lumiera_/doc/technical/howto/crackNuts.txt

how to crack nut #47
====================
:toc:

.collection of successfully employed technical solutions
Some nasty problems are recurring time and again. Maybe a trick could be found somewhere
in the net, and a library function was created to settle this damn topic once and for all.
Maybe even a nice test and demo is provided. And then the whole story will be forgotten.

_Sounds familiar?_  => ☹☻☺👻 ~[red]#... then please consider to leave some traces here ...#~


Methods
-------
Mathematics
~~~~~~~~~~~
- some basic descriptive statistics computations are defined in 'lib/stat/statistic.hpp'
- the simple case for _linear regression_ is also implemented there
- Gnuplot provides also common statistics functions, which may come in handy when the
  goal is anyway to create a visualisation (-> see <<_investigation,below>>)




Situations
----------
Investigation
~~~~~~~~~~~~~
summary test::
  Reformulate the research and the findings into a test, which can be read top-down like a novel.
  Start with documenting the basics, package helpers into a tool class, or package setup into a
  workbench-style class, with individual tool modules. Provide a short version of this test with
  the basic demo, which should be able to run with the regular test suite. Extended long-running
  tests can be started conditionally with commandline-arguments. See 'scheduler-stress-test.cpp'
visualisation::
  Use code generation to visualise data structures or functions and observation statistics.
  Typically these generation statements can be packaged into an invocation helper and included
  into a relevant test, but commented-out there.
+
- generate Graphviz diagrams: 'lib/dot-gen.hpp' provides a simple DSL. See 'test-chain-load-test.cpp'
- generate Gnuplot scripts: use the xref:texttemplate[Text-Template] engine to fill in data, possibly
  from a generated data table

  * 'lib/gnuplot-gen.hpp' provides some pre-canned scripts for statistics plots
  * used by the 'stress-test-rig.cpp', in combination with 'data.hpp' to collect measurement results

Testing
~~~~~~~
verify structured data::
  build a diagnostic output which shows the nesting
  - use configuration by template parameters and use simple numbers or strings as part components
  - render the output and compare it with `""_expect` (the `ExpectStr` -> see 'lib/test/test-helper.hpp')
  - break the expect-string into parts with indentation, by exploiting the C _string gaps_

However, this works only up to a certain degree of complexity.

A completely different approach is to transform the structured result-data into an ETD (`GenNode` tree)
and then directly compare it to an ETD that is created in the test fixture with the DSL notation (`MakeRec()`)

verify floating point data::
+
- either use approximate comparison

  * `almostEqual()` -> see 'lib/test/test-helper.hpp'
  * 'util-quant.hpp' has also an `almostEqual(a,b, ulp)`
  * [yellow-background]#TODO 2024# should be sorted out -> https://issues.lumiera.org/ticket/1360[#1360]

- or render the floating point with the diagnostic-output functions, which deliberately employ
  a built-in rounding to some sensible amount of places (which in most cases helps to weed-out the
  ``number dust'')
- simply match it against an `ExpectStr` -- which implicitly converts to string, thereby
  also applying the aforementioned implicit rounding
+
-> See also <<_formatting,about formatting>> below; in a nutshell, `#include 'lib/format-output.hpp'`

verify fluctuating values::
  A first attempt should be made to bring them into some regular band. This can be achieved by
  automatically calibrating the measurement function (e.g. do a timing calibration beforehand).
  Then the actual value can be matched by the `isLimited(l, val, u)` notation (see 'lib/uitl.hpp')
test with random numbers::
+
- Control the seed! Invoke the seedRand() function once in each test instance.
  This draws an actually random number as seed and re-seeds the `lib::defaultGen`. The seed is written to the log.
- But the seed can be set to a fixed value with the `--seed` parameter of the test runner.
  This is how you reproduce a broken test case.
- Moreover, be sure either to draw all random values from the `defaultGen` or to build a well organised
  tree of PRNG instances, which seed from each other. This is especially valuable when the test starts
  several threads; each thread should use its own generator then (yet this can be handled sloppy if
  the _quality_ of the random number actually does not matter!)



Common Tasks
------------
Data handling
~~~~~~~~~~~~~
persistent data set::
  use the `lib::stat::DataTable` ('data.hpp') with CSV rendering -> see 'data-csv-test.cpp'
structured data::
  represent it as Lumiera ETD, that is as a tree of `GenNode` elements.
+
- be sure to understand the _essential idea:_ the receiver should act based on _knowledge_
  about the structure -- not by _introspection and case-switching_
- however -- for the exceptional case that you _absolutely must discover_ the structure,
  then use the visitor feature. This has the benefit of concentrating the logic at one place.
- can represent the notion of a nested scope, with iteration
- provides a convenient DSL notation, especially for testing, which helps to explain
  the expected structure visually .
- also useful as output format, both for debugging and because it can be matched against
  an expected structure, which is generated with the DSL notation.
- [yellow-background]#(planned)# will be able to map this from/to JSON easily.

Iterating
~~~~~~~~~
Lumiera iterators::
  They are designed for convenient usage with low boilerplate (even while this means wasting
  some CPU cycles or memory). They are deliberately much simpler than STL iterators, can be
  iterated only once, can be bool checked for iteration end, and can be used both in a for-each
  construct and in while-loops.
IterExplorer::
  The function `lib::explore(IT)` builds on top of these features and is meant to basically
  iterate anything that is iterable -- this can be used to level and abstract away the details.
+
- can be filtered and transformed
- can be reduced or collected into a vector
- can be used to build complex layered search- and evaluation schemes
STL-adaptors::
  A set of convenience functions like `eachElm(CON)` -- available in 'iter-adapter-stl.hpp'.
  Also allows to iterate _each key_ or _value_, and to take snapshots
IterSource::
  This is a special Lumiera iterator implementation which delegates through a virtual (OO) interface.
  Thus the source of the data can be abstracted away and switched transparently at runtime.
  Especially relevant for APIs, to produce a data sequence once and without coupling to details;
  even the production-state can be hooked up into the IterSource instance (with a smart-ptr).
  This allows e.g. to send a Diff to the UI through the UI-Bus, while the actual generation
  and application both happen demand-driven or _lazy..._


Formatting
~~~~~~~~~~
- implement a conversion-to-string operator.
- include the C++ IOStreams via 'lib/format-cout.hpp' -> this magically uses the `util::toString()`
- for testing, temporarily include 'lib/test/diagnostic-output' and use the `SHOW_EXPR` macro.
- use 'util::join' ('lib/format-util.hpp') to join arbitrary elements with `util::toString()` conversion
- use _printf-style formatters_ from Boost-format. We provide a light-weight front-end via 'lib/format-string.hpp'

  * the heavyweight boost-format include is only required once, for 'lib/format-string.cpp'.
  * the templated front-end passes-through most basic types and types with string-conversion
  * all invocations are strictly error safe (never throw) and can thus be used from catch-handlers

- use the anchor:texttemplate[lib/text-template]*Text-Template* engine. See 'text-template-test.cpp'.
  Can be used with simple map bindings, or even a _definition string_ `"x=42, y=why-not?"`, but can also
  draw data o from a `lib::GenNode` tree, or even from a custom defined `DataSource` template.
  Supports placeholders, conditionals and simple loops (and that's it --
  because there are way more capable solutions _out there_ ☺)


Language constructs
-------------------
Templates
~~~~~~~~~
build-from-anything::
  use a templated constructor, possibly even with varargs
+
- use a _deduction guide_ to pick the right ctor and arguments -> see

  * `ThreadJoinable` in 'thread.hpp', 698
  * `DataSource<string>` specialisation only when argument can be converted to string,
     in 'text-template.hpp', 745

- prevent shadowing of _automatically generated copy operations._
  See https://issues.lumiera.org/ticket/963[#963]. Based on the ``disable if'' SFINAE technique.
  A ready-made templated typedef `lib::meta::disable_if_self` can be found in 'lib/meta/util.hpp'


Variadics
~~~~~~~~~
pick and manipulate individually::
  The key trick is to define an _index sequence template,_ which can then be matched against
  a processing template for a single argument; and the latter can have partial specialisations
+
- see 'variadic-argument-picker-test.cpp'
- but sometimes it is easier to use the tried and true technique of the Loki-Typelists, which
  can be programmed recursively, similar to LISP. The »bridge« is to unpack the variadic argument pack
  into the `lib::meta::Types<ARGS...>`
+
meta-manipulations::
  When you need to rebind and manipulate a variadic sequence, it helps to transform the sequence
  into one of our meta sequence representations (`lib::meta::Types` or the Loki typelists).
  In `variadic-helper.hpp`, we define a convenient rebinding template `lib::meta::Elms<>`,
  which can work transparently on any type sequence or any tuple-like entity
+
  - to get at the variadics in a sequence representation
  - to get a matching index sequence
  - to rebind into another variadic template, using the same variadic sequence
  - to apply a meta-function on each of the variadic types
  - to compute a conjunction or disjunction of meta-predicates over the sequence
+
tuple-like::
  This is a concept to match on any type in compliance with the »tuple protocol«
+
  - such a type must inject a specialisation of `std::tuple_size<TY>`
  - and it must likewise support `std::tuple_element_t<N, TY>`
  - and, based on that, expose a constexpr _getter function_
  - together this also implies, that such a type can be used in _structured bindings_
    (but note, structured bindings also work on plain arrays and on simple structs,
     which are _not_ considered _tuple-like_ by themselves)
+
apply to tuple-likes::
  Unfortunately, the standard has a glaring deficiency here, insofar it defines an _exposition only_
  concept, which is then hard mapped to support only some fixed types from the STL (tuples, pairs,
  std::array and some range stuff). This was one of the main reasons to define our own `concept tuple_like`
+
  - but unfortunately, `std::apply` was fixed with C++20 to allow only the above mentioned fixed set of
    types from the STL, while in theory there is no reason why not to allow any _tuple-like_ entity
  - this forces us to define our own `lib::meta::apply` as a drop-in replacement for `std::apply`
  - in addition to that, we also define a `lib::meta::getElm<N, TY>`, which is an universal getter
    to work on any _tuple-like_, either with a `get` member function or a free-ADL function `get`
  - note that starting with C++20 it is forbidden to inject function overloads into namespace std,
    and thus a free `get` function must be injected as friend via ADL and used appropriately, i.e.
    unqualified.
+
apply functor to each tuple element::
  A common trick is to use `apply` in combination with a _fold-expression_
+
- provided as `lib::meta::forEach` in 'lib/meta/tuple-helper.hpp
- The design of the `DataTable` with CSV-Formatting is based on this technique, see 'lib/stat/data.hpp'
- 'lib/iter-zip.hpp' uses this to construct a tuple-of-iterators
+
unpack iterator into tuple::
  Under controlled conditions this is possible (even while it seems like time travel from the runtime into
  the compile-time domain). The number of results to extract from the iterator must be known at compile time,
  and the possible result types must be limited, so that a visitor can be used for double-dispatch.
+
- see tuple-record-init-test.cpp
- used in 'command-simple-closure.hpp' to receive parameter values sent via UI-Bus and package them
  into a tuple for invocation of a Steam-Layer command.