add interface about available info of model (V, spiketimes, frequency), move parameter functions to abstractmodel

models/AbstractModel.py

@@ -1,9 +1,93 @@
 
+from stimuli.AbstractStimulus import AbstractStimulus
+
 
 class AbstractModel:
 
     # TODO what information about the model does the ModelParser need to be able to simulate the right kind of data
-    # for further analysis in cell_data/fi_curve etc.
+    #  for further analysis in cell_data/fi_curve etc.
+
+    # TODO change key + values list to a dict
+    KEYS = []
+    VALUES = []
+
+    def __init__(self, params: dict = None):
+        self.parameters = {}
+        if params is None:
+            self._set_default_parameters()
+        else:
+            self._test_given_parameters(params)
+            self.set_parameters(params)
+
+    def simulates_voltage_trace(self) -> bool:
+        raise NotImplementedError("NOT IMPLEMENTED")
+
+    def simulates_frequency(self) -> bool:
+        raise NotImplementedError("NOT IMPLEMENTED")
+
+    def simulates_spiketimes(self) -> bool:
+        raise NotImplementedError("NOT IMPLEMENTED")
+
+    def simulate(self, stimulus: AbstractStimulus, total_time_s):
+        """
+        Simulate the given stimulus in the model
+        and simulate up to the given total time
+        and saves the simulated data in the model.
+
+        :param stimulus: given stimulus
+        :param total_time_s: time to simulate
+        :return: depending on availability: [voltage, spiketimes, frequency]
+        """
+        raise NotImplementedError("NOT IMPLEMENTED")
+
+    def get_voltage_trace(self):
+        raise NotImplementedError("NOT IMPLEMENTED")
+
+    def get_spiketimes(self):
+        raise NotImplementedError("NOT IMPLEMENTED")
+
+    def get_frequency(self):
+        raise NotImplementedError("NOT IMPLEMENTED")
 
     def min_stimulus_strength_to_spike(self):
+        """
+        return the minimal stimulus strength needed for the model to spike
+        :return: min stimulus strength to spike
+        """
         raise NotImplementedError("NOT IMPLEMENTED")
+
+    def get_sampling_interval(self):
+        """
+        return the "sampling" interval of the model: the time step the model simulates by
+        :return: the sampling interval
+        """
+        raise NotImplementedError("NOT IMPLEMENTED")
+
+    def set_parameters(self, params):
+        self._test_given_parameters(params)
+
+        for k in params.keys():
+            self.parameters[k] = params[k]
+
+        for i in range(len(self.KEYS)):
+            if self.KEYS[i] not in self.parameters.keys():
+                self.parameters[self.KEYS[i]] = self.VALUES[i]
+
+    def get_parameters(self):
+        return self.parameters
+
+    def set_variable(self, key, value):
+        if key not in self.KEYS:
+            raise ValueError("Given key is unknown!\n"
+                             "Please check spelling and refer to list LIFAC.KEYS.")
+        self.parameters[key] = value
+
+    def _set_default_parameters(self):
+        for i in range(len(self.KEYS)):
+            self.parameters[self.KEYS[i]] = self.VALUES[i]
+
+    def _test_given_parameters(self, params):
+        for k in params.keys():
+            if k not in self.KEYS:
+                err_msg = "Unknown key in the given parameters:" + str(k)
+                raise ValueError(err_msg)

models/LIFAC.py

@@ -7,22 +7,20 @@ import numpy as np
 class LIFACModel(AbstractModel):
     # all times in milliseconds
     KEYS = ["mem_res", "mem_tau", "v_base", "v_zero", "threshold", "step_size", "delta_a", "tau_a"]
-    VALUES = [100 * 1000000, 0.1 * 200, 0, 0, 10, 0.01, 1, 200]
+    VALUES = [100 * 1000000, 0.1 * 200, 0, 0, 10, 0.01, 1, 20]
 
     # membrane time constant tau = mem_cap*mem_res
     def __init__(self, params: dict = None):
-        self.parameters = {}
-        if params is None:
-            self._set_default_parameters()
-        else:
-            self._test_given_parameters(params)
-            self.set_parameters(params)
-
-        self.last_v = []
-        self.last_adaption = []
-        self.last_spiketimes = []
-
-    def __call__(self, stimulus: AbstractStimulus, total_time_s):
+        super().__init__(params)
+
+        self.voltage_trace = []
+        self.adaption_trace = []
+        self.spiketimes = []
+        self.stimulus = None
+        # self.frequency_trace = []
+
+    def simulate(self, stimulus: AbstractStimulus, total_time_s):
+        self.stimulus = stimulus
         output_voltage = []
         adaption = []
         spiketimes = []
@@ -44,9 +42,9 @@ class LIFACModel(AbstractModel):
             current_v = v_next
             current_a = a_next
 
-        self.last_v = output_voltage
-        self.last_adaption = adaption
-        self.last_spiketimes = spiketimes
+        self.voltage_trace = output_voltage
+        self.adaption_trace = adaption
+        self.spiketimes = spiketimes
 
         return output_voltage, spiketimes
 
@@ -62,6 +60,8 @@ class LIFACModel(AbstractModel):
         return current_a + (step_size * (-current_a)) / self.parameters["tau_a"]
 
     def set_parameters(self, params):
+        self._test_given_parameters(params)
+
         for k in params.keys():
             self.parameters[k] = params[k]
 
@@ -87,3 +87,41 @@ class LIFACModel(AbstractModel):
             if k not in self.KEYS:
                 err_msg = "Unknown key in the given parameters:" + str(k)
                 raise ValueError(err_msg)
+
+    def min_stimulus_strength_to_spike(self):
+        return self.parameters["threshold"] - self.parameters["v_base"]
+
+    def get_sampling_interval(self):
+        return self.parameters["step_size"]
+
+    def get_frequency(self):
+        # TODO also change simulates_frequency() if any calculation is added!
+        raise NotImplementedError("No calculation implemented yet for the frequency.")
+
+    def get_spiketimes(self):
+        return self.spiketimes
+
+    def get_voltage_trace(self):
+        return self.voltage_trace
+
+    def get_adaption_trace(self):
+        return self.adaption_trace
+
+    def simulates_frequency(self) -> bool:
+        return False
+
+    def simulates_spiketimes(self) -> bool:
+        return True
+
+    def simulates_voltage_trace(self) -> bool:
+        return True
+
+    def get_recording_times(self):
+        # [delay, stimulus_start, stimulus_duration, time_to_end]
+        self.stimulus = AbstractStimulus()
+        delay = 0
+        start = self.stimulus.get_stimulus_start_s()
+        duration = self.stimulus.get_stimulus_duration_s()
+        total_time = len(self.voltage_trace) / self.parameters["step_size"]
+
+        return [delay, start, duration, total_time]