Merge pull request #5 from EasyScience/new_job

New job

Author: rozyczko

src/easyscience/Datasets/xarray.py

@@ -612,13 +612,13 @@ def postcompute_func(self, new_computational_fn: Callable):
 
     def fit_prep(self, func_in: Callable, bdims=None, dask_chunks=None) -> Tuple[xr.DataArray, Callable]:
         """
-        Generate boradcasted coordinates for fitting and reform the fitting function into one which can handle xarrays
+        Generate broadcasted coordinates for fitting and reform the fitting function into one which can handle xarrays.
 
-        :param func_in: Function to be wrapped and made xarray fitting compatable.
+        :param func_in: Function to be wrapped and made xarray fitting compatible.
         :type func_in: Callable
         :param bdims: Optional precomputed broadcasted dimensions.
         :type bdims: xarray.DataArray
-        :param dask_chunks: How to split to broadcasted dimensions for dask.
+        :param dask_chunks: How to split the broadcasted dimensions for dask.
         :type dask_chunks: Tuple[int..]
         :return: Tuple of broadcasted fit arrays and wrapped fit function.
         :rtype: xarray.DataArray, Callable

src/easyscience/Objects/Job/Analysis.py

@@ -0,0 +1,64 @@
+#  SPDX-FileCopyrightText: 2023 EasyScience contributors  <core@easyscience.software>
+#  SPDX-License-Identifier: BSD-3-Clause
+#  © 2021-2023 Contributors to the EasyScience project <https://github.com/easyScience/EasyScience
+from abc import ABCMeta
+from abc import abstractmethod
+from typing import Union
+
+import numpy as np
+
+from easyscience.Datasets.xarray import xr  # type: ignore
+from easyscience.fitting.minimizers import MinimizerBase
+from easyscience.Objects.ObjectClasses import BaseObj
+
+
+class AnalysisBase(BaseObj, metaclass=ABCMeta):
+    """
+    This virtual class allows for the creation of technique-specific Analysis objects.
+    """
+    def __init__(self, name: str, interface=None, *args, **kwargs):
+        super(AnalysisBase, self).__init__(name, *args, **kwargs)
+        self.name = name
+        self._calculator = None
+        self._minimizer = None
+        self._fitter = None
+        self.interface = interface
+
+    @abstractmethod
+    def calculate_theory(self,
+                         x: Union[xr.DataArray, np.ndarray],
+                         **kwargs) -> np.ndarray:
+        raise NotImplementedError("calculate_theory not implemented")
+
+    @abstractmethod
+    def fit(self,
+            x: Union[xr.DataArray, np.ndarray],
+            y: Union[xr.DataArray, np.ndarray],
+            e: Union[xr.DataArray, np.ndarray],
+            **kwargs) -> None:
+        raise NotImplementedError("fit not implemented")
+
+    @property
+    def calculator(self) -> str:
+        if self._calculator is None:
+            self._calculator = self.interface.current_interface_name
+        return self._calculator
+
+    @calculator.setter
+    def calculator(self, value) -> None:
+        # TODO: check if the calculator is available for the given JobType
+        self.interface.switch(value, fitter=self._fitter)
+
+    @property
+    def minimizer(self) -> MinimizerBase:
+        return self._minimizer
+
+    @minimizer.setter
+    def minimizer(self, minimizer: MinimizerBase) -> None:
+        self._minimizer = minimizer
+
+    # required dunder methods
+    def __str__(self):
+        return f"Analysis: {self.name}"
+    
+    

src/easyscience/Objects/Job/__init__.py renamed to src/easyscience/Objects/job/__init__.py

@@ -5,9 +5,9 @@
 from abc import ABCMeta
 from abc import abstractmethod
 
-from easyscience.Objects.Job.Analysis import AnalysisBase
-from easyscience.Objects.Job.Experiment import ExperimentBase
-from easyscience.Objects.Job.Theory import TheoryBase
+from easyscience.Objects.job.analysis import AnalysisBase
+from easyscience.Objects.job.experiment import ExperimentBase
+from easyscience.Objects.job.theoreticalmodel import TheoreticalModelBase
 from easyscience.Objects.ObjectClasses import BaseObj
 
 
@@ -29,12 +29,12 @@ def __init__(self, name: str, *args, **kwargs):
     Summary and Info classes are included to store additional information.
     """
     @property
-    def theory(self):
+    def theorerical_model(self):
         return self._theory
 
-    @theory.setter
+    @theorerical_model.setter
     @abstractmethod
-    def theory(self, theory: TheoryBase):
+    def theoretical_model(self, theory: TheoreticalModelBase):
         raise NotImplementedError("theory setter not implemented")
 
     @property
@@ -77,10 +77,8 @@ def analysis(self, analysis: AnalysisBase):
     @abstractmethod
     def calculate_theory(self, *args, **kwargs):
         raise NotImplementedError("calculate_theory not implemented")
-        #pass
 
     @abstractmethod
     def fit(self, *args, **kwargs):
         raise NotImplementedError("fit not implemented")
-        #pass
 

src/easyscience/Objects/job/analysis.py

@@ -6,19 +6,19 @@
 from easyscience.Objects.ObjectClasses import BaseObj
 
 
-class TheoryBase(BaseObj):
+class TheoreticalModelBase(BaseObj):
     """
     This virtual class allows for the creation of technique-specific Theory objects.
     """
     def __init__(self, name: str, *args, **kwargs):
         self._name = name
-        super(TheoryBase, self).__init__(name, *args, **kwargs)
+        super().__init__(name, *args, **kwargs)
 
     # required dunder methods
     def __str__(self):
         raise NotImplementedError("Copy not implemented")
 
     def as_dict(self, skip: list = []) -> dict:
-        this_dict = super(TheoryBase, self).as_dict(skip=skip)
+        this_dict = super().as_dict(skip=skip)
         return this_dict