PTA: Add from_file constructor

2 files changed, 17 insertions, 6 deletions

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())))