56 decide on and implement dag solving strategy

docs/guide/experiment_config.rst

@@ -49,6 +49,8 @@ Configuration files are written in ``YAML`` format and are divided into differen
      - Poisson S-test:
          func: poisson_evaluations.spatial_test
          plot_func: plot_poisson_consistency_test
+   run_mode: parallel
+   force_rerun: true
    postprocess:
      plot_forecasts:
        cmap: magma
@@ -278,3 +280,12 @@ The seismicity catalog can be defined with the ``catalog`` parameter. It represe
 
 .. important::
   The main catalog will be stored, and consecutively filtered to the extent of each testing time-window, as well as to the experiment's spatial domain, and magnitude- and depth- ranges.
+
+
+Run Configuration
+-----------------
+
+The ``run_mode`` parameter allows to perform the experiment tasks in either ``sequential`` (default) or ``parallel``.
+The former is appropriate for staging and testing the experiment is working, whereas ``parallel`` is optimal for heavy computations when the experiment is set to production (e.g., real conditions)
+
+The ``force_rerun`` makes the experiment recompute every forecast. Default is ``false``, which allows when instantiating the experiment to make a self-discovery of existing forecasts.

docs/guide/postprocess_config.rst

@@ -422,19 +422,19 @@ Here are some basic functionalities from **floatCSEP** to access catalogs, forec
               timewindow_str = timewindow2str(timewindow)
               model.get_forecast(timewindow_str)
 
-      * - :attr:`Model.registry.path <floatcsep.infrastructure.registries.ForecastRegistry>`
+      * - :attr:`Model.registry.path <floatcsep.infrastructure.registries.ModelFileRegistry>`
         - Directory of the model file or source code.
-      * - :attr:`Model.registry.database <floatcsep.infrastructure.registries.ForecastRegistry>`
+      * - :attr:`Model.registry.database <floatcsep.infrastructure.registries.ModelFileRegistry>`
         - Database path where forecasts are stored.
       * - :attr:`TimeIndependentModel.forecast_unit <floatcsep.model.TimeIndependentModel>`
         - The forecast unit for a time independent model.
       * - :meth:`TimeDependentModel.func <floatcsep.model.TimeIndependentModel>`
         - The function command to execute a time dependent source code.
       * - :meth:`TimeDependentModel.func_kwargs`
         - The keyword arguments of the model, passed to the arguments file.
-      * - :meth:`TimeDependentModel.registry.args_file <floatcsep.infrastructure.registries.ForecastRegistry>`
+      * - :meth:`TimeDependentModel.registry.args_file <floatcsep.infrastructure.registries.ModelFileRegistry>`
         - The path of the arguments file. Default is ``args.txt``.
-      * - :meth:`TimeDependentModel.registry.input_cat <floatcsep.infrastructure.registries.ForecastRegistry>`
+      * - :meth:`TimeDependentModel.registry.input_cat <floatcsep.infrastructure.registries.ModelFileRegistry>`
         - The path of the input catalog for the model execution.
 
 

docs/reference/api_reference.rst

@@ -52,8 +52,6 @@ instances onto an experimental workflow. The class and its main methods are:
     Experiment.set_models
     Experiment.set_tests
     Experiment.stage_models
-    Experiment.set_input_cat
-    Experiment.set_test_cat
     Experiment.set_tasks
     Experiment.run
     Experiment.read_results
@@ -74,7 +72,6 @@ reading. The abstract and concrete classes, and their main methods are:
    Model.factory
 
    TimeIndependentModel
-   TimeIndependentModel.init_db
    TimeIndependentModel.get_forecast
 
    TimeDependentModel.stage
@@ -128,8 +125,8 @@ These are the helper functions of ``floatCSEP``
     parse_timedelta_string
     read_time_cfg
     read_region_cfg
-    timewindows_ti
-    timewindows_td
+    time_windows_ti
+    time_windows_td
 
 
 Some additional plotting functions to pyCSEP are:
@@ -148,16 +145,26 @@ Some additional plotting functions to pyCSEP are:
 
 A small wrapper for ``pyCSEP`` readers
 
-.. currentmodule:: floatcsep.utils.readers
+.. currentmodule:: floatcsep.utils.file_io
 
 .. autosummary::
    :nosignatures:
 
-    ForecastParsers.dat
-    ForecastParsers.xml
-    ForecastParsers.quadtree
-    ForecastParsers.csv
-    ForecastParsers.hdf5
+    CatalogParser.ascii
+    CatalogParser.json
+
+    CatalogSerializer.ascii
+    CatalogSerializer.json
+
+    GriddedForecastParsers.dat
+    GriddedForecastParsers.xml
+    GriddedForecastParsers.quadtree
+    GriddedForecastParsers.csv
+    GriddedForecastParsers.hdf5
+
+    CatalogForecastParsers.csv
+    CatalogForecastParsers.load_hermes_catalog
+
     HDF5Serializer.grid2hdf5
     serialize
 
@@ -194,20 +201,25 @@ components (e.g., forecasts, catalogs, results, etc.), and allows to be aware of
 .. autosummary::
    :nosignatures:
 
-    ForecastRegistry
-    ForecastRegistry.get_forecast
-    ForecastRegistry.fmt
-    ForecastRegistry.forecast_exists
-    ForecastRegistry.build_tree
-
-    ExperimentRegistry
-    ExperimentRegistry.add_forecast_registry
-    ExperimentRegistry.get_forecast_registry
-    ExperimentRegistry.get_result
-    ExperimentRegistry.get_test_catalog
-    ExperimentRegistry.get_figure
-    ExperimentRegistry.result_exist
-    ExperimentRegistry.build_tree
+    ModelFileRegistry
+    ModelFileRegistry.fmt
+    ModelFileRegistry.get_input_catalog_key
+    ModelFileRegistry.get_forecast_key
+    ModelFileRegistry.get_args_key
+    ModelFileRegistry.get_input_dir
+    ModelFileRegistry.get_forecast_dir
+    ModelFileRegistry.get_args_template_path
+    ModelFileRegistry.forecast_exists
+    ModelFileRegistry.build_tree
+
+    ExperimentFileRegistry
+    ExperimentFileRegistry.add_model_registry
+    ExperimentFileRegistry.get_model_registry
+    ExperimentFileRegistry.get_result_key
+    ExperimentFileRegistry.get_test_catalog_key
+    ExperimentFileRegistry.get_figure_key
+    ExperimentFileRegistry.result_exist
+    ExperimentFileRegistry.build_tree
 
 
 **Repositories**
@@ -225,8 +237,9 @@ catalogs, forecasts), abstracting the experiment logic from the pyCSEP io functi
    CatalogRepository.set_main_catalog
    CatalogRepository.catalog
    CatalogRepository.get_test_cat
-   CatalogRepository.set_test_cat
-   CatalogRepository.set_input_cat
+   CatalogRepository.set_test_cats
+   CatalogRepository.set_input_cats
+   CatalogRepository.filter_catalog
 
    GriddedForecastRepository
    GriddedForecastRepository.load_forecast

docs/reference/infrastructure.rst

@@ -7,12 +7,12 @@ and the required workflow to run an Experiment.
 Registries
 ----------
 
-.. autoclass:: floatcsep.infrastructure.registries.ForecastRegistry
+.. autoclass:: floatcsep.infrastructure.registries.ModelFileRegistry
    :members:
    :undoc-members:
    :show-inheritance:
 
-.. autoclass:: floatcsep.infrastructure.registries.ExperimentRegistry
+.. autoclass:: floatcsep.infrastructure.registries.ExperimentFileRegistry
    :members:
    :undoc-members:
    :show-inheritance:

docs/reference/utilities.rst

@@ -27,11 +27,11 @@ This section documents the `accessors` module.
    :inherited-members:
 
 
-Readers
--------
-This section documents the `readers` module.
+Readers and Parsers
+-------------------
+This section documents the `file_io` module.
 
-.. automodule:: floatcsep.utils.readers
+.. automodule:: floatcsep.utils.file_io
    :members:
    :undoc-members:
    :show-inheritance:

docs/tutorials/case_a.rst

@@ -46,7 +46,7 @@ The source code can be found in the ``tutorials/case_a`` folder or in  `GitHub <
     .. literalinclude:: ../../tutorials/case_a/catalog.csep
        :caption: tutorials/case_a/catalog.csep
 
-* The forecast ``best_model.dat`` to be evaluated is written in the ``.dat`` format (see :doc:`pycsep:concepts/forecasts`). Forecast formats are detected automatically (see :mod:`floatcsep.utils.readers.ForecastParsers`)
+* The forecast ``best_model.dat`` to be evaluated is written in the ``.dat`` format (see :doc:`pycsep:concepts/forecasts`). Forecast formats are detected automatically (see :mod:`floatcsep.utils.file_io.GriddedForecastParsers`)
 
     .. literalinclude:: ../../tutorials/case_a/best_model.dat
         :caption: tutorials/case_a/best_model.dat

floatcsep/experiment.py

@@ -77,7 +77,7 @@ class Experiment:
 
         model_config (str): Path to the models' configuration file
         test_config (str): Path to the evaluations' configuration file
-        run_mode (str): 'serial' or 'parallel'
+        run_mode (str): 'sequential' or 'parallel'
         default_test_kwargs (dict): Default values for the testing
          (seed, number of simulations, etc.)
         postprocess (dict): Contains the instruction for postprocessing
@@ -99,10 +99,11 @@ def __init__(
         catalog: str = None,
         models: str = None,
         tests: str = None,
+        exp_class: str = "ti",
         postprocess: str = None,
         default_test_kwargs: dict = None,
         run_dir: str = "results",
-        run_mode: str = "serial",
+        run_mode: str = "sequential",
         stage_dir: ... = "results",
         report_hook: dict = None,
         **kwargs,
@@ -155,14 +156,19 @@ def __init__(
             f"\tMagnitude range: [{numpy.min(self.magnitudes)},"
             f" {numpy.max(self.magnitudes)}]"
         )
+        exp_class_str = (
+            "Time-Dependent"
+            if self.exp_class in ("td", "time-dependent")
+            else "Time-Independent"
+        )
+        log.info(f"\tExperiment class: {exp_class_str}")
 
         self.catalog = None
         self.models = []
         self.tests = []
 
         self.postprocess = postprocess if postprocess else {}
         self.default_test_kwargs = default_test_kwargs
-
         self.catalog_repo.set_main_catalog(catalog, self.time_config, self.region_config)
 
         self.models = self.set_models(
@@ -347,35 +353,12 @@ def set_tests(self, test_config: Union[str, Dict, List]) -> list:
 
         return tests
 
-    def set_test_cat(self, tstring: str) -> None:
-        """
-        Filters the complete experiment catalog to a test sub-catalog bounded by the test
-        time-window. Writes it to filepath defined in :attr:`Experiment.registry`
-
-        Args:
-            tstring (str): Time window string
-        """
-
-        self.catalog_repo.set_test_cat(tstring)
-
-    def set_input_cat(self, tstring: str, model: Model) -> None:
-        """
-        Filters the complete experiment catalog to an input sub-catalog filtered to the
-        beginning of the test time-window.
-
-        Args:
-            tstring (str): Time window string
-            model (:class:`~floatcsep.model.Model`): Model to give the input
-             catalog
-        """
-        self.catalog_repo.set_input_cat(tstring, model)
-
     def set_tasks(self) -> None:
         """
         Lazy definition of the experiment core tasks by wrapping instances,
         methods and arguments. Creates a graph with task nodes, while assigning
         task-parents to each node, depending on each Evaluation signature.
-        The tasks can then be run in serial as a list or asynchronous
+        The tasks can then be run in sequential as a list or asynchronous
         using the graph's node dependencies.
         For instance:
 
@@ -403,38 +386,39 @@ def set_tasks(self) -> None:
         # Prepare the testing catalogs
         task_graph = TaskGraph()
         for time_i in tw_strings:
-            # The method call Experiment.set_test_cat(time_i) is created lazily
-            task_i = Task(instance=self, method="set_test_cat", tstring=time_i)
-            # An is added to the task graph
+            task_i = Task(instance=self.catalog_repo, method="set_test_cats", tstring=time_i)
             task_graph.add(task_i)
-            # the task will be executed later with Experiment.run()
-            # once all the tasks are defined
+            if self.exp_class in ["td", "time_dependent"]:
+                task_j = Task(
+                    instance=self.catalog_repo,
+                    method="set_input_cats",
+                    tstring=time_i,
+                    models=self.models,
+                )
+
+                task_graph.add(task=task_j)
 
         # Set up the Forecasts creation
         for time_i in tw_strings:
             for model_j in self.models:
-                if isinstance(model_j, TimeDependentModel):
-                    task_tj = Task(
-                        instance=self, method="set_input_cat", tstring=time_i, model=model_j
-                    )
-
-                    task_graph.add(task=task_tj)
-                    # A catalog needs to have been filtered
-
                 task_ij = Task(
                     instance=model_j,
                     method="create_forecast",
                     tstring=time_i,
                     force=self.force_rerun,
                 )
+
                 task_graph.add(task=task_ij)
                 # A catalog needs to have been filtered
                 if isinstance(model_j, TimeDependentModel):
                     task_graph.add_dependency(
-                        task_ij, dep_inst=self, dep_meth="set_input_cat", dkw=(time_i, model_j)
+                        task_ij,
+                        dep_inst=self.catalog_repo,
+                        dep_meth="set_input_cats",
+                        dkw=(time_i, model_j),
                     )
                 task_graph.add_dependency(
-                    task_ij, dep_inst=self, dep_meth="set_test_cat", dkw=time_i
+                    task_ij, dep_inst=self.catalog_repo, dep_meth="set_test_cats", dkw=time_i
                 )
 
         # Set up the Consistency Tests
@@ -450,6 +434,7 @@ def set_tasks(self) -> None:
                             region=self.region,
                         )
                         task_graph.add(task_ijk)
+
                         # the forecast needs to have been created
                         task_graph.add_dependency(
                             task_ijk, dep_inst=model_j, dep_meth="create_forecast", dkw=time_i
@@ -531,7 +516,6 @@ def set_tasks(self) -> None:
                         task_graph.add_dependency(
                             task_k, dep_inst=m_j, dep_meth="create_forecast", dkw=time_str
                         )
-
         self.task_graph = task_graph
 
     def run(self) -> None:
@@ -544,13 +528,16 @@ def run(self) -> None:
          - Memory monitor?
          - Queuer?
         """
-        log.info(f"Running {self.task_graph.ntasks} tasks")
 
         if self.seed:
             numpy.random.seed(self.seed)
 
-        self.task_graph.run()
-        log.info("Calculation completed")
+        if self.run_mode == "parallel":
+            cpu_count = os.cpu_count() or 4
+            workers = getattr(self, "concurrent_tasks", None) or min(cpu_count, 32)
+            self.task_graph.run_parallel(max_workers=workers)
+        else:
+            self.task_graph.run()
         log.debug("Post-run forecast registry")
         log_models_tree(log, self.registry, self.time_windows)
         log.debug("Post-run result summary")
@@ -677,7 +664,7 @@ def from_yml(cls, config_yml: str, repr_dir=None, **kwargs):
         Returns:
             An :class:`~floatcsep.experiment.Experiment` class instance
         """
-        log.info("Initializing experiment from .yml file")
+        log.info(f"Initializing experiment from {config_yml} file")
         with open(config_yml, "r") as yml:
 
             # experiment configuration file