Solved smooth problem and adding test for mlregressor

rampy/spectranization.py

@@ -6,6 +6,8 @@
 from scipy.interpolate import UnivariateSpline
 from scipy.interpolate import interp1d
 
+import rampy
+
 # SPECIFIC FUNCTIONS FOR TREATMENT OF SPECTRA
 
 def spectrarray(name,sh,sf,x):
@@ -163,21 +165,22 @@ def normalise(y,x=0,method="intensity"):
     if method == "minmax":
         y = (y-np.min(y))/(np.max(y)-np.min(y))
 
-def centroid(x,y,smooth = False,**kwargs):
+def centroid(x,y,smoothing=False,**kwargs):
     """calculation of the y signal centroid
     
     as np.sum(y/np.sum(y)*x)
+    if smoothing == 1:
     
     Parameters
     ==========
     x: Numpy array
         x values
     y: Numpy array
         y values, 1 spectrum
-       
+
     Options
     =======
-    smooth : bool
+    smoothing : bool
         True or False. Smooth the signals with arguments provided as kwargs. Default method is whittaker smoothing. See the rampy.smooth function for smoothing options and arguments.
         
     Returns
@@ -190,8 +193,8 @@ def centroid(x,y,smooth = False,**kwargs):
     x = x.reshape(-1)
     y = y.reshape(-1)
 
-    if smooth == True:
-        y_ = rp.smooth(x,y,**kwargs)
+    if smoothing == True:
+        y_ = rampy.smooth(x,y,**kwargs)
     else: 
         y_ = y.copy()
 

rampy/tests/test_mlregressor.py

@@ -0,0 +1,28 @@
+import unittest
+
+import numpy as np
+import scipy
+
+import rampy as rp
+
+class TestSmooth(unittest.TestCase):
+
+    def test_mixing(self):
+
+        # dummy gaussians
+        x = np.arange(0,100,1.0) # a dummy x axis
+        ref1 = 50.0*np.exp(-1/2*((x-40)/20)**2) + np.random.randn(len(x)) # a gaussian with added noise
+        ref2 = 70.0*np.exp(-1/2*((x-60)/15)**2) + np.random.randn(len(x)) # a gaussian with added noise
+
+        # mixed signals
+        F1_true = np.array([0.80,0.60,0.40,0.20])
+        obs = np.dot(ref1.reshape(-1,1),F1_true.reshape(1,-1)) + np.dot(ref2.reshape(-1,1),(1-F1_true.reshape(1,-1)))
+
+        # calculation
+        F1_meas = rp.mixing_sp(obs,ref1,ref2)
+
+        # assertion
+        np.testing.assert_almost_equal(F1_true,F1_meas,decimal=3)
+
+if __name__ == '__main__':
+    unittest.main()

rampy/tests/test_spectranization.py

@@ -40,8 +40,8 @@ def test_centroid(self):
         x = np.arange(0,100,1.).reshape(-1,1)
         y = norm.pdf(x,loc=60,scale=10)
 
-        c1 = rampy.centroid(x,y,smooth=True,method='whittaker')
-        c2 = rampy.centroid(x,y,smooth=False)
+        c1 = rampy.centroid(x,y,smoothing=True,method="whittaker")
+        c2 = rampy.centroid(x,y,smoothing=False)
 
          # Testing 
         np.testing.assert_almost_equal(c1,60.,decimal=1)