- Added new `Statistic`s: Covariance, Pearson, Spearman, R Squared
- Added `covariance_matrix` function
- Added `pearson_correlation` function to compute correlation matrix
- Added `rank_data` and Rank_Method type to create rankings of a Vector
- Added `spearman_correlation` function to compute Spearman Rank correlation matrix
# Important Notes
- Added `Panic.throw_wrapped_if_error` and `Panic.handle_wrapped_dataflow_error` to help with errors within a loop.
- Removed `Array.set_at` use from `Table.Vector_Builder`
The change promotes static methods of `Ref`, `get` and `put`, to be
methods of `Ref` type.
The change also removes `Ref` module from the default namespace.
Had to mostly c&p functional dispatch for now, in order for the methods
to be found. Will auto-generate that code as part of builtins system.
Related to https://www.pivotaltracker.com/story/show/182138899
- Implements various statistics on Vector
# Important Notes
Some minor codebase improvements:
- Some tweaks to Any/Nothing to improve performance
- Fixed bug in ObjectComparator
- Added if_nothing
- Removed Group_By_Key
- Read in Excel files following the specification.
- Support for XLSX and XLS formats.
- Ability to select ranges and sheets.
- Skip Rows and Row Limits.
# Important Notes
- Minor fix to DelimitedReader for Windows
This PR replaces hard-coded `@Builtin_Method` and `@Builtin_Type` nodes in Builtins with an automated solution
that a) collects metadata from such annotations b) generates `BuiltinTypes` c) registers builtin methods with corresponding
constructors.
The main differences are:
1) The owner of the builtin method does not necessarily have to be a builtin type
2) You can now mix regular methods and builtin ones in stdlib
3) No need to keep track of builtin methods and types in various places and register them by hand (a source of many typos or omissions as it found during the process of this PR)
Related to #181497846
Benchmarks also execute within the margin of error.
### Important Notes
The PR got a bit large over time as I was moving various builtin types and finding various corner cases.
Most of the changes however are rather simple c&p from Builtins.enso to the corresponding stdlib module.
Here is the list of the most crucial updates:
- `engine/runtime/src/main/java/org/enso/interpreter/runtime/builtin/Builtins.java` - the core of the changes. We no longer register individual builtin constructors and their methods by hand. Instead, the information about those is read from 2 metadata files generated by annotation processors. When the builtin method is encountered in stdlib, we do not ignore the method. Instead we lookup it up in the list of registered functions (see `getBuiltinFunction` and `IrToTruffle`)
- `engine/runtime/src/main/java/org/enso/interpreter/runtime/callable/atom/AtomConstructor.java` has now information whether it corresponds to the builtin type or not.
- `engine/runtime/src/main/scala/org/enso/compiler/codegen/RuntimeStubsGenerator.scala` - when runtime stubs generator encounters a builtin type, based on the @Builtin_Type annotation, it looks up an existing constructor for it and registers it in the provided scope, rather than creating a new one. The scope of the constructor is also changed to the one coming from stdlib, while ensuring that synthetic methods (for fields) also get assigned correctly
- `engine/runtime/src/main/scala/org/enso/compiler/codegen/IrToTruffle.scala` - when a builtin method is encountered in stdlib we don't generate a new function node for it, instead we look it up in the list of registered builtin methods. Note that Integer and Number present a bit of a challenge because they list a whole bunch of methods that don't have a corresponding method (instead delegating to small/big integer implementations).
During the translation new atom constructors get initialized but we don't want to do it for builtins which have gone through the process earlier, hence the exception
- `lib/scala/interpreter-dsl/src/main/java/org/enso/interpreter/dsl/MethodProcessor.java` - @Builtin_Method processor not only generates the actual code fpr nodes but also collects and writes the info about them (name, class, params) to a metadata file that is read during builtins initialization
- `lib/scala/interpreter-dsl/src/main/java/org/enso/interpreter/dsl/MethodProcessor.java` - @Builtin_Method processor no longer generates only (root) nodes but also collects and writes the info about them (name, class, params) to a metadata file that is read during builtins initialization
- `lib/scala/interpreter-dsl/src/main/java/org/enso/interpreter/dsl/TypeProcessor.java` - Similar to MethodProcessor but handles @Builtin_Type annotations. It doesn't, **yet**, generate any builtin objects. It also collects the names, as present in stdlib, if any, so that we can generate the names automatically (see generated `types/ConstantsGen.java`)
- `engine/runtime/src/main/java/org/enso/interpreter/node/expression/builtin` - various classes annotated with @BuiltinType to ensure that the atom constructor is always properly registered for the builitn. Note that in order to support types fields in those, annotation takes optional `params` parameter (comma separated).
- `engine/runtime/src/bench/scala/org/enso/interpreter/bench/fixtures/semantic/AtomFixtures.scala` - drop manual creation of test list which seemed to be a relict of the old design
Implements https://www.pivotaltracker.com/story/show/181266184
### Important Notes
Changed example image download to only proceed if the file did not exist before - thus cutting on the build time (the build used to download it _every_ time - which completely failed the build if network is down). A redownload can be forced by performing a fresh repository checkout.
- Added Encoding type
- Added `Text.bytes`, `Text.from_bytes` with Encoding support
- Renamed `File.read` to `File.read_text`
- Renamed `File.write` to `File.write_text`
- Added Encoding support to `File.read_text` and `File.write_text`
- Added warnings to invalid encodings
Implements https://www.pivotaltracker.com/story/show/181805693 and finishes the basic set of features of the Aggregate component.
Still not all aggregations are supported everywhere, because for example SQLite has quite limited support for aggregations. Currently the workaround is to bring the table into memory (if possible) and perform the computation locally. Later on, we may add more complex generator features to emulate the missing aggregations with complex sub-queries.
This commit implements `Text.reverse` as an extension on `Text`.
`Text.reverse` reverses strings. For example: `"Hello World!".reverse`
results in `"!dlroW olleH"`.
Strings are reversed by their Extended Grapheme Clusters not by their
characters. This has some performance implications because we need to
find these grapheme cluster boundaries when iterating. To do so,
`BreakIterator.getCharacterInstance` is used.
Implements: https://www.pivotaltracker.com/n/projects/2539304/stories/181265419
Implements infrastructure for new aggregations in the Database. It comes with only some basic aggregations and limited error-handling. More aggregations and problem handling will be added in subsequent PRs.
# Important Notes
This introduces basic aggregations using our existing codegen and sets-up our testing infrastructure to be able to use the same aggregate tests as in-memory backend for the database backends.
Many aggregations are not yet implemented - they will be added in subsequent tasks.
There are some TODOs left - they will be addressed in the next tasks.
- Make it easier to understand the computations.
- Fix issue with First.
- Improve quote handling in Concatenate
- Added validation and warnings to input
The mechanism follows a similar approach to what is being in functions
with default arguments.
Additionally since InstantiateAtomNode wasn't a subtype of EnsoRootNode it
couldn't be used in the application, which was the primary reason for
issue #181449213.
Alternatively InstantiateAtomNode could have been enhanced to extend
EnsoRootNode rather than RootNode to carry scope info but the former
seemed simpler.
See test cases for previously crashing and invalid cases.
- Added Minimum, Maximum, Longest. Shortest, Mode, Percentile
- Added first and last to Map
- Restructured Faker type more inline with FakerJS
- Created 2,500 row data set
- Tests for group_by
- Performance tests for group_by
Following the Slice and Array.Copy experiment, took just the Array.Copy parts out and built into the Vector class.
This gives big performance wins in common operations:
| Test | Ref | New |
| --- | --- | --- |
| New Vector | 41.5 | 41.4 |
| Append Single | 26.6 | 4.2 |
| Append Large | 26.6 | 4.2 |
| Sum | 230.1 | 99.1 |
| Drop First 20 and Sum | 343.5 | 96.9 |
| Drop Last 20 and Sum | 311.7 | 96.9 |
| Filter | 240.2 | 92.5 |
| Filter With Index | 364.9 | 237.2 |
| Partition | 772.6 | 280.4 |
| Partition With Index | 912.3 | 427.9 |
| Each | 110.2 | 113.3 |
*Benchmarks run on an AWS EC2 r5a.xlarge with 1,000,000 item count, 100 iteration size run 10 times.*
# Important Notes
Have generally tried to push the `@Tail_Call` down from the Vector class and move to calling functions on the range class.
- Expanded benchmarks on Vector
- Added `take` method to Vector
- Added `each_with_index` method to Vector
- Added `filter_with_index` method to Vector
Functioning group_by based of Enso Map.
# Important Notes
This is an initial version which will be used to establish the API.
The grouping map will need to be moved to Java code for performance.
* Move to_upper_case and to_lower_case into to_case
* Add an export, not sure about it
* Implement title case
TODO: some more tests would be good
* Add more tests
* explain title case
* fix todo
* changelog
