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|---|---|---|---|---|---|---|---|---|---|---|---|
select_datasource | (
context: DataContext, datasource_name: str = None
) | Select a datasource interactively. | Select a datasource interactively. | def select_datasource(
context: DataContext, datasource_name: str = None
) -> BaseDatasource:
"""Select a datasource interactively."""
# TODO consolidate all the myriad CLI tests into this
data_source: Optional[BaseDatasource] = None
if datasource_name is None:
data_sources: List[BaseDatasource] = cast(
List[BaseDatasource],
list(
sorted(context.datasources.values(), key=lambda x: x.name),
),
)
if len(data_sources) == 0:
cli_message(
string="<red>No datasources found in the context. To add a datasource, run `great_expectations datasource new`</red>"
)
elif len(data_sources) == 1:
datasource_name = data_sources[0].name
else:
choices: str = "\n".join(
[
" {}. {}".format(i, data_source.name)
for i, data_source in enumerate(data_sources, 1)
]
)
option_selection: str = click.prompt(
"Select a datasource" + "\n" + choices + "\n",
type=click.Choice(
[str(i) for i, data_source in enumerate(data_sources, 1)]
),
show_choices=False,
)
datasource_name = data_sources[int(option_selection) - 1].name
if datasource_name is not None:
data_source = context.get_datasource(datasource_name=datasource_name)
return data_source | [
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365,
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] | [
404,
22
] | python | en | ['en', 'en', 'en'] | True |
load_data_context_with_error_handling | (
directory: str, from_cli_upgrade_command: bool = False
) | Return a DataContext with good error handling and exit codes. | Return a DataContext with good error handling and exit codes. | def load_data_context_with_error_handling(
directory: str, from_cli_upgrade_command: bool = False
) -> DataContext:
"""Return a DataContext with good error handling and exit codes."""
try:
context: DataContext = DataContext(context_root_dir=directory)
if from_cli_upgrade_command:
try:
send_usage_message(
data_context=context,
event="cli.project.upgrade.begin",
success=True,
)
except Exception:
# Don't fail for usage stats
pass
ge_config_version: int = context.get_config().config_version
if (
from_cli_upgrade_command
and int(ge_config_version) < CURRENT_GE_CONFIG_VERSION
):
directory = directory or context.root_directory
(
increment_version,
exception_occurred,
) = upgrade_project_one_version_increment(
context_root_dir=directory,
ge_config_version=ge_config_version,
continuation_message=EXIT_UPGRADE_CONTINUATION_MESSAGE,
from_cli_upgrade_command=from_cli_upgrade_command,
)
if not exception_occurred and increment_version:
context = DataContext(context_root_dir=directory)
if from_cli_upgrade_command:
send_usage_message(
data_context=context,
event="cli.project.upgrade.end",
success=True,
)
return context
except ge_exceptions.UnsupportedConfigVersionError as err:
directory = directory or DataContext.find_context_root_dir()
ge_config_version = DataContext.get_ge_config_version(
context_root_dir=directory
)
upgrade_helper_class = (
GE_UPGRADE_HELPER_VERSION_MAP.get(int(ge_config_version))
if ge_config_version
else None
)
if upgrade_helper_class and ge_config_version < CURRENT_GE_CONFIG_VERSION:
upgrade_project(
context_root_dir=directory,
ge_config_version=ge_config_version,
from_cli_upgrade_command=from_cli_upgrade_command,
)
else:
cli_message(string="<red>{}</red>".format(err.message))
sys.exit(1)
except (
ge_exceptions.ConfigNotFoundError,
ge_exceptions.InvalidConfigError,
) as err:
cli_message(string="<red>{}</red>".format(err.message))
sys.exit(1)
except ge_exceptions.PluginModuleNotFoundError as err:
cli_message(string=err.cli_colored_message)
sys.exit(1)
except ge_exceptions.PluginClassNotFoundError as err:
cli_message(string=err.cli_colored_message)
sys.exit(1)
except ge_exceptions.InvalidConfigurationYamlError as err:
cli_message(string=f"<red>{str(err)}</red>")
sys.exit(1) | [
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407,
0
] | [
480,
19
] | python | en | ['en', 'en', 'en'] | True |
confirm_proceed_or_exit | (
confirm_prompt: str = "Would you like to proceed?",
continuation_message: str = "Ok, exiting now. You can always read more at https://docs.greatexpectations.io/ !",
exit_on_no: bool = True,
exit_code: int = 0,
data_context: Optional[DataContext] = None,
usage_stats_event: Optional[str] = None,
) |
Every CLI command that starts a potentially lengthy (>1 sec) computation
or modifies some resources (e.g., edits the config file, adds objects
to the stores) must follow this pattern:
1. Explain which resources will be created/modified/deleted
2. Use this method to ask for user's confirmation
The goal of this standardization is for the users to expect consistency -
if you saw one command, you know what to expect from all others.
If the user does not confirm, the program should exit. The purpose of the exit_on_no parameter is to provide
the option to perform cleanup actions before exiting outside of the function.
|
Every CLI command that starts a potentially lengthy (>1 sec) computation
or modifies some resources (e.g., edits the config file, adds objects
to the stores) must follow this pattern:
1. Explain which resources will be created/modified/deleted
2. Use this method to ask for user's confirmation | def confirm_proceed_or_exit(
confirm_prompt: str = "Would you like to proceed?",
continuation_message: str = "Ok, exiting now. You can always read more at https://docs.greatexpectations.io/ !",
exit_on_no: bool = True,
exit_code: int = 0,
data_context: Optional[DataContext] = None,
usage_stats_event: Optional[str] = None,
) -> Optional[bool]:
"""
Every CLI command that starts a potentially lengthy (>1 sec) computation
or modifies some resources (e.g., edits the config file, adds objects
to the stores) must follow this pattern:
1. Explain which resources will be created/modified/deleted
2. Use this method to ask for user's confirmation
The goal of this standardization is for the users to expect consistency -
if you saw one command, you know what to expect from all others.
If the user does not confirm, the program should exit. The purpose of the exit_on_no parameter is to provide
the option to perform cleanup actions before exiting outside of the function.
"""
confirm_prompt_colorized = cli_colorize_string(confirm_prompt)
continuation_message_colorized = cli_colorize_string(continuation_message)
if not click.confirm(confirm_prompt_colorized, default=True):
if exit_on_no:
cli_message(string=continuation_message_colorized)
cli_message(string=continuation_message_colorized)
if (usage_stats_event is not None) and (data_context is not None):
# noinspection PyBroadException
try:
send_usage_message(
data_context=data_context,
event=usage_stats_event,
event_payload={"cancelled": True},
success=True,
)
except Exception:
# Don't fail on usage stats
pass
sys.exit(exit_code)
else:
return False
return True | [
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633,
0
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675,
15
] | python | en | ['en', 'error', 'th'] | False |
parse_cli_config_file_location | (config_file_location: str) |
Parse CLI yaml config file or directory location into directory and filename.
Uses pathlib to handle windows paths.
Args:
config_file_location: string of config_file_location
Returns:
{
"directory": "directory/where/config/file/is/located",
"filename": "great_expectations.yml"
}
|
Parse CLI yaml config file or directory location into directory and filename.
Uses pathlib to handle windows paths.
Args:
config_file_location: string of config_file_location | def parse_cli_config_file_location(config_file_location: str) -> dict:
"""
Parse CLI yaml config file or directory location into directory and filename.
Uses pathlib to handle windows paths.
Args:
config_file_location: string of config_file_location
Returns:
{
"directory": "directory/where/config/file/is/located",
"filename": "great_expectations.yml"
}
"""
if config_file_location is not None and config_file_location != "":
config_file_location_path = Path(config_file_location)
# If the file or directory exists, treat it appropriately
# This handles files without extensions
if config_file_location_path.is_file():
filename: Optional[str] = fr"{str(config_file_location_path.name)}"
directory: Optional[str] = fr"{str(config_file_location_path.parent)}"
elif config_file_location_path.is_dir():
filename: Optional[str] = None
directory: Optional[str] = config_file_location
else:
raise ge_exceptions.ConfigNotFoundError()
else:
# Return None if config_file_location is empty rather than default output of ""
directory = None
filename = None
return {"directory": directory, "filename": filename} | [
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is_cloud_file_url | (file_path: str) | Check for commonly used cloud urls. | Check for commonly used cloud urls. | def is_cloud_file_url(file_path: str) -> bool:
"""Check for commonly used cloud urls."""
sanitized = file_path.strip()
if sanitized[0:7] == "file://":
return False
if (
sanitized[0:5] in ["s3://", "gs://"]
or sanitized[0:6] == "ftp://"
or sanitized[0:7] in ["http://", "wasb://"]
or sanitized[0:8] == "https://"
):
return True
return False | [
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] | python | en | ['en', 'en', 'en'] | True |
get_relative_path_from_config_file_to_base_path | (
context_root_directory: str, data_path: str
) |
This function determines the relative path from a given data path relative
to the great_expectations.yml file independent of the current working
directory.
This allows a user to use the CLI from any directory, type a relative path
from their current working directory and have the correct relative path be
put in the great_expectations.yml file.
|
This function determines the relative path from a given data path relative
to the great_expectations.yml file independent of the current working
directory. | def get_relative_path_from_config_file_to_base_path(
context_root_directory: str, data_path: str
) -> str:
"""
This function determines the relative path from a given data path relative
to the great_expectations.yml file independent of the current working
directory.
This allows a user to use the CLI from any directory, type a relative path
from their current working directory and have the correct relative path be
put in the great_expectations.yml file.
"""
data_from_working_dir = os.path.relpath(data_path)
context_dir_from_working_dir = os.path.relpath(context_root_directory)
return os.path.relpath(data_from_working_dir, context_dir_from_working_dir) | [
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749,
0
] | [
763,
79
] | python | en | ['en', 'error', 'th'] | False |
ValidationAction.run | (
self,
validation_result_suite,
validation_result_suite_identifier,
data_asset,
**kwargs,
) |
:param validation_result_suite:
:param validation_result_suite_identifier:
:param data_asset:
:param: kwargs - any additional arguments the child might use
:return:
| def run(
self,
validation_result_suite,
validation_result_suite_identifier,
data_asset,
**kwargs,
):
"""
:param validation_result_suite:
:param validation_result_suite_identifier:
:param data_asset:
:param: kwargs - any additional arguments the child might use
:return:
"""
return self._run(
validation_result_suite,
validation_result_suite_identifier,
data_asset,
**kwargs,
) | [
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",",
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] | [
42,
4
] | [
62,
9
] | python | en | ['en', 'error', 'th'] | False |
|
SlackNotificationAction.__init__ | (
self,
data_context,
renderer,
slack_webhook,
notify_on="all",
notify_with=None,
) | Construct a SlackNotificationAction
Args:
data_context:
renderer: dictionary specifying the renderer used to generate a query consumable by Slack API, for example:
{
"module_name": "great_expectations.render.renderer.slack_renderer",
"class_name": "SlackRenderer",
}
slack_webhook: incoming Slack webhook to which to send notification
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
payload: *Optional* payload from other ValidationActions
| Construct a SlackNotificationAction | def __init__(
self,
data_context,
renderer,
slack_webhook,
notify_on="all",
notify_with=None,
):
"""Construct a SlackNotificationAction
Args:
data_context:
renderer: dictionary specifying the renderer used to generate a query consumable by Slack API, for example:
{
"module_name": "great_expectations.render.renderer.slack_renderer",
"class_name": "SlackRenderer",
}
slack_webhook: incoming Slack webhook to which to send notification
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
payload: *Optional* payload from other ValidationActions
"""
super().__init__(data_context)
self.renderer = instantiate_class_from_config(
config=renderer,
runtime_environment={},
config_defaults={},
)
module_name = renderer["module_name"]
if not self.renderer:
raise ClassInstantiationError(
module_name=module_name,
package_name=None,
class_name=renderer["class_name"],
)
self.slack_webhook = slack_webhook
assert slack_webhook, "No Slack webhook found in action config."
self.notify_on = notify_on
self.notify_with = notify_with | [
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"=",
"notify_with"
] | [
108,
4
] | [
145,
38
] | python | en | ['en', 'ca', 'en'] | True |
PagerdutyAlertAction.__init__ | (
self,
data_context,
api_key,
routing_key,
notify_on="failure",
) | Construct a PagerdutyAlertAction
Args:
data_context:
api_key: Events API v2 key for pagerduty.
routing_key: The 32 character Integration Key for an integration on a service or on a global ruleset.
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
| Construct a PagerdutyAlertAction | def __init__(
self,
data_context,
api_key,
routing_key,
notify_on="failure",
):
"""Construct a PagerdutyAlertAction
Args:
data_context:
api_key: Events API v2 key for pagerduty.
routing_key: The 32 character Integration Key for an integration on a service or on a global ruleset.
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
"""
super().__init__(data_context)
if not pypd:
raise DataContextError("ModuleNotFoundError: No module named 'pypd'")
self.api_key = api_key
assert api_key, "No Pagerduty api_key found in action config."
self.routing_key = routing_key
assert routing_key, "No Pagerduty routing_key found in action config."
self.notify_on = notify_on | [
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] | [
215,
4
] | [
237,
34
] | python | en | ['en', 'lb', 'it'] | False |
MicrosoftTeamsNotificationAction.__init__ | (
self,
data_context,
renderer,
microsoft_teams_webhook,
notify_on="all",
) | Construct a MicrosoftTeamsNotificationAction
Args:
data_context:
renderer: dictionary specifying the renderer used to generate a query consumable by teams API, for example:
{
"module_name": "great_expectations.render.renderer.microsoft_teams_renderer",
"class_name": "MicrosoftTeamsRenderer",
}
microsoft_teams_webhook: incoming Microsoft Teams webhook to which to send notifications
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
payload: *Optional* payload from other ValidationActions
| Construct a MicrosoftTeamsNotificationAction | def __init__(
self,
data_context,
renderer,
microsoft_teams_webhook,
notify_on="all",
):
"""Construct a MicrosoftTeamsNotificationAction
Args:
data_context:
renderer: dictionary specifying the renderer used to generate a query consumable by teams API, for example:
{
"module_name": "great_expectations.render.renderer.microsoft_teams_renderer",
"class_name": "MicrosoftTeamsRenderer",
}
microsoft_teams_webhook: incoming Microsoft Teams webhook to which to send notifications
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
payload: *Optional* payload from other ValidationActions
"""
super().__init__(data_context)
self.renderer = instantiate_class_from_config(
config=renderer,
runtime_environment={},
config_defaults={},
)
module_name = renderer["module_name"]
if not self.renderer:
raise ClassInstantiationError(
module_name=module_name,
package_name=None,
class_name=renderer["class_name"],
)
self.teams_webhook = microsoft_teams_webhook
assert (
microsoft_teams_webhook
), "No Microsoft teams webhook found in action config."
self.notify_on = notify_on | [
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] | [
314,
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] | [
351,
34
] | python | en | ['en', 'en', 'en'] | True |
OpsgenieAlertAction.__init__ | (
self,
data_context,
renderer,
api_key,
region=None,
priority="P3",
notify_on="failure",
) | Construct a OpsgenieAlertAction
Args:
data_context:
api_key: Opsgenie API key
region: specifies the Opsgenie region. Populate 'EU' for Europe otherwise do not set
priority: specify the priority of the alert (P1 - P5) defaults to P3
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
| Construct a OpsgenieAlertAction | def __init__(
self,
data_context,
renderer,
api_key,
region=None,
priority="P3",
notify_on="failure",
):
"""Construct a OpsgenieAlertAction
Args:
data_context:
api_key: Opsgenie API key
region: specifies the Opsgenie region. Populate 'EU' for Europe otherwise do not set
priority: specify the priority of the alert (P1 - P5) defaults to P3
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
"""
super().__init__(data_context)
self.renderer = instantiate_class_from_config(
config=renderer,
runtime_environment={},
config_defaults={},
)
module_name = renderer["module_name"]
if not self.renderer:
raise ClassInstantiationError(
module_name=module_name,
package_name=None,
class_name=renderer["class_name"],
)
self.api_key = api_key
assert api_key, "opsgenie_api_key missing in config_variables.yml"
self.region = region
self.priority = priority
self.notify_on = notify_on | [
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EmailAction.__init__ | (
self,
data_context,
renderer,
smtp_address,
smtp_port,
sender_login,
sender_password,
receiver_emails,
sender_alias=None,
use_tls=None,
use_ssl=None,
notify_on="all",
notify_with=None,
) | Construct an EmailAction
Args:
data_context:
renderer: dictionary specifying the renderer used to generate an email, for example:
{
"module_name": "great_expectations.render.renderer.email_renderer",
"class_name": "EmailRenderer",
}
smtp_address: address of the SMTP server used to send the email
smtp_address: port of the SMTP server used to send the email
sender_login: login used send the email
sender_password: password used to send the email
sender_alias: optional alias used to send the email (default = sender_login)
receiver_emails: email addresses that will be receive the email (separated by commas)
use_tls: optional use of TLS to send the email (using either TLS or SSL is highly recommended)
use_ssl: optional use of SSL to send the email (using either TLS or SSL is highly recommended)
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
notify_with: optional list of DataDocs site names to display in the email message
| Construct an EmailAction
Args:
data_context:
renderer: dictionary specifying the renderer used to generate an email, for example:
{
"module_name": "great_expectations.render.renderer.email_renderer",
"class_name": "EmailRenderer",
}
smtp_address: address of the SMTP server used to send the email
smtp_address: port of the SMTP server used to send the email
sender_login: login used send the email
sender_password: password used to send the email
sender_alias: optional alias used to send the email (default = sender_login)
receiver_emails: email addresses that will be receive the email (separated by commas)
use_tls: optional use of TLS to send the email (using either TLS or SSL is highly recommended)
use_ssl: optional use of SSL to send the email (using either TLS or SSL is highly recommended)
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
notify_with: optional list of DataDocs site names to display in the email message
| def __init__(
self,
data_context,
renderer,
smtp_address,
smtp_port,
sender_login,
sender_password,
receiver_emails,
sender_alias=None,
use_tls=None,
use_ssl=None,
notify_on="all",
notify_with=None,
):
"""Construct an EmailAction
Args:
data_context:
renderer: dictionary specifying the renderer used to generate an email, for example:
{
"module_name": "great_expectations.render.renderer.email_renderer",
"class_name": "EmailRenderer",
}
smtp_address: address of the SMTP server used to send the email
smtp_address: port of the SMTP server used to send the email
sender_login: login used send the email
sender_password: password used to send the email
sender_alias: optional alias used to send the email (default = sender_login)
receiver_emails: email addresses that will be receive the email (separated by commas)
use_tls: optional use of TLS to send the email (using either TLS or SSL is highly recommended)
use_ssl: optional use of SSL to send the email (using either TLS or SSL is highly recommended)
notify_on: "all", "failure", "success" - specifies validation status that will trigger notification
notify_with: optional list of DataDocs site names to display in the email message
"""
super().__init__(data_context)
self.renderer = instantiate_class_from_config(
config=renderer,
runtime_environment={},
config_defaults={},
)
module_name = renderer["module_name"]
if not self.renderer:
raise ClassInstantiationError(
module_name=module_name,
package_name=None,
class_name=renderer["class_name"],
)
self.smtp_address = smtp_address
self.smtp_port = smtp_port
self.sender_login = sender_login
self.sender_password = sender_password
if not sender_alias:
self.sender_alias = sender_login
else:
self.sender_alias = sender_alias
self.receiver_emails_list = list(
map(lambda x: x.strip(), receiver_emails.split(","))
)
self.use_tls = use_tls
self.use_ssl = use_ssl
assert smtp_address, "No SMTP server address found in action config."
assert smtp_port, "No SMTP server port found in action config."
assert sender_login, "No login found for sending the email in action config."
assert (
sender_password
), "No password found for sending the email in action config."
assert (
receiver_emails
), "No email addresses to send the email to in action config."
self.notify_on = notify_on
self.notify_with = notify_with | [
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540,
4
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610,
38
] | python | en | ['en', 'en', 'en'] | True |
StoreValidationResultAction.__init__ | (
self,
data_context,
target_store_name=None,
) |
:param data_context: Data Context
:param target_store_name: the name of the param_store in the Data Context which
should be used to param_store the validation result
| def __init__(
self,
data_context,
target_store_name=None,
):
"""
:param data_context: Data Context
:param target_store_name: the name of the param_store in the Data Context which
should be used to param_store the validation result
"""
super().__init__(data_context)
if target_store_name is None:
self.target_store = data_context.stores[data_context.validations_store_name]
else:
self.target_store = data_context.stores[target_store_name] | [
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687,
4
] | [
703,
70
] | python | en | ['en', 'error', 'th'] | False |
|
StoreEvaluationParametersAction.__init__ | (self, data_context, target_store_name=None) |
Args:
data_context: Data Context
target_store_name: the name of the store in the Data Context which
should be used to store the evaluation parameters
| def __init__(self, data_context, target_store_name=None):
"""
Args:
data_context: Data Context
target_store_name: the name of the store in the Data Context which
should be used to store the evaluation parameters
"""
super().__init__(data_context)
if target_store_name is None:
self.target_store = data_context.evaluation_parameter_store
else:
self.target_store = data_context.stores[target_store_name] | [
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|
StoreMetricsAction.__init__ | (
self, data_context, requested_metrics, target_store_name="metrics_store"
) |
Args:
data_context: Data Context
requested_metrics: dictionary of metrics to store. Dictionary should have the following structure:
expectation_suite_name:
metric_name:
- metric_kwargs_id
You may use "*" to denote that any expectation suite should match.
target_store_name: the name of the store in the Data Context which
should be used to store the metrics
| def __init__(
self, data_context, requested_metrics, target_store_name="metrics_store"
):
"""
Args:
data_context: Data Context
requested_metrics: dictionary of metrics to store. Dictionary should have the following structure:
expectation_suite_name:
metric_name:
- metric_kwargs_id
You may use "*" to denote that any expectation suite should match.
target_store_name: the name of the store in the Data Context which
should be used to store the metrics
"""
super().__init__(data_context)
self._requested_metrics = requested_metrics
self._target_store_name = target_store_name
try:
store = data_context.stores[target_store_name]
except KeyError:
raise DataContextError(
"Unable to find store {} in your DataContext configuration.".format(
target_store_name
)
)
if not isinstance(store, MetricStore):
raise DataContextError(
"StoreMetricsAction must have a valid MetricsStore for its target store."
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|
UpdateDataDocsAction.__init__ | (self, data_context, site_names=None, target_site_names=None) |
:param data_context: Data Context
:param site_names: *optional* List of site names for building data docs
|
:param data_context: Data Context
:param site_names: *optional* List of site names for building data docs
| def __init__(self, data_context, site_names=None, target_site_names=None):
"""
:param data_context: Data Context
:param site_names: *optional* List of site names for building data docs
"""
super().__init__(data_context)
if target_site_names:
warnings.warn(
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DeprecationWarning,
)
if site_names:
raise DataContextError(
"Invalid configuration: legacy key target_site_names and site_names key are "
"both present in UpdateDataDocsAction configuration"
)
site_names = target_site_names
self._site_names = site_names | [
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910,
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] | python | en | ['en', 'error', 'th'] | False |
test_parse_cli_config_file_location_posix_paths | (tmp_path_factory) |
What does this test and why?
We want to parse posix paths into their directory and filename parts
so that we can pass the directory to our data context constructor.
We need to be able to do that with all versions of path that can be input.
This tests for posix paths for files/dirs that don't exist and files/dirs that do.
Other tests handle testing for windows support.
|
What does this test and why?
We want to parse posix paths into their directory and filename parts
so that we can pass the directory to our data context constructor.
We need to be able to do that with all versions of path that can be input.
This tests for posix paths for files/dirs that don't exist and files/dirs that do.
Other tests handle testing for windows support.
| def test_parse_cli_config_file_location_posix_paths(tmp_path_factory):
"""
What does this test and why?
We want to parse posix paths into their directory and filename parts
so that we can pass the directory to our data context constructor.
We need to be able to do that with all versions of path that can be input.
This tests for posix paths for files/dirs that don't exist and files/dirs that do.
Other tests handle testing for windows support.
"""
filename_fixtures = [
{
"input_path": "just_a_file.yml",
"expected": {
"directory": "",
"filename": "just_a_file.yml",
},
},
]
absolute_path_fixtures = [
{
"input_path": "/path/to/file/filename.yml",
"expected": {
"directory": "/path/to/file",
"filename": "filename.yml",
},
},
{
"input_path": "/absolute/directory/ending/slash/",
"expected": {
"directory": "/absolute/directory/ending/slash/",
"filename": None,
},
},
{
"input_path": "/absolute/directory/ending/no/slash",
"expected": {
"directory": "/absolute/directory/ending/no/slash",
"filename": None,
},
},
]
relative_path_fixtures = [
{
"input_path": "relative/path/to/file.yml",
"expected": {
"directory": "relative/path/to",
"filename": "file.yml",
},
},
{
"input_path": "relative/path/to/directory/slash/",
"expected": {
"directory": "relative/path/to/directory/slash/",
"filename": None,
},
},
{
"input_path": "relative/path/to/directory/no_slash",
"expected": {
"directory": "relative/path/to/directory/no_slash",
"filename": None,
},
},
]
fixtures = filename_fixtures + absolute_path_fixtures + relative_path_fixtures
for fixture in fixtures:
with pytest.raises(ge_exceptions.ConfigNotFoundError):
toolkit.parse_cli_config_file_location(fixture["input_path"])
# Create files and re-run assertions
root_dir = tmp_path_factory.mktemp("posix")
root_dir = str(root_dir)
for fixture in fixtures:
expected_dir = fixture.get("expected").get("directory")
# Make non-absolute path
if expected_dir is not None and expected_dir.startswith("/"):
expected_dir = expected_dir[1:]
expected_filename = fixture.get("expected").get("filename")
if expected_dir:
test_directory = os.path.join(root_dir, expected_dir)
os.makedirs(test_directory, exist_ok=True)
if expected_filename:
expected_filepath = os.path.join(test_directory, expected_filename)
with open(expected_filepath, "w") as fp:
pass
output = toolkit.parse_cli_config_file_location(expected_filepath)
assert output == {
"directory": os.path.join(root_dir, expected_dir),
"filename": expected_filename,
} | [
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46,
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142,
17
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test_parse_cli_config_file_location_windows_paths | (tmp_path_factory) |
What does this test and why?
Since we are unable to test windows paths on our unix CI, this just
tests that if a file doesn't exist we raise an error.
Args:
tmp_path_factory:
Returns:
|
What does this test and why?
Since we are unable to test windows paths on our unix CI, this just
tests that if a file doesn't exist we raise an error.
Args:
tmp_path_factory:
Returns:
| def test_parse_cli_config_file_location_windows_paths(tmp_path_factory):
"""
What does this test and why?
Since we are unable to test windows paths on our unix CI, this just
tests that if a file doesn't exist we raise an error.
Args:
tmp_path_factory:
Returns:
"""
filename_fixtures = [
{
"input_path": "just_a_file.yml",
"expected": {
"directory": "",
"filename": "just_a_file.yml",
},
},
]
absolute_path_fixtures = [
{
"input_path": r"C:\absolute\windows\path\to\file.yml",
"expected": {
"directory": r"C:\absolute\windows\path\to",
"filename": "file.yml",
},
},
{
"input_path": r"C:\absolute\windows\directory\ending\slash\\",
"expected": {
"directory": r"C:\absolute\windows\directory\ending\slash\\",
"filename": None,
},
},
{
"input_path": r"C:\absolute\windows\directory\ending\no_slash",
"expected": {
"directory": r"C:\absolute\windows\directory\ending\no_slash",
"filename": None,
},
},
]
relative_path_fixtures = [
{
"input_path": r"relative\windows\path\to\file.yml",
"expected": {
"directory": r"relative\windows\path\to",
"filename": "file.yml",
},
},
# Double slash at end of raw string to escape slash
{
"input_path": r"relative\windows\path\to\directory\slash\\",
"expected": {
"directory": r"relative\windows\path\to\directory\slash\\",
"filename": None,
},
},
{
"input_path": r"relative\windows\path\to\directory\no_slash",
"expected": {
"directory": r"relative\windows\path\to\directory\no_slash",
"filename": None,
},
},
]
fixtures = filename_fixtures + absolute_path_fixtures + relative_path_fixtures
for fixture in fixtures:
with pytest.raises(ge_exceptions.ConfigNotFoundError):
toolkit.parse_cli_config_file_location(fixture["input_path"]) | [
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233,
0
] | [
303,
73
] | python | en | ['en', 'error', 'th'] | False |
simulated_project_directories | (tmp_path_factory) |
Using a wacky simulated directory structure allows testing of permutations
of relative, absolute, and current working directories.
/random/pytest/dir/projects/pipeline1/great_expectations
/random/pytest/dir/projects/data/pipeline1
|
Using a wacky simulated directory structure allows testing of permutations
of relative, absolute, and current working directories. | def simulated_project_directories(tmp_path_factory):
"""
Using a wacky simulated directory structure allows testing of permutations
of relative, absolute, and current working directories.
/random/pytest/dir/projects/pipeline1/great_expectations
/random/pytest/dir/projects/data/pipeline1
"""
test_dir = tmp_path_factory.mktemp("projects", numbered=False)
assert os.path.isabs(test_dir)
ge_dir = os.path.join(test_dir, "pipeline1", "great_expectations")
os.makedirs(ge_dir)
assert os.path.isdir(ge_dir)
data_dir = os.path.join(test_dir, "data", "pipeline1")
os.makedirs(data_dir)
assert os.path.isdir(data_dir)
yield ge_dir, data_dir
shutil.rmtree(test_dir) | [
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371,
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] | python | en | ['en', 'error', 'th'] | False |
test_get_relative_path_from_config_file_to_data_base_file_path_from_within_ge_directory_and_relative_data_path | (
monkeypatch, simulated_project_directories
) |
This test simulates using the CLI from within the great_expectations
directory.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1/great_expectations
data: ../../data/pipeline1
expected results in yaml: ../../data/pipeline1
|
This test simulates using the CLI from within the great_expectations
directory. | def test_get_relative_path_from_config_file_to_data_base_file_path_from_within_ge_directory_and_relative_data_path(
monkeypatch, simulated_project_directories
):
"""
This test simulates using the CLI from within the great_expectations
directory.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1/great_expectations
data: ../../data/pipeline1
expected results in yaml: ../../data/pipeline1
"""
ge_dir, data_dir = simulated_project_directories
monkeypatch.chdir(ge_dir)
assert str(os.path.abspath(os.path.curdir)) == str(ge_dir)
obs = get_relative_path_from_config_file_to_base_path(
ge_dir, os.path.join("..", "..", "data", "pipeline1")
)
assert obs == os.path.join("..", "..", "data", "pipeline1") | [
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",",
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")"
] | [
374,
0
] | [
395,
63
] | python | en | ['en', 'error', 'th'] | False |
test_get_relative_path_from_config_file_to_data_base_file_path_from_within_ge_directory_and_absolute_data_path | (
monkeypatch, simulated_project_directories
) |
This test simulates using the CLI from within the great_expectations
directory and using an absolute path.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1/great_expectations
data: /projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
|
This test simulates using the CLI from within the great_expectations
directory and using an absolute path. | def test_get_relative_path_from_config_file_to_data_base_file_path_from_within_ge_directory_and_absolute_data_path(
monkeypatch, simulated_project_directories
):
"""
This test simulates using the CLI from within the great_expectations
directory and using an absolute path.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1/great_expectations
data: /projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
"""
ge_dir, data_dir = simulated_project_directories
monkeypatch.chdir(ge_dir)
assert str(os.path.abspath(os.path.curdir)) == str(ge_dir)
absolute_path = os.path.abspath(os.path.join("..", "..", "data", "pipeline1"))
obs = get_relative_path_from_config_file_to_base_path(ge_dir, absolute_path)
assert obs == os.path.join("..", "..", "data", "pipeline1") | [
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] | [
398,
0
] | [
418,
63
] | python | en | ['en', 'error', 'th'] | False |
test_get_relative_path_from_config_file_to_data_base_file_path_from_adjacent_directory_and_relative_data_path | (
monkeypatch, simulated_project_directories
) |
This test simulates using the CLI from a directory containing the
great_expectations directory.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1
data: ../data/pipeline1
expected results in yaml: ../../data/pipeline1
|
This test simulates using the CLI from a directory containing the
great_expectations directory. | def test_get_relative_path_from_config_file_to_data_base_file_path_from_adjacent_directory_and_relative_data_path(
monkeypatch, simulated_project_directories
):
"""
This test simulates using the CLI from a directory containing the
great_expectations directory.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1
data: ../data/pipeline1
expected results in yaml: ../../data/pipeline1
"""
ge_dir, data_dir = simulated_project_directories
adjacent_dir = os.path.dirname(ge_dir)
monkeypatch.chdir(adjacent_dir)
assert str(os.path.abspath(os.path.curdir)) == str(adjacent_dir)
obs = get_relative_path_from_config_file_to_base_path(
ge_dir, os.path.join("..", "data", "pipeline1")
)
assert obs == os.path.join("..", "..", "data", "pipeline1") | [
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421,
0
] | [
443,
63
] | python | en | ['en', 'error', 'th'] | False |
test_get_relative_path_from_config_file_to_data_base_file_path_from_adjacent_directory_and_absolute_data_path | (
monkeypatch, simulated_project_directories
) |
This test simulates using the CLI from a directory containing the
great_expectations directory and using an absolute path.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1
data: /projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
|
This test simulates using the CLI from a directory containing the
great_expectations directory and using an absolute path. | def test_get_relative_path_from_config_file_to_data_base_file_path_from_adjacent_directory_and_absolute_data_path(
monkeypatch, simulated_project_directories
):
"""
This test simulates using the CLI from a directory containing the
great_expectations directory and using an absolute path.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
cwd: /projects/pipeline1
data: /projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
"""
ge_dir, data_dir = simulated_project_directories
adjacent_dir = os.path.dirname(ge_dir)
monkeypatch.chdir(adjacent_dir)
assert str(os.path.abspath(os.path.curdir)) == str(adjacent_dir)
absolute_path = os.path.abspath(os.path.join("..", "data", "pipeline1"))
obs = get_relative_path_from_config_file_to_base_path(ge_dir, absolute_path)
assert obs == os.path.join("..", "..", "data", "pipeline1") | [
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] | [
446,
0
] | [
467,
63
] | python | en | ['en', 'error', 'th'] | False |
test_get_relative_path_from_config_file_to_data_base_file_path_from_misc_directory_and_relative_data_path | (
monkeypatch, misc_directory, simulated_project_directories
) |
This test simulates using the CLI with the --config flag operating from a
random directory
/projects/pipeline1/great_expectations
/projects/data/pipeline1
/tmp_path/misc
cwd: /tmp_path/random
data: ../../projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
|
This test simulates using the CLI with the --config flag operating from a
random directory | def test_get_relative_path_from_config_file_to_data_base_file_path_from_misc_directory_and_relative_data_path(
monkeypatch, misc_directory, simulated_project_directories
):
"""
This test simulates using the CLI with the --config flag operating from a
random directory
/projects/pipeline1/great_expectations
/projects/data/pipeline1
/tmp_path/misc
cwd: /tmp_path/random
data: ../../projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
"""
ge_dir, data_dir = simulated_project_directories
monkeypatch.chdir(misc_directory)
assert str(os.path.abspath(os.path.curdir)) == str(misc_directory)
obs = get_relative_path_from_config_file_to_base_path(
ge_dir, os.path.join("..", "..", "projects", "data", "pipeline1")
)
assert obs == os.path.join("..", "..", "data", "pipeline1") | [
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470,
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492,
63
] | python | en | ['en', 'error', 'th'] | False |
test_get_relative_path_from_config_file_to_data_base_file_path_from_misc_directory_and_absolute_data_path | (
monkeypatch, misc_directory, simulated_project_directories
) |
This test simulates using the CLI with the --config flag operating from a
random directory and using an absolute path.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
/tmp_path/misc
cwd: /tmp_path/misc
data: /projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
|
This test simulates using the CLI with the --config flag operating from a
random directory and using an absolute path. | def test_get_relative_path_from_config_file_to_data_base_file_path_from_misc_directory_and_absolute_data_path(
monkeypatch, misc_directory, simulated_project_directories
):
"""
This test simulates using the CLI with the --config flag operating from a
random directory and using an absolute path.
/projects/pipeline1/great_expectations
/projects/data/pipeline1
/tmp_path/misc
cwd: /tmp_path/misc
data: /projects/data/pipeline1
expected results in yaml: ../../data/pipeline1
"""
ge_dir, data_dir = simulated_project_directories
monkeypatch.chdir(misc_directory)
assert str(os.path.abspath(os.path.curdir)) == str(misc_directory)
absolute_path = os.path.abspath(
os.path.join("..", "..", "projects", "data", "pipeline1")
)
obs = get_relative_path_from_config_file_to_base_path(ge_dir, absolute_path)
assert obs == os.path.join("..", "..", "data", "pipeline1") | [
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495,
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518,
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] | python | en | ['en', 'error', 'th'] | False |
NDWI | (data, normalize=False, band_pair=0) |
Computes various versions of the Normalized Difference Water Index for an `xarray.Dataset`.
Values should be in the range [-1,1] for valid LANDSAT data (the bands are positive).
Parameters
----------
data: xarray.Dataset or numpy.ndarray
An `xarray.Dataset` containing the bands specified by `band_pair` or
a 2D NumPy array with two columns - the band pair.
normalize: bool
Whether or not to normalize to the range [0,1].
band_pair: int
The band pair to use.
Band pair 0 uses 'nir' and 'swir1': (nir - swir1)/(nir + swir1).
Band pair 1 uses 'green' and 'nir': (green - nir)/(green + nir).
Returns
-------
ndwi: xarray.DataArray
An `xarray.DataArray` with the same shape as `dataset` - the same coordinates in
the same order.
|
Computes various versions of the Normalized Difference Water Index for an `xarray.Dataset`.
Values should be in the range [-1,1] for valid LANDSAT data (the bands are positive).
Parameters
----------
data: xarray.Dataset or numpy.ndarray
An `xarray.Dataset` containing the bands specified by `band_pair` or
a 2D NumPy array with two columns - the band pair.
normalize: bool
Whether or not to normalize to the range [0,1].
band_pair: int
The band pair to use.
Band pair 0 uses 'nir' and 'swir1': (nir - swir1)/(nir + swir1).
Band pair 1 uses 'green' and 'nir': (green - nir)/(green + nir). | def NDWI(data, normalize=False, band_pair=0):
"""
Computes various versions of the Normalized Difference Water Index for an `xarray.Dataset`.
Values should be in the range [-1,1] for valid LANDSAT data (the bands are positive).
Parameters
----------
data: xarray.Dataset or numpy.ndarray
An `xarray.Dataset` containing the bands specified by `band_pair` or
a 2D NumPy array with two columns - the band pair.
normalize: bool
Whether or not to normalize to the range [0,1].
band_pair: int
The band pair to use.
Band pair 0 uses 'nir' and 'swir1': (nir - swir1)/(nir + swir1).
Band pair 1 uses 'green' and 'nir': (green - nir)/(green + nir).
Returns
-------
ndwi: xarray.DataArray
An `xarray.DataArray` with the same shape as `dataset` - the same coordinates in
the same order.
"""
bands = [None] * 2
if band_pair == 0:
bands = ['nir', 'swir1']
elif band_pair == 1:
bands = ['green', 'nir']
else:
raise AssertionError('The band_pair parameter must be in [0,1]')
if isinstance(data, xr.Dataset):
ndwi = (data[bands[0]] - data[bands[1]]) / (data[bands[0]] + data[bands[1]])
if normalize:
ndwi = (ndwi - ndwi.min())/(ndwi.max() - ndwi.min())
else:
ndwi = data[:,0] - data[:,1]
if normalize:
ndwi = (ndwi - np.nanmin(ndwi))/(np.nanmax(ndwi) - np.nanmin(ndwi))
return ndwi | [
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82,
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] | python | en | ['en', 'error', 'th'] | False |
wofs_classify | (dataset_in, clean_mask=None, x_coord='longitude', y_coord='latitude',
time_coord='time', no_data=-9999, mosaic=False, enforce_float64=False) |
Description:
Performs WOfS algorithm on given dataset.
Assumption:
- The WOfS algorithm is defined for Landsat 5/Landsat 7
References:
- Mueller, et al. (2015) "Water observations from space: Mapping surface water from
25 years of Landsat imagery across Australia." Remote Sensing of Environment.
- https://github.com/GeoscienceAustralia/eo-tools/blob/stable/eotools/water_classifier.py
-----
Inputs:
dataset_in (xarray.Dataset) - dataset retrieved from the Data Cube; should contain
coordinates: time, latitude, longitude
variables: blue, green, red, nir, swir1, swir2
x_coord, y_coord, time_coord: (str) - Names of DataArrays in `dataset_in` to use as x, y,
and time coordinates.
Optional Inputs:
clean_mask (nd numpy array with dtype boolean) - true for values user considers clean;
if user does not provide a clean mask, all values will be considered clean
no_data (int/float) - no data pixel value; default: -9999
mosaic (boolean) - flag to indicate if dataset_in is a mosaic. If mosaic = False, dataset_in
should have a time coordinate and wofs will run over each time slice; otherwise, dataset_in
should not have a time coordinate and wofs will run over the single mosaicked image
enforce_float64 (boolean) - flag to indicate whether or not to enforce float64 calculations;
will use float32 if false
Output:
dataset_out (xarray.DataArray) - wofs water classification results: 0 - not water; 1 - water
Throws:
ValueError - if dataset_in is an empty xarray.Dataset.
|
Description:
Performs WOfS algorithm on given dataset.
Assumption:
- The WOfS algorithm is defined for Landsat 5/Landsat 7
References:
- Mueller, et al. (2015) "Water observations from space: Mapping surface water from
25 years of Landsat imagery across Australia." Remote Sensing of Environment.
- https://github.com/GeoscienceAustralia/eo-tools/blob/stable/eotools/water_classifier.py
-----
Inputs:
dataset_in (xarray.Dataset) - dataset retrieved from the Data Cube; should contain
coordinates: time, latitude, longitude
variables: blue, green, red, nir, swir1, swir2
x_coord, y_coord, time_coord: (str) - Names of DataArrays in `dataset_in` to use as x, y,
and time coordinates.
Optional Inputs:
clean_mask (nd numpy array with dtype boolean) - true for values user considers clean;
if user does not provide a clean mask, all values will be considered clean
no_data (int/float) - no data pixel value; default: -9999
mosaic (boolean) - flag to indicate if dataset_in is a mosaic. If mosaic = False, dataset_in
should have a time coordinate and wofs will run over each time slice; otherwise, dataset_in
should not have a time coordinate and wofs will run over the single mosaicked image
enforce_float64 (boolean) - flag to indicate whether or not to enforce float64 calculations;
will use float32 if false
Output:
dataset_out (xarray.DataArray) - wofs water classification results: 0 - not water; 1 - water
Throws:
ValueError - if dataset_in is an empty xarray.Dataset.
| def wofs_classify(dataset_in, clean_mask=None, x_coord='longitude', y_coord='latitude',
time_coord='time', no_data=-9999, mosaic=False, enforce_float64=False):
"""
Description:
Performs WOfS algorithm on given dataset.
Assumption:
- The WOfS algorithm is defined for Landsat 5/Landsat 7
References:
- Mueller, et al. (2015) "Water observations from space: Mapping surface water from
25 years of Landsat imagery across Australia." Remote Sensing of Environment.
- https://github.com/GeoscienceAustralia/eo-tools/blob/stable/eotools/water_classifier.py
-----
Inputs:
dataset_in (xarray.Dataset) - dataset retrieved from the Data Cube; should contain
coordinates: time, latitude, longitude
variables: blue, green, red, nir, swir1, swir2
x_coord, y_coord, time_coord: (str) - Names of DataArrays in `dataset_in` to use as x, y,
and time coordinates.
Optional Inputs:
clean_mask (nd numpy array with dtype boolean) - true for values user considers clean;
if user does not provide a clean mask, all values will be considered clean
no_data (int/float) - no data pixel value; default: -9999
mosaic (boolean) - flag to indicate if dataset_in is a mosaic. If mosaic = False, dataset_in
should have a time coordinate and wofs will run over each time slice; otherwise, dataset_in
should not have a time coordinate and wofs will run over the single mosaicked image
enforce_float64 (boolean) - flag to indicate whether or not to enforce float64 calculations;
will use float32 if false
Output:
dataset_out (xarray.DataArray) - wofs water classification results: 0 - not water; 1 - water
Throws:
ValueError - if dataset_in is an empty xarray.Dataset.
"""
def _band_ratio(a, b):
"""
Calculates a normalized ratio index
"""
return (a - b) / (a + b)
def _run_regression(band1, band2, band3, band4, band5, band7):
"""
Regression analysis based on Australia's training data
TODO: Return type
"""
# Compute normalized ratio indices
ndi_52 = _band_ratio(band5, band2)
ndi_43 = _band_ratio(band4, band3)
ndi_72 = _band_ratio(band7, band2)
#classified = np.ones(shape, dtype='uint8')
classified = np.full(shape, no_data, dtype='uint8')
# Start with the tree's left branch, finishing nodes as needed
# Left branch
r1 = ndi_52 <= -0.01
r2 = band1 <= 2083.5
classified[r1 & ~r2] = 0 #Node 3
r3 = band7 <= 323.5
_tmp = r1 & r2
_tmp2 = _tmp & r3
_tmp &= ~r3
r4 = ndi_43 <= 0.61
classified[_tmp2 & r4] = 1 #Node 6
classified[_tmp2 & ~r4] = 0 #Node 7
r5 = band1 <= 1400.5
_tmp2 = _tmp & ~r5
r6 = ndi_43 <= -0.01
classified[_tmp2 & r6] = 1 #Node 10
classified[_tmp2 & ~r6] = 0 #Node 11
_tmp &= r5
r7 = ndi_72 <= -0.23
_tmp2 = _tmp & ~r7
r8 = band1 <= 379
classified[_tmp2 & r8] = 1 #Node 14
classified[_tmp2 & ~r8] = 0 #Node 15
_tmp &= r7
r9 = ndi_43 <= 0.22
classified[_tmp & r9] = 1 #Node 17
_tmp &= ~r9
r10 = band1 <= 473
classified[_tmp & r10] = 1 #Node 19
classified[_tmp & ~r10] = 0 #Node 20
# Left branch complete; cleanup
del r2, r3, r4, r5, r6, r7, r8, r9, r10
gc.collect()
# Right branch of regression tree
r1 = ~r1
r11 = ndi_52 <= 0.23
_tmp = r1 & r11
r12 = band1 <= 334.5
_tmp2 = _tmp & ~r12
classified[_tmp2] = 0 #Node 23
_tmp &= r12
r13 = ndi_43 <= 0.54
_tmp2 = _tmp & ~r13
classified[_tmp2] = 0 #Node 25
_tmp &= r13
r14 = ndi_52 <= 0.12
_tmp2 = _tmp & r14
classified[_tmp2] = 1 #Node 27
_tmp &= ~r14
r15 = band3 <= 364.5
_tmp2 = _tmp & r15
r16 = band1 <= 129.5
classified[_tmp2 & r16] = 1 #Node 31
classified[_tmp2 & ~r16] = 0 #Node 32
_tmp &= ~r15
r17 = band1 <= 300.5
_tmp2 = _tmp & ~r17
_tmp &= r17
classified[_tmp] = 1 #Node 33
classified[_tmp2] = 0 #Node 34
_tmp = r1 & ~r11
r18 = ndi_52 <= 0.34
classified[_tmp & ~r18] = 0 #Node 36
_tmp &= r18
r19 = band1 <= 249.5
classified[_tmp & ~r19] = 0 #Node 38
_tmp &= r19
r20 = ndi_43 <= 0.45
classified[_tmp & ~r20] = 0 #Node 40
_tmp &= r20
r21 = band3 <= 364.5
classified[_tmp & ~r21] = 0 #Node 42
_tmp &= r21
r22 = band1 <= 129.5
classified[_tmp & r22] = 1 #Node 44
classified[_tmp & ~r22] = 0 #Node 45
# Completed regression tree
return classified
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
# Extract dataset bands needed for calculations
blue = dataset_in.blue
green = dataset_in.green
red = dataset_in.red
nir = dataset_in.nir
swir1 = dataset_in.swir1
swir2 = dataset_in.swir2
# Enforce float calculations - float64 if user specified, otherwise float32 will do
dtype = blue.values.dtype # This assumes all dataset bands will have
# the same dtype (should be a reasonable
# assumption)
if enforce_float64:
if dtype != 'float64':
blue.values = blue.values.astype('float64')
green.values = green.values.astype('float64')
red.values = red.values.astype('float64')
nir.values = nir.values.astype('float64')
swir1.values = swir1.values.astype('float64')
swir2.values = swir2.values.astype('float64')
else:
if dtype == 'float64':
pass
elif dtype != 'float32':
blue.values = blue.values.astype('float32')
green.values = green.values.astype('float32')
red.values = red.values.astype('float32')
nir.values = nir.values.astype('float32')
swir1.values = swir1.values.astype('float32')
swir2.values = swir2.values.astype('float32')
shape = blue.values.shape
classified = _run_regression(blue.values, green.values, red.values, nir.values, swir1.values, swir2.values)
classified_clean = np.full(classified.shape, no_data, dtype='float64')
classified_clean[clean_mask] = classified[clean_mask] # Contains data for clear pixels
# Create xarray of data
x_coords = dataset_in[x_coord]
y_coords = dataset_in[y_coord]
time = None
coords = None
dims = None
if mosaic:
coords = [y_coords, x_coords]
dims = [y_coord, x_coord]
else:
time_coords = dataset_in[time_coord]
coords = [time_coords, y_coords, x_coords]
dims = [time_coord, y_coord, x_coord]
data_array = xr.DataArray(classified_clean, coords=coords, dims=dims)
if mosaic:
dataset_out = xr.Dataset({'wofs': data_array},
coords={y_coord: y_coords, x_coord: x_coords})
else:
dataset_out = xr.Dataset(
{'wofs': data_array},
coords={time_coord: time_coords, y_coord: y_coords, x_coord: x_coords})
return dataset_out | [
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84,
0
] | [
318,
22
] | python | en | ['en', 'error', 'th'] | False |
main | (classifier, platform, product_type, min_lon, max_lon, min_lat, max_lat, start_date, end_date, dc_config) |
Description:
Command-line water detection tool - creates a time-series from
water analysis performed on data retrieved by the Data Cube,
shows plots of the normalized water observations (total water
observations / total clear observations), total water observations,
and total clear observations, and saves a GeoTIFF of the results
Assumptions:
The command-line tool assumes there is a measurement called cf_mask
Inputs:
classifier (str)
platform (str)
product_type (str)
min_lon (str)
max_lon (str)
min_lat (str)
max_lat (str)
start_date (str)
end_date (str)
dc_config (str)
|
Description:
Command-line water detection tool - creates a time-series from
water analysis performed on data retrieved by the Data Cube,
shows plots of the normalized water observations (total water
observations / total clear observations), total water observations,
and total clear observations, and saves a GeoTIFF of the results
Assumptions:
The command-line tool assumes there is a measurement called cf_mask
Inputs:
classifier (str)
platform (str)
product_type (str)
min_lon (str)
max_lon (str)
min_lat (str)
max_lat (str)
start_date (str)
end_date (str)
dc_config (str)
| def main(classifier, platform, product_type, min_lon, max_lon, min_lat, max_lat, start_date, end_date, dc_config):
"""
Description:
Command-line water detection tool - creates a time-series from
water analysis performed on data retrieved by the Data Cube,
shows plots of the normalized water observations (total water
observations / total clear observations), total water observations,
and total clear observations, and saves a GeoTIFF of the results
Assumptions:
The command-line tool assumes there is a measurement called cf_mask
Inputs:
classifier (str)
platform (str)
product_type (str)
min_lon (str)
max_lon (str)
min_lat (str)
max_lat (str)
start_date (str)
end_date (str)
dc_config (str)
"""
# Initialize data cube object
dc = datacube.Datacube(config=dc_config, app='dc-mosaicker')
# Validate arguments
if classifier not in ['cfmask', 'ledaps', 'wofs']:
print('ERROR: Unknown water classifier. Classifier options: cfmask, ledaps, wofs')
return
products = dc.list_products()
platform_names = set([product[6] for product in products.values])
if platform not in platform_names:
print('ERROR: Invalid platform.')
print('Valid platforms are:')
for name in platform_names:
print(name)
return
product_names = [product[0] for product in products.values]
if product_type not in product_names:
print('ERROR: Invalid product type.')
print('Valid product types are:')
for name in product_names:
print(name)
return
try:
min_lon = float(args.min_lon)
max_lon = float(args.max_lon)
min_lat = float(args.min_lat)
max_lat = float(args.max_lat)
except:
print('ERROR: Longitudes/Latitudes must be float values')
return
try:
start_date_str = start_date
end_date_str = end_date
start_date = datetime.strptime(start_date, '%Y-%m-%d')
end_date = datetime.strptime(end_date, '%Y-%m-%d')
except:
print('ERROR: Invalid date format. Date format: YYYY-MM-DD')
return
if not os.path.exists(dc_config):
print('ERROR: Invalid file path for dc_config')
return
# Retrieve data from Data Cube
dataset_in = dc.load(
platform=platform,
product=product_type,
time=(start_date, end_date),
lon=(min_lon, max_lon),
lat=(min_lat, max_lat))
# Get information needed for saving as GeoTIFF
# Spatial ref
crs = dataset_in.crs
spatial_ref = utilities.get_spatial_ref(crs)
# Upper left coordinates
ul_lon = dataset_in.longitude.values[0]
ul_lat = dataset_in.latitude.values[0]
# Resolution
products = dc.list_products()
resolution = products.resolution[products.name == 'ls7_ledaps']
lon_dist = resolution.values[0][1]
lat_dist = resolution.values[0][0]
# Rotation
lon_rtn = 0
lat_rtn = 0
geotransform = (ul_lon, lon_dist, lon_rtn, ul_lat, lat_rtn, lat_dist)
# Run desired classifier
water_class = None
if classifier == 'cfmask': #TODO: implement when cfmask_classify is refactored
return
elif classifier == 'ledaps': #TODO: implement when cfmask_classify is refactored
return
elif classifier == 'wofs':
water_class = wofs_classify(dataset_in)
dataset_out = utilities.perform_timeseries_analysis(water_class)
print(dataset_out)
out_file = (
str(min_lon) + '_' + str(min_lat) + '_' + start_date_str + '_' + end_date_str + '_' + classifier + '_.tif')
utilities.save_to_geotiff(out_file, gdal.GDT_Float32, dataset_out, geotransform, spatial_ref) | [
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367,
0
] | [
483,
97
] | python | en | ['en', 'error', 'th'] | False |
test_validation_operator_run_interactive_golden_path | (
caplog, data_context_simple_expectation_suite, filesystem_csv_2
) |
Interactive mode golden path - pass an existing suite name and an existing validation
operator name, select an existing file.
|
Interactive mode golden path - pass an existing suite name and an existing validation
operator name, select an existing file.
| def test_validation_operator_run_interactive_golden_path(
caplog, data_context_simple_expectation_suite, filesystem_csv_2
):
"""
Interactive mode golden path - pass an existing suite name and an existing validation
operator name, select an existing file.
"""
not_so_empty_data_context = data_context_simple_expectation_suite
root_dir = not_so_empty_data_context.root_directory
os.mkdir(os.path.join(root_dir, "uncommitted"))
runner = CliRunner(mix_stderr=False)
csv_path = os.path.join(filesystem_csv_2, "f1.csv")
result = runner.invoke(
cli,
[
"validation-operator",
"run",
"-d",
root_dir,
"--name",
"default",
"--suite",
"default",
],
input=f"{csv_path}\n",
catch_exceptions=False,
)
stdout = result.stdout
assert "Validation failed" in stdout
assert result.exit_code == 1
assert_no_logging_messages_or_tracebacks(caplog, result) | [
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] | [
13,
0
] | [
44,
60
] | python | en | ['en', 'error', 'th'] | False |
test_validation_operator_run_interactive_pass_non_existing_expectation_suite | (
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
) |
Interactive mode: pass an non-existing suite name and an existing validation
operator name, select an existing file.
|
Interactive mode: pass an non-existing suite name and an existing validation
operator name, select an existing file.
| def test_validation_operator_run_interactive_pass_non_existing_expectation_suite(
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
):
"""
Interactive mode: pass an non-existing suite name and an existing validation
operator name, select an existing file.
"""
not_so_empty_data_context = (
data_context_parameterized_expectation_suite_no_checkpoint_store
)
root_dir = not_so_empty_data_context.root_directory
os.mkdir(os.path.join(root_dir, "uncommitted"))
runner = CliRunner(mix_stderr=False)
csv_path = os.path.join(filesystem_csv_2, "f1.csv")
result = runner.invoke(
cli,
[
"validation-operator",
"run",
"-d",
root_dir,
"--name",
"default",
"--suite",
"this.suite.does.not.exist",
],
input=f"{csv_path}\n",
catch_exceptions=False,
)
stdout = result.stdout
assert "Could not find a suite named" in stdout
assert result.exit_code == 1
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] | [
47,
0
] | [
82,
60
] | python | en | ['en', 'error', 'th'] | False |
test_validation_operator_run_interactive_pass_non_existing_operator_name | (
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
) |
Interactive mode: pass an non-existing suite name and an existing validation
operator name, select an existing file.
|
Interactive mode: pass an non-existing suite name and an existing validation
operator name, select an existing file.
| def test_validation_operator_run_interactive_pass_non_existing_operator_name(
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
):
"""
Interactive mode: pass an non-existing suite name and an existing validation
operator name, select an existing file.
"""
not_so_empty_data_context = (
data_context_parameterized_expectation_suite_no_checkpoint_store
)
root_dir = not_so_empty_data_context.root_directory
os.mkdir(os.path.join(root_dir, "uncommitted"))
runner = CliRunner(mix_stderr=False)
csv_path = os.path.join(filesystem_csv_2, "f1.csv")
result = runner.invoke(
cli,
[
"validation-operator",
"run",
"-d",
root_dir,
"--name",
"this_val_op_does_not_exist",
"--suite",
"my_dag_node.default",
],
input=f"{csv_path}\n",
catch_exceptions=False,
)
stdout = result.stdout
assert "Could not find a validation operator" in stdout
assert result.exit_code == 1
assert_no_logging_messages_or_tracebacks(caplog, result) | [
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] | [
85,
0
] | [
120,
60
] | python | en | ['en', 'error', 'th'] | False |
test_validation_operator_run_noninteractive_golden_path | (
caplog, data_context_simple_expectation_suite, filesystem_csv_2
) |
Non-nteractive mode golden path - use the --validation_config_file argument to pass the path
to a valid validation config file
|
Non-nteractive mode golden path - use the --validation_config_file argument to pass the path
to a valid validation config file
| def test_validation_operator_run_noninteractive_golden_path(
caplog, data_context_simple_expectation_suite, filesystem_csv_2
):
"""
Non-nteractive mode golden path - use the --validation_config_file argument to pass the path
to a valid validation config file
"""
not_so_empty_data_context = data_context_simple_expectation_suite
root_dir = not_so_empty_data_context.root_directory
os.mkdir(os.path.join(root_dir, "uncommitted"))
csv_path = os.path.join(filesystem_csv_2, "f1.csv")
validation_config = {
"validation_operator_name": "default",
"batches": [
{
"batch_kwargs": {
"path": csv_path,
"datasource": "mydatasource",
"reader_method": "read_csv",
},
"expectation_suite_names": ["default"],
}
],
}
validation_config_file_path = os.path.join(
root_dir, "uncommitted", "validation_config_1.json"
)
with open(validation_config_file_path, "w") as f:
json.dump(validation_config, f)
runner = CliRunner(mix_stderr=False)
result = runner.invoke(
cli,
[
"validation-operator",
"run",
"-d",
root_dir,
"--validation_config_file",
validation_config_file_path,
],
catch_exceptions=False,
)
stdout = result.stdout
assert "Validation failed" in stdout
assert result.exit_code == 1
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123,
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171,
60
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test_validation_operator_run_noninteractive_validation_config_file_does_not_exist | (
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
) |
Non-nteractive mode. Use the --validation_config_file argument to pass the path
to a validation config file that does not exist.
|
Non-nteractive mode. Use the --validation_config_file argument to pass the path
to a validation config file that does not exist.
| def test_validation_operator_run_noninteractive_validation_config_file_does_not_exist(
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
):
"""
Non-nteractive mode. Use the --validation_config_file argument to pass the path
to a validation config file that does not exist.
"""
not_so_empty_data_context = (
data_context_parameterized_expectation_suite_no_checkpoint_store
)
root_dir = not_so_empty_data_context.root_directory
os.mkdir(os.path.join(root_dir, "uncommitted"))
validation_config_file_path = os.path.join(
root_dir, "uncommitted", "validation_config_1.json"
)
runner = CliRunner(mix_stderr=False)
result = runner.invoke(
cli,
[
"validation-operator",
"run",
"-d",
root_dir,
"--validation_config_file",
validation_config_file_path,
],
catch_exceptions=False,
)
stdout = result.stdout
assert "Failed to process the --validation_config_file argument" in stdout
assert result.exit_code == 1
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174,
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] | [
209,
60
] | python | en | ['en', 'error', 'th'] | False |
test_validation_operator_run_noninteractive_validation_config_file_does_is_misconfigured | (
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
) |
Non-nteractive mode. Use the --validation_config_file argument to pass the path
to a validation config file that is misconfigured - one of the batches does not
have expectation_suite_names attribute
|
Non-nteractive mode. Use the --validation_config_file argument to pass the path
to a validation config file that is misconfigured - one of the batches does not
have expectation_suite_names attribute
| def test_validation_operator_run_noninteractive_validation_config_file_does_is_misconfigured(
caplog,
data_context_parameterized_expectation_suite_no_checkpoint_store,
filesystem_csv_2,
):
"""
Non-nteractive mode. Use the --validation_config_file argument to pass the path
to a validation config file that is misconfigured - one of the batches does not
have expectation_suite_names attribute
"""
not_so_empty_data_context = (
data_context_parameterized_expectation_suite_no_checkpoint_store
)
root_dir = not_so_empty_data_context.root_directory
os.mkdir(os.path.join(root_dir, "uncommitted"))
csv_path = os.path.join(filesystem_csv_2, "f1.csv")
validation_config = {
"validation_operator_name": "default",
"batches": [
{
"batch_kwargs": {
"path": csv_path,
"datasource": "mydatasource",
"reader_method": "read_csv",
},
"wrong_attribute_expectation_suite_names": ["my_dag_node.default1"],
}
],
}
validation_config_file_path = os.path.join(
root_dir, "uncommitted", "validation_config_1.json"
)
with open(validation_config_file_path, "w") as f:
json.dump(validation_config, f)
runner = CliRunner(mix_stderr=False)
result = runner.invoke(
cli,
[
"validation-operator",
"run",
"-d",
root_dir,
"--validation_config_file",
validation_config_file_path,
],
catch_exceptions=False,
)
stdout = result.stdout
assert (
"is misconfigured: Each batch must have a list of expectation suite names"
in stdout
)
assert result.exit_code == 1
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212,
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268,
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] | python | en | ['en', 'error', 'th'] | False |
_capture_ansi_codes_to_file | (result) |
Use this to capture the ANSI color codes when updating snapshots.
NOT DEAD CODE.
|
Use this to capture the ANSI color codes when updating snapshots.
NOT DEAD CODE.
| def _capture_ansi_codes_to_file(result):
"""
Use this to capture the ANSI color codes when updating snapshots.
NOT DEAD CODE.
"""
with open("ansi.txt", "w") as f:
f.write(result.output.strip()) | [
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BaseYamlConfig.to_yaml | (self, outfile) |
:returns None (but writes a YAML file containing the project configuration)
|
:returns None (but writes a YAML file containing the project configuration)
| def to_yaml(self, outfile):
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BaseYamlConfig.to_yaml_str | (self) |
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:returns a YAML string containing the project configuration
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BaseYamlConfig.to_json_dict | (self) |
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|
:returns a JSON-serialiable dict containing the project configuration
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DataContextConfigSchema.handle_error | (self, exc, data, **kwargs) | Log and raise our custom exception when (de)serialization fails. | Log and raise our custom exception when (de)serialization fails. | def handle_error(self, exc, data, **kwargs):
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docker_pull | (tag: str) | `docker pull` and then return the image ID | `docker pull` and then return the image ID | def docker_pull(tag: str) -> str:
"""`docker pull` and then return the image ID"""
run(['docker', 'pull', tag])
return run_txtcapture(['docker', 'image', 'inspect', tag, '--format={{.Id}}']) | [
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xr_scale | (data, data_vars=None, min_max=None, scaling='norm', copy=False) |
Scales an xarray Dataset or DataArray with standard scaling or norm scaling.
Parameters
----------
data: xarray.Dataset or xarray.DataArray
The NumPy array to scale.
data_vars: list
The names of the data variables to scale.
min_max: tuple
A 2-tuple which specifies the desired range of the final output - the minimum and the maximum, in that order.
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scaling: str
The options are ['std', 'norm'].
The option 'std' standardizes. The option 'norm' normalizes (min-max scales).
copy: bool
Whether or not to copy `data` before scaling.
|
Scales an xarray Dataset or DataArray with standard scaling or norm scaling.
Parameters
----------
data: xarray.Dataset or xarray.DataArray
The NumPy array to scale.
data_vars: list
The names of the data variables to scale.
min_max: tuple
A 2-tuple which specifies the desired range of the final output - the minimum and the maximum, in that order.
If all values are the same, all values will become min_max[0].
scaling: str
The options are ['std', 'norm'].
The option 'std' standardizes. The option 'norm' normalizes (min-max scales).
copy: bool
Whether or not to copy `data` before scaling.
| def xr_scale(data, data_vars=None, min_max=None, scaling='norm', copy=False):
"""
Scales an xarray Dataset or DataArray with standard scaling or norm scaling.
Parameters
----------
data: xarray.Dataset or xarray.DataArray
The NumPy array to scale.
data_vars: list
The names of the data variables to scale.
min_max: tuple
A 2-tuple which specifies the desired range of the final output - the minimum and the maximum, in that order.
If all values are the same, all values will become min_max[0].
scaling: str
The options are ['std', 'norm'].
The option 'std' standardizes. The option 'norm' normalizes (min-max scales).
copy: bool
Whether or not to copy `data` before scaling.
"""
data = data.copy() if copy else data
if isinstance(data, xr.Dataset):
data_arr_names = list(data.data_vars) if data_vars is None else data_vars
for data_arr_name in data_arr_names:
data_arr = data[data_arr_name]
data_arr.values = np_scale(data_arr.values, min_max=min_max, scaling=scaling)
elif isinstance(data, xr.DataArray):
data.values = np_scale(data.values, min_max=min_max, scaling=scaling)
return data | [
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np_scale | (arr, pop_arr=None, pop_min_max=None, pop_mean_std=None, min_max=None, scaling='norm') |
Scales a NumPy array with standard scaling or norm scaling, default to norm scaling.
Parameters
----------
arr: numpy.ndarray
The NumPy array to scale.
pop_arr: numpy.ndarray, optional
The NumPy array to treat as the population.
If specified, all members of `arr` must be within the range of `pop_arr`
or `min_max` must be specified.
pop_min_max: list-like, optional
The population minimum and maximum, in that order.
Supercedes `pop_arr` when normalizing.
pop_mean_std: list-like, optional
The population mean and standard deviation, in that order.
Supercedes `pop_arr` when standard scaling.
min_max: list-like, optional
The desired minimum and maximum of the final output, in that order.
If all values are the same, all values will become `min_max[0]`.
scaling: str, optional
The options are ['std', 'norm'].
The option 'std' standardizes. The option 'norm' normalizes (min-max scales).
|
Scales a NumPy array with standard scaling or norm scaling, default to norm scaling.
Parameters
----------
arr: numpy.ndarray
The NumPy array to scale.
pop_arr: numpy.ndarray, optional
The NumPy array to treat as the population.
If specified, all members of `arr` must be within the range of `pop_arr`
or `min_max` must be specified.
pop_min_max: list-like, optional
The population minimum and maximum, in that order.
Supercedes `pop_arr` when normalizing.
pop_mean_std: list-like, optional
The population mean and standard deviation, in that order.
Supercedes `pop_arr` when standard scaling.
min_max: list-like, optional
The desired minimum and maximum of the final output, in that order.
If all values are the same, all values will become `min_max[0]`.
scaling: str, optional
The options are ['std', 'norm'].
The option 'std' standardizes. The option 'norm' normalizes (min-max scales).
| def np_scale(arr, pop_arr=None, pop_min_max=None, pop_mean_std=None, min_max=None, scaling='norm'):
"""
Scales a NumPy array with standard scaling or norm scaling, default to norm scaling.
Parameters
----------
arr: numpy.ndarray
The NumPy array to scale.
pop_arr: numpy.ndarray, optional
The NumPy array to treat as the population.
If specified, all members of `arr` must be within the range of `pop_arr`
or `min_max` must be specified.
pop_min_max: list-like, optional
The population minimum and maximum, in that order.
Supercedes `pop_arr` when normalizing.
pop_mean_std: list-like, optional
The population mean and standard deviation, in that order.
Supercedes `pop_arr` when standard scaling.
min_max: list-like, optional
The desired minimum and maximum of the final output, in that order.
If all values are the same, all values will become `min_max[0]`.
scaling: str, optional
The options are ['std', 'norm'].
The option 'std' standardizes. The option 'norm' normalizes (min-max scales).
"""
pop_arr = arr if pop_arr is None else pop_arr
if scaling == 'norm':
pop_min, pop_max = (pop_min_max[0], pop_min_max[1]) if pop_min_max is not None else (np.nanmin(pop_arr), np.nanmax(pop_arr))
numerator, denominator = arr - pop_min, pop_max - pop_min
elif scaling == 'std':
mean, std = mean_std if mean_std is not None else (np.nanmean(pop_arr), np.nanstd(pop_arr))
numerator, denominator = arr - mean, std
# Primary scaling
new_arr = arr
if denominator > 0:
new_arr = numerator / denominator
# Optional final scaling.
if min_max is not None:
new_arr = np.interp(new_arr, (np.nanmin(new_arr), np.nanmax(new_arr)), min_max) if denominator > 0 else \
np.full_like(new_arr, min_max[0]) # The values are identical - set all values to the low end of the desired range.
return new_arr | [
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32,
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] | [
72,
18
] | python | en | ['en', 'error', 'th'] | False |
qrot | (q, v) |
Rotate vector(s) v about the rotation described by quaternion(s) q.
Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
where * denotes any number of dimensions.
Returns a tensor of shape (*, 3).
|
Rotate vector(s) v about the rotation described by quaternion(s) q.
Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
where * denotes any number of dimensions.
Returns a tensor of shape (*, 3).
| def qrot(q, v):
"""
Rotate vector(s) v about the rotation described by quaternion(s) q.
Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
where * denotes any number of dimensions.
Returns a tensor of shape (*, 3).
"""
assert q.shape[-1] == 4
assert v.shape[-1] == 3
assert q.shape[:-1] == v.shape[:-1]
qvec = q[..., 1:]
uv = torch.cross(qvec, v, dim=len(q.shape) - 1)
uuv = torch.cross(qvec, uv, dim=len(q.shape) - 1)
return (v + 2 * (q[..., :1] * uv + uuv)) | [
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23,
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] | python | en | ['en', 'error', 'th'] | False |
MacTool.Dispatch | (self, args) | Dispatches a string command to a method. | Dispatches a string command to a method. | def Dispatch(self, args):
"""Dispatches a string command to a method."""
if len(args) < 1:
raise Exception("Not enough arguments")
method = "Exec%s" % self._CommandifyName(args[0])
return getattr(self, method)(*args[1:]) | [
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41,
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MacTool._CommandifyName | (self, name_string) | Transforms a tool name like copy-info-plist to CopyInfoPlist | Transforms a tool name like copy-info-plist to CopyInfoPlist | def _CommandifyName(self, name_string):
"""Transforms a tool name like copy-info-plist to CopyInfoPlist"""
return name_string.title().replace('-', '') | [
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45,
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] | python | en | ['en', 'pl', 'en'] | True |
MacTool.ExecCopyBundleResource | (self, source, dest, convert_to_binary) | Copies a resource file to the bundle/Resources directory, performing any
necessary compilation on each resource. | Copies a resource file to the bundle/Resources directory, performing any
necessary compilation on each resource. | def ExecCopyBundleResource(self, source, dest, convert_to_binary):
"""Copies a resource file to the bundle/Resources directory, performing any
necessary compilation on each resource."""
extension = os.path.splitext(source)[1].lower()
if os.path.isdir(source):
# Copy tree.
# TODO(thakis): This copies file attributes like mtime, while the
# single-file branch below doesn't. This should probably be changed to
# be consistent with the single-file branch.
if os.path.exists(dest):
shutil.rmtree(dest)
shutil.copytree(source, dest)
elif extension == '.xib':
return self._CopyXIBFile(source, dest)
elif extension == '.storyboard':
return self._CopyXIBFile(source, dest)
elif extension == '.strings':
self._CopyStringsFile(source, dest, convert_to_binary)
else:
shutil.copy(source, dest) | [
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66,
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] | python | en | ['en', 'en', 'en'] | True |
MacTool._CopyXIBFile | (self, source, dest) | Compiles a XIB file with ibtool into a binary plist in the bundle. | Compiles a XIB file with ibtool into a binary plist in the bundle. | def _CopyXIBFile(self, source, dest):
"""Compiles a XIB file with ibtool into a binary plist in the bundle."""
# ibtool sometimes crashes with relative paths. See crbug.com/314728.
base = os.path.dirname(os.path.realpath(__file__))
if os.path.relpath(source):
source = os.path.join(base, source)
if os.path.relpath(dest):
dest = os.path.join(base, dest)
args = ['xcrun', 'ibtool', '--errors', '--warnings', '--notices',
'--output-format', 'human-readable-text', '--compile', dest, source]
ibtool_section_re = re.compile(r'/\*.*\*/')
ibtool_re = re.compile(r'.*note:.*is clipping its content')
ibtoolout = subprocess.Popen(args, stdout=subprocess.PIPE)
current_section_header = None
for line in ibtoolout.stdout:
if ibtool_section_re.match(line):
current_section_header = line
elif not ibtool_re.match(line):
if current_section_header:
sys.stdout.write(current_section_header)
current_section_header = None
sys.stdout.write(line)
return ibtoolout.returncode | [
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68,
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92,
31
] | python | en | ['en', 'en', 'en'] | True |
MacTool._CopyStringsFile | (self, source, dest, convert_to_binary) | Copies a .strings file using iconv to reconvert the input into UTF-16. | Copies a .strings file using iconv to reconvert the input into UTF-16. | def _CopyStringsFile(self, source, dest, convert_to_binary):
"""Copies a .strings file using iconv to reconvert the input into UTF-16."""
input_code = self._DetectInputEncoding(source) or "UTF-8"
# Xcode's CpyCopyStringsFile / builtin-copyStrings seems to call
# CFPropertyListCreateFromXMLData() behind the scenes; at least it prints
# CFPropertyListCreateFromXMLData(): Old-style plist parser: missing
# semicolon in dictionary.
# on invalid files. Do the same kind of validation.
import CoreFoundation
s = open(source, 'rb').read()
d = CoreFoundation.CFDataCreate(None, s, len(s))
_, error = CoreFoundation.CFPropertyListCreateFromXMLData(None, d, 0, None)
if error:
return
fp = open(dest, 'wb')
fp.write(s.decode(input_code).encode('UTF-16'))
fp.close()
if convert_to_binary == 'True':
self._ConvertToBinary(dest) | [
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119,
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] | python | en | ['en', 'en', 'en'] | True |
MacTool._DetectInputEncoding | (self, file_name) | Reads the first few bytes from file_name and tries to guess the text
encoding. Returns None as a guess if it can't detect it. | Reads the first few bytes from file_name and tries to guess the text
encoding. Returns None as a guess if it can't detect it. | def _DetectInputEncoding(self, file_name):
"""Reads the first few bytes from file_name and tries to guess the text
encoding. Returns None as a guess if it can't detect it."""
fp = open(file_name, 'rb')
try:
header = fp.read(3)
except e:
fp.close()
return None
fp.close()
if header.startswith("\xFE\xFF"):
return "UTF-16"
elif header.startswith("\xFF\xFE"):
return "UTF-16"
elif header.startswith("\xEF\xBB\xBF"):
return "UTF-8"
else:
return None | [
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138,
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MacTool.ExecCopyInfoPlist | (self, source, dest, convert_to_binary, *keys) | Copies the |source| Info.plist to the destination directory |dest|. | Copies the |source| Info.plist to the destination directory |dest|. | def ExecCopyInfoPlist(self, source, dest, convert_to_binary, *keys):
"""Copies the |source| Info.plist to the destination directory |dest|."""
# Read the source Info.plist into memory.
fd = open(source, 'r')
lines = fd.read()
fd.close()
# Insert synthesized key/value pairs (e.g. BuildMachineOSBuild).
plist = plistlib.readPlistFromString(lines)
if keys:
plist = dict(plist.items() + json.loads(keys[0]).items())
lines = plistlib.writePlistToString(plist)
# Go through all the environment variables and replace them as variables in
# the file.
IDENT_RE = re.compile(r'[/\s]')
for key in os.environ:
if key.startswith('_'):
continue
evar = '${%s}' % key
evalue = os.environ[key]
lines = string.replace(lines, evar, evalue)
# Xcode supports various suffices on environment variables, which are
# all undocumented. :rfc1034identifier is used in the standard project
# template these days, and :identifier was used earlier. They are used to
# convert non-url characters into things that look like valid urls --
# except that the replacement character for :identifier, '_' isn't valid
# in a URL either -- oops, hence :rfc1034identifier was born.
evar = '${%s:identifier}' % key
evalue = IDENT_RE.sub('_', os.environ[key])
lines = string.replace(lines, evar, evalue)
evar = '${%s:rfc1034identifier}' % key
evalue = IDENT_RE.sub('-', os.environ[key])
lines = string.replace(lines, evar, evalue)
# Remove any keys with values that haven't been replaced.
lines = lines.split('\n')
for i in range(len(lines)):
if lines[i].strip().startswith("<string>${"):
lines[i] = None
lines[i - 1] = None
lines = '\n'.join(filter(lambda x: x is not None, lines))
# Write out the file with variables replaced.
fd = open(dest, 'w')
fd.write(lines)
fd.close()
# Now write out PkgInfo file now that the Info.plist file has been
# "compiled".
self._WritePkgInfo(dest)
if convert_to_binary == 'True':
self._ConvertToBinary(dest) | [
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140,
2
] | [
195,
33
] | python | en | ['en', 'fr', 'en'] | True |
MacTool._WritePkgInfo | (self, info_plist) | This writes the PkgInfo file from the data stored in Info.plist. | This writes the PkgInfo file from the data stored in Info.plist. | def _WritePkgInfo(self, info_plist):
"""This writes the PkgInfo file from the data stored in Info.plist."""
plist = plistlib.readPlist(info_plist)
if not plist:
return
# Only create PkgInfo for executable types.
package_type = plist['CFBundlePackageType']
if package_type != 'APPL':
return
# The format of PkgInfo is eight characters, representing the bundle type
# and bundle signature, each four characters. If that is missing, four
# '?' characters are used instead.
signature_code = plist.get('CFBundleSignature', '????')
if len(signature_code) != 4: # Wrong length resets everything, too.
signature_code = '?' * 4
dest = os.path.join(os.path.dirname(info_plist), 'PkgInfo')
fp = open(dest, 'w')
fp.write('%s%s' % (package_type, signature_code))
fp.close() | [
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197,
2
] | [
218,
14
] | python | en | ['en', 'en', 'en'] | True |
MacTool.ExecFlock | (self, lockfile, *cmd_list) | Emulates the most basic behavior of Linux's flock(1). | Emulates the most basic behavior of Linux's flock(1). | def ExecFlock(self, lockfile, *cmd_list):
"""Emulates the most basic behavior of Linux's flock(1)."""
# Rely on exception handling to report errors.
fd = os.open(lockfile, os.O_RDONLY|os.O_NOCTTY|os.O_CREAT, 0o666)
fcntl.flock(fd, fcntl.LOCK_EX)
return subprocess.call(cmd_list) | [
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] | [
220,
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225,
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] | python | en | ['en', 'da', 'en'] | True |
MacTool.ExecFilterLibtool | (self, *cmd_list) | Calls libtool and filters out '/path/to/libtool: file: foo.o has no
symbols'. | Calls libtool and filters out '/path/to/libtool: file: foo.o has no
symbols'. | def ExecFilterLibtool(self, *cmd_list):
"""Calls libtool and filters out '/path/to/libtool: file: foo.o has no
symbols'."""
libtool_re = re.compile(r'^.*libtool: file: .* has no symbols$')
libtool_re5 = re.compile(
r'^.*libtool: warning for library: ' +
r'.* the table of contents is empty ' +
r'\(no object file members in the library define global symbols\)$')
env = os.environ.copy()
# Ref:
# http://www.opensource.apple.com/source/cctools/cctools-809/misc/libtool.c
# The problem with this flag is that it resets the file mtime on the file to
# epoch=0, e.g. 1970-1-1 or 1969-12-31 depending on timezone.
env['ZERO_AR_DATE'] = '1'
libtoolout = subprocess.Popen(cmd_list, stderr=subprocess.PIPE, env=env)
_, err = libtoolout.communicate()
for line in err.splitlines():
if not libtool_re.match(line) and not libtool_re5.match(line):
print >>sys.stderr, line
# Unconditionally touch the output .a file on the command line if present
# and the command succeeded. A bit hacky.
if not libtoolout.returncode:
for i in range(len(cmd_list) - 1):
if cmd_list[i] == "-o" and cmd_list[i+1].endswith('.a'):
os.utime(cmd_list[i+1], None)
break
return libtoolout.returncode | [
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227,
2
] | [
253,
32
] | python | en | ['en', 'en', 'en'] | True |
MacTool.ExecPackageFramework | (self, framework, version) | Takes a path to Something.framework and the Current version of that and
sets up all the symlinks. | Takes a path to Something.framework and the Current version of that and
sets up all the symlinks. | def ExecPackageFramework(self, framework, version):
"""Takes a path to Something.framework and the Current version of that and
sets up all the symlinks."""
# Find the name of the binary based on the part before the ".framework".
binary = os.path.basename(framework).split('.')[0]
CURRENT = 'Current'
RESOURCES = 'Resources'
VERSIONS = 'Versions'
if not os.path.exists(os.path.join(framework, VERSIONS, version, binary)):
# Binary-less frameworks don't seem to contain symlinks (see e.g.
# chromium's out/Debug/org.chromium.Chromium.manifest/ bundle).
return
# Move into the framework directory to set the symlinks correctly.
pwd = os.getcwd()
os.chdir(framework)
# Set up the Current version.
self._Relink(version, os.path.join(VERSIONS, CURRENT))
# Set up the root symlinks.
self._Relink(os.path.join(VERSIONS, CURRENT, binary), binary)
self._Relink(os.path.join(VERSIONS, CURRENT, RESOURCES), RESOURCES)
# Back to where we were before!
os.chdir(pwd) | [
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255,
2
] | [
282,
17
] | python | en | ['en', 'en', 'en'] | True |
MacTool._Relink | (self, dest, link) | Creates a symlink to |dest| named |link|. If |link| already exists,
it is overwritten. | Creates a symlink to |dest| named |link|. If |link| already exists,
it is overwritten. | def _Relink(self, dest, link):
"""Creates a symlink to |dest| named |link|. If |link| already exists,
it is overwritten."""
if os.path.lexists(link):
os.remove(link)
os.symlink(dest, link) | [
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284,
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289,
26
] | python | en | ['en', 'en', 'en'] | True |
MacTool.ExecCompileXcassets | (self, keys, *inputs) | Compiles multiple .xcassets files into a single .car file.
This invokes 'actool' to compile all the inputs .xcassets files. The
|keys| arguments is a json-encoded dictionary of extra arguments to
pass to 'actool' when the asset catalogs contains an application icon
or a launch image.
Note that 'actool' does not create the Assets.car file if the asset
catalogs does not contains imageset.
| Compiles multiple .xcassets files into a single .car file. | def ExecCompileXcassets(self, keys, *inputs):
"""Compiles multiple .xcassets files into a single .car file.
This invokes 'actool' to compile all the inputs .xcassets files. The
|keys| arguments is a json-encoded dictionary of extra arguments to
pass to 'actool' when the asset catalogs contains an application icon
or a launch image.
Note that 'actool' does not create the Assets.car file if the asset
catalogs does not contains imageset.
"""
command_line = [
'xcrun', 'actool', '--output-format', 'human-readable-text',
'--compress-pngs', '--notices', '--warnings', '--errors',
]
is_iphone_target = 'IPHONEOS_DEPLOYMENT_TARGET' in os.environ
if is_iphone_target:
platform = os.environ['CONFIGURATION'].split('-')[-1]
if platform not in ('iphoneos', 'iphonesimulator'):
platform = 'iphonesimulator'
command_line.extend([
'--platform', platform, '--target-device', 'iphone',
'--target-device', 'ipad', '--minimum-deployment-target',
os.environ['IPHONEOS_DEPLOYMENT_TARGET'], '--compile',
os.path.abspath(os.environ['CONTENTS_FOLDER_PATH']),
])
else:
command_line.extend([
'--platform', 'macosx', '--target-device', 'mac',
'--minimum-deployment-target', os.environ['MACOSX_DEPLOYMENT_TARGET'],
'--compile',
os.path.abspath(os.environ['UNLOCALIZED_RESOURCES_FOLDER_PATH']),
])
if keys:
keys = json.loads(keys)
for key, value in keys.iteritems():
arg_name = '--' + key
if isinstance(value, bool):
if value:
command_line.append(arg_name)
elif isinstance(value, list):
for v in value:
command_line.append(arg_name)
command_line.append(str(v))
else:
command_line.append(arg_name)
command_line.append(str(value))
# Note: actool crashes if inputs path are relative, so use os.path.abspath
# to get absolute path name for inputs.
command_line.extend(map(os.path.abspath, inputs))
subprocess.check_call(command_line) | [
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] | [
291,
2
] | [
341,
39
] | python | en | ['en', 'en', 'en'] | True |
MacTool.ExecMergeInfoPlist | (self, output, *inputs) | Merge multiple .plist files into a single .plist file. | Merge multiple .plist files into a single .plist file. | def ExecMergeInfoPlist(self, output, *inputs):
"""Merge multiple .plist files into a single .plist file."""
merged_plist = {}
for path in inputs:
plist = self._LoadPlistMaybeBinary(path)
self._MergePlist(merged_plist, plist)
plistlib.writePlist(merged_plist, output) | [
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343,
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] | [
349,
45
] | python | en | ['en', 'et', 'en'] | True |
MacTool.ExecCodeSignBundle | (self, key, resource_rules, entitlements, provisioning) | Code sign a bundle.
This function tries to code sign an iOS bundle, following the same
algorithm as Xcode:
1. copy ResourceRules.plist from the user or the SDK into the bundle,
2. pick the provisioning profile that best match the bundle identifier,
and copy it into the bundle as embedded.mobileprovision,
3. copy Entitlements.plist from user or SDK next to the bundle,
4. code sign the bundle.
| Code sign a bundle. | def ExecCodeSignBundle(self, key, resource_rules, entitlements, provisioning):
"""Code sign a bundle.
This function tries to code sign an iOS bundle, following the same
algorithm as Xcode:
1. copy ResourceRules.plist from the user or the SDK into the bundle,
2. pick the provisioning profile that best match the bundle identifier,
and copy it into the bundle as embedded.mobileprovision,
3. copy Entitlements.plist from user or SDK next to the bundle,
4. code sign the bundle.
"""
resource_rules_path = self._InstallResourceRules(resource_rules)
substitutions, overrides = self._InstallProvisioningProfile(
provisioning, self._GetCFBundleIdentifier())
entitlements_path = self._InstallEntitlements(
entitlements, substitutions, overrides)
subprocess.check_call([
'codesign', '--force', '--sign', key, '--resource-rules',
resource_rules_path, '--entitlements', entitlements_path,
os.path.join(
os.environ['TARGET_BUILD_DIR'],
os.environ['FULL_PRODUCT_NAME'])]) | [
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351,
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] | [
372,
46
] | python | en | ['en', 'su', 'en'] | True |
MacTool._InstallResourceRules | (self, resource_rules) | Installs ResourceRules.plist from user or SDK into the bundle.
Args:
resource_rules: string, optional, path to the ResourceRules.plist file
to use, default to "${SDKROOT}/ResourceRules.plist"
Returns:
Path to the copy of ResourceRules.plist into the bundle.
| Installs ResourceRules.plist from user or SDK into the bundle. | def _InstallResourceRules(self, resource_rules):
"""Installs ResourceRules.plist from user or SDK into the bundle.
Args:
resource_rules: string, optional, path to the ResourceRules.plist file
to use, default to "${SDKROOT}/ResourceRules.plist"
Returns:
Path to the copy of ResourceRules.plist into the bundle.
"""
source_path = resource_rules
target_path = os.path.join(
os.environ['BUILT_PRODUCTS_DIR'],
os.environ['CONTENTS_FOLDER_PATH'],
'ResourceRules.plist')
if not source_path:
source_path = os.path.join(
os.environ['SDKROOT'], 'ResourceRules.plist')
shutil.copy2(source_path, target_path)
return target_path | [
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374,
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] | [
393,
22
] | python | en | ['en', 'en', 'en'] | True |
MacTool._InstallProvisioningProfile | (self, profile, bundle_identifier) | Installs embedded.mobileprovision into the bundle.
Args:
profile: string, optional, short name of the .mobileprovision file
to use, if empty or the file is missing, the best file installed
will be used
bundle_identifier: string, value of CFBundleIdentifier from Info.plist
Returns:
A tuple containing two dictionary: variables substitutions and values
to overrides when generating the entitlements file.
| Installs embedded.mobileprovision into the bundle. | def _InstallProvisioningProfile(self, profile, bundle_identifier):
"""Installs embedded.mobileprovision into the bundle.
Args:
profile: string, optional, short name of the .mobileprovision file
to use, if empty or the file is missing, the best file installed
will be used
bundle_identifier: string, value of CFBundleIdentifier from Info.plist
Returns:
A tuple containing two dictionary: variables substitutions and values
to overrides when generating the entitlements file.
"""
source_path, provisioning_data, team_id = self._FindProvisioningProfile(
profile, bundle_identifier)
target_path = os.path.join(
os.environ['BUILT_PRODUCTS_DIR'],
os.environ['CONTENTS_FOLDER_PATH'],
'embedded.mobileprovision')
shutil.copy2(source_path, target_path)
substitutions = self._GetSubstitutions(bundle_identifier, team_id + '.')
return substitutions, provisioning_data['Entitlements'] | [
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395,
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] | [
416,
59
] | python | en | ['en', 'en', 'en'] | True |
MacTool._FindProvisioningProfile | (self, profile, bundle_identifier) | Finds the .mobileprovision file to use for signing the bundle.
Checks all the installed provisioning profiles (or if the user specified
the PROVISIONING_PROFILE variable, only consult it) and select the most
specific that correspond to the bundle identifier.
Args:
profile: string, optional, short name of the .mobileprovision file
to use, if empty or the file is missing, the best file installed
will be used
bundle_identifier: string, value of CFBundleIdentifier from Info.plist
Returns:
A tuple of the path to the selected provisioning profile, the data of
the embedded plist in the provisioning profile and the team identifier
to use for code signing.
Raises:
SystemExit: if no .mobileprovision can be used to sign the bundle.
| Finds the .mobileprovision file to use for signing the bundle. | def _FindProvisioningProfile(self, profile, bundle_identifier):
"""Finds the .mobileprovision file to use for signing the bundle.
Checks all the installed provisioning profiles (or if the user specified
the PROVISIONING_PROFILE variable, only consult it) and select the most
specific that correspond to the bundle identifier.
Args:
profile: string, optional, short name of the .mobileprovision file
to use, if empty or the file is missing, the best file installed
will be used
bundle_identifier: string, value of CFBundleIdentifier from Info.plist
Returns:
A tuple of the path to the selected provisioning profile, the data of
the embedded plist in the provisioning profile and the team identifier
to use for code signing.
Raises:
SystemExit: if no .mobileprovision can be used to sign the bundle.
"""
profiles_dir = os.path.join(
os.environ['HOME'], 'Library', 'MobileDevice', 'Provisioning Profiles')
if not os.path.isdir(profiles_dir):
print >>sys.stderr, (
'cannot find mobile provisioning for %s' % bundle_identifier)
sys.exit(1)
provisioning_profiles = None
if profile:
profile_path = os.path.join(profiles_dir, profile + '.mobileprovision')
if os.path.exists(profile_path):
provisioning_profiles = [profile_path]
if not provisioning_profiles:
provisioning_profiles = glob.glob(
os.path.join(profiles_dir, '*.mobileprovision'))
valid_provisioning_profiles = {}
for profile_path in provisioning_profiles:
profile_data = self._LoadProvisioningProfile(profile_path)
app_id_pattern = profile_data.get(
'Entitlements', {}).get('application-identifier', '')
for team_identifier in profile_data.get('TeamIdentifier', []):
app_id = '%s.%s' % (team_identifier, bundle_identifier)
if fnmatch.fnmatch(app_id, app_id_pattern):
valid_provisioning_profiles[app_id_pattern] = (
profile_path, profile_data, team_identifier)
if not valid_provisioning_profiles:
print >>sys.stderr, (
'cannot find mobile provisioning for %s' % bundle_identifier)
sys.exit(1)
# If the user has multiple provisioning profiles installed that can be
# used for ${bundle_identifier}, pick the most specific one (ie. the
# provisioning profile whose pattern is the longest).
selected_key = max(valid_provisioning_profiles, key=lambda v: len(v))
return valid_provisioning_profiles[selected_key] | [
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418,
2
] | [
471,
52
] | python | en | ['en', 'en', 'en'] | True |
MacTool._LoadProvisioningProfile | (self, profile_path) | Extracts the plist embedded in a provisioning profile.
Args:
profile_path: string, path to the .mobileprovision file
Returns:
Content of the plist embedded in the provisioning profile as a dictionary.
| Extracts the plist embedded in a provisioning profile. | def _LoadProvisioningProfile(self, profile_path):
"""Extracts the plist embedded in a provisioning profile.
Args:
profile_path: string, path to the .mobileprovision file
Returns:
Content of the plist embedded in the provisioning profile as a dictionary.
"""
with tempfile.NamedTemporaryFile() as temp:
subprocess.check_call([
'security', 'cms', '-D', '-i', profile_path, '-o', temp.name])
return self._LoadPlistMaybeBinary(temp.name) | [
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485,
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] | python | en | ['en', 'en', 'en'] | True |
MacTool._MergePlist | (self, merged_plist, plist) | Merge |plist| into |merged_plist|. | Merge |plist| into |merged_plist|. | def _MergePlist(self, merged_plist, plist):
"""Merge |plist| into |merged_plist|."""
for key, value in plist.iteritems():
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if isinstance(merged_value, dict):
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merged_plist[key] = merged_value
else:
merged_plist[key] = value
else:
merged_plist[key] = value | [
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487,
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498,
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] | python | en | ['it', 'et', 'en'] | False |
MacTool._LoadPlistMaybeBinary | (self, plist_path) | Loads into a memory a plist possibly encoded in binary format.
This is a wrapper around plistlib.readPlist that tries to convert the
plist to the XML format if it can't be parsed (assuming that it is in
the binary format).
Args:
plist_path: string, path to a plist file, in XML or binary format
Returns:
Content of the plist as a dictionary.
| Loads into a memory a plist possibly encoded in binary format. | def _LoadPlistMaybeBinary(self, plist_path):
"""Loads into a memory a plist possibly encoded in binary format.
This is a wrapper around plistlib.readPlist that tries to convert the
plist to the XML format if it can't be parsed (assuming that it is in
the binary format).
Args:
plist_path: string, path to a plist file, in XML or binary format
Returns:
Content of the plist as a dictionary.
"""
try:
# First, try to read the file using plistlib that only supports XML,
# and if an exception is raised, convert a temporary copy to XML and
# load that copy.
return plistlib.readPlist(plist_path)
except:
pass
with tempfile.NamedTemporaryFile() as temp:
shutil.copy2(plist_path, temp.name)
subprocess.check_call(['plutil', '-convert', 'xml1', temp.name])
return plistlib.readPlist(temp.name) | [
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] | python | en | ['en', 'en', 'en'] | True |
MacTool._GetSubstitutions | (self, bundle_identifier, app_identifier_prefix) | Constructs a dictionary of variable substitutions for Entitlements.plist.
Args:
bundle_identifier: string, value of CFBundleIdentifier from Info.plist
app_identifier_prefix: string, value for AppIdentifierPrefix
Returns:
Dictionary of substitutions to apply when generating Entitlements.plist.
| Constructs a dictionary of variable substitutions for Entitlements.plist. | def _GetSubstitutions(self, bundle_identifier, app_identifier_prefix):
"""Constructs a dictionary of variable substitutions for Entitlements.plist.
Args:
bundle_identifier: string, value of CFBundleIdentifier from Info.plist
app_identifier_prefix: string, value for AppIdentifierPrefix
Returns:
Dictionary of substitutions to apply when generating Entitlements.plist.
"""
return {
'CFBundleIdentifier': bundle_identifier,
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"'AppIdentifierPrefix'",
":",
"app_identifier_prefix",
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"}"
] | [
525,
2
] | [
538,
5
] | python | en | ['en', 'en', 'en'] | True |
MacTool._GetCFBundleIdentifier | (self) | Extracts CFBundleIdentifier value from Info.plist in the bundle.
Returns:
Value of CFBundleIdentifier in the Info.plist located in the bundle.
| Extracts CFBundleIdentifier value from Info.plist in the bundle. | def _GetCFBundleIdentifier(self):
"""Extracts CFBundleIdentifier value from Info.plist in the bundle.
Returns:
Value of CFBundleIdentifier in the Info.plist located in the bundle.
"""
info_plist_path = os.path.join(
os.environ['TARGET_BUILD_DIR'],
os.environ['INFOPLIST_PATH'])
info_plist_data = self._LoadPlistMaybeBinary(info_plist_path)
return info_plist_data['CFBundleIdentifier'] | [
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MacTool._InstallEntitlements | (self, entitlements, substitutions, overrides) | Generates and install the ${BundleName}.xcent entitlements file.
Expands variables "$(variable)" pattern in the source entitlements file,
add extra entitlements defined in the .mobileprovision file and the copy
the generated plist to "${BundlePath}.xcent".
Args:
entitlements: string, optional, path to the Entitlements.plist template
to use, defaults to "${SDKROOT}/Entitlements.plist"
substitutions: dictionary, variable substitutions
overrides: dictionary, values to add to the entitlements
Returns:
Path to the generated entitlements file.
| Generates and install the ${BundleName}.xcent entitlements file. | def _InstallEntitlements(self, entitlements, substitutions, overrides):
"""Generates and install the ${BundleName}.xcent entitlements file.
Expands variables "$(variable)" pattern in the source entitlements file,
add extra entitlements defined in the .mobileprovision file and the copy
the generated plist to "${BundlePath}.xcent".
Args:
entitlements: string, optional, path to the Entitlements.plist template
to use, defaults to "${SDKROOT}/Entitlements.plist"
substitutions: dictionary, variable substitutions
overrides: dictionary, values to add to the entitlements
Returns:
Path to the generated entitlements file.
"""
source_path = entitlements
target_path = os.path.join(
os.environ['BUILT_PRODUCTS_DIR'],
os.environ['PRODUCT_NAME'] + '.xcent')
if not source_path:
source_path = os.path.join(
os.environ['SDKROOT'],
'Entitlements.plist')
shutil.copy2(source_path, target_path)
data = self._LoadPlistMaybeBinary(target_path)
data = self._ExpandVariables(data, substitutions)
if overrides:
for key in overrides:
if key not in data:
data[key] = overrides[key]
plistlib.writePlist(data, target_path)
return target_path | [
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MacTool._ExpandVariables | (self, data, substitutions) | Expands variables "$(variable)" in data.
Args:
data: object, can be either string, list or dictionary
substitutions: dictionary, variable substitutions to perform
Returns:
Copy of data where each references to "$(variable)" has been replaced
by the corresponding value found in substitutions, or left intact if
the key was not found.
| Expands variables "$(variable)" in data. | def _ExpandVariables(self, data, substitutions):
"""Expands variables "$(variable)" in data.
Args:
data: object, can be either string, list or dictionary
substitutions: dictionary, variable substitutions to perform
Returns:
Copy of data where each references to "$(variable)" has been replaced
by the corresponding value found in substitutions, or left intact if
the key was not found.
"""
if isinstance(data, str):
for key, value in substitutions.iteritems():
data = data.replace('$(%s)' % key, value)
return data
if isinstance(data, list):
return [self._ExpandVariables(v, substitutions) for v in data]
if isinstance(data, dict):
return {k: self._ExpandVariables(data[k], substitutions) for k in data}
return data | [
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606,
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] | python | en | ['nl', 'en', 'en'] | True |
ValidationResultsPageRenderer_render_with_run_info_at_end | () |
Rendered validation results with run info at the end
Returns:
json string of rendered validation results
|
Rendered validation results with run info at the end
Returns:
json string of rendered validation results
| def ValidationResultsPageRenderer_render_with_run_info_at_end():
"""
Rendered validation results with run info at the end
Returns:
json string of rendered validation results
"""
fixture_filename = file_relative_path(
__file__,
"./fixtures/ValidationResultsPageRenderer_render_with_run_info_at_end.json",
)
with open(fixture_filename) as infile:
rendered_validation_results = json.load(infile)
return rendered_validation_results | [
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478,
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] | python | en | ['en', 'error', 'th'] | False |
ValidationResultsPageRenderer_render_with_run_info_at_start | () |
Rendered validation results with run info at the start
Returns:
json string of rendered validation results
|
Rendered validation results with run info at the start
Returns:
json string of rendered validation results
| def ValidationResultsPageRenderer_render_with_run_info_at_start():
"""
Rendered validation results with run info at the start
Returns:
json string of rendered validation results
"""
fixture_filename = file_relative_path(
__file__,
"./fixtures/ValidationResultsPageRenderer_render_with_run_info_at_start.json",
)
with open(fixture_filename) as infile:
rendered_validation_results = json.load(infile)
return rendered_validation_results | [
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] | python | en | ['en', 'error', 'th'] | False |
construct_data_context_config | () |
Construct a DataContextConfig fixture given the modifications in the input parameters
Returns:
Dictionary representation of a DataContextConfig to compare in tests
|
Construct a DataContextConfig fixture given the modifications in the input parameters
Returns:
Dictionary representation of a DataContextConfig to compare in tests
| def construct_data_context_config():
"""
Construct a DataContextConfig fixture given the modifications in the input parameters
Returns:
Dictionary representation of a DataContextConfig to compare in tests
"""
def _construct_data_context_config(
data_context_id: str,
datasources: Dict,
config_version: float = float(
DataContextConfigDefaults.DEFAULT_CONFIG_VERSION.value
),
expectations_store_name: str = DataContextConfigDefaults.DEFAULT_EXPECTATIONS_STORE_NAME.value,
validations_store_name: str = DataContextConfigDefaults.DEFAULT_VALIDATIONS_STORE_NAME.value,
evaluation_parameter_store_name: str = DataContextConfigDefaults.DEFAULT_EVALUATION_PARAMETER_STORE_NAME.value,
checkpoint_store_name: str = DataContextConfigDefaults.DEFAULT_CHECKPOINT_STORE_NAME.value,
plugins_directory: Optional[str] = None,
stores: Optional[Dict] = None,
validation_operators: Optional[Dict] = None,
data_docs_sites: Optional[Dict] = None,
):
if stores is None:
stores = copy.deepcopy(DataContextConfigDefaults.DEFAULT_STORES.value)
if data_docs_sites is None:
data_docs_sites = copy.deepcopy(
DataContextConfigDefaults.DEFAULT_DATA_DOCS_SITES.value
)
return {
"config_version": config_version,
"datasources": datasources,
"expectations_store_name": expectations_store_name,
"validations_store_name": validations_store_name,
"evaluation_parameter_store_name": evaluation_parameter_store_name,
"checkpoint_store_name": checkpoint_store_name,
"plugins_directory": plugins_directory,
"validation_operators": validation_operators,
"stores": stores,
"data_docs_sites": data_docs_sites,
"notebooks": None,
"config_variables_file_path": None,
"anonymous_usage_statistics": {
"data_context_id": data_context_id,
"enabled": True,
},
}
return _construct_data_context_config | [
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30,
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] | [
78,
41
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_BaseStoreBackendDefaults_and_simple_defaults | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Ensure that a very simple DataContextConfig setup with many defaults is created accurately
and produces a valid DataContextConfig
|
What does this test and why?
Ensure that a very simple DataContextConfig setup with many defaults is created accurately
and produces a valid DataContextConfig
| def test_DataContextConfig_with_BaseStoreBackendDefaults_and_simple_defaults(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Ensure that a very simple DataContextConfig setup with many defaults is created accurately
and produces a valid DataContextConfig
"""
store_backend_defaults = BaseStoreBackendDefaults()
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
checkpoint_store_name=store_backend_defaults.checkpoint_store_name,
)
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources=default_pandas_datasource_config,
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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] | [
155,
74
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_S3StoreBackendDefaults | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Make sure that using S3StoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_bucket_name getting propagated to all stores.
|
What does this test and why?
Make sure that using S3StoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_bucket_name getting propagated to all stores.
| def test_DataContextConfig_with_S3StoreBackendDefaults(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Make sure that using S3StoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_bucket_name getting propagated to all stores.
"""
store_backend_defaults = S3StoreBackendDefaults(
default_bucket_name="my_default_bucket"
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
desired_stores_config = {
"evaluation_parameter_store": {"class_name": "EvaluationParameterStore"},
"expectations_S3_store": {
"class_name": "ExpectationsStore",
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "expectations",
},
},
"validations_S3_store": {
"class_name": "ValidationsStore",
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "validations",
},
},
"checkpoint_S3_store": {
"class_name": "CheckpointStore",
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "checkpoints",
},
},
}
desired_data_docs_sites_config = {
"s3_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "data_docs",
},
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources=default_pandas_datasource_config,
expectations_store_name="expectations_S3_store",
validations_store_name="validations_S3_store",
evaluation_parameter_store_name=DataContextConfigDefaults.DEFAULT_EVALUATION_PARAMETER_STORE_NAME.value,
checkpoint_store_name="checkpoint_S3_store",
stores=desired_stores_config,
data_docs_sites=desired_data_docs_sites_config,
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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] | [
158,
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] | [
247,
74
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_S3StoreBackendDefaults_using_all_parameters | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Make sure that S3StoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_bucket_name is ignored if individual bucket names are passed
|
What does this test and why?
Make sure that S3StoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_bucket_name is ignored if individual bucket names are passed
| def test_DataContextConfig_with_S3StoreBackendDefaults_using_all_parameters(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Make sure that S3StoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_bucket_name is ignored if individual bucket names are passed
"""
store_backend_defaults = S3StoreBackendDefaults(
default_bucket_name="custom_default_bucket_name",
expectations_store_bucket_name="custom_expectations_store_bucket_name",
validations_store_bucket_name="custom_validations_store_bucket_name",
data_docs_bucket_name="custom_data_docs_store_bucket_name",
checkpoint_store_bucket_name="custom_checkpoint_store_bucket_name",
expectations_store_prefix="custom_expectations_store_prefix",
validations_store_prefix="custom_validations_store_prefix",
data_docs_prefix="custom_data_docs_prefix",
checkpoint_store_prefix="custom_checkpoint_store_prefix",
expectations_store_name="custom_expectations_S3_store_name",
validations_store_name="custom_validations_S3_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="custom_checkpoint_S3_store_name",
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
module_name="great_expectations.datasource",
data_asset_type={
"module_name": "great_expectations.dataset",
"class_name": "PandasDataset",
},
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
desired_stores_config = {
"custom_evaluation_parameter_store_name": {
"class_name": "EvaluationParameterStore"
},
"custom_expectations_S3_store_name": {
"class_name": "ExpectationsStore",
"store_backend": {
"bucket": "custom_expectations_store_bucket_name",
"class_name": "TupleS3StoreBackend",
"prefix": "custom_expectations_store_prefix",
},
},
"custom_validations_S3_store_name": {
"class_name": "ValidationsStore",
"store_backend": {
"bucket": "custom_validations_store_bucket_name",
"class_name": "TupleS3StoreBackend",
"prefix": "custom_validations_store_prefix",
},
},
"custom_checkpoint_S3_store_name": {
"class_name": "CheckpointStore",
"store_backend": {
"bucket": "custom_checkpoint_store_bucket_name",
"class_name": "TupleS3StoreBackend",
"prefix": "custom_checkpoint_store_prefix",
},
},
}
desired_data_docs_sites_config = {
"s3_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"bucket": "custom_data_docs_store_bucket_name",
"class_name": "TupleS3StoreBackend",
"prefix": "custom_data_docs_prefix",
},
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources=default_pandas_datasource_config,
expectations_store_name="custom_expectations_S3_store_name",
validations_store_name="custom_validations_S3_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="custom_checkpoint_S3_store_name",
stores=desired_stores_config,
data_docs_sites=desired_data_docs_sites_config,
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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358,
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] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_FilesystemStoreBackendDefaults_and_simple_defaults | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Ensure that a very simple DataContextConfig setup using FilesystemStoreBackendDefaults is created accurately
This test sets the root_dir parameter
|
What does this test and why?
Ensure that a very simple DataContextConfig setup using FilesystemStoreBackendDefaults is created accurately
This test sets the root_dir parameter
| def test_DataContextConfig_with_FilesystemStoreBackendDefaults_and_simple_defaults(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Ensure that a very simple DataContextConfig setup using FilesystemStoreBackendDefaults is created accurately
This test sets the root_dir parameter
"""
test_root_directory = "test_root_dir"
store_backend_defaults = FilesystemStoreBackendDefaults(
root_directory=test_root_directory
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
data_context_id = data_context_config.anonymous_usage_statistics.data_context_id
desired_config = construct_data_context_config(
data_context_id=data_context_id, datasources=default_pandas_datasource_config
)
# Add root_directory to stores and data_docs
desired_config["stores"][desired_config["expectations_store_name"]][
"store_backend"
]["root_directory"] = test_root_directory
desired_config["stores"][desired_config["validations_store_name"]]["store_backend"][
"root_directory"
] = test_root_directory
desired_config["stores"][desired_config["checkpoint_store_name"]]["store_backend"][
"root_directory"
] = test_root_directory
desired_config["data_docs_sites"]["local_site"]["store_backend"][
"root_directory"
] = test_root_directory
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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361,
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417,
74
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_FilesystemStoreBackendDefaults_and_simple_defaults_no_root_directory | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Ensure that a very simple DataContextConfig setup using FilesystemStoreBackendDefaults is created accurately
This test does not set the optional root_directory parameter
|
What does this test and why?
Ensure that a very simple DataContextConfig setup using FilesystemStoreBackendDefaults is created accurately
This test does not set the optional root_directory parameter
| def test_DataContextConfig_with_FilesystemStoreBackendDefaults_and_simple_defaults_no_root_directory(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Ensure that a very simple DataContextConfig setup using FilesystemStoreBackendDefaults is created accurately
This test does not set the optional root_directory parameter
"""
store_backend_defaults = FilesystemStoreBackendDefaults()
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
checkpoint_store_name=store_backend_defaults.checkpoint_store_name,
)
# Create desired config
data_context_id = data_context_config.anonymous_usage_statistics.data_context_id
desired_config = construct_data_context_config(
data_context_id=data_context_id, datasources=default_pandas_datasource_config
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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] | [
420,
0
] | [
460,
74
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_GCSStoreBackendDefaults | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Make sure that using GCSStoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_bucket_name & default_project_name getting propagated
to all stores.
|
What does this test and why?
Make sure that using GCSStoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_bucket_name & default_project_name getting propagated
to all stores.
| def test_DataContextConfig_with_GCSStoreBackendDefaults(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Make sure that using GCSStoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_bucket_name & default_project_name getting propagated
to all stores.
"""
store_backend_defaults = GCSStoreBackendDefaults(
default_bucket_name="my_default_bucket",
default_project_name="my_default_project",
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
module_name="great_expectations.datasource",
data_asset_type={
"module_name": "great_expectations.dataset",
"class_name": "PandasDataset",
},
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
data_context_id = data_context_config.anonymous_usage_statistics.data_context_id
desired_stores_config = {
"evaluation_parameter_store": {"class_name": "EvaluationParameterStore"},
"expectations_GCS_store": {
"class_name": "ExpectationsStore",
"store_backend": {
"bucket": "my_default_bucket",
"project": "my_default_project",
"class_name": "TupleGCSStoreBackend",
"prefix": "expectations",
},
},
"validations_GCS_store": {
"class_name": "ValidationsStore",
"store_backend": {
"bucket": "my_default_bucket",
"project": "my_default_project",
"class_name": "TupleGCSStoreBackend",
"prefix": "validations",
},
},
"checkpoint_GCS_store": {
"class_name": "CheckpointStore",
"store_backend": {
"bucket": "my_default_bucket",
"project": "my_default_project",
"class_name": "TupleGCSStoreBackend",
"prefix": "checkpoints",
},
},
}
desired_data_docs_sites_config = {
"gcs_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"bucket": "my_default_bucket",
"project": "my_default_project",
"class_name": "TupleGCSStoreBackend",
"prefix": "data_docs",
},
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_id,
datasources=default_pandas_datasource_config,
expectations_store_name="expectations_GCS_store",
validations_store_name="validations_GCS_store",
checkpoint_store_name="checkpoint_GCS_store",
evaluation_parameter_store_name=DataContextConfigDefaults.DEFAULT_EVALUATION_PARAMETER_STORE_NAME.value,
stores=desired_stores_config,
data_docs_sites=desired_data_docs_sites_config,
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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] | [
463,
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] | [
564,
74
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_GCSStoreBackendDefaults_using_all_parameters | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Make sure that GCSStoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_bucket_name is ignored if individual bucket names are passed
|
What does this test and why?
Make sure that GCSStoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_bucket_name is ignored if individual bucket names are passed
| def test_DataContextConfig_with_GCSStoreBackendDefaults_using_all_parameters(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Make sure that GCSStoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_bucket_name is ignored if individual bucket names are passed
"""
store_backend_defaults = GCSStoreBackendDefaults(
default_bucket_name="custom_default_bucket_name",
default_project_name="custom_default_project_name",
expectations_store_bucket_name="custom_expectations_store_bucket_name",
validations_store_bucket_name="custom_validations_store_bucket_name",
data_docs_bucket_name="custom_data_docs_store_bucket_name",
checkpoint_store_bucket_name="custom_checkpoint_store_bucket_name",
expectations_store_project_name="custom_expectations_store_project_name",
validations_store_project_name="custom_validations_store_project_name",
data_docs_project_name="custom_data_docs_store_project_name",
checkpoint_store_project_name="custom_checkpoint_store_project_name",
expectations_store_prefix="custom_expectations_store_prefix",
validations_store_prefix="custom_validations_store_prefix",
data_docs_prefix="custom_data_docs_prefix",
checkpoint_store_prefix="custom_checkpoint_store_prefix",
expectations_store_name="custom_expectations_GCS_store_name",
validations_store_name="custom_validations_GCS_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="custom_checkpoint_GCS_store_name",
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
module_name="great_expectations.datasource",
data_asset_type={
"module_name": "great_expectations.dataset",
"class_name": "PandasDataset",
},
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
desired_stores_config = {
"custom_evaluation_parameter_store_name": {
"class_name": "EvaluationParameterStore"
},
"custom_expectations_GCS_store_name": {
"class_name": "ExpectationsStore",
"store_backend": {
"bucket": "custom_expectations_store_bucket_name",
"project": "custom_expectations_store_project_name",
"class_name": "TupleGCSStoreBackend",
"prefix": "custom_expectations_store_prefix",
},
},
"custom_validations_GCS_store_name": {
"class_name": "ValidationsStore",
"store_backend": {
"bucket": "custom_validations_store_bucket_name",
"project": "custom_validations_store_project_name",
"class_name": "TupleGCSStoreBackend",
"prefix": "custom_validations_store_prefix",
},
},
"custom_checkpoint_GCS_store_name": {
"class_name": "CheckpointStore",
"store_backend": {
"bucket": "custom_checkpoint_store_bucket_name",
"project": "custom_checkpoint_store_project_name",
"class_name": "TupleGCSStoreBackend",
"prefix": "custom_checkpoint_store_prefix",
},
},
}
desired_data_docs_sites_config = {
"gcs_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"bucket": "custom_data_docs_store_bucket_name",
"project": "custom_data_docs_store_project_name",
"class_name": "TupleGCSStoreBackend",
"prefix": "custom_data_docs_prefix",
},
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources=default_pandas_datasource_config,
expectations_store_name="custom_expectations_GCS_store_name",
validations_store_name="custom_validations_GCS_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="custom_checkpoint_GCS_store_name",
stores=desired_stores_config,
data_docs_sites=desired_data_docs_sites_config,
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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] | [
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0
] | [
683,
74
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_DatabaseStoreBackendDefaults | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Make sure that using DatabaseStoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_credentials getting propagated to stores and not data_docs
|
What does this test and why?
Make sure that using DatabaseStoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_credentials getting propagated to stores and not data_docs
| def test_DataContextConfig_with_DatabaseStoreBackendDefaults(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Make sure that using DatabaseStoreBackendDefaults as the store_backend_defaults applies appropriate
defaults, including default_credentials getting propagated to stores and not data_docs
"""
store_backend_defaults = DatabaseStoreBackendDefaults(
default_credentials={
"drivername": "postgresql",
"host": os.getenv("GE_TEST_LOCAL_DB_HOSTNAME", "localhost"),
"port": "65432",
"username": "ge_tutorials",
"password": "ge_tutorials",
"database": "ge_tutorials",
},
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
module_name="great_expectations.datasource",
data_asset_type={
"module_name": "great_expectations.dataset",
"class_name": "PandasDataset",
},
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
desired_stores_config = {
"evaluation_parameter_store": {"class_name": "EvaluationParameterStore"},
"expectations_database_store": {
"class_name": "ExpectationsStore",
"store_backend": {
"class_name": "DatabaseStoreBackend",
"credentials": {
"drivername": "postgresql",
"host": os.getenv("GE_TEST_LOCAL_DB_HOSTNAME", "localhost"),
"port": "65432",
"username": "ge_tutorials",
"password": "ge_tutorials",
"database": "ge_tutorials",
},
},
},
"validations_database_store": {
"class_name": "ValidationsStore",
"store_backend": {
"class_name": "DatabaseStoreBackend",
"credentials": {
"drivername": "postgresql",
"host": os.getenv("GE_TEST_LOCAL_DB_HOSTNAME", "localhost"),
"port": "65432",
"username": "ge_tutorials",
"password": "ge_tutorials",
"database": "ge_tutorials",
},
},
},
"checkpoint_database_store": {
"class_name": "CheckpointStore",
"store_backend": {
"class_name": "DatabaseStoreBackend",
"credentials": {
"drivername": "postgresql",
"host": os.getenv("GE_TEST_LOCAL_DB_HOSTNAME", "localhost"),
"port": "65432",
"username": "ge_tutorials",
"password": "ge_tutorials",
"database": "ge_tutorials",
},
},
},
}
desired_data_docs_sites_config = {
"local_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"base_directory": "uncommitted/data_docs/local_site/",
"class_name": "TupleFilesystemStoreBackend",
},
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources=default_pandas_datasource_config,
expectations_store_name="expectations_database_store",
validations_store_name="validations_database_store",
checkpoint_store_name="checkpoint_database_store",
evaluation_parameter_store_name=DataContextConfigDefaults.DEFAULT_EVALUATION_PARAMETER_STORE_NAME.value,
stores=desired_stores_config,
data_docs_sites=desired_data_docs_sites_config,
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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] | [
686,
0
] | [
804,
74
] | python | en | ['en', 'error', 'th'] | False |
test_DataContextConfig_with_DatabaseStoreBackendDefaults_using_all_parameters | (
construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Make sure that DatabaseStoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_credentials is ignored if individual store credentials are passed
|
What does this test and why?
Make sure that DatabaseStoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_credentials is ignored if individual store credentials are passed
| def test_DataContextConfig_with_DatabaseStoreBackendDefaults_using_all_parameters(
construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Make sure that DatabaseStoreBackendDefaults parameters are handled appropriately
E.g. Make sure that default_credentials is ignored if individual store credentials are passed
"""
store_backend_defaults = DatabaseStoreBackendDefaults(
default_credentials={
"drivername": "postgresql",
"host": os.getenv("GE_TEST_LOCAL_DB_HOSTNAME", "localhost"),
"port": "65432",
"username": "ge_tutorials",
"password": "ge_tutorials",
"database": "ge_tutorials",
},
expectations_store_credentials={
"drivername": "custom_expectations_store_drivername",
"host": "custom_expectations_store_host",
"port": "custom_expectations_store_port",
"username": "custom_expectations_store_username",
"password": "custom_expectations_store_password",
"database": "custom_expectations_store_database",
},
validations_store_credentials={
"drivername": "custom_validations_store_drivername",
"host": "custom_validations_store_host",
"port": "custom_validations_store_port",
"username": "custom_validations_store_username",
"password": "custom_validations_store_password",
"database": "custom_validations_store_database",
},
checkpoint_store_credentials={
"drivername": "custom_checkpoint_store_drivername",
"host": "custom_checkpoint_store_host",
"port": "custom_checkpoint_store_port",
"username": "custom_checkpoint_store_username",
"password": "custom_checkpoint_store_password",
"database": "custom_checkpoint_store_database",
},
expectations_store_name="custom_expectations_database_store_name",
validations_store_name="custom_validations_database_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="custom_checkpoint_database_store_name",
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
module_name="great_expectations.datasource",
data_asset_type={
"module_name": "great_expectations.dataset",
"class_name": "PandasDataset",
},
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
desired_stores_config = {
"custom_evaluation_parameter_store_name": {
"class_name": "EvaluationParameterStore"
},
"custom_expectations_database_store_name": {
"class_name": "ExpectationsStore",
"store_backend": {
"class_name": "DatabaseStoreBackend",
"credentials": {
"database": "custom_expectations_store_database",
"drivername": "custom_expectations_store_drivername",
"host": "custom_expectations_store_host",
"password": "custom_expectations_store_password",
"port": "custom_expectations_store_port",
"username": "custom_expectations_store_username",
},
},
},
"custom_validations_database_store_name": {
"class_name": "ValidationsStore",
"store_backend": {
"class_name": "DatabaseStoreBackend",
"credentials": {
"database": "custom_validations_store_database",
"drivername": "custom_validations_store_drivername",
"host": "custom_validations_store_host",
"password": "custom_validations_store_password",
"port": "custom_validations_store_port",
"username": "custom_validations_store_username",
},
},
},
"custom_checkpoint_database_store_name": {
"class_name": "CheckpointStore",
"store_backend": {
"class_name": "DatabaseStoreBackend",
"credentials": {
"database": "custom_checkpoint_store_database",
"drivername": "custom_checkpoint_store_drivername",
"host": "custom_checkpoint_store_host",
"password": "custom_checkpoint_store_password",
"port": "custom_checkpoint_store_port",
"username": "custom_checkpoint_store_username",
},
},
},
}
desired_data_docs_sites_config = {
"local_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"base_directory": "uncommitted/data_docs/local_site/",
"class_name": "TupleFilesystemStoreBackend",
},
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources=default_pandas_datasource_config,
expectations_store_name="custom_expectations_database_store_name",
validations_store_name="custom_validations_database_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="custom_checkpoint_database_store_name",
stores=desired_stores_config,
data_docs_sites=desired_data_docs_sites_config,
)
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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] | [
807,
0
] | [
955,
74
] | python | en | ['en', 'error', 'th'] | False |
test_override_general_defaults | (
construct_data_context_config,
default_pandas_datasource_config,
default_spark_datasource_config,
) |
What does this test and why?
A DataContextConfig should be able to be created by passing items into the constructor that override any defaults.
It should also be able to handle multiple datasources, even if they are configured with a dictionary or a DatasourceConfig.
|
What does this test and why?
A DataContextConfig should be able to be created by passing items into the constructor that override any defaults.
It should also be able to handle multiple datasources, even if they are configured with a dictionary or a DatasourceConfig.
| def test_override_general_defaults(
construct_data_context_config,
default_pandas_datasource_config,
default_spark_datasource_config,
):
"""
What does this test and why?
A DataContextConfig should be able to be created by passing items into the constructor that override any defaults.
It should also be able to handle multiple datasources, even if they are configured with a dictionary or a DatasourceConfig.
"""
data_context_config = DataContextConfig(
config_version=999,
plugins_directory="custom_plugins_directory",
config_variables_file_path="custom_config_variables_file_path",
datasources={
"my_spark_datasource": {
"data_asset_type": {
"class_name": "SparkDFDataset",
"module_name": "great_expectations.dataset",
},
"class_name": "SparkDFDatasource",
"module_name": "great_expectations.datasource",
"batch_kwargs_generators": {},
},
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "../data/",
}
},
),
},
stores={
"expectations_S3_store": {
"class_name": "ExpectationsStore",
"store_backend": {
"class_name": "TupleS3StoreBackend",
"bucket": "REPLACE_ME",
"prefix": "REPLACE_ME",
},
},
"expectations_S3_store2": {
"class_name": "ExpectationsStore",
"store_backend": {
"class_name": "TupleS3StoreBackend",
"bucket": "REPLACE_ME",
"prefix": "REPLACE_ME",
},
},
"validations_S3_store": {
"class_name": "ValidationsStore",
"store_backend": {
"class_name": "TupleS3StoreBackend",
"bucket": "REPLACE_ME",
"prefix": "REPLACE_ME",
},
},
"validations_S3_store2": {
"class_name": "ValidationsStore",
"store_backend": {
"class_name": "TupleS3StoreBackend",
"bucket": "REPLACE_ME",
"prefix": "REPLACE_ME",
},
},
"custom_evaluation_parameter_store": {
"class_name": "EvaluationParameterStore"
},
"checkpoint_S3_store": {
"class_name": "CheckpointStore",
"store_backend": {
"class_name": "TupleS3StoreBackend",
"bucket": "REPLACE_ME",
"prefix": "REPLACE_ME",
},
},
},
expectations_store_name="custom_expectations_store_name",
validations_store_name="custom_validations_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="checkpoint_S3_store",
data_docs_sites={
"s3_site": {
"class_name": "SiteBuilder",
"store_backend": {
"class_name": "TupleS3StoreBackend",
"bucket": "REPLACE_ME",
},
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
},
"local_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"base_directory": "uncommitted/data_docs/local_site/",
"class_name": "TupleFilesystemStoreBackend",
},
},
},
validation_operators={
"custom_action_list_operator": {
"class_name": "ActionListValidationOperator",
"action_list": [
{
"name": "custom_store_validation_result",
"action": {"class_name": "CustomStoreValidationResultAction"},
},
{
"name": "store_evaluation_params",
"action": {"class_name": "StoreEvaluationParametersAction"},
},
{
"name": "update_data_docs",
"action": {"class_name": "UpdateDataDocsAction"},
},
],
}
},
anonymous_usage_statistics={"enabled": True},
)
desired_stores = {
"custom_evaluation_parameter_store": {"class_name": "EvaluationParameterStore"},
"expectations_S3_store": {
"class_name": "ExpectationsStore",
"store_backend": {
"bucket": "REPLACE_ME",
"class_name": "TupleS3StoreBackend",
"prefix": "REPLACE_ME",
},
},
"expectations_S3_store2": {
"class_name": "ExpectationsStore",
"store_backend": {
"bucket": "REPLACE_ME",
"class_name": "TupleS3StoreBackend",
"prefix": "REPLACE_ME",
},
},
"validations_S3_store": {
"class_name": "ValidationsStore",
"store_backend": {
"bucket": "REPLACE_ME",
"class_name": "TupleS3StoreBackend",
"prefix": "REPLACE_ME",
},
},
"validations_S3_store2": {
"class_name": "ValidationsStore",
"store_backend": {
"bucket": "REPLACE_ME",
"class_name": "TupleS3StoreBackend",
"prefix": "REPLACE_ME",
},
},
"checkpoint_S3_store": {
"class_name": "CheckpointStore",
"store_backend": {
"bucket": "REPLACE_ME",
"class_name": "TupleS3StoreBackend",
"prefix": "REPLACE_ME",
},
},
}
desired_data_docs_sites_config = {
"local_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"base_directory": "uncommitted/data_docs/local_site/",
"class_name": "TupleFilesystemStoreBackend",
},
},
"s3_site": {
"class_name": "SiteBuilder",
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"bucket": "REPLACE_ME",
"class_name": "TupleS3StoreBackend",
},
},
}
desired_validation_operators = {
"custom_action_list_operator": {
"class_name": "ActionListValidationOperator",
"action_list": [
{
"name": "custom_store_validation_result",
"action": {"class_name": "CustomStoreValidationResultAction"},
},
{
"name": "store_evaluation_params",
"action": {"class_name": "StoreEvaluationParametersAction"},
},
{
"name": "update_data_docs",
"action": {"class_name": "UpdateDataDocsAction"},
},
],
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources={
**default_pandas_datasource_config,
**default_spark_datasource_config,
},
config_version=999.0,
expectations_store_name="custom_expectations_store_name",
validations_store_name="custom_validations_store_name",
evaluation_parameter_store_name="custom_evaluation_parameter_store_name",
checkpoint_store_name="checkpoint_S3_store",
stores=desired_stores,
validation_operators=desired_validation_operators,
data_docs_sites=desired_data_docs_sites_config,
plugins_directory="custom_plugins_directory",
)
desired_config["config_variables_file_path"] = "custom_config_variables_file_path"
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config) | [
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test_DataContextConfig_with_S3StoreBackendDefaults_and_simple_defaults_with_variable_sub | (
monkeypatch, construct_data_context_config, default_pandas_datasource_config
) |
What does this test and why?
Ensure that a very simple DataContextConfig setup with many defaults is created accurately
and produces a valid DataContextConfig
|
What does this test and why?
Ensure that a very simple DataContextConfig setup with many defaults is created accurately
and produces a valid DataContextConfig
| def test_DataContextConfig_with_S3StoreBackendDefaults_and_simple_defaults_with_variable_sub(
monkeypatch, construct_data_context_config, default_pandas_datasource_config
):
"""
What does this test and why?
Ensure that a very simple DataContextConfig setup with many defaults is created accurately
and produces a valid DataContextConfig
"""
monkeypatch.setenv("SUBSTITUTED_BASE_DIRECTORY", "../data/")
store_backend_defaults = S3StoreBackendDefaults(
default_bucket_name="my_default_bucket"
)
data_context_config = DataContextConfig(
datasources={
"my_pandas_datasource": DatasourceConfig(
class_name="PandasDatasource",
batch_kwargs_generators={
"subdir_reader": {
"class_name": "SubdirReaderBatchKwargsGenerator",
"base_directory": "${SUBSTITUTED_BASE_DIRECTORY}",
}
},
)
},
store_backend_defaults=store_backend_defaults,
)
# Create desired config
desired_stores_config = {
"evaluation_parameter_store": {"class_name": "EvaluationParameterStore"},
"expectations_S3_store": {
"class_name": "ExpectationsStore",
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "expectations",
},
},
"validations_S3_store": {
"class_name": "ValidationsStore",
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "validations",
},
},
"checkpoint_S3_store": {
"class_name": "CheckpointStore",
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "checkpoints",
},
},
}
desired_data_docs_sites_config = {
"s3_site": {
"class_name": "SiteBuilder",
"show_how_to_buttons": True,
"site_index_builder": {
"class_name": "DefaultSiteIndexBuilder",
},
"store_backend": {
"bucket": "my_default_bucket",
"class_name": "TupleS3StoreBackend",
"prefix": "data_docs",
},
}
}
desired_config = construct_data_context_config(
data_context_id=data_context_config.anonymous_usage_statistics.data_context_id,
datasources=default_pandas_datasource_config,
expectations_store_name="expectations_S3_store",
validations_store_name="validations_S3_store",
checkpoint_store_name="checkpoint_S3_store",
evaluation_parameter_store_name=DataContextConfigDefaults.DEFAULT_EVALUATION_PARAMETER_STORE_NAME.value,
stores=desired_stores_config,
data_docs_sites=desired_data_docs_sites_config,
)
desired_config["datasources"]["my_pandas_datasource"]["batch_kwargs_generators"][
"subdir_reader"
]["base_directory"] = "${SUBSTITUTED_BASE_DIRECTORY}"
data_context_config_schema = DataContextConfigSchema()
assert filter_properties_dict(
properties=data_context_config_schema.dump(data_context_config),
clean_falsy=True,
) == filter_properties_dict(
properties=desired_config,
clean_falsy=True,
)
assert DataContext.validate_config(project_config=data_context_config)
data_context = BaseDataContext(project_config=data_context_config)
assert (
data_context.datasources["my_pandas_datasource"]
.get_batch_kwargs_generator("subdir_reader")
._base_directory
== "../data/"
) | [
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dt_to_str | (date, fmt='%Y-%m-%d') |
Converts a datetime object to a string.
|
Converts a datetime object to a string.
| def dt_to_str(date, fmt='%Y-%m-%d'):
"""
Converts a datetime object to a string.
"""
return date.strftime(fmt) | [
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_n64_to_datetime | (n64) |
Converts Numpy 64 bit timestamps to datetime objects. Units in seconds
|
Converts Numpy 64 bit timestamps to datetime objects. Units in seconds
| def _n64_to_datetime(n64):
"""
Converts Numpy 64 bit timestamps to datetime objects. Units in seconds
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TransformersReader.__init__ | (
self,
model_name_or_path: str = "distilbert-base-uncased-distilled-squad",
model_version: Optional[str] = None,
tokenizer: Optional[str] = None,
context_window_size: int = 70,
use_gpu: int = 0,
top_k_per_candidate: int = 4,
return_no_answers: bool = True,
max_seq_len: int = 256,
doc_stride: int = 128
) |
Load a QA model from Transformers.
Available models include:
- ``'distilbert-base-uncased-distilled-squad`'``
- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
See https://huggingface.co/models for full list of available QA models
:param model_name_or_path: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
See https://huggingface.co/models for full list of available models.
:param model_version: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
:param tokenizer: Name of the tokenizer (usually the same as model)
:param context_window_size: Num of chars (before and after the answer) to return as "context" for each answer.
The context usually helps users to understand if the answer really makes sense.
:param use_gpu: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
:param top_k_per_candidate: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
Note that this is not the number of "final answers" you will receive
(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
and that no_answer can be included in the sorted list of predictions.
:param return_no_answers: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
If you would like to set no_answer_boost, use a `FARMReader`.
:param max_seq_len: max sequence length of one input text for the model
:param doc_stride: length of striding window for splitting long texts (used if len(text) > max_seq_len)
|
Load a QA model from Transformers.
Available models include: | def __init__(
self,
model_name_or_path: str = "distilbert-base-uncased-distilled-squad",
model_version: Optional[str] = None,
tokenizer: Optional[str] = None,
context_window_size: int = 70,
use_gpu: int = 0,
top_k_per_candidate: int = 4,
return_no_answers: bool = True,
max_seq_len: int = 256,
doc_stride: int = 128
):
"""
Load a QA model from Transformers.
Available models include:
- ``'distilbert-base-uncased-distilled-squad`'``
- ``'bert-large-cased-whole-word-masking-finetuned-squad``'
- ``'bert-large-uncased-whole-word-masking-finetuned-squad``'
See https://huggingface.co/models for full list of available QA models
:param model_name_or_path: Directory of a saved model or the name of a public model e.g. 'bert-base-cased',
'deepset/bert-base-cased-squad2', 'deepset/bert-base-cased-squad2', 'distilbert-base-uncased-distilled-squad'.
See https://huggingface.co/models for full list of available models.
:param model_version: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
:param tokenizer: Name of the tokenizer (usually the same as model)
:param context_window_size: Num of chars (before and after the answer) to return as "context" for each answer.
The context usually helps users to understand if the answer really makes sense.
:param use_gpu: If < 0, then use cpu. If >= 0, this is the ordinal of the gpu to use
:param top_k_per_candidate: How many answers to extract for each candidate doc that is coming from the retriever (might be a long text).
Note that this is not the number of "final answers" you will receive
(see `top_k` in TransformersReader.predict() or Finder.get_answers() for that)
and that no_answer can be included in the sorted list of predictions.
:param return_no_answers: If True, the HuggingFace Transformers model could return a "no_answer" (i.e. when there is an unanswerable question)
If False, it cannot return a "no_answer". Note that `no_answer_boost` is unfortunately not available with TransformersReader.
If you would like to set no_answer_boost, use a `FARMReader`.
:param max_seq_len: max sequence length of one input text for the model
:param doc_stride: length of striding window for splitting long texts (used if len(text) > max_seq_len)
"""
self.model = pipeline('question-answering', model=model_name_or_path, tokenizer=tokenizer, device=use_gpu, revision=model_version)
self.context_window_size = context_window_size
self.top_k_per_candidate = top_k_per_candidate
self.return_no_answers = return_no_answers
self.max_seq_len = max_seq_len
self.doc_stride = doc_stride | [
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TransformersReader.predict | (self, query: str, documents: List[Document], top_k: Optional[int] = None) |
Use loaded QA model to find answers for a query in the supplied list of Document.
Returns dictionaries containing answers sorted by (desc.) probability.
Example:
```python
|{
| 'query': 'Who is the father of Arya Stark?',
| 'answers':[
| {'answer': 'Eddard,',
| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
| 'offset_answer_start': 147,
| 'offset_answer_end': 154,
| 'probability': 0.9787139466668613,
| 'score': None,
| 'document_id': '1337'
| },...
| ]
|}
```
:param query: Query string
:param documents: List of Document in which to search for the answer
:param top_k: The maximum number of answers to return
:return: Dict containing query and answers
|
Use loaded QA model to find answers for a query in the supplied list of Document. | def predict(self, query: str, documents: List[Document], top_k: Optional[int] = None):
"""
Use loaded QA model to find answers for a query in the supplied list of Document.
Returns dictionaries containing answers sorted by (desc.) probability.
Example:
```python
|{
| 'query': 'Who is the father of Arya Stark?',
| 'answers':[
| {'answer': 'Eddard,',
| 'context': " She travels with her father, Eddard, to King's Landing when he is ",
| 'offset_answer_start': 147,
| 'offset_answer_end': 154,
| 'probability': 0.9787139466668613,
| 'score': None,
| 'document_id': '1337'
| },...
| ]
|}
```
:param query: Query string
:param documents: List of Document in which to search for the answer
:param top_k: The maximum number of answers to return
:return: Dict containing query and answers
"""
# get top-answers for each candidate passage
answers = []
no_ans_gaps = []
best_overall_score = 0
for doc in documents:
transformers_query = {"context": doc.text, "question": query}
predictions = self.model(transformers_query,
topk=self.top_k_per_candidate,
handle_impossible_answer=self.return_no_answers,
max_seq_len=self.max_seq_len,
doc_stride=self.doc_stride)
# for single preds (e.g. via top_k=1) transformers returns a dict instead of a list
if type(predictions) == dict:
predictions = [predictions]
# assemble and format all answers
best_doc_score = 0
# because we cannot ensure a "no answer" prediction coming back from transformers we initialize it here with 0
no_ans_doc_score = 0
# TODO add no answer bias on haystack side after getting "no answer" scores from transformers
for pred in predictions:
if pred["answer"]:
if pred["score"] > best_doc_score:
best_doc_score = pred["score"]
context_start = max(0, pred["start"] - self.context_window_size)
context_end = min(len(doc.text), pred["end"] + self.context_window_size)
answers.append({
"answer": pred["answer"],
"context": doc.text[context_start:context_end],
"offset_start": pred["start"],
"offset_end": pred["end"],
"probability": pred["score"],
"score": None,
"document_id": doc.id,
"meta": doc.meta
})
else:
no_ans_doc_score = pred["score"]
if best_doc_score > best_overall_score:
best_overall_score = best_doc_score
no_ans_gaps.append(no_ans_doc_score - best_doc_score)
# Calculate the score for predicting "no answer", relative to our best positive answer score
no_ans_prediction, max_no_ans_gap = self._calc_no_answer(no_ans_gaps, best_overall_score)
if self.return_no_answers:
answers.append(no_ans_prediction)
# sort answers by their `probability` and select top-k
answers = sorted(
answers, key=lambda k: k["probability"], reverse=True
)
answers = answers[:top_k]
results = {"query": query,
"answers": answers}
return results | [
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66,
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] | [
153,
22
] | python | en | ['en', 'error', 'th'] | False |
IRAmbassadorTLS.__init__ | (self, ir: 'IR', aconf: Config,
rkey: str="ir.tlsmodule",
kind: str="IRTLSModule",
name: str="ir.tlsmodule",
enabled: bool=True,
**kwargs) |
Initialize an IRAmbassadorTLS from the raw fields of its Resource.
|
Initialize an IRAmbassadorTLS from the raw fields of its Resource.
| def __init__(self, ir: 'IR', aconf: Config,
rkey: str="ir.tlsmodule",
kind: str="IRTLSModule",
name: str="ir.tlsmodule",
enabled: bool=True,
**kwargs) -> None:
"""
Initialize an IRAmbassadorTLS from the raw fields of its Resource.
"""
ir.logger.debug("IRAmbassadorTLS __init__ (%s %s %s)" % (kind, name, kwargs))
super().__init__(
ir=ir, aconf=aconf, rkey=rkey, kind=kind, name=name,
enabled=enabled,
**kwargs
) | [
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40,
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] | [
57,
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] | python | en | ['en', 'error', 'th'] | False |
critical_suite_with_citations | () |
This hand made fixture has a wide range of expectations, and has a mix of
metadata including an BasicSuiteBuilderProfiler entry, and citations.
|
This hand made fixture has a wide range of expectations, and has a mix of
metadata including an BasicSuiteBuilderProfiler entry, and citations.
| def critical_suite_with_citations() -> ExpectationSuite:
"""
This hand made fixture has a wide range of expectations, and has a mix of
metadata including an BasicSuiteBuilderProfiler entry, and citations.
"""
schema: ExpectationSuiteSchema = ExpectationSuiteSchema()
critical_suite: dict = {
"expectation_suite_name": "critical",
"meta": {
"great_expectations_version": "0.13.15+7252.g32fa98e2a.dirty",
"columns": {
"npi": {"description": ""},
"nppes_provider_last_org_name": {"description": ""},
"nppes_provider_first_name": {"description": ""},
"nppes_provider_mi": {"description": ""},
"nppes_credentials": {"description": ""},
"nppes_provider_gender": {"description": ""},
"nppes_entity_code": {"description": ""},
"nppes_provider_street1": {"description": ""},
"nppes_provider_street2": {"description": ""},
"nppes_provider_city": {"description": ""},
},
"citations": [
{
"citation_date": "2020-02-28T17:34:31.307271",
"batch_request": {
"datasource_name": "files_datasource",
"data_connector_name": "files_data_connector",
"data_asset_name": "10k",
},
"batch_markers": {
"ge_load_time": "20200229T013430.655026Z",
"pandas_data_fingerprint": "f6037d92eb4c01f976513bc0aec2420d",
},
"batch_parameters": None,
"comment": "BasicSuiteBuilderProfiler added a citation based on the current batch.",
}
],
"notes": {
"format": "markdown",
"content": [
"#### This is an _example_ suite\n\n- This suite was made by quickly glancing at 1000 rows of your data.\n- This is **not a production suite**. It is meant to show examples of expectations.\n- Because this suite was auto-generated using a very basic profiler that does not know your data like you do, many of the expectations may not be meaningful.\n"
],
},
"BasicSuiteBuilderProfiler": {
"created_by": "BasicSuiteBuilderProfiler",
"created_at": 1582838223.843476,
"batch_request": {
"datasource_name": "files_datasource",
"data_connector_name": "files_data_connector",
"data_asset_name": "10k",
},
},
},
"expectations": [
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "npi"},
"meta": {
"question": True,
"Notes": "There are empty strings that should probably be nulls",
"BasicSuiteBuilderProfiler": {"confidence": "very low"},
},
},
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "provider_type"},
},
],
"data_asset_type": "Dataset",
}
return schema.loads(json.dumps(critical_suite)) | [
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] | [
27,
0
] | [
98,
51
] | python | en | ['en', 'error', 'th'] | False |
suite_with_multiple_citations | () |
A handmade suite with multiple citations each with different batch_request.
The most recent citation does not have batch_request
|
A handmade suite with multiple citations each with different batch_request. | def suite_with_multiple_citations() -> ExpectationSuite:
"""
A handmade suite with multiple citations each with different batch_request.
The most recent citation does not have batch_request
"""
schema: ExpectationSuiteSchema = ExpectationSuiteSchema()
critical_suite: dict = {
"expectation_suite_name": "critical",
"meta": {
"great_expectations_version": "0.13.15+7252.g32fa98e2a.dirty",
"citations": [
{
"citation_date": "2000-01-01T00:00:01.000001",
"batch_request": {
"datasource_name": "files_datasource",
"data_connector_name": "files_data_connector",
"data_asset_name": "1k",
},
},
# This citation is the most recent and has no batch_request
{
"citation_date": "2020-01-01T00:00:01.000001",
},
{
"citation_date": "1999-01-01T00:00:01.000001",
"batch_request": {
"datasource_name": "files_datasource",
"data_connector_name": "files_data_connector",
"data_asset_name": "2k",
},
},
],
},
"expectations": [
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "npi"},
},
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "provider_type"},
},
],
"data_asset_type": "Dataset",
}
return schema.loads(json.dumps(critical_suite)) | [
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] | [
102,
0
] | [
148,
51
] | python | en | ['en', 'error', 'th'] | False |
warning_suite | () |
This hand made fixture has a wide range of expectations, and has a mix of
metadata including BasicSuiteBuilderProfiler entries.
|
This hand made fixture has a wide range of expectations, and has a mix of
metadata including BasicSuiteBuilderProfiler entries.
| def warning_suite() -> ExpectationSuite:
"""
This hand made fixture has a wide range of expectations, and has a mix of
metadata including BasicSuiteBuilderProfiler entries.
"""
schema: ExpectationSuiteSchema = ExpectationSuiteSchema()
warning_suite: dict = {
"expectation_suite_name": "warning",
"meta": {
"great_expectations_version": "0.13.15+7252.g32fa98e2a.dirty",
"citations": [
{
"citation_date": "2020-02-28T17:34:31.307271",
"batch_request": {
"datasource_name": "files_datasource",
"data_connector_name": "files_data_connector",
"data_asset_name": "10k",
},
"batch_markers": {
"ge_load_time": "20200229T013430.655026Z",
"pandas_data_fingerprint": "f6037d92eb4c01f976513bc0aec2420d",
},
"batch_parameters": None,
"comment": "BasicSuiteBuilderProfiler added a citation based on the current batch.",
}
],
},
"expectations": [
{
"expectation_type": "expect_table_row_count_to_be_between",
"kwargs": {"min_value": 800000, "max_value": 1200000},
},
{
"expectation_type": "expect_table_column_count_to_equal",
"kwargs": {"value": 71},
},
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "npi"},
"meta": {"BasicSuiteBuilderProfiler": {"confidence": "very low"}},
},
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "provider_type"},
"meta": {"BasicSuiteBuilderProfiler": {"confidence": "very low"}},
},
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "nppes_provider_last_org_name"},
},
{
"expectation_type": "expect_column_values_to_be_in_set",
"kwargs": {
"column": "nppes_provider_gender",
"value_set": ["M", "F", ""],
},
},
{
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"kwargs": {"column": "nppes_entity_code"},
},
{
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"kwargs": {"column": "nppes_entity_code", "value_set": ["I", "O"]},
},
{
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"kwargs": {
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"partition_object": {
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"weights": [0.9431769750233306, 0.056823024976669335],
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"threshold": 0.1,
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{
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"kwargs": {
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"mostly": 0.999,
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{
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"kwargs": {"column": "medicare_participation_indicator"},
},
{
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"kwargs": {
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"value_set": ["Y", "N"],
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{
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"kwargs": {"column": "number_of_hcpcs"},
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"min_value": 0,
"max_value": 500,
"mostly": 0.999,
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},
{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "total_unique_benes"},
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{
"expectation_type": "expect_column_values_to_be_between",
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"min_value": 0,
"max_value": 2000,
"mostly": 0.95,
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"max_value": 90,
"mostly": 0.995,
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},
{
"expectation_type": "expect_column_kl_divergence_to_be_less_than",
"kwargs": {
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"partition_object": {
"bins": [8, 16.5, 25, 33.5, 42, 50.5, 59, 67.5, 76, 84.5, 93],
"weights": [
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"threshold": 0.9,
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{
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"kwargs": {
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"min_value": 2000,
"max_value": 5000000,
"mostly": 0.98,
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{
"expectation_type": "expect_column_values_to_not_be_null",
"kwargs": {"column": "nppes_provider_first_name", "mostly": 0.9},
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{
"expectation_type": "expect_column_values_to_match_regex",
"kwargs": {
"column": "nppes_provider_zip",
"regex": "^\\d*$",
"mostly": 0.999,
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],
"data_asset_type": "Dataset",
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] | [
152,
0
] | [
391,
50
] | python | en | ['en', 'error', 'th'] | False |
test_notebook_execution_with_pandas_backend | (
titanic_v013_multi_datasource_pandas_data_context_with_checkpoints_v1_with_empty_store_stats_enabled,
) |
To set this test up we:
- create a suite
- add a few expectations (both table and column level)
- verify that no validations have happened
- create the suite edit notebook by hijacking the private cli method
We then:
- execute that notebook (Note this will raise various errors like
CellExecutionError if any cell in the notebook fails
- create a new context from disk
- verify that a validation has been run with our expectation suite
|
To set this test up we: | def test_notebook_execution_with_pandas_backend(
titanic_v013_multi_datasource_pandas_data_context_with_checkpoints_v1_with_empty_store_stats_enabled,
):
"""
To set this test up we:
- create a suite
- add a few expectations (both table and column level)
- verify that no validations have happened
- create the suite edit notebook by hijacking the private cli method
We then:
- execute that notebook (Note this will raise various errors like
CellExecutionError if any cell in the notebook fails
- create a new context from disk
- verify that a validation has been run with our expectation suite
"""
# Since we'll run the notebook, we use a context with no data docs to avoid the renderer's default
# behavior of building and opening docs, which is not part of this test.
context: DataContext = titanic_v013_multi_datasource_pandas_data_context_with_checkpoints_v1_with_empty_store_stats_enabled
root_dir: str = context.root_directory
uncommitted_dir: str = os.path.join(root_dir, "uncommitted")
expectation_suite_name: str = "warning"
context.create_expectation_suite(expectation_suite_name=expectation_suite_name)
batch_request: dict = {
"datasource_name": "my_datasource",
"data_connector_name": "my_basic_data_connector",
"data_asset_name": "Titanic_1912",
}
validator: Validator = context.get_validator(
batch_request=BatchRequest(**batch_request),
expectation_suite_name=expectation_suite_name,
)
validator.expect_table_column_count_to_equal(1)
validator.expect_table_row_count_to_equal(1313)
validator.expect_column_values_to_be_in_set("Sex", ["female", "male"])
validator.save_expectation_suite(discard_failed_expectations=False)
# Sanity check test setup
original_suite: ExpectationSuite = context.get_expectation_suite(
expectation_suite_name=expectation_suite_name
)
assert len(original_suite.expectations) == 3
assert context.list_expectation_suite_names() == [expectation_suite_name]
assert context.list_datasources() == [
{
"name": "my_datasource",
"class_name": "Datasource",
"module_name": "great_expectations.datasource",
"execution_engine": {
"class_name": "PandasExecutionEngine",
"module_name": "great_expectations.execution_engine",
},
"data_connectors": {
"my_basic_data_connector": {
"module_name": "great_expectations.datasource.data_connector",
"base_directory": f"{root_dir}/../data/titanic",
"default_regex": {
"pattern": "(.*)\\.csv",
"group_names": ["data_asset_name"],
},
"class_name": "InferredAssetFilesystemDataConnector",
},
"my_special_data_connector": {
"glob_directive": "*.csv",
"assets": {
"users": {
"pattern": "(.+)_(\\d+)_(\\d+)\\.csv",
"group_names": ["name", "timestamp", "size"],
"class_name": "Asset",
"base_directory": f"{root_dir}/../data/titanic",
"module_name": "great_expectations.datasource.data_connector.asset",
}
},
"module_name": "great_expectations.datasource.data_connector",
"base_directory": f"{root_dir}/../data/titanic",
"default_regex": {"pattern": "(.+)\\.csv", "group_names": ["name"]},
"class_name": "ConfiguredAssetFilesystemDataConnector",
},
"my_other_data_connector": {
"glob_directive": "*.csv",
"assets": {
"users": {
"class_name": "Asset",
"module_name": "great_expectations.datasource.data_connector.asset",
}
},
"module_name": "great_expectations.datasource.data_connector",
"base_directory": f"{root_dir}/../data/titanic",
"default_regex": {"pattern": "(.+)\\.csv", "group_names": ["name"]},
"class_name": "ConfiguredAssetFilesystemDataConnector",
},
"my_runtime_data_connector": {
"module_name": "great_expectations.datasource.data_connector",
"batch_identifiers": ["pipeline_stage_name", "airflow_run_id"],
"class_name": "RuntimeDataConnector",
},
},
},
{
"name": "my_additional_datasource",
"class_name": "Datasource",
"module_name": "great_expectations.datasource",
"execution_engine": {
"module_name": "great_expectations.execution_engine",
"class_name": "PandasExecutionEngine",
},
"data_connectors": {
"my_additional_data_connector": {
"module_name": "great_expectations.datasource.data_connector",
"default_regex": {
"pattern": "(.*)\\.csv",
"group_names": ["data_asset_name"],
},
"base_directory": f"{root_dir}/../data/titanic",
"class_name": "InferredAssetFilesystemDataConnector",
}
},
},
]
assert context.get_validation_result(expectation_suite_name="warning") == {}
# Create notebook
# do not want to actually send usage_message, since the function call is not the result of actual usage
_suite_edit_workflow(
context=context,
expectation_suite_name=expectation_suite_name,
profile=False,
usage_event="test_notebook_execution",
interactive=False,
no_jupyter=True,
create_if_not_exist=False,
datasource_name=None,
batch_request=batch_request,
additional_batch_request_args=None,
suppress_usage_message=True,
)
edit_notebook_path: str = os.path.join(uncommitted_dir, "edit_warning.ipynb")
assert os.path.isfile(edit_notebook_path)
run_notebook(
notebook_path=edit_notebook_path,
notebook_dir=uncommitted_dir,
string_to_be_replaced="context.open_data_docs(resource_identifier=validation_result_identifier)",
replacement_string="",
)
# Assertions about output
context = DataContext(context_root_dir=root_dir)
obs_validation_result: ExpectationSuiteValidationResult = (
context.get_validation_result(expectation_suite_name="warning")
)
assert obs_validation_result.statistics == {
"evaluated_expectations": 3,
"successful_expectations": 2,
"unsuccessful_expectations": 1,
"success_percent": 66.66666666666666,
}
suite: ExpectationSuite = context.get_expectation_suite(
expectation_suite_name=expectation_suite_name
)
suite["meta"].pop("citations", None)
assert suite == original_suite | [
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":",
"\"great_expectations.datasource\"",
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"\"execution_engine\"",
":",
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"\"class_name\"",
":",
"\"PandasExecutionEngine\"",
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"\"module_name\"",
":",
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"\"data_connectors\"",
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",",
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"\"class_name\"",
":",
"\"InferredAssetFilesystemDataConnector\"",
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"}",
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"\"my_special_data_connector\"",
":",
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"\"assets\"",
":",
"{",
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"\"module_name\"",
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"\"class_name\"",
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"\"ConfiguredAssetFilesystemDataConnector\"",
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"}",
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"\"my_other_data_connector\"",
":",
"{",
"\"glob_directive\"",
":",
"\"*.csv\"",
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"\"assets\"",
":",
"{",
"\"users\"",
":",
"{",
"\"class_name\"",
":",
"\"Asset\"",
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"\"module_name\"",
":",
"\"great_expectations.datasource.data_connector.asset\"",
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"}",
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"\"module_name\"",
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"\"base_directory\"",
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"f\"{root_dir}/../data/titanic\"",
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"\"ConfiguredAssetFilesystemDataConnector\"",
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"}",
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"\"my_runtime_data_connector\"",
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"{",
"\"module_name\"",
":",
"\"great_expectations.datasource.data_connector\"",
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"}",
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"}",
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"}",
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"{",
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":",
"\"my_additional_datasource\"",
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"\"class_name\"",
":",
"\"Datasource\"",
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"\"module_name\"",
":",
"\"great_expectations.datasource\"",
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"\"execution_engine\"",
":",
"{",
"\"module_name\"",
":",
"\"great_expectations.execution_engine\"",
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"\"class_name\"",
":",
"\"PandasExecutionEngine\"",
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"}",
",",
"\"data_connectors\"",
":",
"{",
"\"my_additional_data_connector\"",
":",
"{",
"\"module_name\"",
":",
"\"great_expectations.datasource.data_connector\"",
",",
"\"default_regex\"",
":",
"{",
"\"pattern\"",
":",
"\"(.*)\\\\.csv\"",
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"\"group_names\"",
":",
"[",
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"]",
",",
"}",
",",
"\"base_directory\"",
":",
"f\"{root_dir}/../data/titanic\"",
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"\"class_name\"",
":",
"\"InferredAssetFilesystemDataConnector\"",
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"}",
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"]",
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"profile",
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"usage_event",
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"\"test_notebook_execution\"",
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"interactive",
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"\"success_percent\"",
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"}",
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] | [
818,
0
] | [
982,
34
] | python | en | ['en', 'error', 'th'] | False |
test_notebook_execution_with_custom_notebooks_wrong_module | (
suite_with_multiple_citations, data_context_v3_custom_bad_notebooks
) |
Test the error message in case of "bad" custom module is clear
|
Test the error message in case of "bad" custom module is clear
| def test_notebook_execution_with_custom_notebooks_wrong_module(
suite_with_multiple_citations, data_context_v3_custom_bad_notebooks
):
"""
Test the error message in case of "bad" custom module is clear
"""
with pytest.raises(
SuiteEditNotebookCustomTemplateModuleNotFoundError, match=r"invalid\.module"
):
SuiteEditNotebookRenderer.from_data_context(
data_context=data_context_v3_custom_bad_notebooks
).render(suite=suite_with_multiple_citations) | [
"def",
"test_notebook_execution_with_custom_notebooks_wrong_module",
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985,
0
] | [
996,
53
] | python | en | ['en', 'error', 'th'] | False |
test_notebook_execution_with_custom_notebooks | (
suite_with_multiple_citations, data_context_v3_custom_notebooks
) |
Test the different parts of the notebooks can be modified
|
Test the different parts of the notebooks can be modified
| def test_notebook_execution_with_custom_notebooks(
suite_with_multiple_citations, data_context_v3_custom_notebooks
):
"""
Test the different parts of the notebooks can be modified
"""
batch_request: dict = {
"datasource_name": "files_datasource",
"data_connector_name": "files_data_connector",
"data_asset_name": "1k",
}
obs: NotebookNode = SuiteEditNotebookRenderer.from_data_context(
data_context=data_context_v3_custom_notebooks
).render(suite=suite_with_multiple_citations, batch_request=batch_request)
expected: dict = {
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {},
"cells": [
{
"id": "intellectual-inspection",
"cell_type": "markdown",
"source": "# Edit Your Expectation Suite\nUse this notebook to recreate and modify your expectation suite:\n\n**Expectation Suite Name**: `critical`",
"metadata": {},
},
{
"id": "collaborative-transfer",
"cell_type": "code",
"metadata": {},
"execution_count": None,
"source": 'import datetime\n\nimport pandas as pd\n\nimport great_expectations as ge\nimport great_expectations.jupyter_ux\nfrom great_expectations.core.batch import BatchRequest\nfrom great_expectations.checkpoint import SimpleCheckpoint\nfrom great_expectations.exceptions import DataContextError\n\ncontext = ge.data_context.DataContext()\n\nbatch_request = {\n "datasource_name": "files_datasource",\n "data_connector_name": "files_data_connector",\n "data_asset_name": "1k",\n}\n\n\n# Feel free to change the name of your suite here. Renaming this will not remove the other one.\nexpectation_suite_name = "critical"\ntry:\n suite = context.get_expectation_suite(expectation_suite_name=expectation_suite_name)\n print(\n f\'Loaded ExpectationSuite "{suite.expectation_suite_name}" containing {len(suite.expectations)} expectations.\'\n )\nexcept DataContextError:\n suite = context.create_expectation_suite(\n expectation_suite_name=expectation_suite_name\n )\n print(f\'Created ExpectationSuite "{suite.expectation_suite_name}".\')\n\n\nvalidator = context.get_validator(\n batch_request=BatchRequest(**batch_request),\n expectation_suite_name=expectation_suite_name,\n)\ncolumn_names = [f\'"{column_name}"\' for column_name in validator.columns()]\nprint(f"Columns: {\', \'.join(column_names)}.")\nvalidator.head(n_rows=5, fetch_all=False)',
"outputs": [],
},
{
"id": "legal-beauty",
"cell_type": "markdown",
"source": "## Create & Edit Expectations\n\n\nAdd expectations by calling specific expectation methods on the `validator` object. They all begin with `.expect_` which makes autocompleting easy using tab.\n\nBecause you selected interactive mode, you are now creating or editing an Expectation Suite with validator feedback from the sample batch of data that you specified (see `batch_request`).\n\nNote that if you select manual mode you may still create or edit an Expectation Suite directly, without feedback from the `validator`. See our documentation for more info and examples: [How to create a new Expectation Suite without a sample batch](https://docs.greatexpectations.io/en/latest/guides/how_to_guides/creating_and_editing_expectations/how_to_create_a_new_expectation_suite_without_a_sample_batch.html).\n\n\n\nYou can see all the available expectations in the **[expectation glossary](https://docs.greatexpectations.io/en/latest/reference/glossary_of_expectations.html?utm_source=notebook&utm_medium=create_expectations)**.",
"metadata": {},
},
{
"id": "smoking-bangladesh",
"cell_type": "markdown",
"source": "### Table Expectation(s)",
"metadata": {},
},
{
"id": "irish-storm",
"cell_type": "markdown",
"source": "No table level expectations are in this suite. Feel free to add some here.\n\nThey all begin with `validator.expect_table_...`.\n",
"metadata": {},
},
{
"id": "injured-differential",
"cell_type": "markdown",
"source": "### Column Expectation(s)",
"metadata": {},
},
{
"id": "eleven-football",
"cell_type": "markdown",
"source": "#### `npi`",
"metadata": {},
},
{
"id": "eleven-solid",
"cell_type": "code",
"metadata": {},
"execution_count": None,
"source": 'validator.expect_column_values_to_not_be_null(column="npi")',
"outputs": [],
},
{
"id": "responsible-coverage",
"cell_type": "markdown",
"source": "#### `provider_type`",
"metadata": {},
},
{
"id": "compressed-indiana",
"cell_type": "code",
"metadata": {},
"execution_count": None,
"source": 'validator.expect_column_values_to_not_be_null(column="provider_type")',
"outputs": [],
},
{
"id": "scheduled-freeware",
"cell_type": "markdown",
"source": "## Save & Review Your Expectations\n\nLet's save the expectation suite as a JSON file in the `great_expectations/expectations` directory of your project.\n\nLet's now rebuild your Data Docs, which helps you communicate about your data with both machines and humans.",
"metadata": {},
},
{
"id": "useful-hearts",
"cell_type": "code",
"metadata": {},
"execution_count": None,
"source": 'print(validator.get_expectation_suite(discard_failed_expectations=False))\nvalidator.save_expectation_suite(discard_failed_expectations=False)\n\ncheckpoint_config = {\n "class_name": "SimpleCheckpoint",\n "validations": [\n {\n "batch_request": batch_request,\n "expectation_suite_name": expectation_suite_name\n }\n ]\n}\ncheckpoint = SimpleCheckpoint(\n f"_tmp_checkpoint_{expectation_suite_name}",\n context,\n **checkpoint_config\n)\ncheckpoint_result = checkpoint.run()\n\ncontext.build_data_docs()\n\nvalidation_result_identifier = checkpoint_result.list_validation_result_identifiers()[0]\ncontext.open_data_docs(resource_identifier=validation_result_identifier)',
"outputs": [],
},
],
}
del expected["nbformat_minor"]
del obs["nbformat_minor"]
for obs_cell, expected_cell in zip(obs["cells"], expected["cells"]):
obs_cell.pop("id", None)
expected_cell.pop("id", None)
assert obs_cell == expected_cell
assert obs == expected | [
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"'import datetime\\n\\nimport pandas as pd\\n\\nimport great_expectations as ge\\nimport great_expectations.jupyter_ux\\nfrom great_expectations.core.batch import BatchRequest\\nfrom great_expectations.checkpoint import SimpleCheckpoint\\nfrom great_expectations.exceptions import DataContextError\\n\\ncontext = ge.data_context.DataContext()\\n\\nbatch_request = {\\n \"datasource_name\": \"files_datasource\",\\n \"data_connector_name\": \"files_data_connector\",\\n \"data_asset_name\": \"1k\",\\n}\\n\\n\\n# Feel free to change the name of your suite here. Renaming this will not remove the other one.\\nexpectation_suite_name = \"critical\"\\ntry:\\n suite = context.get_expectation_suite(expectation_suite_name=expectation_suite_name)\\n print(\\n f\\'Loaded ExpectationSuite \"{suite.expectation_suite_name}\" containing {len(suite.expectations)} expectations.\\'\\n )\\nexcept DataContextError:\\n suite = context.create_expectation_suite(\\n expectation_suite_name=expectation_suite_name\\n )\\n print(f\\'Created ExpectationSuite \"{suite.expectation_suite_name}\".\\')\\n\\n\\nvalidator = context.get_validator(\\n batch_request=BatchRequest(**batch_request),\\n expectation_suite_name=expectation_suite_name,\\n)\\ncolumn_names = [f\\'\"{column_name}\"\\' for column_name in validator.columns()]\\nprint(f\"Columns: {\\', \\'.join(column_names)}.\")\\nvalidator.head(n_rows=5, fetch_all=False)'",
",",
"\"outputs\"",
":",
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"]",
",",
"}",
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"\"id\"",
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"\"legal-beauty\"",
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"\"cell_type\"",
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"\"markdown\"",
",",
"\"source\"",
":",
"\"## Create & Edit Expectations\\n\\n\\nAdd expectations by calling specific expectation methods on the `validator` object. They all begin with `.expect_` which makes autocompleting easy using tab.\\n\\nBecause you selected interactive mode, you are now creating or editing an Expectation Suite with validator feedback from the sample batch of data that you specified (see `batch_request`).\\n\\nNote that if you select manual mode you may still create or edit an Expectation Suite directly, without feedback from the `validator`. See our documentation for more info and examples: [How to create a new Expectation Suite without a sample batch](https://docs.greatexpectations.io/en/latest/guides/how_to_guides/creating_and_editing_expectations/how_to_create_a_new_expectation_suite_without_a_sample_batch.html).\\n\\n\\n\\nYou can see all the available expectations in the **[expectation glossary](https://docs.greatexpectations.io/en/latest/reference/glossary_of_expectations.html?utm_source=notebook&utm_medium=create_expectations)**.\"",
",",
"\"metadata\"",
":",
"{",
"}",
",",
"}",
",",
"{",
"\"id\"",
":",
"\"smoking-bangladesh\"",
",",
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":",
"\"markdown\"",
",",
"\"source\"",
":",
"\"### Table Expectation(s)\"",
",",
"\"metadata\"",
":",
"{",
"}",
",",
"}",
",",
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"\"id\"",
":",
"\"irish-storm\"",
",",
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":",
"\"markdown\"",
",",
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":",
"\"No table level expectations are in this suite. Feel free to add some here.\\n\\nThey all begin with `validator.expect_table_...`.\\n\"",
",",
"\"metadata\"",
":",
"{",
"}",
",",
"}",
",",
"{",
"\"id\"",
":",
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"\"markdown\"",
",",
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":",
"\"### Column Expectation(s)\"",
",",
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":",
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",",
"\"outputs\"",
":",
"[",
"]",
",",
"}",
",",
"{",
"\"id\"",
":",
"\"responsible-coverage\"",
",",
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":",
"\"markdown\"",
",",
"\"source\"",
":",
"\"#### `provider_type`\"",
",",
"\"metadata\"",
":",
"{",
"}",
",",
"}",
",",
"{",
"\"id\"",
":",
"\"compressed-indiana\"",
",",
"\"cell_type\"",
":",
"\"code\"",
",",
"\"metadata\"",
":",
"{",
"}",
",",
"\"execution_count\"",
":",
"None",
",",
"\"source\"",
":",
"'validator.expect_column_values_to_not_be_null(column=\"provider_type\")'",
",",
"\"outputs\"",
":",
"[",
"]",
",",
"}",
",",
"{",
"\"id\"",
":",
"\"scheduled-freeware\"",
",",
"\"cell_type\"",
":",
"\"markdown\"",
",",
"\"source\"",
":",
"\"## Save & Review Your Expectations\\n\\nLet's save the expectation suite as a JSON file in the `great_expectations/expectations` directory of your project.\\n\\nLet's now rebuild your Data Docs, which helps you communicate about your data with both machines and humans.\"",
",",
"\"metadata\"",
":",
"{",
"}",
",",
"}",
",",
"{",
"\"id\"",
":",
"\"useful-hearts\"",
",",
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",",
"\"metadata\"",
":",
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"}",
",",
"\"execution_count\"",
":",
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",",
"\"source\"",
":",
"'print(validator.get_expectation_suite(discard_failed_expectations=False))\\nvalidator.save_expectation_suite(discard_failed_expectations=False)\\n\\ncheckpoint_config = {\\n \"class_name\": \"SimpleCheckpoint\",\\n \"validations\": [\\n {\\n \"batch_request\": batch_request,\\n \"expectation_suite_name\": expectation_suite_name\\n }\\n ]\\n}\\ncheckpoint = SimpleCheckpoint(\\n f\"_tmp_checkpoint_{expectation_suite_name}\",\\n context,\\n **checkpoint_config\\n)\\ncheckpoint_result = checkpoint.run()\\n\\ncontext.build_data_docs()\\n\\nvalidation_result_identifier = checkpoint_result.list_validation_result_identifiers()[0]\\ncontext.open_data_docs(resource_identifier=validation_result_identifier)'",
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] | [
1107,
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] | python | en | ['en', 'error', 'th'] | False |
ProfilingColumnPropertiesTableContentBlockRenderer.render | (cls, ge_object, header_row=None) | Each expectation method should return a list of rows | Each expectation method should return a list of rows | def render(cls, ge_object, header_row=None):
"""Each expectation method should return a list of rows"""
if header_row is None:
header_row = []
table_rows = []
if isinstance(ge_object, list):
for sub_object in ge_object:
expectation_type = cls._get_expectation_type(sub_object)
if expectation_type in cls.expectation_renderers:
new_rows = [
get_renderer_impl(expectation_type, renderer_type)[1](
result=sub_object
)
for renderer_type in cls.expectation_renderers.get(
expectation_type
)
]
table_rows.extend(new_rows)
else:
expectation_type = cls._get_expectation_type(ge_object)
if expectation_type in cls.expectation_renderers:
new_rows = [
get_renderer_impl(expectation_type, renderer_type)[1](
result=ge_object
)
for renderer_type in cls.expectation_renderers.get(expectation_type)
]
table_rows.extend(new_rows)
return RenderedTableContent(
**{
"content_block_type": "table",
"header_row": header_row,
"table": table_rows,
}
) | [
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27,
4
] | [
64,
9
] | python | en | ['en', 'ga', 'en'] | True |
member_calldef_t._get__cmp__call_items | (self) | implementation details | implementation details | def _get__cmp__call_items(self):
"""implementation details"""
return [self.virtuality, self.has_static, self.has_const] | [
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] | [
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] | python | da | ['eo', 'da', 'en'] | False |
member_calldef_t.virtuality | (self) | Describes the "virtuality" of the member (as defined by the
string constants in the class :class:VIRTUALITY_TYPES).
@type: str | Describes the "virtuality" of the member (as defined by the
string constants in the class :class:VIRTUALITY_TYPES).
| def virtuality(self):
"""Describes the "virtuality" of the member (as defined by the
string constants in the class :class:VIRTUALITY_TYPES).
@type: str"""
return self._virtuality | [
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] | [
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] | [
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] | python | en | ['en', 'en', 'en'] | True |
member_calldef_t.access_type | (self) | Return the access type of the member (as defined by the
string constants in the class :class:ACCESS_TYPES.
@type: str | Return the access type of the member (as defined by the
string constants in the class :class:ACCESS_TYPES.
| def access_type(self):
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string constants in the class :class:ACCESS_TYPES.
@type: str"""
return self.parent.find_out_member_access_type(self) | [
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] | [
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] | python | en | ['en', 'en', 'en'] | True |
member_calldef_t.has_const | (self) | describes, whether "callable" has const modifier or not | describes, whether "callable" has const modifier or not | def has_const(self):
"""describes, whether "callable" has const modifier or not"""
return self._has_const | [
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] | [
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] | [
89,
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] | python | en | ['en', 'gl', 'en'] | True |
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