* add mutliseries support to d3
* fix types
* fix scales when multiseries
* fix tests
* fix all tests
* basic hardcoded legend working, on only one plot at a time
* basic legend
* legend populating correctly with wrong label names
* legend with tooltip for full length names
* remove unused variale
* key on all nodes
* tooltips on points and labels
* turn on/off d3 'brush' so tool tip is availible to the user
* use colourScale
* fix type problems
* use default colour
* rename variables and remove unneeded code
* fix label
* sort labels in legend
* is multiseries show legend and dont show y label
* address comments
* fix type problem
* Update distribution/lib/Standard/Visualization/0.0.0-dev/src/Scatter_Plot.enso
Co-authored-by: James Dunkerley <jdunkerley@users.noreply.github.com>
---------
Co-authored-by: James Dunkerley <jdunkerley@users.noreply.github.com>
- Removes `First` and `Last` from the `Standard.Base` exports.
- Enable auto-scoping for all `Index_Sub_Range` and `Text_Sub_Range`.
- Update all use of those methods to use auto-scoping.
Simplify the `Test.Suite.run_with_filter` to accept a single filter parameter that searches for all the groups and specs that matches that filter. This filter can be a simple text provided from the command line.
# Important Notes
- Pending groups are now printed at the end of the run
- `Test.Suite.run_with_filter` is simplified to accept a single filter parameter that is either `Text` or `Nothing`. See the docs.
- Passing a filter from the command line is therefore straightforward, it is treated as a regex.
- For convenience, I have left all the `main` methods in all the test sources. I have just refactored them to accept the `filter` argument from the command line.
- For example, to run only a single spec from `Vector_Spec.enso`, invoke `enso --run test/Base_Tests/src/Data/Vector_Spec.enso "should allow vector creation with a programmatic constructor"`
- **Majority of the PR is a regex replace** of `^main =` for `main filter=Nothing =` and of `suite.run_with_filter` for `suite.run_with_filter filter`.
- **Fixed some internal engine bugs:**
- `AtomWithHole` allows to specify only one hole - https://github.com/enso-org/enso/pull/9065/files#diff-0f7bb7e85cf86a965de133aa7e6b5958ceb889bd1921c01e00d3a9ceb19626ef
- NaN keys in hash maps are handled in polyglot maps as well - c5257f6c2b78f893214ff67300893b593ea05e21..db4b3c0e9828ee79208d52e02586b24bb845b0d6
Follow-up of #8890
Refactor the rest of the tests to the builder API (`Test_New`):
- `Image_Tests`
- `Geo_Tests`
- `Google_Api_Test`
- `Examples_Test`
- `AWS_Tests`
- `Meta_Test_Suite_Tests`
- `Visualization_Tests`
# Important Notes
- Unrelated: Fix NPE in `File.new "/" . name`
Fixes#5233 by removing `EconomicMap` & co. and using plain old good _linear hashing_. Fixes#8090 by introducing `StorageEntry.removed()` rather than copying the builder on each removal.
* Hash codes prototype
* Remove Any.hash_code
* Improve caching of hashcode in atoms
* [WIP] Add Hash_Map type
* Implement Any.hash_code builtin for primitives and vectors
* Add some values to ValuesGenerator
* Fix example docs on Time_Zone.new
* [WIP] QuickFix for HashCodeTest before PR #3956 is merged
* Fix hash code contract in HashCodeTest
* Add times and dates values to HashCodeTest
* Fix docs
* Remove hashCodeForMetaInterop specialization
* Introduce snapshoting of HashMapBuilder
* Add unit tests for EnsoHashMap
* Remove duplicate test in Map_Spec.enso
* Hash_Map.to_vector caches result
* Hash_Map_Spec is a copy of Map_Spec
* Implement some methods in Hash_Map
* Add equalsHashMaps specialization to EqualsAnyNode
* get and insert operations are able to work with polyglot values
* Implement rest of Hash_Map API
* Add test that inserts elements with keys with same hash code
* EnsoHashMap.toDisplayString use builder storage directly
* Add separate specialization for host objects in EqualsAnyNode
* Fix specialization for host objects in EqualsAnyNode
* Add polyglot hash map tests
* EconomicMap keeps reference to EqualsNode and HashCodeNode.
Rather than passing these nodes to `get` and `insert` methods.
* HashMapTest run in polyglot context
* Fix containsKey index handling in snapshots
* Remove snapshots field from EnsoHashMapBuilder
* Prepare polyglot hash map handling.
- Hash_Map builtin methods are separate nodes
* Some bug fixes
* Remove ForeignMapWrapper.
We would have to wrap foreign maps in assignments for this to be efficient.
* Improve performance of Hash_Map.get_builtin
Also, if_nothing parameter is suspended
* Remove to_flat_vector.
Interop API requires nested vector (our previous to_vector implementation). Seems that I have misunderstood the docs the first time I read it.
- to_vector does not sort the vector by keys by default
* Fix polyglot hash maps method dispatch
* Add tests that effectively test hash code implementation.
Via hash map that behaves like a hash set.
* Remove Hashcode_Spec
* Add some polyglot tests
* Add Text.== tests for NFD normalization
* Fix NFD normalization bug in Text.java
* Improve performance of EqualsAnyNode.equalsTexts specialization
* Properly compute hash code for Atom and cache it
* Fix Text specialization in HashCodeAnyNode
* Add Hash_Map_Spec as part of all tests
* Remove HashMapTest.java
Providing all the infrastructure for all the needed Truffle nodes is no longer manageable.
* Remove rest of identityHashCode message implementations
* Replace old Map with Hash_Map
* Add some docs
* Add TruffleBoundaries
* Formatting
* Fix some tests to accept unsorted vector from Map.to_vector
* Delete Map.first and Map.last methods
* Add specialization for big integer hash
* Introduce proper HashCodeTest and EqualsTest.
- Use jUnit theories.
- Call nodes directly
* Fix some specializations for primitives in HashCodeAnyNode
* Fix host object specialization
* Remove Any.hash_code
* Fix import in Map.enso
* Update changelog
* Reformat
* Add truffle boundary to BigInteger.hashCode
* Fix performance of HashCodeTest - initialize DataPoints just once
* Fix MetaIsATest
* Fix ValuesGenerator.textual - Java's char is not Text
* Fix indent in Map_Spec.enso
* Add maps to datapoints in HashCodeTest
* Add specialization for maps in HashCodeAnyNode
* Add multiLevelAtoms to ValuesGenerator
* Provide a workaround for non-linear key inserts
* Fix specializations for double and BigInteger
* Cosmetics
* Add truffle boundaries
* Add allowInlining=true to some truffle boundaries.
Increases performance a lot.
* Increase the size of vectors, and warmup time for Vector.Distinct benchmark
* Various small performance fixes.
* Fix Geo_Spec tests to accept unsorted Map.to_vector
* Implement Map.remove
* FIx Visualization tests to accept unsorted Map.to_vector
* Treat java.util.Properties as Map
* Add truffle boundaries
* Invoke polyglot methods on java.util.Properties
* Ignore python tests if python lang is missing
Implements https://www.pivotaltracker.com/story/show/184032869
# Important Notes
- Currently we get failures in Full joins on Postgres which show a more serious problem - amending equality to ensure that `[NULL = NULL] == True` breaks hash/merge based indexing - so such joins will be extremely inefficient. All our joins currently rely on this notion of equality which will mean all of our DB joins will be extremely inefficient.
- We need to find a solution that will support nulls and still work OK with indices (but after exploring a few approaches: `COALESCE(a = b, a IS NULL AND b is NULL)`, `a IS NOT DISTINCT FROM b`, `(a = b) OR (a IS NULL AND b is NULL)`; all of which did not work (they all result in `ERROR: FULL JOIN is only supported with merge-joinable or hash-joinable join conditions`) I'm less certain that it is possible. Alternatively, we may need to change the NULL semantics to align it with SQL - this seems like likely the simpler solution, allowing us to generate simple, reliable SQL - the NULL=NULL solution will be cornering us into nasty workarounds very dependent on the particular backend.
Use JavaScript to parse and serialise to JSON. Parses to native Enso object.
- `.to_json` now returns a `Text` of the JSON.
- Json methods now `parse`, `stringify` and `from_pairs`.
- New `JSON_Object` representing a JavaScript Object.
- `.to_js_object` allows for types to custom serialize. Returning a `JS_Object`.
- Default JSON format for Atom now has a `type` and `constructor` property (or method to call for as needed to deserialise).
- Removed `.into` support for now.
- Added JSON File Format and SPI to allow `Data.read` to work.
- Added `Data.fetch` API for easy Web download.
- Default visualization for JS Object trunctes, and made Vector default truncate children too.
Fixes defect where types with no constructor crashed on `to_json` (e.g. `Matching_Mode.Last.to_json`.
Adjusted default visualisation for Vector, so it doesn't serialise an array of arrays forever.
Likewise, JS_Object default visualisation is truncated to a small subset.
New convention:
- `.get` returns `Nothing` if a key or index is not present. Takes an `other` argument allowing control of default.
- `.at` error if key or index is not present.
- `Nothing` gains a `get` method allowing for easy propagation.
# Important Notes
Note that one cannot
```
import Standard.Table as Table_Module
```
because of the 2-component name restriction that gets desugared to `Standard.Table.Main` and we have to write
```
import Standard.Table.Main as Table_Module
```
in a few places. Once we move `Json.to_table` extension this can be improved.
- Removed `Dubious constructor export` from Examples, Geo, Google_Api, Image and Test.
- Updated Google_Api project to meet newer code standards.
- Restructured `Standard.Test`:
- `Main.enso` now exports `Bench`, `Faker`, `Problems`, `Test`, `Test_Suite`
- `Test.Suite` methods moved into a `Test_Suite` type.
- Moved `Bench.measure` into `Bench` type.
- Separated the reporting to a `Test_Reporter` module.
- Moved `Faker` methods into `Faker` type.
- Removed `Verbs` and `.should` method.
- Added `should_start_with` and `should_contain` extensions to `Any`.
- Restructured `Standard.Image`:
- Merged Codecs methods into `Image`.
- Export `Image`, `Read_Flag`, `Write_Flag` and `Matrix` as types from `Main.enso`.
- Merged the internal methods into `Matrix` and `Image`.
- Fixed `Day_Of_Week` to be exported as a type and sort the `from` method.
- Generally export types not modules from the `Standard.Table` import.
- Moved `new`, `from_rows` the `Standard.Table` library into the `Table` type.
- Renames `Standard.Table.Data.Storage.Type` to `Standard.Table.Data.Storage.Storage`
- Removed the internal `from_columns` method.
- Removed `join` and `concat` and merged into instance methods.
- Removed `Table` and `Column` from the `Standard.Database` exports.
- Removed `Standard.Table.Data.Column.Aggregate_Column` as not used any more.
Turns that if you import a two-part import we had special code that would a) add Main submodule b) add an explicit rename.
b) is problematic because sometimes we only want to import specific names.
E.g.,
```
from Bar.Foo import Bar, Baz
```
would be translated to
```
from Bar.Foo.Main as Foo import Bar, Baz
```
and it should only be translated to
```
from Bar.Foo.Main import Bar, Baz
```
This change detects this scenario and does not add renames in that case.
Fixes [183276486](https://www.pivotaltracker.com/story/show/183276486).
Changes following Marcin's work. Should be back to very similar public API as before.
- Add an "interface" type: `Standard.Base.System.File_Format.File_Format`.
- All `File_Format` types now have a `can_read` method to decide if they can read a file.
- Move `Standard.Table.IO.File_Format.Text.Text_Data` to `Standard.Base.System.File_Format.Plain_Text_Format.Plain_Text`.
- Move `Standard.Table.IO.File_Format.Bytes` to `Standard.Base.System.File_Format.Bytes`.
- Move `Standard.Table.IO.File_Format.Infer` to `Standard.Base.System.File_Format.Infer`. **(doesn't belong here...)**
- Move `Standard.Table.IO.File_Format.Unsupported_File_Type` to `Standard.Base.Error.Common.Unsupported_File_Type`.
- Add `Infer`, `File_Format`, `Bytes`, `Plain_Text`, `Plain_Text_Format` to `Standard.Base` exports.
- Fold extension methods of `Standard.Base.Meta.Unresolved_Symbol` into type.
- Move `Standard.Table.IO.File_Format.Auto` to `Standard.Table.IO.Auto_Detect.Auto_Detect`.
- Added a `types` Vector of all the built in formats.
- `Auto_Detect` asks each type if they `can_read` a file.
- Broke up and moved `Standard.Table.IO.Excel` into `Standard.Table.Excel`:
- Moved `Standard.Table.IO.File_Format.Excel.Excel_Data` to `Standard.Table.Excel.Excel_Format.Excel_Format.Excel`.
- Renamed `Sheet` to `Worksheet`.
- Internal types `Reader` and `Writer` providing the actual read and write methods.
- Created `Standard.Table.Delimited` with similar structure to `Standard.Table.Excel`:
- Moved `Standard.Table.IO.File_Format.Delimited.Delimited_Data` to `Standard.Table.Delimited.Delimited_Format.Delimited_Format.Delimited`.
- Moved `Standard.Table.IO.Quote_Style` to `Standard.Table.Delimited.Quote_Style`.
- Moved the `Reader` and `Writer` internal types into here. Renamed methods to have unique names.
- Add `Aggregate_Column`, `Auto_Detect`, `Delimited`, `Delimited_Format`, `Excel`, `Excel_Format`, `Sheet_Names`, `Range_Names`, `Worksheet` and `Cell_Range` to `Standard.Table` exports.
First of all this PR demonstrates how to implement _lazy visualization_:
- one needs to write/enhance Enso visualization libraries - this PR adds two optional parameters (`bounds` and `limit`) to `process_to_json_text` function.
- the `process_to_json_text` can be tested by standard Enso test harness which this PR also does
- then one has to modify JavaScript on the IDE side to construct `setPreprocessor` expression using the optional parameters
The idea of _scatter plot lazy visualization_ is to limit the amount of points the IDE requests. Initially the limit is set to `limit=1024`. The `Scatter_Plot.enso` then processes the data and selects/generates the `limit` subset. Right now it includes `min`, `max` in both `x`, `y` axis plus randomly chosen points up to the `limit`.
The D3 visualization widget is capable of _zooming in_. When that happens the JavaScript widget composes new expression with `bounds` set to the newly visible area. By calling `setPreprocessor` the engine recomputes the visualization data, filters out any data outside of the `bounds` and selects another `limit` points from the new data. The IDE visualization then updates itself to display these more detailed data. Users can zoom-in to see the smallest detail where the number of points gets bellow `limit` or they can select _Fit all_ to see all the data without any `bounds`.
# Important Notes
Randomly selecting `limit` samples from the dataset may be misleading. Probably implementing _k-means clustering_ (where `k=limit`) would generate more representative approximation.
