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The "Change Log Writer" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
CSV Datastream Dissemination Actor
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Screenshot :
Required Packages :
- JyFedoREST
- FCRepoKepler - Uses SimpleHTMLFormDialog to display and manage the form.
Source file :
CSVDatastreamDisseminationActor.py
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The "CSV Datastream Dissemination" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Error Log Writer Actor
This actor writes a log file with a summary of errors encountered during the latest run of the workflow.
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The "Error Log Writer" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Normalize Date Actor
This actor looks at each column that contains a date value and removes extraneous time data when present. It
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Required Packages :
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- FCRepoKepler - script is based on the RowAnalyzer class in fcrepo.kepler.RowAnalyzer.
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Source file :
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- an ordered list of columns in the row.
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Other inputs :
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The "Normalize Date" script also needs a list of indexes for the columns that contain dates. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'indexes'
OR
A port named 'indexes' containing a StringToken.
In either case, the string must contain either a comma-separated list of column numbers or a formula describing a regular sequence that can be used to generate the list. The format of the formula is START + INCREMENT * COUNT. For example, the formula 7+4*10 means there are 10 columns in the list, dates occur every 4 columns starting with column 7. This would generate the list 7,11,15,19,23,27,31,35,39,43.
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Output port :
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- output : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
The "Normalize Date" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
Normalize Latitude Actor
This actor looks at each column that contains a latitude value and assures that all values are valid and in the same format.
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Required Packages :
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- FCRepoKepler - script is based on the RowAnalyzer class in fcrepo.kepler.RowAnalyzer.
Source file
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- an ordered list of columns in the row.
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Other inputs :
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The "Normalize Latitude" script also needs a list of indexes for the columns that contain latitudes. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'indexes'
OR
A port named 'indexes' containing a StringToken.
In either case, the string must contain either a comma-separated list of column numbers or a formula describing a regular sequence that can be used to generate the list. The format of the formula is START + INCREMENT * COUNT. For example, the formula 7+4*10 means there are 10 columns in the list, latitudes occur every 4 columns starting with column 7. This would generate the list 7,11,15,19,23,27,31,35,39,43.
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Output port :
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- output : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
The "Normalize Latitude" PythonActor is used in the following workflows:
- FCRepoLatNormalizer
Normalize Longitude Actor
This actor looks at each column that contains a longitude value and assures that all values are valid and in the same format.
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Required Packages :
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- FCRepoKepler - script is based on the RowAnalyzer class in fcrepo.kepler.RowAnalyzer.
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Source file :
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NormalizeLongitudeActorNormalizeDateActor.py
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- an ordered list of columns in the row.
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Other inputs :
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The "Normalize DateLongitude" script also needs a list of indexes for the columns that contain dateslongitudes. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'indexes'
OR
A port named 'indexes' containing a StringToken.
In either case, the string must contain either a comma-separated list of column numbers or a formula describing a regular sequence that can be used to generate the list. The format of the formula is START + INCREMENT * COUNT. For example, the formula 7+4*10 means there are 10 columns in the list, dates longitudes occur every 4 columns starting with column 7. This would generate the list 7,11,15,19,23,27,31,35,39,43.
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Output port :
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- output : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
The "Normalize DateLongitude" PythonActor is used in the following workflows:
- FCRepoDateNormalizerFCRepoLongNormalizer
Output Prep Actor
This actor sorts through the output created by a RowAnalysis script and routes the data to the proper output writer.
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Source file :
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Input port :
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- input : ObjectToken containing a Python tuple or Java array with 4 items :
- the current row number as an integer.
- a tuple/array of values for each column in the row.
- a tuple/array of changes made.
- a tuple/array of errors encountered.
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Other inputs :
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The "Normalize DateOutput Prep" script also needs the character to be used as a separator between columns in the output text string. This can be acquired in one of two ways:
A string parameter on the PythonActor named 'separator'
OR
A port named 'separator' containing a StringToken. In either case, the string must contain a comma-separated list of column numbers.
Output ports :
- output : StringToken containg a string representing the ouput row in a CSV file. It is constructed by concatenating the values in the columns array using a separator character.
- changes : ObjectToken containing a tuple with 2 items :
- the current row number as an integer.
- the tuple/array of changes made received on the input port.
- errors : ObjectToken containing a tuple with 2 items :
- the current row number as an integer.
- the tuple/array of errors encountered received on the input port.
The "Output Prep" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Row To Columns Actor
This actor splits a text string representing a 'row' into 'columns' using a separator character such as ','.
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Source file :
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Input port :
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- row: StringToken containing a string representation of a single row in a spreadsheet or other data matrix.
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Output port :
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- columns - ObjectToken containing a Python tuple or Java array with 2 items :
- the current row number as an integer.
- a tuple containing an ordered list of values for each column in the row.
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The "Row To Columns" PythonActor is used in the following workflows:
- FCRepoDateNormalizer
- FCRepoLatitudeNormalizer
- FCRepoLongitudeNormalizer
- FCRepoLatLongSplitter
Kepler Workflows
Kepler workflows were developed to illustrate how Kepler might be used as an accessioner's workbench.
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Retrieves a CSV datastream from an object in a Fedora Repository, processes the date column columns to satandardize the standardize their format and saves the results to a local file in CSV format.
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Screenshots :
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Source file :
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FCRepoLatitudeNormalizer
Retrieves a CSV datastream from an object in a Fedora Repository, processes latitude columns to standardize their format and saves the results to a local file in CSV format.
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Screenshots :
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Source file :
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FCRepoLongitudeNormalizer
Retrieves a CSV datastream from an object in a Fedora Repository, processes longitude columns to standardize their format and saves the results to a local file in CSV format.
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Screenshots :
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Source file :
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