MAINT: NPV: Remove typed constants

These were originally added when the code was written as one function without numba. As numba
is now being used, and the functions are therefore seperated. This is no longer required and
makes the code more complex.

Author: Kai-Striega

numpy_financial/_financial.py

@@ -857,23 +857,23 @@ def irr(values, *, guess=None, tol=1e-12, maxiter=100, raise_exceptions=False):
 
 
 @nb.njit(parallel=True)
-def _npv_native(rates, values, out, zero, one):
+def _npv_native(rates, values, out):
     for i in nb.prange(rates.shape[0]):
         for j in nb.prange(values.shape[0]):
-            acc = zero
+            acc = 0.0
             for t in range(values.shape[1]):
-                acc += values[j, t] / ((one + rates[i]) ** t)
+                acc += values[j, t] / ((1.0 + rates[i]) ** t)
             out[i, j] = acc
 
 
 # We require ``forceobj=True`` here to support decimal.Decimal types
 @nb.jit(forceobj=True)
-def _npv_decimal(rates, values, out, zero, one):
+def _npv_decimal(rates, values, out):
     for i in range(rates.shape[0]):
         for j in range(values.shape[0]):
-            acc = zero
+            acc = Decimal("0.0")
             for t in range(values.shape[1]):
-                acc += values[j, t] / ((one + rates[i]) ** t)
+                acc += values[j, t] / ((Decimal("1.0") + rates[i]) ** t)
             out[i, j] = acc
 
 
@@ -972,16 +972,14 @@ def npv(rate, values):
     if dtype == Decimal:
         rates = _to_decimal_array_1d(rates)
         values = _to_decimal_array_2d(values)
-    zero = dtype("0.0")
-    one = dtype("1.0")
 
     shape = _get_output_array_shape(rates, values)
     out = np.empty(shape=shape, dtype=dtype)
 
     if dtype == Decimal:
-        _npv_decimal(rates, values, out, zero, one)
+        _npv_decimal(rates, values, out)
     else:
-        _npv_native(rates, values, out, zero, one)
+        _npv_native(rates, values, out)
 
     return _return_ufunc_like(out)