class FuncsTest(unittest.TestCase):
def setUp(self):
self.a=ImageArray(join(thisdir,'coretestdata/im2_noannotations.png'))
self.a1=ImageArray(join(thisdir,'coretestdata/im1_annotated.png'))
self.a2=STXMImage(join(thisdir,"..","..","..","sample-data","Sample_Image_2017-10-15_100.hdf5"))
self.a3=STXMImage(join(thisdir,"..","..","..","sample-data","Sample_Image_2017-10-15_101.hdf5"))
def test_imagefile_ops(self):
self.a2.gridimage()
self.a3.gridimage()
self.a2.crop(5,-15,5,-5,_=True)
self.a3.crop(5,-15,5,-5,_=True)
self.b=self.a2//self.a3
self.assertEqual(self.b.shape,(90,80),"Failure to crop image correctly.")
self.assertGreater(self.b.max(),0.047,"XMCD Ratio calculation failed")
self.assertLess(self.b.min(),-0.05,"XMCD Ratio calculation failed")
self.b.normalise()
self.assertEqual(self.b.max(),1.0,"Normalise Image failed")
self.assertEqual(self.b.min(),-1.0,"Normalise Image failed")
self.profile=self.b.profile_line((0,0),(100,100))
self.profile.plot()
self.b.mask=self.a2.image>25E3
self.hist=self.b.hist(bins=200)
self.hist.column_headers=["XMCD Signal","Frequency"]
self.hist.labels=None
g1=LorentzianModel(prefix="g1_")
g2=LorentzianModel(prefix="g2_")
params=g1.make_params()
params.update(g2.make_params())
double_peak=g1+g2
g1=np.argmax(self.hist.y[:100]) # Location of first peak
g2=np.argmax(self.hist.y[100:])+100
for k, p in zip(params,[0.25,self.hist.x[g1],self.hist.y[g1]/np.sqrt(2),0.5,self.hist.y[g1],
0.25,self.hist.x[g2],self.hist.y[g2]/np.sqrt(2),0.5,self.hist.y[g2]]):
params[k].value=p
print(g1,g2,params)
self.res=self.hist.lmfit(double_peak,p0=params,output="report")
self.hist.add_column(self.res.init_fit,header="Initial Fit")
self.hist.add_column(self.res.best_fit,header="Best Fit")
self.hist.setas="xyyy"
self.hist.plot(fmt=["b+","b--","r-"])
plt.close("all")
#self.b.adnewjust_contrast((0.1,0.9),percent=True)z
def test_funcs(self):
b=self.a.translate((2.5,3))
self.c=b.correct_drift(ref=self.a)
self.d=b.align(self.a,method="scharr")
tv=np.array(self.d["tvec"])*10
cd=np.array(self.c["correct_drift"])*10
shift=np.array([25,30])
d1=mag(cd-shift)
d2=mag(tv-shift)
self.assertLess(d1,1.5,"Drift Correct off by more than 0.1 pxiels.")
self.assertLess(d2,1.5,"Scharr Alignment off by more than 0.1 pxiels.")
class FuncsTest(unittest.TestCase):
def setUp(self):
self.a = ImageArray(join(thisdir,
'coretestdata/im2_noannotations.png'))
self.a1 = ImageArray(join(thisdir, 'coretestdata/im1_annotated.png'))
def test_funcs(self):
b = self.a.translate((2.5, 3))
c = b.correct_drift(ref=self.a)
class FuncsTest(unittest.TestCase):
def setUp(self):
self.a=ImageArray(join(thisdir,'coretestdata/im2_noannotations.png'))
self.a1=ImageArray(join(thisdir,'coretestdata/im1_annotated.png'))
self.a2=STXMImage(join(thisdir,"..","..","..","sample-data","Sample_Image_2017-10-15_100.hdf5"))
self.a3=STXMImage(join(thisdir,"..","..","..","sample-data","Sample_Image_2017-10-15_101.hdf5"))
def test_imagefile_ops(self):
self.a2.gridimage()
self.a3.gridimage()
self.a2.crop(5,-15,5,-5,_=True)
self.a3.crop(5,-15,5,-5,_=True)
self.b=self.a2//self.a3
self.assertEqual(self.b.shape,(90,80),"Failure to crop image correctly.")
self.assertGreater(self.b.max(),0.047,"XMCD Ratio calculation failed")
self.assertLess(self.b.min(),-0.05,"XMCD Ratio calculation failed")
self.b.normalise()
self.assertEqual(self.b.max(),1.0,"Normalise Image failed")
self.assertEqual(self.b.min(),-1.0,"Normalise Image failed")
self.profile=self.b.profile_line((0,0),(100,100))
self.profile.plot()
self.b.mask=self.a2.image>25E3
self.hist=self.b.hist(bins=200)
self.hist.column_headers=["XMCD Signal","Frequency"]
self.hist.labels=None
g1=LorentzianModel(prefix="g1_")
g2=LorentzianModel(prefix="g2_")
params=g1.make_params()
params.update(g2.make_params())
double_peak=g1+g2
g1=np.argmax(self.hist.y[:100]) # Location of first peak
g2=np.argmax(self.hist.y[100:])+100
for k, p in zip(params,[0.25,self.hist.x[g1],self.hist.y[g1]/np.sqrt(2),0.5,self.hist.y[g1],
0.25,self.hist.x[g2],self.hist.y[g2]/np.sqrt(2),0.5,self.hist.y[g2]]):
params[k].value=p
print(g1,g2,params)
self.res=self.hist.lmfit(double_peak,p0=params,output="report")
self.hist.add_column(self.res.init_fit,header="Initial Fit")
self.hist.add_column(self.res.best_fit,header="Best Fit")
self.hist.setas="xyyy"
self.hist.plot(fmt=["b+","b--","r-"])
plt.close("all")
#self.b.adnewjust_contrast((0.1,0.9),percent=True)z
def test_funcs(self):
b=self.a.translate((2.5,3))
self.c=b.correct_drift(ref=self.a)
self.d=b.align(self.a,method="scharr")
tv=np.array(self.d["tvec"])*10
cd=np.array(self.c["correct_drift"])*10
shift=np.array([25,30])
d1=mag(cd-shift)
d2=mag(tv-(-shift[::-1]))
self.assertLess(d1,1.5,"Drift Correct off by more than 0.1 pxiels.")
self.assertLess(d2,1.5,"Scharr Alignment off by more than 0.1 pxiels.")
a1=ImageFile(self.a1.clone)
a1.as_float()
a1.image=np.sqrt(a1.image)/2+0.25
a1.adjust_contrast()
self.assertEqual(a1.span(),(0.0,1.0),"Either adjust_contrast or span failed with an ImageFile")
# print("#"*80)
# print(self.a.metadata)
# print(self.a1.metadata)
# print(all([k in self.a.metadata.keys() for k in self.a1.metadata.keys()]))
def test_imagefuncs(self):
self.a2.subtract_image(self.a2.image,offset=0)
self.assertTrue(np.all(self.a2.image<=0.0001),"Failed to subtract image from itself")
x=np.linspace(-3*np.pi,3*np.pi,101)
X,Y=np.meshgrid(x,x)
i=ImageFile(np.sin(X)*np.cos(Y))
i2=i.clone
j=i.fft()
self.assertTrue(np.all(np.unique(np.argmax(j,axis=1))==np.array([47,53])),"FFT of image test failed")
j.imshow()
self.assertTrue(len(plt.get_fignums())==1,"Imshow didn't open one window")
plt.close("all")
self.a2.imshow(title=None,figure=None)
self.a2.imshow(title="Hello",figure=1)
self.assertTrue(len(plt.get_fignums())==1,"Imshow with arguments didn't open one window")
plt.close("all")
i=i2
k=i+0.2*X-0.1*Y+0.2
k.level_image(mode="norm")
j=k-i
self.assertLess(np.max(j),0.01,"Level Image failed")
i2=i.clone
i2.quantize([-0.5,0,0.5])
self.assertTrue(np.all(np.unique(i2.data)==np.array([-0.5,0,0.5])),"Quantise levels failed")
i2=i.clone
i2.quantize([-0.5,0,0.5],levels=[-0.25,0.25])
self.assertTrue(np.all(np.unique(i2.data)==np.array([-0.5,0,0.5])),"Quantise levels failed")
i2=i.clone
i2.rotate(np.pi/4)
i2.fft()
self.assertTrue(np.all(np.unique(np.argmax(i2,axis=1))==np.array([46, 47, 49, 50, 51, 53])),"FFT of image test failed")
