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Writing applications

As the name says, the ASpecD framework in itself only provides the scaffold for applications aiming at reproducible processing and analysis of spectroscopic data. However, that does not mean that you need to start from scratch when implementing a Python package for your preferred spectroscopic method. ASpecD comes with (some) “batteries included”, namely an increasing list of generally applicable processing and analysis steps.

Writing applications based on the ASpecD framework should be fairly straight-forward once familiar with its concepts.


Most users of a package based on the ASpecD framework will not bother about how to further develop these packages. They will usually only work with the package by means of “recipe-driven data analysis”. However, if you are interested in writing or further developing packages for the analysis of spectroscopic data based on the ASpecD framework, continue reading.

Before you start

ASpecD is all about reproducible data analysis, hence more reproducible and reliable science. Therefore, before you start writing your own applications based on the ASpecD framework, make sure to have a minimal infrastructure available and use it for your developments. Without a version control system (VCS) and a scheme for version numbers that you follow thoroughly, fundamental aspects of the ASpecD framework simply won’t work. Additionally, do choose an appropriate license for your program. After all, the time frame of reproducibility is not the typical length of a PhD thesis, but rather decades.

If you have experience already with version control systems and version numbering schemes, go ahead with what you are familiar with. However, if you fancy some hints what to use, here are our suggestions:

  • Version control system: git

  • Version numbering scheme: SemVer

This is what ASpecD is developed with and what it uses – for good reasons.

How to start

Often, we assume that developing a package starts with actual coding. However, more often than not this is a mistake. And it is probably a big mistake when starting to develop packages based on the ASpecD framework – and a certain misunderstanding of what this framework is all about. Why that?

At the core of the ASpecD framework and reproducibility is the dataset, the unit of actual (numerical) data and accompanying metadata. Datasets are a very powerful abstraction of the (vendor) data formats the raw data are usually initially stored in. Therefore, it is essential to have a thorough understanding of the data and metadata you are dealing with. Only with such thorough understanding you will be able to abstract from the number of different file formats and create a metadata structure that suits all your current needs. Furthermore, such a metadata structure should be designed to be easily extendible, without need to change existing parts (an application of the “open-closed principle” from software engineering). Therefore, before you start to think about programming, think about the information (metadata) that you need to be available to process and analyse your data.

Therefore, some steps you may take before actually starting to code include:

  • Have a look at the (vendor) file formats of your raw data and see what metadata are stored therein (and whether there is any chance to extract this information)

  • Think about the metadata (information about measurements) you would like to have stored along with your data to gain reproducibility.

  • Take pencil and paper and draft a hierarchical structure of these metadata. Beware that it usually takes a few passes and quite a bit of thinking to converge to a reasonable solution.

Actually implementing a package based on the ASpecD framework that handles your specific data is not that hard after all, as it is mostly straight-forward coding. Furthermore, ASpecD comes with a growing body of functionality for standard spectroscopic tasks already builtin. However, thinking about your particular kind of data and the structure of their accompanying metadata is the actual intellectual and creative task you cannot pass on to a computer.

Eventually, it is all about finding good abstractions that help you understand and describe and finally handle the complex reality. This is what science (and programming) is all about, as Edsger W. Dijkstra kept insisting on.

Package structure

Applications based on the ASpecD framework should be Python packages following the standards laid out by the Python Packaging Authority (PyPA) in their Python Packaging User Guide.

Generally, you will probably start off with deciding about a name for your application. Names, particularly for programs and packages, need care in choosing. It is a good idea to check the Python Package Index for similar packages and possible name clashes, in case you plan to eventually publish your package there (always a good idea to keep in mind).


For the sake of argument, we will choose the name “spectro” for the hypothetical new package here. Of course, you will never choose this name for an actual package, as it is pretty meaningless. Hence, a good choice here…

Having decided upon a name for your new package, continue by creating a basic directory structure and a Python virtual environment for your package, and installing the ASpecD framework [1] (and your package) within this virtual environment. The basic directory structure of your new package may look like follows:

├── docs
├── spectro
│   ├──
│   ├──
│   ├──
│   ├──
│   ├──
│   ├──
│   └──
├── tests
│   ├──
│   ├──
│   ├──
│   ├──
│   ├──
│   └──
├── README.rst
├── Requirements.txt

Directories and files should be pretty self-explaining. If in doubt, consult the Python Packaging Authority (PyPA) and their Python Packaging User Guide or have a look at the ASpecD source code.


If you are understandably not very keen on creating all these structures on your own, but fancy having a Python package that helps you creating and maintaining Python packages, have a look at the pymetacode package.

There are even plans to incorporate/adapt this package to the specific use case of creating and maintaining packages based on the ASpecD framework.

To create the virtual environment and install ASpecD and your package, open a terminal and type something like the following commands:

python3 -m venv spectro_venv
source spectro_venv/bin/activate
pip install aspecd

Make sure to install your package in an editable fashion, using the -e switch of the pip command:

pip install -e spectro

With this, you should be ready to start developing your application.


Before starting to write your own classes, make sure that you have obtained a decent understanding of the role and interactions of each of the different classes in the ASpecD framework. Many aspects rely on “convention over configuration”, and therefore, it is crucial to understand and follow these conventions, as detailed in the API documentation. The ultimate goal of a good object-oriented design is a set of coherent and loosely-coupled classes and units that allow to easily extend and modify a program in response to new requirements. Whereas far from perfect, the ASpecD framework tries to follow these guidelines as set out in the respective literature.


Probably the most fundamental unit of the ASpecD framework is the dataset. Hence, you should first create a dataset class of your own that inherits from the dataset class of the ASpecD framework. Here, we assume that you start with experimental datasets, as opposed to datasets containing calculated data. Therefore, create a module named dataset and include the following code:

import aspecd.dataset

class ExperimentalDataset(aspecd.dataset.ExperimentalDataset):

    def __init__(self):

This was easy, and in most cases, this is all you need to do to have a full-fledged dataset. Of course, you should document your newly created dataset class appropriately. Make sure to obey the rules laid out in PEP 257.

However, life is a bit more complicated to get things working properly and to be able to actually work on data. Next steps include creating importers for raw data and metadata, and creating appropriate metadata classes for storing these metadata within the dataset. Eventually, this means that you will need to modify your newly created dataset class very slightly to reflect the changes you made to your metadata. For details, see the metadata section below.


To actually be able to work on (numeric) data and to store them together with their accompanying metadata in a dataset, you need to write importer classes specific for each type of raw data. To do so, create a module named io and include the following code:


class DatasetImporter(

    def __init__(self, source=''):

    def _import(self):
        # And here goes your code actually importing the data and metadata

Of course, you need to add appropriate code to the non-public function _import of the importer class you just created. And if you have more than one type of raw data, make sure to give your classes better names than just “DatasetImporter”. Even if you start with one type of raw data, naming the importer class closer to the actual file format is always helpful. This prevents you from having to change your depending code later on.

The importer should make sure not only to import the numeric data appropriately into the dataset object (they go into its attribute), but to also create appropriate axes and to read the metadata accompanying the (raw) data. For the necessary structures within the dataset’s metadata attribute and how to eventually fill the metadata into this hierarchy of objects, see the metadata section.

In the (usual) case where you have more than one raw format data are stored in, you would like to create a single class that takes care of returning the correct importer, given a string specifying the source of the data. This is what factories are good for: Returning different subtypes of a common basetype depending on the particular needs. To achieve this for the importers of your application, create a class DatasetImporterFactory that inherits from


class DatasetImporterFactory(

    def _get_importer(self, source):
        # And here goes your code actually choosing the correct importer


Note that in order for recipe-driven data analysis to work, you will need to implement a DatasetImporterFactory class, even if you only implement a single importer for now.


The metadata attribute of the (experimental) dataset is actually an instance of aspecd.metadata.ExperimentalDatasetMetadata that in itself contains a list of attributes found in any case, namely general information about the measurement (measurement), the sample (sample) and the temperature control (temperature_control). Each of these attributes are instances of their respective classes defined as well within the ASpecD framework.

In order to store all the metadata usually contained in files written at the time of data acquisition, you will need to create additional metadata classes and extend aspecd.metadata.ExperimentalDatasetMetadata by writing your own “ExperimentalDatasetMetadata” class subclassing the one from the ASpecD framework:

import aspecd.metadata

class ExperimentalDatasetMetadata(aspecd.metadata.ExperimentalDatasetMetadata):

    def __init__(self, path=''):
        # Add here attributes that are instances of your metadata classes

Your metadata classes should be based on the generic aspecd.metadata.Metadata class. Additionally, all physical quantities appearing somewhere in your metadata should be stored in objects of the class aspecd.metadata.PhysicalQuantity. Note that it might be useful to define the attributes in each of the metadata classes in the order they would be contained in a metadata file and should be included in a report. The aspecd.metadata.Metadata class provides means to include the information contained in its attributes that preserves the order in which they were originally defined within the respective class.

Eventually, you will need to extend your Dataset class that you have defined as described in the corresponding section accordingly:

import aspecd.dataset

class ExperimentalDataset(aspecd.dataset.ExperimentalDataset):

    def __init__(self):
        self.metadata = ExperimentalDatasetMetadata()

Once you have created all the necessary classes for the different groups of metadata, the actual import of the metadata can become quite simple. The only prerequisite here is to have them initially stored in a Python dictionary whose structure resembles that of the hierarchy of objects contained in your ExperimentalDatasetMetadata class. Therefore, make sure that at least the top-level keys of this dictionary have names corresponding to the (public) attributes of your ExperimentalDatasetMetadata class. [2]


The organisation of metadata in a metadata file that gets created during measurement and the representation of the very same metadata within the Dataset class need not be the same, and they will most probably diverge at least over time. To nevertheless be able to map the metadata read from a file and contained in a dictionary (ideally in a collections.OrderedDict), there exists the aspecd.metadata.MetadataMapper class allowing to map the dictionary to the structure of the class hierarchy in your ExperimentalDatasetMetadata class.

Once you have a dictionary, e.g. metadata_dict, with all your metadata and with (top-level) keys corresponding to the the attributes of your ExperimentalDatasetMetadata class, you can import the metadata into your dataset with just one line:


All your metadata classes share this very same method, as long as they are based on aspecd.metadata.Metadata. This allows to traverse the dictionary containing your metadata.


The from_dict() method is rather forgiving, only copying those values of the dict to the corresponding metadata object that are attributes of the object, and neither caring about additional keys in the dictionary nor additional attributes in the object. Therefore, it is your sole responsibility to check that the metadata contained in the dictionary and your metadata classes have corresponding keys/attributes.


Comments are often found (for good reason) in metadata files that accompany raw data and get written during data acquisition. While usually part of the metadata files, they should not be put in the metadata property of the Dataset class. Technically, comments are annotations, and for this very purpose, a whole set of classes is available within the ASpecD framework, namely in the aspecd.annotation module. Usually, you will not need to subclass any of the classes provided in that module.

To add a comment to a dataset, you will need to instantiate an object of class aspecd.annotation.Comment, assign the comment to it, and finally annotate your dataset:

import aspecd.annotation

comment = aspecd.annotation.Comment()
comment.comment = metadata_dict["comment"]

Here, we assumed for simplicity that your metadata are contained in the dictionary metadata_dict, and that your dataset resides in dataset. If you implement this very functionality within your Importer class in its _import() method (see above), as you should do, [3] you will have to adjust some of the variable names accordingly.

Processing steps

After having created classes for the dataset and storing the accompanying metadata, it is time to think of processing your data. As set out in the introduction already in quite some detail, reproducibility is both, at the heart of good scientific practice as well as the ASpecD framework.

Therefore, both, as a developer writing analysis software based on the ASpecD framework as well as its user, you need not bother about such aspects as having processing steps writing a history containing all their parameters. All you need to do is to subclass aspecd.processing.SingleProcessingStep (in most cases, and in some rare cases aspecd.processing.MultiProcessingStep) and adhere to a few basic rules when implementing your own data processing classes.

Let’s assume for simplicity that you want to write a processing step called “MyProcessing”. Generally, you would start out creating a module processing within your Python project, if it does not exist already, and add some basic code to it:

import aspecd.processing

class MyProcessing(aspecd.processing.SingleProcessingStep):

    def __init__(self):
        self.description = 'My processing step'
        self.undoable = True

    def _perform_task(self):
        # And here goes your code performing the actual processing step

A few comments on this code stub:

  • Always set the description attribute appropriately, as it gets stored in the history and is intended to give the user a first impression of what the processing step was good for. Be concise. More than about 60 characters are definitely too exhaustive.

  • Usually, the processing steps are undoable, hence, set the attribute undoable appropriately. For safety reasons, it is set to False in the base class.

  • Store all parameters, implicit and explicit, in the public attribute parameters of the ProcessingStep class. This application of the “convention over configuration” strategy greatly facilitates automatic processing of your data and proper handling of the history.

  • Put all the actual processing into the _perform_task() method. Usually, this will contain a series of calls to other non-public methods performing each their respective part of the processing step.

  • Your classes inheriting from aspecd.processing.ProcessingStep should have no more public attributes than their parent class.

  • Put all your processing steps into the processing module, as this is a prerequisite for reproducing your data processing afterwards. This is another application of the “convention over configuration” strategy greatly facilitating the automatic handling of your data.

If you need to sanitise the parameters before applying the actual processing step to your data, override the non-public method _sanitise_parameters() that will be called straight before _perform_task() when calling the process() method on either the ProcessingStep object or the Dataset object. Furthermore, if you need to set some default parameters, override the non-public method _set_defaults() that will be called even before _sanitise_parameters(). Therefore, a more complex example of a processing step could look like this:

import aspecd.processing

class MyProcessing(aspecd.processing.SingleProcessingStep):

    def __init__(self):
        self.description = 'My processing step'
        self.undoable = True
        self.parameters["type"] = None

    def applicable(dataset):
        return len( <= 3

    def _sanitise_parameters(self):
        if not self.parameters["type"]:
            raise ValueError("No type provided.")

    def _perform_task(self):
        # And here goes your code performing the actual processing step

As processing steps tend to become complex, at least from a programmer’s perspective if parameters need to be checked, and easily if you need to deal with different cases such as 1D and ND with N>1 separately, developing these classes test-first (i.e., applying test-driven development, TDD) is a good idea and will help you and your users being confident in the correct functioning of your code.

From own experience implementing a list of concrete processing steps in the ASpecD framework, the following steps proved useful and may serve as a starting point for own developments:

import unittest

import spectro.processing

class TestMyProcessingStep(unittest.TestCase):
    def setUp(self):
        self.processing = spectro.processing.MyProcessingStep()

    def test_instantiate_class(self):

    def test_has_appropriate_description(self):
        self.assertIn('<whatever describes it>',

    def test_is_undoable(self):

A few comments on this code stub:

  • Use the unittest module of the Python standard library and make yourself familiar with its basic operation and features if not already done.

  • Import your module you want to test.

  • Write (at least) one test class for each class you want to test, adhering to a strict naming convention, starting with Test and containing the name of the class you want to test. This class needs to inherit from unittest.TestCase.

  • Always use a method setUp that at least instantiates an object of your processing step class and assigns it to the attribute self.processing. This convention makes it very convenient to test your processing steps and to decouple the actual test code (in the test methods of your test class) from the name of the class under test—very helpful for some “copy&paste” with modifications afterwards.

  • Always start with instantiating your class as a first test. Only then start to implement the class.

  • Always test for the appropriate description in the parameter description and for the correct setting of the flag undoable.

Of course, this only gets you started, and until now, we have not tested a single line of code actually processing your data. The latter of course highly depends on your actual processing step. But there are usually a few more things to test before you start implementing the actual processing step. This includes applicability to a certain type of datasets, mostly this is a check for the dimensions of your data, and the corresponding code needs to be implemented in aspecd.processing.ProcessingStep.applicable(). Other things contain tests for correct parameters, with the corresponding code being implemented in aspecd.processing.ProcessingStep._sanitise_parameters(). The latter can involve quite a number of tests, and the better you test here, the better the user experience will become. Taking into account the same aspects as shown above in the second example for implementing a processing step, your additional tests may be:

import unittest

import numpy as np

import spectro.processing

class TestMyProcessingStep(unittest.TestCase)

    # All the code from above, including setUp

    def test_process_with_3d_dataset_raises(self): = np.random.random([5, 5, 5])
        with self.assertRaises(aspecd.exceptions.NotApplicableToDatasetError):

    def test_process_without_type_parameter_raises(self):
        with self.assertRaisesRegex(ValueError, "No type provided"):

Of course, you need to import numpy in this case, for having the data assigned random numbers in this case, but you will anyway often use numpy for your actual processing. Furthermore, you can see here why not defining the standard parameters for a processing step in the setUp method is quite helpful, as it helps you see in your tests explicitly how to actually use your class. Using assertRaisesRegex is a good idea to enforce sensible error messages of the exceptions raised. For more details, you may have a look into the test classes of the ASpecD framework for now.


Logging gets quite important as soon as you implement more complex functionality. In order to have your logging play seamlessly with the ASpecD framework, and particularly with recipe-driven data analysis, you need to have the loggers for your modules to be below the root logger of the ASpecD framework. Hence, whenever you need a logger in a module of your package, create one using the aspecd.utils.get_logger() function. This is most conveniently done by adding the following lines of code towards the top of your module:

import aspecd.utils

logger = aspecd.utils.get_logger(__name__)

This will both, create a logger that is located below the root logger of the ASpecD framework in the logger hierarchy, and with a logging.NullHandler handler added, as advocated for in the Python Logging HOWTO.

What’s next?

Of course, there is much more to a full-fledged application for processing and analysis of spectroscopic data, but the steps described so far should get you somehow started.

Additional aspects you may want to consider and that will be detailed here a bit more in the future include:

  • Analysis steps

  • Plotting

  • Reports based on pre-defined templates

  • Recipe-driven data processing and analysis

Make sure to understand the underlying concepts of the ASpecD framework and have a look at its API documentation as well as the source code.