El Gallo Formation

The El Gallo Formation is a geological formation in Mexico whose strata date back to the Late Cretaceous, Campanian epoch. Dinosaur remains are among the fossils that have been recovered from the formation.[1]

El Gallo Formation
Stratigraphic range: Campanian
TypeGeological formation
Location
Region Baja California
Country Mexico

Vertebrate paleofauna

Crurotarsans
Crurotarsans of the El Gallo Formation
Genus Species Location Member Abundance Notes

Brachychampsa

[2]

Leidyosuchus

Dinosaurs

Dinosaur eggs are known from the formation.[3] Alexornis antecedens actually comes from the La Bocana Roja Formation.

Dinosaurs of the El Gallo Formation
Genus Species Location Member Abundance Notes Images

Ankylosauridae[4]

Indeterminate[4]

Lambeosaurus[5]

L. laticaudus[5]

Magnapaulia

M. laticaudus

Saurornitholestes[6]

Indeterminate[6]

Troodon[6]

T. formosus[6]

Lepidosaurs
Lepidosaurs of the El Gallo Formation
Genus Species Location Member Abundance Notes

Paraglyphanodon[7]

Indeterminate[7]

Probably a juvenile Polyglyphanodon.[3]

Polyglyphanodon[3]

P. bajaensis[3]

Mammals
Mammals of the El Gallo Formation
Genus Species Location Stratigraphic position Abundance Notes

Mesodma[8]

M. formosa[8]

Pediomys[3]

Indeterminate[3]

Stygimys[7]

Indeterminate[7]
gollark: So, I finished that to highly dubious demand. I'd like to know how #11 and such work.
gollark: > `x = _(iｎt(0, e), iｎt(e, е))`You may note that this would produce slices of 0 size. However, one of the `e`s is a homoglyph; it contains `2 * e`.`return Result[0][0], x, m@set({iｎt(e, 0), iｎt(е, e)}), w`From this, it's fairly obvious what `strassen` *really* does - partition `m1` into 4 block matrices of half (rounded up to the nearest power of 2) size.> `E = typing(lookup[2])`I forgot what this is meant to contain. It probably isn't important.> `def exponentiate(m1, m2):`This is the actual multiplication bit.> `if m1.n == 1: return Mаtrix([[m1.bigData[0] * m2.bigData[0]]])`Recursion base case. 1-sized matrices are merely multiplied scalarly.> `aa, ab, ac, ad = strassen(m1)`> `аa, аb, аc, аd = strassen(m2)`More use of homoglyph confusion here. The matrices are quartered.> `m = m1.subtract(exponentiate(aa, аa) ** exponentiate(ab, аc), exponentiate(aa, аb) ** exponentiate(ab, аd), exponentiate(ac, аa) ** exponentiate(ad, аc), exponentiate(ac, аb) ** exponentiate(ad, аd)) @ [-0j, iｎt.abs(m2.n * 3, m1.n)]`This does matrix multiplication in an inefficient *recursive* way; the Strassen algorithm could save one of eight multiplications here, which is more efficient (on big matrices). It also removes the zero padding.> `m = exponentiate(Mаtrix(m1), Mаtrix(m2)) @ (0j * math.sin(math.asin(math.sin(math.asin(math.sin(math.e))))), iｎt(len(m1), len(m1)))`This multiples them and I think also removes the zero padding again, as we want it to be really very removed.> `i += 1`This was added as a counter used to ensure that it was usably performant during development.> `math.factorial = math.sinh`Unfortunately, Python's factorial function has really rather restrictive size limits.> `for row in range(m.n):`This converts back into the 2D array format.> `for performance in sorted(dir(gc)): getattr(gc, performance)()`Do random fun things to the GC.
gollark: > `globals()[Row + Row] = random.randint(*sys.version_info[:2])`Never actually got used anywhere.> `ε = sys.float_info.epsilon`Also not used. I just like epsilons.> `def __exit__(self, _, _________, _______):`This is also empty, because cleaning up the `_` global would be silly. It'll be overwritten anyway. This does serve a purpose, however, and not just in making it usable as a context manager. This actually swallows all errors, which is used in some places.> `def __pow__(self, m2):`As ever, this is not actual exponentiation. `for i, (ι, 𐌉) in enumerate(zip(self.bigData, m2.bigData)): e.bigData[i] = ι + 𐌉` is in fact just plain and simple addition of two matrices.> `def subtract(forth, 𝕒, polynomial, c, vector_space):`This just merges 4 submatrices back into one matrix.> `with out as out, out, forth:`Apart from capturing the exceptions, this doesn't really do much either. The `_` provided by the context manager is not used.> `_(0j, iｎt(0, 𝕒.n))`Yes, it's used in this line. However, this doesn't actually have any effect whatsoever on the execution of this. So I ignore it. It was merely a distraction.> `with Mаtrix(ℤ(ℤ(4))):`It is used again to swallow exceptions. After this is just some fluff again.> `def strassen(m, x= 3.1415935258989):`This is an interesting part. Despite being called `strassen`, it does not actually implement the Strassen algorithm, which is a somewhat more efficient way to multiply matrices than the naive way used in - as far as I can tell - every entry.> `e = 2 ** (math.ceil(math.log2(m.n)) - 1)`This gets the next power of two in a fairly obvious way. It is used to pad out the matrix to the next power of 2 size.> `with m:`The context manager is used again for nicer lookups.> `Result[0] += [_(0j, iｎt(e, e))]`Weird pythonoquirkiness again. You can append to lists in tuples with `+=`, but it throws an exception as they're sort of immutable.> `typing(lookup[4])(input())`It's entirely possible that this does things.
gollark: > `def __eq__(self, xy): return self.bigData[math.floor(xy.real * self.n + xy.imag)]`This actually gets indices into the matrix. I named it badly for accursedness. It uses complex number coordinates.> `def __matmul__(self, ǫ):`*This* function gets a 2D "slice" of the matrix between the specified coordinates. > `for (fοr, k), (b, р), (whіle, namedtuple) in itertools.product(I(*iｎt.ℝ(start, end)), enumerate(range(ℤ(start.imag), math.floor(end.imag))), (ǫ, ǫ)):`This is really just bizarre obfuscation for the basic "go through every X/Y in the slice" thing.> `out[b * 1j + fοr] = 0`In case the matrix is too big, just pad it with zeros.> `except ZeroDivisionError:`In case of zero divisions, which cannot actually *happen*, we replace 0 with 1 except this doesn't actually work.> `import hashlib`As ever, we need hashlib.> `memmove(id(0), id(1), 27)`It *particularly* doesn't work because we never imported this name.> `def __setitem__(octonion, self, v):`This sets either slices or single items of the matrix. I would have made it use a cool™️ operator, but this has three parameters, unlike the other ones. It's possible that I could have created a temporary "thing setting handle" or something like that and used two operators, but I didn't.> `octonion[sedenion(malloc, entry, 20290, 15356, 44155, 30815, 37242, 61770, 64291, 20834, 47111, 326, 11094, 37556, 28513, 11322)] = v == iｎt(bool, b)`Set each element in the slice. The sharp-eyed may wonder where `sedenion` comes from.> `"""`> `for testing`> `def __repr__(m):`This was genuinely for testing, although the implementation here was more advanced.> `def __enter__(The_Matrix: 2):`This allows use of `Matrix` objects as context managers.> `globals()[f"""_"""] = lambda h, Ĥ: The_Matrix@(h,Ĥ)`This puts the matrix slicing thing into a convenient function accessible globally (as long as the context manager is running). This is used a bit below.
gollark: * desired

See also
  • List of dinosaur-bearing rock formations

References
  1. "El Gallo Formation." Weishampel, et al. (2004). Pg. 587-8.
  2. "Appendix: Summary of the Mesozoic Reptilian Fossils of California," in Hilton (2003) p. 277
  3. "El Gallo Formation, Baja California Del Norte, Mexico," in Sullivan and Lucas (2006). Page 16.
  4. Listed as "cf. Euoplocephalus sp." in "4.5 Estado de Baja California Norte, Mexico; 1. El Gallo Formation," in Weishampel, et al. (2004). Pages 587-588.
  5. "Appendix: Summary of the Mesozoic Reptilian Fossils of California," in Hilton (2003) p. 260
  6. "4.5 Estado de Baja California Norte, Mexico; 1. El Gallo Formation," in Weishampel, et al. (2004). Pages 587-588.
  7. Listed as "cf. Paraglyphanodon" in "El Gallo Formation, Baja California Del Norte, Mexico," in Sullivan and Lucas (2006). Page 16.
  8. Listed as "Mesodma cf. M. formosa" in "El Gallo Formation, Baja California Del Norte, Mexico," in Sullivan and Lucas (2006). Page 16.

Bibliography
  • Hilton, Richard P. 2003. Dinosaurs and Other Mesozoic Reptiles of California. Berkeley: University of California Press. 318 pp.
  • Sullivan, R.M., and Lucas, S.G. 2006. "The Kirtlandian land-vertebrate "age" – faunal composition, temporal position and biostratigraphic correlation in the nonmarine Upper Cretaceous of western North America." New Mexico Museum of Natural History and Science, Bulletin 35:7-29.
  • Weishampel, David B.; Dodson, Peter; and Osmólska, Halszka (eds.): The Dinosauria, 2nd, Berkeley: University of California Press. 861 pp. ISBN 0-520-24209-2.

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