Workload is an EventSequenceModel, actually

1 files changed, 0 insertions, 77 deletions

diff --git a/lib/workload.py b/lib/workload.py
deleted file mode 100644
index 3e4f1f8..0000000
--- a/lib/workload.py
+++ /dev/null
@@ -1,77 +0,0 @@
-#!/usr/bin/env python3
-
-import logging
-from . import utils
-
-logger = logging.getLogger(__name__)
-
-
-class Workload:
-    def __init__(self, models):
-        self.models = models
-
-    def _event_normalizer(self, event):
-        event_normalizer = lambda p: p
-        if "/" in event:
-            v1, v2 = event.split("/")
-            if utils.is_numeric(v1):
-                event = v2.strip()
-                event_normalizer = lambda p: utils.soft_cast_float(v1) / p
-            elif utils.is_numeric(v2):
-                event = v1.strip()
-                event_normalizer = lambda p: p / utils.soft_cast_float(v2)
-            else:
-                raise RuntimeError(f"Cannot parse '{event}'")
-        return event, event_normalizer
-
-    def eval_strs(self, events, aggregate="sum", aggregate_init=0, use_lut=False):
-        for event in events:
-            event, event_normalizer = self._event_normalizer(event)
-            nn, param = event.split("(")
-            name, action = nn.split(".")
-            param_model = None
-            ref_model = None
-
-            for model in self.models:
-                if name in model.names and action in model.attributes(name):
-                    ref_model = model
-                    if use_lut:
-                        param_model = model.get_param_lut(allow_none=True)
-                    else:
-                        param_model, param_info = model.get_fitted()
-                    break
-
-            if param_model is None:
-                raise RuntimeError(f"Did not find a model for {name}.{action}")
-
-            param = param.removesuffix(")")
-            if param == "":
-                param = dict()
-            else:
-                param = utils.parse_conf_str(param)
-
-            param_list = utils.param_dict_to_list(param, ref_model.parameters)
-
-            if not use_lut and not param_info(name, action).is_predictable(param_list):
-                logging.warning(
-                    f"Cannot predict {name}.{action}({param}), falling back to static model"
-                )
-
-            try:
-                event_output = event_normalizer(
-                    param_model(
-                        name,
-                        action,
-                        param=param_list,
-                    )
-                )
-            except KeyError:
-                logging.error(f"Cannot predict {name}.{action}({param}) from LUT model")
-                raise
-
-            if aggregate == "sum":
-                aggregate_init += event_output
-            else:
-                raise RuntimeError(f"Unknown aggregate type: {aggregate}")
-
-        return aggregate_init