instead of writing result line by line, implemented random.choices for randomisation of multiple stations and writing large batche ot the disk, also instead of "round" just using :.1f which is probably quicker on a large scale, because it's not a mathematical function
Check List:
not applicable, this just improves generation of test data with Python
[ ] You have run ./mvnw verify and the project builds successfully
[ ] Tests pass (./test.sh <username> shows no differences between expected and actual outputs)
[ ] All formatting changes by the build are committed
[ ] Your launch script is named calculate_average_<username>.sh (make sure to match casing of your GH user name) and is executable
[ ] Output matches that of calculate_average_baseline.sh
[ ] For new entries, or after substantial changes: When implementing custom hash structures, please point to where you deal with hash collisions (line number)
instead of writing result line by line, implemented random.choices for randomisation of multiple stations and writing large batche ot the disk, also instead of "round" just using :.1f which is probably quicker on a large scale, because it's not a mathematical function
Check List:
not applicable, this just improves generation of test data with Python
[ ] You have run
./mvnw verify
and the project builds successfully[ ] Tests pass (
./test.sh <username>
shows no differences between expected and actual outputs)[ ] All formatting changes by the build are committed
[ ] Your launch script is named
calculate_average_<username>.sh
(make sure to match casing of your GH user name) and is executable[ ] Output matches that of
calculate_average_baseline.sh
[ ] For new entries, or after substantial changes: When implementing custom hash structures, please point to where you deal with hash collisions (line number)
Execution time:
Execution time of reference implementation: