def find_best_grip_scale(self, grip_list):
""" Finds the positive value level for the previous 30 second block.
"""
sortedList = sorted(abs_val_list(grip_list))
scale = self.get_scale_points(sortedList)
temp_list = []
for i in sortedList:
if scale[0] <= i <= scale[1]:
temp_list.append(i)
#print("scale is between {} and {}".format(scale[0], scale[1]))
#print("over_all_avg = ", self.new_overall_avg())
scale = self.get_scale_points(temp_list)
if scale[2] <= self.new_overall_avg() <= scale[3]:
return 3
elif scale[1] <= self.new_overall_avg() <= scale[2]:
return 2
return 1
def select_response(self) -> (str, str):
""" Chooses an appropriate response based upon user's performance. Returns that response string"""
print("RIVA msg number: ", self._RIVA_message_num)
overall_avg = self.user_stats.new_overall_avg()
scale_points = self.user_stats.get_scale_points(abs_val_list(grip_times(self._all_grips)))
#print("overall avg: {}\n scale point 1: {} scale point 2: {}".format(overall_avg,scale_points[1],scale_points[2]))
# if self._RIVA_message_num == 1:
# return self.response_welcome()
if self._last_response == "training_prompt":
return self.response_training_response()
'''elif self.user_stats.get_grip_avg(grip_number=self._last_worst_grip) > \
self.user_stats.get_scale_points(abs_val_list(grip_times(self._all_grips)))[2]:
return self.response_training_prompt()
'''
elif overall_avg > scale_points[2]:
#print("Q2", scale_points[2])
return self.response_negative()
elif overall_avg < scale_points[1]:
#print("Q1", scale_points[1])
return self.response_positive()
return self.response_training_prompt()
