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Creating chainable functions and New Classes to calculate the Metrics #62
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I like the approach. Maybe later we need specialized versions of these functions, such as for example when we have different records to consider in some specific case, or parameters for the functions. But for now, I guess we can try reimplementing with this, and we will already have a big win ;-) We still need to comment how to integrate this in a giant pr with everything, or in separate ones, but I like it as a proof of concept. Could you please write also some examples of use of these chainable methods, so that they are easier to understand to others? |
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Can we close this, @aswanipranjal? |
Yeah, it's about time. |
This issue proposes creating functions to segregate the metrics according to different fields, such as:
by_author
,by_organizations
andby_period
(weeks, months, years) which can be applied directly to metrics objects and the corresponding aggregations can be obtained.These functions will make it easier for the user to do analysis on the basis of different users or orgs and to see the metrics evolve over periodic intervals.
They've been currently implemented in this file as a part of the Metric class.
We will also be creating new classes to calculate the metrics.
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