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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
875396509fb67856ffbf8603bcb702cbf0caf0ef
| 7,105
|
py
|
Python
|
tests/Unit/Evolution/Systems/RelativisticEuler/Valencia/BoundaryCorrections/Rusanov.py
|
nilsvu/spectre
|
1455b9a8d7e92db8ad600c66f54795c29c3052ee
|
[
"MIT"
] | 117
|
2017-04-08T22:52:48.000Z
|
2022-03-25T07:23:36.000Z
|
tests/Unit/Evolution/Systems/RelativisticEuler/Valencia/BoundaryCorrections/Rusanov.py
|
GitHimanshuc/spectre
|
4de4033ba36547113293fe4dbdd77591485a4aee
|
[
"MIT"
] | 3,177
|
2017-04-07T21:10:18.000Z
|
2022-03-31T23:55:59.000Z
|
tests/Unit/Evolution/Systems/RelativisticEuler/Valencia/BoundaryCorrections/Rusanov.py
|
geoffrey4444/spectre
|
9350d61830b360e2d5b273fdd176dcc841dbefb0
|
[
"MIT"
] | 85
|
2017-04-07T19:36:13.000Z
|
2022-03-01T10:21:00.000Z
|
# Distributed under the MIT License.
# See LICENSE.txt for details.
import numpy as np
import Evolution.Systems.RelativisticEuler.Valencia.TestFunctions as valencia
def dg_package_data_tilde_d(
tilde_d, tilde_tau, tilde_s, flux_tilde_d, flux_tilde_tau, flux_tilde_s,
lapse, shift, spatial_metric, rest_mass_density, specific_internal_energy,
specific_enthalpy, spatial_velocity, normal_covector, normal_vector,
mesh_velocity, normal_dot_mesh_velocity, use_polytropic_fluid):
return tilde_d
def dg_package_data_tilde_tau(
tilde_d, tilde_tau, tilde_s, flux_tilde_d, flux_tilde_tau, flux_tilde_s,
lapse, shift, spatial_metric, rest_mass_density, specific_internal_energy,
specific_enthalpy, spatial_velocity, normal_covector, normal_vector,
mesh_velocity, normal_dot_mesh_velocity, use_polytropic_fluid):
return tilde_tau
def dg_package_data_tilde_s(
tilde_d, tilde_tau, tilde_s, flux_tilde_d, flux_tilde_tau, flux_tilde_s,
lapse, shift, spatial_metric, rest_mass_density, specific_internal_energy,
specific_enthalpy, spatial_velocity, normal_covector, normal_vector,
mesh_velocity, normal_dot_mesh_velocity, use_polytropic_fluid):
return tilde_s
def dg_package_data_normal_dot_flux_tilde_d(
tilde_d, tilde_tau, tilde_s, flux_tilde_d, flux_tilde_tau, flux_tilde_s,
lapse, shift, spatial_metric, rest_mass_density, specific_internal_energy,
specific_enthalpy, spatial_velocity, normal_covector, normal_vector,
mesh_velocity, normal_dot_mesh_velocity, use_polytropic_fluid):
return np.einsum("i,i", normal_covector, flux_tilde_d)
def dg_package_data_normal_dot_flux_tilde_tau(
tilde_d, tilde_tau, tilde_s, flux_tilde_d, flux_tilde_tau, flux_tilde_s,
lapse, shift, spatial_metric, rest_mass_density, specific_internal_energy,
specific_enthalpy, spatial_velocity, normal_covector, normal_vector,
mesh_velocity, normal_dot_mesh_velocity, use_polytropic_fluid):
return np.einsum("i,i", normal_covector, flux_tilde_tau)
def dg_package_data_normal_dot_flux_tilde_s(
tilde_d, tilde_tau, tilde_s, flux_tilde_d, flux_tilde_tau, flux_tilde_s,
lapse, shift, spatial_metric, rest_mass_density, specific_internal_energy,
specific_enthalpy, spatial_velocity, normal_covector, normal_vector,
mesh_velocity, normal_dot_mesh_velocity, use_polytropic_fluid):
return np.einsum("i,ij->j", normal_covector, flux_tilde_s)
def dg_package_data_abs_char_speed(
tilde_d, tilde_tau, tilde_s, flux_tilde_d, flux_tilde_tau, flux_tilde_s,
lapse, shift, spatial_metric, rest_mass_density, specific_internal_energy,
specific_enthalpy, spatial_velocity, normal_covector, normal_vector,
mesh_velocity, normal_dot_mesh_velocity, use_polytropic_fluid):
spatial_velocity_squared = np.einsum("ij,i,j", spatial_metric,
spatial_velocity, spatial_velocity)
# Note that the relativistic sound speed squared has a 1/enthalpy
if use_polytropic_fluid:
polytropic_constant = 1.0e-3
polytropic_exponent = 2.0
sound_speed_squared = polytropic_constant * polytropic_exponent * pow(
rest_mass_density, polytropic_exponent - 1.0) / specific_enthalpy
else:
adiabatic_index = 1.3
chi = specific_internal_energy * (adiabatic_index - 1.0)
kappa_times_p_over_rho_squared = ((adiabatic_index - 1.0)**2 *
specific_internal_energy)
sound_speed_squared = (
chi + kappa_times_p_over_rho_squared) / specific_enthalpy
char_speeds = valencia.characteristic_speeds(lapse, shift,
spatial_velocity,
spatial_velocity_squared,
sound_speed_squared,
normal_covector)
if normal_dot_mesh_velocity is None:
return np.max(np.abs(char_speeds))
else:
return np.max(np.abs(char_speeds - normal_dot_mesh_velocity))
def dg_boundary_terms_tilde_d(
interior_tilde_d, interior_tilde_tau, interior_tilde_s,
interior_normal_dot_flux_tilde_d, interior_normal_dot_flux_tilde_tau,
interior_normal_dot_flux_tilde_s, interior_abs_char_speed,
exterior_tilde_d, exterior_tilde_tau, exterior_tilde_s,
exterior_normal_dot_flux_tilde_d, exterior_normal_dot_flux_tilde_tau,
exterior_normal_dot_flux_tilde_s, exterior_abs_char_speed,
use_strong_form):
if use_strong_form:
return (-0.5 * (interior_normal_dot_flux_tilde_d +
exterior_normal_dot_flux_tilde_d) - 0.5 *
np.maximum(interior_abs_char_speed, exterior_abs_char_speed) *
(exterior_tilde_d - interior_tilde_d))
else:
return (0.5 * (interior_normal_dot_flux_tilde_d -
exterior_normal_dot_flux_tilde_d) - 0.5 *
np.maximum(interior_abs_char_speed, exterior_abs_char_speed) *
(exterior_tilde_d - interior_tilde_d))
def dg_boundary_terms_tilde_tau(
interior_tilde_d, interior_tilde_tau, interior_tilde_s,
interior_normal_dot_flux_tilde_d, interior_normal_dot_flux_tilde_tau,
interior_normal_dot_flux_tilde_s, interior_abs_char_speed,
exterior_tilde_d, exterior_tilde_tau, exterior_tilde_s,
exterior_normal_dot_flux_tilde_d, exterior_normal_dot_flux_tilde_tau,
exterior_normal_dot_flux_tilde_s, exterior_abs_char_speed,
use_strong_form):
if use_strong_form:
return (-0.5 * (interior_normal_dot_flux_tilde_tau +
exterior_normal_dot_flux_tilde_tau) - 0.5 *
np.maximum(interior_abs_char_speed, exterior_abs_char_speed) *
(exterior_tilde_tau - interior_tilde_tau))
else:
return (0.5 * (interior_normal_dot_flux_tilde_tau -
exterior_normal_dot_flux_tilde_tau) - 0.5 *
np.maximum(interior_abs_char_speed, exterior_abs_char_speed) *
(exterior_tilde_tau - interior_tilde_tau))
def dg_boundary_terms_tilde_s(
interior_tilde_d, interior_tilde_tau, interior_tilde_s,
interior_normal_dot_flux_tilde_d, interior_normal_dot_flux_tilde_tau,
interior_normal_dot_flux_tilde_s, interior_abs_char_speed,
exterior_tilde_d, exterior_tilde_tau, exterior_tilde_s,
exterior_normal_dot_flux_tilde_d, exterior_normal_dot_flux_tilde_tau,
exterior_normal_dot_flux_tilde_s, exterior_abs_char_speed,
use_strong_form):
if use_strong_form:
return (-0.5 * (interior_normal_dot_flux_tilde_s +
exterior_normal_dot_flux_tilde_s) - 0.5 *
np.maximum(interior_abs_char_speed, exterior_abs_char_speed) *
(exterior_tilde_s - interior_tilde_s))
else:
return (0.5 * (interior_normal_dot_flux_tilde_s -
exterior_normal_dot_flux_tilde_s) - 0.5 *
np.maximum(interior_abs_char_speed, exterior_abs_char_speed) *
(exterior_tilde_s - interior_tilde_s))
| 47.366667
| 78
| 0.734975
| 971
| 7,105
| 4.802266
| 0.095778
| 0.110015
| 0.092001
| 0.127386
| 0.83294
| 0.814497
| 0.790264
| 0.779112
| 0.760455
| 0.757881
| 0
| 0.006914
| 0.206052
| 7,105
| 149
| 79
| 47.684564
| 0.819713
| 0.017875
| 0
| 0.625
| 0
| 0
| 0.002724
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.083333
| false
| 0
| 0.016667
| 0.05
| 0.216667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
875a61cb4b0d6096e19e2fc6bba23b0311b1d7d0
| 20,063
|
py
|
Python
|
tests/test_client.py
|
freshbooks/freshbooks-python-sdk
|
62a3943a968819fc52aaeac7fee4575e177e8597
|
[
"MIT"
] | 3
|
2020-12-14T18:40:16.000Z
|
2021-11-27T14:55:48.000Z
|
tests/test_client.py
|
freshbooks/freshbooks-python-sdk
|
62a3943a968819fc52aaeac7fee4575e177e8597
|
[
"MIT"
] | 8
|
2020-12-17T10:51:32.000Z
|
2021-12-14T15:45:23.000Z
|
tests/test_client.py
|
freshbooks/freshbooks-python-sdk
|
62a3943a968819fc52aaeac7fee4575e177e8597
|
[
"MIT"
] | 2
|
2020-12-14T16:31:02.000Z
|
2021-08-04T19:23:41.000Z
|
from datetime import datetime
import json
from unittest.mock import patch
import httpretty
import pytest
from freshbooks import Client as FreshBooksClient
from freshbooks import FreshBooksError
from freshbooks.api.accounting import AccountingResource
from freshbooks.api.comments import CommentsResource, CommentsSubResource
from freshbooks.api.projects import ProjectsResource
from freshbooks.api.resource import HttpVerbs
from freshbooks.api.timetracking import TimetrackingResource
from freshbooks.client import API_BASE_URL
from freshbooks.errors import FreshBooksNotImplementedError, FreshBooksClientConfigError
from tests import get_fixture
class TestClientAuth:
def setup_method(self, method):
self.freshBooksClient = FreshBooksClient(
client_id="some_client",
client_secret="some_secret",
redirect_uri="https://example.com"
)
def test_get_auth_request_url(self):
auth_url = self.freshBooksClient.get_auth_request_url()
assert (
auth_url == "https://auth.freshbooks.com/service/auth/oauth/authorize?"
"client_id=some_client&response_type=code&redirect_uri=https%3A%2F%2Fexample.com"
)
def test_get_auth_request_url__with_scopes(self):
scopes = ["some:scope", "another:scope"]
auth_url = self.freshBooksClient.get_auth_request_url(scopes)
assert (
auth_url == "https://auth.freshbooks.com/service/auth/oauth/authorize?"
"client_id=some_client&response_type=code&redirect_uri=https%3A%2F%2Fexample.com"
"&scope=some%3Ascope+another%3Ascope"
)
@httpretty.activate
def test_get_auth_request_url__redirect_not_provided(self):
freshBooksClient = FreshBooksClient(client_id="some_client", client_secret="some_secret")
with pytest.raises(FreshBooksClientConfigError):
freshBooksClient.get_auth_request_url()
@httpretty.activate
def test_get_access_token(self):
url = "{}/auth/oauth/token".format(API_BASE_URL)
httpretty.register_uri(
httpretty.POST,
url,
body=json.dumps(get_fixture("auth_token_response")),
status=200
)
result = self.freshBooksClient.get_access_token("some_grant")
assert httpretty.last_request().body == (
"client_id=some_client&client_secret=some_secret&grant_type=authorization_code"
"&redirect_uri=https%3A%2F%2Fexample.com&code=some_grant").encode("utf-8")
assert self.freshBooksClient.access_token == "my_access_token"
assert result.access_token == "my_access_token"
assert self.freshBooksClient.refresh_token == "my_refresh_token"
assert result.refresh_token == "my_refresh_token"
assert self.freshBooksClient.access_token_expires_at == datetime(2010, 10, 17)
assert result.access_token_expires_at == datetime(2010, 10, 17)
@httpretty.activate
def test_get_access_token__failure(self):
url = "{}/auth/oauth/token".format(API_BASE_URL)
httpretty.register_uri(httpretty.POST, url, status=500)
try:
self.freshBooksClient.get_access_token("some_grant")
except FreshBooksError as e:
assert str(e) == "Failed to fetch access_token"
assert e.status_code == 500
@httpretty.activate
def test_get_access_token__secret_not_provided(self):
freshBooksClient = FreshBooksClient(client_id="some_client", redirect_uri="https://example.com")
with pytest.raises(FreshBooksClientConfigError):
freshBooksClient.get_access_token("some_grant")
@httpretty.activate
def test_get_access_token__redirect_not_provided(self):
freshBooksClient = FreshBooksClient(client_id="some_client", client_secret="some_secret")
with pytest.raises(FreshBooksClientConfigError):
freshBooksClient.get_access_token("some_grant")
@httpretty.activate
def test_get_refresh_token(self):
self.freshBooksClient = FreshBooksClient(
client_id="some_client",
client_secret="some_secret",
redirect_uri="https://example.com",
access_token="an_old_token",
refresh_token="an_old_refresh_token"
)
url = "{}/auth/oauth/token".format(API_BASE_URL)
httpretty.register_uri(
httpretty.POST,
url,
body=json.dumps(get_fixture("auth_token_response")),
status=200
)
result = self.freshBooksClient.refresh_access_token()
assert httpretty.last_request().body == (
"client_id=some_client&client_secret=some_secret&grant_type=refresh_token"
"&redirect_uri=https%3A%2F%2Fexample.com&refresh_token=an_old_refresh_token").encode("utf-8")
assert self.freshBooksClient.access_token == "my_access_token"
assert result.access_token == "my_access_token"
assert self.freshBooksClient.refresh_token == "my_refresh_token"
assert result.refresh_token == "my_refresh_token"
assert self.freshBooksClient.access_token_expires_at == datetime(2010, 10, 17)
assert result.access_token_expires_at == datetime(2010, 10, 17)
@httpretty.activate
def test_get_refresh_token__uninitialized_client(self):
url = "{}/auth/oauth/token".format(API_BASE_URL)
httpretty.register_uri(
httpretty.POST,
url,
body=json.dumps(get_fixture("auth_token_response")),
status=200
)
result = self.freshBooksClient.refresh_access_token("an_old_refresh_token")
assert httpretty.last_request().body == (
"client_id=some_client&client_secret=some_secret&grant_type=refresh_token"
"&redirect_uri=https%3A%2F%2Fexample.com&refresh_token=an_old_refresh_token").encode("utf-8")
assert self.freshBooksClient.access_token == "my_access_token"
assert result.access_token == "my_access_token"
assert self.freshBooksClient.refresh_token == "my_refresh_token"
assert result.refresh_token == "my_refresh_token"
assert self.freshBooksClient.access_token_expires_at == datetime(2010, 10, 17)
assert result.access_token_expires_at == datetime(2010, 10, 17)
@httpretty.activate
def test_get_refresh_token__uninitialized_client_not_provided(self):
with pytest.raises(FreshBooksClientConfigError):
self.freshBooksClient.refresh_access_token()
class TestClientResources:
def setup_method(self, method):
self.freshBooksClient = FreshBooksClient(client_id="some_client", redirect_uri="https://example.com")
@pytest.mark.parametrize(
"resource_name, single_name, delete_via_update",
[
("bills", "bill", True),
("bill_payments", "bill_payment", True),
("bill_vendors", "bill_vendor", True),
("clients", "client", True),
("credit_notes", "credit_note", True),
("estimates", "estimate", False),
("expenses", "expense", True),
("invoices", "invoice", False),
("invoice_profiles", "invoice_profile", True),
("items", "item", True),
("other_income", "other_income", False),
("payments", "payment", True),
("tasks", "task", True),
("taxes", "tax", False)
]
)
@patch.object(AccountingResource, "_get_url", return_value="some_url")
def test_accounting_resource_methods(self, mock_url, resource_name, single_name, delete_via_update):
"""Test general methods on accounting resources"""
account_id = 1234
resource_id = 2345
resource_ = getattr(self.freshBooksClient, resource_name)
list_response = {resource_name: [], "page": 1, "pages": 0, "per_page": 15, "total": 0}
single_response = {single_name: {}}
with patch.object(AccountingResource, "_request", return_value=list_response) as mock_request:
resource_.list(account_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(AccountingResource, "_request", return_value=single_response) as mock_request:
resource_.get(account_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
resource_.create(account_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.POST, data={single_name: {}})
resource_.update(account_id, resource_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={single_name: {}})
resource_.delete(account_id, resource_id)
if delete_via_update:
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={single_name: {"vis_state": 1}})
else:
mock_request.assert_called_with("some_url", HttpVerbs.DELETE)
@patch.object(AccountingResource, "_get_url", return_value="some_url")
def test_accounting_expense_categories_resource_methods(self, mock_url):
"""Test methods on accounting expense categories resource, which has only list and get"""
account_id = 1234
resource_id = 2345
list_response = {"categories": [], "page": 1, "pages": 0, "per_page": 15, "total": 0}
single_response = {"category": {}}
with patch.object(AccountingResource, "_request", return_value=list_response) as mock_request:
self.freshBooksClient.expenses_categories.list(account_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(AccountingResource, "_request", return_value=single_response) as mock_request:
self.freshBooksClient.expenses_categories.get(account_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.expenses_categories.create(account_id, {})
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.expenses_categories.update(account_id, resource_id, {})
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.expenses_categories.delete(account_id, resource_id)
@patch.object(AccountingResource, "_get_url", return_value="some_url")
def test_accounting_gateways_resource_methods(self, mock_url):
"""Test methods on accounting systems resource, which has only get"""
account_id = 1234
resource_id = 2345
list_response = {"gateways": [], "page": 1, "pages": 0, "per_page": 15, "total": 0}
single_response = {}
with patch.object(AccountingResource, "_request", return_value=list_response) as mock_request:
self.freshBooksClient.gateways.list(account_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(AccountingResource, "_request", return_value=single_response) as mock_request:
self.freshBooksClient.gateways.delete(account_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.DELETE)
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.gateways.get(account_id, resource_id)
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.gateways.create(account_id, {})
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.gateways.update(account_id, resource_id, {})
@patch.object(AccountingResource, "_get_url", return_value="some_url")
def test_accounting_staff_resource_methods(self, mock_url):
"""Test methods on accounting staff resource, which has no create"""
account_id = 1234
resource_id = 2345
list_response = {"staffs": [], "page": 1, "pages": 0, "per_page": 15, "total": 0}
single_response = {"staff": {}}
with patch.object(AccountingResource, "_request", return_value=list_response) as mock_request:
self.freshBooksClient.staff.list(account_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(AccountingResource, "_request", return_value=single_response) as mock_request:
self.freshBooksClient.staff.get(account_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.staff.create(account_id, {})
self.freshBooksClient.staff.update(account_id, resource_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={"staff": {}})
self.freshBooksClient.staff.delete(account_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={"staff": {"vis_state": 1}})
@patch.object(AccountingResource, "_get_url", return_value="some_url")
def test_accounting_system_resource_methods(self, mock_url):
"""Test methods on accounting systems resource, which has only get"""
account_id = 1234
resource_id = 2345
single_response = {"system": {}}
with patch.object(AccountingResource, "_request", return_value=single_response) as mock_request:
self.freshBooksClient.systems.get(account_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.systems.list(account_id)
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.systems.create(account_id, {})
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.systems.update(account_id, resource_id, {})
with pytest.raises(FreshBooksNotImplementedError):
self.freshBooksClient.systems.delete(account_id, resource_id)
@pytest.mark.parametrize(
"resource_name, single_name",
[
("projects", "project")
]
)
@patch.object(ProjectsResource, "_get_url", return_value="some_url")
def test_project_resource_methods(self, mock_url, resource_name, single_name):
"""Test general methods on project resources"""
business_id = 1234
resource_id = 2345
resource_ = getattr(self.freshBooksClient, resource_name)
list_response = {resource_name: [], "meta": {"page": 1, "pages": 0, "per_page": 15, "total": 0}}
single_response = {single_name: {}}
with patch.object(ProjectsResource, "_request", return_value=list_response) as mock_request:
resource_.list(business_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(ProjectsResource, "_request", return_value=single_response) as mock_request:
resource_.get(business_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
resource_.create(business_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.POST, data={single_name: {}})
resource_.update(business_id, resource_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={single_name: {}})
resource_.delete(business_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.DELETE)
@pytest.mark.parametrize(
"resource_name, single_name",
[
("time_entries", "time_entry")
]
)
@patch.object(TimetrackingResource, "_get_url", return_value="some_url")
def test_timetracking_resource_methods(self, mock_url, resource_name, single_name):
"""Test general methods on timetracking resources"""
business_id = 1234
resource_id = 2345
resource_ = getattr(self.freshBooksClient, resource_name)
list_response = {resource_name: [], "meta": {"page": 1, "pages": 0, "per_page": 15, "total": 0}}
single_response = {single_name: {}}
with patch.object(TimetrackingResource, "_request", return_value=list_response) as mock_request:
resource_.list(business_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(TimetrackingResource, "_request", return_value=single_response) as mock_request:
resource_.get(business_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
resource_.create(business_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.POST, data={single_name: {}})
resource_.update(business_id, resource_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={single_name: {}})
resource_.delete(business_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.DELETE)
@pytest.mark.parametrize(
"resource_name, single_name",
[
("services", "service"),
]
)
@patch.object(CommentsResource, "_get_url", return_value="some_url")
def test_comment_resource_methods(self, mock_url, resource_name, single_name):
"""Test general methods on comments resources"""
business_id = 1234
resource_id = 2345
resource_ = getattr(self.freshBooksClient, resource_name)
list_response = {resource_name: [], "meta": {"page": 1, "pages": 0, "per_page": 15, "total": 0}}
single_response = {single_name: {}}
with patch.object(CommentsResource, "_request", return_value=list_response) as mock_request:
resource_.list(business_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(CommentsResource, "_request", return_value=single_response) as mock_request:
resource_.get(business_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
resource_.create(business_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.POST, data={single_name: {}})
resource_.update(business_id, resource_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={single_name: {}})
resource_.delete(business_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.DELETE)
@pytest.mark.parametrize(
"resource_name, single_name",
[
("service_rates", "service_rate")
]
)
@patch.object(CommentsSubResource, "_get_url", return_value="some_url")
def test_comment_subresource_methods(self, mock_url, resource_name, single_name):
"""Test general methods on comments sub-resources"""
business_id = 1234
resource_id = 2345
resource_ = getattr(self.freshBooksClient, resource_name)
list_response = {resource_name: [], "meta": {"page": 1, "pages": 0, "per_page": 15, "total": 0}}
single_response = {single_name: {}}
with patch.object(CommentsSubResource, "_request", return_value=list_response) as mock_request:
resource_.list(business_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
with patch.object(CommentsSubResource, "_request", return_value=single_response) as mock_request:
resource_.get(business_id, resource_id)
mock_request.assert_called_with("some_url", HttpVerbs.GET)
resource_.create(business_id, resource_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.POST, data={single_name: {}})
resource_.update(business_id, resource_id, {})
mock_request.assert_called_with("some_url", HttpVerbs.PUT, data={single_name: {}})
with pytest.raises(FreshBooksNotImplementedError):
resource_.delete(business_id, resource_id)
| 46.016055
| 112
| 0.683946
| 2,236
| 20,063
| 5.80322
| 0.085868
| 0.043234
| 0.044544
| 0.060265
| 0.861051
| 0.850262
| 0.843018
| 0.797703
| 0.77443
| 0.720484
| 0
| 0.012424
| 0.20964
| 20,063
| 435
| 113
| 46.121839
| 0.80589
| 0.024822
| 0
| 0.55848
| 0
| 0
| 0.136841
| 0.03161
| 0
| 0
| 0
| 0
| 0.172515
| 1
| 0.061404
| false
| 0
| 0.04386
| 0
| 0.111111
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
5e54972385b92cde578c8cdece6b9497781364d4
| 96
|
py
|
Python
|
logonet_app/w-serve.py
|
jagadeesh-kotra/LogoNet
|
c47d63a963ba76576708511ad45e452e2cedd222
|
[
"MIT"
] | 5
|
2019-07-25T14:47:05.000Z
|
2021-10-31T13:00:46.000Z
|
logonet_app/w-serve.py
|
jagadeesh-kotra/LogoNet
|
c47d63a963ba76576708511ad45e452e2cedd222
|
[
"MIT"
] | null | null | null |
logonet_app/w-serve.py
|
jagadeesh-kotra/LogoNet
|
c47d63a963ba76576708511ad45e452e2cedd222
|
[
"MIT"
] | 1
|
2019-07-09T17:21:06.000Z
|
2019-07-09T17:21:06.000Z
|
from waitress import serve
import ln_main_app
serve(ln_main_app.app, host='0.0.0.0', port=8000)
| 24
| 49
| 0.78125
| 20
| 96
| 3.55
| 0.55
| 0.084507
| 0.253521
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091954
| 0.09375
| 96
| 3
| 50
| 32
| 0.724138
| 0
| 0
| 0
| 0
| 0
| 0.072917
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
5eb16b730873710eef9df6ebefe0e1a6e2a904f4
| 96
|
py
|
Python
|
src/utoolbox/transform/__init__.py
|
liuyenting/utoolbox-legacy
|
dfcb24701ca25a37a223cc3c14b4433e6c296bfd
|
[
"Apache-2.0"
] | 2
|
2020-09-03T06:22:14.000Z
|
2020-10-04T10:14:56.000Z
|
src/utoolbox/transform/__init__.py
|
liuyenting/utoolbox-legacy
|
dfcb24701ca25a37a223cc3c14b4433e6c296bfd
|
[
"Apache-2.0"
] | null | null | null |
src/utoolbox/transform/__init__.py
|
liuyenting/utoolbox-legacy
|
dfcb24701ca25a37a223cc3c14b4433e6c296bfd
|
[
"Apache-2.0"
] | null | null | null |
from .imresize import *
#from .mip import *
#from .pyramids import *
#from .transpose import *
| 16
| 25
| 0.708333
| 12
| 96
| 5.666667
| 0.5
| 0.441176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177083
| 96
| 5
| 26
| 19.2
| 0.860759
| 0.677083
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0d8ab696cc9dc5477fbdead406789ffe70f67e6a
| 21
|
py
|
Python
|
weight/visits/views/__init__.py
|
aleducode/lose_weightapp
|
1210cf3104c0fc929509ff6e51337dbd2dfa157e
|
[
"MIT"
] | 3
|
2019-08-21T06:18:57.000Z
|
2019-11-04T03:00:43.000Z
|
weight/visits/serializers/__init__.py
|
alejandroduquec/lose_weightapp
|
1210cf3104c0fc929509ff6e51337dbd2dfa157e
|
[
"MIT"
] | 11
|
2019-12-20T17:17:27.000Z
|
2022-03-12T00:09:01.000Z
|
weight/visits/views/__init__.py
|
SeptumDevs/lose_weightapp
|
2c39ba45aa6aef37820b385c3060c83a73f8f910
|
[
"MIT"
] | 2
|
2019-08-07T14:56:57.000Z
|
2019-09-03T00:13:31.000Z
|
from .visits import *
| 21
| 21
| 0.761905
| 3
| 21
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 21
| 1
| 21
| 21
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0ddd30b9175786708bd8ed7ebcec8d552e95b7ca
| 48
|
py
|
Python
|
users/models/__init__.py
|
lynnetsy/ms_users
|
d51bec4bb6326752889bc9e1ea1f6fb5ecef3cbb
|
[
"MIT"
] | null | null | null |
users/models/__init__.py
|
lynnetsy/ms_users
|
d51bec4bb6326752889bc9e1ea1f6fb5ecef3cbb
|
[
"MIT"
] | null | null | null |
users/models/__init__.py
|
lynnetsy/ms_users
|
d51bec4bb6326752889bc9e1ea1f6fb5ecef3cbb
|
[
"MIT"
] | null | null | null |
from .model import Model
from .user import User
| 16
| 24
| 0.791667
| 8
| 48
| 4.75
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 48
| 2
| 25
| 24
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
21f89e4ddd390aa80edde17e7cf787e39f018a58
| 154
|
py
|
Python
|
yaaf/agents/dqn/__init__.py
|
ilvieira/yaaf
|
931bde3dbad521bf5fb2744793f54791ca7add11
|
[
"Apache-2.0"
] | 18
|
2019-06-01T17:17:25.000Z
|
2022-01-21T16:09:20.000Z
|
yaaf/agents/dqn/__init__.py
|
ilvieira/yaaf
|
931bde3dbad521bf5fb2744793f54791ca7add11
|
[
"Apache-2.0"
] | null | null | null |
yaaf/agents/dqn/__init__.py
|
ilvieira/yaaf
|
931bde3dbad521bf5fb2744793f54791ca7add11
|
[
"Apache-2.0"
] | 2
|
2021-02-15T10:11:41.000Z
|
2021-03-20T21:38:34.000Z
|
from yaaf.agents.dqn.DQNAgent import DQNAgent
from yaaf.agents.dqn.DQNAgent import MLPDQNAgent
from yaaf.agents.dqn.DQNAgent import DeepMindAtariDQNAgent
| 38.5
| 58
| 0.863636
| 21
| 154
| 6.333333
| 0.380952
| 0.180451
| 0.315789
| 0.383459
| 0.699248
| 0.699248
| 0
| 0
| 0
| 0
| 0
| 0
| 0.077922
| 154
| 3
| 59
| 51.333333
| 0.93662
| 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
| 0
| 0
|
0
| 6
|
df3ddf51259d657fc43f347b6e0f0c1dec459c26
| 160
|
py
|
Python
|
player/__init__.py
|
JosselinSomervilleRoberts/SpeechBubbleSubtitles
|
a4467b042919f34fdd47648ae31af7df5247b6d1
|
[
"MIT"
] | 1
|
2022-01-27T19:46:02.000Z
|
2022-01-27T19:46:02.000Z
|
player/__init__.py
|
JosselinSomervilleRoberts/SpeechBubbleSubtitles
|
a4467b042919f34fdd47648ae31af7df5247b6d1
|
[
"MIT"
] | null | null | null |
player/__init__.py
|
JosselinSomervilleRoberts/SpeechBubbleSubtitles
|
a4467b042919f34fdd47648ae31af7df5247b6d1
|
[
"MIT"
] | null | null | null |
from player.videoPlayer import VideoPlayer
from player.videoPlayerWithBubbles import VideoPlayerWithBubbles
from player.videoPlayerMesh import VideoPlayerMesh
| 53.333333
| 65
| 0.9
| 15
| 160
| 9.6
| 0.4
| 0.208333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08125
| 160
| 3
| 66
| 53.333333
| 0.979592
| 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
| 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
| 6
|
df429864f7b14b85bb5b4cafa152afe464b2aa61
| 133
|
py
|
Python
|
clmm/__init__.py
|
lbaumo/CLMM
|
678422fd173c27a2bad7017b0c095a7c833bbd32
|
[
"BSD-3-Clause"
] | null | null | null |
clmm/__init__.py
|
lbaumo/CLMM
|
678422fd173c27a2bad7017b0c095a7c833bbd32
|
[
"BSD-3-Clause"
] | null | null | null |
clmm/__init__.py
|
lbaumo/CLMM
|
678422fd173c27a2bad7017b0c095a7c833bbd32
|
[
"BSD-3-Clause"
] | null | null | null |
from .galaxycluster import *
from .gcdata import *
from .polaraveraging import *
from .profilemodelling import *
from . import lsst
| 19
| 31
| 0.774436
| 15
| 133
| 6.866667
| 0.466667
| 0.38835
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 133
| 6
| 32
| 22.166667
| 0.919643
| 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
| 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
| 6
|
df6c154be9af00058d8372bc01a2e766e6950e29
| 32,868
|
py
|
Python
|
src/hapPyTango/AttNotification/__init__.py
|
mguijarr/hapPyTango
|
2506c8e83d93fbd2c0a0115983489d59c74caa2f
|
[
"MIT"
] | 1
|
2020-10-28T16:57:36.000Z
|
2020-10-28T16:57:36.000Z
|
src/hapPyTango/AttNotification/__init__.py
|
mguijarr/hapPyTango
|
2506c8e83d93fbd2c0a0115983489d59c74caa2f
|
[
"MIT"
] | null | null | null |
src/hapPyTango/AttNotification/__init__.py
|
mguijarr/hapPyTango
|
2506c8e83d93fbd2c0a0115983489d59c74caa2f
|
[
"MIT"
] | null | null | null |
""" Module: IDL:att.com/AttNotification:1.0
Automagically generated by:-
The ORB called Fnorb v1.1.Return.of.Fnorb
"""
_FNORB_ID = "IDL:att.com/AttNotification:1.0"
# Fnorb modules.
import Fnorb.orb.CORBA
import Fnorb.orb.TypeManager
import Fnorb.orb.Util
# Alias: IDL:att.com/AttNotification/IactSeq:1.0
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/IactSeq:1.0", "000000000000001300000054000000000000000E00000044000000000000002C49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F496E7465726163746976653A312E30000000000C496E7465726163746976650000000000", None)
# Alias: IDL:att.com/AttNotification/NameSeq:1.0
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/NameSeq:1.0", "00000000000000130000001000000000000000120000000000000000", None)
class Interactive(Fnorb.orb.CORBA.Object):
""" Interface: IDL:att.com/AttNotification/Interactive:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/Interactive:1.0"
def do_command(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Interactive/do_command:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
inputs.append(Fnorb.orb.CORBA.TC_string)
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.TC_string)
outputs.append(Fnorb.orb.CORBA.TC_boolean)
outputs.append(Fnorb.orb.CORBA.TC_boolean)
outputs.append(Fnorb.orb.CORBA.TC_Object)
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("do_command", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def my_name(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Interactive/my_name:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:att.com/AttNotification/NameSeq:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("my_name", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def child_names(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Interactive/child_names:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:att.com/AttNotification/NameSeq:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("child_names", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def children(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Interactive/children:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
inputs.append(Fnorb.orb.CORBA.TC_boolean)
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:att.com/AttNotification/IactSeq:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("children", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def safe_cleanup(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Interactive/safe_cleanup:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.TC_boolean)
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("safe_cleanup", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/Interactive:1.0", "000000000000000E00000044000000000000002C49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F496E7465726163746976653A312E30000000000C496E74657261637469766500", Interactive)
# Import base interface packages.
import CosEventChannelAdmin
class CosEvProxyPushSupplier(Fnorb.orb.CORBA.Object, Interactive, CosEventChannelAdmin.ProxyPushSupplier):
""" Interface: IDL:att.com/AttNotification/CosEvProxyPushSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/CosEvProxyPushSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/CosEvProxyPushSupplier:1.0", "000000000000000E0000005B000000000000003749444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F436F73457650726F787950757368537570706C6965723A312E30000000000017436F73457650726F787950757368537570706C69657200", CosEvProxyPushSupplier)
# Import base interface packages.
import CosEventChannelAdmin
class CosEvProxyPullSupplier(Fnorb.orb.CORBA.Object, Interactive, CosEventChannelAdmin.ProxyPullSupplier):
""" Interface: IDL:att.com/AttNotification/CosEvProxyPullSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/CosEvProxyPullSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/CosEvProxyPullSupplier:1.0", "000000000000000E0000005B000000000000003749444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F436F73457650726F787950756C6C537570706C6965723A312E30000000000017436F73457650726F787950756C6C537570706C69657200", CosEvProxyPullSupplier)
# Import base interface packages.
import CosEventChannelAdmin
class CosEvProxyPushConsumer(Fnorb.orb.CORBA.Object, Interactive, CosEventChannelAdmin.ProxyPushConsumer):
""" Interface: IDL:att.com/AttNotification/CosEvProxyPushConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/CosEvProxyPushConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/CosEvProxyPushConsumer:1.0", "000000000000000E0000005B000000000000003749444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F436F73457650726F787950757368436F6E73756D65723A312E30000000000017436F73457650726F787950757368436F6E73756D657200", CosEvProxyPushConsumer)
# Import base interface packages.
import CosEventChannelAdmin
class CosEvProxyPullConsumer(Fnorb.orb.CORBA.Object, Interactive, CosEventChannelAdmin.ProxyPullConsumer):
""" Interface: IDL:att.com/AttNotification/CosEvProxyPullConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/CosEvProxyPullConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/CosEvProxyPullConsumer:1.0", "000000000000000E0000005B000000000000003749444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F436F73457650726F787950756C6C436F6E73756D65723A312E30000000000017436F73457650726F787950756C6C436F6E73756D657200", CosEvProxyPullConsumer)
# Import base interface packages.
import CosNotifyChannelAdmin
class ProxyPushSupplier(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.ProxyPushSupplier):
""" Interface: IDL:att.com/AttNotification/ProxyPushSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/ProxyPushSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/ProxyPushSupplier:1.0", "000000000000000E00000052000000000000003249444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F50726F787950757368537570706C6965723A312E300000000000001250726F787950757368537570706C69657200", ProxyPushSupplier)
# Import base interface packages.
import CosNotifyChannelAdmin
class ProxyPullSupplier(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.ProxyPullSupplier):
""" Interface: IDL:att.com/AttNotification/ProxyPullSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/ProxyPullSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/ProxyPullSupplier:1.0", "000000000000000E00000052000000000000003249444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F50726F787950756C6C537570706C6965723A312E300000000000001250726F787950756C6C537570706C69657200", ProxyPullSupplier)
# Import base interface packages.
import CosNotifyChannelAdmin
class ProxyPushConsumer(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.ProxyPushConsumer):
""" Interface: IDL:att.com/AttNotification/ProxyPushConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/ProxyPushConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/ProxyPushConsumer:1.0", "000000000000000E00000052000000000000003249444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F50726F787950757368436F6E73756D65723A312E300000000000001250726F787950757368436F6E73756D657200", ProxyPushConsumer)
# Import base interface packages.
import CosNotifyChannelAdmin
class ProxyPullConsumer(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.ProxyPullConsumer):
""" Interface: IDL:att.com/AttNotification/ProxyPullConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/ProxyPullConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/ProxyPullConsumer:1.0", "000000000000000E00000052000000000000003249444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F50726F787950756C6C436F6E73756D65723A312E300000000000001250726F787950756C6C436F6E73756D657200", ProxyPullConsumer)
# Import base interface packages.
import CosNotifyChannelAdmin
class StructuredProxyPushSupplier(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.StructuredProxyPushSupplier):
""" Interface: IDL:att.com/AttNotification/StructuredProxyPushSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/StructuredProxyPushSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/StructuredProxyPushSupplier:1.0", "000000000000000E00000064000000000000003C49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F5374727563747572656450726F787950757368537570706C6965723A312E30000000001C5374727563747572656450726F787950757368537570706C69657200", StructuredProxyPushSupplier)
# Import base interface packages.
import CosNotifyChannelAdmin
class StructuredProxyPullSupplier(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.StructuredProxyPullSupplier):
""" Interface: IDL:att.com/AttNotification/StructuredProxyPullSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/StructuredProxyPullSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/StructuredProxyPullSupplier:1.0", "000000000000000E00000064000000000000003C49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F5374727563747572656450726F787950756C6C537570706C6965723A312E30000000001C5374727563747572656450726F787950756C6C537570706C69657200", StructuredProxyPullSupplier)
# Import base interface packages.
import CosNotifyChannelAdmin
class StructuredProxyPushConsumer(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.StructuredProxyPushConsumer):
""" Interface: IDL:att.com/AttNotification/StructuredProxyPushConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/StructuredProxyPushConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/StructuredProxyPushConsumer:1.0", "000000000000000E00000064000000000000003C49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F5374727563747572656450726F787950757368436F6E73756D65723A312E30000000001C5374727563747572656450726F787950757368436F6E73756D657200", StructuredProxyPushConsumer)
# Import base interface packages.
import CosNotifyChannelAdmin
class StructuredProxyPullConsumer(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.StructuredProxyPullConsumer):
""" Interface: IDL:att.com/AttNotification/StructuredProxyPullConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/StructuredProxyPullConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/StructuredProxyPullConsumer:1.0", "000000000000000E00000064000000000000003C49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F5374727563747572656450726F787950756C6C436F6E73756D65723A312E30000000001C5374727563747572656450726F787950756C6C436F6E73756D657200", StructuredProxyPullConsumer)
# Import base interface packages.
import CosNotifyChannelAdmin
class SequenceProxyPushSupplier(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.SequenceProxyPushSupplier):
""" Interface: IDL:att.com/AttNotification/SequenceProxyPushSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/SequenceProxyPushSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/SequenceProxyPushSupplier:1.0", "000000000000000E00000062000000000000003A49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F53657175656E636550726F787950757368537570706C6965723A312E300000000000001A53657175656E636550726F787950757368537570706C69657200", SequenceProxyPushSupplier)
# Import base interface packages.
import CosNotifyChannelAdmin
class SequenceProxyPullSupplier(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.SequenceProxyPullSupplier):
""" Interface: IDL:att.com/AttNotification/SequenceProxyPullSupplier:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/SequenceProxyPullSupplier:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/SequenceProxyPullSupplier:1.0", "000000000000000E00000062000000000000003A49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F53657175656E636550726F787950756C6C537570706C6965723A312E300000000000001A53657175656E636550726F787950756C6C537570706C69657200", SequenceProxyPullSupplier)
# Import base interface packages.
import CosNotifyChannelAdmin
class SequenceProxyPushConsumer(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.SequenceProxyPushConsumer):
""" Interface: IDL:att.com/AttNotification/SequenceProxyPushConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/SequenceProxyPushConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/SequenceProxyPushConsumer:1.0", "000000000000000E00000062000000000000003A49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F53657175656E636550726F787950757368436F6E73756D65723A312E300000000000001A53657175656E636550726F787950757368436F6E73756D657200", SequenceProxyPushConsumer)
# Import base interface packages.
import CosNotifyChannelAdmin
class SequenceProxyPullConsumer(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.SequenceProxyPullConsumer):
""" Interface: IDL:att.com/AttNotification/SequenceProxyPullConsumer:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/SequenceProxyPullConsumer:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/SequenceProxyPullConsumer:1.0", "000000000000000E00000062000000000000003A49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F53657175656E636550726F787950756C6C436F6E73756D65723A312E300000000000001A53657175656E636550726F787950756C6C436F6E73756D657200", SequenceProxyPullConsumer)
# Import base interface packages.
import CosNotifyChannelAdmin
class SupplierAdmin(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.SupplierAdmin):
""" Interface: IDL:att.com/AttNotification/SupplierAdmin:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/SupplierAdmin:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/SupplierAdmin:1.0", "000000000000000E0000004A000000000000002E49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F537570706C69657241646D696E3A312E300000000000000E537570706C69657241646D696E00", SupplierAdmin)
# Import base interface packages.
import CosNotifyChannelAdmin
class ConsumerAdmin(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.ConsumerAdmin):
""" Interface: IDL:att.com/AttNotification/ConsumerAdmin:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/ConsumerAdmin:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/ConsumerAdmin:1.0", "000000000000000E0000004A000000000000002E49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F436F6E73756D657241646D696E3A312E300000000000000E436F6E73756D657241646D696E00", ConsumerAdmin)
class ChannelStats:
""" Struct: IDL:att.com/AttNotification/ChannelStats:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/ChannelStats:1.0"
def __init__(self, _num_announcements, _num_notifications):
""" Constructor. """
self.num_announcements = _num_announcements
self.num_notifications = _num_notifications
return
def __getinitargs__(self):
""" Return the constructor arguments for unpickling. """
return (self.num_announcements, self.num_notifications)
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/ChannelStats:1.0", "000000000000000F00000088000000000000002D49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F4368616E6E656C53746174733A312E30000000000000000D4368616E6E656C53746174730000000000000002000000126E756D5F616E6E6F756E63656D656E747300000000000003000000126E756D5F6E6F74696669636174696F6E7300000000000003", ChannelStats)
# Import base interface packages.
import CosNotifyChannelAdmin
class EventChannel(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.EventChannel):
""" Interface: IDL:att.com/AttNotification/EventChannel:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/EventChannel:1.0"
def obtain_subscription_types(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/EventChannel/obtain_subscription_types:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/EventTypeSeq:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("obtain_subscription_types", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def obtain_offered_types(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/EventChannel/obtain_offered_types:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotification/EventTypeSeq:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("obtain_offered_types", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def obtain_stats(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/EventChannel/obtain_stats:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:att.com/AttNotification/ChannelStats:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("obtain_stats", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/EventChannel:1.0", "000000000000000E00000049000000000000002D49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F4576656E744368616E6E656C3A312E30000000000000000D4576656E744368616E6E656C00", EventChannel)
# Import base interface packages.
import CosNotifyChannelAdmin
import CosNotification
import CosNotification
class EventChannelFactory(Fnorb.orb.CORBA.Object, Interactive, CosNotifyChannelAdmin.EventChannelFactory, CosNotification.QoSAdmin, CosNotification.AdminPropertiesAdmin):
""" Interface: IDL:att.com/AttNotification/EventChannelFactory:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/EventChannelFactory:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/EventChannelFactory:1.0", "000000000000000E00000054000000000000003449444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F4576656E744368616E6E656C466163746F72793A312E3000000000144576656E744368616E6E656C466163746F727900", EventChannelFactory)
# Import base interface packages.
import CosNotifyFilter
class Filter(Fnorb.orb.CORBA.Object, Interactive, CosNotifyFilter.Filter):
""" Interface: IDL:att.com/AttNotification/Filter:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/Filter:1.0"
def MyFID(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Filter/MyFID:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:omg.org/CosNotifyFilter/FilterID:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("MyFID", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/Filter:1.0", "000000000000000E0000003B000000000000002749444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F46696C7465723A312E3000000000000746696C74657200", Filter)
# Import base interface packages.
import CosNotifyFilter
class MappingFilter(Fnorb.orb.CORBA.Object, Interactive, CosNotifyFilter.MappingFilter):
""" Interface: IDL:att.com/AttNotification/MappingFilter:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/MappingFilter:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/MappingFilter:1.0", "000000000000000E0000004A000000000000002E49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F4D617070696E6746696C7465723A312E300000000000000E4D617070696E6746696C74657200", MappingFilter)
# Import base interface packages.
import CosNotifyFilter
class FilterFactory(Fnorb.orb.CORBA.Object, Interactive, CosNotifyFilter.FilterFactory):
""" Interface: IDL:att.com/AttNotification/FilterFactory:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/FilterFactory:1.0"
pass
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/FilterFactory:1.0", "000000000000000E0000004A000000000000002E49444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F46696C746572466163746F72793A312E300000000000000E46696C746572466163746F727900", FilterFactory)
# Alias: IDL:att.com/AttNotification/ServerProperties:1.0
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/ServerProperties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one)
class UnsupportedServerProp(Fnorb.orb.CORBA.UserException):
""" Exception: IDL:att.com/AttNotification/UnsupportedServerProp:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/UnsupportedServerProp:1.0"
def __init__(self, _server_prop_err):
""" Constructor. """
self.server_prop_err = _server_prop_err
return
def __getinitargs__(self):
""" Return the constructor arguments for unpickling. """
return (self.server_prop_err,)
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/UnsupportedServerProp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nsupportedServerProp)
class Server(Fnorb.orb.CORBA.Object, Interactive):
""" Interface: IDL:att.com/AttNotification/Server:1.0 """
_FNORB_ID = "IDL:att.com/AttNotification/Server:1.0"
def destroy(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Server/destroy:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("destroy", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def get_filter_factory(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Server/get_filter_factory:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.TC_Object)
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("get_filter_factory", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def get_channel_factory(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Server/get_channel_factory:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.TC_Object)
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("get_channel_factory", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def get_default_channel(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Server/get_default_channel:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.TC_Object)
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("get_default_channel", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def results_to_file(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Server/results_to_file:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.TC_boolean)
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("results_to_file", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def get_server_props(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Server/get_server_props:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
outputs.append(Fnorb.orb.CORBA.typecode("IDL:att.com/AttNotification/ServerProperties:1.0"))
# Typecodes for user exceptions.
exceptions = []
# Create a request object.
request = self._create_request("get_server_props", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
def set_server_props(self, *args, **kw):
""" Operation: IDL:att.com/AttNotification/Server/set_server_props:1.0 """
# Typecodes for 'in' and 'inout' parameters.
inputs = []
inputs.append(Fnorb.orb.CORBA.typecode("IDL:att.com/AttNotification/ServerProperties:1.0"))
# Typecodes for the result, 'inout' and 'out' parameters.
outputs = []
# Typecodes for user exceptions.
exceptions = []
exceptions.append(Fnorb.orb.CORBA.typecode("IDL:att.com/AttNotification/UnsupportedServerProp:1.0"))
# Create a request object.
request = self._create_request("set_server_props", inputs, outputs, exceptions)
# Make the request!
apply(request.invoke, args, kw)
# Return the results.
return request.results()
Fnorb.orb.TypeManager.TypeManager_init().add_type("IDL:att.com/AttNotification/Server:1.0", "000000000000000E0000003B000000000000002749444C3A6174742E636F6D2F4174744E6F74696669636174696F6E2F5365727665723A312E3000000000000753657276657200", Server)
#############################################################################
| 46.423729
| 2,261
| 0.780577
| 2,709
| 32,868
| 9.374677
| 0.062385
| 0.009057
| 0.039691
| 0.105843
| 0.587927
| 0.576193
| 0.517759
| 0.372893
| 0.334108
| 0.317609
| 0
| 0.263106
| 0.128332
| 32,868
| 707
| 2,262
| 46.489392
| 0.623316
| 0.21419
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| 0.510791
| 1
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| 0.466138
| 0.458442
| 0
| 1
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| 1
| 0.071942
| false
| 0.07554
| 0.100719
| 0
| 0.438849
| 0
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| null | 0
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| null | 1
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| 0
| 0
| 0
|
0
| 6
|
10d0f7e95d8bcd3b794892efd28748c521446d81
| 6,179
|
py
|
Python
|
src/TeamProj/myapp/view/fileprivilege.py
|
DbettKK/1821-D4-backend
|
3c06dc8080b64baaeb1d9e2663d7ac0277ea2155
|
[
"MIT"
] | null | null | null |
src/TeamProj/myapp/view/fileprivilege.py
|
DbettKK/1821-D4-backend
|
3c06dc8080b64baaeb1d9e2663d7ac0277ea2155
|
[
"MIT"
] | null | null | null |
src/TeamProj/myapp/view/fileprivilege.py
|
DbettKK/1821-D4-backend
|
3c06dc8080b64baaeb1d9e2663d7ac0277ea2155
|
[
"MIT"
] | null | null | null |
from rest_framework.views import APIView, Response
from myapp.models import User, File, UserBrowseFile, UserKeptFile, Team, Comment
from myapp.serializers import CommentSer, FileSer
from myapp.views import chk_token
from .userfile import chk_file_id
class SetPriviFile(APIView):
def post(self, request):
token = request.META.get('HTTP_TOKEN')
file_id = request.POST.get('file_id')
privilege = request.POST.get('privilege')
if not all([privilege, file_id]):
return Response({
'info': '参数不完整',
'code': 400
}, status=400)
pri = int(privilege)
if pri < 1 or pri > 4:
return Response({
'info': '权限有误',
'code': 403
}, status=403)
user_id = chk_token(token)
if isinstance(user_id, Response):
return user_id
# u = User.objects.get(pk=user_id)
f = chk_file_id(file_id)
if isinstance(f, Response):
return f
f.permission = pri
f.save()
return Response({
'info': 'success',
'code': 200,
'data': FileSer(f).data
}, status=200)
class SetPriviFileTeam(APIView):
def post(self, request):
token = request.META.get('HTTP_TOKEN')
file_id = request.POST.get('file_id')
team_id = request.POST.get('team_id')
privilege = request.POST.get('privilege')
pri = int(privilege)
if not all([team_id, privilege, file_id]):
return Response({
'info': '参数不完整',
'code': 400
}, status=400)
if pri < 1 or pri > 4:
return Response({
'info': '权限有误',
'code': 403
}, status=403)
user_id = chk_token(token)
if isinstance(user_id, Response):
return user_id
f = chk_file_id(file_id)
if isinstance(f, Response):
return f
if f.type != 'team':
return Response({
'info': '文档类型有误',
'code': 403
}, status=403)
t = Team.objects.filter(pk=team_id)
if t:
f.team_permission = pri
f.save()
return Response({
'info': 'success',
'code': 200,
'data': FileSer(f).data
}, status=200)
return Response({
'info': '不存在该团队',
'code': 403
}, status=403)
class ChangeTeamToPri(APIView):
def get(self, request):
token = request.META.get('HTTP_TOKEN')
user_id = chk_token(token)
file_id = request.GET.get('file_id')
if isinstance(user_id, Response):
return user_id
u = User.objects.get(pk=user_id)
f = chk_file_id(file_id)
if f.type == 'team' or f.team_belong:
f.team_belong = None
f.type = 'private'
f.save()
return Response({
'info': 'success',
'code': 200,
'data': FileSer(f).data
}, status=200)
return Response({
'info': '文档类型有误',
'code': 403
}, status=403)
class ChangePriToTeam(APIView):
def get(self, request):
token = request.META.get('HTTP_TOKEN')
user_id = chk_token(token)
file_id = request.GET.get('file_id')
team_id = request.GET.get('team_id')
if isinstance(user_id, Response):
return user_id
u = User.objects.get(pk=user_id)
f = chk_file_id(file_id)
if isinstance(f, Response):
return f
t = Team.objects.get(pk=team_id)
f.type = 'team'
f.team_belong = t
f.save()
return Response({
'info': 'success',
'code': 200,
'data': FileSer(f).data
}, status=200)
class JudgePriviPri(APIView):
def get(self, request):
token = request.META.get('HTTP_TOKEN')
user_id = chk_token(token)
file_id = request.GET.get('file_id')
if isinstance(user_id, Response):
return user_id
u = User.objects.get(pk=user_id)
f = chk_file_id(file_id)
if isinstance(f, Response):
return f
if f.creator.id == user_id:
f.is_edit_now = True
f.save()
return Response({
'info': 'success',
'code': 200,
'data': {'pri': 4}
}, status=200)
else:
if f.permission >= 2:
f.is_edit_now = True
f.save()
return Response({
'info': 'success',
'code': 200,
'data': {'pri': f.permission}
}, status=200)
class JudgePriviTeam(APIView):
def get(self, request):
token = request.META.get('HTTP_TOKEN')
user_id = chk_token(token)
file_id = request.GET.get('file_id')
if isinstance(user_id, Response):
return user_id
u = User.objects.get(pk=user_id)
f = chk_file_id(file_id)
if isinstance(f, Response):
return f
if f.type != 'team':
return Response({
'info': '非团队文档',
'code': 403,
}, status=403)
if f.team_belong.creator.id == user_id:
f.is_edit_now = True
f.save()
return Response({
'info': 'success',
'code': 200,
'data': {'pri': 4}
}, status=200)
if f.creator.id == user_id:
f.is_edit_now = True
f.save()
return Response({
'info': 'success',
'code': 200,
'data': {'pri': 4}
}, status=200)
else:
if f.team_permission >= 2:
f.is_edit_now = True
f.save()
return Response({
'info': 'success',
'code': 200,
'data': {'pri': f.team_permission}
}, status=200)
| 30.741294
| 80
| 0.486648
| 687
| 6,179
| 4.234352
| 0.114993
| 0.055689
| 0.105191
| 0.058783
| 0.797525
| 0.788587
| 0.756961
| 0.756961
| 0.741836
| 0.741836
| 0
| 0.029592
| 0.392944
| 6,179
| 200
| 81
| 30.895
| 0.745934
| 0.005179
| 0
| 0.823529
| 0
| 0
| 0.072905
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| 1
| 0.032086
| false
| 0
| 0.026738
| 0
| 0.240642
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
10e21e4da45c04da6110f5dc4a65f9d10a76975a
| 39
|
py
|
Python
|
axtoolbox/__init__.py
|
jcsgo/axtoolbox
|
cd4e433dbe224bf68f5f5ffbb481cf30e5aff5c1
|
[
"MIT"
] | null | null | null |
axtoolbox/__init__.py
|
jcsgo/axtoolbox
|
cd4e433dbe224bf68f5f5ffbb481cf30e5aff5c1
|
[
"MIT"
] | null | null | null |
axtoolbox/__init__.py
|
jcsgo/axtoolbox
|
cd4e433dbe224bf68f5f5ffbb481cf30e5aff5c1
|
[
"MIT"
] | null | null | null |
from .core import *
from .keys import *
| 19.5
| 19
| 0.717949
| 6
| 39
| 4.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179487
| 39
| 2
| 20
| 19.5
| 0.875
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
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| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
10fdb6dca3a86dff1081c09a5168caf90b9a8bbd
| 4,794
|
py
|
Python
|
OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/raw/GLES2/OES/texture_compression_astc.py
|
JE-Chen/je_old_repo
|
a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5
|
[
"MIT"
] | null | null | null |
OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/raw/GLES2/OES/texture_compression_astc.py
|
JE-Chen/je_old_repo
|
a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5
|
[
"MIT"
] | null | null | null |
OpenGLWrapper_JE/venv/Lib/site-packages/OpenGL/raw/GLES2/OES/texture_compression_astc.py
|
JE-Chen/je_old_repo
|
a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5
|
[
"MIT"
] | null | null | null |
'''Autogenerated by xml_generate script, do not edit!'''
from OpenGL import platform as _p, arrays
# Code generation uses this
from OpenGL.raw.GLES2 import _types as _cs
# End users want this...
from OpenGL.raw.GLES2._types import *
from OpenGL.raw.GLES2 import _errors
from OpenGL.constant import Constant as _C
import ctypes
_EXTENSION_NAME = 'GLES2_OES_texture_compression_astc'
def _f( function ):
return _p.createFunction( function,_p.PLATFORM.GLES2,'GLES2_OES_texture_compression_astc',error_checker=_errors._error_checker)
GL_COMPRESSED_RGBA_ASTC_10x10_KHR=_C('GL_COMPRESSED_RGBA_ASTC_10x10_KHR',0x93BB)
GL_COMPRESSED_RGBA_ASTC_10x5_KHR=_C('GL_COMPRESSED_RGBA_ASTC_10x5_KHR',0x93B8)
GL_COMPRESSED_RGBA_ASTC_10x6_KHR=_C('GL_COMPRESSED_RGBA_ASTC_10x6_KHR',0x93B9)
GL_COMPRESSED_RGBA_ASTC_10x8_KHR=_C('GL_COMPRESSED_RGBA_ASTC_10x8_KHR',0x93BA)
GL_COMPRESSED_RGBA_ASTC_12x10_KHR=_C('GL_COMPRESSED_RGBA_ASTC_12x10_KHR',0x93BC)
GL_COMPRESSED_RGBA_ASTC_12x12_KHR=_C('GL_COMPRESSED_RGBA_ASTC_12x12_KHR',0x93BD)
GL_COMPRESSED_RGBA_ASTC_3x3x3_OES=_C('GL_COMPRESSED_RGBA_ASTC_3x3x3_OES',0x93C0)
GL_COMPRESSED_RGBA_ASTC_4x3x3_OES=_C('GL_COMPRESSED_RGBA_ASTC_4x3x3_OES',0x93C1)
GL_COMPRESSED_RGBA_ASTC_4x4_KHR=_C('GL_COMPRESSED_RGBA_ASTC_4x4_KHR',0x93B0)
GL_COMPRESSED_RGBA_ASTC_4x4x3_OES=_C('GL_COMPRESSED_RGBA_ASTC_4x4x3_OES',0x93C2)
GL_COMPRESSED_RGBA_ASTC_4x4x4_OES=_C('GL_COMPRESSED_RGBA_ASTC_4x4x4_OES',0x93C3)
GL_COMPRESSED_RGBA_ASTC_5x4_KHR=_C('GL_COMPRESSED_RGBA_ASTC_5x4_KHR',0x93B1)
GL_COMPRESSED_RGBA_ASTC_5x4x4_OES=_C('GL_COMPRESSED_RGBA_ASTC_5x4x4_OES',0x93C4)
GL_COMPRESSED_RGBA_ASTC_5x5_KHR=_C('GL_COMPRESSED_RGBA_ASTC_5x5_KHR',0x93B2)
GL_COMPRESSED_RGBA_ASTC_5x5x4_OES=_C('GL_COMPRESSED_RGBA_ASTC_5x5x4_OES',0x93C5)
GL_COMPRESSED_RGBA_ASTC_5x5x5_OES=_C('GL_COMPRESSED_RGBA_ASTC_5x5x5_OES',0x93C6)
GL_COMPRESSED_RGBA_ASTC_6x5_KHR=_C('GL_COMPRESSED_RGBA_ASTC_6x5_KHR',0x93B3)
GL_COMPRESSED_RGBA_ASTC_6x5x5_OES=_C('GL_COMPRESSED_RGBA_ASTC_6x5x5_OES',0x93C7)
GL_COMPRESSED_RGBA_ASTC_6x6_KHR=_C('GL_COMPRESSED_RGBA_ASTC_6x6_KHR',0x93B4)
GL_COMPRESSED_RGBA_ASTC_6x6x5_OES=_C('GL_COMPRESSED_RGBA_ASTC_6x6x5_OES',0x93C8)
GL_COMPRESSED_RGBA_ASTC_6x6x6_OES=_C('GL_COMPRESSED_RGBA_ASTC_6x6x6_OES',0x93C9)
GL_COMPRESSED_RGBA_ASTC_8x5_KHR=_C('GL_COMPRESSED_RGBA_ASTC_8x5_KHR',0x93B5)
GL_COMPRESSED_RGBA_ASTC_8x6_KHR=_C('GL_COMPRESSED_RGBA_ASTC_8x6_KHR',0x93B6)
GL_COMPRESSED_RGBA_ASTC_8x8_KHR=_C('GL_COMPRESSED_RGBA_ASTC_8x8_KHR',0x93B7)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x10_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x10_KHR',0x93DB)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x5_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x5_KHR',0x93D8)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x6_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x6_KHR',0x93D9)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x8_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_10x8_KHR',0x93DA)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_12x10_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_12x10_KHR',0x93DC)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_12x12_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_12x12_KHR',0x93DD)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_3x3x3_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_3x3x3_OES',0x93E0)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x3x3_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x3x3_OES',0x93E1)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x4_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x4_KHR',0x93D0)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x4x3_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x4x3_OES',0x93E2)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x4x4_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_4x4x4_OES',0x93E3)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x4_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x4_KHR',0x93D1)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x4x4_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x4x4_OES',0x93E4)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x5_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x5_KHR',0x93D2)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x5x4_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x5x4_OES',0x93E5)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x5x5_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_5x5x5_OES',0x93E6)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x5_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x5_KHR',0x93D3)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x5x5_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x5x5_OES',0x93E7)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x6_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x6_KHR',0x93D4)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x6x5_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x6x5_OES',0x93E8)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x6x6_OES=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_6x6x6_OES',0x93E9)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_8x5_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_8x5_KHR',0x93D5)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_8x6_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_8x6_KHR',0x93D6)
GL_COMPRESSED_SRGB8_ALPHA8_ASTC_8x8_KHR=_C('GL_COMPRESSED_SRGB8_ALPHA8_ASTC_8x8_KHR',0x93D7)
| 76.095238
| 132
| 0.895494
| 799
| 4,794
| 4.622028
| 0.151439
| 0.311942
| 0.207961
| 0.259951
| 0.847549
| 0.811265
| 0.451124
| 0
| 0
| 0
| 0
| 0.119862
| 0.035878
| 4,794
| 62
| 133
| 77.322581
| 0.679143
| 0.020859
| 0
| 0
| 1
| 0
| 0.390571
| 0.390571
| 0
| 0
| 0.062284
| 0
| 0
| 1
| 0.017544
| false
| 0
| 0.105263
| 0.017544
| 0.140351
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
804b1a25ca96b35d8f8c71bc827f5a35e30fea2e
| 41
|
py
|
Python
|
projects/databricks-cli/test.py
|
fleimgruber/python
|
2e735762c73651cffc027ca850b2a58d87d54b49
|
[
"Unlicense"
] | 25
|
2021-10-30T19:54:59.000Z
|
2022-03-29T06:11:02.000Z
|
projects/databricks-cli/test.py
|
fleimgruber/python
|
2e735762c73651cffc027ca850b2a58d87d54b49
|
[
"Unlicense"
] | 21
|
2021-10-19T01:09:38.000Z
|
2022-03-24T16:08:53.000Z
|
projects/databricks-cli/test.py
|
fleimgruber/python
|
2e735762c73651cffc027ca850b2a58d87d54b49
|
[
"Unlicense"
] | 3
|
2022-01-25T20:25:13.000Z
|
2022-03-08T02:58:50.000Z
|
import databricks_cli
import integration
| 13.666667
| 21
| 0.902439
| 5
| 41
| 7.2
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097561
| 41
| 2
| 22
| 20.5
| 0.972973
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
3379bd34a10cfa2a0dd3b45ca89e5a2f502fbd20
| 215
|
py
|
Python
|
E-Boutique/src/product/admin.py
|
MDRCS/Fullstack-Django
|
20cbae6e1b70d7051662b579e7967061e529d71f
|
[
"MIT"
] | null | null | null |
E-Boutique/src/product/admin.py
|
MDRCS/Fullstack-Django
|
20cbae6e1b70d7051662b579e7967061e529d71f
|
[
"MIT"
] | 19
|
2020-07-14T07:04:43.000Z
|
2022-03-12T00:41:14.000Z
|
E-Boutique/src/product/admin.py
|
MDRCS/Fullstack-Django
|
20cbae6e1b70d7051662b579e7967061e529d71f
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Product, Category, Brand, ProductImages
admin.site.register(Product)
admin.site.register(Category)
admin.site.register(Brand)
admin.site.register(ProductImages)
| 26.875
| 59
| 0.827907
| 28
| 215
| 6.357143
| 0.428571
| 0.202247
| 0.382022
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.074419
| 215
| 7
| 60
| 30.714286
| 0.894472
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
33829c5ce9d8017f12b87ff6ade2573165f5aee1
| 1,720
|
py
|
Python
|
toir/formats/script/script/instructions/camera.py
|
FistingUranus/innocence-r
|
786e1fca75155027e5875363f0b17e7c3cdefced
|
[
"MIT"
] | 2
|
2021-06-26T16:44:58.000Z
|
2021-09-09T22:32:13.000Z
|
toir/formats/script/script/instructions/camera.py
|
FistingUranus/innocence-r
|
786e1fca75155027e5875363f0b17e7c3cdefced
|
[
"MIT"
] | 4
|
2021-08-29T18:12:17.000Z
|
2022-03-28T08:54:29.000Z
|
toir/formats/script/script/instructions/camera.py
|
FistingUranus/innocence-r
|
786e1fca75155027e5875363f0b17e7c3cdefced
|
[
"MIT"
] | 3
|
2021-07-20T01:00:19.000Z
|
2021-09-09T22:32:14.000Z
|
from . import ScriptInstruction, ScriptInstructionWithArgs
import struct
class ScriptCameraDefault(ScriptInstruction):
pass
class ScriptCameraLockRelease(ScriptInstruction):
pass
class ScriptCameraSet(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<B', opcode)
class ScriptCameraScenePlay(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<B', opcode)
class ScriptCameraSceneWait(ScriptInstruction):
pass
class ScriptCameraLockPlayer(ScriptInstruction):
pass
class ScriptCameraLockObject(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<B', opcode)
class ScriptCameraShake(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<HH', opcode)
class ScriptCameraShakeWait(ScriptInstruction):
pass
class ScriptCameraMoveObject(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<BH', opcode)
class ScriptCameraMovePoint(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<BH', opcode)
class ScriptCameraMoveWait(ScriptInstruction):
pass
class ScriptCameraMovePlayer(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<H', opcode)
class ScriptCameraMovePointSpeed(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<BL', opcode)
class ScriptCameraMovePlayerSpeed(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<L', opcode)
class ScriptCameraMoveObjectSpeed(ScriptInstructionWithArgs):
def __init__(self, opcode):
super().__init__('<BL', opcode)
| 28.196721
| 61
| 0.744767
| 140
| 1,720
| 8.578571
| 0.242857
| 0.233139
| 0.266445
| 0.29975
| 0.489592
| 0.489592
| 0.489592
| 0.362198
| 0.362198
| 0.263947
| 0
| 0
| 0.150581
| 1,720
| 60
| 62
| 28.666667
| 0.82204
| 0
| 0
| 0.522727
| 0
| 0
| 0.014535
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.227273
| false
| 0.136364
| 0.045455
| 0
| 0.636364
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
33977c33810486c32858470c043be702fab8ea3c
| 109
|
py
|
Python
|
app/cogs/blacklist/__init__.py
|
fossabot/Starboard-2
|
798e2d04995ae7d920e76708b9ea8fae6f4af319
|
[
"MIT"
] | 16
|
2021-01-19T19:12:00.000Z
|
2021-12-21T12:00:04.000Z
|
app/cogs/blacklist/__init__.py
|
Davi-the-Mudkip/Starboard-2
|
4de3c689ffef007e4f4a279251d107d890b69b15
|
[
"MIT"
] | 15
|
2021-04-02T16:58:48.000Z
|
2022-03-28T06:09:49.000Z
|
app/cogs/blacklist/__init__.py
|
Davi-the-Mudkip/Starboard-2
|
4de3c689ffef007e4f4a279251d107d890b69b15
|
[
"MIT"
] | 13
|
2021-01-21T14:26:00.000Z
|
2021-09-29T18:55:17.000Z
|
from app.classes.bot import Bot
from . import bl_commands
def setup(bot: Bot):
bl_commands.setup(bot)
| 13.625
| 31
| 0.733945
| 18
| 109
| 4.333333
| 0.5
| 0.25641
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.174312
| 109
| 7
| 32
| 15.571429
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
1d3665b4a1a2e43a597bed16a3b6448c58cbd1d4
| 52
|
py
|
Python
|
rcnn/modeling/uv_rcnn/heads/__init__.py
|
rs9899/Parsing-R-CNN
|
a0c9ed8850abe740eedf8bfc6e1577cc0aa3fc7b
|
[
"MIT"
] | 289
|
2018-10-25T09:42:57.000Z
|
2022-03-30T08:31:50.000Z
|
rcnn/modeling/uv_rcnn/heads/__init__.py
|
qzane/Parsing-R-CNN
|
8c4d940dcd322bf7a8671f8b0faaabb3259bd384
|
[
"MIT"
] | 28
|
2019-01-07T02:39:49.000Z
|
2022-01-25T08:54:36.000Z
|
rcnn/modeling/uv_rcnn/heads/__init__.py
|
qzane/Parsing-R-CNN
|
8c4d940dcd322bf7a8671f8b0faaabb3259bd384
|
[
"MIT"
] | 44
|
2018-12-20T07:36:46.000Z
|
2022-03-16T14:30:20.000Z
|
from .convx_heads import *
from .gce_heads import *
| 17.333333
| 26
| 0.769231
| 8
| 52
| 4.75
| 0.625
| 0.578947
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 52
| 2
| 27
| 26
| 0.863636
| 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
| 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
| 6
|
1d5f8b3cfcddb2805f33de85f200898cf4dae7e7
| 44
|
py
|
Python
|
code/norm/io/__init__.py
|
danilonumeroso/norm
|
aebe8c6e186723ed048ad0a9c44fbf9c3e45a78b
|
[
"MIT"
] | 1
|
2022-03-04T15:32:15.000Z
|
2022-03-04T15:32:15.000Z
|
code/norm/io/__init__.py
|
danilonumeroso/norm
|
aebe8c6e186723ed048ad0a9c44fbf9c3e45a78b
|
[
"MIT"
] | null | null | null |
code/norm/io/__init__.py
|
danilonumeroso/norm
|
aebe8c6e186723ed048ad0a9c44fbf9c3e45a78b
|
[
"MIT"
] | null | null | null |
from ._save import dump, load # noqa: F401
| 22
| 43
| 0.704545
| 7
| 44
| 4.285714
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 0.204545
| 44
| 1
| 44
| 44
| 0.771429
| 0.227273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
1d76c56a35ee5a0d658b631333f29677facd30ab
| 102
|
py
|
Python
|
intvalpy/__init__.py
|
SShary/intvalpy
|
42f4c8f6b23e6481f4032b0a0f7cc0d798fda3be
|
[
"MIT"
] | null | null | null |
intvalpy/__init__.py
|
SShary/intvalpy
|
42f4c8f6b23e6481f4032b0a0f7cc0d798fda3be
|
[
"MIT"
] | null | null | null |
intvalpy/__init__.py
|
SShary/intvalpy
|
42f4c8f6b23e6481f4032b0a0f7cc0d798fda3be
|
[
"MIT"
] | null | null | null |
from .MyClass import Interval
from .intoper import *
from .linear import *
from .nonlinear import *
| 14.571429
| 29
| 0.754902
| 13
| 102
| 5.923077
| 0.538462
| 0.25974
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 102
| 6
| 30
| 17
| 0.916667
| 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
| 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
| 6
|
d58a4069a1205f2b05ed9a4701040cbc53c300d4
| 203
|
py
|
Python
|
yaml2sbml/__init__.py
|
alex-treebeard/yaml2sbml
|
1afbc73b81f311e1eb852fde8b6760709639669a
|
[
"MIT"
] | null | null | null |
yaml2sbml/__init__.py
|
alex-treebeard/yaml2sbml
|
1afbc73b81f311e1eb852fde8b6760709639669a
|
[
"MIT"
] | null | null | null |
yaml2sbml/__init__.py
|
alex-treebeard/yaml2sbml
|
1afbc73b81f311e1eb852fde8b6760709639669a
|
[
"MIT"
] | null | null | null |
from yaml2sbml.yaml2sbml import yaml2sbml
from yaml2sbml.yaml2PEtab import yaml2petab, validate_petab_tables
from yaml2sbml.yaml_validation import validate_yaml
from yaml2sbml.YamlModel import YamlModel
| 40.6
| 66
| 0.891626
| 25
| 203
| 7.08
| 0.4
| 0.293785
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043011
| 0.083744
| 203
| 4
| 67
| 50.75
| 0.908602
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 1
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| null | 1
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| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d59ea9aa03d8bdc8818ccecebd76da8cc739b696
| 74
|
py
|
Python
|
tests/models.py
|
meshy/django-sans-db
|
ee52199adb12f4235f0a8744acc30d8aaa89ad67
|
[
"MIT"
] | 3
|
2021-12-16T22:28:06.000Z
|
2021-12-16T22:28:26.000Z
|
tests/models.py
|
meshy/django-sans-db
|
ee52199adb12f4235f0a8744acc30d8aaa89ad67
|
[
"MIT"
] | null | null | null |
tests/models.py
|
meshy/django-sans-db
|
ee52199adb12f4235f0a8744acc30d8aaa89ad67
|
[
"MIT"
] | null | null | null |
from django.db import models
class ExampleModel(models.Model):
pass
| 12.333333
| 33
| 0.756757
| 10
| 74
| 5.6
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175676
| 74
| 5
| 34
| 14.8
| 0.918033
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
63419a6d90aa7a881a5c1673fb52a0d9b1b4d3fe
| 146
|
py
|
Python
|
extoracle/__init__.py
|
pltrdy/extoracle_summarization
|
a7d94edc6d06f08aca7a25009c479ef89319e3af
|
[
"Apache-2.0"
] | 7
|
2020-08-12T10:56:00.000Z
|
2022-01-28T21:14:44.000Z
|
extoracle/__init__.py
|
pltrdy/extoracle_summarization
|
a7d94edc6d06f08aca7a25009c479ef89319e3af
|
[
"Apache-2.0"
] | 2
|
2020-07-29T20:14:35.000Z
|
2021-12-15T05:33:22.000Z
|
extoracle/__init__.py
|
pltrdy/extoracle_summarization
|
a7d94edc6d06f08aca7a25009c479ef89319e3af
|
[
"Apache-2.0"
] | 1
|
2020-10-23T16:05:09.000Z
|
2020-10-23T16:05:09.000Z
|
import extoracle # noqa
import extoracle.utils # noqa
import extoracle.bin # noqa
from extoracle.extoracle import METHODS, from_files # noqa
| 24.333333
| 59
| 0.780822
| 19
| 146
| 5.947368
| 0.421053
| 0.39823
| 0.336283
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.164384
| 146
| 5
| 60
| 29.2
| 0.92623
| 0.130137
| 0
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| 0
| 0
| 0
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| 0
| 0
| 1
| 0
| true
| 0
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| null | 1
| 1
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| 0
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| 0
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| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
636705baadb550a7532d3b7f2d79f7c8fa64ebdc
| 28
|
py
|
Python
|
shake2py/__init__.py
|
markreidvfx/shake2py
|
8965727a3baf623e0b3c35a2c298142f80242550
|
[
"MIT"
] | 1
|
2016-06-22T01:23:49.000Z
|
2016-06-22T01:23:49.000Z
|
shake2py/__init__.py
|
markreidvfx/shake2py
|
8965727a3baf623e0b3c35a2c298142f80242550
|
[
"MIT"
] | null | null | null |
shake2py/__init__.py
|
markreidvfx/shake2py
|
8965727a3baf623e0b3c35a2c298142f80242550
|
[
"MIT"
] | null | null | null |
from script_parser import *
| 14
| 27
| 0.821429
| 4
| 28
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 28
| 1
| 28
| 28
| 0.916667
| 0
| 0
| 0
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| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
639197a41a6d451910701e7fa819f6abed4aaa0e
| 50
|
py
|
Python
|
ocrsite/ocrlab/tests/__init__.py
|
mikesname/python-ocrlab
|
435cc2548b38d92f8ffdc4bee8845f5a58d655ce
|
[
"MIT"
] | 4
|
2016-01-04T09:36:05.000Z
|
2020-10-18T01:33:39.000Z
|
ocrsite/ocrlab/tests/__init__.py
|
mikesname/python-ocrlab
|
435cc2548b38d92f8ffdc4bee8845f5a58d655ce
|
[
"MIT"
] | null | null | null |
ocrsite/ocrlab/tests/__init__.py
|
mikesname/python-ocrlab
|
435cc2548b38d92f8ffdc4bee8845f5a58d655ce
|
[
"MIT"
] | 3
|
2017-05-04T08:46:45.000Z
|
2021-10-06T19:25:11.000Z
|
from test_core import *
from test_nodes import *
| 12.5
| 24
| 0.78
| 8
| 50
| 4.625
| 0.625
| 0.432432
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.18
| 50
| 3
| 25
| 16.666667
| 0.902439
| 0
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| 1
| 0
| 1
| 0
|
0
| 6
|
63940635c5e7acfab8123b4c675728b636dbb35d
| 32
|
py
|
Python
|
houndify/__init__.py
|
oyeun/houndify
|
559c1c5c9be678fa8d4f29e722d2e461d7cd5159
|
[
"MIT"
] | null | null | null |
houndify/__init__.py
|
oyeun/houndify
|
559c1c5c9be678fa8d4f29e722d2e461d7cd5159
|
[
"MIT"
] | null | null | null |
houndify/__init__.py
|
oyeun/houndify
|
559c1c5c9be678fa8d4f29e722d2e461d7cd5159
|
[
"MIT"
] | null | null | null |
from houndify.houndify import *
| 16
| 31
| 0.8125
| 4
| 32
| 6.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 32
| 1
| 32
| 32
| 0.928571
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
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| true
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| null | 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
63b459186e50708df2ea53117aa9c2d459a390e9
| 44
|
py
|
Python
|
qcodes/utils/__init__.py
|
nulinspiratie/Qcodes
|
d050d38ac83f532523a39549c3247dfa6096a36e
|
[
"MIT"
] | 2
|
2017-02-27T06:02:39.000Z
|
2019-06-03T04:56:59.000Z
|
qcodes/utils/__init__.py
|
nulinspiratie/Qcodes
|
d050d38ac83f532523a39549c3247dfa6096a36e
|
[
"MIT"
] | 50
|
2017-04-12T04:03:15.000Z
|
2022-03-09T00:41:43.000Z
|
qcodes/utils/__init__.py
|
nulinspiratie/Qcodes
|
d050d38ac83f532523a39549c3247dfa6096a36e
|
[
"MIT"
] | null | null | null |
from .helpers import *
from .debug import *
| 14.666667
| 22
| 0.727273
| 6
| 44
| 5.333333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 44
| 2
| 23
| 22
| 0.888889
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| true
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| 1
| 0
| 1
| 0
|
0
| 6
|
89400dd27ad183fdf343400f5d8621570f2d42da
| 5,458
|
py
|
Python
|
utils/dataset.py
|
samiemostafavi/conditional-latency-probability-prediction
|
a196f2db8c6f30f8613797b6a23bffd77a01e1e3
|
[
"MIT"
] | null | null | null |
utils/dataset.py
|
samiemostafavi/conditional-latency-probability-prediction
|
a196f2db8c6f30f8613797b6a23bffd77a01e1e3
|
[
"MIT"
] | null | null | null |
utils/dataset.py
|
samiemostafavi/conditional-latency-probability-prediction
|
a196f2db8c6f30f8613797b6a23bffd77a01e1e3
|
[
"MIT"
] | null | null | null |
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pyarrow.compute as pc
import tensorflow as tf
import tensorflow_io as tfio
def parquet_tf_pipeline(
file_addr,
feature_names,
label_name,
dataset_size,
train_size,
batch_size,
dtype=tf.float32,
):
# We prefetch with a buffer the same size as the dataset because th dataset
# is very small and fits into memory.
dataset = (
tfio.IODataset.from_parquet(
filename = file_addr,
)
.prefetch(buffer_size=dataset_size)
)
def read_parquet(features):
# features is an OrderedDict
# prepare empty tensors
keys = tf.TensorArray(dtype=dtype, size=0, dynamic_size=True)
values_dict = {}
for a in features.items():
# look for the features
for idx,feature_name in enumerate(feature_names):
# a is a tuple, first item is the key, second is the tensor
if a[0].decode("utf-8")==feature_name:
values = tf.TensorArray(dtype=dtype, size=0, dynamic_size=True)
values = values.write(values.size(), tf.cast(a[1],dtype=dtype))
# important to have the squeeze to get (None,) tensor shape
values_dict[feature_name] = tf.squeeze(values.stack(),axis=0)
# look for the keys
if a[0].decode("utf-8")==label_name:
keys = keys.write(keys.size(), tf.cast(a[1],dtype=dtype))
# important to have the squeeze to get (None,) tensor shape
return (values_dict, tf.squeeze(keys.stack(),axis=0))
dataset = dataset.map(read_parquet)
# We shuffle with a buffer the same size as the dataset.
train_dataset = (
dataset.take(train_size).cache().shuffle(buffer_size=train_size).batch(batch_size)
)
test_dataset = dataset.skip(train_size).take(dataset_size-train_size).cache().shuffle(buffer_size=train_size).batch(batch_size)
# to check what is being read:
#for ds in train_dataset:
# print(tfds.as_numpy(ds))
#for ds in test_dataset:
# print(tfds.as_numpy(ds))
return train_dataset, test_dataset
def parquet_tf_pipeline_2(
file_addr,
feature_names,
label_name,
dataset_size,
train_size,
batch_size,
dtype=tf.float32,
):
# We prefetch with a buffer the same size as the dataset because th dataset
# is very small and fits into memory.
dataset = (
tfio.IODataset.from_parquet(
filename = file_addr,
)
.prefetch(buffer_size=dataset_size)
)
def read_parquet(features):
# features is an OrderedDict
# prepare empty tensors
keys = tf.TensorArray(dtype=dtype, size=0, dynamic_size=True)
values_dict = {}
for a in features.items():
# look for the features
for idx,feature_name in enumerate(feature_names):
# a is a tuple, first item is the key, second is the tensor
if a[0].decode("utf-8")==feature_name:
values = tf.TensorArray(dtype=dtype, size=0, dynamic_size=True)
values = values.write(values.size(), tf.cast(a[1],dtype=dtype))
# important to have the squeeze to get (None,) tensor shape
values_dict[feature_name] = tf.squeeze(values.stack(),axis=0)
# look for the keys
if a[0].decode("utf-8")==label_name:
keys = tf.TensorArray(dtype=dtype, size=0, dynamic_size=True)
keys = keys.write(keys.size(), tf.cast(a[1],dtype=dtype))
values_dict['y_input'] = tf.squeeze(keys.stack(),axis=0)
# important to have the squeeze to get (None,) tensor shape
return (values_dict, tf.squeeze(keys.stack(),axis=0))
dataset = dataset.map(read_parquet)
# We shuffle with a buffer the same size as the dataset.
train_dataset = (
dataset.take(train_size).cache().shuffle(buffer_size=train_size).batch(batch_size)
)
test_dataset = dataset.skip(train_size).take(dataset_size-train_size).cache().shuffle(buffer_size=train_size).batch(batch_size)
# to check what is being read:
#for ds in train_dataset:
# print(tfds.as_numpy(ds))
#for ds in test_dataset:
# print(tfds.as_numpy(ds))
return train_dataset, test_dataset
def create_dataset(n_samples = 300, x_dim=3, x_max = 10, x_level=2, dtype = 'float64', dist = 'normal'):
# generate random sample, two components
X = np.array(np.random.randint(x_max, size=(n_samples, x_dim))*x_level).astype(dtype)
if dist is 'normal':
Y = np.array([
np.random.normal(loc=x_sample[0]+x_sample[1]+x_sample[2],scale=(x_sample[0]+x_sample[1]+x_sample[2])/5)
for x_sample in X
]
).astype(dtype)
elif dist is 'gamma':
Y = np.array([
np.random.gamma(shape=x_sample[0]+x_sample[1]+x_sample[2],scale=(x_sample[0]+x_sample[1]+x_sample[2])/5)
for x_sample in X
]
).astype(dtype)
return X,Y
""" load parquet dataset """
def load_parquet(file_addresses, read_columns=None):
table = pa.concat_tables(
pq.read_table(
file_address,columns=read_columns,
) for file_address in file_addresses
)
return table.to_pandas()
| 34.544304
| 131
| 0.619641
| 759
| 5,458
| 4.295125
| 0.183136
| 0.030061
| 0.031902
| 0.033129
| 0.828834
| 0.819018
| 0.811963
| 0.811963
| 0.811963
| 0.811963
| 0
| 0.012626
| 0.27446
| 5,458
| 157
| 132
| 34.764331
| 0.810606
| 0.212166
| 0
| 0.677083
| 0
| 0
| 0.012028
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.0625
| false
| 0
| 0.0625
| 0
| 0.1875
| 0
| 0
| 0
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| null | 0
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| 1
| 1
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| 0
| 0
| 0
|
0
| 6
|
89b2c76ff6bdb36e8b1787d6f3e9014856ae1f57
| 44
|
py
|
Python
|
src/__init__.py
|
0xffea/ray-rate-limiter
|
0898a7813af87bba1f0fae42ea198e17931f1003
|
[
"MIT"
] | null | null | null |
src/__init__.py
|
0xffea/ray-rate-limiter
|
0898a7813af87bba1f0fae42ea198e17931f1003
|
[
"MIT"
] | null | null | null |
src/__init__.py
|
0xffea/ray-rate-limiter
|
0898a7813af87bba1f0fae42ea198e17931f1003
|
[
"MIT"
] | null | null | null |
from .leaky_bucket import LeakyBucketActor
| 14.666667
| 42
| 0.863636
| 5
| 44
| 7.4
| 1
| 0
| 0
| 0
| 0
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| 0
| 0.113636
| 44
| 2
| 43
| 22
| 0.948718
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9864249786cf44ebdab11a5c8680e377eeb4dee7
| 101
|
py
|
Python
|
argparse_compat.py
|
gares/coq-tools
|
f270f5ad7e09661e191499bc028165e71300b157
|
[
"MIT"
] | 31
|
2015-11-28T18:23:20.000Z
|
2022-02-14T16:40:06.000Z
|
argparse_compat.py
|
gares/coq-tools
|
f270f5ad7e09661e191499bc028165e71300b157
|
[
"MIT"
] | 88
|
2015-02-11T18:37:36.000Z
|
2022-03-02T01:09:04.000Z
|
argparse_compat.py
|
gares/coq-tools
|
f270f5ad7e09661e191499bc028165e71300b157
|
[
"MIT"
] | 5
|
2015-07-07T15:08:09.000Z
|
2021-04-07T01:08:57.000Z
|
import sys
if sys.version_info < (3,):
import argparse_py2 as argparse
else:
import argparse
| 16.833333
| 35
| 0.722772
| 15
| 101
| 4.733333
| 0.666667
| 0.394366
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025
| 0.207921
| 101
| 5
| 36
| 20.2
| 0.8625
| 0
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| 0
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| 0
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| 1
| 0
| true
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| 0.6
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| 0.6
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| 0
| null | 1
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7f2644e4b51ca3d29d0943c07e14ce3da4a78623
| 170
|
py
|
Python
|
accounts/forms.py
|
VanWade/spinach
|
1da5fe554e1572729c92fc6b0f39a50e286e50d2
|
[
"Apache-2.0"
] | null | null | null |
accounts/forms.py
|
VanWade/spinach
|
1da5fe554e1572729c92fc6b0f39a50e286e50d2
|
[
"Apache-2.0"
] | null | null | null |
accounts/forms.py
|
VanWade/spinach
|
1da5fe554e1572729c92fc6b0f39a50e286e50d2
|
[
"Apache-2.0"
] | null | null | null |
import re
from django import forms
from django.utils.translation import ugettext_lazy as _
from django.contrib.auth.models import User
from django.conf import settings
| 21.25
| 55
| 0.835294
| 26
| 170
| 5.384615
| 0.615385
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129412
| 170
| 7
| 56
| 24.285714
| 0.945946
| 0
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| null | 1
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| 1
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7f5f389161ab9a2cd6a4612c725122cb7e67860c
| 215
|
py
|
Python
|
backend/chat_user_profile/admin.py
|
crowdbotics-apps/dsfs-28863
|
fea2672275927bd37d23e2267273e0eae54340d2
|
[
"FTL",
"AML",
"RSA-MD"
] | null | null | null |
backend/chat_user_profile/admin.py
|
crowdbotics-apps/dsfs-28863
|
fea2672275927bd37d23e2267273e0eae54340d2
|
[
"FTL",
"AML",
"RSA-MD"
] | null | null | null |
backend/chat_user_profile/admin.py
|
crowdbotics-apps/dsfs-28863
|
fea2672275927bd37d23e2267273e0eae54340d2
|
[
"FTL",
"AML",
"RSA-MD"
] | null | null | null |
from django.contrib import admin
from .models import Profile, Contact, VerificationCode
admin.site.register(VerificationCode)
admin.site.register(Profile)
admin.site.register(Contact)
# Register your models here.
| 23.888889
| 54
| 0.823256
| 27
| 215
| 6.555556
| 0.481481
| 0.152542
| 0.288136
| 0.372881
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 215
| 8
| 55
| 26.875
| 0.907692
| 0.12093
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| true
| 0
| 0.4
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| null | 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
7f75c77d3c83283bd682402cd89d9cc661b4f2a9
| 25
|
py
|
Python
|
bpemb/__init__.py
|
hartb/bpemb
|
c2f26483e61ccf8c169cd83bf316221c3226106c
|
[
"MIT"
] | 1,080
|
2017-10-09T14:04:42.000Z
|
2022-03-24T06:10:56.000Z
|
bpemb/__init__.py
|
hartb/bpemb
|
c2f26483e61ccf8c169cd83bf316221c3226106c
|
[
"MIT"
] | 60
|
2017-10-11T18:46:07.000Z
|
2021-12-09T09:15:05.000Z
|
bpemb/__init__.py
|
hartb/bpemb
|
c2f26483e61ccf8c169cd83bf316221c3226106c
|
[
"MIT"
] | 87
|
2017-10-27T09:18:00.000Z
|
2022-03-20T00:43:39.000Z
|
from .bpemb import BPEmb
| 12.5
| 24
| 0.8
| 4
| 25
| 5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 1
| 25
| 25
| 0.952381
| 0
| 0
| 0
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| 0
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| 0
| 0
| 1
| 0
| true
| 0
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| null | 0
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| 0
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| 0
| 0
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| 0
| 1
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f68ad151d2faecc23e208fbbd5db55d75173efe9
| 1,916
|
py
|
Python
|
src/conductor/client/http/models/__init__.py
|
conductor-sdk/conductor-python
|
b3e4e0ae196f9963316a829fe42d9e7e01a390e2
|
[
"Apache-2.0"
] | 3
|
2022-03-10T18:24:46.000Z
|
2022-03-22T20:49:30.000Z
|
src/conductor/client/http/models/__init__.py
|
conductor-sdk/conductor-python
|
b3e4e0ae196f9963316a829fe42d9e7e01a390e2
|
[
"Apache-2.0"
] | 6
|
2022-03-08T17:48:28.000Z
|
2022-03-30T00:39:22.000Z
|
src/conductor/client/http/models/__init__.py
|
conductor-sdk/conductor-python
|
b3e4e0ae196f9963316a829fe42d9e7e01a390e2
|
[
"Apache-2.0"
] | null | null | null |
from conductor.client.http.models.action import Action
from conductor.client.http.models.bulk_response import BulkResponse
from conductor.client.http.models.event_handler import EventHandler
from conductor.client.http.models.external_storage_location import ExternalStorageLocation
from conductor.client.http.models.health import Health
from conductor.client.http.models.health_check_status import HealthCheckStatus
from conductor.client.http.models.poll_data import PollData
from conductor.client.http.models.rerun_workflow_request import RerunWorkflowRequest
from conductor.client.http.models.search_result_task import SearchResultTask
from conductor.client.http.models.search_result_task_summary import SearchResultTaskSummary
from conductor.client.http.models.search_result_workflow import SearchResultWorkflow
from conductor.client.http.models.search_result_workflow_summary import SearchResultWorkflowSummary
from conductor.client.http.models.skip_task_request import SkipTaskRequest
from conductor.client.http.models.start_workflow import StartWorkflow
from conductor.client.http.models.start_workflow_request import StartWorkflowRequest
from conductor.client.http.models.sub_workflow_params import SubWorkflowParams
from conductor.client.http.models.task import Task
from conductor.client.http.models.task_def import TaskDef
from conductor.client.http.models.task_details import TaskDetails
from conductor.client.http.models.task_exec_log import TaskExecLog
from conductor.client.http.models.task_result import TaskResult
from conductor.client.http.models.task_summary import TaskSummary
from conductor.client.http.models.token import Token
from conductor.client.http.models.workflow import Workflow
from conductor.client.http.models.workflow_def import WorkflowDef
from conductor.client.http.models.workflow_summary import WorkflowSummary
from conductor.client.http.models.workflow_task import WorkflowTask
| 68.428571
| 99
| 0.887265
| 251
| 1,916
| 6.633466
| 0.227092
| 0.210811
| 0.308108
| 0.372973
| 0.56997
| 0.413213
| 0.163363
| 0.112913
| 0
| 0
| 0
| 0
| 0.056367
| 1,916
| 27
| 100
| 70.962963
| 0.920907
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| null | 1
| 1
| 1
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| 0
| 0
| 0
| 0
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| 0
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| null | 0
| 0
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| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f691ea5f2f8fc99d89c9ef6d51d9219dfc37103b
| 123,636
|
py
|
Python
|
examples/readJson.py
|
pexip/os-pyparsing
|
e7230e6d9dbd50defeb1c1f1f74296c0d4c8db42
|
[
"MIT"
] | 1
|
2019-01-06T21:51:21.000Z
|
2019-01-06T21:51:21.000Z
|
examples/readJson.py
|
pexip/os-pyparsing
|
e7230e6d9dbd50defeb1c1f1f74296c0d4c8db42
|
[
"MIT"
] | 1
|
2019-08-24T21:25:49.000Z
|
2019-08-26T22:44:40.000Z
|
examples/readJson.py
|
pexip/os-pyparsing
|
e7230e6d9dbd50defeb1c1f1f74296c0d4c8db42
|
[
"MIT"
] | 2
|
2019-03-21T03:47:03.000Z
|
2019-09-30T23:59:22.000Z
|
#~ url = "http://cmsdoc.cern.ch/cms/test/aprom/phedex/dev/gowri/datasvc/tbedi/requestDetails"
#~ params = {'format':'json'}
#~ import urllib
#~ eparams = urllib.urlencode(params)
#~ import urllib2
#~ request = urllib2.Request(url,eparams)
#~ response = urllib2.urlopen(request)
#~ s = response.read()
#~ response.close()
#~ print s
s = """
{"phedex":{"request":[{"last_update":"1188037561", "numofapproved":"1",
"id":"7425"}, {"last_update":"1188751826", "numofapproved":"1",
"id":"8041"}, {"last_update":"1190116795", "numofapproved":"1",
"id":"9281"}, {"last_update":"1190248781", "numofapproved":"1",
"id":"9521"}, {"last_update":"1192615612", "numofapproved":"1",
"id":"12821"}, {"last_update":"1192729887", "numofapproved":"1",
"id":"13121"}, {"last_update":"1193152971", "numofapproved":"1",
"id":"13501"}, {"last_update":"1194022054", "numofapproved":"1",
"id":"14782"}, {"last_update":"1194429365", "numofapproved":"1",
"id":"15081"}, {"last_update":"1195069848", "numofapproved":"1",
"id":"16661"}, {"last_update":"1178403225", "numofapproved":"1",
"id":"1281"}, {"last_update":"1179239056", "numofapproved":"1",
"id":"1387"}, {"last_update":"1179842205", "numofapproved":"1",
"id":"1665"}, {"last_update":"1179842040", "numofapproved":"1",
"id":"1661"}, {"last_update":"1179935333", "numofapproved":"1",
"id":"1741"}, {"last_update":"1183151195", "numofapproved":"1",
"id":"3841"}, {"last_update":"1187031531", "numofapproved":"1",
"id":"6601"}, {"last_update":"1188820478", "numofapproved":"1",
"id":"8121"}, {"last_update":"1190652719", "numofapproved":"1",
"id":"9983"}, {"last_update":"1192628950", "numofapproved":"1",
"id":"12841"}, {"last_update":"1193075426", "numofapproved":"1",
"id":"13341"}, {"last_update":"1194214609", "numofapproved":"1",
"id":"14882"}, {"last_update":"1194387864", "numofapproved":"1",
"id":"15062"}, {"last_update":"1195134504", "numofapproved":"1",
"id":"16741"}, {"last_update":"1182431453", "numofapproved":"1",
"id":"3421"}, {"last_update":"1183448188", "numofapproved":"1",
"id":"4061"}, {"last_update":"1184588081", "numofapproved":"1",
"id":"4908"}, {"last_update":"1184681258", "numofapproved":"1",
"id":"4913"}, {"last_update":"1188039048", "numofapproved":"1",
"id":"7426"}, {"last_update":"1192699041", "numofapproved":"1",
"id":"12982"}, {"last_update":"1193219685", "numofapproved":"1",
"id":"13529"}, {"last_update":"1193401408", "numofapproved":"1",
"id":"14081"}, {"last_update":"1194454724", "numofapproved":"1",
"id":"15201"}, {"last_update":"1194937690", "numofapproved":"1",
"id":"16044"}, {"last_update":"1194947125", "numofapproved":"1",
"id":"16103"}, {"last_update":"1195134890", "numofapproved":"1",
"id":"16761"}, {"last_update":"1195486898", "numofapproved":"1",
"id":"17301"}, {"last_update":"1195497774", "numofapproved":"1",
"id":"17341"}, {"last_update":"1184744080", "numofapproved":"1",
"id":"4941"}, {"last_update":"1186558911", "numofapproved":"1",
"id":"6321"}, {"last_update":"1189524520", "numofapproved":"1",
"id":"8802"}, {"last_update":"1192683178", "numofapproved":"1",
"id":"12921"}, {"last_update":"1193260655", "numofapproved":"1",
"id":"13530"}, {"last_update":"1194280038", "numofapproved":"1",
"id":"15002"}, {"last_update":"1182077478", "numofapproved":"1",
"id":"3162"}, {"last_update":"1183386650", "numofapproved":"1",
"id":"3961"}, {"last_update":"1192063369", "numofapproved":"1",
"id":"12182"}, {"last_update":"1181931262", "numofapproved":"1",
"id":"3101"}, {"last_update":"1178648271", "numofapproved":"1",
"id":"1308"}, {"last_update":"1179239923", "numofapproved":"1",
"id":"1405"}, {"last_update":"1184370745", "numofapproved":"1",
"id":"4861"}, {"last_update":"1185280568", "numofapproved":"1",
"id":"5302"}, {"last_update":"1187875115", "numofapproved":"1",
"id":"7344"}, {"last_update":"1189140441", "numofapproved":"1",
"id":"8541"}, {"last_update":"1189180903", "numofapproved":"1",
"id":"8661"}, {"last_update":"1189767643", "numofapproved":"1",
"id":"9001"}, {"last_update":"1190726167", "numofapproved":"1",
"id":"10101"}, {"last_update":"1190972990", "numofapproved":"1",
"id":"10661"}, {"last_update":"1190990720", "numofapproved":"1",
"id":"10712"}, {"last_update":"1192004838", "numofapproved":"1",
"id":"12021"}, {"last_update":"1192612211", "numofapproved":"1",
"id":"12803"}, {"last_update":"1194441407", "numofapproved":"1",
"id":"15103"}, {"last_update":"1194792356", "numofapproved":"1",
"id":"15681"}, {"last_update":"1194860650", "numofapproved":"1",
"id":"15801"}, {"last_update":"1194877395", "numofapproved":"1",
"id":"15881"}, {"last_update":"1194950552", "numofapproved":"1",
"id":"16124"}, {"last_update":"1194992714", "numofapproved":"1",
"id":"16421"}, {"last_update":"1195054500", "numofapproved":"1",
"id":"16581"}, {"last_update":"1195228524", "numofapproved":"1",
"id":"17001"}, {"last_update":"1195469382", "numofapproved":"1",
"id":"17161"}, {"last_update":"1178035947", "numofapproved":"1",
"id":"1202"}, {"last_update":"1178869668", "numofapproved":"1",
"id":"1356"}, {"last_update":"1183563268", "numofapproved":"1",
"id":"4201"}, {"last_update":"1185314677", "numofapproved":"1",
"id":"5361"}, {"last_update":"1188467567", "numofapproved":"1",
"id":"7781"}, {"last_update":"1190011821", "numofapproved":"1",
"id":"9202"}, {"last_update":"1190206214", "numofapproved":"1",
"id":"9481"}, {"last_update":"1190973037", "numofapproved":"1",
"id":"10663"}, {"last_update":"1190819127", "numofapproved":"1",
"id":"10342"}, {"last_update":"1192154959", "numofapproved":"1",
"id":"12381"}, {"last_update":"1192634509", "numofapproved":"1",
"id":"12862"}, {"last_update":"1194004677", "numofapproved":"1",
"id":"14722"}, {"last_update":"1195548191", "numofapproved":"1",
"id":"17501"}, {"last_update":"1195548953", "numofapproved":"1",
"id":"17502"}, {"last_update":"1195559809", "numofapproved":"1",
"id":"17541"}, {"last_update":"1177589103", "numofapproved":"1",
"id":"1044"}, {"last_update":"1183416879", "numofapproved":"1",
"id":"4041"}, {"last_update":"1186646977", "numofapproved":"1",
"id":"6342"}, {"last_update":"1189656586", "numofapproved":"1",
"id":"8902"}, {"last_update":"1190150645", "numofapproved":"1",
"id":"9421"}, {"last_update":"1190409040", "numofapproved":"1",
"id":"9741"}, {"last_update":"1190973011", "numofapproved":"1",
"id":"10662"}, {"last_update":"1190993896", "numofapproved":"1",
"id":"10761"}, {"last_update":"1193973610", "numofapproved":"1",
"id":"14661"}, {"last_update":"1193973848", "numofapproved":"1",
"id":"14664"}, {"last_update":"1194539978", "numofapproved":"1",
"id":"15381"}, {"last_update":"1194947356", "numofapproved":"1",
"id":"16105"}, {"last_update":"1195399589", "numofapproved":"1",
"id":"17101"}, {"last_update":"1195464953", "numofapproved":"1",
"id":"17141"}, {"last_update":"1171962221", "numofapproved":"1",
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"request_timestamp":1206363392.08521, "request_call":"requestDetails",
"instance":"tbedi", "call_time":"0.10059", "request_date":"2008-03-2412:56:32 UTC", "request_url":"http://cmsdoc.cern.ch/cms/test/aprom/phedex/dev/gowri/datasvc/tbedi/requestDetails?format=json"}}
"""
from jsonParser import jsonObject
data = jsonObject.parseString(s)
#~ from pprint import pprint
#~ pprint( data[0].asList() )
#~ print
#~ print data.dump()
print(data.phedex.call_time)
print(data.phedex.instance)
print(data.phedex.request_call)
print(len(data.phedex.request))
for req in data.phedex.request[:10]:
#~ print req.dump()
print("-", req.id, req.last_update)
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0
| 6
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f6947cb95396093cc925da70a91acfb38312fda4
| 4,434
|
py
|
Python
|
analysis/parabola.py
|
keflavich/W51_VLA-16B-202
|
4c743c5d001e097f1d184a550e6d21441ca02f2d
|
[
"BSD-3-Clause"
] | null | null | null |
analysis/parabola.py
|
keflavich/W51_VLA-16B-202
|
4c743c5d001e097f1d184a550e6d21441ca02f2d
|
[
"BSD-3-Clause"
] | null | null | null |
analysis/parabola.py
|
keflavich/W51_VLA-16B-202
|
4c743c5d001e097f1d184a550e6d21441ca02f2d
|
[
"BSD-3-Clause"
] | null | null | null |
import os
from astropy.io import fits
from astropy import wcs
from astropy import units as u
import regions
import numpy as np
import pylab as pl
if not os.path.exists('W51e2w_ALMAB3_cutout.fits'):
fh = fits.open('/Users/adam/work/w51/alma/FITS/longbaseline/w51e2_sci.spw0_1_2_3_4_5_6_7_8_9_10_11_12_13_14_15_16_17_18_19.mfs.I.manual.image.tt0.pbcor.fits')
ww = wcs.WCS(fh[0].header).celestial
pr0 = regions.read_ds9('/Users/adam/work/w51/vla_q/regions/e2w_ellipse.reg')[0].to_pixel(ww)
pr0.width *= 2.5
pr0.height *= 2.5
msk = pr0.to_mask()
img_95ghz = msk.multiply(fh[0].data.squeeze())
header = fh[0].header
ww_cutout = ww[msk.bbox.slices]
header.update(ww_cutout.to_header())
fits.PrimaryHDU(data=img_95ghz, header=header).writeto('W51e2w_ALMAB3_cutout.fits', overwrite=True)
else:
img_95ghz = fits.getdata('W51e2w_ALMAB3_cutout.fits')
if not os.path.exists('W51e2w_ALMAB6_cutout.fits'):
fh = fits.open('/Users/adam/work/w51/alma/FITS/longbaseline/W51e2_cont_briggsSC_tclean_allspw.image.fits')
ww = wcs.WCS(fh[0].header).celestial
pr0 = regions.read_ds9('/Users/adam/work/w51/vla_q/regions/e2w_ellipse.reg')[0].to_pixel(ww)
pr0.width *= 2.5
pr0.height *= 2.5
msk = pr0.to_mask()
img_224ghz = msk.multiply(fh[0].data.squeeze())
header = fh[0].header
ww_cutout = ww[msk.bbox.slices]
header.update(ww_cutout.to_header())
fits.PrimaryHDU(data=img_224ghz, header=header).writeto('W51e2w_ALMAB6_cutout.fits', overwrite=True)
else:
img_224ghz = fits.getdata('W51e2w_ALMAB6_cutout.fits')
if not os.path.exists('W51e2w_VLA_Q_cutout.fits'):
fh = fits.open('/Users/adam/work/w51/vla_q/FITS/W51e2w_QbandAarray_cont_spws_continuum_cal_clean_2terms_robust0_wproj_selfcal9.image.tt0.pbcor.fits')
ww = wcs.WCS(fh[0].header).celestial
pr0 = regions.read_ds9('/Users/adam/work/w51/vla_q/regions/e2w_ellipse.reg')[0].to_pixel(ww)
pr0.width *= 2.5
pr0.height *= 2.5
msk = pr0.to_mask()
img_45ghz = msk.multiply(fh[0].data.squeeze())
header = fh[0].header
ww_cutout = ww[msk.bbox.slices]
header.update(ww_cutout.to_header())
fits.PrimaryHDU(data=img_45ghz, header=header).writeto('W51e2w_VLA_Q_cutout.fits', overwrite=True)
else:
img_45ghz = fits.getdata('W51e2w_VLA_Q_cutout.fits')
cy,cx = 127,42
angle = (180+315) * u.deg
xx = np.linspace(0,50,1000)
yy = xx**2 / 28
xx_, yy_ = np.dot([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]], [xx,yy])
xx2_, yy2_ = np.dot([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]], [-xx,yy])
pl.figure(3)
pl.clf()
pl.imshow(img_95ghz, origin='lower', interpolation='none')
pl.plot(xx_+cx,yy_+cy, linewidth=0.5, color='w', linestyle='--')
pl.plot(xx2_+cx,yy2_+cy, linewidth=0.5, color='w', linestyle='--')
data_on_path = img_95ghz[(yy_+cy).astype('int'), (xx_+cx).astype('int')]
data_on_path2 = img_95ghz[(yy_+cy).astype('int'), (-xx_+cx).astype('int')]
prj_dist = (xx**2+yy**2)**0.5
pl.figure(4)
pl.clf()
pl.plot(prj_dist, data_on_path)
pl.plot(prj_dist, data_on_path2)
core = data_on_path[:10].mean()
profile = (core*(prj_dist/prj_dist[250])**-0.1 * (prj_dist >= prj_dist[250])/10.
+ core * (1-(prj_dist/prj_dist[580])**2) * (prj_dist<=prj_dist[580])
)
profile = core * (1-(prj_dist/prj_dist[580])**2) * (prj_dist<=prj_dist[580])
pl.plot(prj_dist, profile)
cy,cx = 62,23
xx = np.linspace(0,25,1000)
yy = xx**2 / 17
xx_, yy_ = np.dot([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]], [xx,yy])
xx2_, yy2_ = np.dot([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]], [-xx,yy])
pl.figure(1)
pl.clf()
pl.imshow(img_45ghz, origin='lower', interpolation='none')
pl.plot(xx_+cx,yy_+cy, linewidth=0.5, color='w', linestyle='--')
pl.plot(xx2_+cx,yy2_+cy, linewidth=0.5, color='w', linestyle='--')
data_on_path = img_45ghz[(yy_+cy).astype('int'), (xx_+cx).astype('int')]
data_on_path2 = img_45ghz[(yy_+cy).astype('int'), (-xx_+cx).astype('int')]
prj_dist = (xx**2+yy**2)**0.5
pl.figure(2)
pl.clf()
pl.plot(prj_dist, data_on_path)
pl.plot(prj_dist, data_on_path2)
core = data_on_path[:10].mean()
profile = (core*(prj_dist/prj_dist[250])**-0.1 * (prj_dist >= prj_dist[250])/10.
+ core * (1-(prj_dist/prj_dist[580])**2) * (prj_dist<=prj_dist[580])
)
profile = core * (1-(prj_dist/prj_dist[580])**2) * (prj_dist<=prj_dist[580])
pl.plot(prj_dist, profile)
| 37.260504
| 162
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0
| 6
|
f6bca771c67d05d17260d3b95312bf83a4073ad8
| 63
|
py
|
Python
|
models/__init__.py
|
duylecampos/easy-alert
|
2d30793223001b5a55b34507da0bd3d200716227
|
[
"MIT"
] | 1
|
2018-02-23T10:13:50.000Z
|
2018-02-23T10:13:50.000Z
|
models/__init__.py
|
duylecampos/easy-alert
|
2d30793223001b5a55b34507da0bd3d200716227
|
[
"MIT"
] | null | null | null |
models/__init__.py
|
duylecampos/easy-alert
|
2d30793223001b5a55b34507da0bd3d200716227
|
[
"MIT"
] | null | null | null |
from models.auth import Auth
from models.channel import Channel
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| 34
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| 10
| 63
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| 0.111111
| 63
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0
| 6
|
1007ddf9e529d01bcc7646bff1629ac7b421eb8e
| 12,569
|
py
|
Python
|
test/linkloading.py
|
Shapes/pisa-fix-django
|
62d7a0c1639ed8e9a45b756c30480771311e1dae
|
[
"Apache-2.0"
] | 1
|
2017-04-26T17:50:10.000Z
|
2017-04-26T17:50:10.000Z
|
test/linkloading.py
|
Shapes/pisa-fix-django
|
62d7a0c1639ed8e9a45b756c30480771311e1dae
|
[
"Apache-2.0"
] | 5
|
2016-05-06T00:04:03.000Z
|
2019-04-05T21:37:30.000Z
|
test/linkloading.py
|
Shapes/pisa-fix-django
|
62d7a0c1639ed8e9a45b756c30480771311e1dae
|
[
"Apache-2.0"
] | 1
|
2020-07-20T13:53:15.000Z
|
2020-07-20T13:53:15.000Z
|
# -*- coding: ISO-8859-1 -*-
# Copyright 2010 Dirk Holtwick, holtwick.it
#
# 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.
__version__ = "$Revision: 194 $"
__author__ = "$Author: holtwick $"
__date__ = "$Date: 2008-04-18 18:59:53 +0200 (Fr, 18 Apr 2008) $"
import ho.pisa as pisa
import logging
log = logging.getLogger(__file__)
def dummyLoader(name):
return '\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00F\x00\x00\x00\x89\x04\x03\x00\x00\x00c\xbeS\xd6\x00\x00\x000PLTE\x00\x00\x00\n\x06\x04\x18\x14\x0f-&\x1eLB6w`E\x8f\x80q\xb2\x9c\x82\xbe\xa1{\xc7\xb0\x96\xd1\xbd\xa9\xd9\xd0\xc6\xef\xeb\xe6\xf8\xf3\xef\xff\xfb\xf7\xff\xff\xffZ\x83\x0b|\x00\x00\x0c\xedIDATx^u\x97]l\x1bWv\xc7g\xe2`\x81\xbe\xcd%Gr\xd3\xa7P\x12e\xb7\x01\x8a\xd0")E\x01\x02\x8f\xf8!\x8bI\x17\x10\xc5!))5`\xf1C\xb4\xb25`S\xb2l\xb95\x90H\xa4.\xb9/u$K3\xe3\xa2\x80W\x12\xc59L\xf6a\xb3\x8dcN\xd6@\xb7\x1f\x01\x8a\x85\x16\x9b-\xfa\x81M\xb8@\x83l\xd1\xd8\xbc|)\xd0\x97\x82\xea\xb93\x92\xec"\xce\x11 \t3?\xfe\xcf\xff\x9e{\xce\x01(\x1c>7\x18\xfb\xc2\xfaE\xffk_\xb6\x18\xeb\x1e>\x8f\xe92d\xfe%T\xa8\x98\xfa\x07\x1f 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class myLinkLoader:
"""
This object is just a wrapper to track additional informations
and handle temporary files after they are not needed any more.
"""
def __init__(self, **kw):
"""
The self.kw could be used in getFileName if you like
"""
self.kw = kw
self.tmpFileList = []
def __del__(self):
for path in self.tmpFileList:
os.remove(path)
self.tmpFileList = []
def getFileName(self, path, relative=None):
import os
import tempfile
log.info("myLinkLoader.getFileName: %r %r %r", path, relative, self.kw)
try:
if "." in path:
new_suffix = "." + path.split(".")[-1].lower()
if new_suffix in (".css", ".gif", ".jpg", ".png"):
suffix = new_suffix
tmpPath = tempfile.mktemp(prefix="pisa-", suffix = suffix)
tmpFile = file(tmpPath, "wb")
try:
# Here you may add your own stuff
tmpFile.write(dummyLoader(path))
finally:
tmpFile.close()
self.tmpFileList.append(tmpPath)
return tmpPath
except Exception, e:
log.exception("myLinkLoader.getFileName")
return None
def helloWorld():
filename = __file__ + ".pdf"
lc = myLinkLoader(database="some_name", port=666).getFileName
pdf = pisa.CreatePDF(
u"""
<p>
Hello <strong>World</strong>
<p>
<img src="apath/some.png">
""",
file(filename, "wb"),
link_callback = lc,
)
if not pdf.err:
pisa.startViewer(filename)
if __name__=="__main__":
pisa.showLogging()
helloWorld()
# print repr(open("img/denker.png", "rb").read())
| 133.712766
| 9,883
| 0.705148
| 2,595
| 12,569
| 3.393834
| 0.341426
| 0.008175
| 0.004088
| 0.003633
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.200867
| 0.064046
| 12,569
| 93
| 9,884
| 135.150538
| 0.547773
| 0.053385
| 0
| 0.081633
| 0
| 0.142857
| 0.698517
| 0.684817
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.081633
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
63e49d34613a460c644b0309a9709e16fbce1773
| 3,548
|
py
|
Python
|
tests/test_routes.py
|
chrishoage/arrsync
|
b2410ba91e4101d1f1fc7266c038891c32780a3c
|
[
"MIT"
] | 1
|
2020-12-11T17:36:59.000Z
|
2020-12-11T17:36:59.000Z
|
tests/test_routes.py
|
chrishoage/arrsync
|
b2410ba91e4101d1f1fc7266c038891c32780a3c
|
[
"MIT"
] | 6
|
2020-12-12T01:46:14.000Z
|
2020-12-13T06:57:50.000Z
|
tests/test_routes.py
|
chrishoage/arrsync
|
b2410ba91e4101d1f1fc7266c038891c32780a3c
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
from contextlib import nullcontext as does_not_raise
from typing import Any, Union
import pytest
from arrsync import routes
from arrsync.common import JobType
@pytest.mark.parametrize(
"job_type,url,expected,excpetion",
[
(JobType.Sonarr, "http://host/", "api/v3/system/status", does_not_raise()),
(JobType.Radarr, "http://host/", "api/v3/system/status", does_not_raise()),
(JobType.Lidarr, "http://host/", "api/v1/system/status", does_not_raise()),
(None, None, None, pytest.raises(Exception)),
],
)
def test_status(
job_type: JobType, url: str, expected: Union[str, None], excpetion: Any
) -> None:
with excpetion:
assert routes.status(job_type, url) == f"{url}{expected}"
@pytest.mark.parametrize(
"job_type,url,expected,excpetion",
[
(JobType.Sonarr, "http://host/", "api/v3/series", does_not_raise()),
(JobType.Radarr, "http://host/", "api/v3/movie", does_not_raise()),
(JobType.Lidarr, "http://host/", "api/v1/artist", does_not_raise()),
(None, None, None, pytest.raises(Exception)),
],
)
def test_content(
job_type: JobType, url: str, expected: Union[str, None], excpetion: Any
) -> None:
with excpetion:
assert routes.content(job_type, url) == f"{url}{expected}"
@pytest.mark.parametrize(
"job_type,url,expected,excpetion",
[
(JobType.Sonarr, "http://host/", "api/v3/qualityprofile", does_not_raise()),
(JobType.Radarr, "http://host/", "api/v3/qualityprofile", does_not_raise()),
(JobType.Lidarr, "http://host/", "api/v1/qualityprofile", does_not_raise()),
(None, None, None, pytest.raises(Exception)),
],
)
def test_profile(
job_type: JobType, url: str, expected: Union[str, None], excpetion: Any
) -> None:
with excpetion:
assert routes.profile(job_type, url) == f"{url}{expected}"
@pytest.mark.parametrize(
"job_type,url,expected,excpetion",
[
(JobType.Sonarr, "http://host/", "api/v3/tag", does_not_raise()),
(JobType.Radarr, "http://host/", "api/v3/tag", does_not_raise()),
(JobType.Lidarr, "http://host/", "api/v1/tag", does_not_raise()),
(None, None, None, pytest.raises(Exception)),
],
)
def test_tag(
job_type: JobType, url: str, expected: Union[str, None], excpetion: Any
) -> None:
with excpetion:
assert routes.tag(job_type, url) == f"{url}{expected}"
@pytest.mark.parametrize(
"job_type,url,expected,excpetion",
[
(JobType.Sonarr, "http://host/", "api/v3/languageprofile", does_not_raise()),
(JobType.Radarr, "http://host/", "api/v3/language", does_not_raise()),
(JobType.Lidarr, None, None, pytest.raises(Exception)),
(None, None, None, pytest.raises(Exception)),
],
)
def test_language(
job_type: JobType, url: str, expected: Union[str, None], excpetion: Any
) -> None:
with excpetion:
assert routes.language(job_type, url) == f"{url}{expected}"
@pytest.mark.parametrize(
"job_type,url,expected,excpetion",
[
(JobType.Sonarr, None, None, pytest.raises(Exception)),
(JobType.Radarr, None, None, pytest.raises(Exception)),
(JobType.Lidarr, "http://host/", "api/v1/metadataprofile", does_not_raise()),
(None, None, None, pytest.raises(Exception)),
],
)
def test_metadata(
job_type: JobType, url: str, expected: Union[str, None], excpetion: Any
) -> None:
with excpetion:
assert routes.metadata(job_type, url) == f"{url}{expected}"
| 33.471698
| 85
| 0.638952
| 442
| 3,548
| 5.002262
| 0.128959
| 0.056988
| 0.086839
| 0.058797
| 0.862053
| 0.832203
| 0.781095
| 0.781095
| 0.763003
| 0.616011
| 0
| 0.005176
| 0.183202
| 3,548
| 105
| 86
| 33.790476
| 0.757764
| 0.005637
| 0
| 0.47191
| 0
| 0
| 0.20017
| 0.083073
| 0
| 0
| 0
| 0
| 0.067416
| 1
| 0.067416
| false
| 0
| 0.05618
| 0
| 0.123596
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
1218a6b5caf314b32e867d9861554e1585bf1478
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/setuptools/_distutils/command/bdist_msi.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/setuptools/_distutils/command/bdist_msi.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/setuptools/_distutils/command/bdist_msi.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/11/51/50/60dfd7f84c5e78ff2099d57d25c20db2e506b0b254cfd69f314d11b7c7
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.427083
| 0
| 96
| 1
| 96
| 96
| 0.46875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
1255869863f0958475601c5888a4c08ae7992711
| 26
|
py
|
Python
|
python/module/b2d/testbed/backend/no_gui/__init__.py
|
pyb2d/pyb2d
|
5d0f9f581d93c3681ee4f518a5d7fd6be900e695
|
[
"MIT"
] | 26
|
2021-12-10T12:08:39.000Z
|
2022-03-29T17:45:31.000Z
|
python/module/b2d/testbed/backend/no_gui/__init__.py
|
pyb2d/pyb2d
|
5d0f9f581d93c3681ee4f518a5d7fd6be900e695
|
[
"MIT"
] | 14
|
2021-11-18T23:58:55.000Z
|
2022-01-06T09:44:58.000Z
|
python/module/b2d/testbed/backend/no_gui/__init__.py
|
DerThorsten/pybox2d
|
5d0f9f581d93c3681ee4f518a5d7fd6be900e695
|
[
"MIT"
] | 3
|
2021-12-16T05:52:12.000Z
|
2021-12-21T08:58:54.000Z
|
from .no_gui import NoGui
| 13
| 25
| 0.807692
| 5
| 26
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 26
| 1
| 26
| 26
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
1263b5f5967c9389ef80274028d29c5669b67ac0
| 154
|
py
|
Python
|
cases/admin.py
|
MaxHarrington/ecasefile
|
4b1621941ed1fbb56779d4264216df3ebe8949fe
|
[
"MIT"
] | 2
|
2018-06-18T03:29:15.000Z
|
2020-07-26T19:22:33.000Z
|
cases/admin.py
|
MaxHarrington/ecasefile
|
4b1621941ed1fbb56779d4264216df3ebe8949fe
|
[
"MIT"
] | null | null | null |
cases/admin.py
|
MaxHarrington/ecasefile
|
4b1621941ed1fbb56779d4264216df3ebe8949fe
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from django.db import models
from .models import Case, CaseFile
admin.site.register(Case)
admin.site.register(CaseFile)
| 22
| 34
| 0.818182
| 23
| 154
| 5.478261
| 0.478261
| 0.15873
| 0.269841
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.103896
| 154
| 6
| 35
| 25.666667
| 0.913043
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
89cc254eb7f84bf9e7421b6ec0a4985db629ab0e
| 67
|
py
|
Python
|
sensors/light.py
|
dhvie/micropython-sensors
|
faf0e1965b6858a75b8864d0462fc121348dc889
|
[
"MIT"
] | null | null | null |
sensors/light.py
|
dhvie/micropython-sensors
|
faf0e1965b6858a75b8864d0462fc121348dc889
|
[
"MIT"
] | null | null | null |
sensors/light.py
|
dhvie/micropython-sensors
|
faf0e1965b6858a75b8864d0462fc121348dc889
|
[
"MIT"
] | null | null | null |
from .sensor import Sensor
class PhotoResistor(Sensor):
pass
| 11.166667
| 28
| 0.746269
| 8
| 67
| 6.25
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.19403
| 67
| 5
| 29
| 13.4
| 0.925926
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
89d20874a9854fc4a37a4df24ca8d43e5012abdc
| 122
|
py
|
Python
|
apps/post/queries/_t_post.py
|
rmdes/tanzawa
|
d53baa10bd6c217cd18628437a88a43e3bd02b70
|
[
"Apache-2.0"
] | 25
|
2021-06-13T03:38:44.000Z
|
2022-03-15T15:53:31.000Z
|
apps/post/queries/_t_post.py
|
rmdes/tanzawa
|
d53baa10bd6c217cd18628437a88a43e3bd02b70
|
[
"Apache-2.0"
] | 59
|
2021-06-12T23:35:06.000Z
|
2022-03-24T21:40:24.000Z
|
apps/post/queries/_t_post.py
|
rmdes/tanzawa
|
d53baa10bd6c217cd18628437a88a43e3bd02b70
|
[
"Apache-2.0"
] | null | null | null |
from .. import models
def get_t_post_by_uuid(uuid: str) -> models.TPost:
return models.TPost.objects.get(uuid=uuid)
| 20.333333
| 50
| 0.737705
| 20
| 122
| 4.3
| 0.65
| 0.186047
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.139344
| 122
| 5
| 51
| 24.4
| 0.819048
| 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 | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
89d943c5e9833c2e3aadd60ed22492f53f4fedc8
| 94
|
py
|
Python
|
jupyterlab_bigquery/jupyterlab_bigquery/details_handler/__init__.py
|
shunr/jupyter-extensions
|
a2fb310215664e29fd7252e5fe353f60a91a0aba
|
[
"Apache-2.0"
] | null | null | null |
jupyterlab_bigquery/jupyterlab_bigquery/details_handler/__init__.py
|
shunr/jupyter-extensions
|
a2fb310215664e29fd7252e5fe353f60a91a0aba
|
[
"Apache-2.0"
] | 1
|
2020-07-20T23:09:46.000Z
|
2020-07-20T23:09:46.000Z
|
jupyterlab_bigquery/jupyterlab_bigquery/details_handler/__init__.py
|
shunr/jupyter-extensions
|
a2fb310215664e29fd7252e5fe353f60a91a0aba
|
[
"Apache-2.0"
] | null | null | null |
from .details_handler import DatasetDetailsHandler, TableDetailsHandler, TablePreviewHandler
| 47
| 93
| 0.893617
| 7
| 94
| 11.857143
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.074468
| 94
| 1
| 94
| 94
| 0.954023
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d60636b4a615db9faa8adb9308e288bebe3144fd
| 25
|
py
|
Python
|
pommerman/cli/__init__.py
|
alekseynp/playground
|
523cc924fe9fd269a8eb3e29c45ace1c5c85b12c
|
[
"Apache-2.0"
] | 1
|
2019-01-04T13:36:04.000Z
|
2019-01-04T13:36:04.000Z
|
pommerman/cli/__init__.py
|
alekseynp/playground
|
523cc924fe9fd269a8eb3e29c45ace1c5c85b12c
|
[
"Apache-2.0"
] | null | null | null |
pommerman/cli/__init__.py
|
alekseynp/playground
|
523cc924fe9fd269a8eb3e29c45ace1c5c85b12c
|
[
"Apache-2.0"
] | 1
|
2018-03-21T15:21:52.000Z
|
2018-03-21T15:21:52.000Z
|
from . import run_battle
| 12.5
| 24
| 0.8
| 4
| 25
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 1
| 25
| 25
| 0.904762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d61939055cc5db7bebcf8b311ac65eabd1670c6c
| 44
|
py
|
Python
|
src/010-summation-of-primes/python/solve.py
|
xfbs/ProjectEulerRust
|
e26768c56ff87b029cb2a02f56dc5cd32e1f7c87
|
[
"MIT"
] | 1
|
2018-01-26T21:18:12.000Z
|
2018-01-26T21:18:12.000Z
|
src/010-summation-of-primes/python/solve.py
|
xfbs/ProjectEulerRust
|
e26768c56ff87b029cb2a02f56dc5cd32e1f7c87
|
[
"MIT"
] | 3
|
2017-12-09T14:49:30.000Z
|
2017-12-09T14:59:39.000Z
|
src/010-summation-of-primes/python/solve.py
|
xfbs/ProjectEulerRust
|
e26768c56ff87b029cb2a02f56dc5cd32e1f7c87
|
[
"MIT"
] | null | null | null |
import solver
print(solver.solve(2000000))
| 11
| 28
| 0.795455
| 6
| 44
| 5.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175
| 0.090909
| 44
| 3
| 29
| 14.666667
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 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
| 0
| 1
|
0
| 6
|
d61ebb3d64dec7d031633f6346c6289bd38ccbb2
| 9,902
|
py
|
Python
|
examples/simple_expression/exp_definition.py
|
m-colombo/tf_tree
|
10e22583d178b25067b13f3c989946b37731f23d
|
[
"MIT"
] | null | null | null |
examples/simple_expression/exp_definition.py
|
m-colombo/tf_tree
|
10e22583d178b25067b13f3c989946b37731f23d
|
[
"MIT"
] | 1
|
2019-09-10T14:45:47.000Z
|
2019-09-10T14:45:47.000Z
|
examples/simple_expression/exp_definition.py
|
m-colombo/tf_tree
|
10e22583d178b25067b13f3c989946b37731f23d
|
[
"MIT"
] | null | null | null |
import random
from tensorflow_trees.definition import TreeDefinition, NodeDefinition, Tree
import tensorflow as tf
import typing as T
def create_num_value(max_value):
"""Build a Value class that handle numbers in [0, max_value] encoded as 1ofk"""
size = max_value + 1
class NumValue(NodeDefinition.Value):
representation_shape = size
class_value = True
@staticmethod
def representation_to_abstract_batch(t: tf.Tensor):
return (tf.argmax(t, axis=-1)).numpy()
@staticmethod
def abstract_to_representation_batch(v: T.List[T.Any]):
return tf.one_hot(v, size, axis=-1)
return NumValue
class OpValue(NodeDefinition.Value):
representation_shape = 2
class_value = True
@staticmethod
def representation_to_abstract_batch(t: tf.Tensor):
ops = ['+', '-']
return ops[(tf.argmax(t, axis=-1)).numpy()[0]]
@staticmethod
def abstract_to_representation_batch(v: T.List[T.Any]):
return tf.one_hot(list(map(lambda x: 0 if x == '+' else 1, v)), 2, axis=-1)
class BinaryExpressionTreeGen:
def __init__(self, max_value):
self.NumValue = create_num_value(max_value)
self.tree_def = TreeDefinition(
node_types=[
NodeDefinition("add_bin", may_root=True, arity=NodeDefinition.FixedArity(2)),
NodeDefinition("sub_bin", may_root=True, arity=NodeDefinition.FixedArity(2)),
NodeDefinition("num_value", may_root=True, arity=NodeDefinition.FixedArity(0), value_type=self.NumValue)
])
self.leaf_values = list(range(0, max_value+1))
self.node_types = self.tree_def.node_types
def generate(self, max_depth):
"""Generate a random arithmetic expression tree, using just binary plus and minus
Args:
max_depth: integer > 0
Returns:
expression tree where leaves are int.
"""
if max_depth == 1: # recursion base case
v = random.sample(self.leaf_values, 1)[0]
return Tree(node_type_id='num_value', value=self.NumValue(abstract_value=v))
elif max_depth > 1:
types = self.node_types
node_type = random.sample(types, 1)[0]
if node_type.id == 'num_value':
return self.generate(1)
else:
return Tree(node_type.id, children=[
self.generate(max_depth - 1),
self.generate(max_depth - 1)], value=None)
def evaluate(self, et):
"""Evaluate the result of the arithmetic expression
Args:
et: expression tree
Returns:
an integer, the result
"""
if et.node_type_id == 'num_value':
return et.value.abstract_value
elif et.node_type_id == 'sub_bin':
return self.evaluate(et.children[0]) - self.evaluate(et.children[1])
elif et.node_type_id == 'add_bin':
return self.evaluate(et.children[0]) + self.evaluate(et.children[1])
class LabelledBinaryExpressionTreeGen(BinaryExpressionTreeGen):
def __init__(self, max_value):
super(LabelledBinaryExpressionTreeGen, self).__init__(max_value)
self.NumValue = create_num_value(max_value)
self.tree_def = TreeDefinition(
node_types=[
NodeDefinition("op_bin", may_root=True, arity=NodeDefinition.FixedArity(2), value_type=OpValue),
NodeDefinition("num_value", may_root=True, arity=NodeDefinition.FixedArity(0), value_type=self.NumValue)
])
self.node_types = self.tree_def.node_types
def generate(self, max_depth):
"""Generate a random arithmetic expression tree, using just binary plus and minus
Args:
max_depth: integer > 0
Returns:
expression tree where leaves are int.
"""
if max_depth == 1: # recursion base case
v = random.sample(self.leaf_values, 1)[0]
return Tree(node_type_id='num_value', value=self.NumValue(abstract_value=v))
elif max_depth > 1:
types = self.node_types
node_type = random.sample(types, 1)[0]
if node_type.id == 'num_value':
return self.generate(1)
else:
o = random.sample(['+', '-'], 1)[0]
return Tree(node_type.id, children=[
self.generate(max_depth - 1),
self.generate(max_depth - 1)], value=OpValue(abstract_value=o))
def evaluate(self, et):
"""Evaluate the result of the arithmetic expression
Args:
et: expression tree
Returns:
an integer, the result
"""
if et.node_type_id == 'num_value':
return et.value.abstract_value
elif et.node_type_id == 'op_bin' and et.value.abstract_value == '-':
return self.evaluate(et.children[0]) - self.evaluate(et.children[1])
elif et.node_type_id == 'op_bin' and et.value.abstract_value == '+':
return self.evaluate(et.children[0]) + self.evaluate(et.children[1])
class NaryExpressionTreeGen(BinaryExpressionTreeGen):
def __init__(self, max_value, max_arity):
super(NaryExpressionTreeGen, self).__init__(max_value)
self.tree_def = TreeDefinition(
node_types=[
NodeDefinition("add_n", may_root=True,
arity=NodeDefinition.VariableArity(min_value=2, max_value=max_arity),
value_type=None),
NodeDefinition("sub_bin", may_root=True, arity=NodeDefinition.FixedArity(2),
value_type=None),
NodeDefinition("num_value", may_root=True, arity=NodeDefinition.FixedArity(0),
value_type=self.NumValue)
]
)
self.node_types = self.tree_def.node_types
def generate(self, max_depth):
"""Generate a random arithmetic expression tree, using just n-ary plus and binary minus
Args:
max_depth: integer > 0
Returns:
expression tree where leaves are int.
"""
if max_depth == 1: # recursion base case
v = random.sample(self.leaf_values, 1)[0]
return Tree(node_type_id='num_value', value=self.NumValue(abstract_value=v))
elif max_depth > 1:
types = self.node_types
node_type = random.sample(types, 1)[0]
if node_type.id == 'num_value':
return self.generate(1)
elif node_type.id == 'add_n':
n = random.randint(node_type.arity.min_value, node_type.arity.max_value)
return Tree(node_type.id, children=[self.generate(max_depth - 1) for _ in range(n)])
elif node_type.id == 'sub_bin':
return Tree(node_type.id, children=[
self.generate(max_depth - 1),
self.generate(max_depth - 1)])
def evaluate(self, t: Tree):
if len(t.children) > 0:
if t.node_type_id == 'add_n':
return sum(map(self.evaluate, t.children))
if t.node_type_id == 'sub_bin':
return self.evaluate(t.children[0]) - self.evaluate(t.children[1])
else:
return t.value.abstract_value
class LabelledNaryExpressionTreeGen(NaryExpressionTreeGen):
def __init__(self, max_value, max_arity):
super(LabelledNaryExpressionTreeGen, self).__init__(max_value, max_arity)
self.tree_def = TreeDefinition(
node_types=[
NodeDefinition("op_n", may_root=True,
arity=NodeDefinition.VariableArity(min_value=2, max_value=max_arity),
value_type=OpValue),
NodeDefinition("num_value", may_root=True, arity=NodeDefinition.FixedArity(0),
value_type=self.NumValue)
]
)
self.node_types = self.tree_def.node_types
def generate(self, max_depth):
"""Generate a random arithmetic expression tree, using just n-ary plus and binary minus
Args:
max_depth: integer > 0
Returns:
expression tree where leaves are int.
"""
if max_depth == 1: # recursion base case
v = random.sample(self.leaf_values, 1)[0]
return Tree(node_type_id='num_value', value=self.NumValue(abstract_value=v))
elif max_depth > 1:
types = self.node_types
node_type = random.sample(types, 1)[0]
o = random.sample(['+', '-'], 1)[0]
if node_type.id == 'num_value':
return self.generate(1)
elif node_type.id == 'op_n' and o == "+":
n = random.randint(node_type.arity.min_value, node_type.arity.max_value)
return Tree(node_type.id, children=[self.generate(max_depth - 1) for _ in range(n)],
value=OpValue(abstract_value='+'))
elif node_type.id == 'op_n' and o == "-":
return Tree(node_type.id, children=[
self.generate(max_depth - 1),
self.generate(max_depth - 1)],
value=OpValue(abstract_value='-'))
def evaluate(self, t: Tree):
if len(t.children) > 0:
if t.node_type_id == 'op_n' and t.value.abstract_value == "+":
return sum(map(self.evaluate, t.children))
if t.node_type_id == 'sub_bin' and t.value.abstract_value == '-':
return self.evaluate(t.children[0]) - self.evaluate(t.children[1])
else:
return t.value.abstract_value
| 37.65019
| 120
| 0.583216
| 1,179
| 9,902
| 4.685327
| 0.099237
| 0.052136
| 0.050688
| 0.028965
| 0.880159
| 0.867125
| 0.83092
| 0.830558
| 0.803946
| 0.795981
| 0
| 0.012319
| 0.311351
| 9,902
| 262
| 121
| 37.793893
| 0.797771
| 0.105837
| 0
| 0.670659
| 0
| 0
| 0.029033
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.101796
| false
| 0
| 0.023952
| 0.017964
| 0.371257
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
d6495f4d1362d68471caa416271d9afcbbdc1718
| 194
|
py
|
Python
|
juniorPython/apps/api/admin.py
|
CatOnDrugs/junior-test
|
7809d4726b7b39d5c0a69addc56aaf1e81d26bd7
|
[
"MIT"
] | null | null | null |
juniorPython/apps/api/admin.py
|
CatOnDrugs/junior-test
|
7809d4726b7b39d5c0a69addc56aaf1e81d26bd7
|
[
"MIT"
] | null | null | null |
juniorPython/apps/api/admin.py
|
CatOnDrugs/junior-test
|
7809d4726b7b39d5c0a69addc56aaf1e81d26bd7
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from .models import Customer, Deal, Gem, DealCSV
admin.site.register(Gem)
admin.site.register(Customer)
admin.site.register(Deal)
admin.site.register(DealCSV)
| 21.555556
| 48
| 0.804124
| 28
| 194
| 5.571429
| 0.428571
| 0.230769
| 0.435897
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.087629
| 194
| 8
| 49
| 24.25
| 0.881356
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 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
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
c388a507754f1397f30d3c301e7a75b8357e990e
| 47
|
py
|
Python
|
chapter_2/comment.py
|
superbe/PythonCrashCourse
|
c8781f68b0e9e68e54d48cce5224ecb6a5625ae2
|
[
"MIT"
] | null | null | null |
chapter_2/comment.py
|
superbe/PythonCrashCourse
|
c8781f68b0e9e68e54d48cce5224ecb6a5625ae2
|
[
"MIT"
] | null | null | null |
chapter_2/comment.py
|
superbe/PythonCrashCourse
|
c8781f68b0e9e68e54d48cce5224ecb6a5625ae2
|
[
"MIT"
] | null | null | null |
# Упражнение 10.
print("Hello Python people!")
| 15.666667
| 29
| 0.723404
| 6
| 47
| 5.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04878
| 0.12766
| 47
| 2
| 30
| 23.5
| 0.780488
| 0.297872
| 0
| 0
| 0
| 0
| 0.645161
| 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
| 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
| 6
|
7f20190d77071ace9b1fbbe31f06c0a1b7708317
| 1,831
|
bzl
|
Python
|
tensorflow/lite/core/shims/cc_library_with_tflite.bzl
|
koreybea/tensorflow
|
e252fffb16f2706688604dc91c426bae367ae5e8
|
[
"Apache-2.0"
] | 6
|
2021-03-30T07:42:04.000Z
|
2022-03-23T02:42:36.000Z
|
tensorflow/lite/core/shims/cc_library_with_tflite.bzl
|
koreybea/tensorflow
|
e252fffb16f2706688604dc91c426bae367ae5e8
|
[
"Apache-2.0"
] | 7
|
2021-02-21T21:05:59.000Z
|
2022-02-10T01:39:06.000Z
|
tensorflow/lite/core/shims/cc_library_with_tflite.bzl
|
koreybea/tensorflow
|
e252fffb16f2706688604dc91c426bae367ae5e8
|
[
"Apache-2.0"
] | 4
|
2019-06-15T01:13:28.000Z
|
2020-12-16T02:28:45.000Z
|
"""Definitions for cc_library/cc_test targets that use the TFLite shims."""
def cc_library_with_tflite(
name,
deps = [],
tflite_deps = [],
**kwargs):
"""Defines a cc_library that uses the TFLite shims.
This is a hook to allow applying different build flags (etc.)
for targets that use the TFLite shims.
Note that this build rule doesn't itself add any dependencies on
TF Lite; this macro should normally be used in conjunction with a
direct or indirect 'tflite_deps' dependency on one of the "shim"
library targets from //tensorflow/lite/core/shims:*.
Args:
name: as for cc_library.
deps: as for cc_library.
tflite_deps: dependencies on rules that are themselves defined using
'cc_library_with_tflite'.
**kwargs: Additional cc_library parameters.
"""
native.cc_library(
name = name,
deps = deps + tflite_deps,
**kwargs
)
def cc_test_with_tflite(
name,
deps = [],
tflite_deps = [],
**kwargs):
"""Defines a cc_test that uses the TFLite shims.
This is a hook to allow applying different build flags (etc.)
for targets that use the TFLite shims.
Note that this build rule doesn't itself add any dependencies on
TF Lite this macro should normally be used in conjunction with a
direct or indirect 'tflite_deps' dependency on one of the "shim"
library targets from //third_party/tensorflow/lite/core/shims:*.
Args:
name: as for cc_test.
deps: as for cc_test.
tflite_deps: dependencies on rules that are themselves defined using
'cc_library_with_tflite'.
**kwargs: Additional cc_test parameters.
"""
native.cc_test(
name = name,
deps = deps + tflite_deps,
**kwargs
)
| 31.568966
| 75
| 0.656472
| 252
| 1,831
| 4.638889
| 0.27381
| 0.06929
| 0.05988
| 0.068435
| 0.85201
| 0.85201
| 0.828058
| 0.773311
| 0.773311
| 0.708298
| 0
| 0
| 0.271436
| 1,831
| 57
| 76
| 32.122807
| 0.876312
| 0.702348
| 0
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0
| 0
| 0.1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
4ef0870989fc1798debd40dae361788aadaca50d
| 4,122
|
py
|
Python
|
src/api/tests/test_logging.py
|
Dabble-of-DevOps-Bio/ella
|
e38631d302611a143c9baaa684bcbd014d9734e4
|
[
"MIT"
] | null | null | null |
src/api/tests/test_logging.py
|
Dabble-of-DevOps-Bio/ella
|
e38631d302611a143c9baaa684bcbd014d9734e4
|
[
"MIT"
] | null | null | null |
src/api/tests/test_logging.py
|
Dabble-of-DevOps-Bio/ella
|
e38631d302611a143c9baaa684bcbd014d9734e4
|
[
"MIT"
] | null | null | null |
import datetime
import pytest
import json
from vardb.datamodel import log
from .util import FlaskClientProxy
@pytest.fixture
def client():
return FlaskClientProxy()
def test_resourcelog(client, test_database, session):
"""
Test that requests to the API are logged correctly in the 'requestloq' table.
These tests are by default logged in as testuser1, with usersession_id of 1.
"""
test_database.refresh()
usersession_id = 1
remote_addr = "127.0.0.1"
# Without payload
r = client.get("/api/v1/config/")
statuscode = r.status_code
response_size = int(r.headers.get("Content-Length"))
rlogs = session.query(log.ResourceLog).all()
assert len(rlogs) == 2 # 2 entries since API did a login as first entry
rl = rlogs[-1]
assert rl.remote_addr == remote_addr
assert rl.usersession_id == usersession_id
assert rl.method == "GET"
assert rl.resource == "/api/v1/config/"
assert rl.statuscode == statuscode
assert rl.response_size == response_size
assert rl.payload is None
assert rl.payload_size == 0
assert rl.query == ""
assert rl.duration > 0
assert isinstance(rl.time, datetime.datetime)
# With payload
payload_data = {
"allele_ids": [1],
"gp_name": "HBOCUTV",
"gp_version": "v01",
"referenceassessments": [],
}
r = client.post("/api/v1/acmg/alleles/?dummy=data", payload_data)
payload = json.dumps(payload_data)
payload_size = len(payload)
statuscode = r.status_code
response_size = int(r.headers.get("Content-Length"))
rlogs = session.query(log.ResourceLog).all()
assert len(rlogs) == 4 # 4 since /currentuser is called to check whether logged in
rl = rlogs[-1]
assert statuscode == 200
assert rl.remote_addr == remote_addr
assert rl.usersession_id == usersession_id
assert rl.method == "POST"
assert rl.resource == "/api/v1/acmg/alleles/"
assert rl.statuscode == statuscode
assert rl.response_size == response_size
assert rl.payload == payload
assert rl.payload_size == payload_size
assert rl.query == "dummy=data"
assert rl.duration > 0
assert isinstance(rl.time, datetime.datetime)
# Make sure /login doesn't log passwords
payload_data = {"username": "abc", "password": "123"}
r = client.post("/api/v1/users/actions/login/", payload_data)
statuscode = r.status_code
response_size = int(r.headers.get("Content-Length"))
rlogs = session.query(log.ResourceLog).all()
assert len(rlogs) == 6 # 6 since /currentuser is called to check whether logged in
rl = rlogs[-1]
assert statuscode == 401 # User doesn't exist
assert rl.remote_addr == remote_addr
assert rl.usersession_id == usersession_id
assert rl.method == "POST"
assert rl.resource == "/api/v1/users/actions/login/"
assert rl.statuscode == statuscode
assert rl.response_size == response_size
assert rl.payload is None
assert rl.payload_size == 0
assert rl.query == ""
assert rl.duration > 0
assert isinstance(rl.time, datetime.datetime)
# Test logging when not logged in
payload_data = {
"allele_ids": [1],
"gp_name": "HBOCUTV",
"gp_version": "v01",
"referenceassessments": [],
}
client.logout()
r = client.post("/api/v1/acmg/alleles/?dummy=data", payload_data, username=None)
payload = json.dumps(payload_data)
payload_size = len(payload)
statuscode = r.status_code
response_size = int(r.headers.get("Content-Length"))
rlogs = session.query(log.ResourceLog).all()
assert len(rlogs) == 9 # logout counts as 1
rl = rlogs[-1]
assert statuscode == 403
assert rl.remote_addr == remote_addr
assert rl.usersession_id is None
assert rl.method == "POST"
assert rl.resource == "/api/v1/acmg/alleles/"
assert rl.statuscode == statuscode
assert rl.response_size == response_size
assert rl.payload == payload
assert rl.payload_size == payload_size
assert rl.query == "dummy=data"
assert isinstance(rl.time, datetime.datetime)
| 31.953488
| 87
| 0.670306
| 545
| 4,122
| 4.961468
| 0.225688
| 0.115385
| 0.044379
| 0.031065
| 0.767751
| 0.732618
| 0.720784
| 0.720784
| 0.720784
| 0.720784
| 0
| 0.016039
| 0.213489
| 4,122
| 128
| 88
| 32.203125
| 0.818014
| 0.110869
| 0
| 0.69
| 0
| 0
| 0.117679
| 0.044542
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.02
| false
| 0.01
| 0.05
| 0.01
| 0.08
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
f614dd5b74053d5be8eb1cfb2995b6aa3fcb9711
| 70
|
py
|
Python
|
smi2img/__init__.py
|
KrisJanssen/smi2img
|
cd1d861375d87c2209c19bace0e0e21de02083c4
|
[
"MIT"
] | null | null | null |
smi2img/__init__.py
|
KrisJanssen/smi2img
|
cd1d861375d87c2209c19bace0e0e21de02083c4
|
[
"MIT"
] | null | null | null |
smi2img/__init__.py
|
KrisJanssen/smi2img
|
cd1d861375d87c2209c19bace0e0e21de02083c4
|
[
"MIT"
] | null | null | null |
from smi2img.smiles2img import SmilesEncoder
from smi2img import util
| 23.333333
| 44
| 0.871429
| 9
| 70
| 6.777778
| 0.666667
| 0.360656
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.048387
| 0.114286
| 70
| 2
| 45
| 35
| 0.935484
| 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
| 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
| 6
|
f666ef2e649fec773c06fbcca21b972731707082
| 9,773
|
py
|
Python
|
tests/test_resource_find.py
|
ColinKennedy/ways
|
1eb44e4aa5e35fb839212cd8cb1c59c714ba10d3
|
[
"MIT"
] | 2
|
2019-11-10T18:35:38.000Z
|
2020-05-12T10:37:42.000Z
|
tests/test_resource_find.py
|
ColinKennedy/ways
|
1eb44e4aa5e35fb839212cd8cb1c59c714ba10d3
|
[
"MIT"
] | 5
|
2017-11-27T18:05:25.000Z
|
2021-06-01T21:57:48.000Z
|
tests/test_resource_find.py
|
ColinKennedy/ways
|
1eb44e4aa5e35fb839212cd8cb1c59c714ba10d3
|
[
"MIT"
] | 1
|
2017-11-27T17:54:53.000Z
|
2017-11-27T17:54:53.000Z
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''Tests for auto-finding Ways objects.'''
# IMPORT STANDARD LIBRARIES
import textwrap
# IMPORT WAYS LIBRARIES
import ways.api
# IMPORT LOCAL LIBRARIES
from . import common_test
class FindContextTestCase(common_test.ContextTestCase):
'''Test for whenever the user tries to get Ways objects without a Context.'''
def test_string(self):
'''Get a Context/Asset automatically, using a string.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/versioned_asset
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
''')
self._make_plugin_sheet(contents)
versioned = '/tmp/foo/ttt/8'
asset = ways.api.get_asset(versioned)
self.assertNotEqual(None, asset)
self.assertEqual(('job', 'versioned_asset'), asset.context.get_hierarchy())
def test_string_tied(self):
'''Resolve a tie between two Contexts.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/library
mapping: '/tmp/{JOB}/library'
another_plugin:
hierarchy: job/config
mapping: '/tmp/{JOB}/config'
''')
self._make_plugin_sheet(contents)
self.assertNotEqual(None, ways.api.get_asset('/tmp/foo/library'))
def test_child_tokens_failure(self):
'''Raise an exception because all Contexts return bad parse values.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/library
mapping: '/tmp/{JOB}/{SCENE}/library'
mapping_details:
SCENE:
mapping: '{SCENE_PREFIX}_{SCENE_SUFFIX}'
SCENE_SUFFIX:
parse:
regex: '[a-z]+'
another_plugin:
hierarchy: job/config
mapping: '/tmp/{JOB}/{SCENE}/config'
mapping_details:
SCENE:
mapping: '{SCENE_PREFIX}_{SCENE_SUFFIX}'
SCENE_SUFFIX:
parse:
regex: '[a-z]+'
''')
self._make_plugin_sheet(contents)
info = {
'JOB': 'foo',
'SCENE_PREFIX': 'something',
'SCENE_SUFFIX': '0010',
}
with self.assertRaises(ValueError):
ways.api.get_asset(info)
def test_child_tokens(self):
'''Get a Context from an Asset that only has child tokens defined.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/library
mapping: '/tmp/{JOB}/{SCENE}/library'
mapping_details:
SCENE:
mapping: '{SCENE_PREFIX}_{SCENE_SUFFIX}'
SCENE_SUFFIX:
parse:
regex: '\d+'
another_plugin:
hierarchy: job/config
mapping: '/tmp/{JOB}/{SCENE}/config'
mapping_details:
SCENE:
mapping: '{SCENE_PREFIX}_{SCENE_SUFFIX}'
SCENE_SUFFIX:
parse:
regex: '[a-z]+'
''')
self._make_plugin_sheet(contents)
info = {
'JOB_NAME': 'foo',
'JOB_ID': '6',
'SCENE_PREFIX': 'something',
'SCENE_SUFFIX': '0010',
}
self.assertNotEqual(None, ways.api.get_asset(info))
def test_string_tied_fails(self):
'''Raise an error if Ways cannot decide the best Context.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/versioned_asset
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
another_plugin:
hierarchy: something/completely/different
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
''')
self._make_plugin_sheet(contents)
versioned = '/tmp/foo/ttt/8'
with self.assertRaises(ValueError):
ways.api.get_asset(versioned)
def test_string_tie_break(self):
'''Use a parser to break a tie between two Contexts.'''
contents = textwrap.dedent(
r'''
plugins:
some_plugin:
hierarchy: foo/bar
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
mapping_details:
ASSET_VERSION:
parse:
regex: tttt
version_plugin:
hierarchy: job/versioned_asset
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
mapping_details:
ASSET_VERSION:
parse:
regex: \d+
another_plugin:
hierarchy: something/completely/different
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
mapping_details:
ASSET_VERSION:
parse:
regex: '[a-z]+'
''')
self._make_plugin_sheet(contents)
self.assertNotEqual(None, ways.api.get_asset('/tmp/foo/ttt/8'))
def test_from_dict(self):
'''Get the correct Context/Asset even if only a dict was given.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/versioned_asset
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
''')
self._make_plugin_sheet(contents)
versioned = {
'JOB': 'foo',
'SOMETHING': 'ttt',
'ASSET_VERSION': '8',
}
self.assertNotEqual(None, ways.api.get_asset(versioned))
def test_tie(self):
'''Raise an error if Ways cannot decide the best Context.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/versioned_asset
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
another_plugin:
hierarchy: something/completely/different
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
''')
self._make_plugin_sheet(contents)
versioned = {
'JOB': 'foo',
'SOMETHING': 'ttt',
'ASSET_VERSION': '8',
}
with self.assertRaises(ValueError):
ways.api.get_asset(versioned)
def test_tie_break_dict(self):
'''Get the correct Context/Asset when two Contexts have the same mapping.'''
contents = textwrap.dedent(
r'''
plugins:
some_plugin:
hierarchy: foo/bar
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
mapping_details:
ASSET_VERSION:
parse:
regex: tttt
version_plugin:
hierarchy: job/versioned_asset
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
mapping_details:
ASSET_VERSION:
parse:
regex: \d+
another_plugin:
hierarchy: something/completely/different
mapping: '/tmp/{JOB}/{SOMETHING}/{ASSET_VERSION}'
mapping_details:
ASSET_VERSION:
parse:
regex: '[a-z]+'
''')
self._make_plugin_sheet(contents)
versioned = {
'JOB': 'foo',
'SOMETHING': 'ttt',
'ASSET_VERSION': '8',
}
self.assertNotEqual(None, ways.api.get_asset(versioned))
def test_fails_no_mapping_string(self):
'''If no Context could be found that has a mapping, raise Exception.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/versioned_asset
another_plugin:
hierarchy: job/vvvv
''')
self._make_plugin_sheet(contents)
versioned = '/tmp/thing'
with self.assertRaises(ValueError):
ways.api.get_asset(versioned)
def test_fails_no_mapping(self):
'''If no Context could be found that has a mapping, raise Exception.'''
contents = textwrap.dedent(
r'''
plugins:
version_plugin:
hierarchy: job/versioned_asset
another_plugin:
hierarchy: job/vvvv
''')
self._make_plugin_sheet(contents)
versioned = {
'JOB': 'foo',
'SOMETHING': 'ttt',
'ASSET_VERSION': '8',
}
with self.assertRaises(ValueError):
ways.api.get_asset(versioned)
| 32.685619
| 84
| 0.471708
| 830
| 9,773
| 5.362651
| 0.153012
| 0.074141
| 0.052572
| 0.059313
| 0.827679
| 0.822287
| 0.806336
| 0.777578
| 0.756235
| 0.756235
| 0
| 0.003047
| 0.429039
| 9,773
| 298
| 85
| 32.795302
| 0.794624
| 0.088202
| 0
| 0.69
| 0
| 0
| 0.089143
| 0
| 0
| 0
| 0
| 0
| 0.12
| 1
| 0.11
| false
| 0
| 0.03
| 0
| 0.15
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
9ca8295ec324632475b58b8cf8818ecb2718831c
| 37
|
py
|
Python
|
mirage/convert/rails_model.py
|
fossabot/django-mirage
|
814b3f2486af31f9dca42ef4bb0215655fe0aea6
|
[
"Apache-2.0"
] | null | null | null |
mirage/convert/rails_model.py
|
fossabot/django-mirage
|
814b3f2486af31f9dca42ef4bb0215655fe0aea6
|
[
"Apache-2.0"
] | null | null | null |
mirage/convert/rails_model.py
|
fossabot/django-mirage
|
814b3f2486af31f9dca42ef4bb0215655fe0aea6
|
[
"Apache-2.0"
] | null | null | null |
def convert_rails_model():
pass
| 9.25
| 26
| 0.702703
| 5
| 37
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.216216
| 37
| 3
| 27
| 12.333333
| 0.827586
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
2136f00b85ed9b87dd30bad6c33cff47274a183a
| 254
|
py
|
Python
|
geneticpython/models/__init__.py
|
ngocjr7/geneticpython
|
4b4157523ce13b3da56cef61282cb0a984cd317b
|
[
"MIT"
] | null | null | null |
geneticpython/models/__init__.py
|
ngocjr7/geneticpython
|
4b4157523ce13b3da56cef61282cb0a984cd317b
|
[
"MIT"
] | null | null | null |
geneticpython/models/__init__.py
|
ngocjr7/geneticpython
|
4b4157523ce13b3da56cef61282cb0a984cd317b
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import
from .tree import *
from .binary_individual import BinaryIndividual
from .float_individual import FloatIndividual
from .int_individual import IntIndividual
from .permutation_individual import PermutationIndividual
| 31.75
| 57
| 0.877953
| 28
| 254
| 7.642857
| 0.5
| 0.299065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.098425
| 254
| 7
| 58
| 36.285714
| 0.934498
| 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
| 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
| 6
|
2138b62cca33db2d29f4a9f47dd6231b1ce16335
| 68
|
py
|
Python
|
shelfcache/__init__.py
|
cristoper/feedfetch
|
180aa2d02f9fa9ece93f72d9303a0cbc3b652b81
|
[
"WTFPL"
] | 5
|
2020-01-23T04:16:35.000Z
|
2021-04-13T02:11:43.000Z
|
shelfcache/__init__.py
|
cristoper/shelfcache
|
180aa2d02f9fa9ece93f72d9303a0cbc3b652b81
|
[
"WTFPL"
] | null | null | null |
shelfcache/__init__.py
|
cristoper/shelfcache
|
180aa2d02f9fa9ece93f72d9303a0cbc3b652b81
|
[
"WTFPL"
] | null | null | null |
from .cache_get import cache_get
from .shelfcache import ShelfCache
| 22.666667
| 34
| 0.852941
| 10
| 68
| 5.6
| 0.5
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 68
| 2
| 35
| 34
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 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
| 6
|
dcc1031f17dbb3e77be4743be21e7a8191370cf8
| 12,478
|
py
|
Python
|
bluebottle/initiatives/tests/test_states.py
|
jayvdb/bluebottle
|
305fea238e6aa831598a8b227223a1a2f34c4fcc
|
[
"BSD-3-Clause"
] | null | null | null |
bluebottle/initiatives/tests/test_states.py
|
jayvdb/bluebottle
|
305fea238e6aa831598a8b227223a1a2f34c4fcc
|
[
"BSD-3-Clause"
] | null | null | null |
bluebottle/initiatives/tests/test_states.py
|
jayvdb/bluebottle
|
305fea238e6aa831598a8b227223a1a2f34c4fcc
|
[
"BSD-3-Clause"
] | null | null | null |
from django.core import mail
from bluebottle.events.tests.factories import EventFactory
from bluebottle.funding.tests.factories import FundingFactory, BudgetLineFactory
from bluebottle.events.states import EventStateMachine
from bluebottle.funding.states import FundingStateMachine
from bluebottle.funding_stripe.tests.factories import (
StripePayoutAccountFactory,
ExternalAccountFactory,
)
from bluebottle.fsm.state import TransitionNotPossible
from bluebottle.test.factory_models.accounts import BlueBottleUserFactory
from bluebottle.test.factory_models.geo import LocationFactory
from bluebottle.test.utils import BluebottleTestCase
from bluebottle.initiatives.tests.factories import InitiativeFactory
from bluebottle.initiatives.states import ReviewStateMachine
from bluebottle.test.factory_models.organizations import OrganizationFactory, OrganizationContactFactory
class InitiativeReviewStateMachineTests(BluebottleTestCase):
def setUp(self):
super(InitiativeReviewStateMachineTests, self).setUp()
self.user = BlueBottleUserFactory.create(first_name='Bart', last_name='Lacroix')
self.initiative = InitiativeFactory.create(
has_organization=False,
owner=self.user,
organization=None
)
payout_account = StripePayoutAccountFactory.create(status='verified')
self.bank_account = ExternalAccountFactory.create(connect_account=payout_account, status='verified')
def test_default_status(self):
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
def test_create_incomplete(self):
self.initiative = InitiativeFactory.create(
title='',
has_organization=False,
owner=self.user,
organization=None
)
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
self.assertRaises(
TransitionNotPossible,
self.initiative.states.submit
)
def test_make_complete(self):
self.initiative = InitiativeFactory.create(
title='',
has_organization=False,
owner=self.user,
organization=None
)
self.initiative.title = 'Some title'
self.initiative.save()
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
self.initiative.states.submit()
self.assertEqual(
self.initiative.status, ReviewStateMachine.submitted.value
)
def test_missing_organization(self):
self.initiative = InitiativeFactory.create(
has_organization=True,
owner=self.user,
organization=None
)
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
self.assertRaises(
TransitionNotPossible,
self.initiative.states.submit
)
def test_missing_organization_contact(self):
self.initiative = InitiativeFactory.create(
has_organization=True,
owner=self.user,
organization=OrganizationFactory.create(),
organization_contact=None
)
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
self.assertRaises(
TransitionNotPossible,
self.initiative.states.submit
)
def test_missing_organization_contact_name(self):
self.initiative = InitiativeFactory.create(
has_organization=True,
owner=self.user,
organization=OrganizationFactory.create(),
organization_contact=OrganizationContactFactory.create(name=None)
)
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
self.assertRaises(
TransitionNotPossible,
self.initiative.states.submit
)
def test_has_organization(self):
self.initiative = InitiativeFactory.create(
has_organization=True,
owner=self.user,
organization=OrganizationFactory.create(),
organization_contact=OrganizationContactFactory.create()
)
self.initiative.states.submit()
self.assertEqual(
self.initiative.status, ReviewStateMachine.submitted.value
)
def test_has_organization_no_phone(self):
self.initiative = InitiativeFactory.create(
has_organization=True,
owner=self.user,
organization=OrganizationFactory.create(),
organization_contact=OrganizationContactFactory.create(phone=None)
)
self.initiative.states.submit()
self.assertEqual(
self.initiative.status, ReviewStateMachine.submitted.value
)
def test_missing_place(self):
self.initiative = InitiativeFactory.create(
has_organization=False,
owner=self.user,
place=None,
organization=None
)
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
self.assertRaises(
TransitionNotPossible,
self.initiative.states.submit
)
def test_submit_contact_without_location_has_locations(self):
LocationFactory.create_batch(5)
self.initiative = InitiativeFactory.create(
has_organization=False,
owner=self.user,
place=None,
location=None,
organization=None
)
self.assertEqual(
self.initiative.status, ReviewStateMachine.draft.value
)
self.assertRaises(
TransitionNotPossible,
self.initiative.states.submit
)
def test_submit_contact_location_has_locations(self):
locations = LocationFactory.create_batch(5)
self.initiative = InitiativeFactory.create(
has_organization=False,
owner=self.user,
place=None,
location=locations[0],
organization=None
)
self.initiative.states.submit()
self.assertEqual(
self.initiative.status, ReviewStateMachine.submitted.value
)
def test_submit_with_activities(self):
event = EventFactory.create(initiative=self.initiative)
funding = FundingFactory.create(initiative=self.initiative, bank_account=self.bank_account)
BudgetLineFactory.create(activity=funding)
incomplete_event = EventFactory.create(initiative=self.initiative, title='')
self.initiative.states.submit(save=True)
event.refresh_from_db()
self.assertEqual(
event.status, ReviewStateMachine.submitted.value
)
funding.refresh_from_db()
self.assertEqual(
funding.status, ReviewStateMachine.submitted.value
)
incomplete_event.refresh_from_db()
self.assertEqual(
incomplete_event.status, ReviewStateMachine.draft.value
)
def test_needs_work(self):
self.initiative.states.submit()
self.initiative.states.request_changes(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.needs_work.value
)
def test_needs_work_resubmit(self):
self.initiative.states.submit()
self.initiative.states.request_changes(save=True)
self.initiative.title = 'Something else'
self.initiative.save()
self.assertEqual(
self.initiative.status, ReviewStateMachine.needs_work.value
)
self.initiative.states.submit(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.submitted.value
)
def test_approve(self):
self.initiative.states.submit()
self.initiative.states.approve(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.approved.value
)
self.assertEqual(len(mail.outbox), 1)
subject = 'Your initiative "{}" has been approved!'.format(self.initiative.title)
self.assertEqual(mail.outbox[0].subject, subject)
self.assertTrue('Hi Bart' in mail.outbox[0].body)
def test_approve_with_activities(self):
event = EventFactory.create(initiative=self.initiative)
incomplete_event = EventFactory.create(initiative=self.initiative, title='')
funding = FundingFactory.create(initiative=self.initiative, bank_account=self.bank_account)
BudgetLineFactory.create(activity=funding)
self.initiative.states.submit(save=True)
self.initiative.states.approve(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.approved.value
)
event.refresh_from_db()
self.assertEqual(
event.status, EventStateMachine.open.value
)
incomplete_event.refresh_from_db()
self.assertEqual(
incomplete_event.status, EventStateMachine.draft.value
)
funding.refresh_from_db()
self.assertEqual(
funding.status, FundingStateMachine.submitted.value
)
def test_reject(self):
self.initiative.states.reject(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.rejected.value
)
self.assertEqual(len(mail.outbox), 1)
subject = 'Your initiative "{}" has been rejected.'.format(self.initiative.title)
self.assertEqual(mail.outbox[0].subject, subject)
self.assertTrue('Hi Bart' in mail.outbox[0].body)
def test_reject_with_activities(self):
event = EventFactory.create(initiative=self.initiative)
self.initiative.states.reject(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.rejected.value
)
event.refresh_from_db()
self.assertEqual(
event.status, EventStateMachine.rejected.value
)
def test_cancel(self):
self.initiative.states.submit()
self.initiative.states.approve(save=True)
mail.outbox = []
self.initiative.states.cancel(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.cancelled.value
)
self.assertEqual(len(mail.outbox), 1)
subject = 'The initiative "{}" has been cancelled.'.format(self.initiative.title)
self.assertEqual(mail.outbox[0].subject, subject)
self.assertTrue('Hi Bart' in mail.outbox[0].body)
def test_cancel_with_activities(self):
self.initiative.states.submit(save=True)
self.initiative.states.approve()
event = EventFactory.create(initiative=self.initiative)
event.states.submit(save=True)
self.initiative.states.cancel(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.cancelled.value
)
event.refresh_from_db()
self.assertEqual(
event.status, EventStateMachine.cancelled.value
)
def test_delete(self):
self.initiative.states.delete(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.deleted.value
)
def test_delete_with_activities(self):
event = EventFactory.create(initiative=self.initiative)
self.initiative.states.delete(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.deleted.value
)
event.refresh_from_db()
self.assertEqual(
event.status, EventStateMachine.deleted.value
)
def test_restore(self):
self.initiative.states.reject(save=True)
self.initiative.states.restore(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.needs_work.value
)
def test_restore_with_activities(self):
event = EventFactory.create(initiative=self.initiative)
self.initiative.states.reject(save=True)
self.initiative.states.restore(save=True)
self.assertEqual(
self.initiative.status, ReviewStateMachine.needs_work.value
)
event.refresh_from_db()
self.assertEqual(
event.status, EventStateMachine.needs_work.value
)
| 32.750656
| 108
| 0.658759
| 1,153
| 12,478
| 7.019948
| 0.105811
| 0.150482
| 0.084013
| 0.089573
| 0.803435
| 0.789227
| 0.77403
| 0.767853
| 0.730912
| 0.681245
| 0
| 0.001294
| 0.257012
| 12,478
| 380
| 109
| 32.836842
| 0.871751
| 0
| 0
| 0.601286
| 0
| 0
| 0.015147
| 0
| 0
| 0
| 0
| 0
| 0.160772
| 1
| 0.080386
| false
| 0
| 0.041801
| 0
| 0.125402
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dccfe05f1674658a48bc0148b58a03d80352dc47
| 108
|
py
|
Python
|
tests/test_dummy.py
|
ocefpaf/xroms
|
763d6e678e28fe074e0aaab26fecd2b74e51a8b0
|
[
"MIT"
] | 4
|
2020-01-21T21:24:17.000Z
|
2020-10-02T03:09:32.000Z
|
tests/test_dummy.py
|
ocefpaf/xroms
|
763d6e678e28fe074e0aaab26fecd2b74e51a8b0
|
[
"MIT"
] | 1
|
2020-04-08T00:11:39.000Z
|
2020-04-25T08:03:45.000Z
|
tests/test_dummy.py
|
ocefpaf/xroms
|
763d6e678e28fe074e0aaab26fecd2b74e51a8b0
|
[
"MIT"
] | 1
|
2020-04-06T06:42:36.000Z
|
2020-04-06T06:42:36.000Z
|
# To be replaced with real tests soon
def test_imports():
import xroms
import xroms.roms_seawater
| 15.428571
| 37
| 0.731481
| 16
| 108
| 4.8125
| 0.875
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 108
| 6
| 38
| 18
| 0.916667
| 0.324074
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 1
| 0
| 1.333333
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
dcd687de17901f15436467e5d2c335fe55d931d3
| 38,016
|
py
|
Python
|
instances/passenger_demand/pas-20210421-2109-int18e/40.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210421-2109-int18e/40.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210421-2109-int18e/40.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 4114
passenger_arriving = (
(7, 11, 9, 4, 3, 0, 7, 12, 6, 2, 4, 0), # 0
(12, 8, 9, 4, 2, 0, 7, 11, 10, 5, 1, 0), # 1
(6, 14, 4, 8, 1, 0, 8, 13, 8, 6, 1, 0), # 2
(0, 14, 12, 1, 5, 0, 10, 7, 5, 6, 6, 0), # 3
(3, 8, 14, 5, 1, 0, 8, 16, 9, 4, 0, 0), # 4
(4, 13, 7, 7, 2, 0, 9, 10, 3, 7, 2, 0), # 5
(3, 9, 6, 5, 1, 0, 3, 10, 8, 5, 0, 0), # 6
(4, 6, 3, 5, 3, 0, 5, 8, 4, 5, 2, 0), # 7
(1, 7, 10, 5, 3, 0, 4, 7, 7, 7, 1, 0), # 8
(10, 15, 8, 4, 2, 0, 13, 12, 4, 7, 2, 0), # 9
(2, 14, 10, 8, 3, 0, 11, 10, 7, 5, 1, 0), # 10
(7, 16, 9, 3, 4, 0, 12, 9, 5, 9, 5, 0), # 11
(2, 12, 12, 5, 3, 0, 8, 10, 13, 4, 1, 0), # 12
(2, 8, 11, 7, 0, 0, 12, 15, 5, 3, 3, 0), # 13
(3, 10, 8, 5, 3, 0, 8, 8, 4, 5, 4, 0), # 14
(4, 14, 8, 3, 4, 0, 4, 8, 2, 5, 3, 0), # 15
(5, 7, 8, 5, 6, 0, 12, 7, 8, 7, 6, 0), # 16
(4, 14, 11, 5, 5, 0, 4, 13, 5, 5, 2, 0), # 17
(1, 11, 9, 5, 3, 0, 2, 13, 6, 5, 2, 0), # 18
(4, 6, 18, 6, 4, 0, 8, 12, 8, 3, 2, 0), # 19
(3, 13, 11, 8, 2, 0, 12, 8, 9, 3, 2, 0), # 20
(3, 5, 11, 3, 4, 0, 9, 11, 8, 6, 3, 0), # 21
(3, 13, 11, 1, 5, 0, 3, 15, 3, 2, 2, 0), # 22
(14, 15, 5, 6, 1, 0, 5, 9, 7, 11, 1, 0), # 23
(7, 9, 13, 3, 3, 0, 10, 13, 8, 6, 1, 0), # 24
(6, 7, 12, 3, 2, 0, 7, 10, 6, 10, 2, 0), # 25
(5, 12, 8, 3, 3, 0, 14, 16, 9, 5, 4, 0), # 26
(10, 11, 7, 6, 3, 0, 9, 9, 5, 6, 3, 0), # 27
(2, 13, 15, 4, 2, 0, 8, 5, 15, 7, 5, 0), # 28
(8, 17, 9, 4, 2, 0, 7, 11, 10, 2, 3, 0), # 29
(3, 17, 15, 6, 3, 0, 9, 11, 7, 3, 2, 0), # 30
(5, 15, 10, 4, 4, 0, 9, 13, 5, 9, 4, 0), # 31
(3, 13, 11, 7, 2, 0, 11, 10, 8, 7, 3, 0), # 32
(5, 13, 11, 2, 5, 0, 7, 14, 5, 5, 2, 0), # 33
(4, 12, 9, 8, 1, 0, 6, 17, 6, 3, 3, 0), # 34
(1, 9, 11, 8, 2, 0, 11, 11, 8, 3, 2, 0), # 35
(4, 17, 11, 6, 2, 0, 6, 18, 6, 6, 5, 0), # 36
(10, 6, 13, 3, 2, 0, 7, 9, 7, 8, 2, 0), # 37
(8, 16, 12, 4, 5, 0, 6, 12, 6, 3, 0, 0), # 38
(3, 11, 9, 5, 1, 0, 10, 15, 7, 8, 5, 0), # 39
(3, 13, 16, 5, 1, 0, 9, 9, 9, 7, 4, 0), # 40
(6, 15, 13, 5, 7, 0, 4, 10, 9, 8, 2, 0), # 41
(5, 11, 11, 4, 4, 0, 7, 13, 9, 9, 4, 0), # 42
(5, 15, 8, 7, 3, 0, 4, 11, 7, 5, 1, 0), # 43
(3, 19, 7, 4, 0, 0, 5, 12, 6, 3, 1, 0), # 44
(4, 9, 8, 9, 0, 0, 3, 13, 6, 9, 2, 0), # 45
(3, 12, 7, 4, 2, 0, 9, 12, 5, 4, 2, 0), # 46
(6, 10, 8, 4, 4, 0, 6, 12, 6, 7, 5, 0), # 47
(8, 11, 7, 8, 2, 0, 11, 9, 12, 7, 2, 0), # 48
(6, 13, 10, 3, 2, 0, 8, 12, 7, 8, 2, 0), # 49
(8, 5, 6, 6, 4, 0, 13, 11, 7, 5, 7, 0), # 50
(5, 12, 4, 6, 7, 0, 6, 19, 13, 5, 1, 0), # 51
(4, 12, 9, 9, 2, 0, 11, 8, 11, 7, 3, 0), # 52
(9, 12, 15, 2, 2, 0, 4, 13, 9, 5, 4, 0), # 53
(10, 15, 18, 7, 2, 0, 10, 9, 10, 4, 0, 0), # 54
(9, 25, 5, 3, 2, 0, 10, 9, 10, 7, 2, 0), # 55
(10, 13, 5, 1, 4, 0, 6, 17, 10, 9, 2, 0), # 56
(8, 8, 10, 7, 5, 0, 8, 14, 11, 6, 7, 0), # 57
(6, 8, 5, 5, 2, 0, 9, 16, 11, 3, 4, 0), # 58
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59
)
station_arriving_intensity = (
(4.769372805092186, 12.233629261363635, 14.389624839331619, 11.405298913043477, 12.857451923076923, 8.562228260869567), # 0
(4.81413961808604, 12.369674877683082, 14.46734796754499, 11.46881589673913, 12.953819711538461, 8.559309850543478), # 1
(4.8583952589991215, 12.503702525252525, 14.54322622107969, 11.530934782608696, 13.048153846153847, 8.556302173913043), # 2
(4.902102161984196, 12.635567578125, 14.617204169344474, 11.591602581521737, 13.14036778846154, 8.553205638586958), # 3
(4.94522276119403, 12.765125410353535, 14.689226381748071, 11.650766304347826, 13.230375, 8.550020652173911), # 4
(4.987719490781387, 12.892231395991162, 14.759237427699228, 11.708372961956522, 13.318088942307691, 8.546747622282608), # 5
(5.029554784899035, 13.01674090909091, 14.827181876606687, 11.764369565217393, 13.403423076923078, 8.54338695652174), # 6
(5.0706910776997365, 13.138509323705808, 14.893004297879177, 11.818703125, 13.486290865384618, 8.5399390625), # 7
(5.1110908033362605, 13.257392013888888, 14.956649260925452, 11.871320652173912, 13.56660576923077, 8.536404347826087), # 8
(5.1507163959613695, 13.373244353693181, 15.018061335154243, 11.922169157608696, 13.644281249999999, 8.532783220108696), # 9
(5.1895302897278315, 13.485921717171717, 15.077185089974291, 11.971195652173915, 13.719230769230771, 8.529076086956522), # 10
(5.227494918788412, 13.595279478377526, 15.133965094794343, 12.018347146739131, 13.791367788461539, 8.525283355978262), # 11
(5.2645727172958745, 13.701173011363636, 15.188345919023137, 12.063570652173912, 13.860605769230768, 8.521405434782608), # 12
(5.3007261194029835, 13.803457690183082, 15.240272132069407, 12.106813179347826, 13.926858173076925, 8.51744273097826), # 13
(5.335917559262511, 13.90198888888889, 15.289688303341899, 12.148021739130433, 13.99003846153846, 8.513395652173912), # 14
(5.370109471027217, 13.996621981534089, 15.336539002249355, 12.187143342391304, 14.050060096153846, 8.509264605978261), # 15
(5.403264288849868, 14.087212342171718, 15.380768798200515, 12.224124999999999, 14.10683653846154, 8.50505), # 16
(5.4353444468832315, 14.173615344854797, 15.422322260604112, 12.258913722826087, 14.16028125, 8.500752241847827), # 17
(5.46631237928007, 14.255686363636363, 15.461143958868895, 12.291456521739132, 14.210307692307696, 8.496371739130435), # 18
(5.496130520193152, 14.333280772569443, 15.4971784624036, 12.321700407608695, 14.256829326923079, 8.491908899456522), # 19
(5.524761303775241, 14.40625394570707, 15.530370340616965, 12.349592391304348, 14.299759615384616, 8.487364130434782), # 20
(5.552167164179106, 14.47446125710227, 15.56066416291774, 12.375079483695652, 14.339012019230768, 8.482737839673913), # 21
(5.578310535557506, 14.537758080808082, 15.588004498714653, 12.398108695652175, 14.374499999999998, 8.47803043478261), # 22
(5.603153852063214, 14.595999790877526, 15.612335917416454, 12.418627038043478, 14.40613701923077, 8.473242323369567), # 23
(5.62665954784899, 14.649041761363636, 15.633602988431875, 12.43658152173913, 14.433836538461538, 8.468373913043479), # 24
(5.648790057067603, 14.696739366319445, 15.651750281169667, 12.451919157608696, 14.457512019230768, 8.463425611413044), # 25
(5.669507813871817, 14.738947979797977, 15.66672236503856, 12.464586956521739, 14.477076923076922, 8.458397826086957), # 26
(5.688775252414398, 14.77552297585227, 15.6784638094473, 12.474531929347828, 14.492444711538463, 8.453290964673915), # 27
(5.7065548068481124, 14.806319728535353, 15.68691918380463, 12.481701086956523, 14.503528846153845, 8.448105434782608), # 28
(5.722808911325724, 14.831193611900254, 15.69203305751928, 12.486041440217392, 14.510242788461538, 8.44284164402174), # 29
(5.7375, 14.85, 15.69375, 12.4875, 14.512500000000001, 8.4375), # 30
(5.751246651214834, 14.865621839488634, 15.692462907608693, 12.487236580882353, 14.511678590425532, 8.430077267616193), # 31
(5.7646965153452685, 14.881037215909092, 15.68863804347826, 12.486451470588234, 14.509231914893617, 8.418644565217393), # 32
(5.777855634590792, 14.896244211647728, 15.682330027173915, 12.485152389705883, 14.50518630319149, 8.403313830584706), # 33
(5.790730051150895, 14.91124090909091, 15.67359347826087, 12.483347058823531, 14.499568085106382, 8.38419700149925), # 34
(5.803325807225064, 14.926025390624996, 15.662483016304348, 12.481043198529411, 14.492403590425532, 8.361406015742128), # 35
(5.815648945012788, 14.940595738636366, 15.649053260869564, 12.478248529411767, 14.48371914893617, 8.335052811094453), # 36
(5.8277055067135555, 14.954950035511365, 15.63335883152174, 12.474970772058823, 14.47354109042553, 8.305249325337332), # 37
(5.839501534526853, 14.969086363636364, 15.615454347826088, 12.471217647058824, 14.461895744680852, 8.272107496251873), # 38
(5.851043070652174, 14.983002805397728, 15.595394429347825, 12.466996875000001, 14.44880944148936, 8.23573926161919), # 39
(5.862336157289003, 14.99669744318182, 15.573233695652176, 12.462316176470589, 14.434308510638296, 8.196256559220389), # 40
(5.873386836636828, 15.010168359374997, 15.549026766304348, 12.457183272058824, 14.418419281914893, 8.153771326836583), # 41
(5.88420115089514, 15.023413636363639, 15.522828260869566, 12.451605882352942, 14.401168085106384, 8.108395502248875), # 42
(5.894785142263428, 15.03643135653409, 15.494692798913043, 12.445591727941178, 14.38258125, 8.060241023238381), # 43
(5.905144852941176, 15.049219602272727, 15.464675, 12.439148529411764, 14.36268510638298, 8.009419827586207), # 44
(5.915286325127877, 15.061776455965909, 15.432829483695656, 12.43228400735294, 14.341505984042554, 7.956043853073464), # 45
(5.925215601023019, 15.074100000000003, 15.39921086956522, 12.425005882352941, 14.319070212765958, 7.90022503748126), # 46
(5.934938722826087, 15.086188316761364, 15.363873777173913, 12.417321874999999, 14.295404122340427, 7.842075318590705), # 47
(5.944461732736574, 15.098039488636365, 15.326872826086957, 12.409239705882353, 14.27053404255319, 7.7817066341829095), # 48
(5.953790672953963, 15.10965159801136, 15.288262635869566, 12.400767095588236, 14.24448630319149, 7.71923092203898), # 49
(5.96293158567775, 15.121022727272724, 15.248097826086958, 12.391911764705883, 14.217287234042553, 7.65476011994003), # 50
(5.971890513107417, 15.132150958806818, 15.206433016304347, 12.38268143382353, 14.188963164893616, 7.588406165667167), # 51
(5.980673497442456, 15.143034375, 15.163322826086954, 12.373083823529411, 14.159540425531915, 7.5202809970015), # 52
(5.989286580882353, 15.153671058238638, 15.118821875, 12.363126654411765, 14.129045345744682, 7.450496551724138), # 53
(5.9977358056266, 15.164059090909088, 15.072984782608694, 12.352817647058824, 14.09750425531915, 7.379164767616192), # 54
(6.00602721387468, 15.174196555397728, 15.02586616847826, 12.342164522058825, 14.064943484042553, 7.306397582458771), # 55
(6.014166847826087, 15.184081534090907, 14.977520652173913, 12.331175, 14.031389361702129, 7.232306934032984), # 56
(6.022160749680308, 15.193712109375003, 14.92800285326087, 12.319856801470587, 13.996868218085105, 7.15700476011994), # 57
(6.030014961636829, 15.203086363636363, 14.877367391304347, 12.308217647058825, 13.961406382978723, 7.0806029985007495), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_arriving_acc = (
(7, 11, 9, 4, 3, 0, 7, 12, 6, 2, 4, 0), # 0
(19, 19, 18, 8, 5, 0, 14, 23, 16, 7, 5, 0), # 1
(25, 33, 22, 16, 6, 0, 22, 36, 24, 13, 6, 0), # 2
(25, 47, 34, 17, 11, 0, 32, 43, 29, 19, 12, 0), # 3
(28, 55, 48, 22, 12, 0, 40, 59, 38, 23, 12, 0), # 4
(32, 68, 55, 29, 14, 0, 49, 69, 41, 30, 14, 0), # 5
(35, 77, 61, 34, 15, 0, 52, 79, 49, 35, 14, 0), # 6
(39, 83, 64, 39, 18, 0, 57, 87, 53, 40, 16, 0), # 7
(40, 90, 74, 44, 21, 0, 61, 94, 60, 47, 17, 0), # 8
(50, 105, 82, 48, 23, 0, 74, 106, 64, 54, 19, 0), # 9
(52, 119, 92, 56, 26, 0, 85, 116, 71, 59, 20, 0), # 10
(59, 135, 101, 59, 30, 0, 97, 125, 76, 68, 25, 0), # 11
(61, 147, 113, 64, 33, 0, 105, 135, 89, 72, 26, 0), # 12
(63, 155, 124, 71, 33, 0, 117, 150, 94, 75, 29, 0), # 13
(66, 165, 132, 76, 36, 0, 125, 158, 98, 80, 33, 0), # 14
(70, 179, 140, 79, 40, 0, 129, 166, 100, 85, 36, 0), # 15
(75, 186, 148, 84, 46, 0, 141, 173, 108, 92, 42, 0), # 16
(79, 200, 159, 89, 51, 0, 145, 186, 113, 97, 44, 0), # 17
(80, 211, 168, 94, 54, 0, 147, 199, 119, 102, 46, 0), # 18
(84, 217, 186, 100, 58, 0, 155, 211, 127, 105, 48, 0), # 19
(87, 230, 197, 108, 60, 0, 167, 219, 136, 108, 50, 0), # 20
(90, 235, 208, 111, 64, 0, 176, 230, 144, 114, 53, 0), # 21
(93, 248, 219, 112, 69, 0, 179, 245, 147, 116, 55, 0), # 22
(107, 263, 224, 118, 70, 0, 184, 254, 154, 127, 56, 0), # 23
(114, 272, 237, 121, 73, 0, 194, 267, 162, 133, 57, 0), # 24
(120, 279, 249, 124, 75, 0, 201, 277, 168, 143, 59, 0), # 25
(125, 291, 257, 127, 78, 0, 215, 293, 177, 148, 63, 0), # 26
(135, 302, 264, 133, 81, 0, 224, 302, 182, 154, 66, 0), # 27
(137, 315, 279, 137, 83, 0, 232, 307, 197, 161, 71, 0), # 28
(145, 332, 288, 141, 85, 0, 239, 318, 207, 163, 74, 0), # 29
(148, 349, 303, 147, 88, 0, 248, 329, 214, 166, 76, 0), # 30
(153, 364, 313, 151, 92, 0, 257, 342, 219, 175, 80, 0), # 31
(156, 377, 324, 158, 94, 0, 268, 352, 227, 182, 83, 0), # 32
(161, 390, 335, 160, 99, 0, 275, 366, 232, 187, 85, 0), # 33
(165, 402, 344, 168, 100, 0, 281, 383, 238, 190, 88, 0), # 34
(166, 411, 355, 176, 102, 0, 292, 394, 246, 193, 90, 0), # 35
(170, 428, 366, 182, 104, 0, 298, 412, 252, 199, 95, 0), # 36
(180, 434, 379, 185, 106, 0, 305, 421, 259, 207, 97, 0), # 37
(188, 450, 391, 189, 111, 0, 311, 433, 265, 210, 97, 0), # 38
(191, 461, 400, 194, 112, 0, 321, 448, 272, 218, 102, 0), # 39
(194, 474, 416, 199, 113, 0, 330, 457, 281, 225, 106, 0), # 40
(200, 489, 429, 204, 120, 0, 334, 467, 290, 233, 108, 0), # 41
(205, 500, 440, 208, 124, 0, 341, 480, 299, 242, 112, 0), # 42
(210, 515, 448, 215, 127, 0, 345, 491, 306, 247, 113, 0), # 43
(213, 534, 455, 219, 127, 0, 350, 503, 312, 250, 114, 0), # 44
(217, 543, 463, 228, 127, 0, 353, 516, 318, 259, 116, 0), # 45
(220, 555, 470, 232, 129, 0, 362, 528, 323, 263, 118, 0), # 46
(226, 565, 478, 236, 133, 0, 368, 540, 329, 270, 123, 0), # 47
(234, 576, 485, 244, 135, 0, 379, 549, 341, 277, 125, 0), # 48
(240, 589, 495, 247, 137, 0, 387, 561, 348, 285, 127, 0), # 49
(248, 594, 501, 253, 141, 0, 400, 572, 355, 290, 134, 0), # 50
(253, 606, 505, 259, 148, 0, 406, 591, 368, 295, 135, 0), # 51
(257, 618, 514, 268, 150, 0, 417, 599, 379, 302, 138, 0), # 52
(266, 630, 529, 270, 152, 0, 421, 612, 388, 307, 142, 0), # 53
(276, 645, 547, 277, 154, 0, 431, 621, 398, 311, 142, 0), # 54
(285, 670, 552, 280, 156, 0, 441, 630, 408, 318, 144, 0), # 55
(295, 683, 557, 281, 160, 0, 447, 647, 418, 327, 146, 0), # 56
(303, 691, 567, 288, 165, 0, 455, 661, 429, 333, 153, 0), # 57
(309, 699, 572, 293, 167, 0, 464, 677, 440, 336, 157, 0), # 58
(309, 699, 572, 293, 167, 0, 464, 677, 440, 336, 157, 0), # 59
)
passenger_arriving_rate = (
(4.769372805092186, 9.786903409090908, 8.63377490359897, 4.56211956521739, 2.5714903846153843, 0.0, 8.562228260869567, 10.285961538461537, 6.843179347826086, 5.755849935732647, 2.446725852272727, 0.0), # 0
(4.81413961808604, 9.895739902146465, 8.680408780526994, 4.587526358695651, 2.5907639423076922, 0.0, 8.559309850543478, 10.363055769230769, 6.881289538043478, 5.786939187017995, 2.4739349755366162, 0.0), # 1
(4.8583952589991215, 10.00296202020202, 8.725935732647814, 4.612373913043478, 2.609630769230769, 0.0, 8.556302173913043, 10.438523076923076, 6.918560869565217, 5.817290488431875, 2.500740505050505, 0.0), # 2
(4.902102161984196, 10.1084540625, 8.770322501606683, 4.636641032608694, 2.628073557692308, 0.0, 8.553205638586958, 10.512294230769232, 6.954961548913042, 5.846881667737789, 2.527113515625, 0.0), # 3
(4.94522276119403, 10.212100328282828, 8.813535829048842, 4.66030652173913, 2.6460749999999997, 0.0, 8.550020652173911, 10.584299999999999, 6.990459782608696, 5.875690552699228, 2.553025082070707, 0.0), # 4
(4.987719490781387, 10.313785116792928, 8.855542456619537, 4.6833491847826085, 2.663617788461538, 0.0, 8.546747622282608, 10.654471153846153, 7.025023777173913, 5.90369497107969, 2.578446279198232, 0.0), # 5
(5.029554784899035, 10.413392727272727, 8.896309125964011, 4.705747826086957, 2.680684615384615, 0.0, 8.54338695652174, 10.72273846153846, 7.058621739130436, 5.930872750642674, 2.603348181818182, 0.0), # 6
(5.0706910776997365, 10.510807458964646, 8.935802578727506, 4.72748125, 2.697258173076923, 0.0, 8.5399390625, 10.789032692307693, 7.0912218750000005, 5.95720171915167, 2.6277018647411614, 0.0), # 7
(5.1110908033362605, 10.60591361111111, 8.97398955655527, 4.7485282608695645, 2.7133211538461537, 0.0, 8.536404347826087, 10.853284615384615, 7.122792391304347, 5.982659704370181, 2.6514784027777774, 0.0), # 8
(5.1507163959613695, 10.698595482954543, 9.010836801092546, 4.768867663043478, 2.7288562499999993, 0.0, 8.532783220108696, 10.915424999999997, 7.153301494565217, 6.007224534061697, 2.6746488707386358, 0.0), # 9
(5.1895302897278315, 10.788737373737373, 9.046311053984574, 4.7884782608695655, 2.743846153846154, 0.0, 8.529076086956522, 10.975384615384616, 7.182717391304348, 6.030874035989716, 2.697184343434343, 0.0), # 10
(5.227494918788412, 10.87622358270202, 9.080379056876605, 4.807338858695652, 2.7582735576923074, 0.0, 8.525283355978262, 11.03309423076923, 7.2110082880434785, 6.053586037917737, 2.719055895675505, 0.0), # 11
(5.2645727172958745, 10.960938409090907, 9.113007551413881, 4.825428260869565, 2.7721211538461534, 0.0, 8.521405434782608, 11.088484615384614, 7.238142391304347, 6.0753383676092545, 2.740234602272727, 0.0), # 12
(5.3007261194029835, 11.042766152146465, 9.144163279241644, 4.8427252717391305, 2.7853716346153847, 0.0, 8.51744273097826, 11.141486538461539, 7.264087907608696, 6.096108852827762, 2.760691538036616, 0.0), # 13
(5.335917559262511, 11.121591111111112, 9.173812982005138, 4.859208695652173, 2.7980076923076918, 0.0, 8.513395652173912, 11.192030769230767, 7.288813043478259, 6.115875321336759, 2.780397777777778, 0.0), # 14
(5.370109471027217, 11.19729758522727, 9.201923401349612, 4.874857336956521, 2.810012019230769, 0.0, 8.509264605978261, 11.240048076923076, 7.312286005434782, 6.134615600899742, 2.7993243963068175, 0.0), # 15
(5.403264288849868, 11.269769873737372, 9.228461278920308, 4.88965, 2.8213673076923076, 0.0, 8.50505, 11.28546923076923, 7.334474999999999, 6.152307519280206, 2.817442468434343, 0.0), # 16
(5.4353444468832315, 11.338892275883836, 9.253393356362468, 4.903565489130434, 2.83205625, 0.0, 8.500752241847827, 11.328225, 7.3553482336956515, 6.168928904241644, 2.834723068970959, 0.0), # 17
(5.46631237928007, 11.40454909090909, 9.276686375321336, 4.916582608695652, 2.842061538461539, 0.0, 8.496371739130435, 11.368246153846156, 7.374873913043479, 6.184457583547558, 2.8511372727272724, 0.0), # 18
(5.496130520193152, 11.466624618055553, 9.298307077442159, 4.928680163043477, 2.8513658653846155, 0.0, 8.491908899456522, 11.405463461538462, 7.393020244565217, 6.198871384961439, 2.866656154513888, 0.0), # 19
(5.524761303775241, 11.525003156565655, 9.318222204370178, 4.939836956521739, 2.859951923076923, 0.0, 8.487364130434782, 11.439807692307692, 7.409755434782609, 6.212148136246785, 2.8812507891414136, 0.0), # 20
(5.552167164179106, 11.579569005681815, 9.336398497750643, 4.95003179347826, 2.8678024038461536, 0.0, 8.482737839673913, 11.471209615384614, 7.425047690217391, 6.224265665167096, 2.894892251420454, 0.0), # 21
(5.578310535557506, 11.630206464646465, 9.352802699228791, 4.95924347826087, 2.8748999999999993, 0.0, 8.47803043478261, 11.499599999999997, 7.438865217391305, 6.235201799485861, 2.907551616161616, 0.0), # 22
(5.603153852063214, 11.67679983270202, 9.367401550449872, 4.967450815217391, 2.8812274038461534, 0.0, 8.473242323369567, 11.524909615384614, 7.451176222826087, 6.244934366966581, 2.919199958175505, 0.0), # 23
(5.62665954784899, 11.719233409090908, 9.380161793059125, 4.974632608695652, 2.8867673076923075, 0.0, 8.468373913043479, 11.54706923076923, 7.461948913043478, 6.25344119537275, 2.929808352272727, 0.0), # 24
(5.648790057067603, 11.757391493055556, 9.391050168701799, 4.980767663043478, 2.8915024038461534, 0.0, 8.463425611413044, 11.566009615384614, 7.471151494565217, 6.260700112467866, 2.939347873263889, 0.0), # 25
(5.669507813871817, 11.79115838383838, 9.400033419023135, 4.985834782608695, 2.8954153846153843, 0.0, 8.458397826086957, 11.581661538461537, 7.478752173913043, 6.266688946015424, 2.947789595959595, 0.0), # 26
(5.688775252414398, 11.820418380681815, 9.40707828566838, 4.989812771739131, 2.8984889423076923, 0.0, 8.453290964673915, 11.593955769230769, 7.484719157608696, 6.271385523778919, 2.9551045951704538, 0.0), # 27
(5.7065548068481124, 11.84505578282828, 9.412151510282778, 4.992680434782609, 2.9007057692307687, 0.0, 8.448105434782608, 11.602823076923075, 7.489020652173913, 6.274767673521851, 2.96126394570707, 0.0), # 28
(5.722808911325724, 11.864954889520202, 9.415219834511568, 4.994416576086956, 2.902048557692307, 0.0, 8.44284164402174, 11.608194230769229, 7.491624864130435, 6.276813223007712, 2.9662387223800506, 0.0), # 29
(5.7375, 11.879999999999999, 9.41625, 4.995, 2.9025, 0.0, 8.4375, 11.61, 7.4925, 6.277499999999999, 2.9699999999999998, 0.0), # 30
(5.751246651214834, 11.892497471590906, 9.415477744565216, 4.994894632352941, 2.9023357180851064, 0.0, 8.430077267616193, 11.609342872340426, 7.492341948529411, 6.276985163043476, 2.9731243678977264, 0.0), # 31
(5.7646965153452685, 11.904829772727274, 9.413182826086956, 4.994580588235293, 2.901846382978723, 0.0, 8.418644565217393, 11.607385531914892, 7.49187088235294, 6.275455217391303, 2.9762074431818184, 0.0), # 32
(5.777855634590792, 11.916995369318181, 9.40939801630435, 4.994060955882353, 2.9010372606382977, 0.0, 8.403313830584706, 11.60414904255319, 7.491091433823529, 6.272932010869566, 2.9792488423295453, 0.0), # 33
(5.790730051150895, 11.928992727272727, 9.40415608695652, 4.993338823529412, 2.899913617021276, 0.0, 8.38419700149925, 11.599654468085104, 7.490008235294118, 6.269437391304347, 2.9822481818181816, 0.0), # 34
(5.803325807225064, 11.940820312499996, 9.39748980978261, 4.9924172794117645, 2.898480718085106, 0.0, 8.361406015742128, 11.593922872340425, 7.488625919117647, 6.264993206521739, 2.985205078124999, 0.0), # 35
(5.815648945012788, 11.952476590909091, 9.389431956521738, 4.9912994117647065, 2.896743829787234, 0.0, 8.335052811094453, 11.586975319148936, 7.486949117647059, 6.259621304347825, 2.988119147727273, 0.0), # 36
(5.8277055067135555, 11.96396002840909, 9.380015298913044, 4.989988308823529, 2.8947082180851056, 0.0, 8.305249325337332, 11.578832872340422, 7.484982463235293, 6.253343532608695, 2.9909900071022726, 0.0), # 37
(5.839501534526853, 11.97526909090909, 9.369272608695653, 4.988487058823529, 2.89237914893617, 0.0, 8.272107496251873, 11.56951659574468, 7.4827305882352935, 6.246181739130434, 2.9938172727272727, 0.0), # 38
(5.851043070652174, 11.986402244318182, 9.357236657608695, 4.98679875, 2.8897618882978717, 0.0, 8.23573926161919, 11.559047553191487, 7.480198125, 6.23815777173913, 2.9966005610795454, 0.0), # 39
(5.862336157289003, 11.997357954545455, 9.343940217391305, 4.984926470588235, 2.886861702127659, 0.0, 8.196256559220389, 11.547446808510635, 7.477389705882353, 6.22929347826087, 2.999339488636364, 0.0), # 40
(5.873386836636828, 12.008134687499997, 9.329416059782607, 4.982873308823529, 2.8836838563829783, 0.0, 8.153771326836583, 11.534735425531913, 7.474309963235294, 6.219610706521738, 3.002033671874999, 0.0), # 41
(5.88420115089514, 12.01873090909091, 9.31369695652174, 4.980642352941176, 2.880233617021277, 0.0, 8.108395502248875, 11.520934468085107, 7.4709635294117644, 6.209131304347826, 3.0046827272727277, 0.0), # 42
(5.894785142263428, 12.02914508522727, 9.296815679347825, 4.978236691176471, 2.8765162499999994, 0.0, 8.060241023238381, 11.506064999999998, 7.467355036764706, 6.1978771195652165, 3.0072862713068176, 0.0), # 43
(5.905144852941176, 12.03937568181818, 9.278805, 4.975659411764705, 2.8725370212765955, 0.0, 8.009419827586207, 11.490148085106382, 7.4634891176470575, 6.1858699999999995, 3.009843920454545, 0.0), # 44
(5.915286325127877, 12.049421164772726, 9.259697690217394, 4.972913602941176, 2.8683011968085106, 0.0, 7.956043853073464, 11.473204787234042, 7.459370404411764, 6.1731317934782615, 3.0123552911931815, 0.0), # 45
(5.925215601023019, 12.059280000000001, 9.239526521739132, 4.970002352941176, 2.8638140425531913, 0.0, 7.90022503748126, 11.455256170212765, 7.455003529411765, 6.159684347826087, 3.0148200000000003, 0.0), # 46
(5.934938722826087, 12.06895065340909, 9.218324266304347, 4.966928749999999, 2.859080824468085, 0.0, 7.842075318590705, 11.43632329787234, 7.450393124999999, 6.145549510869564, 3.0172376633522724, 0.0), # 47
(5.944461732736574, 12.07843159090909, 9.196123695652174, 4.9636958823529405, 2.854106808510638, 0.0, 7.7817066341829095, 11.416427234042551, 7.445543823529412, 6.130749130434782, 3.0196078977272727, 0.0), # 48
(5.953790672953963, 12.087721278409088, 9.17295758152174, 4.960306838235294, 2.8488972606382976, 0.0, 7.71923092203898, 11.39558904255319, 7.4404602573529415, 6.115305054347826, 3.021930319602272, 0.0), # 49
(5.96293158567775, 12.096818181818177, 9.148858695652175, 4.956764705882353, 2.8434574468085105, 0.0, 7.65476011994003, 11.373829787234042, 7.43514705882353, 6.099239130434783, 3.0242045454545443, 0.0), # 50
(5.971890513107417, 12.105720767045453, 9.123859809782608, 4.953072573529411, 2.837792632978723, 0.0, 7.588406165667167, 11.351170531914892, 7.429608860294118, 6.082573206521738, 3.026430191761363, 0.0), # 51
(5.980673497442456, 12.114427499999998, 9.097993695652173, 4.949233529411764, 2.8319080851063827, 0.0, 7.5202809970015, 11.32763234042553, 7.4238502941176465, 6.065329130434781, 3.0286068749999995, 0.0), # 52
(5.989286580882353, 12.122936846590909, 9.071293125, 4.945250661764706, 2.8258090691489364, 0.0, 7.450496551724138, 11.303236276595745, 7.417875992647058, 6.04752875, 3.030734211647727, 0.0), # 53
(5.9977358056266, 12.13124727272727, 9.043790869565216, 4.941127058823529, 2.8195008510638297, 0.0, 7.379164767616192, 11.278003404255319, 7.411690588235294, 6.0291939130434775, 3.0328118181818176, 0.0), # 54
(6.00602721387468, 12.139357244318182, 9.015519701086955, 4.93686580882353, 2.8129886968085103, 0.0, 7.306397582458771, 11.251954787234041, 7.405298713235295, 6.010346467391304, 3.0348393110795455, 0.0), # 55
(6.014166847826087, 12.147265227272724, 8.986512391304348, 4.9324699999999995, 2.8062778723404254, 0.0, 7.232306934032984, 11.225111489361701, 7.398705, 5.991008260869565, 3.036816306818181, 0.0), # 56
(6.022160749680308, 12.154969687500001, 8.95680171195652, 4.927942720588234, 2.7993736436170207, 0.0, 7.15700476011994, 11.197494574468083, 7.391914080882352, 5.9712011413043475, 3.0387424218750003, 0.0), # 57
(6.030014961636829, 12.16246909090909, 8.926420434782608, 4.923287058823529, 2.792281276595744, 0.0, 7.0806029985007495, 11.169125106382976, 7.384930588235295, 5.950946956521738, 3.0406172727272724, 0.0), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_allighting_rate = (
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 258194110137029475889902652135037600173
#index for seed sequence child
child_seed_index = (
1, # 0
39, # 1
)
| 113.480597
| 213
| 0.730008
| 5,147
| 38,016
| 5.389742
| 0.235283
| 0.311452
| 0.246566
| 0.467179
| 0.328034
| 0.327097
| 0.327097
| 0.326448
| 0.326448
| 0.326448
| 0
| 0.819712
| 0.118739
| 38,016
| 334
| 214
| 113.820359
| 0.008328
| 0.031855
| 0
| 0.202532
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| false
| 0.015823
| 0
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| 0
| null | 1
| 1
| 1
| 0
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| 0
| 0
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| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dce20baeb74c1c17594309fed973d896e98ac4fd
| 198
|
py
|
Python
|
tests/conftest.py
|
immerrr/fastapi-security
|
f67023377f34a5821a3e8b6579a64d82e5d62647
|
[
"MIT"
] | 46
|
2020-02-12T01:31:31.000Z
|
2021-11-07T17:41:30.000Z
|
tests/conftest.py
|
immerrr/fastapi-security
|
f67023377f34a5821a3e8b6579a64d82e5d62647
|
[
"MIT"
] | 6
|
2020-02-18T19:14:03.000Z
|
2021-08-31T08:06:02.000Z
|
tests/conftest.py
|
immerrr/fastapi-security
|
f67023377f34a5821a3e8b6579a64d82e5d62647
|
[
"MIT"
] | 5
|
2020-02-15T23:07:19.000Z
|
2021-06-15T08:12:19.000Z
|
import pytest
from fastapi import FastAPI
from starlette.testclient import TestClient
@pytest.fixture
def app():
return FastAPI()
@pytest.fixture
def client(app):
return TestClient(app)
| 14.142857
| 43
| 0.762626
| 25
| 198
| 6.04
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| 13
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| 1
| 1
| 1
| 0
|
0
| 6
|
0d1dfc618bdf068eec75f2d5d1cebe69a2639169
| 99
|
py
|
Python
|
aws_mock/requests/modify_vpc_attribute.py
|
enaydanov/aws_mock
|
4ad3dca270ad164693e85741d5e92f845c34aa01
|
[
"Apache-2.0"
] | null | null | null |
aws_mock/requests/modify_vpc_attribute.py
|
enaydanov/aws_mock
|
4ad3dca270ad164693e85741d5e92f845c34aa01
|
[
"Apache-2.0"
] | null | null | null |
aws_mock/requests/modify_vpc_attribute.py
|
enaydanov/aws_mock
|
4ad3dca270ad164693e85741d5e92f845c34aa01
|
[
"Apache-2.0"
] | null | null | null |
from aws_mock.lib import aws_response
@aws_response
def modify_vpc_attribute() -> None:
pass
| 14.142857
| 37
| 0.767677
| 15
| 99
| 4.733333
| 0.8
| 0.309859
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| 0.161616
| 99
| 6
| 38
| 16.5
| 0.855422
| 0
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| 0
| 1
| 0.25
| true
| 0.25
| 0.25
| 0
| 0.5
| 0
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| 0
| null | 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
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| 0
| 1
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
0d37c1d5f1ecb4a0990dfd3104f97884e865a33a
| 3,000
|
py
|
Python
|
tests/test_optimizers/test_random_state.py
|
0liu/Gradient-Free-Optimizers
|
d057bb5e2521cae3dd19737a57fe9412a166e380
|
[
"MIT"
] | 860
|
2020-06-10T08:53:41.000Z
|
2022-03-30T14:22:20.000Z
|
tests/test_optimizers/test_random_state.py
|
0liu/Gradient-Free-Optimizers
|
d057bb5e2521cae3dd19737a57fe9412a166e380
|
[
"MIT"
] | 24
|
2021-01-25T08:06:54.000Z
|
2022-01-24T13:46:48.000Z
|
tests/test_optimizers/test_random_state.py
|
0liu/Gradient-Free-Optimizers
|
d057bb5e2521cae3dd19737a57fe9412a166e380
|
[
"MIT"
] | 52
|
2020-06-25T09:36:15.000Z
|
2022-03-18T18:11:52.000Z
|
import pytest
import time
import numpy as np
import pandas as pd
from ._parametrize import optimizers
from surfaces.test_functions import AckleyFunction
ackkley_function = AckleyFunction()
def objective_function(para):
score = -(para["x0"] * para["x0"] + para["x1"] * para["x1"])
return score
search_space = {
"x0": np.arange(-75, 100, 1),
"x1": np.arange(-100, 75, 1),
}
err = 0.000001
n_iter = 10
n_random = 2
n_last = n_iter - n_random
@pytest.mark.parametrize(*optimizers)
def test_random_state_0(Optimizer):
opt0 = Optimizer(search_space, initialize={"random": n_random})
opt0.search(
ackkley_function,
n_iter=n_iter,
random_state=1,
)
opt1 = Optimizer(search_space, initialize={"random": n_random})
opt1.search(
ackkley_function,
n_iter=n_iter,
random_state=1,
)
n_last_scores0 = list(opt0.results["score"].values)[-n_last:]
n_last_scores1 = list(opt1.results["score"].values)[-n_last:]
assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err
@pytest.mark.parametrize(*optimizers)
def test_random_state_1(Optimizer):
opt0 = Optimizer(search_space, initialize={"random": n_random})
opt0.search(
ackkley_function,
n_iter=n_iter,
random_state=10,
)
opt1 = Optimizer(search_space, initialize={"random": n_random})
opt1.search(
ackkley_function,
n_iter=n_iter,
random_state=10,
)
n_last_scores0 = list(opt0.results["score"].values)[-n_last:]
n_last_scores1 = list(opt1.results["score"].values)[-n_last:]
assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) < err
@pytest.mark.parametrize(*optimizers)
def test_random_state_2(Optimizer):
opt0 = Optimizer(search_space, initialize={"random": n_random})
opt0.search(
ackkley_function,
n_iter=n_iter,
random_state=1,
)
opt1 = Optimizer(search_space, initialize={"random": n_random})
opt1.search(
ackkley_function,
n_iter=n_iter,
random_state=10,
)
print("\n opt0.results \n", opt0.results)
print("\n opt1.results \n", opt1.results)
n_last_scores0 = list(opt0.results["score"].values)[-n_last:]
n_last_scores1 = list(opt1.results["score"].values)[-n_last:]
assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) > err
@pytest.mark.parametrize(*optimizers)
def test_no_random_state_0(Optimizer):
opt0 = Optimizer(search_space, initialize={"random": n_random})
opt0.search(ackkley_function, n_iter=n_iter)
opt1 = Optimizer(search_space, initialize={"random": n_random})
opt1.search(ackkley_function, n_iter=n_iter)
print("\n opt0.results \n", opt0.results)
print("\n opt1.results \n", opt1.results)
n_last_scores0 = list(opt0.results["score"].values)[-n_last:]
n_last_scores1 = list(opt1.results["score"].values)[-n_last:]
assert abs(np.sum(n_last_scores0) - np.sum(n_last_scores1)) > err
| 25.862069
| 69
| 0.676333
| 416
| 3,000
| 4.615385
| 0.132212
| 0.065104
| 0.028125
| 0.125
| 0.838021
| 0.838021
| 0.838021
| 0.838021
| 0.818229
| 0.818229
| 0
| 0.036431
| 0.185667
| 3,000
| 115
| 70
| 26.086957
| 0.749488
| 0
| 0
| 0.634146
| 0
| 0
| 0.057333
| 0
| 0
| 0
| 0
| 0
| 0.04878
| 1
| 0.060976
| false
| 0
| 0.073171
| 0
| 0.146341
| 0.04878
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
0d42f6f407fed73f39f8904143f0af697cedaeb9
| 148
|
py
|
Python
|
beta/test/Constant_test.py
|
Odusseus/python
|
98380cfab0f147fa0b3ac652006e11f482ebbcfc
|
[
"MIT"
] | null | null | null |
beta/test/Constant_test.py
|
Odusseus/python
|
98380cfab0f147fa0b3ac652006e11f482ebbcfc
|
[
"MIT"
] | null | null | null |
beta/test/Constant_test.py
|
Odusseus/python
|
98380cfab0f147fa0b3ac652006e11f482ebbcfc
|
[
"MIT"
] | null | null | null |
import sys
import Constant
def test_Constant():
assert Constant.BLACK == 0
assert Constant.WHITE == 1
assert Constant.MAX_ELEMENT == 8
| 18.5
| 36
| 0.709459
| 20
| 148
| 5.15
| 0.65
| 0.407767
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0.209459
| 148
| 7
| 37
| 21.142857
| 0.854701
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.166667
| true
| 0
| 0.333333
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
b4ba67eca8309774fed06d9292e0cfbf91d1fa11
| 26
|
py
|
Python
|
terrascript/oneandone/__init__.py
|
GarnerCorp/python-terrascript
|
ec6c2d9114dcd3cb955dd46069f8ba487e320a8c
|
[
"BSD-2-Clause"
] | null | null | null |
terrascript/oneandone/__init__.py
|
GarnerCorp/python-terrascript
|
ec6c2d9114dcd3cb955dd46069f8ba487e320a8c
|
[
"BSD-2-Clause"
] | null | null | null |
terrascript/oneandone/__init__.py
|
GarnerCorp/python-terrascript
|
ec6c2d9114dcd3cb955dd46069f8ba487e320a8c
|
[
"BSD-2-Clause"
] | 1
|
2018-11-15T16:23:05.000Z
|
2018-11-15T16:23:05.000Z
|
"""2019-05-28 10:50:07"""
| 13
| 25
| 0.538462
| 6
| 26
| 2.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.583333
| 0.076923
| 26
| 1
| 26
| 26
| 0
| 0.730769
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
b4d9970ce36e8cb6231d8f23acc267d88c4dbfd1
| 163
|
py
|
Python
|
contribauthproxy/admin.py
|
parksandwildlife/fdwfunhouse
|
f08d73e4cdd1cdc9e2df194f1da24f32d15c9cda
|
[
"Apache-2.0"
] | null | null | null |
contribauthproxy/admin.py
|
parksandwildlife/fdwfunhouse
|
f08d73e4cdd1cdc9e2df194f1da24f32d15c9cda
|
[
"Apache-2.0"
] | null | null | null |
contribauthproxy/admin.py
|
parksandwildlife/fdwfunhouse
|
f08d73e4cdd1cdc9e2df194f1da24f32d15c9cda
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib import admin
from .models import ForeignAuthUser, UserComment
# Register your models here.
admin.site.register([ForeignAuthUser, UserComment])
| 32.6
| 51
| 0.828221
| 19
| 163
| 7.105263
| 0.631579
| 0.385185
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09816
| 163
| 5
| 51
| 32.6
| 0.918367
| 0.159509
| 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 | 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
| 6
|
2ec62b997f2dabc26d4589555ca51d04deda1a59
| 1,675
|
py
|
Python
|
returns/pointfree.py
|
kenjihiraoka/returns
|
4589973520d7226b18acd7295d1a9a10ff032759
|
[
"BSD-2-Clause"
] | null | null | null |
returns/pointfree.py
|
kenjihiraoka/returns
|
4589973520d7226b18acd7295d1a9a10ff032759
|
[
"BSD-2-Clause"
] | null | null | null |
returns/pointfree.py
|
kenjihiraoka/returns
|
4589973520d7226b18acd7295d1a9a10ff032759
|
[
"BSD-2-Clause"
] | null | null | null |
from returns._generated.pointfree.alt import _alt as alt
from returns._generated.pointfree.apply import _apply as apply
from returns._generated.pointfree.bind import _bind as bind
from returns._generated.pointfree.bind_async import _bind_async as bind_async
from returns._generated.pointfree.bind_async_future import (
_bind_async_future as bind_async_future,
)
from returns._generated.pointfree.bind_async_future_result import (
_bind_async_future_result as bind_async_future_result,
)
from returns._generated.pointfree.bind_awaitable import (
_bind_awaitable as bind_awaitable,
)
from returns._generated.pointfree.bind_context import (
_bind_context as bind_context,
)
from returns._generated.pointfree.bind_context_ioresult import (
_bind_context_ioresult as bind_context_ioresult,
)
from returns._generated.pointfree.bind_context_result import (
_bind_context_result as bind_context_result,
)
from returns._generated.pointfree.bind_future import _bind_future as bind_future
from returns._generated.pointfree.bind_future_result import (
_bind_future_result as bind_future_result,
)
from returns._generated.pointfree.bind_io import _bind_io as bind_io
from returns._generated.pointfree.bind_ioresult import (
_bind_ioresult as bind_ioresult,
)
from returns._generated.pointfree.bind_result import _bind_result as bind_result
from returns._generated.pointfree.fix import _fix as fix
from returns._generated.pointfree.map import _map as map_
from returns._generated.pointfree.rescue import _rescue as rescue
from returns._generated.pointfree.unify import _unify as unify
from returns._generated.pointfree.value_or import _value_or as value_or
| 45.27027
| 80
| 0.849552
| 237
| 1,675
| 5.590717
| 0.092827
| 0.166038
| 0.301887
| 0.437736
| 0.439245
| 0.363019
| 0.13434
| 0
| 0
| 0
| 0
| 0
| 0.100299
| 1,675
| 36
| 81
| 46.527778
| 0.87923
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.555556
| 0
| 0.555556
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2ec9a99ac635d763ac5f9cd3f2f34adc8b47aa1f
| 10,835
|
py
|
Python
|
test/jsonrpc/test_api.py
|
thomas-neuman/nkn-client-python
|
a64dd17fde55442c4904fe00a2448b9aeb734cf6
|
[
"Apache-2.0"
] | null | null | null |
test/jsonrpc/test_api.py
|
thomas-neuman/nkn-client-python
|
a64dd17fde55442c4904fe00a2448b9aeb734cf6
|
[
"Apache-2.0"
] | null | null | null |
test/jsonrpc/test_api.py
|
thomas-neuman/nkn-client-python
|
a64dd17fde55442c4904fe00a2448b9aeb734cf6
|
[
"Apache-2.0"
] | null | null | null |
import functools
import json
import responses
import unittest
import nkn_client.jsonrpc.api
class TestNknJsonRpcApi(unittest.TestCase):
def setUp(self):
self._host = "hostname"
self._api = nkn_client.jsonrpc.api.NknJsonRpcApi(self._host)
@responses.activate
def _with_rpc_response(self, method, resp_cb):
responses.add_callback(
responses.POST,
"http://%s/" % (self._host),
callback=resp_cb
)
return method()
def _with_success_response(self, method, expected_result):
expected_json = {
"jsonpc": "2.0",
"result": expected_result,
"id": 1
}
expected_status = 200
def resp_callback(request):
req_body = json.loads(request.body)
resp_body = expected_json.copy()
resp_body.update({"id": req_body["id"]})
return (expected_status, {}, json.dumps(resp_body))
return self._with_rpc_response(method, resp_callback)
def _with_wrong_id_response(self, method, expected_result):
expected_json = {
"jsonpc": "2.0",
"result": expected_result,
"id": 1
}
expected_status = 200
def resp_callback(request):
req_body = json.loads(request.body)
resp_body = expected_json.copy()
resp_id = "BAD:%s" % (req_body["id"])
resp_body.update({"id": resp_id})
return (expected_status, {}, json.dumps(resp_body))
return self._with_rpc_response(method, resp_callback)
def _with_wrong_result_response(self, method, unexpected_result):
pass
def test_get_latest_block_height_succeeds(self):
method = self._api.get_latest_block_height
expected = 5
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_latest_block_height_fails_with_wrong_id(self):
method = self._api.get_latest_block_height
expected = 5
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_latest_block_hash_succeeds(self):
method = self._api.get_latest_block_hash
expected = "6cf00422b02f3d99f5c006fcdb36bfb7cc8b2c345b2f34274e50a3d8f3bb8193"
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_latest_block_hash_fails_with_wrong_id(self):
method = self._api.get_latest_block_hash
expected = "6cf00422b02f3d99f5c006fcdb36bfb7cc8b2c345b2f34274e50a3d8f3bb8193"
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_block_count_succeeds(self):
method = self._api.get_block_count
expected = 270
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_block_count_fails_with_wrong_id(self):
method = self._api.get_block_count
expected = 270
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_block_with_height_param_fails_with_wrong_id(self):
method = functools.partial(self._api.get_block, height=1)
expected = {
"hash": "5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6"
}
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_block_with_height_param_succeeds(self):
method = functools.partial(self._api.get_block, height=1)
expected = {
"hash": "5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6"
}
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_block_with_hash_param_fails_with_wrong_id(self):
method = functools.partial(
self._api.get_block,
hash="5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6"
)
expected = {
"hash": "5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6"
}
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_block_with_hash_param_succeeds(self):
method = functools.partial(
self._api.get_block,
hash="5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6"
)
expected = {
"hash": "5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6"
}
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_block_transactions_by_height_fails_with_wrong_id(self):
method = functools.partial(
self._api.get_block_transactions_by_height,
1
)
expected = {
"Hash": "5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6",
"Height": 1,
"Transactions": [
"327bb43c2e40ccb2f83011d35602829872ab190171b79047397d000eddda18a9"
]
}
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_block_transactions_by_height_succeeds(self):
method = functools.partial(
self._api.get_block_transactions_by_height,
1
)
expected = {
"Hash": "5f85d1286801c2f1129a02b0b19a3312f8113aaa073b5987346c59e27a12bdc6",
"Height": 1,
"Transactions": [
"327bb43c2e40ccb2f83011d35602829872ab190171b79047397d000eddda18a9"
]
}
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_connection_count_fails_with_wrong_id(self):
method = self._api.get_connection_count
expected = 8
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_connection_count_succeeds(self):
method = self._api.get_connection_count
expected = 8
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_raw_mempool_fails_with_wrong_id(self):
method = self._api.get_raw_mempool
expected = []
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_raw_mempool_succeeds(self):
method = self._api.get_raw_mempool
expected = []
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_transaction_fails_with_wrong_id(self):
method = functools.partial(
self._api.get_transaction,
"327bb43c2e40ccb2f83011d35602829872ab190171b79047397d000eddda18a9"
)
expected = {
"hash": "327bb43c2e40ccb2f83011d35602829872ab190171b79047397d000eddda18a9"
}
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_transaction_succeeds(self):
method = functools.partial(
self._api.get_transaction,
"327bb43c2e40ccb2f83011d35602829872ab190171b79047397d000eddda18a9"
)
expected = {
"hash": "327bb43c2e40ccb2f83011d35602829872ab190171b79047397d000eddda18a9"
}
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_websocket_address_fails_with_wrong_id(self):
method = functools.partial(
self._api.get_websocket_address,
"identifier.pubkey"
)
expected = "127.0.0.1:30002"
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_websocket_address_succeeds(self):
method = functools.partial(
self._api.get_websocket_address,
"identifier.pubkey"
)
expected = "127.0.0.1:30002"
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_version_fails_with_wrong_id(self):
method = self._api.get_version
expected = "v0.1-alpha-26-gf7b7"
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_version_succeeds(self):
method = self._api.get_version
expected = "v0.1-alpha-26-gf7b7"
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_neighbor_fails_with_wrong_id(self):
method = self._api.get_neighbor
expected = [
{"IpAddr":[0,0,0,0,0,0,0,0,0,0,255,255,127,0,0,1],"Port":30013,"ID":8408941800585506307},
{"IpAddr":[0,0,0,0,0,0,0,0,0,0,255,255,127,0,0,1],"Port":30005,"ID":2956232338651871234},
{"IpAddr":[0,0,0,0,0,0,0,0,0,0,255,255,127,0,0,1],"Port":30009,"ID":9027538565785539587}
]
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_neighbor_succeeds(self):
method = self._api.get_neighbor
expected = [
{"IpAddr":[0,0,0,0,0,0,0,0,0,0,255,255,127,0,0,1],"Port":30013,"ID":8408941800585506307},
{"IpAddr":[0,0,0,0,0,0,0,0,0,0,255,255,127,0,0,1],"Port":30005,"ID":2956232338651871234},
{"IpAddr":[0,0,0,0,0,0,0,0,0,0,255,255,127,0,0,1],"Port":30009,"ID":9027538565785539587}
]
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_node_state_fails_with_wrong_id(self):
method = self._api.get_node_state
expected = {
"State": 0,
"Port": 30001,
"ID": 4697163132361310211,
"Time": 1530087472382892000,
"Version": 0,
"Services": 0,
"Relay": True,
"Height": 0,
"TxnCnt": 0,
"RxTxnCnt": 0,
"ChordID": "04629f17a6a0ec9a573ecfccb60fa42b104212dd1ec9cdb131993cbb4e15fe5e"
}
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_node_state_succeeds(self):
method = self._api.get_node_state
expected = {
"State": 0,
"Port": 30001,
"ID": 4697163132361310211,
"Time": 1530087472382892000,
"Version": 0,
"Services": 0,
"Relay": True,
"Height": 0,
"TxnCnt": 0,
"RxTxnCnt": 0,
"ChordID": "04629f17a6a0ec9a573ecfccb60fa42b104212dd1ec9cdb131993cbb4e15fe5e"
}
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
def test_get_chord_ring_info_fails_with_wrong_id(self):
method = self._api.get_chord_ring_info
expected = {
"Vnodes": [
{
"Id": "BGKfF6ag7JpXPs/Mtg+kKxBCEt0eyc2xMZk8u04V/l4=",
"Host": "127.0.0.1:30000",
"NodePort": 30001,
"HttpWsPort": 30002
}
]
}
with self.assertRaises(RuntimeError):
_ = self._with_wrong_id_response(method, expected)
def test_get_chord_ring_info_succeeds(self):
method = self._api.get_chord_ring_info
expected = {
"Vnodes": [
{
"Id": "BGKfF6ag7JpXPs/Mtg+kKxBCEt0eyc2xMZk8u04V/l4=",
"Host": "127.0.0.1:30000",
"NodePort": 30001,
"HttpWsPort": 30002
}
]
}
actual = self._with_success_response(method, expected)
self.assertEqual(actual, expected)
if __name__ == "__main__":
unittest.main()
| 30.181058
| 92
| 0.716105
| 1,238
| 10,835
| 5.921648
| 0.102585
| 0.01746
| 0.019643
| 0.022916
| 0.93848
| 0.931387
| 0.909971
| 0.899468
| 0.880371
| 0.847906
| 0
| 0.142377
| 0.176742
| 10,835
| 358
| 93
| 30.265363
| 0.679484
| 0
| 0
| 0.692308
| 0
| 0
| 0.163359
| 0.114444
| 0
| 0
| 0
| 0
| 0.097902
| 1
| 0.122378
| false
| 0.003497
| 0.017483
| 0
| 0.160839
| 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
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| null | 0
| 0
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| 0
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| 0
| 0
| 0
| 0
|
0
| 6
|
2eea938777461449dec408a567d3facef9077d5e
| 17,736
|
py
|
Python
|
ikasl/util/cluster_viewer.py
|
odigous-labs/self-learning-algorithm
|
83fd7e3cbd0daab77ea94fc4bc21b28e2c510d19
|
[
"Apache-2.0"
] | 6
|
2019-06-29T14:33:46.000Z
|
2021-08-06T11:41:57.000Z
|
ikasl/util/cluster_viewer.py
|
odigous-labs/self-learning-algorithm
|
83fd7e3cbd0daab77ea94fc4bc21b28e2c510d19
|
[
"Apache-2.0"
] | null | null | null |
ikasl/util/cluster_viewer.py
|
odigous-labs/self-learning-algorithm
|
83fd7e3cbd0daab77ea94fc4bc21b28e2c510d19
|
[
"Apache-2.0"
] | 5
|
2019-08-26T08:48:08.000Z
|
2020-07-16T08:41:22.000Z
|
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import random
class Viewer:
def __init__(self, image_files_root_folder, width, height, frame_sequence):
self.image_files_root_folder = image_files_root_folder
self.blank_image_filename = '../resources/blank.jpg'.replace('\\', '/')
self.single_image_width = width
self.single_image_height = height
self.frame_sequence = frame_sequence
def view(self, raw_clusters):
child_clusters = {}
for key, cluster in raw_clusters.items():
child_clusters[key] = Viewer.get_index_array(cluster)
child_frame_id = Viewer.select_frame_id(self.frame_sequence)
child_cluster_image_files = {}
for key, child_cluster in child_clusters.items():
child_cluster_image_files[key] = self._get_image_file_names(child_cluster, child_frame_id)
counter = 1
for key, image_file in child_cluster_image_files.items():
if len(image_file) > 0:
img_cluster = self._get_image_cluster(image_file)
Viewer.display_image_clusters(img_cluster, key, counter)
counter += 1
plt.show()
def save(self, raw_clusters, root_folder):
child_clusters = {}
for key, cluster in raw_clusters.items():
child_clusters[key] = Viewer.get_index_array(cluster)
child_frame_id = Viewer.select_frame_id(self.frame_sequence)
child_cluster_image_files = {}
for key, child_cluster in child_clusters.items():
child_cluster_image_files[key] = self._get_image_file_names(child_cluster, child_frame_id)
counter = 1
for key, image_file in child_cluster_image_files.items():
if len(image_file) > 0:
img_cluster = self._get_image_cluster(image_file)
Viewer.save_image_clusters(img_cluster, key, root_folder)
counter += 1
plt.show()
def _get_image_file_names(self, cluster, frame_id):
cluster_image_files = []
for image_id in cluster:
folder_name = Viewer.get_folder_name(image_id)
cluster_image_files.append(self.image_files_root_folder + folder_name + frame_id)
return cluster_image_files
# Compose Image files
def _get_image_set(self, image_list):
overflow = len(image_list) % 6
if overflow != 0:
for _ in range(0, (6 - overflow)):
image_list.append(self.blank_image_filename)
imgs = [Image.open(i) for i in image_list]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
# min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
min_shape = (self.single_image_width, self.single_image_height)
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
return imgs_comb
def _get_image_cluster(self, list_im):
if len(list_im) < 7:
imgs_comb = self._get_image_set(list_im)
elif len(list_im) < 13:
list_im_1 = list_im[0:6]
list_im_2 = list_im[6:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb = np.vstack((imgs_comb_1, imgs_comb_2))
elif len(list_im) < 19:
list_im_1 = list_im[0:6]
list_im_2 = list_im[6:12]
list_im_3 = list_im[12:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), imgs_comb_3))
elif len(list_im) < 25:
list_im_1 = list_im[0:6]
list_im_2 = list_im[6:12]
list_im_3 = list_im[12:18]
list_im_4 = list_im[18:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((imgs_comb_3, imgs_comb_4))))
elif len(list_im) < 31:
list_im_1 = list_im[0:6]
list_im_2 = list_im[6:12]
list_im_3 = list_im[12:18]
list_im_4 = list_im[18:24]
list_im_5 = list_im[24:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb_5 = self._get_image_set(list_im_5)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((imgs_comb_3, np.vstack((imgs_comb_4, imgs_comb_5))))))
elif len(list_im) < 37:
list_im_1 = list_im[0:6]
list_im_2 = list_im[6:12]
list_im_3 = list_im[12:18]
list_im_4 = list_im[18:24]
list_im_5 = list_im[24:30]
list_im_6 = list_im[30:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb_5 = self._get_image_set(list_im_5)
imgs_comb_6 = self._get_image_set(list_im_6)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((np.vstack((imgs_comb_3, imgs_comb_4)), np.vstack((imgs_comb_5, imgs_comb_6))))))
else:
list_im_1 = list_im[0:6]
list_im_2 = list_im[6:12]
list_im_3 = list_im[12:18]
list_im_4 = list_im[18:24]
list_im_5 = list_im[24:30]
list_im_6 = list_im[30:36]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb_5 = self._get_image_set(list_im_5)
imgs_comb_6 = self._get_image_set(list_im_6)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((np.vstack((imgs_comb_3, imgs_comb_4)), np.vstack((imgs_comb_5, imgs_comb_6))))))
return Image.fromarray(imgs_comb)
@staticmethod
def get_index_array(my_list):
if len(my_list) == 0:
return []
return [x for x in map(int, my_list.strip().split(' '))]
# Compose the frame number to be displayed
@staticmethod
def select_frame_id(sequence_id):
frame_number = sequence_id * 5
if frame_number < 10:
frame_number = 'frame000' + str(frame_number) + '.jpg'
elif frame_number < 100:
frame_number = 'frame00' + str(frame_number) + '.jpg'
else:
frame_number = 'frame0' + str(frame_number) + '.jpg'
return frame_number
# Compose image files for parent and chile clusters
@staticmethod
def get_folder_name(video_id):
if video_id < 10:
return 'seq0' + str(video_id) + '/'
else:
return 'seq' + str(video_id) + '/'
@staticmethod
def save_image_clusters(img_clstr, filename, root_folder):
img_clstr.save(root_folder + '/' + filename + '.jpg')
# # for a vertical stacking it is simple: use vstack
# imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))
# imgs_comb = Image.fromarray(imgs_comb)
# imgs_comb.save(filename+'.jpg')
@staticmethod
def display_image_clusters(img_clstr, filename, plt_id):
plt.figure(plt_id)
plt.title(filename)
plt.xticks([])
plt.yticks([])
plt.imshow(img_clstr)
class SceneViewer:
def __init__(self, image_files_root_folder, width, height):
self.image_files_root_folder = image_files_root_folder
self.blank_image_filename = 'E:/Projects/unsupervised/self_organizing/som_py/ikasl/ikasl_v2/resources/blank.jpg'.replace('\\', '/')
self.single_image_width = width
self.single_image_height = height
self.display_image_length = 12
def view(self, raw_clusters):
child_clusters = {}
for key, cluster in raw_clusters.items():
child_clusters[key] = SceneViewer.get_index_array(cluster)
child_cluster_image_files = {}
for key, child_cluster in child_clusters.items():
child_cluster_image_files[key] = self._get_image_file_names(child_cluster)
counter = 1
for key, image_file in child_cluster_image_files.items():
if len(image_file) > 0:
img_cluster = self._get_image_cluster(image_file)
SceneViewer.display_image_clusters(img_cluster, key, counter)
counter += 1
plt.show()
def _get_image_file_names(self, cluster):
cluster_image_files = []
for image_id in cluster:
filename = SceneViewer.select_frame_id(image_id)
cluster_image_files.append(self.image_files_root_folder + filename)
return cluster_image_files
# Compose Image files
def _get_image_set(self, image_list):
overflow = len(image_list) % self.display_image_length
if overflow != 0:
for _ in range(0, (self.display_image_length - overflow)):
image_list.append(self.blank_image_filename)
imgs = [Image.open(i) for i in image_list]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
# min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
min_shape = (self.single_image_width, self.single_image_height)
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
return imgs_comb
def _get_image_cluster(self, list_im):
root_size = self.display_image_length
if len(list_im) <= root_size:
imgs_comb = self._get_image_set(list_im)
elif len(list_im) <= root_size*2:
list_im_1 = list_im[0:root_size]
list_im_2 = list_im[root_size:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb = np.vstack((imgs_comb_1, imgs_comb_2))
elif len(list_im) <= root_size*3:
list_im_1 = list_im[0:root_size]
list_im_2 = list_im[root_size:root_size*2]
list_im_3 = list_im[root_size*2:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), imgs_comb_3))
elif len(list_im) <= root_size*4:
list_im_1 = list_im[0:root_size]
list_im_2 = list_im[root_size:root_size*2]
list_im_3 = list_im[root_size*2:root_size*3]
list_im_4 = list_im[root_size*3:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((imgs_comb_3, imgs_comb_4))))
elif len(list_im) <= root_size*5:
list_im_1 = list_im[0:root_size]
list_im_2 = list_im[root_size:root_size*2]
list_im_3 = list_im[root_size*2:root_size*3]
list_im_4 = list_im[root_size*3:root_size*4]
list_im_5 = list_im[root_size*4:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb_5 = self._get_image_set(list_im_5)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((imgs_comb_3, np.vstack((imgs_comb_4, imgs_comb_5))))))
elif len(list_im) <= root_size*6:
list_im_1 = list_im[0:root_size]
list_im_2 = list_im[root_size:root_size*2]
list_im_3 = list_im[root_size*2:root_size*3]
list_im_4 = list_im[root_size*3:root_size*4]
list_im_5 = list_im[root_size*4:root_size*5]
list_im_6 = list_im[root_size*5:]
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb_5 = self._get_image_set(list_im_5)
imgs_comb_6 = self._get_image_set(list_im_6)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((np.vstack((imgs_comb_3, imgs_comb_4)), np.vstack((imgs_comb_5, imgs_comb_6))))))
else:
total_imgs = len(list_im)
width_imgs = 6
list_im_1 = []
list_im_2 = []
list_im_3 = []
list_im_4 = []
list_im_5 = []
list_im_6 = []
for i in random.sample(range(0, int(total_imgs/width_imgs)), width_imgs):
list_im_1.append(list_im[i])
for i in random.sample(range(int(total_imgs/width_imgs), int(total_imgs/width_imgs)*2), width_imgs):
list_im_2.append(list_im[i])
for i in random.sample(range(int(total_imgs/width_imgs)*2, int(total_imgs/width_imgs)*3), width_imgs):
list_im_3.append(list_im[i])
for i in random.sample(range(int(total_imgs/width_imgs)*3, int(total_imgs/width_imgs)*4), width_imgs):
list_im_4.append(list_im[i])
for i in random.sample(range(int(total_imgs/width_imgs)*4, int(total_imgs/width_imgs)*5), width_imgs):
list_im_5.append(list_im[i])
for i in random.sample(range(int(total_imgs/width_imgs)*5, int(total_imgs/width_imgs)*6), width_imgs):
list_im_6.append(list_im[i])
imgs_comb_1 = self._get_image_set(list_im_1)
imgs_comb_2 = self._get_image_set(list_im_2)
imgs_comb_3 = self._get_image_set(list_im_3)
imgs_comb_4 = self._get_image_set(list_im_4)
imgs_comb_5 = self._get_image_set(list_im_5)
imgs_comb_6 = self._get_image_set(list_im_6)
imgs_comb = np.vstack((np.vstack((imgs_comb_1, imgs_comb_2)), np.vstack((np.vstack((imgs_comb_3, imgs_comb_4)), np.vstack((imgs_comb_5, imgs_comb_6))))))
return Image.fromarray(imgs_comb)
@staticmethod
def get_index_array(my_list):
if len(my_list) == 0:
return []
return [x for x in map(int, my_list.strip().split(' '))]
# Compose the frame number to be displayed
@staticmethod
def select_frame_id(sequence_id):
if sequence_id == 0:
return 'frame00000.jpg'
else:
sequence_id *= 5
if sequence_id < 10:
frame_number = 'frame0000' + str(sequence_id) + '.jpg'
elif sequence_id < 100:
frame_number = 'frame000' + str(sequence_id) + '.jpg'
elif sequence_id < 1000:
frame_number = 'frame00' + str(sequence_id) + '.jpg'
elif sequence_id < 10000:
frame_number = 'frame0' + str(sequence_id) + '.jpg'
else:
frame_number = 'frame' + str(sequence_id) + '.jpg'
return frame_number
@staticmethod
def save_image_clusters(img_clstr, filename, root_folder):
img_clstr.save(root_folder + '/' + filename + '.jpg')
# # for a vertical stacking it is simple: use vstack
# imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))
# imgs_comb = Image.fromarray(imgs_comb)
# imgs_comb.save(filename+'.jpg')
@staticmethod
def display_image_clusters(img_clstr, filename, plt_id):
plt.figure(plt_id)
plt.title(filename)
plt.xticks([])
plt.yticks([])
plt.imshow(img_clstr)
if __name__ == "__main__":
image_files_root_folder = 'E:\Projects\scene_analyzer\\raw_data\\frames/'.replace('\\', '/')
single_image_width = 480
single_image_height = 360
parent_sequence = 1
viewer = SceneViewer(image_files_root_folder, single_image_width, single_image_height)
child_clusters_raw = {
"Pathway-1": "13 23 24 25 26 27 30 33 35 36 38 39 42 49 52 53 54 55 56 57 58 59 60 61 62 63 64 66 67 68 69 72 73 74 76 79 80 87 90 91 110 112 113 130 132 133 134 135 138 139 140 141 142 161",
"Pathway-2": "17 18 19 20 22 41 46 47 65 70 71 75 128 146 147 148 149 150 151 152 153 154 155 156 157 158 159 162 165 167 169 172 174",
"Pathway-3": "77 78 81 82 83 85 86 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 111 114 115 166 176",
"Pathway-4": "21 28 29 31 32 34 37 40 43 44 45 48 50 51 84 88 89 125 126 127 129 131 136 137 143 144 145 160 163 164 168",
"Pathway-5": "1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 116 117 118 119 171 178 184 192",
"Pathway-6": "120 121 122 123 124 170 173 175 177 179 180 181 182 183 185 186 187 188 189 190 191",
"Pathway-7": "",
"Pathway-8": "",
"Pathway-9": "",
"Pathway-10": ""
}
viewer.view(child_clusters_raw)
| 39.066079
| 199
| 0.628157
| 2,741
| 17,736
| 3.662897
| 0.122948
| 0.106972
| 0.071713
| 0.080677
| 0.812948
| 0.795817
| 0.774701
| 0.761355
| 0.754183
| 0.745817
| 0
| 0.069564
| 0.272158
| 17,736
| 453
| 200
| 39.152318
| 0.708188
| 0.051026
| 0
| 0.649231
| 0
| 0.018462
| 0.061507
| 0.008863
| 0
| 0
| 0
| 0
| 0
| 1
| 0.061538
| false
| 0
| 0.012308
| 0
| 0.126154
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
25a7354472da0203b667fda69378ed2c03101ab8
| 91
|
py
|
Python
|
postblog/cli.py
|
machineandme/postblog
|
5d9b547916eece4eb03438d709cdbb3c55562376
|
[
"MIT"
] | 1
|
2019-12-29T00:12:22.000Z
|
2019-12-29T00:12:22.000Z
|
postblog/cli.py
|
machineandme/postblog
|
5d9b547916eece4eb03438d709cdbb3c55562376
|
[
"MIT"
] | null | null | null |
postblog/cli.py
|
machineandme/postblog
|
5d9b547916eece4eb03438d709cdbb3c55562376
|
[
"MIT"
] | null | null | null |
from .interface import Interface
from fire import Fire
def main():
Fire(Interface())
| 13
| 32
| 0.725275
| 12
| 91
| 5.5
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.186813
| 91
| 6
| 33
| 15.166667
| 0.891892
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
25fb96401e89cc827fe926947928dd959ec34e48
| 45
|
py
|
Python
|
execquine.py
|
hanss314/random-stuff
|
606c0393292e1ff08c99cdace16fecdcd7a2203b
|
[
"MIT"
] | null | null | null |
execquine.py
|
hanss314/random-stuff
|
606c0393292e1ff08c99cdace16fecdcd7a2203b
|
[
"MIT"
] | null | null | null |
execquine.py
|
hanss314/random-stuff
|
606c0393292e1ff08c99cdace16fecdcd7a2203b
|
[
"MIT"
] | null | null | null |
x="print('x='+repr(x)+'\\nexec(x)')"
exec(x)
| 15
| 36
| 0.511111
| 9
| 45
| 2.555556
| 0.555556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.044444
| 45
| 2
| 37
| 22.5
| 0.534884
| 0
| 0
| 0
| 0
| 0
| 0.711111
| 0.711111
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
25fce85b7730944fa39d7dcadd13c6b978a7b947
| 194
|
py
|
Python
|
Server/Python/src/dbs/dao/MySQL/DatasetRun/Insert.py
|
vkuznet/DBS
|
14df8bbe8ee8f874fe423399b18afef911fe78c7
|
[
"Apache-2.0"
] | 8
|
2015-08-14T04:01:32.000Z
|
2021-06-03T00:56:42.000Z
|
Server/Python/src/dbs/dao/MySQL/DatasetRun/Insert.py
|
yuyiguo/DBS
|
14df8bbe8ee8f874fe423399b18afef911fe78c7
|
[
"Apache-2.0"
] | 162
|
2015-01-07T21:34:47.000Z
|
2021-10-13T09:42:41.000Z
|
Server/Python/src/dbs/dao/MySQL/DatasetRun/Insert.py
|
yuyiguo/DBS
|
14df8bbe8ee8f874fe423399b18afef911fe78c7
|
[
"Apache-2.0"
] | 16
|
2015-01-22T15:27:29.000Z
|
2021-04-28T09:23:28.000Z
|
#!/usr/bin/env python
""" DAO Object for DatasetRuns table """
from dbs.dao.Oracle.DatasetRun.Insert import Insert as OraDatasetRunInsert
class Insert(OraDatasetRunInsert):
pass
| 21.555556
| 74
| 0.731959
| 23
| 194
| 6.173913
| 0.826087
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175258
| 194
| 8
| 75
| 24.25
| 0.8875
| 0.278351
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
d36789f1c768d32f4cff3cc88138509367858e5a
| 42
|
py
|
Python
|
clygon/__init__.py
|
KrishayR/clygon
|
f6a2b1cfe9a423820392668bc05623deb0ab1ab7
|
[
"MIT"
] | 1
|
2022-03-17T21:57:12.000Z
|
2022-03-17T21:57:12.000Z
|
clygon/__init__.py
|
KrishayR/clygon
|
f6a2b1cfe9a423820392668bc05623deb0ab1ab7
|
[
"MIT"
] | null | null | null |
clygon/__init__.py
|
KrishayR/clygon
|
f6a2b1cfe9a423820392668bc05623deb0ab1ab7
|
[
"MIT"
] | null | null | null |
from clygon.shapes import Polygon, Circle
| 21
| 41
| 0.833333
| 6
| 42
| 5.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119048
| 42
| 1
| 42
| 42
| 0.945946
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d372470d9e8a5bc36ece703efe5e0f650f7746d8
| 121
|
py
|
Python
|
Logic/Logging/LoggerInterface.py
|
TahaNKhan/DeviceDetector
|
46402510c40944a68d7e1b8ebcb7962da7609dbd
|
[
"MIT"
] | null | null | null |
Logic/Logging/LoggerInterface.py
|
TahaNKhan/DeviceDetector
|
46402510c40944a68d7e1b8ebcb7962da7609dbd
|
[
"MIT"
] | null | null | null |
Logic/Logging/LoggerInterface.py
|
TahaNKhan/DeviceDetector
|
46402510c40944a68d7e1b8ebcb7962da7609dbd
|
[
"MIT"
] | null | null | null |
class LoggerInterface:
def log(self, to_log: str) -> None:
pass
def publish(self) -> None:
pass
| 17.285714
| 39
| 0.570248
| 15
| 121
| 4.533333
| 0.666667
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.322314
| 121
| 6
| 40
| 20.166667
| 0.829268
| 0
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0.4
| 0
| 0
| 0.6
| 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
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
d3969d8f20a941ec07dcffe96b016615c3748abf
| 286
|
py
|
Python
|
slam_recognition/util/attractor/__init__.py
|
SimLeek/pySILEnT
|
feec2d1fb654d7c8dc25f610916f4e9b202a1092
|
[
"Apache-2.0",
"MIT"
] | 5
|
2018-11-18T17:35:59.000Z
|
2019-02-13T20:25:58.000Z
|
slam_recognition/util/attractor/__init__.py
|
SimLeek/slam_recognition
|
feec2d1fb654d7c8dc25f610916f4e9b202a1092
|
[
"Apache-2.0",
"MIT"
] | 12
|
2018-10-31T01:57:55.000Z
|
2019-02-07T05:49:36.000Z
|
slam_recognition/util/attractor/__init__.py
|
SimLeek/pySILEnT
|
feec2d1fb654d7c8dc25f610916f4e9b202a1092
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
from .log_attractor_function import log_attractor_function
from .piecewise_attractor_function import piecewise_attractor_function
from .euclidian_attractor_function import euclidian_attractor_function_generator
from .linear_attractor_function import linear_attractor_function_generator
| 57.2
| 80
| 0.93007
| 34
| 286
| 7.294118
| 0.264706
| 0.548387
| 0.370968
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.055944
| 286
| 4
| 81
| 71.5
| 0.918519
| 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
| 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
| 6
|
6ca6ad9054b568b254f6c5f63c4bcc1591f0cf26
| 37
|
py
|
Python
|
Python/long awaited/1.py
|
domreddy/learning
|
692c37367dc2463c9f154cca6236df7d36b6a6f8
|
[
"MIT"
] | null | null | null |
Python/long awaited/1.py
|
domreddy/learning
|
692c37367dc2463c9f154cca6236df7d36b6a6f8
|
[
"MIT"
] | null | null | null |
Python/long awaited/1.py
|
domreddy/learning
|
692c37367dc2463c9f154cca6236df7d36b6a6f8
|
[
"MIT"
] | null | null | null |
print('lets start with this already')
| 37
| 37
| 0.783784
| 6
| 37
| 4.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 37
| 1
| 37
| 37
| 0.878788
| 0
| 0
| 0
| 0
| 0
| 0.736842
| 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
| 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
| 6
|
6cc2ff1e5de93abcdc0353fe0889d895a5509d82
| 18
|
py
|
Python
|
pmatrix/__init__.py
|
hopkira/prioritymatrix-python
|
e42ec144f6e11dfbdc75c08de35079056123c0a7
|
[
"Apache-2.0"
] | 1
|
2021-01-18T19:45:48.000Z
|
2021-01-18T19:45:48.000Z
|
pmatrix/__init__.py
|
hopkira/prioritymatrix-python
|
e42ec144f6e11dfbdc75c08de35079056123c0a7
|
[
"Apache-2.0"
] | 1
|
2020-12-10T00:43:11.000Z
|
2020-12-10T03:02:13.000Z
|
pmatrix/__init__.py
|
hopkira/prioritymatrix-python
|
e42ec144f6e11dfbdc75c08de35079056123c0a7
|
[
"Apache-2.0"
] | 3
|
2016-04-19T19:24:24.000Z
|
2022-03-04T11:54:00.000Z
|
from pm import PM
| 9
| 17
| 0.777778
| 4
| 18
| 3.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 18
| 1
| 18
| 18
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
6cc3770c456cd47f458ca6264a23c709ae0e1aa8
| 214
|
py
|
Python
|
iteration_protocols.py
|
mammykins/pyfund
|
d7de8ae5be7a376dc4daca990f6f55c7e5f6d0be
|
[
"MIT"
] | null | null | null |
iteration_protocols.py
|
mammykins/pyfund
|
d7de8ae5be7a376dc4daca990f6f55c7e5f6d0be
|
[
"MIT"
] | null | null | null |
iteration_protocols.py
|
mammykins/pyfund
|
d7de8ae5be7a376dc4daca990f6f55c7e5f6d0be
|
[
"MIT"
] | null | null | null |
iterable = ['spring', 'summer', 'autumn', 'winter']
print(iterable)
iterator = iter(iterable)
print(next(iterator))
print(next(iterator))
print(next(iterator))
print(next(iterator))
print(next(iterator))
| 21.4
| 52
| 0.700935
| 25
| 214
| 6
| 0.36
| 0.3
| 0.566667
| 0.586667
| 0.566667
| 0.566667
| 0.566667
| 0.566667
| 0.566667
| 0.566667
| 0
| 0
| 0.116822
| 214
| 9
| 53
| 23.777778
| 0.793651
| 0
| 0
| 0.625
| 0
| 0
| 0.117073
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.75
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
9f0be19488b74f7f53b38901e00ff48cf87a6ef0
| 51
|
py
|
Python
|
txt2txtgen/parse/__init__.py
|
kristianwoodsend/txt2txtgen
|
711274db112821cb9f0e37885d8392e8e44a50e5
|
[
"MIT"
] | 1
|
2018-11-03T08:04:21.000Z
|
2018-11-03T08:04:21.000Z
|
txt2txtgen/parse/__init__.py
|
kristianwoodsend/txt2txtgen
|
711274db112821cb9f0e37885d8392e8e44a50e5
|
[
"MIT"
] | null | null | null |
txt2txtgen/parse/__init__.py
|
kristianwoodsend/txt2txtgen
|
711274db112821cb9f0e37885d8392e8e44a50e5
|
[
"MIT"
] | null | null | null |
import StanfordCoreNLP
import PhraseDependencyTree
| 17
| 27
| 0.921569
| 4
| 51
| 11.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078431
| 51
| 2
| 28
| 25.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9f1ff8268d2c598520fc1239b5d51ce064eb37aa
| 65
|
py
|
Python
|
without-attrs/conanfile.py
|
grisumbras/conan-promote
|
f1d746bbd134f64af0d9f6b4cf6d579c99980a1f
|
[
"BSL-1.0"
] | 1
|
2020-02-02T16:39:43.000Z
|
2020-02-02T16:39:43.000Z
|
without-attrs/conanfile.py
|
grisumbras/conan-promote
|
f1d746bbd134f64af0d9f6b4cf6d579c99980a1f
|
[
"BSL-1.0"
] | null | null | null |
without-attrs/conanfile.py
|
grisumbras/conan-promote
|
f1d746bbd134f64af0d9f6b4cf6d579c99980a1f
|
[
"BSL-1.0"
] | null | null | null |
from conans import ConanFile
class MyConan(ConanFile):
pass
| 13
| 28
| 0.769231
| 8
| 65
| 6.25
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.184615
| 65
| 4
| 29
| 16.25
| 0.943396
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
9f2c41832424ecd8ef5b935f3fc0aede8a7e08e0
| 44,920
|
py
|
Python
|
autoencoders/tower.py
|
pouyaAB/Accept_Synthetic_Objects_as_Real
|
127172fbfbac0af01184eff8cabba3d6afd2ac0b
|
[
"MIT"
] | 6
|
2019-11-11T23:25:56.000Z
|
2020-11-02T03:30:52.000Z
|
autoencoders/tower.py
|
pouyaAB/Accept_Synthetic_Objects_as_Real
|
127172fbfbac0af01184eff8cabba3d6afd2ac0b
|
[
"MIT"
] | null | null | null |
autoencoders/tower.py
|
pouyaAB/Accept_Synthetic_Objects_as_Real
|
127172fbfbac0af01184eff8cabba3d6afd2ac0b
|
[
"MIT"
] | 1
|
2022-02-17T11:16:32.000Z
|
2022-02-17T11:16:32.000Z
|
import math
import numpy as np
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import cuda, Variable
from chainer.initializers import Normal
class text_encoder(chainer.Chain):
def __init__(self, latent_size=64, num_objects=10, num_descriptions=10):
super(text_encoder, self).__init__(
l1=L.Linear(num_objects + num_descriptions, 4 * latent_size, initialW=Normal(0.02)),
norm1 = L.BatchNormalization(4 * latent_size),
l2=L.Linear(4 * latent_size, 4 * latent_size, initialW=Normal(0.02)),
norm2 = L.BatchNormalization(4 * latent_size),
mean=L.Linear(4 * latent_size, latent_size, initialW=Normal(0.02)),
var=L.Linear(4 * latent_size, latent_size, initialW=Normal(0.02)),
)
def __call__(self, objects_one_hot, descs_one_hot, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
xp = cuda.get_array_module(objects_one_hot.data)
h1 = F.leaky_relu(self.norm1(self.l1(F.concat((objects_one_hot, descs_one_hot), axis=-1))))
h2 = F.leaky_relu(self.norm2(self.l2(h1)))
mean = self.mean(h2)
var = F.tanh(self.var(h2))
rand = xp.random.normal(0, 1, var.data.shape).astype(np.float32)
z = mean + F.exp(var) * Variable(rand)
return z, mean, var
class text_generator(chainer.Chain):
def __init__(self, latent_size=64, num_objects=10, num_descriptions=10):
super(text_generator, self).__init__(
l3=L.Linear(latent_size, 4 * latent_size, initialW=Normal(0.02)),
norm3 = L.BatchNormalization(4 * latent_size),
l2=L.Linear(4 * latent_size, 4 * latent_size, initialW=Normal(0.02)),
norm2 = L.BatchNormalization(4 * latent_size),
l1_0=L.Linear(4 * latent_size, num_objects, initialW=Normal(0.02)),
l1_1=L.Linear(4 * latent_size, num_descriptions, initialW=Normal(0.02)),
)
def __call__(self, latent, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
xp = cuda.get_array_module(latent.data)
h3 = F.leaky_relu(self.norm3(self.l3(latent)))
h2 = F.leaky_relu(self.norm2(self.l2(h3)))
return self.l1_0(h2), self.l1_1(h2)
class Encoder_double_z(chainer.Chain):
"""
An implementation of the "Tower" model of the VAE encoder from the paper
'Neural scene representation and rendering, by S. M. Ali Eslami and others at DeepMind.
The exact numbers of the layers and were changed.
It is basically a cVAE with multi-dimensional conditions.
v - human level properties HLP, human classification ???? find a good name
This system takes as input an image and two one-hot vectors corresponding to factorized
features of the main object encoded in the image. For instance, if the image contains a
red sphere, the inputs will <image>,"red","round".
Intent: The hypothesis is that by providing HLPs during training and also during testing,
we get a better encoding _of the particular object_.
Validation: We can check the reconstruction error metric, but we can also check this
with visual inspection of the reconstructed version for different HLP inputs
"""
def __init__(self, density=1, size=64, latent_size=100, channel=3):
"""
density - a scaling factor for the number of channels in the convolutional layers. It is multiplied by at least
16,32,64 and 128 as we go deeper.
Use: using density=8 when training the VAE separately. using density=4 when training end to end
Intent: increase the number of features in the convolutional layers.
"""
assert (size % 16 == 0)
self.second_size = size // 4
initial_size = size // 16
super(Encoder_double_z, self).__init__(
dc1=L.Convolution2D(channel, int(16 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
dc2=L.Convolution2D(int(16 * density), int(32 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
dc1_=L.Convolution2D(int(16 * density), int(16 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
dc2_=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(32 * density)),
norm2_=L.BatchNormalization(int(32 * density)),
dc2_p=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(32 * density)),
dc3=L.Convolution2D(int(32 * density), int(64 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(64 * density)),
dc3_=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(64 * density)),
dc3_p=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(64 * density)),
dc4=L.Convolution2D(int(64 * density), int(128 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(128 * density)),
mean_robot=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
initialW=Normal(0.02)),
# var_robot=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
# initialW=Normal(0.02)),
mean_scene=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
initialW=Normal(0.02)),
# var_scene=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
# initialW=Normal(0.02)),
)
def __call__(self, x, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
xp = cuda.get_array_module(x.data)
h1 = F.leaky_relu(self.dc1(x))
h1_ = F.leaky_relu(self.dc1_(h1))
h2 = F.leaky_relu(self.norm2(self.dc2(h1_)))
h2_ = F.leaky_relu(self.norm2_(self.dc2_(h2)))
h2_p = F.leaky_relu(self.norm2_p(self.dc2_p(h2_)))
h2_ = h2_ + h2_p
h3 = F.leaky_relu(self.norm3(self.dc3(h2_)))
h3_ = F.leaky_relu(self.norm3_(self.dc3_(h3)))
h3_p = F.leaky_relu(self.norm3_p(self.dc3_p(h3)))
h3_ = h3_ + h3_p
h4 = F.leaky_relu(self.norm4(self.dc4(h3_)))
mean_robot = self.mean_robot(h4)
# var_robot = F.tanh(self.var_robot(h4))
# rand = xp.random.normal(0, 1, var_robot.data.shape).astype(np.float32)
# z_robot = mean_robot + F.exp(var_robot) * Variable(rand)
mean_scene = self.mean_scene(h4)
# var_scene = F.tanh(self.var_scene(h4))
# rand = xp.random.normal(0, 1, var_scene.data.shape).astype(np.float32)
# z_scene = mean_scene + F.exp(var_scene) * Variable(rand)
return F.normalize(mean_robot, axis=1), F.normalize(mean_scene, axis=1)
class Encoder(chainer.Chain):
"""
An implementation of the "Tower" model of the VAE encoder from the paper
'Neural scene representation and rendering, by S. M. Ali Eslami and others at DeepMind.
The exact numbers of the layers and were changed.
It is basically a cVAE with multi-dimensional conditions.
v - human level properties HLP, human classification ???? find a good name
This system takes as input an image and two one-hot vectors corresponding to factorized
features of the main object encoded in the image. For instance, if the image contains a
red sphere, the inputs will <image>,"red","round".
Intent: The hypothesis is that by providing HLPs during training and also during testing,
we get a better encoding _of the particular object_.
Validation: We can check the reconstruction error metric, but we can also check this
with visual inspection of the reconstructed version for different HLP inputs
"""
def __init__(self, density=1, size=64, latent_size=100, channel=3):
"""
density - a scaling factor for the number of channels in the convolutional layers. It is multiplied by at least
16,32,64 and 128 as we go deeper.
Use: using density=8 when training the VAE separately. using density=4 when training end to end
Intent: increase the number of features in the convolutional layers.
"""
assert (size % 16 == 0)
self.second_size = size // 4
initial_size = size // 16
super(Encoder, self).__init__(
dc1=L.Convolution2D(channel, int(16 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
dc2=L.Convolution2D(int(16 * density), int(32 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
dc1_=L.Convolution2D(int(16 * density), int(16 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
dc2_=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(32 * density)),
norm2_=L.BatchNormalization(int(32 * density)),
dc2_p=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(32 * density)),
dc3=L.Convolution2D(int(32 * density), int(64 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(64 * density)),
dc3_=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(64 * density)),
dc3_p=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(64 * density)),
dc4=L.Convolution2D(int(64 * density), int(128 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(128 * density)),
mean=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
initialW=Normal(0.02)),
var=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
initialW=Normal(0.02)),
)
def __call__(self, x, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
xp = cuda.get_array_module(x.data)
h1 = F.leaky_relu(self.dc1(x))
h1_ = F.leaky_relu(self.dc1_(h1))
h2 = F.leaky_relu(self.norm2(self.dc2(h1_)))
h2_ = F.leaky_relu(self.norm2_(self.dc2_(h2)))
h2_p = F.leaky_relu(self.norm2_p(self.dc2_p(h2_)))
h2_ = h2_ + h2_p
h3 = F.leaky_relu(self.norm3(self.dc3(h2_)))
h3_ = F.leaky_relu(self.norm3_(self.dc3_(h3)))
h3_p = F.leaky_relu(self.norm3_p(self.dc3_p(h3)))
h3_ = h3_ + h3_p
h4 = F.leaky_relu(self.norm4(self.dc4(h3_)))
mean = self.mean(h4)
var = F.tanh(self.var(h4))
rand = xp.random.normal(0, 1, var.data.shape).astype(np.float32)
z = mean + F.exp(var) * Variable(rand)
return z, mean, var, h3_p
class Encoder_text_tower(chainer.Chain):
"""
An implementation of the "Tower" model of the VAE encoder from the paper
'Neural scene representation and rendering, by S. M. Ali Eslami and others at DeepMind.
The exact numbers of the layers and were changed.
It is basically a cVAE with multi-dimensional conditions.
v - human level properties HLP, human classification ???? find a good name
This system takes as input an image and two one-hot vectors corresponding to factorized
features of the main object encoded in the image. For instance, if the image contains a
red sphere, the inputs will <image>,"red","round".
Intent: The hypothesis is that by providing HLPs during training and also during testing,
we get a better encoding _of the particular object_.
Validation: We can check the reconstruction error metric, but we can also check this
with visual inspection of the reconstructed version for different HLP inputs
"""
def __init__(self, density=1, size=64, latent_size=100, channel=3, hidden_dim=100, num_objects=10, num_descriptions=10):
"""
density - a scaling factor for the number of channels in the convolutional layers. It is multiplied by at least
16,32,64 and 128 as we go deeper.
Use: using density=8 when training the VAE separately. using density=4 when training end to end
Intent: increase the number of features in the convolutional layers.
"""
assert (size % 16 == 0)
self.second_size = size // 4
initial_size = size // 16
super(Encoder_text_tower, self).__init__(
dc1=L.Convolution2D(channel, int(16 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
dc2=L.Convolution2D(int(16 * density), int(32 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
dc1_=L.Convolution2D(int(16 * density), int(16 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
dc2_=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(32 * density)),
norm2_=L.BatchNormalization(int(32 * density)),
dc2_p=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(32 * density)),
dc3=L.Convolution2D(int(32 * density + 7), int(64 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(64 * density)),
dc3_=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(64 * density)),
dc3_p=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(64 * density)),
dc4=L.Convolution2D(int(64 * density), int(128 * density), 3, stride=2, pad=1,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(128 * density)),
toConv=L.Linear(num_objects + num_descriptions, self.second_size * self.second_size * 7, initialW=Normal(0.02)),
norm_toConv=L.BatchNormalization(7),
mean=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
initialW=Normal(0.02)),
var=L.Linear(initial_size * initial_size * int(128 * density), latent_size,
initialW=Normal(0.02)),
)
def __call__(self, x, objects_one_hot, descs_one_hot, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
xp = cuda.get_array_module(x.data)
# h0 = F.concat((x, objects, descs), axis=1)
h0 = self.toConv(F.concat((objects_one_hot, descs_one_hot), axis=-1))
h0 = F.reshape(h0, (h0.shape[0], 7, self.second_size, self.second_size))
h0 = F.leaky_relu(self.norm_toConv(h0))
h1 = F.leaky_relu(self.dc1(x))
h1_ = F.leaky_relu(self.dc1_(h1))
h2 = F.leaky_relu(self.norm2(self.dc2(h1_)))
h2_ = F.leaky_relu(self.norm2_(self.dc2_(h2)))
h2_p = F.leaky_relu(self.norm2_p(self.dc2_p(h2_)))
h2_ = h2_ + h2_p
h2_ = F.concat((h2_, h0), axis=1)
h3 = F.leaky_relu(self.norm3(self.dc3(h2_)))
h3_ = F.leaky_relu(self.norm3_(self.dc3_(h3)))
h3_p = F.leaky_relu(self.norm3_p(self.dc3_p(h3)))
h3_ = h3_ + h3_p
h4 = F.leaky_relu(self.norm4(self.dc4(h3_)))
mean = self.mean(h4)
var = F.tanh(self.var(h4))
rand = xp.random.normal(0, 1, var.data.shape).astype(np.float32)
z = mean + F.exp(var) * Variable(rand)
return z, mean, var, h4
class Generator_text(chainer.Chain):
"""
This implemention is very similar to the encoder_text_tower.
Convolution layers has been replaced with deconvolution layers.
This implemention receives a latent vector plus two and two one-hot vectors corresponding to factorized
features of the main object encoded in the image. For instance, if the image contains a
red sphere, the inputs will <image>,"red","round".
Intent: The hypothesis is that by providing HLPs during training and also during testing,
we get a better generative results _of the particular object_.
Validation: We can check the reconstruction error metric, but we can also check this
with visual inspection of the reconstructed version for different HLP inputs
"""
def __init__(self, density=1, size=64, latent_size=100, channel=3, num_objects=10, num_descriptions=10):
filter_size = 2
self.intermediate_size = size // 8
assert (size % 16 == 0)
initial_size = size // 16
super(Generator_text, self).__init__(
g0=L.Linear(num_objects + num_descriptions, self.intermediate_size * self.intermediate_size * 7, initialW=Normal(0.02)),
g1=L.Linear(latent_size, initial_size * initial_size * int(128 * density),
initialW=Normal(0.02)),
norm1=L.BatchNormalization(initial_size * initial_size * int(128 * density)),
g2=L.Deconvolution2D(int(128 * density), int(64 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(64 * density)),
g2_=L.Deconvolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_=L.BatchNormalization(int(64 * density)),
g2_p=L.Deconvolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(64 * density)),
g3=L.Deconvolution2D(int(64 * density + 7), int(32 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(32 * density)),
g3_=L.Deconvolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(32 * density)),
g3_p=L.Deconvolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(32 * density)),
g4=L.Deconvolution2D(int(32 * density), int(16 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(16 * density)),
g4_=L.Deconvolution2D(int(16 * density), int(16 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm4_=L.BatchNormalization(int(16 * density)),
g5=L.Deconvolution2D(int(16 * density), channel, filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
)
self.density = density
self.latent_size = latent_size
self.initial_size = initial_size
def __call__(self, z, objs, descs, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
h0 = self.g0(F.concat((objs, descs), axis=-1))
h0 = F.reshape(h0, (h0.shape[0], 7, self.intermediate_size, self.intermediate_size))
h1 = F.reshape(F.relu(self.norm1(self.g1(z))),
(z.data.shape[0], int(128 * self.density), self.initial_size, self.initial_size))
h2 = F.relu(self.norm2(self.g2(h1)))
h2_ = F.relu(self.norm2_(self.g2_(h2)))
h2_p = F.relu(self.norm2_p(self.g2_p(h2_)))
h2_ = h2_ + h2_p
h2_ = F.concat((h2_, h0), axis=1)
h3 = F.relu(self.norm3(self.g3(h2_)))
h3_ = F.relu(self.norm3_(self.g3_(h3)))
h3_p = F.relu(self.norm3_p(self.g3_p(h3_)))
h3_ = h3_ + h3_p
h4 = F.relu(self.norm4(self.g4(h3_)))
h4_ = F.relu(self.norm4_(self.g4_(h4)))
return F.tanh(self.g5(h4_))
class Generator_text_att(chainer.Chain):
"""
This implemention is very similar to the encoder_text_tower.
Convolution layers has been replaced with deconvolution layers.
This implemention receives a latent vector plus two and two one-hot vectors corresponding to factorized
features of the main object encoded in the image. For instance, if the image contains a
red sphere, the inputs will <image>,"red","round".
Intent: The hypothesis is that by providing HLPs during training and also during testing,
we get a better generative results _of the particular object_.
Validation: We can check the reconstruction error metric, but we can also check this
with visual inspection of the reconstructed version for different HLP inputs
"""
def __init__(self, density=1, size=64, latent_size=100, channel=3, num_objects=10, num_descriptions=10):
filter_size = 2
self.size = size
self.intermediate_size = size // 8
assert (size % 16 == 0)
self.att_size = 5
initial_size = size // 16
super(Generator_text_att, self).__init__(
FC0=L.Linear(num_objects + num_descriptions, 128, initialW=Normal(0.02), nobias=True),
FC1=L.Linear(128, 16, initialW=Normal(0.02), nobias=True),
FC2=L.Linear(16, 32, initialW=Normal(0.02), nobias=True),
FC00=L.Linear(64 * 8 * 8, 32 * self.att_size * self.att_size, initialW=Normal(0.02), nobias=True),
FC01=L.Linear(32 * self.att_size * self.att_size, 16 * self.att_size * self.att_size, initialW=Normal(0.02), nobias=True),
FC02=L.Linear(16 * self.att_size * self.att_size, 32 * self.att_size * self.att_size, initialW=Normal(0.02), nobias=True),
FC22=L.Linear(32 * self.att_size * self.att_size, 1 * self.att_size * self.att_size, initialW=Normal(0.02), nobias=True),
att_norm0=L.BatchNormalization(128),
att_norm1=L.BatchNormalization(16),
att_norm2=L.BatchNormalization(32),
att_norm00=L.BatchNormalization(32),
att_norm01=L.BatchNormalization(16),
att_norm02=L.BatchNormalization(32),
att_norm11=L.BatchNormalization(32),
g2_extra=L.Convolution2D(int(512), int(64), 3, stride=2, pad=1,
initialW=Normal(0.02)),
norm2_extra=L.BatchNormalization(int(64)),
g1=L.Linear(latent_size, initial_size * initial_size * int(128 * density),
initialW=Normal(0.02)),
norm1=L.BatchNormalization(initial_size * initial_size * int(128 * density)),
g2=L.Deconvolution2D(int(128 * density), int(64 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(64 * density)),
g2_=L.Deconvolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_=L.BatchNormalization(int(64 * density)),
g2_p=L.Deconvolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(64 * density)),
norm2_att=L.BatchNormalization(int(16 * density)),
g1_att=L.Linear(self.att_size * self.att_size * int(16 * density), self.att_size * self.att_size * 1, initialW=Normal(0.02)),
g3=L.Deconvolution2D(int(64 * density), int(32 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(32 * density)),
g3_=L.Deconvolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(32 * density)),
g3_p=L.Deconvolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(32 * density)),
g4=L.Deconvolution2D(int(32 * density), int(16 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(16 * density)),
g4_=L.Deconvolution2D(int(16 * density), int(16 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm4_=L.BatchNormalization(int(16 * density)),
g5=L.Deconvolution2D(int(16 * density), channel, filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
)
self.density = density
self.latent_size = latent_size
self.initial_size = initial_size
def __call__(self, z, objs, descs, features, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
h1 = F.relu(self.norm1(self.g1(z)))
h1 = F.reshape(h1, (z.data.shape[0], int(128 * self.density), self.initial_size, self.initial_size))
h2 = F.relu(self.norm2(self.g2(h1)))
h2_ = F.relu(self.norm2_(self.g2_(h2)))
h2_p = F.relu(self.norm2_p(self.g2_p(h2_)))
h2_ = h2_ + h2_p
h3 = F.relu(self.norm3(self.g3(h2_)))
h3_ = F.relu(self.norm3_(self.g3_(h3)))
h3_p = F.relu(self.norm3_p(self.g3_p(h3_)))
h3_ = h3_ + h3_p
h4 = F.relu(self.norm4(self.g4(h3_)))
h4_ = F.relu(self.norm4_(self.g4_(h4)))
#Attention Part
att_h0 = F.tanh(self.att_norm0(self.FC0(F.concat((objs, descs), axis=-1))))
att_h1 = F.tanh(self.att_norm1(self.FC1(att_h0)))
att_h2 = F.tanh(self.att_norm2(self.FC2(att_h1)))
att_h2 = F.tile(F.expand_dims(att_h2, axis=2), (1, 1, self.att_size * self.att_size))
# features = F.transpose(features, (0, 2, 3, 1))
# features = F.reshape(features, (-1, features.shape[1] * features.shape[2], features.shape[3]))
features = F.leaky_relu(self.norm2_extra(self.g2_extra(features)))
att_f0 = F.reshape(self.FC00(features), (-1, 32, self.att_size * self.att_size))
att_f0 = F.tanh(self.att_norm00(att_f0))
original_features = att_f0
att_f1 = F.reshape(self.FC01(att_f0), (-1, 16, self.att_size * self.att_size))
att_f1 = F.tanh(self.att_norm01(att_f1))
att_f2 = F.reshape(self.FC02(att_f1), (-1, 32, self.att_size * self.att_size))
att_f2 = F.tanh(self.att_norm02(att_f2))
att_ff = att_h2 + att_f2
att_ff = F.tanh(self.att_norm11(att_ff))
att_att = F.reshape(self.FC22(att_ff), (-1, self.att_size * self.att_size))
att_att = F.softmax(att_att, axis=1)
h1_att = F.reshape(att_att, (-1, 1, self.att_size, self.att_size))
pooled_features = F.einsum('ijk,ik -> ij', original_features, att_att)
# h0 = F.expand_dims(h0, axis=2)
# h0 = F.reshape(F.tile(h0, (1, 1, self.att_size * self.att_size)), (-1, h0.shape[1], self.att_size, self.att_size))
# h2_att = F.tanh(self.norm2_extra(self.g2_extra(features)))
# h2_att_orig = F.reshape(features, (-1, int(512), self.att_size * self.att_size))
# h2_comb = F.tanh(self.norm2_att(h2_att + h0))
# h2_comb = F.reshape(h2_comb, (-1, int(16 * self.density), self.att_size * self.att_size))
# h1_b = self.g1_att(h2_comb)
# h1_att = F.softmax(h1_b/16, axis=1)
# h1_att = F.reshape(h1_att, (-1, 1, self.att_size, self.att_size))
# # h1_att_fit = F.unpooling_2d(h1_att, 4, outsize=(16,16))
# h1_att_fit = F.reshape(h1_att, (-1, self.att_size * self.att_size))
# pooled_features = F.einsum('ijk,ik -> ij', h2_att_orig, h1_att_fit)
return F.tanh(self.g5(h4_)), F.resize_images(h1_att, (self.size, self.size)), h1_att, pooled_features
class Generator_latent_att(chainer.Chain):
"""
This implemention is very similar to the encoder_text_tower.
Convolution layers has been replaced with deconvolution layers.
This implemention receives a latent vector plus two and two one-hot vectors corresponding to factorized
features of the main object encoded in the image. For instance, if the image contains a
red sphere, the inputs will <image>,"red","round".
Intent: The hypothesis is that by providing HLPs during training and also during testing,
we get a better generative results _of the particular object_.
Validation: We can check the reconstruction error metric, but we can also check this
with visual inspection of the reconstructed version for different HLP inputs
"""
def __init__(self, density=1, size=64, latent_size=100, channel=3, num_objects=10, num_descriptions=10):
filter_size = 2
self.size = size
self.intermediate_size = size // 8
assert (size % 16 == 0)
self.att_size = 16
initial_size = size // 16
super(Generator_latent_att, self).__init__(
g0_att=L.Linear(latent_size, 512, initialW=Normal(0.02)),
g0=L.Linear(num_objects + num_descriptions, 64, initialW=Normal(0.02)),
norm0=L.BatchNormalization(64),
g00=L.Linear(latent_size, 64, initialW=Normal(0.02)),
norm00=L.BatchNormalization(64),
norm00_att=L.BatchNormalization(512),
norm000=L.BatchNormalization(64),
g1=L.Linear(latent_size, initial_size * initial_size * int(128 * density),
initialW=Normal(0.02)),
norm1=L.BatchNormalization(initial_size * initial_size * int(128 * density)),
g2=L.Deconvolution2D(int(128 * density), int(64 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(64 * density)),
g2_=L.Deconvolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_=L.BatchNormalization(int(64 * density)),
g2_p=L.Deconvolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(64 * density)),
g2_extra=L.Deconvolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_extra=L.BatchNormalization(int(64 * density)),
norm2_att=L.BatchNormalization(int(64 * density)),
g1_att=L.Linear(16 * 16 * int(64 * density), self.att_size * self.att_size * 1, initialW=Normal(0.02)),
g3=L.Deconvolution2D(int(64 * density), int(32 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(32 * density)),
g3_=L.Deconvolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(32 * density)),
g3_p=L.Deconvolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(32 * density)),
g4=L.Deconvolution2D(int(32 * density), int(16 * density), filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(16 * density)),
g4_=L.Deconvolution2D(int(16 * density), int(16 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm4_=L.BatchNormalization(int(16 * density)),
g5=L.Deconvolution2D(int(16 * density), channel, filter_size, stride=2, pad=0,
initialW=Normal(0.02)),
)
self.density = density
self.latent_size = latent_size
self.initial_size = initial_size
def __call__(self, z, objs, descs, features, train=True):
with chainer.using_config('train', train), chainer.using_config('enable_backprop', train):
h0 = F.tanh(self.norm0(self.g0(F.concat((objs, descs), axis=-1))))
h00 = F.tanh(self.norm00(self.g00(z)))
z_mask = F.softmax(self.norm000(h00 + h0))
z_pr = z * z_mask
h1 = F.relu(self.norm1(self.g1(z_pr)))
h1 = F.reshape(h1, (z.data.shape[0], int(128 * self.density), self.initial_size, self.initial_size))
h2 = F.relu(self.norm2(self.g2(h1)))
h2_ = F.relu(self.norm2_(self.g2_(h2)))
h2_p = F.relu(self.norm2_p(self.g2_p(h2_)))
h2_ = h2_ + h2_p
h3 = F.relu(self.norm3(self.g3(h2_)))
h3_ = F.relu(self.norm3_(self.g3_(h3)))
h3_p = F.relu(self.norm3_p(self.g3_p(h3_)))
h3_ = h3_ + h3_p
h4 = F.relu(self.norm4(self.g4(h3_)))
h4_ = F.relu(self.norm4_(self.g4_(h4)))
#Attention Part
h000 = F.tanh(self.norm00_att(self.g0_att(z_pr)))
h0000 = F.expand_dims(h000, axis=2)
h0000 = F.reshape(F.tile(h0000, (1, 1, 16 * 16)), (-1, h0000.shape[1], 16, 16))
h2_att = F.tanh(self.norm2_extra(self.g2_extra(h2_)))
h2_att_orig = F.reshape(h2_, (-1, int(64 * self.density), 16 * 16))
h2_comb = F.tanh(self.norm2_att(h2_att + h0000))
h2_comb = F.reshape(h2_comb, (-1, int(64 * self.density), 16 * 16))
h1_b = self.g1_att(h2_comb)
h1_att = F.softmax(h1_b, axis=1)
h1_att = F.reshape(h1_att, (-1, 1, self.att_size, self.att_size))
# h1_att_fit = F.unpooling_2d(h1_att, 4, outsize=(16,16))
h1_att_fit = F.reshape(h1_att, (-1, 16 * 16))
pooled_features = F.einsum('ijk,ik -> ij', h2_att_orig, h1_att_fit)
return F.tanh(self.g5(h4_)), F.resize_images(h1_att, (self.size, self.size)), h1_att, pooled_features
class Discriminator_texual(chainer.Chain):
"""
A discriminator for classifying the both the regular and masked images into their HLP groups.
The discrimiantor can receive both regular and masked images as input and it will try to
classify them based on the object of interest in the image. The discriminator will either
mark the image as fake or it will match it to one the shapes and colors.
The regular and masked images have will go through shared convolution and separate FF layers at
the end. The first 8 convoltion layers are shared and there is separated Fully-connected layers
for regular and masked images.
The discriminator will be used in an adversarial setup with the encoder and the generator.
The discriminator tries to mark the images generated by the generator as fake and classify
real images to their correct class.
Validation: One can check the classification error for real and fake images
"""
def __init__(self, density=1, size=64, channel=3, num_words=32, num_objects=10, num_descriptions=10):
assert (size % 16 == 0)
self.num_objects = num_objects
self.num_descriptions = num_descriptions
initial_size = size // 16
super(Discriminator_texual, self).__init__(
dc1=L.Convolution2D(channel, int(16 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
dc2=L.Convolution2D(int(16 * density), int(32 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(32 * density)),
# An extra layer to make the network deeper and not changing the feature sizes
dc2_=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_=L.BatchNormalization(int(32 * density)),
# "plus layer" another extra layer added to make it deeper with stride = 1 but this one has
# a skip connection between input and output
dc2_p=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(32 * density)),
dc3=L.Convolution2D(int(32 * density), int(64 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(64 * density)),
# An extra layer to make the network deeper and not changing the feature sizes
dc3_=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(64 * density)),
# "plus layer" another extra layer added to make it deeper with stride = 1 but this one has
# a skip connection between input and output
dc3_p=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(64 * density)),
dc4=L.Convolution2D(int(64 * density), int(128 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(128 * density)),
dc5=L.Linear(initial_size * initial_size * int(128 * density), num_objects, initialW=Normal(0.02)),
dc6=L.Linear(initial_size * initial_size * int(128 * density), num_descriptions, initialW=Normal(0.02)),
dc8=L.Linear(initial_size * initial_size * int(128 * density), num_objects, initialW=Normal(0.02)),
dc9=L.Linear(initial_size * initial_size * int(128 * density), num_descriptions, initialW=Normal(0.02)),
)
def __call__(self, x, att=True, train=True):
with chainer.using_config('train', train):
h1 = F.leaky_relu(self.dc1(x))
h2 = F.leaky_relu(self.norm2(self.dc2(h1)))
h2_ = F.leaky_relu(self.norm2_(self.dc2_(h2)))
h2_p = F.leaky_relu(self.norm2_p(self.dc2_p(h2_)))
h2_ = h2_ + h2_p
h3 = F.leaky_relu(self.norm3(self.dc3(h2_)))
h3_ = F.leaky_relu(self.norm3_(self.dc3_(h3)))
h3_p = F.leaky_relu(self.norm3_p(self.dc3_p(h3_)))
h3_ = h3_ + h3_p
h4 = F.leaky_relu(self.norm4(self.dc4(h3_)))
if att:
return self.dc5(h4), self.dc6(h4), h3
else:
return self.dc8(h4), self.dc9(h4), h3
class Discriminator(chainer.Chain):
"""
A discriminator for classifying the both the regular and masked images into their HLP groups.
The discrimiantor can receive both regular and masked images as input and it will try to
classify them based on the object of interest in the image. The discriminator will either
mark the image as fake or it will match it to one the shapes and colors.
The regular and masked images have will go through shared convolution and separate FF layers at
the end. The first 8 convoltion layers are shared and there is separated Fully-connected layers
for regular and masked images.
The discriminator will be used in an adversarial setup with the encoder and the generator.
The discriminator tries to mark the images generated by the generator as fake and classify
real images to their correct class.
Validation: One can check the classification error for real and fake images
"""
def __init__(self, density=1, size=64, channel=3):
assert (size % 16 == 0)
initial_size = size // 16
super(Discriminator, self).__init__(
dc1=L.Convolution2D(channel, int(16 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
dc2=L.Convolution2D(int(16 * density), int(32 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
norm2=L.BatchNormalization(int(32 * density)),
# An extra layer to make the network deeper and not changing the feature sizes
dc2_=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_=L.BatchNormalization(int(32 * density)),
# "plus layer" another extra layer added to make it deeper with stride = 1 but this one has
# a skip connection between input and output
dc2_p=L.Convolution2D(int(32 * density), int(32 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm2_p=L.BatchNormalization(int(32 * density)),
dc3=L.Convolution2D(int(32 * density), int(64 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
norm3=L.BatchNormalization(int(64 * density)),
# An extra layer to make the network deeper and not changing the feature sizes
dc3_=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_=L.BatchNormalization(int(64 * density)),
# "plus layer" another extra layer added to make it deeper with stride = 1 but this one has
# a skip connection between input and output
dc3_p=L.Convolution2D(int(64 * density), int(64 * density), 3, stride=1, pad=1,
initialW=Normal(0.02)),
norm3_p=L.BatchNormalization(int(64 * density)),
dc4=L.Convolution2D(int(64 * density), int(128 * density), 4, stride=2, pad=1,
initialW=Normal(0.02)),
norm4=L.BatchNormalization(int(128 * density)),
dc5=L.Linear(initial_size * initial_size * int(128 * density), 2, initialW=Normal(0.02)),
dc6=L.Linear(initial_size * initial_size * int(128 * density), 2, initialW=Normal(0.02)),
)
def __call__(self, x, att=True, train=True):
with chainer.using_config('train', train):
h1 = F.leaky_relu(self.dc1(x))
h2 = F.leaky_relu(self.norm2(self.dc2(h1)))
h2_ = F.leaky_relu(self.norm2_(self.dc2_(h2)))
h2_p = F.leaky_relu(self.norm2_p(self.dc2_p(h2_)))
h2_p = h2_ + h2_p
h3 = F.leaky_relu(self.norm3(self.dc3(h2_p)))
h3_ = F.leaky_relu(self.norm3_(self.dc3_(h3)))
h3_p = F.leaky_relu(self.norm3_p(self.dc3_p(h3_)))
h3_p = h3_ + h3_p
h4 = F.leaky_relu(self.norm4(self.dc4(h3_p)))
if att:
return self.dc5(h4), h3
else:
return self.dc6(h4), h3
| 57.812098
| 137
| 0.597796
| 6,189
| 44,920
| 4.190176
| 0.057037
| 0.031311
| 0.064204
| 0.072764
| 0.911696
| 0.904369
| 0.876489
| 0.858212
| 0.846836
| 0.832491
| 0
| 0.068697
| 0.282213
| 44,920
| 776
| 138
| 57.886598
| 0.735602
| 0.219101
| 0
| 0.698905
| 0
| 0
| 0.005628
| 0
| 0
| 0
| 0
| 0
| 0.014599
| 1
| 0.036496
| false
| 0
| 0.012774
| 0
| 0.089416
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
9f4fe0c4f3a5ac9ce68a404275e9cc1e94d8a418
| 19,124
|
py
|
Python
|
tests/kind_test.py
|
globocom/globomap-loader-napi
|
a3621e1396c14730d131315ae50ce2d7c1228765
|
[
"Apache-2.0"
] | 3
|
2017-08-31T13:35:49.000Z
|
2019-07-11T11:37:21.000Z
|
tests/kind_test.py
|
globocom/globomap-loader-napi
|
a3621e1396c14730d131315ae50ce2d7c1228765
|
[
"Apache-2.0"
] | 4
|
2017-09-06T22:34:49.000Z
|
2019-07-11T12:33:12.000Z
|
tests/kind_test.py
|
globocom/globomap-loader-napi
|
a3621e1396c14730d131315ae50ce2d7c1228765
|
[
"Apache-2.0"
] | 2
|
2017-09-06T20:46:33.000Z
|
2019-07-11T12:12:38.000Z
|
"""
Copyright 2018 Globo.com
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 unittest2
from mock import patch
from globomap_driver_napi.kind import Kind
from tests.util import open_json
class TestKind(unittest2.TestCase):
maxDiff = None
def tearDown(self):
patch.stopall()
def test_father_environment(self):
self._mock_environment()
data = self._queue_message(
'tests/json/messages/queue/environment.json')
data_ret = self._update_message(
'tests/json/messages/updates/father_environment.json')
for i in range(3):
kind = Kind()
res = kind.father_environment(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_environment(self):
self._mock_environment()
data = self._queue_message(
'tests/json/messages/queue/environment.json')
data_ret = self._update_message(
'tests/json/messages/updates/environment.json')
for i in range(3):
kind = Kind()
res = kind.environment(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_environment_vlan(self):
self._mock_vlan()
data = self._queue_message(
'tests/json/messages/queue/vlan.json')
data_ret = self._update_message(
'tests/json/messages/updates/environment_vlan.json')
for i in range(3):
kind = Kind()
res = kind.environment_vlan(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_vlan(self):
self._mock_vlan()
data = self._queue_message(
'tests/json/messages/queue/vlan.json')
data_ret = self._update_message(
'tests/json/messages/updates/vlan.json')
for i in range(3):
kind = Kind()
res = kind.vlan(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_vlan_networkv4(self):
self._mock_networkv4()
data = self._queue_message(
'tests/json/messages/queue/networkv4.json')
data_ret = self._update_message(
'tests/json/messages/updates/vlan_networkv4.json')
for i in range(3):
kind = Kind()
res = kind.vlan_network_v4(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_vlan_networkv6(self):
self._mock_networkv6()
data = self._queue_message(
'tests/json/messages/queue/networkv6.json')
data_ret = self._update_message(
'tests/json/messages/updates/vlan_networkv6.json')
for i in range(3):
kind = Kind()
res = kind.vlan_network_v6(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_networkv4(self):
self._mock_networkv4()
data = self._queue_message(
'tests/json/messages/queue/networkv4.json')
data_ret = self._update_message(
'tests/json/messages/updates/networkv4.json')
for i in range(3):
kind = Kind()
res = kind.network_v4(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_networkv6(self):
self._mock_networkv6()
data = self._queue_message(
'tests/json/messages/queue/networkv6.json')
data_ret = self._update_message(
'tests/json/messages/updates/networkv6.json')
for i in range(3):
kind = Kind()
res = kind.network_v6(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_ipv4_eqpt(self):
self._mock_ipv4()
data = self._queue_message(
'tests/json/messages/queue/ipv4_eqpt.json')
data_ret = self._update_message(
'tests/json/messages/updates/networkv4_comp_unit.json')
for i in range(2):
kind = Kind()
res = kind.network_v4_comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_ipv4_eqpt_acs(self):
self._mock_ipv4()
data = self._queue_message(
'tests/json/messages/queue/ipv4_eqpt_acs.json')
data_ret = self._update_message(
'tests/json/messages/updates/networkv4_comp_unit_acs.json')
for i in range(2):
kind = Kind()
res = kind.network_v4_comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_ipv6_eqpt(self):
self._mock_ipv6()
data = self._queue_message(
'tests/json/messages/queue/ipv6_eqpt.json')
data_ret = self._update_message(
'tests/json/messages/updates/networkv6_comp_unit.json')
for i in range(2):
kind = Kind()
res = kind.network_v6_comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_ipv6_eqpt_acs(self):
self._mock_ipv6()
data = self._queue_message(
'tests/json/messages/queue/ipv6_eqpt_acs.json')
data_ret = self._update_message(
'tests/json/messages/updates/networkv6_comp_unit_acs.json')
for i in range(2):
kind = Kind()
res = kind.network_v6_comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_comp_unit(self):
self._mock_equipment()
data = self._queue_message(
'tests/json/messages/queue/equipment.json')
data_ret = self._update_message(
'tests/json/messages/updates/comp_unit.json')
for i in range(3):
kind = Kind()
res = kind.comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_comp_unit_acs(self):
self._mock_equipment_acs()
data = self._queue_message(
'tests/json/messages/queue/equipment_acs.json')
data_ret = self._update_message(
'tests/json/messages/updates/comp_unit_acs.json')
for i in range(3):
kind = Kind()
res = kind.comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_vip(self):
self._mock_vip()
data = self._queue_message(
'tests/json/messages/queue/vip_request.json')
data_ret = self._update_message('tests/json/messages/updates/vip.json')
for i in range(3):
kind = Kind()
res = kind.vip(data[i])
self.assertDictEqual(res[0], data_ret[i])
# TODO
# def test_vip_port(self):
# self._mock_vip_by_portpool_id()
# data = self._queue_message(
# 'tests/json/messages/queue/vip_request_port.json')
# data_ret = self._update_message('tests/json/messages/updates/port.json')
# for i in range(3):
# kind = Kind()
# res = kind.port(data[i])
# self.assertDictEqual(res[0], data_ret[i])
def test_port(self):
self._mock_vip_by_portpool_id()
data = self._queue_message(
'tests/json/messages/queue/vip_request_port_pool.json')
data_ret = self._update_message(
'tests/json/messages/updates/port.json')
for i in range(3):
kind = Kind()
res = kind.port(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_pool(self):
self._mock_pool()
data = self._queue_message(
'tests/json/messages/queue/server_pool.json')
data_ret = self._update_message(
'tests/json/messages/updates/pool.json')
for i in range(3):
kind = Kind()
res = kind.pool(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_pool_comp_unit(self):
self._mock_pool_member_id()
data = self._queue_message(
'tests/json/messages/queue/server_pool_member.json')
data_ret = self._update_message(
'tests/json/messages/updates/pool_comp_unit.json')
for i in range(3):
kind = Kind()
res = kind.pool_comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
def test_pool_comp_unit_acs(self):
self._mock_pool_member_id_acs()
data = self._queue_message(
'tests/json/messages/queue/server_pool_member.json')
data_ret = self._update_message(
'tests/json/messages/updates/pool_comp_unit_acs.json')
for i in range(3):
kind = Kind()
res = kind.pool_comp_unit(data[i])
self.assertDictEqual(res[0], data_ret[i])
################
# Non Existent #
################
def test_vip_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_vip').start()
napi_mock.return_value = []
data = self._queue_message(
'tests/json/messages/queue/vip_request.json')
kind = Kind()
res = kind.vip(data[1])
self.assertEqual(res, [])
def test_port_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_vip_by_portpool_id').start()
napi_mock.return_value = []
data = self._queue_message(
'tests/json/messages/queue/vip_request_port_pool.json')
kind = Kind()
res = kind.port(data[1])
self.assertEqual(res, [])
def test_pool_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_pool').start()
napi_mock.return_value = []
data = self._queue_message(
'tests/json/messages/queue/server_pool.json')
kind = Kind()
res = kind.pool(data[1])
self.assertEqual(res, [])
def test_pool_comp_unit_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_pool_by_member_id').start()
napi_mock.return_value = []
data = self._queue_message(
'tests/json/messages/queue/server_pool_member.json')
kind = Kind()
res = kind.pool_comp_unit(data[1])
self.assertEqual(res, [])
def test_environment_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_environment').start()
napi_mock.return_value = []
data = self._queue_message(
'tests/json/messages/queue/environment.json')
kind = Kind()
res = kind.environment(data[1])
self.assertEqual(res, [])
def test_father_environment_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_environment').start()
napi_mock.return_value = []
data = self._queue_message(
'tests/json/messages/queue/environment.json')
kind = Kind()
res = kind.father_environment(data[1])
self.assertEqual(res, [])
def test_environment_vlan_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_vlan').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/vlan.json')
kind = Kind()
res = kind.environment_vlan(data[1])
self.assertEqual(res, [])
def test_vlan_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_vlan').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/vlan.json')
kind = Kind()
res = kind.vlan(data[1])
self.assertEqual(res, [])
def test_vlan_networkv4_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_network_ipv4_id').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/networkv4.json')
kind = Kind()
res = kind.vlan_network_v4(data[1])
self.assertEqual(res, [])
def test_vlan_networkv6_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_network_ipv6_id').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/networkv6.json')
kind = Kind()
res = kind.vlan_network_v6(data[1])
self.assertEqual(res, [])
def test_networkv4_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_network_ipv4_id').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/networkv4.json')
kind = Kind()
res = kind.network_v4(data[1])
self.assertEqual(res, [])
def test_networkv6_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_network_ipv6_id').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/networkv6.json')
kind = Kind()
res = kind.network_v6(data[1])
self.assertEqual(res, [])
def test_network_v4_comp_unit_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_ipv4_by_ip_equipment_id').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/ipv4_eqpt.json')
kind = Kind()
res = kind.network_v4_comp_unit(data[0])
self.assertEqual(res, [])
def test_network_v6_comp_unit_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_ipv6_by_ip_equipment_id').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/ipv6_eqpt.json')
kind = Kind()
res = kind.network_v6_comp_unit(data[0])
self.assertEqual(res, [])
def test_comp_unit_non_existent(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_equipment').start()
napi_mock.return_value = []
data = self._queue_message('tests/json/messages/queue/equipment.json')
kind = Kind()
res = kind.comp_unit(data[0])
self.assertEqual(res, [])
def test_network_v4_comp_unit_update(self):
kind = Kind()
res = kind.network_v4_comp_unit({
'action': 'Alterar',
'data': {'id_object': 1}
})
self.assertEqual(res, [])
def test_network_v6_comp_unit_update(self):
kind = Kind()
res = kind.network_v6_comp_unit({
'action': 'Alterar',
'data': {'id_object': 1}
})
self.assertEqual(res, [])
#########
# MOCKS #
#########
def _mock_environment(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_environment').start()
data = open_json('tests/json/messages/networkapi/get_environment.json')
napi_mock.return_value = data['environments'][0]
def _mock_vlan(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_vlan').start()
data = open_json('tests/json/messages/networkapi/get_vlan.json')
napi_mock.return_value = data['vlans'][0]
def _mock_networkv4(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_network_ipv4_id').start()
data = open_json('tests/json/messages/networkapi/get_networkv4.json')
napi_mock.return_value = data['networks'][0]
def _mock_networkv6(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_network_ipv6_id').start()
data = open_json('tests/json/messages/networkapi/get_networkv6.json')
napi_mock.return_value = data['networks'][0]
def _mock_ipv4(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_ipv4_by_ip_equipment_id').start()
data = open_json('tests/json/messages/networkapi/get_ipv4.json')
napi_mock.return_value = data['ips'][0]
def _mock_ipv6(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_ipv6_by_ip_equipment_id').start()
data = open_json('tests/json/messages/networkapi/get_ipv6.json')
napi_mock.return_value = data['ips'][0]
def _mock_equipment(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_equipment').start()
data = open_json('tests/json/messages/networkapi/get_equipment.json')
napi_mock.return_value = data['equipments'][0]
def _mock_equipment_acs(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_equipment').start()
data = open_json(
'tests/json/messages/networkapi/get_equipment_acs.json')
napi_mock.return_value = data['equipments'][0]
def _mock_vip(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_vip').start()
data = open_json('tests/json/messages/networkapi/get_vip.json')
napi_mock.return_value = data['vips'][0]
def _mock_pool_member_id(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_pool_by_member_id').start()
data = open_json('tests/json/messages/networkapi/get_pool.json')
napi_mock.return_value = data['server_pools'][0]
def _mock_pool_member_id_acs(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_pool_by_member_id').start()
data = open_json('tests/json/messages/networkapi/get_pool_acs.json')
napi_mock.return_value = data['server_pools'][0]
def _mock_vip_by_portpool_id(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_vip_by_portpool_id').start()
data = open_json('tests/json/messages/networkapi/get_vip.json')
napi_mock.return_value = data['vips'][0]
def _mock_pool(self):
napi_mock = patch(
'globomap_driver_napi.networkapi.NetworkAPI.get_pool').start()
data = open_json('tests/json/messages/networkapi/get_pool.json')
napi_mock.return_value = data['server_pools'][0]
def _queue_message(self, file_name):
data = open_json(file_name)
return data
def _update_message(self, file_name):
data = open_json(file_name)
return data
if __name__ == '__main__':
unittest2.main()
| 32.304054
| 93
| 0.624294
| 2,369
| 19,124
| 4.741241
| 0.061207
| 0.054487
| 0.10292
| 0.117521
| 0.919338
| 0.916578
| 0.899217
| 0.892628
| 0.871795
| 0.818198
| 0
| 0.011363
| 0.259099
| 19,124
| 591
| 94
| 32.358714
| 0.781354
| 0.048735
| 0
| 0.6691
| 0
| 0
| 0.261415
| 0.252405
| 0
| 0
| 0
| 0.001692
| 0.087591
| 1
| 0.126521
| false
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| 0.145985
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
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| 0
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| 0
| 0
|
0
| 6
|
9f84c0e25bbbf25e6a86d84f4037159f082ecdde
| 210
|
py
|
Python
|
smartlearner/direction_modifiers/__init__.py
|
MarcCote/smartlearner
|
0afdcd3b38dddfee16330b8324eb3b0e224f1c2b
|
[
"BSD-3-Clause"
] | null | null | null |
smartlearner/direction_modifiers/__init__.py
|
MarcCote/smartlearner
|
0afdcd3b38dddfee16330b8324eb3b0e224f1c2b
|
[
"BSD-3-Clause"
] | null | null | null |
smartlearner/direction_modifiers/__init__.py
|
MarcCote/smartlearner
|
0afdcd3b38dddfee16330b8324eb3b0e224f1c2b
|
[
"BSD-3-Clause"
] | null | null | null |
from .decreasing_learning_rate import DecreasingLearningRate
from .constant_learning_rate import ConstantLearningRate
from .gradient_noise import GradientNoise
from .direction_clipping import DirectionClipping
| 42
| 60
| 0.904762
| 22
| 210
| 8.363636
| 0.636364
| 0.130435
| 0.195652
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| 0.07619
| 210
| 4
| 61
| 52.5
| 0.948454
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| true
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9fc354ba6c85b0179e6daf0943e469e3e7d83bc0
| 254
|
py
|
Python
|
openmdp/scenarios/__init__.py
|
GAIPS/OpenMDP
|
8f690a014e592a8206ec8d4a89155390658cd5d8
|
[
"Apache-2.0"
] | null | null | null |
openmdp/scenarios/__init__.py
|
GAIPS/OpenMDP
|
8f690a014e592a8206ec8d4a89155390658cd5d8
|
[
"Apache-2.0"
] | null | null | null |
openmdp/scenarios/__init__.py
|
GAIPS/OpenMDP
|
8f690a014e592a8206ec8d4a89155390658cd5d8
|
[
"Apache-2.0"
] | null | null | null |
from openmdp.scenarios.CliffWalkMDP import CliffWalkMDP
from openmdp.scenarios.DuoNavigationMDP import DuoNavigationMDP
from openmdp.scenarios.DuoNavigationPOMDP import DuoNavigationPOMDP
from openmdp.scenarios.WindyGridWorldMDP import WindyGridWorldMDP
| 50.8
| 67
| 0.905512
| 24
| 254
| 9.583333
| 0.333333
| 0.191304
| 0.347826
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.062992
| 254
| 4
| 68
| 63.5
| 0.966387
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 1
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| 0
| null | 0
| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4c9325e9de4df7fb7677a38ce0366cc3865f645e
| 43
|
py
|
Python
|
maestro/backends/mongo/contrib/__init__.py
|
estudio89/maestro-python
|
331079cb3f0c10de2e19210cbade793544510f33
|
[
"BSD-3-Clause"
] | null | null | null |
maestro/backends/mongo/contrib/__init__.py
|
estudio89/maestro-python
|
331079cb3f0c10de2e19210cbade793544510f33
|
[
"BSD-3-Clause"
] | null | null | null |
maestro/backends/mongo/contrib/__init__.py
|
estudio89/maestro-python
|
331079cb3f0c10de2e19210cbade793544510f33
|
[
"BSD-3-Clause"
] | null | null | null |
from .factory import create_mongo_provider
| 21.5
| 42
| 0.883721
| 6
| 43
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 43
| 1
| 43
| 43
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4cec1b3c4eb7fdace1878ef486b86741f0b5eaed
| 93
|
py
|
Python
|
lib/core/__init__.py
|
JohnEskimSmith/jarm
|
fc2bcbd6fd5c6587522a97d583b3985ccdcde406
|
[
"BSD-3-Clause"
] | 2
|
2020-11-28T12:22:52.000Z
|
2020-12-17T09:10:09.000Z
|
lib/core/__init__.py
|
JohnEskimSmith/jarm
|
fc2bcbd6fd5c6587522a97d583b3985ccdcde406
|
[
"BSD-3-Clause"
] | null | null | null |
lib/core/__init__.py
|
JohnEskimSmith/jarm
|
fc2bcbd6fd5c6587522a97d583b3985ccdcde406
|
[
"BSD-3-Clause"
] | null | null | null |
from .stats import *
from .templates import *
from .configs import *
from .jarm_calc import *
| 23.25
| 24
| 0.752688
| 13
| 93
| 5.307692
| 0.538462
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16129
| 93
| 4
| 25
| 23.25
| 0.884615
| 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
| 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
| 6
|
4cf8a265f3dab4070d25b70d985201d4b26ee8b7
| 43
|
py
|
Python
|
ardy/core/invoke/__init__.py
|
avara1986/ardy
|
1942413f12e117b991278cada69f478474b9b94b
|
[
"Apache-2.0"
] | 3
|
2017-07-07T06:39:36.000Z
|
2017-11-29T23:09:37.000Z
|
ardy/core/invoke/__init__.py
|
avara1986/ardy
|
1942413f12e117b991278cada69f478474b9b94b
|
[
"Apache-2.0"
] | 3
|
2017-07-06T20:23:30.000Z
|
2018-11-05T21:15:48.000Z
|
ardy/core/invoke/__init__.py
|
avara1986/ardy
|
1942413f12e117b991278cada69f478474b9b94b
|
[
"Apache-2.0"
] | null | null | null |
from ardy.core.invoke.invoke import Invoke
| 21.5
| 42
| 0.837209
| 7
| 43
| 5.142857
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 43
| 1
| 43
| 43
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
e2388d1857d18d89e4c90be86639a6c4fed6b854
| 93
|
py
|
Python
|
MyLibrary/__init__.py
|
lachlangrose/python_template
|
9871a4ccd0e17fc87937a58e7753be54311eec9a
|
[
"MIT"
] | null | null | null |
MyLibrary/__init__.py
|
lachlangrose/python_template
|
9871a4ccd0e17fc87937a58e7753be54311eec9a
|
[
"MIT"
] | 16
|
2021-09-07T03:42:33.000Z
|
2021-12-06T04:58:43.000Z
|
MyLibrary/__init__.py
|
lachlangrose/python_template
|
9871a4ccd0e17fc87937a58e7753be54311eec9a
|
[
"MIT"
] | null | null | null |
import importlib.metadata
__version__ = importlib.metadata.version('python_template')
# bump
| 23.25
| 59
| 0.827957
| 10
| 93
| 7.2
| 0.7
| 0.472222
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075269
| 93
| 3
| 60
| 31
| 0.837209
| 0.043011
| 0
| 0
| 0
| 0
| 0.172414
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
e23b2843f8ee9cc97cad7dd37e90ad03adb2e0a5
| 12,620
|
py
|
Python
|
ParamGenerator/Phoenics/BayesianNeuralNetwork/pymc3_interface_backup.py
|
Tabor-Research-Group/ChemOS
|
50117f572e95e68dc4dccb624cedb28dbfc6e419
|
[
"Apache-2.0"
] | 72
|
2018-01-19T21:08:38.000Z
|
2022-03-26T08:44:49.000Z
|
ParamGenerator/Phoenics/BayesianNeuralNetwork/pymc3_interface_backup.py
|
Tabor-Research-Group/ChemOS
|
50117f572e95e68dc4dccb624cedb28dbfc6e419
|
[
"Apache-2.0"
] | 6
|
2018-12-14T02:44:51.000Z
|
2022-02-16T08:01:12.000Z
|
ParamGenerator/Phoenics/BayesianNeuralNetwork/pymc3_interface_backup.py
|
Tabor-Research-Group/ChemOS
|
50117f572e95e68dc4dccb624cedb28dbfc6e419
|
[
"Apache-2.0"
] | 16
|
2018-06-20T11:34:30.000Z
|
2022-01-07T17:51:22.000Z
|
#!/usr/bin/env python
__author__ = 'Florian Hase'
#========================================================================
import theano
import theano.tensor as T
import numpy as np
import pymc3 as pm
from Utils.utils import VarDictParser
from BayesianNeuralNetwork.distributions import DiscreteLaplace
#========================================================================
class Pymc3Network(VarDictParser):
def __init__(self, var_dicts, observed_params, observed_losses, batch_size, model_details):
VarDictParser.__init__(self, var_dicts)
self.observed_params = observed_params
self.observed_losses = observed_losses
self.num_obs = len(self.observed_losses)
self.batch_size = batch_size
self.model_details = model_details
for key, value in self.model_details.items():
setattr(self, str(key), value)
self._get_weights_and_bias_shapes()
self._process_network_inputs()
def __get_weights(self, index, shape, scale = None):
return pm.Normal('w%d' % index, self.weight_loc, self.weight_scale, shape = shape)
def __get_biases(self, index, shape, scale = None):
return pm.Normal('b%d' % index, self.weight_loc, self.weight_scale, shape = shape)
def weight(self, index):
return getattr(self, 'w%d' % index)
def bias(self, index):
return getattr(self, 'b%d' % index)
def _get_weight_and_bias_shapes(self):
self.weight_shapes = [[self.observed_params.shape[1], self.hidden_shape]]
self.bias_shapes = [[self.hidden_shape]]
for index in range(1, self.num_layers - 1):
self.weight_shapes.append([self.hidden_shape, self.hidden_shape])
self.bias_shapes.append([self.hidden_shape])
self.weight_shapes.append([self.hidden_shape, self.observed_params.shape[1]])
self.bias_shapes.append([self.observed_params.shape[1]])
def _process_network_inputs(self):
print('OBS', self.observed_params)
quit()
def _get_rescalings(self):
# compute rescaling factors for the different variables in the system
# these rescaling factors will eventually substitute the 1.2 and 0.1 in the model below
self.upper_rescalings = np.empty(self.total_size)
self.lower_rescalings = np.empty(self.total_size)
for var_p_index, var_p_name in enumerate(self.var_p_names):
high = self.var_p_highs[var_p_index]
low = self.var_p_lows[var_p_index]
if self.var_p_types[var_p_index] == 'float':
self.upper_rescalings[var_p_index] = high + 0.1 * (high - low)
self.lower_rescalings[var_p_index] = low - 0.1 * (high - low)
elif self.var_p_types[var_p_index] == 'integer':
self.upper_rescalings[var_p_index] = high# + np.ceil(0.1 * (high - low))
self.lower_rescalings[var_p_index] = low# - np.ceil(0.1 * (high - low))
# and don't forget to rescale the network input
self.network_input = 2. * (self.observed_params - self.lower_rescalings) / (self.upper_rescalings - self.lower_rescalings) - 1.
print('OBSERVED_PARAMS', self.observed_params)
print('NETWORK_INPUT', self.network_input)
quit()
#========================================================================
class Pymc3Network(VarDictParser):
def __init__(self, var_dicts, observed_params, observed_losses, batch_size, model_details):
VarDictParser.__init__(self, var_dicts)
self.observed_params = observed_params
self.observed_losses = observed_losses
self.num_obs = len(self.observed_losses)
self.batch_size = batch_size
self.model_details = model_details
for key, value in self.model_details.items():
setattr(self, str(key), value)
self._get_weight_and_bias_shapes()
def _get_weight_and_bias_shapes(self):
self.weight_shapes = [[self.observed_params.shape[1], self.hidden_shape]]
self.bias_shapes = [[self.hidden_shape]]
for index in range(1, self.num_layers - 1):
self.weight_shapes.append([self.hidden_shape, self.hidden_shape])
self.bias_shapes.append([self.hidden_shape])
self.weight_shapes.append([self.hidden_shape, self.observed_params.shape[1]])
self.bias_shapes.append([self.observed_params.shape[1]])
def __get_weights(self, index, shape, scale = None):
return pm.Normal('w%d' % index, self.weight_loc, self.weight_scale, shape = shape)
def __get_biases(self, index, shape, scale = None):
return pm.Normal('b%d' % index, self.weight_loc, self.weight_scale, shape = shape)
def weight(self, index):
return getattr(self, 'w%d' % index)
def bias(self, index):
return getattr(self, 'b%d' % index)
def _get_rescalings(self):
# compute rescaling factors for the different variables in the system
# these rescaling factors will eventually substitute the 1.2 and 0.1 in the model below
self.upper_rescalings = np.empty(self.total_size)
self.lower_rescalings = np.empty(self.total_size)
for var_p_index, var_p_name in enumerate(self.var_p_names):
high = self.var_p_highs[var_p_index]
low = self.var_p_lows[var_p_index]
if self.var_p_types[var_p_index] == 'float':
self.upper_rescalings[var_p_index] = high + 0.1 * (high - low)
self.lower_rescalings[var_p_index] = low - 0.1 * (high - low)
elif self.var_p_types[var_p_index] == 'integer':
self.upper_rescalings[var_p_index] = high# + np.ceil(0.1 * (high - low))
self.lower_rescalings[var_p_index] = low# - np.ceil(0.1 * (high - low))
# and don't forget to rescale the network input
self.network_input = 2. * (self.observed_params - self.lower_rescalings) / (self.upper_rescalings - self.lower_rescalings) - 1.
print('OBSERVED_PARAMS', self.observed_params)
print('NETWORK_INPUT', self.network_input)
quit()
def _get_categorical_observations(self):
# note that we might have multiple categorical variables with a different number of categories
cat_obs = []
for var_p_index, var_p_type in enumerate(self.var_p_types):
if var_p_type == 'categorical':
new_cat_obs = np.zeros((self.num_obs, len(self.var_p_options[var_p_index])))
for obs_index, obs in enumerate(self.observed_params[:, var_p_index]):
new_cat_obs[obs_index, int(obs)] += 1
cat_obs.append(new_cat_obs.copy())
self.cat_obs = cat_obs
def _create_model(self):
self._get_rescalings()
self._get_categorical_observations()
with pm.Model() as self.model:
# getting the location
for layer_index in range(self.num_layers):
setattr(self, 'w%d' % layer_index, self.__get_weights(layer_index, self.weight_shapes[layer_index]))
setattr(self, 'b%d' % layer_index, self.__get_biases(layer_index, self.bias_shapes[layer_index]))
if layer_index == 0:
fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(self.network_input, self.weight(layer_index)) + self.bias(layer_index)))
setattr(self, 'fc%d' % layer_index, fc)
elif 0 < layer_index < self.num_layers - 1:
fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index)))
setattr(self, 'fc%d' % layer_index, fc)
else:
# self.loc = pm.Deterministic('loc', (self.upper_rescalings - self.lower_rescalings) * pm.math.sigmoid(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index)) + self.lower_rescalings)
self._loc = pm.Deterministic('_loc', pm.math.sigmoid(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index)) )
# getting the precision / standard deviation / variance
self.tau_rescaling = np.zeros((self.num_obs, self.observed_params.shape[1]))
for obs_index in range(self.num_obs):
self.tau_rescaling[obs_index] += self.var_p_ranges
self.tau_rescaling = self.tau_rescaling**2
self.tau = pm.Gamma('tau', self.num_obs**2, 1., shape = (self.num_obs, self.observed_params.shape[1]))
# self.tau = pm.Gamma('tau', self.num_obs**1.5, 1., shape = (self.num_obs, self.observed_params.shape[1]))
self.tau = self.tau / self.tau_rescaling
# self.sd = pm.Deterministic('sd', 0.05 + 1. / pm.math.sqrt(self.tau))
self.scale = pm.Deterministic('scale', 1. / pm.math.sqrt(self.tau))
# learn the floats
self.loc = pm.Deterministic('loc', (self.upper_rescalings - self.lower_rescalings) * self._loc + self.lower_rescalings)
self.out_floats = pm.Normal('out_floats', self.loc[:, self._floats], tau = self.tau[:, self._floats], observed = self.observed_params[:, self._floats])
# learn the integers
self.out_ints = DiscreteLaplace('out_ints', loc = self.loc[:, self._ints], scale = self.scale[:, self._ints], observed = self.observed_params[:, self._ints])
# learn the categories
# alpha = self.loc * (self.loc * (1 - self.loc) * self.tau - 1)
# beta = (1 - self.loc) * (self.loc * (1 - self.loc) * self.tau - 1)
# self.alpha = pm.Deterministic('alpha', alpha)
# self.beta = pm.Deterministic('beta', beta)
# self.p = pm.Beta('p', alpha = self.alpha, beta = self.beta)
# print('ALL_PARAMS', self.observed_params)
# print('OBSERV', self.observed_params[:, self._cats])
self.probs = pm.Deterministic('a_dirich', self._loc * self.tau)
for cat_obs_index in range(len(self.cat_obs)):
# print(self._cats)
# print(self._cats[cat_obs_index])
# indices = np.array([self._cats[cat_obs_index]])
# print('INDICES', indices)
# print(self.probs[:, self._cats])
# cat_specific_indices =
out_cats = pm.Dirichlet('out_cats_%d' % cat_obs_index, a = self.probs[:, cat_specific_indices], observed = self.cat_obs[cat_obs_index])
setattr(self, 'out_cats_%d' % cat_obs_index, out_cats)
# self.out_cats = pm.Dirichlet('out_cats', a = self.probs[:, self._cats], observed = self.observed_params[:, self._cats])
# self.out_cats = pm.Normal('p', loc = self.loc[:, self._cats], tau = self.tau[:, self._cats], observed = self.observed_params[:, self._cats]) # perhaps constrain this to only positive numbers!
# self.out_cats = pm.Categorical('out_cats', p = self.p, observed = self.observed_params[:, self._cats])
def _create_model_old(self):
self._get_rescalings()
with pm.Model() as self.model:
# getting the location
for layer_index in range(self.num_layers):
setattr(self, 'w%d' % layer_index, self.__get_weights(layer_index, self.weight_shapes[layer_index]))
setattr(self, 'b%d' % layer_index, self.__get_biases(layer_index, self.bias_shapes[layer_index]))
if layer_index == 0:
fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(self.network_input, self.weight(layer_index)) + self.bias(layer_index)))
setattr(self, 'fc%d' % layer_index, fc)
elif 0 < layer_index < self.num_layers - 1:
fc = pm.Deterministic('fc%d' % layer_index, pm.math.tanh(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index)))
setattr(self, 'fc%d' % layer_index, fc)
else:
self.loc = pm.Deterministic('loc', (self.upper_rescalings - self.lower_rescalings) * pm.math.sigmoid(pm.math.dot(getattr(self, 'fc%d' % (layer_index - 1)), self.weight(layer_index)) + self.bias(layer_index)) + self.lower_rescalings)
# getting the standard deviation (or rather precision)
self.tau_rescaling = np.zeros((self.num_obs, self.observed_params.shape[1]))
for obs_index in range(self.num_obs):
self.tau_rescaling[obs_index] += self.domain_ranges
self.tau_rescaling = self.tau_rescaling**2
self.tau = pm.Gamma('tau', self.num_obs**2, 1., shape = (self.num_obs, self.observed_params.shape[1]))
self.tau = self.tau / self.tau_rescaling
# self.sd = pm.Deterministic('sd', 0.05 + 1. / pm.math.sqrt(self.tau))
self.scale = pm.Deterministic('scale', 1. / pm.math.sqrt(self.tau))
print(self.observed_params.shape)
print(self._floats)
print(self._integers)
quit()
# now that we got all locations and scales we can start getting the distributions
# floats are easy, as we can take loc and scale as they are
self.out = pm.Normal('out', self.loc, tau = self.tau, observed = self.observed_params)
# integers are a bit more tricky and require the following transformation for the beta binomial
alpha = ((n - mu) / sigma**2 - 1) / (n / mu - (n - mu) / sigma**2)
beta = (n / mu - 1) * alpha
self.alpha = pm.Deterministic('alpha', alpha)
self.beta = pm.Deterministic('beta', beta)
def _sample(self, num_epochs = None, num_draws = None):
if not num_epochs: num_epochs = self.num_epochs
if not num_draws: num_draws = self.num_draws
with self.model:
approx = pm.fit(n = num_epochs, obj_optimizer = pm.adam(learning_rate = self.learning_rate))
self.trace = approx.sample(draws = num_draws)
| 41.788079
| 238
| 0.699683
| 1,896
| 12,620
| 4.412975
| 0.111814
| 0.053783
| 0.060237
| 0.020198
| 0.765268
| 0.732999
| 0.713278
| 0.713278
| 0.700849
| 0.700849
| 0
| 0.007998
| 0.14794
| 12,620
| 301
| 239
| 41.92691
| 0.770111
| 0.221712
| 0
| 0.71345
| 0
| 0
| 0.028235
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0
| 0.035088
| 0.046784
| 0.204678
| 0.046784
| 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
| 0
| 0
|
0
| 6
|
e2a74dbe11a69ce1123a5f493329b594d41cc68e
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/poetry/console/commands/config.py
|
GiulianaPola/select_repeats
|
17a0d053d4f874e42cf654dd142168c2ec8fbd11
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/poetry/console/commands/config.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/poetry/console/commands/config.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/e0/3c/d7/56913e00137a9bdab4e9cb1d3d22b887617c9d3eea1861f0c7cf25618b
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.40625
| 0
| 96
| 1
| 96
| 96
| 0.489583
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2c5981046de830e6f0599bb7e5c886f933a60466
| 42
|
py
|
Python
|
pywick/modules/__init__.py
|
achaiah/pywick
|
9d663faf0c1660a9b8359a6472c164f658dfc8cb
|
[
"MIT"
] | 408
|
2019-05-16T16:12:41.000Z
|
2022-03-26T17:27:12.000Z
|
pywick/modules/__init__.py
|
ashishpatel26/pywick
|
1afffd1c21c2b188836d3599e802146182757bb5
|
[
"MIT"
] | 13
|
2019-05-17T05:47:06.000Z
|
2021-06-21T19:02:30.000Z
|
pywick/modules/__init__.py
|
ashishpatel26/pywick
|
1afffd1c21c2b188836d3599e802146182757bb5
|
[
"MIT"
] | 42
|
2019-05-16T19:57:12.000Z
|
2022-03-06T15:23:18.000Z
|
from .module_trainer import ModuleTrainer
| 21
| 41
| 0.880952
| 5
| 42
| 7.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 42
| 1
| 42
| 42
| 0.947368
| 0
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| 0
| 0
| 0
| 0
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| true
| 0
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| null | 0
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| 0
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| 0
| 1
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2c7d5c98364c1da8139d6a63051b9a8fe315209d
| 336
|
py
|
Python
|
insights/tests/test_canonical_facts.py
|
lhuett/insights-core
|
1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8
|
[
"Apache-2.0"
] | null | null | null |
insights/tests/test_canonical_facts.py
|
lhuett/insights-core
|
1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8
|
[
"Apache-2.0"
] | null | null | null |
insights/tests/test_canonical_facts.py
|
lhuett/insights-core
|
1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8
|
[
"Apache-2.0"
] | null | null | null |
from insights.util.canonical_facts import _filter_falsy
def test_identity():
assert {"foo": "bar"} == _filter_falsy({"foo": "bar"})
def test_drops_none():
assert {"foo": "bar"} == _filter_falsy({"foo": "bar", "baz": None})
def test_drops_empty_list():
assert {"foo": "bar"} == _filter_falsy({"foo": "bar", "baz": []})
| 24
| 71
| 0.627976
| 44
| 336
| 4.454545
| 0.431818
| 0.183673
| 0.183673
| 0.27551
| 0.47449
| 0.47449
| 0.47449
| 0.326531
| 0
| 0
| 0
| 0
| 0.14881
| 336
| 13
| 72
| 25.846154
| 0.685315
| 0
| 0
| 0
| 0
| 0
| 0.125
| 0
| 0
| 0
| 0
| 0
| 0.428571
| 1
| 0.428571
| true
| 0
| 0.142857
| 0
| 0.571429
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
2c91c5815282c1bd5bfe35b399a12b07525fe604
| 8,257
|
py
|
Python
|
hfnet/export_predictions.py
|
CaiYingFeng/hfnet
|
b430d0fb192fccbd42e6a19e06eeda5b805e2d1c
|
[
"MIT"
] | null | null | null |
hfnet/export_predictions.py
|
CaiYingFeng/hfnet
|
b430d0fb192fccbd42e6a19e06eeda5b805e2d1c
|
[
"MIT"
] | null | null | null |
hfnet/export_predictions.py
|
CaiYingFeng/hfnet
|
b430d0fb192fccbd42e6a19e06eeda5b805e2d1c
|
[
"MIT"
] | null | null | null |
# import numpy as np
# import argparse
# import yaml
# import logging
# from pathlib import Path
# from tqdm import tqdm
# from pprint import pformat
# logging.basicConfig(format='[%(asctime)s %(levelname)s] %(message)s',
# datefmt='%m/%d/%Y %H:%M:%S',
# level=logging.INFO)
# from hfnet.models import get_model # noqa: E402
# from hfnet.datasets import get_dataset # noqa: E402
# from hfnet.utils import tools # noqa: E402
# from hfnet.settings import EXPER_PATH, DATA_PATH # noqa: E402
# if __name__ == '__main__':
# parser = argparse.ArgumentParser()
# parser.add_argument('config', type=str)
# parser.add_argument('export_name', type=str)
# parser.add_argument('--keys', type=str, default='*')
# parser.add_argument('--exper_name', type=str)
# parser.add_argument('--as_dataset', action='store_true')
# args = parser.parse_args()
# export_name = args.export_name
# exper_name = args.exper_name
# with open(args.config, 'r') as f:
# config = yaml.load(f)
# keys = '*' if args.keys == '*' else args.keys.split(',')
# if args.as_dataset:
# base_dir = Path(DATA_PATH, export_name)
# else:
# base_dir = Path(EXPER_PATH, 'exports')
# base_dir = Path(base_dir, ((exper_name+'/') if exper_name else '') + export_name)
# base_dir.mkdir(parents=True, exist_ok=True)
# if exper_name:
# # Update only the model config (not the dataset)
# with open(Path(EXPER_PATH, exper_name, 'config.yaml'), 'r') as f:
# config['model'] = tools.dict_update(
# yaml.load(f)['model'], config.get('model', {}))
# checkpoint_path = Path(EXPER_PATH, exper_name)
# if config.get('weights', None):
# checkpoint_path = Path(checkpoint_path, config['weights'])
# else:
# if config.get('weights', None):
# checkpoint_path = Path(DATA_PATH, 'weights', config['weights'])
# else:
# checkpoint_path = None
# logging.info('No weights provided.')
# logging.info(f'Starting export with configuration:\n{pformat(config)}')
# with get_model(config['model']['name'])(
# data_shape={'image': [None, None, None, config['model']['image_channels']]},
# **config['model']) as net:
# if checkpoint_path is not None:
# net.load(str(checkpoint_path))
# dataset = get_dataset(config['data']['name'])(**config['data'])
# test_set = dataset.get_test_set()
# for data in tqdm(test_set):
# predictions = net.predict(data, keys=keys)
# predictions['input_shape'] = data['image'].shape
# name = data['name'].decode('utf-8')
# Path(base_dir, Path(name).parent).mkdir(parents=True, exist_ok=True)
# np.savez(Path(base_dir, '{}.npz'.format(name)), **predictions)
import sys
import os
sys.path.append("./")
import numpy as np
import argparse
import yaml
import logging
from pathlib import Path
from tqdm import tqdm
from pprint import pformat
import h5py
import os
logging.basicConfig(format='[%(asctime)s %(levelname)s] %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
from hfnet.models import get_model # noqa: E402
from hfnet.datasets import get_dataset # noqa: E402
from hfnet.utils import tools # noqa: E402
from hfnet.settings import EXPER_PATH, DATA_PATH # noqa: E402
if __name__ == '__main__':
# os.environ["CUDA_VISIBLE_DEVICES"] = "2"
parser = argparse.ArgumentParser()
parser.add_argument('method',type=str)
# parser.add_argument('config', type=str)
# parser.add_argument('export_name', type=str)
# parser.add_argument('--keys', type=str, default='*')
# parser.add_argument('--exper_name', type=str)
parser.add_argument('--as_dataset', action='store_true')
args = parser.parse_args()
#method='superpoint_queries'
method=args.method
if method=='hfnet_db':
export_name = 'aachen'
exper_name = 'hfnet'
myconfig='hfnet/configs/hfnet_export_aachen_db.yaml'
mykeys='keypoints,scores,local_descriptor_map,global_descriptor'
if method=='hfnet_queries':
export_name = 'aachen'
exper_name = 'hfnet'
myconfig='hfnet/configs/hfnet_export_aachen_queries.yaml'
mykeys='keypoints,scores,local_descriptor_map,global_descriptor'
if method=='superpoint_db':
export_name = 'superpoint/aachen'
exper_name=''
myconfig='hfnet/configs/superpoint_export_aachen_db.yaml'
mykeys='keypoints,scores,local_descriptor_map'
if method=='superpoint_queries':
export_name = 'superpoint/aachen'
exper_name=''
myconfig='hfnet/configs/superpoint_export_aachen_queries.yaml'
mykeys='keypoints,scores,local_descriptor_map'
if method=='netvlad':
export_name = 'netvlad/aachen'
exper_name=''
myconfig='hfnet/configs/netvlad_export_aachen.yaml'
mykeys='global_descriptor'
if method=='superpoint':
export_name = 'google_landmarks/superpoint_predictions'
exper_name=''
myconfig='hfnet/configs/superpoint_export_distill.yaml'
mykeys='local_descriptor_map,dense_scores'
# export_name = args.export_name
# exper_name = args.exper_name
with open(myconfig, 'r') as f:
config = yaml.load(f)
keys = '*' if mykeys == '*' else mykeys.split(',')
if args.as_dataset:
base_dir = Path(DATA_PATH, export_name)
else:
base_dir = Path(EXPER_PATH, 'exports')
base_dir = Path(base_dir, ((exper_name+'/') if exper_name else '') + export_name)
base_dir.mkdir(parents=True, exist_ok=True)
if exper_name:
# Update only the model config (not the dataset)
with open(Path(EXPER_PATH, exper_name, 'config.yaml'), 'r') as f:
print(Path(EXPER_PATH, exper_name, 'config.yaml'))
config['model'] = tools.dict_update(
yaml.load(f)['model'], config.get('model', {}))
print(config['model'])
checkpoint_path = Path(EXPER_PATH, exper_name)
print(checkpoint_path)
if config.get('weights', None):
checkpoint_path = Path(checkpoint_path, config['weights'])
print(checkpoint_path)
else:
if config.get('weights', None):
checkpoint_path = Path(DATA_PATH, 'weights', config['weights'])
else:
checkpoint_path = None
logging.info('No weights provided.')
logging.info(f'Starting export with configuration:\n{pformat(config)}')
with get_model(config['model']['name'])(
data_shape={'image': [None, None, None, config['model']['image_channels']]},
**config['model']) as net:
if checkpoint_path is not None:
net.load(str(checkpoint_path))
dataset = get_dataset(config['data']['name'])(**config['data'])
test_set = dataset.get_test_set()
# feature_file = h5py.File(Path(base_dir, 'all_1.h5'), 'a')#生成h5所需
for data in tqdm(test_set):
# print (data)
# break
# print(name)
predictions = net.predict(data, keys=keys)
predictions['input_shape'] = data['image'].shape
name = data['name'].decode('utf-8')#name: db/1606404423.00735318
Path(base_dir, Path(name).parent).mkdir(parents=True, exist_ok=True)
np.savez(Path(base_dir, '{}.npz'.format(name)), **predictions)
###########################
###生成pairs需要的h5文件####
###########################
# if(name.split('.',-1)[-1]=='jpg'):
# name+='.png'
# else:
# name+='.jpg'
# grp=feature_file.create_group(name)
# grp.create_dataset('global_descriptor',data=predictions['global_descriptor'])
# grp.create_dataset('input_shape',data=predictions['input_shape'])
# break
##########################
##########################
##########################
# feature_file.close()
| 37.876147
| 91
| 0.603004
| 975
| 8,257
| 4.90359
| 0.151795
| 0.043296
| 0.039113
| 0.023426
| 0.848986
| 0.838318
| 0.807362
| 0.791257
| 0.775152
| 0.761347
| 0
| 0.008471
| 0.242219
| 8,257
| 217
| 92
| 38.050691
| 0.755634
| 0.43563
| 0
| 0.242105
| 0
| 0
| 0.23023
| 0.125847
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.157895
| 0
| 0.157895
| 0.052632
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2cc3d0eb30bbd847b22f931349107c5d7600015f
| 92
|
py
|
Python
|
app/reviews/__init__.py
|
Tsmith18256/simploo-server
|
afecd96c87cf4f092d5e0373d1106babb3964461
|
[
"MIT"
] | null | null | null |
app/reviews/__init__.py
|
Tsmith18256/simploo-server
|
afecd96c87cf4f092d5e0373d1106babb3964461
|
[
"MIT"
] | null | null | null |
app/reviews/__init__.py
|
Tsmith18256/simploo-server
|
afecd96c87cf4f092d5e0373d1106babb3964461
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
reviews = Blueprint('reviews', __name__)
from . import routes
| 15.333333
| 40
| 0.771739
| 11
| 92
| 6.090909
| 0.636364
| 0.477612
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.152174
| 92
| 5
| 41
| 18.4
| 0.858974
| 0
| 0
| 0
| 0
| 0
| 0.076087
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0.666667
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
e2f7703dd74c33f4de25381d82fbc2ab20e780e2
| 9,958
|
py
|
Python
|
tests/amazon/aws/triggers/test_s3_triggers.py
|
astronomer/astronomer-providers
|
e19c656daab19f3e881f140495e2184c16eaafe0
|
[
"Apache-2.0"
] | 27
|
2022-03-02T04:49:54.000Z
|
2022-03-30T13:19:02.000Z
|
tests/amazon/aws/triggers/test_s3_triggers.py
|
astronomer/astronomer-providers
|
e19c656daab19f3e881f140495e2184c16eaafe0
|
[
"Apache-2.0"
] | 92
|
2022-03-02T08:01:31.000Z
|
2022-03-31T19:47:33.000Z
|
tests/amazon/aws/triggers/test_s3_triggers.py
|
astronomer/astronomer-providers
|
e19c656daab19f3e881f140495e2184c16eaafe0
|
[
"Apache-2.0"
] | 2
|
2022-03-07T17:39:41.000Z
|
2022-03-18T20:37:03.000Z
|
import asyncio
from datetime import datetime
from unittest import mock
import pytest
from airflow.triggers.base import TriggerEvent
from astronomer.providers.amazon.aws.triggers.s3 import (
S3KeySizeTrigger,
S3KeysUnchangedTrigger,
S3KeyTrigger,
S3PrefixTrigger,
)
def test_s3_key_trigger_serialization():
"""
Asserts that the TaskStateTrigger correctly serializes its arguments
and classpath.
"""
trigger = S3KeyTrigger(bucket_key="s3://test_bucket/file", bucket_name="test_bucket", wildcard_match=True)
classpath, kwargs = trigger.serialize()
assert classpath == "astronomer.providers.amazon.aws.triggers.s3.S3KeyTrigger"
assert kwargs == {
"bucket_name": "test_bucket",
"bucket_key": "s3://test_bucket/file",
"wildcard_match": True,
"aws_conn_id": "aws_default",
"hook_params": {},
}
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
async def test_s3_key_trigger_run(mock_client):
"""
Test if the task is run is in triggerr successfully.
"""
mock_client.return_value.check_key.return_value = True
trigger = S3KeyTrigger(bucket_key="s3://test_bucket/file", bucket_name="test_bucket")
with mock_client:
task = asyncio.create_task(trigger.run().__anext__())
await asyncio.sleep(0.5)
assert task.done() is True
asyncio.get_event_loop().stop()
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
async def test_s3_key_trigger_run_exception(mock_client):
"""Test if the task is run is in case of exception."""
mock_client.side_effect = Exception("Unable to locate credentials")
trigger = S3KeyTrigger(bucket_key="s3://test_bucket/file", bucket_name="test_bucket")
generator = trigger.run()
actual = await generator.asend(None)
assert (
TriggerEvent(
{
"message": "Unable to locate credentials",
"status": "error",
}
)
== actual
)
def test_s3_key_size_trigger_serialization():
"""
Asserts that the TaskStateTrigger correctly serializes its arguments
and classpath.
"""
trigger = S3KeySizeTrigger(
bucket_key="s3://test_bucket/file", bucket_name="test_bucket", wildcard_match=True
)
classpath, kwargs = trigger.serialize()
assert classpath == "astronomer.providers.amazon.aws.triggers.s3.S3KeySizeTrigger"
assert kwargs == {
"bucket_name": "test_bucket",
"bucket_key": "s3://test_bucket/file",
"wildcard_match": True,
"aws_conn_id": "aws_default",
"hook_params": {},
"check_fn_user": None,
}
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
async def test_s3_key_size_trigger_run(mock_client):
"""
Test if the task is run is in trigger successfully.
:return:
"""
mock_client.return_value.check_key.return_value = True
trigger = S3KeySizeTrigger(bucket_key="s3://test_bucket/file", bucket_name="test_bucket")
with mock_client:
task = asyncio.create_task(trigger.run().__anext__())
await asyncio.sleep(0.5)
assert task.done() is True
asyncio.get_event_loop().stop()
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_files")
async def test_s3_key_size_trigger_run_check_fn_user_success(mock_get_files, mock_client):
"""
Test if the task is run is in triggerr with check_fn_user defined by user.
"""
mock_get_files.return_value = True
def dummy_check_fn(list_obj):
return True
mock_client.return_value.check_key.return_value = True
trigger = S3KeySizeTrigger(
bucket_key="s3://test_bucket/file", bucket_name="test_bucket", check_fn=dummy_check_fn
)
generator = trigger.run()
actual = await generator.asend(None)
assert TriggerEvent({"status": "success"}) == actual
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_files")
async def test_s3_key_size_trigger_run_check_fn_success(mock_get_files, mock_client):
"""Test if the task is run is in trigger with check_fn."""
mock_get_files.return_value = ["test"]
mock_client.return_value.check_key.return_value = True
trigger = S3KeySizeTrigger(bucket_key="s3://test_bucket/file", bucket_name="test_bucket")
generator = trigger.run()
actual = await generator.asend(None)
assert TriggerEvent({"status": "success"}) == actual
def test_s3_key_size_check_fn_trigger_run():
"""Test if the _check_fn returns True."""
trigger = S3KeySizeTrigger(bucket_key="s3://test_bucket/file", bucket_name="test_bucket")
response = trigger._check_fn(data=[])
assert response is True
def test_s3_keys_unchanged_trigger_serialization():
"""
Asserts that the TaskStateTrigger correctly serializes its arguments
and classpath.
"""
trigger = S3KeysUnchangedTrigger(
bucket_name="test_bucket",
prefix="test",
inactivity_period=1,
min_objects=1,
inactivity_seconds=0,
previous_objects=None,
)
classpath, kwargs = trigger.serialize()
assert classpath == "astronomer.providers.amazon.aws.triggers.s3.S3KeysUnchangedTrigger"
assert kwargs == {
"bucket_name": "test_bucket",
"prefix": "test",
"inactivity_period": 1,
"min_objects": 1,
"inactivity_seconds": 0,
"previous_objects": set(),
"allow_delete": 1,
"aws_conn_id": "aws_default",
"last_activity_time": None,
}
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
async def test_s3_keys_unchanged_trigger_run(mock_client):
"""Test if the task is run is in trigger successfully."""
mock_client.return_value.check_key.return_value = True
trigger = S3KeysUnchangedTrigger(bucket_name="test_bucket", prefix="test")
with mock_client:
task = asyncio.create_task(trigger.run().__anext__())
await asyncio.sleep(0.5)
assert task.done() is True
asyncio.get_event_loop().stop()
def test_s3_keys_unchanged_trigger_raise_value_error():
"""
Test if the S3KeysUnchangedTrigger raises Value error for negative inactivity_period.
"""
with pytest.raises(ValueError):
S3KeysUnchangedTrigger(bucket_name="test_bucket", prefix="test", inactivity_period=-100)
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.is_keys_unchanged")
async def test_s3_keys_unchanged_trigger_run_success(mock_is_keys_unchanged, mock_client):
"""
Test if the task is run is in triggerer successfully.
"""
mock_is_keys_unchanged.return_value = {"status": "success"}
trigger = S3KeysUnchangedTrigger(bucket_name="test_bucket", prefix="test")
generator = trigger.run()
actual = await generator.asend(None)
assert TriggerEvent({"status": "success"}) == actual
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.is_keys_unchanged")
async def test_s3_keys_unchanged_trigger_run_pending(mock_is_keys_unchanged, mock_client):
"""Test if the task is run is in triggerer successfully."""
mock_is_keys_unchanged.return_value = {"status": "pending", "last_activity_time": datetime.now()}
trigger = S3KeysUnchangedTrigger(bucket_name="test_bucket", prefix="test")
task = asyncio.create_task(trigger.run().__anext__())
await asyncio.sleep(0.5)
# TriggerEvent was not returned
assert task.done() is False
asyncio.get_event_loop().stop()
def test_s3_prefix_sensor_trigger_serialization():
"""
Asserts that the S3 prefix trigger correctly serializes its arguments
and classpath.
"""
trigger = S3PrefixTrigger(bucket_name="test-bucket", prefix="test")
classpath, kwargs = trigger.serialize()
assert classpath == "astronomer.providers.amazon.aws.triggers.s3.S3PrefixTrigger"
assert kwargs == {
"bucket_name": "test-bucket",
"prefix": ["test"],
"delimiter": "/",
"aws_conn_id": "aws_default",
"verify": None,
"hook_params": {},
}
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
@mock.patch("astronomer.providers.amazon.aws.hooks.s3.S3HookAsync._check_for_prefix")
async def test_s3_prefix_sensor_trigger_success(mock_check_for_prefix, mock_client):
"""Test if the S3 prefix trigger fires correct event in case of success."""
mock_check_for_prefix.return_value = True
mock_client.return_value.check_key.return_value = True
trigger = S3PrefixTrigger(bucket_name="test-bucket", prefix="test")
generator = trigger.run()
actual = await generator.asend(None)
assert TriggerEvent({"status": "success", "message": "Success criteria met. Exiting."}) == actual
@pytest.mark.asyncio
@mock.patch("astronomer.providers.amazon.aws.triggers.s3.S3HookAsync.get_client_async")
async def test_s3_prefix_sensor_trigger_failure(mock_client):
"""Test if the S3 prefix trigger fires correct event in case of failure."""
mock_client.side_effect = Exception("Test exception")
trigger = S3PrefixTrigger(bucket_name="test-bucket", prefix="test")
task = [i async for i in trigger.run()]
assert len(task) == 1
assert TriggerEvent({"status": "error", "message": "Test exception"}) in task
| 37.29588
| 110
| 0.716409
| 1,248
| 9,958
| 5.455929
| 0.116987
| 0.044059
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| 0.848876
| 0.814951
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| 9,958
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| 111
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0
| 6
|
392c581240800cad72d28f81242639c3bbfc185a
| 79
|
py
|
Python
|
robot/JavaLib/src/test/robotframework/acceptance/calculator.py
|
Zhou6ang/TA
|
016db64c94bd7dae7b734795eb88b6a5ef0a6f2b
|
[
"Apache-2.0"
] | null | null | null |
robot/JavaLib/src/test/robotframework/acceptance/calculator.py
|
Zhou6ang/TA
|
016db64c94bd7dae7b734795eb88b6a5ef0a6f2b
|
[
"Apache-2.0"
] | null | null | null |
robot/JavaLib/src/test/robotframework/acceptance/calculator.py
|
Zhou6ang/TA
|
016db64c94bd7dae7b734795eb88b6a5ef0a6f2b
|
[
"Apache-2.0"
] | null | null | null |
__author__ = 'ganzhou'
def result(input1,input2):
return input1+input2+1;
| 15.8
| 27
| 0.721519
| 10
| 79
| 5.3
| 0.8
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0
| 6
|
3933cb5de0960b5b18fb3bf6a3144f2518edc267
| 92
|
py
|
Python
|
notebooks/ipython-master/setupext/__init__.py
|
burkesquires/jupyter_training_2020
|
63505d9b8133f80330fe92a74b7641066dba420c
|
[
"MIT"
] | 2
|
2020-11-18T19:29:20.000Z
|
2021-09-09T13:52:29.000Z
|
ipython-7.29.0/setupext/__init__.py
|
JohnLauFoo/clc_packages_Yu
|
259f01d9b5c02154ce258734d519ae8995cd0991
|
[
"MIT"
] | null | null | null |
ipython-7.29.0/setupext/__init__.py
|
JohnLauFoo/clc_packages_Yu
|
259f01d9b5c02154ce258734d519ae8995cd0991
|
[
"MIT"
] | 2
|
2020-11-18T19:39:31.000Z
|
2021-11-17T07:49:09.000Z
|
# load extended setup modules for distutils
from .install_data_ext import install_data_ext
| 23
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0
| 6
|
1a48f9fe1024ca5b68910751dee2743bc1abb0ff
| 100
|
py
|
Python
|
http/http_site/url_dispatcher_app/views.py
|
domenicosolazzo/practice-django
|
44e74c973384c38bd71e7c8a1aacd1e10d6a6893
|
[
"MIT"
] | null | null | null |
http/http_site/url_dispatcher_app/views.py
|
domenicosolazzo/practice-django
|
44e74c973384c38bd71e7c8a1aacd1e10d6a6893
|
[
"MIT"
] | 2
|
2021-06-10T19:42:02.000Z
|
2021-06-10T19:50:52.000Z
|
http/http_site/url_dispatcher_app/views.py
|
domenicosolazzo/practice-django
|
44e74c973384c38bd71e7c8a1aacd1e10d6a6893
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
# Create your views here.
def page(request, num="1"):
pass
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0
| 6
|
1a6372d4e155397761608f8acbcee405e59e107d
| 17,688
|
py
|
Python
|
traffic_control/tests/test_operational_area_api_permission.py
|
City-of-Helsinki/city-infrastructure-platform
|
c14513a9e54405412085f1047f91ec58b263eac0
|
[
"CC0-1.0"
] | 2
|
2020-11-23T22:08:58.000Z
|
2022-03-02T13:13:20.000Z
|
traffic_control/tests/test_operational_area_api_permission.py
|
City-of-Helsinki/city-infrastructure-platform
|
c14513a9e54405412085f1047f91ec58b263eac0
|
[
"CC0-1.0"
] | 170
|
2019-12-31T13:37:04.000Z
|
2022-03-12T14:03:35.000Z
|
traffic_control/tests/test_operational_area_api_permission.py
|
City-of-Helsinki/city-infrastructure-platform
|
c14513a9e54405412085f1047f91ec58b263eac0
|
[
"CC0-1.0"
] | 3
|
2020-05-08T05:58:02.000Z
|
2022-03-15T16:07:25.000Z
|
import json
import pytest
from django.conf import settings
from django.contrib.auth.models import Permission
from django.contrib.gis.geos import MultiPolygon, Point, Polygon
from django.urls import reverse
from django.utils.translation import gettext_lazy as _
from rest_framework import status
from traffic_control import models
from traffic_control.models import BarrierPlan, Lifecycle
from traffic_control.tests.factories import (
get_additional_sign_plan,
get_additional_sign_real,
get_api_client,
get_barrier_plan,
get_barrier_real,
get_mount_plan,
get_mount_real,
get_operational_area,
get_owner,
get_plan,
get_road_marking_plan,
get_road_marking_real,
get_signpost_plan,
get_signpost_real,
get_traffic_control_device_type,
get_traffic_light_plan,
get_traffic_light_real,
get_traffic_sign_plan,
get_traffic_sign_real,
get_user,
)
test_point_inside_area = Point(20.0, 20.0, 0.0, srid=settings.SRID)
test_point_outside_area = Point(-20.0, -20.0, 0.0, srid=settings.SRID)
test_3d_point_inside_area = Point(20.0, 20.0, 0.0, srid=settings.SRID)
test_3d_point_outside_area = Point(-20.0, -20.0, 0.0, srid=settings.SRID)
test_multipolygon_inside_area = MultiPolygon(
Polygon(
(
(20.0, 20.0, 0.0),
(20.0, 30.0, 0.0),
(30.0, 30.0, 0.0),
(30.0, 20.0, 0.0),
(20.0, 20.0, 0.0),
),
srid=settings.SRID,
),
srid=settings.SRID,
)
test_multipolygon_outside_area = MultiPolygon(
Polygon(
(
(-20.0, -20.0, 0.0),
(-20.0, -30.0, 0.0),
(-30.0, -30.0, 0.0),
(-30.0, -20.0, 0.0),
(-20.0, -20.0, 0.0),
),
srid=settings.SRID,
),
srid=settings.SRID,
)
model_factory_map = {
"AdditionalSignPlan": get_additional_sign_plan,
"AdditionalSignReal": get_additional_sign_real,
"BarrierPlan": get_barrier_plan,
"BarrierReal": get_barrier_real,
"MountPlan": get_mount_plan,
"MountReal": get_mount_real,
"Plan": get_plan,
"RoadMarkingPlan": get_road_marking_plan,
"RoadMarkingReal": get_road_marking_real,
"SignpostPlan": get_signpost_plan,
"SignpostReal": get_signpost_real,
"TrafficLightPlan": get_traffic_light_plan,
"TrafficLightReal": get_traffic_light_real,
"TrafficSignPlan": get_traffic_sign_plan,
"TrafficSignReal": get_traffic_sign_real,
}
@pytest.mark.django_db
@pytest.mark.parametrize(
"model,location,success",
(
("AdditionalSignPlan", test_3d_point_inside_area, True),
("AdditionalSignPlan", test_3d_point_outside_area, False),
("AdditionalSignPlan", None, False),
("AdditionalSignReal", test_3d_point_inside_area, True),
("AdditionalSignReal", test_3d_point_outside_area, False),
("AdditionalSignReal", None, False),
("BarrierPlan", test_point_inside_area, True),
("BarrierPlan", test_point_outside_area, False),
("BarrierPlan", None, False),
("BarrierReal", test_point_inside_area, True),
("BarrierReal", test_point_outside_area, False),
("BarrierReal", None, False),
("MountPlan", test_point_inside_area, True),
("MountPlan", test_point_outside_area, False),
("MountPlan", None, False),
("MountReal", test_point_inside_area, True),
("MountReal", test_point_outside_area, False),
("MountReal", None, False),
("Plan", test_multipolygon_inside_area, True),
("Plan", test_multipolygon_outside_area, False),
("Plan", None, True),
("RoadMarkingPlan", test_point_inside_area, True),
("RoadMarkingPlan", test_point_outside_area, False),
("RoadMarkingPlan", None, False),
("RoadMarkingReal", test_point_inside_area, True),
("RoadMarkingReal", test_point_outside_area, False),
("RoadMarkingReal", None, False),
("SignpostPlan", test_point_inside_area, True),
("SignpostPlan", test_point_outside_area, False),
("SignpostPlan", None, False),
("SignpostReal", test_point_inside_area, True),
("SignpostReal", test_point_outside_area, False),
("SignpostReal", None, False),
("TrafficLightPlan", test_point_inside_area, True),
("TrafficLightPlan", test_point_outside_area, False),
("TrafficLightPlan", None, False),
("TrafficLightReal", test_point_inside_area, True),
("TrafficLightReal", test_point_outside_area, False),
("TrafficLightReal", None, False),
("TrafficSignPlan", test_3d_point_inside_area, True),
("TrafficSignPlan", test_3d_point_outside_area, False),
("TrafficSignPlan", None, False),
("TrafficSignReal", test_3d_point_inside_area, True),
("TrafficSignReal", test_3d_point_outside_area, False),
("TrafficSignReal", None, False),
),
)
def test__api_operational_area_permission__create(model, location, success):
operational_area = get_operational_area()
user = get_user()
perms = Permission.objects.filter(codename__contains=model.lower())
user.operational_areas.add(operational_area)
user.user_permissions.add(*perms)
device_type = get_traffic_control_device_type()
location = location.ewkt if location else None
if model == "Plan":
data = {
"name": "Test plan",
"plan_number": "2020_1",
"location": location,
"planner": user.pk,
"decision_maker": user.pk,
"linked_objects": {
"barrier_plan_ids": [],
"mount_plan_ids": [],
"road_marking_plan_ids": [],
"signpost_plan_ids": [],
"traffic_light_plan_ids": [],
"traffic_sign_plan_ids": [],
"additional_sign_plan_ids": [],
},
}
else:
data = {
"location": location,
"device_type": device_type.pk,
"lifecycle": Lifecycle.ACTIVE.value,
"owner": get_owner().pk,
}
if model in ["BarrierPlan", "BarrierReal"]:
data["road_name"] = "testroad"
elif model in ["RoadMarkingPlan", "RoadMarkingReal"]:
data["source_id"] = 1
data["source_name"] = "test source"
api_client = get_api_client(user=user)
response = api_client.post(
reverse(f"v1:{model.lower()}-list"), data=data, format="json"
)
ModelClass = getattr(models, model) # noqa: N806
if success:
assert response.status_code == status.HTTP_201_CREATED
assert ModelClass.objects.count() == 1
elif not location:
assert response.status_code == status.HTTP_400_BAD_REQUEST
assert response.json() == {"location": [_("This field may not be null.")]}
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert ModelClass.objects.count() == 0
@pytest.mark.django_db
@pytest.mark.parametrize(
"location,success",
((test_point_inside_area, True), (test_point_outside_area, False), (None, False)),
)
def test__api_operational_area_permission__create__geojson(location, success):
operational_area = get_operational_area()
user = get_user()
perms = Permission.objects.filter(codename__contains="barrierplan")
user.operational_areas.add(operational_area)
user.user_permissions.add(*perms)
device_type = get_traffic_control_device_type()
if location:
location = json.loads(location.geojson)
location.update(
{"crs": {"type": "name", "properties": {"name": f"EPSG:{settings.SRID}"}}}
)
data = {
"location": location,
"device_type": device_type.pk,
"lifecycle": Lifecycle.ACTIVE.value,
"owner": get_owner().pk,
"road_name": "testroad",
}
api_client = get_api_client(user=user)
response = api_client.post(
f"{reverse('v1:barrierplan-list')}?geo_format=geojson", data=data, format="json"
)
if success:
assert response.status_code == status.HTTP_201_CREATED
assert BarrierPlan.objects.count() == 1
elif not location:
assert response.status_code == status.HTTP_400_BAD_REQUEST
assert response.json() == {"location": [_("This field may not be null.")]}
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert BarrierPlan.objects.count() == 0
@pytest.mark.django_db
@pytest.mark.parametrize(
"model,location,success",
(
("AdditionalSignPlan", test_3d_point_inside_area, True),
("AdditionalSignPlan", test_3d_point_outside_area, False),
("AdditionalSignReal", test_3d_point_inside_area, True),
("AdditionalSignReal", test_3d_point_outside_area, False),
("BarrierPlan", test_point_inside_area, True),
("BarrierPlan", test_point_outside_area, False),
("BarrierReal", test_point_inside_area, True),
("BarrierReal", test_point_outside_area, False),
("MountPlan", test_point_inside_area, True),
("MountPlan", test_point_outside_area, False),
("MountReal", test_point_inside_area, True),
("MountReal", test_point_outside_area, False),
("Plan", test_multipolygon_inside_area, True),
("Plan", test_multipolygon_outside_area, False),
("RoadMarkingPlan", test_point_inside_area, True),
("RoadMarkingPlan", test_point_outside_area, False),
("RoadMarkingReal", test_point_inside_area, True),
("RoadMarkingReal", test_point_outside_area, False),
("SignpostPlan", test_point_inside_area, True),
("SignpostPlan", test_point_outside_area, False),
("SignpostReal", test_point_inside_area, True),
("SignpostReal", test_point_outside_area, False),
("TrafficLightPlan", test_point_inside_area, True),
("TrafficLightPlan", test_point_outside_area, False),
("TrafficLightReal", test_point_inside_area, True),
("TrafficLightReal", test_point_outside_area, False),
("TrafficSignPlan", test_3d_point_inside_area, True),
("TrafficSignPlan", test_3d_point_outside_area, False),
("TrafficSignReal", test_3d_point_inside_area, True),
("TrafficSignReal", test_3d_point_outside_area, False),
),
)
def test__api_operational_area_permission__update(model, location, success):
operational_area = get_operational_area()
user = get_user()
perms = Permission.objects.filter(codename__contains=model.lower())
user.operational_areas.add(operational_area)
user.user_permissions.add(*perms)
device_type = get_traffic_control_device_type()
instance = model_factory_map[model](location=location)
if model == "Plan":
data = {
"name": "Test plan",
"plan_number": "2020_1",
"location": location.ewkt,
"planner": user.pk,
"decision_maker": user.pk,
"linked_objects": {
"barrier_plan_ids": [],
"mount_plan_ids": [],
"road_marking_plan_ids": [],
"signpost_plan_ids": [],
"traffic_light_plan_ids": [],
"traffic_sign_plan_ids": [],
"additional_sign_plan_ids": [],
},
}
else:
data = {
"location": location.ewkt,
"device_type": device_type.pk,
"lifecycle": Lifecycle.ACTIVE.value,
"owner": get_owner().pk,
}
if model in ["BarrierPlan", "BarrierReal"]:
data["road_name"] = "testroad"
elif model in ["RoadMarkingPlan", "RoadMarkingReal"]:
data["source_id"] = 1
data["source_name"] = "test source"
api_client = get_api_client(user=user)
response = api_client.put(
reverse(f"v1:{model.lower()}-detail", kwargs={"pk": instance.pk}),
data,
format="json",
)
instance.refresh_from_db()
if success:
assert response.status_code == status.HTTP_200_OK
assert instance.updated_by == user
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert instance.updated_by != user
@pytest.mark.django_db
@pytest.mark.parametrize(
"model,location,success",
(
("AdditionalSignPlan", test_3d_point_inside_area, True),
("AdditionalSignPlan", test_3d_point_outside_area, False),
("AdditionalSignReal", test_3d_point_inside_area, True),
("AdditionalSignReal", test_3d_point_outside_area, False),
("BarrierPlan", test_point_inside_area, True),
("BarrierPlan", test_point_outside_area, False),
("BarrierReal", test_point_inside_area, True),
("BarrierReal", test_point_outside_area, False),
("MountPlan", test_point_inside_area, True),
("MountPlan", test_point_outside_area, False),
("MountReal", test_point_inside_area, True),
("MountReal", test_point_outside_area, False),
("Plan", test_multipolygon_inside_area, True),
("Plan", test_multipolygon_outside_area, False),
("RoadMarkingPlan", test_point_inside_area, True),
("RoadMarkingPlan", test_point_outside_area, False),
("RoadMarkingReal", test_point_inside_area, True),
("RoadMarkingReal", test_point_outside_area, False),
("SignpostPlan", test_point_inside_area, True),
("SignpostPlan", test_point_outside_area, False),
("SignpostReal", test_point_inside_area, True),
("SignpostReal", test_point_outside_area, False),
("TrafficLightPlan", test_point_inside_area, True),
("TrafficLightPlan", test_point_outside_area, False),
("TrafficLightReal", test_point_inside_area, True),
("TrafficLightReal", test_point_outside_area, False),
("TrafficSignPlan", test_3d_point_inside_area, True),
("TrafficSignPlan", test_3d_point_outside_area, False),
("TrafficSignReal", test_3d_point_inside_area, True),
("TrafficSignReal", test_3d_point_outside_area, False),
),
)
def test__api_operational_area_permission__partial_update(model, location, success):
operational_area = get_operational_area()
user = get_user()
perms = Permission.objects.filter(codename__contains=model.lower())
user.operational_areas.add(operational_area)
user.user_permissions.add(*perms)
instance = model_factory_map[model](location=location)
data = {
"location": location.ewkt,
}
api_client = get_api_client(user=user)
response = api_client.patch(
reverse(f"v1:{model.lower()}-detail", kwargs={"pk": instance.pk}),
data,
format="json",
)
instance.refresh_from_db()
if success:
assert response.status_code == status.HTTP_200_OK
assert instance.updated_by == user
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert instance.updated_by != user
@pytest.mark.django_db
@pytest.mark.parametrize(
"model,location,success",
(
("AdditionalSignPlan", test_3d_point_inside_area, True),
("AdditionalSignPlan", test_3d_point_outside_area, False),
("AdditionalSignReal", test_3d_point_inside_area, True),
("AdditionalSignReal", test_3d_point_outside_area, False),
("BarrierPlan", test_point_inside_area, True),
("BarrierPlan", test_point_outside_area, False),
("BarrierReal", test_point_inside_area, True),
("BarrierReal", test_point_outside_area, False),
("MountPlan", test_point_inside_area, True),
("MountPlan", test_point_outside_area, False),
("MountReal", test_point_inside_area, True),
("MountReal", test_point_outside_area, False),
("Plan", test_multipolygon_inside_area, True),
("Plan", test_multipolygon_outside_area, False),
("RoadMarkingPlan", test_point_inside_area, True),
("RoadMarkingPlan", test_point_outside_area, False),
("RoadMarkingReal", test_point_inside_area, True),
("RoadMarkingReal", test_point_outside_area, False),
("SignpostPlan", test_point_inside_area, True),
("SignpostPlan", test_point_outside_area, False),
("SignpostReal", test_point_inside_area, True),
("SignpostReal", test_point_outside_area, False),
("TrafficLightPlan", test_point_inside_area, True),
("TrafficLightPlan", test_point_outside_area, False),
("TrafficLightReal", test_point_inside_area, True),
("TrafficLightReal", test_point_outside_area, False),
("TrafficSignPlan", test_3d_point_inside_area, True),
("TrafficSignPlan", test_3d_point_outside_area, False),
("TrafficSignReal", test_3d_point_inside_area, True),
("TrafficSignReal", test_3d_point_outside_area, False),
),
)
def test__api_operational_area_permission__delete(model, location, success):
operational_area = get_operational_area()
user = get_user()
perms = Permission.objects.filter(codename__contains=model.lower())
user.operational_areas.add(operational_area)
user.user_permissions.add(*perms)
instance = model_factory_map[model](location=location)
api_client = get_api_client(user=user)
response = api_client.delete(
reverse(f"v1:{model.lower()}-detail", kwargs={"pk": instance.pk})
)
instance.refresh_from_db()
if success:
assert response.status_code == status.HTTP_204_NO_CONTENT
assert not instance.is_active
else:
assert response.status_code == status.HTTP_403_FORBIDDEN
assert instance.is_active
| 39.394209
| 88
| 0.66107
| 1,969
| 17,688
| 5.55612
| 0.083799
| 0.069104
| 0.078062
| 0.098995
| 0.847715
| 0.834095
| 0.824954
| 0.817916
| 0.817916
| 0.808775
| 0
| 0.014903
| 0.21851
| 17,688
| 448
| 89
| 39.482143
| 0.776532
| 0.000565
| 0
| 0.685851
| 0
| 0
| 0.175888
| 0.023365
| 0
| 0
| 0
| 0
| 0.057554
| 1
| 0.01199
| false
| 0
| 0.026379
| 0
| 0.038369
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
1a7bf38c2082dde45ff73103e533406fa131ed4e
| 140
|
py
|
Python
|
cacheman/utils.py
|
Jenyay/py_cache_manager
|
aa7fa33dcbc30dbd6b2e62cade5f371f6dbbe2cb
|
[
"BSD-2-Clause"
] | null | null | null |
cacheman/utils.py
|
Jenyay/py_cache_manager
|
aa7fa33dcbc30dbd6b2e62cade5f371f6dbbe2cb
|
[
"BSD-2-Clause"
] | 5
|
2015-02-18T22:17:52.000Z
|
2018-01-23T05:30:09.000Z
|
cacheman/utils.py
|
Jenyay/py_cache_manager
|
aa7fa33dcbc30dbd6b2e62cade5f371f6dbbe2cb
|
[
"BSD-2-Clause"
] | 2
|
2021-05-31T15:18:50.000Z
|
2022-01-15T16:50:25.000Z
|
import string
import random
def random_name(length = 18):
return ''.join(random.choice(string.ascii_uppercase) for _ in range(length))
| 23.333333
| 80
| 0.757143
| 20
| 140
| 5.15
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.016529
| 0.135714
| 140
| 5
| 81
| 28
| 0.834711
| 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 | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
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