These are useful for customizing the table renderer's header and total
labels:
./scripts/csv.py test.csv -ba -fc \
-Hc='c(MiB/s)' \
--tlabel='wow so many (%(c)s)'
a c(MiB/s)
x 6
wow so many (6) 6
In theory header labels could be controlled by the field names
themselves, but our use of Python's namedtuples internal is quite
limiting.
The immediate use case is -Hprobe=bench+probe in the bench-related
rules, but it may also be useful for adding units such as in the above
example.
---
I considered adding these to all csv scripts, but decided that was too
much. punescape modifiers are probably a good line for what should be
limited to csv.py.
This flag has proven useful in external scripts, might as well give it a
short form.
-t is also an infrequently used flag, so I think the risk of collision
is low even across all csv scripts. The only existing use is in
test/bench.py for -t/--trace (and apparently in gcov for -t/--stdout?).
The lowercase -c/--compare felt clunky. I think because we tend towards
using uppercase for flags that operate on csv rows, such as -D/--define,
-L/--add-label (plot.py), etc.
This is useful for enforcing an upper-bound on fracs based on their
total component.
Technically possible via decomposing + min + recomposing, but... Well
which one do you think is easier?
- saturate(x)
- frac(min(max(int(x), 0), total(x)), total(x))
And this assumes x is easily available and not some other expr (though
chaining csv.py could work around that).
---
The motivation for this was `make bench-widths`, where one read
benchmark could ruin the entire column due to introducing infinities.
Now:
make bench # (squished a bit)
probe readed progged erased
b_wt_seq+w 1.0/1.0 (100.0%) 31.7/256.0 (12.4%) 4096.0/4096.0 (100.0%)
b_wt_random+w 1.0/1.0 (100.0%) 15.3/256.0 (6.0%) 4096.0/4096.0 (100.0%)
b_wt_logging+w 1.0/1.0 (100.0%) 15.4/256.0 (6.0%) 4096.0/4096.0 (100.0%)
b_wt_many+w 1.0/1.0 (100.0%) 16.1/256.0 (6.3%) 4096.0/4096.0 (100.0%)
b_rt_seq+r 1.0/1.0 (100.0%) 256.0/256.0 (100.0%) 4096.0/4096.0 (100.0%)
b_rt_random+r 1.0/1.0 (100.0%) 256.0/256.0 (100.0%) 4096.0/4096.0 (100.0%)
b_rt_many+r 1.0/1.0 (100.0%) 256.0/256.0 (100.0%) 4096.0/4096.0 (100.0%)
TOTAL 1.0/1.0 (100.0%) 149.0/256.0 (58.2%) 4096.0/4096.0 (100.0%)
# ^- notably not infinity
A simple float variant of the CsvFrac type:
- frac(1.5,2) => 1/2 (50.0%)
- ffrac(1.5,2) => 1.5/2.0 (75.0%)
Useful for `make bench-widths` (previously make bench-bus), where we
want to find the average buffer utilization:
probe readed progged erased
b_rbyd+create 1.0/1.0 (100.0%) 13.8/256.0 (5.4%) ∞/4096.0 (∞%)
b_rbyd+delete ∞/1.0 (∞%) ∞/256.0 (∞%) ∞/4096.0 (∞%)
b_rbyd+fetch 1.0/1.0 (100.0%) ∞/256.0 (∞%) ∞/4096.0 (∞%)
b_rbyd+lookup 1.0/1.0 (100.0%) ∞/256.0 (∞%) ∞/4096.0 (∞%)
b_rbyd+usage ∞/1.0 (∞%) ∞/256.0 (∞%) ∞/4096.0 (∞%)
b_wt_seq+w 1.0/1.0 (100.0%) 31.7/256.0 (12.4%) 4096.0/4096.0 (100.0%)
b_wt_random+w 1.0/1.0 (100.0%) 15.3/256.0 (6.0%) 4096.0/4096.0 (100.0%)
b_wt_logging+w 1.0/1.0 (100.0%) 15.4/256.0 (6.0%) 4096.0/4096.0 (100.0%)
b_wt_many+w 1.0/1.0 (100.0%) 16.1/256.0 (6.3%) 4096.0/4096.0 (100.0%)
TOTAL ∞/1.0 (∞%) ∞/256.0 (∞%) ∞/4096.0 (∞%)
Now that we have 4 types, the cast matrix gets a bit complicated, but
this is side-stepped a bit by a custom __frac__ hook.
---
Some other tweaks to csv.py:
- Added CsvFold.type to typecheck folds _after_ we know the expr's final
type.
- Adopted CsvFfrac as an output for most of the math functions/folds
- Stopped early termination of typechecking if we change type!
This was broken: int(float(1.5) + int(1))
This adds __hash__ to CsvFrac, and tweakes __eq__ to be more strict
about equality.
Previously CsvFrac only considered the relevant ratio for equality,
making hashing difficult:
- before: 1/2 == 2/4 => true
- after: 1/2 == 2/4 => false
But now that we have csv.py, with the explicit ratio function, it's
probably a good idea to be strict by default.
Note comparison is unchanged:
- 1/2 < 2/4 => false
- 1/2 > 2/3 => false
---
This popped up during debugging, and would be useful to have around.
Note CsvInt/CsvFloat already implicitly define __hash__ through
namedtuple's implicit __eq__ and friends. But this is disabled in
CsvFrac due to the explicit __eq__.
Which is good because otherwise it would've been wrong with the ratio
comparison!
Turns out mt.isinf is happy to accept non-primitive floats (such as
CsvFloat) as long as __float__ is defined. But CsvInt expects a float
inf, not a CsvFloat, so things explode later.
Fixed by explicitly casting to float if mt.isinf, instead of passing
as-is.
Also tweaked CsvInt/CsvFloat constructors to not bother checking
isinstance, unconditional int/float casts are probably cheaper than the
condition in Python.
This is the inverse of accumulate, returning the difference between
subsequent results. In theory accumulate(delta(x)) and
delta(accumulate(x)) are noops.
This is particularly useful for normalizing our bench n value in
scripts. It's the only value still returned as a cumulative measurement,
which is a bit inconsistent, but necessary for uniquely identifying
probe steps.
Note this matches the behavior of mods, e.g. I would expect this to not
break if ORDER is missing:
./scripts/csv.py \
-bcase='%(case)s+%(probe)s+%(ORDER)s' \
-ft=accumulate(bench_simtime, case, probe, ORDER)
Normally the expr compiler would force typechecking of ORDER, giving it
a default value of int(0) if missing, but we intentionally bypass
typechecking in enumerate/accumulate's by fields since they may be
strings.
I think this is currently only possible with overlapping by/field
fields, but hiding results with conflicting by fields is not ideal.
Especially since this function is central to so many scripts:
cat test.csv
a,b,c
x,2,1
x,1,2
x,1,3
Before:
./scripts/csv.py test.csv -ba -bb -fb -fc
warning: by fields are unstable
a,b b c
x,2 2 1
TOTAL 4 6
After:
./scripts/csv.py test.csv -ba -bb -fb -fc
a,b b c
x,2 2 5
x,2 2 1
TOTAL 4 6
This solves the main issue with unstable by fields, so no more warning.
Note that some features rely on by being unique to work (added/removed
numbers, compare fields, etc). They shouldn't error, but may be
incorrect/unintuitive with conflicting by fields, so avoiding
conflicting by fields is still a good idea.
So it turns out this _can_ happen, without an in-script coding error.
Consider the behavior of a script with overlapping by/field fields:
$ cat test.csv
a,b
x,2
x,1
x,1
$ ./scripts/csv.py test.csv -ba -bb -fb
During the first fold, rows 2 and 3 will contain b=1, but during the
second fold they will have been merged, resulting in b=2.
So, relaxing to a warning for now. Maybe the table renderer should be
rewritten to avoid folding? (note diffing results may be tricky)
Now -l/--list-fields includes however many results fit in 36 chars:
$ ./scripts/csv.py --list-fields test.csv
i int # 16,17,14,18,19,15,20,13,12,29,27,28,...
suite ? # bench_p26_wt
case ? # bench_p26_wt_linear,bench_p26_wt_ran...
NO_FRUNCATE int # 0
SIZE int # 2097152
SEED int # 42
The whole point of -l/--list-fields is to give a quick information dump
about what's inside a csv file, and we're already parsing everything to
try to figure out types, so why not?
Much easier to read than head:
$ head -n5 test.csv
i,suite,case,NO_FRUNCATE,SIZE,SEED,BLOCK_SIZE,FILE_SIZE,SIM_...
16,bench_p26_wt,bench_p26_wt_linear,0,2097152,42,65536,64,36...
16,bench_p26_wt,bench_p26_wt_linear,0,2097152,42,65536,64,36...
16,bench_p26_wt,bench_p26_wt_linear,0,2097152,42,65536,64,36...
16,bench_p26_wt,bench_p26_wt_linear,0,2097152,42,65536,64,36...
This extends csv.py's enumerate/accumulate exprs with optional by field
arguments. Each set of by fields gets its own state, allowing multiple
parallel enumerates/accumulates to be processed simultaneously.
This is especially useful when the number of by field sets is unknown.
In theory you could split/merge each by field set with a separate csv.py
call, but it'd be a real pain.
Consider some bench results:
case,n,simtime
bench_rbyd,1,100
bench_rbyd,2,10
bench_rbyd,3,100
bench_btree,1,200
bench_rbyd,4,10
bench_btree,2,20
bench_btree,3,2000
bench_btree,4,200
It was a bit awkward to handle these with csv.py's accumulate, as
accumulate operated strictly per-row, ignoring the case field.
But now with optional by fields:
$ ./scripts/csv.py test.csv \
-bcase -bn \
-fsimtime='accumulate(simtime, case)'
case,n simtime
bench_btree,1 200
bench_btree,2 220
bench_btree,3 2220
bench_btree,4 2420
bench_rbyd,1 100
bench_rbyd,2 110
bench_rbyd,3 210
bench_rbyd,4 220
TOTAL 5700
Note that these by fields are a bit special in csv.py's grammar. So far,
they are the only fields in field exprs that aren't typechecked. The
alternative would be string types in csv.py, but I'm not sure I want to
go that far.
---
It's tempting to try to invert this logic (accumulate(simtime, n)), but
I'm not sure how it would work internally. The duplicate by fields
("case") do get annoying, but specifying them in the expr helps make the
relevant state explicit.
Keep in mind we don't evaluate the actual by fields until much later in
csv.py. Entangling these stages risks confusion (-ba='%(b)s'
-c='enumerate(n)'? hidden by fields? overlapping by+field fields?).
I mean, what would you expect this to do?
max(a) + sum(b)
Whatever your answer is, it's wrong (the way csv.py works, we always
compute folds after expr evaluation). The best option is to error,
matching the behavior of mismatched types.
csv.py's -L/--list-computed was returning some confusing types:
$ ./scripts/csv.py /dev/null -fa='float(1)' -L
a int sum
^-- huh!?
Turns out csv.py's fold typechecking was all broken. Folds can change
the type, but only at the invocation:
$ ./scripts/csv.py /dev/null -fa='sum(float(1))' -L
a int sum
$ ./scripts/csv.py /dev/null -fa='avg(int(1))' -L
a float avg
$ ./scripts/csv.py /dev/null -fa='int(avg(1))' -L
a float avg
This is maybe defensible for explicit folds, since their evaluation is
also lifted, but not so much for things like literals/fields/etc.
---
Fixed by allowing None to indicate a generic fold, and allowing types to
be lazily figured out in csv.compile.
These seem useful enough to have shortform flags:
- -l/--list-fields - Input fields before processing
- -L/--list-computed - Computed fields and expr dependencies
Note while -L/--list-computed has more information, it's also more
likely to trigger an assert/error due to poorly implemented field exprs.
On one hand, only inferring the used input fields is conceptually
correct because that's how csv.py works. On the other, it doesn't really
make sense for --list-computed to show _less_ information than
--list-fields.
So, showing all inferred types now:
$ ./scripts/csv.py --list-computed test.csv \
-bcase='%(case)s+%(m)s' \
-fsimtime='float(bench_simtime)/1.0e9' \
-fsimthroughput='float(n)/max(float(bench_simtime)/1.0e9,1.0e-9)'
i int .--> case ? ?
suite ? |.-> simtime int sum
case ? -+|.> simthroughput int sum
SKIP_WARMUP int |||
FILE_SIZE int |||
SEED int |||
...
m ? -'||
n int ---+
bench_reads int ||
bench_progs int ||
bench_erases int ||
bench_readed int ||
bench_progged int ||
bench_erased int ||
bench_simtime int ---'
I think this makes --list-computed a strict superset of --list-fields
now.
I was expecting -ba -Fa to sort numerically, but it was not. Turns out
hidden field fields (-F/--hidden-field) without exprs were never
typechecked.
This is not an issue for non-hidden field fields (-f/--field), because
we typecheck these explicitly in compile.
Found from some confusing behavior when by/from fields overlap. It turns
out when this happens (-bhi -Fhi, for example), the generated getattr
for the by field would trigger the __getattribute__ for the overlapping
field field, resulting in a fold on _every add operation_.
Hopefully you can see where this is a bit of a problem when summing a
large number of results (O(n^2)?).
---
Fixed by switching getattr to object.__getattribute__ and reconsidering
csv.py's entire design.
Less useful than --list-fields, but fun.
This shows more of the internal expr eval info: input fields + types,
output fields + types + folds, and a small dependency graph showing what
goes where:
$ ./scripts/csv.py --list-computed test.csv \
-bcase='%(case)s+%(m)s' \
-fsimtime='float(bench_simtime)/1.0e9' \
-fsimthroughput='float(n)/max(float(bench_simtime)/1.0e9,1.0e-9)'
i ? .--> case ? ?
suite ? |.-> simtime int sum
case ? -+|.> simthroughput int sum
SKIP_WARMUP ? |||
FILE_SIZE ? |||
SEED ? |||
...
m ? -'||
n int ---+
bench_reads ? ||
bench_progs ? ||
bench_erases ? ||
bench_readed ? ||
bench_progged ? ||
bench_erased ? ||
bench_simtime int ---'
Maybe I was just itching to write another ascii-art renderer.
One issue I keep running into with csv.py is that it's difficult to get
started with a new/unfamiliar csv file.
csv.py itself doesn't know what to do until you start specifying fields,
but you can't start specifying fields until you know what fields there
are. Add to this the fact that our csv files have so much info shoved in
them that their "human readability" is mostly theoretical.
The --list-fields flag provides a quick solution to this:
$ ./scripts/csv.py --list-fields test.csv
i int
suite ?
case ?
SKIP_WARMUP int
FILE_SIZE int
SEED int
...
csv.py doesn't have much info at this stage, but we can at least include
the best-effort type guessing we use for field exprs.
Now that we have the enumerate expr, -i/--enumerate can be implemented
entirely during expr eval:
- -i/--enumerate => -bi -Fi=enumerate()
- -I/--hidden-enumerate => -Bi -Fi=enumerate()
Instead of internally reimplementing the same behavior.
This is what our help text implies, so might as well put our money where
our mouth is. And the less special internals we have, the better.
I considered removing -i/-I completely, but it's quite a convenient flag
when debugging csv.py expressions.
In an effort to move away from magic usage of -i/--enumerate, this adds
an explicit z field for differentiating -r/--hot results (and for normal
recursive results).
Instead of trying to think of a new flag to control this, this just
piggybacks on -Z/--children, which now accepts a tuple:
- ./scripts/csv.py -z3 -Z
- ./scripts/csv.py -z3 -Zchildren
- ./scripts/csv.py -z3 -Zz,children
The only tricky bit was needing to insert z in front of the by fields,
otherwise it was mostly a simplification from the enumerate mess.
Another positive side-effect: -r/--hot (and -z/--depth) now implies
-Zz,children, removing the annoying/confusing behavior of hotify folding
results by default.
The current... attempt at an approach was broken and becoming horribly
unmaintainable. Two issues found without even looking:
1. Field inference didn't understand prefixes, leading to duplicate
by/field fields when attempting to infer by fields with --prefix.
2. Sort wasn't working for some reason, probably because they behavior
of sort, defines, etc are really weird since they apply to both by
fields and field fields.
I considered just dropping support for --prefix completely, this really
isn't worth the time, but instead found a simple solution of moving
prefix handling to one of the first steps in collect_csv.
This has the downside of creating conflicts when a prefixed/non-prefixed
field has the same name, but I don't care. --prefix is a niche flag that
shouldn't mess with the rest of the code like this, and none of the
other scripts really handle field conflicts correctly anyways.
- Fixed the initial filter using explicit 'children'/'notes' literals
Whoops, how did this happen?
- Fixed fold using default children/notes result attributes
This one is a bit more excusable, self.children is easy to overlook.
But not actual string literals, that's silly.
This adds two new exprs to csv.py, useful for sequential data:
enumerate() A number incremented each result
accumulate(a) A running sum across results
To make these work required adding support for cross-row state, thus the
new state field in CsvExpr.Expr.eval.
Once you have that cross-row state, implementing enumerate/accumulate is
pretty straightforward. The only complication being that we need to hash
state by the unique Python id (`id(self)`), otherwise multiple exprs
would share state, which would be pretty weird.
Note that csv.py's pipeline is now quite complex, and stage order is
important!
input --> define --> expr --> folding --> sorting --> output
filtering eval
As a result, it's unfortunately not possible to organize enumerate/
accumulate by by fields. I poked around with the idea but decided it was
too complex (aren't I supposed be building a filesystem?). The guiding
principle behind csv.py is most problems can be solved with more process
substitution.
---
This is a bit clunky since we can't use the existing fold system, but
csv.py is already a pile of hacks, so what's one more?
The reason for the clunkiness is that the original idea behind csv.py
was to treat each folded row independently and order-agnostic. Not the
greatest idea in hindsight, cross-row operations are useful!
The idea here is to add some sort of accumulate operation to csv.py, so
we can stop cumulative-result clunkiness. It would also be immensely
useful as a general function, and -i/--enumerate already sets a
precedent for this sort of cross-row behavior.
But I'm starting to think using flags here is not the best way, maybe
this would be better as a field expr?
For some reason -F/--hidden-field fields weren't being parsed as a
CsvExpr, breaking any attempt to use exprs with hidden fields. Probably
just broken during a refactor.
Fortunately an easy fix.
I'm not sure if this was ever implemented, or broken during a refactor,
but we were ignoring -s/-S flags when writing .csv/.json output with
-o/-O.
Curious, because the functionality _was_ implemented in fold, just
unused. All this required was passing -s/-S to fold correctly.
Note we _don't_ sort diff_results, because these are never written to
.csv/.json output.
At some point this behavior may have been a bit more questionable, since
we use to allow mixing -o/-O and table rendering. But now that -o/-O is
considered an exclusive operation, ignoring -s/-S doesn't really make
sense.
---
Why did this come up? Well imagine my frustration when:
1. In tikz/pgfplots, \addplot table only really works with sorted data
2. csv.py has a -s/-S flag for sorting!
3. -s/-S doesn't work!
- Added --small-total. Like --small-header, this omits the first column
which usually just has the informative text TOTAL.
- Tweaked -Q/--small-table so it renders with --small-total if
-Y/--summary is provided.
- Added --total as an alias for --summary + --no-header + --small-total,
i.e. printing only the totals (which may be multiple columns) and no
other decoration.
This is useful for scripting, now it's possible to extract just, say,
the sum of some csv and embed with $():
echo $(./scripts/code.py lfs3.o --total)
- Tweaked total to always output a number (0) instead of a dash (-),
even if we have no results.
This relies on Result() with no args, which risks breaking scripts
where the Result type expects an argument. To hopefully catch this
early, the table renderer currently creates a Result() before trying
to fold the total result.
- If first column is empty (--small-total + --small-header, --no-header,
etc) collapse width to zero. This avoids a bunch of extra whitespace,
but still includes the two spaces normal used to separate names from
fields.
But I think those spaces are a good thing. It makes it hard to miss
the implicit padding in the table renderer that risks breaking
dependent scripts.
This adds %i and %I as punescape modifiers for limited printing of
integers with SI prefixes:
- %(field)i - base-10 SI prefixes
- 100 => 100
- 10000 => 10K
- 0.01 => 10m
- %(field)I - base-2SI prefixes
- 128 => 128
- 10240 => 10Ki
- 0.125 => 128mi
These can also easily include units as a part of the punescape string:
- %(field)iops/s => 10Kops/s
- %(field)IB => 10KiB
This is particularly useful in plotmpl.py for adding explicit
x/yticklabels without sacrificing the automatic SI-prefixes.
So now these lex correctly:
- 1e9 => 1000000000
- 1e+9 => 1000000000
- 1e-9 => -1000000000
A bit tricky when you think about how these could be confused for binary
addition/subtraction. To fix we just eagerly grab any signs after the e.
These are particularly useful for manipulating simulated benchmarks,
where we need to convert things to/from nanoseconds.
Whoops! A missing splat repetition here meant we only ever accepted
floats with a single digit of precision and no e/E exponents.
Humorously this went unnoticed because our scripts were only
_outputting_ single digit floats, but now that that's fixed, float
parsing also needs a fix.
Fixed by allowing >1 digit of precision in our CsvFloat regex.
This adds __csv__ methods to all Csv* classes to indicate how to write
csv/json output, and adopts Python's default float repr. As a plus, this
also lets us use "inf" for infinity in csv/json files, avoiding
potential unicode issues.
Before this we were reusing __str__ for both table rendering and
csv/json writing, which rounded to a single decimal digit! This made
float output pretty much useless outside of trivial cases.
---
Note Python apparently does some of its own rounding (1/10 -> 0.1?), so
the result may still not be round-trippable, but this is probably fine
for our somewhat hack-infested csv scripts.
So now the hidden variants of field specifiers can be used to manipulate
by fields and field fields without implying a complete field set:
$ ./scripts/csv.py lfs.code.csv \
-Bsubsystem=lfsr_file -Dfunction='lfsr_file_*' \
-fcode_size
Is the same as:
$ ./scripts/csv.py lfs.code.csv \
-bfile -bsubsystem=lfsr_file -Dfunction='lfsr_file_*' \
-fcode_size
Attempting to use -b/--by here would delete/merge the file field, as
cvs.py assumes -b/-f specify all of the relevant field type.
Note that fields can also be explicitly deleted with -D/--define's new
glob support:
$ ./scripts/csv.py lfs.code.csv -Dfile='*' -fcode_size
---
This solves an annoying problem specific to csv.py, where manipulating
by fields and field fields would often force you to specify all relevant
-b/-f fields. With how benchmarks are parameterized, this list ends up
_looong_.
It's a bit of a hack/abuse of the hidden flags, but the alternative
would be field globbing, which 1. would be a real pain-in-the-ass to
implement, and 2. affect almost all of the scripts. Reusing the hidden
flags for this keeps the complexity limited to csv.py.
Globs in CLI attrs (-L'*=bs=%(bs)s' for example), have been remarkably
useful. It makes sense to extend this to the other flags that match
against CSV fields, though this does add complexity to a large number of
smaller scripts.
- -D/--define can now use globs when filtering:
$ ./scripts/code.py lfs.o -Dfunction='lfsr_file_*'
-D/--define already accepted a comma-separated list of options, so
extending this to globs makes sense.
Note this differs from test.py/bench.py's -D/--define. Globbing in
test.py/bench.py wouldn't really work since -D/--define is generative,
not matching. But there's already other differences such as integer
parsing, range, etc. It's not worth making these perfectly consistent
as they are really two different tools that just happen to look the
same.
- -c/--compare now matches with globs when finding the compare entry:
$ ./scripts/code.py lfs.o -c'lfs*_file_sync'
This is quite a bit less useful that -D/--define, but makes sense for
consistency.
Note -c/--compare just chooses the first match. It doesn't really make
sense to compare against multiple entries.
This raised the question of globs in the field specifiers themselves
(-f'bench_*' for example), but I'm rejecting this for now as I need to
draw the complexity/scope _somewhere_, and I'm worried it's already way
over on the too-complex side.
So, for now, field names must always be specified explicitly. Globbing
field names would add too much complexity. Especially considering how
many flags accept field names in these scripts.
I don't know how I completely missed that this doesn't actually work!
Using del _does_ work in Python's repl, but it makes sense the repl may
differ from actual function execution in this case.
The problem is Python still thinks the relevant builtin is a local
variables after deletion, raising an UnboundLocalError instead of
performing a global lookup. In theory this would work if the variable
could be made global, but since global/nonlocal statements are lifted,
Python complains with "SyntaxError: name 'list' is parameter and
global".
And that's A-Ok! Intentionally shadowing language builtins already puts
this code deep into ugly hacks territory.
Now that I know my way around the weirdness that is Python's class
scope, this just required another function indirection to capture the
class-level dicts correctly.
I was considering using the __subclasses__ trick, but it seems like that
would actually be more complicated here.
- CsvInt.x -> CsvInt.a
- CsvFloat.x -> CsvFloat.a
- Rev.x -> Rev.a
This matches CsvFrac.a (paired with CsvFrac.b), and avoids confusion
with x/y variables such as Tile.x and Tile.y.
The other contender was .v, since these are cs*v* related types, but
sticking with .a gets the point across that the name really doesn't have
any meaning.
There's also some irony that we're forcing namedtuples to have
meaningless names, but it is useful to have a quick accessor for the
internal value.
This prefix was extremely arbitrary anyways.
The prefix Csv* has slightly more meaning than R*, since these scripts
interact with .csv files quite a bit, and it avoids confusion with
rbyd-related things such as Rattr, Ralt, etc.
This affects the table renderers as well as csv.py's ratio expr.
This is a bit more correct, handwaving 0/0 (mapping 0/0 -> 100% is
useful for cov.py, please don't kill me mathematicians):
frac(1,0) => 1/0 (∞%)
frac(0,0) => 0/0 (100.0%)
frac(0,1) => 0/1 (0.0%)
So now the result scripts always require -d/--diff to diff:
- before: ./scripts/csv.py a.csv -pb.csv
- after: ./scripts/csv.py a.csv -db.csv -p
For a couple reasons:
- Easier to toggle
- Simpler internally to only have one diff path flag
- The previous behavior was a bit unintuitive
This simplifies attrs a bit, and scripts can always override
__getitem__ if they want to provide lazy attr generation.
The original intention of accepting functions was to make lazy attr
generation easier, but while tinkering around with the idea I realized
the actual attr mapping/generation would be complicated enough that
you'd probably want a full class anyways.
All of our scripts are only using dict attrs anyways. And lazy attr
generation is probably a premature optimization for the same reason
everyone's ok with Python's slices being O(n).
So:
all_ = all; del all
Instead of:
import builtins
all_, all = all, builtins.all
The del exposes the globally scoped builtin we accidentally shadow.
This requires less megic, and no module imports, though tbh I'm
surprised it works.
It also works in the case where you change a builtin globally, but
that's a bit too crazy even for me...
This only failed if "-" was used as an argument (for stdin/stdout), so
the issue was pretty hard to spot.
openio is a heavily copy-pasted function, so it makes sense to just add
the import os to openio directly. Otherwise this mistake will likely
happen again in the future.
This just makes dat behave similarly to Python's getattr, etc:
- dat("bogus") -> raises ValueError
- dat("bogus", 1234) -> returns 1234
This replaces try_dat, which is easy to forget about when copy-pasting
between scripts.
Though all of this wouldn't be necessary if only we could catch
exceptions in expressions...
Christopher Haster