Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
select_with_join_query | (self, user_name, grant_target_name, node=None) | Grant SELECT on the view and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
| Grant SELECT on the view and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
| def select_with_join_query(self, user_name, grant_target_name, node=None):
"""Grant SELECT on the view and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
"""
view_name = f"view_{getuid()}"
table0_name = f"table0_{getuid()}"
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"exitcode"... | [
754,
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] | [
789,
62
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select_with_join_subquery_privilege_granted_directly_or_via_role | (self, node=None) | Check that user is able to select from a view with a stored query that includes `JOIN` and two subqueries
if and only if the user has SELECT privilege on all the tables and the view, either directly or through a role.
| Check that user is able to select from a view with a stored query that includes `JOIN` and two subqueries
if and only if the user has SELECT privilege on all the tables and the view, either directly or through a role.
| def select_with_join_subquery_privilege_granted_directly_or_via_role(self, node=None):
"""Check that user is able to select from a view with a stored query that includes `JOIN` and two subqueries
if and only if the user has SELECT privilege on all the tables and the view, either directly or through a role.
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select_with_join_subquery | (self, grant_target_name, user_name, node=None) | Grant SELECT on the view and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
| Grant SELECT on the view and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
| def select_with_join_subquery(self, grant_target_name, user_name, node=None):
"""Grant SELECT on the view and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
"""
view_name = f"view_{getuid()}"
table0_name = f"table0_{getuid()}"
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select_with_nested_views_privilege_granted_directly_or_via_role | (self, node=None) | Check that user is able to select from a view with a stored query that includes other views if and only if
the user has SELECT privilege on all of the views and the source tables for those views, either directly or through a role.
| Check that user is able to select from a view with a stored query that includes other views if and only if
the user has SELECT privilege on all of the views and the source tables for those views, either directly or through a role.
| def select_with_nested_views_privilege_granted_directly_or_via_role(self, node=None):
"""Check that user is able to select from a view with a stored query that includes other views if and only if
the user has SELECT privilege on all of the views and the source tables for those views, either directly or through ... | [
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852,
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869,
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select_with_nested_views | (self, grant_target_name, user_name, node=None) | Grant SELECT on views and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
| Grant SELECT on views and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
| def select_with_nested_views(self, grant_target_name, user_name, node=None):
"""Grant SELECT on views and tables in the stored query and check the user is able to SELECT if and only if they have SELECT privilege on all of them.
"""
view0_name = f"view0_{getuid()}"
view1_name = f"view1_{getuid()}"
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"table0_na... | [
872,
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67
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drop | (self, node=None) | Test the RBAC functionality of the `DROP VIEW` command.
| Test the RBAC functionality of the `DROP VIEW` command.
| def drop(self, node=None):
"""Test the RBAC functionality of the `DROP VIEW` command.
"""
Scenario(run=drop_with_privilege_granted_directly_or_via_role, setup=instrument_clickhouse_server_log)
Scenario(run=drop_with_revoked_privilege_revoked_directly_or_from_role, setup=instrument_clickhouse_server_log) | [
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drop_with_privilege_granted_directly_or_via_role | (self, node=None) | Check that user is able to drop view with DROP VIEW privilege if the user has privilege directly or through a role.
| Check that user is able to drop view with DROP VIEW privilege if the user has privilege directly or through a role.
| def drop_with_privilege_granted_directly_or_via_role(self, node=None):
"""Check that user is able to drop view with DROP VIEW privilege if the user has privilege directly or through a role.
"""
user_name = f"user_{getuid()}"
role_name = f"role_{getuid()}"
if node is None:
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929,
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945,
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] | python | en | ['en', 'en', 'en'] | True |
drop_with_privilege | (self, grant_target_name, user_name, node=None) | Grant DROP VIEW privilege and check the user is able to successfully drop a view.
| Grant DROP VIEW privilege and check the user is able to successfully drop a view.
| def drop_with_privilege(self, grant_target_name, user_name, node=None):
"""Grant DROP VIEW privilege and check the user is able to successfully drop a view.
"""
view_name = f"view_{getuid()}"
exitcode, message = errors.table_does_not_exist(name=f"default.{view_name}")
if node is None:
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drop_with_revoked_privilege_revoked_directly_or_from_role | (self, node=None) | Check that user is unable to drop view with DROP VIEW privilege revoked directly or from a role.
| Check that user is unable to drop view with DROP VIEW privilege revoked directly or from a role.
| def drop_with_revoked_privilege_revoked_directly_or_from_role(self, node=None):
"""Check that user is unable to drop view with DROP VIEW privilege revoked directly or from a role.
"""
user_name = f"user_{getuid()}"
role_name = f"role_{getuid()}"
if node is None:
node = self.context.node
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drop_with_revoked_privilege | (self, grant_target_name, user_name, node=None) | Revoke DROP VIEW privilege and check the user is unable to DROP a view.
| Revoke DROP VIEW privilege and check the user is unable to DROP a view.
| def drop_with_revoked_privilege(self, grant_target_name, user_name, node=None):
"""Revoke DROP VIEW privilege and check the user is unable to DROP a view.
"""
view_name = f"view_{getuid()}"
exitcode, message = errors.not_enough_privileges(name=f"{user_name}")
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refresh | (self, node=None) | Test the RBAC functionality of the `ALTER LIVE VIEW REFRESH` command.
| Test the RBAC functionality of the `ALTER LIVE VIEW REFRESH` command.
| def refresh(self, node=None):
"""Test the RBAC functionality of the `ALTER LIVE VIEW REFRESH` command.
"""
Scenario(run=refresh_with_privilege_granted_directly_or_via_role, setup=instrument_clickhouse_server_log)
Scenario(run=refresh_with_privilege_revoked_directly_or_from_role, setup=instrument_clickho... | [
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refresh_with_privilege_granted_directly_or_via_role | (self, node=None) | Check that user is able to refresh a live view with REFRESH privilege
granted either directly or through a role.
| Check that user is able to refresh a live view with REFRESH privilege
granted either directly or through a role.
| def refresh_with_privilege_granted_directly_or_via_role(self, node=None):
"""Check that user is able to refresh a live view with REFRESH privilege
granted either directly or through a role.
"""
user_name = f"user_{getuid()}"
role_name = f"role_{getuid()}"
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refresh_with_privilege | (self, grant_target_name, user_name, node=None) | Grant REFRESH privilege and check that user is able to refresh a live view.
| Grant REFRESH privilege and check that user is able to refresh a live view.
| def refresh_with_privilege(self, grant_target_name, user_name, node=None):
"""Grant REFRESH privilege and check that user is able to refresh a live view.
"""
view_name = f"view_{getuid()}"
if node is None:
node = self.context.node
try:
with Given("I have a view"):
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refresh_with_privilege_revoked_directly_or_from_role | (self, node=None) | Check that user is unable to refresh a live view with REFRESH privilege
revoked either directly or through a role.
| Check that user is unable to refresh a live view with REFRESH privilege
revoked either directly or through a role.
| def refresh_with_privilege_revoked_directly_or_from_role(self, node=None):
"""Check that user is unable to refresh a live view with REFRESH privilege
revoked either directly or through a role.
"""
user_name = f"user_{getuid()}"
role_name = f"role_{getuid()}"
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refresh_with_revoked_privilege | (self, grant_target_name, user_name, node=None) | Revoke REFRESH privilege and check that user is unable to refresh a live view.
| Revoke REFRESH privilege and check that user is unable to refresh a live view.
| def refresh_with_revoked_privilege(self, grant_target_name, user_name, node=None):
"""Revoke REFRESH privilege and check that user is unable to refresh a live view.
"""
view_name = f"view_{getuid()}"
exitcode, message = errors.not_enough_privileges(name=f"{user_name}")
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empty_user_name | (self, timeout=300) | Check that empty string as a user name is not allowed.
| Check that empty string as a user name is not allowed.
| def empty_user_name(self, timeout=300):
"""Check that empty string as a user name is not allowed.
"""
servers = {"openldap1": {
"host": "openldap1", "port": "389", "enable_tls": "no",
"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com"
}}
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empty_server_name | (self, timeout=300) | Check that if server name is an empty string then login is not allowed.
| Check that if server name is an empty string then login is not allowed.
| def empty_server_name(self, timeout=300):
"""Check that if server name is an empty string then login is not allowed.
"""
message = "Exception: LDAP server name cannot be empty for user"
servers = {"openldap1": {
"host": "openldap1", "port": "389", "enable_tls": "no",
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empty_server_not_defined | (self) | Check that if server is not defined then login is not allowed.
| Check that if server is not defined then login is not allowed.
| def empty_server_not_defined(self):
"""Check that if server is not defined then login is not allowed.
"""
servers = {"openldap1": {
"host": "openldap1", "port": "389", "enable_tls": "no",
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valid_user_config | (self) | Check syntax of valid user configuration of LDAP authenticated user. | Check syntax of valid user configuration of LDAP authenticated user. | def valid_user_config(self):
"""Check syntax of valid user configuration of LDAP authenticated user."""
servers = {"openldap1": {
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"auth_dn_prefix": "cn=", "auth_dn_suffix": ",ou=users,dc=company,dc=com"
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multiple_servers | (self) | Check that user configuration allows to specify only one LDAP server for a given user
and if multiple servers are specified then the first one is used. | Check that user configuration allows to specify only one LDAP server for a given user
and if multiple servers are specified then the first one is used. | def multiple_servers(self):
"""Check that user configuration allows to specify only one LDAP server for a given user
and if multiple servers are specified then the first one is used."""
servers = {
"openldap1": {
"host": "openldap1", "port": "389", "enable_tls": "no",
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ldap_and_password | (self) | Check that user can't be authenticated if both `ldap` and `password`
is specified for the same user. We expect an error message to be present in the log
and login attempt to fail.
| Check that user can't be authenticated if both `ldap` and `password`
is specified for the same user. We expect an error message to be present in the log
and login attempt to fail.
| def ldap_and_password(self):
"""Check that user can't be authenticated if both `ldap` and `password`
is specified for the same user. We expect an error message to be present in the log
and login attempt to fail.
"""
node = self.context.node
servers = {
"openldap1": {
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":",
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":",
"\"389\"",
",",
"\"enable_tls\"",
":",
"\"no\"",
",... | [
114,
0
] | [
149,
80
] | python | en | ['en', 'en', 'en'] | True |
feature | (self, node="clickhouse1") | Check that server returns an error and prohibits
user login if LDAP users configuration is not valid.
| Check that server returns an error and prohibits
user login if LDAP users configuration is not valid.
| def feature(self, node="clickhouse1"):
"""Check that server returns an error and prohibits
user login if LDAP users configuration is not valid.
"""
self.context.node = self.context.cluster.node(node)
for scenario in loads(current_module(), Scenario):
scenario() | [
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] | [
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allow_experimental_bigint | (node) | Enable experimental big int setting in Clickhouse.
| Enable experimental big int setting in Clickhouse.
| def allow_experimental_bigint(node):
"""Enable experimental big int setting in Clickhouse.
"""
setting = ("allow_experimental_bigint_types", 1)
default_query_settings = None
try:
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default_query_settings ... | [
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] | python | en | ['en', 'en', 'en'] | True |
allow_experimental_map_type | (self) | Set allow_experimental_map_type = 1
| Set allow_experimental_map_type = 1
| def allow_experimental_map_type(self):
"""Set allow_experimental_map_type = 1
"""
setting = ("allow_experimental_map_type", 1)
default_query_settings = None
try:
with By("adding allow_experimental_map_type to the default query settings"):
default_query_settings = getsattr(curren... | [
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execute_query | (sql, expected=None, format="TabSeparatedWithNames", compare_func=None) | Execute SQL query and compare the output to the snapshot.
| Execute SQL query and compare the output to the snapshot.
| def execute_query(sql, expected=None, format="TabSeparatedWithNames", compare_func=None):
"""Execute SQL query and compare the output to the snapshot.
"""
name = basename(current().name)
with When("I execute query", description=sql):
r = current().context.node.query(sql + " FORMAT " + format)
... | [
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72,
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] | python | en | ['en', 'en', 'en'] | True |
table | (self, data_type, name="table0") | Create a table.
| Create a table.
| def table(self, data_type, name="table0"):
"""Create a table.
"""
node = current().context.node
try:
with By("creating table"):
node.query(f"CREATE TABLE {name}(a {data_type}) ENGINE = Memory")
yield
finally:
with Finally("drop the table"):
node.quer... | [
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87,
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] | python | en | ['en', 'ga', 'en'] | True |
getuid | () | Create a unique variable name based on the test it is called from.
| Create a unique variable name based on the test it is called from.
| def getuid():
"""Create a unique variable name based on the test it is called from.
"""
if current().subtype == TestSubType.Example:
testname = f"{basename(parentname(current().name)).replace(' ', '_').replace(',','')}"
else:
testname = f"{basename(current().name).replace(' ', '_').repla... | [
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89,
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] | [
100,
63
] | python | en | ['en', 'en', 'en'] | True |
to_data_type | (data_type, value) | Return a conversion statement based on the data type provided
| Return a conversion statement based on the data type provided
| def to_data_type(data_type, value):
"""Return a conversion statement based on the data type provided
"""
if data_type in ['Decimal256(0)']:
return f'toDecimal256(\'{value}\',0)'
else:
return f'to{data_type}(\'{value}\')' | [
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109,
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] | python | en | ['en', 'en', 'en'] | True |
EpidemicDiscrete.__init__ | (self,
cost_function,
model,
simulation_horizon,
ratio_death_to_R=0.005, # death ratio among people who were infected
time_resolution=7,
seed=np.random.randint(1e6)
) |
EpidemicDiscrete environment is based on the Epidemiological SEIRAH model from Prague et al., 2020 and on a bi-objective
cost function (death toll and gdp recess).
Parameters
----------
cost_function: BaseCostFunction
A cost function.
model: BaseModel
... |
EpidemicDiscrete environment is based on the Epidemiological SEIRAH model from Prague et al., 2020 and on a bi-objective
cost function (death toll and gdp recess). | def __init__(self,
cost_function,
model,
simulation_horizon,
ratio_death_to_R=0.005, # death ratio among people who were infected
time_resolution=7,
seed=np.random.randint(1e6)
):
"""
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67,
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] | python | en | ['en', 'error', 'th'] | False |
EpidemicDiscrete._compute_b | (self, times_since_start, times_since_last) |
Computes the transmission rate depending on the number of days since the last lock-down or since beginning of the current lock-down.
Parameters
----------
times_since_start: nd.array of ints
Time since the start of the current lock-down, for each day.
times_since_la... |
Computes the transmission rate depending on the number of days since the last lock-down or since beginning of the current lock-down. | def _compute_b(self, times_since_start, times_since_last):
"""
Computes the transmission rate depending on the number of days since the last lock-down or since beginning of the current lock-down.
Parameters
----------
times_since_start: nd.array of ints
Time since th... | [
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] | [
109,
17
] | python | en | ['en', 'error', 'th'] | False |
EpidemicDiscrete._update_previous_env_state | (self) |
Save previous env state.
|
Save previous env state. | def _update_previous_env_state(self):
"""
Save previous env state.
"""
if self.env_state is not None:
self.previous_env_state = self.env_state.copy()
self.previous_env_state_labelled = self.env_state_labelled.copy() | [
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111,
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] | [
118,
77
] | python | en | ['en', 'error', 'th'] | False |
EpidemicDiscrete._update_env_state | (self) |
Update the environment state.
|
Update the environment state. | def _update_env_state(self):
"""
Update the environment state.
"""
# Update env state
self.env_state_labelled = dict(zip(self.model.internal_states_labels, self.model_state))
self.env_state_labelled.update(previous_lockdown_state=self.previous_lockdown_state,
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141,
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] | python | en | ['en', 'error', 'th'] | False |
EpidemicDiscrete.reset_same_model | (self) |
To call if you want to reset to the same model the next time you call reset.
Will be cancelled after the first reset, it needs to be called again each time.
|
To call if you want to reset to the same model the next time you call reset.
Will be cancelled after the first reset, it needs to be called again each time. | def reset_same_model(self):
"""
To call if you want to reset to the same model the next time you call reset.
Will be cancelled after the first reset, it needs to be called again each time.
"""
self.reset_same = True | [
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143,
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] | [
150,
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] | python | en | ['en', 'error', 'th'] | False |
EpidemicDiscrete.reset | (self) |
Reset the environment and the tracking of data.
Returns
-------
nd.array
The initial environment state.
|
Reset the environment and the tracking of data. | def reset(self):
"""
Reset the environment and the tracking of data.
Returns
-------
nd.array
The initial environment state.
"""
# initialize history of states, internal model states, actions, cost_functions, deaths
self.history = dict(env_st... | [
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EpidemicDiscrete.update_with_action | (self, action) |
Implement effect of action on transmission rate.
Parameters
----------
action: int
Action is 0 (no lock-down) or 1 (lock-down).
|
Implement effect of action on transmission rate. | def update_with_action(self, action):
"""
Implement effect of action on transmission rate.
Parameters
----------
action: int
Action is 0 (no lock-down) or 1 (lock-down).
"""
# Translate actions
self.previous_lockdown_state = self.lockdown_st... | [
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EpidemicDiscrete.step | (self, action) |
Traditional step function from OpenAI Gym envs. Uses the action to update the environment.
Parameters
----------
action: int
Action is 0 (no lock-down) or 1 (lock-down).
Returns
-------
state: nd.array
New environment state.
cos... |
Traditional step function from OpenAI Gym envs. Uses the action to update the environment. | def step(self, action):
"""
Traditional step function from OpenAI Gym envs. Uses the action to update the environment.
Parameters
----------
action: int
Action is 0 (no lock-down) or 1 (lock-down).
Returns
-------
state: nd.array
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327,
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] | python | en | ['en', 'error', 'th'] | False |
restart | (node=None, safe=False, timeout=300) | Restart ClickHouse server and wait for config to be reloaded.
| Restart ClickHouse server and wait for config to be reloaded.
| def restart(node=None, safe=False, timeout=300):
"""Restart ClickHouse server and wait for config to be reloaded.
"""
with When("I restart ClickHouse server node"):
if node is None:
node = current().context.node
with node.cluster.shell(node.name) as bash:
bash.expect... | [
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] | python | en | ['en', 'en', 'en'] | True |
add_config | (config, timeout=300, restart=False, modify=False) | Add dynamic configuration file to ClickHouse.
:param node: node
:param config: configuration file description
:param timeout: timeout, default: 20 sec
| Add dynamic configuration file to ClickHouse. | def add_config(config, timeout=300, restart=False, modify=False):
"""Add dynamic configuration file to ClickHouse.
:param node: node
:param config: configuration file description
:param timeout: timeout, default: 20 sec
"""
node = current().context.node
cluster = current().context.cluster
... | [
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181,
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] | python | en | ['en', 'en', 'en'] | True |
create_ldap_servers_config_content | (servers, config_d_dir="/etc/clickhouse-server/config.d", config_file="ldap_servers.xml") | Create LDAP servers configuration content.
| Create LDAP servers configuration content.
| def create_ldap_servers_config_content(servers, config_d_dir="/etc/clickhouse-server/config.d", config_file="ldap_servers.xml"):
"""Create LDAP servers configuration content.
"""
uid = getuid()
path = os.path.join(config_d_dir, config_file)
name = config_file
root = xmltree.fromstring("<yandex>... | [
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183,
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] | [
203,
57
] | python | en | ['en', 'fr', 'en'] | True |
modify_config | (config, restart=False) | Apply updated configuration file.
| Apply updated configuration file.
| def modify_config(config, restart=False):
"""Apply updated configuration file.
"""
return add_config(config, restart=restart, modify=True) | [
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206,
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209,
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] | python | en | ['en', 'en', 'en'] | True |
ldap_servers | (servers, config_d_dir="/etc/clickhouse-server/config.d", config_file="ldap_servers.xml",
timeout=300, restart=False, config=None) | Add LDAP servers configuration.
| Add LDAP servers configuration.
| def ldap_servers(servers, config_d_dir="/etc/clickhouse-server/config.d", config_file="ldap_servers.xml",
timeout=300, restart=False, config=None):
"""Add LDAP servers configuration.
"""
if config is None:
config = create_ldap_servers_config_content(servers, config_d_dir, config_file)
re... | [
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212,
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218,
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] | python | en | ['en', 'zh', 'en'] | True |
create_ldap_users_config_content | (*users, config_d_dir="/etc/clickhouse-server/users.d", config_file="ldap_users.xml") | Create LDAP users configuration file content.
| Create LDAP users configuration file content.
| def create_ldap_users_config_content(*users, config_d_dir="/etc/clickhouse-server/users.d", config_file="ldap_users.xml"):
"""Create LDAP users configuration file content.
"""
uid = getuid()
path = os.path.join(config_d_dir, config_file)
name = config_file
root = xmltree.fromstring("<yandex><us... | [
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220,
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] | [
241,
56
] | python | en | ['en', 'en', 'en'] | True |
add_users_identified_with_ldap | (*users) | Add one or more users that are identified via
an ldap server using RBAC.
| Add one or more users that are identified via
an ldap server using RBAC.
| def add_users_identified_with_ldap(*users):
"""Add one or more users that are identified via
an ldap server using RBAC.
"""
node = current().context.node
try:
with Given("I create users"):
for user in users:
node.query(f"CREATE USER '{user['username']}' IDENTIFIED... | [
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257,
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ldap_authenticated_users | (*users, config_d_dir="/etc/clickhouse-server/users.d",
config_file=None, timeout=300, restart=True, config=None, rbac=False) | Add LDAP authenticated users.
| Add LDAP authenticated users.
| def ldap_authenticated_users(*users, config_d_dir="/etc/clickhouse-server/users.d",
config_file=None, timeout=300, restart=True, config=None, rbac=False):
"""Add LDAP authenticated users.
"""
if rbac:
return add_users_identified_with_ldap(*users)
else:
if config_file is None:
... | [
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invalid_server_config | (servers, message=None, tail=30, timeout=300) | Check that ClickHouse errors when trying to load invalid LDAP servers configuration file.
| Check that ClickHouse errors when trying to load invalid LDAP servers configuration file.
| def invalid_server_config(servers, message=None, tail=30, timeout=300):
"""Check that ClickHouse errors when trying to load invalid LDAP servers configuration file.
"""
node = current().context.node
if message is None:
message = "Exception: Failed to merge config with '/etc/clickhouse-server/con... | [
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invalid_user_config | (servers, config, message=None, tail=30, timeout=300) | Check that ClickHouse errors when trying to load invalid LDAP users configuration file.
| Check that ClickHouse errors when trying to load invalid LDAP users configuration file.
| def invalid_user_config(servers, config, message=None, tail=30, timeout=300):
"""Check that ClickHouse errors when trying to load invalid LDAP users configuration file.
"""
node = current().context.node
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add_user_to_ldap | (cn, userpassword, givenname=None, homedirectory=None, sn=None, uid=None, uidnumber=None, node=None) | Add user entry to LDAP. | Add user entry to LDAP. | def add_user_to_ldap(cn, userpassword, givenname=None, homedirectory=None, sn=None, uid=None, uidnumber=None, node=None):
"""Add user entry to LDAP."""
if node is None:
node = current().context.ldap_node
if uid is None:
uid = cn
if givenname is None:
givenname = "John"
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delete_user_from_ldap | (user, node=None, exitcode=0) | Delete user entry from LDAP. | Delete user entry from LDAP. | def delete_user_from_ldap(user, node=None, exitcode=0):
"""Delete user entry from LDAP."""
if node is None:
node = current().context.ldap_node
r = node.command(
f"ldapdelete -x -H ldap://localhost -D \"cn=admin,dc=company,dc=com\" -w admin \"{user['dn']}\"")
if exitcode is not None:
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change_user_password_in_ldap | (user, new_password, node=None, exitcode=0) | Change user password in LDAP. | Change user password in LDAP. | def change_user_password_in_ldap(user, new_password, node=None, exitcode=0):
"""Change user password in LDAP."""
if node is None:
node = current().context.ldap_node
ldif = (f"dn: {user['dn']}\n"
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f"userpassword: {new_password}"... | [
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change_user_cn_in_ldap | (user, new_cn, node=None, exitcode=0) | Change user password in LDAP. | Change user password in LDAP. | def change_user_cn_in_ldap(user, new_cn, node=None, exitcode=0):
"""Change user password in LDAP."""
if node is None:
node = current().context.ldap_node
new_user = dict(user)
new_user['dn'] = f"cn={new_cn},ou=users,dc=company,dc=com"
new_user['cn'] = new_cn
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ldap_user | (cn, userpassword, givenname=None, homedirectory=None, sn=None, uid=None, uidnumber=None, node=None) | Add new user to the LDAP server. | Add new user to the LDAP server. | def ldap_user(cn, userpassword, givenname=None, homedirectory=None, sn=None, uid=None, uidnumber=None, node=None):
"""Add new user to the LDAP server."""
try:
user = None
with Given(f"I add user {cn} to LDAP"):
user = add_user_to_ldap(cn, userpassword, givenname, homedirectory, sn, u... | [
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ldap_users | (*users, node=None) | Add multiple new users to the LDAP server. | Add multiple new users to the LDAP server. | def ldap_users(*users, node=None):
"""Add multiple new users to the LDAP server."""
try:
_users = []
with Given("I add users to LDAP"):
for user in users:
with By(f"adding user {user['cn']}"):
_users.append(add_user_to_ldap(**user, node=node))
... | [
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login | (servers, *users, config=None) | Configure LDAP server and LDAP authenticated users and
try to login and execute a query | Configure LDAP server and LDAP authenticated users and
try to login and execute a query | def login(servers, *users, config=None):
"""Configure LDAP server and LDAP authenticated users and
try to login and execute a query"""
with ldap_servers(servers):
with ldap_authenticated_users(*users, restart=True, config=config):
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public_tables | (self, node=None) | Check that a user with no privilege is able to select from public tables.
| Check that a user with no privilege is able to select from public tables.
| def public_tables(self, node=None):
"""Check that a user with no privilege is able to select from public tables.
"""
user_name = f"user_{getuid()}"
if node is None:
node = self.context.node
with user(node, f"{user_name}"):
with When("I check the user is able to select on system.one... | [
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12,
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sensitive_tables | (self, node=None) | Check that a user with no privilege is not able to see from these tables.
| Check that a user with no privilege is not able to see from these tables.
| def sensitive_tables(self, node=None):
"""Check that a user with no privilege is not able to see from these tables.
"""
user_name = f"user_{getuid()}"
if node is None:
node = self.context.node
with user(node, f"{user_name}"):
with Given("I create a query"):
node.query("S... | [
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feature | (self, node="clickhouse1") | Check set default role query syntax.
```sql
SET DEFAULT ROLE {NONE | role [,...] | ALL | ALL EXCEPT role [,...]} TO {user|CURRENT_USER} [,...]
```
| Check set default role query syntax. | def feature(self, node="clickhouse1"):
"""Check set default role query syntax.
```sql
SET DEFAULT ROLE {NONE | role [,...] | ALL | ALL EXCEPT role [,...]} TO {user|CURRENT_USER} [,...]
```
"""
node = self.context.cluster.node(node)
@contextmanager
def setup(users=2,roles=2):
tr... | [
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is_good_response | (resp) |
Returns True if the response seems to be HTML, False otherwise.
|
Returns True if the response seems to be HTML, False otherwise.
| def is_good_response(resp):
"""
Returns True if the response seems to be HTML, False otherwise.
"""
content_type = resp.headers['Content-Type'].lower()
return (resp.status_code == 200
and content_type is not None
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loadClusterFit | (pkl_file, npz_file, outPrefix = "", max_samples = 100000,
use_gpu = False) | Call this to load a fitted model
Args:
pkl_file (str)
Location of saved .pkl file on disk
npz_file (str)
Location of saved .npz file on disk
outPrefix (str)
Output prefix for model to save to (e.g. plots)
max_samples (int)
Maximum samp... | Call this to load a fitted model | def loadClusterFit(pkl_file, npz_file, outPrefix = "", max_samples = 100000,
use_gpu = False):
'''Call this to load a fitted model
Args:
pkl_file (str)
Location of saved .pkl file on disk
npz_file (str)
Location of saved .npz file on disk
outPr... | [
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assign_samples | (chunk, X, y, model, scale, chunk_size, values = False) | Runs a models assignment on a chunk of input
Args:
chunk (int)
Index of chunk to process
X (NumpyShared)
n x 2 array of core and accessory distances for n samples
y (NumpyShared)
An n-vector to store results, with the most likely cluster memberships
... | Runs a models assignment on a chunk of input | def assign_samples(chunk, X, y, model, scale, chunk_size, values = False):
"""Runs a models assignment on a chunk of input
Args:
chunk (int)
Index of chunk to process
X (NumpyShared)
n x 2 array of core and accessory distances for n samples
y (NumpyShared)
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ClusterFit.fit | (self, X = None) | Initial steps for all fit functions.
Creates output directory. If preprocess is set then subsamples passed X
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
preprocess is set.
(default = None)
de... | Initial steps for all fit functions. | def fit(self, X = None):
'''Initial steps for all fit functions.
Creates output directory. If preprocess is set then subsamples passed X
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
preprocess is set.
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ClusterFit.plot | (self, X=None) | Initial steps for all plot functions.
Ensures model has been fitted.
Args:
X (numpy.array)
The core and accessory distances to subsample.
(default = None)
| Initial steps for all plot functions. | def plot(self, X=None):
'''Initial steps for all plot functions.
Ensures model has been fitted.
Args:
X (numpy.array)
The core and accessory distances to subsample.
(default = None)
'''
if not self.fitted:
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ClusterFit.no_scale | (self) | Turn off scaling (useful for refine, where optimization
is done in the scaled space).
| Turn off scaling (useful for refine, where optimization
is done in the scaled space).
| def no_scale(self):
'''Turn off scaling (useful for refine, where optimization
is done in the scaled space).
'''
self.scale = np.array([1, 1], dtype = self.default_dtype) | [
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ClusterFit.copy | (self, prefix) | Copy the model to a new directory
| Copy the model to a new directory
| def copy(self, prefix):
"""Copy the model to a new directory
"""
self.outPrefix = prefix
self.save() | [
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BGMMFit.fit | (self, X, max_components) | Extends :func:`~ClusterFit.fit`
Fits the BGMM and returns assignments by calling
:func:`~PopPUNK.bgmm.fit2dMultiGaussian`.
Fitted parameters are stored in the object.
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
... | Extends :func:`~ClusterFit.fit` | def fit(self, X, max_components):
'''Extends :func:`~ClusterFit.fit`
Fits the BGMM and returns assignments by calling
:func:`~PopPUNK.bgmm.fit2dMultiGaussian`.
Fitted parameters are stored in the object.
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X (numpy.array)
The core and accessory ... | [
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BGMMFit.save | (self) | Save the model to disk, as an npz and pkl (using outPrefix). | Save the model to disk, as an npz and pkl (using outPrefix). | def save(self):
'''Save the model to disk, as an npz and pkl (using outPrefix).'''
if not self.fitted:
raise RuntimeError("Trying to save unfitted model")
else:
np.savez(self.outPrefix + "/" + os.path.basename(self.outPrefix) + '_fit.npz',
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BGMMFit.load | (self, fit_npz, fit_obj) | Load the model from disk. Called from :func:`~loadClusterFit`
Args:
fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (sklearn.mixture.BayesianGaussianMixture)
The saved fit object
| Load the model from disk. Called from :func:`~loadClusterFit` | def load(self, fit_npz, fit_obj):
'''Load the model from disk. Called from :func:`~loadClusterFit`
Args:
fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (sklearn.mixture.BayesianGaussianMixture)
The saved fit object
'''
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BGMMFit.plot | (self, X, y) | Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
:func:`PopPUNK.plot.plot_results` and :func:`PopPUNK.plot.plot_contours`
Args:
X (numpy.array)
Core and accessory distances
y (numpy.array)
Cluster as... | Extends :func:`~ClusterFit.plot` | def plot(self, X, y):
'''Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
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X (numpy.array)
Core and accessory distances
y (numpy.... | [
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BGMMFit.assign | (self, X, values = False, progress=True) | Assign the clustering of new samples using :func:`~PopPUNK.bgmm.assign_samples`
Args:
X (numpy.array)
Core and accessory distances
values (bool)
Return the responsibilities of assignment rather than most likely cluster
num_processes (int)
... | Assign the clustering of new samples using :func:`~PopPUNK.bgmm.assign_samples` | def assign(self, X, values = False, progress=True):
'''Assign the clustering of new samples using :func:`~PopPUNK.bgmm.assign_samples`
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X (numpy.array)
Core and accessory distances
values (bool)
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DBSCANFit.fit | (self, X, max_num_clusters, min_cluster_prop) | Extends :func:`~ClusterFit.fit`
Fits the distances with HDBSCAN and returns assignments by calling
:func:`~PopPUNK.dbscan.fitDbScan`.
Fitted parameters are stored in the object.
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set ... | Extends :func:`~ClusterFit.fit` | def fit(self, X, max_num_clusters, min_cluster_prop):
'''Extends :func:`~ClusterFit.fit`
Fits the distances with HDBSCAN and returns assignments by calling
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Fitted parameters are stored in the object.
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X (numpy.array)
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DBSCANFit.save | (self) | Save the model to disk, as an npz and pkl (using outPrefix). | Save the model to disk, as an npz and pkl (using outPrefix). | def save(self):
'''Save the model to disk, as an npz and pkl (using outPrefix).'''
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DBSCANFit.load | (self, fit_npz, fit_obj) | Load the model from disk. Called from :func:`~loadClusterFit`
Args:
fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (hdbscan.HDBSCAN)
The saved fit object
| Load the model from disk. Called from :func:`~loadClusterFit` | def load(self, fit_npz, fit_obj):
'''Load the model from disk. Called from :func:`~loadClusterFit`
Args:
fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (hdbscan.HDBSCAN)
The saved fit object
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DBSCANFit.plot | (self, X=None, y=None) | Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
:func:`PopPUNK.plot.plot_dbscan_results`
Args:
X (numpy.array)
Core and accessory distances
y (numpy.array)
Cluster assignments from :func:`~BGMMFit.a... | Extends :func:`~ClusterFit.plot` | def plot(self, X=None, y=None):
'''Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
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Args:
X (numpy.array)
Core and accessory distances
y (numpy.array)
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DBSCANFit.assign | (self, X, no_scale = False, progress = True) | Assign the clustering of new samples using :func:`~PopPUNK.dbscan.assign_samples_dbscan`
Args:
X (numpy.array)
Core and accessory distances
no_scale (bool)
Do not scale X
[default = False]
progress (bool)
Show ... | Assign the clustering of new samples using :func:`~PopPUNK.dbscan.assign_samples_dbscan` | def assign(self, X, no_scale = False, progress = True):
'''Assign the clustering of new samples using :func:`~PopPUNK.dbscan.assign_samples_dbscan`
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X (numpy.array)
Core and accessory distances
no_scale (bool)
Do not scale X
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RefineFit.fit | (self, X, sample_names, model, max_move, min_move, startFile = None, indiv_refine = False,
unconstrained = False, score_idx = 0, no_local = False,
betweenness_sample = betweenness_sample_default, use_gpu = False) | Extends :func:`~ClusterFit.fit`
Fits the distances by optimising network score, by calling
:func:`~PopPUNK.refine.refineFit2D`.
Fitted parameters are stored in the object.
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
... | Extends :func:`~ClusterFit.fit` | def fit(self, X, sample_names, model, max_move, min_move, startFile = None, indiv_refine = False,
unconstrained = False, score_idx = 0, no_local = False,
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RefineFit.apply_threshold | (self, X, threshold) | Applies a boundary threshold, given by user. Does not run
optimisation.
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
preprocess is set.
threshold (float)
The value along the x-axis (core distanc... | Applies a boundary threshold, given by user. Does not run
optimisation. | def apply_threshold(self, X, threshold):
'''Applies a boundary threshold, given by user. Does not run
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Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
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threshold (float)
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RefineFit.save | (self) | Save the model to disk, as an npz and pkl (using outPrefix). | Save the model to disk, as an npz and pkl (using outPrefix). | def save(self):
'''Save the model to disk, as an npz and pkl (using outPrefix).'''
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RefineFit.load | (self, fit_npz, fit_obj) | Load the model from disk. Called from :func:`~loadClusterFit`
Args:
fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (None)
The saved fit object (not used)
| Load the model from disk. Called from :func:`~loadClusterFit` | def load(self, fit_npz, fit_obj):
'''Load the model from disk. Called from :func:`~loadClusterFit`
Args:
fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (None)
The saved fit object (not used)
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RefineFit.plot | (self, X, y=None) | Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
:func:`PopPUNK.plot.plot_refined_results`
Args:
X (numpy.array)
Core and accessory distances
y (numpy.array)
Assignments (unused)
| Extends :func:`~ClusterFit.plot` | def plot(self, X, y=None):
'''Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
:func:`PopPUNK.plot.plot_refined_results`
Args:
X (numpy.array)
Core and accessory distances
y (numpy.array)
Ass... | [
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RefineFit.assign | (self, X, slope=None) | Assign the clustering of new samples
Args:
X (numpy.array)
Core and accessory distances
slope (int)
Override self.slope. Default - use self.slope
Set to 0 for a vertical line, 1 for a horizontal line, or
2 to use a slope
... | Assign the clustering of new samples | def assign(self, X, slope=None):
'''Assign the clustering of new samples
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X (numpy.array)
Core and accessory distances
slope (int)
Override self.slope. Default - use self.slope
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LineageFit.__save_sparse__ | (self, data, row, col, rank, n_samples, dtype) | Save a sparse matrix in coo format
| Save a sparse matrix in coo format
| def __save_sparse__(self, data, row, col, rank, n_samples, dtype):
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'''
if self.use_gpu:
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LineageFit.__reduce_rank__ | (self, higher_rank_sparse_mat, lower_rank, n_samples, dtype) | Lowers the rank of a fit and saves it
| Lowers the rank of a fit and saves it
| def __reduce_rank__(self, higher_rank_sparse_mat, lower_rank, n_samples, dtype):
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LineageFit.fit | (self, X, accessory) | Extends :func:`~ClusterFit.fit`
Gets assignments by using nearest neigbours.
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
preprocess is set.
accessory (bool)
Use accessory rather than core dist... | Extends :func:`~ClusterFit.fit` | def fit(self, X, accessory):
'''Extends :func:`~ClusterFit.fit`
Gets assignments by using nearest neigbours.
Args:
X (numpy.array)
The core and accessory distances to cluster. Must be set if
preprocess is set.
accessory (bool)
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LineageFit.save | (self) | Save the model to disk, as an npz and pkl (using outPrefix). | Save the model to disk, as an npz and pkl (using outPrefix). | def save(self):
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LineageFit.load | (self, fit_npz, fit_obj) | Load the model from disk. Called from :func:`~loadClusterFit`
Args:
fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (sklearn.mixture.BayesianGaussianMixture)
The saved fit object
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fit_npz (dict)
Fit npz opened with :func:`numpy.load`
fit_obj (sklearn.mixture.BayesianGaussianMixture)
The saved fit object
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LineageFit.plot | (self, X, y = None) | Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
:func:`PopPUNK.plot.plot_results` and :func:`PopPUNK.plot.plot_contours`
Args:
X (numpy.array)
Core and accessory distances
y (any)
Unused variable fo... | Extends :func:`~ClusterFit.plot` | def plot(self, X, y = None):
'''Extends :func:`~ClusterFit.plot`
Write a summary of the fit, and plot the results using
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X (numpy.array)
Core and accessory distances
y ... | [
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LineageFit.assign | (self, rank) | Get the edges for the network. A little different from other methods,
as it doesn't go through the long form distance vector (as coo_matrix
is basically already in the correct gt format)
Args:
rank (int)
Rank to assign at
Returns:
y (list of tuple... | Get the edges for the network. A little different from other methods,
as it doesn't go through the long form distance vector (as coo_matrix
is basically already in the correct gt format) | def assign(self, rank):
'''Get the edges for the network. A little different from other methods,
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Args:
rank (int)
Rank to assign at
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LineageFit.edge_weights | (self, rank) | Get the distances for each edge returned by assign
Args:
rank (int)
Rank assigned at
Returns:
weights (list)
Distance for each assignment
| Get the distances for each edge returned by assign | def edge_weights(self, rank):
'''Get the distances for each edge returned by assign
Args:
rank (int)
Rank assigned at
Returns:
weights (list)
Distance for each assignment
'''
if not self.fitted:
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LineageFit.extend | (self, qqDists, qrDists) | Update the sparse distance matrix of nearest neighbours after querying
Args:
qqDists (numpy or cupy ndarray)
Two column array of query-query distances
qqDists (numpy or cupy ndarray)
Two column array of reference-query distances
Returns:
... | Update the sparse distance matrix of nearest neighbours after querying | def extend(self, qqDists, qrDists):
'''Update the sparse distance matrix of nearest neighbours after querying
Args:
qqDists (numpy or cupy ndarray)
Two column array of query-query distances
qqDists (numpy or cupy ndarray)
Two column array of refer... | [
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_set_seed | (self, seed) | set the seed
we call both ``torch.manual_seed()`` and ``np.random.seed()``
we also set the ``self.seed`` attribute
Parameters
----------
seed : int
the seed to set
| set the seed | def _set_seed(self, seed):
"""set the seed
we call both ``torch.manual_seed()`` and ``np.random.seed()``
we also set the ``self.seed`` attribute
Parameters
----------
seed : int
the seed to set
"""
self.seed = seed
if seed is not Non... | [
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_init_synthesized_signal | (self, synthesized_signal_data, clamper=RangeClamper((0, 1)),
clamp_each_iter=True) | initialize the synthesized image
set the ``self.synthesized_signal`` attribute to be a parameter with
the user-supplied data, making sure it's the right shape and
calling clamper on it, if set
also initialize the ``self.synthesized_representation`` attribute
Parameters
... | initialize the synthesized image | def _init_synthesized_signal(self, synthesized_signal_data, clamper=RangeClamper((0, 1)),
clamp_each_iter=True):
"""initialize the synthesized image
set the ``self.synthesized_signal`` attribute to be a parameter with
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_init_ctf_and_randomizer | (self, loss_thresh=1e-4, fraction_removed=0, coarse_to_fine=False,
loss_change_fraction=1, loss_change_thresh=1e-2,
loss_change_iter=50) | initialize stuff related to randomization and coarse-to-fine
Parameters
----------
loss_thresh : float, optional
If the loss over the past ``loss_change_iter`` is less than
``loss_thresh``, we stop.
fraction_removed: float, optional
The fraction of th... | initialize stuff related to randomization and coarse-to-fine | def _init_ctf_and_randomizer(self, loss_thresh=1e-4, fraction_removed=0, coarse_to_fine=False,
loss_change_fraction=1, loss_change_thresh=1e-2,
loss_change_iter=50):
"""initialize stuff related to randomization and coarse-to-fine
Paramet... | [
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_init_store_progress | (self, store_progress, save_progress, save_path) | initialize store_progress-related attributes
sets the ``self.save_progress``, ``self.store_progress``, and
``self.save_path`` attributes, as well as changing
``saved_signal, saved_representation, saved_signal_gradient,
saved_representation_gradient`` attibutes all to lists so we can
... | initialize store_progress-related attributes | def _init_store_progress(self, store_progress, save_progress, save_path):
"""initialize store_progress-related attributes
sets the ``self.save_progress``, ``self.store_progress``, and
``self.save_path`` attributes, as well as changing
``saved_signal, saved_representation, saved_signal_g... | [
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_check_nan_loss | (self, loss) | check if loss is nan and, if so, return True
This checks if loss is NaN and, if so, updates
synthesized_signal/representation to be several iterations ago (so
they're meaningful) and then returns True
Parameters
----------
loss : torch.Tensor
the loss from t... | check if loss is nan and, if so, return True | def _check_nan_loss(self, loss):
"""check if loss is nan and, if so, return True
This checks if loss is NaN and, if so, updates
synthesized_signal/representation to be several iterations ago (so
they're meaningful) and then returns True
Parameters
----------
los... | [
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_clamp_and_store | (self, i) | clamp synthesized_signal and store/save, if appropriate
these all happen together because they all happen ``with
torch.no_grad()``
if it's the right iteration, we update: ``saved_signal,
saved_representation, saved_signal_gradient,
saved_representation_gradient``
Param... | clamp synthesized_signal and store/save, if appropriate | def _clamp_and_store(self, i):
"""clamp synthesized_signal and store/save, if appropriate
these all happen together because they all happen ``with
torch.no_grad()``
if it's the right iteration, we update: ``saved_signal,
saved_representation, saved_signal_gradient,
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_check_for_stabilization | (self, i) | r"""Check whether the loss has stabilized and, if so, return True
We check whether the loss has stopped decreasing and return True
if so.
We rely on a handful of attributes to do this, and take the
following steps:
1. Check if we've been synthesizing for at least
``... | r"""Check whether the loss has stabilized and, if so, return True | def _check_for_stabilization(self, i):
r"""Check whether the loss has stabilized and, if so, return True
We check whether the loss has stopped decreasing and return True
if so.
We rely on a handful of attributes to do this, and take the
following steps:
1. Check if we'... | [
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_finalize_stored_progress | (self) | stack the saved_* attributes
if we were storing progress, stack the ``saved_representation,
saved_signal, saved_signal_gradient,
saved_representation_gradient`` attributes so they're a single
tensor
we can't stack the gradients if we used coarse-to-fine
optimization, be... | stack the saved_* attributes | def _finalize_stored_progress(self):
"""stack the saved_* attributes
if we were storing progress, stack the ``saved_representation,
saved_signal, saved_signal_gradient,
saved_representation_gradient`` attributes so they're a single
tensor
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492,
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synthesize | (self, seed=0, max_iter=100, learning_rate=1, scheduler=True, optimizer='Adam',
optimizer_kwargs={}, swa=False, swa_kwargs={}, clamper=RangeClamper((0, 1)),
clamp_each_iter=True, store_progress=False,
save_progress=False, save_path='synthesis.pt', loss_thresh=1e-... | r"""synthesize an image
this is a skeleton of how synthesize() works, just to serve as a
guide -- YOU SHOULD NOT CALL THIS FUNCTION.
You should, however, copy the call signature and then add any
extra arguments specific to the given synthesis method at the
beginning
FO... | r"""synthesize an image | def synthesize(self, seed=0, max_iter=100, learning_rate=1, scheduler=True, optimizer='Adam',
optimizer_kwargs={}, swa=False, swa_kwargs={}, clamper=RangeClamper((0, 1)),
clamp_each_iter=True, store_progress=False,
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653,
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analyze | (self, x, **kwargs) | r"""Analyze the image, that is, obtain the model's representation of it
Any kwargs are passed to the model's forward method
Parameters
----------
x : torch.Tensor
The image to analyze
Returns
-------
y : torch.Tensor
The model's represen... | r"""Analyze the image, that is, obtain the model's representation of it | def analyze(self, x, **kwargs):
r"""Analyze the image, that is, obtain the model's representation of it
Any kwargs are passed to the model's forward method
Parameters
----------
x : torch.Tensor
The image to analyze
Returns
-------
y : torch... | [
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objective_function | (self, synth_rep, ref_rep, synth_img, ref_img) | r"""Calculate the loss
This is what we minimize. We call
``self.loss_function(ref_rep=ref_rep, synth_rep=synth_rep,
ref_img=ref_img, synth_img=synth_img)`` -- by default, this is
the L2-norm of the difference between the two representations:
``torch.norm(ref_rep - synth_rep, p=2... | r"""Calculate the loss | def objective_function(self, synth_rep, ref_rep, synth_img, ref_img):
r"""Calculate the loss
This is what we minimize. We call
``self.loss_function(ref_rep=ref_rep, synth_rep=synth_rep,
ref_img=ref_img, synth_img=synth_img)`` -- by default, this is
the L2-norm of the difference ... | [
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representation_error | (self, iteration=None, **kwargs) | r"""Get the representation error
This is (synthesized_representation - base_representation). If
``iteration`` is not None, we use
``self.saved_representation[iteration]`` for
synthesized_representation
Any kwargs are passed through to self.analyze when computing the
syn... | r"""Get the representation error | def representation_error(self, iteration=None, **kwargs):
r"""Get the representation error
This is (synthesized_representation - base_representation). If
``iteration`` is not None, we use
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