[reproclasses] new Boltzmann fit class

fishbook/reproclasses.py

@@ -4,6 +4,7 @@ import nixio as nix
 from scipy.stats import circstd
 import os
 import subprocess
+from tqdm import tqdm
 
 from IPython import embed
 
@@ -38,6 +39,12 @@ def _unzip_if_needed(dataset, tracename='trace-1.raw'):
         subprocess.check_call(["gunzip", os.path.join(dataset, tracename + ".gz")])
 
 
+def gaussian_kernel(sigma, dt):
+    x = np.arange(-4. * sigma, 4. * sigma, dt)
+    y = np.exp(-0.5 * (x / sigma) ** 2) / np.sqrt(2. * np.pi) / sigma
+    return y
+
+
 class BaselineData:
 
     def __init__(self, dataset: Dataset):
@@ -244,7 +251,6 @@ class BaselineData:
             data = None
         return data
 
-
     def __read_spike_data_from_directory(self, r) -> np.ndarray:
         data = []
         data_source = os.path.join(self.__dataset.data_source, "basespikes1.dat")
@@ -264,7 +270,8 @@ class BaselineData:
             return None
         return np.asarray(data)
 
-    def __do_read(self, f)->np.ndarray:
+    @staticmethod
+    def __do_read(f) -> np.ndarray:
         data = []
         f.readline()
         unit = f.readline().strip("#").strip()
@@ -360,7 +367,8 @@ class FIData:
         b = f.blocks[0]
 
         mt = None
-        for s in stimuli:
+        for i in tqdm(range(len(stimuli))):
+            s = stimuli[i]
             if not mt or mt.id != s.multi_tag_id:
                 mt = b.multi_tags[s.multi_tag_id]
             sp, eod, t, c = self.__do_read_spike_data_from_nix(mt, s, repro)
@@ -377,23 +385,67 @@ class FIData:
         return len(self.__spike_data)
 
     def spikes(self, index=-1):
-        if 0 < index < self.size:
+        if 0 <= index < self.size:
             return self.__spike_data[index]
         else:
             return self.__spike_data
 
     def eod(self, index=-1):
-        if 0 < index < self.size:
+        if 0 <= index < self.size:
             return self.__eod_times[index], self.__eod_data[index]
         else:
             return self.__eod_times, self.__eod_data
 
     def contrast(self, index=-1):
-        if 0 < index < self.size:
+        if 0 <= index < self.size:
             return self.__contrasts[index]
         else:
             return self.__contrasts
 
+    def time_axis(self, index=-1):
+        if 0 <= index < self.size:
+            return self.__eod_times[index]
+        else:
+            return self.__eod_times
+
+    def rate(self, index=0, kernel_width=0.005):
+        """
+        Returns the firing rate for a single trial.
+
+        :param index: The index of the trial. 0 <= index < size
+        :param kernel_width: The width of the gaussian kernel in seconds
+        :return: tuple of time and rate
+        """
+        t = self.time_axis(index)
+        dt = np.mean(np.diff(t))
+        sp = self.spikes(index)
+        binary = np.zeros(t.shape)
+        spike_indices = (sp / dt).astype(int)
+        binary[spike_indices[(spike_indices >= 0) & (spike_indices < len(binary))]] = 1
+        g = gaussian_kernel(kernel_width, dt)
+        rate = np.convolve(binary, g, mode='same')
+        return t, rate
+
+    def boltzmann_fit(self, start_time=0.01, end_time=0.05, kernel_width=0.005):
+        """
+
+        :param start_time:
+        :param end_time:
+        :param kernel_width:
+        :return: object of type BoltzmannFit
+        """
+        contrasts = np.zeros(self.size)
+        rates = np.zeros(self.size)
+        for i in range(self.size):
+            contrasts[i] = np.round(self.contrast(i))
+            t, r = self.rate(i, kernel_width)
+            rates[i] = np.mean(r[(t >= start_time) & (t < end_time)])
+
+        boltzmann_fit = BoltzmannFit(contrasts, rates)
+        return boltzmann_fit
+
+
+
 if __name__ == "__main__":
     #dataset = Dataset(dataset_id='2011-06-14-ag')
     dataset = Dataset(dataset_id="2018-01-19-ac-invivo-1")