release 0.9.0

.prospector.yaml

@@ -1,6 +1,8 @@
 # For general information of how to configure prospector visit
 # https://prospector.landscape.io/en/master/profiles.html
 
+autodetect: false
+
 inherits:
   - strictness_high
 
@@ -17,7 +19,7 @@ pep257:
 pylint:
   options:
     max-attributes: 16
-    max-module-lines: 4500
+    max-module-lines: 5000
 
 pyroma:
   run: true

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2017-22 Till Biskup
+Copyright (c) 2017-24 Till Biskup
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

VERSION

@@ -1 +1 @@
-0.8.3
+0.9.0

aspecd/analysis.py

@@ -113,13 +113,23 @@
   Perform linear regression without fitting the intercept on 1D data. Note
   that this is mathematically different from a polynomial fit of first order.
 
+* :class:`DeviceDataExtraction`
+
+  Extract device data as separate dataset.
+
+  Datasets may contain additional data as device data in
+  :attr:`aspecd.dataset.Dataset.device_data`. For details,
+  see :class:`aspecd.dataset.DeviceData`. To further process and analyse
+  these device data, the most general way is to extract them as
+  individual dataset and perform all further tasks on it.
+
 
 Writing own analysis steps
 ==========================
 
 Each real analysis step should inherit from either
-:class:`aspecd.processing.SingleProcessingStep` in case of operating on a
-single dataset only or from :class:`aspecd.processing.MultiProcessingStep` in
+:class:`aspecd.analysis.SingleAnalysisStep` in case of operating on a
+single dataset only or from :class:`aspecd.analysis.MultiAnalysisStep` in
 case of operating on several datasets at once. Furthermore, all analysis
 steps should be contained in one module named "analysis". This allows for
 easy automation and replay of analysis steps, particularly in context of
@@ -1951,3 +1961,107 @@ def _perform_task(self):
             self.result = [self.parameters["offset"], float(results[0])]
         else:
             self.result = float(results[0])
+
+
+class DeviceDataExtraction(SingleAnalysisStep):
+    """
+    Extract device data as separate dataset.
+
+    Datasets may contain additional data as device data in
+    :attr:`aspecd.dataset.Dataset.device_data`. For details,
+    see :class:`aspecd.dataset.DeviceData`. To further process and analyse
+    these device data, the most general way is to extract them as
+    individual dataset and perform all further tasks on it.
+
+    A reference to the original dataset is stored in
+    :attr:`aspecd.dataset.Dataset.references`.
+
+    Attributes
+    ----------
+    result : :class:`aspecd.dataset.CalculatedDataset`
+        Dataset containing the device data.
+
+        The device the data are extracted for is provided by the parameter
+        ``device``, see below.
+
+    parameters : :class:`dict`
+        All parameters necessary for this step.
+
+        device : :class:`str`
+            Name of the device the data should be extracted for.
+
+            Raises a :class:`KeyError` if the device does not exist.
+
+            Default: ''
+
+    Raises
+    ------
+    KeyError
+        Raised if device is not present in
+        :attr:`aspecd.dataset.Dataset.device_data`
+
+
+    Examples
+    --------
+    For convenience, a series of examples in recipe style (for details of
+    the recipe-driven data analysis, see :mod:`aspecd.tasks`) is given below
+    for how to make use of this class. The examples focus each on a single
+    aspect.
+
+    Suppose you have a dataset that contains device data referenced with
+    the key "timestamp", and you want to extract those device data and
+    make them accessible from within the recipe using the name "timestamp"
+    as well:
+
+    .. code-block:: yaml
+
+       - kind: singleanalysis
+         type: DeviceDataExtraction
+         properties:
+           parameters:
+             device: timestamp
+         result: timestamp
+
+
+    .. versionadded:: 0.9
+
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.description = 'Extract device data'
+        self.parameters["device"] = ''
+
+    @staticmethod
+    def applicable(dataset):
+        """
+        Check whether analysis step is applicable to the given dataset.
+
+        Device data extraction is only possible if device data are present.
+
+        Parameters
+        ----------
+        dataset : :class:`aspecd.dataset.Dataset`
+            Dataset to check
+
+        Returns
+        -------
+        applicable : :class:`bool`
+            Whether dataset is applicable
+
+        """
+        return dataset.device_data
+
+    def _sanitise_parameters(self):
+        device = self.parameters["device"]
+        if device not in self.dataset.device_data.keys():
+            raise KeyError(f"Device '{device}' not found in dataset")
+
+    def _perform_task(self):
+        device = self.parameters["device"]
+        dataset = self.create_dataset()
+        dataset.data = copy.deepcopy(self.dataset.device_data[device])
+        reference = aspecd.dataset.DatasetReference()
+        reference.from_dataset(self.dataset)
+        dataset.references.append(reference)
+        self.result = dataset