Merge branch 'pr/1.4.4' into mpvanderschelling/issue194

.github/workflows/pr_to_pr.yml

@@ -1,9 +1,12 @@
-name: Pull request to pr/** branches
+name: Pull request and push to pr/** branches
 
 on:
   pull_request:
     branches:
       - "pr/**"
+  push:
+    branches:
+      - "pr/**"
 
 jobs:
   check-coding-style:

VERSION

@@ -1 +1 @@
-1.4.3
+1.4.4

docs/source/conf.py

@@ -24,8 +24,8 @@
 project = 'f3dasm'
 author = 'Martin van der Schelling'
 copyright = '2022, Martin van der Schelling'
-version = '1.4.3'
-release = '1.4.3'
+version = '1.4.4'
+release = '1.4.4'
 
 
 # -- General configuration ----------------------------------------------------

docs/source/rst_doc_files/classes/design/experimentsample.rst

@@ -54,6 +54,19 @@ An KeyError will be raised if the key is not found.
     >>> experiment_sample.get('param_1')
     0.0249
 
+Manually iterating over ExperimentData
+----------------------------------------
+
+The :class:`~f3dasm.design.ExperimentData` object can be manually iterated over to get :class:`~f3dasm.design.ExperimentSample` objects for each experiment:
+
+.. code-block:: python
+
+    >>> for experiment_sample in experiment_data:
+    ...     print(experiment_sample)
+    ExperimentSample(0 : {'x0': 0.8184054141827567, 'x1': 0.937852542255321, 'x2': 0.7376563782762678} - {})
+    ExperimentSample(1 : {'x0': 0.7203461491873061, 'x1': 0.7320604457665572, 'x2': 0.2524387342272223} - {})
+    ExperimentSample(2 : {'x0': 0.35449352388104904, 'x1': 0.11413412225748525, 'x2': 0.1467895592274866} - {})
+
 Storing output parameters to the experiment sample
 --------------------------------------------------
 

src/f3dasm/__init__.py

@@ -37,7 +37,7 @@
 # =============================================================================
 
 
-__version__ = '1.4.3'
+__version__ = '1.4.4'
 
 
 # Log welcome message and the version of f3dasm

src/f3dasm/_src/datageneration/datagenerator.py

@@ -88,8 +88,10 @@ def post_process(self, experiment_sample: ExperimentSample, **kwargs) -> None:
         ...
 
     @time_and_log
-    def run(self, experiment_sample: ExperimentSample, **kwargs) -> ExperimentSample:
-        """This function chains the following methods together
+    def _run(self, experiment_sample: ExperimentSample, **kwargs) -> ExperimentSample:
+        """
+        Run the data generator
+        This function chains the following methods together
 
         * pre_process(); to combine the experiment_sample and the parameters
         of the data generator to an input file that can be used to run the data generator

src/f3dasm/_src/datageneration/functions/function.py

@@ -99,7 +99,7 @@ def execute(self, experiment_sample: ExperimentSample) -> ExperimentSample:
         experiment_sample["y"] = self(x).ravel().astype(np.float32)
         return experiment_sample
 
-    def run(self, experiment_sample: ExperimentSample, **kwargs) -> ExperimentSample:
+    def _run(self, experiment_sample: ExperimentSample, **kwargs) -> ExperimentSample:
         return self.execute(experiment_sample)
 
     def _retrieve_original_input(self, x: np.ndarray):

src/f3dasm/_src/design/domain.py

@@ -8,10 +8,11 @@
 from __future__ import annotations
 
 # Standard
+import math
 import pickle
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Dict, Iterator, List, Sequence, Type
+from typing import Any, Dict, Iterable, Iterator, List, Sequence, Type
 
 # Third-party core
 import numpy as np
@@ -252,7 +253,14 @@ def add_int(self, name: str, low: int, high: int, step: int = 1):
         >>> domain.add_int('param1', 0, 10, 2)
         >>> domain.space
         {'param1': DiscreteParameter(lower_bound=0, upper_bound=10, step=2)}
+
+        Note
+        ----
+        If the lower and upper bound are equal, then a constant parameter
+        will be added to the domain!
         """
+        if low == high:
+            self.add_constant(name, low)
         self._add(name, DiscreteParameter(low, high, step))
 
     def add_float(self, name: str, low: float, high: float, log: bool = False):
@@ -275,8 +283,16 @@ def add_float(self, name: str, low: float, high: float, log: bool = False):
         >>> domain.add_float('param1', 0., 10., log=True)
         >>> domain.space
         {'param1': ContinuousParameter(lower_bound=0., upper_bound=10., log=True)}
+
+        Note
+        ----
+        If the lower and upper bound are equal, then a constant parameter
+        will be added to the domain!
         """
-        self._add(name, ContinuousParameter(low, high, log))
+        if math.isclose(low, high):
+            self.add_constant(name, low)
+        else:
+            self._add(name, ContinuousParameter(low, high, log))
 
     def add_category(self, name: str, categories: Sequence[CategoricalType]):
         """Add a new categorical input parameter to the domain.
@@ -573,6 +589,38 @@ def _filter(self, type: Type[Parameter]) -> Domain:
                    if isinstance(parameter, type)}
         )
 
+    def select(self, names: str | Iterable[str]) -> Domain:
+        """Select a subset of parameters from the domain.
+
+        Parameters
+        ----------
+
+        names : str or Iterable[str]
+            The names of the parameters to select.
+
+        Returns
+        -------
+        Domain
+            A new domain with the selected parameters.
+
+        Example
+        -------
+        >>> domain = Domain()
+        >>> domain.space = {
+        ...     'param1': ContinuousParameter(lower_bound=0., upper_bound=1.),
+        ...     'param2': DiscreteParameter(lower_bound=0, upper_bound=8),
+        ...     'param3': CategoricalParameter(categories=['cat1', 'cat2'])
+        ... }
+        >>> domain.select(['param1', 'param3'])
+        Domain({'param1': ContinuousParameter(lower_bound=0, upper_bound=1),
+                'param3': CategoricalParameter(categories=['cat1', 'cat2'])})
+        """
+
+        if isinstance(names, str):
+            names = [names]
+
+        return Domain(space={key: self.space[key] for key in names})
+
 #                                                                 Miscellaneous
 # =============================================================================
 

src/f3dasm/_src/experimentdata/_data.py

@@ -291,6 +291,23 @@ def n_best_samples(self, nosamples: int, column_name: List[str] | str) -> pd.Dat
         """
         return self.data.nsmallest(n=nosamples, columns=column_name)
 
+    def select_columns(self, columns: Iterable[str] | str) -> _Data:
+        """Filter the data on the selected columns.
+
+        Parameters
+        ----------
+        columns : Iterable[str] | str
+            The columns to select.
+
+        Returns
+        -------
+        _Data
+            The data only with the selected columns
+        """
+        # This is necessary otherwise self.data[columns] will be a Series
+        if isinstance(columns, str):
+            columns = [columns]
+        return _Data(self.data[columns])
 #                                                        Append and remove data
 # =============================================================================