adding: recorder.plot + include set type jsonconversion

nrv/backend/_NRV_Class.py

@@ -252,7 +252,6 @@ def save(self, save=False, fname="nrv_save.json", blacklist=[], **kwargs) -> dic
                     key_dic[key] = {}
                     for i in self.__dict__[key]:
                         key_dic[key][i] = self.__dict__[key][i].save(**kwargs)
-
                 else:
                     key_dic[key] = deepcopy(self.__dict__[key])
         if save:
@@ -286,6 +285,8 @@ def load(self, data, blacklist={}, **kwargs) -> None:
                     self.__dict__[key] = load_any(key_dic[key], **kwargs)
                 elif isinstance(self.__dict__[key], np.ndarray):
                     self.__dict__[key] = np.array(key_dic[key])
+                elif isinstance(self.__dict__[key], set):
+                    self.__dict__[key] = set(key_dic[key])
                 elif is_empty_iterable(key_dic[key]):
                     self.__dict__[key] = eval(self.__dict__[key].__class__.__name__)()
                 else:

nrv/backend/_file_handler.py

@@ -167,7 +167,7 @@ def json_load(filename):
         results = json.load(file_to_read)
     return results
 
-
+## TODO add NRV_decoder to simplify NRV_class save/load methode
 class NRV_Encoder(json.JSONEncoder):
     """
     Json encoding class, specific for NRV2 axon
@@ -183,12 +183,16 @@ def default(self, obj):
             result = float(obj)
         elif isinstance(obj, np.ndarray):
             result = obj.tolist()
+        elif isinstance(obj, set):
+            result = list(obj)
         else:
             # Let the base class Encoder handle the object
             result = json.JSONEncoder.default(self, obj)
         return result
 
 
+
+
 ######################
 ## DXF related code ##
 ######################

nrv/fmod/FEM/mesh_creator/_NerveMshCreator.py

@@ -1205,12 +1205,13 @@ def add_CUFF(
                 contact_thickness = self.Nerve_D * 0.1
         if insulator_thickness is None:
             insulator_thickness = min(
-                5 * contact_thickness, 0.4 * (self.Outer_D - self.Nerve_D) / 2
+                5 * contact_thickness, 0.4 * abs(self.Outer_D - self.Nerve_D) / 2
             )
+            if insulator_thickness < 0:
+                insulator_thickness = 5 * contact_thickness
         if insulator_length is None:
             insulator_length = 2 * contact_length
         # self.reshape_outerBox(tresholded_res=True)
-
         if ID is not None:
             self.electrodes[ID]["kwargs"]["contact_length"] = contact_length
             self.electrodes[ID]["kwargs"]["contact_thickness"] = contact_thickness

nrv/fmod/_recording.py

@@ -5,6 +5,7 @@
 import faulthandler
 
 import numpy as np
+import matplotlib.pyplot as plt
 
 from ..backend._log_interface import rise_error, rise_warning
 from ..backend._MCore import MCH, synchronize_processes
@@ -795,3 +796,19 @@ def gather_all_recordings(self, results:list[dict]):
                 for rec in reclist:
                     point.recording += np.array(rec["recording_points"][i]["recording"])
 
+    def plot(self, axes: plt.axes, points:int|np.ndarray|None=None,color: str = "k", **kwgs) -> None:
+        if self.t is None:
+            rise_warning("empty recorder canot be ploted")
+        else:
+            if points is None:
+                points = np.arange(len(self.recording_points))
+            if np.iterable(points):
+                for i_pts in points:
+                    self.plot(axes=axes, points=i_pts, color=color, **kwgs)
+            else:
+                if points > len(self.recording_points):
+                    rise_warning(f"recording point {points} does not exits in recorder, and so canot be ploted")
+                else:
+                    axes.plot(self.t, self.recording_points[points].recording, color=color,**kwgs)
+
+

nrv/nmod/_fascicles.py

@@ -1531,7 +1531,6 @@ def simulate(
                     "save anyway",
                 )
                 self.save_results = True
-
         self.__init_axon_postprocessing()
         if len(self.NoR_relative_position) == 0:
             self.generate_random_NoR_position()

nrv/ui/_axon_postprocessing.py

@@ -446,7 +446,7 @@ def block(my_dict, position_key=None, t_start=0, t_stop=0):
                 else:
                     return False
     else:
-        print("intra_stim_positions is not in dictionnary")
+        pass_info("intra_stim_positions is not in dictionnary")
 
 
 def max_spike_position(blocked_spike_positionlist, position_max, spike_begin="down"):
@@ -1227,8 +1227,10 @@ def sample_keys(results:axon_results, keys_to_sample:str|set[str]={}, t_start_re
     axon_results
         updated results.
     """
-    if not results["record_g_mem"]:
-        rise_error("gmem not recorded nothing will be done")
+    if isinstance(keys_to_sample, str):
+        keys_to_sample = {keys_to_sample}
+    if len(set(keys_to_sample) - set(results.keys())):
+        rise_error(set(keys_to_sample) - set(results.keys()), "keys are missing to apply postprocessing. Please check simulation parameters")
     else:
         if t_stop_rec < 0:
             t_stop_rec=results.t_sim
@@ -1267,7 +1269,12 @@ def sample_keys(results:axon_results, keys_to_sample:str|set[str]={}, t_start_re
         "Nseg_per_sec",
         "axon_path_type",
         "t_sim",
+        "myelinated",
+        "intra_stim_starts",
+        "intra_stim_positions",
+        "recorder"
         }
+
         list_keys.update(keys_to_keep)
         list_keys.update(keys_to_sample)
         if results.ID==0: