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Domain-driven design (DDD) is a major software design approach, focusing on modeling software to match a domain according to input from that domain's experts. Under domain-driven design, the structure and language of software code (class names, class methods, class variables) should match the business domain. For example: if software processes loan applications, it might have classes like "loan application", "customers", and methods such as "accept offer" and "withdraw". Domain-driven design is predicated on the following goals: placing the project's primary focus on the core domain and domain logic; basing complex designs on a model of the domain; initiating a creative collaboration between technical and domain experts to iteratively refine a conceptual model that addresses particular domain problems. Critics of domain-driven design argue that developers must typically implement a great deal of isolation and encapsulation to maintain the model as a pure and helpful construct. While domain-driven design provides benefits such as maintainability, Microsoft recommends it only for complex domains where the model provides clear benefits in formulating a common understanding of the domain. The term was coined by Eric Evans in his book of the same title published in 2003. Domain-driven design articulates a number of high-level concepts and practices. Of primary importance is a domain of the software, the subject area to which the user applies a program. Software's developers build a domain model: a system of abstractions that describes selected aspects of a domain and can be used to solve problems related to that domain. These aspects of domain-driven design aim to foster a common language shared by domain experts, users, and developers—the ubiquitous language. The ubiquitous language is used in the domain model and for describing system requirements. Ubiquitous language is one of the pillars of DDD together with strategic design and tactical design. In domain-driven design, the domain layer is one of the common layers in an object-oriented multilayered architecture.
[2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will implement a simple fractional resampler that
uses local Lagrange interpolation. The resampler can be used to perform
any rational sampling rate change (but beware of aliasing when
downsampling!)
In [1]:
import numpy as np
import IPython
from scipy.io import wavfile
Local Lagrange interpolation[4]¶
To perform resampling we will need to compute subsample values and, in "I defy anybody to find a record... unless it is some old blues
record from 1922... that uses feedback that way. So I claim it for
the Beatles. Before H