apply ruff to changed files

Author: Burgholzer

src/hsp2/hsp2/om_special_action.py

@@ -208,6 +208,8 @@ def hdf5_load_all(hdf_source):
 #     - alternative: save the integer timestamp or timestep of the start, and if step/stamp > value, enable
 # @tbd: add number of repeats, and save the value of repeats in a register
 """
+
+
 @njit(cache=True)
 def step_special_action(op, state_ix, dict_ix, step):
     ix = op[1]  # ID of this op

src/hsp2/hsp2/sedtrn_step.py

@@ -4,31 +4,56 @@
 from numba.types import List
 
 # the following imports added to handle special actions
-from hsp2.hsp2.state import sedtrn_get_ix, sedtrn_init_ix, get_domain_state, set_domain_state
+from hsp2.hsp2.state import (
+    sedtrn_get_ix,
+    sedtrn_init_ix,
+    get_domain_state,
+    set_domain_state,
+)
 from hsp2.hsp2.om import pre_step_model, step_model, model_domain_dependencies
 from numba.typed import Dict
 
+
 @njit
-def step_sedtrn(domain, state_paths, state_ix, dict_ix, ts_ix, op_tokens, model_exec_list, step, ep_list):
+def step_sedtrn(
+    domain,
+    state_paths,
+    state_ix,
+    dict_ix,
+    ts_ix,
+    op_tokens,
+    model_exec_list,
+    step,
+    ep_list,
+):
     # model_exec_list: a list of elements (specl etc.) that influence these SEDTRN end points
     # ep_list = np.asarray(["RSED1", "RSED2", "RSED3", "RSED4", "RSED5", "RSED6"], dtype='U')
     # NOTE: this could be cached in dict_ix
     # call related specl/ops pre-steps, such as loading timeseries values
     pre_step_model(model_exec_list, op_tokens, state_ix, dict_ix, ts_ix, step)
     # call related specl/ops steps
-    step_model( model_exec_list, op_tokens, state_ix, dict_ix, ts_ix, step)
+    step_model(model_exec_list, op_tokens, state_ix, dict_ix, ts_ix, step)
     # get state value at beginning of timestep - python experts will no doubt have a more code efficient method than this
-    sand_rsed1, silt_rsed2, clay_rsed3, sand_wt_rsed4, silt_wt_rsed5, clay_wt_rsed6 = get_domain_state(state_paths, state_ix, domain, ep_list)
-    
+    sand_rsed1, silt_rsed2, clay_rsed3, sand_wt_rsed4, silt_wt_rsed5, clay_wt_rsed6 = (
+        get_domain_state(state_paths, state_ix, domain, ep_list)
+    )
+
     # now, do sedtrn (simplified for demo purposes)
     tsed1 = sand_rsed1 + silt_rsed2 + clay_rsed3
     tsed2 = sand_wt_rsed4 + silt_wt_rsed5 + clay_wt_rsed6
-    sand_t_rsed7 = sand_rsed1 + sand_wt_rsed4 
+    sand_t_rsed7 = sand_rsed1 + sand_wt_rsed4
     silt_t_rsed8 = silt_rsed2 + silt_wt_rsed5
     clay_t_rsed9 = clay_rsed3 + clay_wt_rsed6
     tsed3 = sand_t_rsed7 + silt_t_rsed8 + clay_t_rsed9
-    
+
     # pass values back to state
-    state_vals = [sand_rsed1, silt_rsed2, clay_rsed3, sand_wt_rsed4, silt_wt_rsed5, clay_wt_rsed6]
+    state_vals = [
+        sand_rsed1,
+        silt_rsed2,
+        clay_rsed3,
+        sand_wt_rsed4,
+        silt_wt_rsed5,
+        clay_wt_rsed6,
+    ]
     set_domain_state(state_paths, state_ix, domain, ep_list, state_vals)
     return

src/hsp2/hsp2/state.py

@@ -192,16 +192,16 @@ def state_load_dynamics_hsp2(state, io_manager, siminfo):
 def get_domain_state(state_paths, state_ix, domain, varkeys):
     # get values for a set of variables in a domain
     # will not check for the index in state_ix, and will fail if a non-scalar value is needed (like from dict_ix)
-    # if varkeys = False, assume that we want all the variables 
+    # if varkeys = False, assume that we want all the variables
     # from the domain, that are predetermined ahead of time, and should save performance
     ret_vals = np.zeros(len(varkeys))
     j = 0
     for i in varkeys:
         # var_path = f'{domain}/{i}'
         var_path = domain + "/" + i
-        #print(var_path)
+        # print(var_path)
         ix = state_paths[var_path]
-        #print("ix",ix) 
+        # print("ix",ix)
         ret_vals[j] = state_ix[ix]
         j += 1
     return ret_vals
@@ -211,18 +211,19 @@ def get_domain_state(state_paths, state_ix, domain, varkeys):
 def set_domain_state(state_paths, state_ix, domain, varkeys, state_vals):
     # get values for a set of variables in a domain
     # will not check for the index in state_ix, and will fail if a non-scalar value is needed (like from dict_ix)
-    # if varkeys = False, assume that we want all the variables 
+    # if varkeys = False, assume that we want all the variables
     # from the domain, that are predetermined ahead of time, and should save performance
     j = 0
     for i in varkeys:
         # var_path = f'{domain}/{i}'
         var_path = domain + "/" + i
-        #print(var_path)
+        # print(var_path)
         ix = state_paths[var_path]
         state_ix[ix] = state_vals[j]
         j += 1
     return True
 
+
 def hydr_state_vars():
     return [
         "DEP",