[WIP]

.pep8speaks.yml

@@ -0,0 +1,24 @@
+# File : .pep8speaks.yml
+
+message:  # Customize the comment made by the bot
+    opened:  # Messages when a new PR is submitted
+        header: "Hello @{name}, Thank you for submitting the Pull Request !"
+                # The keyword {name} is converted into the author's username
+        footer: "Do see the [DIPY coding Style guideline](https://github.com/nipy/dipy/blob/master/doc/devel/coding_style_guideline.rst)"
+                # The messages can be written as they would over GitHub
+    updated:  # Messages when new commits are added to the PR
+        header: "Hello @{name}, Thank you for updating !"
+        footer: ""  # Why to comment the link to the style guide everytime? :)
+    no_errors: "Cheers ! There are no PEP8 issues in this Pull Request. :beers: "
+
+scanner:
+    diff_only: True  # If True, errors caused by only the patch are shown
+
+pycodestyle:
+    max-line-length: 80  # Default is 79 in PEP8
+    # ignore:  # Errors and warnings to ignore
+    #    - W391
+    #    - E203
+
+only_mention_files_with_errors: True  # If False, a separate status comment for each file is made.
+descending_issues_order: False # If True, PEP8 issues in message will be displayed in descending order of line numbers in the file

README.rst

@@ -23,7 +23,7 @@
 .. image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
   :target: https://github.com/nipy/dipy/blob/master/LICENSE
 
-DIPY is a python toolbox for analysis of MR diffusion imaging.
+DIPY [DIPYREF]_ is a python library for analysis of MR diffusion imaging.
 
 DIPY is for research only; please do not use results from DIPY for
 clinical decisions.
@@ -83,3 +83,11 @@ Contributing
 ============
 
 We welcome contributions from the community. Please read our `Contributing guidelines <https://github.com/nipy/dipy/blob/master/CONTRIBUTING.md>`_.
+
+Reference
+=========
+
+.. [DIPYREF] E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem,
+    S. Van Der Walt, M. Descoteaux, I. Nimmo-Smith and DIPY contributors,
+    "DIPY, a library for the analysis of diffusion MRI data",
+    Frontiers in Neuroinformatics, vol. 8, p. 8, Frontiers, 2014.

dipy/align/imwarp.py

@@ -1373,9 +1373,9 @@ def _get_energy_derivative(self):
         x = range(self.energy_window)
         y = self.energy_list[(n_iter - self.energy_window):n_iter]
         ss = sum(y)
-        if(ss > 0):
-            ss *= -1
-        y = [v / ss for v in y]
+        if not ss == 0:  # avoid division by zero
+            ss = - ss if ss > 0 else ss
+            y = [v / ss for v in y]
         der = self._approximate_derivative_direct(x, y)
         return der
 

dipy/reconst/dki.py

@@ -121,6 +121,7 @@ def carlson_rd(x, y, z, errtol=1e-4):
     defined as:
 
     .. math::
+
         R_D = \frac{3}{2} \int_{0}^{\infty} (t+x)^{-\frac{1}{2}}
         (t+y)^{-\frac{1}{2}}(t+z)  ^{-\frac{3}{2}}
 
@@ -302,6 +303,7 @@ def _F2m(a, b, c):
     Function $F_2$ is defined as [1]_:
 
     .. math::
+
         F_2(\lambda_1,\lambda_2,\lambda_3)=
         \frac{(\lambda_1+\lambda_2+\lambda_3)^2}
         {3(\lambda_2-\lambda_3)^2}
@@ -574,6 +576,7 @@ def apparent_kurtosis_coef(dki_params, sphere, min_diffusivity=0,
     calculation of AKC is done using formula [1]_:
 
     .. math ::
+
         AKC(n)=\frac{MD^{2}}{ADC(n)^{2}}\sum_{i=1}^{3}\sum_{j=1}^{3}
         \sum_{k=1}^{3}\sum_{l=1}^{3}n_{i}n_{j}n_{k}n_{l}W_{ijkl}
 
@@ -662,36 +665,39 @@ def mean_kurtosis(dki_params, min_kurtosis=-3./7, max_kurtosis=3):
     Notes
     --------
     The MK analytical solution is calculated using the following equation [1]_:
+
     .. math::
 
-    MK=F_1(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{1111}+
-       F_1(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{2222}+
-       F_1(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{3333}+ \\
-       F_2(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2233}+
-       F_2(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{1133}+
-       F_2(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{1122}
+        MK=F_1(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{1111}+
+           F_1(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{2222}+
+           F_1(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{3333}+ \\
+           F_2(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2233}+
+           F_2(\lambda_2,\lambda_1,\lambda_3)\hat{W}_{1133}+
+           F_2(\lambda_3,\lambda_2,\lambda_1)\hat{W}_{1122}
 
     where $\hat{W}_{ijkl}$ are the components of the $W$ tensor in the
     coordinates system defined by the eigenvectors of the diffusion tensor
     $\mathbf{D}$ and
 
-    F_1(\lambda_1,\lambda_2,\lambda_3)=
-    \frac{(\lambda_1+\lambda_2+\lambda_3)^2}
-    {18(\lambda_1-\lambda_2)(\lambda_1-\lambda_3)}
-    [\frac{\sqrt{\lambda_2\lambda_3}}{\lambda_1}
-    R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
-    \frac{3\lambda_1^2-\lambda_1\lambda_2-\lambda_2\lambda_3-
-    \lambda_1\lambda_3}
-    {3\lambda_1 \sqrt{\lambda_2 \lambda_3}}
-    R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-1 ]
-
-    F_2(\lambda_1,\lambda_2,\lambda_3)=
-    \frac{(\lambda_1+\lambda_2+\lambda_3)^2}
-    {3(\lambda_2-\lambda_3)^2}
-    [\frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}
-    R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
-    \frac{2\lambda_1-\lambda_2-\lambda_3}{3\sqrt{\lambda_2 \lambda_3}}
-    R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-2]
+    .. math::
+
+        F_1(\lambda_1,\lambda_2,\lambda_3)=
+        \frac{(\lambda_1+\lambda_2+\lambda_3)^2}
+        {18(\lambda_1-\lambda_2)(\lambda_1-\lambda_3)}
+        [\frac{\sqrt{\lambda_2\lambda_3}}{\lambda_1}
+        R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
+        \frac{3\lambda_1^2-\lambda_1\lambda_2-\lambda_2\lambda_3-
+        \lambda_1\lambda_3}
+        {3\lambda_1 \sqrt{\lambda_2 \lambda_3}}
+        R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-1 ]
+
+        F_2(\lambda_1,\lambda_2,\lambda_3)=
+        \frac{(\lambda_1+\lambda_2+\lambda_3)^2}
+        {3(\lambda_2-\lambda_3)^2}
+        [\frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}
+        R_F(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)+\\
+        \frac{2\lambda_1-\lambda_2-\lambda_3}{3\sqrt{\lambda_2 \lambda_3}}
+        R_D(\frac{\lambda_1}{\lambda_2},\frac{\lambda_1}{\lambda_3},1)-2]
 
     where $R_f$ and $R_d$ are the Carlson's elliptic integrals.
 
@@ -764,6 +770,7 @@ def _G1m(a, b, c):
     Notes
     --------
     Function $G_1$ is defined as [1]_:
+
     .. math::
 
         G_1(\lambda_1,\lambda_2,\lambda_3)=
@@ -829,7 +836,9 @@ def _G2m(a, b, c):
     Notes
     --------
     Function $G_2$ is defined as [1]_:
+
     .. math::
+
         G_2(\lambda_1,\lambda_2,\lambda_3)=
         \frac{(\lambda_1+\lambda_2+\lambda_3)^2}{(\lambda_2-\lambda_3)^2}
         \left ( \frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}-2\right )
@@ -899,14 +908,18 @@ def radial_kurtosis(dki_params, min_kurtosis=-3./7, max_kurtosis=10):
 
     Notes
     --------
-    RK is calculated with the following equation  [1]_::
+    RK is calculated with the following equation  [1]_:
+
     .. math::
+
         K_{\bot} = G_1(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2222} +
                    G_1(\lambda_1,\lambda_3,\lambda_2)\hat{W}_{3333} +
                    G_2(\lambda_1,\lambda_2,\lambda_3)\hat{W}_{2233}
 
     where:
+
     .. math::
+
         G_1(\lambda_1,\lambda_2,\lambda_3)=
         \frac{(\lambda_1+\lambda_2+\lambda_3)^2}{18\lambda_2(\lambda_2-
         \lambda_3)} \left (2\lambda_2 +
@@ -916,6 +929,7 @@ def radial_kurtosis(dki_params, min_kurtosis=-3./7, max_kurtosis=10):
     and
 
     .. math::
+
         G_2(\lambda_1,\lambda_2,\lambda_3)=
         \frac{(\lambda_1+\lambda_2+\lambda_3)^2}{(\lambda_2-\lambda_3)^2}
         \left ( \frac{\lambda_2+\lambda_3}{\sqrt{\lambda_2\lambda_3}}-2\right )
@@ -1220,6 +1234,7 @@ def dki_prediction(dki_params, gtab, S0=1.):
     .. math::
 
         S=S_{0}e^{-bD+\frac{1}{6}b^{2}D^{2}K}
+
     """
     evals, evecs, kt = split_dki_param(dki_params)
 
@@ -1417,13 +1432,15 @@ def akc(self, sphere):
         calculation of AKC is done using formula:
 
         .. math ::
+
             AKC(n)=\frac{MD^{2}}{ADC(n)^{2}}\sum_{i=1}^{3}\sum_{j=1}^{3}
             \sum_{k=1}^{3}\sum_{l=1}^{3}n_{i}n_{j}n_{k}n_{l}W_{ijkl}
 
         where $W_{ijkl}$ are the elements of the kurtosis tensor, MD the mean
         diffusivity and ADC the apparent diffusion coefficent computed as:
 
         .. math ::
+
             ADC(n)=\sum_{i=1}^{3}\sum_{j=1}^{3}n_{i}n_{j}D_{ij}
 
         where $D_{ij}$ are the elements of the diffusion tensor.
@@ -2014,7 +2031,7 @@ def Wcons(k_elements):
     k_elements : (15,)
         elements of the kurtosis tensor in the following order:
 
-        .. math::
+    .. math::
 
     \begin{matrix} ( & W_{xxxx} & W_{yyyy} & W_{zzzz} & W_{xxxy} & W_{xxxz}
                      & ... \\

dipy/reconst/mapmri.py

@@ -401,8 +401,8 @@ def fit(self, data):
                 lopt * cvxpy.quad_form(c, laplacian_matrix)
             )
             M0 = M[self.gtab.b0s_mask, :]
-            constraints = [M0[0] * c == 1,
-                           K * c > -.1]
+            constraints = [(M0[0] * c) == 1,
+                           (K * c) >= -0.1]
             prob = cvxpy.Problem(objective, constraints)
             try:
                 prob.solve(solver=self.cvxpy_solver)

dipy/reconst/shore.py

@@ -269,7 +269,7 @@ def fit(self, data):
                     self.cache_set(
                         'shore_matrix_positive_constraint',
                         (self.pos_grid, self.pos_radius), psi)
-                constraints = [M0[0] * c == 1., psi * c > 1e-3]
+                constraints = [(M0[0] * c) == 1., (psi * c) >= 1e-3]
             prob = cvxpy.Problem(objective, constraints)
             try:
                 prob.solve(solver=self.cvxpy_solver)

dipy/reconst/tests/test_forecast.py

@@ -50,11 +50,11 @@ def test_forecast_positive_constrain():
 
     sphere = get_sphere('repulsion100')
     fodf = f_fit.odf(sphere, clip_negative=False)
-    assert_equal(fodf[fodf < 0].sum(), 0)
+    assert_almost_equal(fodf[fodf < 0].sum(), 0, 2)
 
     coeff = f_fit.sh_coeff
     c0 = np.sqrt(1.0/(4*np.pi))
-    assert_almost_equal(coeff[0], c0, 10)
+    assert_almost_equal(coeff[0], c0, 5)
 
 
 def test_forecast_csd():

dipy/reconst/tests/test_shore.py

@@ -53,7 +53,7 @@ def test_shore_positive_constrain():
                      pos_radius=20e-03)
     asmfit = asm.fit(data.S)
     eap = asmfit.pdf_grid(11, 20e-03)
-    assert_equal(eap[eap < 0].sum(), 0)
+    assert_almost_equal(eap[eap < 0].sum(), 0, 3)
 
 
 def test_shore_fitting_no_constrain_e0():

dipy/tracking/streamline.py

@@ -29,16 +29,19 @@
 
     MEGABYTE = 1024 * 1024
 
-
     class _BuildCache(object):
         def __init__(self, arr_seq, common_shape, dtype):
             self.offsets = list(arr_seq._offsets)
             self.lengths = list(arr_seq._lengths)
             self.next_offset = arr_seq._get_next_offset()
             self.bytes_per_buf = arr_seq._buffer_size * MEGABYTE
             # Use the passed dtype only if null data array
-            self.dtype = dtype if arr_seq._data.size == 0 else arr_seq._data.dtype
-            if arr_seq.common_shape != () and common_shape != arr_seq.common_shape:
+            if arr_seq._data.size == 0:
+                self.dtype = dtype
+            else:
+                arr_seq._data.dtype
+            if (arr_seq.common_shape != () and
+                    common_shape != arr_seq.common_shape):
                 raise ValueError(
                     "All dimensions, except the first one, must match exactly")
             self.common_shape = common_shape
@@ -50,34 +53,32 @@ def update_seq(self, arr_seq):
             arr_seq._offsets = np.array(self.offsets)
             arr_seq._lengths = np.array(self.lengths)
 
-
     class Streamlines(ArraySequence):
-
         def __init__(self, *args, **kwargs):
             super(Streamlines, self).__init__(*args, **kwargs)
 
         def append(self, element, cache_build=False):
-            """ Appends `element` to this array sequence.
+            """
+            Appends `element` to this array sequence.
+
             Append can be a lot faster if it knows that it is appending several
-            elements instead of a single element.  In that case it can cache the
-            parameters it uses between append operations, in a "build cache".  To
-            tell append to do this, use ``cache_build=True``.  If you use
-            ``cache_build=True``, you need to finalize the append operations with
-            :meth:`finalize_append`.
+            elements instead of a single element.  In that case it can cache
+            the parameters it uses between append operations, in a "build
+            cache". To tell append to do this, use ``cache_build=True``.  If
+            you use ``cache_build=True``, you need to finalize the append
+            operations with :meth:`finalize_append`.
+
             Parameters
             ----------
-            element : ndarray
-                Element to append. The shape must match already inserted elements
-                shape except for the first dimension.
-            cache_build : {False, True}
-                Whether to save the build cache from this append routine.  If True,
-                append can assume it is the only player updating `self`, and the
-                caller must finalize `self` after all append operations, with
-                ``self.finalize_append()``.
-            Returns
+            element : ndarray Element to append. The shape must match already
+                inserted elements shape except for the first dimension.
+                cache_build : {False, True} Whether to save the build cache
+                from this append routine.  If True, append can assume it is the
+                only player updating `self`, and the caller must finalize
+                `self` after all append operations, with
+                ``self.finalize_append()``. Returns
             -------
-            None
-            Notes
+            None Notes
             -----
             If you need to add multiple elements you should consider
             `ArraySequence.extend`.
@@ -124,19 +125,20 @@ def extend(self, elements):
             """ Appends all `elements` to this array sequence.
             Parameters
             ----------
-            elements : iterable of ndarrays or :class:`ArraySequence` object
-                If iterable of ndarrays, each ndarray will be concatenated along
-                the first dimension then appended to the data of this
+            elements : iterable of ndarrays or :class:`ArraySequence` instance
+
+                If iterable of ndarrays, each ndarray will be concatenated
+                along the first dimension then appended to the data of this
                 ArraySequence.
-                If :class:`ArraySequence` object, its data are simply appended to
-                the data of this ArraySequence.
+                If :class:`ArraySequence` object, its data are simply appended
+                to the data of this ArraySequence.
+
             Returns
             -------
-            None
-            Notes
+            None Notes
             -----
-            The shape of the elements to be added must match the one of the data of
-            this :class:`ArraySequence` except for the first dimension.
+            The shape of the elements to be added must match the one of the
+            data of this :class:`ArraySequence` except for the first dimension.
             """
             # If possible try pre-allocating memory.
             try:
@@ -597,7 +599,8 @@ def _extract_vals(data, streamlines, affine=None, threedvec=False):
     """
     data = data.astype(np.float)
     if (isinstance(streamlines, list) or
-            isinstance(streamlines, types.GeneratorType)):
+       isinstance(streamlines, types.GeneratorType) or
+       isinstance(streamlines, Streamlines)):
         if affine is not None:
             streamlines = ut.move_streamlines(streamlines,
                                               np.linalg.inv(affine))

dipy/tracking/tests/test_streamline.py

@@ -1027,6 +1027,9 @@ def test_values_from_volume():
             vv = values_from_volume(data, np.array(sl1))
             npt.assert_almost_equal(vv, ans1, decimal=decimal)
 
+            vv = values_from_volume(data, Streamlines(sl1))
+            npt.assert_almost_equal(vv, ans1, decimal=decimal)
+
             affine = np.eye(4)
             affine[:, 3] = [-100, 10, 1, 1]
             x_sl1 = ut.move_streamlines(sl1, affine)