Merge pull request #2 from arkwave/function3and4

finished functions 3 and 4, testing pending

function3.py

@@ -0,0 +1,27 @@
+import numpy as np
+from function4 import getSkipMean
+def removePeriodicMean(signalMat, period, sliderWidth, NoDataCode):
+    """
+    :param signalMat: n - column matrix where variables represent timeseries
+    :param period: length of repeating pattern of data (i.e. seasons, years, etc).
+    :param sliderWidth: Number of periods to base the anomaly on.
+    :param NoDatCode: data value to be ignored
+    :return: matrix of values that accounts for periodic averages
+    """
+    nData, nSignals = np.shape(signalMat)   
+    newLength = nData - period * (sliderWidth - 1)
+    anomalyMat = np.nan * np.zeros(shape = (newLength, nSignals))
+    avg = np.nan * np.zeros(shape=(nData, nSignals))
+
+    for i in range(newLength):
+        for j in range(nSignals):
+            end = i + (sliderWidth*period) - 1
+            avg[i,j] = getSkipMean(signalMat[i:end:period, j], NoDataCode)
+
+        if avg[i,j] == NoDataCode or signalMat[i,j] == NoDataCode:
+            anomalyMat[i,j] = NoDataCode
+        else:
+            anomalyMat[i,j] = signalMat[i,j] - avg[i,j]
+
+    return anomalyMat
+

function4.py

@@ -0,0 +1,19 @@
+import numpy as np
+def getSkipMean(vector, NoDataCode):
+    """
+    :param vector: vector of values.
+    :param NoDataCode: indicates value to be ignored
+    :return: average of the values in the vector ignoring NoData values
+    """
+    N, _ = np.shape(vector)
+    sum = 0
+    nVals = 0
+    for i in range(N):
+        if vector[i] != NoDataCode:
+            sum+=vector[i]
+            nVals += 1
+    if nVals > 0:
+        avg = sum/nVals
+    else:
+        avg = NoDataCode
+    return avg