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Dataset Extraction (Pinset) - Epsilon

Epsilon

Dataset Extraction (Pinset)

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Unit Testing (EUnit)

Dataset Extraction (Pinset)

Table of contents

Model example

Overview

Properties accessors

Row filtering

Multiple columns: grid

Typeless dataset rules

Nested column generators

Column post-processing

Fill nulls

Normalisation

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Picto Integration

Dataset Extraction (Pinset)¶

The Pinset language offers specific syntax constructs to extract table-like datasets from models. The main objective of Pinset is to facilitate the analysis of models data via conventional data mining and machine learning techniques, which impose a tabular input format. In addition, tables can be useful as an extra viewpoint when creating model visualisations.

Model example¶

We use as running example a course model, which contains the enrolled students along with their grades. All models and Pinset scripts shown in this documentation can be found in an example project in the Epsilon repository.

All Pinset scripts query the following metamodel:

classDiagram

class Course {

}

class Student {

ID: String

isRemote: Boolean

}

class ContactDetails {

email: String

phone: String

}

class EvaluationItem {

percentage: int

}

class Grade {

points: int

}

Course *--> Student: students *

Course *--> EvaluationItem: items *

Student *--> ContactDetails: contact

Student *--> Grade: grades *

Grade --> EvaluationItem: item

As for the data shown as a result of the Pinset scripts, we use the following Flexmi model, which conforms to the metamodel above:

Overview¶

This first Pinset example defines a dataset from students data, containing some basic information such as name and student ID, contact details, the number of completed evaluation items, and the final grade for the course:

dataset studentsSummary over s : Student {

column id: s.ID

column name: s.name

column phone: s.contact.phone

column items_completed: s.grades.size

column final_grade : s.getFinalGrade()

column course_outcome {

if (final_grade < 50) {

return "fail";

}

else if (final_grade < 70) {

return "good";

}

else if (final_grade < 90) {

return "notable";

}

else {

return "excellent";

}

@cached

operation Student getFinalGrade() {

return self.grades

.collect(g | g.points * g.item.percentage)

.sum() / 100;

}

From that Pinset script, the following dataset is generated:

name

phone

items_completed

final_grade

course_outcome

Alice

+44 101

notable

Bob

+44 654

good

Charlie

+44 333

fail

Dana

excellent

As the above example shows, Pinset offers a rule-based syntax to declare datasets.

These rules are specified as a set of column generators that capture data from instances of a type included in an input model.

That type is defined as a parameter, after the over keyword.

In the example, the chosen type is Student, which by default means that each Student instance of the input model will be used to populate a row of the output dataset.

Pinset offers different column generators.

This first example uses the column one, which is composed of the name of the column header and an EOL expression to calculate the cell value over the row element.

Other common EOL constructs are also available in Pinset scripts.

For instance, an EOL block can be used for those column calculations that might be better organised in an imperative set of statements, such as the course_outcome column that shows the final course result in a textual format as used in the Spanish education system.

In addition, external operations can be invoked in the column expressions, such as the getFinalGrade() operation used in the example.

As a last comment for the column generator, values of previously calculated columns of an element can be used in subsequent definitions. For instance, the course_outcome column uses the finalGrade

After this overview, next sections describe extra column generators, as well as on other functionalities offered by Pinset for an easier dataset extraction specification.

Properties accessors¶

As a way to facilitate the definition of columns that simply hold element properties, Pinset offers some column generators to access these properties:

dataset studentsContact over s : Student {

properties [ID as StudentId, name]

reference contact[email, phone]

}

The previous dataset rule results in:

StudentId

name

contact_email

contact_phone

Alice

alice@university.com

+44 101

Bob

bob@university.com

+44 654

Charlie

charlie@university.com

+44 333

Dana

dana@university.com

Precisely, Pinset offers two property accessors: the properties generator can be used to generate columns for attributes of the selected type (e.g. ID and name in the example), while the references one allows getting attributes from single references (i.e. upper bound of 1) of the type, such as contact.

When using the properties accessor, the name of the attribute is used as column name, while for the references accessor a combination of the name of the reference with the name of the attribute is used (e.g. contact_phone). This default behaviour can be altered by using the as keyword.

These accessors also offer null safety. If any attributes or the traversed reference point to null, Pinset automatically inserts a blank value in the cell.

Row filtering¶

By default, all elements of the selected type are processed into rows.

As this might sometimes not be desired, Pinset offers some ways to filter out rows from the resulting dataset:

dataset remoteStudents over s : Student {

guard: s.isRemote

properties[ID, name]

}

dataset finalExamAssistants over s : Student

from : Student.all.select(s |

s.grades.exists(g | g.item.name == "Final Exam")) {

properties[ID, name]

}

These dataset rules show the two ways that can be used to perform filtering in Pinset:

The remoteStudents dataset uses a guard to limit the processed students to the remote ones (based in their boolean attribute). Any element not meeting the guard requirements is excluded from the dataset generation step.

The finalExamAssistants dataset uses a from expression to only include those students that took the final exam of the course. A from expression must return a collection of elements of the selected type to be used for the dataset generation. Therefore, this expression can be used for row filtering, and for other things such as performance improvements (i.e. calculate a collection, and use it for multiple dataset generations).

If necessary, both filtering mechanisms can be used simultaneously. For instance, if we combine the guard and from expressions shown above, we would obtain a dataset with the remote students that took the final exam of the course.

Multiple columns: grid¶

In some cases, we might want to generate a set of columns that are calculated using the same expression, just by changing the parameter(s) of such expression. In the course example, this happens when generating a table including the detailed grades of the students for all the evaluated items of the course, such as the following:

name

Lab_1

Lab_2

Partial_Test

Final_Exam

final_grade

Alice

Bob

100

Charlie

Dana

100

Defining this table with the column generator would quickly become very verbose and tedious, as we would need to use one expression for each evaluated item of the course. Also, using that strategy would match the Pinset script to the specific course, as the script would include the name of the grades that are being represented as columns. Any new item added to future editions of the course, or any new course we might want to suport, would require updating the Pinset script / creating a new one.

To prevent this, Pinset offers the grid generator, which allows the batch-definition of similar columns. A grid has three components:

keys: determine the elements to use as seeds or parameters of each column.

header: used to create the name or header of the column, based on the value of each individual key.

body: used to calculate the value of each cell of the column. Generally, both the row element and the grid key intervene here.

This generator is used in the following dataset rule, which generates the grades table depicted above:

dataset studentGrades over s : Student {

properties[ID, name]

grid {

keys: EvaluationItem.all

header: key.name

body: s.grades.selectOne(g | g.item == key)?.points

}

column final_grade : s.getFinalGrade()

}

In that grid generator, the course evaluation items are used as keys, which means that each one of these items would be evaluated over the header and body expressions to generate a new column. The header of the columns uses the item name, and the body is calculated by looking for a grade of the student for the evaluation item. The body uses the ?. safe null navigation operator in case the student does not have a grade for certain item.

Typeless dataset rules¶

The from expression presented above to filter rows during the generation can be also used to define datasets where the row elements are not instances coming from an input model. This can be useful to perform data aggregations, or to generate synthetic tables starting from a custom collection of values.

The following dataset rule generates a basic table using a sequence of numbers as row elements and different column generators:

dataset numbers over n from : 1.to(5) {

column number : n

column squared : n * n

grid {

keys: 2.to(5)

header: "times_" + key

body: n * key

}

number

squared

times_2

times_3

times_4

times_5

Nested column generators¶

When certain intermediate value has to be used in several column calculations, Pinset offers a nested, composite column generator. This generator is defined by a from expression that calculates a value, followed by a block containing column generators that can use that value:

dataset gradesDetails over g : Grade {

properties[points]

reference item[name]

from student : g.eContainer {

column id : student.ID

column final_grade : student.getFinalGrade()

column grade_lowerthan_final : g.points < final_grade

}

The rule above generates a dataset with one row per grade in the course. The rule includes a from expression, which obtains the student that obtained the grade through the containment reference. Then, it is used to obtain the student id and final grade, and an extra column that determines whether a grade contributed negatively to the final grade of the student, by checking if it has less points than the final grade.

The names of the nested column generators are prefixed with the name given to the object calculated by the from expression:

points

item_name

student_id

student_final_grade

student_grade_lowerthan_final

Lab 1

true

Lab 2

false

Partial Test

true

Final Exam

false

Lab 1

false

100

Final Exam

false

Lab 1

false

Lab 2

false

Partial Test

false

100

Lab 1

false

Lab 2

false

Partial Test

true

Final Exam

false

Column post-processing¶

Pinset offers some column post-processing operations that are frequently used to prepare a dataset for an analysis.

These operations are invoked by annotating the column generators.

dataset studentGradesPostProcessed over s : Student {

properties[ID]

@fillNulls 0

grid {

keys: EvaluationItem.all

header: key.name

body: s.grades.selectOne(g | g.item == key)?.points

}

column final_grade : s.getFinalGrade()

@normalize 100

column final_grade_normalized : final_grade

}

Lab_1

Lab_2

Partial_Test

Final_Exam

final_grade

final_grade_normalized

0.81

100

0.59

0.16

100

0.9

Fill nulls¶

It is possible to @fillNulls with a custom value, or with a special and sometimes used value, such as the mean or the mode of the column values. The following dataset rule

By annotating the grid in the detailed grades example, we can fill with zeros those cells where a student did not took an evaluation item.

Normalisation¶

We can @normalize data columns between the [0,1] interval (useful when applying distance-based algorithms with numeric columns in different scales). A value can be provided to the annotation to perform the normalisation. If no value is given, the maximum value encountered in the column is used instead.

The dataset rule above contains a column with the normalised final grade of the course.

Picto Integration¶

Check out this article to learn how Pinset can be used to generate Picto table visualisations.

Unit Testing (EUnit)

Wizard Language (EWL)

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串行外设接口（Serial Peripheral Interface）的缩写。SPI，是一种高速的，全双工，同步的通信总线，

TIM

高级控制定时器(TIM1和TIM8)由一个16位的自动装载计数器组成，它由一个可编程的预分频器驱动。它适合多种用途，包含测量输入信号的脉冲宽度(输入捕获)，或者产生输出波形(输出比较、PWM、嵌入死区时间的互补PWM等)。使用定时器预分频器和RCC时钟控制预分频器，可以实现脉冲宽度和波形周期从几个微秒到几个毫秒的调节。高级控制定时器(TIM1和TIM8)和通用定时器(TIMx)是完全独立的，它们不共享任何资源。

EXTI 外部中断外部中断/ 事件控制器(EXTI)每个输入线可以独立地配置输入类型(脉冲或挂起)和对应的触发事件(上升沿或下降沿或者双边沿都触发)。每个输入线都可以独立地被屏蔽。挂起寄存器保持着状态线的中断请求。

AFIO

复用功能复用功能I/O

PWR

电源控制(PWR)

BKP

备份寄存器(BKP) 备份寄存器是42个16位的寄存器，他们处在备份域里，当V DD 电源被切断，他们仍然由V BAT 维持供电。当系统在待机模式下被唤醒，或系统复位或电源复位时，他们也不会被复位。

bxCAN

控制器局域网(bxCAN) (Basic Extended CAN) 它的设计目标是，以最小的CPU负荷来高效处理大量收到的报文。它也支持报文发送的优先级要求(优先级特性可软件配置)。

I 2 C

(芯片间)总线接口连接微控制器和串行I 2 C总线

USART

通用同步异步收发器通用同步异步收发器(USART)。

UART

通用异步收发传输器（Universal Asynchronous Receiver/Transmitter)，通常称作UART

IWDG、WWDG

独立看门狗(IWDG) 窗口看门狗(WWDG)可用来检测和解决由软件错误引起的故障；当计数器达到给定的超时值时，触发一个中断(仅适用于窗口型看门狗)或产生系统复位。

RTC

实时时钟(RTC) 实时时钟是一个独立的定时器。RTC模块拥有一组连续计数的计数器，在相应软件配置下，可提供时钟日历的功能。修改计数器的值可以重新设置系统当前的时间和日期。

二、GPIO

STM32中拥有七组端口（GPIOA、GPIOB、、、、、、），每组端口最多拥有16个引脚（Pin0,、、、、、、Pin15）例：PA3(GPIOA,Pin3)

两个GPIO输出的库函数 1.电频输出函数三个参数 1.GPIOx：目标引脚的端口号 2.GPIO_Pin:目标引脚的引脚号 3.PinState:高电频——GPIO_PIN_SET; 低电频——GPIO_PIN_RESET 例：向PB8输入高电频 HAL_GPIO_WritePin(GPIOB,GPIO_PIN_8,GPIO_PIN_SET) 2.电频反转函数例：PA3引脚输出电频反转 HAL_GPIO_TogglePin(GPIOA,GPIO_PIN_3)

三、跑马灯代码： HAL_GPIO_WritePin (GPIOB,GPIO_PIN_9,GPIO_PIN_SET); HAL_Delay(500) ; HAL_GPIO_WritePin (GPICB,GPIO_PIN_9,GPIO_PIN_RESET) ; HAL_Delay(500) ;

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STM32GPIO笔记

STM32GPIO笔记一、系统机构图外设介绍：ADCAnalog-to-Digital Converter的缩写，指模/数转换器或者模数转换器SPI串行外设接口（Serial Peripheral Interface）的缩写。SPI，是一种高速的，全双工，同步的通信总线，TIM高级控制定时器(TIM1和TIM8)由一个16位的自动装载计数器组成，它由一个可编程的预分频器驱动。它适合多种用途，包含测量输入信号的脉冲宽度(输入捕获)，或者产生输出波形(输出比较、PWM、嵌入死区时间的互补PWM

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STM32学习笔记（1）：GPIO口的使用

07-19

STM32F103VET6摸索了很久之后终于把ARM开发板上的LED灯点亮了，虽然是很简单的一个IO口操作，但是由于以前从来都没有什么经验，所以浪费了很多时间，也查找了很多资料。现在可以操作IO口了，证明迈出了学习ARM的第一步。

STM32-GPIO学习笔记

07-17

STM32-GPIO学习笔记

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基于HAL库的GPIO端口三个基础函数HAL_GPIO_Init()、HAL_GPIO_ReadPin()和HAL_GPIO_WritePin()

Strange_Head的博客

03-22

1万+

基于HAL库的GPIO端口三个基础函数HAL_GPIO_Init()、HAL_GPIO_ReadPin()和HAL_GPIO_WritePin()

STM32HAL库的基本使用（1）- GPIO引脚配置

qq_47736089的博客

09-22

4674

作者使用的是STM32L431RCT的开发板，Cortex-M4的内核，是大学老师教学用的，原理图如下。

STM32的GPIO配置.pdf

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imtoken在线下载|pinset

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Dataset Extraction (Pinset) - Epsilon

Pinset: A DSL for Extracting Datasets from Models for Data Mining-Based Quality Analysis | IEEE Conference Publication | IEEE Xplore

Pinset是什么意思_Pinset在线翻译_英语_读音_用法_例句_海词词典

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