...which uncovers further deeply nested problems,
especially when referring to non-copyable types.
Thus need to construct a common type that can be used
both to refer to the source elements and the expanded elements,
and use this common type as result type and also attempt to
produce better diagnostic messages on type mismatch....
...the improved const correctness on STL iterators uncovered another
latent problem with out diagnositc format helper, which provide
consistently rounded float and double output, but failed to take
CV-qualifiaction into account
This is a subtle and far reaching fix, which hopefully removes
a roadblock regarding a Dispatcher pipeline: Our type rebinding
template used to pick up nested type definitions, especially
'value_type' and 'reference' from iterators and containers,
took an overly simplistic approach, which was then fixed
at various places driven by individual problems.
Now:
- value_type is conceptually the "thing" exposed by the iterator
- and pointers are treated as simple values, and no longer linked
to their pointee type; rather we handle the twist regarding
STL const_iterator direcly (it defines a non const value_type,
which is sensible from the STL point of view, but breaks our
generic iterator wrapping mechanism)
To complete the mock setup, the next step would be to extend the GenNode-based spec langage
to allow defining prerequisite Mock-JobTickets. Setting this up seems rather straight forward --
however, defining a simple testcase to cover this extension runs into surprisingly tricky problems..
- for one, the singleValIterator from Itertools has serious difficulties handling references
- but even more surprising, it seems impossible to make the "prerequisites iterator"
fit into the Tree-Explorer framework (which I intend to use as replacement
for the monadic approach)
after some extended analysis of generic types and template instances,
it seems that not TreeExplorer as such is the primary problem, but rather
there is a conceptual mismatch somewhere deep down in Itertools or Iter-Adapter
By reasoning and analysis I conclude that the differentiation into
multiple channels is likely misplaced in JobTicket; it belongs ratther
into the Segment and should provide a suitable JobTicket for each ModelPort
Handling of prerequisites also needs to be reshaped entirely after
switching to a pipeline builder for the Job-planning pipeline; as
preliminary access point, just add an iterator over the immediate
prerequisites, thereby shifting the exploration mechanism entirely
out of the JobTicket implementation
Testcase: A simple Sementation with a single and bounded Segment
As aside, figured out how to unpack an iterator such as to
tie a fixed number of references through a structural binding:
auto const& [s1,s2,s3] = seqTuple<3> (mockSegs.eachSeg());
...now able to build a mock segmentation which issues dummy jobs,
and is wired such as to verify the right job is invoked for each segment.
And this allows to build and verify the Dispatcher,
without being able to invoke actual render jobs yet.
- only the parts actually touched by the algo will be re-allocated
- when a segment is split, the clone copies carry on all data
Library: add function to check for a bare address (without type info)
...this is something I should have done since YEARS, really...
Whenever working with symbolically represented data, tests
typically involve checking *hundreds* of expected results,
and thus it can be really hard to find out where the
failure actually happens; it is better for readability
to have the expected result string immediately in the
test code; now this expected result can be marked
with a user-defined literal, and then on mismatch
the expected and the real value will be printed.
There are 12 distinct cases regarding the orientation of two intervals;
The Segmentation::splitSplice() operation shall insert a new Segment
and adjust / truncate / expand / split / delete existing segments
such as to retain the *Invariant* (seamless segmentation covering
the complete time axis)
- how to pass-in a specification given as GenNode
- now this might be translated into a MockJobTicket allocated in the MockSegmentation
Unimplemented: actually build the Segment with suitable start/end time
right now we're lacking a complete working implementation of render node invocation,
and thus the Dispatcher implementation can only be verified with the help
of mocked jobs. However, at least a preliminary implementation of tagging the
invocation instance is available, and thus we're able to verify that
a given job instance indeed belongs to and is "backed" by a specific JobTicket.
This is prerequisite for building up a (likewise mocked) Fixture datastructure,
and this in turn was meant to form the basis for attacking an actual Scheduler
implementation, followed by a real render node invocation.
- can now create a Job from JobTicket::NIL
- on invocation this Job will to nothing
Only when the first real output backend is implemented,
we can decide if this simplistic implementation is enough,
or if an empty output must be explicitly generated...
* using a simplified preliminary implementation of hash chaining (see #1293)
* simplistic implementation of hashing for time values (half-rotation)
* for now just hashing the time into the upper part of the LUID
Maybe we can even live with that implementation for some time,
depending on how important uniform distribution of hash values is
for proper usage of the frame cache.
Needless to say, various further fine points need more consideration,
especially questions of portability (32bit anyone?). Moreover, since
frame times are typically quantised, the search space for the hashed
time values is drastically reduced; conceivably we should rather
research and implement a good hash function for 128bit and then combine
all information into a single hash key....
...using the MockJobTicket setup as point of reference,
since the actual invocation of render nodes will only be drafted
later in this "Vertical Slice" integration effort...
- introduce a JobTicket::NOP (null-object pattern)
- assuming that the function splitSplice() will retain complete coverage allways
Remark:
`Fixture::getPlaylistForRender()` is a leftover from the very early implementation drafts.
This function was more or less based on the way Cinelerra works; it is clear by now
that Lumiera can not possibly work this way, given that we'll build a low-level model
and dispatch precompiled render jobs....
The Fixture and the low-level model backbone deserve a distinct namespace on their own.
Since it's built by the Builder from the Session contents, and also used by the frame dispatch,
we can expect dependence on some types from Steam-Layer, and thus this namespace
needs to reside in Steam-Layer rather, while the actual low-level Model
might become part of Vault-Layer, creating a hierarchy of data structures.
(Remark: likely also the session related namespaces will need a reorganisation)
The idea is to escape a "design deadlock" by using a test-driven prototype
implementation of the data structure to back a further development
of the Dispatcher and Scheduler implementation, which then can be used
to gradually elaborate and switch over to an actual implementation
data structure
...requires a first attempt towards defining a `JobTiket`.
This turns out quite tricky, due to using those `LinkedElements`
(intrusive single linked list), which requires all added records
actually to live elsewhere. Since we want to use a custom allocator
later (the `AllocationCluster`), this boils down to allocating those
records only when about to construct the `JobTicket` itself.
What makes matters even worse: at the moment we use a separate spec
per Media channel (maybe these specs can be collapsed later non).
And thus we need to pass a collection -- or better an iterator
with raw specs, which in turn must reveal yet another nested
sequence for the prerequisite `JobTickets`.
Anyhow, now we're able at least to create an empty `JobTicket`,
backed by a dummy `JobFunctor`....
Looks like we'll actually retain and use this low-level solution
in cases where we just can not afford heap allocations but need
to keep polymorphic objects close to one another in memory.
Since single linked lists are filled by prepending, it is rather
common to need the reversed order of elements for traversal,
which can be achieved in linear time.
And while we're here, we can modernise the templated emplacement functions
- build the reworked Job-planning pipeline more or less from scratch
- back that with mocked `Dispatcher` and `JobTicket`
- then transfer this into a `RenderDrive`, which can be tested as well
- could continue then to a `CalcStream` integration test....
- decision: the Monad-style iteration framework will be abandoned
- the job-planning will be recast in terms of the iter-tree-explorer
- job-planning and frame dispatch will be disentangled
- the Scheduler will deliberately offer a high-level interface
- on this high-level, Scheduler will support dependency management
- the low-level implementation of the Scheduler will be based on Activity verbs
This finishes a long lasting effort to rework the top-level of the Lumiera GTK UI,
to adapt to GTK-3 and the new asynchronous message based architecture.
Special credits and thanks to
* Joel Holdsworth
* Stefan Kangas
Without their relentless foundational work, the Lumiera UI could
never be where it is now. Even if some code was rewritten and several
parts of the old GTK-2 implementation are now obsolete, numerous ideas
solutions and inspirations were drawn from those early contributions
and live on as part of the reworked GUI.
Note: changing behaviour of TimeSpan to possibly flip start and end,
and also to use Offset as Offset and then re-orient,
since this seems the least surprising behaviour.
These changes carry over into changed default and limiting
on ZoomWindow constructor and various mutators, and most
notably shifting the time span always into allowed domain.
...the implementation was way too naive; in some cases we could go
into an infinite loop. In the end, using Newton approximation was not
necessary (and thus there is no loop anymore), but it helped me get
at a much better solution with very small error margin on average case.
All these corner cases are obviously "academic" to some degree,
but it turns out there is no clear-cut point where you'd be able
just so set a limit and be sure that fractional integer arithmetic
works flawless in all cases.
Thus the choice is
- give up (fractional) integers and work with floats and have to
deal with error accumulation
- or do something as chosen here, namely add a boundary zone, where
fractional integer arithmetic can be kept under control, while admitting
small errors, and in turn get the absolutely precise integers in all
everyday standard cases
The value used previously was too conservative, and prevented ZommWindow
from zooming out to the complete Time domain. This was due to missing the
Time::SCALE denominator, which increaded the limit by factor 1e6
In fact the code is able to handle even this extremely reduced limit,
but doing so seems over the top, since now detox() kicks in on several
calculations, leading to rather coarse grained errors.
Thus I decided to use a compromise: lower the limit only by factor 1000;
with typical screen pixel widths, we can reach the full time domain,
while most scaling and zoom calculations can be performed precisely,
without detox() kicking in. Obviously this change requires adjusting
a lot of the test case expectations, since we can now zoom out maximally.
As it turns out, the calculation path initially choosen for the mutateScale(Rat)
was needlessly indirect, and also duplicated several of the safeguards,
meanwhile implemented way better in conformWindowToMetric(Rat)
Thus, instead of relatively re-scaling the window, now we just
limit the given zoomFactor and pass it to conformWindowToMetric()
There is a built-in limitation, which now is even
lowered to 100000 pixels horizontally.
With the techniques introduced in this changeset, it seems possible
to support more -- yet this would be a case of unnecessary genricity;
handling such large numbers will drive more computations into the
danger zone, and doing so incurs cost in terms of testing and debugging.
Placing that into context, contemporary displays are not even 4K on
average, and it does not look likely even for cinema display to go
way beyond 8k -- so yes, I want display hardware with 100000 pixels!!
The key takeaway of this changeset:
- can calculate px = trunc(zoomFactor * duration) step wise,
even when the direct calculation would lead to wrap-around
- can safely adjust and fix the zoomFactor using Newton approximation
...even zooming out to span the complete time domain (~19000 years).
But only under the condition that the display window is sufficiently
large in terms of pixels, so we can handle the computation without
glitches.
This should not be a relevant limitation in practice, since a window
size of some 100 pixels is enough to handle Duration::MAX. Needless to add
that it's hard to imagine a media timeline of such tremendous size...
building on these Library changes, plus the safe-add function
developed some days ago, it is now possible to mark a large displacement
as `time::Offset`, and apply this to yield any valid time position,
even extreme negative values
...building on these Library changes, plus the safe-add function
developed some days ago, it is now possible to mark a large displacement
as `time::Offset`, and apply this to yield any valid time position,
even extreme negative values
The APIs for time quantisation were drafted in an early stage of the project
and then never followed-up. Especially Grid::gridAlign has no
real-world usage yet, and is only massaged in some tests.
When looking at QuantiserBasics_test, I was puzzled and led astray,
since this function suggests to materialise a continuous time into
a quantised time -- which it doesn't (there is another dedicated
function Quantiser::materialise() to that end); so, without engaging
into the discussion if this function is of any use, I'll hereby
choose a name better reflecting what it does.
This is a deep refactoring to allow to represent the distance
between all valid time points as a time::Offset or time::Duration.
By design this is possible, since Time::MAX was defined as 1/30 of
the maximum value technically representable as int64_t. However,
introducing a different limiter for offsets and durations turns
out difficult, due to the inconsistencies in the exiting hierarchy
of temporal entities. Which in turn seems to stem from the unfortunate
decision to make time entities immutable, see #1261
Since the limiter is hard wired into the `time::TimeValue` constructor,
we are forced to create a "backdoor" of sorts, to pass up values
with different limiting from child classes. This would not be so
much of a problem if calculations weren't forced to go through `TimeVar`,
which does not distinguish between time points and time durations.
This solution rearranges all checks to be performed now by time::Offset,
while time::Duration will only take the absolute value at construction,
based on the fact that there is no valid construction path to yield
a duration which does not go through an offset first.
Later, when we're ready to sort out the implementation base of time values
(see #1258), this design issue should be revisited
- either we'll allow derived classes explicitly to invoke the limiter functions
- or we may be able to have an automatic conversion path from clearly
marked base implementation types, in which case we wouldn't use the
buildRaw_() and _raw() "backdoor" functions any more...
While the calculation was already basically under control, I just was not content
with the achieved numeric precision -- and the fact that the test case in fact
misses the bar, making it difficult do demonstrate that the calculation
is not derailed. I just had the gut feeling that it must be somehow possible
to achieve an absolute error level, not just a relative error level of 1e-6
Thus I reworked this part into a generic helper function, see #1262
The end result is:
* partial failure. we can only ''guarantee'' the relative error margin of 1e-6
* but in most cases not out to the most extreme numbers, the sophisticated
solution achieves much better results way below the absolute error level of 1µ-Tick
Thus with using rational numbers, we have now a solution that is absolutely precise
in the regular case, and gradually introduces errors at the domain bound
but with an guaranteed relative error margin of 1e-6 (== Time::SCALE)
...in a similar vein as done for the product calculation.
In this case, we need to check the dimensions carefully and pick
the best calculation path, but as long as the overall result can
be represented, it should be possible to carry out the calculation
with fractional values, albeit introducing a small error.
As a follow-up, I have now also refactored the re-quantisation
functions, to be usable for general requantisation to another grid,
and I used these to replace the *naive* implementation of the
conversion FSecs -> µ-Grid, which caused a lot of integer-wrap-around
However, while the test now works basically without glitch or wrap,
the window position is still numerically of by 1e-6, which becomes
quite noticeably here due to the large overall span used for the test.
