Updated black formatting

examples/snopt_bugfix.py

@@ -23,8 +23,7 @@ def objfunc(xdict):
 
 
 def sens(xdict, funcs):
-    """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
-    """
+    """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3"""
     x = xdict["x"]
     y = xdict["y"]
     funcsSens = {}

pyoptsparse/postprocessing/OptView_baseclass.py

@@ -253,10 +253,10 @@ def DetermineMajorIterations(self, db, OpenMDAO):
                 self.iter_type[:] = 1.0
 
     def SaveDBData(self, db, data_all, data_major, OpenMDAO, data_str):
-        """ Method to save the information within the database corresponding
-            to a certain key to the relevant dictionaries within the Display
-            object. This method is called twice, once for the design variables
-            and the other for the outputs. """
+        """Method to save the information within the database corresponding
+        to a certain key to the relevant dictionaries within the Display
+        object. This method is called twice, once for the design variables
+        and the other for the outputs."""
 
         # Loop over each optimization iteration
         for i, iter_type in enumerate(self.iter_type):
@@ -297,8 +297,8 @@ def SaveDBData(self, db, data_all, data_major, OpenMDAO, data_str):
                         data_major[new_key].append(data)
 
     def SaveOpenMDAOData(self, db):
-        """ Examine the OpenMDAO dict and save tags if the variables are
-            objectives (o), constraints (c), or design variables (dv). """
+        """Examine the OpenMDAO dict and save tags if the variables are
+        objectives (o), constraints (c), or design variables (dv)."""
 
         # Loop over each key in the metadata db
         for tag in db:

pyoptsparse/postprocessing/OptView_dash.py

@@ -250,10 +250,10 @@ def DetermineMajorIterations(self, db, OpenMDAO):
                 self.iter_type[:] = 1.0
 
     def SaveDBData(self, db, data_all, data_major, OpenMDAO, data_str):
-        """ Method to save the information within the database corresponding
-            to a certain key to the relevant dictionaries within the Display
-            object. This method is called twice, once for the design variables
-            and the other for the outputs. """
+        """Method to save the information within the database corresponding
+        to a certain key to the relevant dictionaries within the Display
+        object. This method is called twice, once for the design variables
+        and the other for the outputs."""
 
         # Loop over each optimization iteration
         for i, iter_type in enumerate(self.iter_type):
@@ -294,8 +294,8 @@ def SaveDBData(self, db, data_all, data_major, OpenMDAO, data_str):
                         data_major[new_key].append(data)
 
     def SaveOpenMDAOData(self, db):
-        """ Examine the OpenMDAO dict and save tags if the variables are
-            objectives (o), constraints (c), or design variables (dv). """
+        """Examine the OpenMDAO dict and save tags if the variables are
+        objectives (o), constraints (c), or design variables (dv)."""
 
         # Loop over each key in the metadata db
         for tag in db:

pyoptsparse/pyCONMIN/pyCONMIN.py

@@ -110,7 +110,7 @@ def __call__(
         storeSens : bool
             Flag sepcifying if sensitivities are to be stored in hist.
             This is necessay for hot-starting only.
-            """
+        """
 
         self.callCounter = 0
         self.storeSens = storeSens

pyoptsparse/pyIPOPT/pyIPOPT.py

@@ -413,7 +413,7 @@ def __call__(
         storeSens : bool
             Flag sepcifying if sensitivities are to be stored in hist.
             This is necessay for hot-starting only.
-            """
+        """
 
         self.callCounter = 0
         self.storeSens = storeSens

pyoptsparse/pyNSGA2/pyNSGA2.py

@@ -87,7 +87,7 @@ def __call__(self, optProb, storeHistory=None, hotStart=None, **kwargs):
         -----
         The kwargs are there such that the sens= argument can be
         supplied (but ignored here in nsga2)
-            """
+        """
 
         # ======================================================================
         # NSGA-II - Objective/Constraint Values Function

pyoptsparse/pyOpt_constraint.py

@@ -345,7 +345,10 @@ def finalize(self, variables, dvOffset, index):
                                 dvGroup, self.name
                             )
                             + "Expecting a Jacobian of size ({}, {}) but received a Jacobian of size ({}, {}).".format(
-                                self.ncon, ndvs, self.jac[dvGroup]["shape"][0], self.jac[dvGroup]["shape"][1],
+                                self.ncon,
+                                ndvs,
+                                self.jac[dvGroup]["shape"][0],
+                                self.jac[dvGroup]["shape"][1],
                             )
                         )
                     )

pyoptsparse/pyOpt_gradient.py

@@ -98,7 +98,7 @@ def __call__(self, x, funcs):
 
         fail : bool
             Flag for failure. It currently always returns False
-            """
+        """
 
         # Since this is *very* dumb loop over all the design
         # variables, it is easier to just loop over the x values as an

pyoptsparse/pyOpt_optimization.py

@@ -109,7 +109,7 @@ def checkVarName(self, varName):
         validName : str
             A valid variable name. May be the same as varName it that
             was, in fact, a valid name.
-            """
+        """
         if varName not in self.variables:
             return varName
         else:
@@ -137,7 +137,7 @@ def checkConName(self, conName):
         validName : str
             A valid constraint name. May be the same as conName it that
             was, in fact, a valid name.
-            """
+        """
         if conName not in self.constraints:
             return conName
         else:
@@ -348,7 +348,7 @@ def delVar(self, name):
         ----------
         name : str
            Name of variable or variable group to remove
-           """
+        """
         try:
             self.variables.pop(name)
         except KeyError:
@@ -924,7 +924,7 @@ def getOrdering(self, conOrder, oneSided, noEquality=False):
            Flag to split equality constraints into two inequality
            constraints. Some optimizers (CONMIN for example) can't do
            equality constraints explicitly.
-           """
+        """
 
         # Now for the fun part determine what *actual* order the
         # constraints need to be in: We recognize the following
@@ -1312,7 +1312,7 @@ def evaluateLinearConstraints(self, x, fcon):
         fcon : dict
             Dictionary of the constraints. The linear constraints are
             to be added to this dictionary.
-            """
+        """
 
         # This is actually pretty easy; it's just a matvec with the
         # proper linearJacobian entry we've already computed

pyoptsparse/pyOpt_optimizer.py

@@ -191,7 +191,7 @@ def _masterFunc(self, x, evaluate):
             This list contains at least one of 'fobj', 'fcon', 'gobj'
             or 'gcon'. This list tells this function which of the
             values is required on return
-            """
+        """
 
         # We are hot starting, we should be able to read the required
         # information out of the hot start file, process it and then
@@ -814,7 +814,7 @@ def setOption(self, name, value=None):
             Name of the option to set
         value : varies
             Variable value to set.
-            """
+        """
 
         if name in self.options["defaults"]:
             if type(value) == self.options["defaults"][name][0]:
@@ -850,7 +850,7 @@ def getOption(self, name):
         -------
         value : varies
             value of option for 'name'
-            """
+        """
 
         if name in self.options["defaults"]:
             return self.options[name][1]
@@ -874,7 +874,7 @@ def getInform(self, infocode=None):
         ----------
         infocode : int
             Integer information code
-            """
+        """
 
         if infocode is None:
             return self.informs
@@ -906,7 +906,7 @@ def OPT(optName, *args, **kwargs):
     -------
     opt : pyOpt_optimizer inherited optimizer
        The desired optimizer
-       """
+    """
 
     optName = optName.lower()
     optList = ["snopt", "ipopt", "slsqp", "nlpqlp", "conmin", "nsga2", "psqp", "alpso", "paropt"]

pyoptsparse/pyOpt_utils.py

@@ -212,7 +212,7 @@ def convertToCOO(mat):
     -------
     newMat : dict
         A coo representation of the same matrix
-        """
+    """
 
     if isinstance(mat, dict):
         if "coo" in mat:
@@ -396,7 +396,7 @@ def convertToDense(mat):
 
 
 def scaleColumns(mat, factor):
-    """ d=
+    """d=
     Scale the columns of the matrix. Must be CSR format
     """
     if not isinstance(mat, dict):
@@ -443,7 +443,7 @@ def extractRows(mat, indices):
     -------
     newMat : dic
        pyoptsparse CSR matrix
-        """
+    """
     rowp = mat["csr"][IROWP]
     cols = mat["csr"][ICOLIND]
     data = mat["csr"][IDATA]
@@ -482,7 +482,7 @@ def _denseToCOO(arr):
     -------
     dict : mat
         The pyoptsparse representation of a sparse matrix
-        """
+    """
     nRows = arr.shape[0]
     nCols = arr.shape[1]
     data = arr.flatten()

pyoptsparse/pyPSQP/pyPSQP.py

@@ -126,7 +126,7 @@ def __call__(
         storeSens : bool
             Flag sepcifying if sensitivities are to be stored in hist.
             This is necessay for hot-starting only.
-            """
+        """
 
         self.callCounter = 0
         self.storeSens = storeSens

pyoptsparse/pyParOpt/ParOpt.py

@@ -122,7 +122,7 @@ def __call__(
         storeSens : bool
             Flag sepcifying if sensitivities are to be stored in hist.
             This is necessay for hot-starting only.
-            """
+        """
 
         self.callCounter = 0
         self.storeSens = storeSens

pyoptsparse/pySLSQP/pySLSQP.py

@@ -120,7 +120,7 @@ def __call__(
         storeSens : bool
             Flag sepcifying if sensitivities are to be stored in hist.
             This is necessay for hot-starting only.
-            """
+        """
 
         self.callCounter = 0
         self.storeSens = storeSens

pyoptsparse/pySNOPT/pySNOPT.py

@@ -303,7 +303,7 @@ def __call__(
             Must be in seconds. This can be useful on queue systems when
             you want an optimization to cleanly finish before the
             job runs out of time.
-            """
+        """
 
         self.callCounter = 0
         self.storeSens = storeSens

setup.py

@@ -54,7 +54,10 @@ def configuration(parent_package="", top_path=None):
 
     import re
 
-    __version__ = re.findall(r"""__version__ = ["']+([0-9\.]*)["']+""", open("pyoptsparse/__init__.py").read(),)[0]
+    __version__ = re.findall(
+        r"""__version__ = ["']+([0-9\.]*)["']+""",
+        open("pyoptsparse/__init__.py").read(),
+    )[0]
 
     setup(
         name="pyoptsparse",

test/test_optProb.py

@@ -57,7 +57,13 @@ def setup_optProb(self, nObj=1, nDV=[4], nCon=[2], xScale=[1.0], objScale=[1.0],
             dvName = "x{}".format(iDV)
             self.x0[dvName] = x0
             self.optProb.addVarGroup(
-                dvName, n, lower=lower, upper=upper, value=x0, scale=xScale[iDV], offset=offset[iDV],
+                dvName,
+                n,
+                lower=lower,
+                upper=upper,
+                value=x0,
+                scale=xScale[iDV],
+                offset=offset[iDV],
             )
 
         # Constraints
@@ -66,7 +72,11 @@ def setup_optProb(self, nObj=1, nDV=[4], nCon=[2], xScale=[1.0], objScale=[1.0],
             lower = np.random.uniform(-5, 2, nc)
             upper = np.random.uniform(5, 6, nc)
             self.optProb.addConGroup(
-                "con_{}".format(iCon), nc, lower=lower, upper=upper, scale=conScale[iCon],
+                "con_{}".format(iCon),
+                nc,
+                lower=lower,
+                upper=upper,
+                scale=conScale[iCon],
             )
 
         # Objective
@@ -86,7 +96,13 @@ def test_setDV_getDV(self):
         We just test that setDV and getDV work, even with scaling
         """
         self.setup_optProb(
-            nObj=1, nDV=[4, 8], nCon=[2, 3], xScale=[4, 1], objScale=[0.3], conScale=[0.1, 8], offset=[3, 7],
+            nObj=1,
+            nDV=[4, 8],
+            nCon=[2, 3],
+            xScale=[4, 1],
+            objScale=[0.3],
+            conScale=[0.1, 8],
+            offset=[3, 7],
         )
         # test getDV first
         x0 = self.optProb.getDVs()
@@ -102,7 +118,13 @@ def test_setDV_VarGroup(self):
         Test that setDV works with a subset of VarGroups
         """
         self.setup_optProb(
-            nObj=1, nDV=[4, 8], nCon=[2, 3], xScale=[4, 1], objScale=[0.3], conScale=[0.1, 8], offset=[3, 7],
+            nObj=1,
+            nDV=[4, 8],
+            nCon=[2, 3],
+            xScale=[4, 1],
+            objScale=[0.3],
+            conScale=[0.1, 8],
+            offset=[3, 7],
         )
         oldDV = self.optProb.getDVs()
         # set values for only one VarGroup

test/test_snopt_bugfix.py

@@ -53,8 +53,7 @@ def objfunc_2con(xdict):
 
 
 def sens(xdict, funcs):
-    """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
-    """
+    """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3"""
     x = xdict["x"]
     y = xdict["y"]
     funcsSens = {}

test/test_snopt_user_termination.py

@@ -37,8 +37,7 @@ def objfunc(self, xdict):
         return funcs, fail
 
     def sens(self, xdict, funcs):
-        """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
-        """
+        """f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3"""
         x = xdict["x"]
         y = xdict["y"]
         funcsSens = {}