--- Begin Message ---
Package: release.debian.org
Severity: normal
User: release.debian.org@packages.debian.org
Usertags: unblock
Please unblock package pysal
It contains an upstream patch to fix #848783 (FTBFS with NumPy 1.12),
instead of just ignoring the test failure.
unblock pysal/1.13.0-3
Kind Regards,
Bas
diff -Nru pysal-1.13.0/debian/changelog pysal-1.13.0/debian/changelog
--- pysal-1.13.0/debian/changelog 2016-12-19 22:45:24.000000000 +0100
+++ pysal-1.13.0/debian/changelog 2017-01-28 12:38:13.000000000 +0100
@@ -1,3 +1,12 @@
+pysal (1.13.0-3) unstable; urgency=medium
+
+ * Team upload.
+ * Add upstream patch to fix FTBFS with NumPy 1.12.
+ (closes: #848783)
+ * Don't ignore test failures, numpy compatibility has been fixed.
+
+ -- Bas Couwenberg <sebastic@debian.org> Sat, 28 Jan 2017 12:38:13 +0100
+
pysal (1.13.0-2) unstable; urgency=medium
* Team upload.
diff -Nru pysal-1.13.0/debian/patches/fix-for-numpy-bump-induced-breakage.patch pysal-1.13.0/debian/patches/fix-for-numpy-bump-induced-breakage.patch
--- pysal-1.13.0/debian/patches/fix-for-numpy-bump-induced-breakage.patch 1970-01-01 01:00:00.000000000 +0100
+++ pysal-1.13.0/debian/patches/fix-for-numpy-bump-induced-breakage.patch 2017-01-28 12:36:03.000000000 +0100
@@ -0,0 +1,346 @@
+Description: Fix for numpy bump induced breakage.
+ - casting index to int
+ - casting to ints
+ - fix for numpy bump (casting and reshape behavior)
+Author: Serge Rey <sjsrey@gmail.com>
+Bug: https://github.com/pysal/pysal/issues/896
+Bug-Debian: https://bugs.debian.org/848783
+
+--- a/pysal/esda/smoothing.py
++++ b/pysal/esda/smoothing.py
+@@ -555,11 +555,11 @@ def assuncao_rate(e, b):
+ class _Smoother(object):
+ """
+ This is a helper class that implements things that all smoothers should do.
+- Right now, the only thing that we need to propagate is the by_col function.
++ Right now, the only thing that we need to propagate is the by_col function.
+
+ TBQH, most of these smoothers should be functions, not classes (aside from
+ maybe headbanging triples), since they're literally only inits + one
+- attribute.
++ attribute.
+ """
+ def __init__(self):
+ pass
+@@ -567,7 +567,7 @@ class _Smoother(object):
+ @classmethod
+ def by_col(cls, df, e,b, inplace=False, **kwargs):
+ """
+- Compute smoothing by columns in a dataframe.
++ Compute smoothing by columns in a dataframe.
+
+ Parameters
+ -----------
+@@ -582,15 +582,15 @@ class _Smoother(object):
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+- directly to `df` itself.
++ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+- smoother.
++ smoother.
+
+ Returns
+ ---------
+ a copy of `df` containing the columns. Or, if `inplace`, this returns
+- None, but implicitly adds columns to `df`.
++ None, but implicitly adds columns to `df`.
+ """
+ if not inplace:
+ new = df.copy()
+@@ -718,13 +718,13 @@ class Empirical_Bayes(_Smoother):
+ class _Spatial_Smoother(_Smoother):
+ """
+ This is a helper class that implements things that all the things that
+- spatial smoothers should do.
++ spatial smoothers should do.
+ .
+- Right now, the only thing that we need to propagate is the by_col function.
++ Right now, the only thing that we need to propagate is the by_col function.
+
+ TBQH, most of these smoothers should be functions, not classes (aside from
+ maybe headbanging triples), since they're literally only inits + one
+- attribute.
++ attribute.
+ """
+ def __init__(self):
+ pass
+@@ -732,7 +732,7 @@ class _Spatial_Smoother(_Smoother):
+ @classmethod
+ def by_col(cls, df, e,b, w=None, inplace=False, **kwargs):
+ """
+- Compute smoothing by columns in a dataframe.
++ Compute smoothing by columns in a dataframe.
+
+ Parameters
+ -----------
+@@ -748,19 +748,19 @@ class _Spatial_Smoother(_Smoother):
+ the spatial weights object or objects to use with the
+ event-population pairs. If not provided and a weights object
+ is in the dataframe's metadata, that weights object will be
+- used.
++ used.
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+- directly to `df` itself.
++ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+- smoother.
++ smoother.
+
+ Returns
+ ---------
+ a copy of `df` containing the columns. Or, if `inplace`, this returns
+- None, but implicitly adds columns to `df`.
++ None, but implicitly adds columns to `df`.
+ """
+ if not inplace:
+ new = df.copy()
+@@ -1072,12 +1072,12 @@ class Age_Adjusted_Smoother(_Spatial_Smo
+ r = direct_age_standardization(e_n, b_n, s, w.n, alpha=alpha)
+ self.r = np.array([i[0] for i in r])
+ w.transform = 'o'
+-
++
+ @_requires('pandas')
+ @classmethod
+ def by_col(cls, df, e,b, w=None, s=None, **kwargs):
+ """
+- Compute smoothing by columns in a dataframe.
++ Compute smoothing by columns in a dataframe.
+
+ Parameters
+ -----------
+@@ -1093,22 +1093,22 @@ class Age_Adjusted_Smoother(_Spatial_Smo
+ the spatial weights object or objects to use with the
+ event-population pairs. If not provided and a weights object
+ is in the dataframe's metadata, that weights object will be
+- used.
++ used.
+ s : string or list of strings
+ the name or names of columns to use as a standard population
+- variable for the events `e` and at-risk populations `b`.
++ variable for the events `e` and at-risk populations `b`.
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+- directly to `df` itself.
++ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+- smoother.
++ smoother.
+
+ Returns
+ ---------
+ a copy of `df` containing the columns. Or, if `inplace`, this returns
+- None, but implicitly adds columns to `df`.
++ None, but implicitly adds columns to `df`.
+ """
+ if s is None:
+ raise Exception('Standard population variable "s" must be supplied.')
+@@ -1326,7 +1326,7 @@ class Spatial_Median_Rate(_Spatial_Smoot
+ if not w.id_order_set:
+ raise ValueError("w id_order must be set to align with the order of e and b")
+ e = np.asarray(e).flatten()
+- b = np.asarray(b).flatten()
++ b = np.asarray(b).flatten()
+ self.r = e * 1.0 / b
+ self.aw, self.w = aw, w
+ while iteration:
+@@ -1469,13 +1469,13 @@ class Spatial_Filtering(_Smoother):
+ b_n_f = b_n[[0]]
+ self.r.append(e_n_f[-1] * 1.0 / b_n_f[-1])
+ self.r = np.array(self.r)
+-
++
+ @_requires('pandas')
+ @classmethod
+ def by_col(cls, df, e, b, x_grid, y_grid, geom_col='geometry', **kwargs):
+ """
+ Compute smoothing by columns in a dataframe. The bounding box and point
+- information is computed from the geometry column.
++ information is computed from the geometry column.
+
+ Parameters
+ -----------
+@@ -1493,13 +1493,13 @@ class Spatial_Filtering(_Smoother):
+ number of grid cells to use along the y-axis
+ geom_col: string
+ the name of the column in the dataframe containing the
+- geometry information.
++ geometry information.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+- smoother.
++ smoother.
+ Returns
+ ---------
+- a new dataframe of dimension (x_grid*y_grid, 3), containing the
++ a new dataframe of dimension (x_grid*y_grid, 3), containing the
+ coordinates of the grid cells and the rates associated with those grid
+ cells.
+ """
+@@ -1525,8 +1525,8 @@ class Spatial_Filtering(_Smoother):
+ grid = np.asarray(r.grid).reshape(-1,2)
+ name = '_'.join(('-'.join((ename, bname)), cls.__name__.lower()))
+ colnames = ('_'.join((name, suffix)) for suffix in ['X', 'Y', 'R'])
+- items = [(name, col) for name,col in zip(colnames, [grid[:,0],
+- grid[:,1],
++ items = [(name, col) for name,col in zip(colnames, [grid[:,0],
++ grid[:,1],
+ r.r])]
+ res.append(pd.DataFrame.from_items(items))
+ outdf = pd.concat(res)
+@@ -1821,9 +1821,9 @@ class Headbanging_Median_Rate(object):
+ if hasattr(self, 'extra') and id in self.extra:
+ extra = self.extra
+ trp_r = r[list(triples[0])]
+- # observed rate
++ # observed rate
+ # plus difference in rate scaled by ratio of extrapolated distance
+- # & observed distance.
++ # & observed distance.
+ trp_r[-1] = trp_r[0] + (trp_r[0] - trp_r[-1]) * (
+ extra[id][-1] * 1.0 / extra[id][1])
+ trp_r = sorted(trp_r)
+@@ -1852,14 +1852,14 @@ class Headbanging_Median_Rate(object):
+ trp_r.sort(order='r')
+ lowest.append(trp_r['r'][0])
+ highest.append(trp_r['r'][-1])
+- lowest_aw.append(self.aw[trp_r['w'][0]])
+- highest_aw.append(self.aw[trp_r['w'][-1]])
++ lowest_aw.append(self.aw[int(trp_r['w'][0])])
++ highest_aw.append(self.aw[int(trp_r['w'][-1])])
+ wm_lowest = weighted_median(np.array(lowest), np.array(lowest_aw))
+ wm_highest = weighted_median(
+ np.array(highest), np.array(highest_aw))
+ triple_members = flatten(triples, unique=False)
+ return r[id], wm_lowest, wm_highest, self.aw[id] * len(triples), self.aw[triple_members].sum()
+-
++
+ def __get_median_from_screens(self, screens):
+ if isinstance(screens, float):
+ return screens
+@@ -1884,13 +1884,13 @@ class Headbanging_Median_Rate(object):
+ k, tr[k], weighted=(self.aw is not None))
+ new_r.append(self.__get_median_from_screens(screens))
+ self.r = np.array(new_r)
+-
++
+ @_requires('pandas')
+ @classmethod
+ def by_col(cls, df, e, b, t=None, geom_col='geometry', inplace=False, **kwargs):
+ """
+ Compute smoothing by columns in a dataframe. The bounding box and point
+- information is computed from the geometry column.
++ information is computed from the geometry column.
+
+ Parameters
+ -----------
+@@ -1904,22 +1904,22 @@ class Headbanging_Median_Rate(object):
+ variables to be smoothed
+ t : Headbanging_Triples instance or list of Headbanging_Triples
+ list of headbanging triples instances. If not provided, this
+- is computed from the geometry column of the dataframe.
++ is computed from the geometry column of the dataframe.
+ geom_col: string
+ the name of the column in the dataframe containing the
+- geometry information.
++ geometry information.
+ inplace : bool
+ a flag denoting whether to output a copy of `df` with the
+ relevant smoothed columns appended, or to append the columns
+- directly to `df` itself.
++ directly to `df` itself.
+ **kwargs: optional keyword arguments
+ optional keyword options that are passed directly to the
+- smoother.
++ smoother.
+ Returns
+ ---------
+ a new dataframe containing the smoothed Headbanging Median Rates for the
+ event/population pairs. If done inplace, there is no return value and
+- `df` is modified in place.
++ `df` is modified in place.
+ """
+ import pandas as pd
+ if not inplace:
+@@ -1939,7 +1939,7 @@ class Headbanging_Median_Rate(object):
+
+ #Headbanging_Triples doesn't take **kwargs, so filter its arguments
+ # (self, data, w, k=5, t=3, angle=135.0, edgecor=False):
+-
++
+ w = kwargs.pop('w', None)
+ if w is None:
+ found = False
+@@ -1951,7 +1951,7 @@ class Headbanging_Median_Rate(object):
+ raise Exception('Weights not provided and no weights attached to frame!'
+ ' Please provide a weight or attach a weight to the'
+ ' dataframe')
+-
++
+ k = kwargs.pop('k', 5)
+ t = kwargs.pop('t', 3)
+ angle = kwargs.pop('angle', 135.0)
+@@ -1959,7 +1959,7 @@ class Headbanging_Median_Rate(object):
+
+ hbt = Headbanging_Triples(data, w, k=k, t=t, angle=angle,
+ edgecor=edgecor)
+-
++
+ res = []
+ for ename, bname in zip(e, b):
+ r = cls(df[ename], df[bname], hbt, **kwargs).r
+--- a/pysal/weights/spatial_lag.py
++++ b/pysal/weights/spatial_lag.py
+@@ -170,7 +170,7 @@ def lag_categorical(w, y, ties='tryself'
+ for neighb, weight in diter(neighbors):
+ vals[inty[w.id2i[neighb]]] += weight
+ outidx = _resolve_ties(idx,inty,vals,neighbors,ties, w)
+- output[w.id2i[idx]] = keys[outidx]
++ output[w.id2i[int(idx)]] = keys[int(outidx)]
+ return output.reshape(orig_shape)
+
+ def _resolve_ties(i,inty,vals,neighbors,method,w):
+--- a/pysal/spatial_dynamics/util.py
++++ b/pysal/spatial_dynamics/util.py
+@@ -12,14 +12,14 @@ def shuffle_matrix(X, ids):
+
+ Parameters
+ ----------
+- X : array
++ X : array
+ (k, k), array to be permutated.
+ ids : array
+ range (k, ).
+
+ Returns
+ -------
+- X : array
++ X : array
+ (k, k) with rows and columns randomly shuffled.
+
+ Examples
+@@ -50,7 +50,7 @@ def get_lower(matrix):
+ Returns
+ -------
+ lowvec : array
+- numpy array, the lower half of the distance matrix flattened into
++ numpy array, the lower half of the distance matrix flattened into
+ a vector of length n*(n-1)/2.
+
+ Examples
+@@ -75,6 +75,6 @@ def get_lower(matrix):
+ if i > j:
+ lowerlist.append(matrix[i, j])
+ veclen = n * (n - 1) / 2
+- lowvec = np.reshape(lowerlist, (veclen, 1))
++ lowvec = np.reshape(np.array(lowerlist), (int(veclen), 1))
+ return lowvec
+
diff -Nru pysal-1.13.0/debian/patches/series pysal-1.13.0/debian/patches/series
--- pysal-1.13.0/debian/patches/series 2016-12-09 19:19:52.000000000 +0100
+++ pysal-1.13.0/debian/patches/series 2017-01-28 12:34:25.000000000 +0100
@@ -1 +1,2 @@
unusual-interpreter.patch
+fix-for-numpy-bump-induced-breakage.patch
diff -Nru pysal-1.13.0/debian/rules pysal-1.13.0/debian/rules
--- pysal-1.13.0/debian/rules 2016-12-19 22:44:32.000000000 +0100
+++ pysal-1.13.0/debian/rules 2017-01-28 12:36:16.000000000 +0100
@@ -8,9 +8,6 @@
%:
dh $@ --with python2,python3 --buildsystem pybuild
-override_dh_auto_test:
- dh_auto_test || echo "Ignoring test failures"
-
override_dh_python2:
dh_python2 -ppython-pysal
dh_numpy -ppython-pysal
--- End Message ---