From b81d903c888d98183da63e6cc3ada70668b349ef Mon Sep 17 00:00:00 2001
From: wernerd-cern <138122408+wernerd-cern@users.noreply.github.com>
Date: Tue, 15 Aug 2023 23:56:39 +0200
Subject: [PATCH] fix: Correct handling of strategy 0 for Minuit optimizer
 (#2277)

* Properly guard the `strategy` option for the Minuit optimizer in the case that
  `strategy` option of 0 is used.
  Without checking `self.strategy is not None` the `if self.strategy` for
  self.strategy==0 evaluates to truthiness of False, giving the wrong value of
  `not do_grad` for do_grad of False.
   - Amends PR https://github.com/scikit-hep/pyhf/pull/1183
* Use the corrected behavior to improve testing of default in test_minuit_strategy_global.
* Add Daniel Werner to contributors list.
---
 docs/contributors.rst           | 1 +
 src/pyhf/optimize/opt_minuit.py | 2 +-
 tests/test_optim.py             | 3 +--
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/contributors.rst b/docs/contributors.rst
index 7fdf9fcce0..08a828bf19 100644
--- a/docs/contributors.rst
+++ b/docs/contributors.rst
@@ -31,3 +31,4 @@ Contributors include:
 - Jerry Ling
 - Nathan Simpson
 - Beojan Stanislaus
+- Daniel Werner
diff --git a/src/pyhf/optimize/opt_minuit.py b/src/pyhf/optimize/opt_minuit.py
index 49ee177ae6..9cb0ce3e14 100644
--- a/src/pyhf/optimize/opt_minuit.py
+++ b/src/pyhf/optimize/opt_minuit.py
@@ -102,7 +102,7 @@ def _minimize(
         # 0: Fast, user-provided gradient
         # 1: Default, no user-provided gradient
         strategy = options.pop(
-            'strategy', self.strategy if self.strategy else not do_grad
+            'strategy', self.strategy if self.strategy is not None else not do_grad
         )
         tolerance = options.pop('tolerance', self.tolerance)
         if options:
diff --git a/tests/test_optim.py b/tests/test_optim.py
index 95fe13f521..3efb8ff0e4 100644
--- a/tests/test_optim.py
+++ b/tests/test_optim.py
@@ -182,10 +182,9 @@ def test_minuit_strategy_global(mocker, backend, strategy):
     m = pyhf.simplemodels.uncorrelated_background([50.0], [100.0], [10.0])
     data = pyhf.tensorlib.astensor([125.0] + m.config.auxdata)
 
-    do_grad = pyhf.tensorlib.default_do_grad
     pyhf.infer.mle.fit(data, m)
     assert spy.call_count == 1
-    assert spy.spy_return.minuit.strategy == strategy if do_grad else 1
+    assert spy.spy_return.minuit.strategy == strategy
 
     pyhf.infer.mle.fit(data, m, strategy=0)
     assert spy.call_count == 2