Merge pull request #1671 from naylor-b/dyn_shape

Implementation for POEM_022 - Determining variable shape at runtime based on connections

openmdao/components/exec_comp.py

@@ -15,7 +15,7 @@
 # Names of metadata entries allowed for ExecComp variables.
 _allowed_meta = {'value', 'shape', 'units', 'res_units', 'desc',
                  'ref', 'ref0', 'res_ref', 'lower', 'upper', 'src_indices',
-                 'flat_src_indices', 'tags'}
+                 'flat_src_indices', 'tags', 'shape_by_conn', 'copy_shape'}
 
 # Names that are not allowed for input or output variables (keywords for options)
 _disallowed_names = {'has_diag_partials', 'units', 'shape'}
@@ -289,6 +289,12 @@ def setup(self):
                     init_vals[arg] = val['value']
                     del kwargs2[arg]['value']
 
+                if 'shape_by_conn' in val or 'copy_shape' in val:
+                    if val.get('shape') is not None or val.get('value') is not None:
+                        raise RuntimeError(f"{self.msginfo}: Can't set 'shape' or 'value' for "
+                                           f"variable '{arg}' along with 'copy_shape' or "
+                                           "'shape_by_conn'.")
+
                 if 'shape' in val:
                     if arg not in init_vals:
                         init_vals[arg] = np.ones(val['shape'])
@@ -302,14 +308,14 @@ def setup(self):
                 init_vals[arg] = val
 
         for var in sorted(allvars):
+            meta = kwargs2.get(var, {'units': units, 'shape': shape})
+
             # if user supplied an initial value, use it, otherwise set to 1.0
             if var in init_vals:
                 val = init_vals[var]
             else:
                 init_vals[var] = val = 1.0
 
-            meta = kwargs2.get(var, {'units': units, 'shape': shape})
-
             if var in outs:
                 self.add_output(var, val, **meta)
             else:

openmdao/components/linear_system_comp.py

@@ -49,8 +49,6 @@ def setup(self):
         vec_size = self.options['vec_size']
         vec_size_A = self.vec_size_A = vec_size if self.options['vectorize_A'] else 1
         size = self.options['size']
-        mat_size = size * size
-        full_size = size * vec_size
 
         self._lup = []
         shape = (vec_size, size) if vec_size > 1 else (size, )
@@ -64,7 +62,16 @@ def setup(self):
         self.add_input("b", val=np.ones(shape))
         self.add_output("x", shape=shape, val=.1)
 
-        # Set up the derivatives.
+    def setup_partials(self):
+        """
+        Set up the derivatives.
+        """
+        vec_size = self.options['vec_size']
+        vec_size_A = self.vec_size_A = vec_size if self.options['vectorize_A'] else 1
+        size = self.options['size']
+        mat_size = size * size
+        full_size = size * vec_size
+
         row_col = np.arange(full_size, dtype="int")
 
         self.declare_partials('x', 'b', val=np.full(full_size, -1.0), rows=row_col, cols=row_col)

openmdao/components/meta_model_structured_comp.py

@@ -166,7 +166,7 @@ def _setup_partials(self):
             'dependent': True,
         }
 
-        for name in self._outputs:
+        for name in self._var_rel_names['output']:
             self._declare_partials(of=name, wrt=pnames, dct=dct)
             if self.options['training_data_gradients']:
                 self._declare_partials(of=name, wrt="%s_train" % name, dct={'dependent': True})

openmdao/components/multifi_meta_model_unstructured_comp.py

@@ -188,7 +188,8 @@ def add_input(self, name, val=1.0, shape=None, src_indices=None, flat_src_indice
                     self._input_sizes[fi] += input_size
 
     def add_output(self, name, val=1.0, surrogate=None, shape=None, units=None, res_units=None,
-                   desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=1.0):
+                   desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=1.0, tags=None,
+                   shape_by_conn=False, copy_shape=None):
         """
         Add an output variable to the component.
 
@@ -230,13 +231,23 @@ def add_output(self, name, val=1.0, surrogate=None, shape=None, units=None, res_
         res_ref : float
             Scaling parameter. The value in the user-defined res_units of this output's residual
             when the scaled value is 1. Default is 1.
+        tags : str or list of strs or set of strs
+            User defined tags that can be used to filter what gets listed when calling
+            list_inputs and list_outputs.
+        shape_by_conn : bool
+            If True, shape this output to match its connected input(s).
+        copy_shape : str or None
+            If a str, that str is the name of a variable. Shape this output to match that of
+            the named variable.
         """
         super(MultiFiMetaModelUnStructuredComp, self).add_output(name, val, shape=shape,
                                                                  units=units, res_units=res_units,
                                                                  desc=desc, lower=lower,
                                                                  upper=upper, ref=ref,
                                                                  ref0=ref0, res_ref=res_ref,
-                                                                 surrogate=surrogate)
+                                                                 surrogate=surrogate, tags=tags,
+                                                                 shape_by_conn=shape_by_conn,
+                                                                 copy_shape=copy_shape)
         self._training_output[name] = self._nfi * [np.empty(0)]
 
         # Add train:<outvar>_fi<n>

openmdao/components/tests/test_external_code_comp.py

@@ -315,6 +315,7 @@ def setup(self):
             sys.executable, 'extcode_paraboloid.py', self.input_file, self.output_file
         ]
 
+    def setup_partials(self):
         # this external code does not provide derivatives, use finite difference
         self.declare_partials(of='*', wrt='*', method='fd')
 
@@ -357,6 +358,7 @@ def setup(self):
             self.input_file, self.output_file, self.derivs_file
         ]
 
+    def setup_partials(self):
         # this external code does provide derivatives
         self.declare_partials(of='*', wrt='*')
 
@@ -549,7 +551,6 @@ def initialize(self):
             def setup(self):
                 self.add_input('area_ratio', val=1.0, units=None)
                 self.add_output('mach', val=1., units=None)
-                self.declare_partials(of='mach', wrt='area_ratio', method='fd')
 
                 self.input_file = 'mach_input.dat'
                 self.output_file = 'mach_output.dat'
@@ -570,6 +571,9 @@ def setup(self):
                 # If you want to write your own string command, the code below will also work.
                 # self.options['command_apply'] = ('python extcode_mach.py {} {}').format(self.input_file, self.output_file)
 
+            def setup_partials(self):
+                self.declare_partials(of='mach', wrt='area_ratio', method='fd')
+
             def apply_nonlinear(self, inputs, outputs, residuals):
                 with open(self.input_file, 'w') as input_file:
                     input_file.write('residuals\n')

openmdao/components/tests/test_meta_model_unstructured_comp.py

@@ -890,6 +890,8 @@ def setup(self):
                 surrogate = SinSurrogate()
                 self.add_input('x', 0.)
                 self.add_output('sin_x', 0., surrogate=surrogate)
+
+            def setup_partials(self):
                 self.declare_partials('sin_x', 'x', method='fd',
                                       form='backward', step=1e-7, step_calc='rel')