add some type annotations

neurodesign/generate.py

@@ -65,9 +65,9 @@ def _generate_order_items(probabilities, items):
 def iti(
     ntrials: int,
     model: str,
-    min: float = None,
-    mean: float = None,
-    max: float = None,
+    min: float | None = None,
+    mean: float | None = None,
+    max: float | None = None,
     lam=None,
     resolution: float = 0.1,
     seed: int | None = 1234,

neurodesign/msequence.py

@@ -17,7 +17,7 @@ def __init__(self):
         # read in taps file and count
         self.tapsfnc()
 
-    def GenMseq(self, mLen, stimtypeno, seed):
+    def GenMseq(self, mLen: int, stimtypeno: int, seed: int):
         """Generate a random msequence given the length of the desired sequence \
            and the number of different values.
 

neurodesign/report.py

@@ -22,10 +22,12 @@
     Table,
 )
 
+from neurodesign import Optimisation
+
 plt.switch_backend("agg")
 
 
-def make_report(population, outfile: str | Path = "NeuroDesign.pdf"):
+def make_report(population: Optimisation, outfile: str | Path = "NeuroDesign.pdf"):
     """Create a report of a finished design optimisation."""
     if not isinstance(population.cov, np.ndarray):
         population.evaluate()
@@ -179,15 +181,16 @@ def _form_xo_reader(imgdata):
 
 
 class PdfImage(Flowable):
-    def __init__(self, img_data, width=200, height=200):
+    def __init__(self, img_data, width: int = 200, height: int = 200):
         self.img_width = width
         self.img_height = height
         self.img_data = img_data
 
-    def wrap(self, width, height):
+    def wrap(self):
         return self.img_width, self.img_height
 
     def drawOn(self, canv, x, y, _sW=0):
+        # TODO TA_CENTER TA_RIGHT TA_LEFT are undefined
         if _sW > 0 and hasattr(self, "hAlign"):
             a = self.hAlign
             if a in ("CENTER", "CENTRE", TA_CENTER):