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Re-designing the functions to calculate Metrics. #59
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aswanipranjal
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- new_functions.py file which has the basic classes and functions - derived_classes.py file which has the derived classes - Readme.md describing how to use the above 2 files - sample_metrics.ipynb describing how the classes can be used - tests for the basic functions inside the Query class Addresses issues chaoss#59 and chaoss#62
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- new_functions.py file which has the basic classes and functions - derived_classes.py file which has the derived classes - Readme.md describing how to use the above 2 files - sample_metrics.ipynb describing how the classes can be used - tests for the basic functions inside the Query class Addresses issues chaoss#59 and chaoss#62
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- new_functions.py file which has the basic classes and functions - derived_classes.py file which has the derived classes - Readme.md describing how to use the above 2 files - sample_metrics.ipynb describing how the classes can be used - tests for the basic functions inside the Query class Addresses issues chaoss#59 and chaoss#62
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- new_functions.py file which has the basic classes and functions - derived_classes.py file which has the derived classes - Readme.md describing how to use the above 2 files - sample_metrics.ipynb describing how the classes can be used - tests for the basic functions inside the Query class Addresses issues chaoss#59 and chaoss#62
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- new_functions.py file which has the classes and functions to Query and implement the metrics - derived_classes.py file which has the derived classes: Issues and PullRequests - Readme.md describing how to use the above 2 files - sample_metrics.ipynb listing the current CHAOSS metrics as well as GMD metrics - tests for the basic functions inside the Query class Addresses issues chaoss#59 and chaoss#62
aswanipranjal
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- new_functions.py: contains Query & Index classes and functions to parse the fetched data - derived_classes.py: contains PullRequests and Issues classes - Readme.md describing how to use the above 2 files - tests for the basic functions inside the Query class Also add: - Examples/Sample_metrics.ipynb: contains the CHAOSS and GMD metrics that are being calculated using the new functions Addresses issues chaoss#59 and chaoss#62
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- new_functions.py: contains Query & Index classes and functions to parse the fetched data - derived_classes.py: contains PullRequests and Issues classes - Readme.md describing how to use the above 2 files - tests for the basic functions inside the Query class Also add: - Examples/Sample_metrics.ipynb: contains the CHAOSS and GMD metrics that are being calculated using the new functions Addresses issues #59 and #62
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Right now, the functions and classes (in metrics.py and esquery.py) that we use to calculate the metrics are complex and repetitive. They get us the results but specialising them for specific classes is too complex and code reuse is difficult to achieve.
We are going to simplify them and add more functional and easy to use chain-able functions which let us apply filters easily and give the output in a structured format.
We will try to use only elasticsearch_dsl objects directly and look at different possible methods in which these helper functions and classes can be created.
For example:
If we are counting Closed Issues in a repo, then we be able to do it by just specifying the filters that
item_type="issue"
orpull_request="false"
and then apply the sum aggregation in the Search object created.But right now, we have to create an object from the
GithubIssuesMetrics
class which inherits from the Metrics class and then use that object's functions to get the aggregation. This is rather cumbersome.This Notebook will be the experimentation lab for this issue.
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