docstrings, renaming etc

fishbook/reproclasses.py

@@ -11,6 +11,16 @@ from IPython import embed
 
 
 def _zero_crossings(x, t, interpolate=False):
+    """get the times at which a signal x 
+
+    Args:
+        x ([type]): [description]
+        t ([type]): [description]
+        interpolate (bool, optional): [description]. Defaults to False.
+
+    Returns:
+        [type]: [description]
+    """
     dt = t[1] - t[0]
     x_shift = np.roll(x, 1)
     x_shift[0] = 0.0
@@ -100,7 +110,7 @@ class BaselineData:
 
     def serial_correlation(self, max_lags=50):
         """
-            return the serial correlation for the the spike train provided by spike_times.
+            Returns the serial correlation of the interspike intervals.
         @param max_lags: The number of lags to take into account
         @return: the serial correlation as a function of the lag
         """
@@ -114,29 +124,29 @@ class BaselineData:
         return scs
 
     def circular_std(self):
-        cstds = []
+        circular_stds = []
         for i in range(self.size):
             phases = self.__spike_phases(index=i)
-            cstds.append(circstd(phases))
-        return cstds
+            circular_stds.append(circstd(phases))
+        return circular_stds
 
     def eod_frequency(self):
-        eodfs = []
+        eod_frequencies = []
         for i in range(self.size):
             eod, time = self.eod(i)
             xings = _zero_crossings(eod, time, interpolate=False)
-            eodfs.append(len(xings)/xings[-1])
-        return 0.0 if len(eodfs) < 1 else np.mean(eodfs)
+            eod_frequencies.append(len(xings)/xings[-1])
+        return 0.0 if len(eod_frequencies) < 1 else np.mean(eod_frequencies)
 
     def __spike_phases(self, index=0): # fixme buffer this stuff
-        etimes = self.eod_times(index=index)
-        eod_period = np.mean(np.diff(etimes))
+        e_times = self.eod_times(index=index)
+        eod_period = np.mean(np.diff(e_times))
         phases = np.zeros(len(self.spikes(index)))
         for i, st in enumerate(self.spikes(index)):
-            last_eod_index = np.where(etimes <= st)[0]
+            last_eod_index = np.where(e_times <= st)[0]
             if len(last_eod_index) == 0:
                 continue
-            phases[i] = (st - etimes[last_eod_index[-1]]) / eod_period * 2 * np.pi
+            phases[i] = (st - e_times[last_eod_index[-1]]) / eod_period * 2 * np.pi
         return phases
 
     def eod_times(self, index=0, interpolate=True):
@@ -144,10 +154,10 @@ class BaselineData:
             return None
         if len(self.__eod_times) < len(self.__eod_data):
             eod, time = self.eod(index)
-            etimes = _zero_crossings(eod, time, interpolate=interpolate)
+            times = _zero_crossings(eod, time, interpolate=interpolate)
         else:
-            etimes = self.__eod_times[index]
-        return etimes
+            times = self.__eod_times[index]
+        return times
 
     @property
     def dataset(self):
@@ -164,6 +174,14 @@ class BaselineData:
         return subjects if len(subjects) > 1 else subjects[0]
 
     def spikes(self, index: int=0):
+        """Get the spike times of the spikes recorded in the given baseline recording.
+
+        Args:
+            index (int, optional): If the baseline activity has been recorded several times, the index can be given. Defaults to 0.
+
+        Returns:
+            : [description]
+        """
         return self.__spike_data[index] if len(self.__spike_data) >= index else None
 
     def membrane_voltage(self, index: int=0):
@@ -233,7 +251,7 @@ class BaselineData:
         return len(self.__spike_data)
 
     def __str__(self):
-        str = "Baseline data of cell %s " % self.__cell.id
+        return "Baseline data of cell %s " % self.__cell.id
 
     def __read_eod_data_from_nix(self, r: RePro, duration) -> np.ndarray:
         data_source = os.path.join(self.__dataset.data_source, self.__dataset.id + ".nix")
@@ -248,7 +266,7 @@ class BaselineData:
         try:
             data = t.retrieve_data("EOD")[:]
         except:
-            data = np.empty();
+            data = np.empty()
         f.close()
         return data
 
@@ -364,10 +382,10 @@ class BoltzmannFit:
 
     @property
     def slope(self) -> float:
-        """
+        r"""
         The slope of the linear part of the Boltzmann, i.e.
         .. math::
-            s = f_max \cdot k / 4
+            s = f_max $\cdot$ k / 4
         :return: the slope.
         """
         return self.__fit_params[0] * self.__fit_params[1] / 4
@@ -758,4 +776,4 @@ if __name__ == "__main__":
     dataset = Dataset(dataset_id="2018-09-13-ac-invivo-1")
     # dataset = Dataset(dataset_id='2013-04-18-ac')
     fi_curve = FileStimulusData(dataset)
-    embed()
+    embed()