Fix PEP8 in sims and remove unnecessary imports

dipy/sims/__init__.py

@@ -1 +1 @@
-#init for simulations
+# init for simulations

dipy/sims/phantom.py

@@ -62,7 +62,7 @@ def add_noise(vol, snr=1.0, S0=None, noise_type='rician'):
     return np.reshape(vol_flat, orig_shape)
 
 
-def diff2eigenvectors(dx,dy,dz):
+def diff2eigenvectors(dx, dy, dz):
     """ numerical derivatives 2 eigenvectors
     """
     basis = np.eye(3)
@@ -197,54 +197,54 @@ def orbital_phantom(gtab=None,
 
 if __name__ == "__main__":
 
-    ## TODO: this can become a nice tutorial for generating phantoms
+    # TODO: this can become a nice tutorial for generating phantoms
 
     def f(t):
-        x=np.sin(t)
-        y=np.cos(t)
-        #z=np.zeros(t.shape)
-        z=np.linspace(-1,1,len(x))
-        return x,y,z
+        x = np.sin(t)
+        y = np.cos(t)
+        # z=np.zeros(t.shape)
+        z = np.linspace(-1, 1, len(x))
+        return x, y, z
 
-    #helix
-    vol=orbital_phantom(func=f)
+    # helix
+    vol = orbital_phantom(func=f)
 
     def f2(t):
-        x=np.linspace(-1,1,len(t))
-        y=np.linspace(-1,1,len(t))
-        z=np.zeros(x.shape)
-        return x,y,z
+        x = np.linspace(-1, 1, len(t))
+        y = np.linspace(-1, 1, len(t))
+        z = np.zeros(x.shape)
+        return x, y, z
 
-    #first direction
-    vol2=orbital_phantom(func=f2)
+    # first direction
+    vol2 = orbital_phantom(func=f2)
 
     def f3(t):
-        x=np.linspace(-1,1,len(t))
-        y=-np.linspace(-1,1,len(t))
-        z=np.zeros(x.shape)
-        return x,y,z
+        x = np.linspace(-1, 1, len(t))
+        y = -np.linspace(-1, 1, len(t))
+        z = np.zeros(x.shape)
+        return x, y, z
 
-    #second direction
-    vol3=orbital_phantom(func=f3)
-    #double crossing
-    vol23=vol2+vol3
+    # second direction
+    vol3 = orbital_phantom(func=f3)
+    # double crossing
+    vol23 = vol2 + vol3
 
-    #"""
+    # """
     def f4(t):
-        x=np.zeros(t.shape)
-        y=np.zeros(t.shape)
-        z=np.linspace(-1,1,len(t))
-        return x,y,z
+        x = np.zeros(t.shape)
+        y = np.zeros(t.shape)
+        z = np.linspace(-1, 1, len(t))
+        return x, y, z
 
-    #triple crossing
-    vol4=orbital_phantom(func=f4)
-    vol234=vol23+vol4
+    # triple crossing
+    vol4 = orbital_phantom(func=f4)
+    vol234 = vol23 + vol4
 
-    voln=add_rician_noise(vol234)
+    voln = add_rician_noise(vol234)
 
-    #"""
+    # """
 
-    #r=fvtk.ren()
-    #fvtk.add(r,fvtk.volume(vol234[...,0]))
-    #fvtk.show(r)
-    #vol234n=add_rician_noise(vol234,20)
+    # r=fvtk.ren()
+    # fvtk.add(r,fvtk.volume(vol234[...,0]))
+    # fvtk.show(r)
+    # vol234n=add_rician_noise(vol234,20)

dipy/sims/tests/__init__.py

@@ -1,4 +1,4 @@
 # Test callable
 from numpy.testing import Tester
 test = Tester().test
-del Tester
+del Tester

dipy/sims/tests/test_phantom.py

@@ -1,26 +1,21 @@
 from __future__ import division
 
 import numpy as np
-import nose
-import nibabel as nib
-import numpy.testing.decorators as dec
 
 from numpy.testing import (assert_, assert_equal, assert_array_equal,
                            assert_array_almost_equal, assert_almost_equal,
                            run_module_suite)
 
-from dipy.core.geometry import vec2vec_rotmat
 from dipy.data import get_data
 from dipy.reconst.dti import TensorModel
-from dipy.sims.phantom import orbital_phantom, add_noise
-from dipy.sims.voxel import single_tensor
+from dipy.sims.phantom import orbital_phantom
 from dipy.core.gradients import gradient_table
 
 
-fimg,fbvals,fbvecs=get_data('small_64D')
-bvals=np.load(fbvals)
-bvecs=np.load(fbvecs)
-bvecs[np.isnan(bvecs)]=0
+fimg, fbvals, fbvecs = get_data('small_64D')
+bvals = np.load(fbvals)
+bvecs = np.load(fbvecs)
+bvecs[np.isnan(bvecs)] = 0
 
 gtab = gradient_table(bvals, bvecs)
 
@@ -29,10 +24,10 @@ def f(t):
     """
     Helper function used to define a mapping time => xyz
     """
-    x = np.linspace(-1,1,len(t)) 
-    y = np.linspace(-1,1,len(t))
-    z = np.linspace(-1,1,len(t))
-    return x,y,z
+    x = np.linspace(-1, 1, len(t))
+    y = np.linspace(-1, 1, len(t))
+    z = np.linspace(-1, 1, len(t))
+    return x, y, z
 
 
 def test_phantom():
@@ -55,7 +50,9 @@ def test_phantom():
     FA[np.isnan(FA)] = 0
     # 686 -> expected FA given diffusivities of [1500, 400, 400]
     l1, l2, l3 = 1500e-6, 400e-6, 400e-6
-    expected_fa =  (np.sqrt(0.5) * np.sqrt((l1 - l2)**2 + (l2-l3)**2 + (l3-l1)**2 )/np.sqrt(l1**2 + l2**2 + l3**2))
+    expected_fa = (np.sqrt(0.5) *
+                   np.sqrt((l1 - l2)**2 + (l2-l3)**2 + (l3-l1)**2) /
+                   np.sqrt(l1**2 + l2**2 + l3**2))
 
     assert_array_almost_equal(FA.max(), expected_fa, decimal=2)
 
@@ -85,6 +82,5 @@ def test_add_noise():
         assert_(np.abs(np.var(vol_noise - vol) - sigma ** 2) < 1)
 
 
-
 if __name__ == "__main__":
     run_module_suite()

dipy/sims/voxel.py

@@ -307,7 +307,6 @@ def multi_tensor(gtab, mevals, S0=100, angles=[(0, 0), (90, 0)],
 
 def multi_tensor_dki(gtab, mevals, S0=100, angles=[(90., 0.), (90., 0.)],
                      fractions=[50, 50], snr=20):
-
     r""" Simulate the diffusion-weight signal, diffusion and kurtosis tensors
     based on the DKI model
 
@@ -533,7 +532,6 @@ def DKI_signal(gtab, dt, kt, S0=150, snr=None):
     return S
 
 
-
 def single_tensor_odf(r, evals=None, evecs=None):
     """ Simulated ODF with a single tensor.