Autodata Cd 3 Cd Code
Elisabet Schwartzkopf
For this to work, the docstrings must of course be written in correctreStructuredText. You can then use all of the usual Sphinx markup in thedocstrings, and it will end up correctly in the documentation. Together withhand-written documentation, this technique eases the pain of having to maintaintwo locations for documentation, while at the same time avoidingauto-generated-looking pure API documentation.
If you prefer NumPy or Google style docstrings over reStructuredText,you can also enable the napoleon extension.napoleon is a preprocessor that converts yourdocstrings to correct reStructuredText before autodoc processes them.
autodoc provides several directives that are versions of the usualpy:module, py:class and so forth. On parsing time, theyimport the corresponding module and extract the docstring of the given objects,inserting them into the page source under a suitable py:module,py:class etc. directive.
Another example; If your class Foo has __str__ special method andautodoc directive has both inherited-members and special-members,__str__ will be documented as in the past, but other special methodthat are not implemented in your class Foo.
The automodule, autoclass andautoexception directives also support a flag option calledshow-inheritance. When given, a list of base classes will be insertedjust below the class signature (when used with automodule, thiswill be inserted for every class that is documented in the module).
All autodoc directives support the no-index flag option that has thesame effect as for standard py:function etc. directives: noindex entries are generated for the documented object (and allautodocumented members).
In an automodule directive with the members option set, onlymodule members whose __module__ attribute is equal to the module nameas given to automodule will be documented. This is to preventdocumentation of imported classes or functions. Set theimported-members option if you want to prevent this behavior anddocument all available members. Note that attributes from imported moduleswill not be documented, because attribute documentation is discovered byparsing the source file of the current module.
autodata and autoattribute support the annotationoption. The option controls how the value of variable is shown. If specifiedwithout arguments, only the name of the variable will be printed, and its valueis not shown:
For module data members and class attributes, documentation can either be putinto a comment with special formatting (using a #: to start the commentinstead of just #), or in a docstring after the definition. Commentsneed to be either on a line of their own before the definition, orimmediately after the assignment on the same line. The latter form isrestricted to one line only.
If you document decorated functions or methods, keep in mind that autodocretrieves its docstrings by importing the module and inspecting the__doc__ attribute of the given function or method. That means that ifa decorator replaces the decorated function with another, it must copy theoriginal __doc__ to the new function.
This value selects if automatically documented members are sortedalphabetical (value 'alphabetical'), by member type (value'groupwise') or by source order (value 'bysource'). The default isalphabetical.
This value is a list of autodoc directive flags that should be automaticallyapplied to all autodoc directives. The supported flags are 'members','undoc-members', 'private-members', 'special-members','inherited-members', 'show-inheritance', 'ignore-module-all'and 'exclude-members'.
If this boolean value is set to True (which is the default), autodoc willlook at the first line of the docstring for functions and methods, and if itlooks like a signature, use the line as the signature and remove it from thedocstring content.
This value contains a list of modules to be mocked up. This is useful whensome external dependencies are not met at build time and break the buildingprocess. You may only specify the root package of the dependenciesthemselves and omit the sub-modules:
Return a listener that either keeps, or if exclude is True excludes,lines between lines that match the marker regular expression. If no linematches, the resulting docstring would be empty, so no change will be madeunless keepempty is true.
If more than one enabled extension handles the autodoc-skip-memberevent, autodoc will use the first non-None value returned by a handler.Handlers should return None to fall back to the skipping behavior ofautodoc and other enabled extensions.
Errors will be returned in either JSON or XML according to the response output format specified in the relevant API call. The error responses all follow the same structure with the error code changing depending on the nature of the error experienced.
16 years ago, there was no uniformity in how diagnostic trouble codes were displayed or retrieved from vehicles. Each manufacturer developed their own dedicated systems, with some having one or two digit codes while others had more comprehensive three and four-digit coding systems. In many cases, DTCs in early systems could be displayed by connecting a LED tester or jump wire between two pins in the data link connector (DLC), then counting the LED or malfunction indicator lamp (MIL) flashes. This lack of consistency between manufacturers made diagnostics more difficult and time-consuming for technicians.
Today we seem to have it easy. There are over 50 DTC readers on the market that help us diagnose the faults in a vehicle. Although code readers are helpful tools, DTCs can still be difficult to understand. DTCs often indicate the area of a fault, rather than the fault itself. Many technicians have made the time-consuming and expensive mistake of simply replacing components because a particular DTC was identified; only to find that the problem persists and the same code is being displayed by the code reader.
This document describes Dataplex automatic data quality, whichlets you define and measure the quality of your data. You can automate thescanning of data, validate data against defined rules, and log alerts if yourdata doesn't meet quality requirements. You can manage data quality rules anddeployments as code, improving the integrity of data production pipelines.
You can get started by usingDataplex data profilingrule recommendations, or by building custom rules in the Google Cloud console.Dataplex provides monitoring, troubleshooting, andCloud Logging alerting that's integrated with Dataplex autodata quality.
A data scan is a Dataplex job that samples data fromBigQuery and Cloud Storage and infers various types ofmetadata. To measure the quality of a table using auto data quality, you createa DataScan object of type data quality. The scan runs on onlyone BigQuery table. The scan uses resources in a Googletenant project, so you don'tneed to set up your own infrastructure.
For aggregate rules, the expectation isapplied against a single value aggregated across the entire data. Forexample, Avg(someCol) >= 10. To pass, the check must evaluate to theboolean true. Aggregate rules don't provide an independent pass or failcount for each row.
Specify an expectation for every row by defining a SQL expression in a WHERE clause. The SQL expression should evaluate to true (pass) or false (fail) per row. Dataplex computes the percentage of rows that pass this expectation and compares this value against the passing threshold percentage to determine the success or failure of the rule.
An assertion rule uses a data quality query to find rows that fail one or more conditions specified in the query. Provide a SQL statement that is evaluated to return rows that match the invalid state. If the query returns any rows, the rule fails.
Dimensions let you aggregate the results of multiple data quality rules formonitoring and alerting. You must associate every data quality rule with adimension. Dataplex supports the following dimensions:
When you create a custom SQL rule, you can refer to a data source table and allof its precondition filters by using the data reference parameter $data() inthe rule, instead of explicitly mentioning the source table and its filters.Examples of precondition filters include row filters, sampling percents, andincremental filters. The $data() parameter is case sensitive.
For example, say that you have a data source table calledmy_project_id.dim_dataset.dim_currency. You want to run an incremental dataquality scan that scans only on new daily data. A row filter that filters fortoday's entries, transaction_timestamp >= current_date(), is applied on thetable.
When you use an alias within a subquery to refer to columns in the source table,either use the data reference parameter to refer to the source table, or omitthe table reference. Don't refer to the columns in the source table by using adirect table reference in the WHERE clause.
Dataplex can filter data to be scanned for data quality by using arow filter. Creating a row filter lets you focus on data within a specifictime period or specific segment, such as a certain region. Using filters canreduce execution time and cost, for example, filtering out data with a timestampbefore a certain date.
Dataplex lets you specify a percentage of records from your datato sample for running a data quality scan. Creating data quality scans on asmaller sample of data can reduce execution time and the cost of querying theentire dataset.
You can export the scan results to a BigQuery table for furtheranalysis. To customize reporting, you can connect the BigQuerytable data to a Looker dashboard. You can build an aggregated reportby using the same results table across multiple scans.
The charge depends on the number of rows, the number of columns, the amount ofdata that you've scanned, the data quality rule configuration, thepartitioning and clustering settings on the table, and the frequency of thegscan.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
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