PTA: Add from_file constructor

4 files changed, 25 insertions, 18 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index 685ce92..4551cda 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -70,8 +70,8 @@ Options:
     not been set yet) are discarded. Note that this may remove entire
     function calls from the model.
 
---hwmodel=<hwmodel.json>
-    Load DFA hardware model from JSON
+--hwmodel=<hwmodel.json|hwmodel.dfa>
+    Load DFA hardware model from JSON or YAML
 
 --export-energymodel=<model.json>
     Export energy model. Requires --hwmodel.
@@ -86,6 +86,7 @@ from dfatool import PTAModel, RawData, pta_trace_to_aggregate
 from dfatool import soft_cast_int, is_numeric, gplearn_to_function
 from dfatool import CrossValidator
 from utils import filter_aggregate_by_param
+from automata import PTA
 
 opts = {}
 
@@ -211,7 +212,7 @@ if __name__ == '__main__':
     function_override = {}
     show_models = []
     show_quality = []
-    hwmodel = None
+    pta = None
     energymodel_export_file = None
     xv_method = None
     xv_count = 10
@@ -262,8 +263,7 @@ if __name__ == '__main__':
             safe_functions_enabled = True
 
         if 'hwmodel' in opts:
-            with open(opts['hwmodel'], 'r') as f:
-                hwmodel = json.load(f)
+            pta = PTA.from_file(opts['hwmodel'])
 
     except getopt.GetoptError as err:
         print(err)
@@ -280,7 +280,7 @@ if __name__ == '__main__':
         traces = preprocessed_data,
         discard_outliers = discard_outliers,
         function_override = function_override,
-        hwmodel = hwmodel)
+        pta = pta)
 
     if xv_method:
         xv = CrossValidator(PTAModel, by_name, parameters, arg_count)
@@ -416,7 +416,7 @@ if __name__ == '__main__':
             plotter.plot_param(model, state_or_trans, attribute, model.param_index(param_name), extra_function=function)
 
     if 'export-energymodel' in opts:
-        if not hwmodel:
+        if not pta:
             print('[E] --export-energymodel requires --hwmodel to be set')
             sys.exit(1)
         json_model = model.to_json()
diff --git a/bin/generate-dfa-benchmark.py b/bin/generate-dfa-benchmark.py
index 7f67e1b..3ae51e6 100755
--- a/bin/generate-dfa-benchmark.py
+++ b/bin/generate-dfa-benchmark.py
@@ -302,11 +302,7 @@ if __name__ == '__main__':
 
     modelfile = args[0]
 
-    with open(modelfile, 'r') as f:
-        if '.json' in modelfile:
-            pta = PTA.from_json(json.load(f))
-        else:
-            pta = PTA.from_yaml(yaml.safe_load(f))
+    pta = PTA.from_file(modelfile)
 
     if 'shrink' in opt:
         pta.shrink_argument_values()
diff --git a/lib/automata.py b/lib/automata.py
index 2387734..6d90e4c 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -4,6 +4,7 @@ from functions import AnalyticFunction, NormalizationFunction
 from utils import is_numeric
 import itertools
 import numpy as np
+import json, yaml
 
 def _dict_to_list(input_dict: dict) -> list:
     return [input_dict[x] for x in sorted(input_dict.keys())]
@@ -100,6 +101,8 @@ class State:
         :returns: Generator object for depth-first search. Each access yields a list of (Transition, (arguments)) elements describing a single run through the PTA.
         """
 
+        # TODO parametergewahrer Trace-Filter, z.B. "setHeaterDuration nur wenn bme680 power mode => FORCED und GAS_ENABLED"
+
         # A '$' entry in trace_filter indicates that the trace should (successfully) terminate here regardless of `depth`.
         if trace_filter is not None and next(filter(lambda x: x == '$', map(lambda x: x[0], trace_filter)), None) is not None:
             yield []
@@ -403,6 +406,15 @@ class PTA:
         return normalized_param
 
     @classmethod
+    def from_file(cls, model_file: str):
+        """Return PTA loaded from the provided JSON or YAML file."""
+        with open(model_file, 'r') as f:
+            if '.json' in model_file:
+                return cls.from_json(json.load(f))
+            else:
+                return cls.from_yaml(yaml.safe_load(f))
+
+    @classmethod
     def from_json(cls, json_input: dict):
         """
         Return a PTA created from the provided JSON data.
diff --git a/lib/dfatool.py b/lib/dfatool.py
index a3d5c0f..363c2a2 100644
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -1505,7 +1505,7 @@ class PTAModel:
     - rel_energy_next: transition energy relative to next state mean power in pJ
     """
 
-    def __init__(self, by_name, parameters, arg_count, traces = [], ignore_trace_indexes = [], discard_outliers = None, function_override = {}, verbose = True, use_corrcoef = False, hwmodel = None):
+    def __init__(self, by_name, parameters, arg_count, traces = [], ignore_trace_indexes = [], discard_outliers = None, function_override = {}, verbose = True, use_corrcoef = False, pta = None):
         """
         Prepare a new PTA energy model.
 
@@ -1534,7 +1534,7 @@ class PTAModel:
         verbose -- print informative output, e.g. when removing an outlier
         use_corrcoef -- use correlation coefficient instead of stddev comparison
             to detect whether a model attribute depends on a parameter
-        hwmodel -- hardware model suitable for PTA.from_hwmodel
+        pta -- hardware model as `PTA` object
         """
         self.by_name = by_name
         self.by_param = by_name_to_by_param(by_name)
@@ -1548,7 +1548,7 @@ class PTAModel:
         self._outlier_threshold = discard_outliers
         self.function_override = function_override.copy()
         self.verbose = verbose
-        self.hwmodel = hwmodel
+        self.pta = pta
         self.ignore_trace_indexes = ignore_trace_indexes
         self._aggregate_to_ndarray(self.by_name)
 
@@ -1715,9 +1715,8 @@ class PTAModel:
     def to_json(self):
         static_model = self.get_static()
         _, param_info = self.get_fitted()
-        pta = PTA.from_json(self.hwmodel)
-        pta.update(static_model, param_info)
-        return pta.to_json()
+        self.pta.update(static_model, param_info)
+        return self.pta.to_json()
 
     def states(self):
         return sorted(list(filter(lambda k: self.by_name[k]['isa'] == 'state', self.by_name.keys())))