diff --git a/fishbook/__init__.py b/fishbook/__init__.py
index ee7afae..237b5a0 100644
--- a/fishbook/__init__.py
+++ b/fishbook/__init__.py
@@ -1,4 +1,4 @@
 from .fishbook import *
-from .reproclasses import BaselineData
+from .reproclasses import BaselineData, FIData
 import fishbook.database as database
 __all__ = ['fishbook', 'database']
\ No newline at end of file
diff --git a/fishbook/reproclasses.py b/fishbook/reproclasses.py
index 83f0cb4..5b0f934 100644
--- a/fishbook/reproclasses.py
+++ b/fishbook/reproclasses.py
@@ -2,6 +2,7 @@ from fishbook.fishbook import Dataset, RePro, Stimulus
 import numpy as np
 import nixio as nix
 from scipy.stats import circstd
+from scipy.optimize import curve_fit
 import os
 import subprocess
 from tqdm import tqdm
@@ -283,6 +284,52 @@ class BaselineData:
         return np.asarray(data)
 
 
+class BoltzmannFit():
+    def __init__(self, xvalues: np.ndarray, yvalues: np.ndarray):
+        assert(len(xvalues) == len(yvalues))
+        self.__xvals = xvalues
+        self.__yvals = yvalues
+        self.__fit_params = None
+        self.__x_sorted = np.unique(self.__xvals)
+        self.__y_avg = None
+        self.__y_err = None
+        self.__do_fit()
+
+    @staticmethod
+    def boltzmann(x, f_max, slope, inflection):
+        y = f_max / (1 + np.exp(-slope * (x - inflection)))
+        return y
+
+    def __do_fit(self):
+        self.__y_avg = np.zeros(self.__x_sorted.shape)
+        self.__y_err = np.zeros(self.__x_sorted.shape)
+        for i, c in enumerate(self.__x_sorted):
+            self.__y_avg[i] = np.mean(self.__yvals[self.__xvals == c])
+            self.__y_err[i] = np.std(self.__yvals[self.__xvals == c])
+        self.__fit_params, _ = curve_fit(self.boltzmann, self.__x_sorted, self.__y_avg, [np.max(self.__y_avg), 0, 0])
+
+    @property
+    def slope(self):
+        return self.__fit_params[0] * self.__fit_params[1] / 4
+
+    @property
+    def parameters(self):
+        return self.__fit_params
+
+    @property
+    def x_data(self):
+        return self.__x_sorted
+
+    @property
+    def y_data(self):
+        return self.__y_avg, self.__y_err
+
+    def solve(self, xvalues=None):
+        if not xvalues:
+            xvalues = self.__x_sorted
+        return self.boltzmann(xvalues, *self.__fit_params)
+
+
 class FIData:
     def __init__(self, dataset: Dataset):
         self.__spike_data = []