added median downsampling method

cosanlab · Mar 2, 2017 · 6541044 · 6541044
1 parent a3a1614
commit 6541044
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Showing 2 changed files with 18 additions and 5 deletions.
diff --git a/nltools/stats.py b/nltools/stats.py
@@ -193,22 +193,25 @@ def calc_bpm(beat_interval, sampling_freq):
     '''
     return 60*sampling_freq*(1/(beat_interval))
 
-def downsample(data,sampling_freq=None, target=None, target_type='samples'):
+def downsample(data,sampling_freq=None, target=None, target_type='samples',method='mean'):
     ''' Downsample pandas to a new target frequency or number of samples using averaging.
     
         Args:
             data: Pandas DataFrame or Series
             sampling_freq:  Sampling frequency of data 
             target: downsampling target
             target_type: type of target can be [samples,seconds,hz]
+            method: (str) type of downsample method ['mean','median'], default: mean
         Returns:
             downsampled pandas object
             
     '''
 
     if not isinstance(data,(pd.DataFrame,pd.Series)):
         raise ValueError('Data must by a pandas DataFrame or Series instance.')
-
+    if not (method=='median') | (method=='mean'):
+        raise ValueError("Metric must be either 'mean' or 'median' ")
+
     if target_type is 'samples':
         n_samples = target
     elif target_type is 'seconds':
@@ -222,7 +225,10 @@ def downsample(data,sampling_freq=None, target=None, target_type='samples'):
     # if data.shape[0] % n_samples:
     if data.shape[0] > len(idx):
         idx = np.concatenate([idx, np.repeat(idx[-1]+1,data.shape[0]-len(idx))])
-    return data.groupby(idx).mean()
+    if method=='mean':
+        return data.groupby(idx).mean()
+    elif method=='median':
+        return data.groupby(idx).median()
 
 def fisher_r_to_z(r):
     ''' Use Fisher transformation to convert correlation to z score '''

diff --git a/nltools/tests/test_stats.py b/nltools/tests/test_stats.py
@@ -1,6 +1,6 @@
 import numpy as np
 import pandas as pd
-from nltools.stats import one_sample_permutation,two_sample_permutation,correlation_permutation
+from nltools.stats import one_sample_permutation,two_sample_permutation,correlation_permutation, downsample
 
 def test_permutation():
 	dat = np.random.multivariate_normal([2,6],[[.5,2],[.5,3]],100)
@@ -19,4 +19,11 @@ def test_permutation():
 	assert stats['p']< .001
 	stats = correlation_permutation(x,y,metric='kendall')
 	assert (stats['correlation']>.4) & (stats['correlation']<.85)
-	assert stats['p']< .001
+	assert stats['p']< .001
+
+def test_downsample():
+	dat = pd.DataFrame()
+	dat['x'] = range(0,100)
+	dat['y'] = np.repeat(range(1,11),10)
+	assert((dat.groupby('y').mean().values.ravel() == downsample(data=dat['x'],sampling_freq=10,target=1,target_type='hz',method='mean').values).all)
+	assert((dat.groupby('y').median().values.ravel() == downsample(data=dat['x'],sampling_freq=10,target=1,target_type='hz',method='median').values).all)