diff --git a/toolkit.py b/toolkit.py
index 6d860776a02b3bd2ffd58fdac18005c278a80cfd..0432498e2d800a2cf22c0990ff753d2fcbd9c39b 100755
--- a/toolkit.py
+++ b/toolkit.py
@@ -13,10 +13,10 @@ import pandas as pd
 import h5py
 from sklearn.preprocessing import StandardScaler
 from sklearn.externals import joblib
-
 from keras.models import Sequential
 from keras.layers import Dense
 from keras.models import model_from_json
+import matplotlib.pyplot as plt
 
 # configure number of cores
 # this doesn't seem to work, but at least with these settings keras only uses 4 processes
@@ -75,12 +75,15 @@ class KerasROOTClassification:
 
         self._scaler = None
         self._class_weight = None
+        self._bkg_weights = None
+        self._sig_weights = None
         self._model = None
 
         # track the number of epochs this model has been trained
         self.total_epochs = 0
 
         self.data_loaded = False
+        self.data_transformed = False
 
 
     def _load_data(self):
@@ -177,6 +180,18 @@ class KerasROOTClassification:
         return self._scaler
 
 
+    def _transform_data(self):
+        if not self.data_transformed:
+            # todo: what to do about the outliers? Where do they come from?
+            logger.debug("training data before transformation: {}".format(self.x_train))
+            logger.debug("minimum values: {}".format([np.min(self.x_train[:,i]) for i in range(self.x_train.shape[1])]))
+            logger.debug("maximum values: {}".format([np.max(self.x_train[:,i]) for i in range(self.x_train.shape[1])]))
+            self.x_train = self.scaler.transform(self.x_train)
+            logger.debug("training data after transformation: {}".format(self.x_train))
+            self.x_test = self.scaler.transform(self.x_test)
+            self.data_transformed = True
+
+
     def _read_info(self, key, default):
         filename = os.path.join(self.project_dir, "info.json")
         if not os.path.exists(filename):
@@ -222,6 +237,7 @@ class KerasROOTClassification:
 
         return self._model
 
+
     @property
     def class_weight(self):
         if self._class_weight is None:
@@ -230,11 +246,18 @@ class KerasROOTClassification:
             self._class_weight = [(sumw_sig+sumw_bkg)/(2*sumw_bkg), (sumw_sig+sumw_bkg)/(2*sumw_sig)]
         return self._class_weight
 
+
     def train(self, epochs=10):
 
         if not self.data_loaded:
             self._load_data()
 
+        if not self.data_transformed:
+            self._transform_data()
+
+        for branch_index, branch in enumerate(self.branches):
+            self.plot_input(branch_index)
+
         try:
             self.model.load_weights(os.path.join(self.project_dir, "weights.h5"))
             logger.info("Weights found and loaded")
@@ -244,7 +267,9 @@ class KerasROOTClassification:
 
         self.total_epochs = self._read_info("epochs", 0)
 
-        self.model.fit(self.x_train, self.y_train,
+        self.model.fit(self.x_train,
+                       # the reshape might be unnescessary here
+                       self.y_train.reshape(-1, 1),
                        epochs=epochs,
                        class_weight=self.class_weight,
                        shuffle=True,
@@ -261,6 +286,52 @@ class KerasROOTClassification:
     def writeFriendTree(self):
         pass
 
+
+    @property
+    def bkg_weights(self):
+        """
+        class weights multiplied by event weights (for plotting)
+        TODO: find a better way to do this
+        """
+        if self._bkg_weights is None:
+            logger.debug("Calculating background weights for plotting")
+            self._bkg_weights = np.empty(sum(self.y_train == 0))
+            self._bkg_weights.fill(self.class_weight[0])
+            self._bkg_weights *= self.w_train[self.y_train == 0]
+        return self._bkg_weights
+
+
+    @property
+    def sig_weights(self):
+        """
+        class weights multiplied by event weights (for plotting)
+        TODO: find a better way to do this
+        """
+        if self._sig_weights is None:
+            logger.debug("Calculating signal weights for plotting")
+            self._sig_weights = np.empty(sum(self.y_train == 1))
+            self._sig_weights.fill(self.class_weight[1])
+            self._sig_weights *= self.w_train[self.y_train == 1]
+        return self._sig_weights
+
+
+    def plot_input(self, var_index):
+        "plot a single input variable"
+        branch = self.branches[var_index]
+        fig, ax = plt.subplots()
+        bkg = self.x_train[:,var_index][self.y_train == 0]
+        sig = self.x_train[:,var_index][self.y_train == 1]
+        logger.debug("Plotting bkg (min={}, max={}) from {}".format(np.min(bkg), np.max(bkg), bkg))
+        logger.debug("Plotting sig (min={}, max={}) from {}".format(np.min(sig), np.max(sig), sig))
+        ax.hist(bkg, color="b", alpha=0.5, bins=50, weights=self.bkg_weights)
+        ax.hist(sig, color="r", alpha=0.5, bins=50, weights=self.sig_weights)
+        ax.set_xlabel(branch+" (transformed)")
+        plot_dir = os.path.join(self.project_dir, "plots")
+        if not os.path.exists(plot_dir):
+            os.mkdir(plot_dir)
+        fig.savefig(os.path.join(plot_dir, "var_{}.pdf".format(var_index)))
+
+
     def plotROC(self):
         pass
 
@@ -272,7 +343,8 @@ class KerasROOTClassification:
 if __name__ == "__main__":
 
     logging.basicConfig()
-    logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+    #logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
+    logging.getLogger("KerasROOTClassification").setLevel(logging.DEBUG)
 
     filename = "/project/etp4/nhartmann/trees/allTrees_m1.8_NoSys.root"