load functions for ModelObject and SimTimer

HSP2/om_model_object.py

@@ -3,11 +3,12 @@
 It handles all Dict management functions, but provides for no runtime execution of it's own.
 All runtime exec is done by child classes.
 """
-from HSP2.state import *
-from HSP2.om import *
+from HSP2.state import set_state, get_state_ix
+from HSP2.om import get_exec_order, is_float_digit
 from pandas import Series, DataFrame, concat, HDFStore, set_option, to_numeric
 from pandas import Timestamp, Timedelta, read_hdf, read_csv
-from numpy import pad
+from numpy import pad, asarray, zeros, int32
+from numba import njit
 
 class ModelObject:
     state_ix = {} # Shared Dict with the numerical state of each object 
@@ -75,15 +76,15 @@ def runnable_op_list(op_tokens, meo, debug = False):
         for ix in meo:
             if ix in run_ops.keys():
                 rmeo.append(ix)
-        rmeo = np.asarray(rmeo, dtype="i8") 
+        rmeo = asarray(rmeo, dtype="i8") 
         return rmeo
 
     @staticmethod
     def model_format_ops(ops):
         if (ModelObject.ops_data_type == 'ndarray'):
             ops = pad(ops,(0,ModelObject.max_token_length))[0:ModelObject.max_token_length]
         else:
-            ops = np.asarray(ops, dtype="i8")
+            ops = asarray(ops, dtype="i8")
         return ops
 
     def format_ops(self):
@@ -363,20 +364,6 @@ def step(self, step):
         # easier to understand demonstrations
         step_one(self.op_tokens, self.op_tokens[self.ix], self.state_ix, self.dict_ix, self.ts_ix, step)
         #step_model({self.op_tokens[self.ix]}, self.state_ix, self.dict_ix, self.ts_ix, step)
-
-    def dddstep_model(op_tokens, state_ix, dict_ix, ts_ix, step):
-        for i in op_tokens.keys():
-            if op_tokens[i][0] == 1:
-                state_ix[i] = step_equation(op_tokens[i], state_ix)
-            elif op_tokens[i][0] == 2:
-                state_ix[i] = exec_tbl_eval(op_tokens[i], state_ix, dict_ix)
-            elif op_tokens[i][0] == 3:
-                step_model_link(op_tokens[i], state_ix, ts_ix, step)
-            elif op_tokens[i][0] == 4:
-                return False
-            elif op_tokens[i][0] == 5:
-                step_sim_timer(op_tokens[i], state_ix, dict_ix, ts_ix, step)
-        return 
 
 """
 The class ModelConstant is for storing constants.  It must be loaded here because ModelObject calls it.

HSP2/om_sim_timer.py

@@ -3,11 +3,12 @@
 It is also used to make an implicit parent child link to insure that an object is loaded
 during a model simulation.
 """
-from HSP2.state import *
-from HSP2.om import *
+from HSP2.state import set_state
+from HSP2.om import ModelObject
 from HSP2.om_model_object import ModelObject
 from pandas import DataFrame, DatetimeIndex
 from numba import njit
+from numpy import int64
 
 class SimTimer(ModelObject):
     def __init__(self, name, container, siminfo):
@@ -57,7 +58,7 @@ def dti_to_time_array(self, siminfo):
         dt = siminfo['delt']
         # sim timer is special, one entry for each time component for each timestep
         # convert DateIndex to numbers [int(i) for i in dateindex.year]
-        tdi = { 0: dateindex.astype(np.int64), 1:[float(i) for i in dateindex.year], 2:[float(i) for i in dateindex.month], 3:[float(i) for i in dateindex.day], 4:[float(i) for i in dateindex.hour], 5:[float(i) for i in dateindex.minute], 6:[float(i) for i in dateindex.second], 7:[float(i) for i in dateindex.weekday], 8:[float(dt) for i in dateindex], 9:[float(i) for i in dateindex.day_of_year], 10:[float(i) for i in dateindex.daysinmonth], 11:[float(dt * 60.0) for i in dateindex] }
+        tdi = { 0: dateindex.astype(int64), 1:[float(i) for i in dateindex.year], 2:[float(i) for i in dateindex.month], 3:[float(i) for i in dateindex.day], 4:[float(i) for i in dateindex.hour], 5:[float(i) for i in dateindex.minute], 6:[float(i) for i in dateindex.second], 7:[float(i) for i in dateindex.weekday], 8:[float(dt) for i in dateindex], 9:[float(i) for i in dateindex.day_of_year], 10:[float(i) for i in dateindex.daysinmonth], 11:[float(dt * 60.0) for i in dateindex] }
         #tdi = { 0:dateindex.year, 1:dateindex.month, 2:dateindex.day, 3:dateindex.hour, 4:dateindex.minute, 5:dateindex.second }
         tid = DataFrame(tdi)
         h = 1 # added to increase row count for debug testing.