change parsing to determine expected type ahead of looping user dict,…

… and convert single arguments to list to type check against lists, this allows multiple types to be specified as acceptable arguments.

pyDeltaRCM/default.yml

@@ -11,22 +11,22 @@ verbose:
   type: 'int'
   default: 0
 seed:
-  type: 'int'
-  default: -1
+  type: ['int', 'None']
+  default: null
 Length:
-  type: 'float'
+  type: ['float', 'int']
   default: 1000.
 Width:
-  type: 'float'
+  type: ['float', 'int']
   default: 2000.
 dx:
-  type: 'float'
+  type: ['float', 'int']
   default: 50.
 L0_meters:
-  type: 'float'
+  type: ['float', 'int']
   default: 150.0
 S0:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.0002
 itermax:
   type: 'int'
@@ -35,40 +35,40 @@ Np_water:
   type: 'int'
   default: 2000
 u0:
-  type: 'float'
+  type: ['float', 'int']
   default: 1.0
 N0_meters:
-  type: 'float'
+  type: ['float', 'int']
   default: 200.0
 h0:
-  type: 'float'
+  type: ['float', 'int']
   default: 5.0
 H_SL:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.0
 SLR:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.0
 Np_sed:
   type: 'int'
   default: 1000
 f_bedload:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.5
 C0_percent:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.1
 Csmooth:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.9
 toggle_subsidence:
   type: 'bool'
   default: False
 sigma_max:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.000825
 start_subsidence:
-  type: 'float'
+  type: ['float', 'int']
   default: 2.5
 save_eta_figs:
   type: 'bool'
@@ -101,44 +101,44 @@ save_velocity_grids:
   type: 'bool'
   default: True
 save_dt:
-  type: 'float'
+  type: 'int'
   default: 50
 save_strata:
   type: 'bool'
   default: True
 omega_sfc:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.1
 omega_flow:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.9
 Nsmooth:
   type: 'int'
   default: 5
 theta_water:
-  type: 'float'
+  type: ['float', 'int']
   default: 1.0
 coeff_theta_sand:
-  type: 'float'
+  type: ['float', 'int']
   default: 2.0
 coeff_theta_mud:
-  type: 'float'
+  type: ['float', 'int']
   default: 1.0
 beta:
   type: 'int'
   default: 3
 sed_lag:
-  type: 'float'
+  type: ['float', 'int']
   default: 1.0
 coeff_U_dep_mud:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.3
 coeff_U_ero_mud:
-  type: 'float'
+  type: ['float', 'int']
   default: 1.5
 coeff_U_ero_sand:
-  type: 'float'
+  type: ['float', 'int']
   default: 1.05
 alpha:
-  type: 'float'
+  type: ['float', 'int']
   default: 0.1

pyDeltaRCM/init_tools.py

@@ -51,14 +51,21 @@ def import_files(self):
         input_file_vars = dict()
 
         # Open and access both yaml files --> put in dictionaries
-        # only access the user input file if provided.
-        default_file = open(self.default_file, mode = 'r')
-        default_dict = yaml.load(default_file, Loader = yaml.FullLoader)
+        # parse default yaml and find expected types
+        default_file = open(self.default_file, mode='r')
+        default_dict = yaml.load(default_file, Loader=yaml.FullLoader)
         default_file.close()
-        if self.input_file: # and os.path.exists(self.input_file):
+        for k, v in default_dict.items():
+            if not type(v['type']) is list:
+                default_dict[k]['type'] = [eval(v['type'])]
+            else:
+                default_dict[k]['type'] = [eval(_v) for _v in v['type']]
+
+        # only access the user input file if provided.
+        if self.input_file:
             try:
-                user_file = open(self.input_file, mode = 'r')
-                user_dict = yaml.load(user_file, Loader = yaml.FullLoader)
+                user_file = open(self.input_file, mode='r')
+                user_dict = yaml.load(user_file, Loader=yaml.FullLoader)
                 user_file.close()
             except ValueError as e:
                 raise e
@@ -67,25 +74,29 @@ def import_files(self):
 
         # go through and populate input vars with user and default values,
         # checking user values for correct type.
-        for oo in default_dict.keys():
-            if oo in user_dict:
-                expected_type = eval(default_dict[oo]['type'])
-                if type(user_dict[oo]) is expected_type:
-                    input_file_vars[oo] = user_dict[oo]
+        for k, v in default_dict.items():
+            if k in user_dict:
+                expected_type = v['type']
+                if type(user_dict[k]) in expected_type:
+                    input_file_vars[k] = user_dict[k]
                 else:
-                    raise TypeError('Input for "{_oo}" not of the right type. '
-                                    'Input type was {_wastype}, '
-                                    'but needs to be {_exptype}.'
-                                    .format(_oo = str(oo), _wastype = type(oo),
-                                            _exptype = expected_type))
+                    raise TypeError('Input for "{_k}" not of the right type '
+                                    'in yaml configuration file "{_file}". '
+                                    'Input type was "{_wastype}", '
+                                    'but needs to be "{_exptype}".'
+                                    .format(_k=str(k), _file=self.input_file,
+                                            _wastype=type(k).__name__,
+                                            _exptype=expected_type))
             else:
-                input_file_vars[oo] = default_dict[oo]['default']
+                input_file_vars[k] = default_dict[k]['default']
 
         # now make each one an attribute of the model object.
         for k, v in list(input_file_vars.items()):
             setattr(self, k, v)
 
-        if self.seed >= 0:
+        if self.seed is not None:
+            if self.verbose >= 2:
+                print("setting random seed to %s " % str(self.seed))
             np.random.seed(self.seed)
 
     def set_constants(self):