Anjaneyasana

Āñjaneyāsana (Sanskrit: आञ्जनेयासन, "Son of Anjani pose"), Crescent Moon Pose[1] or Ashwa Sanchalanasana, Equestrian Pose[2] is a lunging back bending asana in modern yoga as exercise.

Anjaneyasana, Crescent Moon pose

It is sometimes included as one of the asanas in the Surya Namaskar sequence, though usually with arms down in that case.

Etymology and origins

The name Anjaneya is a matronymic for Hanuman whose mother's name is Anjani. Hanuman is a central figure in the epic Rāmāyaṇa and an important Iṣṭa-devatā in devotional worship.[3]

Like many standing poses, Anjaneyasana was unknown in medieval hatha yoga, and was brought into modern yoga in the 20th century from Indian martial arts. It is used in schools of modern yoga such as Sivananda Yoga.[1] It is included as one of the asanas in Ashtanga Vinyasa Yoga's type 1 Surya Namaskar sequence.[4]

Description

The pose is entered from a lunge, with the back knee lowered to the ground, the back arched and the arms raised and stretched over the head. The toes of the back foot remain tucked forward, the heel lifted. The front foot remains in standing position, the hips lowered close to the front foot and the front knee fully bent and pointing forwards. In the full pose, the rear foot is lifted and grasped with both hands, the elbows pointing up.[1][5]

Variations
Variation with arms down

A twisting lunge (a preparatory pose for Parivritta Parsvakonasana[6]) is sometimes called Parivṛtta Anjaneyasana. This has the opposite elbow to the bent forward knee and the rear leg straight.[7]

Moving the front foot on to its side so the knee comes to the ground enables a transition to a related back bend, Rajakapotasana.[1]

Some teachers use the name Crescent Moon Pose for a lunge with raised knee and raised hands, as in Virabhadrasana I.[8]

gollark: It uses the function, yes.
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.

See also

References
  1. Lidell, Lucy; The Sivananda Yoga Centre (1983). The Book of Yoga: the complete step-by-step guide. Ebury. pp. 132–133. ISBN 978-0-85223-297-2. OCLC 12457963.
  2. Saraswati, Swami Satyananda (2003). Asana Pranayama Mudra Bandha. Nesma Books India. p. 165. ISBN 978-81-86336-14-4.
  3. Gaia Staff (27 September 2016). "Anjaneyasana: The Lunge Pose". Gaia. Retrieved 14 February 2019.
  4. "Surya Namaskar Variations: How it is done in these 3 popular yoga traditions". Times of India. 23 June 2018. Retrieved 14 April 2019.
  5. Steiner, Ronald (June 2015). "Anjaneyāsana - Learning devotion from Hanuman". Yoga Aktuell (in German) (92 June/July 2015). Retrieved 23 January 2019.
  6. Mehta, Silva; Mehta, Mira; Mehta, Shyam (1990). Yoga: The Iyengar Way. Dorling Kindersley. p. 36.
  7. "Revolved Crescent Lunge | Parivṛtta Aṅjaneyāsana". Pocket Yoga. Retrieved 16 December 2018.
  8. "Asanas: Standing Poses". About-Yoga.com. Retrieved 16 December 2018.
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