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Tawny Rautenberg
A model is an informative representation of an object, person or system. The term originally denoted the plans of a building in late 16th-century English, and derived via French and Italian ultimately from Latin modulus, a measure.[1]
Models can be divided into physical models (e.g. a ship model or a fashion model) and abstract models (e.g. a set of mathematical equations describing the workings of the atmosphere for the purpose of weather forecasting). Abstract or conceptual models are central to philosophy of science.[2][3]
In scholarly research and applied science, a model should not be confused with a theory: while a model seeks only to represent reality with the purpose of better understanding or predicting the world, a theory is more ambitious in that it claims to be an explanation of reality.[4]
A physical model (most commonly referred to simply as a model but in this context distinguished from a conceptual model) is a smaller or larger physical representation of an object, person or system. The object being modelled may be small (e.g., an atom) or large (e.g., the Solar System) or life-size (e.g., a fashion model displaying clothes for similarly-built potential customers).
The geometry of the model and the object it represents are often similar in the sense that one is a rescaling of the other. However, in many cases the similarity is only approximate or even intentionally distorted. Sometimes the distortion is systematic, e.g., a fixed scale horizontally and a larger fixed scale vertically when modelling topography to enhance a region's mountains.
Instrumented physical models are an effective way of investigating fluid flows for engineering design. Physical models are often coupled with computational fluid dynamics models to optimize the design of equipment and processes. This includes external flow such as around buildings, vehicles, people, or hydraulic structures. Wind tunnel and water tunnel testing is often used for these design efforts. Instrumented physical models can also examine internal flows, for the design of ductwork systems, pollution control equipment, food processing machines, and mixing vessels. Transparent flow models are used in this case to observe the detailed flow phenomenon. These models are scaled in terms of both geometry and important forces, for example, using Froude number or Reynolds number scaling (see Similitude). In the pre-computer era, the UK economy was modelled with the hydraulic model MONIAC, to predict for example the effect of tax rises on employment.
A conceptual model is a theoretical representation of a system, e.g. a set of mathematical equations attempting to describe the workings of the atmosphere for the purpose of weather forecasting.[8] It consists of concepts used to help understand or simulate a subject the model represents.
Abstract or conceptual models are central to philosophy of science,[2][3] as almost every scientific theory effectively embeds some kind of model of the physical or human sphere. In some sense, a physical model "is always the reification of some conceptual model; the conceptual model is conceived ahead as the blueprint of the physical one", which is then constructed as conceived.[9] Thus, the term refers to models that are formed after a conceptualization or generalization process.[2][3]
For example, a street map is a model of the actual streets in a city (mapping), showing the course of the streets while leaving out, say, traffic signs and road markings (reduction), made for pedestrians and vehicle drivers for the purpose of finding one's way in the city (pragmatism).
Additional properties have been proposed, like extension and distortion[11] as well as validity.[12] The American philosopher Michael Weisberg differentiates between concrete and mathematical models and proposes computer simulations (computational models) as their own class of models.[13]
This customer/AWS shared responsibility model also extends to IT controls. Just as the responsibility to operate the IT environment is shared between AWS and its customers, so is the management, operation and verification of IT controls shared. AWS can help relieve customer burden of operating controls by managing those controls associated with the physical infrastructure deployed in the AWS environment that may previously have been managed by the customer. As every customer is deployed differently in AWS, customers can take advantage of shifting management of certain IT controls to AWS which results in a (new) distributed control environment. Customers can then use the AWS control and compliance documentation available to them to perform their control evaluation and verification procedures as required. Below are examples of controls that are managed by AWS, AWS Customers and/or both.
PTAC will continue to submit comments and recommendations regarding physician-focused payment models submitted by stakeholders to the Secretary, as required by statute. In addition, we will expand our communications with the Centers for Medicare & Medicaid Services (CMS) and stakeholders to identify opportunities to further inform and prioritize the work CMS, including the Center for Medicare & Medicaid Innovation (CMMI), and other policymakers are undertaking to modernize health care.
For example, if the models for your application live in the modulemyapp.models (the package structure that is created for anapplication by the manage.py startapp script),INSTALLED_APPS should read, in part:
Each field takes a certain set of field-specific arguments (documented in themodel field reference). For example,CharField (and its subclasses) require amax_length argument which specifies the sizeof the VARCHAR database field used to store the data.
For these situations, Django allows you to specify the model that will be usedto govern the many-to-many relationship. You can then put extra fields on theintermediate model. The intermediate model is associated with theManyToManyField using thethrough argument to point to the modelthat will act as an intermediary. For our musician example, the code would looksomething like this:
When you set up the intermediary model, you explicitly specify foreignkeys to the models that are involved in the many-to-many relationship. Thisexplicit declaration defines how the two models are related.
If the custom through table defined by the intermediate model does not enforceuniqueness on the (model1, model2) pair, allowing multiple values, theremove() call willremove all intermediate model instances:
SQL reserved words, such as join, where or select, are allowed asmodel field names, because Django escapes all database table names and columnnames in every underlying SQL query. It uses the quoting syntax of yourparticular database engine.
If one of the existing model fields cannot be used to fit your purposes, or ifyou wish to take advantage of some less common database column types, you cancreate your own field class. Full coverage of creating your own fields isprovided in How to create custom model fields.
Model inheritance in Django works almost identically to the way normalclass inheritance works in Python, but the basics at the beginning of the pageshould still be followed. That means the base class should subclassdjango.db.models.Model.
The only decision you have to make is whether you want the parent models to bemodels in their own right (with their own database tables), or if the parentsare just holders of common information that will only be visible through thechild models.
Abstract base classes are useful when you want to put some commoninformation into a number of other models. You write your base classand put abstract=True in the Metaclass. This model will then not be used to create any databasetable. Instead, when it is used as a base class for other models, itsfields will be added to those of the child class.
The Student model will have three fields: name, age andhome_group. The CommonInfo model cannot be used as a normal Djangomodel, since it is an abstract base class. It does not generate a databasetable or have a manager, and cannot be instantiated or saved directly.
For many uses, this type of model inheritance will be exactly what you want.It provides a way to factor out common information at the Python level, whilestill only creating one database table per child model at the database level.
The second type of model inheritance supported by Django is when each model inthe hierarchy is a model all by itself. Each model corresponds to its owndatabase table and can be queried and created individually. The inheritancerelationship introduces links between the child model and each of its parents(via an automatically-created OneToOneField).For example:
As mentioned, Django will automatically create aOneToOneField linking your childclass back to any non-abstract parent models. If you want to control thename of the attribute linking back to the parent, you can create yourown OneToOneField and setparent_link=Trueto indicate that your field is the link back to the parent class.
This is what proxy model inheritance is for: creating a proxy for theoriginal model. You can create, delete and update instances of the proxy modeland all the data will be saved as if you were using the original (non-proxied)model. The difference is that you can change things like the default modelordering or the default manager in the proxy, without having to alter theoriginal.
You could also use a proxy model to define a different default ordering ona model. You might not always want to order the Person model, but regularlyorder by the last_name attribute when you use the proxy:
There is no way to have Django return, say, a MyPerson object whenever youquery for Person objects. A queryset for Person objects will returnthose types of objects. The whole point of proxy objects is that code relyingon the original Person will use those and your own code can use theextensions you included (that no other code is relying on anyway). It is nota way to replace the Person (or any other) model everywhere with somethingof your own creation.
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