m1

pages/2_Scenario_Planner.py

@@ -618,11 +618,22 @@ def upload_file_prospects_calc(df):
     df.index = df.channel
 #     # print(param_dicts)
     for col in df.channel:
-        
-        x = (df["Spends"][col]/104 - param_dicts["x_min"][col])/(param_dicts["x_max"][col]-param_dicts["x_min"][col])
-        x_out = x**param_dicts["n"][col]/(param_dicts["Kd"][col]**param_dicts["n"][col]+ x**param_dicts["n"][col])
-        x_out_inv = (x_out*(param_dicts["y_max"][col]-param_dicts["y_min"][col])+param_dicts["y_min"][col])*104
-        df["Prospects"][col] = x_out_inv
+        x = df["Spends"][col]
+        dividing_rate = 104
+        # st.write(x)
+        x_inp = ( x/dividing_rate- param_dicts["x_min"][col]) / (param_dicts["x_max"][col] - param_dicts["x_min"][col])
+        x_out = x_inp**param_dicts["n"][col] / (param_dicts["Kd"][col]**param_dicts["n"][col] + x_inp**param_dicts["n"][col]) #self.hill_equation(x_inp,Kd, n)
+            # # print("x_out",x_out)
+
+
+        x_val_inv = (x_out*param_dicts["x_max"][col] + (1 - x_out) * param_dicts["x_min"][col])
+        sales = (x_val_inv*param_dicts["y_min"][col]/param_dicts["y_max"][col])*dividing_rate
+            # sales = ((x_max - x_min)*x_out + x_min)*dividing_rate
+
+        # x = (df["Spends"][col]/104 - param_dicts["x_min"][col])/(param_dicts["x_max"][col]-param_dicts["x_min"][col])
+        # x_out = x**param_dicts["n"][col]/(param_dicts["Kd"][col]**param_dicts["n"][col]+ x**param_dicts["n"][col])
+        # x_out_inv = (x_out*(param_dicts["y_max"][col]-param_dicts["y_min"][col])+param_dicts["y_min"][col])*104
+        df["Prospects"][col] = sales
     # # print(df)
     return df
     

summary_df.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1d9e7203dcb997451266eb58fb210efd25ca008c970664d12d134f29c4d069c6
+oid sha256:1a806d99197353280a0fa5c5d94ade34c962560041ca3986d304dea5f1bb4667
 size 1822

utilities.py

@@ -358,9 +358,7 @@ def initialize_data(
     other_contributions = (
         output_df.drop([*output_cols], axis=1).sum(axis=1, numeric_only=True).values
     )
-    correction = output_df.drop("Date", axis=1).sum(axis=1).values - (
-        sales + other_contributions
-    )
+    correction = output_df.drop("Date", axis=1).sum(axis=1).values - (sales + other_contributions)
     # # print(other_contributions)
     # # print(correction)
     scenario = Scenario(