Pass vals as ndarray

bicytok/MBmodel.py

@@ -16,7 +16,7 @@ def cytBindingModel(
     """Runs binding model for a given mutein, valency, dose, and cell type."""
     # Check that values are in correct placement, can invert
     Kx = getKxStar()
-    ligandConc = dose / (vals[0] * 1e9)
+    ligandConc = dose / (vals[0][0] * 1e9)
 
     output = polyc(ligandConc, Kx, recCount, vals, recXaffs)[0]
 

bicytok/figures/figure1.py

@@ -67,6 +67,7 @@ def makeFigure():
             targets.append(target)
             valencies.append(valency)
             naming.append("{} ({})".format(target, valency))
+        valencies = np.array([valencies])
 
         for _, dose in enumerate(doseVec):
             optParams = optimizeDesign(

bicytok/figures/figure3.py

@@ -38,7 +38,7 @@ def makeFigure():
     epitopesDF = getSampleAbundances(epitopes, cells)
 
     bindings = get_cell_bindings(
-        epitopesDF, secondary, ["CD25", epitope], affs, 0.1, [valency, valency, valency]
+        epitopesDF, secondary, ["CD25", epitope], affs, 0.1, np.ndarray([valency, valency, valency])
     )
     bindings["Percent Bound of Signal Receptor"] = (
         bindings["Receptor Bound"] / bindings[secondary]

bicytok/figures/figure4.py

@@ -54,11 +54,11 @@ def makeFigure():
             if i == j:
                 targetsBoth = [target1]
                 optAffs = [8.0, 8.0]
-                valenciesBoth = [signal[1], valencies[i]]
+                valenciesBoth = np.array([[signal[1], valencies[i]]])
             else:
                 targetsBoth = [target1, target2]
                 optAffs = [8.0, 8.0, 8.0]
-                valenciesBoth = [signal[1], valencies[i], valencies[j]]
+                valenciesBoth = np.array([[signal[1], valencies[i], valencies[j]]])
 
             optParams = optimizeDesign(
                 signal[0],

bicytok/figures/figure5.py

@@ -54,7 +54,7 @@ def makeFigure():
     for val in valencies:
         prevOptAffs = [8.0, 8.0, 8.0]
         for targets in allTargets:
-            vals = [signal_valency, val, val]
+            vals = np.array([[signal_valency, val, val]])
 
             optParams = optimizeDesign(
                 signal_receptor,

bicytok/selectivityFuncs.py

@@ -55,7 +55,7 @@ def minSelecFunc(
     targRecs: pd.DataFrame,
     offTRecs: pd.DataFrame,
     dose: float,
-    vals: list,
+    vals: np.ndarray,
 ):
     """Serves as the function which will have its return value minimized to get optimal selectivity
     To be used in conjunction with optimizeDesign()
@@ -95,7 +95,7 @@ def optimizeDesign(
     offTCells: list,
     selectedDF: pd.DataFrame,
     dose: float,
-    valencies: list,
+    valencies: np.ndarray,
     prevOptAffs: list,
     cellCat="CellType2",
 ):

bicytok/test/test_model.py

@@ -23,5 +23,5 @@ def test_optimize_design():
         selectedDF=epitopesDF,
         dose=0.1,
         valencies=[2, 2],
-        prevOptAffs=[8.0, 8.0],
+        prevOptAffs=np.array([[8.0, 8.0]]),
     )