From 6f1c12e8120b8e4995bc0fdc1c9968a8df6a502b Mon Sep 17 00:00:00 2001
From: Jacan Chaplais <jacan.chaplais@gmail.com>
Date: Sat, 25 Nov 2023 10:23:32 +0000
Subject: [PATCH] added caching to numba compiled funcs

---
 graphicle/calculate.py | 28 ++++++++++++++++++----------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/graphicle/calculate.py b/graphicle/calculate.py
index a29e82f..b395c45 100644
--- a/graphicle/calculate.py
+++ b/graphicle/calculate.py
@@ -425,7 +425,7 @@ def flow_trace(
     return traces
 
 
-@nb.njit("float64[:](float64[:], float64[:], float64)")
+@nb.njit("float64[:](float64[:], float64[:], float64)", cache=True)
 def _rapidity(
     energy: base.DoubleVector, z: base.DoubleVector, zero_tol: float
 ) -> base.DoubleVector:
@@ -457,7 +457,7 @@ def _rapidity(
     return rap
 
 
-@nb.vectorize("float64(float64, float64)")
+@nb.vectorize("float64(float64, float64)", cache=True)
 def _root_diff_two_squares(
     x1: base.DoubleUfunc, x2: base.DoubleUfunc
 ) -> base.DoubleUfunc:
@@ -494,6 +494,7 @@ def _root_diff_two_squares(
 @nb.njit(
     "float64[:, :](float64[:], float64[:], complex128[:], complex128[:])",
     parallel=True,
+    cache=True,
 )
 def _delta_R(
     rapidity_1: base.DoubleVector,
@@ -532,7 +533,7 @@ def _delta_R(
     return result
 
 
-@nb.njit("float64[:, :](float64[:], complex128[:])", parallel=True)
+@nb.njit("float64[:, :](float64[:], complex128[:])", parallel=True, cache=True)
 def _delta_R_symmetric(
     rapidity: base.DoubleVector, xy_pol: base.ComplexVector
 ) -> base.DoubleVector:
@@ -569,7 +570,7 @@ def _delta_R_symmetric(
     return result
 
 
-@nb.njit("float32[:](bool_[:, :])", parallel=True)
+@nb.njit("float32[:](bool_[:, :])", parallel=True, cache=True)
 def _clust_coeffs(adj: base.BoolVector) -> base.FloatVector:
     num_nodes = adj.shape[0]
     coefs = np.empty(num_nodes, dtype=np.float32)
@@ -664,7 +665,7 @@ def aggregate_momenta(
     return momentum_class(list(it.chain.from_iterable(pmu_sums)))
 
 
-@nb.njit("float64(float64, float64, float64)")
+@nb.njit("float64(float64, float64, float64)", cache=True)
 def _three_norm(x: float, y: float, z: float) -> float:
     max_component = max(abs(x), abs(y), abs(z))
     max_recip = 1.0 / max_component
@@ -674,7 +675,7 @@ def _three_norm(x: float, y: float, z: float) -> float:
     return max_component * np.sqrt(x * x + y * y + z * z)
 
 
-@nb.njit("float64[:](float64[:])")
+@nb.njit("float64[:](float64[:])", cache=True)
 def _angles_to_axis(axis_angles: base.DoubleVector) -> base.DoubleVector:
     """Given the azimuth and inclination angles, return the Cartesian
     coordinates a unit vector.
@@ -699,7 +700,10 @@ def _angles_to_axis(axis_angles: base.DoubleVector) -> base.DoubleVector:
     )
 
 
-@nb.njit(nb.types.Tuple((nb.float64, nb.float64[:]))(nb.float64[:], PMU_DTYPE))
+@nb.njit(
+    nb.types.Tuple((nb.float64, nb.float64[:]))(nb.float64[:], PMU_DTYPE),
+    cache=True,
+)
 def _thrust_with_grad(
     axis_angles: base.DoubleVector, momenta: base.VoidVector
 ) -> ty.Tuple[float, base.DoubleVector]:
@@ -855,7 +859,10 @@ def thrust(
     return thrust_val
 
 
-@nb.njit(nb.types.Tuple((nb.float64, nb.float64[:]))(nb.float64[:], PMU_DTYPE))
+@nb.njit(
+    nb.types.Tuple((nb.float64, nb.float64[:]))(nb.float64[:], PMU_DTYPE),
+    cache=True,
+)
 def _spherocity_with_grad(
     axis_angles: base.DoubleVector, momenta: base.VoidVector
 ) -> ty.Tuple[float, base.DoubleVector]:
@@ -1024,7 +1031,7 @@ def spherocity(
     return sph_val
 
 
-@nb.njit(nb.float64(PMU_DTYPE))
+@nb.njit(nb.float64(PMU_DTYPE), cache=True)
 def _c_parameter(momenta: base.VoidVector) -> float:
     output = norm_sum = 0.0
     for idx_i, pmu_i in enumerate(momenta):
@@ -1071,7 +1078,8 @@ def c_parameter(pmu: "MomentumArray") -> float:
     [
         "float64(bool_[:], bool_[:], Optional(float64[:]))",
         "float64(bool_[:], bool_[:], Omitted(None))",
-    ]
+    ],
+    cache=True,
 )
 def _jaccard_distance(
     u: base.BoolVector,