Develop eq

environment.yml

@@ -43,6 +43,8 @@ dependencies:
     - jupyter-lsp-python
     - jupyterlab-lsp  # Docs at https://github.com/krassowski/jupyterlab-lsp
 
+    # Operational Model (om)
+    - pyparsing
 
     # PIP install requirements only if it is not possible with conda
     # https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#using-pip-in-an-environment

environment_dev.yml

@@ -27,6 +27,9 @@ dependencies:
     # - dask  # for future performance enhancements
     - cltoolbox
 
+    # Operational Model (om)
+    - pyparsing
+
     # Interactivity & Visualization via Jupyter Notebooks (optional,
     # but required for tutorials)
     - jupyterlab  # also installs classic Jupyter notbook

pyproject.toml

@@ -15,7 +15,8 @@ dependencies = [
     "numba",
     "numpy<2.0",
     "pandas",
-    "tables"
+    "tables",
+    "pyparsing"
 ]
 authors = [
     {name = "RESPEC, Inc", email = "[email protected]"}

src/hsp2/hsp2/HYDR.py

@@ -1,27 +1,28 @@
 """Copyright (c) 2020 by RESPEC, INC.
 Author: Robert Heaphy, Ph.D.
 License: LGPL2
-Conversion of no category version of HSPF HRCHHYD.FOR into Python
+Conversion of no category version of HSPF HRCHHYD.FOR into Python"""
 
-Development Notes:
+""" Development Notes:
   Categories not implimented in this version
   Irregation only partially implimented in this version
   Only English units currently supported
   FTABLE can come from WDM or UCI file based on FTBDSN 1 or 0
 """
 
-from math import log10, sqrt
 
-from numba import njit, types
-from numba.typed import Dict, List
-from numpy import any, arange, array, asarray, full, int64, nan, zeros
+from numpy import zeros, any, full, nan, array, int64, arange, asarray
 from pandas import DataFrame
-
-from hsp2.hsp2.om import model_domain_dependencies, pre_step_model, step_model
+from math import sqrt, log10
+from numba import njit, types
+from numba.typed import List
+from hsp2.hsp2.utilities import initm, make_numba_dict
 
 # the following imports added by rb to handle dynamic code and special actions
-from hsp2.hsp2.state import hydr_get_ix, hydr_init_ix
-from hsp2.hsp2.utilities import initm, make_numba_dict
+from hsp2.hsp2.state import hydr_get_ix, hydr_init_ix, hydr_state_vars
+from hsp2.hsp2.om import pre_step_model, step_model, model_domain_dependencies
+from numba.typed import Dict
+
 
 ERRMSGS = (
     "HYDR: SOLVE equations are indeterminate",  # ERRMSG0
@@ -164,25 +165,14 @@ def hydr(io_manager, siminfo, uci, ts, ftables, state):
     # must split dicts out of state Dict since numba cannot handle mixed-type nested Dicts
     state_ix, dict_ix, ts_ix = state["state_ix"], state["dict_ix"], state["ts_ix"]
     state_paths = state["state_paths"]
-    ep_list = [
-        "DEP",
-        "IVOL",
-        "O1",
-        "O2",
-        "O3",
-        "OVOL1",
-        "OVOL2",
-        "OVOL3",
-        "PRSUPY",
-        "RO",
-        "ROVOL",
-        "SAREA",
-        "TAU",
-        "USTAR",
-        "VOL",
-        "VOLEV",
-    ]
-    model_exec_list = model_domain_dependencies(state, state_info["domain"], ep_list)
+    ep_list = (
+        hydr_state_vars()
+    )  # define all eligibile for state integration in state.py
+    # note: calling dependencies with 4th arg = True grabs only "runnable" types, which can save time
+    #       in long simulations, as iterating through non-runnables like Constants consumes time.
+    model_exec_list = model_domain_dependencies(
+        state, state_info["domain"], ep_list, True
+    )
     model_exec_list = asarray(model_exec_list, dtype="i8")  # format for use in numba
     op_tokens = state["op_tokens"]
     #######################################################################################
@@ -216,7 +206,8 @@ def hydr(io_manager, siminfo, uci, ts, ftables, state):
     uci["PARAMETERS"]["ROS"] = ui["ROS"]
     for i in range(nexits):
         uci["PARAMETERS"]["OS" + str(i + 1)] = ui["OS" + str(i + 1)]
-
+    # copy back (modified) operational element data
+    state["state_ix"], state["dict_ix"], state["ts_ix"] = state_ix, dict_ix, ts_ix
     return errors, ERRMSGS
 
 
@@ -875,3 +866,12 @@ def expand_HYDR_masslinks(flags, uci, dat, recs):
         rec["SVOL"] = dat.SVOL
         recs.append(rec)
     return recs
+
+
+def hydr_load_om(state, io_manager, siminfo):
+    for i in hydr_state_vars():
+        state["model_data"][seg_name][i] = {
+            "object_class": "ModelVariable",
+            "name": i,
+            "value": 0.0,
+        }

src/hsp2/hsp2/SEDTRN.py

@@ -3,18 +3,16 @@
 License: LGPL2
 """
 
-from math import exp, log10
-
+from numpy import array, zeros, where, int64, asarray
+from math import log10, exp
 from numba import njit, types
-from numba.typed import Dict
-from numpy import array, asarray, int64, where, zeros
-
 from hsp2.hsp2.ADCALC import advect
-from hsp2.hsp2.om import model_domain_dependencies, pre_step_model, step_model
+from hsp2.hsp2.utilities import make_numba_dict
 
 # the following imports added to handle special actions
-from hsp2.hsp2.state import sedtrn_get_ix, sedtrn_init_ix
-from hsp2.hsp2.utilities import make_numba_dict
+from hsp2.hsp2.state import sedtrn_get_ix, sedtrn_init_ix, sedtrn_state_vars
+from hsp2.hsp2.om import pre_step_model, step_model, model_domain_dependencies
+from numba.typed import Dict
 
 ERRMSGS = (
     "SEDTRN: Warning -- bed storage of sediment size fraction sand is empty",  # ERRMSG0
@@ -110,18 +108,22 @@ def sedtrn(io_manager, siminfo, uci, ts, state):
     # # It appears necessary to load this here, instead of from main.py, otherwise,
     # # _hydr_() does not recognize the function state_step_hydr()?
     # if (hsp2_local_py != False):
-    # 	from hsp2_local_py import state_step_hydr
+    #     from hsp2_local_py import state_step_hydr
     # else:
-    # 	from hsp2.hsp2.state_fn_defaults import state_step_hydr
+    #     from hsp2.hsp2.state_fn_defaults import state_step_hydr
     # must split dicts out of state Dict since numba cannot handle mixed-type nested Dicts
     # initialize the sedtrn paths in case they don't already reside here
     sedtrn_init_ix(state, state["domain"])
     state_ix, dict_ix, ts_ix = state["state_ix"], state["dict_ix"], state["ts_ix"]
     state_paths = state["state_paths"]
     op_tokens = state["op_tokens"]
     # Aggregate the list of all SEDTRN end point dependencies
-    ep_list = ["RSED4", "RSED5", "RSED6"]
-    model_exec_list = model_domain_dependencies(state, state_info["domain"], ep_list)
+    ep_list = (
+        sedtrn_state_vars()
+    )  # define all eligibile for state integration in state.py
+    model_exec_list = model_domain_dependencies(
+        state, state_info["domain"], ep_list, True
+    )
     model_exec_list = asarray(model_exec_list, dtype="i8")  # format for use in
     #######################################################################################
 
@@ -274,11 +276,11 @@ def _sedtrn_(
     HRAD = ts["HRAD"]
     TWID = ts["TWID"]
 
-    if not "ISED1" in ts:
+    if "ISED1" not in ts:
         ts["ISED1"] = zeros(simlen)
-    if not "ISED2" in ts:
+    if "ISED2" not in ts:
         ts["ISED2"] = zeros(simlen)
-    if not "ISED3" in ts:
+    if "ISED3" not in ts:
         ts["ISED3"] = zeros(simlen)
 
     ISED1 = ts["ISED1"]  # if present, else ISED is identically zero;  sand
@@ -799,11 +801,13 @@ def bdexch(avdepm, w, tau, taucd, taucs, m, vol, frcsed, susp, bed):
     return depmas - scrmas, susp, bed  # net deposition or scour, susp, bed
 
 
+""" Sediment Transport in Alluvial Channels, 1963-65 by Bruce Colby.
+This report explains the following empirical algorithm."""
+
+
 @njit(cache=True)
 def colby(v, db50, fhrad, fsl, tempr):
-    """Sediment Transport in Alluvial Channels, 1963-65 by Bruce Colby.
-    This report explains the following empirical algorithm."""
-    # 	Colby's method to calculate the capacity of the flow to transport sand.
+    #     Colby's method to calculate the capacity of the flow to transport sand.
     #
     #   The colby method has the following units and applicable ranges of variables.
     #         average velocity.............v.......fps.........1-10 fps
@@ -875,10 +879,10 @@ def colby(v, db50, fhrad, fsl, tempr):
     F[1, 10], F[2, 10], F[3, 10], F[4, 10], F[5, 10] = 1.0, 1.4, 4.9, 22.0, 120.0
 
     # T = array([[-999, -999, -999, -999, -999, -999, -999, -999],
-    # 		   [-999, 1.2,  1.15, 1.10, 0.96, 0.90, 0.85, 0.82],
-    # 		   [-999, 1.35, 1.25, 1.12, 0.92, 0.86, 0.80, 0.75],
-    # 		   [-999, 1.60, 1.40, 1.20, 0.89, 0.80, 0.72, 0.66],
-    # 		   [-999, 2.00, 1.65, 1.30, 0.85, 0.72, 0.63, 0.55]]).T               # Temperature adjustment, Figure 24
+    #            [-999, 1.2,  1.15, 1.10, 0.96, 0.90, 0.85, 0.82],
+    #            [-999, 1.35, 1.25, 1.12, 0.92, 0.86, 0.80, 0.75],
+    #            [-999, 1.60, 1.40, 1.20, 0.89, 0.80, 0.72, 0.66],
+    #            [-999, 2.00, 1.65, 1.30, 0.85, 0.72, 0.63, 0.55]]).T               # Temperature adjustment, Figure 24
 
     T = zeros((8, 5))
     T[0, 0], T[0, 1], T[0, 2], T[0, 3], T[0, 4] = -999, -999, -999, -999, -999

src/hsp2/hsp2/SPECL.py

@@ -7,10 +7,9 @@
 """
 
 from numba import njit
-from pandas import DataFrame, date_range
 
 
-def specl_load_actions(state, io_manager, siminfo):
+def specl_load_om(state, io_manager, siminfo):
     if "ACTIONS" in state["specactions"]:
         dc = state["specactions"]["ACTIONS"]
         for ix in dc.index:
@@ -36,25 +35,27 @@ def specl_load_actions(state, io_manager, siminfo):
 
 
 def specl_load_state(state, io_manager, siminfo):
-    specl_load_actions(state, io_manager, siminfo)
+    specl_load_om(state, io_manager, siminfo)
     # others defined below, like:
     # specl_load_uvnames(state, io_manager, siminfo)
     # ...
     return
 
 
+"""
+# the code specl() is deprecated in favor of execution inside OM
+# see om_special_action.py for example of object support and runtime functions for classic ACTIONS
+CALL: specl(ui, ts, step, state_info, state_paths, state_ix, specactions)
+    store is the Pandas/PyTable open store
+    siminfo is a dictionary with simulation level infor (OP_SEQUENCE for example)
+    ui is a dictionary with RID specific HSPF UCI like data
+    ts is a dictionary with RID specific timeseries
+    state is a dictionary with value of variables at ts[step - 1]
+    specl_actions is a dictionary with all SPEC-ACTIONS entries
+"""
+
+
 @njit
 def specl(ui, ts, step, state_info, state_paths, state_ix, specactions):
-    """
-    # the code specl() is deprecated in favor of execution inside OM
-    # see om_special_action.py for example of object support and runtime functions for classic ACTIONS
-    CALL: specl(ui, ts, step, state_info, state_paths, state_ix, specactions)
-        store is the Pandas/PyTable open store
-        siminfo is a dictionary with simulation level infor (OP_SEQUENCE for example)
-        ui is a dictionary with RID specific HSPF UCI like data
-        ts is a dictionary with RID specific timeseries
-        state is a dictionary with value of variables at ts[step - 1]
-        specl_actions is a dictionary with all SPEC-ACTIONS entries
-    """
     # ther eis no need for _specl_ because this code must already be njit
     return