added auto generated cython c file output to gitignore

.gitignore

@@ -8,4 +8,4 @@ testing/abc.png
 *.egg-info
 fastXPCS/fastXPCS/cython/welford.c
 fastXPCS/fastXPCS/cython/fxpcs_sparse.c
-fastXPCS/fastXPCS/cython/dense.c
+fastXPCS/fastXPCS/cython/dense.c
\ No newline at end of file

testing/test.py

@@ -10,6 +10,8 @@ from fastXPCS.algos import TTCdata, do_sparse_train
 from fastXPCS.fxpcs_sparse import sparsify, sparsify2
 from PIL import Image
 
+from function_call import doXPCS; 
+
 dataShape = (352, 512, 128)
 photonEnergy = 13.3 # in ADU
 # this is a function that generates a dummy panel dataset given a shape
@@ -47,7 +49,6 @@ def getDummyData(shape, chanceOfPhotonPerPixel = 0.01, correlationPerBunch=0.5,
             p=[q**0, q**1, q**2, q**3, q**4, q**5, q**6]/np.sum([q**0, q**1, q**2, q**3, q**4, q**5, q**6])),(shape[1], shape[2]))         
     
     def evolveImageFrom(image, c=correlationPerBunch):
-        print(c)
         randMask = np.reshape(np.random.choice(
             [0, 1], 
             size=numPixelPrImage, 
@@ -58,7 +59,7 @@ def getDummyData(shape, chanceOfPhotonPerPixel = 0.01, correlationPerBunch=0.5,
     data[0,:,:] = np.reshape(getRandomImage(), (shape[1], shape[2]))
 
     for pulseNr in range(1,dataShape[0]):
-        correlationPerBunch += (1-correlationPerBunch)*0.001
+        correlationPerBunch += (1-correlationPerBunch)*0.01
         data[pulseNr, :, :] = evolveImageFrom(data[pulseNr-1,:,:], correlationPerBunch )
 
     # if we do not returned photnoized data, then add a gaussian random variable
@@ -66,7 +67,7 @@ def getDummyData(shape, chanceOfPhotonPerPixel = 0.01, correlationPerBunch=0.5,
         data += sigma * np.random.standard_normal(size=shape)
         data *= energy
 
-    return data
+    return data.astype(np.float32)
 
 
 data = getDummyData(
@@ -142,9 +143,19 @@ t = time.time()
 out = xpcs(data, photonEnergy)
 elapsed = time.time() - t
 print(f"elapsed {elapsed}")
-print(out.shape)
-print(np.min(out))
-print(np.max(out))
+
+
+print("my xpcs")
+output = np.zeros(shape=(2,2),dtype=np.float32)
+errorCode = doXPCS(data, output)
+print(data)
+print(output)
+print(output[0][0])
+print(output[0][1])
+print(output[1][0])
+print(output[1][1])
+print(output.ravel())
+
 def plot(data):
     data = data.astype(np.float32)
     cmap = pl.get_cmap('viridis')