From a2ab3cf6b9b949c6c144ce970789ef765d011b2f Mon Sep 17 00:00:00 2001
From: Jeffrey Larson <jmlarson@anl.gov>
Date: Wed, 24 Feb 2021 11:27:11 -0600
Subject: [PATCH] Removing np.float and np.int usage

---
 dfols/hessian.py      | 2 +-
 dfols/model.py        | 2 +-
 dfols/solver.py       | 6 +++---
 dfols/trust_region.py | 2 +-
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/dfols/hessian.py b/dfols/hessian.py
index 8e93ba2..0329e9e 100644
--- a/dfols/hessian.py
+++ b/dfols/hessian.py
@@ -39,7 +39,7 @@ class Hessian(object):
     def __init__(self, n, vals=None):
         self.n = n
         if vals is None:
-            self.hq = np.zeros((n * (n + 1) // 2,), dtype=np.float)
+            self.hq = np.zeros((n * (n + 1) // 2,), dtype=float)
         else:
             assert isinstance(vals, np.ndarray), "Can only set Hessian from NumPy array"
             assert len(vals.shape) in [1, 2], "Can only set Hessian from vector or matrix"
diff --git a/dfols/model.py b/dfols/model.py
index 4adc190..8f1901c 100644
--- a/dfols/model.py
+++ b/dfols/model.py
@@ -71,7 +71,7 @@ def __init__(self, npt, x0, r0, xl, xu, r0_nsamples, n=None, m=None, abs_tol=1e-
         self.fval = np.inf * np.ones((npt, ))  # overall objective value for each xpt
         self.fval[0] = sumsq(r0)
         self.kopt = 0  # index of current iterate (should be best value so far)
-        self.nsamples = np.zeros((npt,), dtype=np.int)  # number of samples used to evaluate objective at each point
+        self.nsamples = np.zeros((npt,), dtype=int)  # number of samples used to evaluate objective at each point
         self.nsamples[0] = r0_nsamples
         self.fbeg = self.fval[0]  # f(x0), saved to check for sufficient reduction
 
diff --git a/dfols/solver.py b/dfols/solver.py
index 1f8e8e6..8c66f35 100644
--- a/dfols/solver.py
+++ b/dfols/solver.py
@@ -852,7 +852,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
 
 def solve(objfun, x0, args=(), bounds=None, npt=None, rhobeg=None, rhoend=1e-8, maxfun=None, nsamples=None, user_params=None,
           objfun_has_noise=False, scaling_within_bounds=False, do_logging=True, print_progress=False):
-    x0 = x0.astype(np.float)
+    x0 = x0.astype(np.float64)
     n = len(x0)
 
     # Set missing inputs (if not specified) to some sensible defaults
@@ -861,8 +861,8 @@ def solve(objfun, x0, args=(), bounds=None, npt=None, rhobeg=None, rhoend=1e-8,
         xu = None
     else:
         assert len(bounds) == 2, "bounds must be a 2-tuple of (lower, upper), where both are arrays of size(x0)"
-        xl = bounds[0].astype(np.float) if bounds[0] is not None else None
-        xu = bounds[1].astype(np.float) if bounds[1] is not None else None
+        xl = bounds[0].astype(np.float64) if bounds[0] is not None else None
+        xu = bounds[1].astype(np.float64) if bounds[1] is not None else None
 
     if (xl is None or xu is None) and scaling_within_bounds:
         scaling_within_bounds = False
diff --git a/dfols/trust_region.py b/dfols/trust_region.py
index d360974..ec8c975 100644
--- a/dfols/trust_region.py
+++ b/dfols/trust_region.py
@@ -103,7 +103,7 @@ def trsbox(xopt, g, H, sl, su, delta, use_fortran=USE_FORTRAN):
     iterc = 0
     nact = 0  # number of fixed variables
 
-    xbdi = np.zeros((n,), dtype=np.int)  # fix x_i at bounds? [values -1, 0, 1]
+    xbdi = np.zeros((n,), dtype=int)  # fix x_i at bounds? [values -1, 0, 1]
     xbdi[(xopt <= sl) & (g >= 0.0)] = -1
     xbdi[(xopt >= su) & (g <= 0.0)] = 1