diff --git a/code/test.py b/code/test.py
index fe621f7..b08d495 100644
--- a/code/test.py
+++ b/code/test.py
@@ -129,6 +129,61 @@ def remove_poor(files):
             good_files.append(files[i])
     return good_files
 
+def sam_data(sam):
+    '''
+    Gets metadata for each SAM
+
+    Parameters
+    ----------
+    sam : ReproRun object
+        The sam the metdata should be extracted from.
+
+    Returns
+    -------
+    sam_amp : float
+        amplitude in percent, relative to the fish amplitude.
+    sam_am : float
+        Amplitude modulation frequency.
+    sam_df : float
+        Difference from the stimulus to the current fish eodf.
+    sam_eodf : float
+        The current EODf.
+    sam_nyquist : float
+        The Nyquist frequency of the EODf.
+    sam_stim : float
+        The stimulus frequency.
+
+    '''
+    # create lists for the values we want
+    amplitudes = []
+    dfs = []
+    eodfs = []
+    stim_freqs = []
+    amp_mods = []
+    ny_freqs = []
+    
+    # get the stimuli
+    stimuli = sam.stimuli
+    
+    # loop over the stimuli
+    for stim in stimuli:
+        amplitude, df, eodf, stim_freq, amp_mod, ny_freq = extract_stim_data(stim)
+        amplitudes.append(amplitude)
+        dfs.append(df)
+        eodfs.append(eodf)
+        stim_freqs.append(stim_freq)
+        amp_mods.append(amp_mod)
+        ny_freqs.append(ny_freq)
+      
+    # get the means
+    sam_amp = np.mean(amplitudes)
+    sam_am = np.mean(amp_mods)
+    sam_df = np.mean(dfs)
+    sam_eodf = np.mean(eodfs)
+    sam_nyquist = np.mean(ny_freqs)
+    sam_stim = np.mean(stim_freqs)
+    return sam_amp, sam_am,sam_df, sam_eodf, sam_nyquist, sam_stim
+    
 #find example data
 datafolder = "../../data"
 
diff --git a/code/useful_functions.py b/code/useful_functions.py
index 1115ef5..eb750e8 100644
--- a/code/useful_functions.py
+++ b/code/useful_functions.py
@@ -170,4 +170,59 @@ def remove_poor(files):
         if quality != "poor":
             # if its good or fair add it to the good files
             good_files.append(files[i])
-    return good_files
\ No newline at end of file
+    return good_files
+
+def sam_data(sam):
+    '''
+    Gets metadata for each SAM
+
+    Parameters
+    ----------
+    sam : ReproRun object
+        The sam the metdata should be extracted from.
+
+    Returns
+    -------
+    sam_amp : float
+        amplitude in percent, relative to the fish amplitude.
+    sam_am : float
+        Amplitude modulation frequency.
+    sam_df : float
+        Difference from the stimulus to the current fish eodf.
+    sam_eodf : float
+        The current EODf.
+    sam_nyquist : float
+        The Nyquist frequency of the EODf.
+    sam_stim : float
+        The stimulus frequency.
+
+    '''
+    # create lists for the values we want
+    amplitudes = []
+    dfs = []
+    eodfs = []
+    stim_freqs = []
+    amp_mods = []
+    ny_freqs = []
+    
+    # get the stimuli
+    stimuli = sam.stimuli
+    
+    # loop over the stimuli
+    for stim in stimuli:
+        amplitude, df, eodf, stim_freq, amp_mod, ny_freq = extract_stim_data(stim)
+        amplitudes.append(amplitude)
+        dfs.append(df)
+        eodfs.append(eodf)
+        stim_freqs.append(stim_freq)
+        amp_mods.append(amp_mod)
+        ny_freqs.append(ny_freq)
+      
+    # get the means
+    sam_amp = np.mean(amplitudes)
+    sam_am = np.mean(amp_mods)
+    sam_df = np.mean(dfs)
+    sam_eodf = np.mean(eodfs)
+    sam_nyquist = np.mean(ny_freqs)
+    sam_stim = np.mean(stim_freqs)
+    return sam_amp, sam_am,sam_df, sam_eodf, sam_nyquist, sam_stim
\ No newline at end of file