Package: release.debian.org Severity: normal User: release.debian....@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 <sebas...@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 <sjs...@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
