1 files changed, 33 insertions, 0 deletions
diff --git a/utils/sddm/utils.py b/utils/sddm/utils.py
index 2b67e63..e2e2238 100644
--- a/utils/sddm/utils.py
+++ b/utils/sddm/utils.py
@@ -11,6 +11,39 @@ def fast_cdf(x,loc,scale):
     """
     return np.interp((x-loc)/scale,_fast_cdf_x,_fast_cdf_y)
 
+try:
+    from numba import vectorize, float64, njit
+
+    print("numba found! Loading optimized version of fast_cdf()")
+
+    @njit
+    def interp(x,xp,yp):
+        n = len(xp)
+
+        if x <= xp[0]:
+            return yp[0]
+
+        idx = 1.0/(xp[1]-xp[0])
+
+        i = int((x-xp[0])*idx)
+
+        if i > n-2:
+            return yp[n-1]
+
+        return yp[i] + (yp[i+1]-yp[i])*(x-xp[i])*idx
+
+    @vectorize([float64(float64, float64, float64)])
+    def fast_cdf(x,loc,scale):
+        # Here, we optimize for the case that the value is out of bounds since that
+        # is the most likely. We do this check here to avoid an expensive division.
+        if x - loc > _fast_cdf_x[-1]*scale:
+            return _fast_cdf_y[-1]
+        elif x - loc < _fast_cdf_x[0]*scale:
+            return _fast_cdf_y[0]
+        return interp((x-loc)/scale,_fast_cdf_x,_fast_cdf_y)
+except ImportError:
+    print("No numba, loading slower version of fast_cdf()")
+
 # Energy bias of reconstruction relative to Monte Carlo.
 #
 # Note: You can recreate this array using: