04_19_2024: cleanup + pre-commit

.pre-commit-config.yaml

@@ -0,0 +1,5 @@
+repos:
+- repo: https://github.com/astral-sh/ruff-pre-commit
+  rev: v0.4.1
+  hooks:
+    - id: ruff

pyproject.toml

@@ -57,3 +57,8 @@ disable=[
     "redefined-outer-name",
     "not-callable"
 ]
+
+[tool.pytest.ini_options]
+filterwarnings = [
+    "ignore::DeprecationWarning"
+]

twiss/__init__.py

@@ -2,7 +2,35 @@
 Version and aliases
 
 """
-__version__ = '0.2.4'
+__version__ = '0.2.5'
+
+__all__ = [
+    'symplectify',
+    'is_symplectic',
+    'rotation',
+    'twiss',
+    'is_stable',
+    'propagate',
+    'advance',
+    'normal_to_wolski',
+    'wolski_to_normal',
+    'parametric',
+    'lb_normal',
+    'cs_normal',
+    'wolski_to_lb',
+    'lb_to_wolski',
+    'wolski_to_cs',
+    'cs_to_wolski',
+    'invariant',
+    'lb_invariant',
+    'cs_invariant',
+    'transport',
+    'wolski_transport',
+    'lb_transport',
+    'cs_transport',
+    'momenta',
+    'normal'
+]
 
 from twiss.matrix import symplectify
 from twiss.matrix import is_symplectic

twiss/distribution.py

@@ -49,9 +49,9 @@ def normal(mean:Tensor,
     >>> wx, wy = ws
     >>> torch.allclose(db.covariance_matrix, ex*wx + ey*wy)
     True
-    >>> torch.allclose(db.sample((2**22, )).T.cov(), db.covariance_matrix)
+    >>> torch.allclose(db.sample((2**20, )).T.cov(), db.covariance_matrix)
     True
-    
+
     """
     for _ in wolski:
         emittance = emittance.unsqueeze(-1)

twiss/wolski.py

@@ -124,18 +124,18 @@ def twiss(m:Tensor, *,
         m_s = torch.block_diag(*[b_s for _ in range(d)])
         m_c = torch.block_diag(*[b_c for _ in range(d)])
 
-    l, v = torch.linalg.eig(m)
-    l, v = l.reshape(d, -1), v.T.reshape(d, -1, 2*d)
+    e, v = torch.linalg.eig(m)
+    e, v = e.reshape(d, -1), v.T.reshape(d, -1, 2*d)
 
     u = torch.zeros_like(v)
     for i, (v1, v2) in enumerate(v):
         u[i] = v[i]/(-1j*(v1 @ m_s.to(cdtype) @ v2)).abs().sqrt()
 
-    k = torch.zeros_like(l)
+    k = torch.zeros_like(e)
     v = torch.zeros_like(u)
     for i in range(d):
-        o = torch.clone(l[i].log()).imag.argsort()
-        k[i], v[i] = l[i, o], u[i, o]
+        o = torch.clone(e[i].log()).imag.argsort()
+        k[i], v[i] = e[i, o], u[i, o]
 
     t = 1.0 - k.log().abs().mean(-1)/(2.0*pi)
 
@@ -273,6 +273,6 @@ def advance(n:Tensor,
     """
     d = len(n) // 2
     i = torch.arange(d, dtype=torch.int64, device=n.device)
-    l = m @ n
-    f = mod(torch.arctan2(l[2*i, 2*i + 1], l[2*i, 2*i]), 2.0*pi)
-    return f, l @ rotation(*(-f))
+    k = m @ n
+    f = mod(torch.arctan2(k[2*i, 2*i + 1], k[2*i, 2*i]), 2.0*pi)
+    return f, k @ rotation(*(-f))