got plotting mostly set up but encountered error

cal_and_val/thermal/cal_hev.py

@@ -82,10 +82,13 @@
 assert len(cyc_files_for_cal) > 0
 print("\ncyc_files_for_cal:\n", '\n'.join([cf.name for cf in cyc_files_for_cal]), sep='')
 
+time_column = "Time[s]_RawFacilities"
+speed_column = "Dyno_Spd[mph]"
+
 def df_to_cyc(df: pd.DataFrame) -> fsim.Cycle:
     cyc_dict = {
-        "time_seconds": df["Time[s]_RawFacilities"].to_list(),
-        "speed_meters_per_second": (df["Dyno_Spd[mph]"] * mps_per_mph).to_list(),
+        "time_seconds": df[time_column].to_list(),
+        "speed_meters_per_second": (df[speed_column] * mps_per_mph).to_list(),
         "temp_amb_air_kelvin": (df["Cell_Temp[C]"] + celsius_to_kelvin_offset).to_list(),
         # TODO: pipe solar load from `Cycle` into cabin thermal model
         # TODO: use something (e.g. regex) to determine solar load
@@ -122,9 +125,9 @@ def veh_init(cyc_file_stem: str, dfs: Dict[str, pd.DataFrame]) -> fsim.Vehicle:
 }
 for key, df_for_cal in dfs_for_cal.items():
     # filter out "before" time
-    df_for_cal = df_for_cal[df_for_cal["Time[s]_RawFacilities"] >= 0.0]
+    df_for_cal = df_for_cal[df_for_cal[time_column] >= 0.0]
     # TODO: figure out if we should use an integrator for resampling rate vars
-    # df_for_cal = df_for_cal.set_index("Time[s]_RawFacilities")
+    # df_for_cal = df_for_cal.set_index(time_column)
     # df_for_cal = df_for_cal.resample("1s", origin="start").bfill()
     df_for_cal = df_for_cal[::10]
     df_for_cal.reset_index(inplace=True)
@@ -159,9 +162,9 @@ def veh_init(cyc_file_stem: str, dfs: Dict[str, pd.DataFrame]) -> fsim.Vehicle:
 }
 for key, df_for_val in dfs_for_val.items():
     # filter out "before" time
-    df_for_val = df_for_val[df_for_val["Time[s]_RawFacilities"] >= 0.0]
+    df_for_val = df_for_val[df_for_val[time_column] >= 0.0]
     # TODO: figure out if we should use an integrator for resampling rate vars
-    # df_for_val = df_for_val.set_index("Time[s]_RawFacilities")
+    # df_for_val = df_for_val.set_index(time_column)
     # df_for_val = df_for_val.resample("1s", origin="start").bfill()
     df_for_val = df_for_val[::10]
     df_for_val.reset_index(inplace=True)

cal_and_val/thermal/val_hev.py

@@ -1,6 +1,7 @@
 import pandas as pd
+import matplotlib.pyplot as plt
 
-from cal_hev import cal_mod_obj, val_mod_obj, save_path
+from cal_hev import cal_mod_obj, val_mod_obj, save_path, time_column, mps_per_mph, speed_column
 
 res_df = pd.read_csv(save_path / "pymoo_res_df.csv")
 res_df['euclidean'] = (
@@ -9,4 +10,46 @@
 best_df = res_df.iloc[best_row, :]
 param_vals = res_df.iloc[best_row, : len(cal_mod_obj.param_fns)].to_numpy()
 
-# plotting 
+# getting the solved models
+(errors_cal, sds_cal) = cal_mod_obj.get_errors(
+    sim_drives=cal_mod_obj.update_params(param_vals),
+    return_mods=True,
+)
+(errors_val, sds_val) = val_mod_obj.get_errors(
+    sim_drives=val_mod_obj.update_params(param_vals),
+    return_mods=True,
+)
+
+# plotting
+plot_save_path = save_path / "plots"
+plot_save_path.mkdir(exist_ok=True)
+
+for ((key, df_cal), sd_cal) in zip(cal_mod_obj.dfs.items(), sds_cal):
+    for obj_fn in cal_mod_obj.obj_fns:
+        fig, ax = plt.subplots(2, 1, sharex=True)
+        ax[0].plot(
+            sd_cal['veh']['history']['time_seconds'],
+            obj_fn[0](sd_cal),
+            label='mod',
+        )
+        ax[0].plot(
+            df_cal[time_column],
+            obj_fn[1](df_cal),
+            label='exp',
+        )
+        ax[0].legend()
+        ax[0].set_ylabel(obj_fn[0].__name__)
+
+        ax[1].plot(
+            sd_cal['veh']['history']['time_seconds'],
+            sd_cal['veh']['history']['speed_meters_per_second'],
+            label='mod',
+        )
+        ax[1].plot(
+            df_cal[time_column],
+            df_cal[speed_column] * mps_per_mph,
+            label='exp',
+        )
+        ax[1].legend()
+        ax[1].set_ylabel("Speed [m/s]")
+