Merge pull request #357 from convexopt/py3

get to a low level of python3 compatibility

gpkit/_mosek/cli_expopt.py

@@ -101,8 +101,8 @@ def imize(c, A, p_idxs, *args, **kwargs):
                           for x in t_j_Atj])
 
         log = check_output(["mskexpopt", filename])
-        for logline in log.split("\n"):
-            print logline
+        for logline in log.split(b"\n"):
+            print(logline)
 
         with open(filename+".sol") as f:
             assert_line(f, "PROBLEM STATUS      : PRIMAL_AND_DUAL_FEASIBLE\n")

gpkit/geometric_program.py

@@ -58,7 +58,7 @@ def __init__(self, cost, constraints, verbosity=1):
         p_idxs = []
         m_idxs = []
         for i, p_len in enumerate(self.k):
-            m_idxs.append(range(len(p_idxs), len(p_idxs) + p_len))
+            m_idxs.append(list(range(len(p_idxs), len(p_idxs) + p_len)))
             p_idxs += [i]*p_len
         self.p_idxs = np.array(p_idxs)
         self.m_idxs = m_idxs
@@ -215,7 +215,7 @@ def _almost_equal(num1, num2):
             "local almost equal test"
             return num1 == num2 or abs((num1 - num2) / (num1 + num2)) < tol
         cost = sol["cost"]
-        primal_sol = np.log(sol["variables"].values())
+        primal_sol = np.log(list(sol["variables"].values()))
         nu = sol["sensitivities"]["monomials"]
         la = sol["sensitivities"]["posynomials"]
         A = self.A.tocsr()

gpkit/model.py

@@ -291,7 +291,7 @@ def _solve(self, programType, solver, verbosity, skipfailures,
 
         if sweep:
             if len(sweep) == 1:
-                sweep_grids = np.array(sweep.values())
+                sweep_grids = np.array(list(sweep.values()))
             else:
                 sweep_grids = np.meshgrid(*list(sweep.values()))
 
@@ -351,7 +351,7 @@ def solve_pass(i):
         solution.toarray()
         self.solution = solution  # NOTE: SIDE EFFECTS
         if verbosity > 0:
-            print solution.table()
+            print(solution.table())
         return solution
 
     # TODO: add sweepgp(index)?
@@ -433,7 +433,7 @@ def feasibility(self,
                 max_gp = self.gp().feasibility_search("max")
                 infeasibility = max_gp.solve(verbosity=verbosity-1)["cost"]
                 if verbosity > 0:
-                    print "      overall : %.2f" % infeasibility
+                    print("      overall : %.2f" % infeasibility)
                 feasibilities["overall"] = infeasibility
 
             if "constraints" in search:
@@ -442,7 +442,7 @@ def feasibility(self,
                 result = prod_gp.solve(verbosity=verbosity-1)
                 con_infeas = [result["variables"][sv] for sv in slackvars]
                 if verbosity > 0:
-                    print "  constraints : %s" % con_infeas
+                    print("  constraints : %s" % con_infeas)
                 feasibilities["constraints"] = con_infeas
 
         constants = get_constants(unsubbed, allsubs)
@@ -479,7 +479,7 @@ def feasibility(self,
             var_infeas = {addvalue[constvarkeys[i]]: feasible_constvalues[i]
                           for i in np.where(changed_vals)}
             if verbosity > 0:
-                print "    constants : %s" % var_infeas
+                print("    constants : %s" % var_infeas)
             feasibilities["constants"] = var_infeas
 
         if verbosity > 0:

gpkit/nomial_data.py

@@ -36,11 +36,12 @@ def __init__(self, exps=None, cs=None, nomials=None, simplify=True):
         self.varlocs, self.varstrs = locate_vars(self.exps)
         self.values = {vk: vk.descr["value"] for vk in self.varlocs
                        if "value" in vk.descr}
-        # confirm lengths before calling zip
-        assert len(self.exps) == len(self.cs)
-        self._hash = hash(tuple(zip(self.exps, tuple(self.cs))))
 
     def __hash__(self):
+        if not hasattr(self, "_hash"):
+            # confirm lengths before calling zip
+            assert len(self.exps) == len(self.cs)
+            self._hash = hash(tuple(zip(self.exps, tuple(self.cs))))
         return self._hash
 
     def __repr__(self):

gpkit/nomials.py

@@ -118,6 +118,8 @@ def __init__(self, exps=None, cs=1, require_positive=True, simplify=True,
             self.exp = self.exps[0]
             self.c = self.cs[0]
 
+    __hash__ = NomialData.__hash__
+
     @property
     def value(self):
         """Self, with values substituted for variables that have values

gpkit/solution_array.py

@@ -182,7 +182,8 @@ def results_table(data, title, minval=0, printunits=True, fixedcols=True,
         if not fixedcols:
             maxlens = [maxlens[0], 0, 0, 0]
         dirs = ['>', '<', '<', '<']
-        assert len(dirs) == len(maxlens)  # check lengths before using zip
+        # check lengths before using zip
+        assert len(list(dirs)) == len(list(maxlens))
         fmts = ['{0:%s%s}' % (direc, L) for direc, L in zip(dirs, maxlens)]
     lines = [[fmt.format(s) for fmt, s in zip(fmts, line)] for line in lines]
     lines = [title] + ["-"*len(title)] + [''.join(l) for l in lines] + [""]

gpkit/tools.py

@@ -35,7 +35,7 @@ def te_exp_minus1(posy, nterm):
         raise ValueError("Unexpected number of terms, nterm=%s" % nterm)
     res = 0
     factorial_denom = 1
-    for i in xrange(1, nterm + 1):
+    for i in range(1, nterm + 1):
         factorial_denom *= i
         res += posy**i / factorial_denom
     return res

gpkit/variables.py

@@ -3,6 +3,7 @@
 
 from .varkey import VarKey
 from .nomials import Monomial
+from .nomial_data import NomialData
 from .posyarray import PosyArray
 from .small_classes import Strings, Numbers, Quantity
 from .small_scripts import is_sweepvar
@@ -46,6 +47,8 @@ def __init__(self, *args, **descr):
         Monomial.__init__(self, **descr)
         self.__class__ = Variable
 
+    __hash__ = NomialData.__hash__
+
     @property
     def varkey(self):
         """Get the VarKey associated with this Variable"""