diff --git a/testing_scripts/spatial_rippling/ripple.py b/testing_scripts/spatial_rippling/ripple.py
new file mode 100644
index 0000000..57e8248
--- /dev/null
+++ b/testing_scripts/spatial_rippling/ripple.py
@@ -0,0 +1,115 @@
+import pylab as py
+from scipy import signal
+import numpy as np
+import pyfits
+import math
+import sys
+from astropy.stats import sigma_clip
+pi = math.pi
+
+
+def ripplingMetric(file='s160711_a013002_Kbb_035_RMS.fits',row=10,gain=2.75,makePlot=False,minFpow=-2.0,
+                   maxFpow=1.0,numF=1e5,title='line_integrate',sigma=3.0,itime=900.0):
+
+
+    '''
+    Output: (1) STD across selected row in image (electrons) (2) sqrt of mean in row (electrons) (3) ratio of 1 to 2
+    (4) sigma clipped max - min difference across the row (flux) (5) mean in row (flux) (6) ratio of 4 to 5
+    OPTIONAL: output plot of flux vs column value in that row and power spectrum in that row vs period (pixels)
+    '''
+
+    #Note on GAIN keyword is 2.75 for 2016 and later (new detector), change to 5.6 for old detector, pre 2016
+    #####IMPORTANT^^^^^^*********
+
+    #-file is the fits file that has already been integrated over the desired wavelength/channel range
+    #-row is the row over which to test
+    #-makePlot makes a plot that shows the integrated flux over row and the power spectrum over period
+    #-min and maxFpow are the power index ranges for the power spectrum (in FREQUENCY)
+    #-numF is the number of frequencies (or periods) tested in Lomb Scargle
+    #-title is the title for the optional output plot
+    #-sigma value for the sigma for the clipping used for the max min difference calculation
+    #-itime is the integration time of the fits file in question, seconds
+
+    
+    datafile = pyfits.open(file)
+    data = datafile[0].data
+
+    rowVal = data[row,:]
+
+    #compare std to sqrt of mean, in electron
+    std_row = np.std(rowVal)*gain*itime #electrons
+    sqrt_mean = math.sqrt(np.mean(rowVal*gain*itime)) #electrons
+
+    #compare max - min to mean
+    clipped = sigma_clip(rowVal,sigma=sigma)
+    max_min = np.max(clipped)-np.min(clipped)
+    mean_flux = np.mean(rowVal)
+
+    
+    if (makePlot==True):
+        pixels = np.linspace(1.0,len(rowVal),len(rowVal))
+        tfreq = 10**np.linspace(minFpow,maxFpow,numF)
+    
+        noZero = np.where(rowVal > 0.0)[0]
+        rowVal = rowVal[noZero]
+        pixels = pixels[noZero]
+
+        pgram = signal.lombscargle(pixels,rowVal.byteswap().newbyteorder().astype('float64'),tfreq)
+
+        py.clf()
+        py.axes([0.1,0.7,0.85,0.25])
+        py.plot(pixels,rowVal)
+        py.xlabel('Pixels')
+        py.ylabel('Integrated Flux')
+
+        py.axes([0.1,0.12,0.85,0.45])
+        py.plot(2*pi/tfreq,pgram)
+        py.xscale('log')
+        py.xlabel('Period (pixel)')
+
+        py.savefig('../'+title+'_row'+str(row)+'.png')
+
+
+    return std_row, sqrt_mean,std_row/sqrt_mean, max_min, mean_flux, max_min/mean_flux
+
+
+
+def sumLine(filename,min_pixel,max_pixel,sumRow,outname):
+    '''
+    Output - RMS across entire image and RMS across chosen row
+    '''
+
+    #filename - path of file to import
+    #min_ and max_pixel, limits, in pixels, to sum over
+    #sumRow - individual row to test
+    #outname - name of output .fits file with just integrated flux across chosen pixels
+    
+    file = pyfits.open(filename)
+    data = file[0].data
+    hdr = file[0].header
+    sizeLambda = data.shape[2]
+
+    min_image_lambda = hdr['CRVAL1']
+    delta_lambda = hdr['CDELT1']
+
+    if (min_pixel >= max_pixel):
+        sys.exit("Max lambda needs to be greater than min lambda")
+
+    if (min_pixel < 0):
+        sys.exit("Selected minimum wavelength is off detector")
+
+    if (max_pixel > sizeLambda):
+        sys.exit("Selected maximum wavelength is off detector")
+
+    onlyLine = data[:,:,min_pixel:max_pixel]
+    sumLine = np.sum(onlyLine,axis=2)
+
+    sumRowOnly = sumLine[:,sumRow]
+
+    totalRMS = np.std(sumLine)
+    rowRMS = np.std(sumRowOnly)
+
+    sumLine = np.transpose(sumLine)
+    pyfits.writeto(outname,sumLine,clobber=True)
+
+    return totalRMS, rowRMS