#17 Merge pull request from deshima-dev/astropenguin/issue15

Fix to handle cases where fit errors are None

.github/workflows/tests.yaml

@@ -19,12 +19,13 @@ jobs:
       matrix:
         python: ["3.9", "3.10", "3.11", "3.12"]
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           submodules: true
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python }}
+      - run: pip install setuptools==70.0.0  # temporary
       - run: pip install poetry==1.8.3 && poetry install
       - run: deshima-rawdata download 20231117042946 -e
       - run: python scripts/aste/Configure.py cosmos_20231117042946 out

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "kidanalysis-delft"
-version = "2024.6.0"
+version = "2024.7.0"
 description = "KID analysis scripts made in Delft"
 authors = ["Kenichi Karatsu", "Shinji Fujita"]
 maintainers = ["Akio Taniguchi <[email protected]>"]

scripts/aste/FitSweep.py

@@ -190,7 +190,9 @@ def Calc(kids, nfwhm=5, fitter='gaolinbg', verbose=0, force=False,
                 dQi_ = r.params['Qi'].stderr
 
                 #if dfr_/fr_<0. or dQr_/Qr_<0. or dQc_/Qc_<0. or dQi_/Qi_<0.:
-                if dfr_/fr_<0. or dQr_/Qr_<0. or dQc_/Qc_<0.:
+                if dfr_ is None or dQr_ is None or dQc_ is None or dQi_ is None:
+                    failed.append(i)
+                elif dfr_/fr_<0. or dQr_/Qr_<0. or dQc_/Qc_<0.:
                     failed.append(i)
                 elif fr_!=fr_ or dfr_!=dfr_ or Qr_!=Qr_ or dQr_!=dQr_: # reject Nan
                     failed.append(i)

scripts/libs/fit/fitresult.py

@@ -19,10 +19,14 @@ def __init__(self, minimized=None, error_given=None, function=None):
         else:
             self.error_is_asymptotic = True
 
+        if minimized.covar is not None:
+            self.covar = minimized.covar
+        else:
+            self.covar = np.full([minimized.nvarys] * 2, np.nan)
+
         self.function = function
         self.init_vals = minimized.init_vals
         self.params = minimized.params
-        self.covar  = minimized.covar
         self.info   = dict((k, getattr(minimized, k)) for k in
                            # json-friendly data taken from
                            # http://lmfit.github.io/lmfit-py/fitting.html#MinimizerResult