Cleanup error class (#436)

* use builtin Error classes

* finish removing Error

* format?

---------

Co-authored-by: Marco Mangano <[email protected]>
Co-authored-by: Shugo Kaneko <[email protected]>

Author: 3 people

pyoptsparse/pyALPSO/pyALPSO.py

@@ -12,7 +12,6 @@
 
 # Local modules
 from . import alpso
-from ..pyOpt_error import Error
 from ..pyOpt_optimizer import Optimizer
 
 # isort: off
@@ -161,10 +160,10 @@ def objconfunc(x):
                 self.setOption("minInnerIter", opt("maxInnerIter"))
 
             if opt("stopCriteria") not in [0, 1]:
-                raise Error("Incorrect Stopping Criteria Setting")
+                raise ValueError("Incorrect Stopping Criteria Setting")
 
             if opt("fileout") not in [0, 1, 2, 3]:
-                raise Error("Incorrect fileout Setting")
+                raise ValueError("Incorrect fileout Setting")
 
             # Run ALPSO
             t0 = time.time()

pyoptsparse/pyCONMIN/pyCONMIN.py

@@ -12,7 +12,6 @@
 import numpy as np
 
 # Local modules
-from ..pyOpt_error import Error
 from ..pyOpt_optimizer import Optimizer
 from ..pyOpt_utils import try_import_compiled_module_from_path
 
@@ -194,7 +193,7 @@ def cnmngrad(n1, n2, x, f, g, ct, df, a, ic, nac):
             if self.getOption("IPRINT") >= 0 and self.getOption("IPRINT") <= 4:
                 iprint = self.getOption("IPRINT")
             else:
-                raise Error("IPRINT option must be >= 0 and <= 4")
+                raise ValueError("IPRINT option must be >= 0 and <= 4")
 
             iout = self.getOption("IOUT")
             ifile = self.getOption("IFILE")

pyoptsparse/pyNLPQLP/pyNLPQLP.py

@@ -2,6 +2,7 @@
 pyNLPQLP - A pyOptSparse wrapper for Schittkowski's NLPQLP
 optimization algorithm.
 """
+
 # Standard Python modules
 import datetime
 import os
@@ -11,7 +12,6 @@
 import numpy as np
 
 # Local modules
-from ..pyOpt_error import Error
 from ..pyOpt_optimizer import Optimizer
 from ..pyOpt_utils import try_import_compiled_module_from_path
 
@@ -225,10 +225,10 @@ def nlgrad(m, me, mmax, n, f, g, df, dg, x, active, wa):
             d = np.zeros(nmax)
             go = self.getOption
             if go("iPrint") < 0 or go("iPrint") > 4:
-                raise Error("Incorrect iPrint option. Must be >=0 and <= 4")
+                raise ValueError("Incorrect iPrint option. Must be >=0 and <= 4")
 
             if not (go("mode") >= 0 and go("mode") <= 18):
-                raise Error("Incorrect mode option. Must be >= 0 and <= 18.")
+                raise ValueError("Incorrect mode option. Must be >= 0 and <= 18.")
 
             if os.path.isfile(go("iFile")):
                 os.remove(go("iFile"))

pyoptsparse/pyNSGA2/pyNSGA2.py

@@ -11,7 +11,6 @@
 import numpy as np
 
 # Local modules
-from ..pyOpt_error import Error
 from ..pyOpt_optimizer import Optimizer
 from ..pyOpt_utils import try_import_compiled_module_from_path
 
@@ -161,7 +160,7 @@ def objconfunc(nreal, nobj, ncon, x, f, g):
             if self.getOption("PrintOut") >= 0 and self.getOption("PrintOut") <= 2:
                 printout = self.getOption("PrintOut")
             else:
-                raise Error("Incorrect option PrintOut")
+                raise ValueError("Incorrect option PrintOut")
 
             seed = self.getOption("seed")
             if seed == 0:

pyoptsparse/pyOpt_constraint.py

@@ -7,7 +7,7 @@
 import numpy as np
 
 # Local modules
-from .pyOpt_error import Error, pyOptSparseWarning
+from .pyOpt_error import pyOptSparseWarning
 from .pyOpt_types import Dict1DType
 from .pyOpt_utils import INFINITY, _broadcast_to_array, convertToCOO
 
@@ -204,7 +204,7 @@ def finalize(self, variables: OrderedDict, dvOffset, index: int):
                 try:
                     self.wrt = list(self.wrt)
                 except Exception:
-                    raise Error(f"The 'wrt' argument to constraint '{self.name}' must be an iterable list")
+                    raise TypeError(f"The 'wrt' argument to constraint '{self.name}' must be an iterable list")
 
             # We allow 'None' to be in the list...they are null so
             # just pop them out:
@@ -214,7 +214,7 @@ def finalize(self, variables: OrderedDict, dvOffset, index: int):
             # *actually* are variables
             for dvGroup in self.wrt:
                 if dvGroup not in variables:
-                    raise Error(
+                    raise KeyError(
                         f"The supplied dvGroup '{dvGroup}' in 'wrt' for the {self.name} constraint, does not exist. "
                         + "It must be added with a call to addVar() or addVarGroup()."
                     )
@@ -254,7 +254,7 @@ def finalize(self, variables: OrderedDict, dvOffset, index: int):
             # sparse constraints.
 
             if self.linear:
-                raise Error(
+                raise ValueError(
                     "The 'jac' keyword to argument to addConGroup() must be supplied for a linear constraint. "
                     + f"The constraint in error is {self.name}."
                 )
@@ -274,7 +274,7 @@ def finalize(self, variables: OrderedDict, dvOffset, index: int):
         else:
             # First sanitize input:
             if not isinstance(self.jac, dict):
-                raise Error(
+                raise TypeError(
                     "The 'jac' keyword argument to addConGroup() must be a dictionary. "
                     + f"The constraint in error is {self.name}."
                 )
@@ -301,7 +301,7 @@ def finalize(self, variables: OrderedDict, dvOffset, index: int):
 
                 # Generically check the shape:
                 if self.jac[dvGroup]["shape"][0] != self.ncon or self.jac[dvGroup]["shape"][1] != ndvs:
-                    raise Error(
+                    raise ValueError(
                         f"The supplied Jacobian for dvGroup {dvGroup}' in constraint {self.name}, was the incorrect size. "
                         + f"Expecting a Jacobian of size ({self.ncon}, {ndvs}) but received a Jacobian of size "
                         + f"({self.jac[dvGroup]['shape'][0]}, {self.jac[dvGroup]['shape'][1]})."

pyoptsparse/pyOpt_error.py

@@ -5,31 +5,6 @@
 """
 
 
-class Error(Exception):
-    """
-    Format the error message in a box to make it clear this
-    was a expliclty raised exception.
-    """
-
-    def __init__(self, message):
-        msg = "\n+" + "-" * 78 + "+" + "\n" + "| pyOptSparse Error: "
-        i = 21
-        for word in message.split():
-            if len(word) + i + 1 > 78:  # Finish line and start new one
-                msg += " " * (79 - i) + "|\n| " + word + " "
-                i = 2 + len(word) + 1
-            else:
-                msg += word + " "
-                i += len(word) + 1
-        msg += " " * (79 - i) + "|\n" + "+" + "-" * 78 + "+" + "\n"
-        print(msg)
-        self.message = message
-        super().__init__(message)
-
-    def __str__(self):
-        return self.message
-
-
 class pyOptSparseWarning:
     """
     Format a warning message

pyoptsparse/pyOpt_history.py

@@ -8,7 +8,7 @@
 from sqlitedict import SqliteDict
 
 # Local modules
-from .pyOpt_error import Error, pyOptSparseWarning
+from .pyOpt_error import pyOptSparseWarning
 from .pyOpt_utils import EPS
 
 
@@ -53,7 +53,7 @@ def __init__(self, fileName, optProb=None, temp=False, flag="r"):
                 )
             self._processDB()
         else:
-            raise Error("The flag argument to History must be 'r' or 'n'.")
+            raise ValueError("The flag argument to History must be 'r' or 'n'.")
         self.temp = temp
         self.fileName = fileName
 
@@ -578,7 +578,7 @@ def getValues(self, names=None, callCounters=None, major=True, scale=False, stac
             allNames.add("xuser")
         # error if names isn't either a DV, con or obj
         if not names.issubset(allNames):
-            raise Error(
+            raise KeyError(
                 "The names provided are not one of DVNames, conNames or objNames.\n"
                 + f"The names must be a subset of {allNames}"
             )

pyoptsparse/pyOpt_optimization.py

@@ -13,7 +13,6 @@
 # Local modules
 from .pyOpt_MPI import MPI
 from .pyOpt_constraint import Constraint
-from .pyOpt_error import Error
 from .pyOpt_objective import Objective
 from .pyOpt_types import Dict1DType, Dict2DType, NumpyType
 from .pyOpt_utils import (
@@ -232,13 +231,13 @@ def addVarGroup(
         self.finalized = False
         # Check that the nVars is > 0.
         if nVars < 1:
-            raise Error(
+            raise ValueError(
                 f"The 'nVars' argument to addVarGroup must be greater than or equal to 1. The bad DV is {name}."
             )
 
         # Check that the type is ok
         if varType not in ["c", "i", "d"]:
-            raise Error("Type must be one of 'c' for continuous, 'i' for integer or 'd' for discrete.")
+            raise ValueError("Type must be one of 'c' for continuous, 'i' for integer or 'd' for discrete.")
 
         value = _broadcast_to_array("value", value, nVars)
         lower = _broadcast_to_array("lower", lower, nVars, allow_none=True)
@@ -270,10 +269,10 @@ def addVarGroup(
         if name in self.variables:
             # Check that the variables happen to be the same
             if not len(self.variables[name]) == len(varList):
-                raise Error(f"The supplied name '{name}' for a variable group has already been used!")
+                raise KeyError(f"The supplied name '{name}' for a variable group has already been used!")
             for i in range(len(varList)):
                 if not varList[i] == self.variables[name][i]:
-                    raise Error(f"The supplied name '{name}' for a variable group has already been used!")
+                    raise KeyError(f"The supplied name '{name}' for a variable group has already been used!")
             # We we got here, we know that the variables we wanted to
             # add are **EXACTLY** the same so that's cool. We'll just
             # overwrite with the varList below.
@@ -459,7 +458,7 @@ def addConGroup(
         """
         self.finalized = False
         if name in self.constraints:
-            raise Error(f"The supplied name '{name}' for a constraint group has already been used.")
+            raise KeyError(f"The supplied name '{name}' for a constraint group has already been used.")
 
         # Simply add constraint object
         self.constraints[name] = Constraint(name, nCon, linear, wrt, jac, lower, upper, scale)
@@ -550,7 +549,7 @@ def setDVsFromHistory(self, histFile, key=None):
             self.setDVs(hist[key]["xuser"])
             hist.close()
         else:
-            raise Error(f"History file '{histFile}' not found!.")
+            raise FileNotFoundError(f"History file '{histFile}' not found!.")
 
     def printSparsity(self, verticalPrint=False):
         """
@@ -1016,10 +1015,10 @@ def processXtoDict(self, x: ndarray) -> OrderedDict:
                     xg[dvGroup] = x[..., istart]
                 else:
                     xg[dvGroup] = x[..., istart:iend].copy()
-            except IndexError:
-                raise Error("Error processing x. There is a mismatch in the number of variables.")
+            except IndexError as e:
+                raise ValueError("Error processing x. There is a mismatch in the number of variables.") from e
         if imax != self.ndvs:
-            raise Error("Error processing x. There is a mismatch in the number of variables.")
+            raise ValueError("Error processing x. There is a mismatch in the number of variables.")
         return xg
 
     def processXtoVec(self, x: dict) -> ndarray:
@@ -1053,10 +1052,10 @@ def processXtoVec(self, x: dict) -> ndarray:
                     x_array[..., istart] = x[dvGroup]
                 else:
                     x_array[..., istart:iend] = x[dvGroup]
-            except IndexError:
-                raise Error("Error deprocessing x. There is a mismatch in the number of variables.")
+            except IndexError as e:
+                raise ValueError("Error deprocessing x. There is a mismatch in the number of variables.") from e
         if imax != self.ndvs:
-            raise Error("Error deprocessing x. There is a mismatch in the number of variables.")
+            raise ValueError("Error deprocessing x. There is a mismatch in the number of variables.")
 
         return x_array
 
@@ -1084,13 +1083,13 @@ def processObjtoVec(self, funcs: Dict1DType, scaled: bool = True) -> NumpyType:
             if objKey in funcs:
                 try:
                     f = np.squeeze(funcs[objKey]).item()
-                except ValueError:
-                    raise Error(f"The objective return value, '{objKey}' must be a scalar!")
+                except ValueError as e:
+                    raise ValueError(f"The objective return value, '{objKey}' must be a scalar!") from e
                 # Store objective for printing later
                 self.objectives[objKey].value = np.real(f)
                 fobj.append(f)
             else:
-                raise Error(f"The key for the objective, '{objKey}' was not found.")
+                raise KeyError(f"The key for the objective, '{objKey}' was not found.")
 
         # scale the objective
         if scaled:
@@ -1124,8 +1123,8 @@ def processObjtoDict(self, fobj_in: NumpyType, scaled: bool = True) -> Dict1DTyp
             iObj = self.objectiveIdx[objKey]
             try:
                 fobj[objKey] = fobj_in[iObj]
-            except IndexError:
-                raise Error("The input array shape is incorrect!")
+            except IndexError as e:
+                raise ValueError("The input array shape is incorrect!") from e
         if scaled:
             fobj = self._mapObjtoOpt(fobj)
         return fobj
@@ -1177,15 +1176,15 @@ def processContoVec(
                 if c.shape[-1] == self.constraints[iCon].ncon:
                     fcon[..., con.rs : con.re] = c
                 else:
-                    raise Error(
+                    raise ValueError(
                         f"{len(fcon_in[iCon])} constraint values were returned in {iCon}, "
                         + f"but expected {self.constraints[iCon].ncon}."
                     )
 
                 # Store constraint values for printing later
                 con.value = np.real(copy.copy(c))
             else:
-                raise Error(f"No constraint values were found for the constraint '{iCon}'.")
+                raise KeyError(f"No constraint values were found for the constraint '{iCon}'.")
 
         # Perform scaling on the original Jacobian:
         if scaled:
@@ -1330,14 +1329,14 @@ def processObjectiveGradient(self, funcsSens: Dict2DType) -> NumpyType:
                             # Everything checks out so set:
                             gobj[iObj, ss[0] : ss[1]] = tmp
                         else:
-                            raise Error(
+                            raise ValueError(
                                 f"The shape of the objective derivative for dvGroup '{dvGroup}' is the incorrect length. "
                                 + f"Expecting a shape of {(ss[1] - ss[0],)} but received a shape of {funcsSens[objKey][dvGroup].shape}."
                             )
                     else:
-                        raise Error(f"The dvGroup key '{dvGroup}' is not valid")
+                        raise KeyError(f"The dvGroup key '{dvGroup}' is not valid")
             else:
-                raise Error(f"The key for the objective gradient, '{objKey}', was not found.")
+                raise KeyError(f"The key for the objective gradient, '{objKey}', was not found.")
             iObj += 1
 
         # Note that we looped over the keys in funcsSens[objKey]
@@ -1420,21 +1419,20 @@ def processConstraintJacobian(self, gcon):
                 ndvs = ss[1] - ss[0]
 
                 gotDerivative = False
-                try:
-                    if dvGroup in gcon[iCon]:
-                        tmp = convertToCOO(gcon[iCon][dvGroup])
-                        gotDerivative = True
-                except KeyError:
-                    raise Error(
+                if dvGroup in gcon[iCon]:
+                    tmp = convertToCOO(gcon[iCon][dvGroup])
+                    gotDerivative = True
+                else:
+                    raise KeyError(
                         f"The constraint Jacobian entry for '{con.name}' with respect to '{dvGroup}', "
                         + "as was defined in addConGroup(), was not found in constraint Jacobian dictionary provided."
                     )
                 if not gotDerivative:
                     # All keys for this constraint must be returned
                     # since the user has explicitly specified the wrt.
                     if not con.partialReturnOk:
-                        raise Error(
-                            f"Constraint '{con.name}' was expecting a jacobain with respect to dvGroup "
+                        raise ValueError(
+                            f"Constraint '{con.name}' was expecting a Jacobian with respect to dvGroup "
                             + f"'{dvGroup}' as was supplied in addConGroup(). "
                             + "This was not found in the constraint Jacobian dictionary"
                         )
@@ -1445,15 +1443,15 @@ def processConstraintJacobian(self, gcon):
 
                 # Now check that the Jacobian is the correct shape
                 if not (tmp["shape"][0] == con.ncon and tmp["shape"][1] == ndvs):
-                    raise Error(
+                    raise ValueError(
                         f"The shape of the supplied constraint Jacobian for constraint {con.name} with respect to {dvGroup} is incorrect. "
                         + f"Expected an array of shape ({con.ncon}, {ndvs}), but received an array of shape ({tmp['shape'][0]}, {tmp['shape'][1]})."
                     )
 
                 # Now check that supplied coo matrix has same length
                 # of data array
                 if len(tmp["coo"][2]) != len(con.jac[dvGroup]["coo"][2]):
-                    raise Error(
+                    raise ValueError(
                         f"The number of nonzero elements for constraint group '{con.name}' with respect to {dvGroup} was not the correct size. "
                         + f"The supplied Jacobian has {len(tmp['coo'][2])} nonzero entries, but must contain {len(con.jac[dvGroup]['coo'][2])} nonzero entries."
                     )