj)
# First Phase (burn)
burn1 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=4, order=3))
traj.add_phase('burn1', burn1)
burn1.set_time_options(fix_initial=True, duration_bounds=(.5, 10), units='TU')
burn1.add_state('r', fix_initial=True, fix_final=False,
rate_source='r_dot', units='DU')
burn1.add_state('theta', fix_initial=True, fix_final=False,
rate_source='theta_dot', units='rad')
burn1.add_state('vr', fix_initial=True, fix_final=False,
rate_source='vr_dot', units='DU/TU')
burn1.add_state('vt', fix_initial=True, fix_final=False,
rate_source='vt_dot', units='DU/TU')
burn1.add_state('accel', fix_initial=True, fix_final=False,
rate_source='at_dot', units='DU/TU**2')
burn1.add_state('deltav', fix_initial=True, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn1.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn1.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Second Phase (Coast)
coast = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=10, order=3))
traj.add_phase('coast', coast)
coast.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), duration_ref=10, units='TU')
coast.add_state('r', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='r_dot', units='DU')
coast.add_state('theta', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='theta_dot', units='rad')
coast.add_state('vr', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vr_dot', units='DU/TU')
coast.add_state('vt', fix_initial=False, fix_final=False, defect_scaler=100.0,
rate_source='vt_dot', units='DU/TU')
coast.add_state('accel', fix_initial=True, fix_final=True, ref=1.0E-12, defect_ref=1.0E-12,
rate_source='at_dot', units='DU/TU**2')
coast.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
coast.add_control('u1', opt=False, val=0.0, units='deg')
coast.add_parameter('c', opt=False, val=1.5, units='DU/TU')
# Third Phase (burn)
burn2 = dm.Phase(ode_class=FiniteBurnODE, transcription=dm.GaussLobatto(num_segments=3, order=3))
traj.add_phase('burn2', burn2)
burn2.set_time_options(initial_bounds=(0.5, 20), duration_bounds=(.5, 10), initial_ref=10, units='TU')
burn2.add_state('r', fix_initial=False, fix_final=True,
rate_source='r_dot', units='DU')
burn2.add_state('theta', fix_initial=False, fix_final=False,
rate_source='theta_dot', units='rad')
burn2.add_state('vr', fix_initial=False, fix_final=True,
rate_source='vr_dot', units='DU/TU')
burn2.add_state('vt', fix_initial=False, fix_final=True,
rate_source='vt_dot', units='DU/TU')
burn2.add_state('accel', fix_initial=False, fix_final=False, defect_ref=1.0E-6,
rate_source='at_dot', units='DU/TU**2')
burn2.add_state('deltav', fix_initial=False, fix_final=False,
rate_source='deltav_dot', units='DU/TU')
burn2.add_control('u1', rate_continuity=True, rate2_continuity=True,
units='deg', scaler=0.01, lower=-30, upper=30)
burn2.add_parameter('c', opt=False, val=1.5, units='DU/TU')
burn2.add_objective('deltav', loc='final')
# Link Phases
traj.link_phases(phases=['burn1', 'coast', 'burn2'],
vars=['time', 'r', 'theta', 'vr', 'vt', 'deltav'])
with self.assertWarns(UserWarning) as w:
traj.add_linkage_constraint(phase_a='burn1', phase_b='burn2', var_a='accel', var_b='accel',
lower=-5, upper=5, ref0=-5, ref=5, linear=True, connected=True)
expected_warning = 'Invalid option in linkage between burn1:accel and burn2:accel in trajectory traj. ' \
'The following options for add_linkage_constraint were specified but ' \
'not valid when option \'connected\' is True: lower upper ref0 ref linear'
self.assertEqual(expected_warning, str(w.warning))
def test_linkage_units(self):
import numpy as np
from scipy.interpolate import interp1d
import openmdao.api as om
import dymos as dm
from dymos.models.atmosphere.atmos_1976 import USatm1976Data
class CannonballSizeComp(om.ExplicitComponent):
def setup(self):
self.add_input(name='radius', val=1.0, desc='cannonball radius', units='m')
self.add_input(name='dens', val=7870., desc='cannonball density', units='kg/m**3')
self.add_output(name='mass', shape=(1,), desc='cannonball mass', units='kg')
self.add_output(name='S', shape=(1,), desc='aerodynamic reference area', units='m**2')
self.declare_partials(of='mass', wrt='dens')
self.declare_partials(of='mass', wrt='radius')
self.declare_partials(of='S', wrt='radius')
def compute(self, inputs, outputs):
radius = inputs['radius']
dens = inputs['dens']
outputs['mass'] = (4/3.) * dens * np.pi * radius ** 3
outputs['S'] = np.pi * radius ** 2
def compute_partials(self, inputs, partials):
radius = inputs['radius']
dens = inputs['dens']
partials['mass', 'dens'] = (4/3.) * np.pi * radius ** 3
partials['mass', 'radius'] = 4. * dens * np.pi * radius ** 2
partials['S', 'radius'] = 2 * np.pi * radius
class CannonballODE(om.ExplicitComponent):
def initialize(self):
self.options.declare('num_nodes', types=int)
self.options.declare('alt_unit', values=('ft', 'm', 'NM'))
def setup(self):
nn = self.options['num_nodes']
# static parameters
self.add_input('m', units='kg')
self.add_input('S', units='m**2')
# 0.5 good assumption for a sphere
self.add_input('CD', 0.5)
# time varying inputs
self.add_input('h', units=self.options['alt_unit'], shape=nn)
self.add_input('v', units='m/s', shape=nn)
self.add_input('gam', units='rad', shape=nn)
# state rates
self.add_output('v_dot', shape=nn, units='m/s**2', tags=['dymos.state_rate_source:v'])
self.add_output('gam_dot', shape=nn, units='rad/s', tags=['dymos.state_rate_source:gam'])
self.add_output('h_dot', shape=nn, units=self.options['alt_unit']+'/s', tags=['dymos.state_rate_source:h'])
self.add_output('r_dot', shape=nn, units='m/s', tags=['dymos.state_rate_source:r'])
self.add_output('ke', shape=nn, units='J')
self.declare_coloring(wrt='*', method='cs')
alt_data = USatm1976Data.alt * om.unit_conversion('ft', 'm')[0]
rho_data = USatm1976Data.rho * om.unit_conversion('slug/ft**3', 'kg/m**3')[0]
self.rho_interp = interp1d(np.array(alt_data, dtype=complex),
np.array(rho_data, dtype=complex),
kind='linear')
def compute(self, inputs, outputs):
gam = inputs['gam']
v = inputs['v']
h = inputs['h']
m = inputs['m']
S = inputs['S']
CD = inputs['CD']
if self.options['alt_unit'] == 'ft':
h = h*0.3048
elif self.options['alt_unit'] == 'NM':
h = h*1852
GRAVITY = 9.80665 # m/s**2
# handle complex-step gracefully from the interpolant
if np.iscomplexobj(h):
rho = self.rho_interp(inputs['h'])
else:
rho = self.rho_interp(inputs['h']).real
q = 0.5*rho*inputs['v']**2
qS = q * S
D = qS * CD
cgam = np.cos(gam)
sgam = np.sin(gam)
outputs['v_dot'] = - D/m-GRAVITY*sgam
outputs['gam_dot'] = -(GRAVITY/v)*cgam
if self.options['alt_unit'] == 'ft':
outputs['h_dot'] = v*sgam/0.3048
elif self.options['alt_unit'] == 'NM':
outputs['h_dot'] = v*sgam/1852
outputs['h_dot'] = v*sgam
outputs['r_dot'] = v*cgam
outputs['ke'] = 0.5*m*v**2
p = om.Problem(model=om.Group())
p.model.add_subsystem('size_comp', CannonballSizeComp(),
promotes_inputs=['radius', 'dens'])
p.model.set_input_defaults('dens', val=7.87, units='g/cm**3')
p.model.add_design_var('radius', lower=0.01, upper=0.10,
ref0=0.01, ref=0.10, units='m')
traj = p.model.add_subsystem('traj', dm.Trajectory())
transcription = dm.Radau(num_segments=5, order=3, compressed=True)
start = dm.Phase(ode_class=CannonballODE, transcription=transcription,
ode_init_kwargs={'alt_unit': 'm'})
start = traj.add_phase('start', start)
start.set_time_options(fix_initial=True, duration_bounds=(1, 100), duration_ref=100, units='s')
start.set_state_options('r', fix_initial=True, fix_final=False)
start.set_state_options('h', fix_initial=True, fix_final=False)
start.set_state_options('gam', fix_initial=False, fix_final=False)
start.set_state_options('v', fix_initial=False, fix_final=False)
start.add_parameter('S', units='m**2', static_target=True)
start.add_parameter('m', units='kg', static_target=True)
# Limit the muzzle energy
start.add_boundary_constraint('ke', loc='initial', upper=400000, lower=0, ref=100000)
start.add_boundary_constraint('h', loc='final', equals=2, units='m')
ascent = dm.Phase(ode_class=CannonballODE, transcription=transcription,
ode_init_kwargs={'alt_unit': 'm'})
ascent = traj.add_phase('ascent', ascent)
ascent.set_time_options(fix_initial=False, duration_bounds=(1, 100),
duration_ref=100, units='s')
ascent.set_state_options('r', fix_initial=False, fix_final=False)
ascent.set_state_options('h', fix_initial=False, fix_final=False)
ascent.set_state_options('gam', fix_initial=False, fix_final=True)
ascent.set_state_options('v', fix_initial=False, fix_final=False)
ascent.add_parameter('S', units='m**2', static_target=True)
ascent.add_parameter('m', units='kg', static_target=True)
transcription = dm.GaussLobatto(num_segments=5, order=3, compressed=True)
descent = dm.Phase(ode_class=CannonballODE, transcription=transcription,
ode_init_kwargs={'alt_unit': 'ft'})
traj.add_phase('descent', descent)
descent.set_time_options(initial_bounds=(.5, 100), duration_bounds=(.5, 100),
duration_ref=100, units='s')
descent.add_state('r')
descent.add_state('h', fix_initial=False, fix_final=True)
descent.add_state('gam', fix_initial=False, fix_final=False)
descent.add_state('v', fix_initial=False, fix_final=False)
descent.add_parameter('S', units='m**2', static_target=True)
descent.add_parameter('m', units='kg', static_target=True)
descent.add_objective('r', loc='final', scaler=-1.0)
# Add internally-managed design parameters to the trajectory.
traj.add_parameter('CD',
targets={'ascent': ['CD'], 'descent': ['CD']},
val=0.5, units=None, opt=False, static_target=True)
traj.add_parameter('m', units='kg', val=1.0,
targets={'ascent': 'mass', 'descent': 'mass'}, static_target=True)
# In this case, by omitting targets, we're connecting these
# parameters to parameters with the same name in each phase.
traj.add_parameter('S', units='m**2', val=0.005, static_target=True)
# Link Phases (link time and all state variables)
traj.link_phases(phases=['start', 'ascent'], vars=['*'])
traj.add_linkage_constraint('ascent', 'descent', 'h', 'h', 'final', 'initial', units='ft')
# traj.add_linkage_constraint('ascent', 'descent', 'h', 'h', 'final', 'initial')
traj.link_phases(phases=['ascent', 'descent'], vars=['time', 'r', 'v', 'gam'])
# Issue Connections
p.model.connect('size_comp.mass', 'traj.parameters:m')
p.model.connect('size_comp.S', 'traj.parameters:S')
# A linear solver at the top level can improve performance.
p.model.linear_solver = om.DirectSolver()
# Finish Problem Setup
p.setup()
#############################################
# Set constants and initial guesses
#############################################
p.set_val('radius', 0.05, units='m')
p.set_val('dens', 7.87, units='g/cm**3')
p.set_val('traj.parameters:CD', 0.5)
p.set_val('traj.start.t_initial', 0.0)
p.set_val('traj.start.t_duration', 10.0)
p.set_val('traj.ascent.t_initial', 0.0)
p.set_val('traj.ascent.t_duration', 10.0)
p.set_val('traj.start.states:r', ascent.interp('r', [0, 100]))
p.set_val('traj.start.states:h', ascent.interp('h', [0, 50]))
p.set_val('traj.start.states:v', ascent.interp('v', [200, 150]))
p.set_val('traj.start.states:gam', ascent.interp('gam', [25, 0]), units='deg')
p.set_val('traj.ascent.states:r', ascent.interp('r', [0, 100]))
p.set_val('traj.ascent.states:h', ascent.interp('h', [0, 100]))
p.set_val('traj.ascent.states:v', ascent.interp('v', [200, 150]))
p.set_val('traj.ascent.states:gam', ascent.interp('gam', [25, 0]), units='deg')
p.set_val('traj.descent.t_initial', 10.0)
p.set_val('traj.descent.t_duration', 10.0)
p.set_val('traj.descent.states:r', descent.interp('r', [100, 200]))
p.set_val('traj.descent.states:h', descent.interp('h', [200, 0]))
p.set_val('traj.descent.states:v', descent.interp('v', [150, 200]))
p.set_val('traj.descent.states:gam', descent.interp('gam', [0, -45]), units='deg')
#####################################################
# Run the optimization and final explicit simulation
#####################################################
dm.run_problem(p, run_driver=False, simulate=False)
lnk_start_h_m = p.get_val('traj.linkages.start:h', units='m')
lnk_ascent_h_m = p.get_val('traj.linkages.ascent:h', units='m')
lnk_descent_h_m = p.get_val('traj.linkages.descent:h', units='m')
start_h_m = p.get_val('traj.start.timeseries.states:h', units='m')[[0, -1], ...]
ascent_h_m = p.get_val('traj.ascent.timeseries.states:h', units='m')[[0, -1], ...]
descent_h_m = p.get_val('traj.descent.timeseries.states:h', units='m')[[0, -1], ...]
assert_near_equal(lnk_start_h_m, start_h_m, tolerance=1.0E-12)
assert_near_equal(lnk_ascent_h_m, ascent_h_m, tolerance=1.0E-12)
assert_near_equal(lnk_descent_h_m, descent_h_m, tolerance=1.0E-12)
# manual linkage values
lnk_1_manual = - ascent_h_m[0, ...] + start_h_m[-1, ...]
lnk_2_manual = - descent_h_m[0, ...] + ascent_h_m[-1, ...]
lnk_1_output = p.get_val('traj.linkages.start:h_final|ascent:h_initial')
lnk_2_output = p.get_val('traj.linkages.ascent:h_final|descent:h_initial')
assert_near_equal(lnk_1_output, lnk_1_manual)
assert_near_equal(lnk_2_output, lnk_2_manual)
if __name__ == '__main__': # pragma: no cover
unittest.main()
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| 123
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0
| 7
|
bdc99bfca879118a019ce5ad830ab52252e71d5c
| 8,400
|
py
|
Python
|
test/unittest-jsonrpc.py
|
phyunsj/JSONRPC-JSON-Http-Handler
|
43e5f4a40303a41167d9ee85521587a7eb3da997
|
[
"MIT"
] | null | null | null |
test/unittest-jsonrpc.py
|
phyunsj/JSONRPC-JSON-Http-Handler
|
43e5f4a40303a41167d9ee85521587a7eb3da997
|
[
"MIT"
] | null | null | null |
test/unittest-jsonrpc.py
|
phyunsj/JSONRPC-JSON-Http-Handler
|
43e5f4a40303a41167d9ee85521587a7eb3da997
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
import unittest
import requests
import json
import xmlrpclib
import pexpect
import sys, getopt, pprint
IPADDR = '127.0.0.1'
def cmdOptions(argv):
global IPADDR
try:
opts, args = getopt.getopt(argv,"ht:",["target="])
except getopt.GetoptError:
print 'Usage : unittest-appweb.py -t <target>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'Usage : unittest-appweb.py -t <target> '
sys.exit()
elif opt in ("-t", "--target"):
IPADDR = arg
class TestAppweb(unittest.TestCase):
global IPADDR
def setUp(self):
pass
def tearDown(self):
pass
def test_jsonrpc_1(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 401, 'params': { 'first' : 18, 'second' : 13} , 'id': 1}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
self.assertEqual( jsonResponse['result'], 5)
def test_jsonrpc_2(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 402, 'params': { 'first' : 7, 'second' : 9} , 'id': 2}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
self.assertEqual( jsonResponse['result']['first'], -2)
self.assertEqual( jsonResponse['result']['second'], 2)
self.assertEqual( jsonResponse['result']['third'], 4)
def test_jsonrpc_3(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 403, 'params': { 'first' : 16, 'second' : 17, 'third' : 5} , 'id': 3}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
self.assertEqual( jsonResponse['result']['first'], -1)
self.assertEqual( jsonResponse['result']['second'], 12)
def test_test_jsonrpc_4(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 404, 'params': [20,41,63] , 'id': 4}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
print jsonResponse
self.assertEqual( jsonResponse['result'][0], 19)
self.assertEqual( jsonResponse['result'][1], 40)
self.assertEqual( jsonResponse['result'][2], 62)
def test_jsonrpc_5(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 405, 'params': { 'first' : [20,21,23], 'second' : [11,21,31,41,51]} , 'id': 5}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
self.assertEqual( jsonResponse['result']['first'][0], 19)
self.assertEqual( jsonResponse['result']['first'][1], 20)
self.assertEqual( jsonResponse['result']['first'][2], 22)
self.assertEqual( jsonResponse['result']['second'][0], 9)
self.assertEqual( jsonResponse['result']['second'][1], 19)
self.assertEqual( jsonResponse['result']['second'][2], 29)
self.assertEqual( jsonResponse['result']['second'][3], 39)
self.assertEqual( jsonResponse['result']['second'][4], 49)
def test_jsonrpc_6(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 406, 'params': { 'first' : { 'p1' : 44, 'p2' : 5, 'p3' : 110}} , 'id': 6}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
def test_jsonrpc_7(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 407, 'params': { 'first' : { 'p1' : 44, 'p2' : 5, 'p3' : 110}, 'second' : { 'p1' : 33, 'p2' : 75, 'p3' : 20} } , 'id': 7}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
def test_jsonrpc_parse_error(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = "{ 'jsonrpc': '2.0', 'build' : '10101', 'method': '407', 'params': '[ a,b,c,d,'' , 'id': '7'}"
r = requests.post(TargetUrl, data=payload, timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
self.assertEqual( jsonResponse['error']['code'], -32700)
def test_jsonrpc_unknwon_method_error(self):
TargetUrl = 'http://'+IPADDR+':8888/jsonrpc.egi'
self.headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
# login as admin
payload = { 'jsonrpc': "2.0", 'build' : 10101, 'method': 600, 'params': { 'first' : { 'p1' : 44, 'p2' : 5, 'p3' : 110}, 'second' : { 'p1' : 33, 'p2' : 75, 'p3' : 20} } , 'id': 7}
r = requests.post(TargetUrl, data=json.dumps(payload), timeout=60, headers=self.headers, verify=False)
print '[RESULT] HTTP Response Status :', r.status_code
print '[RESULT] HTTP Response Header :', r.headers
print '[RESULT] HTTP Payload :', r.content
jsonResponse = json.loads(r.content)
self.assertEqual( jsonResponse['error']['code'], -32601)
###################################################################################################
#
# __main__ UnitTest Runner
#
if __name__ == '__main__':
cmdOptions(sys.argv[1:])
suite = unittest.TestLoader().loadTestsFromTestCase(TestAppweb)
result = unittest.TextTestRunner(verbosity=2).run(suite)
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| 188
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0
| 7
|
da3b9d533b218a1b7fbb41cc0206210b22e8c82b
| 25,993
|
py
|
Python
|
src/gui/icons.py
|
mtahirtariq/easysub
|
d6083a7120991819719566d1e4daf7714b3db77a
|
[
"MIT"
] | null | null | null |
src/gui/icons.py
|
mtahirtariq/easysub
|
d6083a7120991819719566d1e4daf7714b3db77a
|
[
"MIT"
] | null | null | null |
src/gui/icons.py
|
mtahirtariq/easysub
|
d6083a7120991819719566d1e4daf7714b3db77a
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created: Mon Oct 19 00:00:56 2015
# by: The Resource Compiler for PyQt (Qt v4.8.6)
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore
qt_resource_data = "\
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0
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da53fce0bc98c8fa308f3fd10c9330b6de6bf11c
| 12,305
|
py
|
Python
|
models/goodwin_keen.py
|
mourtadg7/goodwin-keen-model
|
911913fb0b7389b27a1efeb86ce4336551357690
|
[
"MIT"
] | null | null | null |
models/goodwin_keen.py
|
mourtadg7/goodwin-keen-model
|
911913fb0b7389b27a1efeb86ce4336551357690
|
[
"MIT"
] | 1
|
2021-09-12T18:10:53.000Z
|
2021-09-12T18:14:17.000Z
|
models/goodwin_keen.py
|
mourtadg7/goodwin-keen-model
|
911913fb0b7389b27a1efeb86ce4336551357690
|
[
"MIT"
] | 2
|
2021-09-12T15:56:01.000Z
|
2022-01-12T18:58:21.000Z
|
<<<<<<< Updated upstream:models/goodwin_keen.py
import os
import math
import numpy as np
import sympy as sym
from .parameters import *
import matplotlib.pyplot as plt
from sympy import symbols, Matrix
import scipy.integrate as integrate
PATH = os.path.join(os.getcwd(), 'static/imgs')
def j_keen():
"""
The symbolic Jacobian for the Goodwin Keen Mode.
:return:
"""
v, r, alpha, beta, delta, phi0, phi1, kappa0, kappa1, kappa2 = symbols(
'nu r alpha beta delta Phi_0 Phi_1 kappa_0 kappa_1 kappa_2')
L, W, D = symbols('lambda omega d')
phil_curve = phi1 / (1 - L) ** 2 - phi0
kappa = kappa0 + kappa1 * sym.exp(
kappa2 * (1 - W - r * D)) # Acceleration relation for the total real capital stock
system = Matrix([L * (kappa / v - alpha - beta - delta), # dL/dt
W * (phil_curve - alpha), # dW/dt
D * (r - kappa / v + delta) + kappa - (1 - W)]) # dD/dt
funcs = Matrix([L, W, D])
J = system.jacobian(funcs)
return (J)
def num_keen(y: list, t: np.array) -> list:
W, L, D = y
pi = 1 - W - r * D
kappa = kappa0 + kappa1 * math.exp(kappa2 * pi) # Acceleration relation for the total real capital stock
phil_curve = phi1 / (1 - L) ** 2 - phi0 # Phillips curve
system = np.array([
W * (phil_curve - alpha), # dW/dt
L * (kappa / v - alpha - beta - delta), # dL/dt
D * (r - kappa / v + delta) + kappa - (1 - W)]) # dD/dt
return system
def find_eqm_keen(ICs: list, t: np.array) -> list:
"""
Finds the equilibrium points for the Goodwin Keen mode.
:param ICs: Initial conditions
:param t: Time
:return: Returns the equilibrium points.
"""
result = integrate.odeint(num_keen, ICs, t)
W_sol = result[-50:-1, 0] # Wage Share
L_sol = result[-50:-1, 1] # Employment Rate
D_sol = result[-50:-1, 2] # Debt Ratio
W_eqm = sum(W_sol) / len(W_sol)
L_eqm = sum(L_sol) / len(L_sol)
D_eqm = sum(D_sol) / len(D_sol)
return ([W_eqm, L_eqm, D_eqm])
def eig_keen_val(ICs: list):
"""
Phillips Curve Parameters from Keen (1995).
Evaluated Eigenvalues for the Goodwin Keen Model
:param ICs: List of an initial conditions.
:return: The eigen values of the system.
"""
k = 0.05 # Acceleration relation for the total real capital stock
t_eqm = np.arange(0, 1000, 0.01)
W, L, D = find_eqm_keen(ICs, t_eqm)
J = j_keen()
J_eval = J.subs([('nu', v),
('r', r),
('alpha', alpha),
('beta', beta),
('delta', delta),
('k', k),
('Phi_1', phi1),
('Phi_0', phi0),
('kappa_0', kappa0),
('kappa_1', kappa1),
('kappa_2', kappa2),
('lambda', L),
('omega', W),
('d', D)])
J_eig = J_eval.eigenvects()
eigenvals = [J_eig[0][0], J_eig[1][0], J_eig[2][0]]
return (eigenvals)
def plot_num_keen(t: np.array, ICs: list, file_name: str = '') -> None:
result = integrate.odeint(num_keen, ICs, t)
W_sol = result[:, 0] # Wage Share
L_sol = result[:, 1] # Employment Rate
D_sol = result[:, 2] # Debt Ratio
golden_ratio = (1+np.sqrt(5))/2
H = 4.4
fig, ax = plt.subplots(figsize=(int(golden_ratio*H),H), tight_layout=True)
ax.set_title(fr'$\omega_0={ICs[0]}, \lambda_0={ICs[1]}, d_0={ICs[2]}$')
ax.set_xlim( left=t[0], right=t[-1]-1)
lns_L = ax.plot(t, W_sol, label=r'$\omega$')
lns_W = ax.plot(t, L_sol, label=r'$\lambda$')
ax.set_ylabel('Rates')
ax1 = ax.twinx() # instantiate a second axes that shares the same x-axis
color = 'C2'
ax1.set_ylabel('Ratio', color=color) # we already handled the x-label with ax1
lns_D = ax1.plot(t, D_sol, label=r'$d$', color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax.set_xlabel('Time (years)')
lns = lns_L + lns_W + lns_D
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=1)
if file_name:
my_file = f'{file_name}.svg'
fig.savefig(os.path.join(PATH, my_file), dpi=1000)
plt.show()
def phase_plot_keen(t: np.array, ICs: list, eqm: tuple, file_name: str = '') -> None:
result = integrate.odeint(num_keen, ICs, t)
W_sol = result[:, 0] # Wage Share
L_sol = result[:, 1] # Employment Rate
D_sol = result[:, 2] # Debt Ratio
golden_ratio = (1+np.sqrt(5))/2
H = 4.4
fig, axes = plt.subplots(figsize=(int(golden_ratio*H),H), tight_layout=True)
axes.set_axis_off()
axes = fig.add_subplot(111, projection = '3d')
axes.plot3D(W_sol,L_sol,D_sol, c='blue', label='Transient', linewidth=0.5)
axes.view_init(45,-20)
axes.scatter(eqm[0], eqm[1], eqm[2], c='red', label=r'($\lambda^*, \omega^*, d^*$)')
axes.scatter(ICs[0], ICs[1], ICs[2], c='green', label=r'($\lambda_0, \omega_0, d_0$)')
axes.set_title(fr'$\omega_0={ICs[0]}, \lambda_0={ICs[1]}, d_0={ICs[2]}$')
axes.set_xlabel(r'$\lambda$')
axes.set_ylabel(r'$\omega$')
axes.set_zlabel(r'$d$')
axes.legend(loc=0)
if file_name:
my_file = f'{file_name}.svg'
fig.savefig(os.path.join(PATH, my_file), dpi=1000)
=======
import numpy as np
import math
import scipy.integrate as integrate
import matplotlib.pyplot as plt
import sympy as sym
from sympy import symbols
from sympy import Matrix
import os
PATH = os.path.join(os.getcwd(), 'imgs')
alpha = 0.025 # Technological growth rate
beta = 0.02 # Population growth rate
delta = 0.01 # Deprecation rate
# Phillips Curve Parameters from Keen (1995)
phi0 = 0.04 / (1 - 0.04 ** 2)
phi1 = 0.04 ** 3 / (1 - 0.04 ** 2)
# Investment Rate Parameters from Grasselli (2012)
kappa0 = -0.0065
kappa1 = math.exp(-5)
kappa2 = 20
r = 0.03 # Real interest rate
v = 3 # Capital to output ratio
def j_keen():
"""
The symbolic Jacobian for the Goodwin Keen Mode.
:return:
"""
v, r, alpha, beta, delta, phi0, phi1, kappa0, kappa1, kappa2 = symbols(
'nu r alpha beta delta Phi_0 Phi_1 kappa_0 kappa_1 kappa_2')
L, W, D = symbols('lambda omega d')
phil_curve = phi1 / (1 - L) ** 2 - phi0
kappa = kappa0 + kappa1 * sym.exp(
kappa2 * (1 - W - r * D)) # Acceleration relation for the total real capital stock
system = Matrix([L * (kappa / v - alpha - beta - delta), # dL/dt
W * (phil_curve - alpha), # dW/dt
D * (r - kappa / v + delta) + kappa - (1 - W)]) # dD/dt
funcs = Matrix([L, W, D])
J = system.jacobian(funcs)
return (J)
def num_keen(y: list, t: np.array) -> list:
W, L, D = y
pi = 1 - W - r * D
kappa = kappa0 + kappa1 * math.exp(kappa2 * pi) # Acceleration relation for the total real capital stock
phil_curve = phi1 / (1 - L) ** 2 - phi0 # Phillips curve
system = np.array([
W * (phil_curve - alpha), # dW/dt
L * (kappa / v - alpha - beta - delta), # dL/dt
D * (r - kappa / v + delta) + kappa - (1 - W)]) # dD/dt
return system
def find_eqm_keen(ICs: list, t: np.array) -> list:
"""
Finds the equilibrium points for the Goodwin Keen mode.
:param ICs: Initial conditions
:param t: Time
:return: Returns the equilibrium points.
"""
result = integrate.odeint(num_keen, ICs, t)
W_sol = result[-50:-1, 0] # Wage Share
L_sol = result[-50:-1, 1] # Employment Rate
D_sol = result[-50:-1, 2] # Debt Ratio
W_eqm = sum(W_sol) / len(W_sol)
L_eqm = sum(L_sol) / len(L_sol)
D_eqm = sum(D_sol) / len(D_sol)
return ([W_eqm, L_eqm, D_eqm])
def eig_keen_val(ICs: list):
"""
Phillips Curve Parameters from Keen (1995).
Evaluated Eigenvalues for the Goodwin Keen Model
:param ICs: List of an initial conditions.
:return: The eigen values of the system.
"""
k = 0.05 # Acceleration relation for the total real capital stock
t_eqm = np.arange(0, 1000, 0.01)
W, L, D = find_eqm_keen(ICs, t_eqm)
J = j_keen()
J_eval = J.subs([('nu', v),
('r', r),
('alpha', alpha),
('beta', beta),
('delta', delta),
('k', k),
('Phi_1', phi1),
('Phi_0', phi0),
('kappa_0', kappa0),
('kappa_1', kappa1),
('kappa_2', kappa2),
('lambda', L),
('omega', W),
('d', D)])
J_eig = J_eval.eigenvects()
eigenvals = [J_eig[0][0], J_eig[1][0], J_eig[2][0]]
return (eigenvals)
def plot_num_keen(t: np.array, ICs: list, file_name: str = '') -> None:
result = integrate.odeint(num_keen, ICs, t)
W_sol = result[:, 0] # Wage Share
L_sol = result[:, 1] # Employment Rate
D_sol = result[:, 2] # Debt Ratio
golden_ratio = (1+np.sqrt(5))/2
H = 4.4
fig, ax = plt.subplots(figsize=(int(golden_ratio*H),H), tight_layout=True)
ax.set_title(fr'$\omega_0={ICs[0]}, \lambda_0={ICs[1]}, d_0={ICs[2]}$')
ax.set_xlim( left=t[0], right=t[-1]-1)
lns_L = ax.plot(t, W_sol, label=r'$\omega$')
lns_W = ax.plot(t, L_sol, label=r'$\lambda$')
ax.set_ylabel('Rates')
ax1 = ax.twinx() # instantiate a second axes that shares the same x-axis
color = 'darkgreen'
ax1.set_ylabel('Ratio', color=color) # we already handled the x-label with ax1
lns_D = ax1.plot(t, D_sol, label=r'$d$', color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax.set_xlabel('Time (years)')
lns = lns_L + lns_W + lns_D
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=1)
if file_name:
my_file = f'{file_name}.svg'
fig.savefig(os.path.join(PATH, my_file), dpi=1000)
plt.show()
def phase_plot_keen(t: np.array, ICs: list, eqm: tuple, file_name: str = '') -> None:
result = integrate.odeint(num_keen, ICs, t)
W_sol = result[:, 0] # Wage Share
L_sol = result[:, 1] # Employment Rate
D_sol = result[:, 2] # Debt Ratio
golden_ratio = (1+np.sqrt(5))/2
H = 4.4
fig, axes = plt.subplots(figsize=(int(golden_ratio*H),H), tight_layout=True)
axes.set_axis_off()
axes = fig.add_subplot(111, projection = '3d')
axes.plot3D(W_sol,L_sol,D_sol, c='blue', label='Transient', linewidth=0.5)
# axes.view_init(45,-20)
axes.scatter(eqm[0], eqm[1], eqm[2], c='red', label=r'($\lambda^*, \omega^*, d^*$)')
axes.scatter(ICs[0], ICs[1], ICs[2], c='green', label=r'($\lambda_0, \omega_0, d_0$)')
axes.set_title(fr'$\omega_0={ICs[0]}, \lambda_0={ICs[1]}, d_0={ICs[2]}$')
axes.set_xlabel(r'$\lambda$')
axes.set_ylabel(r'$\omega$')
axes.set_zlabel(r'$d$')
axes.legend(loc=0)
if file_name:
my_file = f'{file_name}.svg'
fig.savefig(os.path.join(PATH, my_file), dpi=1000)
plt.show()
def _2D_phase_plot_keen(t: np.array, ICs: list, eqm: tuple, file_name: str = '') -> None:
result = integrate.odeint(num_keen, ICs, t)
W_sol = result[:, 0] # Wage Share
L_sol = result[:, 1] # Employment Rate
D_sol = result[:, 2] # Debt Ratio
golden_ratio = (1+np.sqrt(5))/2
H = 4.4
fig, axes = plt.subplots(figsize=(int(golden_ratio*H),H), tight_layout=True)
axes.set_axis_off()
axes = fig.add_subplot()
axes.plot(W_sol,L_sol, c='blue', label='Transient', linewidth=0.5)
# axes.view_init(45,-20)
# axes.scatter(eqm[0], eqm[1], eqm[2], c='red', label=r'($\lambda^*, \omega^*, d^*$)')
axes.scatter(ICs[0], ICs[1], ICs[2], c='green', label=r'($\lambda_0, \omega_0, d_0$)')
axes.set_title(fr'$\omega_0={ICs[0]}, \lambda_0={ICs[1]}, d_0={ICs[2]}$')
axes.set_xlabel(r'$\lambda$')
axes.set_ylabel(r'$\omega$')
# axes.set_zlabel(r'$d$')
axes.legend(loc=0)
if file_name:
my_file = f'{file_name}.svg'
fig.savefig(os.path.join(PATH, my_file), dpi=1000)
>>>>>>> Stashed changes:scripts/goodwin_keen.py
plt.show()
| 32.55291
| 110
| 0.563023
| 1,891
| 12,305
| 3.528292
| 0.118985
| 0.028327
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| 0.932404
| 0.903627
| 0.891787
| 0.891787
| 0.891787
| 0.891787
| 0
| 0.040524
| 0.274035
| 12,305
| 378
| 111
| 32.55291
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| 0.062745
| null | null | 0
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0
| 8
|
da90762e09dd14483614a366ccf86048a6c35f4e
| 7,323
|
py
|
Python
|
tests/browser_blob.py
|
pekrau/blobserver
|
f4c9411197033df9e8aefc1651dc6009c33e0c90
|
[
"MIT"
] | 1
|
2022-03-09T09:23:28.000Z
|
2022-03-09T09:23:28.000Z
|
tests/browser_blob.py
|
pekrau/blobserver
|
f4c9411197033df9e8aefc1651dc6009c33e0c90
|
[
"MIT"
] | 64
|
2021-02-19T19:31:15.000Z
|
2022-02-25T10:18:26.000Z
|
tests/browser_blob.py
|
pekrau/blobserver
|
f4c9411197033df9e8aefc1651dc6009c33e0c90
|
[
"MIT"
] | 1
|
2021-03-01T13:06:37.000Z
|
2021-03-01T13:06:37.000Z
|
"Test the web site pages for blob creation, update and delete."
import os
import os.path
import time
import unittest
import requests
import selenium
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.alert import Alert
from selenium.webdriver.support.ui import WebDriverWait
from selenium.common.exceptions import NoSuchElementException
import utils
class Blob(utils.BrowserTestCase):
"Test creating, downloading, updating and deleting blob."
def test_1_list_blobs(self):
"Login and list the user's blobs."
# Login page; fill in user name and password in form.
self.driver.get(self.settings["BASE_URL"] + "user/login")
self.assertIn("Login", self.driver.title)
elem = self.driver.find_element_by_name("username")
elem.clear()
elem.send_keys(self.settings["USERNAME"])
elem = self.driver.find_element_by_name("password")
elem.clear()
elem.send_keys(self.settings["PASSWORD"])
elem = self.driver.find_element_by_id("login-form")
elem.send_keys(Keys.RETURN)
# Menu item "My blobs" is visible only when logged in.
elem = self.driver.find_element_by_link_text("My blobs")
elem.click()
# Page listing the account's blobs.
elem = self.driver.find_element_by_id("blobs")
elems = elem.find_elements_by_xpath("//tbody/tr")
self.assertGreaterEqual(len(elems), 0)
def test_2_create_blob(self):
"Login and create a blob by upload."
# Login page; fill in user name and password in form.
self.driver.get(self.settings["BASE_URL"] + "user/login")
self.assertIn("Login", self.driver.title)
elem = self.driver.find_element_by_name("username")
elem.clear()
elem.send_keys(self.settings["USERNAME"])
elem = self.driver.find_element_by_name("password")
elem.clear()
elem.send_keys(self.settings["PASSWORD"])
elem = self.driver.find_element_by_id("login-form")
elem.send_keys(Keys.RETURN)
# Menu item "My blobs" is visible only when logged in.
elem = self.driver.find_element_by_link_text("My blobs")
elem.click()
# Page listing the account's blobs.
elem = self.driver.find_element_by_id("blobs")
elems = elem.find_elements_by_xpath("//tbody/tr")
count = len(elems)
# Go to upload page.
elem = self.driver.find_element_by_link_text("Upload")
elem.click()
# Fill in form.
elem = self.driver.find_element_by_name("description")
elem.clear()
description = "A test blob: this Python file."
elem.send_keys(description)
elem = self.driver.find_element_by_name("file")
elem.clear()
filename = os.path.basename(__file__)
filepath = os.path.join(os.getcwd(), filename)
elem.send_keys(os.path.join(filepath))
elem = self.driver.find_element_by_xpath("//button[@type='submit']")
elem.click()
time.sleep(1)
# Check the uploaded blob, compare to the file.
elem = self.driver.find_element_by_xpath("//div[text()='Blob']/following-sibling::div/a")
self.assertEqual(elem.text, filename)
elem = self.driver.find_element_by_xpath("//div[text()='Description']/following-sibling::div/p")
self.assertEqual(elem.text, description)
# Find href for blob data, get and compare to source file.
elem = self.driver.find_element_by_link_text(filename)
response = requests.get(elem.get_attribute("href"))
self.assertEqual(response.status_code, 200)
with open(filepath, "rb") as infile:
data = infile.read()
self.assertEqual(data, response.content)
# Delete the blob.
elem = self.driver.find_element_by_xpath("//button[@type='submit' and contains(., 'Delete')]")
elem.click()
time.sleep(1)
Alert(self.driver).accept()
time.sleep(1)
# Page listing the account's blobs.
elem = self.driver.find_element_by_id("blobs")
elems = elem.find_elements_by_xpath("//tbody/tr")
self.assertEqual(count, len(elems))
def test_3_update_blob(self):
"Login and create a blob by upload, and then update it."
# Login page; fill in user name and password in form.
self.driver.get(self.settings["BASE_URL"] + "user/login")
self.assertIn("Login", self.driver.title)
elem = self.driver.find_element_by_name("username")
elem.clear()
elem.send_keys(self.settings["USERNAME"])
elem = self.driver.find_element_by_name("password")
elem.clear()
elem.send_keys(self.settings["PASSWORD"])
elem = self.driver.find_element_by_id("login-form")
elem.send_keys(Keys.RETURN)
# Menu item "My blobs" is visible only when logged in.
elem = self.driver.find_element_by_link_text("My blobs")
elem.click()
# Page listing the account's blobs.
elem = self.driver.find_element_by_id("blobs")
elems = elem.find_elements_by_xpath("//tbody/tr")
count = len(elems)
# Go to upload page.
elem = self.driver.find_element_by_link_text("Upload")
elem.click()
# Fill in form.
elem = self.driver.find_element_by_name("description")
elem.clear()
description = "A test blob: this Python file."
elem.send_keys(description)
elem = self.driver.find_element_by_name("file")
elem.clear()
filename = os.path.basename(__file__)
filepath = os.path.join(os.getcwd(), filename)
elem.send_keys(filepath)
elem = self.driver.find_element_by_xpath("//button[@type='submit']")
elem.click()
time.sleep(1)
# Find href for blob data, get and compare to source file.
elem = self.driver.find_element_by_link_text(filename)
response = requests.get(elem.get_attribute("href"))
self.assertEqual(response.status_code, 200)
with open(filepath, "rb") as infile:
data = infile.read()
self.assertEqual(data, response.content)
# Update the blob information via the form on the update page.
elem = self.driver.find_element_by_link_text("Update")
elem.click()
elem = self.driver.find_element_by_name("description")
elem.clear()
description = "An updated test blob."
elem.send_keys(description)
elem = self.driver.find_element_by_xpath("//button[@type='submit']")
elem.click()
time.sleep(1)
# Check the new description.
elem = self.driver.find_element_by_xpath("//div[text()='Description']/following-sibling::div/p")
self.assertEqual(elem.text, description)
# Delete the blob.
elem = self.driver.find_element_by_xpath("//button[@type='submit' and contains(., 'Delete')]")
elem.click()
time.sleep(1)
Alert(self.driver).accept()
time.sleep(1)
# Page listing the account's blobs.
elem = self.driver.find_element_by_id("blobs")
elems = elem.find_elements_by_xpath("//tbody/tr")
self.assertEqual(count, len(elems))
if __name__ == "__main__":
unittest.main()
| 37.943005
| 104
| 0.645637
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0
| 7
|
e5e371fb1883c8aff6a97109b72d83745fa250fe
| 158
|
py
|
Python
|
agents/__init__.py
|
tango4j/Continual-Learning-Benchmark
|
89eb1396c294feebdba818d67707c923667391ad
|
[
"MIT"
] | null | null | null |
agents/__init__.py
|
tango4j/Continual-Learning-Benchmark
|
89eb1396c294feebdba818d67707c923667391ad
|
[
"MIT"
] | null | null | null |
agents/__init__.py
|
tango4j/Continual-Learning-Benchmark
|
89eb1396c294feebdba818d67707c923667391ad
|
[
"MIT"
] | null | null | null |
from . import default
from . import regularization
from . import customization
from . import exp_replay
from . import mem_replay
from . import model_gen_nets
| 22.571429
| 28
| 0.810127
| 22
| 158
| 5.636364
| 0.5
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| 6
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0
| 7
|
f919849136bd0622d6caae8f8f5b2f302f120c17
| 89,216
|
py
|
Python
|
py-proio/proio/model/lcio/lcio_pb2.py
|
chekanov/proio
|
7de5ef21b9e2d5b22c80fad5c0dca108e8750578
|
[
"BSD-3-Clause"
] | null | null | null |
py-proio/proio/model/lcio/lcio_pb2.py
|
chekanov/proio
|
7de5ef21b9e2d5b22c80fad5c0dca108e8750578
|
[
"BSD-3-Clause"
] | null | null | null |
py-proio/proio/model/lcio/lcio_pb2.py
|
chekanov/proio
|
7de5ef21b9e2d5b22c80fad5c0dca108e8750578
|
[
"BSD-3-Clause"
] | null | null | null |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: proio/model/lcio.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='proio/model/lcio.proto',
package='proio.model.lcio',
syntax='proto3',
serialized_pb=_b('\n\x16proio/model/lcio.proto\x12\x10proio.model.lcio\"\x1a\n\tIntParams\x12\r\n\x05\x61rray\x18\x01 \x03(\x05\"\x1c\n\x0b\x46loatParams\x12\r\n\x05\x61rray\x18\x01 \x03(\x02\"\x1d\n\x0cStringParams\x12\r\n\x05\x61rray\x18\x01 \x03(\t\"\x90\x03\n\x06Params\x12\x30\n\x04ints\x18\x01 \x03(\x0b\x32\".proio.model.lcio.Params.IntsEntry\x12\x34\n\x06\x66loats\x18\x02 \x03(\x0b\x32$.proio.model.lcio.Params.FloatsEntry\x12\x36\n\x07strings\x18\x03 \x03(\x0b\x32%.proio.model.lcio.Params.StringsEntry\x1aH\n\tIntsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12*\n\x05value\x18\x02 \x01(\x0b\x32\x1b.proio.model.lcio.IntParams:\x02\x38\x01\x1aL\n\x0b\x46loatsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12,\n\x05value\x18\x02 \x01(\x0b\x32\x1d.proio.model.lcio.FloatParams:\x02\x38\x01\x1aN\n\x0cStringsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12-\n\x05value\x18\x02 \x01(\x0b\x32\x1e.proio.model.lcio.StringParams:\x02\x38\x01\"\\\n\nParticleID\x12\x12\n\nlikelihood\x18\x01 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_INTPARAMS = _descriptor.Descriptor(
name='IntParams',
full_name='proio.model.lcio.IntParams',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='array', full_name='proio.model.lcio.IntParams.array', index=0,
number=1, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=44,
serialized_end=70,
)
_FLOATPARAMS = _descriptor.Descriptor(
name='FloatParams',
full_name='proio.model.lcio.FloatParams',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='array', full_name='proio.model.lcio.FloatParams.array', index=0,
number=1, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=72,
serialized_end=100,
)
_STRINGPARAMS = _descriptor.Descriptor(
name='StringParams',
full_name='proio.model.lcio.StringParams',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='array', full_name='proio.model.lcio.StringParams.array', index=0,
number=1, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=102,
serialized_end=131,
)
_PARAMS_INTSENTRY = _descriptor.Descriptor(
name='IntsEntry',
full_name='proio.model.lcio.Params.IntsEntry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='key', full_name='proio.model.lcio.Params.IntsEntry.key', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='value', full_name='proio.model.lcio.Params.IntsEntry.value', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=304,
serialized_end=376,
)
_PARAMS_FLOATSENTRY = _descriptor.Descriptor(
name='FloatsEntry',
full_name='proio.model.lcio.Params.FloatsEntry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='key', full_name='proio.model.lcio.Params.FloatsEntry.key', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='value', full_name='proio.model.lcio.Params.FloatsEntry.value', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=378,
serialized_end=454,
)
_PARAMS_STRINGSENTRY = _descriptor.Descriptor(
name='StringsEntry',
full_name='proio.model.lcio.Params.StringsEntry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='key', full_name='proio.model.lcio.Params.StringsEntry.key', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='value', full_name='proio.model.lcio.Params.StringsEntry.value', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=456,
serialized_end=534,
)
_PARAMS = _descriptor.Descriptor(
name='Params',
full_name='proio.model.lcio.Params',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='ints', full_name='proio.model.lcio.Params.ints', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='floats', full_name='proio.model.lcio.Params.floats', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='strings', full_name='proio.model.lcio.Params.strings', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[_PARAMS_INTSENTRY, _PARAMS_FLOATSENTRY, _PARAMS_STRINGSENTRY, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=134,
serialized_end=534,
)
_PARTICLEID = _descriptor.Descriptor(
name='ParticleID',
full_name='proio.model.lcio.ParticleID',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='likelihood', full_name='proio.model.lcio.ParticleID.likelihood', index=0,
number=1, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.ParticleID.type', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='PDG', full_name='proio.model.lcio.ParticleID.PDG', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='algType', full_name='proio.model.lcio.ParticleID.algType', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='params', full_name='proio.model.lcio.ParticleID.params', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=536,
serialized_end=628,
)
_MCPARTICLE = _descriptor.Descriptor(
name='MCParticle',
full_name='proio.model.lcio.MCParticle',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='parents', full_name='proio.model.lcio.MCParticle.parents', index=0,
number=1, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='children', full_name='proio.model.lcio.MCParticle.children', index=1,
number=2, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='PDG', full_name='proio.model.lcio.MCParticle.PDG', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='vertex', full_name='proio.model.lcio.MCParticle.vertex', index=3,
number=4, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.MCParticle.time', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='p', full_name='proio.model.lcio.MCParticle.p', index=5,
number=6, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='mass', full_name='proio.model.lcio.MCParticle.mass', index=6,
number=7, type=1, cpp_type=5, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='charge', full_name='proio.model.lcio.MCParticle.charge', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='PEndPoint', full_name='proio.model.lcio.MCParticle.PEndPoint', index=8,
number=9, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='spin', full_name='proio.model.lcio.MCParticle.spin', index=9,
number=10, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='colorFlow', full_name='proio.model.lcio.MCParticle.colorFlow', index=10,
number=11, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='genStatus', full_name='proio.model.lcio.MCParticle.genStatus', index=11,
number=12, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='simStatus', full_name='proio.model.lcio.MCParticle.simStatus', index=12,
number=13, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=631,
serialized_end=852,
)
_SIMTRACKERHIT = _descriptor.Descriptor(
name='SimTrackerHit',
full_name='proio.model.lcio.SimTrackerHit',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.SimTrackerHit.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.SimTrackerHit.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.SimTrackerHit.pos', index=2,
number=3, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='EDep', full_name='proio.model.lcio.SimTrackerHit.EDep', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.SimTrackerHit.time', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='mc', full_name='proio.model.lcio.SimTrackerHit.mc', index=5,
number=6, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='p', full_name='proio.model.lcio.SimTrackerHit.p', index=6,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pathLength', full_name='proio.model.lcio.SimTrackerHit.pathLength', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='quality', full_name='proio.model.lcio.SimTrackerHit.quality', index=8,
number=9, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=855,
serialized_end=1005,
)
_TRACKERRAWDATA = _descriptor.Descriptor(
name='TrackerRawData',
full_name='proio.model.lcio.TrackerRawData',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.TrackerRawData.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.TrackerRawData.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.TrackerRawData.time', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='ADCs', full_name='proio.model.lcio.TrackerRawData.ADCs', index=3,
number=4, type=13, cpp_type=3, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1007,
serialized_end=1085,
)
_TRACKERDATA = _descriptor.Descriptor(
name='TrackerData',
full_name='proio.model.lcio.TrackerData',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.TrackerData.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.TrackerData.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.TrackerData.time', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='charges', full_name='proio.model.lcio.TrackerData.charges', index=3,
number=4, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1087,
serialized_end=1165,
)
_TRACKERHIT = _descriptor.Descriptor(
name='TrackerHit',
full_name='proio.model.lcio.TrackerHit',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.TrackerHit.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.TrackerHit.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.TrackerHit.type', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.TrackerHit.pos', index=3,
number=4, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cov', full_name='proio.model.lcio.TrackerHit.cov', index=4,
number=5, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='EDep', full_name='proio.model.lcio.TrackerHit.EDep', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='EDepErr', full_name='proio.model.lcio.TrackerHit.EDepErr', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.TrackerHit.time', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='quality', full_name='proio.model.lcio.TrackerHit.quality', index=8,
number=9, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='rawHits', full_name='proio.model.lcio.TrackerHit.rawHits', index=9,
number=10, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1168,
serialized_end=1333,
)
_TRACKERPULSE = _descriptor.Descriptor(
name='TrackerPulse',
full_name='proio.model.lcio.TrackerPulse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.TrackerPulse.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.TrackerPulse.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.TrackerPulse.time', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='charge', full_name='proio.model.lcio.TrackerPulse.charge', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cov', full_name='proio.model.lcio.TrackerPulse.cov', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='quality', full_name='proio.model.lcio.TrackerPulse.quality', index=5,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='TPC', full_name='proio.model.lcio.TrackerPulse.TPC', index=6,
number=7, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1335,
serialized_end=1456,
)
_TRACKERHITPLANE = _descriptor.Descriptor(
name='TrackerHitPlane',
full_name='proio.model.lcio.TrackerHitPlane',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.TrackerHitPlane.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.TrackerHitPlane.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.TrackerHitPlane.type', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.TrackerHitPlane.pos', index=3,
number=4, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='U', full_name='proio.model.lcio.TrackerHitPlane.U', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='V', full_name='proio.model.lcio.TrackerHitPlane.V', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dU', full_name='proio.model.lcio.TrackerHitPlane.dU', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dV', full_name='proio.model.lcio.TrackerHitPlane.dV', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='EDep', full_name='proio.model.lcio.TrackerHitPlane.EDep', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='EDepErr', full_name='proio.model.lcio.TrackerHitPlane.EDepErr', index=9,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.TrackerHitPlane.time', index=10,
number=11, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='quality', full_name='proio.model.lcio.TrackerHitPlane.quality', index=11,
number=12, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='rawHits', full_name='proio.model.lcio.TrackerHitPlane.rawHits', index=12,
number=13, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1459,
serialized_end=1662,
)
_TRACKERHITZCYLINDER = _descriptor.Descriptor(
name='TrackerHitZCylinder',
full_name='proio.model.lcio.TrackerHitZCylinder',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.TrackerHitZCylinder.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.TrackerHitZCylinder.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.TrackerHitZCylinder.type', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.TrackerHitZCylinder.pos', index=3,
number=4, type=1, cpp_type=5, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='center', full_name='proio.model.lcio.TrackerHitZCylinder.center', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dRPhi', full_name='proio.model.lcio.TrackerHitZCylinder.dRPhi', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dZ', full_name='proio.model.lcio.TrackerHitZCylinder.dZ', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='EDep', full_name='proio.model.lcio.TrackerHitZCylinder.EDep', index=7,
number=8, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='EDepErr', full_name='proio.model.lcio.TrackerHitZCylinder.EDepErr', index=8,
number=9, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.TrackerHitZCylinder.time', index=9,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='quality', full_name='proio.model.lcio.TrackerHitZCylinder.quality', index=10,
number=11, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='rawHits', full_name='proio.model.lcio.TrackerHitZCylinder.rawHits', index=11,
number=12, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1665,
serialized_end=1869,
)
_TRACK_TRACKSTATE = _descriptor.Descriptor(
name='TrackState',
full_name='proio.model.lcio.Track.TrackState',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='loc', full_name='proio.model.lcio.Track.TrackState.loc', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='d0', full_name='proio.model.lcio.Track.TrackState.d0', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='phi', full_name='proio.model.lcio.Track.TrackState.phi', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='omega', full_name='proio.model.lcio.Track.TrackState.omega', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='z0', full_name='proio.model.lcio.Track.TrackState.z0', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='tanL', full_name='proio.model.lcio.Track.TrackState.tanL', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cov', full_name='proio.model.lcio.Track.TrackState.cov', index=6,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='ref', full_name='proio.model.lcio.Track.TrackState.ref', index=7,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2071,
serialized_end=2188,
)
_TRACK = _descriptor.Descriptor(
name='Track',
full_name='proio.model.lcio.Track',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.Track.type', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='chi2', full_name='proio.model.lcio.Track.chi2', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='NDF', full_name='proio.model.lcio.Track.NDF', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dEdx', full_name='proio.model.lcio.Track.dEdx', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dEdxErr', full_name='proio.model.lcio.Track.dEdxErr', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='radius', full_name='proio.model.lcio.Track.radius', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='subDetHits', full_name='proio.model.lcio.Track.subDetHits', index=6,
number=7, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='states', full_name='proio.model.lcio.Track.states', index=7,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='hits', full_name='proio.model.lcio.Track.hits', index=8,
number=9, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='tracks', full_name='proio.model.lcio.Track.tracks', index=9,
number=10, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[_TRACK_TRACKSTATE, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1872,
serialized_end=2188,
)
_SIMCALORIMETERHIT_CONTRIB = _descriptor.Descriptor(
name='Contrib',
full_name='proio.model.lcio.SimCalorimeterHit.Contrib',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='MCParticle', full_name='proio.model.lcio.SimCalorimeterHit.Contrib.MCParticle', index=0,
number=1, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='energy', full_name='proio.model.lcio.SimCalorimeterHit.Contrib.energy', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.SimCalorimeterHit.Contrib.time', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='PDG', full_name='proio.model.lcio.SimCalorimeterHit.Contrib.PDG', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='stepPos', full_name='proio.model.lcio.SimCalorimeterHit.Contrib.stepPos', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2343,
serialized_end=2432,
)
_SIMCALORIMETERHIT = _descriptor.Descriptor(
name='SimCalorimeterHit',
full_name='proio.model.lcio.SimCalorimeterHit',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.SimCalorimeterHit.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.SimCalorimeterHit.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='energy', full_name='proio.model.lcio.SimCalorimeterHit.energy', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.SimCalorimeterHit.pos', index=3,
number=4, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='contributions', full_name='proio.model.lcio.SimCalorimeterHit.contributions', index=4,
number=5, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[_SIMCALORIMETERHIT_CONTRIB, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2191,
serialized_end=2432,
)
_RAWCALORIMETERHIT = _descriptor.Descriptor(
name='RawCalorimeterHit',
full_name='proio.model.lcio.RawCalorimeterHit',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.RawCalorimeterHit.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.RawCalorimeterHit.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='amplitude', full_name='proio.model.lcio.RawCalorimeterHit.amplitude', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='timeStamp', full_name='proio.model.lcio.RawCalorimeterHit.timeStamp', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2434,
serialized_end=2525,
)
_CALORIMETERHIT = _descriptor.Descriptor(
name='CalorimeterHit',
full_name='proio.model.lcio.CalorimeterHit',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='cellID0', full_name='proio.model.lcio.CalorimeterHit.cellID0', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cellID1', full_name='proio.model.lcio.CalorimeterHit.cellID1', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='energy', full_name='proio.model.lcio.CalorimeterHit.energy', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='energyErr', full_name='proio.model.lcio.CalorimeterHit.energyErr', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='time', full_name='proio.model.lcio.CalorimeterHit.time', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.CalorimeterHit.pos', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.CalorimeterHit.type', index=6,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='raw', full_name='proio.model.lcio.CalorimeterHit.raw', index=7,
number=8, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2528,
serialized_end=2667,
)
_CLUSTER = _descriptor.Descriptor(
name='Cluster',
full_name='proio.model.lcio.Cluster',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.Cluster.type', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='energy', full_name='proio.model.lcio.Cluster.energy', index=1,
number=2, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='energyErr', full_name='proio.model.lcio.Cluster.energyErr', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.Cluster.pos', index=3,
number=4, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='posErr', full_name='proio.model.lcio.Cluster.posErr', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='theta', full_name='proio.model.lcio.Cluster.theta', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='phi', full_name='proio.model.lcio.Cluster.phi', index=6,
number=7, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dirErr', full_name='proio.model.lcio.Cluster.dirErr', index=7,
number=8, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='shape', full_name='proio.model.lcio.Cluster.shape', index=8,
number=9, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='PIDs', full_name='proio.model.lcio.Cluster.PIDs', index=9,
number=10, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='clusters', full_name='proio.model.lcio.Cluster.clusters', index=10,
number=11, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='hits', full_name='proio.model.lcio.Cluster.hits', index=11,
number=12, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='weights', full_name='proio.model.lcio.Cluster.weights', index=12,
number=13, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='subDetEnes', full_name='proio.model.lcio.Cluster.subDetEnes', index=13,
number=14, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2670,
serialized_end=2929,
)
_RECPARTICLE = _descriptor.Descriptor(
name='RecParticle',
full_name='proio.model.lcio.RecParticle',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='proio.model.lcio.RecParticle.type', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='p', full_name='proio.model.lcio.RecParticle.p', index=1,
number=2, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='energy', full_name='proio.model.lcio.RecParticle.energy', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cov', full_name='proio.model.lcio.RecParticle.cov', index=3,
number=4, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='mass', full_name='proio.model.lcio.RecParticle.mass', index=4,
number=5, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='charge', full_name='proio.model.lcio.RecParticle.charge', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='ref', full_name='proio.model.lcio.RecParticle.ref', index=6,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='PIDs', full_name='proio.model.lcio.RecParticle.PIDs', index=7,
number=8, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='PIDUsed', full_name='proio.model.lcio.RecParticle.PIDUsed', index=8,
number=9, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='goodnessOfPID', full_name='proio.model.lcio.RecParticle.goodnessOfPID', index=9,
number=10, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='recs', full_name='proio.model.lcio.RecParticle.recs', index=10,
number=11, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='tracks', full_name='proio.model.lcio.RecParticle.tracks', index=11,
number=12, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='clusters', full_name='proio.model.lcio.RecParticle.clusters', index=12,
number=13, type=4, cpp_type=4, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='startVtx', full_name='proio.model.lcio.RecParticle.startVtx', index=13,
number=14, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=2932,
serialized_end=3192,
)
_VERTEX = _descriptor.Descriptor(
name='Vertex',
full_name='proio.model.lcio.Vertex',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='primary', full_name='proio.model.lcio.Vertex.primary', index=0,
number=1, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='algType', full_name='proio.model.lcio.Vertex.algType', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='chi2', full_name='proio.model.lcio.Vertex.chi2', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='prob', full_name='proio.model.lcio.Vertex.prob', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pos', full_name='proio.model.lcio.Vertex.pos', index=4,
number=5, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='cov', full_name='proio.model.lcio.Vertex.cov', index=5,
number=6, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='params', full_name='proio.model.lcio.Vertex.params', index=6,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='recPart', full_name='proio.model.lcio.Vertex.recPart', index=7,
number=8, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=3195,
serialized_end=3324,
)
_RELATION = _descriptor.Descriptor(
name='Relation',
full_name='proio.model.lcio.Relation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='from', full_name='proio.model.lcio.Relation.from', index=0,
number=1, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='to', full_name='proio.model.lcio.Relation.to', index=1,
number=2, type=4, cpp_type=4, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='weight', full_name='proio.model.lcio.Relation.weight', index=2,
number=3, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=3326,
serialized_end=3378,
)
_PARAMS_INTSENTRY.fields_by_name['value'].message_type = _INTPARAMS
_PARAMS_INTSENTRY.containing_type = _PARAMS
_PARAMS_FLOATSENTRY.fields_by_name['value'].message_type = _FLOATPARAMS
_PARAMS_FLOATSENTRY.containing_type = _PARAMS
_PARAMS_STRINGSENTRY.fields_by_name['value'].message_type = _STRINGPARAMS
_PARAMS_STRINGSENTRY.containing_type = _PARAMS
_PARAMS.fields_by_name['ints'].message_type = _PARAMS_INTSENTRY
_PARAMS.fields_by_name['floats'].message_type = _PARAMS_FLOATSENTRY
_PARAMS.fields_by_name['strings'].message_type = _PARAMS_STRINGSENTRY
_TRACK_TRACKSTATE.containing_type = _TRACK
_TRACK.fields_by_name['states'].message_type = _TRACK_TRACKSTATE
_SIMCALORIMETERHIT_CONTRIB.containing_type = _SIMCALORIMETERHIT
_SIMCALORIMETERHIT.fields_by_name['contributions'].message_type = _SIMCALORIMETERHIT_CONTRIB
_CLUSTER.fields_by_name['PIDs'].message_type = _PARTICLEID
_RECPARTICLE.fields_by_name['PIDs'].message_type = _PARTICLEID
DESCRIPTOR.message_types_by_name['IntParams'] = _INTPARAMS
DESCRIPTOR.message_types_by_name['FloatParams'] = _FLOATPARAMS
DESCRIPTOR.message_types_by_name['StringParams'] = _STRINGPARAMS
DESCRIPTOR.message_types_by_name['Params'] = _PARAMS
DESCRIPTOR.message_types_by_name['ParticleID'] = _PARTICLEID
DESCRIPTOR.message_types_by_name['MCParticle'] = _MCPARTICLE
DESCRIPTOR.message_types_by_name['SimTrackerHit'] = _SIMTRACKERHIT
DESCRIPTOR.message_types_by_name['TrackerRawData'] = _TRACKERRAWDATA
DESCRIPTOR.message_types_by_name['TrackerData'] = _TRACKERDATA
DESCRIPTOR.message_types_by_name['TrackerHit'] = _TRACKERHIT
DESCRIPTOR.message_types_by_name['TrackerPulse'] = _TRACKERPULSE
DESCRIPTOR.message_types_by_name['TrackerHitPlane'] = _TRACKERHITPLANE
DESCRIPTOR.message_types_by_name['TrackerHitZCylinder'] = _TRACKERHITZCYLINDER
DESCRIPTOR.message_types_by_name['Track'] = _TRACK
DESCRIPTOR.message_types_by_name['SimCalorimeterHit'] = _SIMCALORIMETERHIT
DESCRIPTOR.message_types_by_name['RawCalorimeterHit'] = _RAWCALORIMETERHIT
DESCRIPTOR.message_types_by_name['CalorimeterHit'] = _CALORIMETERHIT
DESCRIPTOR.message_types_by_name['Cluster'] = _CLUSTER
DESCRIPTOR.message_types_by_name['RecParticle'] = _RECPARTICLE
DESCRIPTOR.message_types_by_name['Vertex'] = _VERTEX
DESCRIPTOR.message_types_by_name['Relation'] = _RELATION
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
IntParams = _reflection.GeneratedProtocolMessageType('IntParams', (_message.Message,), dict(
DESCRIPTOR = _INTPARAMS,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.IntParams)
))
_sym_db.RegisterMessage(IntParams)
FloatParams = _reflection.GeneratedProtocolMessageType('FloatParams', (_message.Message,), dict(
DESCRIPTOR = _FLOATPARAMS,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.FloatParams)
))
_sym_db.RegisterMessage(FloatParams)
StringParams = _reflection.GeneratedProtocolMessageType('StringParams', (_message.Message,), dict(
DESCRIPTOR = _STRINGPARAMS,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.StringParams)
))
_sym_db.RegisterMessage(StringParams)
Params = _reflection.GeneratedProtocolMessageType('Params', (_message.Message,), dict(
IntsEntry = _reflection.GeneratedProtocolMessageType('IntsEntry', (_message.Message,), dict(
DESCRIPTOR = _PARAMS_INTSENTRY,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Params.IntsEntry)
))
,
FloatsEntry = _reflection.GeneratedProtocolMessageType('FloatsEntry', (_message.Message,), dict(
DESCRIPTOR = _PARAMS_FLOATSENTRY,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Params.FloatsEntry)
))
,
StringsEntry = _reflection.GeneratedProtocolMessageType('StringsEntry', (_message.Message,), dict(
DESCRIPTOR = _PARAMS_STRINGSENTRY,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Params.StringsEntry)
))
,
DESCRIPTOR = _PARAMS,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Params)
))
_sym_db.RegisterMessage(Params)
_sym_db.RegisterMessage(Params.IntsEntry)
_sym_db.RegisterMessage(Params.FloatsEntry)
_sym_db.RegisterMessage(Params.StringsEntry)
ParticleID = _reflection.GeneratedProtocolMessageType('ParticleID', (_message.Message,), dict(
DESCRIPTOR = _PARTICLEID,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.ParticleID)
))
_sym_db.RegisterMessage(ParticleID)
MCParticle = _reflection.GeneratedProtocolMessageType('MCParticle', (_message.Message,), dict(
DESCRIPTOR = _MCPARTICLE,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.MCParticle)
))
_sym_db.RegisterMessage(MCParticle)
SimTrackerHit = _reflection.GeneratedProtocolMessageType('SimTrackerHit', (_message.Message,), dict(
DESCRIPTOR = _SIMTRACKERHIT,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.SimTrackerHit)
))
_sym_db.RegisterMessage(SimTrackerHit)
TrackerRawData = _reflection.GeneratedProtocolMessageType('TrackerRawData', (_message.Message,), dict(
DESCRIPTOR = _TRACKERRAWDATA,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.TrackerRawData)
))
_sym_db.RegisterMessage(TrackerRawData)
TrackerData = _reflection.GeneratedProtocolMessageType('TrackerData', (_message.Message,), dict(
DESCRIPTOR = _TRACKERDATA,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.TrackerData)
))
_sym_db.RegisterMessage(TrackerData)
TrackerHit = _reflection.GeneratedProtocolMessageType('TrackerHit', (_message.Message,), dict(
DESCRIPTOR = _TRACKERHIT,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.TrackerHit)
))
_sym_db.RegisterMessage(TrackerHit)
TrackerPulse = _reflection.GeneratedProtocolMessageType('TrackerPulse', (_message.Message,), dict(
DESCRIPTOR = _TRACKERPULSE,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.TrackerPulse)
))
_sym_db.RegisterMessage(TrackerPulse)
TrackerHitPlane = _reflection.GeneratedProtocolMessageType('TrackerHitPlane', (_message.Message,), dict(
DESCRIPTOR = _TRACKERHITPLANE,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.TrackerHitPlane)
))
_sym_db.RegisterMessage(TrackerHitPlane)
TrackerHitZCylinder = _reflection.GeneratedProtocolMessageType('TrackerHitZCylinder', (_message.Message,), dict(
DESCRIPTOR = _TRACKERHITZCYLINDER,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.TrackerHitZCylinder)
))
_sym_db.RegisterMessage(TrackerHitZCylinder)
Track = _reflection.GeneratedProtocolMessageType('Track', (_message.Message,), dict(
TrackState = _reflection.GeneratedProtocolMessageType('TrackState', (_message.Message,), dict(
DESCRIPTOR = _TRACK_TRACKSTATE,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Track.TrackState)
))
,
DESCRIPTOR = _TRACK,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Track)
))
_sym_db.RegisterMessage(Track)
_sym_db.RegisterMessage(Track.TrackState)
SimCalorimeterHit = _reflection.GeneratedProtocolMessageType('SimCalorimeterHit', (_message.Message,), dict(
Contrib = _reflection.GeneratedProtocolMessageType('Contrib', (_message.Message,), dict(
DESCRIPTOR = _SIMCALORIMETERHIT_CONTRIB,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.SimCalorimeterHit.Contrib)
))
,
DESCRIPTOR = _SIMCALORIMETERHIT,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.SimCalorimeterHit)
))
_sym_db.RegisterMessage(SimCalorimeterHit)
_sym_db.RegisterMessage(SimCalorimeterHit.Contrib)
RawCalorimeterHit = _reflection.GeneratedProtocolMessageType('RawCalorimeterHit', (_message.Message,), dict(
DESCRIPTOR = _RAWCALORIMETERHIT,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.RawCalorimeterHit)
))
_sym_db.RegisterMessage(RawCalorimeterHit)
CalorimeterHit = _reflection.GeneratedProtocolMessageType('CalorimeterHit', (_message.Message,), dict(
DESCRIPTOR = _CALORIMETERHIT,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.CalorimeterHit)
))
_sym_db.RegisterMessage(CalorimeterHit)
Cluster = _reflection.GeneratedProtocolMessageType('Cluster', (_message.Message,), dict(
DESCRIPTOR = _CLUSTER,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Cluster)
))
_sym_db.RegisterMessage(Cluster)
RecParticle = _reflection.GeneratedProtocolMessageType('RecParticle', (_message.Message,), dict(
DESCRIPTOR = _RECPARTICLE,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.RecParticle)
))
_sym_db.RegisterMessage(RecParticle)
Vertex = _reflection.GeneratedProtocolMessageType('Vertex', (_message.Message,), dict(
DESCRIPTOR = _VERTEX,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Vertex)
))
_sym_db.RegisterMessage(Vertex)
Relation = _reflection.GeneratedProtocolMessageType('Relation', (_message.Message,), dict(
DESCRIPTOR = _RELATION,
__module__ = 'proio.model.lcio_pb2'
# @@protoc_insertion_point(class_scope:proio.model.lcio.Relation)
))
_sym_db.RegisterMessage(Relation)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\n\013proio.modelB\004LcioZ2github.com/decibelcooper/proio/go-proio/model/lcio'))
_PARAMS_INTSENTRY.has_options = True
_PARAMS_INTSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
_PARAMS_FLOATSENTRY.has_options = True
_PARAMS_FLOATSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
_PARAMS_STRINGSENTRY.has_options = True
_PARAMS_STRINGSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
# @@protoc_insertion_point(module_scope)
| 43.371901
| 7,682
| 0.725755
| 11,944
| 89,216
| 5.194826
| 0.03642
| 0.068335
| 0.058665
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| 0.846955
| 0.815043
| 0.750431
| 0.726465
| 0.721163
| 0.71578
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| 0.049387
| 0.138697
| 89,216
| 2,056
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| 43.392996
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| 0.744593
| 1
| 0.00103
| 0.195076
| 0.164344
| 0
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| 1
| 0
| false
| 0
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| 0.00309
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| null | 0
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0
| 9
|
da9fcf335021d98e14468118101b39c6ae001d68
| 21,570
|
py
|
Python
|
code/object/Surface.py
|
goldleaf3i/declutter-reconstruct
|
954b755a1eced34af50d31ee2e3938a0b751cc4d
|
[
"MIT"
] | 2
|
2022-03-14T07:37:45.000Z
|
2022-03-25T09:01:48.000Z
|
code/object/Surface.py
|
goldleaf3i/declutter-reconstruct
|
954b755a1eced34af50d31ee2e3938a0b751cc4d
|
[
"MIT"
] | 1
|
2022-03-24T07:34:16.000Z
|
2022-03-27T11:20:27.000Z
|
code/object/Surface.py
|
goldleaf3i/declutter-reconstruct
|
954b755a1eced34af50d31ee2e3938a0b751cc4d
|
[
"MIT"
] | 1
|
2022-03-08T05:37:22.000Z
|
2022-03-08T05:37:22.000Z
|
# Una superficie e' un poligono atomico che non puo' essere scomposto nelle sue parti.
# L'unione di piu' superfici forma uno spazio.
from __future__ import division
import object.Segment as sg
# oggetto di tipo Faccia, ha come attributo una lista di edges che formano un poligono chiuso.
# La dimensione della lista e' >3.
class Cell(object):
def __init__(self, edges):
self.borders = edges
self.out = None
self.partial = None
def set_out(self, o):
self.out = o
def set_partial(self, p):
self.partial = p
def create_cells(edges):
# create faces as closed chain of adjacent edges
faces = []
face = create_3_faces(faces, edges)
while face:
face = create_3_faces(faces, edges)
face = create_4_faces(faces, edges)
while face:
face = create_4_faces(faces, edges)
face = create_5_faces(faces, edges)
while face:
face = create_5_faces(faces, edges)
face = create_6_faces(faces, edges)
while face:
face = create_6_faces(faces, edges)
face = create_7_faces(faces, edges)
while face:
face = create_7_faces(faces, edges)
return faces
def create_3_faces(faces, edges):
# if find a faces of 3 side not already present in faces, create it and add it to faces and return True.
boards = []
remaining_edges = []
# list of all edges associated to less than 2 faces, still good to create a face.
for edge in edges:
if edge.num_faces < 2:
remaining_edges.append(edge)
for edge in remaining_edges:
x1 = edge.x1
y1 = edge.y1
x2 = edge.x2
y2 = edge.y2
common_x_edge = x1
common_y_edge = y1
adjacent = sg.adjacent_edges(common_x_edge, common_y_edge, edge, remaining_edges)
# edges that have as extreme point x1,y1 and m different from edge
for a in adjacent:
x3 = a.x1
y3 = a.y1
x4 = a.x2
y4 = a.y2
# if there is already face with these 2 side stop and search another adjacent.
if face_exist(edge, a, faces):
break
# check edge that have as extreme the one different from previous one
if (x3 == common_x_edge) and (y3 == common_y_edge):
common_x_a = x4
common_y_a = y4
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
else:
common_x_a = x3
common_y_a = y3
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
for b in adjacent2:
x5 = b.x1
y5 = b.y1
x6 = b.x2
y6 = b.y2
if (x5 == common_x_a) and (y5 == common_y_a):
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x6 == x2) and (y6 == y2):
boards.extend((edge, a, b))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
else:
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x5 == x2) and (y5 == y2):
boards.extend((edge, a, b))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
return False
def create_4_faces(faces, edges):
# if find a faces of 4 side not already present in faces, create it and add it to faces and return True.
boards = []
remaining_edges = []
# list of all edges associated to less than 2 faces, still good to create a face.
for edge in edges:
if edge.num_faces < 2:
remaining_edges.append(edge)
for edge in remaining_edges:
x1 = edge.x1
y1 = edge.y1
x2 = edge.x2
y2 = edge.y2
common_x_edge = x1
common_y_edge = y1
adjacent = sg.adjacent_edges(common_x_edge, common_y_edge, edge, remaining_edges)
# edges that have as extreme point x1,y1 and m different from edge
for a in adjacent:
x3 = a.x1
y3 = a.y1
x4 = a.x2
y4 = a.y2
# check edge that have as extreme the one different from previous one
if face_exist(edge, a, faces):
break
if (x3 == common_x_edge) and (y3 == common_y_edge):
common_x_a = x4
common_y_a = y4
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
else:
common_x_a = x3
common_y_a = y3
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
for b in adjacent2:
x5 = b.x1
y5 = b.y1
x6 = b.x2
y6 = b.y2
if (x5 == common_x_a) and (y5 == common_y_a):
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x6 == x2) and (y6 == y2):
adjacent3 = []
# choose another b of previous for loop
# if a face of 3 side is not closed.
else:
common_x_b = x6
common_y_b = y6
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
else:
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x5 == x2) and (y5 == y2):
adjacent3 = []
# if a face of 3 side is not closed.
else:
common_x_b = x5
common_y_b = y5
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
for c in adjacent3:
x7 = c.x1
y7 = c.y1
x8 = c.x2
y8 = c.y2
if (x7 == common_x_b) and (y7 == common_y_b):
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x8 == x2) and (y8 == y2):
boards.extend((edge, a, b, c))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
else:
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x7 == x2) and (y7 == y2):
boards.extend((edge, a, b, c))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
return False
def create_5_faces(faces, edges):
# if find a faces of 5 side not already present in faces, create it and add it to faces and return True.
boards = []
remaining_edges = []
# list of all edges associated to less than 2 faces, still good to create a face.
for edge in edges:
if edge.num_faces < 2:
remaining_edges.append(edge)
for edge in remaining_edges:
x1 = edge.x1
y1 = edge.y1
x2 = edge.x2
y2 = edge.y2
common_x_edge = x1
common_y_edge = y1
adjacent = sg.adjacent_edges(common_x_edge, common_y_edge, edge, remaining_edges)
# edges that have as extreme point x1,y1 and m different from edge
for a in adjacent:
x3 = a.x1
y3 = a.y1
x4 = a.x2
y4 = a.y2
# check edge that have as extreme the one different from previous one
if face_exist(edge, a, faces):
break
if (x3 == common_x_edge) and (y3 == common_y_edge):
common_x_a = x4
common_y_a = y4
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
else:
common_x_a = x3
common_y_a = y3
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
for b in adjacent2:
x5 = b.x1
y5 = b.y1
x6 = b.x2
y6 = b.y2
if (x5 == common_x_a) and (y5 == common_y_a):
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x6 == x2) and (y6 == y2):
adjacent3 = []
# choose another b of previous for loop
# if a face of 3 side is not closed.
else:
common_x_b = x6
common_y_b = y6
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
else:
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x5 == x2) and (y5 == y2):
adjacent3 = []
# if a face of 3 side is not closed.
else:
common_x_b = x5
common_y_b = y5
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
for c in adjacent3:
x7 = c.x1
y7 = c.y1
x8 = c.x2
y8 = c.y2
if (x7 == common_x_b) and (y7 == common_y_b):
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x8 == x2) and (y8 == y2):
adjacent4 = []
# if a face of 4 side is not closed.
else:
common_x_c = x8
common_y_c = y8
adjacent4 = sg.adjacent_edges(common_x_c, common_y_c, c, remaining_edges)
else:
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x7 == x2) and (y7 == y2):
adjacent4 = []
# if a face of 4 side is not closed.
else:
common_x_c = x7
common_y_c = y7
adjacent4 = sg.adjacent_edges(common_x_c, common_y_c, c, remaining_edges)
for d in adjacent4:
x9 = d.x1
y9 = d.y1
x10 = d.x2
y10 = d.y2
if (x9 == common_x_c) and (y9 == common_y_c):
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x10 == x2) and (y10 == y2) and (d != edge):
boards.extend((edge,a,b,c,d))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
d.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
else:
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x9 == x2) and (y9 == y2) and (d != edge):
boards.extend((edge, a, b, c, d))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
d.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
return False
def create_6_faces(faces, edges):
# if find a faces of 5 side not already present in faces, create it and add it to faces and return True.
boards = []
remaining_edges = []
# list of all edges associated to less than 2 faces, still good to create a face.
for edge in edges:
if edge.num_faces < 2:
remaining_edges.append(edge)
for edge in remaining_edges:
x1 = edge.x1
y1 = edge.y1
x2 = edge.x2
y2 = edge.y2
common_x_edge = x1
common_y_edge = y1
adjacent = sg.adjacent_edges(common_x_edge, common_y_edge, edge, remaining_edges)
# edges that have as extreme point x1,y1 and m different from edge
for a in adjacent:
x3 = a.x1
y3 = a.y1
x4 = a.x2
y4 = a.y2
# check edge that have as extreme the one different from previous one
if face_exist(edge, a, faces):
break
if (x3 == common_x_edge) and (y3 == common_y_edge):
common_x_a = x4
common_y_a = y4
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
else:
common_x_a = x3
common_y_a = y3
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
for b in adjacent2:
x5 = b.x1
y5 = b.y1
x6 = b.x2
y6 = b.y2
if (x5 == common_x_a) and (y5 == common_y_a):
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x6==x2) and (y6==y2):
adjacent3 = []
# choose another b of previous for loop
# if a face of 3 side is not closed.
else:
common_x_b = x6
common_y_b = y6
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
else:
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x5 == x2) and (y5 == y2):
adjacent3 = []
# if a face of 3 side is not closed.
else:
common_x_b = x5
common_y_b = y5
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
for c in adjacent3:
x7 = c.x1
y7 = c.y1
x8 = c.x2
y8 = c.y2
if (x7 == common_x_b) and (y7 == common_y_b):
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x8 == x2) and (y8 == y2):
adjacent4 = []
# if a face of 4 side is not closed.
else:
common_x_c = x8
common_y_c = y8
adjacent4 = sg.adjacent_edges(common_x_c, common_y_c, c, remaining_edges)
else:
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x7 == x2) and (y7 == y2):
adjacent4 = []
# if a face of 4 side is not closed.
else:
common_x_c = x7
common_y_c = y7
adjacent4 = sg.adjacent_edges(common_x_c, common_y_c, c, remaining_edges)
for d in adjacent4:
x9 = d.x1
y9 = d.y1
x10 = d.x2
y10 = d.y2
if (x9 == common_x_c) and (y9 == common_y_c):
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x10 == x2) and (y10 == y2) and (d != edge):
adjacent5 = []
else:
common_x_d = x10
common_y_d = y10
adjacent5 = sg.adjacent_edges(common_x_d, common_y_d, d, remaining_edges)
else:
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x9 == x2) and (y9 == y2) and (d != edge):
adjacent5 = []
else:
common_x_d = x9
common_y_d = y9
adjacent5 = sg.adjacent_edges(common_x_d, common_y_d, d, remaining_edges)
for e in adjacent5:
x11 = e.x1
y11 = e.y1
x12 = e.x2
y12 = e.y2
if (x11 == common_x_d) and (y11 == common_y_d):
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x12 == x2) and (y12 == y2) and (e != edge):
boards.extend((edge,a,b,c,d,e))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
d.num_faces += 1
e.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
else:
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x11 == x2) and (y11 == y2) and (e != edge):
boards.extend((edge,a,b,c,d,e))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
d.num_faces += 1
e.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
return False
def create_7_faces(faces, edges):
# if find a faces of 5 side not already present in faces, create it and add it to faces and return True.
boards = []
remaining_edges = []
# list of all edges associated to less than 2 faces, still good to create a face.
for edge in edges:
if edge.num_faces < 2:
remaining_edges.append(edge)
for edge in remaining_edges:
x1 = edge.x1
y1 = edge.y1
x2 = edge.x2
y2 = edge.y2
common_x_edge = x1
common_y_edge = y1
adjacent = sg.adjacent_edges(common_x_edge, common_y_edge, edge, remaining_edges)
# edges that have as extreme point x1,y1 and m different from edge
for a in adjacent:
x3 = a.x1
y3 = a.y1
x4 = a.x2
y4 = a.y2
# check edge that have as extreme the one different from previous one
if face_exist(edge, a, faces):
break
if (x3 == common_x_edge) and (y3 == common_y_edge):
common_x_a = x4
common_y_a = y4
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
else:
common_x_a = x3
common_y_a = y3
adjacent2 = sg.adjacent_edges(common_x_a, common_y_a, a, remaining_edges)
for b in adjacent2:
x5 = b.x1
y5 = b.y1
x6 = b.x2
y6 = b.y2
if (x5 == common_x_a) and (y5 == common_y_a):
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x6==x2) and (y6==y2):
adjacent3 = []
# choose another b of previous for loop
# if a face of 3 side is not closed.
else:
common_x_b = x6
common_y_b = y6
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
else:
# it could be a face of 3 side, check if returned to coordinates of first edge.
if (x5 == x2) and (y5 == y2):
adjacent3 = []
# if a face of 3 side is not closed.
else:
common_x_b = x5
common_y_b = y5
adjacent3 = sg.adjacent_edges(common_x_b, common_y_b, b, remaining_edges)
for c in adjacent3:
x7 = c.x1
y7 = c.y1
x8 = c.x2
y8 = c.y2
if (x7 == common_x_b) and (y7 == common_y_b):
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x8 == x2) and (y8 == y2):
adjacent4 = []
# if a face of 4 side is not closed.
else:
common_x_c = x8
common_y_c = y8
adjacent4 = sg.adjacent_edges(common_x_c, common_y_c, c, remaining_edges)
else:
# it could be a face of 4 side, check if returned to coordinates of first edge.
if (x7 == x2) and (y7 == y2):
adjacent4 = []
# if a face of 4 side is not closed.
else:
common_x_c = x7
common_y_c = y7
adjacent4 = sg.adjacent_edges(common_x_c, common_y_c, c, remaining_edges)
for d in adjacent4:
x9 = d.x1
y9 = d.y1
x10 = d.x2
y10 = d.y2
if (x9 == common_x_c) and (y9 == common_y_c):
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x10 == x2) and (y10 == y2) and (d != edge):
adjacent5 = []
else:
common_x_d = x10
common_y_d = y10
adjacent5 = sg.adjacent_edges(common_x_d, common_y_d, d, remaining_edges)
else:
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x9 == x2) and (y9 == y2) and (d != edge):
adjacent5 = []
else:
common_x_d = x9
common_y_d = y9
adjacent5 = sg.adjacent_edges(common_x_d, common_y_d, d, remaining_edges)
for e in adjacent5:
x11 = e.x1
y11 = e.y1
x12 = e.x2
y12 = e.y2
if (x11 == common_x_c) and (y11 == common_y_c):
# it could be a face of 6 side, check if returned to coordinates of first edge.
if (x12 == x2) and (y12 == y2) and (e != edge):
adjacent6 = []
else:
common_x_e = x12
common_y_e = y12
adjacent6 = sg.adjacent_edges(common_x_e, common_y_e, e, remaining_edges)
else:
# it could be a face of 6 side, check if returned to coordinates of first edge.
if (x11 == x2) and (y11 == y2) and (e != edge):
adjacent6 = []
else:
common_x_e = x11
common_y_e = y11
adjacent6 = sg.adjacent_edges(common_x_e, common_y_e, e, remaining_edges)
for f in adjacent6:
x13 = f.x1
y13 = f.y1
x14 = f.x2
y14 = f.y2
if (x13 == common_x_e) and (y13 == common_y_e):
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x14 == x2) and (y14 == y2) and (f != edge):
boards.extend((edge,a,b,c,d,e,f))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
d.num_faces += 1
e.num_faces += 1
f.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
else:
# it could be a face of 5 side, check if returned to coordinates of first edge.
if (x13 == x2) and (y13 == y2) and (f != edge):
boards.extend((edge,a,b,c,d,e,f))
face = Cell(boards)
if not (check_belonging(face, faces)):
edge.num_faces += 1
a.num_faces += 1
b.num_faces += 1
c.num_faces += 1
d.num_faces += 1
e.num_faces += 1
f.num_faces += 1
faces.append(face)
return True
else:
del boards[:]
return False
def face_exist(edge, a, faces):
# return True if already exist a face with sides edge and a.
for face in faces:
if (edge in face.borders) and (a in face.borders):
return True
return False
def check_belonging(face, faces):
# return true if face is already inside faces or there is already one with 2 common edge
sides = face.borders
for face2 in faces:
sides2 = face2.borders
if (all(inside(side, sides2) for side in sides)) and (all(inside(side1, sides) for side1 in sides2)):
return True
if len(common_edge(face, face2)) >= 2:
return True
return False
def inside(side, sides):
# return True if side is inside sides
if side in sides:
return True
else:
return False
def common_edge(face1, face2):
# return list of edges in common between the 2 faces
borders1 = []
borders2 = []
for border in face1.borders:
borders1.append(border)
for border in face2.borders:
borders2.append(border)
return list(set(borders1).intersection(borders2))
def adjacent(face1, face2):
# return true if the 2 faces are adjacent
borders1 = []
borders2 = []
for border in face1.borders:
borders1.append(border)
for border in face2.borders:
borders2.append(border)
common = list(set(borders1).intersection(borders2))
if len(common) == 0:
return False
return True
| 31.674009
| 105
| 0.60751
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| 0.858099
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| 0
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| 0.298516
| 21,570
| 680
| 106
| 31.720588
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0
| 7
|
c13f55b25c36ff7035514f4adfaa4d1f5d27fcac
| 235
|
py
|
Python
|
ProsperFlask/{{cookiecutter.project_name}}/{{cookiecutter.library_name}}/launch_gunicorn.py
|
EVEprosper/ProsperCookiecutters
|
569ca0c311a5ead2b49f0cdde4cb2ad14dcd3a2c
|
[
"MIT"
] | null | null | null |
ProsperFlask/{{cookiecutter.project_name}}/{{cookiecutter.library_name}}/launch_gunicorn.py
|
EVEprosper/ProsperCookiecutters
|
569ca0c311a5ead2b49f0cdde4cb2ad14dcd3a2c
|
[
"MIT"
] | null | null | null |
ProsperFlask/{{cookiecutter.project_name}}/{{cookiecutter.library_name}}/launch_gunicorn.py
|
EVEprosper/ProsperCookiecutters
|
569ca0c311a5ead2b49f0cdde4cb2ad14dcd3a2c
|
[
"MIT"
] | null | null | null |
# AUTOGENERATED BY: ProsperCookiecutters/ProsperFlask
# TEMPLATE VERSION: {{cookiecutter.template_version}}
# AUTHOR: {{cookiecutter.author_name}}
"""bare-bones framework for launching Flask with Gunicorn"""
from endpoints import APP
| 33.571429
| 60
| 0.8
| 25
| 235
| 7.44
| 0.84
| 0.16129
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| 0.097872
| 235
| 7
| 61
| 33.571429
| 0.877358
| 0.834043
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| 1
| 0
|
0
| 7
|
c1429b9cbb3d1cb617d82373eaffb10b20ba1f43
| 1,865
|
py
|
Python
|
dict_curation_projects/conversion/autocomplete.py
|
indic-dict/dict-curation
|
d91ce7d0fa28b158267d6e65b41730d8e0d81b41
|
[
"MIT"
] | null | null | null |
dict_curation_projects/conversion/autocomplete.py
|
indic-dict/dict-curation
|
d91ce7d0fa28b158267d6e65b41730d8e0d81b41
|
[
"MIT"
] | null | null | null |
dict_curation_projects/conversion/autocomplete.py
|
indic-dict/dict-curation
|
d91ce7d0fa28b158267d6e65b41730d8e0d81b41
|
[
"MIT"
] | null | null | null |
import dict_curation.babylon.exporter
from dict_curation import babylon
def dump_vedic_dicts():
# babylon.dump_headwords_file(in_path="/home/vvasuki/indic-dict/stardict-sanskrit-kAvya/kyv-ts-padasvara/kyv-ts-padasvara.babylon", out_path="/home/vvasuki/sanskrit-coders/autocomplete-sa/dicts/kyv-ts.csv")
# babylon.dump_definitions_file(in_path="/home/vvasuki/indic-dict/stardict-sanskrit-kAvya/kyv-ts-padasvara/kyv-ts-padasvara.babylon", out_path="/home/vvasuki/sanskrit-coders/autocomplete-sa/dicts/kyv-ts-padasvara.csv")
dict_curation.babylon.exporter.dump_headwords_file(in_path="/home/vvasuki/indic-dict/stardict-sanskrit-kAvya/rv-padasvara-dev/rv-padasvara-dev.babylon", out_path="/home/vvasuki/sanskrit-coders/autocomplete-sa/dicts/kaavya/rv.csv")
dict_curation.babylon.exporter.dump_definitions_file(in_path="/home/vvasuki/indic-dict/stardict-sanskrit-kAvya/rv-padasvara-dev/rv-padasvara-dev.babylon", out_path="/home/vvasuki/sanskrit-coders/autocomplete-sa/dicts/svara/rv.csv")
def dump_declined():
# babylon.dump_headwords_file(in_path="/home/vvasuki/indic-dict/stardict-sanskrit-vyAkaraNa/dhaval-tiNanta/dhaval-tiNanta.babylon", out_path="/home/vvasuki/sanskrit-coders/autocomplete-sa/dicts/declined/tiNanta-dhaval.csv")
dict_curation.babylon.exporter.dump_headwords_file(in_path="/home/vvasuki/indic-dict/stardict-sanskrit/sa-head/sa-entries/kalpadruma-sa/kalpadruma-original-hw.babylon", sanskrit_dataout_path="/home/vvasuki/sanskrit-coders/autocomplete-sa/dicts/declined/kalpadruma.csv")
def dump_stem():
dict_curation.babylon.exporter.dump_headwords_file(in_path="/home/vvasuki/indic-dict/stardict-sanskrit-kAvya/dcs-frequency/dcs-frequency.babylon", out_path="/home/vvasuki/sanskrit-coders/autocomplete-sa/dicts/stem/dcs.csv")
if __name__ == '__main__':
dump_declined()
# dump_vedic_dicts()
# dump_stem()
| 81.086957
| 273
| 0.808579
| 267
| 1,865
| 5.456929
| 0.183521
| 0.07687
| 0.144132
| 0.067262
| 0.75978
| 0.75978
| 0.73919
| 0.73919
| 0.73919
| 0.695264
| 0
| 0
| 0.04504
| 1,865
| 23
| 274
| 81.086957
| 0.81808
| 0.361394
| 0
| 0
| 0
| 0.363636
| 0.544688
| 0.537943
| 0
| 0
| 0
| 0
| 0
| 1
| 0.272727
| true
| 0
| 0.181818
| 0
| 0.454545
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
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| 1
| null | 0
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| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
c1889f89f38c6fb78c8f0e8c62ab4dde30c78af9
| 82
|
py
|
Python
|
tests/test_bar.py
|
dawsonbooth/dustpan
|
b92c95e67c1bff74d2258fdc426ed145ef1901c0
|
[
"MIT"
] | null | null | null |
tests/test_bar.py
|
dawsonbooth/dustpan
|
b92c95e67c1bff74d2258fdc426ed145ef1901c0
|
[
"MIT"
] | null | null | null |
tests/test_bar.py
|
dawsonbooth/dustpan
|
b92c95e67c1bff74d2258fdc426ed145ef1901c0
|
[
"MIT"
] | null | null | null |
def test_func_1():
assert bool(1)
def test_func_2():
assert not bool(0)
| 11.714286
| 22
| 0.646341
| 15
| 82
| 3.266667
| 0.6
| 0.285714
| 0.44898
| 0
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| 0
| 0
| 0
| 0.063492
| 0.231707
| 82
| 6
| 23
| 13.666667
| 0.714286
| 0
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| 0.5
| 1
| 0.5
| true
| 0
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| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c1b0a98f85b41269ce90c28b7a3ed458d72d6772
| 22,844
|
py
|
Python
|
algorithms.py
|
qwe123coder/fliscopt
|
423aa694a2d45e04d98672fe305bd129019a9ee2
|
[
"MIT"
] | 23
|
2021-09-30T19:14:39.000Z
|
2022-03-18T16:23:54.000Z
|
algorithms.py
|
qwe123coder/fliscopt
|
423aa694a2d45e04d98672fe305bd129019a9ee2
|
[
"MIT"
] | 27
|
2021-09-30T19:01:05.000Z
|
2022-03-08T08:36:38.000Z
|
algorithms.py
|
qwe123coder/fliscopt
|
423aa694a2d45e04d98672fe305bd129019a9ee2
|
[
"MIT"
] | 16
|
2021-09-30T20:21:02.000Z
|
2022-01-14T01:09:26.000Z
|
import heapq
import math
import random
import sys
from utils.ga_utils import crossover, mutation
def random_search(domain, fitness_function, seed=random.randint(10, 100), seed_init=True, init=[], epochs=100):
""" Random search algorithm implemented
Args:
domain (list): List containing the upper and lower bound.i.e domain of our inputs
fitness_function (function): This parameter accepts a fitness function of given optimization problem.
seed (int,optional): Set the seed value of the random seed generator. Defaults to random integer value.
seed_init(bool,optional): True set's the seed of only population init generator, False sets all generators
init (list, optional): List for initializing the initial solution. Defaults to [].
epochs (int, optional): Number of times the algorithm runs. Defaults to 100.
Returns:
list: List containing the best_solution,
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
int: The number of function evaluations(NFE) after running the algorithm
int: Seed value used by random generators.
"""
if seed_init:
# Set the seed for initial population only
r_init = random.Random(seed)
else:
# Same seeds for both init and other random generators
r_init = random.Random(seed)
random.seed(seed)
best_cost = sys.maxsize
scores = []
nfe = 0
if len(init) > 0:
solution = init
else:
solution = [r_init.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
for i in range(epochs):
if i != 0:
solution = [random.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
if not fitness_function.__name__ == 'fitness_function':
cost = fitness_function(solution)
else:
cost = fitness_function(solution, 'FCO')
nfe += 1
if cost < best_cost:
best_cost = cost
best_solution = solution
scores.append(best_cost)
return best_solution, best_cost, scores, nfe, seed
def hill_climb(domain, fitness_function, seed=random.randint(10, 100), seed_init=True, init=[], epochs=100):
""" Simple Hill Climbing algorithm implemented
Args:
domain (list): List containing the upper and lower bound.i.e domain of our inputs
fitness_function (function): This parameter accepts a fitness function of given optimization problem.
seed (int,optional): Set the seed value of the random seed generator. Defaults to random integer value.
seed_init(bool,optional): True set's the seed of only population init generator, False sets all generators
init (list, optional): List for initializing the initial solution. Defaults to [].
epochs (int, optional): Number of times the algorithm runs. Defaults to 100.
Returns:
list: List containing the best_solution,
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
int: The number of function evaluations(NFE) after running the algorithm
int: Seed value used by random generators.
"""
if seed_init:
# Set the seed for initial population only
r_init = random.Random(seed)
else:
# Same seeds for both init and other random generators
r_init = random.Random(seed)
random.seed(seed)
count = 0
scores = []
nfe = 0
if len(init) > 0:
solution = init
else:
solution = [r_init.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
while True:
neighbors = []
for i in range(len(domain)):
if solution[i] > domain[i][0]:
if solution[i] != domain[i][1]: # cannot change value of 9 to 10
neighbors.append(
solution[0:i] + [solution[i] + 1] + solution[i + 1:])
if solution[i] < domain[i][1]:
if solution[i] != domain[i][0]:
neighbors.append(
solution[0:i] + [solution[i] - 1] + solution[i + 1:])
# actual = fitness_function(solution, 'FCO')
if not fitness_function.__name__ == 'fitness_function':
actual = fitness_function(solution)
else:
actual = fitness_function(solution, 'FCO')
nfe += 1
best = actual
for i in range(len(neighbors)):
count += 1
# cost = fitness_function(neighbors[i], 'FCO')
if not fitness_function.__name__ == 'fitness_function':
cost = fitness_function(neighbors[i])
else:
cost = fitness_function(neighbors[i], 'FCO')
nfe += 1
if cost < best:
best = cost
solution = neighbors[i]
scores.append(best)
if best == actual:
print('Count: ', count)
# print('NFE: ',nfe)
break
return solution, best, scores, nfe, seed
def simulated_annealing(domain, fitness_function, seed=random.randint(10, 100), seed_init=True, init=[],
temperature=50000.0, cooling=0.95, step=1):
""" Simulated annealing algorithm implemented with temeperature and cooling parameters.
Args:
domain (list): List containing the upper and lower bound.i.e domain of our inputs
fitness_function (function): This parameter accepts a fitness function of given optimization problem.
init (list, optional): List for initializing the initial solution. Defaults to [].
seed (int,optional): Set the seed value of the random seed generator. Defaults to random integer value.
seed_init(bool,optional): True set's the seed of only population init generator, False sets all generators
epochs (int, optional): Number of times the algorithm runs. Defaults to 100.
temperature (float, optional): This parameter controls the degree of randomness.Increasing it increases the search space. Defaults to 50000.0.
cooling (float, optional): The margin by which temperature decreases at each epoch. Defaults to 0.95.
step (int, optional): Number of steps to the right or left to make changes in given solution. Defaults to 1.
Returns:
list: List containing the best_solution,
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
int: The number of function evaluations(NFE) after running the algorithm
int: Seed value used by random generators.
"""
if seed_init:
# Set the seed for initial population only
r_init = random.Random(seed)
else:
# Same seeds for both init and other random generators
r_init = random.Random(seed)
random.seed(seed)
count = 0
nfe = 0
scores = []
simulated_annealing.temp = []
if len(init) > 0:
solution = init
else:
solution = [r_init.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
while temperature > 0.1:
i = random.randint(0, len(domain) - 1)
direction = random.randint(-step, step)
temp_solution = solution[:]
temp_solution[i] += direction
if temp_solution[i] < domain[i][0]:
temp_solution[i] = domain[i][0]
elif temp_solution[i] > domain[i][1]:
temp_solution[i] = domain[i][1]
count += 1
# cost = fitness_function(solution, 'FCO')
if not fitness_function.__name__ == 'fitness_function':
cost = fitness_function(solution)
else:
cost = fitness_function(solution, 'FCO')
nfe += 1
# cost_temp = fitness_function(temp_solution, 'FCO')
if not fitness_function.__name__ == 'fitness_function':
cost_temp = fitness_function(solution)
else:
cost_temp = fitness_function(solution, 'FCO')
nfe += 1
try:
prob = pow(math.e, (-cost_temp - cost) / temperature)
except OverflowError:
prob = float('inf')
best = cost
if (cost_temp < cost or random.random() < prob):
best = cost_temp
solution = temp_solution
scores.append(best)
simulated_annealing.temp.append(temperature)
temperature = temperature * cooling
print('Count: ', count)
return solution, best, scores, nfe, seed
def genetic_algorithm(domain, fitness_function, seed=random.randint(10, 100), seed_init=True, init=[],
population_size=100, step=1,
probability_mutation=0.2, elitism=0.2,
number_generations=500, search=False):
""" Genetic algorithm implemented with elitisim.
Args:
domain (list): List containing the upper and lower bound.i.e domain of our inputs
fitness_function (function): This parameter accepts a fitness function of given optimization problem.
init (list, optional): List for initializing the initial solution. Defaults to [].
seed (int,optional): Set the seed value of the random seed generator. Defaults to random integer value.
seed_init(bool,optional): True set's the seed of only population init generator, False sets all generators
population_size (int, optional): The maximum size of the population to generate. Defaults to 100.
probability_mutation (float, optional): Controls the rate of mutation of genes. Defaults to 0.2.
elitism (float, optional): The percentage of population which proceeds onto next iter without changes. Defaults to 0.2.
number_generations (int, optional): Analgous to epochs, but in this context refers to number of generations the algorithm evolves to . Defaults to 500.
search (bool, optional): If True solution is initialized as the result of a RandomSearch . Defaults to False.
step (int, optional): Number of steps to the right or left to make changes in given solution. Defaults to 1.
Returns:
list: List containing the best_solution,
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
int: The number of function evaluations(NFE) after running the algorithm
int: Seed value used by random generators.
"""
if seed_init:
# Set the seed for initial population only
r_init = random.Random(seed)
else:
# Same seeds for both init and other random generators
r_init = random.Random(seed)
random.seed(seed)
population = []
scores = []
nfe = 0
for i in range(population_size):
if search == True:
solution, b_c, sc, r_nfe, s = random_search(
domain, fitness_function, seed)
nfe += r_nfe
if len(init) > 0:
solution = init
else:
solution = [r_init.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
population.append(solution)
number_elitism = int(elitism * population_size)
for i in range(number_generations):
if not fitness_function.__name__ == 'fitness_function':
costs = [(fitness_function(individual), individual)
for individual in population]
else:
costs = [(fitness_function(individual, 'FCO'), individual)
for individual in population]
# costs = [(fitness_function(individual, 'FCO'), individual)
# for individual in population]
nfe += 1
# costs.sort()
heapq.heapify(costs)
ordered_individuals = [individual for (cost, individual) in costs]
population = ordered_individuals[0:number_elitism]
if not fitness_function.__name__ == 'fitness_function':
scores.append(fitness_function(population[0]))
else:
scores.append(fitness_function(population[0], 'FCO'))
# scores.append(fitness_function(population[0], 'FCO'))
nfe += 1
while len(population) < population_size:
if random.random() < probability_mutation:
m = random.randint(0, number_elitism)
population.append(
mutation(domain, step, ordered_individuals[m]))
else:
i1 = random.randint(0, number_elitism)
i2 = random.randint(0, number_elitism)
population.append(
crossover(domain, ordered_individuals[i1], ordered_individuals[i2]))
return costs[0][1], costs[0][0], scores, nfe, seed
def genetic_algorithm_reversed(domain, fitness_function, seed=random.randint(10, 100), seed_init=True, init=[],
population_size=100, step=1,
probability_crossover=0.2, elitism=0.2,
number_generations=500, search=False):
""" Genetic algorithm implemented with elitisim.
Args:
domain (list): List containing the upper and lower bound.i.e domain of our inputs
fitness_function (function): This parameter accepts a fitness function of given optimization problem.
init (list, optional): List for initializing the initial solution. Defaults to [].
seed (int,optional): Set the seed value of the random seed generator. Defaults to random integer value.
seed_init(bool,optional): True set's the seed of only population init generator, False sets all generators
population_size (int, optional): The maximum size of the population to generate. Defaults to 100.
probability_crossover (float, optional): Controls the rate of crossover of genes. Defaults to 0.2.
elitism (float, optional): The percentage of population which proceeds onto next iter without changes. Defaults to 0.2.
number_generations (int, optional): Analgous to epochs, but in this context refers to number of generations the algorithm evolves to . Defaults to 500.
search (bool, optional): If True solution is initialized as the result of a RandomSearch . Defaults to False.
step (int, optional): Number of steps to the right or left to make changes in given solution. Defaults to 1.
Returns:
list: List containing the best_solution,
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
int: The number of function evaluations(NFE) after running the algorithm
int: Seed value used by random generators.
"""
if seed_init:
# Set the seed for initial population only
r_init = random.Random(seed)
else:
# Same seeds for both init and other random generators
r_init = random.Random(seed)
random.seed(seed)
population = []
scores = []
nfe = 0
for i in range(population_size):
if search == True:
solution, b_c, sc, r_nfe, s = random_search(
domain, fitness_function, seed)
nfe += r_nfe
if len(init) > 0:
solution = init
else:
solution = [r_init.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
population.append(solution)
number_elitism = int(elitism * population_size)
for i in range(number_generations):
if not fitness_function.__name__ == 'fitness_function':
costs = [(fitness_function(individual), individual)
for individual in population]
else:
costs = [(fitness_function(individual, 'FCO'), individual)
for individual in population]
nfe += 1
# costs.sort()
heapq.heapify(costs)
ordered_individuals = [individual for (cost, individual) in costs]
population = ordered_individuals[0:number_elitism]
if not fitness_function.__name__ == 'fitness_function':
scores.append(fitness_function(population[0]))
else:
scores.append(fitness_function(population[0], 'FCO'))
# scores.append(fitness_function(population[0], 'FCO'))
nfe += 1
while len(population) < population_size:
if random.random() < probability_crossover:
i1 = random.randint(0, number_elitism)
i2 = random.randint(0, number_elitism)
population.append(
crossover(domain, ordered_individuals[i1], ordered_individuals[i2]))
else:
m = random.randint(0, number_elitism)
population.append(
mutation(domain, step, ordered_individuals[m]))
return costs[0][1], costs[0][0], scores, nfe, seed
def genetic_algorithm_with_reversals(domain, fitness_function, seed=random.randint(10, 100), seed_init=True, init=[],
population_size=100, step=1,
probability_mutation=0.2, elitism=0.2, n_k=250, step_length=100,
number_generations=500, search=False):
""" Genetic algorithm implemented with elitisim with n number of reversals.
No. of reversals= number_generations/n_keach of n iter=step_length i.e n step reversal.
Args:
domain (list): List containing the upper and lower bound.i.e domain of our inputs
fitness_function (function): This parameter accepts a fitness function of given optimization problem.
init (list, optional): List for initializing the initial solution. Defaults to [].
seed (int,optional): Set the seed value of the random seed generator. Defaults to random integer value.
seed_init(bool,optional): True set's the seed of only population init generator, False sets all generators
population_size (int, optional): The maximum size of the population to generate. Defaults to 100.
probability_mutation (float, optional): Controls the rate of mutation of genes. Defaults to 0.2.
elitism (float, optional): The percentage of population which proceeds onto next iter without changes. Defaults to 0.2.
number_generations (int, optional): Analgous to epochs, but in this context refers to number of generations the algorithm evolves to . Defaults to 500.
n_k (int, optional): Divides number of generations to get actual no of reversals. Defaults to 50.
step_length (int,optional): The number of reversal steps in a given reversal. Defaults to 120.
search (bool, optional): If True solution is initialized as the result of a RandomSearch . Defaults to False.
step (int, optional): Number of steps to the right or left to make changes in given solution. Defaults to 1.
Returns:
list: List containing the best_solution,
int: The final cost after running the algorithm,
list: List containing all costs during all epochs.
int: The number of function evaluations(NFE) after running the algorithm
int: Seed value used by random generators.
"""
if seed_init:
# Set the seed for initial population only
r_init = random.Random(seed)
else:
# Same seeds for both init and other random generators
r_init = random.Random(seed)
random.seed(seed)
population = []
scores = []
nfe = 0
rev = 0
for i in range(population_size):
if search == True:
solution, b_c, sc, r_nfe, s = random_search(
domain, fitness_function, seed)
nfe += r_nfe
if len(init) > 0:
solution = init
else:
solution = [r_init.randint(domain[i][0], domain[i][1])
for i in range(len(domain))]
population.append(solution)
number_elitism = int(elitism * population_size)
for i in range(number_generations):
if not fitness_function.__name__ == 'fitness_function':
costs = [(fitness_function(individual), individual)
for individual in population]
else:
costs = [(fitness_function(individual, 'FCO'), individual)
for individual in population]
nfe += 1
if i % n_k == 0 and i != 0:
if step_length == 1:
costs.sort(reverse=True)
rev += 1
else:
rev += 1
for _ in range(step_length - 1):
costs.sort(reverse=True)
ordered_individuals = [
individual for (cost, individual) in costs]
population = ordered_individuals[0:number_elitism]
if not fitness_function.__name__ == 'fitness_function':
scores.append(fitness_function(population[0]))
else:
scores.append(fitness_function(population[0], 'FCO'))
nfe += 1
while len(population) < population_size:
if random.random() < probability_mutation:
i1 = random.randint(0, number_elitism)
i2 = random.randint(0, number_elitism)
population.append(
crossover(domain, ordered_individuals[i1], ordered_individuals[i2]))
else:
m = random.randint(0, number_elitism)
population.append(
mutation(domain, step, ordered_individuals[m]))
print(rev) # To print the number of reversals
else:
heapq.heapify(costs)
ordered_individuals = [individual for (cost, individual) in costs]
population = ordered_individuals[0:number_elitism]
if not fitness_function.__name__ == 'fitness_function':
scores.append(fitness_function(population[0]))
else:
scores.append(fitness_function(population[0], 'FCO'))
nfe += 1
while len(population) < population_size:
if random.random() < probability_mutation:
i1 = random.randint(0, number_elitism)
i2 = random.randint(0, number_elitism)
population.append(
crossover(domain, ordered_individuals[i1], ordered_individuals[i2]))
else:
m = random.randint(0, number_elitism)
population.append(
mutation(domain, step, ordered_individuals[m]))
return costs[0][1], costs[0][0], scores, nfe, seed
| 46.430894
| 159
| 0.61885
| 2,764
| 22,844
| 5.009768
| 0.073806
| 0.082328
| 0.023399
| 0.01271
| 0.885679
| 0.879107
| 0.855709
| 0.84206
| 0.84206
| 0.84206
| 0
| 0.016659
| 0.298415
| 22,844
| 491
| 160
| 46.525458
| 0.84732
| 0.401681
| 0
| 0.767742
| 0
| 0
| 0.018587
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.019355
| false
| 0
| 0.016129
| 0
| 0.054839
| 0.009677
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
de0cc20e059136977ef6ce1e31fc13cf86456bf4
| 210
|
py
|
Python
|
6 kyu/Exclamation marks series 17 Put the exclamation marks and question marks to the balance Are they balanced.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 6
|
2020-09-03T09:32:25.000Z
|
2020-12-07T04:10:01.000Z
|
6 kyu/Exclamation marks series 17 Put the exclamation marks and question marks to the balance Are they balanced.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 1
|
2021-12-13T15:30:21.000Z
|
2021-12-13T15:30:21.000Z
|
6 kyu/Exclamation marks series 17 Put the exclamation marks and question marks to the balance Are they balanced.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | null | null | null |
dict={"!":2, "?":3}
def balance(left, right):
return "Left" if sum(dict[i] for i in left)>sum(dict[i] for i in right) else "Right" if sum(dict[i] for i in left)<sum(dict[i] for i in right) else "Balance"
| 52.5
| 161
| 0.633333
| 43
| 210
| 3.093023
| 0.325581
| 0.210526
| 0.240602
| 0.330827
| 0.646617
| 0.646617
| 0.646617
| 0.646617
| 0.646617
| 0.646617
| 0
| 0.011561
| 0.17619
| 210
| 4
| 162
| 52.5
| 0.757225
| 0
| 0
| 0
| 0
| 0
| 0.085308
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 0.666667
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
a9b6aee4ceed79d6e5f517b21fb88a57395656fb
| 32,028
|
py
|
Python
|
tests/browser/elements/select_list_tests.py
|
lmtierney/watir-snake
|
e959a508ecbe63662900e66a3fb768bf569d1524
|
[
"MIT"
] | null | null | null |
tests/browser/elements/select_list_tests.py
|
lmtierney/watir-snake
|
e959a508ecbe63662900e66a3fb768bf569d1524
|
[
"MIT"
] | null | null | null |
tests/browser/elements/select_list_tests.py
|
lmtierney/watir-snake
|
e959a508ecbe63662900e66a3fb768bf569d1524
|
[
"MIT"
] | null | null | null |
import re
import pytest
import nerodia
from nerodia.exception import Error, NoValueFoundException, UnknownObjectException
@pytest.fixture
def reset_form(browser_manager):
yield
browser_manager.instance.link().click()
pytestmark = [pytest.mark.page('forms_with_input_elements.html'),
pytest.mark.usefixtures('reset_form')]
class TestSelectListExists(object):
def test_returns_true_if_the_select_list_exists(self, browser):
assert browser.select_list(id='new_user_country').exists is True
assert browser.select_list(id=re.compile('new_user_country')).exists is True
assert browser.select_list(name='new_user_country').exists is True
assert browser.select_list(name=re.compile('new_user_country')).exists is True
assert browser.select_list(class_name='country').exists is True
assert browser.select_list(class_name=re.compile('country')).exists is True
assert browser.select_list(index=0).exists is True
assert browser.select_list(xpath="//select[@id='new_user_country']").exists is True
def test_returns_the_first_select_list_if_given_no_args(self, browser):
assert browser.select_list().exists
def test_returns_false_if_the_select_list_doesnt_exist(self, browser):
assert browser.select_list(id='no_such_id').exists is False
assert browser.select_list(id=re.compile('no_such_id')).exists is False
assert browser.select_list(name='no_such_name').exists is False
assert browser.select_list(name=re.compile('no_such_name')).exists is False
assert browser.select_list(value='no_such_value').exists is False
assert browser.select_list(value=re.compile('no_such_value')).exists is False
assert browser.select_list(text='no_such_text').exists is False
assert browser.select_list(text=re.compile('no_such_id')).exists is False
assert browser.select_list(class_name='no_such_class').exists is False
assert browser.select_list(class_name=re.compile('no_such_class')).exists is False
assert browser.select_list(index=1337).exists is False
assert browser.select_list(xpath="//select[@id='no_such_id']").exists is False
def test_raises_correct_exception_when_what_argument_is_invalid(self, browser):
with pytest.raises(TypeError):
browser.select_list(id=3.14).exists
class TestSelectListAttributes(object):
# id
def test_returns_the_id_if_the_select_list_exists_and_has_id(self, browser):
assert browser.select_list(index=0).id == 'new_user_country'
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_id_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(index=1337).id
# name
def test_returns_the_name_if_the_select_list_exists_and_has_name(self, browser):
assert browser.select_list(index=0).name == 'new_user_country'
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_name_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(index=1337).name
# multiple
def test_knows_whether_the_select_list_allows_multiple_selections(self, browser):
assert not browser.select_list(index=0).multiple
assert browser.select_list(index=1).multiple
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_multiple_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(index=1337).multiple
# value
def test_returns_the_value_if_the_select_list_exists_and_has_value(self, browser):
browser.select_list(index=0).select(re.compile('Norway'))
assert browser.select_list(index=0).value == '2'
browser.select_list(index=0).select(re.compile('Sweden'))
assert browser.select_list(index=0).value == '3'
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_value_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(index=1337).value
# text
def test_returns_the_text_if_the_select_list_exists_and_has_text(self, browser):
browser.select_list(index=0).select(re.compile('Norway'))
assert browser.select_list(index=0).text == 'Norway'
browser.select_list(index=0).select(re.compile('Sweden'))
assert browser.select_list(index=0).text == 'Sweden'
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_text_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(index=1337).text
def test_finds_all_attribute_methods(browser):
assert hasattr(browser.select_list(index=0), 'class_name')
assert hasattr(browser.select_list(index=0), 'id')
assert hasattr(browser.select_list(index=0), 'name')
assert hasattr(browser.select_list(index=0), 'value')
class TestSelectListAccess(object):
# enabled
def test_returns_true_if_the_select_list_is_enabled(self, browser):
assert browser.select_list(name='new_user_country').enabled is True
def test_returns_false_if_the_select_list_is_disabled(self, browser):
assert browser.select_list(name='new_user_role').enabled is False
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_enabled_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(name='no_such_name').enabled
# disabled
def test_returns_true_if_the_select_list_is_disabled(self, browser):
assert browser.select_list(index=2).disabled is True
def test_returns_false_if_the_select_list_is_enabled(self, browser):
assert browser.select_list(index=0).disabled is False
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_disabled_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(index=1337).disabled
class TestSelectListOther(object):
# option
def test_returns_an_instance_of_option(self, browser):
from nerodia.elements.option import Option
option = browser.select_list(name='new_user_country').option(text='Denmark')
assert isinstance(option, Option)
assert option.value == '1'
# options
def test_returns_all_the_options(self, browser):
options = browser.select_list(name='new_user_country').options()
assert [opt.text for opt in options] == ['Denmark', 'Norway', 'Sweden',
'United Kingdom', 'USA', 'Germany']
# selected_options
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_selected_options_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(name='no_such_name').selected_options
def test_returns_the_currently_selected_items(self, browser):
selected = browser.select_list(name='new_user_country').selected_options
assert [opt.text for opt in selected] == ['Norway']
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN', 'NO']
# clear
def test_clears_the_selection_when_possible(self, browser):
browser.select_list(name='new_user_languages').clear()
assert not browser.select_list(name='new_user_languages').selected_options
def test_does_not_clear_selections_if_the_select_list_does_not_allow_multiple_selections(self, browser):
with pytest.raises(Error):
browser.select_list(name='new_user_country').clear()
opts = browser.select_list(name='new_user_country').selected_options
assert [opt.text for opt in opts] == ['Norway']
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_clear_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(name='no_such_name').clear()
def test_clear_fires_onchange_event(self, browser, messages):
browser.select_list(name='new_user_languages').clear()
assert len(messages.list) == 2
def test_clear_doesnt_fire_onchange_event_for_already_cleared_option(self, browser, messages):
browser.select_list(name='new_user_languages').option().clear()
assert len(messages.list) == 0
# include
def test_returns_true_if_the_given_option_exists_by_text(self, browser):
assert browser.select_list(name='new_user_country').includes('Denmark') is True
def test_returns_true_if_the_given_option_exists_by_label(self, browser):
assert browser.select_list(name='new_user_country').includes('Germany') is True
def test_returns_false_if_the_given_option_doesnt_exist(self, browser):
assert browser.select_list(name='new_user_country').includes('Ireland') is False
# is_selected
def test_returns_true_if_the_given_option_is_selected_by_text(self, browser):
browser.select_list(name='new_user_country').select('Denmark')
assert browser.select_list(name='new_user_country').is_selected('Denmark') is True
def test_returns_false_if_the_given_option_is_not_selected_by_text(self, browser):
assert browser.select_list(name='new_user_country').is_selected('Sweden') is False
def test_returns_true_if_the_given_option_is_selected_by_label(self, browser):
browser.select_list(name='new_user_country').select('Germany')
assert browser.select_list(name='new_user_country').is_selected('Germany') is True
def test_returns_false_if_the_given_option_is_not_selected_by_label(self, browser):
browser.select_list(name='new_user_country').option(label='Germany').clear()
assert browser.select_list(name='new_user_country').is_selected('Germany') is False
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_for_selected_if_the_select_list_doesnt_exist(self, browser):
with pytest.raises(UnknownObjectException):
browser.select_list(name='new_user_country').is_selected('missing_option')
class TestSelectListSelect(object):
# finding by value
def test_selects_an_option_by_value_with_a_string(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select('2')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN']
def test_selects_an_option_by_value_with_a_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select(re.compile(r'1|3'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'NO']
# finding by text
def test_selects_an_option_by_text_with_a_string(self, browser):
browser.select_list(name='new_user_country').select('Denmark')
selected = browser.select_list(name='new_user_country').selected_options
assert [opt.text for opt in selected] == ['Denmark']
def test_selects_an_option_by_text_with_a_regexp(self, browser):
browser.select_list(name='new_user_country').select(re.compile(r'Denmark'))
selected = browser.select_list(name='new_user_country').selected_options
assert [opt.text for opt in selected] == ['Denmark']
# finding by label
def test_selects_an_option_by_label_with_a_string(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select('NO')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['NO']
def test_selects_an_option_by_label_with_a_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select(re.compile(r'^N'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['NO']
def test_selects_multiple_options_successively(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select('Danish')
browser.select_list(name='new_user_languages').select('Swedish')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'Swedish']
def test_selects_empty_options(self, browser):
browser.select_list(id='delete_user_username').select('')
selected = browser.select_list(name='delete_user_username').selected_options
assert [opt.text for opt in selected] == ['']
def test_returns_the_value_selected(self, browser):
assert browser.select_list(name='new_user_languages').select('Danish') == 'Danish'
def test_fires_onchange_event_when_selecting_an_item(self, browser, messages):
browser.select_list(id='new_user_languages').select('Danish')
assert messages.list == ['changed language']
def test_doesnt_fires_onchange_event_when_selecting_an_item(self, browser, messages):
browser.select_list(id='new_user_languages').clear() # removes the two pre-selected options
browser.select_list(id='new_user_languages').select('English')
assert len(messages) == 3
browser.select_list(id='new_user_languages').select('English')
assert len(messages) == 3
def test_an_empty_string_when_selecting_an_option_that_disappears_when_selected(self, browser):
assert browser.select_list(id='obsolete').select('sweden') == ''
def test_selects_options_with_a_single_quoted_value(self, browser):
browser.select_list(id='single-quote').select("'foo'")
selected = browser.select_list(id='single-quote').selected_options
assert [opt.text for opt in selected] == ["'foo'"]
@pytest.mark.skipif('nerodia.relaxed_locate is False',
reason='only applicable when relaxed locating')
@pytest.mark.usefixtures('timeout_reset')
@pytest.mark.page('wait.html')
def test_waits_to_select_an_option(self, browser):
from time import time
browser.link(id='add_select').click()
select_list = browser.select_list(id='languages')
nerodia.default_timeout = 2
start_time = time()
with pytest.raises(NoValueFoundException):
select_list.select('No')
assert time() - start_time > 2
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_if_the_option_doesnt_exist(self, browser):
message_parts = ["#<Select: located: False;",
"'name': 'new_user_country'",
"'tag_name': 'select'"]
with pytest.raises(NoValueFoundException) as e:
browser.select_list(name='new_user_country').select('missing_option')
assert all(part in e.value.args[0] for part in message_parts)
message_parts = ["#<Select: located: False;",
"'name': {}".format(re.compile('new_user_country')),
"'tag_name': 'select'"]
with pytest.raises(NoValueFoundException) as e:
browser.select_list(name=re.compile('new_user_country')).select('missing_option')
assert all(part in e.value.args[0] for part in message_parts)
# This test doesn't like quick timeout
def test_raises_correct_exception_if_the_option_is_disabled(self, browser):
from nerodia.exception import ObjectDisabledException
with pytest.raises(ObjectDisabledException):
browser.select_list(name='new_user_languages').select('Russian')
def test_raises_correct_exception_if_argument_is_not_a_string_regexp_numeric_list(self, browser):
with pytest.raises(TypeError):
browser.select_list(name='new_user_languages').select({})
class TestSelectListSelectMultipleOptions():
def test_selects_each_item_in_a_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select(['Danish', 'Swedish'])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'Swedish']
def test_selects_each_item_in_a_parameter_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select('Danish', 'Swedish')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'Swedish']
def test_selects_each_item_based_on_text(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select([re.compile(r'ish')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish']
def test_selects_each_item_based_on_label_and_single_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select([re.compile(r'NO|EN')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN', 'NO']
def test_selects_each_item_based_on_label_and_multiple_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select([re.compile(r'NO'),
re.compile(r'EN')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN', 'NO']
def test_selects_each_item_from_a_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select([re.compile(r'ish'),
re.compile(r'Latin')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin',
'Latin']
def test_selects_each_item_from_multiple_arguments(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select(re.compile(r'ish'),
re.compile(r'Latin'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin',
'Latin']
def test_returns_the_first_matching_value_if_there_are_multiple_matches(self, browser):
browser.select_list(name='new_user_languages').clear()
selected = browser.select_list(name='new_user_languages').select([re.compile(r'ish')])
assert selected == 'Danish'
class TestSelectListJsSelectMultipleOptions():
def test_selects_each_item_in_a_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select(['Danish', 'Swedish'])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'Swedish']
def test_selects_each_item_in_a_parameter_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select('Danish', 'Swedish')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'Swedish']
def test_selects_each_item_based_on_text(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select([re.compile(r'ish')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish']
def test_selects_each_item_based_on_label_and_single_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select([re.compile(r'NO|EN')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN', 'NO']
def test_selects_each_item_based_on_label_and_multiple_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select([re.compile(r'NO'),
re.compile(r'EN')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN', 'NO']
def test_selects_each_item_from_a_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select([re.compile(r'ish'),
re.compile(r'Latin')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin',
'Latin']
def test_selects_each_item_from_multiple_arguments(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select(re.compile(r'ish'),
re.compile(r'Latin'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin',
'Latin']
def test_returns_the_first_matching_value_if_there_are_multiple_matches(self, browser):
browser.select_list(name='new_user_languages').clear()
selected = browser.select_list(name='new_user_languages').js_select([re.compile(r'ish')])
assert selected == 'Danish'
class TestSelectListJsSelect(object):
# finding by value
def test_js_selects_an_option_by_value_with_a_string(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select('2')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN']
def test_js_selects_an_option_by_value_with_a_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select(re.compile(r'2'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN']
# finding by text
def test_js_selects_an_option_by_text_with_a_string(self, browser):
browser.select_list(name='new_user_country').js_select('Denmark')
selected = browser.select_list(name='new_user_country').selected_options
assert [opt.text for opt in selected] == ['Denmark']
def test_js_selects_an_option_by_text_with_a_regexp(self, browser):
browser.select_list(name='new_user_country').js_select(re.compile(r'Denmark'))
selected = browser.select_list(name='new_user_country').selected_options
assert [opt.text for opt in selected] == ['Denmark']
# finding by label
def test_js_selects_an_option_by_label_with_a_string(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select('NO')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['NO']
def test_js_selects_an_option_by_label_with_a_regexp(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select(re.compile(r'NO'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['NO']
def test_js_selects_multiple_options_successively(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select('Danish')
browser.select_list(name='new_user_languages').js_select('Swedish')
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'Swedish']
def test_js_selects_empty_options(self, browser):
browser.select_list(id='delete_user_username').js_select('')
selected = browser.select_list(name='delete_user_username').selected_options
assert [opt.text for opt in selected] == ['']
def test_returns_the_value_js_selected(self, browser):
assert browser.select_list(name='new_user_languages').js_select('Danish') == 'Danish'
def test_js_selects_options_with_a_single_quoted_value(self, browser):
browser.select_list(id='single-quote').js_select("'foo'")
selected = browser.select_list(id='single-quote').selected_options
assert [opt.text for opt in selected] == ["'foo'"]
def test_selects_exact_matches_when_using_string(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select('Latin')
selected_options = [el.text for el in browser.select_list(name='new_user_languages').selected_options]
assert 'Azeri - Latin' not in selected_options
assert 'Latin' in selected_options
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_if_the_option_doesnt_exist(self, browser):
with pytest.raises(NoValueFoundException):
browser.select_list(name='new_user_country').js_select('missing_option')
with pytest.raises(NoValueFoundException):
browser.select_list(name=re.compile('new_user_country')).js_select('missing_option')
@pytest.mark.usefixtures('quick_timeout')
def test_raises_correct_exception_if_the_option_is_disabled(self, browser):
from nerodia.exception import ObjectDisabledException
with pytest.raises(ObjectDisabledException):
browser.select_list(name='new_user_languages').js_select('Russian')
def test_raises_correct_exception_if_argument_is_not_a_string_regexp_numeric_list(self, browser):
with pytest.raises(TypeError):
browser.select_list(name='new_user_languages').js_select({})
class TestSelectListSelectAll(object):
def test_selects_multiple_options_based_on_text(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select_all(re.compile(r'ish'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish']
def test_selects_multiple_options_based_on_labels(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select_all(re.compile(r'NO|EN'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN', 'NO']
def test_selects_all_options_in_a_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select_all([re.compile(r'ish'), re.compile(r'Latin')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin', 'Latin']
def test_selects_all_options_in_a_parameter_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').select_all(re.compile(r'ish'), re.compile(r'Latin'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin', 'Latin']
def test_returns_the_first_matching_value_if_there_are_multiple_matches(self, browser):
browser.select_list(name='new_user_languages').clear()
assert browser.select_list(name='new_user_languages').select_all(re.compile(r'ish')) == 'Danish'
class TestSelectListJsSelectAll(object):
def test_selects_multiple_options_based_on_value(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select_all(re.compile(r'\d+'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'NO', 'Russian']
def test_selects_multiple_options_based_on_text(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select_all(re.compile(r'ish'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish']
def test_selects_multiple_options_based_on_labels(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select_all(re.compile(r'NO|EN'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['EN', 'NO']
def test_selects_all_options_in_a_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select_all([re.compile(r'ish'), re.compile(r'Latin')])
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin', 'Latin']
def test_selects_all_options_in_a_parameter_list(self, browser):
browser.select_list(name='new_user_languages').clear()
browser.select_list(name='new_user_languages').js_select_all(re.compile(r'ish'), re.compile(r'Latin'))
selected = browser.select_list(name='new_user_languages').selected_options
assert [opt.text for opt in selected] == ['Danish', 'EN', 'Swedish', 'Azeri - Latin', 'Latin']
def test_returns_the_first_matching_value_if_there_are_multiple_matches(self, browser):
browser.select_list(name='new_user_languages').clear()
assert browser.select_list(name='new_user_languages').js_select_all(re.compile(r'ish')) == 'Danish'
| 53.558528
| 112
| 0.716155
| 4,220
| 32,028
| 5.080095
| 0.051185
| 0.112417
| 0.171285
| 0.15869
| 0.890895
| 0.876388
| 0.867292
| 0.839957
| 0.812109
| 0.790326
| 0
| 0.002481
| 0.169539
| 32,028
| 597
| 113
| 53.648241
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| 0.003467
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0
| 7
|
a9b8fedc7ce75cbff2f080290191922ad5a6267b
| 8,061
|
py
|
Python
|
Birds_TrainingCode/DataPreprocess/CreateTFRecords.py
|
CraneHzm/Birds_InceptionV3_BCNN
|
45abc5a0a0293506540f7f75809de86a5f05fa10
|
[
"MIT"
] | 12
|
2018-04-24T09:28:34.000Z
|
2021-11-09T07:05:55.000Z
|
Birds_TrainingCode/DataPreprocess/CreateTFRecords.py
|
sy-sunmoon/Birds_InceptionV3_BCNN
|
99b620aaf03f2f47482757d801a37031da8d1b57
|
[
"MIT"
] | null | null | null |
Birds_TrainingCode/DataPreprocess/CreateTFRecords.py
|
sy-sunmoon/Birds_InceptionV3_BCNN
|
99b620aaf03f2f47482757d801a37031da8d1b57
|
[
"MIT"
] | 7
|
2018-05-28T14:41:01.000Z
|
2021-11-09T07:01:00.000Z
|
# Copyright (c) 2018/4/12 Hu Zhiming JimmyHu@pku.edu.cn All Rights Reserved.
# Create the train, validation & test dataset from the given train, validation & test images.
# import the libs(libs & future libs).
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
# Image is used to process images.
from PIL import Image
# Get the current work directory.
cwd = os.getcwd()
# a class to write records to a TFRecords file.
validation_writer = tf.python_io.TFRecordWriter("validation.tfrecords")
test_writer = tf.python_io.TFRecordWriter("test.tfrecords")
# the augmented train set.
train_aug_writer = tf.python_io.TFRecordWriter("train_aug.tfrecords")
# the width & height to resize the images.
# (299, 299) is the image size required by Inception V3.
ImageWidth = 299
ImageHeight= 299
# create the validation tfrecords.
# read the whole 200 classes and write them into validation tfrecords.
for i in range(1, 201):
# create a list of the files in the given class directory.
class_dir = os.listdir(cwd + "/Validation/" + str(i) + "/")
for name in class_dir:
# the path of an image.
image_path = cwd + "/Validation/" + str(i) + "/" + name
# print(image_path)
# read the image.
img = Image.open(image_path)
# print(image_path)
# print(img.mode)
# resize the image.
img = img.resize((ImageWidth, ImageHeight))
# convert the image to bytes(raw data).
img_raw = img.tobytes()
# calculate the label of this image.
label = i-1
# print('num: {}, Number: {}, label: {}'.format(num, Number, label))
# example defines the data format.
example = tf.train.Example(features=tf.train.Features(feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
}))
# Serialize the data to string and write it.
validation_writer.write(example.SerializeToString())
print('Image Name: {}, label: {}'.format(name, label))
# close the TFRecords writer.
validation_writer.close()
# create the test tfrecords.
# read the whole 200 classes and write them into test tfrecords.
for i in range(1, 201):
# create a list of the files in the given class directory.
class_dir = os.listdir(cwd + "/Test/" + str(i) + "/")
for name in class_dir:
# the path of an image.
image_path = cwd + "/Test/" + str(i) + "/" + name
# print(image_path)
# read the image.
img = Image.open(image_path)
# resize the image.
img = img.resize((ImageWidth, ImageHeight))
# convert the image to bytes(raw data).
img_raw = img.tobytes()
# calculate the label of this image.
label = i-1
# print('num: {}, Number: {}, label: {}'.format(num, Number, label))
# example defines the data format.
example = tf.train.Example(features=tf.train.Features(feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
}))
# Serialize the data to string and write it.
test_writer.write(example.SerializeToString())
print('Image Name: {}, label: {}'.format(name, label))
# close the TFRecords writer.
test_writer.close()
# Create the augmented train dataset, i.e., write the train data, trainFlip data, trainCrop data & trainNoise data into the dataset.
# Write the train data into train_aug tfrecords.
# read the whole 200 classes and write them into train_aug tfrecords.
for i in range(1, 201):
# create a list of the files in the given class directory.
class_dir = os.listdir(cwd + "/Train/" + str(i) + "/")
for name in class_dir:
# the path of an image.
image_path = cwd + "/Train/" + str(i) + "/" + name
# print(image_path)
# read the image.
img = Image.open(image_path)
# resize the image.
img = img.resize((ImageWidth, ImageHeight))
# convert the image to bytes(raw data).
img_raw = img.tobytes()
# calculate the label of this image.
label = i-1
# print('num: {}, Number: {}, label: {}'.format(num, Number, label))
# example defines the data format.
example = tf.train.Example(features=tf.train.Features(feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
}))
# Serialize the data to string and write it.
train_aug_writer.write(example.SerializeToString())
print('Image Name: {}, label: {}'.format(name, label))
# Write the trainFlip data into train_aug tfrecords.
# read the whole 200 classes and write them into train_aug tfrecords.
for i in range(1, 201):
# create a list of the files in the given class directory.
class_dir = os.listdir(cwd + "/TrainFlip/" + str(i) + "/")
for name in class_dir:
# the path of an image.
image_path = cwd + "/TrainFlip/" + str(i) + "/" + name
# print(image_path)
# read the image.
img = Image.open(image_path)
# resize the image.
img = img.resize((ImageWidth, ImageHeight))
# convert the image to bytes(raw data).
img_raw = img.tobytes()
# calculate the label of this image.
label = i-1
# print('num: {}, Number: {}, label: {}'.format(num, Number, label))
# example defines the data format.
example = tf.train.Example(features=tf.train.Features(feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
}))
# Serialize the data to string and write it.
train_aug_writer.write(example.SerializeToString())
print('Image Name: {}, label: {}'.format(name, label))
# Write the trainCrop data into train_aug tfrecords.
# read the whole 200 classes and write them into train_aug tfrecords.
for i in range(1, 201):
# create a list of the files in the given class directory.
class_dir = os.listdir(cwd + "/TrainCrop/" + str(i) + "/")
for name in class_dir:
# the path of an image.
image_path = cwd + "/TrainCrop/" + str(i) + "/" + name
# print(image_path)
# read the image.
img = Image.open(image_path)
# resize the image.
img = img.resize((ImageWidth, ImageHeight))
# convert the image to bytes(raw data).
img_raw = img.tobytes()
# calculate the label of this image.
label = i-1
# print('num: {}, Number: {}, label: {}'.format(num, Number, label))
# example defines the data format.
example = tf.train.Example(features=tf.train.Features(feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
}))
# Serialize the data to string and write it.
train_aug_writer.write(example.SerializeToString())
print('Image Name: {}, label: {}'.format(name, label))
# Write the trainNoise data into train_aug tfrecords.
# read the whole 200 classes and write them into train_aug tfrecords.
for i in range(1, 201):
# create a list of the files in the given class directory.
class_dir = os.listdir(cwd + "/TrainNoise/" + str(i) + "/")
for name in class_dir:
# the path of an image.
image_path = cwd + "/TrainNoise/" + str(i) + "/" + name
# print(image_path)
# read the image.
img = Image.open(image_path)
# resize the image.
img = img.resize((ImageWidth, ImageHeight))
# convert the image to bytes(raw data).
img_raw = img.tobytes()
# calculate the label of this image.
label = i-1
# print('num: {}, Number: {}, label: {}'.format(num, Number, label))
# example defines the data format.
example = tf.train.Example(features=tf.train.Features(feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
}))
# Serialize the data to string and write it.
train_aug_writer.write(example.SerializeToString())
print('Image Name: {}, label: {}'.format(name, label))
# close the TFRecords writer.
train_aug_writer.close()
| 37.319444
| 132
| 0.688748
| 1,175
| 8,061
| 4.635745
| 0.104681
| 0.046264
| 0.024234
| 0.030843
| 0.824858
| 0.808335
| 0.808335
| 0.808335
| 0.808335
| 0.808335
| 0
| 0.013883
| 0.177894
| 8,061
| 215
| 133
| 37.493023
| 0.808058
| 0.428111
| 0
| 0.707071
| 0
| 0
| 0.089602
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.060606
| 0
| 0.060606
| 0.070707
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e71d4ff07d8d9ea7b810f70a10b573326e386ad9
| 40
|
py
|
Python
|
tests/test_afacinemas_scraper.py
|
douglasgusson/afacinemas-scraper
|
22ffbdb8ac62535c133c3273d790144529d2cdd2
|
[
"MIT"
] | 1
|
2021-01-29T01:33:13.000Z
|
2021-01-29T01:33:13.000Z
|
tests/test_afacinemas_scraper.py
|
douglasgusson/afacinemas-scraper
|
22ffbdb8ac62535c133c3273d790144529d2cdd2
|
[
"MIT"
] | null | null | null |
tests/test_afacinemas_scraper.py
|
douglasgusson/afacinemas-scraper
|
22ffbdb8ac62535c133c3273d790144529d2cdd2
|
[
"MIT"
] | null | null | null |
def test_assert_true():
assert True
| 13.333333
| 23
| 0.725
| 6
| 40
| 4.5
| 0.666667
| 0.740741
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 40
| 2
| 24
| 20
| 0.84375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0
| 1
| 1
| 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
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e74ed73d47bde79b98bfd7a9b6bffedf6ce583a6
| 388
|
py
|
Python
|
colour/algebra/coordinates/__init__.py
|
aurelienpierre/colour
|
3ac45c12fbc0493e49ba4d4b2cb253df9fe14c47
|
[
"BSD-3-Clause"
] | 1
|
2022-02-12T06:28:15.000Z
|
2022-02-12T06:28:15.000Z
|
colour/algebra/coordinates/__init__.py
|
aurelienpierre/colour
|
3ac45c12fbc0493e49ba4d4b2cb253df9fe14c47
|
[
"BSD-3-Clause"
] | null | null | null |
colour/algebra/coordinates/__init__.py
|
aurelienpierre/colour
|
3ac45c12fbc0493e49ba4d4b2cb253df9fe14c47
|
[
"BSD-3-Clause"
] | null | null | null |
from .transformations import (
cartesian_to_spherical,
spherical_to_cartesian,
cartesian_to_polar,
polar_to_cartesian,
cartesian_to_cylindrical,
cylindrical_to_cartesian,
)
__all__ = [
"cartesian_to_spherical",
"spherical_to_cartesian",
"cartesian_to_polar",
"polar_to_cartesian",
"cartesian_to_cylindrical",
"cylindrical_to_cartesian",
]
| 21.555556
| 31
| 0.742268
| 40
| 388
| 6.5
| 0.225
| 0.253846
| 0.307692
| 0.338462
| 0.892308
| 0.892308
| 0.892308
| 0.892308
| 0.892308
| 0.892308
| 0
| 0
| 0.180412
| 388
| 17
| 32
| 22.823529
| 0.81761
| 0
| 0
| 0
| 0
| 0
| 0.329897
| 0.237113
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.0625
| 0
| 0.0625
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
e76a598bc27b62a3dfbb58cb32dffeaa03c6e061
| 110
|
py
|
Python
|
mongozen/queries/__init__.py
|
shaypal5/mongozen
|
78f419c88ea187cac49572c4ffc13f821e01ae85
|
[
"MIT"
] | 4
|
2018-01-11T11:21:24.000Z
|
2018-07-21T08:53:47.000Z
|
mongozen/queries/__init__.py
|
shaypal5/mongozen
|
78f419c88ea187cac49572c4ffc13f821e01ae85
|
[
"MIT"
] | 2
|
2018-02-05T00:11:51.000Z
|
2018-02-12T13:52:25.000Z
|
mongozen/queries/__init__.py
|
shaypal5/mongozen
|
78f419c88ea187cac49572c4ffc13f821e01ae85
|
[
"MIT"
] | 2
|
2018-02-05T09:21:17.000Z
|
2019-06-23T08:39:45.000Z
|
import mongozen.queries.common
import mongozen.queries.time
try:
del mongozen
except NameError:
pass
| 13.75
| 30
| 0.772727
| 14
| 110
| 6.071429
| 0.714286
| 0.329412
| 0.494118
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.172727
| 110
| 7
| 31
| 15.714286
| 0.934066
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.166667
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
99d27da2c66633b02ddeda1673e93db9ea754991
| 7,754
|
py
|
Python
|
postgresPy/pgCRUD.py
|
holamtruong/s1_ricemap
|
5914e37cfdd819db8441e2530c40a5cc2a3594ff
|
[
"ISC"
] | 1
|
2021-05-18T14:37:19.000Z
|
2021-05-18T14:37:19.000Z
|
postgresPy/pgCRUD.py
|
holamtruong/s1_ricemap
|
5914e37cfdd819db8441e2530c40a5cc2a3594ff
|
[
"ISC"
] | null | null | null |
postgresPy/pgCRUD.py
|
holamtruong/s1_ricemap
|
5914e37cfdd819db8441e2530c40a5cc2a3594ff
|
[
"ISC"
] | null | null | null |
'''
Some functions to CRUD Postgres database by Truong Ho
'''
import psycopg2
from postgresPy import pgDBConfig
# Check PostgreSQL database version
def check_db_version():
""" Connect to the PostgreSQL database server """
conn = None
try:
# read connection parameters
params = pgDBConfig.config()
# connect to the PostgreSQL server
print('Connecting to the PostgreSQL database...')
conn = psycopg2.connect(**params)
# create a cursor
cur = conn.cursor()
# execute a statement
print('PostgreSQL database version:')
cur.execute('SELECT version()')
# display the PostgreSQL database server version
db_version = cur.fetchone()
print(db_version)
# close the communication with the PostgreSQL
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
print('Database connection closed.')
# Select all rows of a table
def select_all_row(schema, table):
conn = None
try:
# read connection parameters
params = pgDBConfig.config()
# connect to the PostgreSQL server
print('Connecting to the PostgreSQL database...')
conn = psycopg2.connect(**params)
# create a cursor
cur = conn.cursor()
# create a SQL command
sql_command = "SELECT * FROM {}.{};".format(str(schema), str(table))
print(sql_command)
# execute a statement
cur.execute(sql_command)
records = cur.fetchall()
conn.close()
return records
# close the communication with the PostgreSQL
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
print('Database connection closed.')
# Select all rows of a table
def select_row_where(schema, table, select_col, select_value):
conn = None
try:
# read connection parameters
params = pgDBConfig.config()
# connect to the PostgreSQL server
print('Connecting to the PostgreSQL database...')
conn = psycopg2.connect(**params)
# create a cursor
cur = conn.cursor()
sql_command = "SELECT * FROM {}.{} WHERE {} = {};".format(str(schema), str(table), str(select_col),
str(select_value))
print(sql_command)
# execute a statement
cur.execute(sql_command)
records = cur.fetchall()
conn.close()
return records
# close the communication with the PostgreSQL
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
print('Database connection closed.')
# insert into multi column
def insert_multi_column(schema, table, list_col, list_value):
conn = None
try:
# read connection parameters
params = pgDBConfig.config()
# connect to the PostgreSQL server
print('Connecting to the PostgreSQL database...')
conn = psycopg2.connect(**params)
# create a cursor
cur = conn.cursor()
# get list of columns, values
fieldList = str()
valueList = str()
for field in list_col:
fieldList = fieldList + ", " + field
for value in list_value:
valueList = valueList + ", \'" + str(value) + "\'"
# create a SQL command
sql_command = '''INSERT INTO {}.{} ({}) VALUES ({});'''.format(str(schema), str(table),
str(fieldList[1:]), str(valueList[1:]))
print(sql_command)
# execute a statement
cur.execute(sql_command)
# commit the changes to the database
conn.commit()
# count successfully result
print(cur.rowcount, "Record inserted successfully into the table.")
# close the communication with the PostgreSQL
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
# insert into multi column and Geometry
def insert_multi_column_and_geometryPolygon(schema, table, list_col, list_value, wkt_poylygon, srid):
conn = None
try:
# read connection parameters
params = pgDBConfig.config()
# connect to the PostgreSQL server
print('Connecting to the PostgreSQL database...')
conn = psycopg2.connect(**params)
# create a cursor
cur = conn.cursor()
# get list of columns, values
fieldList = str()
valueList = str()
for field in list_col:
fieldList = fieldList + ", " + field
for value in list_value:
valueList = valueList + ", \'" + str(value) + "\'"
# create a SQL command
sql_command = '''INSERT INTO {}.{} (the_geom, {})
VALUES ( ST_GeomFromText('{}',{}), {});'''.format(str(schema), str(table),
str(fieldList[1:]), wkt_poylygon, srid,
str(valueList[1:]))
print(sql_command)
# execute a statement
cur.execute(sql_command)
# commit the changes to the database
conn.commit()
# count successfully result
print(cur.rowcount, "Record inserted successfully into the table.")
# close the communication with the PostgreSQL
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
# insert into multi column with list key-value
def insert_multi_column_keyvalue(schema, table, list_col, list_value):
conn = None
try:
# read connection parameters
params = pgDBConfig.config()
# connect to the PostgreSQL server
print('Connecting to the PostgreSQL database...')
conn = psycopg2.connect(**params)
# create a cursor
cur = conn.cursor()
# get list of columns, values
fieldList = str()
valueList = str()
for field in list_col:
fieldList = fieldList + ", " + field
for value in list_value:
valueList = valueList + ", \'" + str(value) + "\'"
# create a SQL command
sql_command = '''INSERT INTO {}.{} ({}) VALUES ({});'''.format(str(schema), str(table),
str(fieldList[1:]), str(valueList[1:]))
print(sql_command)
# execute a statement
cur.execute(sql_command)
# commit the changes to the database
conn.commit()
# count successfully result
print(cur.rowcount, "Record inserted successfully into the table.")
# close the communication with the PostgreSQL
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
if __name__ == '__main__':
'''
list_col = ['name', 'properties', 'weight', 'birthday']
list_val = ['a small dog', 'Bad dog', 4.543113, '2017-01-21']
insert_multi_column('public', 'lusuno', list_col, list_val)
'''
'''
rs = select_all_row('public', 'lusuno')
print(rs)
'''
'''
rs = select_row_where('public', 'lusuno', 'id', 1)
print(rs)
'''
| 29.938224
| 111
| 0.56861
| 817
| 7,754
| 5.310894
| 0.148103
| 0.059922
| 0.044941
| 0.035492
| 0.81263
| 0.807329
| 0.788891
| 0.788891
| 0.780595
| 0.780595
| 0
| 0.00674
| 0.330281
| 7,754
| 258
| 112
| 30.054264
| 0.828808
| 0.197446
| 0
| 0.856115
| 0
| 0
| 0.128744
| 0.004303
| 0
| 0
| 0
| 0
| 0
| 1
| 0.043165
| false
| 0
| 0.014388
| 0
| 0.071942
| 0.179856
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
99edadbd8853ca2b06e60a51bb53293fe3ccc2eb
| 3,320
|
py
|
Python
|
model/subnet.py
|
kyuyeonpooh/objects-that-sound
|
962031567f7e5657637d5518dff4f9a44af1c7eb
|
[
"BSD-3-Clause"
] | 18
|
2020-05-11T06:38:14.000Z
|
2022-03-08T02:01:08.000Z
|
model/subnet.py
|
kyuyeonpooh/objects-that-sound
|
962031567f7e5657637d5518dff4f9a44af1c7eb
|
[
"BSD-3-Clause"
] | 6
|
2020-11-13T17:46:51.000Z
|
2021-04-30T21:13:55.000Z
|
model/subnet.py
|
kyuyeonpooh/objects-that-sound
|
962031567f7e5657637d5518dff4f9a44af1c7eb
|
[
"BSD-3-Clause"
] | 5
|
2020-05-27T07:36:45.000Z
|
2022-03-08T02:01:09.000Z
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class ImageConvNet(nn.Module):
def __init__(self):
super(ImageConvNet, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, 3, stride=2, padding=1)
self.bn1_1 = nn.BatchNorm2d(64)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.bn1_2 = nn.BatchNorm2d(64)
self.pool1 = nn.MaxPool2d(2)
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.bn2_1 = nn.BatchNorm2d(128)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.bn2_2 = nn.BatchNorm2d(128)
self.pool2 = nn.MaxPool2d(2)
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.bn3_1 = nn.BatchNorm2d(256)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.bn3_2 = nn.BatchNorm2d(256)
self.pool3 = nn.MaxPool2d(2)
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.bn4_1 = nn.BatchNorm2d(512)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.bn4_2 = nn.BatchNorm2d(512)
def forward(self, x):
x = F.relu(self.bn1_1(self.conv1_1(x)))
x = F.relu(self.bn1_2(self.conv1_2(x)))
x = self.pool1(x)
x = F.relu(self.bn2_1(self.conv2_1(x)))
x = F.relu(self.bn2_2(self.conv2_2(x)))
x = self.pool2(x)
x = F.relu(self.bn3_1(self.conv3_1(x)))
x = F.relu(self.bn3_2(self.conv3_2(x)))
x = self.pool3(x)
x = F.relu(self.bn4_1(self.conv4_1(x)))
x = F.relu(self.bn4_2(self.conv4_2(x)))
return x
class AudioConvNet(nn.Module):
def __init__(self):
super(AudioConvNet, self).__init__()
self.conv1_1 = nn.Conv2d(1, 64, 3, stride=2, padding=1)
self.bn1_1 = nn.BatchNorm2d(64)
self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)
self.bn1_2 = nn.BatchNorm2d(64)
self.pool1 = nn.MaxPool2d(2)
self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)
self.bn2_1 = nn.BatchNorm2d(128)
self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)
self.bn2_2 = nn.BatchNorm2d(128)
self.pool2 = nn.MaxPool2d(2)
self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)
self.bn3_1 = nn.BatchNorm2d(256)
self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)
self.bn3_2 = nn.BatchNorm2d(256)
self.pool3 = nn.MaxPool2d(2)
self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)
self.bn4_1 = nn.BatchNorm2d(512)
self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)
self.bn4_2 = nn.BatchNorm2d(512)
def forward(self, x):
x = F.relu(self.bn1_1(self.conv1_1(x)))
x = F.relu(self.bn1_2(self.conv1_2(x)))
x = self.pool1(x)
x = F.relu(self.bn2_1(self.conv2_1(x)))
x = F.relu(self.bn2_2(self.conv2_2(x)))
x = self.pool2(x)
x = F.relu(self.bn3_1(self.conv3_1(x)))
x = F.relu(self.bn3_2(self.conv3_2(x)))
x = self.pool3(x)
x = F.relu(self.bn4_1(self.conv4_1(x)))
x = F.relu(self.bn4_2(self.conv4_2(x)))
return x
# forward propagation test
if __name__ == "__main__":
x = torch.rand((1, 3, 224, 224))
icn = ImageConvNet()
print(icn(x).shape)
x = torch.rand((1, 1, 257, 200))
acn = AudioConvNet()
print(acn(x).shape)
| 31.619048
| 63
| 0.583133
| 553
| 3,320
| 3.341772
| 0.097649
| 0.064935
| 0.103896
| 0.060606
| 0.87013
| 0.87013
| 0.844156
| 0.816017
| 0.816017
| 0.816017
| 0
| 0.147454
| 0.260542
| 3,320
| 104
| 64
| 31.923077
| 0.605295
| 0.007229
| 0
| 0.8
| 0
| 0
| 0.002429
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.05
| false
| 0
| 0.0375
| 0
| 0.1375
| 0.025
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
4166f044593729367607c1461ccf0fb331db8e0a
| 149
|
py
|
Python
|
ApproPO/envs/gym_frozenmarsrover/envs/__init__.py
|
annanguyen19/ApproPO
|
1e563de4d8728c55d082b09203a2eec9d5731aff
|
[
"MIT"
] | 11
|
2020-05-25T16:50:12.000Z
|
2021-09-16T17:22:38.000Z
|
ApproPO/envs/gym_frozenmarsrover/envs/__init__.py
|
annanguyen19/ApproPO
|
1e563de4d8728c55d082b09203a2eec9d5731aff
|
[
"MIT"
] | null | null | null |
ApproPO/envs/gym_frozenmarsrover/envs/__init__.py
|
annanguyen19/ApproPO
|
1e563de4d8728c55d082b09203a2eec9d5731aff
|
[
"MIT"
] | 6
|
2020-09-24T12:28:24.000Z
|
2022-03-06T14:07:55.000Z
|
from ApproPO.envs.gym_frozenmarsrover.envs.frozenmarsrover_env import FrozenMarsRoverEnv
from ApproPO.envs.gym_frozenmarsrover.envs.maps import MAPS
| 49.666667
| 88
| 0.892617
| 19
| 149
| 6.842105
| 0.473684
| 0.169231
| 0.230769
| 0.276923
| 0.569231
| 0.569231
| 0
| 0
| 0
| 0
| 0
| 0
| 0.053691
| 149
| 2
| 89
| 74.5
| 0.921986
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
41a78011fa4eaeead6eb351bc5bb468f3e78d1c2
| 29
|
py
|
Python
|
package/subpackage/module_c.py
|
tagler/Data_Science_Project_Template_Python
|
0c60a06e81df2e01db771393a17504f28f4fa432
|
[
"MIT"
] | null | null | null |
package/subpackage/module_c.py
|
tagler/Data_Science_Project_Template_Python
|
0c60a06e81df2e01db771393a17504f28f4fa432
|
[
"MIT"
] | null | null | null |
package/subpackage/module_c.py
|
tagler/Data_Science_Project_Template_Python
|
0c60a06e81df2e01db771393a17504f28f4fa432
|
[
"MIT"
] | null | null | null |
def print_c():
print('c')
| 14.5
| 14
| 0.551724
| 5
| 29
| 3
| 0.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.206897
| 29
| 2
| 15
| 14.5
| 0.652174
| 0
| 0
| 0
| 0
| 0
| 0.033333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 1
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
68c7616b62d818ff2b876b279d3a67725afef40d
| 902
|
py
|
Python
|
lib/asn1/test/asn1_SUITE_data/LargeConstraints.py
|
jjhoo/otp
|
808eccc796a05caf2f2a244db385df83c2680b9f
|
[
"Apache-2.0"
] | 8,238
|
2015-01-02T01:05:29.000Z
|
2022-03-30T04:09:46.000Z
|
lib/asn1/test/asn1_SUITE_data/LargeConstraints.py
|
jjhoo/otp
|
808eccc796a05caf2f2a244db385df83c2680b9f
|
[
"Apache-2.0"
] | 2,988
|
2015-01-02T11:40:59.000Z
|
2022-03-31T18:29:54.000Z
|
lib/asn1/test/asn1_SUITE_data/LargeConstraints.py
|
jjhoo/otp
|
808eccc796a05caf2f2a244db385df83c2680b9f
|
[
"Apache-2.0"
] | 2,459
|
2015-01-01T18:54:55.000Z
|
2022-03-31T08:58:25.000Z
|
LargeConstraints DEFINITIONS ::=
BEGIN
-- Maximum number that can be encoded as a constrained whole number: 1 bsl (255*8)
-- The number of octets needed to represent a number cannot be more than 255
-- As the length field is encoded as a 8-bit bitfield.
RangeMax ::= INTEGER (1..126238304966058622268417487065116999845484776053576109500509161826268184136202698801551568013761380717534054534851164138648904527931605160527688095259563605939964364716019515983399209962459578542172100149937763938581219604072733422507180056009672540900709554109516816573779593326332288314873251559077853068444977864803391962580800682760017849589281937637993445539366428356761821065267423102149447628375691862210717202025241630303118559188678304314076943801692528246980959705901641444238894928620825482303431806955690226308773426829503900930529395181208739591967195841536053143145775307050594328881077553168201547776)
END
| 90.2
| 641
| 0.91796
| 48
| 902
| 17.25
| 0.729167
| 0.021739
| 0.024155
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.735294
| 0.05765
| 902
| 9
| 642
| 100.222222
| 0.238824
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ec49a5bb21bb0ed2a8ff2b08e2f731d606bba086
| 127
|
py
|
Python
|
gcd/inference/constraints/__init__.py
|
CogComp/gcd
|
ab8736346b383b2fc9fbe57274b70ed1cd1b9422
|
[
"Apache-2.0"
] | 3
|
2021-05-23T23:48:40.000Z
|
2021-11-09T06:56:39.000Z
|
gcd/inference/constraints/__init__.py
|
danieldeutsch/gcd
|
fdf1a0a8628272fca8dd5c9ce182d16428b1ad38
|
[
"Apache-2.0"
] | null | null | null |
gcd/inference/constraints/__init__.py
|
danieldeutsch/gcd
|
fdf1a0a8628272fca8dd5c9ce182d16428b1ad38
|
[
"Apache-2.0"
] | 1
|
2021-11-27T16:38:20.000Z
|
2021-11-27T16:38:20.000Z
|
from gcd.inference.constraints.constraint import Constraint
from gcd.inference.constraints.constraint_set import ConstraintSet
| 42.333333
| 66
| 0.889764
| 15
| 127
| 7.466667
| 0.533333
| 0.125
| 0.285714
| 0.482143
| 0.660714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.062992
| 127
| 2
| 67
| 63.5
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ec56961aeb7a6167eadbf5713f708ffd4085baa4
| 1,606
|
py
|
Python
|
request-management-api/request_api/schemas/foidocument.py
|
bcgov/foi-flow
|
7f9897b3aad4ba91fbc8edcb8f526906efb490df
|
[
"Apache-2.0"
] | null | null | null |
request-management-api/request_api/schemas/foidocument.py
|
bcgov/foi-flow
|
7f9897b3aad4ba91fbc8edcb8f526906efb490df
|
[
"Apache-2.0"
] | 1,579
|
2021-04-14T18:27:45.000Z
|
2022-03-31T23:49:42.000Z
|
request-management-api/request_api/schemas/foidocument.py
|
bcgov/foi-flow
|
7f9897b3aad4ba91fbc8edcb8f526906efb490df
|
[
"Apache-2.0"
] | 1
|
2022-03-01T20:17:47.000Z
|
2022-03-01T20:17:47.000Z
|
from marshmallow import EXCLUDE, Schema, fields, validate
"""
This class consolidates schemas of document operations.
__author__ = "sumathi.thirumani@aot-technologies.com"
"""
class RenameDocumentSchema(Schema):
class Meta: # pylint: disable=too-few-public-methods
"""Exclude unknown fields in the deserialized output."""
unknown = EXCLUDE
filename = fields.Str(data_key="filename",required=True,allow_none=False)
class ReplaceDocumentSchema(Schema):
class Meta: # pylint: disable=too-few-public-methods
"""Exclude unknown fields in the deserialized output."""
unknown = EXCLUDE
filename = fields.Str(data_key="filename",required=True,allow_none=False)
documentpath = fields.Str(data_key="documentpath",required=True,allow_none=False)
category = fields.Str(data_key="category",allow_none=True)
class DocumentSchema(Schema):
class Meta: # pylint: disable=too-few-public-methods
"""Exclude unknown fields in the deserialized output."""
unknown = EXCLUDE
filename = fields.Str(data_key="filename",required=True,allow_none=False)
documentpath = fields.Str(data_key="documentpath",required=True,allow_none=False)
category = fields.Str(data_key="category",allow_none=True)
class CreateDocumentSchema(Schema):
class Meta: # pylint: disable=too-few-public-methods
"""Exclude unknown fields in the deserialized output."""
unknown = EXCLUDE
documents = fields.Nested(DocumentSchema, many=True, validate=validate.Length(min=1), required=True,allow_none=False)
| 38.238095
| 121
| 0.720423
| 189
| 1,606
| 6.021164
| 0.280423
| 0.063269
| 0.079965
| 0.098418
| 0.745167
| 0.72232
| 0.72232
| 0.72232
| 0.72232
| 0.72232
| 0
| 0.000749
| 0.168742
| 1,606
| 42
| 121
| 38.238095
| 0.851685
| 0.224159
| 0
| 0.714286
| 0
| 0
| 0.058394
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.047619
| 0
| 0.809524
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
6b6eb2f0a86de459ef8ea280e8df077a4297ab1f
| 145,331
|
py
|
Python
|
cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_infra_statsd_oper.py
|
tkamata-test/ydk-py
|
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_infra_statsd_oper.py
|
tkamata-test/ydk-py
|
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
cisco-ios-xr/ydk/models/cisco_ios_xr/_meta/_Cisco_IOS_XR_infra_statsd_oper.py
|
tkamata-test/ydk-py
|
b637e7853a8edbbd31fbc05afa3aa4110b31c5f9
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
import re
import collections
from enum import Enum
from ydk._core._dm_meta_info import _MetaInfoClassMember, _MetaInfoClass, _MetaInfoEnum
from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict
from ydk._core._dm_meta_info import ATTRIBUTE, REFERENCE_CLASS, REFERENCE_LIST, REFERENCE_LEAFLIST, REFERENCE_IDENTITY_CLASS, REFERENCE_ENUM_CLASS, REFERENCE_BITS, REFERENCE_UNION
from ydk.errors import YPYError, YPYModelError
from ydk.providers._importer import _yang_ns
_meta_table = {
'InfraStatistics.Interfaces.Interface.Cache.Protocols.Protocol' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Cache.Protocols.Protocol',
False,
[
_MetaInfoClassMember('protocol-name', ATTRIBUTE, 'str' , None, None,
[], ['[\\w\\-\\.:,_@#%$\\+=\\|;]+'],
''' Name of the protocol
''',
'protocol_name',
'Cisco-IOS-XR-infra-statsd-oper', True),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocol', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Protocol number
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocol',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Cache.Protocols' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Cache.Protocols',
False,
[
_MetaInfoClassMember('protocol', REFERENCE_LIST, 'Protocol' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Cache.Protocols.Protocol',
[], [],
''' Interface counters per protocol
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocols',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Cache.InterfacesMibCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Cache.InterfacesMibCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'interfaces-mib-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Cache.DataRate' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Cache.DataRate',
False,
[
_MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Bandwidth (in kbps)
''',
'bandwidth',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Input load as fraction of 255
''',
'input_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('load-interval', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of 30-sec intervals less one
''',
'load_interval',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Output load as fraction of 255
''',
'output_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input data rate
''',
'peak_input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input packet rate
''',
'peak_input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output data rate
''',
'peak_output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output packet rate
''',
'peak_output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('reliability', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Reliability coefficient
''',
'reliability',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'data-rate',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Cache.GenericCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Cache.GenericCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'generic-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Cache' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Cache',
False,
[
_MetaInfoClassMember('data-rate', REFERENCE_CLASS, 'DataRate' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Cache.DataRate',
[], [],
''' Datarate information
''',
'data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('generic-counters', REFERENCE_CLASS, 'GenericCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Cache.GenericCounters',
[], [],
''' Generic set of interface counters
''',
'generic_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('interfaces-mib-counters', REFERENCE_CLASS, 'InterfacesMibCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Cache.InterfacesMibCounters',
[], [],
''' Set of interface counters as displayed by the
InterfacesMIB
''',
'interfaces_mib_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocols', REFERENCE_CLASS, 'Protocols' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Cache.Protocols',
[], [],
''' List of protocols
''',
'protocols',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'cache',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Latest.Protocols.Protocol' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Latest.Protocols.Protocol',
False,
[
_MetaInfoClassMember('protocol-name', ATTRIBUTE, 'str' , None, None,
[], ['[\\w\\-\\.:,_@#%$\\+=\\|;]+'],
''' Name of the protocol
''',
'protocol_name',
'Cisco-IOS-XR-infra-statsd-oper', True),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocol', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Protocol number
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocol',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Latest.Protocols' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Latest.Protocols',
False,
[
_MetaInfoClassMember('protocol', REFERENCE_LIST, 'Protocol' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Latest.Protocols.Protocol',
[], [],
''' Interface counters per protocol
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocols',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Latest.InterfacesMibCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Latest.InterfacesMibCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'interfaces-mib-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Latest.DataRate' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Latest.DataRate',
False,
[
_MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Bandwidth (in kbps)
''',
'bandwidth',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Input load as fraction of 255
''',
'input_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('load-interval', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of 30-sec intervals less one
''',
'load_interval',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Output load as fraction of 255
''',
'output_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input data rate
''',
'peak_input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input packet rate
''',
'peak_input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output data rate
''',
'peak_output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output packet rate
''',
'peak_output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('reliability', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Reliability coefficient
''',
'reliability',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'data-rate',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Latest.GenericCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Latest.GenericCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'generic-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Latest' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Latest',
False,
[
_MetaInfoClassMember('data-rate', REFERENCE_CLASS, 'DataRate' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Latest.DataRate',
[], [],
''' Datarate information
''',
'data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('generic-counters', REFERENCE_CLASS, 'GenericCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Latest.GenericCounters',
[], [],
''' Generic set of interface counters
''',
'generic_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('interfaces-mib-counters', REFERENCE_CLASS, 'InterfacesMibCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Latest.InterfacesMibCounters',
[], [],
''' Set of interface counters as displayed by the
InterfacesMIB
''',
'interfaces_mib_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocols', REFERENCE_CLASS, 'Protocols' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Latest.Protocols',
[], [],
''' List of protocols
''',
'protocols',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'latest',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Total.Protocols.Protocol' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Total.Protocols.Protocol',
False,
[
_MetaInfoClassMember('protocol-name', ATTRIBUTE, 'str' , None, None,
[], ['[\\w\\-\\.:,_@#%$\\+=\\|;]+'],
''' Name of the protocol
''',
'protocol_name',
'Cisco-IOS-XR-infra-statsd-oper', True),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocol', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Protocol number
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocol',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Total.Protocols' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Total.Protocols',
False,
[
_MetaInfoClassMember('protocol', REFERENCE_LIST, 'Protocol' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Total.Protocols.Protocol',
[], [],
''' Interface counters per protocol
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocols',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Total.InterfacesMibCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Total.InterfacesMibCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'interfaces-mib-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Total.DataRate' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Total.DataRate',
False,
[
_MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Bandwidth (in kbps)
''',
'bandwidth',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Input load as fraction of 255
''',
'input_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('load-interval', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of 30-sec intervals less one
''',
'load_interval',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Output load as fraction of 255
''',
'output_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input data rate
''',
'peak_input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input packet rate
''',
'peak_input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output data rate
''',
'peak_output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output packet rate
''',
'peak_output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('reliability', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Reliability coefficient
''',
'reliability',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'data-rate',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Total.GenericCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Total.GenericCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'generic-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Total' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Total',
False,
[
_MetaInfoClassMember('data-rate', REFERENCE_CLASS, 'DataRate' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Total.DataRate',
[], [],
''' Datarate information
''',
'data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('generic-counters', REFERENCE_CLASS, 'GenericCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Total.GenericCounters',
[], [],
''' Generic set of interface counters
''',
'generic_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('interfaces-mib-counters', REFERENCE_CLASS, 'InterfacesMibCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Total.InterfacesMibCounters',
[], [],
''' Set of interface counters as displayed by the
InterfacesMIB
''',
'interfaces_mib_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocols', REFERENCE_CLASS, 'Protocols' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Total.Protocols',
[], [],
''' List of protocols
''',
'protocols',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'total',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Protocols.Protocol' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Protocols.Protocol',
False,
[
_MetaInfoClassMember('protocol-name', ATTRIBUTE, 'str' , None, None,
[], ['[\\w\\-\\.:,_@#%$\\+=\\|;]+'],
''' Name of the protocol
''',
'protocol_name',
'Cisco-IOS-XR-infra-statsd-oper', True),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocol', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Protocol number
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocol',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.Protocols' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.Protocols',
False,
[
_MetaInfoClassMember('protocol', REFERENCE_LIST, 'Protocol' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Protocols.Protocol',
[], [],
''' Interface counters per protocol
''',
'protocol',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'protocols',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.InterfacesMibCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.InterfacesMibCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'interfaces-mib-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.DataRate' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.DataRate',
False,
[
_MetaInfoClassMember('bandwidth', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Bandwidth (in kbps)
''',
'bandwidth',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input data rate in 1000's of bps
''',
'input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Input load as fraction of 255
''',
'input_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Input packets per second
''',
'input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('load-interval', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of 30-sec intervals less one
''',
'load_interval',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output data rate in 1000's of bps
''',
'output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-load', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Output load as fraction of 255
''',
'output_load',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Output packets per second
''',
'output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input data rate
''',
'peak_input_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-input-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak input packet rate
''',
'peak_input_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-data-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output data rate
''',
'peak_output_data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('peak-output-packet-rate', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Peak output packet rate
''',
'peak_output_packet_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('reliability', ATTRIBUTE, 'int' , None, None,
[('0', '255')], [],
''' Reliability coefficient
''',
'reliability',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'data-rate',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface.GenericCounters' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface.GenericCounters',
False,
[
_MetaInfoClassMember('applique', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Applique
''',
'applique',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('availability-flag', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Availability bit mask
''',
'availability_flag',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets received
''',
'broadcast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('broadcast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Broadcast packets sent
''',
'broadcast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes received
''',
'bytes_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('bytes-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Bytes sent
''',
'bytes_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('carrier-transitions', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Carrier transitions
''',
'carrier_transitions',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('crc-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input CRC errors
''',
'crc_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('framing-errors-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Framing-errors received
''',
'framing_errors_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('giant-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received giant packets
''',
'giant_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-aborts', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input aborts
''',
'input_aborts',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input drops
''',
'input_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total input errors
''',
'input_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-ignored-packets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input ignored packets
''',
'input_ignored_packets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-overruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input overruns
''',
'input_overruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('input-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Input queue drops
''',
'input_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-data-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Time when counters were last written (in
seconds)
''',
'last_data_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('last-discontinuity-time', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' SysUpTime when counters were last reset (in
seconds)
''',
'last_discontinuity_time',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets received
''',
'multicast_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('multicast-packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Multicast packets sent
''',
'multicast_packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffer-failures', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffer failures
''',
'output_buffer_failures',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-buffers-swapped-out', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output buffers swapped out
''',
'output_buffers_swapped_out',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output drops
''',
'output_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-errors', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Total output errors
''',
'output_errors',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-queue-drops', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output queue drops
''',
'output_queue_drops',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('output-underruns', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Output underruns
''',
'output_underruns',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets received
''',
'packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('packets-sent', ATTRIBUTE, 'int' , None, None,
[('0', '18446744073709551615')], [],
''' Packets sent
''',
'packets_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('parity-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received parity packets
''',
'parity_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('resets', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of board resets
''',
'resets',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('runt-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received runt packets
''',
'runt_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-last-clear-counters', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Number of seconds since last clear counters
''',
'seconds_since_last_clear_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet received
''',
'seconds_since_packet_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('seconds-since-packet-sent', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Seconds since packet sent
''',
'seconds_since_packet_sent',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('throttled-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Received throttled packets
''',
'throttled_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('unknown-protocol-packets-received', ATTRIBUTE, 'int' , None, None,
[('0', '4294967295')], [],
''' Unknown protocol packets received
''',
'unknown_protocol_packets_received',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'generic-counters',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces.Interface' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces.Interface',
False,
[
_MetaInfoClassMember('interface-name', ATTRIBUTE, 'str' , None, None,
[], ['(([a-zA-Z0-9_]*\\d+/){3,4}\\d+)|(([a-zA-Z0-9_]*\\d+/){3,4}\\d+\\.\\d+)|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]*\\d+))|(([a-zA-Z0-9_]*\\d+/){2}([a-zA-Z0-9_]+))|([a-zA-Z0-9_-]*\\d+)|([a-zA-Z0-9_-]*\\d+\\.\\d+)|(mpls)|(dwdm)'],
''' Name of the interface
''',
'interface_name',
'Cisco-IOS-XR-infra-statsd-oper', True),
_MetaInfoClassMember('cache', REFERENCE_CLASS, 'Cache' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Cache',
[], [],
''' Cached stats data of interfaces
''',
'cache',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('data-rate', REFERENCE_CLASS, 'DataRate' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.DataRate',
[], [],
''' Datarate information
''',
'data_rate',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('generic-counters', REFERENCE_CLASS, 'GenericCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.GenericCounters',
[], [],
''' Generic set of interface counters
''',
'generic_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('interfaces-mib-counters', REFERENCE_CLASS, 'InterfacesMibCounters' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.InterfacesMibCounters',
[], [],
''' Set of interface counters as displayed by the
InterfacesMIB
''',
'interfaces_mib_counters',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('latest', REFERENCE_CLASS, 'Latest' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Latest',
[], [],
''' Latest stats data of interfaces
''',
'latest',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('protocols', REFERENCE_CLASS, 'Protocols' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Protocols',
[], [],
''' List of protocols
''',
'protocols',
'Cisco-IOS-XR-infra-statsd-oper', False),
_MetaInfoClassMember('total', REFERENCE_CLASS, 'Total' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface.Total',
[], [],
''' Total stats data of interfaces
''',
'total',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'interface',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics.Interfaces' : {
'meta_info' : _MetaInfoClass('InfraStatistics.Interfaces',
False,
[
_MetaInfoClassMember('interface', REFERENCE_LIST, 'Interface' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces.Interface',
[], [],
''' Statistics of an interface
''',
'interface',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'interfaces',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
'InfraStatistics' : {
'meta_info' : _MetaInfoClass('InfraStatistics',
False,
[
_MetaInfoClassMember('interfaces', REFERENCE_CLASS, 'Interfaces' , 'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper', 'InfraStatistics.Interfaces',
[], [],
''' List of interfaces
''',
'interfaces',
'Cisco-IOS-XR-infra-statsd-oper', False),
],
'Cisco-IOS-XR-infra-statsd-oper',
'infra-statistics',
_yang_ns._namespaces['Cisco-IOS-XR-infra-statsd-oper'],
'ydk.models.cisco_ios_xr.Cisco_IOS_XR_infra_statsd_oper'
),
},
}
_meta_table['InfraStatistics.Interfaces.Interface.Cache.Protocols.Protocol']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Cache.Protocols']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Cache.Protocols']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Cache']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Cache.InterfacesMibCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Cache']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Cache.DataRate']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Cache']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Cache.GenericCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Cache']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Latest.Protocols.Protocol']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Latest.Protocols']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Latest.Protocols']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Latest']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Latest.InterfacesMibCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Latest']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Latest.DataRate']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Latest']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Latest.GenericCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Latest']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Total.Protocols.Protocol']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Total.Protocols']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Total.Protocols']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Total']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Total.InterfacesMibCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Total']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Total.DataRate']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Total']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Total.GenericCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Total']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Protocols.Protocol']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface.Protocols']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Cache']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Latest']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Total']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.Protocols']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.InterfacesMibCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.DataRate']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface.GenericCounters']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces.Interface']['meta_info']
_meta_table['InfraStatistics.Interfaces.Interface']['meta_info'].parent =_meta_table['InfraStatistics.Interfaces']['meta_info']
_meta_table['InfraStatistics.Interfaces']['meta_info'].parent =_meta_table['InfraStatistics']['meta_info']
| 51.66406
| 245
| 0.47592
| 11,269
| 145,331
| 5.98172
| 0.014287
| 0.066936
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| 0.987509
| 0.98647
| 0.984468
| 0.96895
| 0.96177
| 0.953581
| 0
| 0.061167
| 0.381735
| 145,331
| 2,812
| 246
| 51.682432
| 0.689037
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| 0.756221
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| 0.000422
| 0.379188
| 0.258045
| 0
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| false
| 0
| 0.003374
| 0
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0
| 7
|
d47f43a4fb8e7a7c66a95ca0a52131e0de86a2bf
| 16,959
|
py
|
Python
|
sdk/python/pulumi_newrelic/alert_channel.py
|
bob-bins/pulumi-newrelic
|
f8a121fb7d6e6ad979d3ccf72467b9e89769e305
|
[
"ECL-2.0",
"Apache-2.0"
] | 6
|
2019-09-17T20:41:26.000Z
|
2022-01-13T23:54:14.000Z
|
sdk/python/pulumi_newrelic/alert_channel.py
|
bob-bins/pulumi-newrelic
|
f8a121fb7d6e6ad979d3ccf72467b9e89769e305
|
[
"ECL-2.0",
"Apache-2.0"
] | 136
|
2019-04-29T21:34:57.000Z
|
2022-03-30T17:07:03.000Z
|
sdk/python/pulumi_newrelic/alert_channel.py
|
bob-bins/pulumi-newrelic
|
f8a121fb7d6e6ad979d3ccf72467b9e89769e305
|
[
"ECL-2.0",
"Apache-2.0"
] | 3
|
2019-10-05T10:33:59.000Z
|
2021-06-15T16:37:49.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
from . import outputs
from ._inputs import *
__all__ = ['AlertChannelArgs', 'AlertChannel']
@pulumi.input_type
class AlertChannelArgs:
def __init__(__self__, *,
type: pulumi.Input[str],
config: Optional[pulumi.Input['AlertChannelConfigArgs']] = None,
name: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a AlertChannel resource.
:param pulumi.Input[str] type: The type of channel. One of: `email`, `slack`, `opsgenie`, `pagerduty`, `victorops`, or `webhook`.
:param pulumi.Input['AlertChannelConfigArgs'] config: A nested block that describes an alert channel configuration. Only one config block is permitted per alert channel definition. See Nested config blocks below for details.
:param pulumi.Input[str] name: The name of the channel.
"""
pulumi.set(__self__, "type", type)
if config is not None:
pulumi.set(__self__, "config", config)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter
def type(self) -> pulumi.Input[str]:
"""
The type of channel. One of: `email`, `slack`, `opsgenie`, `pagerduty`, `victorops`, or `webhook`.
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: pulumi.Input[str]):
pulumi.set(self, "type", value)
@property
@pulumi.getter
def config(self) -> Optional[pulumi.Input['AlertChannelConfigArgs']]:
"""
A nested block that describes an alert channel configuration. Only one config block is permitted per alert channel definition. See Nested config blocks below for details.
"""
return pulumi.get(self, "config")
@config.setter
def config(self, value: Optional[pulumi.Input['AlertChannelConfigArgs']]):
pulumi.set(self, "config", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the channel.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@pulumi.input_type
class _AlertChannelState:
def __init__(__self__, *,
config: Optional[pulumi.Input['AlertChannelConfigArgs']] = None,
name: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering AlertChannel resources.
:param pulumi.Input['AlertChannelConfigArgs'] config: A nested block that describes an alert channel configuration. Only one config block is permitted per alert channel definition. See Nested config blocks below for details.
:param pulumi.Input[str] name: The name of the channel.
:param pulumi.Input[str] type: The type of channel. One of: `email`, `slack`, `opsgenie`, `pagerduty`, `victorops`, or `webhook`.
"""
if config is not None:
pulumi.set(__self__, "config", config)
if name is not None:
pulumi.set(__self__, "name", name)
if type is not None:
pulumi.set(__self__, "type", type)
@property
@pulumi.getter
def config(self) -> Optional[pulumi.Input['AlertChannelConfigArgs']]:
"""
A nested block that describes an alert channel configuration. Only one config block is permitted per alert channel definition. See Nested config blocks below for details.
"""
return pulumi.get(self, "config")
@config.setter
def config(self, value: Optional[pulumi.Input['AlertChannelConfigArgs']]):
pulumi.set(self, "config", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the channel.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def type(self) -> Optional[pulumi.Input[str]]:
"""
The type of channel. One of: `email`, `slack`, `opsgenie`, `pagerduty`, `victorops`, or `webhook`.
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "type", value)
class AlertChannel(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
config: Optional[pulumi.Input[pulumi.InputType['AlertChannelConfigArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Use this resource to create and manage New Relic alert channels.
## Example Usage
### Email
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
include_json_attachment="true",
recipients="foo@example.com",
),
type="email")
```
### Slack
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
channel="example-alerts-channel",
url="https://hooks.slack.com/services/XXXXXXX/XXXXXXX/XXXXXXXXXX",
),
type="slack")
```
### OpsGenie
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
api_key="abc123",
recipients="user1@domain.com, user2@domain.com",
tags="tag1, tag2",
teams="team1, team2",
),
type="opsgenie")
```
### PagerDuty
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
service_key="abc123",
),
type="pagerduty")
```
### VictorOps
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
key="abc123",
route_key="/example",
),
type="victorops")
```
### Webhook
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
type="webhook",
config=newrelic.AlertChannelConfigArgs(
base_url="http://www.test.com",
payload_type="application/json",
payload={
"condition_name": "$CONDITION_NAME",
"policy_name": "$POLICY_NAME",
},
headers={
"header1": value1,
"header2": value2,
},
))
```
### Webhook with complex payload
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
base_url="http://www.test.com",
payload_string=\"\"\"{
"my_custom_values": {
"condition_name": "$CONDITION_NAME",
"policy_name": "$POLICY_NAME"
}
}
\"\"\",
payload_type="application/json",
),
type="webhook")
```
## Import
Alert channels can be imported using the `id`, e.g. bash
```sh
$ pulumi import newrelic:index/alertChannel:AlertChannel main <id>
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['AlertChannelConfigArgs']] config: A nested block that describes an alert channel configuration. Only one config block is permitted per alert channel definition. See Nested config blocks below for details.
:param pulumi.Input[str] name: The name of the channel.
:param pulumi.Input[str] type: The type of channel. One of: `email`, `slack`, `opsgenie`, `pagerduty`, `victorops`, or `webhook`.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: AlertChannelArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Use this resource to create and manage New Relic alert channels.
## Example Usage
### Email
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
include_json_attachment="true",
recipients="foo@example.com",
),
type="email")
```
### Slack
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
channel="example-alerts-channel",
url="https://hooks.slack.com/services/XXXXXXX/XXXXXXX/XXXXXXXXXX",
),
type="slack")
```
### OpsGenie
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
api_key="abc123",
recipients="user1@domain.com, user2@domain.com",
tags="tag1, tag2",
teams="team1, team2",
),
type="opsgenie")
```
### PagerDuty
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
service_key="abc123",
),
type="pagerduty")
```
### VictorOps
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
key="abc123",
route_key="/example",
),
type="victorops")
```
### Webhook
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
type="webhook",
config=newrelic.AlertChannelConfigArgs(
base_url="http://www.test.com",
payload_type="application/json",
payload={
"condition_name": "$CONDITION_NAME",
"policy_name": "$POLICY_NAME",
},
headers={
"header1": value1,
"header2": value2,
},
))
```
### Webhook with complex payload
```python
import pulumi
import pulumi_newrelic as newrelic
foo = newrelic.AlertChannel("foo",
config=newrelic.AlertChannelConfigArgs(
base_url="http://www.test.com",
payload_string=\"\"\"{
"my_custom_values": {
"condition_name": "$CONDITION_NAME",
"policy_name": "$POLICY_NAME"
}
}
\"\"\",
payload_type="application/json",
),
type="webhook")
```
## Import
Alert channels can be imported using the `id`, e.g. bash
```sh
$ pulumi import newrelic:index/alertChannel:AlertChannel main <id>
```
:param str resource_name: The name of the resource.
:param AlertChannelArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(AlertChannelArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
config: Optional[pulumi.Input[pulumi.InputType['AlertChannelConfigArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = AlertChannelArgs.__new__(AlertChannelArgs)
__props__.__dict__["config"] = config
__props__.__dict__["name"] = name
if type is None and not opts.urn:
raise TypeError("Missing required property 'type'")
__props__.__dict__["type"] = type
super(AlertChannel, __self__).__init__(
'newrelic:index/alertChannel:AlertChannel',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
config: Optional[pulumi.Input[pulumi.InputType['AlertChannelConfigArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
type: Optional[pulumi.Input[str]] = None) -> 'AlertChannel':
"""
Get an existing AlertChannel resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['AlertChannelConfigArgs']] config: A nested block that describes an alert channel configuration. Only one config block is permitted per alert channel definition. See Nested config blocks below for details.
:param pulumi.Input[str] name: The name of the channel.
:param pulumi.Input[str] type: The type of channel. One of: `email`, `slack`, `opsgenie`, `pagerduty`, `victorops`, or `webhook`.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _AlertChannelState.__new__(_AlertChannelState)
__props__.__dict__["config"] = config
__props__.__dict__["name"] = name
__props__.__dict__["type"] = type
return AlertChannel(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def config(self) -> pulumi.Output[Optional['outputs.AlertChannelConfig']]:
"""
A nested block that describes an alert channel configuration. Only one config block is permitted per alert channel definition. See Nested config blocks below for details.
"""
return pulumi.get(self, "config")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
The name of the channel.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
The type of channel. One of: `email`, `slack`, `opsgenie`, `pagerduty`, `victorops`, or `webhook`.
"""
return pulumi.get(self, "type")
| 36.314775
| 252
| 0.581107
| 1,691
| 16,959
| 5.667061
| 0.126552
| 0.049358
| 0.040906
| 0.037567
| 0.815194
| 0.793488
| 0.780445
| 0.772201
| 0.747156
| 0.743504
| 0
| 0.003344
| 0.312342
| 16,959
| 466
| 253
| 36.392704
| 0.818385
| 0.499971
| 0
| 0.62585
| 1
| 0
| 0.09687
| 0.041315
| 0
| 0
| 0
| 0
| 0
| 1
| 0.14966
| false
| 0.006803
| 0.047619
| 0
| 0.285714
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2e12f14c4db8c9d46779b35c9b6008032e7faa8f
| 2,184
|
py
|
Python
|
schoolNet/AutoCheckIn/encrypt.py
|
qhlai/hitsz_srun_autoconnect
|
b76489f825f7197b8d72cafcf0581e047478c7ed
|
[
"MIT"
] | 2
|
2021-07-30T09:12:57.000Z
|
2021-11-19T03:18:49.000Z
|
schoolNet/AutoCheckIn/encrypt.py
|
qhlai/hitsz_srun_autoconnect
|
b76489f825f7197b8d72cafcf0581e047478c7ed
|
[
"MIT"
] | null | null | null |
schoolNet/AutoCheckIn/encrypt.py
|
qhlai/hitsz_srun_autoconnect
|
b76489f825f7197b8d72cafcf0581e047478c7ed
|
[
"MIT"
] | null | null | null |
import os,sys
#加密
def encrypt(path,password,GivenNewFileName):
#因为刚学可能有库可以直接获取这些信息吧，不过自己写个算法获取这些信息也没什么难度
fileFullName = path.split(os.path.sep)#os.path.sep为操作系统的文件分隔符
fileName = fileFullName[len(fileFullName)-1].split(".")[0]
fileSuffix = fileFullName[len(fileFullName)-1].split(".")[1]
# print("文件全名称:",fileFullName[len(fileFullName)-1])
# print("文件名称:",fileName)
# print("文件后缀:",fileSuffix)
fileParent = path[0:len(path)-len(fileFullName[len(fileFullName)-1])]
#newFileName="encrypt_"+fileFullName[len(fileFullName)-1]
newFileName=GivenNewFileName
newFilePath=fileParent+newFileName
# print("文件父路径:",fileParent)
# print("新的文件名称:",newFileName)
# print("新的文件全路径:", newFilePath)
f_read = open(path,"rb")
f_write = open(newFilePath,"wb")
count=0 #当前密码加密索引
#我们采用异或循环加密
for now in f_read:
for nowByte in now:
newByte=nowByte^ord(password[count%len(password)])
count+=1
f_write.write(bytes([newByte]))
f_read.close()
f_write.close()
#解密（因为我们采取的异或解密，所以其实和加密算法一样）
def decrypt(path, password,GivenNewFileName):
# 因为刚学可能有库可以直接获取这些信息吧，不过自己写个算法获取这些信息也没什么难度
fileFullName = path.split(os.path.sep) # os.path.sep为操作系统的文件分隔符
fileName = fileFullName[len(fileFullName) - 1].split(".")[0]
fileSuffix = fileFullName[len(fileFullName) - 1].split(".")[1]
# print("文件全名称:", fileFullName[len(fileFullName)-1])
# print("文件名称:", fileName)
# print("文件后缀:", fileSuffix)
fileParent = path[0:len(path) - len(fileFullName[len(fileFullName)-1])]
#newFileName = "decrypt_" + fileFullName[len(fileFullName) - 1]
newFileName=GivenNewFileName
newFilePath = fileParent + newFileName
# print("文件父路径:", fileParent)
# print("新的文件名称:", newFileName)
# print("新的文件全路径:", newFilePath)
f_read = open(path, "rb")
f_write = open(newFilePath, "wb")
count = 0 # 当前密码加密索引
# 我们采用异或循环加密
for now in f_read:
for nowByte in now:
newByte = nowByte ^ ord(password[count % len(password)])
count += 1
f_write.write(bytes([newByte]))
f_read.close()
f_write.close()
| 30.760563
| 75
| 0.656136
| 237
| 2,184
| 5.987342
| 0.21519
| 0.12685
| 0.190275
| 0.197322
| 0.949965
| 0.949965
| 0.949965
| 0.949965
| 0.949965
| 0.949965
| 0
| 0.011383
| 0.195513
| 2,184
| 70
| 76
| 31.2
| 0.796244
| 0.315934
| 0
| 0.914286
| 0
| 0
| 0.008163
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057143
| false
| 0.114286
| 0.028571
| 0
| 0.085714
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 10
|
2e49d370775c14c362bd8a28461c205544179183
| 178
|
py
|
Python
|
aux/protocol/soap/__init__.py
|
bischjer/auxiliary
|
e42d8a4af43c9bd4d816c03edc2465640635b46b
|
[
"BSD-3-Clause"
] | null | null | null |
aux/protocol/soap/__init__.py
|
bischjer/auxiliary
|
e42d8a4af43c9bd4d816c03edc2465640635b46b
|
[
"BSD-3-Clause"
] | null | null | null |
aux/protocol/soap/__init__.py
|
bischjer/auxiliary
|
e42d8a4af43c9bd4d816c03edc2465640635b46b
|
[
"BSD-3-Clause"
] | null | null | null |
from aux.protocol.soap.soap import SOAPRequest, SOAPResponse
#TODO: REFACTOR from aux.protocol.http.https import HTTPSConnection
# from aux.protocol.soap.wsdl import WSDLClient
| 35.6
| 67
| 0.831461
| 24
| 178
| 6.166667
| 0.583333
| 0.141892
| 0.304054
| 0.256757
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095506
| 178
| 4
| 68
| 44.5
| 0.919255
| 0.629213
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
2e616db57b4dbc7fd40db0d01670ec0842353abc
| 24,853
|
py
|
Python
|
graphical_analysis/query_scenario_additional_layer_analysis.py
|
sjuenger/WikiMETA
|
13ed293b4bda8ff0fc10b532907ca35c24a12616
|
[
"MIT"
] | null | null | null |
graphical_analysis/query_scenario_additional_layer_analysis.py
|
sjuenger/WikiMETA
|
13ed293b4bda8ff0fc10b532907ca35c24a12616
|
[
"MIT"
] | null | null | null |
graphical_analysis/query_scenario_additional_layer_analysis.py
|
sjuenger/WikiMETA
|
13ed293b4bda8ff0fc10b532907ca35c24a12616
|
[
"MIT"
] | null | null | null |
import os
import json
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# plot only the NON-redundant data
def plot_additional_layer_information_about_scenarios_per_timeframe_for_OPTIONAL_MINUS_SUBSELECT_UNION_FILTER(timeframes,
metadata,
scenario):
if metadata not in ["reference_metadata", "rank_metadata", "qualifier_metadata"]:
raise Exception
if scenario not in ["optional", "minus", "subselect", "union", "filter"]:
raise Exception
csv_ready_scenario_dict = {}
csv_ready_scenario_dict["timeframe"] = []
csv_ready_scenario_dict["datatype"] = []
csv_ready_scenario_dict["base scenario"] = []
csv_ready_scenario_dict["scenario name"] = []
csv_ready_scenario_dict["scenario count"] = []
csv_ready_scenario_dict["scenario percentage"] = []
csv_ready_scenario_dict["total scenarios"] = []
for timeframe in timeframes:
# get the path to the location information of the timeframe scenario data per
# datatype
information_path = "data/" + timeframe[:21] + "/" + timeframe[22:] + \
"/" + metadata + \
"/scenarios/non_redundant/" + scenario + "_statistical_information.json"
if os.path.exists(information_path):
with open(information_path, "r") as stat_info_scenarios_data:
stat_info_scenarios_dict = json.load(stat_info_scenarios_data)
for ID in stat_info_scenarios_dict["metadata_found_in_scenarios"]:
# to exclude the datatypes, e.g. 'total' or 'only_derived
if ID != "datatype":
csv_ready_scenario_dict["scenario name"].append(ID)
csv_ready_scenario_dict["scenario count"]. \
append(stat_info_scenarios_dict["metadata_found_in_scenarios"][ID])
total_occurrences = stat_info_scenarios_dict["total_found_metadata"]
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(int(stat_info_scenarios_dict["metadata_found_in_scenarios"][ID]) /
total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["timeframe"]. \
append(timeframe[:21].replace("_", "-\n"))
csv_ready_scenario_dict["datatype"].append(metadata)
csv_ready_scenario_dict["base scenario"].append(scenario)
# insert the total data
overall_information_path = "data/statistical_information/query_research/" + "non_redundant" + \
"/" + metadata + "/scenarios/additional_layer/" \
+ scenario +"_statistical_information.json"
with open(overall_information_path, "r") as overall_data:
overall_dict = json.load(overall_data)
for ID in overall_dict["metadata_found_in_scenarios"]:
csv_ready_scenario_dict["timeframe"].append("total")
csv_ready_scenario_dict["scenario name"].append(ID)
csv_ready_scenario_dict["scenario count"]. \
append(overall_dict["metadata_found_in_scenarios"][ID])
total_occurrences = overall_dict["total_found_metadata"]
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(int(overall_dict["metadata_found_in_scenarios"][ID]) /
total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["datatype"].append(metadata)
csv_ready_scenario_dict["base scenario"].append(scenario)
# plot the data in a heatmap
tmp_dict = {}
tmp_dict["scenario name"] = []
tmp_dict["timeframe"] = []
tmp_dict["scenario percentage"] = []
for i in range(len(csv_ready_scenario_dict["timeframe"])):
tmp_dict["scenario name"].append(csv_ready_scenario_dict["scenario name"][i])
tmp_dict["timeframe"].append(csv_ready_scenario_dict["timeframe"][i])
tmp_dict["scenario percentage"].append(\
csv_ready_scenario_dict["scenario percentage"][i])
df = pd.DataFrame(tmp_dict)
df = pd.pivot_table(data=df,
index='scenario name',
values='scenario percentage',
columns='timeframe', sort=False)
mask = (df == 0)
fig, ax = plt.subplots(figsize=(9, 10)) # 16 10
tmp = sns.heatmap(df, ax=ax, annot=True, vmin = 0, vmax = 1, mask=mask, cmap="YlOrRd",
linewidths=.5)
tmp.figure.tight_layout()
#tmp.figure.subplots_adjust(left=0.2, bottom=-0.2)
plt.gcf().autofmt_xdate()
save_path = "data/statistical_information/query_research/non_redundant/" \
+ metadata + "/scenarios/additional_layer/" + scenario + ".pdf"
tmp.get_figure().savefig(save_path)
plt.close()
# plot only the NON-redundant data
def plot_additional_additional_layer_information_about_scenarios_per_timeframe_for_SUBSELECT_UNION(timeframes,
metadata,
scenario):
if metadata not in ["reference_metadata", "rank_metadata", "qualifier_metadata"]:
raise Exception
if scenario not in ["subselect", "union"]:
raise Exception
csv_ready_scenario_dict = {}
csv_ready_scenario_dict["timeframe"] = []
csv_ready_scenario_dict["datatype"] = []
csv_ready_scenario_dict["base scenario"] = []
csv_ready_scenario_dict["scenario name"] = []
csv_ready_scenario_dict["scenario count"] = []
csv_ready_scenario_dict["scenario percentage"] = []
csv_ready_scenario_dict["total scenarios"] = []
for timeframe in timeframes:
# get the path to the location information of the timeframe scenario data per
# datatype
information_path = "data/" + timeframe[:21] + "/" + timeframe[22:] + \
"/" + metadata + \
"/scenarios/non_redundant/" + scenario + "_statistical_information.json"
if os.path.exists(information_path):
with open(information_path, "r") as stat_info_scenarios_data:
stat_info_scenarios_dict = json.load(stat_info_scenarios_data)
if scenario == "union":
tmpID = "scenarios_found_in_second_level_subselect"
elif scenario == "subselect":
tmpID = "scenarios_found_in_second_level_union"
for ID in stat_info_scenarios_dict[tmpID]:
# to exclude the datatypes, e.g. 'total' or 'only_derived
if ID != "datatype":
# only show scenarios in the data, that occur at least once
# lazy evaluation
if (scenario == "union" and stat_info_scenarios_dict["scenarios_found_in_second_level_subselect"][ID] > 0)\
or\
(scenario == "subselect" and stat_info_scenarios_dict["scenarios_found_in_second_level_union"][ID] > 0):
csv_ready_scenario_dict["scenario name"].append(ID)
if scenario == "union":
csv_ready_scenario_dict["scenario count"]. \
append(stat_info_scenarios_dict["scenarios_found_in_second_level_subselect"][ID])
total_occurrences = stat_info_scenarios_dict["metadata_found_in_scenarios"]["subselect"]
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(
int(stat_info_scenarios_dict["scenarios_found_in_second_level_subselect"][ID]) /
total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
elif scenario == "subselect":
csv_ready_scenario_dict["scenario count"]. \
append(stat_info_scenarios_dict["scenarios_found_in_second_level_union"][ID])
total_occurrences = stat_info_scenarios_dict["metadata_found_in_scenarios"]["union"]
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(
int(stat_info_scenarios_dict["scenarios_found_in_second_level_union"][ID]) /
total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
csv_ready_scenario_dict["timeframe"]. \
append(timeframe[:21].replace("_", "-\n"))
csv_ready_scenario_dict["datatype"].append(metadata)
csv_ready_scenario_dict["base scenario"].append(scenario)
# insert the total data
overall_information_path = "data/statistical_information/query_research/" + "non_redundant" + \
"/" + metadata + "/scenarios/additional_layer/" \
+ scenario + "_statistical_information.json"
with open(overall_information_path, "r") as overall_data:
overall_dict = json.load(overall_data)
if scenario == "union":
tmpID = "scenarios_found_in_second_level_subselect"
elif scenario == "subselect":
tmpID = "scenarios_found_in_second_level_union"
for ID in overall_dict[tmpID]:
# only show scenarios in the data, that occur at least once
# lazy evaluation
if (scenario == "union" and overall_dict["scenarios_found_in_second_level_subselect"][
ID] > 0) \
or \
(scenario == "subselect" and overall_dict["scenarios_found_in_second_level_union"][
ID] > 0):
csv_ready_scenario_dict["scenario name"].append(ID)
if scenario == "union":
csv_ready_scenario_dict["scenario count"]. \
append(overall_dict["scenarios_found_in_second_level_subselect"][ID])
total_occurrences = overall_dict["metadata_found_in_scenarios"]["subselect"]
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(
int(overall_dict["scenarios_found_in_second_level_subselect"][ID]) /
total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
elif scenario == "subselect":
csv_ready_scenario_dict["scenario count"]. \
append(overall_dict["scenarios_found_in_second_level_union"][ID])
total_occurrences = overall_dict["metadata_found_in_scenarios"]["union"]
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(
int(overall_dict["scenarios_found_in_second_level_union"][ID]) /
total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
csv_ready_scenario_dict["timeframe"]. \
append("total")
csv_ready_scenario_dict["datatype"].append(metadata)
csv_ready_scenario_dict["base scenario"].append(scenario)
# plot the data in a heatmap
tmp_dict = {}
tmp_dict["scenario name"] = []
tmp_dict["timeframe"] = []
tmp_dict["scenario percentage"] = []
for i in range(len(csv_ready_scenario_dict["timeframe"])):
tmp_dict["scenario name"].append(csv_ready_scenario_dict["scenario name"][i])
tmp_dict["timeframe"].append(csv_ready_scenario_dict["timeframe"][i])
tmp_dict["scenario percentage"].append(\
csv_ready_scenario_dict["scenario percentage"][i])
df = pd.DataFrame(tmp_dict)
df = pd.pivot_table(data=df,
index='scenario name',
values='scenario percentage',
columns='timeframe', sort=False)
mask = (df == 0)
tmp = None
if scenario == "union":
fig, ax = plt.subplots(figsize=(9, 8)) # 16 10
tmp = sns.heatmap(df, ax=ax, annot=True, vmin=0, vmax=1, mask=mask, cmap="OrRd",
linewidths=.5)
elif scenario == "subselect":
fig, ax = plt.subplots(figsize=(9, 6)) # 16 10
tmp = sns.heatmap(df, ax=ax, annot=True, vmin=0, vmax=1, mask=mask, cmap="OrRd",
linewidths=.5)
tmp.figure.tight_layout()
#tmp.figure.subplots_adjust(left=0.45, bottom=0.6)
plt.gcf().autofmt_xdate()
if scenario == "union":
tmpFileName = "_additional_subselect"
elif scenario == "subselect":
tmpFileName = "_additional_union"
save_path = "data/statistical_information/query_research/non_redundant/" \
+ metadata + "/scenarios/additional_layer/" + \
scenario + tmpFileName + ".pdf"
tmp.get_figure().savefig(save_path)
plt.close()
# plot only the NON-redundant data
def plot_additional_layer_information_about_scenarios_per_datatype_for_OPTIONAL_MINUS_SUBSELECT_UNION_FILTER(timeframes,
metadata,
scenario):
if metadata not in ["reference_metadata", "rank_metadata", "qualifier_metadata"]:
raise Exception
if scenario not in ["optional", "minus", "subselect", "union", "filter"]:
raise Exception
csv_ready_scenario_dict = {}
csv_ready_scenario_dict["datatype"] = []
csv_ready_scenario_dict["base scenario"] = []
csv_ready_scenario_dict["scenario name"] = []
csv_ready_scenario_dict["scenario count"] = []
csv_ready_scenario_dict["scenario percentage"] = []
csv_ready_scenario_dict["total scenarios"] = []
# insert the total data
overall_information_path = "data/statistical_information/query_research/" + "non_redundant" + \
"/" + metadata + "/scenarios/additional_layer/" \
+ scenario +"_statistical_information.json"
with open(overall_information_path, "r") as overall_data:
overall_dict = json.load(overall_data)
for datatype in overall_dict["metadata_per_datatype_found_in_scenarios"]:
for ID in overall_dict["metadata_per_datatype_found_in_scenarios"][datatype]:
csv_ready_scenario_dict["scenario name"].append(ID)
csv_ready_scenario_dict["scenario count"]. \
append(overall_dict["metadata_per_datatype_found_in_scenarios"][datatype][ID])
total_occurrences = overall_dict["total_found_metadata"]
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(int(overall_dict["metadata_per_datatype_found_in_scenarios"][datatype][ID]) /
total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["datatype"].append(datatype.split("/")[1].
replace("_+_", " +\n").
replace("e_", "e\n"))
csv_ready_scenario_dict["base scenario"].append(scenario)
# plot the data in a heatmap
tmp_dict = {}
tmp_dict["scenario name"] = []
tmp_dict["datatype"] = []
tmp_dict["scenario percentage"] = []
for i in range(len(csv_ready_scenario_dict["datatype"])):
tmp_dict["scenario name"].append(csv_ready_scenario_dict["scenario name"][i])
tmp_dict["datatype"].append(csv_ready_scenario_dict["datatype"][i])
tmp_dict["scenario percentage"].append(\
csv_ready_scenario_dict["scenario percentage"][i])
df = pd.DataFrame(tmp_dict)
df = pd.pivot_table(data=df,
index='scenario name',
values='scenario percentage',
columns='datatype', sort=False)
mask = (df == 0)
if metadata == "rank_metadata":
fig, ax = plt.subplots(figsize=(12, 10)) # 16 10
else:
fig, ax = plt.subplots(figsize=(8, 10)) # 16 10
tmp = sns.heatmap(df, ax=ax, annot=True, vmin = 0, vmax = 1, mask=mask, cmap="BuPu",
linewidths=.5)
tmp.figure.tight_layout()
#tmp.figure.subplots_adjust(left=0.2, bottom=-0.2)
plt.gcf().autofmt_xdate()
save_path = "data/statistical_information/query_research/non_redundant/" \
+ metadata + "/scenarios/additional_layer/" + scenario + "_per_datatype.pdf"
tmp.get_figure().savefig(save_path)
plt.close()
# plot only the NON-redundant data
def plot_additional_additional_layer_information_about_scenarios_per_datatype_for_SUBSELECT_UNION(timeframes,
metadata,
scenario):
if metadata not in ["reference_metadata", "rank_metadata", "qualifier_metadata"]:
raise Exception
if scenario not in ["subselect", "union"]:
raise Exception
csv_ready_scenario_dict = {}
csv_ready_scenario_dict["datatype"] = []
csv_ready_scenario_dict["base scenario"] = []
csv_ready_scenario_dict["scenario name"] = []
csv_ready_scenario_dict["scenario count"] = []
csv_ready_scenario_dict["scenario percentage"] = []
csv_ready_scenario_dict["total scenarios"] = []
# insert the total data
overall_information_path = "data/statistical_information/query_research/" + "non_redundant" + \
"/" + metadata + "/scenarios/additional_layer/" \
+ scenario + "_statistical_information.json"
with open(overall_information_path, "r") as overall_data:
overall_dict = json.load(overall_data)
if scenario == "union":
tmpID = "scenarios_per_datatype_found_in_second_level_subselect"
elif scenario == "subselect":
tmpID = "scenarios_per_datatype_found_in_second_level_union"
for datatype in overall_dict[tmpID]:
for ID in overall_dict[tmpID][datatype]:
csv_ready_scenario_dict["scenario name"].append(ID)
if scenario == "union":
csv_ready_scenario_dict["scenario count"]. \
append(overall_dict["scenarios_per_datatype_found_in_second_level_subselect"]\
[datatype][ID])
total_occurrences = overall_dict["metadata_found_in_scenarios"]["subselect"]
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(
int(overall_dict["scenarios_per_datatype_found_in_second_level_subselect"]\
[datatype][ID]) / total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
elif scenario == "subselect":
csv_ready_scenario_dict["scenario count"]. \
append(overall_dict["scenarios_per_datatype_found_in_second_level_union"]\
[datatype][ID])
total_occurrences = overall_dict["metadata_found_in_scenarios"]["union"]
if total_occurrences > 0:
csv_ready_scenario_dict["scenario percentage"]. \
append(
int(overall_dict["scenarios_per_datatype_found_in_second_level_union"]\
[datatype][ID]) / total_occurrences)
else:
csv_ready_scenario_dict["scenario percentage"]. \
append(0)
csv_ready_scenario_dict["total scenarios"].append(total_occurrences)
csv_ready_scenario_dict["datatype"].append(datatype.split("/")[1].
replace("_+_", " +\n").
replace("e_", "e\n"))
csv_ready_scenario_dict["base scenario"].append(scenario)
# plot the data in a heatmap
tmp_dict = {}
tmp_dict["scenario name"] = []
tmp_dict["datatype"] = []
tmp_dict["scenario percentage"] = []
for i in range(len(csv_ready_scenario_dict["datatype"])):
tmp_dict["scenario name"].append(csv_ready_scenario_dict["scenario name"][i])
tmp_dict["datatype"].append(csv_ready_scenario_dict["datatype"][i])
tmp_dict["scenario percentage"].append(\
csv_ready_scenario_dict["scenario percentage"][i])
df = pd.DataFrame(tmp_dict)
df = pd.pivot_table(data=df,
index='scenario name',
values='scenario percentage',
columns='datatype', sort=False)
mask = (df == 0)
fig, ax = plt.subplots(figsize=(8, 10)) # 16 10
tmp = sns.heatmap(df, ax=ax, annot=True, vmin = 0, vmax = 1, mask=mask, cmap="PuBu",
linewidths=.5)
tmp.figure.tight_layout()
#tmp.figure.subplots_adjust(left=0.45, bottom=0.6)
plt.gcf().autofmt_xdate()
if scenario == "union":
tmpFileName = "_additional_subselect"
elif scenario == "subselect":
tmpFileName = "_additional_union"
save_path = "data/statistical_information/query_research/non_redundant/" \
+ metadata + "/scenarios/additional_layer/" + \
scenario + tmpFileName + "_per_datatype.pdf"
tmp.get_figure().savefig(save_path)
plt.close()
# plot only the NON-redundant data
def plot_additional_layer_information_about_scenarios(timeframes, metadata):
return
def intTryParse(value):
try:
return int(value), True
except ValueError:
return value, False
| 42.702749
| 132
| 0.554621
| 2,410
| 24,853
| 5.371369
| 0.06639
| 0.063654
| 0.127308
| 0.159135
| 0.978525
| 0.974508
| 0.964079
| 0.960603
| 0.951024
| 0.940672
| 0
| 0.007054
| 0.349777
| 24,853
| 581
| 133
| 42.776248
| 0.793998
| 0.041283
| 0
| 0.825641
| 0
| 0
| 0.208776
| 0.097432
| 0.010256
| 0
| 0
| 0
| 0
| 1
| 0.015385
| false
| 0
| 0.012821
| 0.002564
| 0.035897
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
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