black + dead code

batch/inference_job_launcher.py

@@ -392,7 +392,6 @@ def launch_batch(
         continuation_run_id,
     )
 
-
     seir_modifiers_scenarios = None
     outcome_modifiers_scenarios = None
     # here the config is a dict
@@ -712,7 +711,9 @@ def launch(self, job_name, config_file, seir_modifiers_scenarios, outcome_modifi
             cur_env_vars = base_env_vars.copy()
             cur_env_vars.append({"name": "FLEPI_SEIR_SCENARIOS", "value": s})
             cur_env_vars.append({"name": "FLEPI_OUTCOME_SCENARIOS", "value": d})
-            cur_env_vars.append({"name": "FLEPI_PREFIX", "value": f"{config['name']}_{s}_{d}"}) # TODO: get it from gempyor and makes it contains run_id also in scripts
+            cur_env_vars.append(
+                {"name": "FLEPI_PREFIX", "value": f"{config['name']}_{s}_{d}"}
+            )  # TODO: get it from gempyor and makes it contains run_id also in scripts
             cur_env_vars.append({"name": "FLEPI_BLOCK_INDEX", "value": "1"})
             cur_env_vars.append({"name": "FLEPI_RUN_INDEX", "value": f"{self.run_id}"})
             if not (self.restart_from_location is None):

flepimop/gempyor_pkg/src/gempyor/cli.py

@@ -2,6 +2,7 @@
 from .compartments import compartments
 from gempyor.utils import config
 
+
 @click.group()
 @click.option(
     "-c",
@@ -17,11 +18,9 @@ def cli(config_file):
     config.read(user=False)
     config.set_file(config_file)
 
-cli.add_command(compartments)
 
+cli.add_command(compartments)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     cli()
-
-

flepimop/gempyor_pkg/src/gempyor/compartments.py

@@ -248,7 +248,9 @@ def parse_single_transition(self, seir_config, single_transition_config, fake_co
 
         return rc
 
-    def toFile(self, compartments_file='compartments.parquet', transitions_file='transitions.parquet', write_parquet=True):
+    def toFile(
+        self, compartments_file="compartments.parquet", transitions_file="transitions.parquet", write_parquet=True
+    ):
         out_df = self.compartments.copy()
         if write_parquet:
             pa_df = pa.Table.from_pandas(out_df, preserve_index=False)
@@ -294,7 +296,7 @@ def get_comp_idx(self, comp_dict: dict, error_info: str = "no information") -> i
         comp_idx = self.compartments[mask].index.values
         if len(comp_idx) != 1:
             raise ValueError(
-                f"The provided dictionary does not allow to isolate a compartment: {comp_dict} isolate {self.compartments[mask]} from options {self.compartments}. The get_comp_idx function was called by'{error_info}'." 
+                f"The provided dictionary does not allow to isolate a compartment: {comp_dict} isolate {self.compartments[mask]} from options {self.compartments}. The get_comp_idx function was called by'{error_info}'."
             )
         return comp_idx[0]
 
@@ -493,7 +495,7 @@ def parse_parameter_strings_to_numpy_arrays_v2(self, parameters, parameter_names
             # in this case we find the next array and set it to that size,
             # TODO: instead of searching for the next array, better to just use the parameter shape.
             if not isinstance(substituted_formulas[i], np.ndarray):
-                for k in range(len(substituted_formulas)): 
+                for k in range(len(substituted_formulas)):
                     if isinstance(substituted_formulas[k], np.ndarray):
                         substituted_formulas[i] = substituted_formulas[i] * np.ones_like(substituted_formulas[k])
 
@@ -650,40 +652,41 @@ def list_recursive_convert_to_string(thing):
     return str(thing)
 
 
-
 @click.group()
 def compartments():
     pass
 
+
 # TODO: CLI arguments
 @compartments.command()
 def plot():
-    assert config["compartments"].exists() 
-    assert config["seir"].exists() 
+    assert config["compartments"].exists()
+    assert config["seir"].exists()
     comp = Compartments(seir_config=config["seir"], compartments_config=config["compartments"])
-    
+
     # TODO: this should be a command like build compartments.
     (
         unique_strings,
         transition_array,
         proportion_array,
         proportion_info,
     ) = comp.get_transition_array()
-    
+
     comp.plot(output_file="transition_graph", source_filters=[], destination_filters=[])
 
     print("wrote file transition_graph")
 
+
 @compartments.command()
 def export():
-    assert config["compartments"].exists() 
-    assert config["seir"].exists() 
+    assert config["compartments"].exists()
+    assert config["seir"].exists()
     comp = Compartments(seir_config=config["seir"], compartments_config=config["compartments"])
     (
         unique_strings,
         transition_array,
         proportion_array,
         proportion_info,
     ) = comp.get_transition_array()
-    comp.toFile('compartments_file.csv', 'transitions_file.csv')
-    print("wrote files 'compartments_file.csv', 'transitions_file.csv' ")
+    comp.toFile("compartments_file.csv", "transitions_file.csv")
+    print("wrote files 'compartments_file.csv', 'transitions_file.csv' ")

flepimop/gempyor_pkg/src/gempyor/outcomes.py

@@ -151,32 +151,14 @@ def read_parameters_from_config(modinf: model_info.ModelInfo):
                         f"Places in seir input files does not correspond to subpops in outcome probability file {branching_file}"
                     )
 
-        # subclasses = [""]
-        # if modinf.outcomes_config["subclasses"].exists():
-        #    subclasses = modinf.outcomes_config["subclasses"].get()
-
         parameters = {}
         for new_comp in outcomes_config:
             if outcomes_config[new_comp]["source"].exists():
-                # for subclass in subclasses:
-                #    class_name = new_comp + subclass
-                #    parameters[class_name] = {}
                 parameters[new_comp] = {}
                 # Read the config for this compartement
                 src_name = outcomes_config[new_comp]["source"].get()
                 if isinstance(src_name, str):
-                    #    if src_name != "incidI":
-                    #        parameters[class_name]["source"] = src_name + subclass
-                    #        else:
-                    #            parameters[class_name]["source"] = src_name
                     parameters[new_comp]["source"] = src_name
-                # else:
-                # else:
-                #    if subclasses != [""]:
-                #        raise ValueError("Subclasses not compatible with outcomes from compartments ")
-                #    elif ("incidence" in src_name.keys()) or ("prevalence" in src_name.keys()):
-                #        parameters[class_name]["source"] = dict(src_name)
-
                 elif ("incidence" in src_name.keys()) or ("prevalence" in src_name.keys()):
                     parameters[new_comp]["source"] = dict(src_name)
 

flepimop/gempyor_pkg/src/gempyor/seeding_ic.py

@@ -35,7 +35,7 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict:
     n_seeding_ignored_before = 0
     n_seeding_ignored_after = 0
 
-    #id_seed = 0
+    # id_seed = 0
     for idx, (row_index, row) in enumerate(df.iterrows()):
         if row["subpop"] not in setup.subpop_struct.subpop_names:
             raise ValueError(
@@ -44,17 +44,21 @@ def _DataFrame2NumbaDict(df, amounts, setup) -> nb.typed.Dict:
 
         if (row["date"].date() - setup.ti).days >= 0:
             if (row["date"].date() - setup.ti).days < len(nb_seed_perday):
-
                 nb_seed_perday[(row["date"].date() - setup.ti).days] = (
                     nb_seed_perday[(row["date"].date() - setup.ti).days] + 1
                 )
                 source_dict = {grp_name: row[f"source_{grp_name}"] for grp_name in cmp_grp_names}
                 destination_dict = {grp_name: row[f"destination_{grp_name}"] for grp_name in cmp_grp_names}
-                seeding_dict["seeding_sources"][idx] = setup.compartments.get_comp_idx(source_dict, error_info = f"(seeding source at idx={idx}, row_index={row_index}, row=>>{row}<<)")
-                seeding_dict["seeding_destinations"][idx] = setup.compartments.get_comp_idx(destination_dict, error_info = f"(seeding destination at idx={idx}, row_index={row_index}, row=>>{row}<<)")
+                seeding_dict["seeding_sources"][idx] = setup.compartments.get_comp_idx(
+                    source_dict, error_info=f"(seeding source at idx={idx}, row_index={row_index}, row=>>{row}<<)"
+                )
+                seeding_dict["seeding_destinations"][idx] = setup.compartments.get_comp_idx(
+                    destination_dict,
+                    error_info=f"(seeding destination at idx={idx}, row_index={row_index}, row=>>{row}<<)",
+                )
                 seeding_dict["seeding_subpops"][idx] = setup.subpop_struct.subpop_names.index(row["subpop"])
                 seeding_amounts[idx] = amounts[idx]
-                #id_seed+=1
+                # id_seed+=1
             else:
                 n_seeding_ignored_after += 1
         else:
@@ -303,14 +307,14 @@ def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict:
             raise NotImplementedError(f"unknown seeding method [got: {method}]")
 
         # Sorting by date is very important here for the seeding format necessary !!!!
-        #print(seeding.shape)
+        # print(seeding.shape)
         seeding = seeding.sort_values(by="date", axis="index").reset_index()
-        #print(seeding)
-        mask = (seeding['date'].dt.date > setup.ti) & (seeding['date'].dt.date <= setup.tf)
+        # print(seeding)
+        mask = (seeding["date"].dt.date > setup.ti) & (seeding["date"].dt.date <= setup.tf)
         seeding = seeding.loc[mask].reset_index()
-        #print(seeding.shape)
-        #print(seeding)
-        
+        # print(seeding.shape)
+        # print(seeding)
+
         # TODO: print.
 
         amounts = np.zeros(len(seeding))
@@ -322,11 +326,10 @@ def draw_seeding(self, sim_id: int, setup) -> nb.typed.Dict:
         elif method == "FolderDraw" or method == "FromFile":
             amounts = seeding["amount"]
 
-
         return _DataFrame2NumbaDict(df=seeding, amounts=amounts, setup=setup)
 
     def load_seeding(self, sim_id: int, setup) -> nb.typed.Dict:
-        """ only difference with draw seeding is that the sim_id is now sim_id2load"""
+        """only difference with draw seeding is that the sim_id is now sim_id2load"""
         return self.draw_seeding(sim_id=sim_id, setup=setup)
 
     def load_ic(self, sim_id: int, setup) -> nb.typed.Dict:
@@ -336,19 +339,21 @@ def load_ic(self, sim_id: int, setup) -> nb.typed.Dict:
     def seeding_write(self, seeding, fname, extension):
         raise NotImplementedError(f"It is not yet possible to write the seeding to a file")
 
+
 class SimulationComponent:
     def __init__(self, config: confuse.ConfigView):
         raise NotImplementedError("This method should be overridden in subclasses.")
-        
+
     def load(self, sim_id: int, setup) -> np.ndarray:
         raise NotImplementedError("This method should be overridden in subclasses.")
-    
+
     def draw(self, sim_id: int, setup) -> np.ndarray:
         raise NotImplementedError("This method should be overridden in subclasses.")
-    
+
     def write_to_file(self, sim_id: int, setup):
         raise NotImplementedError("This method should be overridden in subclasses.")
-
+
+
 class Seeding(SimulationComponent):
     def __init__(self, config: confuse.ConfigView):
         self.seeding_config = config
@@ -393,14 +398,14 @@ def draw(self, sim_id: int, setup) -> nb.typed.Dict:
             raise NotImplementedError(f"unknown seeding method [got: {method}]")
 
         # Sorting by date is very important here for the seeding format necessary !!!!
-        #print(seeding.shape)
+        # print(seeding.shape)
         seeding = seeding.sort_values(by="date", axis="index").reset_index()
-        #print(seeding)
-        mask = (seeding['date'].dt.date > setup.ti) & (seeding['date'].dt.date <= setup.tf)
+        # print(seeding)
+        mask = (seeding["date"].dt.date > setup.ti) & (seeding["date"].dt.date <= setup.tf)
         seeding = seeding.loc[mask].reset_index()
-        #print(seeding.shape)
-        #print(seeding)
-        
+        # print(seeding.shape)
+        # print(seeding)
+
         # TODO: print.
 
         amounts = np.zeros(len(seeding))
@@ -412,17 +417,17 @@ def draw(self, sim_id: int, setup) -> nb.typed.Dict:
         elif method == "FolderDraw" or method == "FromFile":
             amounts = seeding["amount"]
 
-
         return _DataFrame2NumbaDict(df=seeding, amounts=amounts, setup=setup)
-    
+
     def load(self, sim_id: int, setup) -> nb.typed.Dict:
-        """ only difference with draw seeding is that the sim_id is now sim_id2load"""
+        """only difference with draw seeding is that the sim_id is now sim_id2load"""
         return self.draw(sim_id=sim_id, setup=setup)
-
+
+
 class InitialConditions(SimulationComponent):
     def __init__(self, config: confuse.ConfigView):
         self.initial_conditions_config = config
-        
+
     def draw(self, sim_id: int, setup) -> np.ndarray:
         method = "Default"
         if self.initial_conditions_config is not None and "method" in self.initial_conditions_config.keys():
@@ -600,6 +605,6 @@ def draw(self, sim_id: int, setup) -> np.ndarray:
                 """ Ignoring the previous population mismatch errors because you added flag 'ignore_population_checks'. This is dangerous"""
             )
         return y0
-    
+
     def load(self, sim_id: int, setup) -> nb.typed.Dict:
-        return self.draw(sim_id=sim_id, setup=setup)
+        return self.draw(sim_id=sim_id, setup=setup)

flepimop/gempyor_pkg/src/gempyor/simulate.py

@@ -274,7 +274,7 @@
     help="write parquet file output at end of simulation",
 )
 # @profile_options
-#@profile()
+# @profile()
 def simulate(
     config_file,
     in_run_id,

flepimop/gempyor_pkg/tests/outcomes/test_outcomes.py

@@ -546,8 +546,8 @@ def test_outcomes_read_write_hnpi2_custom_pname():
         first_sim_index=1,
         outcome_modifiers_scenario="Some",
         stoch_traj_flag=False,
-out_run_id=107,
-)
+        out_run_id=107,
+    )
 
     outcomes.onerun_delayframe_outcomes(sim_id2write=1, modinf=inference_simulator.modinf, load_ID=True, sim_id2load=1)
 

flepimop/gempyor_pkg/tests/seir/dev_new_test0.py

@@ -20,7 +20,7 @@
 
 
 def test_parameters_from_timeserie_file():
-#   if True:
+    #   if True:
     config.clear()
     config.read(user=False)
     config.set_file(f"{DATA_DIR}/config_compartmental_model_format_with_covariates.yml")
@@ -32,19 +32,20 @@ def test_parameters_from_timeserie_file():
         outcome_scenario="high_death_rate",
         stoch_traj_flag=False,
     )
-    
+
     p = parameters.Parameters(
-       parameter_config=config["seir"]["parameters"], 
-       ti=config["start_date"].as_date(),
-       tf=config["end_date"].as_date(),
-       nodenames=inference_simulator.s.spatset.nodenames,
-       config_version="v3")
-
-    #p = inference_simulator.s.parameters
+        parameter_config=config["seir"]["parameters"],
+        ti=config["start_date"].as_date(),
+        tf=config["end_date"].as_date(),
+        nodenames=inference_simulator.s.spatset.nodenames,
+        config_version="v3",
+    )
+
+    # p = inference_simulator.s.parameters
     p_draw = p.parameters_quick_draw(n_days=inference_simulator.s.n_days, nnodes=inference_simulator.s.nnodes)
 
     p_df = p.getParameterDF(p_draw)["parameter"]
-    
+
     for pn in p.pnames:
         if pn == "R0s":
             assert pn not in p_df
@@ -54,7 +55,7 @@ def test_parameters_from_timeserie_file():
     initial_df = read_df("data/r0s_ts.csv").set_index("date")
 
     assert (p_draw[p.pnames2pindex["R0s"]] == initial_df.values).all()
-    
+
     ### test what happen when the order of geoids is not respected (expected: reput them in order)
 
     ### test what happens with incomplete data (expected: fail)